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==== Front
Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_04_0064
Essay
Developing Academic–Practice Partnerships to Enhance the Integration of Genomics Into Public Health
Edwards Karen L PhD Department of Epidemiology
The author is also affiliated with the Institute for Public Health Genetics, School of Public Health and Community Medicine, University of Washington, Seattle, Wash
Box 357236, School of Public Health and Community Medicine, University of Washington, Seattle WA 98195 [email protected]
206-616-1258
Raup Sarah F MPH Department of Epidemiology, School of Public Health and Community Medicine, University of Washington, Seattle, Wash
Peterson Oehlke Kristin MS Minnesota Department of Health, Minneapolis, Minn
4 2005
15 3 2005
2 2 A022005
The sequencing of the human genome has provided tools to gain a better understanding of the role of genes and their interaction with environmental factors in the development of disease. However, much work remains in translating discoveries into new opportunities for disease prevention and health promotion. Both public health academia and practice have important roles to play in bridging the gap between the growth in knowledge stemming from the Human Genome Project and its application in public health. Recognizing this, the Centers for Disease Control and Prevention, through the Association of Schools of Public Health, established Centers for Genomics and Public Health at three schools of public health in 2001: the University of Michigan, the University of North Carolina, and the University of Washington. This paper describes the experience of the University of Washington Center for Genomics and Public Health in forging partnerships with public health practitioners to translate genomic advances into public health practice.
==== Body
Introduction
The sequencing of the human genome has provided tools to gain a better understanding of the role of genes and their interaction with environmental factors in disease development (1). This understanding is predicted to improve methods for targeting interventions aimed at preventing disease and improving health. In the future, for instance, some believe that individuals will be screened for genetic susceptibility to common disorders such as cancer, heart disease, and diabetes, thus yielding recommendations for personalized prevention strategies. Primary prevention strategies (such as dietary changes) and secondary prevention strategies (such as more frequent or earlier initiation of medical screening) might be used to minimize disease risk (2). Disease management is expected to improve through pharmacogenomics, which promises safer and more efficacious drugs through the customization of drug therapies based upon an individual's genetic makeup (3).
Much work remains, however, in translating discoveries into new opportunities for disease prevention and health promotion. While new reports of gene–disease associations are published almost daily, most are not replicable (4). Most chronic diseases are caused not by a single gene but by the complex interplay among several genes and numerous environmental factors. Therefore, population-based studies that assess the prevalence of genotypes, the disease risk associated with gene variants, and gene–gene and gene–environment interactions are needed (5). Furthermore, as new genetic tests are developed, information on their analytical validity (accuracy with which a genetic characteristic can be detected in a given laboratory test), clinical validity (accuracy with which a test predicts a clinical outcome), clinical utility (likelihood that the test will lead to an improved health outcome), and ethical, legal, and social consequences will be needed to make decisions about their use in clinical and public health practice (6). The impact of genomic information on risk perception and health behavior change needs to be better studied, and the added value of targeted interventions based upon genetic susceptibility compared with population-based prevention recommendations needs to be determined (7). Health care providers and public health professionals will need to be educated about genomics (7) and the public will need to be "genetically literate" if genomics is to be used as a tool for disease prevention (8). Concerns about the use of genomic information (e.g., fear of employment or insurance discrimination) will need to be well understood through community input and adequately addressed as programs or policies incorporating genomics are developed (9).
The Centers for Genomics and Public Health: Linking Academia and Practice
Both public health academia and practice have important roles to play in bridging the gap between the growth in knowledge stemming from the Human Genome Project and its application in improving health and preventing disease. Recognizing this, the Centers for Disease Control and Prevention's Office of Genomics and Disease Prevention (CDC OGDP), through the Association of Schools of Public Health, established Centers for Genomics and Public Health at three schools of public health in 2001: the University of Michigan, the University of North Carolina, and the University of Washington. CDC OGDP, which since 1997 has taken the lead in promoting the use of genomics to improve health and prevent disease across the lifespan by integrating genomics into public health research, policy, and programs (10), established the centers with the mission of further integrating genomics into public health practice by increasing the genomics and public health knowledge base; providing technical assistance to local, state, and regional public health systems; and training the public health workforce (11). In 2002, the Institute of Medicine released the report Who Will Keep the Public Healthy? Educating Public Health Professionals for the 21st Century. The report acknowledged genomics as an important component of public health and called upon schools of public health to provide students and practicing public health professionals with a framework for understanding the importance of genomics to public health (12). Although the centers were established prior to development of this report, they are clearly playing a role in answering this call.
To accomplish their mission of further integrating genomics into public health practice, particularly in the area of chronic disease prevention, the centers have developed strong ties with public health practitioners within state public health agencies and to a lesser extent with practitioners at the local level. Both academics and practitioners have benefited from partnerships through new concepts and applications. For instance, though some researchers within schools of public health have long been engaged in a variety of research activities related to genomics in the areas of biostatistics, epidemiology, environmental and occupational health, health policy, health services, and the behavioral sciences, these researchers rarely engage in public health activities at the state or local level, and many do not have a solid understanding of public health in the real world. State health departments have also been engaged in genomics activities for a number of years, ranging from newborn screening programs to the provision of genetic services. However, a 2000 survey of state health departments conducted by the Council of State and Territorial Epidemiologists indicated that few state health departments had begun to consider opportunities for using genomics outside of the context of maternal and child health, despite an increasing awareness of the potential application of genomics in broader public health efforts. Survey respondents identified lack of resources, proven disease prevention measures, and outcomes data as potential barriers (13). Through the centers, academic researchers and public health practitioners have begun to collaborate more closely, and opportunities for using genomics to improve public health, particularly in the area of chronic disease prevention, have been identified as a result. This paper describes the experience of the University of Washington Center for Genomics and Public Health (UWCGPH) in forging partnerships with public health practitioners to translate genomic advances into public health practice.
The University of Washington Center for Genomics and Public Health
At UWCGPH, we have learned a tremendous amount about how to develop genomics-centered collaborations with state public health agencies, and these collaborations enhance the work of both researchers and practitioners. Relationship building is a first step toward identifying opportunities for collaboration. Despite the distance that may lie between universities and state health agencies, face-to-face contact and regular communication is important to developing relationships. We traveled from Seattle to the Washington State Health Department (WA DOH) in Olympia, Wash, numerous times to meet with chronic disease program staff. We also traveled to the Oregon Health Division (OHD) in Portland to meet with genetics program staff; Oregon is one of four states currently leading efforts to integrate genomics into chronic disease with recent funding from the CDC's National Center for Chronic Disease Prevention and Health Promotion (14). We attended several public health conferences and participated in evaluating state public health programs to learn more about public health activities in Washington. All these efforts provided us with the opportunity to better understand the role of state public health programs and the knowledge and skills held by public health practitioners, as well as to share information about our expertise and to work together to identify ways in which genomics might be integrated into state public health efforts.
We have found that both formal and informal educational efforts are effective for stimulating interest in genomics and encouraging ongoing learning about genomics terms and concepts. For example, we collaborated with the CDC and other Centers to develop an animated Web-based module, Genomics for Public Health Practitioners: The Practical Application of Genomics in Public Health Practice. Pilot testing of this module indicates that it addresses many questions raised by public health practitioners about the use of genomics in public health. We also identified genomics training courses within the Pacific Northwest for interested public health practitioners to attend. While such formal educational efforts are effective in some instances, we also believe that adding genomics terms and concepts to the public health lexicon can be accomplished through ongoing joint efforts between the centers and state health departments. Over time, a common understanding of terms and concepts begin to emerge as academics and practitioners work together to tackle issues.
To address the common notion that genomics is a separate field, rather than an area that is becoming increasingly relevant to almost every disease and public health program area, we have learned the value of framing genomics as an additional tool for informing and addressing public health issues. For example, we conducted a project to examine the impact of genomics on public health efforts to reduce asthma morbidity and mortality by using a consultative process that engaged public health professionals, researchers, health care providers, and community representatives in dialogue about this issue. The final conclusions and recommendations drawn from this process are summarized in a final report, Asthma Genomics: Implications for Public Health (15). We hope that by engaging a variety of experts in the examination and dialogue process and by broadly disseminating the final report, those involved in asthma public health efforts at the local, state, and national levels will be more prepared to manage issues that may arise with the use of genomics to prevent, diagnose, and treat asthma and will think more about how genomics might play a role in reducing the effects of this common disease.
Family history, which reflects the consequences of genetic susceptibilities, shared environment, and common behaviors, is a risk factor for almost all chronic diseases (7) and can be incorporated into efforts to address many diseases of public health importance. Although family history has been long collected within the medical setting, there have been few public health efforts promoting the use of family history as a tool for disease prevention (16). In 2003, the CDC funded three sites to answer many important questions regarding the use of family history in public heath and preventive medicine (17). In the meantime, however, state health agencies appear interested in playing a role in answering important questions, including the following: Can a simple family history tool accurately and reliably collect information from family members? Can disease information about an individual's relatives be used to inform their risk for disease? If so, would individuals found to be at increased risk be more likely to adopt lifestyle changes and participate in early detection and prevention strategies (16)?
Family history was a topic for which we identified opportunities for collaboration with the Washington State Diabetes Prevention and Control Program (DPCP). For example, we took part in an assessment of the Washington State Diabetes Public Health System Performance, which was aimed at identifying the strengths and weaknesses of the statewide diabetes public health program. Opportunities for improvement resulting from the assessment included capturing the nonidentified diabetics in the state, developing a robust research agenda relevant to diabetes public health practice, and developing strategies to work more closely with academic partners. To help the WA DOH address these identified gaps, we proposed to develop a research project involving the use of family history as a public health tool. The Washington State Collaborative (WSC) Adult Preventive Services, a systematic approach to health care quality improvement in which organizations and providers test procedural innovations and then share their experiences to accelerate learning and promote widespread implementation of best practices, was an ideal setting in which to perform this project. We plan to collaborate with DPCP to collect more structured family histories as part of the WSC's quality improvement efforts, as well as to provide training to physicians regarding the utility of family history information.
After solid relationships with public health practitioners have been developed, in which practitioners begin to understand how genomics can be used as a tool for addressing issues of public health importance and academics begin to understand how genomics fits within the context of current public health programs and priorities, collaborative projects aimed at integrating genomics into public health are more easily identified. Funding for collaborative projects is an important issue, as state health departments often do not have the resources to carry out special projects.
In some instances, genomics-related projects can be proposed within the context of larger public health program proposals. For example, as a result of our efforts to demonstrate the relevance of family history to diabetes public health efforts, we were invited by the WA DOH to present information on the use of family history as a potential public health tool to those communities submitting proposals to the U.S. Department of Health and Human Services initiative, Steps to a HealthierUS. We suggested several potential community projects, including a community campaign to increase knowledge of family history. By incorporating family history into a comprehensive disease prevention and health promotion strategy, the communities would address many objectives of Steps to a HealthierUS. The Washington communities received funding, and we plan to assist them in identifying ways in which family history can be used to meet their goals.
Proposals also can be developed around other projects. For example, to evaluate the potential use of family history information, we are in the process of identifying funding to pilot a family history tool in a sample of clinics participating in the WSC. In addition, some projects can be best implemented in conjunction with the four states that have obtained funding to address genomics and chronic disease (14). For example, we are working with the health departments of the four states in reviewing existing family history questions included on various state surveys (e.g., Behavioral Risk Factor Surveillance System) and in developing new questions about family history for such surveys. Lastly, we have had discussions with OHD about collaborating on the development of a genomics awareness campaign for the health agency.
Benefits of Using Academic–Practice Collaborations
Because they have only been in existence for two and a half years, it is difficult to fully assess the benefits of the collaborations the centers have developed with their state public health agency partners. Several benefits of these academic–practice partnerships, however, have been casually observed. Academics have gained an awareness of what public health practice means, including an appreciation for the valuable expertise held by program staff and the day-to-day work they carry out. As a result, many have become involved in practice-based research, teaching, and service activities that they would have been less likely to consider prior to the exposure to public health practice afforded to them through these centers.
State public health departments are also taking better advantage of the expertise held by public health genomics researchers. For example, requests to the centers for guest speakers at conferences and representatives for statewide committees and taskforces have increased. Both academics and practitioners have come together to identify collaborative projects and opportunities to participate in research aimed at questions of public health importance.
Lastly, public health students have gained invaluable experience with real-world public health genomics issues through their involvement in projects carried out through the centers, creating a new generation of public health professionals with exposure to genomics in practice. In some cases, public health departments have benefited because these students have tackled valuable projects that they otherwise would not have had resources to address.
The academic–practice partnerships created through the centers for Genomics and Public Health are slowly transforming the landscape of public health genomics research and practice. The well-traveled bridge between the centers and state health departments has created partnerships in which both sides benefit and flourish. The work of academics is informed and enriched by real-world issues that affect real populations while the work of public health practitioners is sharpened with the developing knowledge and new approaches the academy has to offer. This burgeoning synergy, strengthened by time, shared experiences, and successes, is ultimately greater than the sum of its parts and will be an important element for the genomics revolution to maximally benefit public health.
This project is supported by a cooperative agreement with the Centers for Disease Control and Prevention through the Association of Schools of Public Health, Grant Number U36/CCU300430-23.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Raup SF, Oehlke KP, Edwards KL. Developing academic–practice partnerships to enhance the integration of genomics into public health. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0064.htm
==== Refs
1 Collins FS Green ED Guttmacher AE Guyer MS 2003 422 835 847 Nature A vision for the future of genomics research 12695777
2 Khoury MJ McCabe LL McCabe ER 2003 348 50 58 N Engl J Med Population screening in the age of genomic medicine 12510043
3 Weinshilboum R 2003 348 529 537 N Engl J Med Inheritance and Drug Response 12571261
4 Colhoun HM McKeigue PM Davey Smith G 2003 865 872 361 Lancet Problems of reporting genetic associations with complex outcomes 12642066
5 Little J Khoury MJ Bradley L Clyne M Lin B Lindegren ML 2003 667 673 157 Am J Epidemiol The human genome project is complete. How do we develop a handle for the pump? 12697570
6 Burke W Atkins D Gwinn M Guttmacher A Haddow J Lau J 2002 156 311 318 Am J Epidemiol Genetic test evaluation: information needs for clinicians, policy makers, and the public 12181100
7 Khoury MJ 2003 5 261 268 Genet Med Genetics and genomics in practice: the continuum from genetic disease to genetic information in health and disease 12865755
8 Kaye CI Laxova R Livingston JE Lloyd-Puryear MA Mann M McCabe ER 2001 4 175 196 Community Genet Integrating genetic services into public health—guidance for state and territorial programs from the National Newborn Screening and Genetics Resource Center (NNSGRC) 14960911
9 French ME Moore JB 2003 Washington (DC) Partnership for Prevention Harnessing genetics to prevent disease and promote health
10 About the Office of Genomics and Disease Prevention, CDC [Internet] Atlanta (GA) Centers for Disease Control and Prevention cited 2004 April 9
11 Centers for Disease Control and Prevention awards funds for genetics programs [Internet] Atlanta (GA) Centers for Disease Control and Prevention, Office of Genomics and Disease Prevention cited 2004 April 9
12 Gebbie K Rosenstock L Hernandez LM 2002 Who will keep the public healthy? Educating public health professionals in the 21st century Washington (DC) Institute of Medicine of The National Academies, The National Academies Pres
13 Piper MA Lindenmayer JM Lengerich EJ Pass KA Brown WG Crowder WB 2001 116 22 31 Public Health Rep The role of state public health agencies in genetics and disease prevention: results of a national survey 11571405
14 Centers for Disease Control and Prevention awards funds for genomics and chronic disease prevention [Internet] Atlanta (GA) Centers for Disease Control and Prevention, Office of Genomics and Disease Prevention cited 2004 April 9
15 Consultative process final report [Internet] Seattle (WA) University of Washington Center for Genomics and Public Health cited 2004 April 9 Available from: URL: http://depts.washington.edu/cgph/workinggroups/ subhead.php?fid=24
16 Yoon PW Scheuner MT Peterson-Oehlke KL Gwinn M Faucett A Khoury MJ 2002 4 304 310 Genet Med Can family history be used as a tool for public health and preventive medicine? 12172397
17 CDC genomic funding [Internet] Atlanta (GA) Centers for Disease Control and Prevention, Office of Genomics and Disease Prevention cited 2004 April 9
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==== Front
Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_04_0143
Essay
Using Evidence-based Community and Behavioral Interventions to Prevent Skin Cancer: Opportunities and Challenges for Public Health Practice
Glanz Karen PhD, MPH, Department of Behavioral Sciences and Health Education, Rollins School of Public Health, Emory University
1518 Clifton Rd NE, Atlanta, GA 30322 [email protected]
404-727-7536
Saraiya Mona MD, MPH Division of Cancer Prevention and Control, Centers for Disease Control and Prevention, Atlanta, Ga
4 2005
15 3 2005
2 2 A032005
==== Body
Introduction
Skin cancer is the most common cancer in the United States and is increasing in incidence (1). In 2004, more than 1 million people were expected to be diagnosed with squamous cell or basal cell carcinoma, and more than 2200 deaths were expected (2). Another 54,200 people were estimated to be diagnosed with melanoma, the most lethal of all skin cancers, and 7600 persons were expected to die from that disease during 2004. High levels of exposure to ultraviolet radiation (UVR) increase the risk of all three major forms of skin cancer, and approximately 65% to 90% of melanomas are caused by UVR exposure. Other risk factors for skin cancer include having fair skin, hair, and eyes; growing up closer to the equator; and having a large number of moles or nevi (3).
Fortunately, skin cancer is one of the most preventable cancers. State and local health departments can play an important role in preventing skin cancer by developing population-based programs to prevent the disease; assuring sun-safe environments and policies; and regulating exposure where appropriate. Behaviors that reduce risk include limiting or minimizing exposure to the sun during midday hours; wearing protective clothing; and using a broad-spectrum sunscreen when outside (3).
The Task Force on Community Preventive Services conducted an evidence-based review of the efficacy of interventions for sun protection in varied segments of the population across various settings (4,5). Reviewers examined the methodology of identified studies to see whether their design was suitable and their execution good enough to be included in the Task Force's review and also to inform the later determination of whether the evidence was sufficient to recommend a particular intervention (6,7). Given the increasing emphasis on basing policy and practice on evidence, public health leaders and practitioners should be familiar with this evidence review, its findings, and its implications for policy and practice.
This paper summarizes the state of knowledge about the effectiveness of interventions to reduce UVR exposure among various groups to prevent skin cancer and suggests strategies and resources for translating the evidence into action to improve population health.
State of the Evidence in Settings Most Influenced by Public Health Agencies
Methods
The Task Force on Community Preventive Services conducted systematic evidence reviews of the effectiveness of interventions for reducing UVR exposure to prevent skin cancer, using rigorous but standard methodology developed for the Guide to Community Preventive Services (Community Guide) (6) and methodology specific to this review (5). These reviews examined behavioral, educational, policy, and environmental strategies for changing behaviors to reduce skin cancer risk (5). In establishing the criteria for the evidence review, the task force accepted several premises: 1) exposure to sun helps cause skin cancer; 2) covering up and avoiding exposure to UVR plays a protective role; and 3) an outcome of using sunscreen by itself is not an indicator of intervention effectiveness (4).
A conceptual model, or analytic framework, was developed to show the relationship of the interventions to relevant intermediate outcomes (e.g., knowledge, attitudes, intentions regarding sun-protective behaviors) to actual behaviors and the prevention of skin cancer. Outcome data extracted from the studies were aligned with the analytic framework to answer research questions.
Key outcome targets identified in the analytic framework were improvements in knowledge, attitudes, and intentions relative to reducing UVR exposure or increasing protection from the sun; changes in exposure and protection; reduction of sunburn; and changes in policies and environments aimed at reducing exposure (e.g., limiting exposure during peak sun hours, increasing shade, providing sunscreen). The review team considered sunscreen use to be a secondary outcome because, although sunscreens prevent sunburn, their role in preventing melanoma has not been unequivocally shown (8,9). Also, although none of the studies identified measured incidence of precancer, nevi, photodamage, or skin cancer, the review team assumed that behavioral changes and reduction of sunburn, if achieved, would lead to lower rates of cancer (5).
To give a positive recommendation, the task force requires at least two high-quality studies showing positive effects. The evidence reviews covered nine categories of interventions. Six focused on distinct settings: health care and health care providers, the workplace, recreation/tourism, secondary schools and colleges, primary schools, and child care centers. The other three categories focused on a target population (e.g., children's parents and caregivers) or broad interventions (e.g., media campaigns, community-wide multicomponent interventions). The focus was strictly on prevention, not early detection.
Main findings
Of particular interest to health departments are the findings for settings in which health departments have advisory, collaborative, or regulatory roles: day care, recreation centers, primary schools, work sites, community-wide programs, and media campaigns. These findings are summarized here.
In two settings, evidence was sufficient to recommend interventions: primary schools and recreation/tourism. Educational and policy interventions in primary schools had sufficient evidence of increasing children's covering-up behavior — specifically, wearing protective clothing and hats. Approaches included interactive classroom and take-home activities about sun protection, brochures for parents, and a working session to develop plans and policies for sun protection. These approaches provided sufficient evidence of improvement in covering-up behavior, with a median relative increase of 25% across six studies of good quality (the Appendix provides definition of relative increase). Evidence was insufficient to determine the effectiveness of interventions in improving other behaviors, such as avoiding the sun, because of inconsistent results; evidence was also not sufficient to determine effectiveness in decreasing sunburns because there was only one study, which was limited in design and execution.
Evidence was also sufficient for the effectiveness of interventions in recreation/tourism settings, specifically for increasing adult covering-up behavior, with a median net increase of 11.2% across five studies. These interventions included one or more of these strategies: training in sun safety and role modeling by outdoor recreation staff and lifeguards; providing lessons in sun safety, interactive activities, and programs for parents; increasing available shaded areas; providing sunscreen and educational brochures; and offering point-of-purchase prompts. In contrast, intervention studies yielded insufficient evidence to determine effectiveness in affecting children's sun-protective behavior; results were inconsistent.
The Task Force on Community Preventive Services found insufficient evidence on which to make recommendations for or against interventions to reduce exposure to UVR in the following settings and populations: child care centers, secondary schools and colleges, recreation/tourism settings for children, occupational settings, media campaigns alone, and community-wide multicomponent interventions (4). A finding of insufficient evidence, however, does not suggest that an intervention does not or cannot work; rather, it indicates that the available evidence base was insufficient in quality or quantity to make a determination (10). Furthermore, many of the studies had multiple components that could not be evaluated separately (4); some strategies for which effectiveness was not evaluated independently might be part of an effective community program.
Translating Evidence Into Action
The findings of the evidence review for the Community Guide on interventions to reduce UVR exposure have an important place in evidence-based decision making among public health officials. They should be considered when identifying legislative and policy approaches that support prevention and in developing research agendas (10,11). While evidence-based policy and practice is an increasing priority, it is equally necessary to mobilize community partnerships to identify and address health problems (12).
One evaluation of the process of disseminating earlier Community Guide findings found that city and county health department program directors believed that rigorous information about the effectiveness of interventions was important, but the directors noted that evidence-based recommendations alone do not assure the implementation of effective interventions (13). These evidence reviews clearly fill a gap, however: an analysis of the data-based planning activities of state health agencies in the mid-1990s found that there were few useful sources of data on proven preventive interventions and how to implement them (14).
Efforts to translate Community Guide evidence review into action should use local data, the recommendations, and resources available from federal agencies, voluntary health organizations, and academic sources. In particular, public health planners and program directors can benefit from several program models and ready-made tools for program planning, implementation, and evaluation in the prevention of skin cancer. The "Guidelines for School Programs to Prevent Skin Cancer" (15) can be used to help shape policy and curricular interventions. The Centers for Disease Control and Prevention offers free online resources for skin cancer prevention and education (16), and the Cancer Control PLANET Web site includes a step-by-step model for effective planning of skin cancer control (17).
The National Comprehensive Cancer Control Program provides a model, a framework, and funding to develop state cancer prevention plans. The planning process involves leadership from state health departments using data-driven priorities and multisectoral cooperation (18,19). A review of available state cancer plans shows a range of objectives and actions, including 1) plans to determine the prevalence of sunburn using data from national surveys such as the National Health Interview Survey or state-based data from the Behavior Risk Factor Surveillance System (20); 2) the establishment of objectives related to awareness, policy change, and reduction of sunburns (21) and 3) detailed analyses of incidence and trends for melanoma in population subgroups, analysis of barriers, and clear goals and action plans (22).
Research and evaluation in states and local communities are important to the continuing growth of the evidence base in preventing skin cancer and can be accomplished by health department personnel with academic and other public health system partners (12). In Hawaii, a survey of elementary school principals showed that most were aware of the risks of excess UVR exposure, but few policies were in place; still, these principals were receptive to statewide leadership for prevention (23). In Georgia, a statewide cancer control program focused initially on breast and cervical cancer, but it planned to expand into preventing skin cancer (24). In addition, a Maine project to prevent skin cancer using components from various well-researched strategies (25) could provide useful information to other states by adding a structured program evaluation.
Conclusion
Both opportunities and challenges emerge from the evidence review on interventions to prevent skin cancer conducted for the Community Guide. First, readers should note that the absence of sufficient data to prove the efficacy of primary prevention efforts in specific settings or subpopulations is not proof of inefficacy. Rather, the findings reveal the need for additional evaluation of efforts to achieve primary prevention. Public health agencies have room for improvement and involvement. Opportunities for involvement include taking a leadership role in developing policies and regulations to reduce UVR exposure, especially among children; working with the media to communicate consistent and effective messages about sun protection; and engaging with the private sector to encourage adoption of protections and policies for outdoor workers.
Public health departments also have opportunities to contribute to areas in which there is sufficient evidence that strategies to prevent skin cancer have been effective. Divisions charged with preventing chronic diseases can work with schools and recreational settings by helping them to set policies and adopt prevention curricula. The credibility of school and recreation administrators as community leaders can enable them to be powerful communicators about how skin cancer may affect their populations.
Although the Community Guide does not show that interventions to prevent skin cancer are useful in many settings, it does support an effect in primary schools and outdoor recreation. These findings suggest that public health agencies should allocate resources to primary schools and outdoor recreation while refining and confirming the efficacy of interventions in other settings. Ultimately, the importance of the Community Guide evidence review "will be determined by its impact on enhancing health and quality of life in communities" (26).
This work was supported in part by the Centers for Disease Control and Prevention. The authors thank Peter Briss for his leadership on the evidence review, Cornelia White, Phyllis Nichols, Debjani Das, the Cancer Chapter team and consultants, and the Task Force on Community Preventive Services for their contributions to the evidence review and recommendations. We also thank Peter L. Taylor for editorial assistance.
Appendix
Summary Effect Measuresa
Before-and-after-only design Study with comparison group
(RCT, cohort design, nonrandomized trial)
Absolute effect measure: post − pre ΔI − ΔC
Relative effect measure: (post − pre)/pre × 100 (ΔI/Ipre − ΔC/Cpre) × 100
a RCT indicates randomized controlled trial; I indicates intervention; C indicates control; Δ indicates change.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Glanz K, Saraiya M. Using evidence-based community and behavioral interventions to prevent skin cancer: opportunities and challenges for public health practice. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0143.htm
==== Refs
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9 Dennis LK Beane Freeman LE VanBeek MJ 2003 139 966 978 Ann Intern Med Sunscreen use and the risk for melanoma: a quantitative review 14678916
10 Briss PA Brownson RC Fielding JE Zaza S 2004 25 281 302 Annu Rev Public Health Developing and using the Guide to Community Preventive Services: lessons learned about evidence-based public health 15015921
11 Guide to Community Preventive Services: an essential resource for state and local health departments [homepage on the Internet] Atlanta (GA) Centers for Disease Control and Prevntion cited 2004 Sep 4 Available from: URL: http://www.thecommunityguide.org
12 Committee on Assuring the Health of the Public in the 21st Century, Institute of Medicine Washington (DC) The National Academies Press 2003 The future of the public's health in the 21st century
13 Martinez RM McHugh M Kliman R Roschwalb S 1999 Community preventive services in ten health departments and their receptivity to evidence-based guidelines Mathematica Policy Research Washington (DC)
14 Alciati MH Glanz K 1996 111 165 172 Publ Health Rep Using data to plan public health programs: experience from state cancer prevention and control programs
15 Glanz K Saraiya M Wechsler H Centers for Disease Control and Prevention 51 RR4 2002 1 18 MMWR Recomm Rep Guidelines for school programs to prevent skin cancer 11995901
16 Centers for Disease Control and Prevention Choose your cover campaign [Internet] Atlanta (GA) Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion cited 2004 Aug 22
17 Cancer Control PLANET Sun safety: 5 steps to effective cancer control planning [Internet] Cited 2004 Aug 22
18 Abed J Reilley B Butler MO Kean T Wong F Hohman K 2000 6 67 78 J Public Health Manag Pract Developing a framework for comprehensive cancer prevention and control in the United States: an initiative of the Centers for Disease Control and Prevention 10787781
19 Centers for Disease Control and Prevention National comprehensive cancer control program [Internet] Atlanta (GA) cited 2004 Aug 22 Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion
20 2004 New Mexico cancer plan, 2002-2006: a document to guide collaborative cancer control efforts throughout the state New Mexico Department of Health Santa Fe (NM)
21 Colorado Cancer Coalition 2004 Colorado cancer plan 2005 Colorado Department of Public Health and Environment Denver (CO)
22 The North Carolina Advisory Committee on Cancer Coordination and Control 2004 The North Carolina cancer control plan, 2001-2006 Raleigh (NC) North Carolina Department of Health and Human Services
23 Eakin P Maddock J Techur-Pedro A Kaliko R Derauf DC Prev Chronic Dis [serial online] Sun protection policy in elementary schools in Hawaii 7 2004
24 Parker DM Prev Chronic Dis [serial online] Georgia's Cancer Awareness and Education Campaign: combining public health models and private sector communication strategies 7 2004
25 Hayden CA A model community skin cancer prevention project in Maine Prev Chronic Dis [serial online] 4 2004
26 Kohatsu ND Melton RJ 18 1 Suppl 2000 3 4 Am J Prev Med A health department perspective on the Guide to Community Preventive Services 10806970
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Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_05_0008
Editorial
Blazing a Trail: A Public Health Research Agenda in Genomics and Chronic Disease
McBride Colleen M PhD Chief, Social and Behavioral Research Branch National Human Genome Research Institute
31 Center Dr, Building 31, Room B2B37, Bethesda, MD 20892-2030 [email protected]
301-594-6788
4 2005
15 3 2005
2 2 A042005
==== Body
Whether and when genomics will lead to public health benefit via reductions in chronic disease burden has provided fodder for debate (1,2). A point of agreement among both proponents and skeptics is that directing genomics research to achieve this end will require integration of knowledge across multiple disciplines and levels of analysis (i.e., biological, behavioral, social, and environmental) (3). Getting started on building these collaborations while the territory is new could temper the disciplinary hegemony that so often presents formidable barriers to transdisciplinary research (4). That said, when it comes to genomics, which has been the bastion of bench scientists and most recently epidemiologists, it may be especially challenging to attract the array of chronic disease researchers with expertise in health education, health psychology, health services delivery, and community-based intervention that will be critical to further this research agenda.
Vociferous pessimism expressed by some scientific leaders about the future application of genomic discovery to public health improvements (2) may be scaring off some public health scientists from pursuing genomics research (5). As has been said, "mud sticks whatever its veracity" (6). However, some public health researchers (7) would contend that waiting until genomic discovery is further along to get involved will relegate us to the role of translators, stuck with disseminating the technologies that evolve, even if they are poorly suited to populations or limited in their impact on chronic disease outcomes. Indeed, public health scientists must be among the trailblazers in step with or a step ahead of the science, with a voice in directing genomics research toward public health benefit.
Unfortunately, the emerging public health research agenda for chronic disease is giving relatively little consideration to the future of genomic discovery (8). An informal review of the American Journal of Public Health over the past decade shows that from 1995 to 1999 only eight articles related to genetics were published, a number that increased only to 22 between 2000 and 2004. Publications related to obesity, another area recognized during the same time to be important for chronic disease, increased fivefold from 26 to 138.
So how do we enlist public health scientists in transdisciplinary collaborations that further a public health research agenda? First, it is time for a frame change. The past decade's research agenda was framed to anticipate and protect the public from the potential negative ethical, social, and psychological implications of genomic discovery. Not surprisingly, scientists in the vanguard of this research have been bioethicists, lawyers, and public policy experts. To enlist public health researchers in genomics research, the agenda must be reframed to understand the practical and proximal benefits of genomics for chronic disease. Specifically, we should be figuring out how genomic discovery might help us to address three persistent challenges for chronic disease prevention and management: 1) reducing prevalent behavioral risk factors, 2) reducing disparities in chronic disease outcomes, and 3) improving chronic disease care delivery at reduced cost. Below I suggest examples of research in genomics and chronic disease that could galvanize the transdisciplinary research collaborations needed to address these challenges.
Reducing prevalent behavioral risk factors
The predicted broad array of genetic susceptibility tests that will identify populations and individuals at increased risk of chronic disease raise myriad research questions. Most notably, how can these tests and related feedback be used to motivate adoption of risk-reducing behaviors?
At the social environment level, public education about genomics will be a priority. Fewer than half of Americans are aware of currently available genetic testing for cancer susceptibility (9). Not surprisingly, awareness is greatest among the most highly educated. Contrast this to a recent Institute of Medicine report suggesting that nearly half of Americans cannot read complex text and may lack the skills needed to evaluate the risks and benefits of health-related technologies (10). Development and evaluation of health education approaches for individuals with low literacy is needed generally, and testing strategies to communicate the complexity of genomic risk may be especially fertile ground for this research. The increasing direct-to-consumer advertising of susceptibility testing and popular press coverage of genomic discovery provide a number of "interventions" and natural experiment opportunities for exploring the public's understanding of genomic risk and examining factors that influence interest in testing and its association with risk-reduction outcomes across different target groups.
At an individual level, a number of social and psychological theories support debate about whether genomic risk information will be viewed as more motivational for risk-factor reduction than other risk feedback (e.g., measurement of blood pressure and cholesterol levels, family history). Important questions remain about whether genetic risk information can help us improve upon state-of-the-art risk communications by personalizing risk in different or more effective ways than current risk indicators.
An important challenge will be how to communicate information on small incremental risk increases conferred by emerging genetic markers for chronic disease risk. Currently the little empiric evidence available on these risk communications is confined to highly selected samples of well-educated patients for genes that confer high levels of risk. The increasing evidence base for common genetic polymorphisms that interact with common environmental risk factors to modestly increase chronic disease risk (e.g., GSTM1 for smoking-related diseases, PPARG for diabetes, COL1A1 for osteoporosis) offer research tools that can be used now to understand broader populations' response to genetic risk and to address other important public health questions (11).
Three decades of research in developing and testing behavior-change interventions for risk reduction tell us it is unlikely that a genetic test result alone will prompt behavior change. Yes, genetic test results might provide a cue to action to be capitalized upon and integrated with evidence-based multicomponent interventions already shown to influence behavior change. Moreover, consideration of who might be most interested in genetic testing and their motivations for such testing also could be explored to adapt intervention approaches accordingly.
Reducing disparities in chronic disease outcomes
The prediction that genomic discovery may enable future population-risk stratification for chronic diseases raises understandable uneasiness about the use of genetic determinism to explain health disparities (12). This makes it all the more important that research now test how to use knowledge about the remarkable similarity of the human genome across time, continents, and populations to inform the discussion about what is social and what is biologic in our constructions of race, ethnicity, and other social groupings; such research could help us begin to clarify the individual and joint effects of these factors on chronic disease outcomes and health disparities. Scientists now suggest that at best, genetic predispositions may account for a third or less of chronic disease mortality (13). Communicating about the complex, probabilistic, and relatively weaker role of genetics in chronic disease could naturally open a dialogue about the stronger role of environment and, in turn, might be used to strengthen the potency of behavior-change interventions and to address the socially determined causes of disparities.
There are many fascinating and critical research questions about what are the most effective methods to increase the public's skills for evaluating the relative contribution of genetics to chronic disease outcomes, the fallibilities and strengths of genomics research, and to which groups these interventions should be targeted (e.g., racial or ethnic communities, patients, health care providers, health insurers, journalists, bench scientists). Moreover, how might these educational and skills-building interventions influence a target community's opinions and receptivity to emerging genomic discoveries and research?
Methodological research also should be a priority. For example, most of the large population genetics registries, despite earnest efforts, have had poor minority representation (14). From a scientific perspective, the external validity of study results based on these registries, as well as their credibility to minority communities, is lessened. Thus, it is critical now to evaluate different approaches to recruitment for genetic studies that augment minority and population-based recruitment. Moreover, exploring public education interventions to improve study recruitment could be a fruitful area of research. In this regard, research might also explore whether genetic susceptibility testing for chronic disease is viewed as a monolith, or whether participation in genomic research related to population-wide diseases (e.g., cancer, diabetes) and race- or ethnicity-associated diseases (e.g., cystic fibrosis, sickle cell anemia) are viewed differently by target groups.
Equal access to genomic technologies also will be important to reducing disparities in chronic disease outcomes. Again, getting started early will be critical if we are to design technologies that have any potential for dissemination (15). To this end, it is important to evaluate genetic testing and feedback in naturalistic settings such as public health clinics to better understand system barriers and facilitators that must be considered as we develop genomic technologies for broad-based dissemination (16). In each case, rigorous evaluation of delivery approaches that increase the likelihood that genomic technologies are accompanied by appropriate support services and are affordable to individuals and/or systems will be key to success.
Improving chronic disease care effectiveness and efficiency
Interventions to help patients manage the physical and psychological consequences of their chronic conditions and make requisite lifestyle changes have shown benefits for a variety of patient and system-level outcomes (17). Yet effective self-management involves trial and error, as clinical recommendations are based on broad and heterogeneous phenotypes of chronic disease. An important question is whether genetically customized management recommendations could improve patient self-management of chronic illness above current standard-of-care approaches. For example, psychological theories tell us it is plausible and testable that genetically customized self-management interventions might empower patients to be better self-managers and consumers of health care. Accordingly, genetic tailoring might improve patient–provider relationships in ways that reduce visit time and follow-up needs. Additionally, we might ask what genetic information patients and providers need to make them better collaborators. Answers to these questions "upstream" might be used to direct bench science to genomics research where products have the best potential for dissemination to these target groups.
Also important to consider is that health care providers are expected to deliver an increasing number of preventive services during their visits with patients (18). Thus, the potential for genomic risk stratification to enable efficiencies in health care delivery that reduce cost without compromising care is an important area for research. Research related to current genetic testing applications (e.g., BRCA1, BRCA2, hereditary nonpolyposis colorectal cancer [HNPCC]) has been conducted in specialized care settings where certified genetic counselors provide one- to three-hour sessions to support patient decision making and communicate test results. This research tells us little about how these applications might be incorporated into primary care or community health settings. Evaluating different counseling delivery models that have been shown in previous health promotion research to be effective (e.g., lay advisors, telephone counseling, Web-based information) is a good place to start. This evaluation will require us to involve genetic counselors to balance what is best practice for communicating about chronic disease markers against what can be effectively integrated into a variety of care settings.
Cost also will be important to consider. Current studies have shown some pharmacogenomic interventions to be worth the cost, but these studies are too few in number to evaluate the implications of genomic medicine broadly (19). The importance of research for evaluating the interventions that might be most cost-effective upstream of genomic technology development cannot be overstated (19).
What do we need to move forward a public health research agenda?
Special journal editions like this one and the research that is highlighted is a good start. Bringing the theme of genomics to national public health and behavioral medicine meetings and featuring public health scientists in the vanguard of this research from the Public Health Genomics Centers, funded by the Centers for Disease Control and Prevention, and the Centers of Excellence for ELSI Research, funded by the National Institutes of Health, as keynote speakers also could increase buy-in.
The slower pace of genomic discovery in chronic disease means that for the time being we will be using imperfect genomic-risk prototypes (20). Certainly, we must have standards for choosing which prototypes to evaluate (e.g., meta-analyses as an evidence base) but not hold them now to standards such as clinical validity or utility that ultimately may be the goal for dissemination. Indeed, why put the cart before the horse if the technologies in their prototypic form cannot accomplish goals that will affect public health outcomes? Genome scientists, clinicians, and public health researchers could collaborate in developing working standards for selecting promising genomic-risk applications to be used in chronic disease research. It will be important to secure buy-in for this research from institutional review boards that may be uncomfortable with the use of experimental genomic technologies in public health and clinical settings.
Compromising on prototypes does not mean that our research should compromise on rigor. It is time to move beyond descriptive and exploratory studies to conceptually based, hypothesis-driven public health research. Public health researchers have a trailblazing role to play in these earliest phases of framing an agenda for genomics research that puts public health challenges front and center. The time for action is now!
I gratefully acknowledge Ms Stephanie Moller, Dr Karen Emmons, Dr Jesse Gruman, Dr Alan Guttmacher, Dr Muin Khoury, and Dr Benjamin Wilfond for their helpful comments.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: McBride CM. Blazing a trail: a public health research agenda in genomics and chronic disease. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/05_0008.htm
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2 Merikangas KR Risch N 10 24 302 2003 599 601 Science Genomic priorities and public health 14576422
3 Khoury MJ Little J Burke W Khoury MJ Little J Burke W 2004 Oxford University Press New York (NY) Human genome epidemiology: a scientific foundation for using genetic information to improve health and prevent disease Human genome epidemiology: scope and strategies
4 Rosenfield P Kessel F Kessel F Rosenfield PL Anderson NB New York (NY) Oxford University Press 2003 378 413 Expanding the boundaries of health and social science: case studies in interdisciplinary innovation Fostering interdisciplinary research: the way forward
5 Hay DA 2003 2 321 326 Genes Brain Behav Who should fund and control the direction of human behavior genetics? Review of Nuffield Council on Bioethics 2002 Report, genetics and human behaviour: the ethical context 14653302
6 Curry O Human Nature Review Evolutionary psychology: "fashionable ideology" or "new foundation"? 2003 81 92
7 Lerman C Shields AE 3 4 2004 235 241 Nat Rev Cancer Genetic testing for cancer susceptibility: the promise and the pitfall 14993905
8 Smith TW Orleans CT Jenkins CD 23 2 2004 126 131 Health Psychol Prevention and health promotion: decades of progress, new challenges, and an emerging agenda 15008655
9 Wideroff L Vadaparampil ST Breen N Croyle RT Freedman AN 2003 6 147 156 Community Genet Awareness of genetic testing for increased cancer risk in the year 2000 National Health Interview Survey 15237199
10 Institute of Medicine 2004 Health literacy: a prescription to end confusion Washington (DC) National Academies Press
11 Lohmueller KE Pearce CL Pike M Lander ES Hirschhorn JN 2 33 2003 177 182 Nat Genet Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease 12524541
12 Sankar P Cho MK Condit CM Hunt LM Koenig B Marshall P 291 24 6 2004 2985 2989 JAMA Genetic research and health disparities 15213210
13 McGinnis JM Williams-Russo P Knickman JR 21 2 2002 78 93 Health Aff (Millwood) The case for more active policy attention to health promotion 11900188
14 Moorman PG Skinner CS Evans JP Newman B Sorenson JR Calingaert B 13 8 2004 1349 1354 Cancer Epidemiol Biomarkers Prev Racial differences in enrolment in a cancer genetics registry 15298957
15 Glasgow RE Lichtenstein E Marcus AC 93 8 8 2003 1261 1267 Am J Public Health Why don't we see more translation of health promotion research practice? Rethinking the efficacy-to-effectiveness transition 12893608
16 Freund CL Clayton EW Wilfond BS 2004 32 106 110 J Law Med Ethics Natural settings trials — improving the introduction of clinical genetic tests 15152432
17 Newman S Steed L Mulligan K 23 2004 10 364 1523 1537 Lancet Self-management interventions for chronic illness 15500899
18 Yarnall KS Pollak KI Ostbye T Krause KM Michener JL 93 4 4 2003 635 641 Am J Public Health Primary care: is there enough time for prevention? 12660210
19 Phillips KA Van Bebber SL 5 8 2004 1139 1149 Pharmacogenomics A systematic review of cost-effectiveness analyses of pharmacogenomic interventions 15584880
20 Haga SB Khoury MJ Burke W 34 4 8 2003 347 350 Nat Genet Genomic profiling to promote a healthy lifestyle: not ready for prime time 12923535
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Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_05_0011
Editorial
Genomics and the Prevention and Control of Common Chronic Diseases: Emerging Priorities for Public Health Action
Khoury Muin J MD, PhD Office of Genomics and Disease Prevention, Coordinating Center for Health Promotion, Centers for Disease Control and Prevention
4770 Buford Hwy, Mail Stop E-89, Atlanta, GA 30341 [email protected]
770-488-8510
Mensah George A MD National Center for Chronic Disease Prevention and Health Promotion, Coordinating Center for Health Promotion, Centers for Disease Control and Prevention, Atlanta, Ga
4 2005
15 3 2005
2 2 A052005
==== Body
The completion of the Human Genome Project in 2003 continues to raise expectations on near-term applications of human genome discoveries in personalized disease prevention, especially in the area of common chronic diseases (1,2). In fact, almost daily we are confronted with stories of scientific discoveries of human genetic variants that are suggested to affect our risks for one or more of the major common chronic diseases. (See Table 1 for an illustrative sample of news stories published online during December 2004 [3].) Yet the immediate significance of most of these discoveries remains elusive. Despite the scientific excitement and the predictions for personalized prevention and drug treatment, the promise of human gene discovery for health promotion and disease prevention is yet to be fulfilled (4).
Increasingly, public health practitioners from academic, government, and other organizations have taken a proactive leadership role in assessing the relevance of this technology to population health and to community-based interventions (a new field often referred to as public health genetics, or genomics) (5). This issue of Preventing Chronic Disease contains several articles illustrating various processes developed and applied by schools of public health and state health departments to evaluate the role of genomics and its relevance to the prevention of chronic diseases in the population (6-11).
Johnson et al (6) demonstrate the feasibility and success of using family history as a simple genomic tool to inform and motivate high-risk families to make long-term lifestyle behavior changes for preventing a variety of chronic diseases. Annis et al (7) show that existing population-based databases contain valuable genomic information and can serve as a reliable source for chronic disease program recommendations for early detection, prevention, and risk assessment. Irwin et al (8) examine the genomic content of state Comprehensive Cancer Control programs and show that many states have genomic components in their written plans. Importantly, about 67% of programs that included family history in their plans have already begun implementing their stated goals. Harrison et al (9) describe a process for synthesizing genomics information and for sharing knowledge and lessons learned. Novel educational approaches, such as the one presented by Theisen et al (10), and innovative training tools, such as those highlighted by Bodzin et al (11), will be crucial in efforts to provide continuing education for the public and health care professionals. A central theme in all of these papers is the importance of family history as a tool for chronic disease prevention and health promotion.
Why should public health focus on family history in the genomics era?
In November 2004, the United States Surgeon General launched a public education campaign urging all citizens to know their family medical history and to discuss it with health care providers using an online family history collection tool (12). With all the excitement about genomics, one may wonder why we are still using an old-fashioned tool such as family history (13). Nonetheless, several basic facts about family history make it ideal for use in public health practice:
1) A history of one or more chronic diseases is common in the population, although awareness of the details and accurate reporting of the disease may be suboptimal.
2) Family history is the most consistent risk factor for almost all human diseases across the lifespan (14). Thus, the presence of a disease in a family, especially among first-degree relatives, increases the risk for that disease.
3) Family history reflects the complex interaction among many shared genes, shared behaviors, shared cultures, and shared environments among families, all of which could also be disease risk factors. In fact, only a small fraction of people with family history of a common chronic disease have a "genetic disease" (with high lifetime risk of disease). A total of 188 such diseases have been identified as of 2004, accounting for a relatively small burden of chronic disease (15). Most people with family history of a disease have a moderately increased risk of the disease.
4) Although family history cannot be changed, knowledge of it provides an opportunity to personalize and target our myriad disease prevention and health promotion messages (12,16).
5) Today, family history is the best genomic tool available, and compared with other genetic tests, it can be relatively inexpensively collected.
Although eliciting family history should be a routine component of patient medical records and encounters with health care providers, the completeness of such information and its use in practice are less than optimal. Despite the implications of family history for public health, the public's knowledge, attitudes, and behaviors related to family history have some way to go. In a recent national survey of 4345 adults, the Centers for Disease Control and Prevention (CDC) found that although most respondents (97%) considered knowledge of family history either very important or somewhat important to their personal health, a strikingly smaller proportion (30%) reported actively engaging in collection of information to develop a family health history (17). Routine collection and use of the family history by clinicians remains suboptimal. Acheson et al have shown that family history is discussed in only half of new visits to primary care physicians and 22% of follow-up visits. Also, the average duration of the family history discussion is only 2.5 minutes, focusing mostly on psychosocial and not health-related issues (18). More recently, Walter et al reviewed lay understanding of familial risk for common chronic diseases and how each person's sense of disease vulnerability depends not only on family history but also on personal models of disease causation and inheritance (19). These findings underscore the need for more public and health provider education to improve the effectiveness of using family history as a risk communication tool for personal health and disease prevention.
Despite the importance of family history for health promotion, our public health strategy in preventing common chronic diseases continues to be a one-size-fits-all approach to promoting healthy lifestyles in the population at large. Most people do not get enough physical activity, are overweight, and do not adhere to health screening recommendations. With development and validation of the right family history tools, public health can begin to develop, test, and apply personalized prevention messages. Because a large fraction of the population has a family history of one or more common diseases, augmenting and not replacing the population approach to prevention with an approach focused on higher-risk families may help achieve overall population health goals. For example, we know from population studies conducted in Utah that 14% of families have almost 72% of the burden of early heart disease (under 50 years of age) and 48% of the burden of all heart disease in the whole population (20). A distinct advantage of a family-centered approach to prevention is that it does not focus exclusively on genetic factors but works within an overall framework of biologic and sociocultural relationships to effect behavioral change and risk factor reduction. As discussed in this issue by Johnson et al (6), family history can begin to build a bridge between the one-size-fits-all approach to prevention and the one-person-at-a-time approach to genetics.
What are the emerging public health priorities in genomics?
Three emerging priorities for public health action in genomics highlighted in this issue of Preventing Chronic Disease are 1) the conduct and support of population-based research and databases in genomics and health; 2) the development of the evidence base for genomic applications in health promotion and disease prevention; and 3) the assurance of an adequate public health capacity in genomics. These public health genomics priorities provide a road map for long-term translation of human genome discoveries into chronic disease prevention and health promotion across the lifespan.
1) Conduct and support of population-based research and databases on genomics and health. Generally, additional public health research is needed to asses the impact of the thousands of genetic variants (and their interactions with modifiable risk factors) on the burden of chronic diseases (incidence and prevalence as well as morbidity, disability, and mortality). Although gene–disease associations continue to be investigated in the context of family studies, association studies using mostly case-control designs are becoming more common. As shown in Table 2 for a sample of common chronic diseases, the number of published epidemiologic articles on gene–disease association is increasing over time. These data are derived from the Genomics and Disease Prevention Information System (GDPInfo) (21), an online searchable and continuously updated information system developed by the CDC. Between October 1, 2000, and December 30, 2004, GDPInfo captured records of 13,858 published epidemiologic articles on genes and disease outcomes. The population-level implications of findings from many such studies are unclear. Often, the potential importance of a reported association is impossible to evaluate because basic population-based genotype prevalence data are not available. The CDC and many collaborators in the Human Genome Epidemiology Network (HuGE Net) are currently developing methodologic guidance and systematic reviews for integrating data from these studies and developing inference for research, policy, and practice (22).
Another example of the use of national surveys is the existing DNA repository containing specimens from more than 7000 participants in the second phase of National Health and Nutrition Examination Survey III (1991–1994). The CDC determined the prevalence of gene variants associated with hereditary hemochromatosis (23,24). The CDC in collaboration with the Juvenile Diabetes Research Foundation and others created the type 1 diabetes DNA repository to study genetic risk factors for sequelae of type 1 diabetes (25-27). Also, the CDC developed a DNA repository as part of the population-based National Birth Defects Prevention Survey (28).
In addition to national surveys, state public health programs can provide valuable population data to assess the impact of genes on the burden of chronic disease. An example of state-based data collection for chronic disease is the cancer registries that are a component of state-based comprehensive control programs (see Irwin et al in this issue). These registries can truly provide a population-level assessment of the impact of family history and individual genes on the burden of cancer in the United States.
2) Development of the evidence base for genomic applications in health promotion and disease prevention. Public health is beginning to evaluate the added value of using genetic tests as tools for disease prevention. For decades, public health practice has downplayed the "high risk" model of prevention in favor of a population approach, which may not benefit most individuals but can have a large impact on the burden of disease (29,30). For example, a small downward shift in mean serum cholesterol distribution could reduce the burden of coronary heart disease in the population more than treating people with high cholesterol levels, since most of the burden of heart disease occurs among persons with values in the normal range (29). As the discussion of family history illustrates, we can combine a high risk strategy with a population strategy to achieve overall public health goals. Nevertheless, the use of individual genes as risk factors for disease or the use of a combination of genetic variants along biologic pathways (so-called genomic profile) for testing for chronic disease susceptibility should be quantitatively evaluated for its potential impact on individuals and the population (31,32). In general, existing quantitative evaluations of combinations of genetic variants and their expressions are still in the theoretical realm and are based on many assumptions that have not been validated.
As in other areas of health practice, public health is becoming the convener of multidisciplinary deliberations needed to determine the role, if any, of genomics information, above and beyond a population approach to the prevention of chronic diseases (e.g., smoking control programs, diet, physical activity, early detection programs). The recent surge of direct-to-consumer marketing of genetic tests, such as genomic profiles for susceptibility to cardiovascular disease and bone health (33) and for testing for breast and ovarian cancer (34), will increasingly necessitate a public health response for building the evidence base for use of genomic applications in population health and for measuring the impact of test marketing on consumers' and providers' knowledge, attitudes, and behaviors.
For the past five years, the CDC and many collaborators have developed model approaches for obtaining and synthesizing available information on genetic tests. The Foundation for Blood Research developed key data elements needed for genetic tests by intended use and applied a model approach to five conditions (35). The CDC and many partners are currently exploring the development of a more sustainable public–private partnership process to summarize evidence and identify gaps in knowledge to stimulate further research. In addition, public health assessment will be needed to monitor current and future levels of use of genetic tests, as well as knowledge, attitudes, and behaviors of consumers and health care providers. A population-based approach in collecting valid clinical and laboratory data will ensure that consumers, practitioners, and policy makers have access to timely and current information on genetic tests and their impact on the public's health. These efforts will also allow a smoother integration of validated genetic tests into practice.
One example of a public health assessment in genomics is a 1997 expert panel workshop jointly held by the National Institutes of Health (NIH) and the CDC to explore issues on population screening for iron overload due to hereditary hemochromatosis (a small but preventable cause of multiple chronic diseases including heart disease, liver cancer, diabetes, and arthritis) (36). This collaboration led to the identification of important gaps in research, funding of further studies, and implementation of a nationwide physician training program that promotes early detection of hemochromatosis (37).
3) Assurance of an adequate public health capacity in genomics. Clearly, the integration of genomic information into practice and programs requires resources, a competent workforce, a robust public health system that can address health disparities, and an informed public. Educational and planning resources for public health genomics have been developed over the past three years (11). The Association of State and Territorial Health Officials' guide to genomics for public health practitioners (38) and the Web-based introduction to genomics (Six Weeks to Genomic Awareness) developed by the University of Michigan (39) are examples of the available resources. Another useful state policy guide for genomics and chronic diseases was developed by the Partnership for Prevention™, a public–private coalition focused on disease prevention and health promotion (40).
A 2003 report by the Institute of Medicine identified genomics as one of the eight crosscutting priorities for the training of all public health professionals in the 21st century (41). In October 2003, the Association of Schools of Public Health administered an online survey to representatives of all 33 accredited U.S. schools of public health. The survey provided a baseline assessment of the extent to which the schools were offering curriculum content in the eight areas recommended by the Institute of Medicine, including genomics (42). Although 52% of the schools offer courses in genomics, only 15% of the schools require genomics to be part of their core curriculum, clearly the lowest figure for all eight crosscutting areas (highest was policy with 79%), indicating a definite need for improved integration of genomics into public health education (42).
Over the past five years, the CDC has promoted the integration of genomics across all public health functions, including training and workforce development. In collaboration with many partners, the CDC hosted the development of public health workforce competencies in genomics (43), established three Centers for Genomics and Public Health at schools of public health to develop training (44), provided funding and technical assistance to state and local health departments (45), and actively engaged in offering training and career development opportunities in genomics and public health. As discussed by Dr Bill Roper, previous Dean of the School of Public Health at the University of North Carolina at Chapel Hill, at the CDC public health genomics symposium in May 2003, genomics is becoming an integral component of the concept of "public health preparedness" in the 21st century (46).
Conclusion
Although a nascent field, the application of genomics to chronic disease prevention and health promotion is already occurring at the state and local levels. Success in this endeavor will require building appropriate capacity and maintaining a skilled public health workforce competent in the evaluation and use of genomic information for preventing common chronic diseases. Of equal importance is the development of innovative tools and evidence-based processes that allow the differentiation between genomic technology that is ready for use in population health and technology that is not ready for prime time. Health policies need to be developed to ensure the appropriate use of genomic information for health promotion while avoiding psychosocial and financial harms to individuals and population groups. As exemplified in this issue of Preventing Chronic Disease, we believe that the continued leadership and collaboration among schools of public health, state health departments, and other public health partners will take us a long way to ensure that human genomic information will be used for preventing chronic diseases and promoting health in individuals, families, and communities.
Figures and Tables
Table 1 Examples of News Stories on Human Genome Discoveries Relevant to Common Chronic Diseasesa
Story Headline Source Date
Faulty gene signaling linked to Crohn's HealthDay News Dec 28, 2004
Genetic difference at opiate receptor gene affects a person's response to alcohol Medical News Today Dec 15, 2004
Important discovery of gene involved in breast and prostate cancer PR Newswire Dec 15, 2004
Mutant gene linked to treatment-resistant depression NIH news release Dec 14, 2004
Molecular test can predict both the risk of breast cancer recurrence and who will benefit from chemotherapy NCI news release Dec 10, 2004
Important genetic risk factor for amyotrophic lateral sclerosis News-Medical.Net Dec 6, 2004
Association of vitamin D receptor gene polymorphisms with childhood and adult asthma RedNova News Dec 2, 2004
a Web-based stories posted on the CDC Genomics and Health Weekly Update, December 2004. Available from http://www.cdc.gov/genomics/update/current.htm. NIH indicates National Institutes of Health; NCI indicates National Cancer Institute.
Table 2 Number of Gene–Disease Association Studies Reported in the Medical Literature, by Year and Disease, CDC Genomics and Disease Prevention Information System, 2001–2004a
Disease No. published articles
2001 2002 2003 2004
Coronary heart disease (and stroke) 197 205 262 238
Diabetes 128 154 156 165
Breast cancer 61 111 136 146
Colorectal cancer 53 55 46 70
Lung cancer 35 52 60 55
Alzheimer’s 88 100 111 145
Asthma 38 40 49 71
a Search was conducted on December 30, 2004, at http://www2a.cdc.gov/genomics/GDPQueryTool/frmQueryAdvPage.asp.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Khoury MJ, Mensah GA. Genomics and the prevention and control of common chronic diseases: emerging priorities for public health action. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/05_0011.htm
==== Refs
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4 Haga S Khoury MJ Burke W 2003 34 347 350 Nat Genet Genomic profiling to promote a healthy lifestyle: not ready for prime time 12923535
5 Khoury MJ Burke W Thomson E 2000 Genetics and public health in the 21st century: using genetic information to improve health and prevent disease New York Oxford University Press
6 Johnson J Giles RT Larsen L Ware J Adams T Hunt SC 2005 4 Prev Chronic Dis [serial online] Utah's Family High Risk Program: bridging the gap between genomics and public health
7 Annis AM Caulder MS Cook ML Duquette D 2005 4 Prev Chronic Dis [serial online] Associations among diabetes, family history, and other demographic and risk factors among participants of the National Health and Nutrition Examination Survey 1999–2002
8 Irwin DE Zuiker ES Rakhra-Burris T Millikan RC 2005 4 Prev Chronic Dis [serial online] Review of state Comprehensive Cancer Control plans for genomics content.
9 Harrison TA Burke W Edwards KL 2005 4 Prev Chronic Dis [serial online] The asthma consultative process: a collaborative approach to integrating genomics into public health practice
10 Theisen V Duquette D Kardia S Wang C Beene-Harris R Bach J 2005 4 Prev Chronic Dis [serial online] Blood pressure Sunday: introducing genomics to the community through family history
11 Bodzin J Kardia SLR Goldenberg A Raup SF Bach JV Citrin T 2005 4 Prev Chronic Dis [serial online] Genomics and public health: development of Web-based training tools for increasing genomic awareness
12 U.S. Department of Health and Human Services U.S. Surgeon General's family history initiative Washington (DC) U.S. Department of Health and Human Services Accessed 2004 Dec 30
13 Guttmacher AE Collins FS Carmona RH 2004 351 2333 2336 N Engl J Med The family history--more important than ever 15564550
14 Yoon PW Scheuner MT Khoury MJ 2003 24 128 135 Am J Prev Med Research priorities for evaluating family history in the prevention of common chronic diseases 12568818
15 Scheuner MT Yoon PW Khoury MJ 125 1 2 15 2004 50 65 Am J Med Genet C Semin Med Genet Contribution of Mendelian disorders to common chronic disease: opportunities for recognition, intervention, and prevention 14755434
16 Khoury MJ 2003 5 261 268 Genet Med Genetics and genomics in practice: the continuum from genetic disease to genetic information in health and disease 12865755
17 Yoon PW Scheuner MT Gwinn M Khoury MJ Jorgensen C Hariri S 2004 53 1044 1047 MMWR Awareness of family health history as a risk factor for disease --- United States, 2004
18 Acheson LS Wiesner GL Zyzanski SJ Goodwin MA Stange KC 2000 2 180 185 Genet Med Family history-taking in community family practice: implications for genetic screening 11256663
19 Walter FM Emery J Braithwaite D Marteau TM 2004 2 583 594 Ann Fam Med Lay understanding of familial risk of common chronic diseases: a systematic review and synthesis of qualitative research 15576545
20 Hunt SC Gwinn M Adams TD 2003 24 136 142 Am J Prev Med Family history assessment: strategies for prevention of cardiovascular disease 12568819
21 Centers for Disease Control and Prevention Genomics and disease prevention information system (GDPInfo) Accessed 2004 Dec 30 U.S. Department of Health and Human Services, Centers for Disease Control and Prevention Atlanta (GA)
22 Centers for Disease Control and Prevention The Human Genome Epidemiology Network (HuGENet) Atlanta (GA) U.S. Department of Health and Human Services, Centers for Disease Control and Prevntion Accessed 2004 Dec 30
23 Cogswell ME Gallagher ML Steinberg KK Caudill SP Looker AC Bowman BA 2003 5 304 310 Genet Med HFE genotype and transferrin saturation in the United States 12865759
24 Steinberg KK Cogswell ME Chang JC Caudill SP McQuillan GM Bowman BA 2001 285 2216 2222 JAMA The prevalence of C282Y and H63D mutations in the hemochromatosis (HFE) gene in the United States 11325323
25 (SK Cordovado, unpublished data, Tissue Antigens 2005) High-resolution genotyping of HLA-DQA1 exon 1, 2 and 3 in the GoKinD Study and the identification of novel alleles DQA1*040102, *0402 and *0404
26 Greene CN Cordovado SK Mueller PW 2004 65 737 744 Hum Immunol Polymorphism scan for differences between transmitted and nontransmitted DRB1 *030101 alleles outside of exon 2 for type 1 diabetes: the frequency of polymorphisms is similar 15301864
27 Cordovado SK Simone AE Mueller PW 2001 58 308 314 Tissue Antigens High-resolution sequence-based typing strategy for HLA-DQA1 using SSP-PCR and subsequent genotyping analysis with novel spreadsheet program 11844141
28 Yoon PW Rasmussen SA Lynberg MC Moore CA Anderka M Carmichael SL 2001 116 Suppl 1 32 40 Public Health Rep The National Birth Defects Prevention Study 11889273
29 Rose G 1985 14 32 38 Int J Epidemiol Sick individuals and sick populations 3872850
30 Rockhill B 2005 16 124 129 Epidemiology Theorizing about causes at the individual level while estimating effects at the population level: implications for prevention 15613957
31 Khoury MJ Yang Q Gwinn M Little J Dana Flanders W 2004 6 38 47 Genet Med An epidemiologic assessment of genomic profiling for measuring susceptibility to common diseases and targeting interventions 14726808
32 Wacholder S 1 16 2005 1 3 Epidemiology The impact of a prevention effort on the community 15613938
33 Vineis P Christiani DC 2004 15 3 5 Epidemiology Genetic testing for sale 14712140
34 Centers for Disease Control and Prevention 2004 53 603 606 MMWR Genetic testing for breast and ovarian cancer susceptibility: evaluating direct-to-consumer marketing --- Atlanta, Denver, Raleigh-Durham, and Seattle, 2003
35 Centers for Disease Control and Prevention Model System for collecting, analyzing and disseminating information on genetic tests Atlanta (GA) U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, Office of Genomics and Disease Prevention Accessed 2004 Dec 30
36 Burke W Thomson E Khoury MJ McDonnell SM Press N Adams PC 1998 280 172 178 JAMA Hereditary hemochromatosis: gene discovery and its implications for population-based screening 9669792
37 Reyes M Dunet D Blanck HM Grossniklaus D Genomics and population health, United States 2003 Hemochromatosis: information and resources for health care providers. Atlanta (GA) U.S. Department of Health and Human Services, Centers for Disease Control and Prevention Accessed 2004 Dec 30
38 Association of State and Territorial Health Officials Genomics: a guide for public health Accessed 2004 Dec 30 Washington (DC) Association of State and Territorial Health Officials Available from: URL:http://www.genomicstoolkit.org/index.shtml
39 University of Michigan Center for Genomics and Public Health Six weeks to genomics awareness (Webcast) Ann Arbor (MI) University of Michigan Center for Genomics and Public Health Accessed 2004 Dec 30 Available from: URL:http://www.genomicawareness.org/index.htm
40 Partnership for Prevention Harnessing genetics to prevent disease and improve health: a state policy guide 2003 Washington (DC) Partnership for Prevention Accessed 2004 Dec 30 Available from: URL:http://www.prevent.org/publications/GeneticsReport.pdf
41 Institute of Medicine Who will keep the public healthy? Educating public health professionals for the 21st century Bethesda (MD) National Academies Press 2003
42 Shortell SM Weist EM Sow MS Foster A Tahir R 2004 94 1671 1674 Am J Public Health Implementing the Institute of Medicine's recommended curriculum content in schools of public health: a baseline assessment 15451728
43 Centers for Disease Control and Prevention Genomics competencies for the public health workforce Atlanta (GA) U.S. Department of Health and Human Services, Centers for Disease Control and Prevention Accessed 2004 Dec 30
44 Centers for Disease Control and Prevention Centers for Genomics and Public Health [homepage on the Internet] Atlanta (GA) U.S. Department of Health and Human Services, Centers for Disease Control and Prevention Accessed 2004 Dec 30
45 Sing J Hutsell CA State capacity grants for integrating genomics into chronic disease prevention programs. In: Genomics and population health, United States 2003 Atlanta (GA) U.S. Department of Health and Human Services, Centers for Disease Control and Prevention Accessed 2004 Dec 30
46 Roper W Presentation at the Centers for Disease Control and Prevention's Public Health Genomics Symposium 2004 May 4 Atlanta (GA) Centers for Disease Control and Prevention
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Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_05_0017
Editorial
How Do We Ensure the Quality of the Public Health Workforce?
Thacker Stephen B MD, MSc Director Office of Workforce and Career Development, Centers for Disease Control and Prevention
Mail Stop E-94, Atlanta, GA 30333 [email protected]
404-498-6010
4 2005
15 3 2005
2 2 A062005
==== Body
The events of September 11, 2001, brought unprecedented attention to public health in the United States. The national response to these events included a large infusion of resources into the public health system that enhanced the capacity for the system to respond to terrorist threats and other public health emergencies. However, as illustrated by the emerging epidemics of obesity and diabetes in this country, a disproportionate burden of disease, death, and disability in this century will continue to be attributable to chronic disease. To address this burden effectively requires the development of a workforce with new skills in addition to maintenance of evolving traditional competencies. In 2002, the Institute of Medicine (IOM) published a report, Who Will Keep the Public Healthy?, that targeted the training needs of the public health workforce in this century (1). The IOM report included a recommendation for federal agencies to provide incentives for developing academic–practice partnerships.
Two contributions to this issue of Preventing Chronic Disease address this recommendation and offer excellent illustrations of the benefits of such partnerships. Franks et al also address a second IOM recommendation for developing curricula in emerging areas of public health practice; they share their work in social marketing, physical activity, and evidence-based public health (2). Bodzin et al address both recommendations as they develop competencies and curricula in genomics, a field that few people understand well but one that will probably redefine both clinical medicine and public health practice (3). These investigators recognize both the training needs resulting from new challenges in public health and the opportunities to adopt new strategies and technologies to reach public health workers who are thirsty for the knowledge and skills that will enable them to do their jobs effectively.
The need to identify, train, and support the public health workforce has been long recognized (4-6). Although the Centers for Disease Control and Prevention (CDC), along with several other organizations in and outside of government, acknowledge the workforce as the essential element in public health, support for training in public health has been inconsistent and poorly funded. The director of the CDC in 2001 declared that the first priority of the agency was to build the capacity of the public health workforce (7). Following an extended process during 2003 and 2004 that included input from outside partners and the public, the CDC formed the Office of Workforce and Career Development (OWCD) to address needs within the agency and nationally.
To identify the target population and determine its training needs, the CDC has supported efforts in government and academia to update and supplement previous assessments (8,9). The OWCD has a leadership role in environmental scanning of learning methods and of new needs and disciplines in public health, evaluation of competency-based training activities, and research and innovation in learning methods. The OWCD is conducting similar activities for the CDC workforce while working with partners to develop competencies and multiple methods to deliver training effectively. The office also serves as an innovator in approaches to targeted recruitment, leadership development, succession planning, and retention. Through this new office, the CDC will serve as a focal point for developing the national public health workforce, coordinating information, sharing cutting-edge learning tools, and advocating for more general recognition of the importance of training that is conducted and evaluated rigorously. An essential element of this national role will be to strengthen current academic, private, and public partnerships and establish new partnership networks in workforce development and learning.
The workforce is the engine that makes the public health system function. In the face of new challenges in terrorism, injury prevention, chronic disease, and emerging infections, public health researchers are identifying effective prevention tools, using new disciplines such as genomics and informatics. In the context of globalization and heightened expectations, the public health workforce is aging and understaffed. Still, this workforce must be trained appropriately and supplemented with new highly trained workers prepared to meet these challenges. Unfortunately, training continues to be underappreciated and poorly funded. It is our challenge to make workforce recruitment and development and succession planning respected and supported priorities at the CDC and in the nation. Innovative work, such as that presented in this issue, is important, but far more needs to done. For it is, indeed, our responsibility as public health workers to meet our assurance obligation and make the development of ourselves and our coworkers a national priority.
Author Information
Corresponding Author: Stephen B. Thacker, MD, MSc, Director, Office of Workforce and Career Development, Centers for Disease Control and Prevention, Mail Stop E-94, Atlanta, GA 30333. Telephone: 404-498-6010. E-mail: [email protected].
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Thacker SB. How do we ensure the quality of the public health workforce? Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/05_0017.htm
==== Refs
1 Gebbie KM Rosenstock L Hernandez LM 2003 Who will keep the public healthy? Educating public health professionals for the 21st century Washington (DC) National Academies Press
2 Franks AL Brownson RC Bryant C Brown KM Hooker SP Pluto DM Prevention research centers: contributions to updating the public health workforce through training Prev Chronic Dis [serial online] 2005 4
3 Bodzin J Kardia SLR Goldenberg A Raup SF Bach JV Citrin T Prev Chronic Dis [serial online] 2005 4 Genomics and public health: development of Web-based training tools for increasing genomic awareness
4 U.S. Department of Health and Human Services, U.S. Public Health Service The public health workforce: an agenda for the 21st century, a report of the Public Health Functions Project Washington (DC) U.S. Government Printing Office 1997
5 Potter MA Pistella CL Fertman CI Dato VM 2000 90 1294 1296 Am J Public Health Needs assessment and a model agenda for training the public health workforce 10937012
6 Gebbie KM 1999 89 660 661 Am J Public Health The public health workforce: key to public health infrastructure 10224974
7 Koplan JP cited 2005 Jan 29 Centers for Disease Control and Prevention Atlanta (GA) Building infrastructure to protect the public's health: a public health training network broadcast sponsored by the Association of State and Territorial Health Officials in partnership with HHS, CDC, HRSA, and FDA
8 Centers for Disease Control and Prevention 1992 41 221 223 MMWR Leadership development survey of state health officers — United States, 1988
9 Council of State and Territorial Epidemiologists (CSTE) 2004 2004 national assessment of epidemiologic capacity: findings and recommendations Atlanta (GA) CSTE Available from: URL:www.cste.org
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Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_05_0002
Editorial
Reducing Health Disparities: What Is Being Done, What Works
FEATURED ABSTRACTS FROM THE 19th NATIONAL CONFERENCE ON CHRONIC DISEASE PREVENTION AND CONTROL
Baldyga William DrPH, MA Associate Director Institute for Health Research and Policy (MC 275)
1747 W Roosevelt Rd, Room 572, Chicago, IL 60608 [email protected]
312-996-0786
Petersmarck Karen PhD, MPH Michigan Department of Community Health, Lansing, Mich
4 2005
15 3 2005
2 2 A072005
==== Body
If necessity is the mother of invention, creativity in public health has never been more important. Fortunately, the ability of the public health thinkforce (as compared to workforce) to respond to the challenges inherent in assuring the public's health is remarkable. The willingness of the thinkers to share information has always been a strength of the field, and now new technologies have enhanced our abilities to communicate what works, for whom, and under what conditions.
Challenges to population health continue to mount. Risk factor increases (e.g., obesity) and poorer access to services (e.g., percentage of population without insurance) conspire with multiple other health determinants to create monumental challenges for public health, particularly in the area of health disparities. Understanding disparities — their roots and their implications — is a difficult challenge; our future success will be largely determined by our response to this challenge. Correcting disparities will require, in part, the best application of chronic disease program knowledge to the populations at greatest risk.
The planners of the 19th National Conference on Chronic Disease Prevention and Control invited state and federal public health leadership, academic researchers, and others to think about solutions for the disparities that exist and continue despite our efforts. Some of the most impressive responses to that invitation appear here. Creativity and curiosity are features of the work presented in this section, and, it is hoped, they will spark those virtues in the readership.
One year ago, shortly after the launch of Preventing Chronic Disease, the decision was made to incorporate the best abstracts from the annual Chronic Disease Conference as a regular feature. The abstracts capture the field of public health now, offering a glimpse of what is being done, what works, and how it does so. They come from state and local chronic disease prevention programs and the academic community, including the Prevention Research Centers. Furthermore, they reflect the seven conference tracks: Partnerships; Evidence-based Programs: Research, Translation, and Evaluation; Health System Change; Social Determinants of Health Inequities; Communications and Technology; Methods and Surveillance; and Policy and Legal. These abstracts represent some of the best current work in the field of public health.
Two years ago, those not participating in the conference would be hard pressed, by both time and access, to find this information. Today, this issue of Preventing Chronic Disease brings the information to your desktop. The abstracts can inform you, challenge you, and connect you to colleagues who share your interests. You are invited to take advantage of this opportunity and to react with your own ideas — those that best meet the needs of your communities.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Baldyga W, Petersmarck K. Reducing health disparities: what is being done, what works. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/05_0002.htm
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Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_04_0128
Original Research
PEER REVIEWEDReview of State Comprehensive Cancer Control Plans for Genomics Content
Irwin Debra E PhD, MSPH Research Assistant Professor North Carolina Center for Genomics and Public Health, Department of Epidemiology, School of Public Health
CB 7435, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 [email protected]
919-218-3612
Shaughnessy Zuiker Erin MPH Research Associate North Carolina Center for Genomics and Public Health, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
Rakhra-Burris Tejinder MA Project Director North Carolina Center for Genomics and Public Health, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
Millikan Robert C DVM, PhD Associate Professor Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
4 2005
15 3 2005
2 2 A082005
Introduction
The goals of this study were to determine U.S. states with Comprehensive Cancer Control plans that include genomics in some capacity and to review successes with and barriers to implementation of genomics-related cancer control initiatives.
Methods
This study was conducted in two phases. Phase one included a content analysis of written state Comprehensive Cancer Control plans (n = 30) for terms related to genomics, or "genomic components" (n = 18). The second phase involved telephone interviews with the Comprehensive Cancer Control plan coordinators in states with plans that contained genomic components (n = 16). The interview was designed to gather more detailed information about the genomics-related initiatives within the state's Comprehensive Cancer Control plan and the successes with and barriers to plan implementation, as defined by each state.
Results
Eighteen of the 30 Comprehensive Cancer Control plans analyzed contained genomics components. We noted a large variability among these 18 plans in the types of genomics components included. Nine (56%) of the 16 states interviewed had begun to implement the genomics components in their plan. Most states emphasized educating health care providers and the public about the role of genomics in cancer control. Many states consider awareness of family history to be an important aspect of their Comprehensive Cancer Control plan. Approximately 67% of states with family history components in their plans had begun to implement these goals. Virtually all states reported they would benefit from additional training in cancer genetics and general public health genomics.
Conclusion
The number of states incorporating genomics into their Comprehensive Cancer Control plans is increasing. Family history is a public health application of genomics that could be implemented more fully into Comprehensive Cancer Control plans.
==== Body
Introduction
Comprehensive Cancer Control (CCC) is an emerging public health model that seeks to bring together public and private stakeholders to efficiently use limited resources to reduce the burden of cancer. The CCC program allows states and territories to facilitate their own partnerships to address their unique cancer burdens. CCC results in many benefits, including increased efficiency for delivering public health messages and services to the public. The Centers for Disease Control and Prevention's (CDC's) National CCC Program (NCCCP) is a resource for supporting CCC efforts. Since 1998, the number of programs participating in NCCCP has grown from six to 61 (1). With this support, state, tribal, and territorial health agencies continue to establish broad-based CCC coalitions, to assess the burden of cancer, to determine priorities for cancer prevention and control, and to develop and implement CCC plans. State planners play a large and important role in CCC programs as the cancer burden increases for the population and advances in cancer genomics continue to challenge public health specialists.
For public health purposes, genetics may be defined as the "study of single gene hereditability," whereas genomics is the study of functions and interactions of all the genes in the genome, including their interactions with environmental factors (2). It is estimated that 5% to 10% of cancer is caused by autosomal dominant inherited genetic changes, such as BRCA1 and BRCA2 mutations in breast and ovarian cancer (3). Family history of cancer in a first-degree relative has been shown to confer an increased cancer risk (e.g., the relative risk of breast cancer conferred by a first-degree relative with breast cancer is 2.1) (4). Individuals who may have a genetic susceptibility because of cancer in their family can be distinguished from individuals in the general population by the relatively straightforward process of taking a family history. The American Society of Clinical Oncology supports integrating cancer risk assessment and management, including genetic testing for cancer predisposition genes, into the practice of oncology and preventive medicine (5).
The states are committed to reducing the burden of cancer among their populations, and the emerging contribution of genetics and genomics to the field of cancer control cannot be ignored. The goals of this study were to determine U.S. states that include genomics in some capacity in their CCC plans and to review the successes with and barriers to implementation of these genomics-related state cancer control initiatives.
Methods
This study was conducted in two phases. The first phase was a content review of written state cancer control plans. In collaboration with the CDC, the North Carolina Center for Genomics and Public Health (NCCGPH) identified state CCC plans funded by the CDC from 1997 to 2004. Each plan was searched for the words "genetics," "genomics," "genes," "family history," "DNA," "first-degree relative," and "heritability." The search terms were identical to those chosen by the CDC for an earlier content analysis. These search terms were used to create a comprehensive list of potential genomics-related topics found within the state plans. Throughout this document, these terms will be referred to as "genomic components."
Several themes from among the genomic components were detected across plans, and these were tabulated. A report was written summarizing overall themes, supplying standardized definitions of genomic components, and detailing genomic components found in each state's CCC plan.
Once the written CCC plans were reviewed, five topic areas were identified as areas needing more information to provide a more complete picture of the genomic components within the plans. The five topic areas were 1) the CCC plan writing process; 2) successes with and barriers to implementation; 3) general public and health care provider education programs that may have been implemented; 4) priority of genomics in the state health department; and 5) additional partnerships, training, and technical assistance that would be useful for CCC coordinators, coalitions, and state cancer control planners. NCCGPH staff, in consultation with the CDC, developed a telephone interview to gather additional information on these topics. The Institutional Review Board of the University of North Carolina approved the interview component of the study.
The second phase of the study involved telephone interviews with the CCC plan coordinators in states with genomic components in their cancer control plans. Sixteen of the 18 states agreed to be interviewed. The summary report from the content review of the written CCC plans (Phase 1) was sent to all 16 of the state CCC coordinators for their review prior to their scheduled interview. At the beginning of the interview, the CCC coordinators were asked to verify that the summary of genomic components for their state was accurate and complete. No changes or additions were made by any of the states interviewed. All interviews were audiotaped and transcribed, and copies of the transcriptions were sent back to each state for quality control purposes and their final approval.
The interview used a semistructured questionnaire (Appendix) and gathered information about only the genomic components within the CCC plan. (The other elements of the CCC plans were not discussed in the interview.) The questions addressed the five topics listed above. Each state was allowed to determine if implementation of the "genomic components" had begun based on the context of their state plan. In addition, standardized definitions of "success and barriers" were not imposed; each state was allowed to determine success based on its plan and goals.
Results
Summary of written Comprehensive Cancer Control plans
Of the 30 CCC plans analyzed, 18 contained genomic components. Among these 18 plans, we found large variability in the types of genomic components included. Table 1 summarizes the frequency of the main themes among the CCC plans. Most states used the terms "genetics" or "family history," while only one state referred to "genomics" in the written CCC plan. Half of the states intended to monitor advances in the cancer genomics field by publishing new information through their in-house newsletters, convening advisory panels, working with statewide experts in the field, and providing professional educational programs. Slightly more than one quarter of the states discussed gene–environment interactions in any context. Gene–environment interactions were discussed under a variety of topics; for example, variations seen in incidence rates among racial and ethnic groups for certain cancers, genetic research studies on various nutrients, and the relationship between inherited susceptibility and environmental factors for some cancers.
One theme, education, consistently presented itself in two forms: 1) increasing awareness about genomics among health care providers, and 2) providing education about genomics and its role in cancer control to patients and the general public. Approximately 44% of the CCC plans targeted education of health care providers and the public to promote early screening for those individuals identified at higher risk of cancer based on family history (data not shown). Also, one third of plans (33%) mentioned training health care professionals in the use of cancer risk assessment, including the use of family history tools.
Summary of interviews
Nine out of the 16 states interviewed had begun implementation of the genomic components of their plan at the time of our interview (Table 2). All of these states were funded through implementation-type grants. States that reported initiation of implementation projects did so largely through educational forums or seminars, presentations at professional meetings, publication and distribution of fact sheets on specific cancers, and public service announcements (PSAs) that included issues of family history. Only two states reported that genomics was somewhat not a priority within their state health department (Table 2). Many states (43.7%) reported implementing education efforts aimed at health care providers, and 25% of the states reported providing some form of public education about genomics. Educational efforts have been accomplished mainly through holding open meetings and seminars, attending public health fairs, publishing fact sheets, issuing PSAs, and developing Web sites (Table 2).
Six states (data not shown) discussed implementation of their objectives to educate the public about family history. Initiatives included developing fact sheets, brochures, and Web sites discussing individual cancers and the role that family history plays as an important risk factor to consider when assessing cancer risk and the need for early screening. Some of these states held forums for health care professionals to discuss the importance of family history as a tool in assessing cancer risk. One state has convened a panel and developed a pilot to use the state cancer registry to help identify families at high risk for cancer development.
The primary reasons cited for successful implementation of genomic components within the state CCC plans were the following: 1) establishing strong partnerships within the state; 2) obtaining additional funding for implementation; and 3) making genomics a high priority within the state health department (Table 3). The types of partnerships varied and included private industry, major medical centers within the state, public research institutions, and universities. Many of the advisory committees had members who convened an array of partners within the state. Funding was obtained from national and local organizations, private industry, academic institutions, and other public resources. As expected, lack of additional funding and competing priorities were the major reasons cited as barriers to successful implementation (Table 3).
Virtually all of the states interviewed indicated that they would welcome additional training (n = 14) and/or technical assistance (n = 6) in genomics. States requesting additional training preferred some level of interpersonal interaction (100%), with the essential component being a live person to field questions, whether that be via phone, video, the Internet, or a face-to-face training session.
A basic public health genomics course was requested by 12 states, with topics including 1) the definition of genetics vs genomics; 2) risk assessment and family history issues; 3) proteomics; and 4) gene–environment interaction (Table 4). Three states requested training in the ethical, legal, and social issues (ELSI) of cancer genomics (Table 4). And six states requested a template or "how to" guide for implementing genomics issues into cancer control (Table 4). In addition to training requests, six states requested technical assistance; five of these six states requested program planning, implementation, and evaluation services (Table 4).
Discussion
As expected, we noted a great deal of inconsistency in both the overall content and the level of detail within specific action plans. It is important to note that the dates and coverage of the plans range from 1997 to 2008. The more recently published plans have more extensive genomics content. For example, early plans (published in 1997 or 1998) do not include a section on breast cancer and genetic testing for susceptibility. The primary genomic components within these earlier plans are related to family history as a cancer risk factor. Conversely, plans published after 2000 provide more information on genetic testing for inherited breast cancer susceptibility (BRCA1 and BRCA2 genes) as well as brief discussions of familial risk assessment.
Individuals involved in writing the plans and the process that each state underwent to write the plans may also have contributed to the variability in genomic components seen among the CCC plans. Some of the individuals interviewed for this review were not on staff at the time the plans were written and could only provide limited information regarding the process. However, all of the states used a collaborative writing process, involving several individuals with varying expertise who came together to draft the plans. In addition, the individuals interviewed may not be aware of all the programs that are ongoing within their state, so these results may reflect a subset of genomics-related activities within the state health department.
Nine of the 16 states we interviewed had begun to implement genomics-related projects within their CCC plan. Implementation was not strictly defined for the states, but instead states were allowed to determine whether or not implementation had begun based on the context of their state plan. The states were given the opportunity to define "successful" for the context of their program. Similarly, a standardized definition of "success and barriers" was not imposed. Hence, there is most likely variability in the interpretation of these terms.
Some of the state implementation projects were specifically designed to address genomics-related goals and objectives within the CCC plan, while other states have chosen to implement broader programs to address genomics-related CCC components as well as other CCC plan goals. These implementation projects varied greatly among the states and included such activities as creating Web sites and fact sheets and developing innovative public and health care provider educational programs. For example, one state trained local barbers in an ethnic community as "lay educators" to promote prostate cancer awareness, including risk from family history. Using the barbershop as a gathering place, the lay educators provided literature and information to clientele about the importance of early screening and family history risk. A video was also created to play in the barbershop to provide more information about prostate cancer.
It is interesting to note that only one of the plans reviewed actually used the term "genomics"; the others used the term "genetics." The one plan that used the term "genomics" was recently updated, reflecting the fact that genomics is a relatively new concept. The understanding of and use of this term may not yet be fully incorporated into public health practice. In addition, because "genomics" is new terminology, some states may have chosen not to use the term in order to make their plans more reader friendly.
Education is a theme that consistently presented itself in two forms: 1) increasing awareness about genomics among legislators and health care providers, and 2) providing education about genomics and its role in cancer control to patients, providers, and the general public. All of the plans discuss educating the public about early screening and prevention, specifically for breast, ovarian, colon, and prostate cancers. Most plans discuss the emerging field of cancer genetics, and all of the plans mention the need to monitor ongoing research and advances within the field. Educational programs were implemented as part of ongoing seminars or as stand-alone events, including fairs, athletic events, and social hours. Their success was reported as being largely dependent on aspects of their presentation (appropriate topics, dynamic speakers), timing (appropriate length for the event) and successful advertising.
The emphasis placed on raising awareness and educating health care providers and the public may reflect the time at which the plans were written, which was still early in the process of integrating genetics into public health cancer control efforts. This result seems appropriate given the early stages of the field of cancer genomics and available public health applications at the time of publication. Also, there were few commercially available tests for cancer genes that showed a significant public health benefit at the time these state plans were developed.
Using family history as a risk assessment tool is an important component within cancer genetics and one of the most amenable public health applications of genomics at this time (5-8). Genetic testing should be offered when an individual has a family history suggesting a genetic cancer susceptibility condition (5). Several states simply mention that family history is a risk factor for specific cancers, such as breast, colon, and prostate cancer. Other states dedicate entire sections to family history and call for educating providers about its use in cancer risk assessment and training them to detect patterns of inheritance and differentiating hereditary syndromes. Family history is a public health application of genomics that could be implemented more fully into CCC plans through awareness and education efforts.
In its 2003 annual report, the CDC identified the premature commercialization of genetic tests — before safety, efficacy, and cost-effectiveness had been established — as one of the key issues in genetic testing (9). The year 2003 brought the first direct-to-consumer advertising for an inherited breast and ovarian cancer susceptibility genetic test (BRCA1 and BRCA2). Given this recent development, it is not surprising that none of the reviewed plans discussed the impact of commercialization of genetic testing and direct-to-consumer marketing for genetic susceptibility tests. As technology advances and more tests are available to the public, there will likely be an increase in this type of marketing activity by commercial entities. This development highlights the increasing importance of providing education about informed uses of genetic testing as it relates to cancer.
Some states (approximately one third) identified reasons for success in implementation of the genomic components of their state plans. Predominantly, these included securing adequate funding, developing excellent partnerships, and having genetics deemed a high priority within the state health department. Two states noted that the resources within the states, such as having staff dedicated to public health genomics, increased the likelihood of successful implementation. As expected, the primary barriers to successful implementation of the genomic components were lack of funding and competing priorities. Almost 90% of states (14/16) interviewed were interested in obtaining additional genomics-related training and/or technical assistance.
In summary, the number of states incorporating genomic components into their CCC plans is increasing. These states are beginning to implement these objectives. Periodic reviews of the successes and barriers related to implementation of genomic components should continue so as to document progress and share the lessons learned from these experiences.
We thank Melanie Myers, Phyllis Rochester, and Nikki Hayes for their valuable contributions to this work. The Center for Genomics and Public Health at the University of North Carolina was supported by a cooperative agreement from the CDC through the Association of Schools of Public Health, Grant U36/CCU300430-22. The contents of this document are solely the responsibility of the authors and do not necessarily represent the official views of the CDC or ASPH.
Appendix: Questions for State CCC Plan Interviews
Have you had a chance to read through the fact sheet we mailed to you?
_______no (read through and review contents of the fact sheet)
_______yes
Do you have any questions at this point?
_____no
_____yes (resolve/answer questions)
Do you agree to participate in this study?
_____no (stop interview)
_____yes (continue with interview)
Thank you. On behalf of NCCGPH, we are grateful that you have agreed to participate in this study of genomics content in your state’s Comprehensive Cancer Control plan. This interview should take about 45 minutes of your time. I would like to remind you that the following questions refer to the genomics components of your CCC plan and not to other activities. Upon completion of the interview, a written summary of your interview will be sent to you for verification and approval.
Do I have your permission to audiotape this interview to facilitate accurate recording of your answers?
_____no (do not turn on the tape recorder)
_____yes
Let’s begin.
Have you had a chance to review the materials we sent you about your cancer plan?
_____no (reschedule the interview)
_____yes
If yes, are there any sections in your plan relating to genetics/genomics that were missed or that are inaccurate in our review? If so, please describe these sections.
Do you have any additional comments regarding the review?
What type of grant does your state have for CCC development?
_____planning
_____implementation
_____other (specify ______________________)
Who was involved in writing the genetics/genomics sections of your CCC plan?
What is their background as it relates to genetics?
How were these individuals selected?
How long did the writing process take for the genetic sections?
What was the general process/procedure used to write the genetic sections?
To date, have you had the opportunity to implement the genetic components within your action plans?
_____no (go to 6)
_____yes
If yes, what has been accomplished thus far?
If you have begun to implement the genetic components detailed in your state plan, how have you been successful in your implementation? Please explain.
If you have begun to implement the genetic components detailed in your state plan, have you encountered any barriers?
_____no (go to 7)
_____yes
If yes, please explain.
Have you provided any activities or programs to educate the public about genetics and cancer?
_____no (go to 8)
_____yes
If yes, please list or describe these activities or programs.
Using the following 4-point scale where 1 = received positively, 2 = somewhat positively, 3 = somewhat not positively, or 4 = not positively at all, how have the activities or programs been received? Received positively, somewhat positively, somewhat not positively, or not positively at all?
Why do you think these results were obtained?
Have you provided any activities or programs to educate health care providers about genetics and cancer?
_____no (go to 9)
_____yes
If yes, please list or describe these activities or programs.
On the same 4-point scale, how have these activities or programs been received: received positively, somewhat positively, somewhat not positively, or not positively at all?
Why do you think these results were obtained?
Please rank genomics in terms of priority level for your state health department on the 4-point scale, where 1 = high priority, 2 = somewhat a priority, 3 = somewhat not a priority and 4 = not a priority. Is genomics a priority, somewhat of a priority, somewhat not a priority, or not a priority?
Explain your answer.
Have you begun the process of drafting a subsequent CCC plan?
_____no (go to 11)
_____yes
If yes, has the genomics components changed?
_____no
_____yes
_____not sure — plan not completed
Why or why not?
What is the planned date of issue for the new CCC plan?
Is it an implementation grant?
_____yes
_____no (if no, specify what the type)
Does your state have genetic nondiscrimination legislation in place currently?
_____no
_____yes
_____don’t know
As you move forward, what types of partnerships would be helpful to you in implementing the genomics components of your state plan?
Do you or your staff need additional training in genomics to assist in implementation of the genetic components of your state plan?
_____no (go to 14)
_____yes
_____not sure
If yes, what specific topics would you need training in to assist in implementation of the genetic components of your state plan?
Which of the following training formats would you most prefer?
_____Interactive videoconference
_____CD-ROM (interactive)
_____Phone conference call
_____Internet accessible
_____Standard videotape (not interactive)
Are you aware of the currently funded resources available to you through the three national genomic centers funded by the CDC?
_____yes (end interview)
_____no
_____not sure
If not, interviewer should explain services.
Would these types of services be of use to you?
_____no (end interview)
_____yes
_____not sure
What type of partnership with the genomic centers would be the most feasible?
Follow up with asking specifics that they would like or need help with.
Thank you very much for your time today. Do you have any questions for me? I’ll be sending you a summary of our interview for you to review and approve. Where should I send this?
Record Address:
_______________________________________
Interviewer Signature
_______________________________________
Figures and Tables
Table 1 Summary of Genomics-related Themes Among 18 State Comprehensive Cancer Control (CCC) Plansa
Theme No. Plans (%)
Used term “genomics”a 1 (5.6)
Used term “genetics”b 17 (94.4)
Discussed “family history”c 17 (94.4)
Discussed training health care professionals to use family history for cancer risk assessmentd 6 (33.3)
Discussed providing general education for health care providerse 10 (55.6)
Discussed providing general education for publicf 8 (44.4)
Discussed monitoring ongoing research and advancesg 9 (50.0)
Discussed gene–environment interactionsh 5 (27.8)
a Plan mentioned “genomics” in any context.
b Plan used the term “genetic” or “genetics” in any context.
c Plan discussed “family history” as related to cancer, most often stated as a risk factor for cancer.
d Plan specifically discussed training any health care providers to use family history as a tool for assessing risk.
e Plan mentioned providing some form of education for any type of health care providers about cancer genetics.
f Plan mentioned providing some form of general education for the public about cancer genetics, genetic predisposition for cancer, or the importance of genetic markers.
g Plan mentioned monitoring ongoing research/advances in cancer genetics, the field of genetics, or ethical, legal, and social implications of cancer genetics.
h Plan mentioned gene–environment interactions as related to cancer.
Table 2 Profile of 16 State Comprehensive Cancer Control (CCC) Plans with Genomics Components
CCC Plan No. Plans (%)
Type of Grant
Planning 3 (18.8)
Implementation 13 (81.2)
Have begun to put genomic components of plan into action 9 (56.2)
Have begun to draft a new CCC plan 9 (56.2)
Provided general public education about genomics 4 (25.0)
Provided health care provider education about genomics 7 (43.8)
Perceived priority level of genomics within the state health department
High priority 5 (31.2)
Somewhat a priority 9 (56.2)
Somewhat not a priority 2 (12.5)
Not a priority 0 (0)
Table 3 Identified Successes With and Barriers to Implementation of State Comprehensive Cancer Control (CCC) Plansa
Successes No. Plans (%) Barriers No. Plans (%)
Excellent partnerships and strong research/medical community in state 5 (31.2) Lack sufficient funding 6 (37.5)
High priority 3 (18.8) Misperceptions/ misinformation among public about genomics 4 (25.0)
Genetic counselor on staff 2 (12.5) Lack of sufficient staff/leadership 3 (18.8)
Additional funding sources 2 (12.5) Low priority 3 (18.8)
Provide continuing credits for professionals (i.e., CMEs, CEUs) 2 (12.5) Time constraints 2 (12.5)
Lack model/template to apply genetics 2 (12.5)
a A state could report more than one success and/or barrier; percentages are based on 16 plans.
Table 4 Training and Technical Assistance Needs in Genomics of 16 States with Genomics Components of Comprehensive Cancer Control Plan
No. (%)
States requesting training 14 (87.5)
Preferred topicsa
Basic public health genomics concepts 11 (68.8)
Genomics vs genetics defined 4 (25.0)
ELSIb 3 (18.8)
“How to” guide for implementation of genomics components 6 (37.5)
States requesting technical assistance 6 (37.5)
Program planning, implementation, and evaluation services 5 (31.3)
a States may have responded with more than one training topic; percentages are based on 16 state plans.
b ELSI = ethical, legal, and social implications.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Irwin DE, Zuiker ES, Rakhra-Burris T, Millikan RC. Review of state Comprehensive Cancer Control plans for genomics content. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0128.htm
==== Refs
1 Centers for Disease Control and Prevention National Comprehensive Cancer Control Program Atlanta (GA) U.S. Department of Health and Human Services, Centers for Disease Control and Prevention cited 2004 Nov
2 World Health Organization WHO definitions of genetics and genomics Geneva (Switzerland) World Health Organization cited 2004 Nov
3 National Cancer Institute Genetics of breast and ovarian cancer - general information Bethesda (MD) U.S. National Institutes of Health, National Cancer Institute cited 2005 Jan
4 National Cancer Institute Genetics of breast and ovarian cancer - family history as a risk factor for breast cancer Bethesda (MD) U.S. National Institutes of Health, National Cancer Institute cited 2005 Jan
5 American Society of Clinical Oncology Genetic testing Alexandria (VA) American Society of Clinical Oncology cited 2004 Nov Available from: URL: http://www.asco.org/ac/1,1003,_12-002151-00_18-0011612-00_19-00-00_20-001,00.asp
6 Khoury MJ 2003 5 261 268 Gent Med Genetics and genomics in practice: the continuum from genetic disease to genetic information in health and disease
7 Yoon PW Scheuner MT Khoury MJ Am J Prev Med Research priorities for evaluating family history in the prevention of common chronic disease 24 2 128 135 12568818
8 Yoon PW Scheuner MT Peterson-Oehlke KL Gwinn M Faucett A Khoury MJ 4 4 2002 304 310 Genet Med Can family history be used as a tool for public health and preventive medicine? 12172397
9 Centers for Disease Control and Prevention Genomics and population health: United States 2003 Atlanta (GA) U.S. Department of Health and Human Services, Centers for Disease Control and Prevention cited 2004 Nov
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Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_04_0049
Original Research
PEER REVIEWEDAdherence to Oral Hypoglycemic Agents in Hawaii
Lee Rachel John A. Burns School of Medicine
2015 Mott-Smith Dr, Honolulu, HI 96822 [email protected]
808-533-3125
Taira Deborah A ScD John A. Burns School of Medicine, Honolulu, Hawaii, Hawaii Medical Service Association
4 2005
15 3 2005
2 2 A092005
Introduction
Adherence to oral hypoglycemic agents is essential to reducing the poor health outcomes of populations at high risk for developing diabetes and its chronic complications. The goal of this study was to identify characteristics of patients in Hawaii least likely to adhere to oral hypoglycemic agents.
Methods
This retrospective administrative data analysis included prescription refill claims for oral hypoglycemic agents from January 1, 1999, through June 30, 2003 (n = 20,685). Multivariate logistic regression analysis was used to examine the relationship between adherence and patient characteristics.
Results
Adherence was found to be strongly associated with age and ethnicity. Relative to the age subset 55 to 64 years, adherence increased as age increased, reaching a peak at age 74 (odds ratio [OR] 1.1; 95% confidence interval [CI], 1.0–1.20). Past the age of 85, adherence declined (OR 0.90; 95% CI, 0.82–0.98). Relative to white patients, the odds ratio of adherence was highest for Japanese patients (OR 1.20; 95% CI, 1.0–1.30) and lowest for Filipino patients (OR 0.78; 95% CI, 0.68–0.90). Gender was not associated with adherence.
Conclusion
Differences in adherence to oral hypoglycemic agents were found to be related to ethnicity and age. Adherence was found to be lowest in younger patients and Filipino patients. This is a significant finding considering that younger diabetic patients have been shown to have the poorest glycemic control and worst health outcomes. Although the literature on adherence to oral hypoglycemic agents and health outcomes in Filipino patients is limited, studies support an increased risk for developing diabetes in this group. This information can be used to target younger patients and Filipino patients to improve their adherence to oral hypoglycemic agents.
==== Body
Introduction
Patient adherence to a prescribed regimen of oral hypoglycemic agents to prevent diabetes is generally low and difficult to maintain, even in populations with adequate access to health care and drug coverage (1,2). This problem poses serious consequences for Asian-Pacific Islanders, who have a higher genetic predisposition than whites for developing diabetes and its chronic complications (3-6). The Asian-Pacific Islander population in Hawaii is large and heterogeneous, composed of individuals of Japanese, Chinese, Filipino, Korean, and Hawaiian ancestry. Comparing the different ethnic groups in Hawaii that compose the Asian-Pacific Islander category reveals significant health disparities among them. Diabetes has been found to be three to seven times more prevalent in Hawaiians and three to four times more prevalent in Filipinos and Japanese than whites (7). In addition, Hawaiians have the highest prevalence of diabetes reported for any Polynesian or part-Polynesian group, and mixed ancestry has not been shown to diminish the risk for type 2 diabetes (8,9). Native Hawaiians on average have less education and lower income and are more likely to live in rural areas with less access to medical services than other ethnic groups living in Hawaii. These aspects of the Native Hawaiian population further elevate its risk for developing diabetes and its chronic complications.
Evidence suggests that dietary and lifestyle changes place ethnic minorities at higher risk for developing diabetes. The Honolulu Heart Program, a study of 8006 Japanese men born from 1900 to 1919, correlated a westernized and sedentary lifestyle with an increased risk for developing diabetes. These studies found that Japanese men who retained a Japanese lifestyle and diet were less likely to develop diabetes than those Japanese men who followed a westernized lifestyle (10,11).
Different cultural beliefs and dietary practices among the various ethnic groups in Hawaii affect the outcome of diabetes and adherence to medications. The literature suggests that traditional cultural beliefs about family and the social support it provides when family members are ill play an important role in either encouraging or preventing individuals from seeking medical care for diabetes (12,13). Because minority populations and patients facing socioeconomic barriers to health care access have been shown to have the worst adherence to medications and poor glycemic control (14), determining the association between ethnicity and adherence to oral hypoglycemic agents among Japanese, Chinese, Filipino, Hawaiian, and white patients in Hawaii will help reveal the ethnic disparities that exist in Hawaii and identify those groups who most need to be targeted for intervention.
Methods
Study population
The patients in this study were enrollees in a large health care plan in Hawaii from January 1, 1999, to June 30, 2003, who met their algorithm for diabetes, had drug coverage, and filled at least one prescription for one of the following oral hypoglycemic agents: sulfonylurea, metformin, thiazolidinedione, and α-glucosidase inhibitors. To determine if a patient had diabetes, we used a diabetes algorithm that followed two decision paths. The first path looked for the presence of a diabetes diagnosis with specific treatment services; the second identified the presence of diabetes based on specific drugs and medical supplies, provided the member did not have gestational diabetes. The characteristics that we controlled for were age, gender, ethnicity, island of residence, morbidity level, year of treatment, and type of coverage (i.e., HMO, PPO, Medicare cost contract).
Morbidity level was assessed using the Johns Hopkins University's Ambulatory Care Groups (ACGs), derived from the mix of a member's diagnoses. In the original study, 51 combinations or ACGs resulted from applying multivariate techniques to maximize variance explained in use of services and ambulatory care charges (15). This method can be applied to large populations with numerous types of diagnoses to predict ambulatory care use and cost of care and to determine the burden of morbidity. High morbidity was defined as a morbidity level of four or five on a five-point scale.
Sources of data
Patient age, gender, island of residence, morbidity level, comorbid conditions, and type of coverage were obtained from administrative data. This data did not include ethnicity of the patient. Self-reported ethnicity information was available for a portion of members in the study population from existing health-plan member satisfaction surveys. The satisfaction survey was a mailed questionnaire filled out by members to describe their experiences with health care services; on that survey, members were asked to check all that apply among 17 ethnic categories. These categories were chosen to be consistent with the Hawaii Department of Health's Hawaii Health Surveillance Program. In most cases, members who marked more than one race or ethnicity were categorized as "mixed." Any member who marked Hawaiian, however, was classified as Hawaiian regardless of what other categories he or she may have marked, because so few persons are of Hawaiian-only ancestry. We examined ethnic differences in oral adherence for the six main ethnic/racial groups in Hawaii: Japanese, Chinese, whites, Hawaiians, Filipinos, and Koreans. Members who marked other ethnicities, no ethnicity, or were mixed but non-Hawaiian were excluded from the analyses.
Calculation of adherence
Treatment adherence for this study was calculated allowing small gaps between prescriptions. The maximum allowable gaps were based upon possession ratios, calculated as follows:
Possession ratio = days supplied for first prescription/(fill date of second prescription − fill date of first prescription)
A claim separated from a previous claim with a possession ratio of 0.8 or greater was considered adherent.
Days of adherence were calculated from the date of the first prescription until the end-of-supply of the last claim. For isolated claims, claims not within a possession ratio of 0.8 of other claims, the days of adherence were taken as the days of supply on the claim. The days of adherence for other claims were calculated as follows:
Days of adherence = date of last adherent prescription − date of first prescription + days of supply on last compliant prescription
The average days of adherence per year were calculated as the days of adherence divided by the days of enrollment in a drug plan since the first prescription date. Patients needed to have some drug enrollment and medical enrollment to be in the study. They did not need to be continuously enrolled for the entire study period. Days of adherence were looked at and adjusted by days of enrollment on an annual basis. The average number of days of enrollment per year was 354 days.
Statistical analysis
Multivariate logistic regression analysis was used to examine the relationship between adherence and patient characteristics. All analyses were performed using Stata V7.0 statistical software (StataCorp, College Town, Tex).
Results
Among the 39,536 patients on an oral hypoglycemic agent, 20,685 unique numbers met the inclusion criteria for the study (Table 1). The mean age (± SD) of the study sample was 63.7 years (± 12.01). Of the study sample, 54.4% were male and 45.6% were female. Among the study sample, 20.7% had high morbidity; 77.3% lived on Oahu, 12.1% on Hawaii, 5.8% on Kauai, 6.3% on Maui, 0.4% on Lanai, and 0.7% on Molokai. The distributions of oral hypoglycemic agents were as follows: sulfonylurea 44.0%, metformin 31.2%, α-glucosidase inhibitors 1.6%, and thiazolidinedione 23.2%. During the study period, 19.9% of patients also used insulin. Of these patients, 16.0% used sulfonylurea, 19.8% used metformin, 31.0% used α-glucosidase inhibitors, and 33.0% used thiazolidinedione. Patients on thiazolidinedione and α-glucosidase inhibitors have a higher percentage of insulin use.
Adherence differed according to drug class, age, ethnicity, and island of residence (Table 2). Overall adherence to oral hypoglycemic agents was low at 61.4%. Relative to sulfonylurea, the odds ratio of adherence was highest for metformin, followed by thiazolidinedione, and lowest for α-glucosidase inhibitors. Relative to the age subset 55 to 64 years, adherence increased with older patients, reaching a peak at age 74, then decreased for patients aged 85 and older. Age was controlled for because metformin and thiazolidinedione are less likely to be used among the elderly due to contraindications and adverse effects. Japanese patients were the most likely to be adherent, followed by Chinese, whites (referent group), Hawaiians, and Filipinos. Relative to Oahu, Lanai had the lowest odds ratio.
Compared with the fee-for-service members, HMO members were less likely to adhere to medication therapy. Adherence of members with Medicare coverage was similar to that of members in the fee-for-service plan. Members with the lowest morbidity level (i.e., fewest comorbid conditions) tended to be the least adherent to medication. Adherence tended to improve with increased morbidity but declined for those with the highest morbidity level. Gender and the year of treatment were not significantly associated with adherence.
Discussion
Patient adherence to oral hypoglycemic agents is integral to reducing the health care costs and chronic complications of diabetes. Identifying which patients are at greatest risk for nonadherence to oral hypoglycemic agents is an important first step toward developing interventions that improve adherence.
Older patients had better adherence than younger patients to oral hypoglycemic agents, with adherence declining after age 85. A possible explanation for the better adherence among the older patients is that they are more knowledgeable and experienced with using the medications. However, with increasing age and burden of disease, adherence becomes more difficult to maintain over time. The finding of an association between age and adherence to oral hypoglycemic agents has helped to identify younger diabetic patients as a susceptible group requiring intervention to improve adherence. The diagnosis of diabetes made at an earlier age means these individuals will have an increased duration of exposure to hyperglycemia and as a consequence increased severity of microvascular complications (16). In addition, recent trends show an increased incidence of type 2 diabetes in younger individuals belonging to minority groups (17). This is reflected in current screening guidelines that recommend diabetes testing earlier (before the age of 45) for anyone belonging to a high-risk group, such as Asian-Pacific Islanders.
Ethnicity was also found to be a significant factor in adherence to oral hypoglycemic agents. Japanese patients had the highest adherence, and Filipino patients had the lowest adherence. Worrisome about this finding is that Filipinos in Hawaii have the highest prevalence of diabetes among the four largest Asian groups, which include Chinese, Japanese, Filipino and Korean (18). Among Filipino males living in Hawaii, diabetes is the third leading cause of death, and in Filipino females it is the second leading cause of death (19). More research is needed to understand the risk factors that contribute to Filipino susceptibility to developing diabetes and the reasons behind the low rates of adherence to oral hypoglycemic therapy in this group.
More studies also need to be done on patients living on the island of Lanai to find out the reasons behind the low rates of adherence. One possibility to consider is the rural setting of the island, which poses barriers to health care access for persons with diabetes.
This study did not examine the effects on adherence of combination drug therapy either with or without concomitant insulin use. Combination therapy has been shown to be associated with poorer rates of adherence than monotherapy (20-21). In clinical practice, sulfonylurea and metformin are usually prescribed by themselves as a first-line therapy because these two drugs are the only ones to demonstrate decreased vascular risk. Furthermore, although thiazolidinedione and α-glucosidase inhibitors are indicated for use as monotherapy, they are used more in combination therapy, thereby contributing to the decreased rates of adherence in these two medications. Insulin has been associated with lower adherence rates; however, it would be premature to make any conclusion at this point without further data on the use of combination therapy in this population.
Other limitations to our results are that the database for ethnicity is not complete, patients may have received free samples from their physicians, and the use of medical refill claims is an indirect method of measuring adherence. It is not known if patients are actually ingesting their medications; however, filling a prescription is a necessary first step for doing so. Another limitation was the lack of HbA1c levels, which would have provided a means for linking nonadherence with poor health outcomes in the population. To be able to link poor health outcomes with nonadherence in susceptible groups, such as younger patients and Filipino patients, would strengthen the conclusion that these groups are at high risk for the chronic complications of diabetes and need to be targeted for intervention. In addition, this study did not examine the effects of combination drug use on adherence.
Despite these limitations, prescription refill claims can be used as a tool to predict patient characteristics at highest risk for low adherence. Previous studies that have attempted to correlate demographic characteristics such as race, age, and gender with adherence have had inconsistent results (22-24). However, these studies used a small sample population. This study used a large sample population, and the trends demonstrated for race and age were consistent with other studies that used a large sample population (25-27).
The findings in this study highlight the need to target younger patients, Filipino patients, and patients living in rural areas, such as the island of Lanai, for better glycemic control. Even with adequate access to health care services and drug coverage, there was low adherence to oral diabetic medications. This is a disturbing finding considering that the benefit of intensive glycemic control has been demonstrated by the U.K. Prospective Diabetes Study (28).
Funding for this study was received from Hawaii Medical Service Association.
Figures and Tables
Table 1 Characteristics of Patients with Diabetes Taking Oral Hypoglycemic Agents in Hawaii (n=20,685), January 1, 1999, to June 30, 2003a
Mean age, years (±SD) 63.7 (± 12.01)
Male 54.4
Female 45.6
High morbidityb 20.7
Island of residence
Oahu 77.3
Hawaii 12.1
Kauai 5.8
Maui 6.3
Lanai 0.4
Molokai 0.7
Oral hypoglycemic agent
Sulfonylurea 44.0
Metformin 31.2
α-glucosidase inhibitors 1.6
Thiazolidinedione 23.2
Insulin use and oral hypoglycemic agent 19.9
Sulfonlyurea 16.0
Metformin 19.8
α-glucosidase inhibitors 31.0
Thiazolidinedione 33.0
a Values are percentages unless otherwise indicated.
b High morbidity was defined as a morbidity level of four or five on a 5-point scale using the Johns Hopkins University’s Ambulatory Care Groups (ACGs).
Table 2 The Impact of Drug Class and Patient Characteristics on Adherence to Oral Hypoglycemic Agents in Hawaii, January 1, 1999, to June 30, 2003
Characteristics Adherence OR (95% CI)a
Oral hypoglycemic agent
Sulfonylurea 1.0 (ref)
Metformin 0.76 (0.73-0.78)
α-glucosidase inhibitors 0.42 (0.37-0.46)
Thiazolidinedione 0.48 (0.46-0.49)
Insulin use 0.82 (0.79-0.85)
Sex
Male 1.0 (ref)
Female 0.97 (0.94-1.0)
Age, years
1-18 0.20 (0.02-1.90)
19-24 0.30 (0.13-0.66)
25-34 0.47 (0.39-0.57)
35-44 0.72 (0.67-0.78)
45-54 0.86 (0.83-0.90)
55-64 1.0 (ref)
65-74 1.1 (1.10-1.20)
75-84 1.1 (1.0-1.10)
85+ 0.90 (0.82-0.98)
Race/ethnicity
White 1.0 (ref)
Japanese 1.20 (1.0-1.30)
Chinese 1.0 (0.89-1.20)
Filipino 0.78 (0.68-0.90)
Hawaiian 0.89 (0.77-1.0)
Other 0.87 (0.74-1.0)
Missing data 0.85 (0.76-0.96)
Island of residence
Oahu 1.0 (ref)
Hawaii 1.10 (1.10-1.20)
Kauai 1.10 (1.0-1.20)
Maui 0.98 (0.92-1.0)
Lanai 0.79 (0.62-1.0)
Molokai 0.94 (0.79-1.10)
Morbidityb
Morbidity 1 1.0 (ref)
Morbidity 2 1.30 (1.10-1.50)
Morbidity 3 1.40 (1.20-1.60)
Morbidity 4 1.40 (1.20-1.60)
Morbidity 5 1.30 (1.10-1.60)
Morbidity 6 1.10 (0.95-1.30)
Year
1999 1.0 (ref)
2000 0.97 (0.93-1.0)
2001 1.10 (1.0-1.10)
2002 0.92 (0.87-0.97)
Coverage
Fee-for-service 1.0 (ref)
HMO 0.89 (0.85-0.93)
Medicare 0.98 (0.87-1.10)
a OR = odds ratio; CI = confidence interval.
b Morbidity level was assessed using the Johns Hopkins University’s Ambulatory Care Groups.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Lee R, Taira DA. Adherence to oral hypoglycemic agents in Hawaii. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0049.htm
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19 Braun KL Yang H Onaka AT Horiuchi BY 55 12 1996 278 283 Hawaii Med J Life and death in Hawaii: ethnic variations in life expectancy and mortality, 1980 and 1990 9009460
20 Dailey G Kim MS Lian JF 23 8 2001 1311 1320 Clin Ther Patient compliance and persistence with antihyperglycemic drug regimens: evaluation of a Medicaid patient population with type 2 diabetes mellitus 11558867
21 Melikian C White TJ Vanderplas A Dezii CM Chang E 24 3 2002 460 467 Clin Ther Adherence to oral antidiabetic therapy in a managed care organization: a comparison of monotherapy, combination therapy, and fixed-dose combination therapy 11952029
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23 Steiner JF Prochazka AV 1997 50 105 116 J Clin Epidemiol The assessment of refill compliance using pharmacy records: methods, validity, and applications 9048695
24 Vermiere E Hearnshaw H Royen PV Denekens J 2001 26 331 342 J Clin Pharm Ther Patient adherence to treatment: three decades of research. A comprehensive review 11679023
25 Schectman JM Bovbjerg VE Voss JD 2002 40 1294 1300 Med Care Predictors of medication-refill adherence in an indigent rural population 12458310
26 Monane M Bohn RL Gurwitz JH Glynn RJ Levin R Avorn J 1996 86 1805 1808 Am J Public Health Compliance with antihypertensive therapy among elderly Medicaid enrollees: the roles of age, gender, and race 9003143
27 Charles H Good CB Hanusa BH Chang CC Whittle J 96 1 2003 17 27 J Natl Med Assoc Racial differences in adherence to cardiac medications
28 UK Prospective Diabetes Study (UKPDS) Group 352 9131 1998 837 853 Lancet Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33) 9742976
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Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_04_0090
Original Research
PEER REVIEWEDUsing the State Plan Index to Evaluate the Quality of State Plans to Prevent Obesity and Other Chronic Diseases
Dunět Diane O PhD, MPA Health Scientist, Centers for Disease Control and Prevention (CDC), National Center for Chronic Disease Prevention and Health Promotion, Division of Nutrition and Physical Activity, Chronic Disease Nutrition Branch
4770 Buford Hwy NE, Mail Stop K-26, Atlanta, GA 30341-3719 [email protected]
770-488-5566
Butterfoss Frances D PhD Professor and Head Health Promotion & Disease Prevention, Center for Pediatric Research, Eastern Virginia Medical School, Norfolk, Va
Hamre Robin RD, MPH Team Leader Nutrition and Physical Activity Program to Prevent Obesity and Other Chronic Diseases, Centers for Disease Control and Prevention (CDC), National Center for Chronic Disease Prevention and Health Promotion, Division of Nutrition and Physical Activity, Atlanta, Ga
Kuester Sarah MS, RD Nutrition and Physical Activity Program to Prevent Obesity and Other Chronic Diseases, Centers for Disease Control and Prevention (CDC), National Center for Chronic Disease Prevention and Health Promotion, Division of Nutrition and Physical Activity, Atlanta, Ga
4 2005
15 3 2005
2 2 A102005
Introduction
Implicit in public health planning models is the assumption that good public health plans lead to good programs, and good programs lead to desired health outcomes. Despite considerable resources that are devoted to developing plans, public health agencies and organizations have lacked a tool for evaluating the finished product of their planning efforts — the written plan itself — as an important indicator of progress. To address the need for an instrument to assess the quality of state plans designed to prevent and control chronic diseases, we created and tested the State Plan Index and used it to evaluate the quality of nine state plans aimed at preventing and reducing obesity.
Methods
The State Plan Index was developed under the auspices of the Centers for Disease Control and Prevention (CDC) in collaboration with public health experts in federal, state, and academic settings. The State Plan Index included 55 items related to plan quality arranged into nine components. Each item was rated on a Likert scale from 0 to 5, with 5 being the highest rating. Each plan also received a separate overall plan quality score using the same scale. Each state plan was evaluated by four or five raters using the State Plan Index. For each plan, the 55 items were averaged to calculate an item average score, and a subscore was calculated for each State Plan Index component. Finally, five states also self-rated their own plans (self score).
Results
The mean item average score for all plans was 2.4 out of 5.0. The range of item average scores was 1.0 to 3.0. The component of the State Plan Index with the highest mean component score (3.3) was Presentation of Epidemiologic Data on Disease Burden. The components with the lowest component scores were Resources for Plan Implementation (0.7); Integration of Obesity Efforts with Other Chronic Disease Efforts (1.7); and Program Evaluation (2.0). Plan quality was rated higher when based on the single overall plan quality score assigned by raters. In addition, self scores were consistently and substantially higher than rater-assigned scores.
Conclusion
Evaluation of plans early in the life of programs can be used to strengthen existing programs and to guide programs newly engaged in chronic disease prevention planning. The CDC has used the State Plan Index evaluation results to guide technical assistance, plan training sessions, and enhance communication with state staff about plan content, quality, and public health approach. Some state program directors self-evaluated their obesity draft plan and used the evaluation results to strengthen their planning process and to guide plan revisions. Other states have adapted the State Plan Index as a framework for new planning efforts to prevent obesity as well as other chronic diseases.
==== Body
Introduction
Public health experts promote planning at the state and community levels in order to achieve desired public health outcomes (1). With the support of the Centers for Disease Control and Prevention (CDC), substantial public health resources have been devoted to state planning in areas such as bioterrorism preparedness, school health, and tobacco control (2). Drawing on long traditions of planning and organizational science, public health and policy experts have developed an array of planning models (3-7) as well as tools with which to assess infrastructure (8-10), design interventions (11-13), and manage ongoing data collection (14). The availability of different planning models provides public health practitioners with the flexibility to not only match an appropriate model with an intended goal but also to use a model (or a combination of model elements) that is compatible with the norms, expectations, and acceptability of organizations and community stakeholders (15). Implicit in planning models is the assumption that good plans lead to good programs, and good programs lead to desired health outcomes. Thus, the quality of a plan deserves focused attention from evaluators and public health practitioners.
Although many planning models include evaluation, this evaluation is often in the context of assessing the effectiveness of strategies selected through the planning process or tracking the status of the plan's implementation. Recent attention has turned to the plans themselves — the finished products of the planning process — as indicators of progress. However, plan evaluations have been limited to inventories and descriptions of the content of state plans that address various chronic diseases; they have not directly addressed plan quality. (See, for example, Abed et al [16].) More typically, evaluation of state plans is informal, as when program staff judge a plan primarily on the basis of their own expertise. In the current study, comprehensive state obesity prevention plans were systematically evaluated for quality. The plans were developed by states that receive funding and technical assistance under CDC cooperative agreements for obesity through the CDC's Obesity Prevention Program described below.
The CDC Obesity Prevention Program
Obesity in the United States has reached epidemic proportions. Among adults in the United States, the prevalence of overweight is approximately 65.7%, and the prevalence of obesity is approximately 30.6% (17). Body mass index (BMI) is calculated as a person's body weight in kilograms divided by the height squared in meters. Adults with a BMI of 25 to 29.9 are considered overweight, while adults with a BMI of ≥30 are considered obese (18).
In children, weight status is determined using sex-specific growth charts for BMI-for-age, with overweight status defined as a BMI at or greater than the 95th percentile (19). Since 1980, the prevalence of overweight has doubled for children aged two to 11 years, while in adolescents aged 12 to 19 years, the prevalence of overweight has more than tripled (17). The Surgeon General's Call to Action to Prevent and Decrease Overweight and Obesity (20), published in 2001, states that the cost in the United States of overweight and obesity and their complications is estimated at $117 billion annually. Obesity in this country is both epidemic and costly.
The factors that contribute to obesity are many and varied, as are the public health strategies needed to address obesity. In 2000, the CDC launched its state-based Nutrition and Physical Activity Program to Prevent Obesity and Other Chronic Diseases (CDC Obesity Prevention Program). The CDC Obesity Prevention Program maintains a Web site with detailed information about the program and links to resources available from www.cdc.gov/nccdphp/dnpa/obesity/state_programs.
State plans for obesity prevention
The CDC Obesity Prevention Program is based on a familiar public health model: state health departments are funded by a federal agency to develop comprehensive plans and are provided with ongoing technical assistance, training, and other resources. In 2000, the CDC Obesity Prevention Program awarded six state health departments (California, Connecticut, Massachusetts, North Carolina, Rhode Island, and Texas) an average of $350,000 per year through cooperative agreements. In 2001, an additional six states (Colorado, Florida, Michigan, Montana, Pennsylvania, and Washington) received similar awards. As of July 2004, 23 state health departments were funded for obesity program planning and capacity building, and five were funded by the CDC to implement their state plan for obesity.
The CDC promotes a three-pronged approach to obesity planning: behavior change, environmental change, and policy change. The cooperative agreements between the CDC and states have the following goals: promote development and implementation of community nutrition and physical activity plans for obesity prevention and control; decrease levels of obesity or reduce the rates of growth of obesity in communities reached through interventions; increase physical activity and improve dietary behaviors in communities reached through interventions; and increase interventions, policies, environmental supports, and/or legislative actions for improved nutrition and physical activity.
In developing obesity plans, states are encouraged to draw upon local resources, develop community support, and identify political, economic, and environmental factors that may act as barriers or facilitators to change. Thus, flexibility is important in planning models, and approaches should be compatible with the needs of a state and its current circumstances. At the same time, however, sound principles of public health practice and evidence-based strategies must be used to benefit from theory, scientific research, and program evaluation. Because obesity is a factor in many other chronic diseases, such as diabetes, cardiovascular disease, cancer, and arthritis, states are strongly encouraged to find ways to integrate strategies for obesity control into existing program structures. Although this approach adds complexity because of the many and varied stakeholder interests involved, integrated strategies can facilitate unified public health messages and offer potential cost savings through shared resources (21).
Every state funded by the CDC Obesity Prevention Program is required to develop and implement a state plan. Assessing plan quality serves as an important early indicator of whether states are on track. In addition, the quality of state plans is one of several indicators used by the CDC Obesity Prevention Program to self-monitor its effectiveness in providing guidance and technical assistance for planning.
Because no satisfactory evaluation instrument was available to assess state plan quality, a new instrument was developed for this purpose called the State Plan Index (SPI). As described in detail in "State Plan Index: A Tool for Assessing the Quality of State Public Health Plans" in this issue of Preventing Chronic Disease (22), the SPI was developed by a team at the CDC led by Butterfoss and Dun?t in collaboration with more than 100 public health experts in federal, state, and academic settings. The objective for this evaluation was to assess the quality of state plans developed by states that receive funding under the CDC cooperative agreements described above.
Methods
The SPI used for this evaluation consisted of 55 items arranged into nine components:
1) Involvement of Stakeholders; 2) Presentation of Data on Disease Burden and Existing Efforts to Control Obesity; 3) Goals; 4) Objectives; 5) Selecting Population(s) and Strategies for Intervention; 6) Integration of Strategies with Other Programs and Implementation of Plan; 7) Resources for Implementation of Plan; 8) Evaluation; and 9) Accessibility of Plan. A six-point Likert scale was used to score each item, each component, and the overall quality of a plan. "Not Addressed" was scored as 0. Consistent with the findings of the formative evaluation on weighting conducted during SPI development (22), SPI items were weighted equally, as were the nine components.
Nine of the ten state plans used in the pilot test of the SPI (22) were evaluated. The nine states were Colorado, Florida, Massachusetts, Michigan, North Carolina, Oregon, Pennsylvania, Texas, and Washington. As of June 1, 2003, these were the only states in the United States to have completed a full draft or final version of a state plan for obesity. One plan from the pilot test that was not a full draft was not included in the evaluation. With the exception of Oregon's, all of the plans were developed with the support of the CDC's cooperative agreement for obesity. Both the SPI and state plans were developed during the same time frame; therefore, state staff did not have the benefit of the SPI as a tool during their planning process. State staff were invited to voluntarily participate in this evaluation by sharing their plans for review and by serving as raters.
Written state plans were provided directly to the CDC by each state or were downloaded from the state's Web sites. During August and September 2003, 41 SPI ratings were completed on the nine state obesity plans by 18 volunteer raters. The raters were recruited as follows: nine from states funded through the CDC's Obesity Prevention Program; five from nonfunded states recruited through the Association of State and Territorial Public Health Nutrition Directors (ASTPHND); one paid independent public health consultant who rated all nine plans; and three CDC Obesity Prevention Program staff, who rated five to nine plans each.
The intention was to have each plan assessed by five raters consisting of one to two state staff from CDC-funded states; one member of ASTPHND; the independent public health expert consultant; and one CDC Obesity Prevention Program staff member. Because some raters did not complete the ratings within the time allotted, the total number of ratings for this analysis was 41 rather than 45. Raters were assigned plans on the basis of suggestions from the CDC Obesity Prevention Program staff members, who matched state plans with raters they believed were least likely to be familiar with the obesity prevention efforts in that state. This approach was intended to avoid raters' consideration of any background information not included in the written plan. An initial plan to blind reviewers to the names of states on hard copies of plans was determined impracticable, especially because some SPI items involve considering how well plans respond to local conditions. Also, information throughout the plan such as epidemiologic data, the names of partners, or a governor's endorsement made it impossible to conceal state names without the possibility of distorting important details in the plan.
Written instructions were provided to raters on the use of the SPI, and a telephone conference was held for orientation. No formal training session was conducted because the SPI was designed to be used without special training. Raters were given a three-week time period to review and rate plans. No instructions were given on the order in which plans were to be reviewed by each rater. Raters provided scores by marking paper copies of the SPI. For these ratings of state obesity prevention plans, the standard was not a comparison or control group; rather, each state plan was measured against the explicit ideal set forth in the SPI.
In the process of planning this evaluation and developing the SPI, the CDC expanded the scope of the evaluation beyond its initial objective of assessing plans to include a process for providing narrative feedback to states. Raters' narrative comments on individual SPI items and major plan components, as well as overall impressions of plans, were provided to the CDC electronically and compiled by the authors.
After the ratings were completed, telephone debriefings were held to discuss raters' experiences in the evaluation process and their reactions to using the SPI to assess state plans. The evaluation also served as one of the field tests of the SPI. As a result of comments received from raters, the SPI was slightly modified by subdividing five of the 55 SPI items used for this evaluation. The final 60-item version of the SPI is available from the CDC Obesity Prevention Program Web site.
States were encouraged to use the SPI to evaluate their own plan, especially for plans that were not yet finalized and disseminated. Five state program directors or program coordinators conducted self ratings and shared the results with the CDC.
SPSS (SPSS Inc, Chicago, Ill), a statistical software program, was used for analysis of the results. The item average score was calculated for each state; the item average score is the mean of the 55 individual item scores assigned by each rater, averaged across raters for each state. Raters also provided an additional score to represent their judgment of the overall quality of a plan (overall plan quality score). An average overall plan quality score was calculated for each state. The correlation coefficient was calculated to measure the association between scores based on an average of 55 SPI items rated individually and scores based on the single overall plan quality score assigned by the raters.
Because of the small number of states evaluated, the results were not stratified on demographics or other variables. In discussing preliminary results of this evaluation with stakeholders, suggestions were made on variables that might have influenced a state's ability to produce a quality plan. Therefore, correlation analysis was used to explore the potential relationship between the quality of state plans and other variables, including prevalence of adult obesity in the state, state population size, personal income per capita, the length of time the state had received CDC funding for obesity, and the objective review panel score received on the state application for funding under the CDC cooperative agreement.
Results
Item average scores and overall plan quality scores
As shown in Table 1, item average scores (average of 55 SPI items) ranged from a high of 3.0 to a low of 1.0 on a scale of 0 to 5. The mean item average score for the nine plans was 2.4, and the median score was 2.6.
State averages of overall plan quality score ranged from a high of 4.3 to a low of 2.3 on a scale of 0 to 5 (Table 1). The mean overall plan quality score was 3.4, and the median score was 3.5. Although overall plan quality scores and the item average scores were highly correlated (Pearson r2 = 0.88, P ≤ .01), the raters consistently made an upward adjustment when assigning an overall plan quality score.
Self-rating scores were consistently — and substantially — higher than the scores assigned by the other raters. As shown in Table 1, the mean of rater-assigned scores for these five states was 2.6, whereas the mean for self ratings was 4.7, or almost double (median score = 5.0).
Component scores
Table 2 shows state scores organized by SPI component. A component score is the average of rater-assigned scores for all of the items within that component by all raters of that plan. Mean scores for the SPI components ranged from a high of 3.3 for Presentation of Data on Disease Burden and Existing Efforts to Control Obesity to a low of 0.7 for Resources for Implementation of Plan. Scores in the Resources component ranged from 0 to 1.6, with even the highest scores falling well below ideal. Examples of SPI items in this component included whether or not the lead agency for the plan was identified, how resources would be provided to local partners, and whether the plan addressed issues related to sustainability of efforts.
The component Integration of Strategies with Other Programs and Implementation of Plan was the only other SPI component where the highest score achieved by a state plan was less than 3.0. The mean score for this component was 1.7, with state component scores ranging from a low of 0.7 to a high of 2.6. Examples of SPI items in this component included how strategies will be integrated with existing programs that focus on chronic diseases, prevention, education, and service delivery; and how existing or potential partners (government, community-based, faith-based, business/industry, and private organizations) will be involved to implement the plan. The SPI component with the greatest variability in the range of scores was Presentation of Data on Disease Burden and Existing Efforts to Control Obesity; one state scored 0.0 because no epidemiologic data were presented in the plan, and another state scored 4.5.
At least one state plan scored 4.0 or higher in at least one of the following components: Involvement of Stakeholders (one state), Presentation of Data on Disease Burden (two states), Objectives (one state), and Accessibility of Plan (two states).
Consistency among raters
For the nine plans evaluated, federal staff assigned slightly higher average ratings than state staff (federal mean of nine states = 3.5 vs 3.2 for state staff). The paid independent public health expert's average score fell between federal and state scores (mean = 3.3). The interclass correlation coefficient (Shrout–Fleiss) for the overall plan quality scores was 0.78.
Plan quality and other variables
Spearman rank correlation coefficients (rs) were consistently low and not significant at P ≤.05 between state plan quality and 1) prevalence of adult obesity in the state (23) (rs = 0.43; P = .24); 2) state resident population size as of the year 2000 (24) (rs = 0.30; P = .43); 3) personal income per capita as of the year 2000 (24) (rs = −0.32; P = .41); 4) the number of months elapsed from initial funding of a state by the CDC Obesity Prevention Program cooperative agreement and June 1, 2003, when the evaluation commenced (rs = 0.54; P = .13); and 5) the objective review panel score received on the state application for funding under the CDC cooperative agreement (rs = 0.21, P = .57).
Discussion
Limitations
One limitation of this evaluation is that only nine state plans were assessed. Another important limitation is that, although the SPI provides a systematic and detailed format for assessing plan quality, the items require that determinations be made on the basis of the professional judgment of the rater. Because raters were not instructed on the order in which to review plans, a rater's assessment could have been influenced by the sequence in which he or she reviewed plans.
Another limitation of this evaluation is that raters in general assigned a higher overall plan quality score than the mean of their scores on the 55 items of the SPI. During telephone debriefing sessions, raters were asked whether this difference resulted from their weighting some SPI items more heavily than others. Raters indicated that they did not adjust weighting for particular items or components; rather, some raters said they were reluctant to assign a low overall score, fearing it might demoralize the state staff who wrote the plan. Therefore, the item average score rather than the overall plan quality score may provide a more unbiased assessment of plan quality.
In addition, for the five self ratings shared with the CDC, all five self-rated item average scores and overall plan quality scores were higher than the average scores assigned by outside raters. State staff had background knowledge about their own plan that was neither contained in the written plan nor available to outside raters. Even though all raters were instructed to rate only the information contained in the written plan, the background knowledge of state staff may still be reflected in the high scores on self-assessment. The discrepancy in scores may also have resulted from difficulty in objectively rating one's own work. Just as grade inflation in an academic classroom may gloss over opportunities for improvement, the tendency to raise summary scores and assign high scores during self-assessment can divert attention from aspects of a state plan that could be strengthened.
The low average component score for Resources for Implementation of Plan may not accurately reflect plan quality in this area. Although some state staff indicated that lack of information in their plan for this component did reflect a lack of development of resources for implementation, a few state staff indicated a desire to keep confidential — and secure — the resources and partnerships they had worked hard to build. They expressed reluctance to reveal details about resources to anyone outside the planning group, saying they were concerned that others might try to tap into innovative resources and thereby decrease those available for obesity efforts. For some state staff, withholding resource information from their written plan represented a strategic decision rather than a lack of planning. In contrast, state staff indicated that low scores on the component Integration of Strategies with Other Programs and Implementation of Plan reflected a true lack of fully developed plans for these activities.
To accommodate state needs and preferences for the way in which information is shared, future evaluations might assess relevant background materials as well as the state's written plan. Importantly, both state and federal staff who participated in the evaluation agreed that all SPI items should remain, especially if the SPI is used for self-ratings or to guide planning. The results of this evaluation served as the basis for further dialogue between the CDC and funded states to clarify expectations regarding plan content.
All state plans have components that could be strengthened; however, the fact that at least one of the plans scored 4.0 or higher in at least one component indicates that some state plans already contain components that are "consistently strong and often close to ideal," according to the scoring rubric of the SPI. This result is especially noteworthy since state staff wrote their plans as the SPI was being developed and did not have the benefit of the recommendations for each of the SPI components.
Use of State Plan Index to support program improvement
From November 2003 to April 2004, states were provided with summaries of the ratings of their plan and an anonymous compilation of comments from raters. Technical assistance was provided by CDC project officers, who discussed SPI results with state staff on routine telephone calls or site visits. As a result of this evaluation, some CDC project officers informally reported to the authors that the results of this evaluation helped them by identifying plan components rated as near ideal that could be recommended as resources to states engaged in writing or revising a plan.
Process use of evaluation
Evaluation expert Michael Q. Patton asserts that people often benefit more from skills learned by virtue of their participation in an evaluation process than from the results of an evaluation (25). Patton calls this "process use" of evaluation and notes its potential for organizational learning and development. Although this evaluation was intended to systematically assess the quality of state plans, state staff have reported benefits from process use of this evaluation. State staff who evaluated plans from other states and self-rated their own plans indicated that they gained useful insights into sound planning practices. In several instances, state staff reported that the ideal standards in the SPI helped them clarify their own expectations on the content, quality, format, and public health approach to be used in the planning process and to better translate this to their partners and stakeholders. State staff also reported that they have used the SPI as a final checklist before publishing their plan.
Other benefits of using the State Plan Index
Throughout the process of developing the State Plan Index and conducting this evaluation, information was shared with state staff, not only in states funded by the CDC for obesity but also with nonfunded states. One state that did not receive CDC funding for planning independently conducted a self-assessment of its plan using the SPI and later shared some of the results with the CDC. In this state, all members of the state obesity planning task force reviewed the state's draft plan using the SPI as a guide. Based on this review, the task force planned specific actions they would take to address potential weaknesses: for example, adding faith-based organizations and consumers as stakeholders, restating plan objectives in measurable and time-based terms, and identifying specific ways to integrate obesity efforts with other chronic disease areas as well as across systems and agencies.
Several state staff have requested copies of the SPI used for this evaluation, indicating a desire to conduct a similar evaluation of the plans of local entities. In another state not funded for obesity programs by the CDC, staff are adapting the SPI for use in state diabetes planning efforts and intend to conduct an evaluation similar to that reported here.
Finally, the results of this evaluation have been used as a training tool. In reviewing plans that were rated high, medium, and low, new staff quickly honed their understanding of the elements of a quality plan. The use of the evaluation results as a basis for focused technical assistance and evidence-based planning of future training for state staff is a program improvement already underway at the federal level.
Plan quality and broader public health issues
Although the evaluation reported here focuses on the CDC's Obesity Prevention Program, its results can also be related to broader public health issues. For example, although the SPI provides a comprehensive list of plan attributes that public health experts and scholars identified as ideal, future evaluations can be designed to pinpoint any components that appear particularly critical to achieving desired public health outcomes. From there, future planning processes might be streamlined or focused on key components. Even more broadly, understanding the relationship between plan quality and health outcomes also contributes to a better understanding of the return on investment for public health planning efforts. Additional long-term evaluation studies could address the following:
Does the quality of a plan affect its utility? For example, do states with better plans use them to leverage resources more effectively?
How can planning processes be streamlined?
Do better state plans lead to better health outcomes?
The process of engaging stakeholders, examining data, identifying and choosing interventions, building partnerships for implementation, and organizing the writing of a plan is time-consuming and complex. This evaluation showed that when compared with a set of ideal standards, the quality of state plans was variable, and some components of every state plan examined could be strengthened. As a result of this evaluation and the feedback and comments received from outside raters, several states reported informally to the CDC that they will refine and strengthen their plans, especially when plans undergo periodic updating. In general, areas where attention should be focused to strengthen existing plans were identified as resource planning, evaluation, and integrating intervention strategies across related chronic disease programs.
The participation by state staff in this evaluation demonstrates a successful evaluation partnership and a willingness of state staff to engage in program evaluation with other states as well as with the CDC. The peer ratings of plans added credibility to the process. Moreover, the time invested in reading and rating the plans of other states and applying the SPI offered an opportunity for staff to hone their planning expertise and to become more familiar with the SPI instrument and its inherent recommendations.
Perhaps most useful to public health practice is that evaluation conducted early in the life of a program can be rapidly translated into concrete program improvements with the potential to strengthen public health efforts. The agility and ease with which state staff have adapted the CDC's evaluation process and evaluation tools to guide their new planning efforts demonstrates the resourcefulness of state and local public health professionals and their genuine commitment to quality and effectiveness.
The authors gratefully acknowledge the contributions of those who participated in this evaluation as raters: Susanne Gregory, Claire Heiser, James Guwani, Sue Lin Yee, Maria Bettencourt, Sara Bonam, Cathy Brewton, Dorothy Caldwell, Sherry Clark, Jane Cotner, Donna F. McLean, Jane Moore, Karen Oby, Rachel Oys, Vaheedha Prabhakher, Julie Robarts, Kimberly Swanson, and Kyle Unland. We also acknowledge the support of the Association of State and Territorial Public Health Nutrition Directors and Diane Thompson and L. Michele Maynard at the CDC. During this project, Frances Butterfoss was under contract with the CDC through the Oak Ridge Institute for Science and Education fellowship program.
Figures and Tables
Table 1 Comparison of State Plan Index (SPI)a Scores For Obesity Plans for Nine U.S. Statesb
Range Mean Median
Item average scorec 1.0–3.0 2.4 2.6
Average overall plan quality scored 2.3–4.3 3.4 3.5
Self scoree (5 states, rater mean = 2.6) 4.0–5.0 4.7 5.0
a The SPI is available in this issue of Preventing Chronic Disease (22). SPI ratings are assigned on a Likert scale from 0 to 5 points.
b Colorado, Florida, Massachusetts, Michigan, North Carolina, Oregon, Pennsylvania, Texas, and Washington.
c Item average score is the mean of raters’ scores for the 55 individual items in the SPI, averaged for each state.
d Overall plan quality score is a single numeric rating that represents a rater’s overall evaluation of a state plan.
e Self score is a single numeric rating made by state staff of their own plan. The self score is the overall evaluation of a plan and corresponds to overall plan quality score assigned by other raters.
Table 2 Evaluation of Nine State Obesity Plansa Using the State Plan Index (SPI)b
State Plan Index Components Range Mean Median
A. Involvement of Stakeholders 1.0–4.3 2.8 2.9
B. Presentation of Data on Disease Burden and Existing Efforts to Control Obesity 0–4.5 3.3 3.6
C. Goals 2.5–3.7 3.0 3.0
D. Objectives 2.4–4.2 2.9 2.8
E. Selecting Population(s) and Strategies for Intervention 0.1–3.1 2.1 2.2
F. Integration of Strategies with Other Programs and Implementation of Plan 0.7–2.6 1.7 1.6
G. Resources for Implementation of Plan 0–1.6 0.7 0.5
H. Evaluation 0.6–3.6 2.0 2.0
I. Accessibility of Plan 0.8–4.5 2.9 3.6
a Colorado, Florida, Massachusetts, Michigan, North Carolina, Oregon, Pennsylvania, Texas, and Washington.
b The SPI is available in this issue of Preventing Chronic Disease (22). SPI ratings are assigned on a Likert scale from 0 to 5 points.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Dunět DO, Butterfoss FD, Hamre R, Kuester S. Using the State Plan Index to evaluate the quality of state plans to prevent obesity and other chronic diseases. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0090.htm
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5 National Association of County and City Health Officials Mobilizing for action through planning and partnerships (MAPP) Washington (DC) cited 2004 Jun 28 National Association of County and City Health Officials Available from: URL: http://www.naccho.org/project77.cfm
6 Sussman S 2001 13 Handbook of program development for health behavior research and practice The six-step program development chain model Thousand Oaks (CA) SAGE Publications
7 Institute of Medicine 2002 409 The future of the public's health in the 21st century The CHIP Model Washington (DC) National Academies Press
8 National Association of County and City Health Officials Assessment protocol for excellence in public health (APEXPH) cited 2004 Jun 28 Washington (DC) National Association of County and City Health Officials Available from: URL: http://www.naccho.org/project47.cfm
9 Centers for Disease Control and Prevention. School health index [Internet] Atlanta (GA) Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion cited 2004 Jun 28 reviewed 2004 Apr 22
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16 Abed J Reilley B Butler MO Kean T Wong F Hohman K 6 2 2000 67 78 J Public Health Manag Pract Developing a framework for comprehensive cancer prevention and control in the United States: an initiative of the Centers for Disease Control and Prevention 10787781
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18 National Institutes of Health 9 1998 Bethesda (MD) National Insitutes of Health; National Heart, Lung and Blood Institute Obesity education initiative: clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in Adults: the evidence report
19 Kuczmarski RJ Flegal KM 2000 72 1074 1081 Am J Clin Nutr Criteria for definition of overweight in transition: background and recommendations for the United States 11063431
20 U.S. Department of Health and Human Services 2001 Public Health Service, U.S. Department of Health and Human Services, Office of the Surgeon General The Surgeon General's call to action to prevent and decrease overweight and obesity 2001 Rockville (MD)
21 Mansley EC Dunět DO May DS Chattopadhyay SK McKenna MT 22 Suppl 2000 S67 S79 Med Decis Making Variation in average costs among federally sponsored state-organized cancer detection programs: economies of scale?
22 Butterfoss FD Dunět DO Prev Chronic Dis [serial online] 2005 Apr [2005 March 15] State Plan Index: a tool for assessing the quality of state public health plans
23 Mokdad AH Ford ES Bowman BA Dietz WH Vinicor F Bales VS 289 1 2003 76 79 JAMA Prevalence of obesity, diabetes, and obesity-related health risk factors 12503980
24 U.S. Census Bureau 2001 Washington (DC) Statistical abstract of the United States: 2001 U.S. Department of Commerce
25 Patton MQ 1997 Thousand Oaks (CA) SAGE Publications Utilization-focused evaluation 3rd ed
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Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_04_0104
Original Research
PEER REVIEWEDDevelopment of a Brief Survey on Colon Cancer Screening Knowledge and Attitudes Among Veterans
Wolf Michael S PhD, MPH Assistant Professor of Medicine Institute for Health Services Research and Policy Studies, Northwestern University, Feinberg School of Medicine
Dr. Wolf is also affiliated with The VA Midwest Center for Health Services and Policy Research, the VA Chicago Healthcare System, Chicago, Ill, and the Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, Ill
676 N St Clair St, Suite 200, Chicago, IL 60611 [email protected]
312-695-0459
Rademaker Alfred PhD Feinberg School of Medicine
Bennett Charles L MD, PhD The VA Midwest Center for Health Services and Policy Research, Feinberg School of Medicine, and Robert H. Lurie Comprehensive Cancer Center
Ferreira M. Rosario MD The VA Midwest Center for Health Services and Policy Research, Feinberg School of Medicine, and Robert H. Lurie Comprehensive Cancer Center
Dolan Nancy C MD Feinberg School of Medicine and Robert H. Lurie Comprehensive Cancer Center
Fitzgibbon Marian PhD Feinberg School of Medicine and Robert H. Lurie Comprehensive Cancer Center
Davis Terry C PhD Louisiana State University Medical School, Shreveport, La
Medio Franklin PhD Medical University of South Carolina, Charleston, SC
Liu Dachao MA Feinberg School of Medicine
Lee June The VA Midwest Center for Health Services and Policy Research
4 2005
15 3 2005
2 2 A112005
Introduction
Poor knowledge of and negative attitudes toward available screening tests may account in part for colorectal cancer screening rates being the lowest among 17 quality measures reported for the Department of Veterans Affairs health care system, the largest integrated health system in the United States. The purpose of this study was to develop a brief assessment tool to evaluate knowledge and attitudes among veterans toward colorectal cancer screening options.
Methods
A 44-item questionnaire was developed to assess knowledge, attitudes, and beliefs about colorectal cancer and screening and was then administered as part of an ongoing randomized controlled trial among 388 veterans receiving care in a general medicine clinic. Sixteen candidate items on colorectal cancer knowledge, attitudes, and beliefs were selected for further evaluation using principal components analysis. Two sets of items were then further analyzed.
Results
Because the Cronbach α for beliefs was low (α = 0.06), the beliefs subscale was deleted from further consideration. The final scale consisted of seven items: a four-item attitude subscale (α = 0.73) and a three-item knowledge subscale (α = 0.59). Twelve-month follow-up data were used to evaluate predictive validity; improved knowledge and attitudes were significantly associated with completion of flexible sigmoidoscopy (P = .004) and completion of either flexible sigmoidoscopy or colonoscopy (P = .02).
Conclusion
The two-factor scale offers a parsimonious and reliable measure of colorectal cancer screening knowledge and attitudes among veterans. This colorectal Cancer Screening Survey (CSS) may especially be useful as an evaluative tool in developing and testing of interventions designed to improve screening rates within this population.
==== Body
Introduction
Colorectal cancer is the third most common cancer and the third leading cause of cancer death in the United States (1). The U.S. Preventive Services Task Force, the American Cancer Society, and the American Gastroenterological Association have developed guidelines for colorectal cancer screening and recommend that persons aged 50 years or older who are at average risk for the disease be screened periodically (2-4). Despite these recommendations and multiple studies finding that colorectal cancer screening is cost-effective, screening rates are the lowest for any other cancer screening test, with only half of persons aged 50 years and older having received any of the available methods (5).
One potential barrier to effective screening is inadequate knowledge of both the disease and the possible options for undergoing various types of screening tests (6-17). Poor knowledge related to colorectal cancer is associated with compromised perceptions of cancer risk and low rates of screening services use (6,7,12,14,16). Targeted efforts are needed to improve both the overall awareness of colorectal cancer and the availability of often limited resources for invasive screening procedures, such as flexible sigmoidoscopy or colonoscopy. As health education and colorectal cancer screening programs are developed, valid and reliable measures of knowledge and attitude are needed to explicitly assess the efficacy of these efforts.
One prior study reports on a brief instrument that measures beliefs and attitudes toward colorectal cancer screening (17). This assessment was conducted in a mailed survey of primarily white, employed men. However, it has not been evaluated in other settings characterized by higher rates of racial/ethnic minorities or among persons of lower socioeconomic status; these groups have previously been found to be at greater risk for low screening compliance (18-21). Another population at greater risk for low screening compliance is veterans who receive care in the Department of Veterans Affairs (VA) health care system, the largest integrated delivery system in the country. Out of 17 quality measures routinely included in a nationwide VA quality improvement effort, colorectal cancer screening rates are the lowest (22).
As part of a randomized clinical trial effort to improve colorectal cancer screening rates within a VA outpatient general medicine clinic, we recently reported on knowledge and attitudinal barriers to screening participation among veterans (23). Our intervention targeted improvements in both veterans' perceptions about the disease and screening options in addition to their compliance. In this study, we developed and validated a brief measurement tool for evaluating knowledge and attitudes toward colorectal cancer screening among veterans.
Methods
Recruitment of participants
We designed a 44-item questionnaire to measure patient knowledge, attitudes, and beliefs associated with colorectal cancer screening and administered the questionnaire to 388 veterans. Male veterans aged 50 years and older who had not received colorectal cancer screening (defined as having a fecal occult blood test [FOBT] within one year or a flexible sigmoidoscopy or colonoscopy within five years) were recruited from general medicine clinics at the VA Chicago Health System between May 1, 2001, and December 31, 2002. Patients were ineligible if they 1) had received a FOBT within one year; 2) received a flexible sigmoidoscopy or colonoscopy within five years; 3) had a personal or family history of colorectal cancer or polyps; or 4) had a personal history of inflammatory bowel disease. In addition, individuals with dementia, impaired vision, hearing problems, or acute illness were deemed ineligible to participate in the study. We excluded patients with impaired vision because the instrument we employed to assess health literacy required the ability to view a list of words. The study protocol was approved by the Northwestern University Institutional Review Board.
Between May 2001 and December 2002, research assistants approached 589 eligible participants as they waited for their scheduled outpatient visit. Of these, 156 (26.4%) refused to be in the study, and 56 (9.6%) did not complete the study questionnaire primarily because their general medicine physician was ready to begin their visit. In all, 388 (65.9%) individuals completed the entire baseline interview, including the questionnaire. No compensation was offered for participation. After the informed consent process, participants took part in a 10- to 15-minute, face-to-face interview that included sociodemographic items, a literacy assessment, and the 44-item questionnaire. The literacy assessment consisted of administering the Rapid Estimate of Adult Literacy in Medicine (REALM), a screening instrument used to determine the ability of patients to read and pronounce common medical terminology and lay terms for body parts and illnesses (24,25). Raw REALM scores are converted to grade ranges: 0–18 = third grade and below, 19–44 = fourth to sixth grade, 45–60 = seventh to eighth grade, and 61–66 = high school. Follow-up interviews were conducted with 227 of these patients six to 12 months after the baseline interview, beginning November 2001 through December 2003. Patients' screening status was obtained through medical record review also during this period.
Development of the colorectal cancer questionnaire
The 44-item questionnaire included items designed to assess knowledge of colorectal cancer and specific screening tests and attitudes and beliefs toward colorectal cancer and available screening options. Knowledge questions were adapted from the 1992 National Health Interview Survey (NHIS) Cancer Control Supplement, with modifications to reflect current terminology (e.g., use of the term flexible sigmoidoscopy or flex sig instead of proctoscopy) (26). Attitudinal and belief items were developed based on findings from focus groups conducted among this same population of veterans (27). Reader comprehension of the questionnaire items was evaluated using five one-hour cognitive interviews among a convenience sample of community-based, screening-eligible adults. All interviews were conducted by one of the research investigators (Ferreira) and followed available guidelines established for properly conducting cognitive interviews in survey development (28). Interview techniques included both "concurrent think-aloud" and specific probes. The interviews were tape-recorded and abstracted for relevant information, which was used to modify the questionnaire. The modified questionnaire was then administered to a pilot group of 15 patients who were approached in the general medicine clinics (29).
During the pilot process, we obtained patient feedback to items in the pilot test and maintained reading levels of instructions, items, and response options appropriate for lower-literate patients; we used a common measure of document readability (Flesch–Kincaid) to gauge reading levels. Principles described by Doak et al were also applied to maximize item comprehension (30). The final version of the questionnaire registered as having a fifth-grade level of reading comprehension. Even though we planned to administer the instrument through an interview, the readability of the document provided us additional assurance that the questionnaire could be appropriately understood by most patients.
Of the 44 items in the questionnaire administered to the 388 veterans, 10 were associated with knowledge, 29 were associated with attitudes, and five were associated with beliefs. After the administration of the questionnaire, five of the knowledge items were selected by the research team as appropriate for analysis; other knowledge items were excluded because they were conditional questions that were not answered by everyone. Of the 29 attitude items, six were selected for analysis; again, other attitude items were excluded because they were conditional questions not answered by everyone. All five of the belief questions were selected for analysis. Thus, a total of 16 items were selected for analysis.
To prepare for data analysis, we scored questions so that low values reflected high knowledge and attitudes consistent with screening, or "correct" beliefs; high values reflected low knowledge and attitudes inconsistent with screening, or "incorrect" beliefs. Scoring for the knowledge scale was dichotomous (1 = yes, 2 = no); a response of yes required follow-up patient confirmation of understanding of the concept in question. For subjects who responded no or who were determined to have inadequate knowledge of the test in question, simple standard descriptions of both FOBT and flexible sigmoidoscopy were provided by the interviewer to ensure a proper frame of reference. The attitude scale was scored from 1 to 3 on level of worry (1 = not very or not at all worried, 2 = somewhat worried, 3 = very or extremely worried). For both subscales and the total scale, the score was determined by the sum of all nonmissing items. Items on the belief scale were scored for an initial analysis, but the belief construct was not included in a final analysis.
Psychometric analyses
Principal components (PC) analysis was used to assess the construct validity of the 16 items selected for initial analysis. Cronbach a was used to examine reliability (internal consistency) of the derived knowledge, attitudes, and beliefs subscales. The value of Cronbach a ranges between 0 and 1; if items within a scale are perfectly correlated, then a = 1; if items are totally unrelated, then α = 0. An α coefficient of 0.70 or higher is considered to be acceptably reliable, indicating that items within the same scale measure the same underlying construct. A low Cronbach α for the belief scale and low factor loadings of the belief variables resulted in deletion of this subscale. Final PC analysis on the remaining seven knowledge and attitude items was performed to determine whether these items followed the knowledge and attitude pattern. To confirm reliability of the knowledge and attitudes subscales, correlations between the full scale and items within the subscales were calculated.
An additional question of interest was: Do individuals who improve their knowledge and attitude exhibit different screening behavior than individuals who do not improve knowledge and attitude? To assess the predictive validity of the total score with screening behavior, change in the total score between two time points (initial questionnaire and follow-up questionnaire) was related to screening behavior using Fisher's exact test. It was postulated that an improvement in knowledge and attitudes would be related to an improvement in screening behavior.
Results
Respondents had a mean age of 67.3 years (SEM = 0.52); 41.4% were African American; 59.6% had completed high school, and 22% had completed college. Respondents' reading abilities averaged at the seventh- to eighth-grade level (mean REALM score = 57.3, SEM = −0.7), with 36% having reading skills lower than the eighth-grade level. More than two thirds (69.1%) of the men in the study were unemployed or retired, and 38% were married. One third of respondents reported their health as either very good or excellent.
Initial analysis consisted of the evaluation of 16 candidate items on colorectal cancer knowledge, attitudes, and beliefs using PC analysis. Item factor loadings for the three-factor solution are shown in Table 1. Because factor loadings for beliefs were low, the belief subscale was deleted from further consideration. Also, decisions were made to remove additional items based on lower factor loadings and/or conceptual fit with remaining items. Thus, the items "likely to get a flexible sigmoidoscopy (or FOBT) if friend recommended," "know testing age," and "heard of colorectal cancer" were deleted from further consideration.
The plan and procedure of item retention resulted in provisional compositions that could be mapped to two factors: knowledge and attitudes. These two sets of items were further analyzed using PC analysis to assess construct validity and Cronbach a to evaluate internal consistency (Table 2). All seven items were retained. The final scale consisted of seven items: a four-item attitude subscale and a three-item knowledge subscale (Table 3).
Higher correlations were observed between items within subscales and their corresponding full scale, while low correlations were expected and subsequently attained between items within subscales and the noncorresponding full scale (Table 4).
Twelve-month follow-up data were used to evaluate the predictive validity of the knowledge and attitudes scale and each of the two subscales for completion of a colorectal cancer screening test (Table 5). Because low values of all items in the attitudes subscale reflected favorable attitudes consistent with screening, and low values of all items in the knowledge subscale represented high knowledge, decrements over time on these subscales and the overall knowledge and attitude scale represented an improvement in attitudes consistent with screening and/or an improvement in knowledge.
We would assume such improvements in knowledge and attitudes would be associated with screening completion among eligible individuals noncompliant with existing screening recommendations. A minimum decrement over time (i.e., an improvement) of more than four points in the total knowledge and attitude summary scale was significantly associated with higher levels of colorectal cancer screening completion. A decrease of more than four points over time on the full scale was significantly associated with completion of flexible sigmoidoscopy (P = .004) and completion of either flexible sigmoidoscopy or colonoscopy (P = .02).
Discussion
We have developed a seven-item scale that can be used to measure knowledge and attitudes toward colorectal cancer screening among U.S. veterans. This instrument (Appendix), the Colorectal Cancer Screening Survey (CSS), was designed to be a brief and simple measure of knowledge and attitudes of veterans toward colorectal cancer screening tests. The results of this study suggest that the two-factor solution offers a parsimonious and reliable measure. It is the first psychometric tool to our knowledge to measure colorectal cancer screening knowledge and attitudes among veterans, a population that is predominantly low-income; nearly half in this study were African American. The CSS was also developed for all levels of literacy. Items were determined to be at a fifth-grade reading level and had simple response options. Moreover, the instrument was interviewer-administered.
Although adequate knowledge and positive attitudes alone may not be sufficient to ensure completion of colorectal cancer screening tests, both are common barriers that have been previously linked to noncompliance. Several studies have found that the absence of clinical symptoms was the most important factor associated with noncompliance with returning FOBTs or undergoing a flexible sigmoidoscopy procedure (7-16). Other attitudinal barriers include fear and anxiety about cancer and perceptions that colorectal cancer screening tests are uncomfortable, embarrassing, or generally unpleasant. The goal of many patient-directed interventions has been to overcome these barriers; the CSS could serve as a valuable indicator of an intervention's efficacy to improve intermediary outcomes.
Interestingly, the CSS had the highest predictive validity with the completion of a flexible sigmoidoscopy screening test, and was less likely to predict screening use when return of FOBTs was considered. This discrepancy may reflect both the level of difficulty of personal endorsement for colorectal cancer screening participation between the available testing options, as well as resources within the VA health care system. For example, the decision to have an FOBT may depend less on knowledge and attitudes than the decision to agree to a more invasive procedure such as flexible sigmoidoscopy or colonoscopy. It may be easier to agree to complete an FOBT with poorer knowledge and a less positive attitude toward colorectal cancer screening than to agree to complete a flexible sigmoidoscopy and colonoscopy, since an FOBT asks less of a patient. Patients may complete the procedure based on physician recommendation without recognizing it as a colorectal cancer screening test. However, flexible sigmoidoscopy and colonoscopy procedures require repeat visits and extensive preparation and take more time to explain and to engage subjects in decision making. Although the relationship did not reach significance, it is noteworthy that those with improved knowledge and attitude scores on the CSS scale had higher rates of colonoscopy screening, a test that is often exceedingly difficult to receive in a timely manner within the VA healthcare system because of limited trained clinical staff and resources.
Limitations to this study should be noted. First, our study is based on a cohort of male veterans. Additional assessments in other settings that provide care for large numbers of racial/ethnic minorities, both male and female, and/or who are of low socioeconomic status, such as the county medical systems, are needed. Second, the CSS may benefit from further psychometric evaluation that could improve upon the knowledge subscale and also evaluate the reliability of CSS scores over time. Further evaluation might also include test–retest reliability and discriminant validity assessments. Evidence of sensitivity to change will be necessary to eventually determine whether the CSS is an applicable evaluative tool for screening interventions.
In conclusion, the CSS may be a useful tool for testing the effect of interventions designed to improve colorectal cancer screening among veterans through improving patient knowledge and attitudes. Because veterans with low knowledge and negative attitudes toward screening tests may not be quickly or easily identified in clinical settings, the CSS might eventually be considered for use as a screening assessment to identify veterans who are at risk for colorectal cancer screening noncompliance.
This project was funded by the Department of Veterans Affairs (PCI 99-158) and the National Cancer Institute (R01 CA86424).
Appendix. The Colorectal Cancer Screening Survey (CSS)
1. Have you heard of any medical tests to find colon or rectal cancer?
Yes [1] No [2]
Colon or rectal cancer is a type of cancer of the large intestine, that is, the part of the body where the stool (or BM or poop) is made; and of the rectum, which is the part of the body the stool (or BM or poop) goes through when you have a bowel movement.
2. Do you know what a flexible sigmoidoscopy is (also called a “sigmoidoscopy” or “flex sig”)?
Yes [1] Go to A No [2] Go to B
A. Can you tell me what it is? [Open-ended. Check items mentioned. Prompt further explanation without suggestion.]
Test done by doctor
With tube, with light, camera
The tube is inserted in the rectum
To look inside for problems/growths/cancer/polyps
Other (specify) ________________________
B. A flex sig is a test that the doctor does using a flexible tube with a light at the end. The doctor puts the tube in the rectum to check for problems in the rectum or colon.
[If respondent is confusing flex sig and colonoscopy]:
Colonoscopy: uses sedation, patient drinks a gallon of bad-tasting liquid to clean out colon.
Flex sig: does not use sedation, patient is awake, patient gets an enema to clean out colon.
3. How worried are you that a flex sig might be embarrassing? Would you say . . .
Don’t Know Flex Sig [0] Not at All Worried [1] Not Very Worried [2] Somewhat Worried [3] Very Worried [4] Extremely Worried [5]
4. How worried are you that a flex sig might be painful? Would you say . . .
Don’t Know Flex Sig [0] Not at All Worried [1] Not Very Worried [2] Somewhat Worried [3] Very Worried [4] Extremely Worried [5]
5. Do you know what a Fecal Occult Blood Test or Hemoccult Test is (also called an FOBT)?
Yes [1] Go to A No [2] Go to B
A. Can you tell me what it is? [Open-ended. Check items mentioned. Prompt further explanation without suggestion].
Collect stool sample at home
Put it on special cards
Send to hospital/doctor
To test if there is blood in the stool
Other (specify) _____________________________
B. An FOBT is done at home. A person takes a small sample of stool (or BM or poop) and puts it on a special card. Then the card is sent to the hospital and is tested to see if there is blood in the stool (or BM or poop).
[Make sure respondent is not confusing FOBT with digital rectal exam]:
Digital rectal exam: done by doctor in exam room. Doctor puts stool on special card to test for blood.
FOBT: taken home by patient. Patient puts poop onto special card and sends card in to be tested.
6. How worried are you that an FOBT might be messy? Would you say . . .
Don’t Know FOBT [0] Not at All Worried [1] Not Very Worried [2] Somewhat Worried [3] Very Worried [4] Extremely Worried [5]
7. How worried are you that an FOBT might be inconvenient? Would you say . . .
Don’t Know FOBT [0] Not at All Worried [1] Not Very Worried [2] Somewhat Worried [3] Very Worried [4] Extremely Worried [5]
Figures and Tables
Table 1 Initial Principal Components Analysis of 16 Knowledge, Attitude, and Belief Items, Survey on Colorectal Cancer Screening Among U.S. Veteransa
Factor Factor loading Proportion of variance explained (Eigen value) Cronbach α
Attitudes 0.22 (3.50) 0.63
Likely to get FS if friend recommended 0.69
Worried FS would be embarrassing 0.71
Worried FS would be painful 0.69
Likely to get FOBT if friend recommended 0.65
Worried FOBT would be embarrassing 0.65
Worried FOBT would be painful 0.68
Beliefs 0.10 (1.68) 0.06
How serious if found early −0.04
Chances of survival if found early −0.26
How serious if found late −0.17
Chances of survival if found late −0.12
Chances of getting colorectal cancer −0.14
Knowledge 0.10 (1.60) 0.53
Heard of colorectal cancer 0.44
Heard of tests for colorectal cancer 0.37
Know of FS 0.22
Know of FOBT 0.47
Know testing age 0.31
Total percent of variance explained by three factors = 42%.
a FS = flexible sigmoidoscopy; FOBT = fecal occult blood test.
Table 2 Final Principal Components Analysis of Seven Knowledge and Attitude Items, Survey on Colorectal Cancer Screening Among U.S. Veterans
Factor Factor loading Proportion of variance explained(Eigen value) Cronbach α
Attitudes 0.38 (2.67) 0.73
Worried FS would be embarrassing 0.71
Worried FS would be painful 0.68
Worried FOBT would be embarrassing 0.69
Worried FOBT would be painful 0.73
Knowledge 0.19 (1.33) 0.59
Heard of tests for colorectal cancer 0.50
Know of FS 0.58
Know of FOBT 0.53
Total percent of variance explained by two factors = 57%.
a FS = flexible sigmoidoscopy; FOBT = fecal occult blood test.
Table 3 Scores for Participants Responding to Survey on Colorectal Cancer Screening Among U.S. Veterans
No. Respondents Mean Score SD Range
7-Item knowledge and attitudes scale 323 9.3 2.2 5-17
3-item knowledge subscale 382 4.7 1.1 2-6
4-item attitude subscale 323 4.7 2.0 2-12
Table 4 Item-total Correlations for Scale and Subscales, Survey on Colorectal Cancer Screening Among U.S. Veteransa
Subscales and items Full Scale Subscale
Attitudes Knowledge
Attitude subscale 0.88 1.00 −0.10
Worried FS would be embarrassing 0.54 0.60 0.02
Worried FS would be painful 0.59 0.63 0.06
Worried FOBT would be embarrassing 0.51 0.53 0.06
Worried FOBT would be painful 0.61 0.64 0.07
Knowledge subscale 0.39 −0.10 1.00
Heard of tests for colorectal cancer 0.25 −0.09 0.73
Know of FS 0.18 −0.17 0.69
Know of FOBT 0.33 0.00 0.67
a FS = flexible sigmoidoscopy; FOBT = fecal occult blood test.
Table 5 Relationship Between Changes in Scale and Colorectal Cancer Screening Behavior Among U.S. Veterans Participating in Survey and One-year Follow-up Interview (n=227)a
Screening behavior Decrease in Scale >4 points n=69 Decrease in Scale ⩽4 points n=158 P
Flexible sigmoidoscopy obtained 16 (23.2) 13 (8.2) .004
Colonoscopy obtained 13 (18.8) 20 (12.7) .23
Flexible sigmoidoscopy or colonoscopy obtained 25 (36.2) 33 (20.9) .02
Flexible sigmoidoscopy, colonoscopy, or FOBT performed 37 (53.6) 87 (55.1) .89
a Decrease in scale represents improvement in knowledge and attitudes. All values represent numbers (percentages) unless otherwise indicated. FOBT = fecal occult blood test.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Wolf MS, Rademaker A, Bennett CL, Ferreira MR, Dolan NC, Davis TC, et al. Development of a brief survey on colon cancer screening knowledge and attitudes among veterans. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0104.htm
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4 Byers T Levin B Rothenberger D Dodd GD Smith RA 1997 47 154 160 CA Cancer J Clin American Cancer Society guidelines for screening and surveillance for early detection of colorectal polyps and cancer: update 1997. American Cancer Society Detection and Treatment Advisory Group on Colorectal Cancer 9152173
5 48 2 19 6 1999 116 121 MMWR Morb Mortal Wkly Rep Screening for colorectal cancer – United States, 1997 10073920
6 Brown ML Potosky AL Thompson GB Kesseler LG 1990 19 562 574 Prev Med The knowledge and use of screening tests for colorectal and prostate cancer: data from the 1987 National Health Interview Survey 2235923
7 Farrands PA Hardcastle JD Chamberlain J Moss S 1984 6 12 19 Community Med Factors affecting compliance with screening for colorectal cancer 6705494
8 Price JH 1993 18 347 362 J Community Health Perceptions of colorectal cancer in a socioeconomically disadvantaged population 8120177
9 Wong NY Nenny S Guy RJ Seow-Choen F 45 7 2002 946 950 Dis Colon Rectum Adults in a high-risk area are unaware of the importance of colorectal cancer screening: a telephone and mail survey 12130885
10 Kelly RS Shank C 30 1992 1029 1042 Med Care Adherence to screening flexible sigmoidoscopy in asymptomatic patients 1434956
11 Myers RE Balshem AM Wolf TA Ross EA Millner L 31 6 6 1993 508 519 Med Care Adherence to continuous screening for colorectal neoplasia 8501998
12 Brenes GA Paskett ED 2000 31 416 410 Prev Med Predictors of stage of adoption for colorectal cancer screening 11006067
13 Ling BS Moskowitz MA Wachs D Pearson B Schroy PC 16 12 2001 822 830 J Gen Intern Med Attitudes toward colorectal cancer screening tests 11903761
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16 Vernon SW 1997 89 1406 1422 J Natl Cancer Inst Participation in colorectal cancer screening: a review 9326910
17 Vernon SW Myers RE Tilley BC 1997 6 825 832 Cancer Epidemiol Biomarkers Prev Development and validation of an instrument to measure factors related to colorectal cancer screening adherence 9332766
18 Mandelblatt J Andrews H Kao R Wallace R Kerner J 86 12 12 1996 1794 1797 Am J Public Health The late-stage diagnosis of colorectal cancer: demographic and socioeconomic factors 9003140
19 Cooper GS Yuan Z Rimm AA 6 4 1997 283 285 Cancer Epidemiol Biomarkers Prev Racial disparity in the incidence and case-fatality of colorectal cancer: analysis of 329 United States counties 9107433
20 Ionescu MV Carey F Tait IS Steele RJ 1998 352 1439 Lancet Socioeconomic status and stage at presentation of colorectal cancer 9807992
21 Tavani A Fioretti F Franceschi S Gallus S Negri E Montella M 28 3 1999 380 385 Int J Epidemiol Education, socioeconomic status and risk of cancer of the colon and rectum 10405837
22 Jha AK Perlin JB Kizer KW Dudley RA 2003 348 2218 2227 N Eng J Med Effect of the transformation of the Veterans Affairs Health Care System on the quality of care
23 Dolan NC Ferreira MR Davis TC Fitzgibbon ML Rademaker A Liu D 22 12 2004 2617 2622 J Clin Oncol Colorectal cancer screening knowledge, attitudes, and beliefs among veterans: does literacy make a difference? 15226329
24 Davis TC Crouch MA Long SW Jackson RH Bates P George RB 23 6 8 1991 433 435 Fam Med Rapid assessment of literacy levels of adult primary care patients 1936717
25 Davis T Crouch M Long S 4 1998 Red Lake Hospital Rapid estimate of adult literacy in medicine (REALM): examiner's instruction sheet Red Lake (MN)
26 Benson V Marano MA 189 1 1994 1 269 Vital Health Stat 10 Current estimates from the National Health Interview Survey, 1992 8154108
27 Davis T Dolan N Ferreira MR Tomori C Green KW Sipler AM 10 1 2001 193 200 Cancer Invest The role of inadequate health literacy skills in colorectal cancer screening
28 Willis GB 3 1994 Atlanta (GA) Centers for Disease Control and Prevention, National Center for Health Care Statistics, Office of Research and Methodology Cognitive interviewing and questionnaire design: a training manual
29 Bennett CL Gorby NS Ferreira MR Dolan NC Fitzgibbon ML Rademaker AW A focused CQI intervention can increase colorectal cancer screening rates among veterans: results from the Chicago VA project Bethesda (MD) 2004 Mar 14-16
30 Doak CC Doak LG Root JH 1996 Teaching patients with low literacy skills 2nd ed Lippincott Williams & Wilkins Philadelphia
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Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_04_0098
Original Research
PEER REVIEWEDKnowledge and Perceptions of Diabetes in an Appalachian Population
Tessaro Irene MA, MSN, DrPH Department of Health Promotion and Risk Reduction, Community Health Initiatives
PO Box 6275, School of Nursing, West Virginia University, Morgantown, WV 26506 [email protected]
304-293-5582
Smith Shannon L MA Department of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC
Rye Sheila MS Department of Health Promotion and Risk Reduction, School of Nursing, West Virginia University, Morgantown, WVa
4 2005
15 3 2005
2 2 A132005
Introduction
Qualitative research on knowledge and perceptions of diabetes is limited in the Appalachian region, where social, economic, and behavioral risk factors put many individuals at high risk for diabetes. The aim of this study was to gain a culturally informed understanding of diabetes in the Appalachian region by 1) determining cultural knowledge, beliefs, and attitudes of diabetes among those who live in the region; 2) identifying concerns and barriers to care for those with diabetes; and 3) determining the barriers and facilitators to developing interventions for the prevention and early detection of diabetes in Appalachia.
Methods
Thirteen focus groups were conducted in 16 counties in West Virginia in 1999. Seven of the groups were composed of persons with diabetes (n = 61), and six were composed of community members without diabetes (n = 40). Participants included 73 women and 28 men (n = 101).
Results
Findings show that among this population there is lack of knowledge about diabetes before and after diagnosis and little perception that a risk of diabetes exists (unless there is a family history of diabetes). Social interactions are negatively affected by having diabetes, and cultural and economic barriers to early detection and care create obstacles to the early detection of diabetes and education of those diagnosed.
Conclusion
Public health education and community-level interventions for primary prevention of diabetes in addition to behavior change to improve the management of diabetes are needed to reduce the health disparities related to diabetes in West Virginia.
==== Body
Introduction
West Virginia is a state with a disproportionately high burden of diabetes. During the last decade, West Virginia has consistently ranked among the highest diabetes prevalence and diabetes-related mortality of all the United States. In 1999, West Virginia had the third highest rate of death due to diabetes among all U.S. states (1) and ranked second in prevalence of diabetes with 7.6% of West Virginians having diabetes (2). Obesity, the most preventable cause of type 2 diabetes, has also been consistently higher in West Virginia than nationally; West Virginia has the second highest obesity rate in the nation (3).
The Appalachian region has a population with many social and health disparities contributing to the high rates of diabetes and obesity. High rates of poverty, low education, high unemployment, an aging population, and limited access to health care characterize many of the regions of Appalachia in general and West Virginia in particular (4). West Virginia is the second most rural state in the nation (5). Disparities in health status and risk factors among rural residents are well documented (6). Among all states, West Virginia has the nation's oldest population; nearly one third of West Virginians are older than 50 years (7). Additionally, 50 of West Virginia's 55 counties are designated as medically underserved (8). Of all states, West Virginia has the fourth highest percentage of adults aged 18 to 64 with no health care coverage (1).
Studies show that diabetes can be prevented or delayed in persons at high risk (primary prevention). For those with a high risk for diabetes, the benefits of interventions for physical activity and weight loss are clear (9,10). In the Diabetes Prevention Program, a large randomized clinical trial of 3234 individuals at 27 centers, a lifestyle intervention (e.g., physical activity, weight loss) was more effective than medicine in preventing or delaying the onset of diabetes in persons at high risk (10).
Community-wide public health interventions for physical activity and weight loss, community resources, and environmental changes can help reduce the morbidity and mortality from diabetes in West Virginia. Social and cultural factors are important elements in planning health promotion programs (11). Studies regarding the social and cultural influences on diabetes awareness and prevention that could help inform the design of community interventions are limited in the Appalachian region.
This paper presents data from focus group discussions with an underserved population of largely white, rural, aging participants in West Virginia, both those with and without diabetes. The existing qualitative research on lay perceptions and knowledge of diabetes focus on those with diabetes. Thus, studies of these perceptions before the onset of diabetes are also important to better understand cultural norms and beliefs about diabetes.
This research was a project of the Appalachian Diabetes Coalition based in West Virginia and funded by the Centers for Disease Control and Prevention. Its aim was to gain a cultural understanding of diabetes in the Appalachian region by 1) determining cultural knowledge, beliefs, and attitudes about diabetes; 2) identifying concerns and barriers to care for those with diabetes; and 3) determining the barriers and facilitators to developing interventions for the prevention and early detection of diabetes in Appalachia.
Methods
Data collection and sample
Thirteen focus groups were conducted in West Virginia over a five-month period in 1999. Seven groups were composed of 61 persons with diabetes, and six were composed of 40 community members without diabetes. All data were self-reported.
A sample of participants from 16 counties in six regions of the state was employed to represent all geographic areas of West Virginia. Research participant recruitment involved three steps: 1) designating communities for data collection, 2) identifying and involving community leaders, and 3) publicizing the focus group project on a local level to generate interest. Church leaders, activity and education coordinators, clinic and hospital staff, extension agents, and diabetes educators in the selected areas assisted with the recruitment process. Each focus group was conducted in a central community location as determined by community leaders who helped to organize the groups in their areas. Locations included health centers, clinics, hospital meeting rooms, churches, and senior centers (12).
Participants included 73 women and 28 men (n = 101) with a mean age of 59.1 years and 72.3% aged 50 or older. Participants with diabetes were on average older (64.5 years) than those without diabetes (53.3 years). The mean education level of all participants was 12.2 years, ranging from third grade to a completed master's degree. Twenty-seven participants (26.7%) did not complete a high school education. The mean education level of the participants with diabetes was 11.7 years, slightly less than the average of 12.9 years for those without diabetes. Overall, the sample was high-school educated. Most of the participants (94.1%) identified themselves as white, which reflects the population of West Virginia as a whole. According to 2000 census data, the population of West Virginia is predominantly (95%) white (13).
The authors moderated all focus groups. Groups were tape recorded, and a research assistant was present to take notes. Incentives to participants included health education print materials, a certified diabetes educator or health professional from the community to answer questions after the focus groups, and $25 to cover their time and transportation costs. The Institutional Review Board at West Virginia University approved the study.
Discussion questions were based on the explanatory model of illness (14), social learning theory (15), the health belief model (16), and social support theory (17). The discussion guide format elicited perspectives on diabetes and its management, including cultural knowledge, attitudes and beliefs about diabetes, prevention issues, early detection and health-seeking behavior, diabetes care issues, community health concerns, and information-seeking (Appendix). For those who did not have diabetes, the discussion guide was adjusted slightly to elicit general information helpful in interpreting cultural norms and attitudes about the disease. A brief eight-question survey was distributed at the beginning of each focus group to profile the participants demographically and assess the perception of risk of developing diabetes among participants from the general population.
Data analysis
Focus group discussions were transcribed and verified with the handwritten notes. Transcripts were reviewed and imported into NUD*IST, a computer software package for qualitative data analysis (QSR International, Melbourne, Australia) (18). Qualitative methods were used to analyze data (19). Analysis began by coding the responses of the participants according to their contexts and relevance to the research question. Patterns arose during the systematic coding process, and themes were then determined by the researchers according to concepts and issues the participants emphasized repeatedly within groups and between groups. These themes are presented and illustrated with quotations from focus group participants. All quotations are taken from participants with diabetes unless [G] is indicated, in which case the quotation was provided by a general population participant without diabetes.
Results
Cultural beliefs and perceived susceptibility
Heredity, obesity, and physical inactivity were all recognized as risk factors for diabetes, with heredity often being mentioned in combination with another cause. Those with and without diabetes said that the disease most likely developed from inactivity (laziness) and lack of self-discipline (eating too much sugar). Because of these beliefs, blame and guilt were often associated with the diabetes diagnosis, along with a perception that diabetes was self-induced. Those with diabetes often recounted a specific period or event in their lives that they attributed to the development of the disease. Accounts of periods of inactivity and weight gain, pregnancy, stress, or a time where specific sugary foods were eaten were recalled. Respondents with the disease thought they could accurately identify their individual causes; however, without the classic risk factors of diabetes — overweight, a sedentary lifestyle, and particularly heredity — many discussants could not understand how they developed diabetes or why others with these risk factors did not have diabetes: "As far as I know, I'm the only one that had diabetes. None of my real blood kin has it. So why did I get it?"
A common belief about heredity was that diabetes only strikes every other generation of a family. Some participants felt that this was true for them or had heard about this happening to others: "Ours is hereditary, but it skips a generation. Not everybody gets it."
Through a short survey, participants in the general population (i.e., those without diabetes) focus groups were asked about their perceptions of risk of acquiring diabetes. As the Table shows, more than one third (35.9%) of participants did not know their risk. Almost half of those who had a family history of diabetes felt they were at high risk for developing diabetes, and no respondents without a family history of diabetes felt they were at high risk. Half of those without a family history of diabetes did not know their risk; the other half considered they were at small or average risk.
Barriers to early detection
The inability to recognize symptoms of diabetes was cited as a major barrier to early detection and diagnosis. Participants generally perceived that if there were no recognizable symptoms, there was no need to go to a doctor or to think they were at risk. Not going to doctors was often mentioned as a barrier to early detection: "There is a lot of people that just never go to the doctor. . . . They were raised that way."
Participants felt that a lot of people did not want to know they had diabetes, particularly because it put a burden on the family. Transportation and inability to afford care were issues mentioned as general barriers to seeking care. For West Virginians, this is especially problematic because this state is the second most rural in the nation, with one of the lowest per capita incomes of all states: "I would say in a lot of rural areas, you have a lot of people who have an inability to go from point A to point B, just actually not being able to get there, period." [G]
Diabetes was considered a burdensome disease by most, a condition to be feared with severe complications. Some felt this fear was why people did not go to the doctor; people did not want to burden their family with their diabetes. There was also fear of the consequences of diabetes, especially amputation and blindness, as in the following example: "I think it's worse than cancer. I put it higher than cancer. Because it is long term. It's a slow process of dying. Where cancer seems to be more quick. . .where cancer, I hate to say it, it's not short and sweet. It's just short." [G]
Knowledge about diabetes
Participants from the general population knew very little about diabetes, and those with diabetes knew little before diagnosis. Unless there was a family member with diabetes, there was little reason to be concerned or to have to know about diabetes. Most participants with diabetes never recognized symptoms before diagnosis and were diagnosed when under care for another health problem or on a routine visit.
Once diagnosed, participants reported they received little information from professionals to help them deal with the disease. They lacked knowledge in many areas — diet, physical activity, and resource information. Participants created alternative approaches to self-management according to their acquired knowledge about diabetes, which frequently appeared to be incorrect or incomplete. Participants felt they had caused the disease themselves, so the responsibility for controlling the disease fell heavily on them.
Participants consistently mentioned as issues lack of education by physicians about diabetes and lack of time spent with clients by physicians. They felt doctors knew little about nutrition, tended to prescribe medicine almost exclusively, and assumed people had money to pay for equipment, such as test strips. Additionally, they felt like they were being rushed and often forgot what they wanted to ask about their diabetes. Doctors did not usually explain what prescribed medicine was for or what the side effects could be.
The cost of care was another major concern for individuals with diabetes. Many felt physicians did not understand or were unwilling to deal with cost issues:
"There is lots of things I have go wrong that I need to tell the doctor. But I know that I can't go out here and pay for all these tests, so I will keep it to myself. I don't even tell him because I know he's going to want extensive blood work of this or that and I don't do it, so I keep it to myself."
"I told my doctor the same thing. If you do not give me my medicine, there is no way I can afford the medicine. I will just have to die early I reckon."
Participants with diabetes wanted more information about their condition but often did not know where to go to get it. Dietitians and diabetes educators offered information when they were available. However, few diabetes educators or nutritionists are located in West Virginia, particularly in rural areas. Participants felt few affordable health professionals or educational programs could give them the information they needed to deal with their diabetes. Some participants sought information from members of their social networks, the pharmacy, or the health department. Many relied on magazines for new information. Almost all had access to a general practitioner, but few to diabetes specialists.
Although persons with diabetes seemed to recognize the relationship between physical activity and blood glucose control, very little discussion of actual exercise behaviors generated around it. Lack of energy from diabetes, the cost associated with exercise because of the need to check blood levels before and after exercise, and lack of community resources were cited as barriers to exercise: "There again is a vicious circle because the more you sit around, the fatter you get. You eat and you sit some more and you just keep going in the circle." Although weight loss was readily identified as a means of controlling the disease, the focus remained on methods other than physical activity, such as a reduced-calorie or reduced-fat diet.
Social relationships
Participants with diabetes discussed the effect the disease had on their social relationships. Because of certain beliefs related to diabetes causation, particularly laziness and eating too much of the wrong foods, diabetes was perceived as a self-induced disease. The stigma associated with a disease perceived as self-induced and not under control, along with the perception that others feel the same way, led to participants' concern that others treated them differently. They also expressed feeling depressed because others did not understand how diabetes was affecting them. Having support from others with diabetes who are dealing with similar issues was mentioned by participants as something that could help them cope with diabetes.
Furthermore, a number of participants expressed that others may not really think they are sick because they do not look sick:
"You know, I think part of the reason people think that way is because we do look healthy. I mean, we don't look any different. You can't see any problem that we have. Diabetes is usually a slow acting disease. You know it takes years and years before you see anything bad come from it."
The effect of diabetes on social relationships was not limited to those with diabetes. Participants from the general population focus groups felt that at times they did treat persons with diabetes differently:
"You find yourself getting short tempered. You get irritated real easy sometimes. You get irritated with other people because they have to eat on time; they take their shots. You get into a restaurant and here they've taken their meds waiting on their food because it has to be in their blood system for at least 30 minutes. They're just taking their time. Yeah, you get irritated [with a person with diabetes]." [G]
Discussion
This qualitative research was conducted to gain insight into the cultural understandings surrounding diabetes in West Virginia. Participants lacked knowledge about diabetes before and after diagnosis and had little perception of risk of diabetes other than a family history of diabetes. Social interactions were reported to be negatively affected by having diabetes, and cultural and economic barriers to early detection and care create obstacles to the early detection of diabetes and education once diagnosed.
Poverty is an integral component of West Virginian culture, and economic circumstances dictate many behaviors. In a resource-poor area such as West Virginia, individuals adopt creative strategies for coping with diabetes because they lack access or have only limited access to diabetes education, health care, health care providers with knowledge to educate patients about diabetes and its management, exercise facilities, and the types of food needed (20). Decisions about early detection of diabetes and care-seeking are frequently made from the integration of cultural values with the pervading poverty. Socioeconomic factors appear to be major influences on health-related decision making, creating disparities in the diagnosis and treatment of diabetes in this Appalachian sample.
Our focus groups found that the health care system provided little information to persons diagnosed with diabetes, making it difficult for those persons to find affordable education to manage diabetes. Participants reported that their physicians knew little about the disease. Those with diabetes lacked knowledge about diet, physical activity, and resource information. It was clear from the focus groups that persons with diabetes needed information tailored to their individual needs, whether it be medication, health behavior change, or coping with a diagnosis of diabetes.
Because of its chronic nature, diabetes requires daily management to control blood glucose levels, including a dietary regimen, regular exercise, routine monitoring of blood glucose levels, regular physician office visits, and for some, daily hypoglycemic agents. A person with diabetes adjusts and adapts to these modifications and restrictions within the framework of his or her cultural influences, economic circumstances, knowledge, and resources, regardless of clinical recommendations (21). Because the concerns of the patient and practitioner surrounding illness and treatment often differ, discord between clinical and lay models is often medically labeled as nonadherence (22,23). What is medically labeled as nonadherent behavior, however, is often a common-sense adaptation for the patient from within his or her belief framework, cultural context, and outside influences, such as financial constraints, limited knowledge, and lack of availability of appropriate medical care and/or facilities. Without knowledge about their disease, nutritional education, and access to affordable and/or appropriate items, people often rely on information that is partial, outdated, or incorrect (23). If persons are not satisfied with the information they are receiving from their health care providers, they will seek alternative sources to help them manage their disease. In short, lack of knowledge and high costs hamper preventive health behaviors in rural Appalachia (24).
The stigma associated with diabetes because of self-blame has been shown to negatively affect social relationships. Cohen et al (21) found that managing diabetes was a major obstacle to social relationships, contributing to such events as divorce, loss of jobs, sexual problems, and infertility. Maclean (22) found that those with diabetes were very concerned about how others would treat them once they discovered they had diabetes, so they avoided situations in which they were likely to be treated differently. The current study found that while those with diabetes did not want to be treated differently, they also felt others did not understand what they were going through. Stigma is a social label, defined within a cultural context, that changes the way individuals view themselves and are viewed by others (25). Persons with diabetes are "blamed" for their disease because of the perception of responsibility both on the part of those with and without diabetes. It is not surprising, then, that support from others with diabetes who were dealing with similar issues was repeatedly brought up as an important strategy for change in these focus groups.
Implications for public health
Several findings of this study point to the need for more public health education and community-level interventions for primary prevention of diabetes and behavior change to improve the management of diabetes and to reduce the health disparities related to diabetes in West Virginia. Primary prevention of diabetes is becoming more important because of findings from the lifestyle modification prevention trials (26). Health promotion programs that combine both behavioral and social and physical environmental change strategies can provide a more comprehensive approach to addressing diabetes (27). Several behavioral and environmental intervention strategies have the potential for the primary prevention of diabetes in addition to behavioral change for those with diabetes in rural West Virginia. Health communications that address a person's unique situation and personal characteristics have been shown effective for changing health behaviors for a variety of health issues and populations (28). Social network interventions, such as lay or natural helpers, build on the naturally existing sources of social and community support to diffuse health information and provide support for behavioral and social change (29). For those with diabetes, support and health information, through formal or informal groups, can be beneficial (30). Group medical visits have been suggested for some chronic diseases and could be an effective strategy to provide support as well as education and disease management (31).
This research was supported by grant U48/CCU310821 from the Centers for Disease Control and Prevention awarded to the Appalachian Diabetes Coalition of the Prevention Research Center of West Virginia University, Morgantown, WVa.
Appendix: Diabetes Focus Group Scripts
Persons Without Diabetes (focus group script)
Cultural knowledge, beliefs, and attitudes about diabetes
What comes to mind when you think about diabetes?
How much of a concern is diabetes for you personally?
What concerns you most about diabetes?*
What do you know about diabetes?
What do you think causes diabetes?*
Who are the people most at risk for developing diabetes?*
When does diabetes develop?*
What are the symptoms or signs of diabetes?
What are some of the problems diabetes can cause?
Do you know persons who have diabetes? How has diabetes affected their life?* Probe: problems, reactions of others
Prevention and early detection of diabetes
What prevents people from finding out they have diabetes?*
What do you think is the best way to detect diabetes early?*
What do you think people can do to prevent getting diabetes?*
What behavior changes can one make to prevent diabetes?* What information or help would you need to make these behavior changes?
Do you think people need more information about diabetes?* What kind of information would be most needed?*
What is the best way to get health information to people about diabetes and early detection?* Probe: community, church, medical, media, print materials
Persons With Diabetes (focus group script)
Cultural knowledge, beliefs, and attitudes about diabetes
What concerns you most about having diabetes?*
How has diabetes affected your life?* Probe: problems, reactions of others, behavior change
What do you think causes diabetes?*
Who are the people most at risk for developing diabetes? *
When does diabetes develop?*
Prevention and early detection of diabetes
What prevents people from finding out they have diabetes?*
What do you think is the best way to detect diabetes early?*
What do you think people can do to prevent getting diabetes?*
What behavior changes can one make to prevent diabetes?*
Do you think people need more information about diabetes?* What kind of information would be most needed?*
What is the best way to get health information to people about diabetes and early detection?* Probe: community, church, medical, media, print materials
Diabetes care issues
What are the ways you take care of your diabetes?Probe: treatment (biomedical), behavior change, alternative/folk medicine
Probe: advantages, disadvantages
What concerns do you have about getting health care for your diabetes?What problems have you encountered getting care and treatment?
What do you want to know about diabetes that you don’t already know?
What kind of information would help you most with your diabetes?
What is the best way to get this information to you?Probe: community, church, medical, media, print materials
Figures and Tables
Table Perceived Risk for Diabetes Among Study Participants Without the Disease, West Virginia, 1999a
Perceived Risk Family History of Diabetes (n=21) No Family History of Diabetes (n=18) Total (N=39)
High 10 (47.6) 0 (0) 10 (25.6)
Average 5 (23.8) 4 (22.2) 9 (23.1)
Small 1 (4.8) 3 (16.7) 4 (10.3)
No risk 0 (0) 2 (11.1) 2 (5.1)
Don’t know risk 5 (23.8) 9 (50.0) 14 (35.9)
a Values are numbers (percentages). N = 39 because responses on family history of diabetes and perceived risk were not provided by one of the 40 participants.
* same question in both groups
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Tessaro I, Smith SL, Rye S. Knowledge and perceptions of diabetes in an Appalachian population. Prev Chronic Dis. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0098.htm
==== Refs
1 Centers for Disease Control and Prevention, U.S. Department of Health and Human Services 2002 The burden of chronic diseases and their risk factors: national and state perspectives Atlanta (GA) Centers for Disease Control and Prevention
2 West Virginia Department of Health and Human Resources 2002 Charleston (WV) West Virginia Department of Health and Human Services The burden of diabetes in West Virginia Available from: URL: http://www.wvdhhr.org/bph/oehp/diabetes/burden
3 Obesity prevalence among U.S. adults [Internet] Washington (DC) The Henry J. Kaiser Family Foundation 2001 Available from: URL: http//www.statehealthfacts.org
4 Appalachia Leadership Initiative on Cancer (ALIC) 1994 Bethesda (MD) Sowing seeds in the mountains National Cancer Institute
5 U.S. Census Bureau 10 1995 Washington (DC) U.S. Census Burea Urban and Rural Population 1900-1990
6 University of Pittsburgh Center for Rural Health Practice 2004 Bradford (PA) Bridging the health divide: the rural public health research agenda University of Pittsburgh Center for Rural Health Practice
7 West Virginia aging health status report [Internet] Charleston (WV) West Virginia Department of Health and Human Resources 2004
8 Medically underserved [Internet] Charleston (WV) West Virginia Department of Health and Human Resources 2004 Available from: URL: http://www.wvrecruitment.org
9 James SA Jamjoum L Raghunathan TE Strogatz DS Furth ED Khazanie PG 1998 21 555 562 Diabetes Care Physical activity and NIDDM in African-Americans 9571342
10 Knowler WC Barrett-Connor E Fowler SE Hamman RF Lachin JM Walker EA Diabetes Prevention Research Group 346 2002 393 403 N Engl J Med Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin 11832527
11 Kreuter MW Lukwago SN Bucholtz RD Clark EM Sanders-Thompson V 2003 30 133 146 Health Educ Behav Achieving cultural appropriateness in health promotion programs: targeted and tailored approaches 12693519
12 Smith SL Blake K Olson CR Tessaro I 2002 18 118 123 J Rural Health Community entry in conducting rural focus groups: process, legitimacy, and lessons learned 12043750
13 West Virginia quick facts [Internet] Washington (DC) U.S. Census Bureau 2000
14 Kleinman A 1980 Berkeley (CA) Patients and healers in the context of culture University of California Press
15 Bandura AJ 1996 Englewood Cliffs (NJ) Prentice-Hall Social foundations of thought and action: a social cognitive theory
16 Janz NK Champion VL Strecher VJ Glanz K Rimer BK Lewis F Jossey-Bass San Francisco 3rd ed 2002 45 66 Health behavior and health education: theory, research and practice The health belief model
17 Berkman LF 1995 57 245 254 Psychosom Med The role of social relations in health promotion 7652125
18 1998 N4 qualitative data analysis program [software] QSR International Melbourne (Australia)
19 Patton MQ 1990 SAGE Publications Thousand Oaks (CA) Qualitative evaluation methods 2nd ed
20 Schoenberg NE Amey CH Coward RT 1998 47 2113 2125 Soc Sci Med Stories of meaning: lay perspectives on the origin and management of noninsulin dependent diabetes mellitus among older women in the United States 10075251
21 Cohen MZ Tripp-Reimer T Smith C Sorofman B Lively S 1993 38 59 66 Explanatory models of diabetes: patient practitioner variation Soc Sci Med
22 MacLean HM 1991 32 689 696 Soc Sci Med Patterns of diet related self-care in diabetes 2035045
23 Murphy E Kinmonth AL 1995 12 184 192 Family Pract No Symptoms, no problem? Patients' understandings of non insulin dependent diabetes
24 Elnicki DM Morris DK Shockcor WT 1995 155 421 424 Arch Intern Med Patient-perceived barriers to preventive health care among indigent, rural Appalachian patients 7848026
25 Goffman E 1963 Prentice-Hall Stigma: notes on the management of spoiled identity Englewood Cliffs (NJ)
26 Bowman BA Gregg EW Williams DE Engelgau MM Jack L 11 2003 Suppl S8 S14 J Public Health Manag Pract Translating the science of primary, secondary, and tertiary prevention to inform the public health response to diabetes
27 Stokols D 1996 10 282 298 Am J Health Promot Translating social ecological theory into guidelines for community health promotion 10159709
28 Kreuter MW Farrell D Olevitch L Brennan L 2000 Mahwah (NJ) Tailoring health messages: customizing communication with computer technology Lawrence Erlbaum
29 Collin AH Pancoast D 1976 Washington (DC) National Association of Social Workers 17 31 Overview of natural helping networks. In: National helping networks
30 Morris DB 1998 24 493 497 Diabetes Educ A rural diabetes support group 9830953
31 Bodenheimer T Lorig K Holman H Grumbach K 2002 19 2469 2475 JAMA Patient self-management of chronic disease in primary care
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Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_04_0095
Original Research
PEER REVIEWEDRural Community Knowledge of Stroke Warning Signs and Risk Factors
Harwell Todd S MPH Montana Department of Public Health and Human Services
Cogswell Building, C-314, PO Box 202951, Helena, MT 59620-2951 [email protected]
406-444-1437
Blades Lynda L MPH, CHES Montana Department of Public Health and Human Services, Helena, Mont
Oser Carrie S MPH Montana Department of Public Health and Human Services, Helena, Mont
Fogle Crystelle C MBA, MS, RD Montana Department of Public Health and Human Services, Helena, Mont
Helgerson Steven D MD, MPH Montana Department of Public Health and Human Services, Helena, Mont
Gohdes Dorothy MD Montana Department of Public Health and Human Services, Helena, Mont
Dietrich Dennis W MD Benefis Healthcare, Great Falls, Mont
Burnett Anne M RN, MN Benefis Healthcare, Great Falls, Mont
Okon Nicholas J DO St Vincent Healthcare, Billings, Mont
Allen Martha J RN, BSN St Vincent Healthcare, Billings, Mont
Rodriguez Daniel V MD Deaconess Billings Clinic, Billings, Mont
Russell Joseph A NREMT-P City of Great Falls Fire and Rescue, Great Falls, Mont
4 2005
15 3 2005
2 2 A142005
Introduction
Rapid identification and treatment of ischemic stroke can lead to improved patient outcomes. Public education campaigns in selected communities have helped to increase knowledge about stroke, but most data represent large metropolitan centers working with academic institutions. Much less is known about knowledge of stroke among residents in rural communities.
Methods
In 2004, 800 adults aged 45 years and older from two Montana counties participated in a telephone survey using unaided questions to assess awareness of stroke warning signs and risk factors. The survey also asked respondents if they had a history of atrial fibrillation, diabetes, high blood pressure, high cholesterol, smoking, heart disease, or stroke.
Results
More than 70% of survey participants were able to correctly report two or more warning signs for stroke: numbness on any side of the face/body (45%) and speech difficulties (38%) were reported most frequently. More than 45% were able to correctly report two or more stroke risk factors: smoking (50%) and high blood pressure (44%) were reported most frequently. Respondents aged 45 to 64 years (odds ratio [OR] 2.44; 95% confidence interval [CI], 1.78–3.46), women (OR 2.02; 95% CI, 1.46–2.80), those with 12 or more years of education (OR 1.96; 95% CI, 1.08–3.56), and those with high cholesterol (OR 1.68; 95% CI, 1.17–2.42) were more likely to correctly identify two or more warning signs compared with respondents without these characteristics. Women (OR 1.48; 95% CI, 1.07–2.05) and respondents aged 45 to 64 years (OR 1.35; 95% CI, 1.01–1.81) were also more likely to correctly identify two or more stroke risk factors compared with men and older respondents.
Conclusion
Residents of two rural counties were generally aware of stroke warning signs, but their knowledge of stroke risk factors was limited.
==== Body
Introduction
Public health efforts to promote stroke awareness and the need to seek urgent treatment have assumed a new importance in the years since the publication of a major clinical trial showing decreased short-term disability and improved outcomes for patients experiencing an ischemic stroke after thrombolytic therapy (1). Prehospital barriers to prompt treatment for ischemic stroke include the lack of awareness of stroke warning signs in patients and family members, underuse of 911 emergency medical services (EMS), and long distances to tertiary-care facilities that provide diagnostic and treatment services (2-5). These barriers can lead to delayed presentation to the emergency department and to ineligibility for time-dependent treatment. Achieving increased use of thrombolytic therapy within the three-hour window in one community required a multilevel intervention to influence the knowledge and behavior of the public, the response of EMS, and the coordination of diagnostic and treatment facilities at the hospitals (6,7).
From a public health perspective, an important component for the success of stroke interventions is to improve public knowledge about stroke, particularly focusing on individuals at high risk and their family members and caregivers. Public education campaigns in selected communities have been effective in increasing the level of knowledge about stroke, but most data have come from large metropolitan centers working with academic institutions (7,8). Much less is known about basic knowledge of stroke symptoms, risk factors, and the need for urgent intervention among residents in rural communities that are relatively isolated from major metropolitan centers.
We conducted a telephone survey in two rural counties in Montana in 2004. This report describes the level of awareness of stroke warning signs and risk factors and the public perception of the need to call 911 EMS for stroke in residents aged 45 years and older.
Methods
Setting
The population for this study included residents living in Cascade and Yellowstone counties, which include the cities of Great Falls (Cascade County) and Billings (Yellowstone County). The 2000 census population for Cascade County was 80,357 (9). Fourteen percent of the population was aged 65 years and older and 23% was aged 45 to 64 years. The majority of residents were white (91%) or American Indian (4%). The 2000 census population for Yellowstone County was 129,352. Thirteen percent of the Yellowstone County population was aged 65 years and older, and 23% was aged 45 to 64 years. Similar to Cascade County, the majority of residents were white (93%) or American Indian (3%). Both Cascade and Yellowstone counties are classified as rural counties — Cascade County has a population density of 29.8 persons per square mile, and Yellowstone County has a population density of 49.1 persons per square mile. These communities are served by three tertiary-care hospitals that provide comprehensive stroke diagnostic and treatment services. These facilities provide services for large multicounty areas that extend across state boundaries.
Telephone survey
From February 2004 through April 2004, the Montana Department of Public Health and Human Services conducted a random-digit–dial telephone survey of adults aged 45 years and older living in Cascade (n = 400) and Yellowstone (n = 400) counties. Eligible persons living in households with more than one eligible respondent were randomly selected. A trained interview team using computer-assisted telephone interviewing software conducted the survey. The survey was field tested to detect potential problems with questions or answer categories and then revised as needed. A total of 3520 calls were made as part of the survey. Of these calls, 1002 (28%) were nonworking numbers, 754 (21%) were households with no eligible respondent, 426 (12%) were not private residences, and 252 (7%) were no answer/answering machine or busy. Of the remaining calls to persons in eligible households (n = 1086), 800 (74%) were completions, 224 (21%) were refusals, 39 (4%) were unable to complete due to communication/language barriers, and 23 (2%) were not completed because the eligible respondent was not available during the interviewing period. Up to 15 attempts were made to complete unanswered calls.
The survey included questions on the warning signs and risk factors for stroke, use of 911 EMS, previous diagnoses of risk factors for stroke, and demographic information. Open-ended questions adapted from Pancioli et al were used to assess respondents' knowledge of the warning signs and risk factors for stroke (2). Respondents were prompted to name up to three warning signs and three risk factors for stroke. Respondents were asked four questions adapted from Yoon and colleagues to identify what they would do if they witnessed someone having a stroke or if they experienced sudden stroke warning signs including numbness, paralysis, and speech problems that would not go away (10). Respondents were also asked a series of questions from the Behavioral Risk Factor Surveillance System Survey to identify if they had a history of heart attack, angina, coronary heart disease, stroke, transient ischemic attack (TIA), atrial fibrillation, diabetes, high blood pressure, or high cholesterol and if they currently smoked cigarettes (11). Respondents who reported a history of a heart attack, angina, or coronary heart disease were classified as having a history of heart disease. Female respondents who had been told only that they had gestational diabetes were not categorized as persons with a current diagnosis of diabetes. Respondents who reported that they smoked cigarettes every day or some days were categorized as current smokers.
Based on current recommendations from national organizations (12-15), we considered the following as established warning signs for stroke: dizziness, difficulty understanding speech or slurred speech, severe headache, problems with vision, weakness on one or both sides of body or face, numbness on one or both sides of body or face, trouble walking, loss of balance, or lack of coordination. We considered high blood pressure, high cholesterol, smoking, diabetes, heavy alcohol use, history of heart disease, and history of stroke or TIA to be established stroke risk factors.
Data analyses
Data analyses were completed using SPSS V11.5 software (SPSS Inc, Chicago, Ill). Chi-square tests were used to compare differences in respondent knowledge of two or more warning signs, two or more risk factors for stroke, and use of 911 EMS by age, sex, and history of stroke risk factors. Multiple logistic regression analyses were conducted to identify demographic and self-reported risk factors independently associated with knowledge of warning signs and risk factors for stroke.
Results
The mean age of respondents (N = 800) was 61 years (range 45 to 95); 60% were female; 96% were white; 2% were American Indian; and 93% reported 12 or more years of education. Ten percent reported a history of atrial fibrillation; 6% reported a history of diabetes; 37% reported a history of high blood pressure; 31% reported a history of high cholesterol; 17% currently smoked cigarettes; and 40% were former smokers. Eight percent reported a history of heart disease, and 6% reported a history of stroke or TIA. Overall, 80% reported one or more risk factors for stroke, and 56% reported two or more risk factors for stroke.
Numbness on any side of the face or body (45%) and speech problems (38%) were the most frequently reported established warning signs for stroke (Table 1). Fewer respondents reported vision problems (18%) or difficulty walking (11%). Overweight (56%), smoking (50%), and high blood pressure (44%) were the most frequently reported risk factors for stroke (Table 1).
The majority of respondents (70%) could identify two or more warning signs for stroke (Table 2). Women (75%) were more likely than men (62%) to identify two or more established warning signs for stroke, and respondents aged 45 to 64 years (76%) were more likely than those aged 65 years and older (59%) to identify two or more established warning signs for stroke. Just under half of the respondents (45%) could identify two or more established risk factors for stroke. Respondents aged 45 to 64 years (48%) were more likely to identify two or more established risk factors for stroke compared with those aged 65 years and older (40%).
Adjusting for multiple factors using logistic regression analyses, women, individuals aged 45 to 64 years, those with 12 or more years of education, and individuals with a history of high cholesterol were more likely to identify two or more established warning signs for stroke compared with respondents without these characteristics (Table 3). Women and respondents aged 45 to 64 years were more likely to identify two or more established risk factors for stroke compared with men and with respondents aged 65 years and older.
Overall, the majority of respondents (76%) indicated they would call 911 EMS if they witnessed someone having a stroke (Table 4). There were no differences by age, sex, or years of education in the proportion of respondents who indicated they would call 911 if they witnessed someone having a stroke (data not shown). When asked what they would do if they were experiencing sudden difficulty speaking, numbness, or weakness or paralysis, 43% to 49% of individuals indicated they would call 911. Depending on the symptom, 17% to 23% indicated they would go to the hospital, 14% to 19% would call their doctor, 11% to 18% would call their spouse or a family member, and 3% to 5% would do something else (Table 4). Respondents aged 65 years and older were more likely than respondents aged less than 65 to indicate they would call 911 if they experienced sudden difficulty speaking (49% vs 42%, P = .04), numbness (50% vs 40%, P = .006), or weakness or paralysis (55% vs 45%, P = .004). There were no differences by sex or years of education in the proportion of respondents who indicated they would call 911 if they experienced any of these warning signs (data not shown).
Discussion
The majority of respondents from these rural counties were aware of the established warning signs for stroke, and awareness was higher in women, younger respondents, those with a higher level of education, and those with a history of high cholesterol compared with respondents without these characteristics. Interestingly, respondents with a history of other major stroke risk factors (e.g., high blood pressure) were no more aware of the warning signs compared with respondents without these conditions. Overall, fewer respondents were aware of the established risk factors for stroke. We also found that the majority of respondents would call 911 if they thought someone was having a stroke, but less than half would call 911 if they were experiencing stroke warning signs.
Individuals responding to the survey lived in communities that are typical of many communities across the United States, where health care for a large region is centered in a nearby town. Awareness of established stroke warning signs and risk factors was higher than awareness levels reported in other community surveys (10,16,17). In 1999, only 30% of adults surveyed in Michigan identified two or more warning signs correctly, and 34% identified two or more risk factors correctly (17). In 2000, more than 40% of respondents in Cincinnati, Ohio, identified two or more warning signs for stroke, and 32% identified two or more established risk factors for stroke (16). A study of adults living in an urban area of Australia in 1999 found that 26% of respondents to a telephone survey identified two or more warning signs for stroke, and 50% identified two or more risk factors for stroke (10).
Our findings on what respondents would do if they witnessed someone having a stroke or if they were experiencing warning signs of stroke are comparable to previous studies from Australia and Michigan (10,17). In Australia, 67% of respondents would call an ambulance if they witnessed someone having a stroke, while less than half of respondents would call an ambulance if they were experiencing sudden stroke warning signs (45% difficulties with speech, 38% numbness/weakness, 35% weakness or paralysis). In Michigan, however, 79% indicated they would call 911 if someone was having a stroke.
There are a number of limitations to this study. First, the survey does not reflect the experience of residents without telephones. Second, self-reported information regarding risk factors for stroke is subject to recall bias. Previous studies, however, have found that self-reported risk factors for cardiovascular disease are reported reliably (18,19). Third, respondents were asked unaided questions to assess respondent knowledge of the warning signs and risk factors for stroke. Some previous studies assessing awareness of stroke warning signs used aided questions and found higher levels of knowledge than the levels found in this study (20). It is possible that unaided questions may underestimate the awareness of the warning signs of stroke, and aided questions may overestimate awareness. Fourth, this study was conducted in a rural non-Hispanic white population, and there may be significant variation in awareness of stroke warning signs and risk factors in other geographic and racial and ethnic communities in the United States.
Our findings about stroke awareness in rural communities are important because they are similar to studies published from academic centers working in urban areas (10,16,17). This effort, however, represents a collaboration between a state public health agency and several regional health care systems not only to understand the levels of stroke awareness in two communities at baseline but also to promote community awareness and increased use of EMS and to define regional approaches for prompt stroke treatment in cooperation with the stroke referral centers in these counties. Based on the findings reported here, the Montana Cardiovascular Health Program and these partners have developed and implemented a multifaceted intervention to increase community awareness of the warning signs of stroke and the need to use 911 adapting strategies that have been shown to be successful (8,21).
It is reassuring that levels of community awareness and knowledge about stroke in rural settings are not markedly different from levels found in urban environments. Others have shown that multifaceted interventions to increase community awareness, use of EMS, and availability of prompt diagnosis and treatment can succeed (7,8). Strategies to reduce barriers to prompt stroke treatment may be somewhat different across large frontier areas compared with urban environments. Cooperative efforts between public health agencies, communities, and prehospital and acute care systems, however, can build on the published experience of others to understand and improve knowledge about stroke warning signs and risk factors as part of broader public health interventions targeted to stroke prevention and treatment. In the United States, public health programs are only beginning to develop partnerships and work toward developing and documenting successful interventions in a wide variety of settings. This report represents the beginning of efforts to meet the challenges in rural communities in Montana and across many rural areas.
The authors thank Linda Priest and staff members from Northwest Resource Consultants for their work and expertise conducting the telephone survey and the Great Falls Stroke Coalition for their support of this project. This project was supported through cooperative agreement with the Centers for Disease Control and Prevention, Division of Adult and Community Health (U50/CCU821287-02) in Atlanta, Ga.
Figures and Tables
Table 1 Perceptions of Stroke Warning Signs and Risk Factors Among Survey Respondents Aged ⩾45 Years (N = 800), Montana, 2004a
Responses No. (%)
Warning signs
Numbness (any side of face or body)b 360 (45)
Speech difficultiesb 300 (38)
Do not know 306 (38)
Dizzinessb 278 (35)
Headacheb 204 (26)
Weakness (any)b 198 (25)
Other 174 (22)
Vision problemsb 140 (18)
Shortness of breath 108 (14)
Difficulties walkingb 85 (11)
Risk factors
Overweight 445 (56)
Smokingb 400 (50)
High blood pressureb 351 (44)
Lack of exercise 208 (26)
High cholesterolb 172 (22)
Do not know 169 (21)
Stress 155 (19)
Alcohol useb 101 (13)
Other 96 (12)
Family history of heart disease 58 (7)
Diabetesb 57 (7)
Family history of stroke 45 (6)
History of heart diseaseb 40 (5)
a Only warning signs and risk factors with at least 5% responding are listed.
b Established warning signs and risk factors.
Table 2 Knowledge of Established Stroke Warning Signs and Risk Factors, Overall and by Age and Sex, Montana, 2004
Total
(N = 800)
No. (%) Male
(n = 322)
No. (%) Female
(n = 478)
No. (%) Aged 45–64
years
(n = 511)
No. (%) Aged 65+
years
(n = 287)
No. (%)
Number of warning signs
One or more 697 (87) 272 (85) 425 (89) 461 (90)a 234 (82)
Two or more 557 (70) 200 (62) 357 (75)a 387 (76)a 169 (59)
Three 311 (39) 111 (35) 200 (42)a 225 (44)a 85 (30)
Number of risk factors
One or more 683 (85) 262 (81) 421 (88)a 443 (87) 238 (83)
Two or more 360 (45) 133 (41) 227 (48) 243 (48)a 116 (40)
Three 55 (7) 16 (5) 39 (8) 41 (8) 14 (5)
a P ⩽.05 for comparisons by sex and age category.
Table 3 Factors Independently Associated With Awareness of Two or More Warning Signs and Risk Factors for Stroke Among Respondents Aged ⩾45 Yearsa
Factor Odds Ratio (95% Confidence Interval)
Knowledge of two or more warning signs
Sex (female) 2.02 (1.46-2.80)
Age (45-64 years) 2.44 (1.78-3.46)
Education level (⩾12 years) 1.96 (1.08-3.56)
Atrial fibrillation 0.77 (0.46-1.29)
Diabetes 0.92 (0.48-1.76)
High blood pressure 0.94 (0.67-1.33)
High cholesterol 1.68 (1.17-2.42)
History of heart diseaseb 1.54 (0.84-2.83)
History of stroke or transient ischemic attack 1.20 (0.62-2.32)
Current smoker 1.00 (0.63-1.59)
Former smoker 1.13 (0.79-1.61)
Knowledge of two or more risk factors
Sex (female) 1.48 (1.07-2.05)
Age (45-64 years) 1.35 (1.01-1.81)
Education level (⩾12 years) 0.75 (0.41-1.35)
Atrial fibrillation 0.75 (0.46-1.22)
Diabetes 1.50 (0.82-2.74)
High blood pressure 1.19 (0.87-1.62)
High cholesterol 1.11 (0.81-1.52)
History of heart diseaseb 1.23 (0.71-2.10)
History of stroke or transient ischemic attack 1.39 (0.76-2.52)
Current smoker 1.18 (0.78-1.78)
Former smoker 1.16 (0.85-1.60)
a Referent groups include the following: males, respondents aged ⩾65 years, respondents with less than 12 years of education, respondents without a history of atrial fibrillation, diabetes, high blood pressure, high cholesterol, heart disease, stroke, or transient ischemic attack, and respondents who reported never smoking cigarettes.
b Includes heart attack, angina, or coronary heart disease.
Table 4 Reactions to Witnessing a Potential Stroke and to Experiencing Potential Warning Signs of a Stroke Among Survey Respondents Aged ⩾45 years, Montana, 2004
If you thought someone was having a stroke, what is the first thing you would do?
No. (%) If you experienced sudden . . . that would not go away, what is the first thing you would do?
Difficulty speaking
No. (%) Numbness, tingling, or dead sensation
No. (%) Weakness or paralysis
No. (%)
Take them/go to hospital 121 (15) 132 (17) 176 (22) 186 (23)
Tell them/call your/their doctor 21 (3) 121 (15) 150 (19) 110 (14)
Call 911 608 (76) 354 (44) 346 (43) 388 (49)
Tell them/call spouse or family member 4 (1) 147 (18) 92 (12) 88 (11)
Do something else 38 (5) 38 (5) 32 (4) 25 (3)
Do not know 8 (1) 8 (1) 4 (1) 3 (0)
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Blades LL, Oser CS, Dietrich DW, Okon NJ, Rodriguez DV, Burnett AM, et al. Rural community knowledge of stroke warning signs and risk factors. Prev Chronic Dis. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0095.htm
==== Refs
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15 Goldstein LB Adams R Becker K Furberg CD Gorelick PB Hademenos G 32 1 2001 280 299 Stroke Primary prevention of ischemic stroke: a statement for healthcare professionals from the Stroke Council of the American Heart Association 11136952
16 Schneider AT Pancioli AM Khoury JC Rademacher E Tuchfarber A Miller R 289 3 2003 343 346 JAMA Trends in community knowledge of the warning signs and risk factors for stroke 12525235
17 Reeves MJ Hogan JG Rafferty AP 2002 59 1547 1552 Neurology Knowledge of stroke risk factors and warning signs among Michigan adults 12451195
18 Kehoe R Wu SY Leske MC Chylack LT Jr 139 8 4 15 1994 813 818 Am J Epidemiol Comparing self-reported and physician-reported medical history 8178794
19 Jackson C Jatulis DE Fortmann SP 82 3 3 1992 412 416 Am J Public Health The Behavioral Risk Factor Survey and the Stanford Five-City Project Survey: a comparison of cardiovascular risk behavior estimates 1536358
20 Greenlund KJ Neff LJ Zheng ZJ Keenan NL Giles WH Ayala CA 25 4 2003 315 319 Am J Prev Med Low public recognition of major stroke symptoms 14580633
21 Goff DC Jr Mitchell P Finnegan J Pandey D Bittner V Feldman H REACT Study Group 38 1 2004 85 93 Prev Med Knowledge of heart attack symptoms in 20 US communities. Results from the Rapid Early Action for Coronary Treatment Community Trial 14672645
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Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_04_0089
Original Research
PEER REVIEWEDState Plan Index: A Tool for Assessing the Quality of State Public Health Plans
Butterfoss Frances D PhD Professor and Head Health Promotion & Disease Prevention, Center for Pediatric Research, Eastern Virginia Medical School
855 W Brambleton Ave, Norfolk, VA 23510 [email protected]
757-668-6429
Dunĕt Diane O PhD, MPA Health Scientist Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Nutrition and Physical Activity, Chronic Disease Nutrition Branch, Atlanta, Ga
4 2005
15 3 2005
2 2 A152005
Introduction
The State Plan Index is an evaluation instrument that uses a Likert scale to assess 60 indicators of the quality of state public health plans. The State Plan Index was needed to enable evaluation of plans that were developed using a variety of public health planning models.
Methods
Federal, state, and academic partners participated in developing and testing the instrument. The authors conducted a literature review, interviews with experts, and several rounds of formative evaluation to assess item inclusion, coverage, weighting, organization of items, and content validity. In two rounds of field testing, public health practitioners at the federal and state levels rated 10 state public health plans for obesity prevention.
Results
Field-test raters took an average of two hours to rate a plan and indicated that the State Plan Index was "easy to use," "comprehensive," and "fair." Mean Cronbach a for components of the State Plan Index was 0.88 (median 0.93). Component scores among the 10 plans rated ranged from 0.2 to 4.8, indicating that raters made distinctions in quality among the components and the plans they rated. Correlations between component scores and overall scores were statistically significant (P < .001), except for one component.
Conclusion
Public health professionals at the federal and state levels found the State Plan Index to be a useful tool for evaluating public health plans that were developed by states using various planning approaches. After the field tests, state staff reported adapting the State Plan Index for use as a planning tool, an evaluation tool for local plans, and a self-assessment tool for drafts of state plans. In addition, the State Plan Index can be revised easily for use in other chronic disease areas.
==== Body
Introduction
Many professionals encourage public health planning as a key step in addressing complex issues such as chronic disease (1). This is especially true when problems require long-term strategies and multiple approaches, such as changes in policy, the environment, or individual behavior. Yet despite the widely held assumption that planning is important and despite the investment of substantial resources in planning at state and community levels, a key question lingers: Do better plans lead to better health outcomes?
In the last 25 years, an array of public health community planning, health education, and program development models have been developed, including PRECEDE–PROCEED (PRECEDE = Predisposing, Reinforcing, and Enabling Constructs in Educational/Ecological Diagnosis and Evaluation, and PROCEED = Policy, Regulatory, and Organizational Constructs in Educational and Environmental Development) (2); MAPP (Mobilizing Action through Planning and Partnership) (3); PATCH (Planned Approach To Community Health) (4); CHIP (Community Health Improvement Program) (5); and the Six-Step Program Development Chain Model (6). Other public health planning models address particular public health strategies, such as the CDCynergy model for planning, managing, and evaluating public health communication programs (7) and Intervention Mapping for designing theory- and evidence-based health promotion programs (8). Still others are focused on planning for a particular public health problem, such as planning for Comprehensive Cancer Control (9) and Getting to Outcomes for substance abuse prevention (10).
The availability of different models provides public health practitioners with the flexibility not only to match the appropriate model with the intended goal but also to use a model that fits within the norms and expectations of an organization and that meets with acceptance in the community involved. A plan also may be designed using more than one model; Breckon et al assert that "model elements can be mixed or matched depending on what fits or is acceptable [italics added]" (11). The possibility of combining elements from different models offers greater flexibility in plan design but also creates a greater need for an evaluation instrument that remains reliable across a diverse and expanding body of public health plans.
Planning models generally prescribe a planning process rather than articulate desired attributes of a finished plan that is the outcome of such a process. To date, evaluation instruments have focused on assessment of planning processes (9,12,13) and methods to inventory or describe the content of community plans (14,15). Criteria to assess plan quality could be derived by implication from the concepts contained in each of the various planning models. However, this task is difficult and time-consuming for practitioners, who need to assess the quality of written plans regardless of the planning process(es) or model(s) used.
Although evaluation instruments for state plans are limited, tools to generally assess public health infrastructure or capacity have been developed and widely disseminated (16-18). For example, the School Health Index developed by the Centers for Disease Control and Prevention (CDC) (17) provides comprehensive questionnaires that schools can use as self-assessment and planning tools to improve the effectiveness of their health and safety policies and programs. On a larger scale, the National Public Health Performance Standards provide a framework for assessment of state and local public health systems (18).
The State Plan Index (SPI) was developed as part of the evaluation of the CDC's Nutrition and Physical Activity Program to Prevent Obesity and Other Chronic Diseases (Obesity Prevention Program) and is available from www.cdc.gov/nccdphp/dnpa/obesity/state_programs The CDC Obesity Prevention Program provides planning support and other assistance to states for obesity prevention and reduction. The SPI was needed to evaluate state plans that were developed by state public health practitioners and their community partners using a variety of public health planning models. In addition, to understand the relationship between plan quality and health outcomes in the long term, an evaluation instrument was needed to assess baseline plan quality. As described below, the SPI development process drew upon a wide array of existing public health planning models, tools, and resources.
Methods
Instrument development
Development of the SPI began in June 2002. The authors reviewed published professional public health literature on planning, community-based planning, plan assessment, and recommended planning methods, including but not limited to the references cited here. Key elements were gleaned from these public health planning models. In addition, planning processes that were considered critical across the models were identified. Other relevant published and unpublished materials were reviewed, including the CDC Obesity Prevention Program guidelines, reports, and existing state plans. One of the authors also conducted in-person key informant interviews with planning experts throughout the CDC's National Center for Chronic Disease Prevention and Health Promotion from the Divisions of Adolescent and School Health, Adult and Community Health, Cancer Prevention and Control, Diabetes Translation, Nutrition and Physical Activity, Oral Health, and Reproductive Health; the Office on Smoking and Health; and in the CDC's National Center for HIV, STD, and TB Prevention.
The authors then developed a set of key indicators of plan quality, intentionally incorporating the concept that a high-quality written plan should reflect both plan attributes as well as evidence of planning processes that experts had identified as critical. The list of key indicators was shared with state-level public health professionals who provided further suggestions for indicators and additional documents for review. State plans for comprehensive cancer control, cardiovascular health, and diabetes that were recommended as exemplary by practitioners were reviewed by the authors to identify common characteristics as potential SPI items. Through an iterative process, the State Plan Index evolved into a set of indicators grouped within major components.
In June 2003, telephone interviews were conducted by one of the authors with seven nationally recognized academic experts in strategic planning, public health, instrument development, and psychometrics. Federal and state public health practitioners and experts also participated in a formal review process to assess the SPI items proposed for inclusion, as well as in a formative evaluation process to recommend whether SPI items and components should be weighted equally. SPI items were also examined for coverage, overlap, weighting, and content validity. In total, approximately 100 public health representatives in federal, state, and academic settings provided suggestions for item inclusion and reviewed and commented on several preliminary drafts of the SPI. A list of the SPI components with the rationale for including each is presented in the Appendix.
Sample, measures, testing, and refinement
A pilot-test version of the SPI, finalized in July 2003, consisted of 55 items grouped within nine components. A 5-point Likert scale was provided for each item, from 1 = low quality to 5 = high quality, with an additional "Not Addressed" option for each item. A similar Likert scale was provided to rate each component and the quality of the plan as a whole. "Not Addressed" was scored as 0 in the analyses described below. The authors conducted a pilot test of the instrument by independently rating two state plans. Based on this pilot test, wording of SPI items was clarified, and an assessment was made of the approximate time that would be needed to read and rate a plan.
The first of two field tests was conducted in July and August 2003 (Table 1). Nineteen raters participated in the first field test: 10 staff members from states funded through the CDC's Obesity Prevention Program, five staff members from other states who were members of the Association of State and Territorial Nutrition Directors, a paid independent public health consultant who rated all 10 plans, and three CDC staff members who rated five or 10 plans each. Raters were provided written instructions and a telephone orientation conducted by the authors to provide background information for the field test. No formal training was provided to raters, because the SPI was developed with the intention that it could be used by practitioners without the need for special training.
At the time of field testing, only 10 states had developed comprehensive plans for obesity prevention; nine of the 10 plans rated were from states funded through the CDC's Obesity Prevention Program. The plans were provided to the CDC or downloaded from the states' Web sites. As summarized in Table 1, each plan had four or five raters who provided a score for each item, each component, and the overall plan quality. Each plan was to have five raters, but two raters did not complete all ratings within the time allotted, resulting in a total of 46 rather than 50 ratings. Raters were assigned plans based on suggestions from the CDC Obesity Prevention Program staff members, who matched state plans with raters who were most likely to be unfamiliar with obesity prevention efforts in that state. Raters were requested to provide both numeric scores for each item as well as written feedback for each SPI component. In addition, written comments were solicited from the raters, and telephone debriefings were held with them to discuss any difficulties encountered in the rating process and to obtain suggestions for further refinements in the instrument.
Based upon the results of Field Test 1, minor changes in wording were made to the SPI, and five items were subdivided. To ensure that the changes to the SPI did not affect rating outcomes, Field Test 2 was conducted in November 2003 with a subset of the plans. Three plans were chosen to represent high-, low-, and average-scoring plans. The final 60-item version of the SPI was used by two raters — the same paid public health expert consultant from Field Test 1 and one new rater from the CDC Obesity Prevention Program who did not participate in Field Test 1.
Analysis
Cronbach α was calculated for each component to assess whether items grouped within the component reliably measured the same dimension. Face validity for SPI items was determined by repeated review by federal, state, and academic planning and public health experts. Because no gold standard exists in the area of criterion validity (20), raters' overall plan scores were used as a proxy measure for criterion validity. Spearman rank correlation coefficients were calculated between raters' component scores and the overall score they assigned for each plan in Field Test 1. Although raters scored individual items before assigning an overall plan quality score, SPI instructions direct: "The [overall] score does not need to be an average of the [component] scores." Thus, raters were free to assign quality scores for each component and for the overall plan independently of their item-by-item ratings. To assess the consistency of plan ratings among raters while taking into account differences in plan quality, the interclass correlation coefficient (Shrout–Fleiss) was calculated for the overall plan score.
Results
The final version of the SPI contains nine components: (A) Involvement of Stakeholders; (B) Presentation of Data on Disease Burden and Existing Efforts to Control Obesity; (C) Goals; (D) Objectives; (E) Selecting Population(s) and Strategies for Intervention; (F) Integration of Strategies with Other Programs and Implementation of Plan; (G) Resources for Implementation of Plan; (H) Evaluation; and (I) Accessibility of Plan. The Appendix provides a brief rationale for each component. A 5-point Likert scale ranging from 1 = low to 5 = high is used to score each item, each component, and the overall quality of a plan. A rating option of "Not Addressed" is also provided. Items are weighted equally, as are the nine SPI components.
The results of Field Test 1 showed a wide range of average score by component (0.2 to 4.8 on a 5.0 scale), indicating that raters made distinctions in quality among the components and among the plans rated. Raters took an average of 2.0 hours to review a plan and complete the SPI, compared to an average of 1.3 hours in the pilot test spent by the authors who had developed the SPI. The plans reviewed contained an average of 40 pages and generally included graphics and illustrative tables that noticeably reduced the volume of text. Thus, 2.0 hours was judged to be a reasonable length of time to review and rate a plan.
Overall, comments from field testers were very positive; raters commented that the SPI was "easy to use," "comprehensive," and "user-friendly" and that it "seemed fair" and made them "look at plans in a new and more systematic way." The most commonly reported problem was that raters were somewhat uncomfortable assigning a very low score when a plan had little detail. For example, several plans lacked detail regarding the development of financial or other resources for plan implementation. However, raters reported that states may have addressed resource issues even though detail was not provided in the plan reviewed.
Table 2 shows the coefficient of reliability (Cronbach α), calculated to assess whether items grouped within each component measured the same dimension. The average Cronbach α was 0.88, higher than the 0.8 level generally considered acceptable for social science data (21).
Table 2 also provides the Spearman rank correlation coefficient for each component, which indicates the correlation between component scores and overall plan scores that raters assigned in Field Test 1. All correlations were statistically significant at P < .001, except for Component G (Resources), a component that lacked detail in nearly all of the plans examined. Moderate to strong correlations were found between component scores and the overall plan quality score. The interclass correlation coefficient (Shrout–Fleiss) for Overall Plan Scores was 0.78 (skewed downward by low scores in the Resources component). The authors judged this to be an acceptable level of agreement among raters who rated the same plan. Data analyses were repeated for Field Test 2 with very similar results (data not shown).
During debriefing telephone conferences, raters were asked to comment further on their impressions of Component G (Resources) and their experience with the SPI ratings for plans that lacked detail. Some state staff reflected on their own plans, commenting that they had indeed addressed resources but were reluctant to reveal information about funding and resources outside of the planning group. They expressed concern that others might be inspired to tap into new resources and creative arrangements that planners had struggled to build. Despite these concerns, state and federal staff who participated in the debriefing agreed that the items in the SPI component for resources were appropriate and should be retained, especially if the SPI were to be translated from an evaluation tool into a guide for planning.
The authors also queried raters about whether they felt comfortable checking "Not Addressed" if an item was merely mentioned in a plan but inadequately addressed. Some raters noted their preference to provide written recommendations for improving a component or item, arguing that concrete suggestions were more important than "grades." However, other raters who checked some SPI boxes for low scores or "Not Addressed" noted that "grade inflation" could mask opportunities to strengthen a plan. To address this issue, future orientation sessions for SPI raters should stress the importance of using the SPI scoring system as a tool for providing clear feedback so that weak areas can be easily identified by states and appropriately addressed.
Discussion
Summary
The final SPI includes 60 items organized within nine components. The SPI can be used to evaluate plans developed using different public health planning models, thus providing a useful means of judging the quality of plans themselves. Moreover, although the SPI was developed for the CDC Obesity Prevention Program, most items can be easily adapted to other chronic disease areas. SPI pilot testers reported that the instrument was easy to use and consistent with the judgments they apply as public health professionals in assessing state plans. After the SPI field tests, some state staff, on their own initiative, used the SPI to self-assess their current plan and to guide development of action steps to address SPI items noted as weaknesses.
Limitations
Although the SPI was judged as useful by experts in state, federal, and academic settings, several limitations remain. First, the concept of plan quality rests on the assumptions inherent in the public health models and literature reviewed. Second, because only 10 states had developed an obesity plan at the time of the SPI field testing, only these 10 plans were reviewed. Third, all testing was conducted on state obesity plans. Fourth, although the analyses generally showed high correlations between the component scores and the overall plan scores to corroborate criterion validity (except for Component G [Resources] that had missing data, as discussed above), the effect may be lessened because raters assigned their overall ratings after assessing individual items. Further, although the SPI is designed to help assess the quality of a written plan, even well-conceived plans may fail during implementation.
Significance
Public health promotion models assume that quality planning will result in better health outcomes. Research in this area has been hampered by the lack of a useful instrument to measure plan quality at the state level. The proliferation of public health planning models and tools provides ideas to suit different planning groups and situations. If the widely held assumption that public health plans make a difference to health outcomes is correct, evaluation of the quality of the end product of planning (a written plan) is an important checkpoint. The SPI is grounded in theory, public health practice, and empirical field testing as well as in the expert opinions of state, federal, and academic collaborators.
Use of a systematic evaluation instrument also promotes the application of consistent standards in assessing state plans. Consistency has been embraced in the objective review panel process where written applications for federal funding are assessed against a detailed set of criteria. The SPI provides an evaluation tool that can be applied no matter who participated in the planning process or what planning approach was used.
Besides its use as an evaluation tool, the SPI has been adapted by state staff for use as a self-assessment tool. After participating in the CDC SPI field testing, one state staff member reported to the CDC that the state's obesity planning steering committee subsequently used the SPI to reassess its current written plan. Based on this review, the committee planned actions they would take to address potential weaknesses, such as adding faith-based organizations and consumers as stakeholders, restating plan objectives in measurable and time-based terms, and identifying ways to integrate obesity efforts with other chronic disease areas as well as across systems and agencies.
In an era of limited resources and increased accountability, linking public health efforts to health outcomes is more critical than ever. The SPI fills the need for an evaluation tool that can be used to systematically evaluate the quality of state plans. This assessment can ultimately be used to better understand the return on investment of resources devoted to planning.
Perhaps most importantly, the SPI provides straightforward, succinct guidance to public health practitioners embarking on a new planning process. Many of the practitioners who participated in the pilot test remarked that the SPI would have been very helpful to them if it had been available when their obesity program planning efforts were launched. As public health practitioners continue to engage in planning to address the growing burden of chronic disease in the United States, we hope that the SPI will prove a useful tool to guide and evaluate planning.
The authors gratefully acknowledge the contributions of Robin Hamre and Sarah Kuester and the project officers of the CDC Obesity Prevention Program for their support, guidance, and thoughtful review of drafts throughout the development of the SPI; the CDC staff members throughout the National Center for Chronic Disease Prevention and Health Promotion who provided review and comment on several drafts of the SPI and served as field testers; state health department staff members who also provided review and comment and served as field testers; members of the Association of State and Territorial Public Health Nutrition Directors who served as reviewers and field testers; and Susanne Gregory, who served as a rater for both rounds of field testing. The authors are especially grateful to the academic experts who were interviewed as key informants: Vincent Francisco, Robert Goodman, Michelle Kegler, Sandra Quinn, Russell Schuh, John Stevenson, and Abe Wandersman. We also acknowledge Cathleen Gillespie and David Freedman at the CDC, who provided statistical expertise and support for the data analyses of the field tests.
Any errors and omissions are the sole responsibility of the authors.
This project was undertaken while Dr. Butterfoss was under contract with the CDC through the Oak Ridge Institute for Science and Education (ORISE) fellowship program.
Appendix
Involvement of Stakeholders. Early involvement increases the likelihood that stakeholders will develop a sense of ownership in the plan and a commitment to making it succeed. The different experiences and perspectives that partners bring will help ensure that the plan is responsive to the needs of all segments of the population. Each partner brings its own contacts and constituents, widening the base of support for the plan and increasing its credibility across the state. Community planning models emphasize the need for meaningful involvement of stakeholders, with some models designed for community-led planning. (See for example, MAPP [3].)
Presentation of Data on Disease Burden and Existing Efforts to Control Obesity. Evidence-based public health practice must include a systematic examination of data on disease burden for population subgroups. Assessing existing resources that address a public health problem identifies opportunities for partnership and the potential to leverage additional resources. The use of reliable data sources lends credibility to the planning process. Evidence-based planning models emphasize the need for data to inform decision making. (See for example, PRECEED–PROCEED [2].)
Goals. Goals provide a vision of what planners intend to achieve. Because planning itself consumes time and other resources, something important should be gained. Goals should unambiguously convey that something new is intended that is likely to lead to desired change in health status indicators. Tools based on community planning models have been developed to assist in developing goals, such as The Community Tool Box (19).
Objectives. Objectives should be specific, measurable, achievable, results-oriented, time-phased, and logically organized. They should be consistent with the overall public health priorities of the state and tied directly to the goals specified in the plan. As with goals, tools that support planning models provide guidance on developing and writing sound objectives (19).
Selecting Population(s) and Strategies for Intervention. Advances in social marketing applied to public health have contributed to the design of interventions better matched to the intended audience. Many planning models emphasize the importance of understanding a community and the unique attributes of its members before selecting strategies. (See, for example, CDCynergy [7].) Setting criteria for a systematic selection of interventions to be undertaken supports an evidence-based approach to public health. Although disease burden may figure prominently among the criteria used to select interventions, other criteria may be even more important, for example, political factors in a community or a subgroup’s readiness to change. Documenting the rationale for selecting strategies clarifies the planning group’s decision making process and informs plan implementers who become involved later.
Integration of Strategies with Other Programs and Implementation of Plan. Public health partnerships and collaborations are key strategies to leverage limited resources. Often, however, a disadvantage with partnerships is having less direct control of action steps. Planning for systematic assessment of implementation steps helps ensure that a plan is carried out as designed and provides feedback useful for midcourse correction. Planning models may emphasize the need to consider how new strategies can be integrated into existing infrastructure. (See, for example, CHIP [5].)
Resources for Implementation of Plan. A plan may serve little purpose unless planners address how to locate, maintain, and sustain resources needed to implement the plan. Although this step is not often explicitly addressed in planning models, public health practitioners provided many examples of promising new initiatives that terminated because of the lack of resources that could sustain efforts for a time period long enough to achieve intended outcomes. In an era when public health resources are stretched thin, planners must consider what resources are currently available as well as what would be needed to implement the plan.
Evaluation. Virtually every planning model reviewed for this study identified evaluation as an important and useful activity. Some planning models also emphasize the importance of incorporating evaluation into a planning process. (See, for example, “Getting to Outcomes” [10].) As part of planning, measures of success can be identified and systems set in place to monitor progress and identify problems once plan implementation begins. Because planning groups may disband after a plan is written, planners should identify those who will carry out an evaluation and the audience for evaluation information.
Accessibility of Plan. Just as varied planning models may be used, a written plan may have several different audiences. A good plan should be understandable and useful. As much as possible, the plan should be designed to elicit interest and support in the reader. Arrangements for distribution of a plan should be made early to ensure timely dissemination to those who can contribute to the plan’s implementation and success.
Figures and Tables
Table 1 Summary of State Plan Index (SPI) Pilot Test and Field Tests, 2003
Pilot Test
55-item prototype version of SPI Two authors each rate two state plans to assess usability of SPI format, clarity of wording, and time needed to read and rate a state plan.
These two ratings were not included in statistical analyses reported here.
Field Test 1
55-item field test version of SPI Ten state obesity plans, each rated by four to five raters from a pool of 19 raters. Each plan was to be rated by: one member of the Association of State and Territorial Public Health Nutrition Directors (from states not receiving CDC funding for obesity).
one volunteer peer rater from a state receiving CDC funding for obesity
one paid public health expert consultant who rated all 10 plans
one CDC staff member from the Obesity Prevention Program who rated all 10 plans
one of two other CDC staff members on the Obesity Prevention Program team who each rated five plans
Number of plans rated = 46. Four states had four rather than five ratings because some ratings were not completed in the allotted time.
Field Test 2
60-item final version of SPI Three state plans (chosen to represent high-, low-, and average-scoring plans from Field Test 1) were rated by the same paid public health expert consultant from Field Test 1 and one new rater from the CDC Obesity Prevention Program team who did not participate in Field Test 1.
Number of plans rated for analysis = 6.
Table 2 Results of Field Test 1 of State Plan Indexa, 2003
State Plan Index Component Reliability of Items Within Each Component Correlation Between Component Score and Overall Plan Score
Cronbach α Spearman rank correlation coefficient (P)
A Stakeholders 0.93 0.49 (<.001)
B Data on Disease Burden 0.92 0.62 (<.001)
C Goals 0.99 0.70 (<.001)
D Objectives 0.95 0.70 (<.001)
E Strategies for Intervention 0.70 0.57 (<.001)
F Integration of Strategies 0.87 0.52 (<.001)
G Resources 0.68 0.07 (.65)
H Evaluation 0.94 0.54 (<.001)
I Accessibility 0.95 0.62 (<.001)
Overall Plan Score 0.83 Does not apply
Mean across components 0.88 0.54
Median of component scores 0.93 0.57
a Field Test 1 included 10 state plans, 46 ratings, and 19 raters.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Butterfoss FD, Dunĕt DO. State Plan Index: a tool for assessing the quality of state public health plans. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0089.htm
==== Refs
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4 U.S. Department of Health and Human Services. Planned approach to community health (PATCH): guide for the local coordinator [Internet] Atlanta (GA) Centers for Disease Control and Prevention 2000
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20 AGREE Collaboration 2003 12 18 23 Qual Saf Health Care Development and validation of an international appraisal instrument for assessing the quality of clinical practice guidelines: the AGREE project 12571340
21 UCLA Academic Technology Services What does Cronbach's Alpha mean? [Internet] Los Angeles (CA) University of California Los Angeles cited 2004 Jul 16 Available from: URL: http://www.ats.ucla.edu/stat/spss/faq/alpha.html
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==== Front
Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_04_0112
Original Research
PEER REVIEWEDAnthropometric Changes Using a Walking Intervention in African American Breast Cancer Survivors: A Pilot Study
Wilson Diane B EdD, MS, RD Department of Internal Medicine and Massey Cancer Center
PO Box 980306, Virginia Commonwealth University, School of Medicine, Richmond, VA 23298-0306 [email protected]
804-828-9891
Porter Jerlym S MS Department of Psychology, Virginia Commonwealth University, Richmond, Va
Parker Gwen MS Massey Cancer Center, Virginia Commonwealth University, Richmond, Va
Kilpatrick James PhD Department of Biostatistics, Virginia Commonwealth University, Richmond, Va
4 2005
15 3 2005
2 2 A162005
Introduction
African American women exhibit a higher mortality rate from breast cancer than do white women. African American women are more likely to gain weight at diagnosis, which may increase their risk of cancer recurrence and comorbidities. Physical activity has been shown to decrease body mass index and improve quality of life in cancer survivors. This study was designed to evaluate the feasibility and impact of a community-based exercise intervention in African American breast cancer survivors.
Methods
A theory-based eight-week community intervention using pedometers with scheduling, goal setting, and self-assessment was tested in a convenience sample of African American breast cancer survivors (n = 24). Data were collected at three time points to examine changes in steps walked per day, body mass index, and other anthropometric measures, attitudes, and demographic variables.
Results
Statistically significant increases in steps walked per day and attitude toward exercise as well as significant decreases in body mass index, body weight, percentage of body fat, and waist, hip, and forearm circumferences, as well as blood pressure, were reported from baseline to immediate post-intervention. Positive changes were retained or improved further at three-month follow-up except for attitude toward exercise. Participant retention rate during eight-week intervention was 92%.
Conclusion
Increasing walking for exercise, without making other changes, can improve body mass index, anthropometric measures, and attitudes, which are associated with improved quality of life and reduced risk of cancer recurrence. The high participant retention rate, along with significant study outcomes, demonstrate that among this sample of African American breast cancer survivors, participants were motivated to improve their exercise habits.
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Introduction
African American women exhibit a higher rate of breast cancer mortality when compared with white women (1,2). Being diagnosed later, variation in treatment response, and larger tumor size have all been identified as factors that may contribute to differences in breast cancer survival time (3). Obesity is also more prevalent among African American women. Although the majority of women report weight gain after breast cancer diagnosis, African American women are at greater risk for this pattern (4). Being overweight is not only associated with increased risk of cancer recurrence but also with comorbid conditions such as heart disease, stroke, diabetes, and depression, all of which may contribute to decreased quality of life and shorter survival time (5-10). By contrast, being more physically active is associated with improved quality of life and decreased body mass index (BMI) in cancer survivors, which in turn may contribute to longer survival time (11,12).
Until recently, women who have completed cancer therapy have been offered little to improve survival or decrease risk of new disease. Yet studies show that as a group, breast cancer survivors are interested in improving their health behaviors and quality of life (4,7). Two randomized trials (13,14) are currently testing healthy lifestyle interventions for cancer survivors. However, very few interventions have been developed and tested specifically among African American women with breast cancer, even though they are a population at high risk for recurrence and comorbid disease.
To address weight gain in African American breast cancer survivors, we designed a theory-based cognitive-behavioral walking program to test its feasibility and impact on steps per day and BMI. The study was pilot tested among African American breast cancer survivors, using a community education model in an urban inner-city setting.
Methods
We pilot tested an eight-week community-based walking program in a convenience sample of African American breast cancer survivors (n = 24) to investigate feasibility and impact on outcome measures over three time points: 1) baseline, 2) immediate post-intervention, and 3) three-month follow-up. African American women who 1) had been diagnosed with breast cancer, 2) had completed treatment at least three months before recruitment, 3) were mobile, and 4) were less than 70 years of age were eligible for the study.
Participant recruitment
Using a broad, organized effort, participants were recruited from Massey Cancer Center clinics, outreach sites, contacts with local churches, community leaders, and breast cancer organizations including breast cancer support groups. A city council member, along with other breast cancer survivors, was also instrumental in communicating study information throughout the community. Flyers, television announcements, and personal communication were used during the three-month recruitment effort. We contacted approximately 230 potentially eligible women. Recruitment rate was approximately 10%. Reasons for nonparticipation included having cancer treatment within the prior three months, not being able to attend community meetings because of work or family commitments, or having comorbid conditions that decreased mobility.
Intervention
The theory-driven intervention was designed with the primary study goal of integrating walking into one's daily routine. The Health Belief Model was used as the theoretical framework for the intervention (15). This well-known model is based on perceived seriousness and perceived susceptibility as the strongest predictors for the implementation of health behaviors. Thus, the intervention was designed on the basis that breast cancer survivors are a population who have experienced a serious disease and perceive their susceptibility toward a cancer recurrence. Eight 75-minute weekly sessions were held at a community center (evening) and at a local church (noontime). Sessions were presented by the same instructor and staff, using a curriculum that described benefits and barriers to exercise, its relationship to health and cancer risk, and personal assessment/problem-solving sessions for motivation. Didactic, interactive, and small-group processes were used during each session. Steps-only pedometers were tested, and progressive step goals were provided. Participants scheduled walking times for the upcoming week and reported steps walked per day for the previous week using scheduler/tracker forms. Patients served as their own controls.
Study variables
Study variables were assessed at three time points: baseline, immediate post-intervention, and at three-month follow-up. The study goal was the integration of walking into the participant's daily routine. The primary study outcomes were changes in number of steps and BMI. Steps per day were measured using a steps-only pedometer. Participants were instructed to wear the pedometer upon rising in the morning until bedtime and to record the number of steps walked. BMI was calculated from weight and height using a calibrated scale. Waist, hip, and upper arm circumferences were measured using a tape measure, and blood pressure was measured with a standard blood pressure cuff. Body-fat percentage was measured using Futrex, a portable near-infrared sensor system (16). All clinical measures were taken by a clinical nurse practitioner.
Participant demographic information, cancer history, and attitudinal measures were assessed using standardized survey items from other study instruments. The instrument was pilot tested in a comparable age group of African American women. Attitudes toward exercise were measured using the Exercise Decisional Balance instrument (17), a 16-item questionnaire focused on avoidance of exercise (cons) and positive perceptions of exercise (pros). Cancer anxiety was measured using the Cancer Anxiety Scale (18), and participants' concern about cancer recurrence was assessed.
Data collection and statistical analysis
Data were entered into a database using SPSS statistical software (SPSS Inc, Chicago, Ill). Descriptive statistics were determined for all study variables. Analysis of variance was performed to test for differences in measures collected at baseline, immediately after intervention, and at three-month follow-up. Paired t tests were used to determine differences in mean anthropometric and attitudinal measures between the three time points. In addition, based on frequency distribution of time since diagnosis, all study variables were tested among those diagnosed three years or less prior to start of the intervention (1999–2002) (n = 10) and those diagnosed earlier (1978–1998) (n = 12), using independent samples t tests.
Results
Twenty-four women were enrolled in the intervention study. One participant dropped out because of scheduling conflicts. One experienced a cancer recurrence, resulting in 22 eligible women completing the intervention.
Table 1 shows the characteristics of the study sample. Mean age was 55 years (range 47–66 years). The majority of the women had post-high school education. The sample was approximately 50% married and 50% divorced, widowed, or single. Receiving both chemotherapy and radiation therapy was most prevalent among participants (46%), with 18% receiving radiation alone, chemotherapy alone, or neither treatment; 23% were currently taking tamoxifen. Forty-five percent of participants (10/22) had been diagnosed with breast cancer in or since 1999, and 55% (12/22) were diagnosed before 1999. For most participants (91%), this was their first cancer diagnosis.
Feasibility
Feasibility was determined by examining attendance at weekly sessions, study retention, and receptivity to pedometer use. Attendance at weekly sessions was excellent, with 70% of the participants attending seven or more intervention sessions. Study retention to the eight-week study was also excellent, with 22 of 24 women completing the intervention and immediate post-assessment. Participants had positive experiences using the pedometers and recording steps per day. Broken or lost pedometers were reported by approximately 25% of the study sample, and they were replaced to ensure continuous data collection. Additional data showed that 95% responded "about right" to a survey item asking whether number of study sessions were too many, too few, or about right.
Impact on study outcomes
Results of ANOVA analyses of repeated measures (baseline, immediate post-intervention, and at three-month follow-up) showed statistically significant differences in steps per day (P < .001), hip circumference (P = .009), forearm circumference (P < .001), systolic blood pressure (P = .002), diastolic blood pressure (P = .001), and attitude toward exercise (P = .005).
Table 2 shows the difference in mean study measures using paired t tests, from baseline to immediate post-intervention. Mean steps per day significantly increased from 4791 to 8297 (P < .001). Other significant decreases included the following: BMI (P = .004), body weight (P = .005), percentage body fat (P = .003), and forearm circumference (P = .007). Increased positive perception of exercise was also reported (P = .03).
Table 3 shows study results among women who completed the three-month follow-up assessment (n = 17). From immediate post-intervention to the three-month follow-up, mean steps per day did not significantly change. There were statistically significant improvements in hip circumference (P = .04), forearm circumference (P = .04), and diastolic blood pressure (P = .02). Thus, all anthropometric measures either stayed the same or showed further improvement by further reduction in measures from immediate post-intervention to three-month follow-up. Of all study variables, only attitude toward exercise significantly changed direction (P < .001), with women showing a more negative opinion of exercise by three-month follow-up compared with immediate post-intervention. There were no differences in mean study outcomes in the participants who did not attend three-month follow-up assessment sessions (n = 5) compared with participants who did attend and had measurements (n = 17).
Time since diagnosis
More recently diagnosed women tended to have higher body measures at all three time points, but only diastolic blood pressure was significantly higher at baseline (P = .02) when compared to earlier diagnosed women. The same effect was true at immediate post-intervention for both diastolic blood pressure (P = .02) and systolic blood pressure (P = .003). At three-month follow-up, recently diagnosed women were significantly more likely to have higher waist measures (P = .048), with trends toward larger hip (P = .06) and body fat (P = .05) measures than earlier diagnosed women.
Discussion
We found statistically significant changes in the main study outcomes of steps per day, BMI, and virtually all of the anthropometric changes measured in the study population after an eight-week intervention, with most results remaining at three-month follow-up. The breast cancer survivor participants were motivated and compliant with the intervention, which likely enhanced their success. Having had cancer and understanding their risk of recurrence may account for the strong motivation we found in this population, as suggested by the constructs of the Health Belief Model. It is also important to note that the sample participants all had more than a high school education, which may also have contributed to their success.
Although we found only a few statistically significant differences in mean body measures in relationship to time since diagnosis, we may have detected more evidence of this pattern had we had a larger study sample. While not significant, the more recently diagnosed women had larger body measures and lower mean steps per day than earlier diagnosed women at both immediately post and at three-month follow-up.
The goal of the study was to have women integrate walking into their daily routines on their own. They attended sessions for education, motivation, and self-assessment; walking did not take place during the study sessions. This was an important feature of the study design because research shows that compliance is likely to decline significantly after an intervention is completed (19). Thus, our results showing that mean steps per day stayed relatively steady even at three-month follow-up was encouraging.
Anthropometric and clinical measures
The mean change in body weight was modest but significantly less than baseline. This level of weight loss supports what similar interventions have reported (20). We were encouraged to see weight loss occur among participants using an exercise-only intervention. Nearly every participant posted decreases in at least one anthropometric measure, so that even among women who did not show weight loss, decreases were noted in body circumferences or blood pressure. We were also encouraged to see that anthropometric improvements did not fall off at three-month follow-up, and some improved further. It is possible that adding dietary modifications to the exercise intervention would contribute to more substantial anthropometric changes.
Attitudinal factors
Women in the study improved their attitude toward exercise from baseline to post-intervention by reporting fewer barriers to exercise over the study period. This was not surprising given the focus of the intervention sessions on overcoming personal obstacles to exercise. However, the attitudinal improvement did not hold at three-month follow-up. Although steps per day did not significantly change at three-month follow-up, one might wonder if the decline in exercise attitude might eventually negatively influence exercise behavior after a longer time interval. Cancer stress scores did not change significantly over the course of the intervention. However, scores for this variable were not particularly high even at the start of the intervention. This could be related to the fact that only 18% of participants were diagnosed during the 12 months prior to the start of the study. Cancer stress may subside as time passes after a woman's diagnosis. Had we studied a group of more recently diagnosed women, we may have found more evidence of cancer stress at baseline and potential for impact after the exercise intervention than we did the with this study population.
Limitations
This study was limited by the study size and lack of control group. For a pilot study, however, the sample size was adequate to study feasibility and study outcomes. In addition, the study reflects the common limitations for relying on self-report data. Anthropometric variables were included in the study in addition to self-reported data to provide measured data for evaluating results. The intervention tested exercise only; thus, even more significant changes among this population are possible if both energy balance components — food intake and physical activity — are modified. Overall, the study objectives were realized, and the study provides interesting pilot data for testing a more comprehensive lifestyle intervention in a similar population. However, the sample does not constitute a representative sample, and the study findings may not be applicable to other breast cancer survivors.
Summary and conclusions
Steps walked per day, BMI, body circumferences, blood pressure, and attitudinal variables all showed improved mean statistically significant changes in this population of African American breast cancer survivors after a theory-based cognitive-behavioral community intervention. The study showed strong feasibility measures in positive response to using pedometers, high participant retention, social support, and excellent compliance after eight weeks. Given data indicating obesity is associated with shorter breast cancer survival time, these study results may position breast cancer survivors to have both improved quality of life and reduced risk of cancer recurrence. Further study is needed to test a randomized comprehensive diet and exercise intervention in African American breast cancer survivors against controls in a longer, larger randomized trial with additional study variables.
This study was funded through a competitive peer-reviewed grant initiative by the Massey Cancer Center at Virginia Commonwealth University.
Figures and Tables
Table 1 Characteristics of African American Breast Cancer Survivors Participating in Eight-Week Walking Intervention (n = 22)
Mean (range)
Age (yrs) 55 (47-66)
Weight (lbs) 191 (142-271)
Body mass index (kg/m2) 32.7 (25.2-47.2)
No. (%)a
Education
<High school 1 (4.5)
High school graduate 1 (4.5)
>High school 20 (91)
Marital status
Married 11 (50)
Single/divorced/widowed 11 (50)
Menopausal status
Premenopausal 3 (14)
Postmenopausal 19 (86)
Time since diagnosis, years
<1 3 (14)
1-3 7 (32)
4-6 4 (18)
7-9 3 (14)
⩾10 5 (23)
Type of treatment
Chemotherapy 4 (18)
Radiation therapy 4 (18)
Both 10 (46)
Neither 4 (18)
Takes tamoxifen 5 (23)
Drinks alcohol 6 (27)
Smokes 2 (9)
a Percentages do not total 100 because of rounding.
Table 2 Change in Group Anthropometric Measures From Baseline to Immediately After Eight-Week Walking Intervention Among African American Breast Cancer Survivors (n=22)
Baseline Change Pa
Steps/day 4791 +3506 <.001
Body mass index (kg/m2) 32.7 −0.38 .004
Weight (lbs) 191.2 −2.0 .005
Body fat (%) 40.1 −3.4 .003
Waist circumference (cm) 99.3 −4.6 .04
Hip circumference (cm) 117.9 −2.1 .02
Forearm circumference (cm) 34.8 −1.5 .007
Systolic blood pressure (mm Hg) 140.9 −10.1 <.001
Diastolic blood pressure (mm Hg) 80.1 −6.2 .005
Waist-to-hip ratio 0.84 −0.02 .16
Exercise attitude totalb 66.2 +3.0 .03
Cancer anxiety totalc 6.8 −0.36 .20
a Paired t test for difference in group means.
b Attitudes toward exercise were measured using the Exercise Decisional Balance instrument (17).
c Cancer anxiety was measured using the Cancer Anxiety Scale (18).
Table 3 Change in Group Anthropometric Measures From Immediately After Eight-Week Walking Intervention to Three-Month Follow-up Among African American Breast Cancer Survivors (n=17)
Immediate Post-Intervention Change Pa
Steps/day 8223 +22 .97
Body mass index (kg/m2) 31.5 +0.14 .32
Weight (lbs) 182.2 +0.62 .47
Body fat (%) 36.1 +2.1 .06
Waist circumference (cm) 95.2 −0.35 .70
Hip circumference (cm) 115.8 −1.9 .04
Forearm circumference (cm) 33.3 −0.91 .04
Systolic blood pressure (mm Hg) 129.9 −1.1 .74
Diastolic blood pressure (mm Hg) 74.1 −4.2 .02
Waist-to-hip ratio 0.82 +0.01 .24
Exercise attitude totalb 68.6 −4.45 <.001
Cancer anxiety totalc 6.4 −0.06 .88
a Paired t test for difference in group means.
b Attitudes toward exercise were measured using the Exercise Decisional Balance instrument (17).
c Cancer anxiety was measured using the Cancer Anxiety Scale (18).
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Wilson DB, Porter JS, Parker G, Kilpatrick J. Anthropometric changes using a walking intervention in African American breast cancer survivors: a pilot study. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0112.htm
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15 Rosenstock IM Strecher VJ Becker MH 1988 15 175 183 Health Educ Q Social learning theory and the health belief model 3378902
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17 Marcus BH Rakowski W Rossi JS 1992 11 257 261 Assessing motivational readiness and decision making for exercise Health Psychol
18 Lerman C Daly M Masny A Balshem A 1994 4 843 850 J Clin Oncol Attitudes about genetic testing for breast and ovarian cancer susceptibility
19 Courneya KS Friedenreich CM Sela RA Quinney HA Rhodes RE Jones LW 2004 11 8 17 Int J Behav Med Exercise motivation and adherence in cancer survivors after participation in a randomized controlled trial: an attribution theory perspective 15194515
20 Chlebowski RT Aiello E McTiernan A 2002 20 1128 1143 J Clin Oncol Weight loss in breast cancer patient management 11844838
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Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_04_0114
Original Research
PEER REVIEWEDPrevalence of Physical Activity in the United States: Behavioral Risk Factor Surveillance System, 2001
Macera Caroline A PhD Professor of Epidemiology Graduate School of Public Health, San Diego State University
5500 Campanile Dr, HT 119, San Diego, CA 92182-4162 [email protected]
619-302-2400
Ham Sandra A MS Centers for Disease Control and Prevention, Division of Nutrition and Physical Activity, Atlanta, Ga
Yore Michelle M MSPH Centers for Disease Control and Prevention, Division of Nutrition and Physical Activity, Atlanta, Ga
Jones Deborah A PhD Centers for Disease Control and Prevention, Division of Nutrition and Physical Activity, Atlanta, Ga
Dexter Kimsey C PhD, MSEH Centers for Disease Control and Prevention, Division of Nutrition and Physical Activity, Atlanta, Ga
Kohl Harold W III PhD Centers for Disease Control and Prevention, Division of Nutrition and Physical Activity, Atlanta, Ga
Ainsworth Barbara E III PhD, MPH San Diego State University, Department of Exercise and Nutritional Sciences, San Diego, Calif
4 2005
15 3 2005
2 2 A172005
Introduction
The health benefits of regular cardiovascular exercise are well-known. Such exercise, however, has traditionally been defined as vigorous physical activity, such as jogging, swimming, or aerobic dance. Exercise of moderate intensity also promotes health, and many U.S. adults may be experiencing the health benefits of exercise through lifestyle activities of moderate intensity, such as yard work, housework, or walking for transportation. Until recently, public health surveillance systems have not included assessments of this type of physical activity, focusing on exercise of vigorous intensity. We used an enhanced surveillance tool to describe the prevalence and amount of both moderate-intensity and vigorous-intensity physical activity among U.S. adults.
Methods
We analyzed data from the 2001 Behavioral Risk Factor Surveillance System, a state-based, random-digit–dialed telephone survey administered to U.S. adults aged 18 years and older (n = 82,834 men and 120,286 women). Physical activity behavior was assessed using questions designed to quantify the frequency of participation in moderate- or vigorous-intensity physical activities performed during leisure time or for household chores and transportation.
Results
Overall, 45% of adults (48% of men and 43% of women) were active at recommended levels during nonworking hours (at least 30 minutes five or more days per week in moderate-intensity activities, equivalent to brisk walking, or at least 20 minutes three or more days per week in vigorous activities, equivalent to running, heavy yard work, or aerobic dance). Less than 16% of adults (15% of men and 17% of women) reported no moderate or vigorous activity in a usual week.
Conclusion
Integrating surveillance of lifestyle activities into national systems is possible, and doing so may provide a more accurate representation of the prevalence of recommended levels of physical activity. These results, however, suggest that the majority of U.S. adults are not active at levels associated with the promotion and maintenance of health.
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Introduction
The 1996 Surgeon General's report on physical activity and health (1) emphasized the health benefits of moderate-intensity physical activities, especially everyday activities. These activities include heavy yard work, brisk walking, and housework in addition to purposeful leisure-time exercise. Participation in activities of at least moderate intensity is associated with numerous health benefits, including lower all-cause mortality, lower cardiovascular mortality, improved function, and enhanced quality of life. Although vigorous-intensity activities (such as running and other aerobic sports) that challenge the cardiovascular system are strongly related to many positive health outcomes, less than 15% of the U.S. population is active at that level, and this prevalence did not change from 1990 to 1998 (2). Several organizations and agencies have supported health-related recommendations of 30 minutes per day of moderate-intensity physical activities on most days of the week (3,4), but this level of physical activity has been difficult to track in the U.S. population.
Historically, surveillance systems for physical activity were designed to measure leisure-time activities with an emphasis on participation in vigorous-intensity sports. They did not assess participation in lifestyle physical activities of moderate intensity that might be related to household, transportation, or occupational activities. Therefore, it is not possible with historical surveillance systems to know how many Americans have been achieving a level of physical activity to ensure health benefits through a broader range of physical activities that occur during nonworking hours. To address this question, we recently documented the prevalence of physical activity during nonworking hours for each state in the United States (5). The purpose of this paper is to extend these findings by describing the epidemiology of physical activity recommendations during nonworking hours for U.S. adults.
Methods
The Behavioral Risk Factor Surveillance System (BRFSS) is a population-based, random-digit–dialed telephone survey administered to U.S. civilian, noninstitutionalized adults aged 18 years and older in the 50 states and the District of Columbia. Questions on physical activity have been included in most years since the survey began in 1984. Between 1997 and 2000, the Physical Activity and Health Branch at the Centers for Disease Control and Prevention developed a new set of questions designed to measure occupational, household, and leisure-time physical activity with a special emphasis on moderate-intensity activities. Questions were validated using activity logs and accelerometers and subsequently modified (6). Additional testing included cognitive testing in 1998 and 1999 and a pilot test in four states (Nebraska, Georgia, Hawaii, and Michigan) in 1999. Questions were modified to reflect changes suggested by the various tests, and because of space constraints, a subset of the questions was implemented in the 2001 BRFSS.
The final questionnaire included items about moderate and vigorous activities that are performed during nonworking hours in a usual week (5). The questions included the number of days per week and number of minutes per day. These questions required the respondent to self-select the intensity of an activity, whereas in previous BRFSS surveys the participant specified an activity and standard intensity values were applied according to the respondent's age and sex. Both approaches generate useful measurements, but the self-assessed intensity method was selected because of the wide individual variation in fitness and energy expenditure required to perform a particular activity. A table comparing the questions used in the BRFSS for 2000 and 2001 has been previously published (5).
In addition to questions on moderate and vigorous activities, a single item was asked of all employed persons. This item classified occupational activity as "mostly sitting or standing," "mostly walking," or "mostly heavy labor."
The criteria for determining compliance with health-related physical activity guidelines were adapted from the Surgeon General's report on physical activity and health (1) and other consensus statements (3,4). Respondents were classified as meeting recommendations if they reported participation in moderate-intensity activities on five or more days per week for 30 or more minutes per day and/or vigorous activity for three or more days per week for 20 minutes or more per day. Respondents were classified as inactive if they reported no moderate or vigorous physical activity on any day during a usual week.
In addition to employment activity status, demographic variables included were age, educational level (less than high school, high school graduate, some college, and college graduate), race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, other), body mass index (BMI calculated as weight [kg]/height [m]2), and region of the country. BMI was categorized into underweight (<18.5), healthy weight (18.5–24.9), overweight (25.0–29.9), and obese (⩾30.0). Region of the country was defined as follows: Midwest (Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, Oklahoma, South Dakota, Wisconsin); Northeast (Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont); South (Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, South Carolina, Tennessee, Texas, Virginia, West Virginia); and West (Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, Wyoming).
All data were stratified by sex, and all prevalence estimates were age-adjusted to the year 2000 standard population. SUDAAN statistical software (Research Triangle Institute, Research Triangle Park, NC) was used to adjust for the complex sample survey design. Logistic regression models were calculated using "meeting physical activity recommendations" as the outcome.
Results
After exclusion of 7370 observations from Guam, Puerto Rico, and the Virgin Islands, the analysis sample included 203,120 respondents (82,834 men and 120,286 women). The median response rate for all of the states included in the 2001 BRFSS was 51.1% (7). The method used to calculate the response rate was based on a formula developed by the Council of American Survey Research Organizations (CASRO) and reflects the efficiency of telephone sampling as well as the degree of cooperation among the eligible respondents contacted (8). Data were weighted by age and sex to reflect each state's most recent estimate of the adult population.
The distributions of age, educational level, occupational status, and other variables for men and women in the sample are shown in Table 1. Sixty-four percent of the sample was aged 30 to 64 years. Eleven percent had less than a high school education, while 30% had graduated from college. The distributions of age and education were similar for men and women. Overall, 38% of the respondents were not currently employed (29% of men and 43% of women), and 41% of men and women had jobs that required mostly sitting or standing.
Overall, 45% of the respondents were active at the recommended levels in their nonworking hours (48% of men and 43% of women) (Table 2). The prevalence of meeting the criteria for moderate activity was similar for both men and women (32%), but men surpassed women in meeting the criteria for vigorous activity (29% for men vs 20% for women). The data indicate that 13% of men and 8% of women met the guidelines for both moderate and vigorous activity, while only 16% of the respondents (15% of men and 17% of women) were inactive (no moderate or vigorous activity at any time during a usual week).
As expected, the prevalence of meeting recommended levels of physical activity was generally lower at older ages. The difference between the youngest (18 to 29 years) group and oldest (⩾75 years) group in meeting recommendations was slightly greater among women than men: 50% of women aged 18 to 29 vs 27% of women aged 75 or older, and 58% of men aged 18 to 29 vs 38% for men aged 75 or older. Also as expected, for both men and women, the prevalence of recommended activity was higher among non-Hispanic whites than non-Hispanic blacks, Hispanics, or "other" racial/ethnic groups. Meeting recommended levels of physical activity was successively higher with greater educational attainment for both men and women.
The prevalence of recommended physical activity varied by BMI, with about half the men classified as healthy weight or overweight meeting recommended levels, while fewer obese or underweight men did so. For women, 50% of those in the healthy weight group met recommended levels vs only 33% of obese women. Regional differences were noted; the West had the highest prevalence of recommended physical activity for both men and women. As for employment status, women who were active on the job (mostly walking or heavy labor) were more active during nonworking hours than those who were less active on the job or the unemployed. For men, those doing mostly heavy labor were more active during nonworking hours than other groups.
Odds ratios for meeting recommendations for moderate or vigorous activity are shown in Table 3 by age, race/ethnicity, education, BMI, region, and occupational activity. In both sexes, younger adults (aged 18 to 29) were more active than older adults, and non-Hispanic whites were more active than the other racial/ethnic groups. Also, for both men and women, activity was higher among those with at least a high school education than among those who did not finish high school. Both the obese and the underweight groups were less active than the healthy weight group. In both sexes, those who were active at work (walking or heavy labor) were more active during nonworking hours than those who mostly sat or stood at work.
Discussion
Because emerging research in the past 15 years has indicated a dose–response relationship between physical activity and health as well as the specific health benefits of moderate-intensity physical activity, surveillance systems must be able to document prevalence and trends for moderate-intensity lifestyle activity. The surveillance system for physical activity used in the 2001 BRFSS broadens the concept of physical activity beyond traditional sports-related vigorous exercise by including examples of housework and yard work. Although these questions provide a more complete picture of the prevalence of health-related physical activity than those previously used, other domains, such as transportation and childcare activities, which are not mentioned in examples, may also account for activity that is not easily remembered or reported. Future work in this area should attempt to quantify all domains so that surveillance systems can monitor and track patterns of lifestyle physical activity.
The National Health Interview Survey (NHIS) measures moderate- and vigorous-intensity leisure-time physical activity for national Healthy People 2010 objectives. However, because of the sampling frame, it is not feasible to generate state-specific estimates of physical activity prevalence using NHIS data. Previous work has shown that state-specific BRFSS data can be weighted and combined to produce prevalence estimates of smoking and alcohol use comparable to national surveys (9). However, the prevalence estimates of physical activity generated by NHIS and combined BRFSS data will be different because of slight changes in question wording that have been shown to affect prevalence (10). In addition, BRFSS can be used by states, some metropolitan areas, and some counties to monitor progress toward the Healthy People 2010 objectives for reducing the proportion of adults who engage in no leisure-time physical activity as well as increasing the proportion of adults who engage in regular physical activity of moderate and/or vigorous intensity.
Although the prevalence of U.S. adults achieving recommended levels of physical activity was higher in 2001 (45.4%) than in 2000 (26.2%) (5), this finding was expected because of the addition of nonsports-related examples (such as heavy yard work and housework). Changes in surveillance systems are often difficult to make and can result in losing the ability to track temporal trends. The 2001 survey, however, also included a tracking question that had been used before 2001: "During the past 30 days, other than your regular job, did you participate in any physical activity or exercise such as running, calisthenics, golf, gardening, or walking for exercise?" The prevalence of inactivity as measured by this question did not change much from 2000 to 2001 (27.4% to 26.0%), suggesting that the increases seen in recommended activity (from 26% in 2000 to 45% in 2001) may be primarily because of the expanded definition of physical activity and the inclusion of the additional examples of yard work and housework (5). Recent data based on 35 states with physical activity data from 1988 to 2002 indicate that the prevalence of physical inactivity continues to slowly decrease (25.1% in 2002), which may suggest that recommended physical activity may increase over time (11).
BRFSS has some limitations. First, it is a telephone-based system that surveys noninstitutionalized adults residing in the United States and is thus limited in its ability to capture people without telephones or those who do not reside at home. Second, all information is self-reported and subject to potential misclassification bias. Respondents may be prone to providing socially desirable answers. Third, the statistical issues involved in combining data from state-specific surveys may have influenced estimates of the prevalence of physical activity.
It is notable that even with expanded definitions of physical activity, less than half of the U.S. adult population is achieving sufficient activity to obtain health benefits. Although the recommended levels of physical activity as defined here are associated with health benefits, these are minimal amounts recommended for adults of all ages; a fully active lifestyle would include aerobic activities as well as those that increase strength and flexibility, which were not measured in this study. Members of the U.S. Preventive Services Task Force have recently reviewed the literature and identified several effective interventions that were shown to increase physical activity among U.S. adults and adolescents (12). Among the recommended interventions are point-of-decision prompts to encourage stair use, social support for physical activity in community settings, individually adapted health behavior change, and creation of places for physical activity combined with informational outreach activities. To more fully understand the nature of physical activity in the population and to assess changes at the population level that may result from suggested interventions, future surveillance systems will need to capture purposeful physical activity (such as stair climbing) that is not usually of a duration to warrant reporting (at least 10 minutes).
In summary, less than half of U.S. adults meet minimal physical activity recommendations, even with more inclusive methods of surveillance that include some lifestyle activities. Even so, this study identified predictable population differences that help point the way for population-based promotion efforts.
Figures and Tables
Table 1 Percent Distribution of Age, Education, and Other Variables for Men and Women, Behavioral Risk Factor Surveillance System, United States, 2001
Men, % (n=82,834) Women, % (n=120,286) Total, % (N=203,120)
Age, years
18-29 17.9 16.3 17.0
30-44 31.8 30.6 31.1
45-64 33.7 32.4 32.9
65-74 10.1 11.0 10.6
⩾75 6.5 9.8 8.4
Education
Less than high school 11.1 11.5 11.3
High school graduate 30.8 32.0 31.5
Some college or technical school 25.3 28.6 27.3
College graduate 32.8 28.0 29.9
Race/ethnicity
Non-Hispanic white 80.1 79.0 79.5
Non-Hispanic black 6.7 8.7 7.9
Hispanic 6.3 6.4 6.4
Other 6.9 5.9 6.3
Body mass index (BMI)
Underweight (<18.5) 0.9 3.0 2.1
Healthy weight (18.5-24.9) 31.9 46.6 40.4
Overweight (25.0-29.9) 45.6 29.5 36.3
Obese (⩾30.0) 21.7 21.0 21.3
Region
South 30.8 33.1 32.1
Midwest 23.3 23.3 23.3
Northeast 22.4 22.2 22.3
West 23.5 21.3 22.2
Employment status/occupational activity
Employed, mostly sitting or standing 40.7 40.7 40.7
Employed, mostly walking 14.5 12.2 13.1
Employed, mostly heavy labor 15.7 3.9 8.7
Not currently employed 29.2 43.3 37.5
Table 2 Age-Adjusted Prevalence of Physical Activity Status by Sex, Behavioral Risk Factor Surveillance System, United States, 2001 (N=203,120)
Demographic Group Inactivea% (SE) Moderateb% (SE) Vigorousc% (SE) Recommendedd% (SE)
Overall 15.9 (0.15) 31.6 (0.18) 24.3 (0.17) 45.4 (0.2)
Men
Age, years
All ages 15.0 (0.22) 31.5 (0.28) 29.2 (0.28) 47.9 (0.31)
18-29 10.3 (0.48) 35.7 (0.68) 43.3 (0.73) 57.8 (0.74)
30-44 11.8 (0.37) 30.3 (0.48) 31.9 (0.50) 48.3 (0.55)
45-64 16.3 (0.40) 29.5 (0.48) 23.7 (0.45) 43.4 (0.54)
65-74 21.4 (0.80) 34.1 (0.87) 18.4 (0.90) 45.7 (1.01)
⩾75 29.7 (1.05) 29.2 (1.00) 11.5 (0.74) 38.4 (1.13)
Race/ethnicity
Non-Hispanic white 13.0 (0.22) 34.3 (0.31) 30.6 (0.30) 50.6 (0.33)
Non-Hispanic black 20.8 (0.90) 22.5 (0.87) 26.2 (0.92) 40.3 (1.05)
Hispanic 21.8 (1.19) 24.6 (1.13) 26.5 (1.51) 41.6 (1.52)
Other 17.0 (1.04) 27.8 (1.19) 25.8 (1.12) 43.1 (1.38)
Education
Less than high school 29.3 (0.94) 23.0 (0.82) 19.6 (0.90) 35.6 (1.04)
High school 18.1 (0.44) 31.6 (0.50) 25.1 (0.47) 46.0 (0.57)
Some college or technical school 12.6 (0.40) 34.4 (0.55) 30.0 (0.53) 50.3 (0.59)
College graduate 8.6 (0.32) 33.1 (0.53) 35.6 (0.54) 52.7 (0.56)
Body mass index (BMI)
Underweight (<18.5) 19.0 (1.41) 29.7 (1.61) 27.7 (1.61) 45.6 (1.82)
Healthy weight (18.5-24.9) 13.7 (0.39) 33.2 (0.49) 31.7 (0.50) 50.4 (0.55)
Overweight (25.0-29.9) 13.1 (0.33) 33.4 (0.44) 31.2 (0.47) 50.8 (0.51)
Obese (⩾30.0) 18.2 (0.51) 26.8 (0.60) 22.7 (0.58) 40.2 (0.67)
Region
South 17.8 (0.36) 32.4 (0.61) 26.9 (0.40) 45.4 (0.46)
Northeast 15.1 (0.52) 32.1 (0.53) 29.6 (0.59) 48.9 (0.68)
Midwest 15.0 (0.43) 29.0 (0.41) 28.9 (0.52) 47.9 (0.58)
West 10.7 (0.52) 34.2 (0.74) 32.6 (0.80) 51.1 (0.85)
Employment status/occupational activity
Employed, mostly sitting or standing 12.1 (0.46) 28.8 (0.51) 30.6 (0.50) 46.9 (0.58)
Employed, mostly walking 14.4 (0.73) 33.1 (0.93) 26.8 (0.77) 47.0 (1.03)
Employed, mostly heavy labor 16.4 (1.07) 37.0 (1.03) 33.0 (0.97) 53.0 (1.17)
Not currently employed 22.8 (0.68) 31.4 (0.67) 24.6 (0.68) 45.3 (0.77)
Women
Age, years
All ages 16.7 (0.20) 31.8 (0.23) 19.6 (0.20) 43.0 (0.26)
18-29 11.8 (0.41) 34.7 (0.59) 28.9 (0.56) 49.8 (0.63)
30-44 11.9 (0.31) 34.5 (0.42) 22.7 (0.37) 46.5 (0.46)
45-64 17.0 (0.39) 31.0 (0.40) 16.4 (0.33) 40.7 (0.45)
65-74 24.5 (0.73) 28.0 (0.65) 9.5 (0.42) 36.1 (0.75)
⩾75 39.6 (0.83) 20.5 (0.63) 5.7 (0.39) 26.9 (0.75)
Race/ethnicity
Non-Hispanic white 13.2 (0.18) 34.5 (0.26) 21.5 (0.23) 46.0 (0.28)
Non-Hispanic black 28.4 (0.73) 21.2 (0.64) 14.3 (0.55) 31.4 (0.74)
Hispanic 27.1 (1.03) 26.2 (0.88) 14.2 (0.67) 35.6 (1.01)
Other 19.1 (1.14) 29.4 (1.16) 18.7 (0.92) 41.2 (1.32)
Education
Less than high school 32.3 (0.83) 26.5 (0.77) 10.5 (0.56) 34.0 (0.87)
High school 18.8 (0.36) 30.3 (0.40) 15.8 (0.33) 40.3 (0.45)
Some college or technical school 14.0 (0.34) 32.8 (0.43) 20.6 (0.38) 44.3 (0.47)
College graduate 9.9 (0.35) 35.7 (0.47) 26.4 (0.43) 49.2 (0.52)
Body mass index (BMI)
Underweight (<18.5) 19.8 (0.98) 32.9 (1.10) 21.2 (0.99) 43.6 (1.20)
Healthy weight (18.5-24.9) 12.8 (0.26) 36.5 (0.35) 25.2 (0.32) 49.9 (0.38)
Overweight (25.0-29.9) 15.7 (0.38) 31.2 (0.46) 17.6 (0.39) 41.8 (0.51)
Obese (⩾30.0) 22.2 (0.51) 25.6 (0.54) 11.4 (0.39) 32.8 (0.59)
Region
South 19.8 (0.29) 32.5 (0.52) 17.8 (0.29) 39.7 (0.37)
Northeast 16.8 (0.45) 32.0 (0.44) 20.5 (0.45) 44.6 (0.58)
Midwest 15.5 (0.36) 28.7 (0.33) 19.5 (0.40) 42.8 (0.49)
West 13.0 (0.54) 36.0 (0.63) 22.1 (0.52) 47.2 (0.69)
Employment status/occupational activity
Employed, mostly sitting or standing 12.8 (0.43) 30.7 (0.46) 20.3 (0.35) 42.3 (0.51)
Employed, mostly walking 15.7 (1.06) 33.1 (0.85) 21.0 (0.62) 44.2 (0.98)
Employed, mostly heavy labor 12.9 (1.34) 40.4 (1.76) 28.2 (1.69) 55.6 (1.80)
Not currently employed 20.5 (0.35) 33.1 (0.42) 17.6 (0.34) 43.1 (0.46)
a Inactive = no moderate or vigorous activity.
b Moderate = participated in 30 minutes per day of moderate-intensity activity on five or more days per usual week.
c Vigorous = participated in 20 minutes per day of vigorous-intensity activity on three or more days per usual week.
d Recommended = met moderate or vigorous recommendations or both. Note that these two categories are not mutually exclusive.
Table 3 Adjusted Odds Ratios for Meeting Recommended Levels of Physical Activity Among Men and Women, Behavioral Risk Factor Surveillance System, United States, 2001a
Men (n=82,834) Women (n=120,286)
OR (95% CI) OR (95% CI)
Age, years
18-29 2.33 (2.06-2.64) 2.77 (2.51-3.06)
30-44 1.55 (1.37-1.75) 2.47 (2.24-2.71)
45-64 1.27 (1.13-1.43) 2.03 (1.86-2.23)
65-74 1.43 (1.25-1.62) 1.63 (1.48-1.81)
⩾75 1.0 (ref) 1.0 (ref)
Race/ethnicity
Non-Hispanic white 1.0 (ref) 1.0 (ref)
Non-Hispanic black 0.74 (0.68-0.82) 0.63 (0.59-0.68)
Hispanic 0.74 (0.66-0.83) 0.73 (0.67-0.81)
Other 0.70 (0.62-0.79) 0.75 (0.66-0.84)
Education
Less than high school 1.0 (ref) 1.0 (ref)
High school 1.40 (1.26-1.56) 1.23 (1.13-1.34)
Some college or technical school 1.71 (1.54-1.90) 1.40 (1.28-1.53)
College graduate 1.84 (1.66-2.04) 1.64 (1.50-1.79)
Body mass index (BMI)
Underweight (<18.5) 0.60 (0.42-0.85) 0.83 (0.73-0.94)
Healthy weight (18.5-24.9) 1.0 (ref) 1.0 (ref)
Overweight (25.0-29.9) 1.01 (0.95-1.07) 0.77 (0.73-0.81)
Obese (⩾30.0) 0.68 (0.63-0.73) 0.53 (0.50-0.56)
Region
South 1.0 (ref) 1.0 (ref)
Northeast 1.11 (1.04-1.19) 1.17 (1.10-1.24)
Midwest 1.03 (0.97-1.10) 1.04 (0.99-1.10)
West 1.24 (1.15-1.34) 1.33 (1.25-1.43)
Employment status/occupational activity
Employed, mostly sitting or standing 1.0 (ref) 1.0 (ref)
Employed, mostly walking 1.17 (1.08-1.27) 1.18 (1.10-1.27)
Employed, mostly heavy labor 1.40 (1.29-1.52) 1.69 (1.51-1.91)
Not currently employed 1.15 (1.07-1.24) 1.20 (1.14-1.27)
a Adjusted for all variables shown. Recommended levels of physical activity = participating in 30 minutes per day of moderate-intensity activity on five or more days per week or 20 minutes per day of vigorous-intensity activity on three or more days per week. OR = odds ratio; CI = confidence interval; ref = referent group.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Macera CA, Ham SA, Yore MM, Jones DA, Ainsworth BE, Kimsey CD, et al. Prevalence of physical activity in the United States: Behavioral Risk Factor Surveillance System, 2001. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0114.htm
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1 U.S. Department of Health and Human Services 1996 Physical activity and health: a report of the Surgeon General Atlanta (GA) U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion
2 Centers for Disease Control and Prevention 50 3 9 2001 166 169 MMWR Physical activity trends — United States, 1990-1998
3 Pate RR Pratt M Blair SN Haskell WL Macera CA Bouchard C 1995 273 402 407 JAMA Physical activity and public health: a recommendation from the Centers for Disease Control and Prevention and the American College of Sports Medicine 7823386
4 NIH Consensus Development Panel on Physical Activity and Cardiovascular Health 1996 276 241 246 JAMA Physical activity and cardiovascular health 8667571
5 Macera CA Jones DA Yore MM Ham SA Kohl HW III Kimsey CD 2003 52 764 769 MMWR Prevalence of physical activity, including lifestyle activities among adults—United States, 2000-2001
6 Ainsworth BE Bassett DR Jr Strath SJ Swartz AM O'Brien WL Thompson RW 32 Suppl 9 2000 S457 S464 Med Sci Sports Exerc Comparison of three methods for measuring the time spent in physical activity 10993415
7 Centers for Disease Control and Prevention 8 2002 16 2001 Behavioral Risk Factor Surveillance System summary data quality report Atlanta (GA) Centers for Disease Control and Prevention
8 Centers for Disease Control and Prevention 1998 7 99 Behavioral Risk Factor Surveillance System user's guide Atlanta (GA) U.S. Department of Health and Human Services, Centers for Disease Control and Prevention
9 Ham SA Macera CA Jones DA Ainsworth BE Turczyn KM 1 2 2004 98 113 Phys Activity Health Considerations for physical activity research: variations on a theme
10 Gentry EM Kalsbeek WD Hogelin GC Jones JT Gaines KL Forman MR 1 6 1985 9 14 Am J Prev Med The behavioral risk factor surveys: II. Design, methods, and estimates from combined state data 3870927
11 2004 53 82 86 Centers for Disease Control and Prevention MMWR Prevalence of no leisure time physical activity — 35 states and the District of Columbia, 1988-2002
12 Kahn EB Ramsey LT Brownson RC Heath GW Howze EH Powell KE 22 4S 2002 73 107 Am J Prev Med The effectiveness of interventions to increase physical activity. A systematic review 11985936
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Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_04_0115
Original Research
PEER REVIEWEDAdherence to Heart-Healthy Behaviors in a Sample of the U.S. Population
Bryson Chris L MD, MS Health Services Research and Development, Northwest Center of Excellence, VA Puget Sound Health Care System
The author is also affiliated with the Department of Medicine, University of Washington, Seattle, Wash
Met Park W, Suite 1400, 1100 Olive Way, Seattle, WA 98101 [email protected]
206-764-2430
Miller Rosalie R MD, MPH Health Services Research and Development Center of Excellence, VA Puget Sound Health Care System, Seattle, Wash
Sales Anne E MSN, PhD Centers for Disease Control and Prevention, Division of Nutrition and Physical Activity, Atlanta, Ga
Kopjar Branko MD, MS, PhD Health Services Research and Development Center of Excellence, VA Puget Sound Health Care System, Seattle, Wash, and Department of Health Services, University of Washington, Seattle, Wash
Fihn Stephan D MD, MPH Health Services Research and Development Center of Excellence, VA Puget Sound Health Care System, Seattle, Wash, and Department of Health Services and Department of Medicine, University of Washington, Seattle, Wash
4 2005
15 3 2005
2 2 A182005
Introduction
Following national recommendations for physical activity, diet, and nonsmoking can reduce both incident and recurrent coronary heart disease. Prevalence data about combinations of behaviors are lacking. This study describes the prevalence of full adherence to national recommendations for physical activity, fruit and vegetable consumption, and nonsmoking among individuals with and without coronary heart disease and examines characteristics associated with full adherence.
Methods
We performed a cross-sectional analysis of data from the 2000 Behavioral Risk Factor Surveillance System, a national population-based survey. We included respondents to the cardiovascular disease module and excluded individuals with poor physical health or activity limitations.
Results
Subjects were most adherent to smoking recommendations (approximately 80%) and less adherent to fruit and vegetable consumption and physical activity (approximately 20% for both). Only 5% of those without coronary heart disease and 7% of those with coronary heart disease were adherent to all three behaviors (P < .01). Among those without a history of coronary heart disease, female sex (odds ratio [OR] 1.47; 95% confidence interval [CI], 1.23–1.76), highest age quintile (OR 1.67; 95% CI, 1.28–2.19), more education (OR 2.48; 95% CI, 1.69–3.64), and more income (OR 1.19; 95% CI, 1.04–1.36) were associated with full adherence. Among those with coronary heart disease, mid-age quintile (OR 3.79; 95% CI, 1.35–10.68), good general health (OR 2.05; 95% CI, 1.07–3.94), and more income (OR 1.51; 95% CI, 1.06–2.16) were associated with full adherence.
Conclusion
These data demonstrate the lack of a heart-healthy lifestyle among a sample of U.S. adults with and without coronary heart disease. Full adherence to combined behaviors is far below adherence to any of the individual behaviors.
==== Body
Introduction
Physical inactivity, unhealthy diet, and smoking are three leading health behaviors contributing to the prevalence of coronary heart disease (CHD) in the United States (1,2). Significant research effort goes into documenting the prevalence of these behaviors and quantifying how much they contribute to CHD morbidity and mortality. While prevalence and risk data are available for individual behaviors, data about combinations of behaviors are lacking.
National recommendations for physical activity, diet, and smoking abstinence are available for all Americans. For most individuals, even those with CHD, walking is practicable. And for all individuals, independent of CHD or health status, maintaining a healthful diet and smoking cessation are achievable goals. The intent of these recommendations is different for those without and for those with CHD, namely to prevent incident cases of heart disease in those at risk and to reduce the risk of subsequent events in those with prevalent disease. While smoking is clearly associated with heart disease, a growing body of literature demonstrates that exercise, including moderate activity such as walking (3,4), and increased fruit and vegetable intake reduce the risk of CHD events (5-7). There are currently some data on the level of adherence to individual health behaviors.
Approximately one in five of the U.S. population adheres to recommendations for fruit and vegetable intake (8), one in four adhere to recommendations for exercise (9), and three in four adheres to recommendations not to smoke (10). Yet adherence to these individual recommendations is still below national goals (2), and there are very limited data on the combinations of these behaviors.
For public health, combinations of risk factors and behaviors are at least as important to understand as the prevalence of single behaviors. Major risk factors, including cholesterol, hypertension, and smoking clearly are responsible for the vast majority of heart disease (11,12). Furthermore, recommendations for diet, exercise, and smoking abstinence impact these major cardiovascular risk factors in different ways. Those with a low-risk profile have a much lower incidence of cardiovascular disease (CVD) than those with one or more single risk factors (13), and the lowest incidence of heart disease appears to be among those who adhere to multiple risk-reducing behaviors (14). The prevalence of individuals with multiple favorable risk factors or who participate in multiple positive health behaviors is known to be relatively rare (12-14). A full picture of modifiable cardiovascular risk cannot be accurately reflected in separate estimates of adherence to single behaviors.
We have sought 1) to describe the prevalence of full adherence to national recommendations for physical activity, fruit and vegetable consumption, and smoking abstinence, and 2) to examine characteristics associated with full adherence in a sample of the U.S. population, stratified by CHD status.
Methods
Design
We used the 2000 Behavioral Risk Factor Surveillance System (BRFSS) survey to provide the data for our study. The BRFSS is a population-based, random-digit–dialed telephone survey of the civilian, noninstitutionalized U.S. population aged 18 years and older. The methodology of BRFSS is described in detail elsewhere (15). The following summary focuses on our analysis of respondents with and without self-reported CHD.
Study population
Thirteen states (Delaware, Georgia, Indiana, Iowa, Kentucky, Mississippi, Montana, Ohio, Oklahoma, Pennsylvania, South Carolina, Virginia, and West Virginia) and the District of Columbia collected information about the prevalence of CVD in 2000. We selected participants who answered the CVD module (module 13) and then divided this group into individuals with and individuals without self-reported heart disease based on the following questions: 1) Has a doctor ever told you that you had a heart attack or myocardial infarction? 2) Has a doctor ever told you that you had angina or coronary heart disease? Individuals who responded yes to questions 1 or 2 were defined as having CHD.
We excluded individuals who reported poor physical health or activity limitation for at least a period of 15 days in the preceding month in both individuals with and individuals without CHD. These individuals would not be expected to participate regularly in exercise because of poor health and are therefore not included in the denominator of people who could potentially participate in these recommendations. Subjects were asked two questions to quantitate the number of days that they had poor physical health: 1) Now thinking about your physical health, which includes physical illness and injury, how many days during the past 30 days was your physical health not good?; 2) During the past 30 days, for about how many days did poor physical or mental health keep you from doing your usual activities, such as self-care, work, or recreation? Individuals who had 15 or more days of poor health from either question were excluded.
Responses of "don't know/not sure" to any of the questions or refusals to give an answer were coded as missing for this analysis.
Ascertaining adherence status
For those individuals with and individuals without self-reported heart disease, we extracted data on the following three health behaviors: physical activity (core question 6), smoking status (core question 7), and fruit and vegetable intake (core question 8) (Appendix). These behaviors were divided into dichotomous categories classified as fully adherent or not fully adherent to national recommendations.
Physical activity
Respondents were asked about the two physical activities that they engage in most often and about the frequency and duration of each activity. Respondents were classified as fully adherent if they engaged in a moderate-intensity physical activity at least five days per week for at least 30 minutes per day or vigorous-intensity activity at least three days per week for at least 20 minutes per day during the preceding month, based on recommendations from the Centers for Disease Control and Prevention (CDC) and the American College of Sports Medicine (16). We defined activity on the basis of metabolic equivalent (MET) intensity levels recorded in the compendium of physical activities: light activity, <3 METs; moderate activity, 3–6 METs; and vigorous activity, >6 METs (16). One MET is defined as the energy expended by sitting quietly and is equivalent to 3.5 ml of O2 uptake per kg body weight per minute, or to 1 kcal/kg of body weight per hour. Using the list of 56 recorded activities in the BRFSS and each activity's associated MET, we computed the self-reported frequency and duration of each activity per individual for both moderate-intensity and vigorous-intensity categories. Respondents were included if they had complete data for either the primary or secondary activity. If the primary or secondary activity met recommendations, or if the summation of primary and secondary activities met recommendations, respondents were counted as fully adherent. Responses with incomplete information about frequency or duration of activity could not be evaluated for adherence and were coded as missing.
Smoking abstinence
Respondents were asked if they had smoked 100 cigarettes in their entire lifetime and to categorize their current smoking pattern. Current nonsmokers (never and former smokers) were classified as fully adherent, without regard to the length of time since ceasing to smoke.
Fruit and vegetable intake
Respondents were classified as fully adherent to fruit and vegetable intake recommendations if they reported consuming five or more servings per day of combined fruits, fruit juice, and vegetables based on recommendations from the U.S. Department of Health and Human Services (17) and the American Heart Association (18). Participants were asked to report their consumption frequency of five foods: fruit, fruit juice (such as orange, grapefruit, or tomato), green salad, carrots, and other vegetables. Each participant was asked how frequently he or she consumed an item. Because the BRFSS did not inquire about portion size, each affirmative response was counted as one portion. Responses with insufficient data to determine the servings per day of a group were coded as missing for that group. Participants with data for at least four out of the five groups were included for the analysis. The groups were added together to produce the total number of fruit and vegetable servings per day. Participants reporting a total of five or more servings per day were counted as fully adherent to dietary recommendations for fruit and vegetable consumption.
We excluded potatoes because a growing body of evidence shows that a high intake of starchy vegetables, including potatoes, does not provide protective benefits against CHD and may increase risk of CHD (6,19,20). While the benefit of fruit juice on incident CHD risk has not been clearly established, fruit juice has not been found to confer an increased risk of CHD and was counted towards the fruit and vegetable total servings.
Combined adherence to physical activity, nonsmoking, and diet
A composite, dichotomous adherence score was calculated for each individual, incorporating physical activity, diet, and nonsmoking data. Respondents were classified as fully adherent to all three behaviors or as not fully adherent. Subjects were included only if they had enough information to count them as either fully adherent or not fully adherent to each individual behavior. A total of 1594 records (4%) were missing full adherence data (diet, 749; physical activity, 746; and current smoking status, 99) and were excluded from the analyses.
Statistical analysis
We computed age- and sex-adjusted prevalence estimates by direct standardization, using the entire group of respondents to the CVD module as the referent population. Broad age categories were used in the adjustment, as attempts to refine the age categories further produced inaccurate results because of the small numbers of fully adherent respondents. Statistical significance was tested using an age- and sex-adjusted logistic regression that accounted for the study design, modeling the characteristic as either a dichotomous or an indicator variable. Because those with CHD comprised 7% of the total population, and the rate of full adherence was low, race and body mass index (BMI) categories were collapsed to produce column proportions. All variables were modeled as categorical indicator variables unless noted.
Logistic regression was used to examine characteristics associated with full adherence, while adjusting for covariates. Multivariate models included the following covariates: age (18–99, in quintiles), gender, race (white, nonwhite), income (<$15,000; $15,000–$24,999; $25,000–$49,999; ⩾$50,000), education (<high school, high school or GED, >high school), general health (good, not good), mental health (good, not good), and BMI (underweight and normal, <18.5 kg/m2 and 18.5–24.9 kg/m2; overweight, 25–29.9 kg/m2; and obese, ⩾30 kg/m2). Age was divided into quintiles to provide sufficient numbers for analysis. Indicator variables were used for age, education, and BMI to model possible non-linear effects. Income was included as a grouped linear variable. Because the multivariable models revealed no difference in the odds of adherence for respondents with greater than or equal to high school education among those with CHD, the two higher educational categories were combined to create a dichotomous variable (<high school, ⩾high school) among those with CHD. Sociodemographic and health status characteristics were chosen a priori. Multivariable modeling with numerous covariates was not possible given the smaller number of respondents within the CHD group and the low prevalence of full adherence overall. Efforts were made to keep models comparable for both those with and those without CHD. All variables were entered into the model simultaneously.
All P values, adjusted odds ratios, and confidence intervals were obtained from logistic regression analyses that accounted for sampling design using Stata 7 SE (Stata Corp, College Station, Tex). All P values are two-tailed.
Results
Of the 43,058 individuals who answered both the CHD questions and the exercise, fruit and vegetable, and smoking questions, 3167 (7.4%) reported CHD. Our final sample included 38,851 subjects. The final sample excluded individuals who were likely to be unable to comply with physical activity recommendations; they reported more than 15 days of limitation because of poor health or they reported physical health as not good for more than 15 days. Among the final sample, 2189 (5.6%) reported CHD. As expected, there were disproportionately more subjects with self-reported CHD who were excluded for 15 or more days of poor health (n = 978, 30.9% of those with CHD) compared with those without CHD (n = 3229, 8.1% of those without CHD). Overall, only 5.1% of those without CHD and 7.2% of those with CHD fully adhered to all three health behaviors (unadjusted P value for difference = .009). When adjusted for age and sex, 5.0% of those without CHD and 5.1% of those with CHD were fully adherent (P = .03 for difference).
We examined how combinations of adherence to individual recommendations overlapped and used Venn diagrams to illustrate the frequency of the patterns of overlap. Figures 1a and 1b describe the weighted, unadjusted proportion of individuals adhering to seven possible individual and combined behaviors and the proportion of individuals who did not adhere to any of the behaviors. Regardless of CHD status, a minority of individuals were fully adherent to all three recommended behaviors. Adherence to single recommendations and to combinations of recommendations was similar in both groups. Adherence to fruit and vegetable intake and smoking abstinence was higher among individuals with CHD (P = .007, fruit and vegetable intake; P = .008, smoking abstinence), while there was no significant difference between CHD and non-CHD groups in the proportion of individuals who exercise (P = .10). Most respondents who were adherent to physical activity recommendations were also nonsmokers. Most respondents who adhered to fruit and vegetable intake recommendations were also nonsmokers. However, among individuals with and individuals without CHD, nearly one half of individuals who were adherent to nonsmoking recommendations did not adhere to either of the other two health behaviors. The proportion of individuals who did not follow any of the recommendations was moderate and similar for both those with CHD (16%) and those without CHD (18%).
Figure 1a Proportion of respondents without heart disease adherent to individual and combinations of fruit and vegetable intake, nonsmoking, and physical activity recommendations (n, sample = 36,772; N, weighted population = 40,725,302).
Venn diagramA text description of this diagram is also available
Smoking abstinence (76%)
Smoking abstinence alone 47%
Smoking abstinence and fruit and vegetable intake 10%
Smoking abstinence and physical activity 14%
Smoking abstinence, fruit and vegetable intake, and physical activity 5%
Physical activity (24%)
Physical activity alone 4%
Physical activity and smoking abstinence 14%
Physical activity and fruit and vegetable intake 1%
Physical activity, smoking abstinence, and fruit and vegetable intake 5%
Fruit and vegetable intake (18%)
Fruit and vegetable intake alone 2%
Fruit and vegetable intake and smoking abstinence 10%
Fruit and vegetable intake and physical activity 1%
Fruit and vegetable intake, smoking abstinence, and physical activity 5%
None (18%)
Figure 1b Proportion of respondents with heart disease adherent to individual and combinations of fruit and vegetable intake, nonsmoking, and physical activity recommendations (n, sample = 2129; N, weighted population = 2,361,137).
Venn diagramA text description of this diagram is also available
Smoking abstinence (80%)
Smoking abstinence alone 48%
Smoking abstinence and physical activity 12%
Smoking abstinence and fruit vegetable intake 12%
Smoking abstinence, physical activity, and fruit and vegetable intake 7%
Physical activity (21%)
Physical activity alone 2%
Physical activity and smoking abstinence 12%
Physical activity and fruit and vegetable intake <1%
Physical activity, smoking abstinence, and fruit and vegetable intake 7%
Fruit and vegetable intake (22%)
Fruit and vegetable intake alone 2%
Fruit and vegetable intake and smoking abstinence 12%
Fruit and vegetable intake and physical activity <1%
Fruit and vegetable intake, smoking abstinence, and physical activity 7%
None (16%)
Tables 1 and 2 show the characteristics of fully adherent individuals (column proportions labeled Full Adherence) and the age- and sex-adjusted proportions of individuals adherent to single recommendations and the combination of the three among specific groups (row proportions labeled Smoking Abstinence, Physical Activity, Fruit and Vegetable Intake, and All). The adjusted proportion of individuals who were fully adherent by individual characteristics varied from 2% to 7% among individuals without CHD (Table 1) and from 1% to 9% among those with CHD (Table 2). Among those without CHD, the higher proportion of adherence observed for women was associated with a greater adherence to fruit and vegetable intake but relatively similar levels of adherence for the other two behaviors. The lower adherence observed for the youngest age group was associated with a combination of lower adherence to smoking and diet recommendations, despite having better adherence to physical activity. Higher levels of income and education were associated with greater adherence to all three behaviors. Similar trends were seen for income and education among those with CHD.
We also examined characteristics independently associated with full adherence using logistic regression (Table 3). Among individuals without a history of CHD, highest age quintile [61–99 years] (OR 1.67; 95% CI, 1.28–2.19), female sex (OR 1.47; 95% CI, 1.23–1.76), nonwhite race (OR 1.29; 95% CI, 1.03–1.63), more education (OR 2.48; 95% CI, 1.69–3.64), more income (OR 1.19; 95% CI, 1.04–1.36), good general health (OR 1.39; 95% CI, 0.96–1.20) and good mental health (OR 1.50; 95%CI, 0.96–2.32) were associated with full adherence. In those with CHD, age quintile 3 [63–69 years] (OR 3.79; 95% CI, 1.35–10.68), age quintile 4 [70–76 years] (OR 3.42; 95% CI, 1.20–9.76), more income (OR 1.51; 95% CI, 1.06–2.16), and good general health (OR 2.05; 95% CI, 1.07–3.94) were associated with full adherence.
Discussion
We evaluated full adherence to three heart-healthy behaviors in a sample of individuals from 13 states and the District of Columbia with and without CHD. We found prevalence estimates for adherence to each single behavior that were similar to other studies (8,21-24). Approximately 18% of those without CHD and 22% of those with CHD were adherent to fruit and vegetable intake recommendations. Li et al report similar rates of adherence of 19%, 22%, and 23% for BRFSS respondents in the years 1990, 1994, and 1996 (8). We also found approximately 76% of those without CHD and 80% of those with CHD were current nonsmokers, similar to a national sample from the 2001 National Health Interview Survey that found approximately 77% of adults overall were nonsmokers. We found that 21% of those with CHD and 24% of those without CHD were adherent to physical activity recommendations, also similar to prior published BRFSS data from 1998 in which 25% of the overall population participated in recommended levels of physical activity (22). Most strikingly, we also observed that only about one in 20 individuals was adherent to all three of these behaviors, far below adherence to any of the individual behaviors.
Similar to other work in health-related behavior (25,26), we found both an education gradient and income gradient, with prevalence for all three behaviors being highest among individuals with more than high school education and greatest income. Higher levels of education may increase the likelihood of obtaining or understanding health-related information needed to develop heart-healthy behaviors. And while recent health gains for the U.S. population as a whole appear to reflect achievements among higher socioeconomic groups, lower socioeconomic groups continue to lag (2).
While we found that most respondents classified as adherent to physical activity or fruit and vegetable intake recommendations were also nonsmokers, the lower full adherence observed for the youngest age group for those without CHD was associated with a combination of lower adherence to nonsmoking and fruit and vegetable intake, despite having better adherence to physical activity. Among those with CHD, the lower full adherence observed for the youngest age group was associated with a combination of lower adherence to all three behaviors, with smoking accounting for the largest absolute difference between the youngest and oldest groups.
Among those without CHD, we also identified greater full adherence for self-identified nonwhite, nonblack racial groups (i.e., "other"), which was largely related to better adherence to fruit and vegetable intake. It is possible that cultural influences on food preferences account for better adherence and would have been recognized had ethnicity, rather than racial categories, been reported.
Consistent with other reports, we found that full adherence to dietary recommendations varied by age and sex, with the better dietary habits reported in women among those without CHD and reported in older adults in both CHD groups (27). However, even among these groups, low adherence to individual recommendations was common, suggesting that there is substantial room for improvement. Full adherence to fruit and vegetable intake was comparable for men and women with CHD and likely represents the greater proportion of men in our sample.
Whether affecting the risk for a first cardiovascular event or recurrent event, studies conclusively show that engaging in lifestyle modification reduces the risk of future coronary events (3-5,28-32). Major risk factors explain the bulk of cases of heart disease, and these risk factors are directly related to diet, physical activity, and smoking (33). Hypertension, cholesterol levels, and smoking are the most potent risk factors for CVD (13,34,35). Smoking remains the leading preventable cause of death in the United States (34) and contributes substantially to cardiovascular mortality and morbidity. The relative risk of CHD associated with physical inactivity ranges from 1.5 to 2.4, an increase in risk comparable to that observed for high blood cholesterol, hypertension, or cigarette smoking (16). Multiple studies have reported inverse relationships between fruit and vegetable intake and risk of CVD (5,6,28). As previous work has shown, combinations of healthy behaviors have an incrementally more protective effect (14). Among a group of women without CVD at baseline, nonsmokers who maintained healthful diet and who followed physical activity recommendations had an incidence of coronary events that was 60% lower than the incidence among the rest of the population. Physical activity and healthy diet have been shown to be independently associated with reductions in CHD risk. These findings strongly argue for monitoring the prevalence of not only individual health behaviors in the general population but also the combination of adherence to diet, exercise, and smoking recommendations.
Health promotion and disease prevention programs aimed at CHD might be more effective if they aimed at particular segments of the population who have more than one behavioral risk factor for CHD. For example, if a large proportion of the population is sedentary and has unhealthy diets but does not smoke, then a two-pronged program that aims at improving diet and physical activity but spends little or no time on smoking abstinence could be considered.
This study had limitations. The BRFSS does not reach individuals without telephones. Telephone coverage has been found to be lower among poorer households, so lower income respondents in both CHD groups may have been underrepresented in BRFSS samples (36). The BRFSS is based on self-reported data and is subject to reporting error. Misclassification within our CHD groups may have occurred because of reporting error in the form of incomplete medical knowledge or reporting bias by the respondent. Because of the social desirability of these behaviors, this study may overestimate the number of people adherent to each recommendation and also combinations of behaviors. We also may have misclassified some individuals based on fruit and vegetable intake because of missing data; however, analyses including subjects with complete data did not differ from analyses including subjects with four out of the five fruit and vegetable responses.
The weighted findings provide prevalence estimates that apply to the 13 states and the District of Columbia that used the BRFSS CVD module in 2000, and there is no guarantee that those 13 states were representative of all 50. However, we have no reason to suspect that these states are dissimilar to the remainder of the states and territories that participate in the BRFSS survey. The response rate for the 2000 BRFSS was approximately 50%. Although some attempt is made through post-stratification reweighting to force the age, sex, and race composition of the weighted BRFSS population to agree with census totals, it is possible that respondents still differ from nonrespondents on other characteristics that may be relevant to CHD risk factors. We used current national guidelines to create definitions of adherence to fruit and vegetable intake, exercise, and smoking recommendations. These were based on the guidelines from the CDC and the American College of Sports Medicine (16) and the American Heart Association (37,38) for exercise; from numerous sources, including the American Heart Association, for not smoking (18,38); and from the U.S. Department of Health and Human Services (17) and the American Heart Association for fruit and vegetable intake (18). While there is no debate on appropriate smoking behavior for cardiovascular risk reduction and some debate on the type and amount of physical activity that is best for risk reduction, there is no single widely accepted consensus yet for the most appropriate dietary regimen. In examining only the amount of fruit and vegetable intake, we ignored other important dietary factors that are known to influence cardiovascular risk factors. These include other dietary patterns and nutrients, such as the Mediterranean diet, which is high in omega-3 fatty acids, and the DASH (Dietary Approaches to Stop Hypertension) diet, which is high in fruit servings but also very low in sodium. Nevertheless, generous fruit and vegetable intake has been associated with reductions in CVD and may reflect generally healthier diets. The consumption of at least five or more servings of fruit and vegetables is also endorsed by the American Heart Association Dietary Guidelines, in part because this dietary pattern generally represents a healthier eating pattern, and also data show reductions in cardiovascular risk associated with this type of eating pattern (18).
In summary, we found markedly low full adherence to combined national recommendations for physical activity, fruit and vegetable intake, and smoking abstinence in this population-based study among people with and people without CHD. Population-wide behavioral strategies that target combinations of unhealthy behaviors and that complement clinical strategies may be more effective in producing desired changes and reducing the risk of CVD, investments which are critically needed in reducing the nation's CHD burden.
Dr. Miller was an Ambulatory Care Fellow in the Department of Veteran Affairs when this work was performed. The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veteran Affairs. Drs Miller and Bryson had full access to the data and take full responsibility for the accuracy of the data analysis.
Appendix. Behavioral Risk Factor Surveillance System Core Questions
Section 6: Exercise
The next few questions are about exercise, recreation, or physical activities other than your regular job duties.
6.1. During the past month, did you participate in any physical activities or exercises such as running, calisthenics, golf, gardening, or walking for exercise?
6.2. What type of exercise or exercise did you spend the most time doing during the past month?
Ask Q6.3 only if answer to Q6.2 is running, jogging, walking, or swimming. All others, go to Q6.4.
6.3. How far did you usually walk/run/jog/swim?
6.4. How many times per week or per month did you take part in this activity during the past month?
6.5. And when you took part in this activity, for how many minutes or hours did you usually keep at it?
6.6. Was there another exercise or exercise that you participated in during the last month?
6.7. What other type of exercise gave you the next most exercise during the past month?
Ask Q6.8 only if answer to Q6.7 is running, jogging, walking, or swimming. All others go to Q6.9.
6.8. How far did you usually walk/run/jog/swim?
6.9. How many times per week or per month did you take part in this activity?
6.10. And when you took part in this activity, for how many minutes or hours did you usually keep at it?
Section 7: Tobacco Use
7.1. Have you smoked at least 100 cigarettes in your entire life?
7.2. Do you now smoke cigarettes every day, some days, or not at all?
7.3. On the average, about how many cigarettes a day do you now smoke?
7.3a. On the average, when you smoked during the past 30 days, about how many cigarettes did you smoke a day?
7.4. During the past 12 months, have you quit smoking for 1 day or longer?
7.5. About how long has it been since you last smoked cigarettes regularly, that is, daily?
Section 8: Fruits and Vegetables
These next questions are about the foods you usually eat or drink. Please tell me how often you eat or drink each one, for example, twice a week, three times a month, and so forth. Remember, I am only interested in the foods you eat. Include all foods you eat, both at home and away from home.
8.1. How often do you drink fruit juices such as orange, grapefruit, or tomato?
8.2. Not counting juice, how often do you eat fruit?
8.3. How often do you eat green salad?
8.4. How often do you eat potatoes not including french fries, fried potatoes, or potato chips?
8.5. How often do you eat carrots?
8.6. Not counting carrots, potatoes, or salad, how many servings of vegetables do you usually eat?
Figures and Tables
Table 1 Adjusteda Prevalence Estimates of Full Adherence to National Recommendations on Smoking Abstinence, Physical Activity, and Fruit and Vegetable Intake for BRFSSb Cardiovascular Disease Module Respondents Without Self-Reported Heart Disease
(Sample, n = 36,722; Population, N = 40,725,302)
Characteristic Full Adherencec (%) Smoking Abstinence (%) Physical Activity (%) Fruit and Vegetable Intake (%) All (%)
Sex
Female 61 77 22 22 6
Male 39 75 25 14 4
Age, years
18-44 51 71 25 16 5
45-64 33 77 22 19 6
⩾65 16 90 21 22 6
Race
White 83 75 24 18 5
Black 11 79 21 19 4
Other 6 78 23 25 6
Married/partnered 63 79 22 18 5
Income
<$15,000 6 74 21 18 5
$15,000-$24,999 14 71 22 17 4
$25,000-$49,999 31 75 22 17 5
⩾$50,000 50 82 26 20 6
Education
<High school 4 59 17 13 2
High school or GED 22 70 19 15 3
>High school 74 82 27 22 7
Employmentd
Employed 79 77 23 18 5
Retired 17 82 23 22 3
Unemployed 3 68 21 17 4
Unable to work 1 67 15 17 2
General healthe
Good 95 77 24 19 5
Fair 5 63 16 15 3
Poor <1 59 13 16 3
Good mental healthf 97 77 24 18 5
BMI (kg/m2)
<18.5 2 73 20 19 6
18.5-24.9 50 73 27 19 6
25-29.9 37 78 23 18 5
⩾30 11 81 17 17 3
Diabetesg 5 81 19 22 6
Strokeh 1 70 21 15 6
a Characteristics are age and sex-adjusted, and percentages are weighted estimates.
b Abbreviations: BRFSS indicates Behavioral Risk Factor Surveillance System; GED indicates general equivalency diploma; BMI indicates body mass index.
c Percentages in the Full Adherence column reflect the proportion of subjects with the row characteristic who are adherent (row percentage), while the following four columns reflect the proportion of subjects who are adherent to smoking abstinence, physical activity, and fruit and vegetable recommendations among subjects defined by each row characteristic (column percentages).
d Employment categories are mutually exclusive; employed includes self-employed, employed by others, homemakers, and students.
e Subjects were asked, “Would you say in general your health is excellent, very good, good, fair, or poor?” Good health includes excellent, very good, and good responses.
f Good mental health is defined as ⩾15 days of self-reported good mental health in the preceding month.
g Subjects were asked, "Have you ever been told by a doctor that you have diabetes?"
h Subjects were asked, "Has a doctor ever told you that you had a [stroke]?"
Table 2 Adjusteda Prevalence Estimates of Full Adherence to National Recommendations on Smoking Abstinence, Physical Activity, and Fruit and Vegetable Intake for BRFSSb Cardiovascular Disease Module Respondents With Self-Reported Heart Disease
(Sample, n = 2129; Population, N = 2,361,137)
Characteristic Full adherencec (%) Smoking Abstinence (%) Physical Activity (%) Fruit and Vegetable Intake (%) All (%)
Sex
Female 31 69 15 19 4
Male 69 65 20 21 6
Age, years
18-44 6 57 15 19 4
45-64 25 73 18 19 5
⩾65 68 89 22 24 9
Race
White 88 66 17 20 5
Nonwhite 12 68 20 15 4
Married/partnered 64 68 17 18 5
Income
<$15,000 3 67 10 22 3
$15,000-$24,999 11 64 15 20 3
$25,000-$49,999 25 70 13 20 4
⩾$50,000 61 69 18 25 8
Education
<High school 9 52 11 13 4
High school or GED 20 65 17 17 3
>High school 71 71 20 24 7
Employmentd
Employed 76 68 17 18 5
Retired 20 64 11 10 4
Unemployed 3 29 12 21 9
Unable to work 1 70 14 35 1
General healthe
Good 82 67 20 19 6
Fair 14 61 12 20 3
Poor 4 82 6 35 2
Good mental healthf 98 69 16 21 5
BMI (kg/m2)
<24.9 45 64 23 18 6
25-29.9 40 63 20 22 4
⩾30 15 73 11 23 3
Diabetesg 24 70 12 29 6
Strokeh 9 69 22 25 5
a Characteristics are age and sex-adjusted, and percentages are weighted estimates.
b Abbreviations: BRFSS indicates Behavioral Risk Factor Surveillance System; GED indicates general equivalency diploma; BMI indicates body mass index.
c Percentages in the Full Adherence column reflect the proportion of subjects with the row characteristic among those who are adherent (row percentage), while the following four columns reflect the proportion of subjects who are adherent to smoking abstinence, physical activity, and fruit and vegetable recommendations, and the combination of all three among subjects defined by each row characteristic (column percentages).
d Employment categories are mutually exclusive; employed includes self-employed, employed by others, homemaker, and students.
e Subjects were asked, “Would you say in general your health is excellent, very good, good, fair, or poor?” Good health includes excellent, very good, and good responses.
f Good mental health is defined as ⩾15 days of self-reported good mental health in the preceding month.
g Subjects were asked, "Have you ever been told by a doctor that you have diabetes?"
h Subjects were asked, "Has a doctor ever told you that you had a [stroke]?"
Table 3 Characteristics Associated With Full Adherence to National Recommendations on Fruit and Vegetable Intake, Smoking, and Physical Activity Among BRFSS Cardiovascular Disease Module Respondentsa
Without CHD With CHD
OR (95% CI) OR (95% CI)
Age (years)
Quintile 1, (18-30) 1.00 (ref) Quintile 1, (18-52) 1.00 (ref)
Quintile 2, (31-39) 0.92 (0.71-1.19) Quintile 2, (53-62) 1.32 (0.47-3.72)
Quintile 3, (40-48) 0.98 (0.75-1.27) Quintile 3, (63-69) 3.79 (1.35-10.68)
Quintile 4, (49-60) 1.26 (0.97-1.64) Quintile 4, (70-76) 3.42 (1.20-9.76)
Quintile 5, (61-99) 1.67 (1.28-2.19) Quintile 5, (77-97) 1.99 (0.54-7.29)
Sex
Male 1.00 (ref) Male 1.00 (ref)
Female 1.47 (1.23-1.76) Female 1.23 (0.63-2.41)
Race
White 1.00 (ref) White 1.00 (ref)
Nonwhite 1.29 (1.03-1.63) Nonwhite 0.83 (0.34-2.02)
Education
<High school 1.00 (ref) <High school 1.00 (ref)
High school or GED 1.21 (0.82-1.79) ⩾High school 1.24 (0.51-3.00)
⩾high school 2.48 (1.69-3.64)
Income
<$15,000 1.00 (ref) <$15,000 1.00 (ref)
>$15,000 1.19 (1.04-1.36) >$15,000 1.51 (1.06-2.16)
General health
Fair or poor 1.00 (ref) Fair or poor 1.00 (ref)
Goodb 1.39 (0.96-1.20) Good 2.05 (1.07-3.94)
Mental healthc
Poor 1.00 (ref) Poor 1.00 (ref)
Good 1.50 (0.96-2.32) Good 0.89 (0.22-3.65)
Body Mass Index
⩽24.9 1.00 (ref) ⩽24.9 1.00 (ref)
25-29.9 0.81 (0.67-0.97) 25-29.9 0.52 (0.26-1.05)
⩾30 0.47 (0.37-0.60) ⩾30 0.88 (0.40-1.94)
a Abbreviations: BRFSS indicates Behavioral Risk Factor Surveillance System; CHD indicates coronary heart disease; OR indicates odds ratio; CI indicates confidence interval; GED indicates general equivalency diploma.
b Subjects were asked, “Would you say in general your health is excellent, very good, good, fair, or poor?” Good health includes excellent, very good, and good responses.
c Good mental health is defined as ⩾15 days of self-reported good mental health in the preceding month. Poor mental health is defined as <15 days of good mental health.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Miller RR, Sales AE, Kopjar B, Fihn SD, Bryson CL. Adherence to heart-healthy behaviors in a sample of the U.S. population. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0115.htm
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Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_04_0131
Original Research
PEER REVIEWEDFamily History, Diabetes, and Other Demographic and Risk Factors Among Participants of the National Health and Nutrition Examination Survey 1999–2002
Annis Ann M RN, MPH Genomics, Michigan Department of Community Health
PO Box 30195, 3423 N MLK Jr Blvd, Lansing, MI 48909 [email protected]
517-335-9296
Caulder Mark S MS, MPH Michigan Department of Community Health, Lansing, Mich
Cook Michelle L MPH Michigan Department of Community Health, Lansing, Mich
Duquette Debra MS, CGC Michigan Department of Community Health, Lansing, Mich
4 2005
15 3 2005
2 2 A192005
Introduction
Family history of diabetes has been recognized as an important risk factor of the disease. Family medical history represents valuable genomic information because it characterizes the combined interactions between environmental, behavioral, and genetic factors. This study examined the strength and effect of having a family history of diabetes on the prevalence of self-reported, previously diagnosed diabetes among adult participants of the National Health and Nutrition Examination Survey 1999–2002.
Methods
The study population included data from 10,283 participants aged 20 years and older. Gender, age, race/ethnicity, poverty income ratio, education level, body mass index, and family history of diabetes were examined in relation to diabetes status. Diabetes prevalence estimates and odds ratios of diabetes were calculated based on family history and other factors.
Results
The prevalence of diabetes among individuals who have a first-degree relative with diabetes (14.3%) was significantly higher than that of individuals without a family history (3.2%), corresponding to a crude odds ratio of five. Both prevalence and odds ratio estimates significantly increased with the number of relatives affected with diabetes. Family history was also associated with several demographic and risk factors.
Conclusion
Family history of diabetes was shown to be a significant predictor of diabetes prevalence in the adult U.S. population. We advocate the inclusion of family history assessment in public health prevention and screening programs as an inexpensive and valuable source of genomic information and measure of diabetes risk.
==== Body
Introduction
Diabetes mellitus presents multiple challenges to public health. An estimated 18.2 million individuals in the United States have diabetes (1). This disease contributes to significant morbidity, including cardiovascular, cerebrovascular, and renal disease, and premature mortality (1-3). In 2002, diabetes was ranked as the sixth leading cause of death (1,4). Another major public health challenge is the increasing prevalence of type 2 diabetes in adults, children, and adolescents during the past two decades (5-7). Additionally, type 2 diabetes may account for 90% to 95% of all diagnosed cases of diabetes (1,6,8), may progress undetected for years, and is often not diagnosed until onset of clinical symptoms or complications (3,6,8).
Undiagnosed diabetes constitutes approximately 29.3% of total diabetes prevalence (5). It is clear that developing strategies to screen and identify high-risk individuals should be an important public health goal. Screening for type 2 diabetes is recommended for individuals aged 45 years and older and/or younger individuals who have one or more risk factors, such as race/ethnicity (i.e., African American, Native American, and Hispanic), overweight or obesity, physical inactivity, previous history of gestational diabetes, and family history of diabetes (9). A primary goal of tailored screening is to recognize high-risk individuals in the presymptomatic stage of diabetes. Research has indicated that diabetes and many of its health complications can be delayed or prevented through medical and lifestyle interventions, such as pharmaceuticals, diet, and exercise (6,10-17).
For prevention efforts to be most effective, public health programs must recognize the factors involved in diabetes susceptibility. Evidence for a strong genetic element of type 2 diabetes susceptibility is suggested by the high incidence in certain racial/ethnic populations (1,3,6,18,19), high concordance in monozygotic twins compared with dizygotic twins (6,20,21), and high incidence among first-degree relatives of persons with type 2 diabetes (3,6,19,22-25). The complex pathophysiologic nature of diabetes supports the idea that multiple biologic and/or chemical pathways are implicated in the development and progression of the disease (26), and numerous genetic loci have been investigated in the search for genetic determinants of the disease (26-30). Identifying susceptibility loci for diabetes, however, has been difficult because of the multiple genes involved and strong environmental contributing factors (26).
Family history of type 2 diabetes is recognized as an important risk factor of the disease (3,6,9,19,22-25). Individuals who have a family history of diabetes can have two to six times the risk of type 2 diabetes compared with individuals with no family history of the disease (6,19). The etiologies of type 2 diabetes are complex: family medical history provides valuable genomic information because it represents the combination of inherited genetic susceptibilities and shared environmental and behavioral factors (31). The use of family history as part of a comprehensive risk assessment for an individual can be crucial in the prevention, early detection, and treatment of type 2 diabetes. On a population level, family history may help tailor health promotion messages for specific population groups (31).
A goal of this study was to assess the feasibility of obtaining and using genomic information from an existing, national population-based data source to provide chronic disease program recommendations. Specifically, our objective was to examine the strength and effect of having a family history of diabetes in first-degree relatives on the prevalence of self-reported, physician-diagnosed diabetes among adult participants in the National Health and Nutrition Examination Survey (NHANES) during 1999 to 2002. We evaluated several risk factors influencing diabetes prevalence in the United States and how these factors relate to family history.
Methods
Population
The National Center for Health Statistics (NCHS), within the Centers for Disease Control and Prevention (CDC), annually conducts NHANES, a continuous, population-based survey of the civilian, noninstitutionalized U.S. population (32). Data for NHANES is collected from U.S. households using two methods: an in-home interview and a physical health examination. Written informed consent is obtained from each participant for both parts of the survey. Information gathered by NHANES is intended for health research purposes, and NHANES documentation and codebooks are provided elsewhere (32).
For the study, data sets from both NHANES 1999–2000 and NHANES 2001–2002 were merged to create a NHANES 1999–2002 data set (n = 21,004) (32). Information on family history of diabetes was not available for participants aged 19 years and younger. Because family history was considered an important predictor of diabetes status, and the main focus was type 2 diabetes, subjects under the age of 20 years (n = 10,713) were excluded from the data set.
Diabetes status
Diabetes status was self-reported by asking whether an individual had ever been told by a doctor or health professional that he/she had diabetes or "sugar diabetes" other than during pregnancy (for female respondents). Because this survey question precluded gestational diabetes, pregnant women (n = 603) were not excluded from the study. The interview process did not discriminate between type 1 and type 2 diabetes. Survey participants from whom diabetes status was not ascertained during the NHANES interview were excluded from this study (n = 8). Among the remaining 10,283 adult respondents, 991 were categorized as having diabetes (including eight pregnant females), and 9292 were categorized as not having diabetes.
Individuals who reported a previous diagnosis of diabetes were asked at which age their diagnosis occurred. Age of diagnosis information was missing for 10 subjects in the sample population. There were 83 subjects who reported an age of diagnosis younger than 20 years. Although type 1 diabetes typically occurs during these younger ages, there was no definitive way to differentiate between type 1 and type 2 diabetes, and therefore we did not exclude any subject based on age of diabetes diagnosis.
Demographics and risk factors
Sex, age, and race were self-reported during the survey interview. Age was recorded as the subject's age in years at the time of interview. The age categories were 20–39 years, 40–59 years, and 60 years and older (33). Race and ethnicity were categorized in the following groups: non-Hispanic white, non-Hispanic black, Mexican American, and "other," which consisted of all other individual and multiracial groups. Statistical results for the "other" category are not described because the wide variability within the group prevents meaningful interpretation of estimates.
Socioeconomic status was assessed by poverty income ratio (PIR) and education level of the participants. The PIR, based on family size, is the ratio of family income to the family's poverty threshold level, determined by the Bureau of the Census (34). NHANES calculated participants' PIR values using self-reported family income data. We used the following categories: PIR <1.00, PIR 1.00–1.85, and PIR ⩾1.86. PIR values less than 1.00 are deemed to be below the poverty threshold. Some federally funded food assistance programs have an eligibility cut point of 1.85 (33,34). Education level was self-reported as the highest level achieved and was categorized as less than high school, high school or general equivalency diploma (GED), and more than high school.
During the NHANES physical examination, survey participants had both standing height (m) and weight (kg) measured, which were used to calculate body mass index (BMI [kg/m2]). Healthy weight was defined as BMI <25, overweight as BMI 25–29, and obesity as BMI ⩾30. Individuals who did not undergo a physical exam or who had missing BMI information and all women who were reported as being pregnant at the time of interview were excluded from analyses that contained BMI.
Family history
Participants were asked whether any biological member of their family, living or deceased, had ever been told he/she had diabetes. Family history information was not available from 216 individuals because of participant refusal (n = 2) and lack of knowledge of family medical history (n = 214). Subjects specified the relationship of any family member with diabetes; however, diabetes in children of the participants was not ascertained. We defined family history as having a first-degree relative (parent and/or sibling) with diabetes and categorized subjects according to parental and sibling diabetes status and number of first-degree relatives with diabetes.
Statistics
Statistical analyses were conducted using SAS version 9.1.3 (SAS Institute Inc, Cary, NC). This newest version permits analyses of complex survey designs. To achieve sufficient sample sizes, NHANES oversamples certain populations (33,34); thus, appropriate NHANES sample weights, stratums, and primary sampling units (PSUs) were used to account for complex sampling design, differential probabilities of selection, and nonresponse. Poststratification adjustments were applied by NHANES to the sample weights based on census population controls (33-35).
Prevalence estimates for diabetes, stratified by demographics and risk factors, were calculated using NHANES sampling weights and are extrapolated to the adult, noninstitutionalized, civilian U.S. population. Comparisons of diabetes prevalence between different groups were performed using F tests based on design-adjusted Rao–Scott chi squares (χ2). Age-adjusted prevalence (not shown) for the gender–race groups, based on the standard U.S. Census 2000 population (36), were deemed unreliable because of large associated standard errors and small sample sizes, especially in the group aged 20–39 years. For subjects with diabetes, the average age at diagnosis was examined by demographic and risk factors.
Crude and adjusted odds ratios (OR) and 95% confidence intervals (CI) for diabetes associated with family history were calculated through logistic regression analyses, which modeled the binary outcome of diabetes status (yes/no). Individual Wald χ2 tests and P values for all β estimates were computed. Four regression models were developed to first analyze family history independently, then in combination with demographic and risk factors demonstrating significant association with diabetes status. Variance estimates and standard errors were calculated using the Taylor expansion method. Any estimate with a relative standard error greater than 30% was considered to be statistically unreliable. Significance testing of interaction terms was performed to assess potential interaction between the factors included in the models. Likelihood ratio tests, multivariate Wald χ2 tests, and F tests were calculated to test for overall model significance. All P values less than .05 were considered statistically significant.
Results
Demographics and risk factors
The frequencies and weighted percentages of adults with diabetes are stratified by demographic and risk factors (Table 1). The overall estimated prevalence of diabetes among adults, representative of the civilian U.S. population, was 6.5%. Among men, the diabetes prevalence of non-Hispanic black men was significantly higher than that of Mexican American men (P = .01). Non-Hispanic black women had the highest prevalence of diabetes (11.4%) among all gender–race groups. The diabetes prevalence of non-Hispanic black women was significantly higher when compared to the prevalence of non-Hispanic white women (P < .001) and Mexican American women (P = .007). Mexican American women had significantly higher diabetes prevalence than non-Hispanic white women (P = .004).
The prevalence of diabetes significantly increased with age at interview (P = .001), and individuals 60 years and older experienced the highest prevalence (15.1%). Among the three PIR categories, adults in the group with the highest PIR level had significantly lower diabetes prevalence than adults at poverty level (P = .008) and in the middle PIR category (P < .001). Additionally, adults with less than a high school education experienced significantly higher diabetes prevalence than both those with a high school education (P < .001) and more than a high school education (P < .001). Finally, diabetes prevalence increased significantly with higher BMI status (P < .001). Overweight adults were almost twice as likely to have diabetes than healthy-weight adults, and obese adults were nearly four times as likely than healthy-weight adults.
For the individuals in the study who had diabetes, self-reported age of diabetes diagnosis was assessed (data not shown). Among men who had diabetes, the average age of diagnosis for the three race/ethnicity categories was similar: 46.4 years (95% CI, 43.3–49.4) for Non-Hispanic whites, 45.1 years (95% CI, 41.4–48.8) for non-Hispanic blacks, and 45.0 years (95% CI, 42.1–47.9) for Mexican Americans. Overall, men with diabetes had a mean age of diabetes diagnosis of 45.7 years (95% CI, 43.2–48.3). In contrast, women who had diabetes showed more striking differences in age of diagnosis among race groups. The mean age of diagnosis was 48.8 years (95% CI, 44.6–53.0) for non-Hispanic white women, 43.6 years (95% CI, 41.6–45.6) for non-Hispanic black women, and 40.4 years (95% CI, 37.5–43.3) for Mexican American women. Overall, women who had diabetes had an average age at diagnosis of 46.4 years (95% CI, 43.9–49.0). In addition, individuals who had diabetes and were obese had a younger mean age of diabetes diagnosis at 43.7 years (95% CI, 40.9–46.6) than overweight (48.6 years; 95% CI, 45.8–51.4) and healthy-weight (47.3 years; 95% CI, 44.1–50.4) individuals with diabetes.
Family history
Table 2 displays the frequencies and percentages of individuals who had diabetes in the study according to family history status: 3172 adult respondents reported having a family history of diabetes in a first-degree relative (parents and siblings) within the study population of 10,283. The diabetes prevalence for individuals with a family history was more than four times higher than the prevalence for individuals without a family history (P < .001). Among adults with a family history, diabetes prevalence increased significantly with a corresponding increase in number of family members with diabetes (P < .001). The diabetes prevalence for individuals with three or more first-degree relatives with diabetes (44.4%) was higher than the prevalence associated with any other demographic or risk factor measured.
Diabetes prevalence associated with parental history significantly increased with the number of affected parents (P < .001). The diabetes prevalence for individuals with a diabetic mother (16.5%) was higher than for individuals with a diabetic father (12.4%). In addition, having a sibling with diabetes conferred a diabetes prevalence approximately 4.5 times higher than the prevalence for individuals without a diabetic sibling (P < .001).
Further assessment of age of diagnosis (data not shown) showed that among individuals with diabetes who had a first-degree relative with diabetes, the mean age of diagnosis was 44.5 years (95% CI, 42.4–46.6) compared with 48.5 years (95% CI, 45.4–51.6) for individuals with diabetes who had no family history of diabetes. Moreover, there was more than an eight-year difference in mean age of diagnosis of individuals with diabetes whose parents had diabetes compared with individuals with diabetes whose parents did not have diabetes: 39.9 years (95% CI, 34.9–45.0) for individuals with two diabetic parents, 44.3 years (95% CI, 42.1–46.6) for individuals with one diabetic parent, and 48.3 years (95% CI, 45.7–51.0) for individuals with neither parent diabetic.
The presence of family history among adults differed by several factors and is depicted in Figures 1–3. A significantly larger proportion of individuals with diabetes reported having a family history of diabetes than individuals without diabetes (P < .001). More women reported a family history than men (P = .006). Compared with non-Hispanic whites, a higher percentage of non-Hispanic blacks (P = .001) and Mexican Americans (P < .001) reported a family history of diabetes. And obese and overweight adults were more likely to have a family history of diabetes than healthy-weight adults (P < .001 for both).
Figure 1 Percentages (95% confidence interval) of U.S. adults aged 20 years and older reporting a family history of diabetes, by self-reported diabetes status, NHANES 1999–2002.
Venn diagram % (95% CI)
Individuals Without Diabetes 27.0 (25.6–28.3)
Individuals With Diabetes 65.1 (61.8–68.4)
Figure 2 Percentages (95% confidence interval) of U.S. adults aged 20 years and older reporting a family history of diabetes, by gender and race/ethnicity, NHANES 1999–2002.
Venn diagram % (95% CI)
Men 27.8 (26.0–29.6)
Women 30.9 (29.2–32.6)
Non-Hispanic whites 27.5 (26.0–29.1)
Non-Hispanic blacks 35.2 (31.5–38.8)
Mexican Americans 34.3 (31.0–37.6)
Figure 3 Percentages (95% Confidence IntervaI) of U.S. adults aged 20 years and older reporting a family history of diabetes, by body mass index (BMI), NHANES 1999–2002.
Venn diagram % (95% CI)
Healthy weight (BMI <25) 22.6 (20.7–24.5)
Overweight (BMI 25–29) 30 (27.5–32.5)
Obese (BMI ⩾30) 37.5 (34.9–40.1)
Multivariate analyses
The association of family history and diabetes was evaluated with four regression models shown in Table 3. Each model used a different variable for family history and analyzed these variables first independently (crude ORs), then with the addition of other demographic and risk factors in the model (adjusted ORs). The family history variable was statistically significant in crude analyses of each model. Adults with a family history of diabetes had five times the odds of having diabetes compared with individuals who did not have a family history of diabetes.
The adjusted models used the categorical factors of gender, age group, race/ethnicity, PIR, and BMI. Since PIR and education level were highly related, education level was not included in the models. Regression analyses were also performed using age, PIR, and BMI as continuous variables; however, this did not appreciably change the parameter estimates corresponding to family history. In each of the four models, all additional variables were statistically significant, with the exception of BMI 25–29, for which the β estimate had a P value of .051 (Model 1) and .052 (Model 2).
After adjusting for the other variables, family history remained significantly associated with diabetes status, though the adjusted ORs were slightly lower than the crude ORs. Adults with a family history of diabetes had four times the odds of having diabetes themselves compared with individuals without a family history (P < .001). The odds of having diabetes were almost 15 times higher for those with three or more diabetic relatives compared with adults with no family history (P < .001). Parental and sibling diabetes history were also significantly associated with increased risk of diabetes (P < .001 for both).
Discussion
Our diabetes prevalence estimates for the gender–race groups were similar to a previous review of data from NHANES III (1988–1994), which showed that for both men and women, non-Hispanic blacks had a higher diabetes prevalence than non-Hispanic whites and Mexican Americans (37). However, we did not find any studies using NHANES data that examined family history of diabetes in relation to diabetes prevalence.
We found that family history of diabetes was a significant predictor of self-reported diabetes among U.S. adults. We estimated that adults with a family history of diabetes in a parent or sibling had four times the odds of having diabetes than adults without a family history of the disease, after adjusting for gender, age, race, PIR, and BMI. These findings are consistent with a recent summary review of 10 studies performed in various countries, which reported that individuals with a positive family history of diabetes had two to six times the risk of type 2 diabetes, compared with individuals without a family history of the disease (19).
Moreover, our study demonstrated that adults with two diabetic parents had more than twice the risk of diabetes than adults with only one diabetic parent. This additive risk association has been described previously in a white U.S. population (22). Through further examination of family history, an elevated diabetes risk was found to be associated with an increased number of first-degree family members affected with diabetes. Among all demographic and risk factors, the presence of three or more diabetic first-degree relatives corresponded to the highest diabetes prevalence and OR for diabetes. With the exception of a few studies, a relatively small amount of literature quantified family history of diabetes in terms of the number of affected relatives.
Because family history was one of the strongest risks for diabetes in our study, individuals with family members who have diabetes should be a screening priority for diabetes. As stated previously, undiagnosed diabetes constitutes approximately 29.3% of total diabetes prevalence (5). A current study demonstrated that the prevalence of diagnosed diabetes has increased, and the prevalence of undiagnosed diabetes has decreased for severely obese individuals (BMI ⩾35), possibly because of a better awareness of BMI as a risk factor among health care providers and improved screening among these individuals (5). Similarly, the use of a family history screening tool could capture many more of these undiagnosed individuals who would benefit from early intervention.
Individuals who have close relatives with diabetes may be more motivated to seek early health screening and thus more likely to be diagnosed than individuals without a family history. Because of earlier screening, individuals with a family history would likely be younger at age of diagnosis than individuals without a family history. This likelihood is supported by both our study (44.5 years at diagnosis for individuals with a family history vs 48.5 years at diagnosis for individuals without a family history) and an Australian study, which found a trend of younger age of diabetes diagnoses with increasing number of family members affected (24). Furthermore, research has shown that individuals with type 2 diabetes are more likely to collect health information from family members (38). However, our study indicated that a higher proportion of adults who had diabetes did not know their family history of diabetes (2.7%) when compared with adults who did not have diabetes (2.0%), although this difference was not statistically significant.
In addition, proportionately more women reported a father, mother, brother, or sister with diabetes than men, and there were more reports of female relatives with diabetes than male relatives with diabetes. A recent study found that women were slightly more likely than men to regard family history as very important to their own health and were more likely to collect family medical information (38). Among men in our study, 2.2% did not know their family history of diabetes, compared with 1.8% of women.
Limitations
Limitations of our study include the inability to discriminate between cases of type 1 and type 2 diabetes. Had stratification been possible, we may have found different relationships among diabetes, family history, and other factors. Subjects in our study were not excluded based on age of diabetes diagnosis; such exclusion could have eliminated many type 1 diabetes cases. It is estimated that approximately one third of children with diabetes aged 12 to 19 years have type 1 diabetes. The prevalence of type 1 diabetes among all ages in the United States is approximately 0.12% (39). Therefore, the exclusion of individuals with type 1 diabetes from our study population would probably not have affected our results appreciably.
Because diabetes diagnoses of participants and family members were self-reported and not verified, the true diabetes prevalence may be misrepresented. Moreover, diabetes is underdiagnosed in the United States, suggesting that the true prevalence is higher than reported prevalence. Subjects also self-reported age of diabetes diagnosis, creating a potential for recall bias. As previously mentioned, survey participants were not asked about family history of diabetes in children, which limited our definition of first-degree relatives to parents and siblings only. Also, NHANES excludes institutionalized persons, including individuals residing in nursing homes, who are likely to be older adults.
Implications
Our findings create several implications for public health. First, diabetes has paralleled the obesity epidemic. Similar to a previous NHANES study (40), we found that non-Hispanic black women had the highest prevalence of obesity (48.7%) compared with non-Hispanic white women (31.1%), Mexican American women (36.8%), non-Hispanic black men (26.8%), non-Hispanic white men (27.9%), and Mexican American men (25.8%). The prevalence of family history was also highest in women and non-Hispanic blacks among genders and races. Both obesity and diabetes have strong environmental components, such as diet and physical activity. Thus, the presence of family history often reflects the shared environment and health-related behaviors among family members in addition to hereditary factors. The recognition of this high correlation among obesity, diabetes, and family history can help guide population-appropriate health promotion activities.
Second, with the current striving for genetic awareness and competency in public health, this study represents a feasible and inexpensive method of extracting genomic information from existing population-based data sources. NHANES, a validated and well-recognized survey, provides a substantial amount of health information on a national level. Other population-based surveys also offer informative data that may pertain to genomics. There are several steps public health practitioners can take now to access and use genomics and incorporate genomics into programs. Because family history encompasses both genetic and environmental factors, it can be applied to other chronic diseases involving multiple complex etiologies, such as cardiovascular disease. Therefore, knowledge gained from family history and diabetes can be translated into other public health program areas.
Finally, at the primary care and public health level, this study supports the promotion of a family history tool for diabetes prevention and early detection strategies as a valuable measure of diabetes risk. Family history is easily available and inexpensive to obtain yet may be underused in health care practice (31). The following three criteria are suggested for incorporating a family history tool into public health screening: 1) the disease represents a significant public health burden, 2) family history is an established risk factor, and 3) there are effective interventions for prevention (31). Type 2 diabetes meets these criteria. It is evident that neither diabetes nor obesity prevalence is decreasing; therefore, it is critical that we use all available resources to quantify individual disease risk as accurately and completely as possible.
We thank Rebecca Malouin, Janice Bach, Corinne Miller, and Earl Watt with the Michigan Department of Community Health for their helpful advice and resources. We also thank Kathy Welch with the University of Michigan for guidance in using SAS programming and analyses. Financial support was provided as part of a CDC genomics cooperative agreement U58/CCU522826 in the Chronic Disease Prevention and Health Promotion Programs, Component 7, Genomics and Chronic Disease Prevention, Program Announcement 03022.
Figures and Tables
Table 1 Frequencies and Percentages of Self-Reported Individuals With Diabetes by Demographic and Risk Factors, Adults Aged 20 Years and Older in the United States, 1999–2002
Total (n) Diabetic (n) Weighteda % (95% CI)
Total 10,283 991 6.5 (5.9-7.1)
Men
All races 4802 481 6.7 (5.9-7.5)
Non-Hispanic white 2396 196 6.2 (5.1-7.3)
Non-Hispanic black 887 108 8.2 (6.5-9.9)
Mexican American 1129 130 5.3 (3.9-6.7)
Women
All races 5481 510 6.3 (5.5-7.2)
Non-Hispanic white 2674 170 5.1 (4.4-5.8)
Non-Hispanic black 1034 140 11.4 (9.2-13.6)
Mexican American 1266 148 7.7 (6.2-9.3)
Age, years
20-39 3618 61 1.7 (1.1-2.2)
40-59 2964 256 6.6 (5.6-7.5)
⩾60 3701 674 15.1 (13.9-16.4)
Poverty income ratio (PIR)
PIR <1.00<(poverty) 1743 208 7.9 (6.0-9.8)
PIR 1.00-1.85 2138 278 8.7 (7.3-10.1)
PIR ⩾1.86 5222 378 5.3 (4.7-6.0)
Education level
Less than high school 3559 514 10.9 (9.5-12.3)
High school/GED 2361 200 6.5 (5.4-7.5)
More than high school 4321 273 4.7 (3.8-5.5)
Body mass index (BMI)b
BMI <25 2752 143 3.1 (2.3-3.9)
BMI 25-29 3087 298 5.9 (4.8-7.0)
BMI ⩾30 2662 386 11.2 (10.1-12.4)
a For extrapolation of diabetes prevalence to the adult, noninstitutionalized, civilian U.S. population, weighted percentages incorporate NHANES sampling weights to account for unequal selection probabilities and nonrandom sampling design.
b Excludes pregnant women.
Table 2 Frequencies and Percentages of Self-Reported Individuals With Diabetes by Family History of Diabetes, Adults Aged 20 Years and Older in the United States, 1999–2002
Total (n)a Diabetic (n) Weightedb % (95% CI)
Family history of diabetes (parents and/or siblings only)
No 6895 344 3.2 (2.8-3.6)
Yes 3172 618 14.3 (12.8-15.9)
Number of relatives with diabetes (parents and/or siblings only)
One relative 2343 354 11.0 (9.5-12.5)
Two relatives 606 148 19.3 (15.4-23.2)
Three or more relatives 223 116 44.4 (37.7-51.0)
Parental history of diabetes
Neither parent has diabetes 7640 512 4.2 (3.7-4.7)
One parent has diabetes 2181 368 12.3 (10.7-13.9)
Both parents have diabetes 246 82 25.4 (18.8-31.9)
Father has diabetes 1046 177 12.4 (10.0-14.7)
Mother has diabetes 1627 355 16.5 (14.6-18.5)
Sibling history of diabetes
No sibling has diabetes 8749 596 4.7 (4.2-5.2)
At least one sibling has diabetes 1318 366 21.7 (19.2-24.3)
Brother(s) has/have diabetes 756 228 23.3 (19.7-26.9)
Sister(s) has/have diabetes 828 257 25.6 (22.2-28.9)
a Family history status was not ascertained for 216 of the 10,283 participants in the National Health and Nutrition Examination Survey 1999–2002.
b For extrapolation of diabetes prevalence to the adult, non-institutionalized, civilian U.S. population, weighted percentages incorporate NHANES sampling weights to account for unequal selection probabilities and nonrandom sampling design.
Table 3 Odds Ratios and 95% Confidence Intervals for Diabetes by Family History, Adults Aged 20 Years and Older in the United States, 1999–2002a
Model Crude Adjustedb
OR 95% CI OR 95% CI
Model 1:Family history of diabetes (parents and/or siblings only)c
No 1.00 (ref) NA 1.00 (ref) NA
Yes 5.06 4.37-5.85 3.95 3.25-4.79
Model 2:Number of relatives with diabetes (parents and/or siblings only)c
None 1.00 (ref) NA 1.00 (ref) NA
One relative 3.74 3.15-4.43 3.05 2.44-3.82
Two relatives 7.25 5.63-9.34 5.14 3.81-6.91
Three or more relatives 24.12 18.24-31.89 14.83 10.95-20.08
Model 3:Parental history of diabetesc
No 1.00 (ref) NA 1.00 (ref) NA
One parent has diabetes 3.17 2.65-3.79 3.04 2.34-3.94
Both parents have diabetes 7.68 5.63-10.48 6.95 4.69-10.29
Model 4: Sibling history of diabetesc
No 1.00 (ref) NA 1.00 (ref) NA
At least one sibling has diabetes 5.59 4.80-6.51 3.52 2.94-4.21
a OR indicates odds ratio; CI indicates confidence interval; ref indicates referent group; NA indicates not applicable.
b Full regression models are adjusted for gender (males, females), age group (20–39, 40–59, ⩾60 years), race/ethnicity (non-Hispanic white, non-Hispanic black, Mexican American, other), poverty income ratio (PIR) (PIR <1.00, PIR 1.00–1.85, PIR ⩾1.86), and body mass index (BMI <25, BMI 25–29, BMI ⩾30). Females, aged 20–39 years, non-Hispanic white, PIR ⩾1.86, and BMI <25 were used as referent groups
c Overall model significance: P < .001.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Annis AM, Caulder MS, Cook ML, Duquette D. Family history, diabetes, and other demographic and risk factors among participants of the National Health and Nutrition Examination Survey 1999–2002. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0131.htm
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22 Meigs JB Cupples A Wilson PW 2000 49 2201 2207 Diabetes Parental transmission of type 2 diabetes: the Framingham Offspring study 11118026
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38 Centers for Disease Control and Prevention 53 44 2004 1044 1047 MMWR Morb Mortal Wkly Rep Awareness of family health history as a risk factor for disease—United States, 2004 15538320
39 LaPorte RE Matsushima M Chang YF 2nd edition 1995 37 46 Bethesda (MD) Prevalence and incidence of insulin-dependent diabetes Diabetes in America National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health
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Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_04_0039
Special Topic
PEER REVIEWEDChildhood Obesity — What We Can Learn From Existing Data on Societal Trends, Part 2
Sturm Roland PhD RAND
1700 Main St, Santa Monica, CA 90401 [email protected]
310-393-0411 ext. 6164
4 2005
15 3 2005
2 2 A202005
The number of overweight and obese youth has increased in recent decades, yet few data assess how the lives of children have changed during the "obesity epidemic." Part 1 of this two-part study discussed trends in time use, studying at home, and media use. Part 2 focuses on transportation, physical education, and diet.
Walking or biking for transportation can expend a large amount of energy, but active transportation is not a major source of physical activity for youth, averaging eight minutes a day in 2001, with little change over the past few decades. For adolescents, there was no clear trend in physical education during the past decade, but there are no data for after-school and day-care programs, which have become more important as children spend more time away from home. For younger children, time spent in organized sports and outdoor activities increased by 73 minutes per week between 1981 and 1997.
Eating as a primary activity declined, suggesting a shift toward snacking or eating as a secondary activity. Statistically significant trends exist for carbohydrate intake, especially for chips/crackers/popcorn/pretzels (intake tripled from the mid-1970s to the mid-1990s) and soft drinks (intake doubled during the same period). Price and income data suggest possible economic reasons for these changes. The percentage of disposable income spent on food has declined continuously, and almost all of the decline has been represented by food consumed at home, yet today's disposable income buys more calories than it has in the past. Relative prices have encouraged shifts across food types. From a baseline of 100 during 1982–84, the price index for fresh fruit and vegetables increased to 258 by 2002 (far exceeding general inflation), whereas the price index for soft drinks increased only to 126 by 2002 (below general inflation).
Part 2 of 2
==== Body
Introduction
The number of overweight and obese youth has been increasing, reflecting changes in social and environmental factors that need to be understood and modified for effective prevention (1-3). This two-part report reviews some data available to track how the lives of children have changed during the "obesity epidemic." Part 1 reviewed changes in time use, homework, and media use; Part 2 reviews transportation, physical education (PE), and diet.
Active transportation, such as walking or biking, can expend a large amount of energy, and it has been hypothesized that increased suburbanization reduces walking and biking. Only recently have transportation patterns and urban design in relation to physical activity and health attracted interest. Although research has been limited to cross-sectional comparisons and adults, it has shown an association between increases in sprawl and decreases in leisure time and utilitarian walking and increases in body mass index and chronic health conditions (4,5). Only the National Household Travel Survey (NHTS) provides national data for youth travel; it is discussed in the next section of this paper.
We saw in Part 1 that children now spend more time away from home than in the past. As a result, physical activity in school, after-school programs, and day-care settings plays a more important role in determining physical activity levels of children. Limited data are available on physical activity among high school students, which are discussed below, and there are few other data.
Many of the most prominent hypotheses on weight gain address changes in food and diet and the roles of such factors as soft drinks, vending machines, snacks, fast food, and portion sizes. In contrast to the availability of data on PE and transportation, there is a vast scholarly literature on dietary change, and this paper cannot do justice to such a broad area. An Institute of Medicine report on preventing childhood obesity provides a more comprehensive list of citations (6). In addition, the U.S. Department of Agriculture's (USDA's) Economic Research Service regularly updates macroeconomic data in the Food Consumption Data System (7). Nevertheless, interesting data are available on relative food price changes and trends in the eating patterns of children that, while well known among researchers in the nutrition field, do not appear to be widely known among the broader research community interested in child health.
Transportation
Transportation is an important part of everyday life. American adults spend more than 10 hours per week traveling, about equally divided among transportation related to occupation (work commute), home activities (child care/shopping/personal care), and leisure-time activities (8). Adult transportation and leisure time have increased at the expense of occupation and household activities, with particularly large increases before 1985.
The only national data that provide somewhat comparable data over multiple years are the Nationwide Personal Transportation Surveys (NPTS), now called the NHTS, conducted in 1969, 1977, 1983, 1990, 1995, and 2001 by the U.S. Department of Transportation (9). Walking is generally more underestimated than other transportation modes, and only the 2001 survey probed for walking trips. For our purposes, we use only three years: 1977, 1990, and 2001. There is no walking information in the 1969 survey; the 1983 sample was small and not representative for children; and the 1995 data neglected walking and biking trips. Although the data are intended to be nationally representative, the consistency of methods and quality of data collection is noticeably lower than for the time-use and education data reported in Part 1 of this paper. Thus, while the transportation data discussed in this article are likely to indicate true secular changes, readers should view them with greater skepticism. We do not show statistical significance tests because the main source of uncertainty stems from design changes, not from statistical uncertainty, and statistical tests would suggest more precision than there really is.
The Table shows the share of trips (one-way short- or long-distance travel) by transportation mode for children aged five to 15 years. No clear trend is visible, especially no precipitous decline of walking. Somewhat surprising is the very large increase in the role of school buses between 1977 and 1990, which subsequently declined (and could very well be a methods artifact).
Travel to and from school is a regular, predictable, and important part of children's travel, even if these trips account only for a minority of all trips. Policy influences how children travel to school more than it influences other travel modes because of decisions on school location or busing, so policy offers better opportunities for interventions in school transportation than it does in other areas. Figure 1 shows a clear and significant decline in the percentage of walking trips for children aged five to 15 years. The trend is slightly stronger for adolescents, dropping from 20.9% in 1977 to 13.6% in 1990 to 10.9% in 2001 (data on adolescents only not shown). Because the total number of school trips does not change much, it seems safe to say that physical activity associated with getting to and from school has declined, despite data limitations. These are probably the best available numbers for youth transportation choices, but they do not provide a complete picture and in isolation are even misleading because school trips are only a small part of total trips, and other trips have not remained constant.
Figure 1 Walking to school as percentage of school trips among U.S. children aged five to 15 years. Author's analysis based on data from National Personal Transportation Survey for 1977 and 1990 and the National Household Travel Survey for 2001 (9).
Bar chart Percentage of school trips
1977 20.2
1990 16.6
1991 12.5
The overall role of walking and biking is more difficult to assess because physical activity depends on the time and distance spent walking or biking. One reliable fact is that the total number of daily trips made by children has substantially increased (Figure 2), so even a decline in the share of walking or biking does not automatically translate into a decline in physical activity. The increase in the number of trips is not surprising because the time-use section in Part 1 showed that children are now spending more time away from home than in the past.
Figure 2 Total number of daily trips among U.S. children aged five to 15 years. Author’s analysis based on data from National Personal Transportation Survey for 1977 and 1990 and the National Household Travel Survey for 2001 (9).
Bar chart Number of trips
1977 2.2
1990 3.1
2001 3.5
Trip distances are not necessarily fixed, and increased suburban sprawl could increase the distance of walking or biking trips (because all destinations are now farther away) or decrease it (if the substitution of driving for walking or biking overcompensates). Here the data quality becomes more questionable. In the NPTS/NHTS, we see little evidence of changes in walking-trip length for children, but biking distances declined noticeably, from about 1.3 miles in 1977 to 0.9 miles in 2001 (Figure 3).
Figure 3 Average trip length (in miles) among U.S. children aged five to 15 years. Author’s analysis based on data from National Personal Transportation Survey for 1977 and 1990 and the National Household Travel Survey for 2001 (9).
Bar chart Miles
Walking Bicycling
1977 0.69 1.31
1990 0.53 0.98
2001 0.59 0.94
The best metric for physical activity is total active travel time, which incorporates changes in number of trips, distances, and travel mode (Figure 4). Based on the available data, active travel time appears to have increased, a consequence of the increase in the total number of trips, even as walking to school unambiguously declined.
Figure 4 Average active travel time (in minutes) among U.S. children aged five to 15 years. Author's analysis based on data from National Personal Transportation Survey for 1977 and 1990 and the National Household Travel Survey for 2001 (9).
Bar chart Minutes
Walking Bicycling
1977 2.7 0.3
1990 3.9 0.6
2001 6.3 2.1
The magnitude of active travel time is important, however. The highest estimates for active travel time (which were recorded for 2001) add up to only about eight minutes of walking and biking combined per day. So even with a 50% increase, the energy expenditure associated with active travel time would be no more than the energy equivalent of a half-can of soft drink.
On the positive side, interventions that increase walking to school could effect a large relative change. If an additional one quarter of children were to walk to school — not an entirely unrealistic scenario because enough children live within walking distance of their schools — total active travel time could increase by 50% nationwide.
Transportation offers promising interventions for other reasons. Transportation patterns depend on public goods, and large externalities are associated with individual automobile use (10). The growth of traffic and accompanying changes in land use reduce incentives to walk or bike because nearby destinations are disappearing and because of perceived (and actual) danger. The absence of good time-series data on the travel patterns and mobility needs of children and the focus on planning for automobiles by federal, state, and local transit and planning agencies indicate neglect. New policies and investments that make cities safer and more convenient for walking and biking may be economically efficient aside from the benefit to the health and mobility of children (10).
Physical Education
Children spend many hours in school, making PE programs in schools a potentially important channel through which physical activity and fitness may be promoted among young children (11,12). Arguably as important, though rarely studied or discussed, is physical activity in day care and after-school programs, where children are spending far more time now than two decades ago.
In 1998, 16% of kindergartners received daily PE instruction in school, and approximately 13% received PE instruction less than once a week or never (13). Results of the 2001 Youth Risk Behavior Surveillance System (YRBSS), a national school-based survey of ninth- to 12th-graders conducted by the Centers for Disease Control and Prevention (CDC) show that nearly one half (45%) do not play team sports during the year; nearly one half (48%) are not enrolled in a PE class; and PE enrollment drops from 74% for ninth-graders to 31% for 12th-graders (14).
Trend data on changes are more difficult to find. The YRBSS goes back only as far as 1991 and then only for a few variables. The quality of these nationally representative data is excellent, but limitations stem from the short time period, the small number of PE items available in all years, and a narrow target population that excludes younger children. The consistency of the YRBSS data, however, is much better than the consistency of the transportation data.
Figure 5 shows the percentage of high school students who attended PE classes at least once a week. Little evidence suggests a continuous trend in either direction. If we were to fit a linear trend to these averages, it would suggest an increase in participation by about two percentage points per decade — not exactly a major increasing trend, but certainly not evidence of a decline either. The absolute level, however, could be considered disappointingly low: almost half of U.S. high school students do not receive regular PE in school.
Figure 5 Percentage of U.S. high school students who attended physical education class one or more days during an average school week. Data from the Youth Risk Behavioral Surveillance System, Centers for Disease Control and Prevention (14).
Bar chart Percentage of students
1991 48.9
1993 52.1
1995 59.6
1997 48.8
1999 56.1
2001 51.7
Despite fluctuations in reported PE participation, the percentage of high school students who get enough physical activity to satisfy minimum guideline levels is essentially constant (the difference between the highest and lowest annual numbers is only two percentage points) and much higher (Figure 6) than the rates of high school students receiving regular PE in school. This variable includes physical activity outside school. Even if total participation is less than optimal, there is no evidence for declining exercise levels.
Figure 6 Percentage of U.S. high school students who exercised or participated in physical activities that made them sweat and breathe hard for at least 20 minutes on three or more of past seven days. Data from the Youth Risk Behavioral Surveillance System, Centers for Disease Control and Prevention (14).
Bar chartA text description of this chart is also available.
Percentage of students
1993 65.8
1995 63.7
1997 63.8
1999 64.7
2001 64.6
Only one variable is inconsistent with the physical activity data described above: namely, the percentage of students participating in daily PE (Figure 7). Participation rates were highest in 1991 and then dropped quickly to bottom out in 1995, followed by a significant and continuous increase since then. The 1991 number itself seems to be out of line with other years and could potentially be an issue of methodology — national changes of that magnitude rarely happen so quickly nor do they immediately reverse themselves. Numbers for 1993 and 2001 are similar.
Figure 7 Percentage of U.S. high school students attending daily physical education classes. Data from the Youth Risk Behavioral Surveillance System, Centers for Disease Control and Prevention (14).
Bar chart Percentage of students
1991 41.6
1993 34.3
1995 25.4
1997 27.4
1999 29.1
2001 32.2
How effective are school PE programs in preventing obesity and promoting physical activity? School boards are receiving mixed messages about PE. On one hand, government organizations like the CDC recommend that all schools require daily PE for all students from kindergarten through 12th grade. On the other hand, the predominant conclusion emerging from research studies is that typical school PE is of low quality when compared with ideal PE instruction. School boards, principals, and teachers facing other competing goals, especially academic achievement, may conclude that if existing PE is of limited value, it should be abolished or at least reduced in favor of other academic instruction. However, PE in elementary schools as currently implemented nationwide (and not ideal instruction) plays an important role in containing excess weight gain among girls (13).
Diet
We can examine trends in dietary change for children by using data from the Continuing Survey of Food Intakes by Individuals (CSFII) 1989–91, 1994–96, and 1998 and the Nationwide Food Consumption Survey 1977–78. Enns et al have published the results of these surveys for children aged six to 11 years (15). There are only two strong and consistent trends. One, the intake of chips/crackers/popcorn/pretzels roughly tripled from the mid-1970s to the mid-1990s: from five grams (1977–78) to nine grams (1989–91) to 14 grams (1994–96, 1998) per day for girls and from five grams to nine grams to 15 grams for boys. Two, the intake of soft drinks roughly doubled during the same period: from 105 grams to 136 grams to 200 grams per day for girls and from 112 grams to 169 grams to 217 grams per day for boys. Other researchers found parallel changes for all age groups, and trends appear similar for different age groups (16). While increased snacking is likely a main cause for the shift across foods, there also has been a shift to larger portion sizes (17,18).
Some researchers believe that high-fructose corn syrup or added caloric sweeteners play an important role in the development of obesity worldwide (19,20). For a typical soft drink, 100 mL (or less than one third of a 12-oz can) has 10.7 g of sugar and provides 43 kcal of energy. This energy value corresponds to the energy expenditure of about eight minutes of walking for an adult and to the daily average that is reported as active travel time among children aged five to 15 years in the 2001 NHTS. Thus, the increase in soft drink consumption alone appears to be at least equal to the total energy expenditure associated with children's active travel in 2001. That this trend in soft drink consumption could be a factor in weight gain is also consistent with cross-sectional data that show an association between the consumption of sugar-sweetened drinks and obesity after controlling for observable characteristics (21). Soft drink consumption is also negatively related to milk, fruit, and vegetable consumption and positively related to higher calorie intake (22-24).
Two significant trends are apparent in the share of energy from fat and carbohydrates. The share of energy from fat fell for both boys and girls, and the share of energy from carbohydrates increased (15). Figure 8 shows changes in grams. Fat intake decreased by about 100 kcal or less, but carbohydrate intake increased by about 150 to 200 kcal. The point estimate of total energy intake in the 1990s was higher than in 1977–78, but the data cannot reject the hypothesis of no change.
Figure 8 Daily fat and carbohydrate intake in grams per day for U.S. boys and girls aged six to 11 years. Data from Continuing Survey of Food Intakes by Individuals for 1989–91, 1994–96, and 1998 and Nationwide Food Consumption Survey 1977–78, published by Enns et al (15).
Bar chart Grams
Boys Girls
Fat Carbohydrates Fat Carbohydrates
1977–78 84.7 226.2 77.8 211.9
1989–91 72.7 245.5 69.8 241.6
1994–96, 1998 75.1 279.6 66.8 250.0
An alternative data series is provided by the USDA's Food Consumption Data System, but it cannot identify trends for subgroups because it is composed of macroeconomic data (Figure 9). Calories per capita remained relatively constant from 1970 until the mid-1980s but then increased. Consistent with the CSFII data, the energy increase is derived almost exclusively from carbohydrates.
Figure 9 U.S. food supply of macronutrients in grams per capita per day, 1970–2000. Data from Food Consumption Data System, Economic Research Service, U.S. Department of Agriculture (7).
Line graphA line graph shows three lines, one indicating protein consumption, one indicating fat consumption, and one indicating carbohydrate consumption over a 31-year period, from 1970 to 2000. The line indicating protein remains fairly constant at the 100-gram level, increasing slightly from 96 grams in 1970 to 112 grams in 2000. The line indicating fat started at 151 grams in 1970, trending slightly upward after 1980, and reached 171 grams in 2000. The line indicating carbohydrates remains fairly constant from 1970 to 1982, starting at 395 grams in 1970 to 399 grams in 1982, but then rises steadily to reach 505 grams in 2000.
Price and income data may be important because they shed light on underlying economic trends. The percentage of disposable income spent by Americans on food has continuously declined since the end of World War II, even as it bought more calories. Almost all of the decline is derived from food prepared and consumed at home; the share of disposable income for food away from home stayed relatively constant. In 1970, Americans spent one third of their food dollars on food away from home; this amount grew to 39% in 1980, 45% in 1990, and 47% in 2001. Away-from-home foods tend to be more energy-dense and contain more fats and sugars than foods at home. USDA researchers have calculated that if food away from home had the same average nutritional densities as food at home in 1995, Americans would have consumed 197 fewer calories per day and reduced their fat intake to 31.5% of calories (instead of the actual 33.6%) (25).
With increasing income, people are shifting to more convenient food away from home. Demographic reasons explain this shift as well. Increased numbers of smaller households (resulting from lower fertility rates) and increased numbers of single-parent households enjoy fewer economies of scale in home-food production than larger families. Preparation of an in-home meal involves a fixed time that differs little with the number of persons served, whereas eating out involves the same marginal costs for each person. This difference in "technology" combined with demographic changes alone would have shifted incentives toward fewer meals prepared at home. In addition, relative price changes have made the consumption of prepared foods cheaper compared with the time costs of preparing food at home and cleaning up.
What is not clear is why the location of consumption should so dramatically alter nutritional content. Lack of information at the point of consumption is probably part of the reason, although this argument would only apply to adults who are presumed to be able to make rational decisions. If adults lack information about nutritional content at the point of consumption, it is not surprising that competition takes place among factors that consumers can evaluate easily (at least with repeat purchases): price, amount, and taste. This type of market failure is well known to economists since Nobel Laureate George Akerlof's "lemon paper" (26). Akerlof argued that if quality is an important dimension but cannot be assessed by a buyer, competition will take place on price and other observed characteristics (e.g., portion size) and drive out higher-quality products even if they would be preferred by buyers with more complete information. When informational problems are sufficiently severe, regulation is needed for an efficient market. Requiring easily available and understandable information about the nutritional content of prepared meals at the point of consumption might address this informational problem.
Another economic trend shown by data collected by the USDA's Economic Research Service (7) also shifts incentives in a direction that does not promote healthier eating patterns. Figure 10 shows relative price changes, using the period 1982–1984 as the baseline (index = 100) for each series. While the consumer price index increased to 180 by 2002, the price index for fresh fruit and vegetables increased to 258. In contrast, sugars, sweets, fats and oils became relatively cheaper than other goods, and their prices increased less than the consumer price index (data not shown for fats and oils). With a 2002 price index of 126, soft drinks were among the items that became (relatively) the cheapest.
Figure 10 Relative price changes for fresh fruits and vegetables, sugars and sweets, and soft drinks, using the period 1982–84 as the baseline (index = 100), 1978–2002. Data from Food Consumption Data System, Economic Research Service, U.S. Department of Agriculture (7).
Bar chartYear Consumer Price Index Fresh fruits and vegetables Sugars and sweets Soft drinks
1978 65.2 70.7 68.3 70.8
1979 72.6 76.1 73.6 77.3
1980 82.4 81.8 90.5 86.6
1981 90.9 91.6 97.7 95.3
1982 96.5 96.7 97.5 97.8
1983 99.6 96.4 99.3 100.3
1984 103.9 106.9 103.2 101.8
1985 107.6 109.7 105.8 102.8
1986 109.6 113.0 109.0 103.6
1987 113.6 126.8 111.0 105.7
1988 118.3 136.1 114.0 105.7
1989 124.0 147.7 119.4 108.4
1990 130.7 161.0 124.7 112.1
1991 136.2 174.1 129.3 113.0
1992 140.3 171.0 133.1 114.9
1993 144.5 178.6 133.4 115.9
1994 148.2 186.7 135.2 115.7
1995 152.4 206.0 137.5 119.5
1996 156.9 211.8 143.7 119.9
1997 160.5 215.4 147.8 118.3
1998 163.0 231.2 150.2 117.5
1999 166.6 237.2 152.3 118.8
2000 172.2 238.8 154.0 123.4
2001 177.1 247.9 155.7 125.4
2002 179.9 258.4 159.0 125.6
Detailed data exist on lunches and breakfasts offered in schools participating in the National School Lunch Program, although only for two data points (school years 1991–92 and 1998–99) (27). Energy content has remained fairly constant for lunches (with an increase of 3% in primary schools and a decrease of 3% in secondary schools) but declined somewhat for breakfast (a decrease of 4% for primary schools and a decrease of 10% for secondary schools). Fat content declined and was replaced by carbohydrates with no change in protein content (27). The overall role of school diet in children's diet is less clear because the data refer to meals offered, not necessarily consumed, and because there have been large increases in participation in the School Breakfast Program. Schools provided approximately 8% of all meals and snacks and contributed 9% of total calories for children aged two to 19 years in 1994–1996, but the importance of school foods in a child's diet was highest among children aged six to 11 years (28).
Discussion
In 1995, "Obesity in Britain: Gluttony or Sloth?," a study published in the BMJ, energized the debate on whether the obesity epidemic is caused by declining physical activity or increasing energy intake (29). The authors came down on the "sloth" side for adults. Even if one could separate energy intake and expenditure, we saw in this review that existing data are too limited to support a conclusive analysis. However, food consumption patterns have changed dramatically while youth physical activity patterns have not. In fact, the data generally show fewer changes in physical activity — changes in time studying at home, participating in exercise, or taking part in high school PE — than commonly thought. Changes went in the opposite direction, even if at a minimal level — less television watching and more active transportation time. These patterns do not exclude the possibility that overall physical activity has declined, because major changes in time use have occurred, and, for example, we have not determined activity levels in after-school and day-care settings where time spent by children has increased substantially. Both dietary and physical activity interventions can affect weight gain. Interventions affecting physical activity can be desirable even if recent increases in obesity among youth have been primarily related to changes in diet.
This report was prepared for the Robert Wood Johnson Foundation. Tania Andreyeva and Hilary Rhodes provided research assistance.
Figures and Tables
Table Percentage of Trips by Transportation Mode Among U.S. Children Aged Five to 15 Yearsa
1977 1990 2001
Personal vehicles 76.0 65.5 71.3
Public transportation 2.7 2.1 1.0
School bus 7.6 15.4 10.2
Bicycle 1.3 2.3 3.3
Walk 11.9 14.1 13.3
Other 0.4 0.4 0.9
a Data based on National Personal Transportation Surveys for 1977 and 1990 and the National Household Travel Survey for 2001 (9).
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Sturm R. Childhood obesity — what we can learn from existing data on societal trends, part 2. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0039.htm
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2 Hill JO Peters JC 280 5368 1998 1371 1374 Science Environmental contributions to the obesity epidemic 9603719
3 Hill JO Wyatt HR Reed GW Peters JC 299 5608 2003 853 855 Science Obesity and the environment: where do we go from here? 12574618
4 Ewing R Schmid T Killingsworth R Zlot A Raudenbush S 18 1 2003 47 57 Am J Health Promot Relationship between urban sprawl and physical activity, obesity, and morbidity 13677962
5 Sturm R Cohen D 118 7 10 2004 488 496 Public Health Suburban sprawl and physical and mental health 15351221
6 Koplan JP Liverman CT Kraak VA Preventing childhood obesity: health in the balance Washington (DC) National Academies Press Forthcoming
7 U.S. Department of Agriculture Food consumption data system [Internet] Washington (DC) Economic Research Service 2004
8 Robinson JP Godbey GG 1999 Time for life: the surprising ways Americans use their time 2nd ed University Park (PA) Pennsylvania State University Press
9 Bureau of Transportation Statistics National household travel survey [Internet] Washington (DC) U.S. Department of Transportation
10 Porter RC 1999 Academic Press; 1999 Economics at the wheel: the costs of cars and drivers London Academic Press
11 Carter RC 288 17 2002 2180 JAMA The impact of public schools on childhood obesity 12413386
12 Centers for Disease Control and Prevention 46 RR-6 1997 1 36 MMWR Recomm Rep Guidelines for school and community programs to promote lifelong physical activity among young people
13 Datar A Sturm R 2004 9 94 9 1501 1506 Am J Public Health Physical education in elementary school and body mass index: evidence from the early childhood longitudinal study 15333302
14 Centers for Disease Control and Prevention 2002 51 1 62 MMWR Morb Mortal Wkly Rep Youth Risk Behavior Surveillance System (YRBSS): United States, 2001
15 Enns CW Mickle SJ Goldman JD 14 2 2002 56 68 Family Economics and Nutrition Review Trends in food and nutrient intakes by children in the United States
16 Nielsen SJ Siega-Riz AM Popkin BM 10 5 5 2002 370 378 Obes Res Trends in energy intake in U.S. between 1977 and 1996: similar shifts seen across age groups 12006636
17 Jahns L Siega-Riz AM Popkin BM 138 4 4 2001 493 498 J Pediatr The increasing prevalence of snacking among US children from 1977 to 1996 11295711
18 Nielsen SJ Popkin BM 289 1 22-29 4 2003 450 453 JAMA Patterns and trends in food portion sizes, 1977-1998 12533124
19 Bray GA Nielsen SJ Popkin BM 79 4 4 2004 537 543 Am J Clin Nutr Consumption of high-fructose corn syrup in beverages may play a role in the epidemic of obesity 15051594
20 Popkin BM Nielsen SJ 11 11 11 2003 1325 1332 Obes Res The sweetening of the world's diet 14627752
21 Ludwig DS Peterson KE Gortmaker SL 357 9255 2001 505 508 Lancet Relation between consumption of sugar-sweetened drinks and childhood obesity: A prospective observational analysis 11229668
22 Harnack L Stang J Story M 99 4 1999 436 441 J Am Diet Assoc Soft drink consumption among U.S. children and adolescents: nutritional consequences 10207395
23 Cullen KW Ash DM Warneke C de Moor C 92 9 2002 1475 1478 Am J Public Health Intake of soft drinks, fruit-flavored beverages, and fruit and vegetables by children in grades 4 through 6 12197978
24 Pereira MA Jacobs DR Jr Van Horn L Slattery ML Kartashov AI Ludwig DS 287 16 2002 2081 2089 JAMA Dairy consumption, obesity, and the insulin resistance syndrome in young adults: the CARDIA Study 11966382
25 Lin BH Guthrie J Frazão E Washington (DC) U.S. Department of Agriculture, Economic Research Service, Food and Rural Economics Division undated America's eating habits: changes and consequences Agriculture Information Bulletin No. 750, USDA/ERS 213 242 Nutrient contribution of food away from home
26 Akerlof GA 84 3 1970 488 500 Quarterly Journal of Economics The market for 'lemons': quality uncertainty and the market mechanism
27 U.S. Department of Agriculture, Food and Nutrition Service 1 2001 Special Nutrition Programs Report No. CN-01-SNDAIIFR School Nutrition Dietary Assessment Study II The Office of Analysis, Nutrition and Evaluation Washington (DC)
28 Lin BH Guthrie J Frazão E Food Review Quality of children's diets at and away from home: 1994-96 1999 Jan–Apr 2 10
29 Prentice AM Jebb SA 311 7002 1995 437 439 BMJ Obesity in Britain: gluttony or sloth? 7640595
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Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
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PCDv22_04_0142
Special Topics
Peer ReviewedFeatured Abstracts From the 19th National Conference on Chronic Disease Prevention and Control
4 2005
15 3 2005
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==== Body
Prev Chronic Dis
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EDITORIAL: FEATURED ABSTRACTS FROM THE 19th NATIONAL CONFERENCE ON CHRONIC DISEASE PREVENTION AND CONTROL
Reducing Health Disparities: What Is Being Done, What Works
Baldyga William DrPH, MA Associate Director Institute for Health Research and Policy (MC 275)
1747 W Roosevelt Rd, Room 572, Chicago, IL 60608 312-996-0786 [email protected]
Petersmarck Karen PhD, MPH Michigan Department of Community Health, Lansing, Mich
4 2005
15 3 2005
2 2 A21
If necessity is the mother of invention, creativity in public health has never been more important. Fortunately, the ability of the public health thinkforce (as compared to workforce) to respond to the challenges inherent in assuring the public’s health is remarkable. The willingness of the thinkers to share information has always been a strength of the field, and now new technologies have enhanced our abilities to communicate what works, for whom, and under what conditions.
Challenges to population health continue to mount. Risk factor increases (e.g., obesity) and poorer access to services (e.g., percentage of population without insurance) conspire with multiple other health determinants to create monumental challenges for public health, particularly in the area of health disparities. Understanding disparities — their roots and their implications — is a difficult challenge; our future success will be largely determined by our response to this challenge. Correcting disparities will require, in part, the best application of chronic disease program knowledge to the populations at greatest risk.
The planners of the 19th National Conference on Chronic Disease Prevention and Control invited state and federal public health leadership, academic researchers, and others to think about solutions for the disparities that exist and continue despite our efforts. Some of the most impressive responses to that invitation appear here. Creativity and curiosity are features of the work presented in this section, and, it is hoped, they will spark those virtues in the readership.
One year ago, shortly after the launch of Preventing Chronic Disease, the decision was made to incorporate the best abstracts from the annual Chronic Disease Conference as a regular feature. The abstracts capture the field of public health now, offering a glimpse of what is being done, what works, and how it does so. They come from state and local chronic disease prevention programs and the academic community, including the Prevention Research Centers. Furthermore, they reflect the seven conference tracks: Partnerships; Evidence-based Programs: Research, Translation, and Evaluation; Health System Change; Social Determinants of Health Inequities; Communications and Technology; Methods and Surveillance; and Policy and Legal. These abstracts represent some of the best current work in the field of public health.
Two years ago, those not participating in the conference would be hard pressed, by both time and access, to find this information. Today, this issue of Preventing Chronic Disease brings the information to your desktop. The abstracts can inform you, challenge you, and connect you to colleagues who share your interests. You are invited to take advantage of this opportunity and to react with your own ideas — those that best meet the needs of your communities.
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Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedPE2GO: A Program to Address Disparities in Youth Physical Activity Opportunities
Martin Maurice W MEd Research Evaluation Specialist Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Diabetes Translation
4770 Buford Highway NE, Mail Stop K-10, Atlanta, GA 30341 770-488-5385 [email protected]
Martin Sarah
Elmer Elmer Ray
4 2005
15 3 2005
2 2 A21
Track: Partnerships
The purpose of this study was to investigate the effectiveness of the PE2GO pilot program in six school districts across the United States (Chicago, Ill; Los Angeles, Calif; Akron, Ohio; New York, NY; Memphis, Tenn; and Portland, Ore). PE2GO is a community affairs initiative of Nike, Inc, the athletic apparel and shoe manufacturer based in Beaverton, Ore. Within the PE2GO program, Nike partners with organizations across the country to offer programs in underserved areas (e.g., Native American Boys & Girls Clubs, African American and Latino communities in Los Angeles) to foster physical activity among youth through their influencers such as parents, teachers, and coaches. PE2GO is a self-contained physical education (PE) program that provides classroom teachers with the tools they need to lead developmentally appropriate, quality PE lessons in their fourth- and fifth-grade classrooms in inner-city schools. The pilot program reached 6000 elementary school students.
In September 2003, experienced trainers from nonprofit Sports, Play, and Active Recreation for Kids (SPARK) conducted a one-day training of PE staff using a playbook created especially for the PE2GO program. Nike provided the curriculum and the necessary equipment. The initial training focused on two themes: building a foundation and disguising fitness. A second training approximately four months later focused on a third theme: simplifying sports. Trained evaluation consultants independent from Nike or SPARK collected data for the program’s evaluation and analyses in three distinct phases: pre-intervention, mid-intervention, and post-intervention. The intervention occurred through May 2004, and all data were reported by the faculty and administrators at the schools where the curricula were implemented (N = 164); this group included classroom teachers (n = 128), PE specialists (n = 22), and school-level administrators (n = 14).
Reported minutes of PE per week increased significantly from pre-intervention to mid-intervention (37 minutes pre-intervention vs 60 minutes mid-intervention; P < .05) and remained significantly higher than pre-intervention at the post-test (73 minutes). Satisfaction increased significantly from pre-intervention to mid-intervention (P < .05) and remained elevated post-intervention. Four of the eight questions assessing barriers showed that barriers decreased significantly from pre-intervention to mid-intervention (P < .05) and remained reduced post-intervention. Almost all administrators reported that they would support staff development (94%) and encourage staff to implement PE2GO (88%); more than half said they would reward staff for implementing PE2GO with fidelity (56%). From the qualitative research, almost all responded that administrators have expressed support for the program, yet about half added that administrators have had little involvement. Classroom teachers were successfully trained and satisfied with the program and the effect it was having on their fourth- and fifth-grade students. Reported minutes of PE increased substantially.
The PE2GO program holds promise in this day of declining opportunities for children to be active during their school hours, especially in schools with limited resources for PE specialists.
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Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedAwareness of Cardiovascular Disease Risk in American Indians
Oser Carrie S MPH Cardiovascular Disease/Diabetes Epidemiologist Montana Department of Public Health and Human Services
1400 Broadway, Cogswell Building, PO Box 202951, Helena, MT 59620-2951 406-444-4002 [email protected]
Blades Lynda
Strasheim Carol
Helgerson Steven
Gohdes Dorothy
Harwell Todd
4 2005
15 3 2005
2 2 A21
Track: Methods and Surveillance
Although cardiovascular disease (CVD) has become the leading cause of death in American Indians, little is known about how Indian communities perceive their risk.
In 2003, a telephone survey was conducted among adult American Indians living on or near Montana’s seven Indian reservations. Respondents were asked about awareness of heart disease risk; history of cardiovascular disease (CVD) such as heart attack, angina, or stroke; and risk factors for CVD.
The prevalence of CVD and risk factors among men and women aged 45 years and older (N = 516) was high: CVD (26% in men and 15% in women), diabetes (24% in men and 26% in women), high blood pressure (48% in men and 46% in women), high cholesterol (34% in men and 40% in women), smoking (28% in men and 33% in women), and obesity (37% in men and 46% in women). Men with a history of certain medical conditions were more likely to be aware of their risk for heart disease than men without these conditions: CVD (87% with vs 46% without), high blood pressure (70% with vs 44% without), high cholesterol (71% with vs 53% without), and obesity (67% with vs 52% without). The same was true of women: CVD (98% with vs 58% without), diabetes (74% with vs 60% without), high blood pressure (73% with vs 56% without), high cholesterol (72% with vs 60% without), and obesity (74% with vs 55% without). Neither men nor women associated smoking with their own risk for heart disease.
The prevalence of CVD risk factors was alarmingly high in this population. Awareness of risk for heart disease was associated with most modifiable CVD risk factors, except smoking.
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Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedRace, Ethnicity, and Linguistic Isolation as Determinants of Participation in Public Health Surveillance Surveys
Link Michael W PhD Senior Survey Methodologist Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Adult and Community Health
4770 Buford Hwy NE, Mail Stop K-66, Atlanta, GA 30341-3717 770-488-5444 [email protected]
Mokdad Ali
Stackhouse Herbert
Flowers Nicole
4 2005
15 3 2005
2 2 A21
Track: Methods and Surveillance
Public health officials and researchers require valid and reliable public health surveillance data to plan, implement, and evaluate programs designed to eliminate health disparities among racial and ethnic minority populations. Monitoring chronic disease and behavioral risk factors among such populations, however, has proven challenging. This research is designed to assess disparities among minority populations in participation levels in public health surveillance efforts and to test alternative methods for reducing these disparities.
We analyzed data from the 2003 Behavioral Risk Factor Surveillance System (BRFSS), which is a monthly, random-digit–dialed telephone survey of the noninstutionalized adult (aged 18 years and older) population in the United States. County-level data from the 2003 BRFSS and 2000 U.S. Census are modeled using ordinary least squares regression to examine the effects of race, ethnicity, and linguistic isolation on six measures of survey participation (e.g., resolution, screening, cooperation, refusal, refusal conversion, response rates).
The study finds that even after adjusting for other factors such as socioeconomic conditions, average commute time, use of call screening technology, and level of data collection effort (other factors thought to be related to survey response), areas with higher percentages of African Americans (regression coefficient, −0.14, P < .001), Hispanics (regression coefficient, −0.57, P < .001), and those who do not speak English — particularly those speaking only Asian (regression coefficient, −1.67, P < .001) or Indo-European (regression coefficient, −2.73, P < .001) languages — were significantly less likely than whites to participate in the public health surveillance.
In response to this finding, the BRFSS is investigating two alternatives for reaching these underrepresented groups: 1) use of alternative survey modes; in particular, providing translated hard-copy versions of the BRFSS by mail, and 2) use of specialized language line translation services to offer real-time translation of the BRFSS into languages beyond English and Spanish.
The collection of valid and reliable data for public health surveillance in the United States is becoming challenging. Current methods increasingly underrepresent racial, ethnic, and linguistically isolated groups. As a result, the health problems and needs of these groups may be significantly underreported. The development of successful public health interventions and programs capable of reducing health disparities requires that monitoring systems be developed that are capable of tracking the public health of all groups.
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Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedMeasuring Population Health Disparities: The Wisconsin County Health Rankings
Kempf Angela M MA Project Assistant and Doctoral Student University of Wisconsin Medical School, Department of Population Health Sciences, Wisconsin Public Health and Health Policy Institute
610 Walnut St, Suite 760, Madison, WI 53726 608-263-7497 [email protected]
Remington Patrick L
Peppard Paul E
Dranger Elizabeth A
Kindig David A
4 2005
15 3 2005
2 2 A21
Track: Methods and Surveillance
The purpose of this project was to rank the population health of counties in Wisconsin to promote use of local population health information, identify disparities between counties, encourage population health improvement, and broaden the understanding of the determinants of health.
The Wisconsin Public Health and Health Policy Institute, with assistance from state government, community, and university groups, annually compiles county data and produces the Wisconsin County Health Rankings report.
This project is modeled after the United Health Foundation's annual America's Health: State Health Rankings and is based on a population health model in which a variety of determinants impact health outcomes. Mortality years of potential life lost (YPLL) and self-reported health status are used to develop a summary measure of county health outcomes. A summary measure of health determinants is developed using 18 measures in four (weighted) categories: health care (10%), health behaviors (40%), socioeconomic factors (40%), and physical environment (10%). Data sources include the Centers for Disease Control and Prevention, the U.S. Census, state vital statistics, and the Wisconsin Department of Health and Family Services. A draft report was developed and shared with local public health officials in late 2003. The report was revised on the basis of feedback, and Wisconsin County Health Rankings, 2003 was released to the public in January 2004. A survey assessing the usefulness of the rankings was sent to all county health officers following its release.
Significant disparities exist in the health outcomes and determinants of Wisconsin counties. We used Pearson product moment correlation and found that, overall, the summary determinant and summary outcome ranks were well correlated (r = 0.75). Compared with the healthiest counties (e.g., Ozaukee), the least healthy counties (e.g., Menominee) showed greater improvement in health over time. Of the county health officers participating in the survey of the rankings (N = 68, 94% response rate), 82% reported that the rankings were useful to their work, and 69% planned to use the rankings in their communities. Suggestions received through this survey and other more informal feedback will be incorporated into the 2004 rankings, such as the expansion and improvement of the environmental health components and the inclusion of additional local survey data.
The Wisconsin County Health Rankings provides a valuable vehicle for delivering and discussing county-level health information and for engaging stakeholders in the discussion of approaches for reducing observed disparities. This report will continue to be produced annually with special attention given to improving population health measures and its use in community health improvement efforts.
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Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedColorectal Cancer Screening in Washington State: Predictors of Current Screening and Explanations for No Screening
Hannon Peggy A PhD, MPH Research Scientist University of Washington
1107 NE 45th St, Suite 200, Seattle, WA 98105 206-676-7859 [email protected]
Harris Jeffrey
Martin Diane
VanEenwyk Juliet
Bowen Deborah
4 2005
15 3 2005
2 2 A21
Track: Social Determinants of Health Inequities
The purpose of this study was to identify predictors of current colorectal cancer screening in Washington State and to examine participants’ reasons for not being screened.
We analyzed data from the 2002 Behavioral Risk Factor Surveillance System for Washington State residents aged 50 years and older (N = 2109). Current colorectal cancer screening was defined as having a fecal occult blood test (FOBT) within the past year and/or sigmoidoscopy or colonoscopy within the past five years. Participants who did not have current FOBT or current endoscopy were asked the primary reason for not obtaining screening.
Overall, current colorectal cancer screening was reported by 51.9% of the sample (FOBT by 25.8%; endoscopy by 42.8%). Univariate analyses showed that several demographic characteristics were significantly associated with screening status, including white race (P = .04), aged 65 years or older (P < .001), annual income more than $75,000 (P < .001), and having a college degree (P = .02).
In a multivariate analysis adjusting for the above characteristics and other likely confounding variables (e.g., sex, marital status), participants were significantly more likely to have current screening if they possessed health insurance (54.2% vs 16.8% for uninsured participants, P < .001) and had discussed colorectal cancer screening with a health care provider (67.3% vs 33.4% for participants who had never discussed screening with a health care provider, P < .001). Participants were also significantly more likely to report current screening if they lived in a large town or urban area (53.0% vs 42.7% for participants living in small towns/rural areas; P = .05).
The majority of participants without current screening cited lack of awareness as the primary reason for not being screened (53.0% for FOBT; 46.9% for endoscopy). An additional group of participants stated their physicians had not recommended screening (24.8% for FOBT; 33.3% for endoscopy). Relatively few participants said they were not willing to be screened (20.4% for FOBT; 18.1% for endoscopy) or cited lack of access (1.8% each for FOBT and endoscopy).
Our results indicate that nearly half of age-appropriate Washington State residents lack current colorectal cancer screening. Awareness of colorectal cancer screening, particularly via speaking with a health care provider, was an important predictor of screening. These findings are consistent with published reports based on National Health Interview Survey data. Interventions should be developed to increase awareness of and physician recommendations for colorectal cancer screening, particularly among disadvantaged patient populations.
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Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedEducating California School Board Members: Aligning Policies for Student Health and Achievement
Agron Peggy MA, RD Program Chief California Department of Health Services, California Project LEAN (Leaders Encouraging Activity and Nutrition)
PO Box 997413, Mail Stop 7211, Sacramento, CA 95899-7413 916-552-9883 [email protected]
Berends Victoria
4 2005
15 3 2005
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Track: Policy and Legal
The objective of this project was to share the development, implementation, and outcomes of a three-year intervention aimed to educate California school board members about the important role school nutrition policies can play to increase the health and academic achievement of students.
California Project LEAN (CPL), the California School Boards Association (CSBA), the University of South Florida, The California Parent Teachers Association, and 10 regional collaboratives worked together to educate school board members across California. Special outreach efforts were directed toward school districts serving low-income children.
Formative research was conducted to understand the factors that influence policy decision making for California school board members and included a literature review, a media analysis, key informant interviews, and a statewide survey of California school board members and superintendents. The formative research served as the foundation for developing a social marketing plan and intervention strategies. Research indicated that CSBA was highly respected by its members. The following activities were implemented jointly by CSBA and CPL: 1) the development of a Healthy Food Policy Resource Guide (Guide); 2) placement of advertisements and articles published in CSBA communications; 3) training of more than 300 school board members across the state; and 4) local mobilization led by CPL regional coordinators.
The evaluation of this three-year project consisted of 1) surveys of school board members who received the guide and attended a training; 2) key informant interviews; and 3) a postsurvey of school board members and superintendents.
Evaluation findings suggested that the Guide was useful to school board members and that members who attended trainings planned to raise the issue of nutrition policy for discussion at a school board meeting. A number of California districts have established policies that support healthy eating. Postsurveys and postinterviews were analyzed, and final data was available by January 1, 2005. The impact of this project continues to be realized as more school districts request assistance for mobilizing to offer healthier foods for their students.
The nation is experiencing an unparalleled obesity epidemic. Many of the foods children eat at school are high in fat, sugar, and calories. These practices can contribute to inadequate diets and the development of poor dietary habits. Developing targeted campaigns with respected education partners can provide local policy makers with resources to help them establish policies to support healthy eating.
Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
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PCDv22_04_0142g
Special Topics
Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedAdherence to Guidelines for Following Up Low-Grade Pap Test Results by Age and Race or Ethnicity
Benard Vicki B PhD Epidemiologist Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Cancer Prevention and Control
4770 Buford Hwy NE, Mail Stop K-55, Atlanta, GA 30341 770-488-1092 [email protected]
Lawson Herschel
Eheman Christie
Anderson Christa
Helsel William
4 2005
15 3 2005
2 2 A21
Track: Health System Change
The objective of this study was to determine if low-income and uninsured women in the National Breast and Cervical Cancer Early Detection Program (NBCCEDP) with Papanicolaou (Pap) test abnormalities of atypical squamous cells of undetermined significance (ASCUS) or low-grade squamous intraepithelial lesions (LSIL) were followed by the recommended interim guidelines for management of abnormal cervical cytology.
For this study period (July 1991 through September 2000), the National Cancer Institute's (NCI's) recommended guidelines for women with a low-grade abnormality Pap test result (ASCUS or LSIL) was follow-up by Pap tests repeated every four to six months for two years. If a second ASCUS or LSIL report occurred, the patient should have been considered for colposcopic evaluation. We analyzed data from 10,004 women in the NBCCEDP with ASCUS or LSIL followed by a second low-grade abnormality. The racial/ethnic groups included in the analysis were white, black, Asian/Pacific Islander, American Indian/Alaska Native, and Hispanic.
Using recommended guidelines, 44% of women in the NBCCEDP were followed appropriately with a colposcopy following two low-grade abnormalities. Younger women (under 30) were more likely to receive a colposcopy following two low-grade abnormalities, and older women (over 60) were more likely to receive a third Pap test. Hispanic or Latino women were more likely than other racial/ethnic groups to receive a colposcopy after two low-grade abnormalities, and American Indian or Alaska Native women were more likely than other racial/ethnic groups to receive a third Pap test.
Less than half of the women studied were followed by the recommended guidelines. Factors such as age and race/ethnicity influence the appropriate follow-up of a woman with cytological abnormalities. From this study, we are not able to determine if these differences occur at the patient or provider level. However, the national program is working with state, territorial, and tribal programs to further investigate the issue and recommend interventions to improve the level of follow-up.
Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
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PCDv22_04_0142h
Special Topics
Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedVERB™ Campaign: Extending the Reach of a National Campaign to Ethnically Diverse Audiences
Bretthauer-Mueller Rosemary VERB Partnership Team Leader Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Adolescent and School Health
4770 Buford Hwy NE, Mail Stop K-85, Atlanta, GA 30341 770-488-6289 [email protected]
Melancon Heidi
4 2005
15 3 2005
2 2 A21
Track: Communications and Technology
The objective of the Centers for Disease Control and Prevention’s (CDC's) “VERB. It’s what you do.” campaign is to increase and maintain physical activity among “tweens,” or children aged nine to 13 years.
VERB™, a national social marketing campaign with ethnic market overlays, reaches all tweens across the nation with messages designed to get them up and moving. To ensure that all segments of the multicultural audiences are reached by the campaign, the CDC worked with four multicultural advertising/marketing agencies to supplement and complement the general market communication with culturally relevant messages and executions through appropriate channels. The campaign’s efforts extend an invitation to Native American, African American, Asian American, and Hispanic/Latino tweens to take part in the VERB campaign. These culturally and linguistically relevant efforts also help to fill in the gaps inherent in general market communication channels that reach tweens in addition to those that reach parents and other adult influencers. The VERB executions expand campaign messages, reach, impact, and effectiveness.
To reach ethnic audiences, the four multicultural agencies have produced a marketing mix that includes television, radio, out-of-home, and print advertising; in-school promotions; viral marketing; events; and public relations. As is the standard for the VERB campaign, these products were developed on the basis of extensive formative and message-testing research. The culturally relevant products are also strongly rooted in the VERB brand strategy to maintain synergy with the general market efforts, which is critical to a seamless campaign.
The campaign’s national longitudinal evaluation indicates that 63% of African American tweens and 70% of Hispanic/Latino tweens are aware of the VERB brand, exceeding the campaign’s goal of 50% awareness. A special survey was administered in-language with Asian language-speaking parents of tweens living in the Los Angeles area. The results indicate that the parents surveyed were more aware of VERB than any other parental ethnic group.
Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
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PCDv22_04_0142i
Special Topics
Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedRacial Differences in Factors That Influence Survival With Oral Cancer in Georgia: 1978–2001
Krishna Ranjitha BDS, MPH Guest Researcher Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Oral Health
4770 Buford Hwy NE, Mail Stop F-10, Atlanta, GA 30341 770-488-3075 [email protected]
Liff Jonathan
Chen Amy
Eke Paul
Valerie Robison
4 2005
15 3 2005
2 2 A21
Track: Health System Change
The purpose of this study was to examine the racial differences in distribution of risk factors associated with oral cancer survival in Georgia (1978–2001).
Studies have shown that the five-year survival rate for people with oral cancers is much lower in blacks than whites. According to the Surveillance, Epidemiology and End Results (SEER) program, the national five-year survival rate for people with cancers of the oral cavity and pharynx from 1992 to 1997 was 36.1% for blacks and 59.7% for whites.
Data from 1503 whites and 531 blacks with oral cancers in five urban and 10 rural counties of Georgia from 1978 to 2001 were analyzed. Data were collected by the Georgia Center for Cancer Statistics, a population-based cancer registry affiliated with the SEER program. Racial disparities were examined in stage at diagnosis, grade of cancer, sex, age, socioeconomic status, rural/urban residence, and type of treatment.
Compared with whites, blacks were twice as likely (Odds Ratio [OR] = 2.5, 95% Confidence Interval [CI], 2.0–3.0) to die during the five-year follow-up time. Compared with whites older than 70 years, blacks were 2.2 times (95% CI, 1.6–3.1) more likely to be diagnosed at age 61 to 70 years, 3.7 times (95% CI, 2.7–5.1) more likely to be diagnosed at age 51 to 60 years, and 4.8 times (95% CI, 3.4–6.7) more likely to be diagnosed at younger than 50 years. Compared with whites who were mostly diagnosed at the localized stage of the disease, blacks were 3.0 times (95% CI, 2.4–3.8) more likely to be diagnosed at the regional stage and 4.8 times (95% CI, 3.4–6.7) more likely to be diagnosed after distant metastasis. Blacks were also more likely to have grade 2 (OR 3.0; 95% CI, 2.3–3.9) and grade 3 (OR 2.2; 95% CI, 1.6–3.1) cancers. Consequently, blacks were 95% more likely than whites to have received radiation (OR 3.1; CI, 2.2–4.3) or both radiation and surgery (OR 2.3; 95% CI, 1.7–2.9). Whites were more likely than blacks to have received surgery only.
In Georgia, diagnoses of oral cancers in black patients occurred at a much younger age and at a more advanced stage and higher grade of disease. Oral cancers in black patients were also treated with other than cancer-directed surgery only. These disparities highlight both a challenge to further understand the reasons for racial disparities in survival rates of patients with oral cancers and an opportunity to reduce those disparities.
Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
15670474
PCDv22_04_0142j
Special Topics
Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedEliminating Disparities in Communities of Color Through the Lifetime Fitness Program
Snyder Susan J MS Director Senior Services of Seattle/King County, Senior Wellness Project
2208 2nd Ave, Suite 100, Seattle, WA 98121 206-727-6297 [email protected]
Belza Basia
4 2005
15 3 2005
2 2 A21
Track: Evidence-based Programs: Research, Translation, and Evaluation
The objective of this study was to convey lessons learned about factors that contribute to sustainable and effective group fitness programs for older adults in ethnic communities.
The University of Washington Health Promotion Research Center conducted focus groups with older adults from seven cultural groups (American Indian/Alaska Native, African American, Chinese, Korean, Spanish-speaking Latinos, Filipinos, and Vietnamese) to generate ideas for programming that would increase the level of physical activity in these communities. After focus group results were compiled and published, an evidence-based group exercise program for older adults — the Lifetime Fitness Program (LFP) — was implemented in 11 focus-group communities that also had a nutrition program. The 11 communities were located in Texas and western and central Washington.
The LFP was designed by researchers and specialists in aging at the University of Washington in Seattle as an easy-to-implement fitness program aimed directly at older adults. Average age of participants at all LFP sites (N = 3258) is 74.3 years (SD ± 8.7). The program is offered in hourly sessions two to three times per week and includes strength, endurance, balance, and flexibility exercises. LFP Testing of Function for each participant is conducted at enrollment and every four months thereafter.
Focus group findings showed that both the key motivator and primary barrier for physical activity were related to health and chronic conditions. Ideal fitness program components that were common across the groups were programs that included peer support and instruction, were offered in locations close to where attendees lived and in a center that was targeted to their ethnicity, and included several options for exercising (e.g., alone, in a group). Detailed results of these focus groups are published in the report Elder Perspectives on Physical Activity: A Multicultural Discussion.
Preliminary data reported here include 226 LFP participants from 11 ethnic sites (average age, 72.8 years; SD ± 8.7). Participants had at least one valid outcomes measure; 27% (n = 62) had four-month follow-up data. At baseline, percentages of participants below normal limits were the following: in arm curls, 23% of participants at ethnic sites and 10% at nonethnic sites; in Up and Go, 68% at ethnic sites, 36% at nonethnic sites; and in chair stands, 30% at ethnic sites and 21% at nonethnic sites. Normal limits were obtained from published age- and sex-based cut points.
Significant improvement was seen in chair stands and arm curl repetitions at ethnic sites at four months. At follow-up (n = 62), percentages of participants below normal limits were the following: arm curls (2% at ethnic sites, 4% at nonethnic sites), Up and Go (49% at ethnic sites, 29% at nonethnic sites), and chair stands (7% at ethnic sites, 12% at nonethnic sites).
Knowledge gained from these focus groups and from the implementation and evaluation of the LFP can inform future interventions to better reach ethnic minority communities. A policy that links senior nutrition sites serving minority communities to evidenced-based programs such as the LFP may be an effective way to reduce health disparities. Since this abstract, additional data have been collected and analyzed.
Prev Chronic Dis
Preventing Chronic Disease
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Centers for Disease Control and Prevention
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PCDv22_04_0142k
Special Topics
Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedDeveloping a Rural Health Promotion Specialist Program to Provide Preventive Health Care to the Medically Indigent
Maltese Joy N RN Chronic Disease Prevention Initiatives Coordinator and Stroke and Heart Attack Prevention Coordinator Georgia Department of Human Resources, Division of Public Health, District 4 Health Services
122A Gordon Commercial Dr, LaGrange, GA 30240 706-845-4035 [email protected]
Brewton Cary
4 2005
15 3 2005
2 2 A21
Track: Partnerships
The objective of this program was to recruit and train individuals to help promote preventive health care in underserved rural areas. The program took place in Troup County and Heard County communities in rural west Georgia.
The District Four Public Health Chronic Disease Prevention Initiative made minor modifications to the Racial and Ethnic Approaches to Community Health (REACH) 2010 program (designed for urban areas) and implemented it in our rural communities. To recruit lay volunteers, we primarily focused on the faith community but also included private businesses such as laundromats and community centers. Volunteers were trained in blood pressure screenings, body mass index (BMI) measuring, diet counseling, exercise, and resources to support individuals screened. A resource library was established with supporting information that included pamphlets and flip charts to help facilitate volunteers’ interaction with individuals at risk. Health promotion specialists collected and submitted contact data for aggregate review to the District Health Services Chronic Disease Prevention staff.
After one-day training was provided to 15 health promotion specialists on February 7, 2004, 134 people were screened over a three-month period between February and June 2004. Of the individuals screened, 98% were African American, lived in rural areas of west Georgia, and were medically indigent or had limited access to health care. The data showed that 39% of the individuals screened were prehypertensive and 37% were either in stage 1 or stage 2 hypertension for their systolic measurement. The BMI measurements showed that 34% were overweight and 40% were considered obese. Overall, 76% of those screened showed hypertensive risks that correlated to 74% that were overweight or obese.
Futhermore, eight individuals who had stage 2 hypertension reported not taking medications as directed by their doctor. The health promotion specialist was able to encourage these people to resume their medications and recorded a return to normal blood pressures usually after two to four weeks of taking medications on a regular basis.
Having lay volunteers trained as health promotion specialists in rural areas is critical in preventing stroke and heart attack and reducing unnecessary emergency department visits in the absence of a health care provider. Individuals screened and counseled responded well to the advice given by people they know and trust in their churches, community centers, and local businesses. We currently have a waiting list for people to be trained and hope to expand this initiative to every community in our health district and, eventually, throughout Georgia.
Prev Chronic Dis
Preventing Chronic Disease
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Centers for Disease Control and Prevention
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PCDv22_04_0142l
Special Topics
Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedRegional/Racial Prevalence of Metabolic Syndrome: The MSM Regional Assessment Health Surveillance Study, 2003–2004
Arroyo Cassandra MS, PhD Research Instructor Morehouse School of Medicine, Social Epidemiology Research Center
720 Westview Dr SW, NCPC 315, Atlanta, GA 30310 404-756-8909 [email protected]
Jones Dennis
Liu Yong
Din-Dzietham Rebecca
Davis Sharon
4 2005
15 3 2005
2 2 A21
Track: Evidence-based Programs: Research, Translation, and Evaluation
The objective of this study was to examine regional and racial variations in the prevalence of metabolic syndrome (MetS) in Fulton, Bulloch, Candler, Evans, and Jenkins counties of Georgia.
Random-digit–dialing data followed by examination data were obtained from 319 African American and white men and women aged 19 years and older from 2002 through 2003. MetS was defined by Adult Treatment Panel III criteria. Correlates included race (African American vs non-Hispanic white), sex, education level, age, and region (urban vs rural). Univariate and multiple regression models were used to assess the interaction between region and race, and the association with correlates setting nominal P value at .05 for main effect and .10 for interaction. SUDAAN (Research Triangle Institute, Triangle Park, NC) was used to account for the complex design and to obtain correct variance and county-representative estimates.
The MetS overall prevalence was 21.2%. Unadjusted prevalence of MetS was significantly higher (P < .001) in urban areas (21.4%) vs rural areas (19.6%) among African Americans (31.1%) vs non-Hispanic whites (9.6%) and among women (22.2%) vs men (19.9%). There was a significant interaction between region and race (P < .001), so separate models were estimated for African Americans and non-Hispanic whites. For African Americans, MetS was 2.47 (95% Confidence Interval [CI], 2.23–2.73) times more prevalent among those living in urban vs rural areas and 0.48 (CI, 0.46–0.50) times less prevalent among men vs women. Prevalence of MetS was also 1.48 (CI, 1.40–1.56) times higher among those with less than 12 years of education and 0.68 (CI, 0.65–0.72) times lower among those with 12 years of education vs those with more than 12 years. Among non-Hispanic whites, MetS was 0.34 (CI, 0.32–0.37) times less prevalent among those living in the urban area, 6.13 (CI, 5.60-6.71) times more prevalent among men, 7.9 (CI, 7.12–8.68) times more prevalent among those with 12 years of education, and 4.6 (CI, 3.82–5.66) times more prevalent among those with less than 12 years of education.
The study suggests that African Americans living in the urban area of Georgia have a higher prevalence of MetS than their white counterparts. National prevalence rate estimates for MetS suggest that whites in general have a higher prevalence of MetS. A more comprehensive database is needed to further explore this interaction between race and region to target more specific groups for intervention.
Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
15670474
PCDv22_04_0142m
Special Topics
Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedA Community-based Partnership to Address Barriers to Physical Activity in an African American Community
Plescia Marcus MD, MPH Chief North Carolina Division of Public Health, Chronic Disease and Injury Section
1915 Mail Service Center, Raleigh, NC 27699 919-715-0125 [email protected]
Groblewski Martha
4 2005
15 3 2005
2 2 A21
Track: Partnerships
The objective of this project was to describe changes in physical activity behaviors resulting from a partnership between a community coalition, lay health advisors, and a YMCA branch.
The Centers for Disease Control and Prevention’s Racial and Ethnic Approaches to Community Health (REACH 2010) project funds 37 communities to engage in participatory, community-based interventions to address racial and ethnic health disparities. Our project targets a geographically defined, urban, medically underserved, African American community of 18,892 in Charlotte, NC.
The focus of the project is to recruit, train, and support lay health advisors to promote health behavior change among community residents. A partnership with a local branch of the YMCA was designed to support the efforts of the lay health advisors and address existing barriers to participation in regular physical activity. Residents are encouraged to participate in community-based YMCA activities that occur in a range of settings and are funded by the REACH 2010 coalition. Evaluation methods include 1) a yearly random survey of community residents by telephone using Behavioral Risk Factor Surveillance System questions on physical activity behaviors; 2) pretest and two-year follow-up surveys by telephone of program participants to measure relative frequency of physical activity and current stage of change for physical activity behavior change; and 3) focus groups to determine motivational factors among community participants in the YMCA program.
The project has maintained an average of 15 active lay health advisors working at least 10 hours per week in the community. In the second year of the project, the percentage of adults in the focus community who indicated they did not meet physical activity recommendations decreased from 32.0% (95% Confidence Interval [CI], 28.6–35.5) at baseline to 24.4% (95% CI, 21.1–28.2). At two-year follow-up of YMCA program participants, 56.6% indicated that they were exercising more than they did two years before, and the percentage who indicated Maintenance Stage of Change increased from 41.67% (95% CI, 37.31–46.03) to 54.24% (95% CI, 48.01–58.65). Focus group respondents identified intrapersonal factors such as improved self-efficacy and interpersonal factors such as social support and fellowship as aspects of the YMCA programs that helped motivate them to continue participation in the program.
Partnership between a REACH 2010 community coalition and a local YMCA branch was instrumental in establishing and supporting a community lay health advisor program and addressing community barriers to physical activity. This multicomponent intervention resulted in significant physical activity behavior change at the individual and community levels.
Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
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PCDv22_04_0142n
Special Topics
Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedGetting the Most Out of Vital Statistics Data: Diabetes-related Heart Disease Mortality in New Mexico
Krapfl R. Heidi MS Epidemiologist New Mexico Department of Health, Diabetes Prevention and Control Program
1190 St Francis Dr, S-1300, Santa Fe, NM 87505-4173 505-827-0325 [email protected]
Gohdes Dorothy
Croft Janet
4 2005
15 3 2005
2 2 A21
Track: Methods and Surveillance
The objective of this study was to illustrate how multiple-cause mortality data can enhance interpretation of heart disease (HD) mortality among racial/ethnic groups.
Multiple-cause mortality files for New Mexico from 1999–2001 were obtained from the National Center for Health Statistics at the Centers for Disease Control and Prevention. Deaths from HD for New Mexico residents were identified by the International Classification of Diseases, Tenth Revision (ICD-10) codes I00–I09, I11, I13, and I20–I51. Premature heart disease (PHD) was defined as any underlying HD death occurring in persons aged less than 65 years. Diabetes-related HD was classified as any death where the underlying cause of death was HD, and diabetes (ICD-10 codes E10–E14) was reported as any of up to 20 contributing causes of death. Residents were grouped into four racial/ethnic categories: non-Hispanic white, Hispanic of any race, non-Hispanic American Indian, and other. All death rates for HD were calculated with bridged-race population estimates and age-adjusted to the 2000 U.S. Standard Population.
From 1999 to 2001, 24% of all deaths in New Mexico reported HD as the leading cause of death. Of these deaths, 16.6% occurred in persons aged less than 65 years and were therefore classified as premature. The proportion of PHD deaths was substantially higher in the American Indian (29.2%) and Hispanic (20.8%) populations than in whites (13.7%). Diabetes contributed to almost 18% of PHD deaths in American Indians and Hispanics and to 10% of PHD deaths among whites.
Multiple-cause mortality data indicate that the contribution of diabetes to PHD is disparate among racial/ethnic groups in New Mexico. These results support continued analysis of these data in a consistent manner and further underscore the growing threat of diabetes to communities in the United States. Much of the progress in decreasing cardiovascular disease in the United States may be lost as increasing diabetes and obesity lead to PHD death in many populations.
Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
15670474
PCDv22_04_0142o
Special Topics
Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedOvercoming Barriers in Access to Specialized Health Care Services
Russell Doran Donna Cardiac Quality Initiatives, Advisor New York State Department of Health, Cardiac Service Program
1 University Pl, Suite 209, Rensselaer, NY 12144-3455 518-402-1016 [email protected]
Gold Jeffrey
Serbaroli Francis
Hannan Edward L
Macielak Paul F
Streck William F
4 2005
15 3 2005
2 2 A21
Track: Policy and Legal
The objective of this interdisciplinary statewide initiative was to identify factors limiting access to specialty care and to develop strategies to overcome these barriers. We focused on barriers that are amenable to resolution through health care policy, regulation, or statute and on innovative strategies that could be implemented within the resource constraints of our health care system.
A Quality Initiatives Workgroup (QIW) of the New York State Public Health Council (PHC) was convened to examine issues and potential resolutions associated with disparities in access to specialty care for New York State residents. The work group was composed of members of the PHC and experts from pertinent fields. In addition to gathering input from QIW panel members on clinical, analytical, and financial issues, the group set out to gather information through roundtable discussions with a broad range of experts (including regulators, insurers, providers, researchers, patient advocates, medical educators, and professional societies), literature reviews, and responses from a survey aimed at gaining a broad perspective on barriers and potential solutions to accessing specialty care.
The QIW’s review of expert testimony, questionnaire responses, and research concluded that disparities in access to care exist and that these disparities have a significant and costly impact on the overall health of New York State residents. Factors associated with barriers to appropriate health care fall into the following categories: health care delivery issues (including provider awareness and implementation, patient-provider communications, and system navigation issues), availability of services, insurance coverage, and applied research and quality initiatives. Specific issues, goals, and recommended actions are identified in each of these areas. Sufficient evidence is available to support implementation of well-planned strategies aimed at reducing existing inequities. In evaluating strategies for overcoming barriers, the QIW recommends an initial focus on seven clinical areas: heart disease, stroke, cancer, major orthopedic conditions, diabetes, end-stage renal disease, and HIV/AIDS. These diseases impact a substantial portion of the population and have relatively mature and accepted clinical treatment protocols (thus making them amenable to monitoring). Additionally, inequities to care have been identified in the literature for these diseases.
Recommended actions — some of which are being implemented — will be discussed. These recommendations will provide a stimulus, foundation, and preliminary framework for groups to build upon; offer guidance for evaluating the provision of specialized services; identify challenges that cut across the health care sector; and provide a road map for remediation.
Prev Chronic Dis
Preventing Chronic Disease
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Centers for Disease Control and Prevention
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PCDv22_04_0142p
Special Topics
Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedReducing Dental Sealant Disparities in School-aged Children Through Better Targeting of Informational Campaigns
Jones Kari A PhD Research Economist Centers for Disease Control and Prevention, Division of Public Private Partnerships
4770 Buford Hwy NE, MS K-39, Atlanta, GA 30341-3724 770-488-2404 [email protected]
Griffin Susan
Moonesinghe Ramal
Jaramillo Freder
Vousden Claudia
4 2005
15 3 2005
2 2 A21
Track: Communications and Technology
The objective of this study was to investigate whether disparities in receipt of dental sealants among school-aged children are linked to caregivers’ knowledge of the preventive purpose of sealants. These findings may be used to better target oral health education campaigns.
Using data from the National Health and Nutrition Examination Survey (NHANES) 1999–2000, we estimated sealant prevalence among children aged six to 17 years who had at least one tooth eligible for a sealant. We then identified the explanatory factors (main effects model) associated with knowledge of sealants among caregivers of children aged less than 18 years using data from the 2003 ConsumerStyles, HealthStyles, and Recontact marketing surveys and logistic regression. We stratified the data from the marketing surveys on sealant knowledge and the NHANES data on sealant prevalence by race/ethnicity and income (whether ⩾ or < 200% of the 2003 federal poverty guidelines), the two significant explanatory factors common to both data sets (P < .001 for race/ethnicity and P = .01 for income).
Over the full study sample, sealant knowledge was 62.5%, and sealant prevalence was 31%. Caregivers’ race/ethnicity, age, marital status, education, income, and sex were significant predictors of sealant knowledge. Both sealant knowledge and prevalence were positively associated with income level. Among higher-income families, 71% of caregivers exhibited sealant knowledge compared with 47% of their low-income counterparts; 42% of higher-income children had sealants compared with 22% of their low-income counterparts. Among higher-income families, sealant prevalence among children was positively associated with caregiver knowledge (r = 0.973). Non-Hispanic whites had the highest caregiver knowledge (78%) and highest sealant prevalence (49%) in this group; non-Hispanic blacks had the lowest caregiver knowledge (41%) and sealant prevalence (22%). Among low-income families, there was no association between caregiver knowledge and sealant prevalence.
Current sealant prevalence is well below the Healthy People 2010 objective of 50%. We found disparities in both knowledge and prevalence of sealants by race/ethnicity and income. The positive association between sealant knowledge and prevalence for higher-income families is consistent with the economic principle that demand for sealants increases with knowledge of their benefit. The lack of an association between sealant prevalence and knowledge among low-income families may reflect higher levels of public provision of sealants to this group. This suggests that informational campaigns could increase demand for sealants in both income groups. Additionally, efficient targeting — targeting groups with the lowest demonstrated knowledge — should also help eliminate disparities. This information is useful to oral health coalitions funded by the Centers for Disease Control and Prevention in many states to promote oral health and eliminate oral health disparities.
Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
15670474
PCDv22_04_0142q
Special Topics
Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedEngaging Older Adults to Be More Active Where They Live: Audit Tool Development
Kruger Judy PhD Epidemiologist Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion
4770 Buford Hwy NE, Mail Stop K-46, Atlanta, GA 30341 770-488-5922 [email protected]
Kealey Melissa
Hunter Rebecca
Ivey Susan
Satariano William
Bayles Constance
Brennan Ramirez Laura
Bryant Lucinda
Johnson Courtney
Lee Chanam
Levinger David
McTigue Kathleen
Moni Gwen
Vernez Moudon Anne
Pluto Delores
Prohaska Thomas
Sible Christen
Tindal Sabrina
Wilcox Sara
Winters Kendra
Williams Kathy
4 2005
15 3 2005
2 2 A21
Track: Social Determinants of Health Inequities
Because of an increase in the prevalence of chronic diseases and an aging population, older adults are at highest risk for poor health. Health disparities by age and socioeconomic status have been shown to exist for many physical activity outcomes and may be explained in part by differences in the built environment in which older adults live, work, and play. Partners of the Environmental Workgroup of the Centers for Disease Control and Prevention Healthy Aging Research Network (HAN) have designed and piloted an instrument that focuses on the relationship between the built environment and physical activity in older adults.
Neighborhoods in seven diverse U.S. communities served by HAN were studied: Alamosa, Colo; Columbia, SC; Hendersonville, NC; Seattle, Wash; McKeesport, Pa; Chicago, Ill; and Berkeley, Calif. An environmental audit instrument, developed to be sensitive to issues of importance to older adults, was designed to assess street-scale factors associated with physical activity across multiple settings. Audit items included land use, destinations, sidewalk and intersection conditions, amenities such as benches, social disorder such as litter, and the types of human activity observed. In the pilot study, 15–30 street segments were audited by two or more trained researchers at each of the seven HAN sites. In addition to the audit, qualitative semi-structured interviews were conducted to identify additional items of importance to adults aged 65 years or older.
Inter-rater reliability among the trained researchers was examined. Researchers across the seven HAN sites examined differences in neighborhood segments audited to determine the distribution of study variables, such as presence of sidewalks and crosswalks. Qualitative interviews of older adults about environmental influences on walking behavior revealed that having destinations to walk to appeared to be an important motivator for walking, although many people reported both walking to reach destinations as well as taking walks just for pleasure. Destinations such as grocery stores, banks, pharmacies, restaurants, and beauty parlors were mentioned, as were churches, libraries, parks, and the homes of family, friends, and neighbors. Personal safety was another frequently mentioned topic, with many residents feeling safe walking during the daytime but not at night. Interviewees indicated that their choice of walking routes was influenced by the length of the route, sidewalk quality, people along the route, amount of traffic, crosswalks and signals for crossing the street, perceived safety from crime, scenery, aesthetics, and presence of interesting things to look at. Future directions include using the environmental audit instrument in a study to see which environmental audit items are correlated with actual walking behavior in an older population.
Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
15670474
PCDv22_04_0142r
Special Topics
Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedConducting a Successful “Through With Chew” Week
Bagdonas Sylvia MA Tobacco Program Consultant Wyoming Department of Health, Substance Abuse Division
6101 Yellowstone Ave, Suite 220, Cheyenne, WY 82002 307-777-3690 [email protected]
Mueller Niki
Russian Amy
4 2005
15 3 2005
2 2 A21
Track: Communications and Technology
An idea of the American Academy of Otolaryngology — Head and Neck Surgery, “Through With Chew” Week is an educational campaign designed to prevent and reduce the use of chew/spit tobacco, a type of tobacco use that has not received as much attention as cigarette smoking. The state of Wyoming successfully conducted a “Through With Chew” Week (including a Great American Spit Out Day) in February 2003. The statewide program targeted Wyoming’s small and rural population, which has a disproportionately high chew-tobacco-use rate — the second highest in the nation (Behavioral Risk Factor Surveillance System [BRFSS], Adult Tobacco Survey [ATS], 2003).
The component aims of the program were the following: 1) to educate the public about the extent of chew-tobacco use in Wyoming and its costly health implications for all citizens; 2) to conduct surveys to establish baseline data on current levels of prevention and intervention; 3) to partner with health care providers to promote quitting; 4) to counteradvertise the tobacco industry; and 5) to determine adaptations necessary for American Indian populations. Wyoming’s comprehensive statewide plan included tool kits, media kits, and “quit spit” kits for use by local program managers. Extensive media coverage included newspaper and pizza-box ads, television coverage, and presentations by Gruen Von Behrens, a 25-year-old man severely marred by oral cancer. Additional “guerrilla” advertising was conducted in barbershops, rodeos, fairgrounds, little league fields, bowling alleys, agriculture shops, and publications.
Follow-up surveys to Tobacco-Free Wyoming Communities and the dental community indicated that dental office interventions increased by 58%, and response volumes to Wyoming’s Quitline and QuitNet from chew tobacco users doubled. Wyoming’s “Through With Chew” campaign, including lessons learned and tangible tools for success, can be replicated easily in other states and communities.
Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
15670474
PCDv22_04_0142s
Special Topics
Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedRetooling an Osteoporosis Prevention Program to Serve Low-Income Populations: A Practical Guide to Bone Health
Cyzman Denise R MS, RD Section Manager Diabetes, Kidney, and Other Chronic Diseases, Michigan Department of Community Health
3423 N Martin Luther King Blvd, PO Box 30195, Lansing, MI 48909 517-335-8369 [email protected]
Bour Norma
McLaury Rachel
Lyles Judith
4 2005
15 3 2005
2 2 A21
Track: Partnerships
We modified an existing osteoporosis prevention program to address the needs of low-income consumers in Michigan and conducted a pilot test to assess the program’s effectiveness.
Building on previously collected focus group data from consumers and educators, we surveyed eight educators with the Michigan State University Extension (MSUE) to help identify barriers encountered delivering an educational osteoporosis prevention program to low-income constituents. The educators recommended program content changes that placed more emphasis on practical information relating to food choices, nutrition, and physical activity. Registered dietitians and staff representing the Michigan Nutrition Network, United Dairy Industry, and the Michigan Department of Community Health developed new material. The resulting program, A Practical Guide to Bone Health, was designed as a 45-minute presentation available in either a flip-chart or PowerPoint format. The content emphasized physical activity, diet quality, and the importance of calcium and vitamin D. A shopping section guided consumers to better food choices. The program also encouraged change in diet and physical activities. A statewide steering committee reviewed the program before a final field test.
We conducted the program at four sites, one urban and three rural, selected by MSUE educators who reach low-income populations. Participants completed pre-tests and post-tests assessing basic knowledge and intent to change behavior. Educators documented program location, number of participants, and audience income level.
A total of 46 people participated in the program. Participants were primarily between the ages of 18 and 44 years (75%), white (93%), and female (79%); at three of four sites, participants were predominantly low-income. A total of 43 participants completed both the pre-test and the post-test (93%). Of those who did not have perfect scores on the knowledge pretest, 73% increased their scores following the session. Prior to the program, 28% reported inadequate calcium intake and 30% reported inadequate physical activity. At post-test, 50% of those with low calcium intake indicated intent to eat three or more servings of calcium-rich foods. Likewise, 62% of those with low activity levels reported intent to increase physical activity to at least 30 minutes three times a week. An additional 13% indicated intent to increase activity to 30 minutes five times a week.
Collaboration among public health practitioners resulted in an easy-to-use and effective osteoporosis prevention program directed toward the needs of low-income populations.
Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
15670474
PCDv22_04_0142t
Special Topics
Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedMaking the Grade on Women’s Health: A National and State-by-State Report Card 2004
Berlin Michelle MD, MPH Director OHSU National Center of Excellence in Women's Health, and Associate Professor, Departments of Obstetrics and Gynecology, Public Health and Preventive Medicine, and Medical Informatics and Clinical Epidemiology, Oregon Health and Sciences University
3181 SW Sam Jackson Park Rd, UHN 50, Portland, OR 97239-3098 503-494-4480 [email protected]
Waxman Judith
4 2005
15 3 2005
2 2 A21
Track: Methods and Surveillance
Making the Grade on Women's Health: A National and State-by-State Report Card is a comprehensive study of the status of women's health in the United States based on selected health status and policy indicators. Health status indicators reflect conditions with a significant impact on quality of life and well-being, affect large numbers of women generally or disproportionately affect a specific population and/or age group, are amenable to prevention or improvement, and are measurable through consistent, reliable data. The 27 indicators fall within four categories: women's access to health care services, preventive health care activities, key women's health conditions, and whether women live in healthy communities.
The states and the nation received grades for each status indicator based on whether the benchmark was met (Satisfactory) or not met (Satisfactory Minus, Unsatisfactory, or Fail, based on scores' distance from the benchmark). In the 2004 Report Card, grades take into account that states and the nation have several years to achieve those benchmarks based upon Healthy People 2010. The Report Card also provides 67 policy indicators to evaluate the performance of state and federal governments in promoting women's health; these are based on state statutes, regulations, and policies and programs that address problems identified by health status indicators.
The nation met only two indicators (mammograms in women aged >40 years and annual dental visits) and received an overall grade of Unsatisfactory. All states and the District of Columbia met one benchmark (annual dental visits) and missed eight (including proportion of women with health insurance, rates of high blood pressure and diabetes, infant mortality, poverty, and wage gap). Twenty-five states improved at least five policies, and the majority of states weakened in one to three policies. The policy most consistently improved was preventing tobacco sales to minors. Only one policy goal, Medicaid coverage for breast and cervical cancer treatment, was met by all the states.
For most health status indicators, the nation and the states fall short of meeting national goals. Despite interest in health disparities, key differences (including race/ethnicity and age) persist. A number of state governments improved policies in key areas to meet women's health needs, but many states have fallen behind. The 2004 Report Card findings identify pressing issues that must be tackled by policy makers, public health administrators, and care providers. To improve and maintain the health of U.S. women, these issues must be addressed swiftly and accurately.
Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
15670474
PCDv22_04_0142u
Special Topics
Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedResponse Shift: The Measurable and Desired Outcome of Chronic Disease Self-management Programs That Violates Pre–Post Assessment
Osborne Richard H BSc, PhD The University of Melbourne Department of Medicine, Centre for Rheumatic Diseases, Royal Melbourne Hospital
Melbourne, Australia 3050 +61-3-9342-8561 [email protected]
Hawkins Melanie
4 2005
15 3 2005
2 2 A21
Track: Evidence-based Programs: Research, Translation, and Evaluation
Chronic disease self-management programs are designed not only to empower participants and increase their knowledge but also to provide participants with existential insights into their health and ability to self manage. The outcome of a self-management program is traditionally evaluated by comparing pre-intervention and post-intervention questionnaires, a methodology that assumes that participants answer questions from the same perspective before and after the program. Systematic reviews of the outcomes from self-management interventions that use self-appraisal outcomes identify small to no effects. This study of community-based self-management programs in Australia aimed to determine if changes in internal values or perspectives (termed a response shift) occurred in participants and if response shift is measurable with a paper-based questionnaire.
The HEI-Q-Perspective Questionnaire, a retrospective nine-item post-test questionnaire, was developed to measure potential benefits of self-management programs. Cognitive interviews with respondents elicited spontaneous statements about the reasons for their paper-based answers across the nine items. The sensitivity, specificity, and overall accuracy of the questionnaire were calculated using the interview as the gold standard. A response shift can be negative (i.e., after the course, participants report they now realize that before the course they were worse than they thought they were), positive (i.e., participants now realize they were better before the course than they thought they were), or neutral (no change).
In-depth interviews (n = 39) and mailed questionnaires (n = 132) reflected that a “true” response shift occurred in about half of the questionnaire items. Of these, 33% had a negative response shift, 18% had a positive response shift, and approximately 32% had no response shift. The presence or absence of response shift could not be determined in approximately 17% of cases. Substantial concordance between interview and questionnaire were observed (average overall accuracy 0.79), indicating the questionnaire effectively identified response shift. A positive or negative response shift was found to have profound effects on patient-reported outcomes — even large positive or large negative program effects revealed in an interview could be concealed in an individual’s pre–post score. This clearly demonstrates that response shift can violate classic outcome assessment of self-management programs.
Response shift occurred in about half of the participants. This result suggests that classic outcome assessment (pre-test vs post-test) in many individuals is flawed. Response shift is a desirable outcome of courses but has not been formally measured. The strong concordance between the questionnaire and cognitive interviews indicates the HEI-Q-Perspective can detect response shift. This new questionnaire will assist researchers and program evaluators to better estimate the impact of self-management programs and to understand the role of response shift in this and other settings.
Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
15670474
PCDv22_04_0142v
Special Topics
Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedMoving People to Move: Midpoint Results of the Walk the Ozarks to Wellness Project
Caito Nikki M MPH, MS, RD, LD Research Coordinator Saint Louis University School of Public Health, Health Communication Research Laboratory
3545 Lafayette Ave, Room 424, St. Louis, MO 63104 314-977-4047 [email protected]
Elliott Michael
Lovegreen Sarah
Klump Paula
Kreuter Matthew W
Brownson Ross
4 2005
15 3 2005
2 2 A21
Track: Communications and Technology
The Walk the Ozarks to Wellness Project is a four-year longitudinal study of walking currently underway in six rural underserved communities in southeast Missouri. The project's objective is to increase time spent walking among low-income, overweight, rural Missourians who have diabetes or who are at risk for developing the disease.
Participants initially enrolled in this study at community health events sponsored by a local steering committee. Throughout the first year of the intervention, beginning in November 2003, participants received a monthly newsletter. In the second year, participants receive a bimonthly newsletter. Newsletter messages were written based on participant surveys completed at baseline and at month nine. Topics addressed in the first nine newsletters included motivation, health history, discussions with doctors, self-efficacy, and barriers. Additional newsletters included topics about social support and physical activity level.
To date, 1065 participants have enrolled in the project in nine groups. We have received midpoint (T2) surveys from 153 participants in the first group. Paired t-test analyses show significant improvement at T2 in those who reported no walking and no moderate activities at baseline, in both days per week and minutes per week (P < .001). Of those who marked having no place to exercise as a barrier at baseline, 81% no longer had this barrier at T2. McNemar tests show significant improvement at T2 in those who had not talked to their doctors about healthy eating (P < .001), exercise (P = .029), and losing weight (P < .001) at baseline. Stages of Change analyses show advancement in those who were in the precontemplation and contemplation stages, where overall, 69% in precontemplation and 67% in contemplation moved forward.
These results show the preliminary impact of a tailored intervention in a high-risk population. This tailored intervention was effective in 1) moving participants forward in the initial stages of change; 2) increasing awareness about the importance of talking with one’s doctor about leading a healthy lifestyle; and 3) decreasing perceived barriers.
Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
15670474
PCDv22_04_0142w
Special Topics
Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedColorectal Cancer Screening Among Latinos From U.S. Cities Along the Texas–Mexico Border: A Qualitative Study
Fernandez Maria E PhD Assistant Professor of Health Promotion and Behavioral Sciences University of Texas Health Science Center at Houston School of Public Health, Center for Health Promotion and Prevention Research
7000 Fannin St, Suite 2558, Houston, TX 77030 713-500-9626 [email protected]
Torres Isabel
Vernon Sally
Byrd Theresa
Hinjosa-Lindsey Marilyn
Wippold Rosario
Bains Yadvindera
4 2005
15 3 2005
2 2 A21
Track: Evidence-based Programs: Research, Translation, and Evaluation
The purpose of this study was to identify factors influencing the decision to undergo colorectal cancer screening among low-income Hispanics living along the Texas–Mexico border. Although colorectal cancer is a leading cause of cancer death among Latinos, most are not getting the recommended colorectal cancer screening (CRCS) tests, and little is known about what types of factors may influence informed decision making for CRCS among Latinos.
Four focus groups with low-income Latino men and women were conducted in January 2004 at each of three sites (Brownsville, El Paso, and Laredo, Tex). Demographic, psychosocial, and cultural factors potentially related to CRCS were addressed, as well as general issues such as access to health care services, perceptions about the importance of preventive health care, and factors surrounding health care decision making.
Both women and men in this study reported a heavy reliance on home remedies, herbal remedies, and prescription drugs bought across the border, in part because of financial barriers and lack of insurance. Many participants in all groups reported feeling more satisfied with the care they received on the Mexican side of the border because of lower-priced medications and office visits and perceptions that care in Mexico is more efficient, flexible, thorough, humane, and because it is offered using the Spanish language. The participants’ knowledge, attitudes, and beliefs related to cancer reflected a mixture of misconceptions and accurate information, and participants expressed varying beliefs about the survivability of cancer. Participants often associated the word cancer with descriptors such as death, fear, pain, ugliness, sadness, and hopelessness.
Most participants in this study knew very little about colorectal cancer and even less about CRCS. Many of them were confused about the differences between colorectal cancer and stomach or prostate cancers. There was also a strong belief that untreated hemorrhoids and constipation were major causes of colorectal cancer. Individual-level barriers to CRCS suggested by participants included embarrassment, machismo, lack of knowledge and information, procrastination, fear of questioning physicians, fear of the actual screening procedures, and fear of receiving a diagnosis of cancer. Other barriers to CRCS identified by participants included lack of health insurance or financial resources, being undocumented, and transportation barriers.
Results from these focus groups have provided much-needed preliminary information about this area of Latino health and will provide guidance for the development of interventions to increase CRCS among low-income Hispanics along the Texas–Mexico border.
Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
15670474
PCDv22_04_0142x
Special Topics
Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedChallenges to Measuring Health Care Disparities in the National Healthcare Disparities Report: Disparities in Data
Moy Ernest MD, MPH Agency for Healthcare Research and Quality, Center for Quality Improvement and Patient Safety
540 Gaither Rd, Rockville, MD 20850 301-427-1329 [email protected]
Arispe Irma
Holmes Julia
4 2005
15 3 2005
2 2 A21
Track: Methods and Surveillance
The objective of this study was to assess the ability of extant national data sets to measure health care disparities in access, use, and quality for different racial and ethnic groups, based on our experiences in developing the Congressionally mandated National Healthcare Disparities Report (NHDR).
For each of the health care measures included in the NHDR, we examined the ability of national data sources to provide information for different groups. We focused on groups specified by 1997 Office of Management and Budget (OMB) Standards: racial minorities, including single-race blacks, Asians, Native Hawaiians and Other Pacific Islanders (NHOPI), American Indians and Alaska Natives (AI/AN), multiple-race individuals, and ethnic minorities (Hispanics).
Measurement challenges were categorized as issues of collection (if data for a particular group were not collected and usable); estimation (if data for a group were collected but suppressed because of small cell size or large relative standard error); and power (if data for a group were collected and adequate to generate estimates but lacked sufficient power to detect relative differences compared with comparison groups of 10% with P < .05).
For almost every NHDR measure, measurement challenges limited our ability to assess disparities for at least one group. Major measurement challenges varied among groups. Collection issues prevented assessments of disparities for NHOPI and for multiple-race individuals for more than 60% of NHDR measures. Estimation issues prevented an assessment of disparities for AI/AN for almost half of measures. Issues of statistical power were common among Asians, NHOPI, AI/AN, and multiple-race individuals. Measures that focus on subsets of the general populations (i.e., women, children, elderly) were particularly vulnerable to measurement challenges.
The goal of reducing disparities in health care depends upon our capacity to measure and track differences in care. For some racial and ethnic minority groups, extant national data are sufficient for assessing many areas of disparity. However, for smaller groups, challenges related to data collection, estimation, and power severely limit our ability to assess disparities. These disparities in data must be addressed to allow design of interventions that reduce disparities in care for all minority groups rather than just the larger groups.
Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
15670474
PCDv22_04_0142y
Special Topics
Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedAn Alternative to Identifying and Engaging the Underserved, Out-of-Care, HIV High-Risk Population
Khan Hadi Erum BSc, MPH Epidemiologist Dutchess County Department of Health
387 Main St, Poughkeepsie, NY 12601 845-486-3750 [email protected]
Jaar-Marzouka Sabrina
Hiemke Trina
Cardinale Lisa
4 2005
15 3 2005
2 2 A21
Track: Health System Change
The objective of this project was to provide and assess the effectiveness of a mobile screening and referral program for the medically underserved, high-risk, and difficult-to-reach population of Dutchess County, New York. By U.S. Census 2000 population estimates, 80% of the population of Dutchess County is white, 9.3% is African American, and 6.4% is Hispanic. However, African Americans are 15 times more likely and Hispanics almost 11 times more likely than whites to have HIV. We used an outreach van for HIV screening and referrals at nontraditional service hours and at high-risk venues in two low-income neighborhoods with the greatest burden of HIV disease.
Mobile van outreach workers surveyed each individual screened, and results were reviewed to assess the needs of the HIV-positive out-of-care clients. Outreach service forms and screening forms were analyzed to quantify the number of clients served. Lastly, follow-up phone calls to designated service providers verified whether clients referred made medical visits.
In 2003, the outreach van screened 179 individuals and identified 35 (19.5%) HIV-positive individuals. Of the 35 HIV-positive individuals, outreach workers linked 22 (62.8%) to primary care. Of these 22, 17 (77.3%) were African American and 5 (22.7%) were Hispanic. Dutchess County has an estimated 657 HIV/AIDS-positive individuals out of care; the outreach van identified and provided services to 5.3% (35/657) of this population.
Two of the biggest challenges the program faces are the need to contact high volumes of people to identify the individuals with HIV infection and the issue of safety of the outreach staff and security of the van in high-risk neighborhoods. The success of the mobile screening program can be attributed to the following factors: 1) basic primary care services are brought to the client; 2) the outreach van is staffed with racially diverse peers, outreach workers, and a nurse; 3) the program does not label the van as an HIV-care provider only but provides various other low-threshold screenings, education, and support services; and 4) over time, trusted community leaders referred others in the community to use the van services.
This initial evaluation indicates that the mobile van outreach program is successful in keeping the minority, out-of-care population in underserved neighborhoods engaged in primary care. In addition, the outreach van provides an opportunity for surveillance of HIV and the general health status of high-risk communities; it also increases access to primary care through unique partnerships among service providers.
Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
15670474
PCDv22_04_0142z
Special Topics
Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedIs Food Insecurity a Price of Smoking Among the Poor?
Armour Brian PhD Health Scientist Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health
4770 Buford Hwy NE, Mail Stop K-50, Atlanta, GA 30341 770-488-5718 [email protected]
Pitts M. Melinda
Lee Chung-won
Woollery Trevor
Caraballo Ralph
4 2005
15 3 2005
2 2 A21
Track: Social Determinants of Health Inequities
The objective of this study was to estimate the share of income that families spend on cigarettes and to determine the association between food insecurity, smoking, and poverty.
Cigarette smoking prevalence is higher among adults living below the poverty level. The opportunity cost of smoking (the goods and services smokers forgo to purchase cigarettes) will be proportionately higher among poor families than nonpoor families since a larger share of their income will go toward the purchase of cigarettes. No study has documented what these opportunity costs might be. This study shows an association between food insecurity and the share of income spent on cigarettes. Evidence supporting this association suggests that state-sponsored smoking cessation programs targeting poor smokers may have an added benefit of reducing food insecurity.
Data from the 2001 Panel Study of Income Dynamics (PSID) were used to identify smokers and families that were food insecure and to determine the share of families’ income spent on cigarettes. The PSID is a nationally representative longitudinal study of U.S. families that collects economic, health, and social behavior data on 7406 families. T tests and chi-square tests were used to assess univariate differences between continuous and categorical variables. Multivariate logistic regression models were used to assess the association between food insecurity, smoking, and poverty.
Approximately 7.6% of families lived in poverty, 6.1% were food insecure, and 26.1% had at least one family member who smoked in 2001. Poor families were more likely to have a family member who smoked cigarettes than nonpoor families (33.3%, poor families vs 22.5%, nonpoor families; P < .001). The share of income spent on cigarettes was significantly higher for poor families than nonpoor families (12.0%, poor families vs 2.0%, nonpoor families; P < .001). Results from the multivariate logistic regression analysis revealed that the odds of being food insecure increased as share of income spent on cigarettes increased (adjusted odds ratio, 1.72; 95% confidence interval, 1.66–1.79).
Having an average annual income of $8624 and average annual cigarette expenditures of $857, poor families with a family member who smokes spend a large share of their income on cigarettes. This study suggests that in addition to the adverse health consequences linked to smoking, poor families may also pay a price of food insecurity.
Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
15670474
PCDv22_04_0142aa
Special Topics
Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedThe Incidence of End-Stage Renal Disease in Georgia, 1999–2002
Abe Karon PhD Epidemic Intelligence Service Officer Georgia Department of Human Resources, Division of Public Health
2 Peachtree St NW, Atlanta, GA 30303 404-657-2577 [email protected]
Mertz Kristen
Powell Kenneth
Wu Manxia
Cho Pyone
4 2005
15 3 2005
2 2 A21
Track: Methods and Surveillance
Each year in the United States, approximately 80,000 people are diagnosed with end-stage renal disease (ESRD), a condition requiring dialysis or kidney transplant to sustain life. The primary causes of the disease for the majority of patients are diabetes and hypertension. We sought to assess racial disparities in the burden of ESRD and its contributing causes in Georgia.
ESRD Network 6 is part of the United States Renal Data System, a nationwide 18-network ESRD surveillance system that collects information on newly diagnosed and chronic ESRD patients. We used data from the ESRD Network 6 Web site to calculate age-adjusted ESRD incidence rates in Georgia and to describe the demographic characteristics of newly diagnosed patients from 1999 through 2002. We also used data from the Behavioral Risk Factor Surveillance System to compare the age-adjusted prevalence of diabetes (2002) and hypertension (2001) among blacks and whites aged 18 years or older in Georgia.
Each year, more than 3000 persons in Georgia are diagnosed with ESRD. From 1999 through 2002, the age-adjusted incidence rate for ESRD was higher in Georgia (42 per 100,000) than in the nation (33 per 100,000). Of the newly diagnosed ESRD patients in Georgia, 57% were older than 65 years, and 50% were female. Diabetes was the primary cause of 40% of ESRD cases, and hypertension was the primary cause of 30% of ESRD cases. Although adult blacks were 1.7 times more likely than whites to have diabetes and 1.4 times more likely than whites to have hypertension, blacks were 4.3 times more likely than whites to develop ESRD.
ESRD is a major public health burden, especially among blacks. Although a higher percentage of blacks than whites suffer from diabetes and hypertension, the racial disparity in the prevalence of ESRD is much greater. The incidence of ESRD might be reduced by 1) educating patients with diabetes and hypertension about the importance of diligent self-management and regular medical care, and by 2) encouraging physicians to monitor the renal function of their patients with diabetes and hypertension.
Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
15670474
PCDv22_04_0142bb
Special Topics
Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedDeveloping, Implementing, and Disseminating Evidence-based Healthy Aging Programs in Community-based Organizations
Whitelaw Nancy A PhD Director Center for Healthy Aging, The National Council on the Aging
300 D St SW, Suite 801, Washington, DC 20024 713-798-3850 [email protected]
Erckenbrack Nancy
Schreiber Robert
Simmons June
Wilson Nancy
4 2005
15 3 2005
2 2 A21
Track: Evidence-based Programs: Research, Translation, and Evaluation
Our objective was to convey findings on the development, implementation, and dissemination of evidence-based healthy aging programs in community-based organizations. Fifteen locations, including care management agencies, senior centers, and churches in Boston, Mass; Houston, Tex; Los Angeles, Calif; and Portland, Ore, implemented one of four evidence-based healthy aging programs. These sites reached a group of highly diverse participants, including Latinos, African Americans, Native Americans, Chinese, as well as other non-English–speaking populations.
The National Council on the Aging conducted a three-year national project to translate health promotion and disease management studies into evidence-based model programs that are feasible for local agencies to operate and attractive to older adults. Expert review panels examined the evidence for effective interventions on a variety of prevention topics and recommended the development of model programs for diabetes self-management, nutrition, depression management, and physical activity. Four regional teams across the country translated each of the review panel’s recommended evidence-based interventions into model programs, two using a lay-leader method and two designed for implementation by care managers. Throughout the translations process, experts from the Centers for Disease Control and Prevention and academic institutions reviewed the programs to ensure fidelity.
The results of this project were evaluated using the RE-AIM framework for assessment of health behavior interventions. Each site succeeded in reaching diverse populations with risk factors relevant to the model program. Approximately 350 older adults were reached during the pilot period. Data gathered on the pilots suggest that the programs were effective — improvements in self-reported health status were reported, and satisfaction levels were high. Of critical importance were the willingness and ability of various groups and participants to adopt the program. Considerable attention was paid to maintaining fidelity to the “proven” intervention during the implementation phases. On-site reviews by outside experts confirmed that implementation was consistent with original studies. Maintenance is still being assessed, but 12 of the 15 pilot sites continue to offer these programs.
The results of this project reinforce the belief that community-based organizations are highly capable of implementing evidence-based health promotion and disease management programs for older adults. Such programs can be incorporated into existing programming, providing excellent opportunities for organizations to expand their reach and quality of offerings. The four model programs are now offered as free toolkits, which guide organizations through the implementation of the programs.
Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
15670474
PCDv22_04_0142cc
Special Topics
Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedDiabetes Awareness, Training, and Action Program: North Carolina’s Response to the Care of School Children With Diabetes Law
Hudson Collins Paula MHDL, RHEd Senior Advisor for Healthy Schools North Carolina Department of Public Instruction
6349 Mail Service Center, Raleigh, NC 27699-6349 919-807-3859 [email protected]
4 2005
15 3 2005
2 2 A21
Track: Policy and Legal
In the fall of 2002, the Care of School Children with Diabetes Act was signed into law in the state of North Carolina. This law requires that all children with diabetes who are enrolled in any of the state’s public schools have an Individual Diabetes Care Plan available upon a parent’s request. The law also requires that schools enrolling children with diabetes provide general training on diabetes and its management for all faculty and support staff and two volunteer emergency care providers for students with diabetes.
To respond to the law, the North Carolina (NC) Department of Public Instruction, NC Department of Health and Human Services, NC Diabetes Advisory Council, American Diabetes Association, Wake Forest Baptist University Medical Center, Wake Area Health Education Centers, BlueCross BlueShield of NC Foundation, NC Healthy Schools, and the Diabetes Prevention and Control Branch of NC Public Health worked together to create the Diabetes Awareness, Training, and Action (DATA) Program. The DATA Program involved developing and producing training materials, designing and implementing six regional trainings across the state using a “train-the-trainer” model, seeking private funding for the project, developing the individual care plans and all reporting forms, informing parents, and preparing evaluation reports for various state agencies.
As a result of the DATA Program, all students with diabetes in North Carolina have a plan of care in place, which is determined by the primary care provider, school, and parent. Emergencies because of diabetes have decreased, and general awareness and acceptance of procedures related to diabetes control are better understood, accepted, and monitored in the school setting. The increased interaction among school personnel, medical personnel, and parents, especially parents of children in homes experiencing health disparities, have yielded the unintended benefits of improved communication and collaboration among these groups. In addition, this cooperative spirit has encouraged the community to work through doctors’ offices as well as public health clinics to make this diabetes information readily available to families that might not otherwise have the means to access health care.
An Individual Health Care Plan should be in place for children with any type of health care need. While the management of diabetes in children was the test case in North Carolina, children with any chronic condition should have access to care plans to avoid the need for a state law that relates to just one chronic condition.
Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
15670474
PCDv22_04_0142dd
Special Topics
Original Research: Featured Abstract from the 19th National Conference on Chronic Disease Prevention and Control
Peer ReviewedTrying to Quit: Low-Income Smokers’ Access to Cessation Care in a Managed Care Environment
Fleming-Moran Millicent PhD Associate Professor Indiana University, Department of Applied Health Science
HPER 116, Bloomington, IN 47401 812-855-8361 [email protected]
Li Kaigang
Gibson Joseph
Garland Miriam
4 2005
15 3 2005
2 2 A21
Track: Methods and Surveillance
This study describes 295 smokers in a managed care safety-net insurance program, where 63% received cessation advice during at least one visit in the previous year. Our study asks: Does longer program enrollment increase a smoker's likelihood of receiving cessation advice?
The study population is drawn from Advantage program clients who are predominantly minority, working poor with Medicaid/Medicare or are under-insured county residents who meet 200% or less of federal poverty guidelines. State medical school practitioners coordinate the program in seven primary care clinics in a Midwestern urban county.
Telephone surveys using Bellview CATI survey software (Pulse Train Software, Ltd, Surrey, UK) were administered in English or Spanish to 731 Advantage enrollees. Of these, 317 were enrolled for less than one year, 281 were enrolled for one year, and 133 were enrolled for more than one year. Of the 731 enrollees, 295 (40.4%) were current smokers. The current smokers were categorized by sex, ethnicity, age, education, knowledge of primary care physician (PCP), and coronary heart disease (CHD) risk other than smoking. The association of each characteristic with cessation advice was determined by chi-square tests of significance. Predisposing factors (sex, age, ethnicity), enabling factors (education, known PCP), health care need (other CHD risk), and program enrollment time were tested in a logistic model of cessation advisement, using a forward selection process.
Advantage smokers who are female (72.0%), white (70.4%), and over age 65 (85.0%) and who know their PCP (68.5%) and have another CHD risk factor (89.3%) report more advice than smokers who are male (49.0%), minority (54.5%), and under age 35 (35.0%) and who do not know their PCP (51.0%) or have any other CHD risk factors (46.2%). Individuals who were enrolled for more than one year (71.2%) report more advice than individuals enrolled for less than one year (53.1%). In logistic analysis, other CHD risk doubled the likelihood of cessation advice (odds ratio [OR] 2.02; 95% confidence interval [CI], 1.5–2.8), as did being female (OR 1.95; CI, 1.1–3.3). Being over age 65 increased the likelihood of advisement (OR 1.5; CI, 1.09–2.12), while minority status reduced the likelihood (OR 0.41; CI, 0.24–.70). Enabling factors of education, enrollment time, or PCP recognition did not enter the model.
Safety-net programs increase access to and continuity of primary care in low-income communities where smoking is most prevalent. Advantage's 63% advisement rate exceeds that reported for other smokers using primary care and indicates appropriate outreach to high CHD risk smokers. More than one third (37%) of smokers in Advantage's program, however, report no cessation counseling. We propose examination of visit patterns, language difficulties, and clinical smoking records as ways to track and target younger, male, and minority smokers for provider prompts and cessation support. Increasing access to cessation care would reduce CHD, respiratory, and adverse reproductive outcomes in this population.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Baldyga W, Petersmarck K. Reducing health disparities: what is being done, what works. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/05_0002.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Martin M, Martin S, Martin ER. PE2GO: a program to address disparities in youth physical activity opportunities [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142a.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Oser C, Blades L, Strasheim C, Helgerson S, Gohdes D, Harwell T. Awareness of cardiovascular disease risk in American Indians [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142b.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Link M, Mokdad A, Stackhouse H, Flowers N. Race, ethnicity, and linguistic isolation as determinants of participation in public health surveillance surveys [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142c.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Kempf AM, Remington PL, Peppard PE, Dranger EA, Kindig DA. Measuring population health disparities: the Wisconsin County Health Rankings [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142d.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Hannon P, Harris J, Martin D, VanEenwyk J, Bowen D. Colorectal cancer screening in Washington State: predictors of current screening and explanations for no screening [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142e.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Hannon P, Harris J, Martin D, VanEenwyk J, Bowen D. Colorectal cancer screening in Washington State: predictors of current screening and explanations for no screening [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142f.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Agron P, Berends V. Educating California school board members: aligning policies for student health and achievement [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142g.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Bretthauer-Mueller R, Melancon H. VERB™ campaign: extending the reach of a national campaign to ethnically diverse audiences [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142h.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Krishna R, Liff J, Chen A, Eke P, Robison V. Racial differences in factors that influence survival with oral cancer in Georgia: 1978–2001 [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142i.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Snyder S, Belza B. Eliminating disparities in communities of color through the Lifetime Fitness Program [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142j.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Maltese JN, Brewton C. Developing a rural health promotion specialist program to provide preventive health care to the medically indigent [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142k.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Arroyo C, Jones D, Liu Y, Din-Dzietham R, Davis S. Regional/racial prevalence of metabolic syndrome: the MSM Regional Assessment Health Surveillance Study, 2003–2004 [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142l.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Plescia M, Groblewski M. A community-based partnership to address barriers to physical activity in an African American community [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142m.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Krapfl H, Gohdes D, Croft J. Getting the most out of vital statistics data: diabetes-related heart disease mortality in New Mexico [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142n.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: KDoran DR, Gold J, Serbaroli F, Hannan EL, Macielak PF, Streck WF. Overcoming barriers in access to specialized health care services [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142o.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Jones K, Griffin S, Moonesinghe R, Jaramillo F, Vousden C. Reducing dental sealant disparities in school-aged children through better targeting of informational campaigns [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142p.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Kealey M, Kruger J, Hunter R, Ivey S, Satariano W, Bayles C, et al. Engaging older adults to be more active where they live: audit tool development [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142q.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Bagdonas S, Mueller N, Russian A. Conducting a successful “Through with Chew” Week [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142r.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Cyzman D, Bour N, McLaury R, Lyles J. Retooling an osteoporosis prevention program to serve low-income populations: A Practical Guide to Bone Health [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142s.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Berlin M, Waxman J. Making the Grade on Women’s Health: A National and State-by-State Report Card 2004 [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142t.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Hawkins M, Osborne R. Response shift: the measurable and desired outcome of chronic disease self-management programs that violates pre-post assessment [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142u.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Caito NM, Elliott M, Lovegreen S, Klump P, Kreuter MW, Brownson R. Moving people to move: midpoint results of the Walk the Ozarks to Wellness Project [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142v.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Fernandez ME, Torres I, Vernon S, Byrd T, Hinjosa-Lindsey M, Wippold R, et al. Colorectal cancer screening among Latinos from U.S. cities along the Texas–Mexico border: a qualitative study [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142w.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Moy E, Arispe I, Holmes J. Challenges to measuring health care disparities in the National Healthcare Disparities Report: disparities in data [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142x.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Khan Hadi E, Jaar-Marzouka S, Hiemke T, Cardinale L. An alternative to identifying and engaging the underserved, out-of-care, HIV high-risk population [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142y.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Armour B, Pitts MM, Lee C, Woollery T, Caraballo R. Is food insecurity a price of smoking among the poor? [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142z.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Abe K, Mertz K, Powell K, Wu M, Cho P. The incidence of end-stage renal disease in Georgia, 1999–2002 [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142aa.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Whitelaw N, Erckenbrack N, Schreiber R, Simmons J, Wilson N. Developing, implementing, and disseminating evidence-based healthy aging programs in community-based organizations [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142bb.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Collins PH. Diabetes Awareness, Training, and Action Program: North Carolina’s response to the Care of School Children with Diabetes Law [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142cc.htm.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Fleming-Moran M, Li K, Gibson J, Garland M. Trying to quit: low-income smokers’ access to cessation care in a managed care environment [abstract]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0142dd.htm.
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==== Front
Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_04_0093
Community Case Study
PEER REVIEWEDCapturing Change in a Community–University Partnership: The ¡Sí Se Puede! Project
Kelley Michele A ScD, MSW Associate Professor University of Illinois at Chicago, School of Public Health, Division of Community Health Sciences
1603 W Taylor St, Room 652, Chicago, IL 60612-4394 [email protected]
312-413-3225
Baldyga William DrPH, MA Associate Director, Institute for Health Research and Policy, Co-director, Illinois Prevention Research Center, University of Illinois at Chicago, Chicago, Ill
Barajas Fabiola MPH Community Health Promotion Coordinator MacNeal Health Network, Berwyn, Ill
Rodriguez-Sanchez Maria MA Project Coordinator Illinois Prevention Research Center, University of Illinois at Chicago, Chicago, Ill
4 2005
15 3 2005
2 2 A222005
Background
Community health interventions are increasingly employing partnerships combined with multilevel intervention models to achieve their objectives. Resources and methods for project evaluation are often limited to changes in population health status or health behaviors, while broader contextual questions that may illuminate mechanisms for change across ecological levels and project sustainability may not be addressed.
Context
This paper describes a project to prevent and control diabetes in a Latino community and presents practical methods for addressing some challenges to evaluation, using data sources that often may be overlooked.
Methods
A case study method was used to examine approaches to capture data that can help explain changes across ecological levels. An ecological framework was used to organize sources of data. Data sources and findings are related to project timelines and goals.
Consequences
Although not a direct focus of the original research, substantial changes in community capacity were observed and measured over the course of the five-year project. Documentation on community change was found in routine project reports, logs, the news media, meeting minutes, and community documents.
Interpretation
A logical progression of community change across ecological levels became evident. A modest post hoc evaluation was feasible, using data routinely available from project and target community sources. Specific questions for future research on how community change occurs and how such changes may relate to population health and sustainability are suggested.
==== Body
Background
Public health goals to reduce health disparities among people with chronic disease pose enormous challenges to health care providers, public health practitioners, academic researchers, and community leaders (1). In response to these challenges, recent community-based approaches to improve health are employing dual strategies of 1) creating partnerships between researchers and affected communities and 2) developing multilevel or ecological conceptual frameworks of health determinants. The rationale for the first strategy — creating community partnerships — is grounded in participatory democracy and recognition of local culture and community assets as necessary for tailoring acceptable and sustainable interventions (2). Although there is no agreed-upon definition of researcher–community partnerships (also referred to as community-based participatory research, or CBPR), key components include community involvement in the research process (i.e., identifying issues to be addressed) and in designing and delivering the interventions. In addition to improving the health behaviors of residents, CBPR aims to develop and strengthen community assets to address self-identified threats to health (3).
Although there is growing consensus that community engagement is a necessary and ethical condition for successful health promotion programs (4-6), there is a paucity of literature on the effectiveness of such engagement (7). The lack of literature may be explained in part by the complexity of and the insufficient detail on the partnerships described in research reports (8). Furthermore, creating partnerships is a necessary but insufficient strategy for improving health because many health determinants lie outside of the influence of the community (9).
Public health initiatives increasingly employ multilevel or ecological approaches to influence community-level change (10-12). McLeroy et al define ecological levels of influence on individual behavior (13). The ecological levels correspond to a series of community influences on intrapersonal factors (e.g., knowledge, attitudes, behavior, self-concept), interpersonal networks (e.g., family, friends, coworkers), institutional processes (e.g., formal and informal social networks, social support systems), community factors (e.g., relationships among organizations and networks), and public policy (e.g., local, state, and national laws and policies) (13). Although interest has grown in designing multilevel approaches to improve community health in areas such as nutrition, physical activity, and smoking prevention and cessation, no adequate, tested framework exists for measuring the effects of such initiatives beyond the individual or group level (14).
Sufficient knowledge of the dynamics and processes of change that occur in multilevel community health initiatives will provide future intervention efforts with an evidence base for making sound judgments on the best use of resources for addressing complex health issues. Because immigrant populations and communities of color are often targets for community-based interventions, multilevel frameworks could significantly enhance the ability of public health programs to close the gap on racial and ethnic health disparities.
In this paper, we demonstrate how a community-wide health promotion effort to prevent and control diabetes in a Latino community has addressed the challenges of capturing community change across ecological levels and the limitations in using available data. Like many chronic disease prevention research efforts, resources for evaluating this health promotion effort focused on measuring individual-level changes in health knowledge and behavior. (This evaluation is in progress.) Here we report on the use of existing data from project sources to perform a post hoc evaluation that captures observed differences in programs, participating organizations, and interorganizational relationships; we also document community-member participation in project development and leadership.
This paper reports on the initial project phase (1999–2004) and focuses on aspects of community change that have traditionally not been captured with data but may be critical in understanding conditions under which such projects are likely to influence individual health behaviors, to continue beyond the life of a specific project, and to enable the community to take on other related critical health issues as they emerge in the future.
We address the following questions: 1) What effects did the collaborative process have on the community? 2) How can we use data routinely collected during CBPR to describe community change beyond the individual level as the project evolves over time? We conclude with recommendations for data-capture strategies and suggest research questions to guide future community-based interventions that employ multilevel-change strategies.
Context
This paper addresses the measurement of community change using a case study of a community-based diabetes prevention and control project. ¡Sí Se Puede! (Yes We Can!, abbreviated as SSP) is a research demonstration project conducted by the Illinois Prevention Research Center (IPRC) at the University of Illinois at Chicago and funded through the Prevention Research Center Program Office and the Division of Diabetes Translation, Centers for Disease Control and Prevention (CDC). According to the CDC, "The Prevention Research Centers (PRCs) conduct participatory, community-based research to prevent disease and promote health. The outcomes are intended to be applicable to public health programs and policies" (15). The IPRC partnered with the Latino Organization of the Southwest (LOS) to conduct a community-based diabetes prevention and control project in two community areas on Chicago's southwest side. The LOS is a stable, engaged community organization dedicated to community improvement through empowerment strategies. The LOS provides leadership on a range of education, immigration, employment, housing, safety, social, and more recently, health issues.
The community of interest is known as Greater Lawn, an area of approximately 6.5 square miles in southwestern Chicago, Ill. Although a more detailed description of the community itself and the selection and engagement process are described elsewhere (16), it is important to note that the Latino community is only somewhat recently established on the southwest side of Chicago, as revealed by changes in the 1990 and 2000 censuses. This means that there were no prior organized community-wide health promotion efforts or mechanisms in place to foster such efforts.
In 1999, the IPRC and the LOS created the SSP Latino Diabetes Project with the goal of developing, implementing, and evaluating a program of activities designed to achieve the following: to raise awareness of the impact of diabetes on Latino patients and families, to enhance the ability of community members to reduce their risk of diabetes and diabetic complications, and to promote protective behaviors (healthy lifestyles) for Latino youth and their families. Four objectives guide the project activities toward this goal: 1) increase family and community awareness of the burden of diabetes and mitigating factors; 2) enhance behaviors that prevent diabetes onset or reduce diabetes complications; 3) improve the self-efficacy/self-management skills of diagnosed diabetics; and 4) enhance the quality of care delivered to diagnosed diabetics and increase opportunities to identify individuals at risk for diabetes. SSP interventions include health and diabetes education programs, school-based risk reduction curricula, physical activity programs such as walking clubs, nutrition education programs, health fairs, and a media campaign.
To address the objectives of the SSP project, a Community Advisory Board (CAB) was convened. The lead agency and project investigators nominated representatives of local schools and the local parks and recreation department, where project efforts were initially focused. The CAB is chaired by the director of the lead partner agency, LOS, and is composed of representatives of advocacy organizations, community nonprofit agencies, public schools, health care organizations, the faith community, local businesses, and resident program participants. Later, representatives from media organizations, local political leadership, and other organizations were added to ensure representation and assist with broad project objectives. A logic model that describes the inputs, outcomes, and impact of the SSP project is presented in the Figure.
Figure Logic model for the ¡Sí Se Puede! project, Chicago, Ill.
This figure illustrates the relationships among intervention characteristics and intermediate and distal outcomes. In general, the figure reads from left to right. The six top headings read from left to right: 1) “Project Theory,” 2) “Intervention Levels,” 3) “Intervention Processes,” 4) “Intermediate Outcomes,” 5) “Outcome,” and 6) “Impact.” Under each heading, there are topics, with arrows pointing from left to right between each set of topics. Under the first heading is a single box, “Ecological Model.” Under the final heading is a single box, “Promote/Increase Healthy Lifestyle in Greater Lawn.” Along the bottom of the figure, a dashed line, “Participatory Research,” joins the first box “Ecological Model” with the final box, “Promote/Increase Healthy Lifestyle in Greater Lawn.”Under “Intervention Levels” are three boxes, “Community,” “Family,” and “Individual.” These categories interact, indicated by arrows. Driven by social ecological theory and using a participatory research model the SSP project intervened on the community, family, and individual levels to achieve desired outcomes. Under the third heading (“Intervention Processes”) are three boxes: “Outreach,” “Capacity Building,” and “Health Education.” For each intervention level, outreach, capacity building, and health education interventions were designed to reach the six project objectives: “Increase Community and Family Linkages to Resources,” “Increase Knowledge and Awareness of Healthy Lifestyles,” “Improve Nutrition,” “Increase Physical Activity,” “Improve Self-management of Diabetes” and “Increase Access to Quality Diabetes Care” (each represented by boxes under the fourth heading, “Intermediate Outcomes.”) Under the fifth heading, “Outcome” are three boxes showing the three anticipated outcomes from the SSP project: “Increase Community Capacity for Health Issues,” “Increase Family Support for Health Lifestyles,” and “Increase Individual Self-Efficacy for Diabetes Prevention and Control and General Health.” Interaction between these three boxes is indicated with arrows. Finally, the outcomes lead to the final impact, “Promote/Increase Healthy Lifestyle in Greater Lawn.”
IPRC researchers used assessment strategies to increase understanding of community leader and resident concerns. A series of focus groups with residents and in-depth, key-informant interviews with community leaders were conducted in 1999. Among the findings from interviews with community leaders was a consensus that bringing single-issue health expertise to the community, even on a high-priority topic such as diabetes, was not seen as sufficient for sustained community involvement. Related desires of community leadership included nurturing a value for health among community residents, integrating health concerns into the agenda of community organizations, and connecting community resources both within the target community and between the community and the broader metropolitan area. Subsequently, in 2001, a fifth objective was added: to develop the capacity of the community to address diabetes care and other self-identified health concerns. As initially conceptualized, enhanced community capacity was seen as a sum of the total effect of all intervention components rather than a separate research question, requiring its own design and data collection strategy.
IPRC researchers proposed analytic strategies to measure progress toward the first four objectives. Also proposed were educational objectives to strengthen community capacity by increasing knowledge of prevention research, for example, through attendance at national meetings. However, unlike other project objectives, no testable hypotheses or specific research questions were developed, and no analysis plan captured the activities related to community capacity or measured their impact. The primary reason was that resources for additional evaluation were not budgeted, as project resources were necessarily devoted to intervention activities and evaluation of the four original objectives. A secondary reason was related to the perception that "community building" would be opportunistic, responding to nonscientific (community) issues and could not be structured in a way that allowed for preplanned, quantitative measurement. Later, as the value of the activities conducted to increase community capacity became clarified and incorporated into project objectives, a strategy to catalogue and measure those changes was developed.
Methods
Data capture strategies for this analysis (objective 5) relied on retrospective reviews of existing qualitative data collected routinely at CAB meetings and other meetings (meeting minutes) and through field notes, community leader interviews, and evaluation reports.
A community forum was held in the project's final months to assess how the SSP project influenced community organizations and their capacity to engage in diabetes health-promotion activities. An IPRC investigator led the discussion. Participants included community leaders representing various sectors, including education, health care, parks and recreation, and advocacy organizations. The discussion explored 1) reasons for joining a health-improvement initiative; 2) how the community thinks about health and diabetes; 3) how the community is organized for health promotion; 4) the relationships among organizations within and outside of the community; 5) the necessary skills and resources needed to improve community health; and 6) other issues of concern to the community.
Data from the forum and other sources were reported to the community through the CAB and presentations to other community advocacy organizations. Data about the program and community change were also found in local newspaper articles, television and radio programs, evaluation summaries from walking and nutrition and diabetes education programs, field reports, staff meeting notes, CAB meeting minutes, and focus group reports during program implementation. Although all of these activities were primarily concerned with how the program benefited individual residents, they also provided community-level contextual information that became the database for staff to begin to identify and catalog the reported community changes (beyond the individual level).
To ascertain the availability of information to describe community change, data from the project process and outcome evaluation were examined and categorized according to the corresponding ecological level. Table 1 provides a summary of the changes during the implementation of the SSP project. The table is organized according to selected ecological levels identified by McLeroy and colleagues (13); these levels are identified along the vertical axis: program, organizational, interorganizational, and community. The horizontal axis identifies the change observed, how it was observed, the source of data for the observation, the project objective addressed, and the time frame for the change.
Consequences
This section reports SSP project results and data challenges related to increases in community capacity described at each ecological level.
Program level
For a health promotion program to be successfully established in a community, the program needs to be judged worthwhile, relevant to the intended audience, easily accessible, and reflective of the values and culture of the target audience. SSP program recruitment was built upon the recognition of the LOS as a credible organization within the community. SSP program activities were presented to community members as joint partnership activities with the LOS. To establish the SSP programs, the IPRC, with the approval and encouragement of community leadership, hired a staff member from a lead community-based organization to assist with program implementation and to voice community concerns. This led to increased organizational commitment for SSP and community resident acceptance of program initiatives. The SSP program also located an office at the LOS, creating a capacity previously unavailable in the community: dedicated staff and space within the community for health promotion activities.
As the SSP project progressed, several changes were observed at the program level. For example, walking club participants began to take on leadership roles. The program successfully encouraged participants to continue walking as a group after the initial eight-week walking program officially ended. In addition to becoming walking club leaders, SSP program participants have been active in promoting the SSP project, including joining the CAB.
Similarly, incorporation of community residents into the media campaign added to credibility and recognition of the project while creating successful role models for change. Community members volunteered to participate in the media campaign, telling of their own struggles to learn and maintain healthy behaviors and sharing their knowledge with other community residents in need of positive reinforcement.
In addition, program participants requested information on health topics not previously covered and suggested new or revised strategies, such as using existing English as a Second Language classes to deliver diabetes-related health education. This unique program configuration is an example of enhancing the ecological validity of an intervention: the program becomes part of the existing setting, rather than artificially imposed or interjected into a community (17,18). Because SSP programs are now integrated in familiar and trusted community structures, it is assumed they are more visible, accessible, and acceptable to the target population.
Perhaps the ultimate measure of enhanced program capacity is full institutionalization of a program within the target community. Community residents have become program leaders, replacing SSP staff to lead exercise programs. The transition from program participant to program leadership is an important change in community capacity and a necessary step to program institutionalization. Indigenous community leadership for program maintenance was not observed during the first three years of the project but became evident in the project's fourth year.
Organizational level
Enhancing community organizational capacity is consistent with community leadership desires to play a lead role in health enhancement programs and with resident preferences for hearing messages from trusted community sources that speak to their experiences. Enhancing community organizational capacity creates change that reflects both an integration of critical categorical health issues into community organizations and the assumption of a leadership role in health for the primary community partner, the LOS. Project researchers believed that focusing on this community organization was the most effective way to enhance health capacity and sustainability.
Significant changes have occurred within the LOS as a result of engaging with the SSP project, including new emphasis on community health. The LOS has identified health as one of four major program areas, as reflected in its revised mission statement, and has created a health subcommittee to move forward its health agenda. LOS leadership and staff have participated in formal and informal training opportunities that have enhanced organizational capacity, and the LOS is now a focal point in the community for health promotion activities and information.
In addition to guiding the SSP project, the CAB continues to be an active forum for community health concerns. An ultimate goal for the SSP project will be to establish the CAB as an ongoing entity within the LOS, independent of SSP activities and funding.
Engagement with SSP has also fostered new health capacity within other community organizations. Local primary schools, a locus for SSP nutrition, activity, and educational programs, have increased awareness of student health needs and the relationship of good health to academic performance. Local school leadership is now a consistent and valued voice on the CAB, and schools participate in more health-promoting activities such as Walk Our Children to School Day, encouraging healthy eating, providing health education materials, and promoting community physical activity programs.
To a lesser extent, the local faith community, park district, and libraries have become more aware of health issues and the community's desire to address them. This increased knowledge has resulted in greater acceptance of health concerns as important to community residents and more willingness to collaborate on programs and activities.
Modification of the structure within each of the organizations identified above is important to helping sustain programs that support desired behavioral changes in individuals and families. The changes described became evident in years 3 to 5 of the SSP project, suggesting that community organizations required significant time to prepare, deliberate, and commit to engage in formal or informal health promotion activities.
Interorganizational level
At the time the CAB was formed, the lead agency, the LOS, was part of a larger umbrella organization that focused on family and youth issues in the community. The SSP project focused on the LOS because the project's target audience was the rapidly growing Latino population in southwestern Chicago. The maturing of the LOS into a separate, nonprofit organizational entity gave the SSP project the necessary focus and organizational commitment required to move forward (16). At the interorganizational level, the SSP project has played a major role in developing significantly enhanced community capacity.
SSP activities to raise community awareness of the epidemic of diabetes and its differential impact on Latinos have resulted in 1) new relationships between the LOS and other community organizations, who are now collaborating on an increasing number of health-related issues and activities, including a community health fair; 2) new and expanded roles among existing partners, such as schools, libraries, and local businesses, which are now engaged in the dissemination of health-promotion information to community residents; 3) new linkages and access to resources previously unavailable or untapped, such as asthma education programs (asthma was identified in focus groups as a critical community concern), links to local health care provider organizations, and access to media outlets; and 4) creation of new networks of health-related projects and programs in the community. Referral patterns and resource networks have been expanded as organizations involved in SSP learn of each other's programs and attempt to coordinate and expand these resources through a community health coalition.
The SSP project has also resulted in changing perceptions about the community and its organizations. Through the community's involvement with SSP, health care providers and organizations, nongovernmental organizations, media outlets, and governmental organizations became aware of the community's interest in improving their health and identified the LOS as an agency with the knowledge, commitment, and resources to improve community health.
The SSP project provided the community a locus for its health-related concerns and the impetus and credibility to engage policy makers. LOS leadership has been recruited by nonprofit health organizations, local health care providers, and representatives of community organizations to participate in collaborative activities such as community forums and health education programs. The LOS has cultivated relationships among members of the news media, thus establishing access to news outlets and increasing opportunities for disseminating information. Community representatives and SSP staff members have been invited to join several local diabetes and Latino health-related coalitions. Our findings on the importance of community networks, coalitions, and linkages among resources in enhancing the effectiveness of health promotion initiatives are consistent with the literature (19,20). The interorganizational connections fostered through the SSP community–university partnership provide the community with a network of contacts to continue health improvement initiatives beyond the limited project period.
Community level
For the SSP project, community-level changes reflect assessment and infrastructure development activities, including the development of the CAB. Changes at the community level were observed during the first year of the project. The community began to experience change by identifying health issues and recognizing diabetes as a concern. Prior to SSP, awareness of health as a community issue rather than an individual issue was not evident in community structures, nor did focus groups or key-leader surveys identify an organized response to any categorical health threats.
"Stages of change" is a concept that can be useful in describing a community's readiness to engage in health promotion (21). It parallels individual stages of change for health behavior as presented by Prochaska et al (22). Determining a community's stage of change can provide direction on strategies to guide and mobilize communities and advance through the next stages. In addressing community readiness issues, it is important to recognize that many community-based organizations address health within the context of social and economic conditions (e.g., immigration rights, housing, public safety) that affect the overall health of residents (23).
During its first year, the SSP community was at the "no awareness" stage regarding diabetes. By working with the community, SSP raised awareness of diabetes as a community issue. As the project matured, the community moved from the contemplation phase, or awareness of diabetes as a critical health issue, to the action phase when collaborative efforts to address health issues are evident, even if only at a beginning stage. The action phase is reflected in the development of a strategic plan by the CAB and the engagement of local political leadership to address diabetes as an important health concern. The development of a strategic plan did not occur until the fourth and fifth years of the project, suggesting that the community had to become knowledgeable about diabetes to effectively address concerns with local political leaders, including the city public health commissioner. All changes observed at the community level represent the initial steps toward building community capacity, and they are evident throughout Table 1.
Interpretation
This paper describes a case study of observed changes in community capacity as a result of engagement in a community-based research program targeting lifestyle interventions for the prevention and control of diabetes in a Latino population. We attempt to address two issues: the effects of the partnership on the community as a whole, beyond the individual level, and how post hoc evaluation methods can capture aspects of change in community capacity.
A seminal paper by Goodman et al (24) outlines several dimensions of community capacity particularly relevant to our observations, including participation, leadership development, skills in intervention design and media advocacy, interorganizational networks and resource sharing, consensus building about values related to health, and organizational self-scrutiny related to strengths and limitations of health initiatives.
Although the importance of community capacity and its relationship to successful health initiatives has been established in the public health literature, measurement strategies and evaluation resources may not be available to many collaborative projects. Our evaluation of community capacity building, although not planned at the beginning of the SSP project, reveals modifications at the community level congruent with Goodman's dimensions. We therefore suggest strategies for measuring change in community capacity using existing data, when resources are limited, or when project planning precludes the use of formative evaluation strategies to capture change beyond individual behavior, knowledge, and skills. Finally, the paper has attempted to organize the data so that the sequential nature of capacity-generating activities is evident and so that timelines are clarified. This information can be useful in planning the next phase of interventions or in developing new interventions with different populations.
Although future project evaluation will need to confirm this, we believe that the momentum developed in the SSP project during the initial phase will be crucial to the project's success in the future as it evolves under new funding initiatives. Knowledge of initial community capacity change can be measured against future changes and can be used to ask focused questions about conditions within and external to the project (e.g., demographic and economic changes) that enabled or constrained the community in its ability to take action on issues critical to the health of its residents.
Ideally, the best practice in evaluating collaborative community-level interventions is to incorporate an evaluation plan at the beginning of the project that recognizes that community-level changes are distinct from individual-level changes and that collaborative processes are distinct from "intervention technologies," or activities designed to change knowledge and skills of individual participants (e.g., walking clubs). In many evaluation reports, it is difficult to distinguish between these separate components and outcomes, in part because they are not well defined or incorporated into logic models. A more specific conceptualization would allow for more focused questions and hypotheses so that researchers can share lessons learned in their attempts to capture change and relate change to program outcomes, both proximal and distal.
Research reports often lack details of collaborative efforts. Also underreported is information on broad community impacts and the consequences of how interventions were implemented. Trickett (8) underscores the importance of sufficient detail if we are to advance the science of collaborative, community-based research. We are challenged to document and "make what actually happens a heuristic for theory." Qualitative methods provide important tools to describe the process of change and the nature of change and to include perceptions and definitions of change from the vantage point of the community.
Ethnography particularly lends itself to participatory methods in that community members can be trained to work with researchers to document processes and issues during a project's evolution (25). Community member involvement is an intervention in and of itself, and the impact on the community workers needs to be documented (26).
In the next phase of scientific inquiry, hypotheses will need to be created to test proposed or alternative methods to enhance a community's ability to adopt and maintain healthier lifestyles. These methods can complement the quantitative strategies; each community intervention will have unique features that cannot be predetermined, controlled for (as in a randomized design strategy), or captured with a set of quantitative measures.
Another critical issue is a commitment of resources that adequately support collaborative work and that capture the processes and outcomes associated with it. Community-based collaborative research has a dual purpose of 1) empowerment and community development, also referred to as capacity building, and 2) the prevention and control of disease and improvement of health status (27-29). Labonte and Laverick (30) describe community capacity building in terms of its utility for more efficient delivery of interventions versus the "strengthened community action" that ensues as a desirable end in its own right. The inherent conflicts in this dual approach in addition to its methodological challenges and community benefits are becoming more apparent as evaluation strategies become more sophisticated in capturing total efforts (inputs and processes) and outcomes.
Ultimately, evaluation resources must be committed to long-term follow-up to capture these more distal effects. With these challenges in mind, we conclude with a proposed set of questions that may be useful in thinking through evaluation strategies at the beginning of a project:
What does the community desire for the future health of its residents and how has this changed over time?
What are the characteristics of community roles and relationships in this project? How do these characteristics change over time as the project is implemented (e.g., intensity, duration)?
How has community involvement (e.g., individual participants, organizations and coalitions, media) with the project changed over time and what accounts for this change?
What are the effects of collaboration on the "intervention technologies" that were introduced to this project?
What are the apparent effects of collaboration on community organizations, organization structures and mission, programs, coalitions, leadership, skills, and readiness to engage in health promotion efforts?
What community structures and resources were most useful in achieving project objectives for individual level changes?
What unanticipated community changes occurred (both desirable and undesirable) during the project and what evidence is there that they are attributable to the project?
What are community perceptions of the project and its accomplishments and how have these perceptions changed over time as the project was implemented?
What aspects of community change appear to be most important for sustainability of the project and its constituent activities?
And finally:
To what extent can community-level changes in capacity be transferred from one categorical health issue to future, emerging health issues?
The authors acknowledge the Centers for Disease Control and Prevention and its National Center for Chronic Disease Prevention and Health Promotion, specifically the Prevention Research Centers Office and the Division of Diabetes Translation for project funding (U48/CCU509661). We express our gratitude toward and appreciation for all the community residents who participated in the SSP and community leadership, including Camille Odeh of the Southwest Youth Collaborative, Hector Rico of the Latino Organization of the Southwest, and all the members of our community advisory board.
Figures and Tables
Table Ecological-Level Community Changes Observed During the SSP Project Implementationa
Ecological Level Nature of Change How Observed Sources of Data Project Objective Time Frame
Program SSP project hires staff member from lead CBO Acceptance of position Appointment papers e Year 1
SSP has joint offices at CBO and university SSP staff have dual roles and presence at university and CBO; informational telephone number established at community organization, publicized in community Staff schedule e Year 2-5
Existing CBO programs (ESL) are used to deliver health information Focus groups; community forum Focus groups report; forum report a, e, b Year 4
Community members take expanded roles in programs/activities (e.g., CAB, walking clubs) Community participants volunteer to lead/participate in activities Field notes, staff meeting minutes a, e, b Year 4
Organizational Lead CBO establishes a health subcommittee Included in their mission statement Mission statement a, e Year 3
Schools increase awareness of health concerns Organizations seek additional health expertise; establish and participate in Walk Our Children to School Day; school offers additional physical activity schedule for students Field reports; program evaluation data a, b, e Year 3-5
Religious institutions collaborate and host programs Organizations participate in additional health programs Field reports; program evaluation data a, b, e Year 3-5
Park district hosts yearly health education event Attendee registration Registration forms; newspaper articles a-e Year 3-5
Libraries support distribution of health information at their agency Project ideas are discussed and developed with library Log of health education materials a, b, e Year 1
School establish an onsite resource center for parents Project ideas are discussed and developed with schools Field reports; program evaluation data a, b, e Year 4-5
Inter-
organizational Director of CBO becomes consultant with School of Public Health Prevention Program Personal communication with CBO Director of CBO e Year 5
Project leadership shifts from a community partnership to single agency (Latino-serving CBO) Strong support and commitment for project from Latino-serving CBO Letters of support; collaboration on various activities; meeting minutes a, c, e Year 2
CBO develops health media links Articles published in press; PSAs; radio spots; and live remotes Local television, magazine, newspaper articles a, c, e Year 2-5
CBO joins national/regional/local health organizations Appointments to committees, work groups, coalitions Meeting minutes (staff, CAB) a, e Year 1-5
CBO develops new/enhanced relationships with local providers Meetings with local providers; development of resource directory CAB meeting minutes; community resource guide d Year 1-5
CAB membership represents business, health, education, religious, NGO sectors Participants discuss formation of local health coalition; community forum CAB meeting minutes/focus group notes; forum report e Year 2-5
Community Identified priority health concerns with the community Focus groups, community leader interviews, and survey Focus group reports, community leader interview reports, survey report a, b Year 1-2
Increase political involvement in health issues Representative from Alderman’s office on board; meetings with public health commissioner, local political leaders; community forum Observation; forum report a, e Year 3-4
Establish health community advisory board (CAB) Monthly meetings, agendas CAB meeting minutes a, e Year 1-5
Residents use local health clubs, park district, CBOs for physical activity Focus group Focus group report; field notes b Year 3-5
Developed strategic plan for CAB Monthly meetings, agendas CAB meeting minutes a, e Year 4-5
a CBO indicates community-based organization and here refers to the lead community partner (Latino-serving) organization; ESL indicates English as a second language; CAB indicates community advisory board; NGO indicates nongovernmental organization; PSAs indicate public service announcements. Program objectives are indicated by the following:Increase family and community awareness of the burden of diabetes and mitigating factors.
Enhance behaviors that prevent diabetes onset or reduce diabetes complications.
Improve the self-efficacy/self-management skills of diagnosed diabetics.
Enhance the quality of care delivered to diagnosed diabetics and the opportunities to identify individuals at risk for diabetes.
Develop the capacity of the community to address diabetes care and other self-identified health concerns.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Kelley MA, Baldyga W, Barajas F, Rodriguez-Sanchez M. Capturing change in a community–university partnership: the ¡Sí Se Puede! project. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0093.htm
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Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_04_0134
Community Case Study
PEER REVIEWED Blood Pressure Sunday: Introducing Genomics to the Community Through Family History
Duquette Debra MS Michigan Department of Community Health
PO Box 30195, 3423 North Martin Luther King Jr Blvd, Lansing, MI 48909 [email protected]
517-335-8286
Theisen Velma MSN Michigan Department of Community Health, Lansing, Mich
Beene-Harris Rosalyn MPH Michigan Department of Community Health, Lansing, Mich
Bach Janice MS Michigan Department of Community Health, Lansing, Mich
Kardia Sharon PhD University of Michigan–Center for Genomics and Public Health, Department of Epidemiology, Ann Arbor, Mich
Wang Catharine PhD University of Michigan–Center for Genomics and Public Health, Department of Health Behavior and Health Education, Ann Arbor, Mich
4 2005
15 3 2005
2 2 A232005
Background
Family history of a chronic disease, such as high blood pressure, is an important predictor of future disease. The integration of genomics information into public health activities offers the opportunity to help raise awareness among populations at high risk for high blood pressure.
Context
The prevalence of high blood pressure in blacks at any age is about twice that of whites. Detroit is second among major U.S. cities in the percentage of residents who are black (81.6%). According to data from the Behavioral Risk Factor Surveillance System 1998–2002, the perceived health status of Detroit respondents was one of the worst in Michigan; 17.4% of Detroit respondents reported no health care coverage; 69.6% reported being obese or overweight; and 33.1% reported no physical activity.
Methods
The Michigan Department of Community Health and the University of Michigan's Center for Genomics and Public Health collaborated on a pilot program to develop a worksheet emphasizing the importance of personal family history of high blood pressure. The handout was distributed to individuals at primarily black, Detroit-area churches during an annual screening event for high blood pressure and stroke.
Consequences
Approximately 500 handouts were distributed; a collaborative effort was achieved; genomics information was integrated into an existing program; the ability to reach churches in a predominantly black community was demonstrated; consumers reported interest in the subject matter; and an appropriate literacy level for the handout was attained.
Interpretation
The strengths of this pilot program and suggested modifications may serve to guide others in genomics and/or chronic disease programs in future endeavors.
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Background
"It is not a question of if, but when and how the advances resulting from the Human Genome Project will be integrated into society, medicine and public health" (1). To integrate genomics into state public health activities, the Coordinating Center for Health Promotion within the Centers for Disease Control and Prevention (CDC) awarded Michigan and three other states a five-year cooperative agreement. The Michigan Department of Community Health's (MDCH's) genomics program is multidisciplinary, relying on internal partnerships with multiple chronic disease programs, including cardiovascular health. MDCH also relies on academic partnerships with the University of Michigan–Center for Genomics and Public Health (UM–CGPH) and two other academic centers. The MDCH Cardiovascular Health and Genomics Programs and UM–CGPH collaborated to translate genomic information about the importance of family history with the goal of raising awareness in a community at high risk for high blood pressure.
High blood pressure
High blood pressure is a common and serious public health problem that affects about 30% of the U.S. adult population (2). High blood pressure, especially if untreated, can result in mortality and morbidity because of the complications of end-stage renal disease, coronary artery disease, and/or stroke. Risk factors for high blood pressure include:
a family history of high blood pressure
African American ancestry
age 65 years or older
low socioeconomic status
overweight or obesity
a sedentary lifestyle
excess intake of dietary sodium and/or insufficient intake of potassium
excess consumption of alcohol (3,4).
Although high blood pressure is serious, the majority of individuals diagnosed do not have their blood pressure controlled (3). Many factors contribute to poor control of blood pressure, such as lack of knowledge of the potential consequences, noncompliance, cost of managing a lifelong condition, and complexity of disease management. Prevention and control of a chronic disease begins with a good understanding of individual risk.
High blood pressure can be prevented by a complementary application of strategies that targets the general population and individuals at high risk. Current national guidelines recommend nonpharmacologic therapy, including lifestyle modifications, for primary prevention and treatment of high blood pressure (5).
Populations at risk for high blood pressure: blacks and the Detroit community
The prevalence of high blood pressure varies among populations. The most systematic comparison of ethnic groups has been between black and white individuals with hypertension (6). The prevalence of high blood pressure in blacks at any age is about twice that of whites (3,6). Blacks have been reported as having an earlier onset and greater frequency of stroke and renal failure, but a lower risk of coronary artery disease than whites (6). Sodium intake appears to be especially important in the etiology of high blood pressure for blacks (6). Genetic factors may influence the response to antihypertensive drugs; blacks are known to respond better to treatment with diuretics than with beta-blockers (6).
The Michigan Behavioral Risk Factor Surveillance System (BRFSS), combining 1997, 1999, and 2001 results, found that the proportion of blacks in Detroit who had ever been told by a health professional that they had high blood pressure was 34.9%, and the proportion of whites was 21.4% (7). The reported proportion of blacks with high blood pressure in Detroit is higher than that reported in Michigan adults (27.1%) and in the national median (25.6%) (7).
Awareness of family history
A family health history reflects the outcome of numerous influences, including genetics, ethnicity, culture, and environment. The family health history holds important clues to current and future health risks for almost all chronic diseases, including high blood pressure. According to Yoon et al, "Evidence suggests that family history is useful for predicting disease when there are multiple family members affected, relationship among relatives is close and disease has an earlier onset than expected" (8).
An annual national mail survey, HealthStyles, recently included questions about the awareness of family history as a risk factor for disease. (HealthStyles is a proprietary database product of social marketing and public relations firm Porter Novelli, licensed by the CDC for audience analysis in health communication planning.) The survey indicated that 96.3% of respondents (n = 4345) considered knowledge of family history important, but only 29.8% were actively collecting the information (9). National efforts have begun to promote the collection and use of family history (9).
It has recently been stated that "certain subgroups of the population might benefit from targeted programs to raise awareness about the collection and recording of family health histories" (9). Information regarding family history of chronic diseases is also inexpensive to incorporate into already existing programs to increase awareness, educate, and encourage healthy behaviors and preventive measures.
Genetics of high blood pressure
Family history has been recognized as a significant risk factor for high blood pressure since the 1930s and confirmed in numerous subsequent observational studies (6). However, the specific inheritance pattern of high blood pressure remains unknown. Most observations support multifactorial inheritance, with blood pressure having a continuous distribution influenced by multiple genes and environmental factors. As expected with multifactorial inheritance, correlation of blood pressure is seen with increasing biologic relatedness (6). Based on family studies, a history of high blood pressure in a first-degree relative increases the likelihood by about twofold that an individual will have high blood pressure (6).
In many families, high blood pressure is most likely a polygenic condition, meaning that multiple genes contribute to the development of high blood pressure. In other families, high blood pressure may be caused by a single gene that strongly influences blood pressure. Susceptibility genes have been localized, and candidate genes include those encoding angiotensinogen, angiotensin receptor-1, the beta-3 subunit of guanine nucleotide-binding protein, and tumor necrosis factor receptor-2 (10,11). In fact, routine DNA-based testing to predict common diseases may not occur for years (8). Yoon et al state, "In the meantime, family medical history represents a 'genomic tool' that can capture genetic susceptibility, shared environment and common behaviors in relation to disease risk" (12).
Family history is a tool that incorporates the genetic risk of an individual and can easily be used in public health practice. Thus, the MDCH and the UM–CGPH collaborated to develop a handout that emphasizes the importance of a personal family history of high blood pressure. The target audience was black adults in Detroit. The goal of the pilot project was to enable consumers to collect individual family history information and then to use this information to identify personal risks and possible health measures to prevent high blood pressure.
Context
The city of Detroit is located in Wayne County, which is in southeast Michigan. It is the tenth largest city in the United States by population and is second among major U.S. cities in the percentage of blacks (81.6%) (Figure) (13). The median age among blacks living in Detroit is 40.4 years (14). Residents of Detroit are primarily from a low-wage workforce. As of 2000, Detroit ranked 88th in household income out of the 100 largest U.S. cities (13).
Figure Black population of Detroit, Mich. Reproduced with permission from Wayne State University, Center for Urban Studies.
This map shows the counties of Detroit to have a predominantly black population.
According to 1998–2002 BRFSS data, the perceived health status of Detroit respondents was one of the worst in Michigan (14). Among southeast Michigan respondents, Detroit had the highest proportion (17.4%) of respondents who reported no health care coverage (14). Additionally, 69.6% of Detroit respondents reported being obese or overweight, and 33.1% reported no physical activity in their leisure time, which was the highest in Michigan (14). In contrast, Detroit had one of the lowest rates of heavy alcohol use (3.9%) in southeast Michigan (14). As reflected in this BRFSS data, behavioral and environmental risk factors in the Detroit community may contribute to the larger proportion of individuals with hypertension (Table).
To test the feasibility of introducing the importance of family history and high blood pressure in a black population, we collaborated with an established project that offers stroke and blood pressure screening and education to faith-based groups in the Detroit area. The event, Blood Pressure Sunday, is a collaborative effort with MDCH and the American Heart Association, Greater Midwest Affiliate, and has been offered every May for five years as part of National Stroke Awareness and High Blood Pressure Month. During this month-long program, churches offer educational material, educational sessions, and screening after church services.
Participating churches identify a contact person, typically a parish nurse or health coordinator. Each contact person receives a workbook, material to distribute during the program, blood pressure measurement equipment, and one-day training by MDCH Cardiovascular Program staff. In the past, there have been up to 90 churches participating, 1907 individuals screened, and 270 individuals trained during the month-long event. The population reached is more than 75% black. A key component of the program is the emphasis on standardized, accurate blood pressure screening.
Based on the demographics of Detroit, Blood Pressure Sunday appeared to be an ideal program for targeted distribution of a general consumer awareness handout about family history of high blood pressure. The distribution at these churches also had the advantage of using an established, respected support system.
Methods
Download the Family History and High Blood Pressure handout (PDF–95K)
MDCH and UM-CGPH collaborated to develop the content of the Family History and High Blood Pressure handout. The content was initially developed by the UM-CGPH staff and later refined by MDCH staff over a six-month period. A worksheet for individuals to identify their own personal family history is a key part of the material. Readability levels using Flesch–Kincaid were conducted, and the material was judged to be at an approximate eighth-grade level. Drafts of the handout were circulated to ten MDCH staff members to test understanding of the directions and provide general feedback on clarity. After modifications, the material was printed in a colorful handout.
The introductory page of the handout includes three questions on knowledge of, attitude toward, and preventive actions for a family history of high blood pressure. The handout includes a worksheet that encourages collection of the family history of high blood pressure, heart disease, and stroke, including age of onset and age and cause of death. The consumer is encouraged to start with first-degree relatives (parents and siblings) and then to extend to second-degree relatives (grandparents and aunts/uncles). This approach of adding affected relatives and age of onset provides a simple overview for risk assessment, which can be used by families and their health care providers. The worksheet also emphasizes the importance of sharing the collected information with medical providers and family and discussing screening and healthy lifestyles with offspring. The last page highlights that high blood pressure is a chronic condition requiring evaluation and treatment.
The proposal for this project was presented to the Blood Pressure Sunday planning group. The planning group includes four parish nurses, two American Heart Association staff members, two professional volunteers, and one MDCH cardiovascular health nurse consultant. Members agreed that the material could be introduced to parish nurses and other health coordinators at the one-day training session. During this training, a 15-minute overview of the Family History and High Blood Pressure pilot was given, and a sample of the handout was distributed to all participants. At the conclusion of the training, 12 churches in Detroit agreed to pilot the Family History and High Blood Pressure handout. A follow-up contact was made to review the pilot and estimate the number of handouts for each church. Family History and High Blood Pressure handouts were sent to each church.
Consequences
Approximately 500 copies of the Family History and High Blood Pressure handout were distributed. The pilot was discussed at a follow-up meeting with the parish nurses; more than half of the participating parish nurses were in attendance. Written feedback was obtained prior to the follow-up meeting. In addition, there was a discussion about successes and challenges in the pilot. Overall, the participating parish nurses expressed similar experiences and concerns about the utility of information collected. The possibility that the consumer may not remember to share the family history information at his or her next medical appointment was discussed. Also, the important question of whether this information would be a motivator to change behavior was raised. The parish nurses thought that consumers understood the material, comprehended its importance, and took interest in the topic. Some consumers reportedly commented to the nurses that they had never thought about how high blood pressure in their family related to their own health.
Overall, this pilot had several strengths: handouts were distributed to a population at high risk for high blood pressure; a collaborative effort was achieved between an academic center and state health department chronic disease and genomics programs; genomics information was successfully integrated into an existing program; the ability to reach churches in a predominantly black community was demonstrated; the consumers reported interest in the subject matter; the difficult task of attaining an appropriate literacy level of the material was advanced; and a worksheet format for a family history tool was developed. The worksheet format is simple, inexpensive, and easily integrated into other chronic disease materials.
Limitations of this pilot include a lack of field testing for consumers' opinion about the readability of the handout. We would like to have evaluated the audience's understanding of the information and whether the proper messages were conveyed. Also, while the MDCH Cardiovascular Health and Genomics Programs received indirect feedback about the tool from the church nurses, the programs and the nurses did not systematically collect information from the consumers on the usefulness of the handout and the worksheet.
Interpretation
The MDCH Genomics Work Group and two additional CDC-funded state genomics programs reviewed this pilot project. Possible suggestions for the future include changing the handout in the following ways:
Modifying the format (e.g., using bullet points to reduce verbiage, separating the educational component from the family history tool for clarity and utility);
Revising current content (e.g., simplifying instructions for family history worksheet, further lowering the literacy level); and
Adding more information (e.g., additional chronic diseases, incorporating culturally appropriate messages and images).
The development and use of a general consumer awareness handout is an iterative process. Therefore, the next steps for the program include further enhancements of the handout, field testing of the handout and worksheet, and collaboration with other possible channels of dissemination such as physicians and local health departments.
This pilot of a general consumer awareness handout on family history and high blood pressure demonstrated the challenges of developing material on this complicated topic. One suggestion is to implement this kind of activity at a place where participants can read the material, complete a feedback form, and return for discussion. Possible settings for this scenario include waiting rooms at physicians' offices prior to routine physicals or waiting rooms at local health departments prior to health screenings and/or immunizations. This context would provide a direct and immediate communication with a medical provider to highlight the family history and prevention messages. Future plans include distributing this handout at a science museum in Detroit, targeting a younger audience in a nonreligious setting.
The circumstances of this pilot limited the ability to collect feedback information on behavior and attitude change. It was hoped that this type of feedback would be gained from the parish nurses. Because of time constraints, however, collection of feedback was not feasible. Future initiatives should include collection of follow-up information. For instance, a focus group with participants could strengthen the content of the handout and provide feedback on methods to collect follow-up information.
In summary, this pilot project was a worthwhile activity that will be adapted for use in other settings. Additionally, the collaborative process of developing and distributing the handout is an example of how to integrate genomics into existing resources within chronic disease programs in other state health departments.
This project was financially supported in part by a CDC cooperative agreement, project U58/CCU522826, from the Chronic Disease Prevention and Health Promotion Programs, Component 7, Genomics and Chronic Disease Prevention, Program Announcement 03022, and by a Center for Genomics and Public Health cooperative agreement, project S1957-21/23, from the Association of Schools of Public Health and the CDC.
We thank the American Heart Association, Greater Midwest Affiliate, Toni Price, and the parish nurses who participated in Blood Pressure Sunday. We also thank Rebecca Malouin, Michelle Cook, Mark Caulder, Ann Annis, Laurie DeDecker, and Valerie Ewald for their support, resources, and assistance with this manuscript. Also, we appreciate the feedback from Jenny Johnson and the Utah Department of Health's Chronic Disease Genomics Program and Heart Disease and Stroke program, and Kris Peterson-Oehlke from the Minnesota Department of Health. We also thank the City of Detroit, participating churches, and most importantly, the church members.
Figures and Tables
Table Possible Risk Factors for High Blood Pressure in Detroita
Suspected Higher Risk Groups for High Blood Pressurea Detroit Populationb
Family history of high blood pressure Unknown
African American ancestry Majority African American
Over 65 years Not relevantc
Lower socioeconomic status One of lowest household incomes in large U.S. cities
Overweight or obese High proportion of obese or overweight respondents
Sedentary lifestyle Highest proportion of no physical activity in leisure time
Excess intake of dietary sodium and/or insufficient intake of potassium Unknown
Excess alcohol consumption Not relevantc
a Source: National High Blood Pressure Education Program (3).
b Sources: Detroit in focus: a profile from Census 2000 (13) and Bureau of Epidemiology, Michigan Department of Community Health (7,14).
c Michigan BRFSS data do not appear to support this risk factor for the Detroit population.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Theisen V, Duquette D, Kardia S, Wang C, Beene-Harris R, Bach J. Blood Pressure Sunday: introducing genomics to the community through family history. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0134.htm
==== Refs
1 French ME Moore JB 2003 Washington (DC) Partnership for Prevention Harnessing genetics to prevent disease & promote health, a state policy guide
2 National Center for Health Statistics Fast stats A to Z, hypertension [Internet] Hyattsville (MD) Centers for Disease Control and Prevention cited 2004 Nov 19 [updated 2004 Oct 27]
3 National High Blood Pressure Education Program Bethesda (MD) The National Institutes of Health; National Heart, Lung, and Blood Institute cited 2004 Nov 19 2004 8 The seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC 7) [monograph on the Internet]
4 Whelton PK He J Appel LJ Cutler JA Havas S Kotchen TA 288 15 2002 1882 1888 JAMA Primary prevention of hypertension: clinical and public health advisory from The National High Blood Pressure Education Program 12377087
5 McGuire HL Svetkey LP Harsha DW Elmer PJ Appel LJ Ard JD 6 7 2004 383 390 J Clin Hypertens Comprehensive lifestyle modification and blood pressure control: a review of the PREMIER trial
6 Burke W Motulsky AG King RA Rotter JI Motulsky AG New York Oxford University Press 1992 170 191 The genetic basis of common diseases Hypertension
7 Bureau of Epidemiology, Michigan Department of Community Health (MDCH) Health indicators and risk estimates by community health assessment regions: Michigan Behavioral Risk Factor Survey 1997-2001 cited 2004 Nov 19 Lansing (MI) MDCH 2003 6
8 Yoon PW Scheuner MT Peterson-Oehlke KL Gwinn M Faucett A Khoury MJ 4 4 2002 304 310 Genet Med Can family history be used as a tool for public health and preventive medicine? 12172397
9 Centers for Disease Control and Prevention 53 44 2014 1044 1047 MMWR Morb Mortal Wkly Rep Awareness of family health history as a risk factor for disease—United States, 2004
10 Online Mendelian Inheritance in Man (OMIM) [homepage on the Internet] Baltimore (MD) Johns Hopkins University cited 2004 Nov 19 MIM Number: 145500 updated 2004 June 17
11 Hopkins PN Hunt SC 5 6 2003 413 429 Genet Med Genetics of hypertension 14614392
12 Yoon P Scheuner M Gwinn M Bedrosian S Ottmann D Khoury MJ 2003 39 45 Genomics and population health: United States 2003 The family history public health initiative Atlanta (GA) Centers for Disease Control and Prevention
13 The Brookings Institution Detroit in focus: a profile from Census 2000 [Internet] Washington (DC) The Brookings Institution 2003 11 cited 2004 Nov 19 Available from: http://www.brookings.edu/es/urban/livingcities/Detroit.htm
14 Bureau of Epidemiology, Michigan Department of Community Health (MDCH) 4 2004 MDCH Lansing (MI) cited 2004 Nov 19 Health indicators and risk estimates by community health assessment geographic area & local health departments, Michigan Behavioral Risk Factor Survey 1998-2002
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Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_04_0132
Community Case Study
PEER REVIEWEDUtah’s Family High Risk Program: Bridging the Gap Between Genomics and Public Health
Johnson Jenny CHES Utah Department of Health Chronic Disease Genomics Program
PO Box 142106, Salt Lake City, UT, 84114-2106 [email protected]
801-538-9416
Giles Rebecca T MPH, CHES Utah Department of Health, Salt Lake City, Utah
Larsen LaDene RN, BSN Utah Department of Health, Salt Lake City, Utah
Ware Joan MSPH, RN Utah Department of Health, Salt Lake City, Utah
Adams Ted PhD, MSPH University of Utah, Cardiovascular Genetics Research Program, Salt Lake City, Utah
Hunt Steven C PhD University of Utah, Cardiovascular Genetics Research Program, Salt Lake City, Utah
4 2005
15 3 2005
2 2 A242005
Background
Family history is a simple yet powerful genomic tool that can identify individuals and entire populations at risk for diseases such as heart disease, cancer, and diabetes. Despite its use for predicting disease, family history has traditionally been underused in the public health setting.
Context
A program for identifying families at risk for a variety of chronic diseases was implemented in Utah. Utah has population characteristics that are unique among the United States. Although the land area is large, most residents live within a relatively small geographic area. The religion of 70% of the residents encourages the recording of detailed family histories, and many families have access to records dating back to the 1800s.
Methods
From 1983 through 1999, the Utah Department of Health, local health departments, school districts, the University of Utah, and the Baylor College of Medicine implemented and conducted the Family High Risk Program, which identified families at risk for chronic diseases using the Health Family Tree Questionnaire in Utah high schools.
Consequences
The collection of family history is a cost-effective method for identifying and intervening with high-risk populations. More than 80% of eligible families consented to fully participate in the program. A total of 80,611 usable trees were collected. Of the 151,188 Utah families who participated, 8546 families identified as high-risk for disease(s) were offered follow-up interventions.
Interpretation
The program was revolutionary in design and demonstrated that family history can bridge the gap between genetic advances and public health practice.
==== Body
Background
With the arrival of the genomics era, we are faced with the challenge of how to apply genetic knowledge to public health practice (1). A challenge of this magnitude also presents a great opportunity to more effectively target health promotion activities to individuals and families at highest risk. Family history holds promise as one of the keys to unlock this opportunity because it captures genetic and environmental components of diseases, including shared cultural and behavioral risks (1,2). However, despite the fact that family history plays a significant role in many chronic diseases of public health concern such as heart disease, asthma, cancer, and diabetes (3), it has traditionally been underused in the public health setting (2,3). Few examples of public health organizations that have used family history as a long-term, cost-effective tool for identifying and intervening with high-risk populations are documented in the current literature.
From 1983 through 1999, the Utah Department of Health (UDOH) partnered with local health departments, school districts, Baylor College of Medicine, and the University of Utah School of Medicine Cardiovascular Genetics Research Clinic (UCVG) to develop and implement the Family High Risk Program (FHRP). The FHRP used the Health Family Tree Questionnaire (HFT) to identify families at increased risk of developing major adult-onset diseases that could be prevented, delayed, or treated effectively with early interventions.
Context
The late Roger R. Williams, MD, former director of UCVG and founder of Make Early Diagnosis to Prevent Early Death (MED PED) (4), was instrumental in developing the FHRP. Williams' research on familial trends in coronary-prone pedigrees showed that approximately 14% of the Utah population contributed to 72% of the state's total early coronary deaths (5). In light of these findings and other epidemiological studies (R. Williams, University of Utah, unpublished data, 1982), Williams joined efforts in 1980 with investigators at the Baylor College of Medicine to further develop the HFT as a tool to accurately collect and analyze familial disease tendencies (6).
There has been some criticism of the FHRP because it was implemented in a unique population compared with the total U.S. population. Although Utah has a large land area, the majority of the state's 2,351,467 (7) citizens live along the Wasatch Front, a stretch of land 90 miles long and 20 miles wide. In 2003, approximately 32% of Utah's population was aged 18 years and older (7), reflective of the increase in public school enrollment since the 1980s. The religious background of 70% (7) of Utah's citizens encourages the recording of detailed family histories, and access to genealogical records dating back to the 1800s is available for many Utahns in the Family History Library of the Church of Jesus Christ of Latter-day Saints (8). Family pedigrees in Utah are typically larger than in other states, and many families reside in the same area for multiple generations. Utahns have a favorable relationship with public health agencies and the state's major universities, which has enabled numerous population studies. Finally, researchers also have access to a variety of records from the Utah Population Database that aid in developing these studies (8).
Despite concerns that such unique characteristics would affect the program's ability to identify and intervene with high-risk populations in other states, data from Texas students showed similar results when compared with data from Utah students (5). These data warrant further exploration for using family history to bridge genetic advances and public health practice on a national scale.
Methods
The original version of the HFT was developed to enhance risk-reduction messages in health education courses (9,10). Baylor investigators used the HFT in Texas from 1980 to 1986 with 6578 families from four multiethnic cities within the Waco Independent School District (5,6). The tool was used in the Waco Family Health Program, which was designed to increase students' knowledge of the risks and prevention of cardiovascular disease and to promote behavior changes. However, little testing was done on the validity of the HFT because of the original intent to use it as an educational tool. From 1982 to 1985, Williams received funding from the Thrasher Research Fund to further develop and assess the HFT in Utah high schools (5,6).
Partnerships among public health, community, and research-based entities played an important and unique role in the FHRP. Previous working relationships between Williams and the UDOH provided the infrastructure needed for program implementation. Key individuals from high schools, school districts, local health departments, hospitals, medical associations, and nonprofit agencies (e.g., American Cancer Society) were recruited to disseminate the FHRP throughout Utah. Voluntary training sessions were conducted with participating teachers and local health department personnel prior to program implementation. During the sessions, teachers received curriculum materials, optical scanner forms, and HFTs for their students at no cost to themselves or the school districts. Training sessions were also available for health care providers working with high-risk families. Continuing medical education was available through grand rounds, a self-study course, and a set of videos.
The program was pilot tested in 1983 with more than 1000 students in seven high schools, far exceeding expectations. Revisions to program materials were then made, and full program implementation began in fall 1983. Material development was supported by the U.S. Department of Health and Human Services through the Centers for Disease Control and Prevention (CDC), National Heart, Lung, and Blood Institute (NHLBI), and Utah State general funds. The HFT was designed to collect three generations of family medical history (Figure 1); its large format (two feet by three feet) was designed to fit comfortably on a kitchen or dining room table to encourage family participation. The information included lifestyle factors and certain disease conditions (Figure 2) for siblings, parents, aunts and uncles, and grandparents of students enrolled in required high school health education classes. In 1995, hip fractures, asthma, and Alzheimer's disease were added to the HFT. The condition "other cancers" was removed in 1996.
Figure 1 The Health Family Tree questionnaire collected family medical history from students enrolled in required high school health education courses in Utah from 1983 through 1999. Reprinted with permission from Elsevier (9).
The Health Family Tree questionnaire is described in the text.
Figure 2 Information collected for the Health Family Tree questionnaire included age of disease onset for a number of chronic diseases as well as lifestyle risk factors for each family member of participating students. Reprinted with permission from Elsevier (9).
Individuals participating in the Utah program for identifying family history of chronic disease were asked to fill out a short questionnaire on numerous topics: age, sex, health problems, cigarette smoking, weight, alcohol use, and routine exercise.
Teachers used the HFT as the focus of a four-part curriculum (Table) on the prevention of common chronic diseases (11). A curriculum guide was written and updated periodically by FHRP staff with input from participating teachers. Students were encouraged to complete the HFT assignment whether or not they were a blood relative to their family members, and parents were required to give consent for their student to participate before data collection. Three participation options were available for selection. Option one gave students consent for full participation in the program. This included an evaluation of the HFT, permission for the UDOH, local health department, or UCVG representatives to contact the family, and permission to store names, addresses, and phone numbers in confidential research files at UCVG for further research. Option two allowed for partial participation that included permission for the student to complete the HFT but receive no evaluation, follow-up visits, or further contact. However, data from the HFT were stored anonymously at UCVG. Option three was nonparticipation in the program, and students were given alternate assignments to complete. Nonparticipation had no effect on the student's grade as long as alternate assignments were completed.
After collecting information for the HFT, students transferred the data onto optical scanner forms and completed a demographic survey. This allowed UCVG researchers to efficiently analyze the information and determine the disease risk for each family, or Family History Score (5,6,9,12). Statistical analyses of family risk were calculated separately for each parent's family, excluding adopted relatives, which helped identify high-risk parental pedigrees even if the student was not a blood relative (6). Computer-generated reports summarizing risk of disease(s) and behavior-change recommendations to reduce risk were mailed by UCVG to families who consented to provide contact information. A list of high-risk families was also sent to the UDOH.
Family-based interventions were offered to families identified by the HFTs as high-risk for a particular disease(s). Williams and the UDOH developed nursing protocols and standards of care (Figure 3) to ensure appropriate and consistent care was provided to all high-risk families. The UDOH coordinated with public health nurses at local health departments in the community where the family lived to provide personalized medical assessments, education, and referrals to health care providers during in-home visits. Behavior-change classes (e.g., smoking cessation, cooking classes), free medical screenings (e.g., blood pressure, cholesterol), and educational resources (e.g., handout on "Questions You Might Want to Ask Your Physician About," Family Health Record Book) (11) were also available to high-risk families.
Figure 3 Standards of care for breast cancer used by local health departments and public health nurses during follow-up care of high-risk families. Reproduced with permission from Oncology Nursing Society (11).
Nurses were provided a list of seven objectives for following up with family members identified as having a high risk of breast cancer. Each process objective includes three to seven additional recommendations. The seven objectives include 1) assessing current knowledge base, 2) providing and reviewing a brochure 3) explaining risk factors, 4) discussing behaviors that may alter risk factors, 5) referring family to a physician, 6) reviewing screening procedures, 7) describing recommendations and planning for follow-up.
The in-home visits were highly effective during the early years of the program because interventions assessed the entire family structure, taking into account not only medical history but also social structure, lifestyle behaviors, and family dynamics. In-home visits allowed nurses to provide individualized care for each family as well as emotional support as they developed healthier behaviors. However, as funding and time constraints were placed on the UDOH and local health departments, fewer families received the care that program planners had intended. Changing family dynamics throughout the period of the FHRP, such as fewer two-parent households and more women working outside the home, proved to be difficult barriers and reduced the effectiveness of interventions.
Evaluations on intervention effectiveness were conducted over a period of ten years with a cohort of high- and average-risk families. FHRP staff also conducted periodic satisfaction surveys with teachers, students, public health nurses, and high-risk families throughout the program to guide program activities.
Consequences
The FHRP demonstrated that the collection of family history is a cost-effective method for identifying and intervening with high-risk populations. Strategies for reducing program costs were identified by UCVG early in program development. By designing optical scanner forms for data input, the time and expense required for analysis decreased dramatically. Costs were reduced from $25 per analysis to less than $10 per analysis for students who completed the HFT but did not receive follow-up interventions (5,6,9). Cost for each high-risk family that received interventions was approximately $27 (5). This cost included data processing, report generation and mailing, and in-kind donations from UDOH and local health departments. Costs for both high- and average-risk families compared favorably with other types of behavior-modifying programs at that time.
Although the UDOH terminated the program in 1999 because of a lack of funding, data from HFTs were collected by UCVG until 2002. A total of 80,611 usable trees were collected from students during the 20-year span. More than 80% of eligible families consented to fully participate in the program (option one). Twelve percent of eligible families consented to partially participate (option two), and only 7% refused to participate (option three) (T. Adams, unpublished data, 2004). Families who refused to participate during later years of the program often did so because older children had already completed an HFT and, if their family was at high risk, they had already been offered follow-up care from local health departments. Teacher participation in the FHRP was also high, with approximately 284 teachers from 55 high schools voluntarily participating, many for the entire length of the program.
Of the 151,188 Utah families who participated in the program, 17,064 were identified as high-risk for coronary heart disease and 13,106 were identified as high-risk for stroke (5), many of which might have otherwise remained undiagnosed by both their health care providers and the public health system. The UDOH offered interventions to 8546 high-risk families. During the early years of the FHRP, an average of 90% of high-risk families had some form of follow-up contact; more than 60% of the contacts were in-home visits (J Ware, oral communication, January 2004). Focus groups and telephone surveys conducted with high-risk families showed that the majority of participants were grateful to be told about their disease risk, and 95% of parents felt the project was a valuable learning experience for their child. Families were motivated to make long-term behavior changes simply by knowing their family history, and families showed compliance with targeted health promotion messages. Preliminary review of the 10-year evaluations showed that both high- and average-risk families reported an increase in healthy lifestyle behaviors, such as obtaining yearly medical exams and blood pressure checks, as a direct result of participating in the FHRP. A higher increase in healthy lifestyle behaviors was seen in families that received interventions (Utah Department of Health Chronic Disease Genomics Program, unpublished data, April 2004). Complete analysis of intervention effectiveness is underway and will be published in a subsequent article.
The long-term success of the program has generated worldwide interest. Countries interested in using the program included Canada, France, Germany, Hungary, Japan, Russia, South Africa, and Sweden. FHRP staff received additional contacts from universities and state health departments in California, Florida, Iowa, Maine, Minnesota, New Jersey, North Carolina, Oregon, and Texas. In 1986, the FHRP was recognized as a "distinguished community health promotion program," receiving the U.S. Department of Health and Human Services Secretary of Health's National Award of Excellence. The FHRP was also a semifinalist in 1986, 1988, and 1990 in the Innovations in State and Local Government Awards, an awards program of the Ford Foundation and the John F. Kennedy School of Government at Harvard University.
Interpretation
As we enter the genomics era, family history will become an increasingly important tool for bridging genetics and disease prevention strategies. The FHRP provides a practical example of what geneticists have long known — that family history can be used to predict disease susceptibility in high-risk individuals, and these individuals can benefit from personalized interventions, thus reducing their risk of disease (2,13,14). In a recent article, Guttmacher et al reiterated the importance of applying this "free, well-proven, personalized genomic tool" in preventive medicine (13). We believe the FHRP successfully demonstrates that family history, used as a simple genomic tool, can bridge the gap between genetic knowledge and public health practice and can serve as a reminder of the importance of utilizing family history information in disease management and prevention (13,14). Use of family history has great potential to educate and motivate individuals to comply with preventive health strategies; this potential is suggested in the literature (15) and by preliminary review of FHRP data.
Experience from the FHRP has provided a springboard for activities in public health, including within the CDC Office of Genomics and Disease Prevention (OGDP), to explore the usefulness of genomics in public health (2,15). The OGDP launched an initiative in 2002 to understand how family history can be used in health promotion and disease prevention and has begun to develop a family history tool that can be used within a strategy to integrate genomics into public health practice (1,2). The U.S. Surgeon General launched a National Family History Initiative to encourage the public to use family history in their health care (13). Data and experience from the program have also enabled further medical genealogical research in the MED PED program (4) and NHLBI Family Heart Study (5).
We believe the long-term success of the FHRP demonstrates that family history can enhance traditional health promotion and disease prevention strategies in public health, community, and health care settings. Health professionals must discover the value of genomics (13,14) by exploring the development of programs similar to the FHRP and integrating recommendations from current family history research into their own practice. Perhaps outcomes of these projects will again prove what we have already learned from the FHRP — that family history holds the key for applying genomics to today's public health concerns.
This publication was supported by cooperative agreement U58-CCU822802 from the CDC. Program activities were supported by grants from the Thrasher Research Fund (Health Family Trees in Utah); NHLBI HL-17269-06 (National Research and Demonstration Center in Texas), HL-21088-10 (Cardiovascular Genetics in Utah), HL-00379-05, and HL-24855; CDC Prevention Block Grants; and state general funds. The contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of the Thrasher Research Fund, NHLBI, or the CDC.
We express gratitude to Karen Nellist for providing technical assistance with the editing of this manuscript.
Figures and Tables
Table Learning Objectives of the Family High Risk Program, Health Family Tree Curriculum for High School Health Education Programs in Utah, 1983–1999
Part 1
Heredity and Your Health The student will be able to: Recognize definitions of various chronic diseases.
Explain basic principles of heredity.
Explain the difference between a medical pedigree and standard pedigree or family tree.
Define familial tendency.
Discuss importance of identifying individuals with familial tendencies for disease.
Part 2
Filling Out the Health Family Tree The student will be able to: Complete the “You” section of the Health Family Tree pedigree form in class.
Complete the Health Family Tree pedigree form at home with parental assistance.
Part 3
Healthy Lifestyles The student will be able to: Recognize that families with familial tendencies should have a physician’s supervision to reduce risk.
Recognize that there are controllable risk factors that impact those with familial tendency as well as those without familial tendency.
Describe the healthy lifestyle choices that will enhance the quality of life and decrease risk of chronic diseases.
Part 4
Checking the Computer Scanner Forms The student will be able to: Accurately edit the Health Family Tree data on the computer scanner sheets.
Summarize the number of relatives who died of heart attacks, strokes, cancer, and diabetes, noting the age when they died. Looking at this summary, the students should decide if there could be a familial tendency.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Johnson J, Giles RT, Larsen L, Ware J, Adams T, Hunt SC. Utah’s Family High Risk Program: bridging the gap between genomics and public health. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0132.htm
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1 Khoury MJ 5 4 2003 261 268 Genet Med Genetics and genomics in practice: the continuum from genetic disease to genetic information in health and disease 12865755
2 Yoon PW Scheuner MT Khoury MJ 24 2 2003 128 135 Am J Prev Med Research priorities for evaluating family history in the prevention of common chronic diseases 12568818
3 Yoon PW Sheuner MT Peterson-Oehlke KL Gwinn M Faucett A Khoury MJ 4 4 2002 304 310 Genet Med Can family history be used as a tool for public health and preventive medicine? 12172397
4 University of Utah Make early diagnosis to prevent early death [Internet] Salt Lake City (UT) University of Utah cited 2004 Oct 29 Available from: http://www.medped.org
5 Williams RR Hunt SC Heiss G Province MA Bensen JT Higgins M 2001 87 129 135 Am J Cardiol Usefulness of cardiovascular family history data for population-based preventive medicine and medical research (the Health Family Tree Study and the NHLBI Family Heart Study) 11152826
6 Williams RR Hunt SC Barlow GK Chamberlain RM Weinberg AD Cooper HP 78 10 1988 1283 1286 Am J Public Health Health family trees: a tool for finding and helping young family members of coronary and cancer prone pedigrees in Texas and Utah 3421383
7 Wikimedia Foundation Inc Wikipedia: the free encyclopedia [homepage on the Internet] St. Petersburg (FL) Wikimedia Foundation Inc cited 2004 5 Nov updated 2004 Nov 2 Available from: URL: http://en.wikipedia.org/wiki/Utah
8 Huntsman Cancer Institute Pedigree and population resource: Utah population database [Internet] Salt Lake City (UT) University of Utah cited 2004 Oct 29 Available from: http://www.hci.utah.edu/groups/ppr/
9 Hunt SC Williams RR Barlow GK 39 10 1986 809 821 J Chronic Dis A comparison of positive family history definitions for defining risk of future disease 3760109
10 Chamberlain RM 1987 Bethesda (MD) NHLBI-Kappa Systems Inc 1986 Forum on cardiovascular disease risk factors in minority populations Family risk reduction through the public school
11 Beck S Breckenridge-Potterf S Wallace S Ware J Asay E Giles RT 15 3 1988 301 306 Oncol Nurs Forum The Family High Risk Program: targeted cancer prevention 3375107
12 Williams RR Dadone MM Hunt SC Jorde LB Hopkins PN Smith JB Rao DC Elston RC Kuller LH Feinleib M Carter C Havlik R 1984 419 442 Genetic epidemiology of coronary heart disease: past, present, and future The genetic epidemiology of hypertension: A review of past studies and current results for 948 persons in 48 Utah pedigrees New York Alan R Liss, Inc
13 Guttmacher AE Collins FS Carmona RH 351 22 2004 2333 2336 New Engl J Med The family history – more important than ever 15564550
14 Scheuner MT Wang SJ Raffel LJ Larabell SK Rotter JI 1997 71 315 324 Am J Med Genet Family history: a comprehensive genetic risk assessment method for the chronic conditions of adulthood 9268102
15 Hunt SC Gwinn M Adams TD 24 2 2003 136 142 Am J Prev Med Family history assessment strategies for prevention of cardiovascular disease 12568819
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Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_04_0133
Tools and Techniques
Genomics and Public Health: Development of Web-based Training Tools for Increasing Genomic Awareness
Kardia Sharon LR PhD Michigan Center for Genomics & Public Health, University of Michigan, School of Public Health, Department of Epidemiology
611 Church St, Room 246, Ann Arbor, MI 48104-3028 [email protected]
734-936-0866
Bodzin Jennifer MPH Michigan Center for Genomics & Public Health, University of Michigan, School of Public Health, Ann Arbor, Mich
Goldenberg Aaron MA, MPH Michigan Center for Genomics & Public Health, University of Michigan, School of Public Health, Ann Arbor, Mich
Citrin Toby JD Michigan Center for Genomics & Public Health, University of Michigan, School of Public Health, Ann Arbor, Mich
Raup Sarah F MPH Center for Genomics & Public Health, University of Washington, Seattle, Wash
Bach Janice V MS Michigan Department of Community Health, Epidemiology Services Division, Genomics Program, Lansing, Mich
4 2005
15 3 2005
2 2 A252005
In 2001, the Centers for Disease Control and Prevention funded three Centers for Genomics and Public Health to develop training tools for increasing genomic awareness. Over the past three years, the centers, working together with the Centers for Disease Control and Prevention's Office of Genomics and Disease Prevention, have developed tools to increase awareness of the impact genomics will have on public health practice, to provide a foundation for understanding basic genomic advances, and to translate the relevance of that information to public health practitioners' own work. These training tools serve to communicate genomic advances and their potential for integration into public heath practice. This paper highlights two of these training tools: 1) Genomics for Public Health Practitioners: The Practical Application of Genomics in Public Health Practice, a Web-based introduction to genomics, and 2) Six Weeks to Genomic Awareness, an in-depth training module on public health genomics. This paper focuses on the processes and collaborative efforts by which these live presentations were developed and delivered as Web-based training sessions.
==== Body
Introduction
As genomics research continues to expand and identify potential applications for disease prevention, departments of health as well as public health practitioners will need to become increasingly aware of genomics as a public health tool. In 2001, the Centers for Disease Control and Prevention (CDC) produced a set of competencies to help integrate genomics into public health practice and training (1,2). Genomic competencies were developed for all members of the public health workforce, along with specific competencies for each functional area of public health: administration, clinical, epidemiology, health education, laboratory, and environmental health (3). These competencies are described in detail and are available from www.cdc.gov/genomics/training/competencies/comps.htm (4). Based on these competencies, a public health worker in any program at any level should be able to demonstrate a basic knowledge of the role genomics plays in disease development. This knowledge is essential for public health practitioners to integrate genomics tools into public health practice and to educate the public.
Currently, the public's understanding of genomics is derived primarily from the media, and the scientific information is often incomplete and/or inaccurate (5,6). As acknowledged in the 2002 Institute of Medicine report, Who Will Keep the Public Healthy?, "because few in the current public health workforce have the level of understanding of genomics that is required today, major continuing educational efforts must be undertaken to ready practicing public health professionals to use genomics effectively" (7).
During the past few years, several genomics training tools have been developed to help public health practitioners increase their awareness of the impact of genomics on public health practice, such as genetic testing for adult cancer, identifying genetically at-risk subgroups susceptible to environmental exposures, or developing new genomic technologies. These training tools aim to provide a foundation for understanding basic genomics (e.g., DNA mutations, inheritance patterns). They also help practitioners identify and translate the relevance of genomics to their own work (e.g., using family history as a genomic–environmental indicator of a person's own risk of chronic diseases). Traditionally, genomics training tools have included books, CD-ROMs, lectures, workshops, and presentations at conferences or meetings. The Genomics Toolkit is a good example of a recently developed guide designed to deliver an inventory of effective genomic tools for public health practitioners to use in program technical assistance (2,8). With improved technologies now available for developing high-tech presentations — such as synching audio, video, and PowerPoint — using the Internet to provide genomics training sessions is a logical, convenient way to increase the genomic literacy of public health professionals.
Two Web-based genomics training tools have been developed to provide genomics education to large numbers of public health practitioners nationwide. Genomics for Public Health Practitioners: The Practical Application of Genomics in Public Health Practice serves as an introduction, while Six Weeks to Genomic Awareness is a more in-depth series of six training modules. Both training tools described in this article resulted from the collaborative efforts of individuals from the CDC's Office of Genomics and Disease Prevention (OGDP), the Michigan Center for Genomics and Public Health (MCGPH), the North Carolina Center for Genomics and Public Health (NCCGPH), and the University of Washington Center for Genomics and Public Health (UWCGPH).
Genomics for Public Health Practitioners: The Practical Application of Genomics in Public Health Practice
Genomics for Public Health Practitioners: The Practical Application of Genomics in Public Health Practice is a Web-based introductory presentation aimed at increasing awareness of the impact of genomics on public health and familiarizing practitioners with some basic concepts of public health genomics. Topic areas covered in this training tool include definitions (e.g., genetics, genomics, the human genome project) and applications of public health genomics (e.g., surveillance, policy, education); potential interventions based upon genomic information (e.g., modification of screening recommendations, exposures to environmental factors); challenges facing public health genomics (e.g., rapid commercialization of genetic tests, equal access to interventions); and a description of how one public health program incorporated genomics (e.g., Utah Health Family Tree Study, CDC Family History Public Health Initiative). Individuals who designed this tool developed content that would be appropriate for public health professionals with no genomics background or exposure. Conference calls, e-mails, and a Web board for posting files were used to communicate ideas among the many individuals and locations. After content was agreed upon, the module was animated and narrated for use as a Web-based presentation. An informal piloting and evaluation stage, during which public health practitioners offered feedback, provided insight on changes for the final product. Following approval by the CDC, the final presentation was launched on the Web sites of OGDP and each Center for Genomics and Public Health on August 19, 2004.
Six Weeks to Genomic Awareness
Creating the Six Weeks to Genomic Awareness series was consistent with one of the main goals of the Centers for Genomics and Public Health — to develop and provide genomics training for the current and future public health workforce. An existing collaborative relationship between the Michigan Department of Community Health (MDCH) and the MCGPH afforded the capacity for developing this kind of training. The Genomics Work Group, a committee established to integrate genetics into chronic disease programs at MDCH, and whose members include representatives from the Bureau of Health Promotion and Disease Control (e.g., cancer, cardiovascular, and diabetes programs), the Bureau of Laboratories, and the Bureau of Epidemiology (including genomics and newborn screening), requested help from the MCGPH in developing a genomics training for MDCH staff. These public health professionals agreed that all program staff within their departments would benefit from introductory information on the impact of genomics on a variety of public health issues. Working with the MCGPH, the state genomics coordinator helped to identify the topic areas most relevant for state health agency employees.
During winter 2002, the plan for Six Weeks to Genomic Awareness began to take form. First, partners at both the MCGPH and the MDCH identified topics that would allow public health practitioners to begin integrating genomics into the functional areas of public health (administration, clinical, epidemiology, health education, laboratory, and environmental health). Twelve initial topics were identified along with detailed learning objectives, terms, and concepts, additional resources (such as Web sites), and interactive exercises. After discussions with MDCH partners about desired format and length, Six Weeks to Genomic Awareness was created as a six-module course to be presented onsite at MDCH in 90-minute sessions during the lunch hour through May and June 2003. Three members of the MCGPH team, two professors at the University of Michigan School of Public Health, two genetic counselors from the University of Michigan, and the state genomics coordinator at MDCH developed and presented the seminar series.
The series was designed to provide an understanding about the role of genomics throughout all public health fields, not just those programs traditionally associated with genetics (e.g., newborn screening, maternal and child health). Additionally, the course aimed to dispel myths about genetic determinism and motivate health professionals to consider the ethical, legal, and social implications of applying genomics within the public health context. The Genomic Competencies for the Public Health Workforce outlined by the CDC (4) were used as a backbone for session development. Modules were designed to reflect the major themes most relevant for public health — molecular genetics, genes in populations and gene–disease associations, genetic testing, gene–environment interactions, ethical, legal, and social implications, and state and national resources (Table 1).
Six Weeks to Genomic Awareness proved to be a successful approach to educating Michigan's public health workforce at the state level. In all, 70 program staff attended at least one session and 32 attended three or more. Attendees represented a variety of bureaus (e.g., Bureau of Laboratories), divisions (e.g., Epidemiology Services Division), and programs (e.g., Cancer, Vital Records). Following each session, evaluations were collected to gather feedback on the content, format, and effectiveness of the presentations based on a five-point rating scale (1 = poor, 5 = excellent). On average, participants rated the sessions as "very good" in terms of overall impression, usefulness of content, effectiveness of presentation, and quality of visual aids and materials. Participants also commented on the most useful part of each session and what might have been confusing or least useful and suggested additional topics to be covered in future genomics training sessions. This information was used to improve and enhance each presentation as part of the process for converting the series into the Web-based training course described below.
Given the success of Six Weeks to Genomic Awareness in providing genomics training to Michigan's public health workforce, MCGPH decided to convert the sessions into an online format for dissemination to public health practitioners nationwide. As work began on converting Six Weeks to Genomic Awareness into an online course, the Association of State and Territorial Chronic Disease Program Directors (CDD) approached the MCGPH with a request for genomics training for its members. Recognizing an ideal opportunity to improve and disseminate Six Weeks to Genomic Awareness to a broader audience, the CDD provided the funding necessary for ensuring the conversion of the series into a Web-based format.
Presentations given during the MDCH sessions were used as the basis for the online distance-learning modules; however, slides needed to be updated to reflect changes in the science and to include improved graphics and animations. In addition, the 90-minute format originally used was not appropriate for viewing on the Web, and the content was repackaged into 20–30-minute modules more suitable for the Web. While the evaluations collected from the MDCH onsite sessions had provided important data on the needs of public health practitioners, wider evaluation data were needed to develop the online sessions. Individuals at the CDD, CDC, NCCGPH, and UWCGPH provided expert and practical review and comment during the development of the online course.
During June and July 2004, each session was filmed and edited, and a Web template was created to synchronize the video and slide presentations. Marketing tools, such as e-mail postcards, were developed and distributed to CDD and CDC staff to announce the launch of Six Weeks to Genomic Awareness on July 19, 2004. A lobby page (available from www.genomicawareness.org) was developed to house all of the Six Weeks to Genomic Awareness training modules and to provide an avenue for individuals with questions, comments, or problems accessing the module to contact the Michigan Center for Genomics and Public Health.
The kick-off to the Web-based version of Six Weeks to Genomic Awareness culminated on August 19, 2004, in a one-hour live Webcast that brought together a panel consisting of Six Weeks to Genomic Awareness presenters, Toby Citrin, JD, and Sharon Kardia, PhD, in addition to Jean Chabut, BSN, MPH, the Chief Administrative Officer at the Michigan Department of Community Health. Public health professionals from around the country called in or submitted questions in advance for the panel to answer during the Webcast.
Between the time the modules became available online on July 19, 2004, and the end of October 2004, almost 3000 unique visitors (as determined by an individual computer's unique IP address) had visited the Six Weeks to Genomic Awareness Web site and had streamed more than 62,000 megabytes of content. (To put this in perspective, a data transfer of 1000 megabytes is equivalent to approximately 15 hours of viewing.)
Six Weeks to Genomic Awareness is also a cost-effective approach to providing genomics education. Total costs for developing the online course and producing the live Webcast were roughly $35,000. This figure does not include payment for instructor time, which was contributed. With almost 3000 individuals accessing Six Weeks to Genomic Awareness in just the three months tracked so far, the per-person cost of providing this genomics training is about $12. We fully expect the number of viewers to increase over time and the per-person cost of this training to decrease.
A voluntary evaluation also accompanied each of the Six Weeks to Genomic Awareness presentations. An analysis of evaluations (n = 41) from the first module, "Introduction to Genomics: The Human Genome" indicated that the majority of individuals viewing the session found it to be "very good" or "excellent" in terms of overall presentation (88%), usefulness of content (76%), appropriateness of Web-based format (88%), and relevance to their area of work (77%). Participants who filled out evaluation forms represented 20 states nationwide and one international location. The majority of the participants (63%) had more than 10 years of experience in their field of work, and 30% worked in public health practice, 22% in public health research, and 24% in health care provision. Other individuals worked in a range of fields from basic science, policy and legislation, nursing, geographic information systems, education, and counseling.
Additional evaluation questions asked participants to measure how knowledgeable they felt, how much new information they had learned, how interested they were in learning more, and how confident they felt in applying the information after viewing the presentation, using a 10-point scale (1 = not at all, 10 = very). Responses suggested that individuals completing the first module felt relatively more knowledgeable about the human genome than before (mean ± SD = 6.73 ± 2.07) and had learned new information about the human genome (mean ± SD = 7.48 ± 2.12). Individuals also demonstrated a great interest in learning more about the human genome (mean ± SD = 9.05 ± 1.30); however, they did not feel as confident that they would be able to apply the information to their area of work (mean ± SD = 7.20 ± 2.42). At this time, the evaluation results from the other modules continue to be compiled and analyzed.
Collaborative Efforts
Both Genomics for Public Health Practitioners: The Practical Application of Genomics in Public Health Practice and Six Weeks to Genomic Awareness demonstrate the importance of collaboration in developing genomics training tools. Effective and appropriate education of professionals on public health genomics requires the expertise and resources of many individuals and organizations working together at all stages of development, evaluation, implementation, and dissemination.
Collaboration is vital because public health professionals nationwide are at different levels of understanding and different levels of integrating genomics into public health. Because public health genomics is a new and emerging field, it is important to encompass the needs of a wide range of audiences. Practitioners should be involved in developing training methods and materials to ensure that the final product will meet the needs of public health professionals and will relate to their work, both in terms of issues covered and language used to teach genomic concepts. Academic institutions need to share genomic research and knowledge and to provide insight into successful teaching methods, as well as to help place science in a public health context and to focus on important ethical, legal, and social issues. Technical experts, who know how to produce a distance-learning course in terms of software, equipment, and technical support, are also needed to develop valuable Web-based training. When the organizations giving and the organizations receiving the training work together, the needs of all audiences can be met more successfully.
Future Projects
The successes of Genomics for Public Health Practitioners: The Practical Application of Genomics in Public Health Practice and Six Weeks to Genomic Awareness have motivated new projects to expand genomics training in specific disease areas. For example, at the UWCGPH, a presentation on the genetics of obesity has been developed and disseminated via the Web. Genetics of Obesity was offered as a live presentation at the Chronic Disease Directors Diet, Nutrition, and Physical Activity teleconference on October 14, 2004, and converted into an online audio-assisted presentation. An accompanying brochure supplements the information found in the presentation and directs public health practitioners to additional articles and Web resources. In addition, the MCGPH, the MDCH, and the Michigan Cancer Genetics Alliance are collaborating to develop a series of modules on cancer genomics for public health professionals in the MDCH Cancer Section. This process began with a needs assessment (developed with help from the NCCGPH) to target specific areas of interest. Genomics for Public Health Practitioners: The Practical Application of Genomics in Public Health Practice, Six Weeks to Genomic Awareness, and the cancer and obesity training sessions describe a three-tiered approach to providing genomics training: introductory, in-depth, and disease-specific. By using this approach, public health practitioners have the opportunity to understand basic concepts before being exposed to more complex topics.
Both the University of Michigan School of Nursing and School of Dentistry have used Genomics for Public Health Practitioners: The Practical Application of Genomics in Public Health Practice and Six Weeks to Genomic Awareness. Faculty from programs outside the University of Michigan have also indicated interest in linking students to the Six Weeks to Genomic Awareness as part of their genetic epidemiology education. One clear advantage of Web-based learning is its wide capacity for distribution; it extends easily to new and larger audiences. There appears to be a great need for genomics information by other health professionals and for training future health professionals.
Conclusion
An introductory module, Genomics for Public Health Practitioners: The Practical Application of Genomics in Public Health Practice, and a more extensive training course, Six Weeks to Genomic Awareness, have been developed to provide quality genomics training to public health practitioners using two Web-based formats. Genomics for Public Health Practitioners: The Practical Application of Genomics in Public Health Practice uses computer-animated graphics with voiceover, whereas Six Weeks to Genomic Awareness employs live instructors in a lecture format that is taped specifically for use on the Internet. Both tools can be used to increase awareness of the impact genomics has on public health practice, to provide a foundation for understanding genomic advances, and to help translate the relevance of this information to public health practitioners' own work. These training tools demonstrate the importance of a collaborative approach, with organizations willing to share resources and expertise throughout project development and implementation. The success of both trainings also demonstrates the effectiveness of using the Web as a tool for disseminating genomics education. By developing Genomics for Public Health Practitioners: The Practical Application of Genomics in Public Health Practice as a Web-based introduction and converting Six Weeks to Genomic Awareness into an online format, more than 3000 public health professionals have been able to access genomics training via the Web. Both trainings can be accessed through links from multiple sources, including the Web sites for each of the Centers for Genomics and Public Health and the CDC's OGDP. Genomics for Public Health Practitioners is available from www.cdc.gov/genomics/training/GPHP/default.htm. Six Weeks to Genomic Awareness is available from www.genomicawareness.org.
The development of Genomics for Public Health Practitioners: The Practical Application of Genomics in Public Health Practice and Six Weeks to Genomic Awareness was funded through several cooperative agreements with the Centers for Disease Control and Prevention's Office of Genomics and Disease Prevention and the Association of Schools of Public Health (S1946-21/23, S1957-21/23, S1958-21/23). The conversion of Six Weeks to Genomic Awareness into an online distance-learning course was also supported by funding from the Association of State and Territorial Chronic Disease Program Directors.
The authors acknowledge and thank the following: Jean C. Chabut, BSN, MPH, and the public health practitioners at the Michigan Department of Community Health for their input and participation in the development of Six Weeks to Genomic Awareness; Pam Clouser McCann, MS, CGC, for her work in helping to develop the Six Weeks to Genomic Awareness concept and the module on genomics resources and for presenting the resources model for both the MDCH sessions and online training; Catharine Wang, PhD, for reviewing this article; the staff of the University of Michigan School of Public Health, Public Health Genetics Program for contributing their time to developing and reviewing slide presentations for Six Weeks to Genomic Awareness; LaDene Larsen, RN, and the professionals of the Association of State and Territorial Chronic Disease Program Directors; Michael Glaza and the staff at Level Four Consulting; and Melanie Myers, PhD, at the Office of Genomics and Disease Prevention. Finally, the authors acknowledge and thank the leadership and staff of all three Centers for Genomics and Public Health for their collaborative efforts in developing both of these genomics training tools.
Figures and Tables
Table Six Weeks to Genomic Awareness Session Goals
Session 1: Introduction to Genomics: The Human Genome
The goals of this session are to provide an overview of basic molecular genetics necessary for an understanding of a genetic approach to public health and to describe how diseases are inherited at the molecular level and transmitted through families. Topics include cell structure, function, and replication; the relationship between protein, DNA, RNA, genes, and chromosomes; effects of mutations on gene and protein expression and health; Mendel’s Laws; inheritance patterns; and pedigrees.
Session 2: Genes in Populations
The goal of this session is to introduce concepts in population genetics and genetic epidemiology and to learn how to apply this knowledge to analysis of the scientific literature and popular media. Topics include population genetics; the Hardy–Weinberg Principle; methodologies to examine the genetic basis of disease; and human genome epidemiology.
Session 3: Genetic Testing
The goal of this session is to provide an overview of genetic testing terms, methods, and issues, including population screening. Topics include laboratory methods; test descriptions; testing limitations and complexities; informed consent process; federal and state oversight; current population-based screening programs; and nonmedical uses of genetic testing technologies.
Session 4: Gene–Environment Interactions
The goal of this session is to demonstrate how a person’s genetic makeup interacts with environmental factors and what this interaction means for public health. Topics include examples of how gene–environment interactions result in disease development and the benefits and implications from research.
Session 5: Ethical, Legal, and Social Issues
The goal of this session is to introduce the ethical, legal, and social issues arising from the application of genetic technologies in medicine and public health. Topics include ethical issues; how racial/ethnic factors affect the use, understanding, and interpretation of genetic information; policies/legislation related to genetic privacy and discrimination; gene patents; and genetic determinism.
Session 6: An Overview of State and National Resources
The goals of this session are to provide an overview of the genetic health care delivery system, including state-specific examples; to provide an overview of the role of state and national agencies/organizations in genomics research and its application to public health practice; and to demonstrate how state and national entities might serve as resources for public health professionals seeking to maintain up-to-date knowledge about genomics and public health. Topics include types and changes to the delivery of genetic services; the genetic health care delivery system; and federal and state genomics resources.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Bodzin J, Kardia SLR, Goldenberg A, Raup SF, Bach JV, Citrin T. Genomics and public health: development of Web-based training tools for increasing genomic awareness. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0133.htm
==== Refs
1 The purpose [Internet] Atlanta (GA) Centers for Disease Control and Prevention, Genomic Competencies for the Public Health Workforce cited 2004 Nov 10 [updated 2004 Aug 14]
2 Centers for Disease Control and Prevention 2004 81 94 Atlanta (GA) Centers for Disease Control and Prevention, Office of Genomics and Disease Prevention Genomics and population health: United States 2003
3 Public health workforce development: developing genomic competencies in public health [Internet] Atlanta (GA) Centers for Disease Control and Prevention, Office of Genomics and Disease Prevention cited 2004 Nov 10 [updated 2004 Aug 14]
4 Genomics workforce competencies 2001 [Internet] Atlanta (GA) Centers for Disease Control and Prevention, Office of Genomics and Disease Prevention [updated 2004 Aug 14 updated 2004 Aug 14 cited 2004 Nov 1
5 Pellechia Marianne G 1997 6 49 68 Public Understand Sci Trends in science coverage: a content analysis of three U.S. newspapers
6 Kua E Reder M Grossel MJ 2004 13 309 322 Science in the news: a study of reporting genomics Public Understand Sci
7 Institute of Medicine 2003 72 The National Academies Press Washington (DC) Who will keep the public healthy?
8 Genomics: a guide for public health [homepage on the Internet] Washington (DC) Association of State and Territorial Health Officials cited 2004 Nov 10 Available from: URL: http://www.genomicstoolkit.org/
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Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_04_0139
Tools and Techniques
Prevention Research Centers: Contributions to Updating the Public Health Workforce Through Training
Franks Adele L MD Michigan Center for Genomics & Public Health, University of Michigan, School of Public Health, Department of Epidemiology
388 Middle St, Amherst, MA 01002 [email protected]
413-587-0025
Brownson Ross C PhD Prevention Research Center, Saint Louis University School of Public Health, St Louis, Mo
Baker Elizabeth A PhD, MPH Prevention Research Center, Saint Louis University School of Public Health, St Louis, Mo
Leet Terry L MPH Prevention Research Center, Saint Louis University School of Public Health, St Louis, Mo
O'Neall Margret A PhD Prevention Research Center, Saint Louis University School of Public Health, St Louis, Mo
Bryant Carol PhD Prevention Research Center, University of South Florida, Tampa, Fla
McCormack Brown Kelli PhD Prevention Research Center, University of South Florida, Tampa, Fla
Hooker Steven P PhD Prevention Research Center, University of South Carolina, Columbia, SC
Shepard Dennis M MAT Prevention Research Center, University of South Carolina, Leesburg, SC
Russell R. Pate PhD Department of Exercise Science, University of South Carolina, Columbia, SC
Gillespie Kathleen N PhD Department of Health Management and Policy, Saint Louis University School of Public Health, St Louis, Mo
Simoes Eduardo J MD, MPH Prevention Research Centers Program, Centers for Disease Control and Prevention, Atlanta, Ga
4 2005
15 3 2005
2 2 A262005
Because public health is a continually evolving field, it is essential to provide ample training opportunities for public health professionals. As a natural outgrowth of the Centers for Disease Control and Prevention's Prevention Research Centers Program, training courses of many types have been developed for public health practitioners working in the field. This article describes three of the Prevention Research Center training program offerings: Evidence-Based Public Health, Physical Activity and Public Health for Practitioners, and Social Marketing. These courses illustrate the commitment of the Prevention Research Centers Program to helping create a better trained public health workforce, thereby enhancing the likelihood of improving public health.
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Introduction
As exemplified by the genomics focus of this issue of Preventing Chronic Disease, public health is a continually evolving field. With an estimated 450,000 people working in salaried public health positions (1), many of whom received their training long ago, ensuring that members of this workforce receive the continuing education necessary to keep their competencies current is a daunting task. As public health incorporates new knowledge, changing methods, and technologic advances, regular training opportunities for public health professionals will become an ever greater necessity (2). While conferences such as the 19th National Conference on Chronic Disease Prevention and Control, the featured abstracts of which are included in this issue, serve to acquaint participants with current matters, skill development usually requires more intensive training that includes active engagement. Therefore, making professional development opportunities and training courses more available to public health practitioners is essential (2).
In the 20 years since its inception, the Prevention Research Centers (PRC) program of the Centers for Disease Control and Prevention (CDC) has been offering training opportunities to public health practitioners in the United States and abroad. The PRCs are based on collaborations among schools of public health or medicine, communities, and public health agencies. The purpose of the collaborations is to design, test, and disseminate health promotion strategies that are effective in real-world settings. The PRCs' training programs have been a natural outgrowth of these collaborations, helping the PRCs carry out their mission to disseminate effective interventions. By training professionals and laypeople who work in the field, the PRCs help ensure that effective new strategies can be put in action for the benefit of communities. Because the prevention researchers themselves focus on applied community research, the training programs reflect their understanding of community needs, further increasing the value of the courses to public health professionals.
In keeping with the broad scope of the PRC program and the wide range of interests among the 28 centers, the PRCs provide many types of training (available from www.cdc.gov/prc/training/index.htm). For this issue of Preventing Chronic Disease, we highlight three training programs to illustrate their relevance to public health practitioners. The first, Evidence-based Public Health, provides a set of concepts and tools to help public health practitioners focus their scarce resources on efforts that stand the best chance of success, regardless of the health problem being addressed. The second, Physical Activity and Public Health for Practitioners, addresses the crucial public health problem of insufficient physical activity and teaches skills that public health practitioners can use to effectively mount physical activity promotion programs. The third, Social Marketing, provides training in a methodology that is insufficiently used in public health, despite the fact that it can increase the chance of success for nearly any health promotion effort.
Evidence-Based Public Health
Public health practice is too often governed by short-term demands, management of crises, or long established habits. Practitioners' lack of sufficient training in more systematic approaches to priority setting and program selection serves to perpetuate a reactive style of public health practice. Recognizing the importance of cultivating a more systematic approach to the practice of public health, the Saint Louis University (SLU) PRC has developed a training course to increase the capacity of public health practitioners to find and use existing information and assessment tools in their daily work and to practice evidence-based public health. Evidence-based public health is a process that engages key stakeholders in "the development, implementation, and evaluation of effective programs and policies in public health through application of principles of scientific reasoning, including systematic uses of data and information systems and appropriate use of behavior science theory and program planning models" (3).
The Evidence-Based Public Health (EBPH) course was first developed in 1997 by the SLU School of Public Health and the Missouri Department of Health and Senior Services. It was later expanded in collaboration with the CDC, the Chronic Disease Directors, as well as the World Health Organization (WHO). In its current configuration, the EBPH course focuses on developing specific skills to improve public health practice. It introduces the basic concepts of evidence-based decision making and addresses how to use strategic planning processes and develop a concise statement of the issue under consideration, how to quantify the issue in accordance with basic principles of epidemiology and apply these principles to the available data, how to search the scientific literature and available databases to systematically review the evidence, how to assess the evidence and prioritize among options, how to develop a program action plan, and how to evaluate programs and policies after they are implemented.
The course is structured to encourage active engagement of students in practice exercises and case studies as well as in examination of ways the curriculum can be applied to the jobs they will be resuming after the training. The course is taught by multidisciplinary faculty that includes experts in epidemiology, behavioral science/health education, and economic evaluation. The course makes extensive use of online databases such as the Missouri Information for Community Assessment Web site (available from www.health.state.mo.us/MICA/nojava.html*), an interactive system that allows users to create a table of data from various data files including births, deaths, and hospital discharges.
Since 1997, this 2.5- to 4.5-day course has been offered 20 times and has reached more than 250 employees of local and state health departments in Missouri, approximately 145 public health practitioners from 36 other states, 80 practitioners in the Russian Federation, and 60 practitioners from Europe. Most of the participants have had no graduate training in public health. The course has been further adapted to meet the local needs and priorities of four states and three countries, and it has also been translated into Russian and Spanish. (For more information, including plans for future courses, contact Ross Brownson at [email protected].)
The course was recorded in 2002, and a set of 16 CDs has been produced, with exercises and case studies for self-study. These materials are available to people who cannot travel to a course location or prefer the self-study format (available from http://prc.slu.edu). In addition, Evidence-Based Public Health, a book published in 2003, now serves to augment the course for both students and teachers (4).
Course evaluations completed by course participants from 2001 to 2004 have shown very high levels of satisfaction with the course (8.5–10 on a 10-point scale) and with the instructors (8–10 on a 10-point scale). Nearly all participants (94%) have said that they expect to use their new skills in their daily work. Plans for the future include expanded formal follow-up of students to evaluate the impact of the course. Several universities and state health departments have initiated similar training courses for their personnel, indicating a recognized need for such courses.
Physical Activity and Public Health for Practitioners
To halt the joint epidemics of obesity and diabetes in the United States, it is imperative to increase physical activity levels among people of all ages (5). Unfortunately, too few public health practitioners have the skills necessary to identify, implement, and evaluate evidence-based physical activity programs (6). To address these gaps in public health expertise, the University of South Carolina's Prevention Research Center developed an intensive six-day training course that has been offered annually since 1996.
The objectives of the course (the Physical Activity and Public Health [PAPH] Practitioner's Course on Community Interventions) are to give course participants the ability to 1) use public health and scientific information to identify and prioritize community-based physical activity interventions; 2) develop and implement community-based partnerships; 3) develop and implement both evidence-based individual behavioral interventions and policy/environmental interventions to promote physical activity; and 4) evaluate interventions to increase physical activity at the local level.
Each year, 25 participants are selected to take the course on the basis of their professional credentials, experience, and potential to enhance public health practice. Since the course began, 228 professionals representing 40 states and seven foreign countries have completed the training. These participants were affiliated with 21 colleges/universities; 31 state departments of health; and numerous other organizations such as hospitals, local health departments, nonprofit organizations, private foundations, research institutes, and federal government agencies. Faculty for the course consists of experts from the University of South Carolina's Arnold School of Public Health, the CDC, and other universities and nongovernmental organizations around the world.
During the 2001 Physical Activity and Public Health (PAPH) course, the rural estuarial town of Bluffton, SC, hosted a team project. The town was preparing to undergo rapid development, and the team saw an opportunity to expand the park system in a manner that could produce a continuous greenway from the historic downtown to the newly developing areas, thus encouraging foot and bicycle traffic as a means of transportation. The team's recommendations to the town included purchasing a privately held parcel of land that was home to an historic oyster-shucking factory. The team presented its recommendations to the community at a well-attended meeting in the local town hall. At that meeting, community groups interested in bicycle and walking trails interacted for the first time with the mayor and other town officials who made a public commitment to hold subsequent joint meetings. These meetings resulted in the formation of the Greater Bluffton Pathways Coalition, which soon became an active group with 150 members.
Primed to purchase the land, the town diligently pursued public funds available from the Beaufort County Open Land Trust. When the land became available for sale, the municipal leaders were ready to secure its purchase. Furthermore, the town was able to arrange for the historic oyster factory to remain in operation on the public land as an historical institution. Bluffton has come to trust the PAPH students as a valuable resource, providing knowledge and an objective perspective, and it continues to serve as an eager host community for the team exercises. The effort of Bluffton residents to increase green space and safe venues for physical activity and to maintain the historic character of their town continues along with the development process.
The program is designed to teach participants how to develop a logic model for evidence-based efforts to improve health through physical activity; how to select, plan and implement physical activity promotion programs in communities; how to measure outcomes; the importance of carefully planning and conducting a program evaluation; and the resources they will need to stay current with the latest developments in the field. The course uses multiple formats including didactic lectures with ample question and answer time, consultation sessions with experts, and a team field project. Course participants also have numerous opportunities for informal networking and individual tutorial sessions.
Local and state health departments and community organizations regularly enter into partnerships with the course organizers to provide opportunities for students to practice what they have learned in the classroom. These partners provide students with important information and insights into the features of the communities that serve as the settings for field projects. Results and recommendations from the field projects are in turn provided to community partners for potential implementation. Community members report having taken several actions on the basis of these recommendations, leading to environmental policy changes in the community (Sidebar).
To date, the course has been evaluated through participants' anonymous responses to a written questionnaire at the close of the course, as well as through exit interviews. Feedback from these efforts has led to a continuous process of course improvement.
The most recent postcourse evaluation results available (2003) showed a very high level of satisfaction among course participants and unanimous agreement that the course will influence their future professional activities. In open-ended questioning, respondents mentioned the opportunities for interaction with faculty and for networking with other participants as especially useful.
Longer-term evaluation based on how participants have subsequently used the training is currently under way. Meanwhile, follow-up surveys conducted by telephone and by mail to elicit feedback from participants in the first four years of the course (1996–1999) have yielded important information. Of the 63 respondents to the mailed survey, 65% reported an increase in their involvement in physical activity interventions after the course. Nearly all respondents (97%) said that they had applied information from the course in their daily work, 74% said that they had changed the way they plan and implement physical activity programs, and 75% said that they had increased their leadership role in physical activity promotion.
The success of this training program is perhaps best exemplified by its use as a model for developing other training programs such as the Nutrition and Public Health course sponsored by the CDC's Division of Nutrition and Physical Activity. The WHO Collaborating Center sponsored a similar course in Brazil last year in which 57 professionals were trained by an expert faculty from Brazil and around the world. Another course is planned for Colombia in 2005. In addition, WHO regularly sends participants to the United States course in hopes of improving their competencies and spawning interest around the world for physical activity promotion. Information about the course and application materials are available from www.prevention.sph.sc.edu/seapines/index.htm.
Social Marketing
Social marketing involves the use of marketing principles in designing and implementing programs to promote socially beneficial behavior change (7-9). At its most basic level, it is a set of methods to increase understanding of a target audience so that practitioners can develop interventions most likely to resonate well with, and elicit the desired behavior change in, the intended audience. Its use as a tool in public health has been growing, although many in the field have incomplete understanding of its techniques, its proper use, or its potential benefits. To help public health professionals to acquire the knowledge, skill, motivation, and experience necessary to use social marketing effectively, the Florida Prevention Research Center (FPRC) at the University of South Florida offers several types of training courses to public health professionals working at the federal, state, and local levels.
To make both introductory training and continuing education in social marketing available, the FPRC offers the annual Social Marketing and Public Health Conference, Social Marketing in Public Health Field School courses, a Social Marketing in Public Health graduate certificate, and a variety of shorter training workshops tailored to meet the needs of a wide variety of public health practitioners.
The Social Marketing in Public Health Conference, which was initiated in 1991, has since attracted more than 3500 public health and social marketing practitioners from 50 states and 14 countries. The main conference is designed for professionals with some expertise in social marketing. To complement plenary sessions on current issues in the field, concurrent sessions feature case studies and analyses of emerging theoretical and methodological issues. The conference culminates in an array of half-day workshops on topics such as health message design principles and practice, formative research methods, low-cost materials development, pretesting techniques, and evaluation methods. The faculty consists of experts in social marketing from around the world (available from www.cme.hsc.usf.edu*.)
Just prior to the main conference, a two-day introductory training session is held to teach participants the basics of social marketing, including its conceptual framework, audience segmentation techniques, the use of formative research to develop a marketing plan, the development and pretesting of marketing materials and tactics, and the principles of program monitoring and evaluation. Participants work in teams, applying marketing concepts to specific public health problems. More than half of the participants in the introductory training session stay for the main conference as well.
The Social Marketing in Public Health Field Schools offer graduate-level courses taught by experts in social marketing, commercial marketing, and public health (available from www.cme.hsc.usf.edu). These courses are offered in an intensive five-day format. One course is offered just prior to, and one just after, the main conference in June to accommodate busy professionals who prefer a concentrated travel schedule. Another five-day course is offered in January. Two to four Field School courses have been offered each year, with enrollments varying from 28 to 110 (in 2004). Each of these five-day courses may be taken for three graduate credits by students enrolled in masters' degree programs or in the social marketing graduate certificate program (see below). Courses address strategic planning, formative research methods, media expertise, special communications skills (e.g., for low literacy and cross-cultural populations), and consumer behavior theory. Pretest–posttest evaluations of these training courses have consistently demonstrated a significant increase in participants' understanding of social marketing concepts and confidence in their ability to apply social marketing techniques.
To meet the growing need for advanced training and credentialing in social marketing and public health, the FPRC faculty has developed an 18-credit–hour graduate certificate program (available from www.outreach.usf.edu/gradcerts/certificates.asp) designed for experienced masters-prepared public health professionals. (Three have graduated to date.) The only such program for public health practitioners in the United States, the graduate certificate program requires students to complete six courses.
Each year, FPRC faculty members also provide intensive, multiday social marketing training workshops tailored to meet the needs of practitioners working in state and local public health departments. Since 2001, the FPRC has offered training in social marketing and community-based prevention marketing to state health departments in Alaska, Arkansas, California, Florida, Colorado, Idaho, Kentucky, North Carolina, Texas, and Missouri, as well as to numerous local health departments around the country. (Recent training workshops are available from http://publichealth.usf.edu/prc/training_matrix.pdf.)
Other social marketing resources relevant to public health practitioners are available from http://publichealth.usf.edu/prc/training.html and from http://turningpointprogram.org?Pages/socialmakt.html.
Summary
A well-trained public health workforce is essential if public health research is to have a tangible impact on populations. The three PRC-generated training courses highlighted here illustrate the commitment of the PRC program to improving public health, not only through innovative research with communities, but through direct training of public health practitioners (more offerings are available from www.cdc.gov/prc/training/index.htm).
The EBPH and PAPH courses build on the successful application of evidence-based approaches in clinical disciplines (10,11), the increased availability of online data, and outstanding new systematic reviews such as the Guide to Community Preventive Services (12,13) (available from www.thecommunityguide.org) that seek to improve the use of scientific information in public health practice settings. Social marketing training offers techniques to help public health practitioners to better understand their target audiences so that public health efforts to change behavior have the best chance of success. These three training courses developed and offered by PRCs constitute a meaningful contribution to the ongoing training of public health practitioners and, by creating a better trained workforce, enhance the likelihood that public health interventions are selected for implementation on the basis of scientific evidence of effectiveness and that the interventions selected are ultimately successful.
Evidence-Based Public Health: This program was funded through CDC contract U48/CCU710806 (Prevention Research Centers Program), the Chronic Disease Directors, and the Missouri Department of Health and Senior Services. We appreciate support from our course advisors, in particular Garland Land, Chris Maylahn, Deborah Porterfield, Paul Siegel, and Eduardo Simoes. We are also grateful to our collaborators: Don Bishop, Claudia Campbell, Lucimar Coser Cannon, Gunter Diem, Kathy Douglas, Vilius Grabauskas, Jim Gurney, Debra Haire-Joshu, Aulikki Nissinen, Aushra Shatchkute, Bill True, and Mike Waller; and to our research assistants: Laura Caisley, Carolyn Harris, Lori Hattan, and Leslie McIntosh.
Physical Activity and Public Health Practitioners Course on Community Interventions: This program was funded through the CDC contract U48/CCU409664 (Prevention Research Centers Program). We express our deepest appreciation to all of the course coordinators and faculty and the many staff from the CDC, the South Carolina Department of Health and Environmental Control, and the Utah Department of Health who have over the years devoted their valuable time and effort to the course. We also thank Janna Borden, Sonja Snowdon, and the several graduate students who have contributed countless hours of exceptional administrative support.
Social Marketing: We thank the CDC for providing stipends for state and local health department professionals to attend the annual Social Marketing and Public Health Conference. We also wish to thank our Field School instructors: Rob Donovan, Gerard Hastings, Richard Krueger, Nancy Lee, Claudia Parvanta, Bonnie Salazar, Paul Solomon, and Charles Weinberg.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Franks AL, Brownson RC, Bryant C, Brown KM, Hooker SP, Pluto DM, et al. Prevention Research Centers: contributions to updating the public health workforce through training. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0139.htm
==== Refs
1 Center for Health Policy2000New YorkColumbia University School of NursingThe public health workforce: enumeration 2000 Center for Health Policy. The public health workforce: enumeration 2000. New York: Columbia University School of Nursing; 2000.
2 Institute of Medicine 2003 National Academies Press Washington (DC) Who will keep the public healthy? Educating public health professionals for the 21st century
3 Brownson RC Gurney JG Land G 1999 5 86 97 J Public Health Manag Pract Evidence-based decision making in public health 10558389
4 Brownson RC Baker EA Leet TL Gillespie KN 2003 New York Oxford University Press Evidence-based public health
5 U.S. Department of Health and Human Services 2001 Rockville (MD) U.S. Department of Health and Human Services, Public Health Service, Office of the Surgeon General The Surgeon General's call to action to prevent and decrease overweight and obesity
6 Brown DR Pate RR Pratt M Wheeler F Buchner D Ainsworth B 2001 116 197 202 Public Health Rep Physical activity and public health: training courses for researchers and practitioners 12034908
7 Kotler P Roberto N Lee N 2002 Social marketing: improving the quality of life Thousand Oaks (CA) SAGE Publications Available from: URL: http://www.sagepub.com/
8 Donovan R Henley N 2003 IP Communications Melbourne (Australia) Social marketing: principles and practices
9 Andreasen A 1995 Marketing social change Jossey-Bass San Francisco (CA) Available from: URL: http://www.josseybass.com/WileyCDA/
10 Evidence-Based Medicine Working Group 1992 17 2420 2425 JAMA Evidence-based medicine. A new approach to teaching the practice of medicine
11 Sackett DL Rosenberg WMC Gray JAM Haynes RB Richardson WS 1996 312 71 72 BMJ Evidence based medicine: what it is and what it isn't 8555924
12 Briss PA Zaza S Pappaioanou M Fielding J Wright-De Aguero L Truman BI 18 1 Suppl 2000 35 43 Am J Prev Med Developing an evidence-based Guide to Community Preventive Services--methods. The Task Force on Community Preventive Services 10806978
13 Briss PA Brownson RC Fielding JE Zaza S 2004 25 281 302 Annu Rev Public Health Developing and using the guide to community preventive services: lessons learned about evidence-based public health 15015921
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Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_04_0135
Tools and Techniques
The Asthma Consultative Process: A Collaborative Approach to Integrating Genomics Into Public Health Practice
Edwards Karen L PhD University of Washington
Box 354695, Seattle, WA 98105 [email protected]
206-616-1258
Harrison Tabitha A MPH Department of Epidemiology, School of Public Health and Community Medicine, University of Washington, Seattle, Wash
Burke Wylie MD, PhD Department of Medical History and Ethics, School of Medicine, University of Washington, Seattle, Wash
4 2005
15 3 2005
2 2 A272005
Genomics research findings on asthma are reported with increasing frequency. As these findings are incorporated into existing knowledge of disease etiology and pathogenesis, the implications for public health practice need to be considered. In 2003, the University of Washington Center for Genomics and Public Health initiated a project to synthesize information about asthma genomics, to examine its relevance to public health research and practice, and to communicate findings to a public health practice audience. This goal was achieved through review of the scientific literature, formation of a working group, and consultations with professionals and community organizations. This paper describes the methods used to conduct these professional and community consultations, referred to as the asthma consultative process, and discusses the lessons learned from this activity.
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Introduction
There is widespread enthusiasm that findings from the Human Genome Project will significantly shape the public health practice of the future (1). In recent years, genomic research has provided new insights into the etiology and pathogenesis of asthma, a major public health burden in the United States. However, while genomics research is leading to new hypotheses about asthma onset and progression, and may alter how asthma is understood at the molecular level, it is not yet clear if and how genomics can be used in asthma prevention, diagnosis, and management. Additionally, the volume of new literature poses a potential barrier for professionals who wish to stay abreast of current findings. To translate basic sciences research findings into relevant applications, it will be important to gather and synthesize the rapidly changing body of information about asthma genomics and to consider research findings in the context of public health principles. Thus, a multidisciplinary approach for summarizing, evaluating, and translating knowledge in asthma genomics for public health practice is needed. To meet this challenge, the University of Washington Center for Genomics and Public Health (UWCGPH) developed a process for summarizing and considering potential implications of asthma genomics within the broad context of public health practice. This paper describes the methods used to conduct professional and community consultations, referred to as the asthma consultative process, and discusses the lessons learned from this activity.
Laying the Groundwork for the Asthma Consultative Process
In December 2002, the UWCGPH initiated a project with support from the Centers for Disease Control and Prevention's (CDC's) Office of Genomics and Disease Prevention (OGDP) and National Center for Environmental Health (NCEH) to gather and synthesize information about asthma genomics, to examine its relevance to public health research and practice, and to translate findings into language appropriate for an audience of public health practitioners. The UWCGPH identified a need to examine the knowledge base in asthma genomics and to involve public health and genomics experts in the project to accomplish these goals. The UWCGPH met these needs through a yearlong project that involved an extensive review of the literature, the formation of a working group, and the asthma consultative process.
The project began with a review of the published scientific literature on asthma genomics. The UWCGPH identified relevant literature through an initial electronic search of PubMed using the search terms "asthma genomics (gene/genetic/genetics)," restricting the search to studies in humans and articles written in English. This literature base was used to identify current findings relevant to the integration of genomics into public health research and practice related to asthma. Periodic reviews of the literature were conducted, and additional publications, including local, national, and federal documents on asthma, were identified throughout the project period. Following the initial literature search, the UWCGPH drafted a document summarizing current asthma research priorities and evidence of genetic contributors to asthma risk.
In addition to the literature review, the UWCGPH formed a working group of 12 professionals knowledgeable in asthma, genomics, and public health practice who contributed expert opinion, provided guidance on how to structure consultations, and helped formulate project findings. Members of the group were selected to ensure broad representation of public health interests, including research and practice at the state and national levels.
Asthma Consultative Frameworks
The Asthma Working Group developed two frameworks: 1) the translational pathway and 2) a matrix of perspectives and health interventions. Both frameworks were used to define stakeholders with an interest in genomics and to broadly characterize types of health care interventions in which genomics could be used. These frameworks also served as guides for focusing consultations to specific areas of expertise or practice. The translational pathway (Figure 1) served to illustrate translation of scientific findings into public health practice and to emphasize the importance of cross-disciplinary interactions in this process. Importantly, this pathway encouraged consultants to view their work as part of a broader effort to use genomics advances to improve health outcomes.
Figure 1 Translational pathway, which serves to illustrate translation of scientific findings into public health practice.
Venn diagramA text description of this chart is also available
Identification of Genetic Component Family history studies Gene discovery Gene function Gene–disease associations
Characteristics of Risk Gene–gene Gene–environment Biologic pathways Multicausal pathways
Intervention Design Clinical trials Clinical management Environmental change Drug development
Implementation & Assessment Education Behavioral change Systems change
Public & Private Policy Assessment of health care delivery Evaluation of harms/benefits of genetic information
The broad spectrum of public health research and practice occurs in many settings, draws upon several disciplines, and affects the population's health through several means. Figure 2 presents a matrix of perspectives and health interventions, outlining four key opportunities for health intervention at different stages of disease prevention and management: population-based prevention, risk-based prevention, diagnosis, and management. In addition to designating areas of intervention, the framework outlines the perspectives of groups who have a vested interest in the research and use of genomics for health care purposes: individuals with asthma and their family members, communities, researchers, health care professionals, commercial developers, and public health practitioners. This framework was used to create discussion probes (Appendix) for the consultations and also helped guide the selection of experts who participated in the asthma consultative process.
Asthma Consultative Process
The primary purpose of the asthma consultative process was to supplement scientific evidence identified in the medical literature and to provide comment on the potential implications genomics may have on public health efforts in asthma. The selection of consultants was guided by the goal of representing a broad range of perspectives. Therefore, we conducted a series of consultations with a variety of professionals and members of public organizations. The process was an iterative one in which the UWCGPH consulted with individuals and, after each round of consultation, incorporated expert commentary into a working document.
Professional consultant sampling
We sought consultation with professionals involved in asthma or genomics research and medical or public health practice. Experts were identified using a snowball sampling technique, beginning with Seattle area researchers, health care providers, and public health practitioners identified by the working group (2). These consultants were asked to recommend additional local and national experts; additional experts were sought until no new experts were identified. A total of 47 professionals who represented a broad range of organizations, disciplines, and interests participated in the asthma consultative process. This total does not include the 12 members of the Asthma Working Group.
Professional consultant format
Consultations were conducted by two authors (Burke and Harrison) between January and September 2003 through telephone or in-person key informant interviews and group discussions. Consultations began with a description of the project, including both frameworks, either formally through a PowerPoint presentation for group consultations or informally for individual or small group discussions. During consultations, approximately 30 minutes to two hours in duration, participants were asked to comment on the frameworks and to address a set of open-ended questions developed by the working group. The questions focused on the potential implications genomics may have for asthma prevention, diagnosis, and management. Written notes were taken and group discussions were tape-recorded with the permission of participants. Some consultants requested additional background materials, including review papers, prior to consultative meetings.
Community consultant sampling
Initial efforts to solicit expert opinion were directed toward professionals working in scientific, public health, or medical fields. However, as indicated in the framework, the perspective of community members is also vital to integrating genomics into public health and medical practice. The working group first identified local community organizations with an interest in asthma. The UWCGPH contacted each organization to assess its interest in participating in the asthma consultative process. A representative from the organization then approached the group's members and asked if they would be willing to participate in a group discussion about genomics and asthma. Because of time and funding constraints, only a subset of organizations identified by the working group were asked to participate in the process. Three community groups, including 18 community members, participated in the group consultations between September and October 2003. Approval for community consultations was obtained by the University of Washington's Human Subjects Division.
Community consultants format
The UWCGPH worked with the community contacts to schedule the group meetings, to discuss the meeting environment, and to learn about the needs and interests of each group. A total of three community groups were consulted at three separate meetings held in the evenings. Foreign language translators were hired to assist with discussions for two meetings and to translate consent forms distributed to participants. Written notes taken by the UWCGPH were used to summarize discussions. Sessions were not tape-recorded.
The format for community consultations differed slightly from professional consultations. Community consultations consisted of group discussions led by a member of the UWCGPH and a representative from each organization. Each meeting began with a description of the project, followed by a general discussion about asthma and genomics. Participants were then read one to three hypothetical scenarios illustrating potential asthma-related uses of genetic information. The hypothetical scenarios were developed by the UWCGPH based on input provided by professional consultants about areas of asthma genomics that could potentially lead to health care applications. Topics represented by the scenarios were pharmacogenomics, newborn screening to identify susceptible individuals for possible prevention efforts, and policy development. Policy development centered on the use of genetic susceptibility information to assist in setting clean air standards. Scenarios were presented to the group and were followed by a discussion period where participants were encouraged to provide comment on the scenario, including identifying areas of concern. At the end of each meeting, participants were asked if they had any questions, were reminded that their input would be confidential, and were thanked for participation.
Discussion
The UWCGPH developed the asthma consultative process to summarize asthma genomics research findings and examine the implications of these findings for public health research and practice. Results from this process, and from the literature review and working group discussions, have been compiled into a final report that is available from http://www.uwcgph.org/. Briefly, findings suggest that public health researchers and practitioners can ensure that genomics research supports public health goals by 1) facilitating the analysis and communication of research in asthma genomics, 2) promoting population-based research that investigates both genetic and environmental risk factors, and 3) conducting advocacy and outreach to promote access to genomics-based therapies and support community-based participatory research methods.
While the asthma consultative process described here was framed in the context of public health, it drew from experts involved in efforts to improve the population's health at several organizational levels and across several disciplines. The diversity of consultants was valuable to the process because it helped to 1) identify unanticipated issues that would not have been apparent from a single perspective or through a literature review alone, 2) identify unforeseen stakeholders, and 3) reflect the diversity of public health in our findings.
The asthma consultative process also allowed for a degree of flexibility. The scheduling of consultations with experts over several months, rather than at a single meeting, allowed for access to a wider range and greater number of experts and allowed us to tailor discussions to each consultant's area of expertise. Additionally, we were able to make efficient use of experts' time by consulting over the telephone, convening at national conferences, and scheduling group or individual appointments at locations and dates convenient for consultants. The iterative process also allowed us to return to issues during the course of the consultation as new insights emerged. Thus, the frameworks and report developed by the working group were continually revised after each consultation and over time led to a document that served as the basis for the final report.
Another advantage of the process was that it provided opportunities to gauge the level of awareness about public health genomics and to assist public health practitioners interested in learning about asthma genomics. Few consultants had considered genomics in the context of public health practice. In some instances, discussions enabled consultants to create new relationships and exchange ideas, providing them with opportunities to learn about research or public health practices and to gain insights into perspectives other than their own. Additionally, the process created opportunities for assisting public health practitioners to learn about asthma genomics. For example, the UWCGPH provided technical assistance on asthma genomics to three state health departments and developed Web pages highlighting information about asthma genomics as part of the CDC Public Health Perspective series.
The asthma consultative process also had some limitations. The process was a voluntary one in which experts were recommended by other consultants. Because of this, the participation of individuals representing each perspective could not be guaranteed. For example, a sixth perspective, that of the commercial developer, was identified during initial consultations and was added to the framework. Although individuals representing the commercial perspective were invited to provide consultations, none participated in the process. Additionally, resource and time constraints did not allow us to conduct as comprehensive a series of consultations with community groups as were conducted with professionals. Future efforts are needed to expand the dialogue about public health genomics with additional community organizations, asthmatic individuals and their families, and commercial developers.
In summary, the asthma consultative process offers an efficient approach for examining the potential implications that genomics will have for the field of public health. The method described in this paper not only enabled the UWCGPH to review the current state of knowledge in asthma genomics, but it allowed for opportunities to engage multiple stakeholders, create linkages among experts from different disciplines, and generate awareness about public health genomics.
The collaboration between professionals from multiple health-related disciplines will be fundamental to bridging the gap between the identification of genetic contributions to disease and the development of new genomics-based interventions to improve health outcomes. In an effort to promote this important collaboration, the UWCGPH carried out the asthma consultative process. Processes such as this one can serve to strengthen the capacity to integrate genomics into public health research and practice.
This paper was supported by a cooperative agreement with the Centers for Disease Control and Prevention through the Association of Schools of Public Health, Grant Number U36/CCU300430-23.
Appendix. Consultation Guide Discussion Probes
How would you define genomics?
Does the framework provided by the translational pathway and the table provide a useful way of framing the discussion of the implications of genomics for asthma disease prevention?
Are there missing perspectives? What different groups/agencies do you see represented in each perspective?
Are there alternative ways to approach the problem?
What perspective(s) do you feel you represent?
From your perspective, is genomics currently a factor in asthma care? If yes, does it affect:
Universal prevention measures
Risk-based prevention measures
Diagnosis
Management
Public health efforts
If no, why not and what would make it applicable? Are there barriers?
Is genomics likely to have an impact on asthma care in the future? If yes, will it affect:
Universal prevention measures
Risk-based prevention measures
Diagnosis
Management
Public health efforts
If no, why not and what would make it applicable? Are there barriers?
What research is most important in the area of asthma genomics? Why is this research most important?
What are the barriers to accomplishing this research?
What could be done to encourage this research?
How could this research contribute to improved asthma outcomes?
Are there potential harms related to asthma genomics?
If yes: What are they?
Are there potential mechanisms/solutions to prevent or control these harms?
If no, why not?
Does your own work involve genomics?
If yes, how?
If no, do you expect it to do so in the future?
Do state agencies and the CDC have a role in the application of genomic information or research to asthma disease prevention?
If yes, how?
If no, why and do you expect it to do so in the future?
Figures and Tables
Figure 2 A matrix of perspectives and health interventions, which outlines four key opportunities for health interventions at different stages of disease prevention and management.
Perspectives Interventions
Population-based prevention Risk-based prevention Diagnosis Management
Population-based intervention or detection efforts to avoid or delay asthma onset Intervention efforts targeted to individuals with susceptibilities to asthma to avoid or delay asthma onset Identification of asthma subtypes and identification of individuals with asthma, including distinguishing asthma from other respiratory diseases Interventions to reduce disease burden of asthma, including pharmaceutical and other therapeutics, environmental modification, and behavioral mechanisms
Patient and family
Community
Researcher
Healthcare professional
Commercial developer
Public health practitioner
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Harrison TA, Burke W, Edwards KL. The asthma consultative process: a collaborative approach to integrating genomics into public health practice. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0135.htm
==== Refs
1 Institute of Medicine 2003 68 72 Who will keep the public healthy? Educating public health professionals for the 21st century National Academies Press Washington
2 Kuzel AJ Crabtree BF Miller WL Inc 1999 33 45 Doing qualitative research 2nd ed Sampling in qualitative inquiry SAGE Publications, Inc Thousand Oaks (CA)
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Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_04_0135
Tools and Techniques
The Asthma Consultative Process: A Collaborative Approach to Integrating Genomics Into Public Health Practice
Edwards Karen L PhD University of Washington
Box 354695, Seattle, WA 98105 [email protected]
206-616-1258
Harrison Tabitha A MPH Department of Epidemiology, School of Public Health and Community Medicine, University of Washington, Seattle, Wash
Burke Wylie MD, PhD Department of Medical History and Ethics, School of Medicine, University of Washington, Seattle, Wash
4 2005
15 3 2005
2 2 A272005
Genomics research findings on asthma are reported with increasing frequency. As these findings are incorporated into existing knowledge of disease etiology and pathogenesis, the implications for public health practice need to be considered. In 2003, the University of Washington Center for Genomics and Public Health initiated a project to synthesize information about asthma genomics, to examine its relevance to public health research and practice, and to communicate findings to a public health practice audience. This goal was achieved through review of the scientific literature, formation of a working group, and consultations with professionals and community organizations. This paper describes the methods used to conduct these professional and community consultations, referred to as the asthma consultative process, and discusses the lessons learned from this activity.
==== Body
Introduction
There is widespread enthusiasm that findings from the Human Genome Project will significantly shape the public health practice of the future (1). In recent years, genomic research has provided new insights into the etiology and pathogenesis of asthma, a major public health burden in the United States. However, while genomics research is leading to new hypotheses about asthma onset and progression, and may alter how asthma is understood at the molecular level, it is not yet clear if and how genomics can be used in asthma prevention, diagnosis, and management. Additionally, the volume of new literature poses a potential barrier for professionals who wish to stay abreast of current findings. To translate basic sciences research findings into relevant applications, it will be important to gather and synthesize the rapidly changing body of information about asthma genomics and to consider research findings in the context of public health principles. Thus, a multidisciplinary approach for summarizing, evaluating, and translating knowledge in asthma genomics for public health practice is needed. To meet this challenge, the University of Washington Center for Genomics and Public Health (UWCGPH) developed a process for summarizing and considering potential implications of asthma genomics within the broad context of public health practice. This paper describes the methods used to conduct professional and community consultations, referred to as the asthma consultative process, and discusses the lessons learned from this activity.
Laying the Groundwork for the Asthma Consultative Process
In December 2002, the UWCGPH initiated a project with support from the Centers for Disease Control and Prevention's (CDC's) Office of Genomics and Disease Prevention (OGDP) and National Center for Environmental Health (NCEH) to gather and synthesize information about asthma genomics, to examine its relevance to public health research and practice, and to translate findings into language appropriate for an audience of public health practitioners. The UWCGPH identified a need to examine the knowledge base in asthma genomics and to involve public health and genomics experts in the project to accomplish these goals. The UWCGPH met these needs through a yearlong project that involved an extensive review of the literature, the formation of a working group, and the asthma consultative process.
The project began with a review of the published scientific literature on asthma genomics. The UWCGPH identified relevant literature through an initial electronic search of PubMed using the search terms "asthma genomics (gene/genetic/genetics)," restricting the search to studies in humans and articles written in English. This literature base was used to identify current findings relevant to the integration of genomics into public health research and practice related to asthma. Periodic reviews of the literature were conducted, and additional publications, including local, national, and federal documents on asthma, were identified throughout the project period. Following the initial literature search, the UWCGPH drafted a document summarizing current asthma research priorities and evidence of genetic contributors to asthma risk.
In addition to the literature review, the UWCGPH formed a working group of 12 professionals knowledgeable in asthma, genomics, and public health practice who contributed expert opinion, provided guidance on how to structure consultations, and helped formulate project findings. Members of the group were selected to ensure broad representation of public health interests, including research and practice at the state and national levels.
Asthma Consultative Frameworks
The Asthma Working Group developed two frameworks: 1) the translational pathway and 2) a matrix of perspectives and health interventions. Both frameworks were used to define stakeholders with an interest in genomics and to broadly characterize types of health care interventions in which genomics could be used. These frameworks also served as guides for focusing consultations to specific areas of expertise or practice. The translational pathway (Figure 1) served to illustrate translation of scientific findings into public health practice and to emphasize the importance of cross-disciplinary interactions in this process. Importantly, this pathway encouraged consultants to view their work as part of a broader effort to use genomics advances to improve health outcomes.
Figure 1 Translational pathway, which serves to illustrate translation of scientific findings into public health practice.
Venn diagramA text description of this chart is also available
Identification of Genetic Component Family history studies Gene discovery Gene function Gene–disease associations
Characteristics of Risk Gene–gene Gene–environment Biologic pathways Multicausal pathways
Intervention Design Clinical trials Clinical management Environmental change Drug development
Implementation & Assessment Education Behavioral change Systems change
Public & Private Policy Assessment of health care delivery Evaluation of harms/benefits of genetic information
The broad spectrum of public health research and practice occurs in many settings, draws upon several disciplines, and affects the population's health through several means. Figure 2 presents a matrix of perspectives and health interventions, outlining four key opportunities for health intervention at different stages of disease prevention and management: population-based prevention, risk-based prevention, diagnosis, and management. In addition to designating areas of intervention, the framework outlines the perspectives of groups who have a vested interest in the research and use of genomics for health care purposes: individuals with asthma and their family members, communities, researchers, health care professionals, commercial developers, and public health practitioners. This framework was used to create discussion probes (Appendix) for the consultations and also helped guide the selection of experts who participated in the asthma consultative process.
Asthma Consultative Process
The primary purpose of the asthma consultative process was to supplement scientific evidence identified in the medical literature and to provide comment on the potential implications genomics may have on public health efforts in asthma. The selection of consultants was guided by the goal of representing a broad range of perspectives. Therefore, we conducted a series of consultations with a variety of professionals and members of public organizations. The process was an iterative one in which the UWCGPH consulted with individuals and, after each round of consultation, incorporated expert commentary into a working document.
Professional consultant sampling
We sought consultation with professionals involved in asthma or genomics research and medical or public health practice. Experts were identified using a snowball sampling technique, beginning with Seattle area researchers, health care providers, and public health practitioners identified by the working group (2). These consultants were asked to recommend additional local and national experts; additional experts were sought until no new experts were identified. A total of 47 professionals who represented a broad range of organizations, disciplines, and interests participated in the asthma consultative process. This total does not include the 12 members of the Asthma Working Group.
Professional consultant format
Consultations were conducted by two authors (Burke and Harrison) between January and September 2003 through telephone or in-person key informant interviews and group discussions. Consultations began with a description of the project, including both frameworks, either formally through a PowerPoint presentation for group consultations or informally for individual or small group discussions. During consultations, approximately 30 minutes to two hours in duration, participants were asked to comment on the frameworks and to address a set of open-ended questions developed by the working group. The questions focused on the potential implications genomics may have for asthma prevention, diagnosis, and management. Written notes were taken and group discussions were tape-recorded with the permission of participants. Some consultants requested additional background materials, including review papers, prior to consultative meetings.
Community consultant sampling
Initial efforts to solicit expert opinion were directed toward professionals working in scientific, public health, or medical fields. However, as indicated in the framework, the perspective of community members is also vital to integrating genomics into public health and medical practice. The working group first identified local community organizations with an interest in asthma. The UWCGPH contacted each organization to assess its interest in participating in the asthma consultative process. A representative from the organization then approached the group's members and asked if they would be willing to participate in a group discussion about genomics and asthma. Because of time and funding constraints, only a subset of organizations identified by the working group were asked to participate in the process. Three community groups, including 18 community members, participated in the group consultations between September and October 2003. Approval for community consultations was obtained by the University of Washington's Human Subjects Division.
Community consultants format
The UWCGPH worked with the community contacts to schedule the group meetings, to discuss the meeting environment, and to learn about the needs and interests of each group. A total of three community groups were consulted at three separate meetings held in the evenings. Foreign language translators were hired to assist with discussions for two meetings and to translate consent forms distributed to participants. Written notes taken by the UWCGPH were used to summarize discussions. Sessions were not tape-recorded.
The format for community consultations differed slightly from professional consultations. Community consultations consisted of group discussions led by a member of the UWCGPH and a representative from each organization. Each meeting began with a description of the project, followed by a general discussion about asthma and genomics. Participants were then read one to three hypothetical scenarios illustrating potential asthma-related uses of genetic information. The hypothetical scenarios were developed by the UWCGPH based on input provided by professional consultants about areas of asthma genomics that could potentially lead to health care applications. Topics represented by the scenarios were pharmacogenomics, newborn screening to identify susceptible individuals for possible prevention efforts, and policy development. Policy development centered on the use of genetic susceptibility information to assist in setting clean air standards. Scenarios were presented to the group and were followed by a discussion period where participants were encouraged to provide comment on the scenario, including identifying areas of concern. At the end of each meeting, participants were asked if they had any questions, were reminded that their input would be confidential, and were thanked for participation.
Discussion
The UWCGPH developed the asthma consultative process to summarize asthma genomics research findings and examine the implications of these findings for public health research and practice. Results from this process, and from the literature review and working group discussions, have been compiled into a final report that is available from http://www.uwcgph.org/. Briefly, findings suggest that public health researchers and practitioners can ensure that genomics research supports public health goals by 1) facilitating the analysis and communication of research in asthma genomics, 2) promoting population-based research that investigates both genetic and environmental risk factors, and 3) conducting advocacy and outreach to promote access to genomics-based therapies and support community-based participatory research methods.
While the asthma consultative process described here was framed in the context of public health, it drew from experts involved in efforts to improve the population's health at several organizational levels and across several disciplines. The diversity of consultants was valuable to the process because it helped to 1) identify unanticipated issues that would not have been apparent from a single perspective or through a literature review alone, 2) identify unforeseen stakeholders, and 3) reflect the diversity of public health in our findings.
The asthma consultative process also allowed for a degree of flexibility. The scheduling of consultations with experts over several months, rather than at a single meeting, allowed for access to a wider range and greater number of experts and allowed us to tailor discussions to each consultant's area of expertise. Additionally, we were able to make efficient use of experts' time by consulting over the telephone, convening at national conferences, and scheduling group or individual appointments at locations and dates convenient for consultants. The iterative process also allowed us to return to issues during the course of the consultation as new insights emerged. Thus, the frameworks and report developed by the working group were continually revised after each consultation and over time led to a document that served as the basis for the final report.
Another advantage of the process was that it provided opportunities to gauge the level of awareness about public health genomics and to assist public health practitioners interested in learning about asthma genomics. Few consultants had considered genomics in the context of public health practice. In some instances, discussions enabled consultants to create new relationships and exchange ideas, providing them with opportunities to learn about research or public health practices and to gain insights into perspectives other than their own. Additionally, the process created opportunities for assisting public health practitioners to learn about asthma genomics. For example, the UWCGPH provided technical assistance on asthma genomics to three state health departments and developed Web pages highlighting information about asthma genomics as part of the CDC Public Health Perspective series.
The asthma consultative process also had some limitations. The process was a voluntary one in which experts were recommended by other consultants. Because of this, the participation of individuals representing each perspective could not be guaranteed. For example, a sixth perspective, that of the commercial developer, was identified during initial consultations and was added to the framework. Although individuals representing the commercial perspective were invited to provide consultations, none participated in the process. Additionally, resource and time constraints did not allow us to conduct as comprehensive a series of consultations with community groups as were conducted with professionals. Future efforts are needed to expand the dialogue about public health genomics with additional community organizations, asthmatic individuals and their families, and commercial developers.
In summary, the asthma consultative process offers an efficient approach for examining the potential implications that genomics will have for the field of public health. The method described in this paper not only enabled the UWCGPH to review the current state of knowledge in asthma genomics, but it allowed for opportunities to engage multiple stakeholders, create linkages among experts from different disciplines, and generate awareness about public health genomics.
The collaboration between professionals from multiple health-related disciplines will be fundamental to bridging the gap between the identification of genetic contributions to disease and the development of new genomics-based interventions to improve health outcomes. In an effort to promote this important collaboration, the UWCGPH carried out the asthma consultative process. Processes such as this one can serve to strengthen the capacity to integrate genomics into public health research and practice.
This paper was supported by a cooperative agreement with the Centers for Disease Control and Prevention through the Association of Schools of Public Health, Grant Number U36/CCU300430-23.
Appendix. Consultation Guide Discussion Probes
How would you define genomics?
Does the framework provided by the translational pathway and the table provide a useful way of framing the discussion of the implications of genomics for asthma disease prevention?
Are there missing perspectives? What different groups/agencies do you see represented in each perspective?
Are there alternative ways to approach the problem?
What perspective(s) do you feel you represent?
From your perspective, is genomics currently a factor in asthma care? If yes, does it affect:
Universal prevention measures
Risk-based prevention measures
Diagnosis
Management
Public health efforts
If no, why not and what would make it applicable? Are there barriers?
Is genomics likely to have an impact on asthma care in the future? If yes, will it affect:
Universal prevention measures
Risk-based prevention measures
Diagnosis
Management
Public health efforts
If no, why not and what would make it applicable? Are there barriers?
What research is most important in the area of asthma genomics? Why is this research most important?
What are the barriers to accomplishing this research?
What could be done to encourage this research?
How could this research contribute to improved asthma outcomes?
Are there potential harms related to asthma genomics?
If yes: What are they?
Are there potential mechanisms/solutions to prevent or control these harms?
If no, why not?
Does your own work involve genomics?
If yes, how?
If no, do you expect it to do so in the future?
Do state agencies and the CDC have a role in the application of genomic information or research to asthma disease prevention?
If yes, how?
If no, why and do you expect it to do so in the future?
Figures and Tables
Figure 2 A matrix of perspectives and health interventions, which outlines four key opportunities for health interventions at different stages of disease prevention and management.
Perspectives Interventions
Population-based prevention Risk-based prevention Diagnosis Management
Population-based intervention or detection efforts to avoid or delay asthma onset Intervention efforts targeted to individuals with susceptibilities to asthma to avoid or delay asthma onset Identification of asthma subtypes and identification of individuals with asthma, including distinguishing asthma from other respiratory diseases Interventions to reduce disease burden of asthma, including pharmaceutical and other therapeutics, environmental modification, and behavioral mechanisms
Patient and family
Community
Researcher
Healthcare professional
Commercial developer
Public health practitioner
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Harrison TA, Burke W, Edwards KL. The asthma consultative process: a collaborative approach to integrating genomics into public health practice. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/04_0135.htm
==== Refs
1 Institute of Medicine 2003 68 72 Who will keep the public healthy? Educating public health professionals for the 21st century National Academies Press Washington
2 Kuzel AJ Crabtree BF Miller WL Inc 1999 33 45 Doing qualitative research 2nd ed Sampling in qualitative inquiry SAGE Publications, Inc Thousand Oaks (CA)
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Prev Chronic Dis. 2005 Mar 15; 2(2):A28
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Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_05_0027
Book Review
Human Genome Epidemiology: A Scientific Foundation for Using Genetic Information to Improve Health and Prevent Disease
4 2005
15 3 2005
2 2 A29Khoury Muin J MD, PhD Little Julian PhD Burke Wylie MD, PhD
Human Genome Epidemiology: A Scientific Foundation for Using Genetic Information to Improve Health and Prevent Disease.
Oxford University Press. 2004. ISBN: 0-19-514674-3 Price: $65.00 549 pages 2005
==== Body
Cover of Human Genome Epidemiology: A Scientific Foundation for Using Genetic Information to Improve Health and Prevent Disease
Human Genome Epidemiology is edited by three well-recognized experts in genetic epidemiology, and their experience shows in the information and organization of this text. While they do not define their intended audience, the material focuses on the complexities of epidemiologic study and design in population-based genomics. Readability characterizes all the chapters, and the concepts will be accessible to anyone with a firm grounding in epidemiologic principles. Hence, the book will be useful for both the graduate student and the experienced practitioner who wish to learn more about this field.
The book contains four parts. Part One is devoted to the basics of common definitions, molecular biology, and the laboratory testing that drove the Human Genome Project and continue to be central in genomic research. Definitions include distinguishing genetics (the study of single genes and their effects) from genomics (the study of both single genes and the functions and interactions of all genes in the genome). An example of the editors' thorough attention to detail is a chapter on the ethics related to genomic studies.
Part Two is the most technical aspect of the text and examines the application of standard epidemiologic research techniques in human genomics. These chapters discuss additional research methods unique to human genome epidemiology and provide comprehensive descriptions of the strengths and drawbacks of each research technique. The final chapter in this section provides a checklist for designing and reporting genomic epidemiologic studies. At this point, the average epidemiologist should have the information necessary for assessing existing studies and perhaps designing credible original studies as well.
How useful will the results of these studies prove to practical disease prevention? Part Three explores three types of validity for human genetic tests. Analytic validity is the sensitivity, specificity, and predictive value of a laboratory test for genotype. Clinical validity refers to the same values with respect to phenotype; that is, is a given test a good measure of gene–disease association? Last, clinical utility refers to whether such tests provide information that can change disease outcomes. The remainder of this section discusses the application of these concepts to clinical medicine, pharmacology, and randomized clinical trials and offers guidelines for assessing these values in different settings.
Part Three leaves the reader with a sense that while genetic testing may be useful in clinical contexts or among select high-risk populations, there is little evidence to support the use of most genetic tests in mass population testing. Part Four reinforces this concept by providing 12 case studies on using human genome epidemiology to improve health. In one example after another, reasonable evidence often supports an association between a health outcome and a gene, a gene–gene interaction, or a gene–environment interaction. But never does the evidence suggest that general population testing would be an appropriate public health response.
One might infer that this text indicates a limited role for human genomic epidemiology in current public health practice. However, the book does offer important points for practical epidemiology. This is a rapidly growing field, and research epidemiologists can play an essential role in designing and conducting research. In fact, we may learn tomorrow of genetic tests with immediate general population applications. When such reports do become available, it will be essential for epidemiologists to quickly grasp the strengths and limitations of the interpreted studies. As genetic testing becomes a high-profile public discussion, epidemiologists will need to provide policy makers with a clear understanding of how to separate the wheat from the chaff in determining public health actions. The editors note that the results of genomic epidemiologic studies can be explored by other disciplines: policy (to decide value added), communication (to explain risk information), economics (to examine cost effectiveness), and outcomes research (to measure impact). For the most part, these aspects are not discussed in this text, but public health professionals will need to address them all to use human genomic epidemiology effectively in practice.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Human genome epidemiology: a scientific foundation for using genetic information to improve health and prevent disease [book review]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/05_0027.htm.
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2022-01-31 23:31:35
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Prev Chronic Dis. 2005 Mar 15; 2(2):A29
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==== Front
Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_05_0014
Letter to the Editor
A Note on “A Catalog of Biases in Questionnaires”
Harrow Jeffrey J MD, PhD Veterans Integrated Service Network (VISN) 8, Patient Safety Center of Inquiry, James A. Haley VA Medical Center, Tampa, Fla
4 2005
15 3 2005
2 2 A302005
==== Body
To the Editor:
The authors of "A Catalog of Biases in Questionnaires" in the January issue (1) provide a useful catalog of questionnaire biases, but in one of their examples, their recommended response is guilty of the same bias. This error occurs in Overlapping Interval.
Example:
How many cigarettes do you smoke per day?
[ ] None [ ] 5 or less [ ] 5–25 [ ] 25 or more
The authors' recommended question was:
How many cigarettes do you smoke per day?
[ ] None [ ] 4 or less [ ] 5–24 [ ] 25 or more
While this corrects the ambiguity of the first question for respondents who smoke exactly 5 or 25 cigarettes per day, it maintains the ambiguity for nonsmokers. The recommended question should be:
How many cigarettes do you smoke per day?
[ ] None [ ] 1–4 [ ] 5–24 [ ] 25 or more
Read the author's reply
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Harrow JJ. A note on “A Catalog of Biases in Questionnaires” [letter to the editor]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/05_0014.htm.
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1 BCK Choi AWP Pak Prev Chronic Dis [serial online] 2005 1 Accessed 2004 Dec 15 A catalog of biases in questionnaires
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Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_05_0016
Letter to the Editor
A Note on “A Catalog of Biases in Questionnaires” [Response to Letter]
Choi Bernard C.K. PhD Centre for Chronic Disease Prevention and Control, Public Health Agency of Canada, Ottawa, Ontario, Canada
Pak Anita W.P. PhD Office of Institutional Research, University of Ottawa, Ottawa, Ontario, Canada
4 2005
15 3 2004
2 2 A312005
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In Reply:
We would like to thank Dr Harrow (1) for pointing out a flawed example in our paper (2). As he notes, the recommended question should be:
How many cigarettes do you smoke per day?[ ] None [ ] 1–4 [ ] 5–24 [ ] 25 or more
The previously recommended category "4 or less" was a typographical error.
Read the original letter
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: Choi BCK, Pak AWP. A note on “A Catalog of Biases in Questionnaires” [response to letter]. Prev Chronic Dis [serial online] 2005 Apr [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2005/apr/05_0016.htm.
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1 Harrow JJ Prev Chronic Dis [serial online] 2005 Apr Accessed 2005 Mar 15 A note on "A Catalog of Biases in Questionnaires" [letter to editor]
2 BCK Choi AWP Pak Prev Chronic Dis [serial online] 2005 1 Accessed 2004 Dec 15 A catalog of biases in questionnaires
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Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv22_05_0013
Errata
Errata, Vol. 2: No. 1
4 2005
15 3 2005
2 2 A322005
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In the article "Childhood Obesity — What We Can Learn From Existing Data on Societal Trends, Part 1," some of the categories along the x-axis in Figures 4 and 5 were incorrectly labeled. In Figure 4, the categories "Sports/Outdoors," "Personal care," "Shopping," "Television," and "Other passive leisure (e.g., conversations, church, visiting)" were incorrectly labeled. In Figure 5, the categories "Shopping," "Television," and "Playing" were incorrectly labeled. The figures were corrected on our Web site on December 20, 2004 and appear online at http://www.cdc.gov/pcd/issues/2005/jan/04_0038.htm. We regret any confusion these errors may have caused.
In addition, the article "A Catalog of Biases in Questionnaires" contained an error in the example of a recommended question that prevents bias from overlapping intervals. The recommended question and response categories should be:
How many cigarettes do you smoke per day?
[   ] None   [   ] 1–4   [   ] 5–24   [   ] 25 or more
The response category "1–4" appeared in the article as "4 or less." The error was corrected on our Web site on January 21, 2005 and appears online at http://www.cdc.gov/pcd/issues/2005/jan/04_0050.htm. We regret any confusion this error may have caused.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
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PLoS GenetPLoS GenetpgenplgeplosgenPLoS Genetics1553-73901553-7404Public Library of Science San Francisco, USA 1642491910.1371/journal.pgen.002000605-PLGE-RA-0196R1plge-02-01-05Research ArticleDiabetes - Endocrinology - MetabolismStatisticsGenetics/Comparative GenomicsGenetics/Genetics of DiseaseGenetics/Gene ExpressionGenetics/Complex Traitsexpression quantative trait mappingtrait correlationsG-protein coupled receptorsstearoyl-CoA desaturasetranscriptional regulationCombined Expression Trait Correlations and Expression Quantitative Trait Locus Mapping Inheritance of Gene Expression in DiabetesLan Hong 1Chen Meng 2Flowers Jessica B 13Yandell Brian S 24Stapleton Donnie S 1Mata Christine M 1Mui Eric Ton-Keen 1Flowers Matthew T 1Schueler Kathryn L 1Manly Kenneth F 5Williams Robert W 5Kendziorski Christina 6Attie Alan D 1*1 Department of Biochemistry, University of Wisconsin, Madison, Wisconsin, United States of America
2 Department of Statistics, University of Wisconsin, Madison, Wisconsin, United States of America
3 Department of Nutritional Sciences, University of Wisconsin, Madison, Wisconsin, United States of America
4 Department of Horticulture, University of Wisconsin, Madison, Wisconsin, United States of America
5 Departments of Anatomy and Neurobiology, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
6 Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin, United States of America
Gibson Greg EditorNorth Carolina State University, United States of America* To whom correspondence should be addressed. E-mail: [email protected] 2006 20 1 2006 2 1 e612 8 2005 6 12 2005 © 2006 Lan et al.2006This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.Coordinated regulation of gene expression levels across a series of experimental conditions provides valuable information about the functions of correlated transcripts. The consideration of gene expression correlation over a time or tissue dimension has proved valuable in predicting gene function. Here, we consider correlations over a genetic dimension. In addition to identifying coregulated genes, the genetic dimension also supplies us with information about the genomic locations of putative regulatory loci. We calculated correlations among approximately 45,000 expression traits derived from 60 individuals in an F2 sample segregating for obesity and diabetes. By combining the correlation results with linkage mapping information, we were able to identify regulatory networks, make functional predictions for uncharacterized genes, and characterize novel members of known pathways. We found evidence of coordinate regulation of 174 G protein–coupled receptor protein signaling pathway expression traits. Of the 174 traits, 50 had their major LOD peak within 10 cM of a locus on Chromosome 2, and 81 others had a secondary peak in this region. We also characterized a Riken cDNA clone that showed strong correlation with stearoyl-CoA desaturase 1 expression. Experimental validation confirmed that this clone is involved in the regulation of lipid metabolism. We conclude that trait correlation combined with linkage mapping can reveal regulatory networks that would otherwise be missed if we studied only mRNA traits with statistically significant linkages in this small cross. The combined analysis is more sensitive compared with linkage mapping alone.
Synopsis
In order to annotate gene function and identify potential members of regulatory networks, the authors explore correlation of expression profiles across a genetic dimension, namely genotypes segregating in a panel of 60 F2 mice derived from a cross used to explore diabetes in obese mice. They first identified 6,016 seed transcripts for which they observe that the gene expression is linked to a particular region of the genome. Then they searched for transcripts whose expression is highly correlated with the seed transcripts and tested for enrichment of common biological functions among the lists of correlated transcripts. They found and explored the properties of 1,341 sets of transcripts that share a particular “gene ontology” term. Thirty-eight seeds in the G protein–coupled receptor protein signaling pathway were correlated with 174 transcripts, all of which are also annotated as G protein–coupled receptor protein signaling pathway and 131 of which share a regulatory locus on Chromosome 2. The authors note many of these findings would have been missed by simple expression quantitative trait loci analysis without the correlation step. The approach was used to identify a common set of genes involved in lipid metabolism.
Citation:Lan H, Chen M, Flowers JB, Yandell BS, Stapleton DS, et al. (2006) Combined expression trait correlations and expression quantitative trait locus mapping. PLoS Genet 2(1): e6.
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Introduction
Biological systems are tightly controlled such that the concentrations of components are precisely balanced in an active biological pathway. The balances are maintained in part by coordinately regulated expression levels of the genes involved in the biological processes. Thus, genes that are coregulated across various physiologic conditions are likely to have similar functions. This creates a context by which the function of unannotated genes can be predicted.
One of the greatest challenges to biologists today is to functionally characterize genes identified by the genome sequencing projects. Traditionally, gene functions are inferred based on similarities in primary sequences, functional domains, or structures of encoded proteins [1]. In contrast to these qualitative approaches, we and others are using quantitative methods to predict gene function and discover gene regulatory networks [2,3].
Eisen and colleagues [4] were among the first to use microarray gene expression profiles for gene function prediction. They used hierarchical clustering algorithms to analyze time-course gene expression profiles of budding yeast and human fibroblasts and found that grouping of transcripts with similar profiles across a time dimension could accurately identify genes of similar function. Recently, Zhang and colleagues [3] generated microarray expression data for nearly 40,000 mRNAs in 55 mouse tissues and found that coordinated expression across a tissue dimension can contribute to the prediction of gene function. Although extremely informative, these approaches do not establish lines of causation among correlated mRNAs and therefore cannot provide the origin of a particular gene regulatory network.
Genetics establishes a one-way line of causation between genotype and phenotype. Brem et al. [5] used microarray data as phenotypes for genetic mapping in a segregating yeast sample. This approach has inspired the mapping of expression quantitative trait loci (eQTL) in mammalian species [6–12]. Each study identified cis and trans traits. A cis trait is one that genetically maps to the physical location of the gene encoding its mRNA, suggesting that variation at the locus is responsible for the heritable changes in gene expression. A trans trait maps to a region distinct from its physical location and thus implies the location of a potential regulator acting in trans. Theoretically, when multiple traits map together in trans (i.e., overlapping eQTL peaks within an approximately 10-cM region), we can hypothesize that they are coregulated by a common regulator encoded by a gene locus underlying the eQTL. Unfortunately, in practice, because mRNAs are typically regulated by multiple factors, individual trans-acting eQTL exert a relatively weak genetic signal and are difficult to detect in a small sample size. This limits the power to detect metabolic regulators.
Schadt et al. [9] surveyed 23,574 genes using Rosetta oligonucleotide arrays. More recent studies used Affymetrix U74A arrays, or RAE230A arrays, which covered about one third of all the transcripts [7,8,11]. The transcripts chosen for measurement were biased toward known genes. Here, we measured the expression levels of the transcripts on the Affymetrix MOE430 Set arrays, which include many genes of unknown function.
We studied an F2 segregating for obesity and diabetes to expose key components of gene regulatory networks. To this end, we combined correlation analysis with linkage mapping. Rather than considering correlation across a time or tissue dimension, we evaluated correlation across a genetic dimension, as created by a segregating F2 sample. An F2 represents an allelic block permutation. Our combined approach increases the power to identify metabolic regulatory networks.
Results
We generated an F2-ob/ob sample from the C57BL/6J (B6) and BTBR founder strains. The B6 and BTBR strains, when made obese, differ in diabetes susceptibility [13]; B6-ob/ob mice are diabetes resistant, whereas BTBR-ob/ob mice develop severe diabetes. The F2 mice were genotyped for 194 markers (average marker interval of approximately 10 cM). A subset of 60 mice was chosen using a selective phenotyping algorithm developed in our group [14]. From each liver, 45,037 expression measurements were obtained using the Affymetrix MOE430 microarrays. According to the December 2004 gene annotation table, 14,487 (32.2%) of the transcripts have unique RefSeq protein identifiers, and 16,466 (36.6%) have been annotated to at least one Gene Ontology (GO) Biological Process (BP). Altogether, about two thirds of the transcripts that we measured were not annotated in either of these two sources.
Mapping of Metabolic Regulatory Loci
After processing the Affymetrix CEL files with Robust Multi-array Average (RMA) [15], we used interval mapping to screen the 45,037 traits. Transcripts with LOD scores of 3.4 or greater at one or more locations in the genome were recorded as mapping transcripts. Use of this LOD threshold roughly corresponds to a genomewide type I error rate of 0.08 [16] and a false discovery rate (FDR) of 0.48 (see Materials and Methods). The high FDR is not surprising, considering that adjustments for multiple tests across transcripts have not been made [17]. We nevertheless keep this LOD threshold since we consider this step as only an initial screen; the threshold reduces the computational burden significantly while maintaining a high true-positive rate. This procedure yielded 6,016 mapping transcripts.
We plotted the mapping locations of these expression traits against their physical locations (Figure 1A). Of the 6,016 mapping transcripts, 723 were classified as cis (the diagonal spots in Figure 1A) and the rest were classified as trans. The cis traits are listed in Table S1. Rearrangement of Figure 1A using hierarchical clustering of the LOD profiles enabled us to identify the putative locations of metabolic regulators (Figure 1B). We identified 15 regions with the highest number of mapping transcripts, and for each region, we tested the list of mapping traits for enrichment in a common functional annotation, using the GO database and a Hypergeometric calculation. For each region, we examined up to 50 GO terms, each of which involved more than ten genes. A region is said to be enriched for a particular GO term if the resulting Hypergeometric P-value is ≤0.01. Using these criteria, we found modest functional enrichment in each region (results not shown). However, when we followed up and examined all eQTLs that have similar maximum LOD positions (whether statistically significant or not), we found clear evidence of enrichment (Figure 3B). We performed the same procedure on randomized data, preserving the correlation structure among the traits. This enrichment test did not reveal results as significant as were observed in the original data.
Figure 1 Expression QTL in the (B6 × BTBR) F2-ob/ob Cross
(A) Expression QTL that regulate gene expression in the (B6 × BTBR) F2-ob/ob cross. The physical locations of the transcripts are organized on the y-axis. The chromosome regions (x-axis) to which those transcripts are mapped were obtained using the scanone function in the R/qtl package with 5-cM intervals. The gray scale reflects the strength of the linkage signals (LOD scores greater than 8 are scaled to 8). Transcripts appearing on the diagonal are inferred to be cis-regulated. The chromosomes were concatenated to form a 2,300-Mb genome, starting from Chromosome 1 and ending with Chromosome 19.
(B) Global display of mapping patterns of linkage clusters. The y-axis shows the 6,016 mapping transcripts. The x-axis shows the physical locations to which these transcripts map. The transcripts are grouped based on the hierarchical clustering of linkage mapping patterns across the genome. Darker areas indicate the regions to which traits are comapped or coregulated. The boxes correspond to the hot spots on Chromosomes 2, 10, and 13. The gray scale reflects the strength of the linkage signals (LOD scores greater than 8 are scaled to 8).
Figure 2 Coordinate Regulation of Genes for GPCR Protein Signaling Pathways
(A) GPCR protein signaling pathway genes show coordinated changes across the 60 F2 mice. The rescaled expression levels of 174 distinct transcripts (y-axis) that belong to the GPCR pathway. The x-axis displays the 60 F2 mice. The plot shows that the subset of GPCR traits identified by our trait correlation-GO analysis has vertical patterns that indicate coordinate expression across the 60 F2 mice (P = 1e10−4 by permutation test).
(B) Histogram of pairwise correlation coefficients of GPCR traits identified with different seeds.
(C) Scaled expression levels of traits from one realization of the permuted lists. Thirty-eight lists of correlated traits were randomly sampled from a total of 1,341 correlated trait lists. The list sizes were adjusted to match those of the GPCR lists. The expression values were scaled to have mean intensity = 0 and variance = 1, as in the GPCR list in Figure 2A.
Combining Mapping and Correlation Analysis Improves Sensitivity to Detect Regulatory Loci
We developed a strategy to explore metabolic regulatory networks on a genomewide scale that incorporates correlated traits that by themselves do not show statistically significant linkage. Considering each of the 6,016 mapping transcripts as a “seed,” we computed the Pearson correlation coefficients (r) across the 60 F2 mice for the 45,037 expression measurements and constructed a list of traits having r ≥ 0.7 (FDR = 1.52e10−6). We then tested for enrichment of any GO BP. Among the 6,016 seed transcripts, 1,341 produced lists of traits enriched for at least one GO term. The lists identified by the seed traits and GO terms are given in Table S2.
Among all of the lists from the 1,341 seed transcripts, we identified 862 unique GO BP terms (15,726 in total). We traced back the lists of correlated traits that are enriched for each unique GO term and concatenated all such lists. Different seeds may have identified overlapping traits enriched for the same GO term. These redundant copies of traits were removed after concatenation. By doing so, we associated a collection of distinct traits with each of the 862 unique GO terms. The full archive of lists is available in Table S3.
To illustrate how a gene mapping approach alone is insufficient, consider a locus on Chromosome 2 that regulates in trans multiple ion transport genes (Figure 1B, box), including six genes encoding subunits of the mitochondrial F1 or F0 ATPase of the proton channel of the electron transport system. It is known that there are 24 other ATP synthase subunits in the genome. None of the 24 expression traits exceeded the LOD threshold of 3.4, but many of them shared the linkage peak on Chromosome 2 (Figure S1). These results exposed a limitation of eQTL mapping; without inspection of the list of correlated trans traits, biological inference, and reconsideration of the 24 statistically insignificant LOD profiles, we would have missed evidence for coregulation of an entire family of mitochondrial respiratory genes. To automate this procedure, we consider augmenting eQTL mapping with trait correlation analysis.
Figure 3 An Expression Quantitative Trait Locus on Chromosome 2 Regulates Many GPCR Protein Pathway Genes
(A) Comapping of GPCR pathway traits to loci on Chromosome 2. The figure shows the peaks on Chromosome 2 versus LOD score for 174 GPCR protein signaling pathway traits. The closed circles correspond to 38 seed traits.
(B) Comapping of GPCR pathway traits. The figure shows the magnitude (y-axis) versus location of LOD peaks (x-axis is scaled by cM) for 174 GPCR protein signaling pathway traits. The closed circles correspond to the 38 seed traits. The interval mapping LOD profiles for all 38 seeds are included in Figure S2.
A Regulator Locus for G Protein–Coupled Receptor Genes
Using the correlation analysis described above, we identified 38 seeds that are in the G protein–coupled receptor (GPCR) protein signaling pathway and found 174 distinct traits correlated with these 38 seeds. Dramatically, all 174 traits were annotated as belonging to the GPCR protein signaling pathway GO term (Table S4). We created a heat plot to visualize the correlation patterns across multiple lists for this pathway by first rescaling the log2 intensity values of each trait across all the 60 F2 mice so that the mean = 0 and variance = 1. This rescaling step is necessary because some genes are overexpressed across all the F2 mice and others are underexpressed, which fails to reveal the coordinate regulation pattern across traits. We plotted the rescaled expression levels of the transcripts across the 60 F2 mice (Figure 2A). A striking feature of Figure 2A is the monochromatic columns, which reflect the coordinate changes in gene expression across the genetic dimension represented by the F2 sample. The traits from different seeds are not all highly correlated with each other (Figure 2B). However, the fact that they are enriched for the same GO term implies that they are coordinately regulated.
To ensure that the vertical patterns that were preserved across multiple lists were indeed functionally relevant, we randomly sampled 38 lists from the total of 1,341 lists and adjusted the sizes to be the same as those of the GPCR lists. We concatenated traits from those 38 randomly sampled lists. The vertical patterns shown in Figure 2A are no longer evident (Figure 2C). This was repeated 10,000 times. The vertical patterns in Figure 2A were shown to be statistically significant (P = 1e10−4; see Materials and Methods).
It is important to note that many of the traits correlated to seed traits did not show significant linkage across the 60 F2 mice and would have been overlooked by linkage mapping alone. When we mapped the GPCR seed with the most significant linkage, we discovered that the expression levels of this GPCR gene were influenced by a regulatory locus on Chromosome 2 at 30 cM, between D2Mit297 and D2Mit9 (Figure 3A). Of the 174 traits, 50 had their major LOD peak within 10 cM of this locus, and 81 others had a secondary peak in this region (Figure 3A). While many of these LOD scores do not exceed our significance threshold, the strong agreement suggests that most are driven by this same regulator or closely linked regulators, thus comprising a regulatory network. The LOD profiles for the 38 seed traits are shown in Figure S2. There appeared to be secondary regulators on Chromosomes 10 and 13 (Figure 3B). Similar evidence was suggested by inspecting the chromosome regions as shown in the boxes in Figure 1B. We picked out all transcripts, whether or not they have LOD scores exceeding 3.4, that share the same LOD peaks on the regions of Chromosome 2, 10, or 13 and tested for BP GO enrichment. We found GPCR protein signaling pathway in the enriched list with very small P-values. In short, we found that trait correlation combined with linkage mapping can reveal regulatory networks that would otherwise be missed if we studied only mRNA traits with statistically significant linkages in this small cross. The combined analysis is more sensitive compared with linkage mapping only.
A Regulator Locus of Lipid Metabolism Genes
The strategy given above can be applied to many metabolic pathways. We found evidence of a regulatory pathway controlling lipid metabolism (Figure 4; P = 4e10−4). These expression patterns were combined from lists using 71 different seeds. As was the case for the GPCR transcripts, these show evidence of coregulation across the 60 F2 mice.
Figure 4 Lipid Metabolism Pathway Genes Show Coordinated Expression across the F2 Mice
The rescaled expression levels of 184 distinct transcripts (y-axis) that belong to the lipid metabolism pathway are shown for each of the 60 F2 mice (x-axis). Plot shows that the subset of lipid metabolism traits identified by our trait correlation-GO analysis has preserved vertical patterns (P = 4e10−4 by permutation test).
We have previously studied eQTL for stearoyl-CoA desaturase-1 (Scd1) [2], an important gene for lipid metabolism and insulin sensitivity [18,19]. When we used Scd1 expression as a seed, the traits that were most closely correlated with Scd1 were clearly enriched in lipid metabolism genes (Table S5).
The traits that are highly correlated also map to the same genomic locations, even though their respective genes are in separate locations (i.e., they are being regulated in trans). Major QTL peaks for most of the 20 lipid metabolism traits were found on Chromosomes 2 (D2Mit263) and 5 (D5Mit240); to a lesser extent, they were found by loci on several other chromosomes. We found strong evidence that these two loci are jointly predictive for all 20 traits (Table 1).
Table 1 Top 20 Gene Expression Traits Correlated with Scd1 Are All Lipid Metabolism Genes
Two genes that ranked high in Table 1, RIKEN cDNA 3110032G18 (Riken32G18 hereafter; r = 0.868) and diazepam binding inhibitor (r = 0.816), did not seem to have obvious functional relevance with Scd1. We used this opportunity to test the predictive power of trait correlations.
Diazepam Binding Inhibitor Is Functionally Related to Scd1
The correlation between diazepam binding inhibitor (Dbi) and Scd1 is stronger than that for the classic lipid biosynthesis genes such as fatty acid synthase, malic enzyme, and fatty acyl-CoA ligase. Interval mapping of the expression levels of Dbi and Scd1 showed highly similar patterns (data not shown), suggesting that they are regulated by common loci. To understand the biological basis for the predicted coregulation, we first sought to use in silico approaches to investigate this gene. The Dbi gene symbol is synonymous with acyl-CoA binding protein (Acbp). ACBP is a 10-kDa cytosolic protein that binds long-chain fatty acyl-CoAs and has a high affinity for oleoyl-CoA, the product of the SCD1 reaction [20]. Our data analysis predicts that a common regulator maintains a fixed ratio of mRNA for SCD1 and for a protein that binds its product, oleoyl-CoA. Recently, Dbi was reported to be regulated by sterol regulatory element binding protein-1c (SREBP-1c), a known master regulator of lipogenic enzymes [21]. Perhaps one or more of the regulators we have mapped are coactivators or corepressors of SREBP-1c, analogous to PPARγ coactivator-1β [22]. Thus, the prediction from our trait correlation analysis that Dbi is functionally related to Scd1 was confirmed by recently published experimental data.
Prediction of Riken32G18 as a Putative Lipid Metabolism Gene
The mRNA most closely correlated to Scd1 is Riken32G18, which maps to the same location on Chromosome 2 as Scd1 expression but with almost twice the LOD score. Notably, Riken32G18 is physically located on Chromosome 5 and Scd1 is on Chromosome 19; thus, the two genes are likely coregulated in trans by the Chromosome 2 locus.
We hypothesized that Riken32G18 plays a role in lipid metabolism with a close functional relationship with Scd1. There is no information available from the online resources to functionally link Riken32G18 to Scd1 or to lipid metabolism. In order to test its functional relationship with lipid metabolism, we asked if Riken32G18 expression is responsive to conditions that regulate expression of lipid metabolism genes.
From our previous results, we know that lipogenic genes are expressed at a lower level in ob/ob adipose tissue than in lean adipose tissue [23]. Whereas Scd1 and fatty acid synthase (Fas) were expressed at a lower level in ob/ob adipose tissue, Riken32G18 expression was higher in obese adipose tissue. Thus, Riken32G18 appeared to be regulated reciprocally with lipogenic genes (Figure 5A). In contrast to adipose tissue, lipogenic genes are more highly expressed in livers of ob/ob mice than in lean mice [24]. Again, Riken32G18 was regulated in the opposite direction; i.e., its expression was reduced in ob/ob liver (Figure 5B). Finally, liver-X-receptor (LXR) α activation increases genes in fatty acid metabolism in the liver. Once again, Riken32G18 expression was down-regulated by LXRα activation (Figure 5C). We conclude that Riken32G18 is very likely a novel gene involved in lipid metabolism. It is coregulated with lipid metabolism genes across the 60 F2 mice and reciprocally regulated with such genes across physiologic conditions.
Figure 5 Regulation of Riken32G18 Inversely Parallels the Regulation of Lipogenic Genes
(A) Metabolic regulation of Riken32G18. Expression of lipogenic genes and Riken32G18 in adipose tissues of lean versus obese mice.
(B) Expression of lipogenic genes and Riken32G18 in livers of lean versus obese mice.
(C) Expression of LXRα target genes and Riken32G18 in mouse livers treated with LXRα agonist T0901317.
Discussion
Macromolecules and metabolites in cells interact with one another across a critical concentration range. The functional concentration must be within boundaries defined by the kinetics and thermodynamics of these interactions. One way cells can maintain the critical concentration range for molecules within a pathway is to maintain them under the control of a common regulatory system. A well-characterized example is the regulation of lipogenic enzymes by SREBP-1c, a master regulator of virtually all enzymes critical to fatty acid synthesis [25]. Pathways that feed into lipogenesis are also coregulated with lipogenic enzymes.
Whereas Eisen et al. [4] studied gene expression correlation over a time dimension and Zhang et al. [3] studied gene expression correlation over a tissue dimension, we performed our studies over a genetic dimension. In addition to identifying coregulated genes, the genetic dimension also supplies us with information about the genomic location of putative regulatory loci. Our method involved a two-step approach. First, we applied a genomewide linkage analysis to identify mapping transcripts using a liberal filter to ensure a high true-positive rate. In the second step, we performed trait correlation analysis, using seeds within linkage groups.
Our approach revealed biologically meaningful relationships among the traits. In particular, we found that traits that are correlated over a genetic dimension are frequently enriched for a physiologically meaningful biological function. For example, we found a gene whose expression was highly correlated with those of lipid metabolism genes and with Scd1 expression as a seed trait. The prediction that this gene is involved in lipid metabolism was borne out by recent studies showing that this protein binds to oleoyl-CoA, the product of the SCD1 reaction. Moreover, the eQTL for this gene maps to the same location on distal Chromosome 2 as did other lipid metabolism eQTL.
The distal region of Chromosome 2 containing the cluster of lipid metabolism traits is quite interesting as several QTL for obesity and related traits have been mapped to this region [13,26–30]. Furthermore, Estrada-Smith et al. [31] have used congenic mouse lines derived from B6 and CAST/Ei strains to identify a 17-Mb region of distal Chromosome 2 that regulates plasma levels of triglycerides. This region directly overlaps with the location of our fatty acid metabolism cluster and our fasting plasma insulin locus.
We also found an entirely unannotated gene highly correlated with lipid metabolism genes. Analysis of tissues from obese animals or animals treated with an LXRα agonist revealed that this gene is regulated inversely under these physiologic conditions with lipid metabolism genes, providing evidence that it participates in lipid metabolism or perhaps is a negative regulator of lipid metabolism.
Another example of coordinated expression was the GPCR protein signaling pathway genes. Here, we found that all of the 174 transcripts that were highly correlated with 38 GPCR seeds encoded GPCR pathway proteins and most mapped to the same locus on Chromosome 2. Fewer than 10% of the transcripts are olfactory receptors. Among the other correlating genes are gamma-aminobutyric acid (GABA) receptor signaling pathway genes, neuropeptide signaling pathway genes, and cAMP signaling genes (Table S6).
The GPCR protein signaling pathway cluster overlaps with a region on proximal Chromosome 2 that was narrowed down to an 8-Mb region regulating body weight differences in the B6 and BTBR strains [32] and a 10-Mb region underlying obesity-related traits in the B6 and CAST/Ei strains [31]. Several of the GPCRs in the cluster are related to obesity traits in mice when mutated or overexpressed [33–36], and others have been proposed as candidate genes for human obesity [37–39] (Figure S3A). An additional subset of the GPCR cluster represents GABA receptor subunits (Figure S3B). Alterations in the GABA system are related to the obesity phenotype seen in obese (fa/fa) Zucker rats [40] and humans with Prader-Willi syndrome [41,42].
Although we have focused our own analysis on lipid metabolism, our database, which is available through WebQTL (www.genenetwork.org) [43], can provide insights into the regulation of metabolic pathways expressed in the liver. A researcher can start with a particular mRNA of interest as a seed trait and quickly identify genes whose expression is highly correlated with the seed trait. By looking for overlapping loci that control the expression of the seed and additional genes of interest, a regulatory pathway can be hypothesized.
The particular implementation of the approach can and should be study specific. For example, there are a number of thresholds that must be determined, each affecting the number of transcripts considered and the overall FDR. The level of tolerable FDR depends on a number of factors. We accepted a very high FDR in our first stage of transcript mapping as we desired only a mild filtering of the data to reduce the computational burden. We have recently developed statistical methods that provide better control of FDR for eQTL mapping [17]. These could be used if a more stringent filtering was desired at this stage. A more conservative filtering in the second stage was used here by imposing a relatively high threshold for the correlation coefficient. Although a correlation coefficient threshold as low as 0.4 would still correspond to an acceptable FDR of 0.05, we chose the more conservative 0.7. We have reported stage-specific FDRs as they were used to guide our analysis. However, we note that the FDR associated with the entire procedure is not known. Indeed, it is an extremely difficult problem to precisely define, not to mention determine, overall FDR for studies involving multiple types of data and multiple analysis methods. Doing so remains an open question of importance to both statisticians and biologists.
In summary, here we used two complementary approaches. First, we started with single QTL mapping to obtain seeds. Second, we used the seeds to obtain correlated traits. Finally, we confirmed genetic architecture for the correlated traits. As we have argued, the combined approach is more sensitive to retrieve biologically meaningful information from the data set. If we just did QTL mapping, those traits with nonsignificant LOD scores would have been missed. If we just performed correlation analysis, we would have lost the causation information that comes from the genetic linkage analysis. Our approach provides an important organizing step in the analysis of expression networks. It yields sets of related traits that can be further analyzed using multiple trait mapping [44] and causal networks [45], as these methods are most effective after screening to obtain a modest number of closely related traits. Moreover, our approach increases the power to formally uncover members of gene expression networks.
Materials and Methods
Animals.
The power of a genetic mapping study depends on the heritability of the trait, the number of individuals included in the analysis, and the genetic dissimilarity among them. In this study, we selected 60 (B6 × BTBR) F2-ob/ob mice (29 males and 31 females) based on the selective phenotyping algorithm that we developed [14]. The algorithm selects a subset of F2 individuals from a mapping panel based on genotype data. The selection achieves substantial improvements in sensitivity compared to a random sample of the same size [14]. The 60 mice used in this study were a subset of F2 mice segregating for phenotypes associated with obesity and diabetes [13]. The F1 mice used to generate the F2 sample were all derived from crosses in which the B6 parent was male and the BTBR parent was female. The framework map consists of 194 microsatellite markers, with an average spacing of 10 cM.
Microarrays.
Liver total RNA was extracted from frozen tissue samples with RNAzol reagent (Tel-Test, Friendswood, Texas, United States). Crude RNA samples were purified with RNeasy mini-columns (Qiagen, Valencia, California, United States) before being subjected to microarray and RT-PCR studies. The RNA samples were processed according to the Affymetrix Expression Analysis Technical Manual. A total of 60 MOE430A and MOE430B arrays were used to monitor the expression levels of approximately 45,000 genes or ESTs. The data were processed with MAS5.0 to generate cell intensity files. Quantitative expression levels of all the transcripts were estimated using the RMA algorithm for normalization [15].
Identifying transcripts with linkages.
To identify traits that have linkages in the 60 F2 mice, we used standard interval mapping implemented in R/qtl [46] to map each of the 45,037 unique probe sets at 5-cM resolution. Transcripts with LOD scores of 3.4 or greater at one or more locations in the genome were recorded as transcripts with linkages, or “seeds.” Using maximum LOD of 3.4 or greater as a threshold roughly corresponds to genomewide type I error rate of 0.08 [16]. We permuted the animal labels five times and for each permutation ran R/qtl interval mapping on a 5-cM grid. The loci-specific LOD scores from the five permutations were pooled together to estimate empirical P-values These P-values were then used for calculation of q-values and estimation of FDR [47]. The FDR corresponding to a LOD score of 3.4 was 0.48. This resulted in 6,016 mapping traits. We found some regions of the genome with strong sex-by-genotype interactions. The results reported here are in regions with little or no evidence for such interactions.
Hierarchical clustering of the LOD score profiles.
LOD score profiles were obtained for each transcript using R/qtl interval mapping with 5-cM intervals. This yielded, for each trait, 465 interval mapping testing points in the entire genome. Treating each LOD score profile as a point in the 465-dimensional space, we calculated pairwise Euclidean distances between LOD score profiles and performed hierarchical clustering based on those distances. Clustering the LOD profiles has the advantage that transcripts having similar mapping patterns across the genome will be grouped together.
Expression correlation.
Using each of the 6,016 transcripts as a seed, Pearson correlation coefficients were calculated against the log-transformed expression intensities (raw intensities were normalized by RMA) of all the 45,037 transcripts in 60 F2 mice. We obtained a 6,016-by-45,037 matrix of correlation coefficients. For each seed, we collected the traits with a correlation coefficient of 0.7 or greater, which forms the list of correlated traits for that seed. For each pair of transcripts, we computed its Pearson correlation together with the P-value. We then computed the corresponding q-values to determine the FDR [47]. A correlation of 0.7 corresponds to an FDR of 1.52e10−6.
Test for enrichment.
Enriched functional groups, defined as GO categories corresponding to BP, were identified from each list with P-values computed from a Hypergeometric calculation, carried out using a custom variant of “GOHyperG” in the “GOstats” package (version 1.1.3; Bioconductor Core Team 2004; http://www.bioconductor.org). Interpretation of these P-values is not straightforward since many dependent hypotheses are tested. Furthermore, the Hypergeometric calculation tends to result in small P-values when groups with few transcripts are considered. It has been suggested that one consider only GO nodes with small P-values and a reasonable number of genes (ten or more) [48]. For each list, we examined up to 50 GO terms, each of which involves more than ten genes and has a Hypergeometric P-value of ≤0.01.
Creating the coordinated expression plot.
From the lists of correlated traits, we identified 15,726 BP GO terms in total, among which 862 are unique. For each of the unique GO terms, we traced back the lists of correlated traits that were enriched (P = 0.01), and we concatenated all such lists. To create the heat plot, we first normalized the log intensity values of each trait across all the 60 F2 mice to mean = 0 and variance = 1. This step is very important because some traits were highly overexpressed or underexpressed across all the F2 mice. We then plotted the rescaled expression values of transcripts across the 60 F2 mice.
Permutation test for coordinated expression.
We developed a statistic to assess the extent of coordinate expression. We first collapsed all the members in a list into a pseudo-member by taking the average across the transcripts in that list. We then computed pairwise correlations among the pseudo-members. The final measure of coordinate expression was the average of those pairwise correlations. For a given GO term, we sampled the same number of lists from the total of 1,341 lists 10,000 times and matched the list sizes to those in the GO term of consideration. We computed the statistic in each random sample. The empirical P-value was calculated as the frequency of the samples where the statistic exceeded the one calculated using the observed data. The P-value for GPCR protein signaling pathway set was 1e10−4, and it was 4e10−4 for the lipid metabolism set.
Genetic architecture of GO groups.
Subsequent QTL analyses were performed on traits within GO groups using one- and two-QTL scans with R/qtl. Possible interacting effects of sex were examined and found not to be significant for the GPCR and lipid metabolism groups reported here (data not shown).
Real-time quantitative PCR for Riken32G18 and lipogenic genes.
First-strand cDNA was synthesized from 1 μg of total RNA using Super Script II Reverse Transcriptase (GIBCO BRL, San Diego, California, United States) primed with a mixture of oligo-dT and random hexamers. Reactions lacking the reverse transcriptase served as a control for amplification of genomic DNA. Representative genes encoding enzymes in lipid metabolism pathways were studied together with Riken32G18, including Scd1, Fas, and phospholipid transfer protein (Pltp). The housekeeping gene β-actin was used as a normalization control.
LXRα agonist treatment of mice.
BTBR male 7-wk-old mice were fed a chow diet (Harlan 7001 4% fat rodent chow) supplemented with DMSO vehicle or DMSO + T0901317 (0.025% by weight; Tularik, San Francisco, California, United States) for 7 d. T0901317 is a nonsteroidal LXR agonist that has previously been shown to potently transactivate LXR-regulated genes in mice [49]. After a 4-h fast, liver samples were collected and frozen in liquid nitrogen.
Supporting Information
Figure S1 LOD Profiles of ATP Synthase Subunit mRNA Traits
The traits exhibiting a linkage peak on Chromosome 2 are grouped together on top rows.
(477 KB PDF)
Click here for additional data file.
Figure S2 LOD Profiles of 38 Seed Traits That Produce the 174 Correlated Traits Belonging to the GPCR Pathway
(495 KB PDF)
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Figure S3 LOD Profiles of GPCR eQTL
(A) LOD profiles of GPCR protein signaling pathway mRNA traits related to obesity. The LOD profiles were generated on WebQTL. The empirical P-values are generated by permutation test (1,000 permutations); the lower line indicates suggestive linkage and the upper line indicates significant linkage. The green line indicates BTBR alleles increase the trait values; the red line indicates that B6 alleles increase the trait values.
(B) LOD profiles of GPCR protein signaling pathway mRNA traits related to obesity, from the GABA receptor subunit family. The LOD profiles were generated on WebQTL. The empirical P-values are generated by permutation test (1,000 permutations); the lower line indicates suggestive linkage and the upper line indicates significant linkage. The green line indicates BTBR alleles increase the trait values; the red line indicates that B6 alleles increase the trait values.
(3.2 MB PDF)
Click here for additional data file.
Table S1
cis Traits Inferred by Using LOD Greater Than 3.4
Columns A, B, and C show the Affymetrix probe set IDs, gene names, and the gene symbols of the cis traits. Columns D and E show the physical chromosome and position (Mb) of the traits. Column F shows the maximum LOD scores of the cis traits.
(46 KB TXT)
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Table S2 Enriched GO Categories for Expression Traits That Are Correlated with the Seed Traits
Using each of the 6,016 transcripts as a seed, Pearson correlation coefficients were calculated against the log-transformed expression intensities (raw intensities were normalized by RMA) of all of the 45,037 transcripts in the 60 F2 mice. For each seed, we collected the traits with correlation coefficients of 0.7 or greater, which form the list of correlated traits for that seed. Enriched functional groups, defined as GO categories corresponding to BP, were identified from each list with P-values computed using a custom variant of “GOHyperG” in the “GOstats” package (Bioconductor Core Team 2004; http://www.bioconductor.org). For each list, we examined up to 50 GO terms, each of which involves more than ten genes and has a P-value of ≤0.01. Column A contains the Affymetrix probe set identifiers. Column B contains the names of the genes. Columns C through AZ are the GO terms identified, and Columns BA through CX are the identifiers of these GO terms.
(948 KB TXT)
Click here for additional data file.
Table S3 GO Enrichments of Traits That Are Correlated with Seed Traits
From the lists of traits correlated with the seed traits in Table S2, we identified 15,726 BP GO terms in total, among which 862 are unique. For each of the unique GO terms, we traced back the lists of correlated traits that were enriched (P = 0.01), and we concatenated all such lists. Columns A and B show the GO category identifiers and the names, respectively. Columns C and D show the probe set IDs and gene names of the correlated traits. Column E shows whether (“T” or “F”) the traits were annotated as belonging to the corresponding GO terms in the public database.
(8.3 MB TXT)
Click here for additional data file.
Table S4 Descriptions of the 174 GPCR Protein Signaling Pathway Traits
Columns A, B, and C show the Affymetrix probe set IDs, gene symbols, and gene titles of the 174 gene traits. Columns D and E show the physical chromosomes and positions (Mb) of the 174 traits. Columns F, G, and H show the chromosomes, positions (Mb), and LOD scores of the maximum LOD peaks of those 174 traits.
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Table S5 Top 100 Traits Whose Expression Levels Are Correlated with Those of Scd1 in the 60 F2 Mice
Column A shows the Affymetrix probe set IDs. Columns B and C show the gene symbols and the titles of the genes. Columns D and E show the genomic locations of these traits. Column F shows the Pearson correlation coefficients. Column G shows the unadjusted P-values for the correlations.
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Table S6 Descriptions of the Gene Ontology “BP” Terms That Are Enriched in the 174 GPCR Protein Signaling Pathway Traits
Columns A and B show the GO category identifiers and the names. Column C shows the enrichment P-values (rounded to five decimal places) calculated using Hypergeometric distribution. Column D shows the GO category size.
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Accession Numbers
The original genotypes, physiologic phenotypes, and microarray-derived gene expression data are available in WebQTL (http://www.genenetwork.org; group: B6BTBRF2 [43]) and in GEO (http://www.ncbi.nlm.nih.gov/geo; accession number GSE3330).
The Gene Ontology (www.geneontology.org) accession numbers are GPCR protein signaling pathway (0007186) and Scd1 (1415965_at).
We thank Mark Craven, Ping Wang, Keith Noto, Dan Klass, and Elias Chaibub Neto for technical assistance and helpful discussions. The authors are grateful to Yanhua Qu and Jintao Wang for depositing our data into WebQTL. This research was supported in part by American Diabetes Association grant 7–03-IG-01, National Institutes of Health (NIH)/National Institute of Diabetes and Digestive and Kidney Diseases grants 5803701 and 66369, and grant HL56593. We are grateful to the Center for Inherited Disease Research Genotyping Laboratory for their genotyping under an NIH-supported project.
Author contributions. HL, JBF, BSY, MTF, CK, and ADA conceived and designed the experiments. HL, JBF, DSS, ETKM, MTF, and KLS performed the experiments. HL, MC, JBF, BSY, CMM, MTF, CK, and ADA analyzed the data. HL, JBF, BSY, KFM, and RWW contributed reagents/materials/analysis tools. HL, MC, JBF, BSY, CK, and ADA wrote the paper.
Competing interests. The authors have declared that no competing interests exist.
A previous version of this article appeared as an Early Online Release on December 7, 2005 (DOI: 10.1371/journal.pgen.0020006.eor).
Abbreviations
BPBiological Process
eQTLexpression quantitative trait loci
FDRfalse discovery rate
GABAgamma-aminobutyric acid
GOGene Ontology
GPCRG protein–coupled receptor
LXRliver-X-receptor
RMARobust Multi-array Average
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PLoS GenetPLoS GenetpgenplgeplosgenPLoS Genetics1553-73901553-7404Public Library of Science San Francisco, USA 1644005510.1371/journal.pgen.002000205-PLGE-RV-0111R2plge-02-01-01ReviewEvolutionPathologyGenetics/GenomicsGenetics/Disease ModelsGenetics/EpigeneticsAnimalsVirusesRetroviral Elements and Their Hosts: Insertional Mutagenesis in the Mouse Germ Line ReviewMaksakova Irina A Romanish Mark T Gagnier Liane Dunn Catherine A van de Lagemaat Louie N. Mager Dixie L ** To whom correspondence should be addressed. E-mail: [email protected] A. Maksakova, Mark T. Romanish, Liane Gagnier, Catherine A. Dunn, Louie N. van de Lagemaat, and Dixie L. Mager are at the Terry Fox Laboratory, BC Cancer Agency, Vancouver, British Columbia, Canada, and Department of Medical Genetics, University of British Columbia, Canada.
1 2006 27 1 2006 2 1 e2 © 2006 Maksakova et al.2006This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.The inbred mouse is an invaluable model for human biology and disease. Nevertheless, when considering genetic mechanisms of variation and disease, it is important to appreciate the significant differences in the spectra of spontaneous mutations that distinguish these species. While insertions of transposable elements are responsible for only ~0.1% of de novo mutations in humans, the figure is 100-fold higher in the laboratory mouse. This striking difference is largely due to the ongoing activity of mouse endogenous retroviral elements. Here we briefly review mouse endogenous retroviruses (ERVs) and their influence on gene expression, analyze mechanisms of interaction between ERVs and the host cell, and summarize the variety of mutations caused by ERV insertions. The prevalence of mouse ERV activity indicates that the genome of the laboratory mouse is presently behind in the “arms race” against invasion.
Citation:Maksakova IA, Romanish MT, Gagnier L, Dunn CA, van de Lagemaat LN, et al. (2006) Retroviral elements and their hosts: Insertional mutagenesis in the mouse germ line. PLoS Genet 2(1): e2.
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Introduction
The activity of transposable elements (TEs) places a variable mutational load upon their host species [1–3]. In species such as Drosophila, TEs comprise approximately 10% of heterochromatic [4] and only 2%–3% of euchromatic DNA [4,5] but cause over 50% of de novo mutations [6]. In contrast, nearly half of the human genome is TE-derived but de novo disease-causing insertions are rare [7,8]. TE activity in the laboratory mouse falls in the middle of these two extremes [8,9], largely because of the activity of endogenous retroviruses (ERVs) and other elements with long terminal repeats (LTRs), which together make up 8%–10% of the genome [7,9–12] (Box 1). A striking difference between the mouse and the human repertoire of ERVs/LTR elements is that the mouse contains many “active” LTR retroelements and a few potentially infectious ERVs that are closely related to exogenous mouse retroviruses [9,13]. Unlike in inbred mice, infectious human ERVs have not been described, no new insertions have been found, and there are no ERVs closely related to human exogenous retroviruses [10,11,14]. In addition to LTR elements, the major classes of retrotransposons in mammals are the non-autonomous short interspersed elements (SINEs) and the autonomous long interspersed elements (LINEs) [7,9,12]. The retrotransposition and genomic effects of these non-LTR retroelements have been extensively discussed in a number of recent reviews [2,15,16].
Since the mouse is widely used as a disease model, it is important to understand the mutagenic events affecting this species and how they differ from those in humans. This article examines mouse ERVs and other LTR retroelements, focusing on insertional mutagenesis of the germ line. For the purposes of this review, LTR retroelements, which amplify via intracellular retrotransposition, and true exogenous retroviruses, which amplify by extracellular infections and retrotransposition, will be considered together as “ERVs” as they have a common evolutionary origin [13]. We discuss mutational mechanisms of different families of ERVs, illustrating significant differences in their effects on genes. We also discuss host responses to curtail ERV activity and, in some cases, to adopt ERVs for normal cell functions. Finally, we present the view that inbred mice are in a transitory state in which ERVs are not at equilibrium with their host genome.
Prevalence of ERV-Induced Mouse Insertional Mutations
Present-day activities are markedly different between human and mouse ERVs. A recent review lists 48 TE insertion mutations in human [17], and estimates of the frequency of novel human TE insertions range from one retrotransposition per 8–33 births [2,18–20], all of them due to L1-mediated non-LTR retrotransposons (reviewed in [21,22]). Given that ~47,000 mutant alleles have been characterized (according to the Human Gene Mutation Database [http://www.hgmd.cf.ac.uk/]; August 2005), these numbers suggest that ~0.1% of human spontaneous mutations are due to TE insertions, but none are due to ERV activity. As in human, a small number of de novo germ line L1 insertions have been reported in mice (reviewed in [22,23]). However, the ongoing activity of ERVs accounts for the majority of new insertional mutations in the mouse. To provide a current estimate of the fraction of spontaneous mutations due to ERV insertion, we tabulated all documented cases and found 63 (Table S1). The Mouse Genome Informatics database (http://www.informatics.jax.org/) lists 1,489 spontaneous mutant alleles (as of August 2005). After removing unannotated cases and numerous non-independent entries and revertants derived from the nonagouti a allele (Box 2), 519 spontaneous alleles with an annotated molecular mechanism remained. This list included 55 of our 63 ERV insertion mutations. Taken at face value, these figures suggest that 10%–12% of all mutations are due to ERV insertions, a fraction very similar to previously reported estimates of 10%–15% based on lower numbers [22,23]. Reversion of ERV-induced mutations has also been observed at a few loci due to LTR–LTR recombination (Box 2). It should be noted that the 10%–15% figure is likely an underestimate because of ascertainment bias. For example, point mutations in coding regions will be more readily detected than ERV insertions in introns or outside gene borders. Regardless of the precise figure, ERV activity in inbred mice is dramatically higher than in modern humans.
Most ERVs are highly transcribed during early zygotic divisions and in germ cells, resulting in an increased likelihood of new heritable proviral integrations (Box 3). Although genomic copy numbers of murine leukemia virus (MLV) are low (see Box 1), this family is the most active mouse ERV on a per provirus basis. New MLV provirus acquisitions are found in 2%–75% of the progeny in the highly susceptible SWR/J-RF/J hybrid mice [24–26]. AKR mice appear to gain one new ecotropic MLV provirus every 50–100 generations [13]. Five germ line mutations or strain variants due to insertions of MLV have been well characterized in other lab strains (Table S1). The somatic effects of MLV, as well as mouse mammary tumor virus, in activating oncogenes via insertional mutagenesis are well known [27]. Indeed, mapping common retroviral integration sites in mouse tumor systems has proven a powerful strategy to identify new genes involved in cancer [28,29].
Intracisternal A particle (IAP) and Early Transposon (ETn)/MusD elements are present in much higher copy numbers than MLVs (Box 1) and are responsible for the majority of ERV-induced de novo germ line mutations (Tables 1 and Table S1). In addition, IAP elements are frequent insertional mutagens in somatic cells, particularly in leukemia, plasmocytoma, and myeloma cell lines, and can activate oncogenes or cytokine genes [30]. Notably, very few new ETn insertions have been reported in somatic cells [31], likely reflecting their restricted expression pattern or limited expression of coding-competent MusD elements, required for ETn retrotransposition.
Table 1 Overview of IAP and ETn Mutagenic Germ Line Insertions
Genetic Background and ERV Subtype Influence on Insertion Probability
Not all mouse strains are equally susceptible to ERV insertions. Most of the IAP insertions have occurred in C3H/HeJ (Tables 1 and Table S1), and nearly all of these cases are of the IAP subtype IΔ1 [32,33]. It seems likely that one or a small number of IΔ1 IAP elements are active in this strain, possibly because of a favorable genomic context or escape from host suppression mechanisms (see below). The IΔ1 subtype, however, requires complementation in trans from coding-competent IAP elements [34], so the latter must also be expressed. A specific strain bias for ETn insertions is not as obvious, although six mutations have occurred in A/J mice and two in each of two other infrequently used strains (SELH/Bc and MRL/MpJ) (Table S1). As with IAP elements, this suggests that some strains harbor more “active” elements and/or allow more ETn or MusD expression. Where sufficient sequence is available, it has been found that most ETn insertions are of a particular structural subtype, ETnII-β, and are nearly identical, suggesting very few currently active elements ([35]; unpublished data). A very definite strain bias has been observed for germ line movement of MLVs, with AKR and SWR/J-RF/J mice being highly active strains (reviewed in [13]) (see Box 3). One of the explanations for such selective activity in particular strains may be the presence of a single highly transcribed master element in a favorable genomic context, as appears to be the case for AKR mice. The other explanation is the difference in host suppression factors, such as methylation levels and the presence of virus-suppressing loci. One long-studied locus involved in suppression of MLV and a number of other ERVs is Fv1 (see below).
As mentioned above, most de novo insertions of IAP and ETn elements are those of defective sequences lacking full coding potential. This fact is curious, given that, in assay systems, coding-competent IAP and MusD elements retrotranspose much more efficiently when proteins and retrotransposing RNA are encoded by the same template (cis preference) [34,36]. Expression patterns of defective and full-length IAP elements vary widely in different cells [30,32,37], but the IΔ1 deleted subtype is preferentially expressed in acute myeloid leukemia cell lines derived from C3H/HeJ mice, despite being present at lower genomic numbers than full-length forms [32]. Non-coding ETnII elements are transcribed at a much higher level than their coding-competent MusD relatives [35], probably explaining their higher likelihood to retrotranspose. Indeed, among the 23 characterized mutagenic ETn/MusD insertions, only two have been reported as MusD (Table S1). However, it is unclear why transcripts from defective elements would predominate in vivo. Possibly they are less likely to be recognized as retroviral elements and to be repressed by host cell silencing machinery.
Mutagenic Mechanisms of ERV Insertions
Most commonly, germ line mutations due to ERV insertions occur in an intron, disrupting gene expression by causing premature polyadenylation, aberrant splicing, or ectopic transcription driven by the ERV LTR (Tables 1 and Table S1). In some cases, small amounts of normal gene transcripts and protein can still be detected. While the number of characterized MLV-induced mutations is too small to perceive general trends, IAP and ETn elements show significant differences in their effects on genes (Figure 1). For ETn insertions, the most commonly reported defect is premature polyadenylation within the ETn, coupled with aberrant splicing due to a few commonly used cryptic splice signals (Figures 1 and 2A). IAP insertions within introns also typically cause aberrant splicing but use a wider variety of cryptic splice signals. In addition, compared to ETns, fewer cases of premature polyadenylation within IAP elements are well documented (Figure 1), but, in many cases, all aberrant gene transcripts have not been well characterized.
Figure 1 Mutagenic Mechanisms of IAP and ETn Insertions
IAP and ETn insertions were classified by their mechanism of gene disruption. Well documented instances of aberrant transcription initiation (5'-terminus) and polyadenylation (3'-terminus) were counted, as well as aberrant splicing and exon skipping (internal disruption). Insertions that cause gene disruption by multiple mechanisms (Table S1) were counted once in each relevant class.
Figure 2 Common Effects of ETn and IAP Insertions on Gene Expression
(A) ETn effects on gene transcript processing. The most common patterns of aberrant transcript processing caused by ETns in gene introns are shown. The natural LTR polyadenylation (polyA) site and a second cryptic polyadenylation site in the internal region, along with four cryptic splice acceptors (SA) and a donor site (SD), are involved in most cases. The number of such cases is an underestimate, since several reports lack sufficient detail of aberrant transcripts. In some cases, several aberrant forms have been found. Boxes denote gene exons, thin lines denote introns, and thick lines denote spliced mRNAs, with direction of transcription from left to right. For clarity, cryptic splice acceptor sites in the 3' LTR are not shown since no documented splicing events involving these sites were found. Intronic mutagenic ETns and the affected gene are most often found in the same orientation (15 of 16 cases).
(B) IAP promoter effects on gene transcription. Ectopic gene expression driven by an antisense promoter in the 5' LTR of an IAP has been reported in eight cases. In some cases, the IAP is located a significant distance upstream of the gene.
A striking difference between the effects of IAP and ETn elements is their tendency to drive ectopic gene expression (Figure 1). For IAP elements, nine cases of LTR-driven gene expression have been reported. Interestingly, eight of these nine cases are driven from an antisense promoter located in the 5' IAP LTR (Figure 2B; Tables 1 and Table S1). Many of the mutant alleles caused by IAP LTR-driven gene expression show variable expressivity among genetically identical mice and have therefore been termed metastable epialleles [38]. The variable expressivity is due to stochastic establishment of the methylation state of the 5' LTR. If the LTR is mostly methylated, its promoter is inactive and little or no effect on the gene is observed. However, if the LTR is unmethylated, its promoter drives ectopic gene expression, resulting in the mutant phenotype. Such cases have been extensively studied by Whitelaw and coworkers, who have proposed the intriguing theory that phenotypic variation in mammals could in part be due to incomplete and variable silencing of retrotransposons in somatic cells [39].
It is unclear why no instances of ETn-promoted ectopic gene expression have been observed, but the lack of such cases could be explained by inactivity of the ETn LTR promoter in somatic cells due to heavy methylation or lack of necessary transcription factors. Expression studies (Box 3) indicate that at least some IAP elements are transcribed in various cell types, a property that would increase the probability of such elements providing promoter function. It is possible that the presence of the cryptic antisense promoter in the IAP LTR also increases the likelihood that an IAP element 5' of a gene will provide promoter function (Figure 2B).
Host Silencing Mechanisms
Transcriptional gene silencing.
To guard against harmful genomic consequences of ERVs and other TEs, an arsenal of cellular defense strategies has evolved to counteract their amplification (Figure 3; Table 2). Transcriptional gene silencing is a principle mechanism for controlling TEs in a broad range of species including mammals, flowering plants, and those fungi whose genomes contain m5C [40]. The best-documented mechanism, DNA methylation of promoters, can directly impede access of transcription factors or lead to an inactive form of chromatin at target loci [41]. Indeed, a majority of genomic CpG dinucleotides and 5-methyl cytosines reside within ERVs and other retroelements in mammals [42]. Several lines of evidence confirm that genomic hypomethylation and TE activation are interrelated. DNA methyltransferase (Dnmt) mutant mice with mutant Dnmt1 or Dnmt3 do not maintain and initiate methylation at existing or new proviral loci, respectively [43,44]. In fact, both MLVs and IAPs become substantially demethylated [43], and IAP transcripts are expressed up to 100-fold higher in Dnmt1−/- mice relative to wild-type [45]. While Dnmt1 is necessary after DNA replication, Dnmt3a and Dnmt3b are essential in the germ line and during development to establish the methylation repertoire [44]. This activity is largely restricted to dispersed and tandem repeats [46]. Dnmt3a and Dnmt3b knockout embryonic stem (ES) cells are unable to establish methylation at new MLV integrations. Knockout ES cells and embryos exhibit a general decrease in methylation at centromere repeats, MLVs, IAPs, and L1s [44]. In addition to the research on promoter methylation, there is a study showing that intragenic methylation reduces the elongation efficiency of RNA polymerase II [47], which suggests that the methylated state of TEs within introns might affect gene expression.
Figure 3 Host Restriction and Silencing of ERVs/LTRs
Blocks to various stages of the retroviral or LTR retroelement life cycle are depicted as are silencing mechanisms affecting activity of integrated elements. Examples of restriction genes and silencing mechanisms: receptor block, Fv4; uncoating block, Trim5; reverse transcription/trafficking block, APOBEC3 and Fv1; transcription block, CpG methylation; and RNA processing block, Nxf1 and RNAi. See text and Table 2 for more details and other examples. An ERV or LTR element within an intron is shown to illustrate common gene-disruptive effects of such sequences through introduction of polyadenylation sites, promoters, and splice donor and acceptor sites. Spliced RNA is depicted with dashed lines. A normal gene transcript driven by the native promoter (P) is shown below the gene. A full-length retroviral transcript, which could be packaged for further rounds of retrotransposition or retroviral infection, is shown above the gene locus. Various potential aberrant or chimeric transcripts are shown above.
Table 2 Mammalian Restriction Genes against Retroviral Activity
It is well established that genomic methylation can serve to recruit chromatin-remodeling proteins [41]. The SWI/SNF family members are components of the trithorax group protein complex and are responsible for maintaining transcriptional activity. A SWI/SNF mammalian catalytic subunit, Brm (SWI/SNF-related, matrix associated, actin-dependent regulator of chromatin), is involved in increased transcription of retroviral RNA, but this is alleviated in cells lacking this protein [48]. Moreover, Brm-deficient cells treated with histone deacetylase inhibitors are unable to silence transcription of retroviral genes. These results suggest that Brm-type SWI/SNF is essential for TE expression and that histone deacetylation is crucial for silencing. Paradoxically, Lsh (lymphoid-specific helicase), also a SWI/SNF family member, preferentially associates with repeats and contributes to their silencing [49]. Lsh−/- mice are hyperacetylated at histones overlying TEs (class I and II LTRs, LINEs, SINEs, and centromeric repeats), and their transcripts are abundant. This defect appears specific to repetitive sequences. A further level of silencing is mediated by histone methylation. Intriguingly, different families of repeats were found to have characteristic repressive histone methylation patterns [50]. Furthermore, histone methyltransferase knockout ES cells exhibited a loss of these repressive marks and an increase in transcription from tandem and interspersed repeats.
Post-transcriptional gene silencing.
Since transcriptional silencing is unlikely to prevent activity of all TEs, it is essential that some processes act at the level of expressed transcripts. An RNA interference (RNAi)–mediated mechanism, the components of which are discussed elsewhere [51], is involved in post-transcriptional gene silencing of repetitive DNA. High levels of sense and antisense IAP and ERV-L transcripts are expressed concurrently in developing mice, but are not detected past the eight-cell stage [52,53]. Moreover, inhibiting the RNAi pathway in preimplantation embryos by RNAi-mediated knockdown of Dicer results in a 50% increase in IAP and ERV-L transcripts [52,53]. Dicer knockout mouse ES cells exhibit increased transcription from centromeric repeats, L1s, and IAPs, combined with severe developmental defects [54]. In an analogous example, silencing of the mammalian X chromosome is dependent upon an antisense transcript and shortly after its detection, histone 3–lysine 9 and CpG methylation is established at Xist [55], connecting double-stranded RNAs to transcriptional gene silencing. The fact that heterochromatin can be established at homologous loci via short interfering RNAs (siRNAs) is well documented. Examples in model organisms such as fission yeast and Arabidopsis have implicated repeat-derived siRNAs in directing such conformational changes. Fission yeast deleted for RNAi pathway components express centromeric-repeat and integrated transgene transcripts, normally heavily silenced by heterochromatinization [56]. Studies in plants show that TEs and tandem repeats specifically become silenced by histone 3-lysine 9 and CpG methylation. These changes are dependent on the chromatin remodeling factor Decrease in DNA Methylation 1 (DDM1) and guided by siRNAs. Indeed, various Arabidopsis genes become subject to RNAi-mediated silencing because of TE proximity to their promoters [57]. Similar results in human cells have demonstrated that non TE-derived siRNAs targeted to the EF1A promoter of a proviral green fluorescent protein reporter inhibits transcription of the transfected EF1A promoter, as well as that of the endogenous copy [58]. Also, siRNAs targeting the E-cadherin promoter induced DNA methylation and heterochromatin [59], but DNA methylation is not a prerequisite, as shown with the CDH1 promoter [60].
Host restriction factors.
Finally, a variety of gene products, some derived from domesticated viral genes, function at various stages of the retroviral life cycle to curtail both exogenous retroviruses and ERVs and have been extensively reviewed recently [61–64] (Table 2). Some particularly relevant examples include Fv1, the Ref/Lv1 family of proteins, APOBEC3G, and Nxf1. Fv1, the “prototypic” retrovirus restriction gene, is an ancient ERV-L gag-like gene that restricts infection by MLV [65]. APOBEC3G encodes a cytidine deaminase that mediates cysteine–uracil transitions when co-packaged with retroviral genomes. It inhibits HIV and MLV replication and also suppresses IAP and MusD/ETn retrotransposition [66]. Nxf1, encoding an mRNA nuclear export factor, has been shown to suppress the hypomorphic effects of intronic IAP insertions, presumably by facilitating accurate splicing [67]. The recently described Ref1/Lv1 family of proteins, including TRIM5α, suppresses HIV and MLV [68,69]. However, effects of these proteins on ERVs are unknown.
Inbred Mice—Out of Balance with Their ERVs?
The evolution of silencing mechanisms by the host likely, in turn, places pressure on TEs/ERVs to evolve means to escape repression, setting up an “arms race,” not unlike that involving the immune system and infectious agents [1,70,71]. In the case of TEs, waves of amplification are countered by host defenses (Figure 3) and negative selection that quench activity until new variants or “master” elements appear that are capable of instigating further genomic expansions [1,3]. The high rate of ERV germ line and somatic insertional mutations in the laboratory mouse indicates that at least some inbred strains are currently in an active phase of ERV genomic expansion. In contrast, ERV-like elements in humans, while present in comparable overall numbers, have long ago ceased activity [7]. It is interesting to speculate which is the more common situation in modern-day mammals. Without detailed analysis of a variety of mammalian genomes and mutational spectra, it is difficult to answer this question. In mouse, the still active ETn and IAP elements likely amplify via intracellular retrotransposition, thereby avoiding the “front line” defense mechanisms, such as Fv1 and Fv4, in place to inhibit early stages of exogenous infections. In contrast, MLV likely amplifies primarily through rounds of infection of germ line cells, allowing more opportunities for the host to evolve resistance and keep proviral copy number low. We propose that inbred mice represent a relatively transitory state in which host silencing mechanisms have not yet adapted to retrotransposition of new ERV variants. The IAP family nicely illustrates this point. The IΔ1 partly deleted subtype is currently the most active IAP element but is a minor fraction of the total number of existing IAPs. This situation suggests that full-length IAPs amplified to high copy numbers during mouse evolution but have recently been essentially silenced. The IΔ1 subtype must have arisen recently and, possibly because of specifics of its structure and/or genomic context, has been freed from suppression and allowed to retrotranspose—mainly in the C3H/HeJ strain. A similar scenario is occurring with respect to ETn/MusD elements, where a minor population of ETnII-β elements is causing the bulk of current retrotranspositional activity. This relatively permissive phase of ERV expansion that is ongoing in inbred mice provides a rare opportunity to study how a mammalian host genome responds to new waves of invasion by mobile elements.
Conclusion
This review has attempted to highlight the mutational impact that ERVs have had and continue to have on the mouse germ line and to discuss host defenses that have evolved to control these elements. Unlike in human, ERVs in the mouse genome are in an expansion phase, with specific IAP and ETn variants currently playing the dominant role. These elements have accumulated to hundreds of copies in the genome, but evidence indicates that only a few have a high probability of retrotransposing. Identification of their genomic location and/or chromatin state may provide insight into host control mechanisms and why particular elements escape suppression. Genetic factors responsible for variable retrotransposition rates in different strains also await discovery and may reveal new host restriction genes or alleles. Given the propensity for these ERVs to affect gene expression, it would be interesting to investigate ERV insertions as mediators of phenotypic differences among inbred strains. Indeed, it may be particularly informative to examine genes harboring polymorphic ERV insertions in their introns.
The epigenetic control of mouse and human ERVs is of substantial interest because of their potential effects on adjacent genes. In addition to obvious gene-disruptive effects, mammalian ERVs may also play a role in tissue-specific gene regulation (see Box 4). Some IAP elements act as metastable epialleles [38] with their methylation state determining effects on neighboring genes. The idea that variable silencing of retrotransposons could contribute to gene expression variability in mammals [39] is attractive but, thus far, IAPs are the only type of retroelement shown to display this effect, and it remains to be determined how widespread this phenomenon may be. Functions for RNA-mediated silencing, including potential roles for RNAi [53] and microRNAs [72] in controlling ERVs and exogenous retroviruses, are rapidly being elucidated. A number of questions, however, including the origins of double-stranded RNA necessary for inducing silencing, are currently unanswered [73].
Although ERV insertions are not a source of new mutations in humans, understanding their effects in mice is important for understanding gene regulatory effects of existing human ERVs/LTRs, thousands of which are located within gene borders [74,75], and in elucidating the disruptive effects of therapeutic retroviral vectors. Retroviral activation of proto-oncogenes has occurred in gene therapy trials, raising major concerns [76]. Therefore, potential long-range promoter or enhancer effects, as displayed by IAP elements, need to be considered and vectors designed to reduce the chances of oncogene activation [77]. The high probability of some retroviruses integrating into introns [78,79] may also limit their usefulness as therapeutic gene delivery systems if aberrant gene splicing and polyadenylation results. Eliminating cryptic splicing and polyadenylation signals within retroviral vectors may be a worthwhile strategy. However, as demonstrated by the mouse ERVs, unique properties and sequence motifs result in distinct mutational mechanisms (see Figure 1), indicating the challenge of attempting to predict a priori the mutagenic behavior of different classes of retroviruses.
Supporting Information
Table S1 Germ Line Mouse Mutations Caused by ERV Insertions
(233 KB DOC)
Click here for additional data file.
Related work in our laboratory was supported by a grant from the Canadian Institutes of Health Research with core support provided by the BC Cancer Agency. We thank Patrik Medstrand and anonymous reviewers for helpful suggestions and Christine Kelly for manuscript preparation. We also thank Susan McClatchy at The Jackson Laboratory for help with the Mouse Genome Informatics database. We regret that space limitations prevented the citing of all relevant work by colleagues.
Abbreviations
DnmtDNA methyltransferase
ERVendogenous retrovirus
ESembryonic stem
ETnearly transposon
IAPintracisternal A particle
LINElong interspersed element
LTRlong terminal repeat
MLVmurine leukemia virus
RNAiRNA interference
SINEshort interspersed element
siRNAshort interfering RNA
TEtransposable element
Box 1. Classification of Mouse ERVs
A plethora of ERV families exist in the mouse and are grouped into three major classes (class I, II, and III) [9,13,80]. ERVs with infectious counterparts, namely MLV and mouse mammary tumor virus, have been studied for decades and are the subject of many excellent reviews [13,81,82]. Here we focus on the less well appreciated class II IAP and ETn elements, which account for the majority of characterized germ line insertional mutations, and briefly touch on representatives of other classes.
Class I retroviruses.
The class I/type C/gammaretroviruses, composing about 0.7% of the genome [9] and grouped based on similarity to MLV [13], were first isolated from lymphomas of AKR mice [83]. MLV entered the germ line of mice approximately 1.5 million years ago and its copy number ranges from 25 to 70 depending on the mouse strain. MLV proviruses are subdivided based on their host ranges determined by their env genes, but only a few encode replication-competent viruses [13,84]. Class I also includes several other families, but it is unclear if any have fully coding-competent members [13].
Class II retroviruses.
The prototype of the much more numerous class II/types B and D/betaretroviral group, composing about 3% of the genome [9], is mouse mammary tumor virus [13]. A wide variety of other mouse betaretroviral ERV families also exist, some of which retain coding capacity and appear to have entered the genome quite recently [80,85].
One of the most extensively studied noninfectious families of ERVs in the mouse is the IAP family. Though there is variation in copy numbers between different mouse strains, about 700 full-length and 300 partially deleted elements are present in the haploid mouse genome. Type I elements encompass full-length members as well as four deleted classes [37]. Of these, the IΔ1 subclass, which has a 1.9-kb deletion in gag-pol, is the most abundant deleted form in the mouse genome and is also responsible for the majority of IAP insertional mutations [32,33]. Type II elements differ from type I elements by a characteristic 500-bp length difference and only comprise partially deleted members [37]. IAP elements were thought to lack an env gene until about 200 env-containing elements were discovered [86].
The other major family of active mouse LTR elements are ETns, first described as a family of non-coding sequences transcribed during early embryogenesis [87]. ETn RNA levels are significantly elevated and restricted to certain tissues during embryonic days 3.5 [87] to 13.5 [88]. Two major subtypes of ETn elements, I and II, differ in the 3' portion of the LTR and a 5' internal segment. As with IAP elements, copy numbers likely vary between strains, but the February 2002 release of the C57BL/6 genome has ~200 ETnI and ~40 ETnII elements [35]. Although ETnI elements are more numerous, ETnII elements are currently more active [31]. The lack of coding potential in ETn elements raised questions as to how they could retrotranspose, but it is now clear that a related coding-competent family of endogenous betaretroviruses, termed MusD [89], that share nearly identical LTRs with ETns, provide the proteins necessary for ETn retrotransposition [36]. No MusD element has an env-related sequence, suggesting amplification exclusively via retrotransposition.
Class III retroviruses.
Mouse class III elements, which may include some active ERVs, consist of the MuERV-L family, which encompasses up to 200 proviral copies of about 7.5 kb per haploid genome [90] and different subgroups of the highly repetitive non-autonomous ORR1 and MT MaLR elements [91]. Class III elements compose approximately 5.4% of the genome [9], significantly contribute to the early mouse transcriptome [52], and affect gene regulation (Box 4). However, only one mutation due to insertion of a class III MaLR element has been documented (Table S1).
Many mouse and human ERVs and solitary LTRs remain essentially unstudied, except for their annotation in the Repbase database of repetitive sequences [92]. Since such annotations are often based on incomplete information, they should be viewed with caution [85].
Box 2. Reversion of ERV-Induced Mutations
In a few instances, reversion to a wild-type phenotype occurs among mice carrying ERV insertional mutations. The mechanism of reversion involves deletion of internal ERV sequences via homologous recombination between the 5' and the 3' LTRs of the provirus in the parental germ line, leaving behind a solitary LTR. Such generation of solitary LTRs has been noted in early studies on MLV [93], and this mechanism has been efficient throughout evolution. Indeed, solitary LTRs are typically present in much higher copy numbers than full proviral forms and make up the bulk of retroviral material in the mouse and human genomes [9,11]. In the case of the hairless mutation [94] and the dilute coat color mutation [95], both of which are caused by aberrant splicing due to an MLV integration into the intron, reversion to wild-type occurs via generation of a solitary LTR. Germ line reversions of the dilute mutation occur at a frequency of 3.9–4.5 x10−6 events per gamete[96], and one somatic revertant, chimeric for about 50% of reverted cells, was encountered, with the frequency of somatic reversion estimated at 9 × 10−7 per animal analyzed [96]. Occasionally, reversions can result in diverse phenotypes because of expression of different forms of transcripts. Such is the case with the nonagouti a allele, which encompasses an insertion of a 5.5-kb VL30 element containing 5.5 kb of additional internal sequence flanked by 526-bp direct repeats. By means of homologous recombination, a can revert to two dominant agouti alleles, black-and-tan (a[t]), containing only the VL30 element with a single internal 526-bp repeat, and the white-bellied agouti (Aw), which only has a solitary VL30 LTR [97]. There is no published evidence of other ERV-induced mutations reverting to wild-type, perhaps because of the difficulties associated with breeding the mutant animals, the time span required to detect reversions, and, for the somatic revertants, restricted tissues where the reversion could be detected.
Box 3. Dynamics of ERV Expression and Transcriptional Restriction
Acquisition of new heritable proviruses requires expression in the germ line and, indeed, transcription of ERVs in different species is elevated in germ line cells, early embryo, and placenta compared to adult or differentiated tissues [98]. Active transcription during early developmental stages is advantageous to ERVs, since it increases the probability of proviral integrations likely to contribute to the germ line and be inherited by the next generation. High transcriptional activity of ERVs in early embryogenesis is partly due to lower levels of suppressive methylation, but expression profiles do not coincide exactly with the patterns of global genomic de- and remethylation [99] and are likely also the result of changing transcription factor repertoire during development.
In AKR mice, a high-leukemic mouse strain, MLV transcripts are detected at high levels in the embryo and throughout the life of the animal, likely originating from one ancestral provirus in this strain [13]. In low-leukemic mouse strains, such as BALB/c, C3H/He, and C57BL/6, MLV proviruses are transcribed at significantly lower or undetectable levels (reviewed in [82]). Transcript levels of ETn and IAP elements are also highest during early embryogenesis [37,87,88,98]. While IAP elements are expressed in many mouse tumors and cell lines [37], expression of ETns is more restricted, elevated only in undifferentiated embryonic carcinoma and ES cells, as well as in primary acute myeloid leukemia cells [100]. Though IAP transcripts are detectable in some normal adult tissues and cell types, such as thymus and activated splenic B cells (for review see [37]), a reporter gene system in transgenic mice found IAP promoter activity to be restricted to undifferentiated spermatogonia [101]. This finding suggests that IAP transcripts produced in differentiated somatic tissues or tumor cells may initiate from only a very limited number of elements in favorable genomic contexts or may be influenced by other genes. Indeed, in most studies on ERV expression, it is unclear how many individual elements contribute to the transcript pool, a fact that limits firm conclusions on transcriptional activity of these large families.
Time-specific restrictions placed on ERV transcription, limited largely to a narrow window of early embryogenesis, are suggestive of extremely tight regulation imposed by complex mechanisms of the host genome in an effort to prevent somatic insertional mutagenesis.
Box 4. Adoption of ERVs to Serve the Host
Although the vast majority of TE and ERV insertions are selectively neutral, allowing them to drift to fixation, or detrimental and subject to negative selection, such elements can occasionally be co-opted by the host to serve important cellular functions [2,3,71,102]. For example, as mentioned in the text, endogenous viral loci can play a role in repelling exogenous retroviral infections (Table 2). Ancient ERVs may also have been co-opted to function in placental development in humans [103,104] and mice [105], prompting the suggestion that expression of different ERVs is partly responsible for the great diversity of mammalian placental structures [106].
A growing number of studies have shown that LTR elements are particularly well suited to donation of enhancers or promoters and, if fixed, can assume roles in gene regulation. Many examples of mammalian genes regulated by ERVs/LTRs or other TEs have been reported [71,74,107–110], such as the mouse Slp (sex-limited protein) gene, which has acquired male-specific expression due to a MuRRS ERV that provides an enhancer [111], and the CYP19 gene, encoding a key enzyme in estrogen biosynthesis, which has high expression in human and primate placenta due to an alternative promoter provided by an ancient LTR [74]. A recent study documenting ERVs present in chimpanzee but not human found that several such elements were associated with genes differentially expressed in the two species [112], raising the possibility that ERVs and other TEs may be critical in driving speciation, as has been discussed by others [2,3,71,102].
Most ERV LTRs seem to be extremely powerful, ready-made promoters active during early embryogenesis and in the germ line, and recent work has fueled speculation that LTR retroelements could play a role in expression of genes essential for early development. Knowles and co-workers reported that about 13% of cDNAs from full-grown mouse oocytes contained retroviral sequences, the majority of which were derived from the poorly understood class III MT MaLR family [52], but by the two-cell stage, transcripts of another class III family, MuERV-L, started to predominate [52,113]. Notably, MT, MuERV-L, and some other LTRs were found to act as alternative promoters for subsets of host genes in full-grown oocytes and cleavage-stage embryos, apparently controlling synchronous, developmentally regulated expression of these genes [52]. An independent study reported that blocking MuERV-L expression inhibits embryonic development to the four-cell stage [113]. Such findings have prompted the proposal that differential gene expression driven by LTRs may trigger sequential reprogramming and genome remodeling during embryonic development [52]. This idea is intriguing, but since LTR retrotransposon families and insertion patterns are generally not conserved across divergent species, it is difficult to envisage a scenario in which such elements evolved to play a critical role in common developmental processes. It is easier to imagine LTRs/TEs being involved in species-specific processes. Nevertheless, it is clear that McClintock's original theory of TEs as “controlling elements” [114] and Britten and Davidson's postulation of repetitive elements as regulatory units [115], views that have been little appreciated for decades, are now gaining increasing attention.
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PLoS GenetPLoS GenetpgenplgeplosgenPLoS Genetics1553-73901553-7404Public Library of Science San Francisco, USA 1644005610.1371/journal.pgen.002000105-PLGE-RV-0108R2plge-02-01-06ReviewBiochemistryBioinformatics - Computational BiologyCell BiologyEvolutionGenetics/Functional GenomicsEukaryotesAnimalsYeast and FungiGenetic Analysis of the Cytoplasmic Dynein Subunit Families ReviewPfister K. Kevin *Shah Paresh R Hummerich Holger Russ Andreas Cotton James Annuar Azlina Ahmad King Stephen M Fisher Elizabeth M. C * To whom correspondence should be addressed. E-mail: [email protected]. Kevin Pfister is in the Department of Cell Biology, School of Medicine, University of Virginia, Charlottesville, Virginia, United States of America. Paresh R. Shah is in the Department of Neurodegenerative Disease and in the MRC Prion Unit, Institute of Neurology, London, United Kingdom. Holger Hummerich is in the MRC Prion Unit, Institute of Neurology, London, United Kingdom. Andreas Russ is in the Genetics Unit, Department of Biochemistry, University of Oxford, Oxford, United Kingdom. James Cotton is in the Department of Zoology, the Natural History Museum, London, United Kingdom. Azlina Ahmad Annuar and Elizabeth M. C. Fisher are in the Department of Neurodegenerative Disease, Institute of Neurology, London, United Kingdom. Stephen M. King is in the Department of Molecular, Microbial, and Structural Biology, University of Connecticut Health Center, Farmington, Connecticut, United States of America.
1 2006 27 1 2006 2 1 e1© 2006 Pfister et al.2006This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.Cytoplasmic dyneins, the principal microtubule minus-end-directed motor proteins of the cell, are involved in many essential cellular processes. The major form of this enzyme is a complex of at least six protein subunits, and in mammals all but one of the subunits are encoded by at least two genes. Here we review current knowledge concerning the subunits, their interactions, and their functional roles as derived from biochemical and genetic analyses. We also carried out extensive database searches to look for new genes and to clarify anomalies in the databases. Our analysis documents evolutionary relationships among the dynein subunits of mammals and other model organisms, and sheds new light on the role of this diverse group of proteins, highlighting the existence of two cytoplasmic dynein complexes with distinct cellular roles.
Citation:Pfister KK, Shah PR, Hummerich H, Russ A, Cotton J, et al. (2006) Genetic analysis of the cytoplasmic dynein subunit families. PLoS Genet 2(1): e1.
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Introduction
Dyneins are large multi-subunit protein complexes that undertake a wide range of roles within the cell. They are adenosine triphosphate (ATP)–driven, microtubule minus-end-directed molecular motors that can be divided, based on function, into two classes: axonemal and cytoplasmic dyneins [1–7] (reviewed in [8,9]). Axonemal dyneins are responsible for the movement of cilia and flagella. Two cytoplasmic dynein complexes have been identified. The most abundant cytoplasmic dynein complex, cytoplasmic dynein 1, is involved in functions as diverse as spindle-pole organization and nuclear migration during mitosis, the positioning and functioning of the endoplasmic reticulum, the Golgi apparatus, and the nucleus, and also the minus-end-directed transport of vesicles, including endosomes and lysosomes, along microtubules and retrograde axonal transport in neurons. A second cytoplasmic dynein complex, cytoplasmic dynein 2, has a role in intraflagellar transport (IFT), a process required for ciliary/flagellar assembly (reviewed in [10]).
The core of the cytoplasmic dynein 1 complex is a homodimer of two heavy chain polypeptides and associated intermediate, light intermediate, and light chain polypeptides, which are defined and named by their molecular mass and mobility in SDS-PAGE gels (Figure 1A). The protein subunits are encoded by families of at least two genes, and the expression patterns of the individual family members are different in various cell types. At least one of the light chains, DYNLL1 (LC8), has multiple cellular roles independent of its participation in a dynein complex. Cytoplasmic dynein 1 interacts with various other proteins including a second multimer, dynactin, to form the dynein–dynactin complex. Dynactin is comprised of at least seven different proteins, which together act as an adaptor that connects the cytoplasmic dynein motor to a range of cargoes (for review, see [11]). Interaction with dynactin also increases dynein motor processivity [12]. Furthermore, dynactin functions independently of dynein, anchoring microtubules at the centrosome [13]. Current evidence suggests that the second cytoplasmic dynein complex, cytoplasmic dynein 2, is also a homodimer of a distinct heavy chain, DYNC2H1, with associated light intermediate chain, DYNC2LI1 (Figure 1B). No other subunits have yet been identified for this complex, and it does not appear to interact with the dynactin complex [14–16].
Figure 1 The Mammalian Cytoplasmic Dynein Complexes
(A) Cytoplasmic dynein. (Left panel) Polypeptides of immunoaffinity-purified rat brain cytoplasmic dynein. Polypeptide mass (in kDa) is indicated on the right side of the gel, and the consensus family names are indicated on the left. (Right panel) Structural model for the association of the cytoplasmic dynein complex subunits. The core of the cytoplasmic dynein complex is made of two DYNC1H1 heavy chains which homodimerize via regions in their N-termini. The motor domains are at the C-termini of the heavy chains, the large globular heads of ~350 kDa that are composed of a ring of seven densities surrounding a central cavity; six of the densities are AAA domains (numbered 1–6). AAA domain 1 is the site of ATP hydrolysis. The microtubule-binding domain is a projection found on the opposite side of the ring between AAA domains 4 and 5. C is the C-terminus of the heavy chain that would form the 7th density. Two DYNC1I intermediate chains (IC74) and DYNC1LI light intermediate chains bind at overlapping regions of the N-terminus of the heavy chain, overlapping with the heavy chain dimerization domains. Dimers of the three light chain families; DYNLT, the Tctex1 light chains; DYNLRB, the Roadblock light chains; and DYNLL, the LC8 light chains, bind to the intermediate chain dimers.
(B) Cytoplasmic dynein 2 complex, structural model for subunit association. This dynein complex has a unique role in IFT and is sometimes known as IFT dynein. Structural predictions indicate that the heavy chain, DYNC2H1, is similar to the cytoplasmic and axonemal dyneins. The only known subunit of this complex is a 33- to 47-kDa polypeptide, DYNC2LI1, which is related to the cytoplasmic dynein light intermediate chains. No intermediate chain or light chains have yet been identified [16].
The cytoplasmic dynein proteins are fundamental to the functioning of all cells, and have recently been shown to be causally mutated in forms of neurodegeneration [17–19]. They are thus of great interest for mammalian genetic, and other, studies. We therefore sought to examine the role of cytoplasmic dynein subunits from a genetic perspective. During this analysis, we noted considerable confusion in the human and mouse gene and protein names and mapping positions. Therefore, we reexamined the mapping locations for the subunit genes and clarified and updated entries in the various sequence databases. In doing so, we utilized the revised consensus nomenclature developed for the cytoplasmic dynein subunits and their genes (Table 1). We also defined, as far as possible with current data, homologous genes in model organisms, including Drosophila, Caenorhabditis elegans, Chlamydomonas, and yeast. To further our understanding of the function of cytoplasmic dynein subunits, we also briefly examined mutations in this group of proteins in a variety of model organisms. We do not discuss dynein-binding proteins such as dynactin, LIS1, or various kinases, which while important for dynein function, have not yet been shown to be stoichiometric components of the cytoplasmic dynein complex.
Table 1 Human and Mouse Cytoplasmic Dynein Genes and Map Positions
Human and mouse cytoplasmic dynein subunit genes.
The subunits of the cytoplasmic dynein complexes are resolved into subunit polypeptides of ~530 kDa (heavy chains), ~74 kDa (intermediate chains), ~33–59 kDa (light intermediate chains), and ~10–14 kDa (light chains) in SDS-PAGE gels (Figure 1A). Research on the cytoplasmic dynein subunits has been undertaken in a wide range of organisms from yeast to humans. The nomenclature of the mammalian genes encoding these proteins has drawn on homologs in other organisms and, consequently, a number of aliases have been found for any given human or mouse cytoplasmic dynein subunit. Much of the early research into dynein genetics was conducted in the biflagellate green alga Chlamydomonas on the dyneins found in the flagellar axoneme, and therefore some cytoplasmic dynein nomenclature derives from these studies. For example, mammalian members of the cytoplasmic light chain families DYNLRB and DYNLL have commonly been referred to as LC7 and LC8, respectively, which are the names of homologous Chlamydomonas axonemal dynein subunits.
Nomenclature.
The revised classification system for mammalian cytoplasmic dynein (Table 1) recognizes the two distinct dynein complexes, cytoplasmic dynein 1 and cytoplasmic dynein 2, and the fact that cytoplasmic dynein light chains are shared with some axonemal dyneins. Cytoplasmic dynein subunits are also classified into polypeptide families according to sequence similarity within groups of similarly sized proteins; thus there is sequence similarity within the dynein gene families (and when cytoplasmic and axonemal members of the same gene families are compared) but not among them.
This nomenclature has been approved by the Human Genome Organization Nomenclature Committee [20] and the International Committee on Standardized Nomenclature for Mice. In accordance with their policy, the designation of each unique cytoplasmic dynein subunit starts with DYNC for dynein, cytoplasmic, followed by the specific dynein complex subtype 1 or 2; for example, cytoplasmic dynein 2 is designated DYNC2. The shared light chains start with DYN. Each subunit is designated with a letter(s) for the size of the polypeptides, H for the heavy chain, I for the intermediate chain, LI for the light intermediate chain, and L for the light chain. Additional letters (T, RB, and L) are used to distinguish the three distinct light chain families as described in the text. Individual members of the gene families are assigned numbers. Standard human and mouse gene nomenclature is used: italicized upper case for human gene symbols (for example, DYNC1H1), italicized initial upper case and then lower-case letters for mouse (Dync1h1), and for proteins of both species, the same symbols in upper case, upright (DYNC1H1). In accordance with the International Union of Pure and Applied Chemistry standards, isoforms of the intermediate chain gene products are referred to with letters. This nomenclature system can be expanded to other subunits as appropriate. We refer to mapping positions using the prefixes Hsa (Homo sapiens) for human and Mmu (Mus musculus) for mouse, followed by the chromosomal localization e.g. Hsa2q11, Mmu11.
Table 1 lists the aliases, map position, and protein/DNA-sequence accession data for each known mouse and human cytoplasmic dynein gene. The greatest number of aliases was observed for the cytoplasmic dynein 1 heavy chain 1 (DYNC1H1) for which we identified 15 different names. Some alternative cytoplasmic dynein gene names have come from large-scale gene and transcript identification efforts such as the partial DYNC1H1 clone KIAA0325 and its mouse homolog “mKIA00325,” generated by the Kazusa cDNA project [21]. A small number of gene names have been derived from the names of DNA markers and cDNA clones used to identify the genes, for example, cytoplasmic dynein 2 light intermediate chain 1, DYNC2LI1, was named DKFZp564A033 after the cDNA sequence and clone of the same name. The heavy chain gene DYNC1H1 has also been referred to by the name of a marker, Hp22, generated from its human cDNA sequence, as well as the rat-derived marker Rk3–8 and a cDNA clone named HL-3.
Cytoplasmic Dynein Heavy Chain Gene Family (DYNC1H1, DYNC2H1)
Figure 2A shows the phylogenetic relationships amongst the dynein heavy chain protein sequences from various organisms. The heavy chain sequences fall into two distinct clades, and the relationships within each clade are generally consistent with known evolutionary distances between the organisms shown. We note that our phylogeny fits well with and extends previous phylogenetic analyses of the heavy chain proteins [22,23]. This analysis indicates that the partial human sequence DNAH12, (AAB09729) [23], is unlikely to be a cytoplasmic dynein.
Figure 2 Panel Showing the Protein-Based Phylogenies of the Cytoplasmic Dynein Subunit Families
Species names are shown with NCBI/GenBank gene/protein names. NCBI/GenBank protein-sequence accession numbers are given in Table S1. Orthologous human, mouse, and rat gene names use the revised systematized consensus nomenclature (e.g. DYNC1H1 in humans, mouse, and rat). Relationships amongst dynein sequences of different species do not necessarily reflect the evolutionary relationships amongst species; see [208] and [209] for further details. Named clades are indicated in the right margins. Bayesian and maximum-likelihood bootstrap values are shown as percentages (top and bottom, respectively), adjacent to branch points. Asterisks denote bootstraps below 50%. Filled circles denote bootstraps at 100%. Scale-bar represents evolutionary distance (estimated numbers of amino-acid substitutions per site).
(A) Cytoplasmic dynein heavy chain family. Chlamydomonas outer arm heavy chain (ODA11) is used as the outgroup. DNAH12frag is the partial axonemal heavy chain fragment taken from [23]. For mouse DYNC2H1, XP_35830, only partial protein sequence (336aa) was available in the GenBank database. Adding this partial sequence to our analysis resulted in spurious clustering, therefore we obtained an extended, putative sequence by using BLAST (TBLASTN) against the mouse genome (Build 32) with human and rat sequences XP_370652 and NP_075413, respectively. Incomplete mouse genomic assembly at the DYNC2H1 locus yielded a truncated sequence 3455 amino acids in length, 85% the length of human DYNC2H1.
(B) Cytoplasmic dynein intermediate chain family. The Chlamydomonas IC2 (ODA6) is used as the outgroup.
(C) Cytoplasmic dynein light intermediate chain family. There does not appear to be a sufficiently distant homolog in Chlamydomonas to be used as an outgroup in this analysis, therefore ODA11 (Q39610, a heavy chain protein) was chosen as the outgroup for this tree.
(D) Cytoplasmic dynein light chain Tctex1 family. The Chlamydomonas LC2 light chain is used as the outgroup.
(E) Cytoplasmic dynein light chain Roadblock family. The Chlamydomonas outer arm dynein LC7a, is used as the outgroup.
(F) Cytoplasmic dynein light chain LC8 family. The Chlamydomonas Q39579 sequence is used as an outgroup. This phylogeny is poorly resolved, with low bootstrap support values and posterior clade probabilities, most likely due to there being little variation amongst the ingroup sequences. We found good support for the LC8 light chain 1 clade, and some support for the LC8 light chain 2 clade, of four vertebrate sequences. The relationships of the two sequences, C. elegans and Takifugu were poorly resolved, and therefore we have not included these in the LC8 light chain 2 clade.
Cytoplasmic dynein heavy chain 1, DYNC1H1.
DYNC1H1, cytoplasmic dynein 1 heavy chain 1, is the largest cytoplasmic dynein subunit, having ~4,600 residues and a molecular weight of >530 kDa. First identified in rat spinal cord and brain and termed Microtubule Associated Protein 1C (MAP1C) [24], DYNC1H1 is a distant member of the AAA family of ATPases and is the cytoplasmic counterpart to axonemal dynein heavy chains [3,25]. DYNC1H1 associates as a homodimer within the cytoplasmic dynein complex and effects the contact and translocation of the dynein complex along microtubules via its large motor domain [8,26] (Figure 1B).
The C-terminal region of DYNC1H1 is the motor domain of the dynein complex and is conserved in all cytoplasmic and axonemal dynein heavy chains. This region is arranged as a heptameric ring with six AAA domains and a seventh domain, the identity of which remains a matter of discussion (Figure 1B) [12,25,27,28]. AAA domains are regions of ATP binding and hydrolysis, and thus they generate the energy required for translocation [29–31]. While the first AAA domain is essential for motor activity [32], reviewed in [30], the first four AAA domains are potentially capable of binding and hydrolysing ATP [33–35]. Contact of the heavy chain with a microtubule is established via an ~15-nm projection that extends between the fourth and fifth AAA domains [28,36]. The N-terminal region of DYNC1H1 is known as the stem, and force production, and therefore translocation, is thought to be achieved through the contact and shift of a 10-nm fold of the stem closest to the first AAA domain [37]. DYNC1H1 dimerization also occurs in the stem, and the intermediate chains and light intermediate chains bind in this region as well [38,39]. The three light chains bind to the intermediate chains [40]. Collectively the five smaller dynein subunits that bind to the N-terminus of DYNC1H1 make up the cargo-binding portion of the dynein complex.
The sequence of full-length mammalian DYNC1H1 was first obtained in rat and mouse [41,42]. Human DYNC1H1 was identified by screening an adenocarcinoma library with a partial human cDNA [23,43]. As yet, the only mutations reported in mammalian heavy chains have been in the mouse: the Loa and Cra1 strains have allelic point mutations in Dync1h1 that cause late-onset motor neuron degeneration in heterozygotes and neuronal apoptosis in homozygotes [17]. The loss of both copies of Dync1h1 has been shown to be lethal during early embryonic development, with disorganization of the Golgi complex, improper distribution of endosomes and lysosomes, and defects in cell proliferation; no phenotype has yet been reported for heterozygote knock-out mice [44].
In Drosophila, the dynein heavy chain gene, Dhc64C, functions in oogenesis [45,46], oocyte differentiation [47], centrosome attachment during mitosis [48], eye development, cell development in thorax, abdomen, and wing [45], and axonal transport [49]. Homozygous mutations induced by the mutagen ethyl methane sulfonate in Dhc64C are larval/pupal lethal, whilst heterozygotes have defects in bristle formation, eye development, and fertility [45]. In C. elegans, dynein heavy chain (dhc-1) is an essential gene, also known as let-354 (LEThal) [50]. Extensive mutational analysis has been conducted on dhc-1 to produce a range of variants from recessive/dominant lethals to temperature-sensitive mutants. The resultant phenotypes invariably include embryonic lethality, spindle orientation defects, polar body abnormalities, and excessive blebbing in the early embryo [51–54].
In the yeast Saccharomyces cerevisiae, heavy chain function ensures the alignment and orientation of mitotic spindles. Mutation of the S. cerevisiae heavy chain gene dyn1, which has 50% similarity (28% identity) to DYNC1H1 over 80% of the protein's length, has been shown to disrupt spindle orientation and reduce the fidelity of nuclear segregation during mitosis [55,56]. Despite this phenotype, dyn1 mutants remain viable, although dyn1 and kinesin double mutants are lethal [57]. This observation suggests some functional redundancy for dynein by kinesin motors in yeast. No cytoplasmic dynein 1 heavy chain 1 homolog has unambiguously been identified in Chlamydomonas, and neither have dyneins been found in either the Arabidopsis or rice genomes [58,59] (reviewed in [60]). There are many dynein heavy chains in the Chlamydomonas genome. However, with the exception of DYNC2H1, they appear to be components of the axonemal dyneins.
Cytoplasmic dynein 2 heavy chain 1, DYNC2H1.
The cytoplasmic dynein 2 heavy chain, DYNC2H1, was originally identified in sea urchin embryos by Gibbons and colleagues and was termed DYH1b [61]. It is much less abundant than DYNC1H1 and does not appear to heterodimerize with DYNC1H1; biochemical analyses suggest that DYNC2H1 is a homodimer [16]. DYNC2H1 contains regions characteristic of cytoplasmic dyneins, for example, human DYNC1H1 and DYNC2H1 sequences are similar within both the motor region and around the light intermediate chain–binding site [15]. However, the expression of its mRNA increases during embryonic reciliation, a property typical of axonemal dyneins, suggesting a flagellar role for an otherwise cytoplasmic-like dynein heavy chain. The flagellar properties of DYNC2H1 were clarified with its identification as the motor responsible for retrograde (tip to base) IFT, in Chlamydomonas, a process required for assembly and maintenance of the eukaryotic cilium/flagellum [6,22]. DYNC2H1 is also important in modified ciliary structures such as nematode mechanosensory neurons [62] and vertebrate photoreceptors [63,64]. In C. elegans, the DYNC2H1 homolog, che-3, is expressed in ciliated sensory neurons which are thought to be involved in odorant chemotaxis [65]. Mutations of che-3 affect IFT, the establishment and maintenance of sensory cilia, which are stunted and swollen in the mutants, [62,66], chemotactic behavior [67], and formation of the third larval stage, dauer formation [68].
The first mammalian DYNC2H1 gene was described in rat, designated DLP4 [69], and full-length sequence has been obtained [15]. Genetic and biochemical studies suggest that DYNC2H1 associates with a member of the light intermediate chain family, DYNC2LI1 (Figure 1B, and see discussion of the light intermediate chain family below) [14,16,70–73] and possibly also with DYNLL1 (LC8) light chain [72]. In mice Dync2h1, mRNA is abundant in the olfactory epithelium and the ependymal layer of the neural tube; antibodies against DYNC2H1 and DYNC2LI1 strongly stain these tissues and connecting cilia in the retina as well as primary cilia of non-neuronal cultured cells [15]. The co-localization of DYNC2H1, DYNC2LI1, and homologs of the IFT pathway in mammalian ciliated tissues supports a specific role for DYNC2H1 in the generation and maintenance of mammalian cilia [14,16]. Other antibody studies suggest that DYNC2H1 localizes to the cytoplasm of apical regions of ciliated rat tracheal epithelial cells, but not in the cilia themselves [74]. In non-ciliated human COS cells, antibodies against DYNC2H1 show Golgi localization and induce Golgi dispersion, suggesting a cytoplasmic role for DYNC2H1 [23].
Cytoplasmic Dynein Intermediate Chain Gene Family (DYNC1I1, DYNC1I2)
Intermediate chains are present in axonemal and/or cytoplasmic dyneins from yeast to mammals (Figure 2B). Protein-sequence data demonstrate evolutionary distant relationships between axonemal and cytoplasmic dynein intermediate chains; for example, rat DYNC1I1 has 48% similarity to the Chlamydomonas IC2 axonemal outer arm dynein intermediate chain encoded by the ODA6 locus [75,76]. Figure 2B shows the dynein intermediate chain protein phylogeny. The intermediate chain sequences fall into two distinct clades, intermediate chains 1 and 2, comprised of vertebrate species only. An alternative placement of a Takifugu sequence, as a member of the intermediate chain 1 clade, is almost as well supported by the data as the placement shown in Figure 2B (49% bootstrap support against 51% support). In view of this and with all non-vertebrate species falling outside these clades, the data suggest a recent evolutionary origin for the split into intermediate chain gene 1 and intermediate chain gene 2, perhaps as part of a “2R” event of genome duplication (see [77] for review). The absence of an amphibian (Xenopus) intermediate chain 1 protein may be due to the current paucity of X. laevis sequences in the GenBank sequence database (http://www.ncbi.nlm.nih.gov/Genbank).
The cytoplasmic dynein 1 intermediate chains have a molecular weight of ~74 kDa [5] and associate in the cytoplasmic dynein complex with a stoichiometry of two intermediate chains per complex [40,78]. DYNC1I1 and DYNC1I2 proteins are thought to help assemble the cytoplasmic dynein complex and to bind various cargoes. The intermediate chains interact with the dynein activator, dynactin, via their conserved N-termini [79]. The DYNC1I C-termini contain a WD repeat domain [76,80,81] that is conserved between cytoplasmic and axonemal intermediate chains and is important for intermediate chain–binding to the heavy chains [76,82]. The dynein light chains, DYNLL1 (LC8) and DYNLT1 (Tctex1), bind near the N-termini of the intermediate chains [83–85], and the DYNLRB (Roadblock) light chains bind just upstream of the WD repeat region [40]. The DYNC1I are phosphorylated, and phosphorylation at one site regulates DYNC1I2 interaction with the p150 subunit of dynactin [86,87].
The Chlamydomonas IC2 axonemal intermediate chain was localized to the base of the dynein heavy chain dimer by immunoelectron microscopy [88]. Steffen and colleagues identified a similar location for the cytoplasmic dynein intermediate chain and found that antibodies to it block dynein binding to membrane-bound organelles [89,90]. These data indicate a role for DYNC1I in targeting the dynein complex to various cargoes, including membranous organelles and kinetochores [76,79,89]. In Drosophila, mutations in dynein intermediate chain, Cdic (also referred to as cDic and Dic), lead to larval lethality, demonstrating that this intermediate chain provides an essential function. Cdic mutations dominantly enhance the rough-eye phenotype of Glued, a dominant mutation in the p150 subunit of dynactin [91]. Shortwing (sw) is an allele of the dynein intermediate chain gene but, unlike other Cdic alleles, sw is homozygous viable and gives rise to a recessive, temperature-sensitive defect in eye and wing development [91].
We note that in Drosophila, the Cdic gene lies in the 19DE region of the X chromosome, adjacent to several dynein intermediate chain-like sequences. These sequences are derived from a 7-kb duplication/deletion event involving Cdic and its proximal gene annexin X, which encodes a cell-surface-adhesion protein [92]. The duplication/deletion of this 7-kb region resulted in the formation of a de novo coding sequence, under the control of a testes-specific promoter, called sperm-specific dynein intermediate chain gene (Sdic) [93]. The de novo region has undergone at least 10-fold tandem duplication, which has given rise to a multi-gene family comprising at least four classes of Sdic gene, of which more than one class is functional [93].
Cytoplasmic dynein 1 intermediate chain 1, DYNC1I1.
Multiple DYNC1I1 isoforms exist in mammals. They are the products of alternative splicing of the N-terminal region of a single DYNC1I1 gene and phosphorylation [76,79,86]. In humans, alternate splicing may arise from cryptic splice-acceptor sites located within exon 4 of this 17-exon gene [94]. Two DYNC1I1 isoforms were found in rat brain and DYNC1I1 mRNA, and protein isoform expression is regulated during rat brain development, and a single DYNC1I1 isoform is found in testis. DYNC1I1 expression is also cell-specific: cultured rat neurons express at least two DYNC1I1 alternative splicing variants and their phosphorylated isoforms, while cultured glial astrocytes do not express any DYNC1I1 gene products [95–97]. In the mouse, expression of Dync1i1 has been shown to be restricted primarily to the brain, with weak expression in testis [94], further supporting possible neuronal specificity for Dync1i1. As with the other dynein subunits, the isoform diversity of the intermediate chain is thought to result in specific populations of dynein molecules that have specific functions; for example, both DYNC1I1 isoforms are components of cytoplasmic dynein found in the slow component of axonal transport in the optic nerves [95]. Multiple isoforms of the Drosophila intermediate chains are also produced by alternative splicing of the single gene [92].
Cytoplasmic dynein 1 intermediate chain 2, DYNC1I2.
Vaughan and Vallee used a partial human cDNA sequence with identity to the already known DYNC1I1 gene as a probe to isolate a rat Dync1i2 cDNA; predicted human and rat DYNC1I2 sequences are 94% identical [79], and the existence of two genes was supported by mapping data that placed Dync1i1 and Dync1i2 at distinct loci within the mouse genome [98]. Like Dync1i1, Dync1i2 produces different splice isoforms: alternative splice sites lie at two positions within the N-terminal region. The expression of Dync1i2 isoforms is ubiquitous with the rat DYNC1I2C isoform being expressed in all tissues and cells examined [79,94,96,97]. During rat brain development, DYNC1I2C is the only isoform found before E14 (embryonic day 14) and it is often the only isoform observed in cultured cells [96,99]. During nerve growth-factor stimulation of PC12 cell differentiation and neurite extension, there is a change in relative expression levels of the DYNC1I2 isoforms [100]. In the rat optic nerve, it has been shown that the DYNC1I2C isoform is the only intermediate chain involved in the fast component of anterograde transport to the axon tip [95,99].
The strong expression of Dync1i2 in the mouse developing limb bud led to the suggestion that DYNC1I2 may play a role in limb development and digit patterning and/or in establishing cell polarity [94]; dynein may not do this directly, but may mediate these processes by orientating intracellular components correctly [101].
Cytoplasmic Dynein Light Intermediate Chain Gene Family (DYNC1LI1, DYNC1LI2, DYNC2LI1)
Figure 2C shows the phylogenetic relationships amongst the dynein light intermediate chain protein sequences from various organisms. The light intermediate chains can be separated into three distinct groups: the two light intermediate chains that are components of cytoplasmic dynein 1, DYNC1LI1 and DYNC1LI2, are more closely related to each other than to the cytoplasmic dynein 2 light intermediate chain, DYNC2LI1. Hughes and colleagues first proposed the name light intermediate chains for these subunits [102], although these polypeptides were also referred to as light chains [103] prior to the discovery of the smaller light chains [104]. The mammalian cytoplasmic dynein complex contains four species with molecular masses of 50–60 kDa that resolve into numerous isoforms on 2D gels [86,102]. The multiple isoforms observed in 1D and 2D gels are thought to be the result of post-translational phosphorylation, although the possibility of alternate splicing has not been eliminated [86,102,103]. A third gene, DYNC2LI1, has recently been described which encodes a protein that appears to exclusively associate with DYNC2H1 in the cytoplasmic dynein 2 complex [14–16,71]. Unlike the other subunits of cytoplasmic dynein, homologs of the DYNC1LIs have not yet been identified in the axonemal dyneins [105]. The function of the DYNC1LIs has yet to be determined, although it has been suggested that they may regulate the interactions of dynein with dynactin, or with sub-cellular cargoes of dynein-mediated motility. DYNC1LI1 and DYNC1LI2 form only homo-oligomers, and their mutually exclusive binding to the N-terminal base of the dynein heavy chain is consistent with a role in cargo binding [38].
C. elegans appears to have one light intermediate chain (dli-1) for cytoplasmic dynein (DYNC1H1-based complexes), and one (xbx-1) for cytoplasmic dynein 2 (DYNC2H1-based complexes) [73]. dli-1 is required for dynein function during mitosis, pronuclear migration, centrosome separation, and centrosome association with the male pronuclear envelope [106], as well as retrograde axonal transport. Mutations in dli-1 lead to an accumulation of cargo at axonal terminals [52]. Disruption of xbx-1 results in ciliary defects and causes behavioral abnormalities that are observed in other cilia mutants [14]. Binding of dli-1 to ZYG-12 is thought to be the mechanism for dynein binding to the nuclear envelope [107].
Cytoplasmic dynein 1 light intermediate chain 1, DYNC1LI1.
DYNC1LI1 was cloned from rat [38] and found to have a P-loop motif, which is one of the major conserved motifs making up the nucleotide-binding domain found in numerous proteins, including ATPases and kinases [108]. DYNC1LI1, however, lacks other essential motifs associated with ATPase activity, which itself has not been assayed. Tynan showed that pericentrin, a known dynein cargo, binds DYNC1LI1 and not DYNC1LI2 [38]. DYNC1LI and its phospho-isoform are exclusively found with dynein in the slow component of axonal transport in rat optic nerves [95]. In HeLa cells, DYNC1LI1 localizes to the microtubule organizing centre and mitotic spindle, co-localizing with the GTPase Rab4a (which interacts with the central domain of DYNC1LI1 [109]); thus DYNC1LI1 may be implicated in the regulation of membrane-receptor recycling. Phosphorylation of the Xenopus DYNC1LI has been implicated in regulation of dynein binding to membrane-bound organelles [110]. It is thought that Xenopus melanosomes contain a distinct dynein light intermediate chain protein, possibly a version of DYNC1LI1 [111]. In the chicken (Gallus gallus) DYNC1LI1 has been called DLC-A, as part of the DLC-A group of light chains [103].
Cytoplasmic dynein 1 light intermediate chain 2, DYNC1LI2.
DYNC1LI2 is paralogous to DYNC1LI1 and is also thought to be post-translationally modified by phosphorylation [86,102,103]. DYNC1LI2 is found in both the fast and slow components of axonal transport in rat optic nerves, although its phospho-isoforms are found only in the slow component of axonal transport. During nerve growth-factor stimulation of PC12 cell differentiation and neurite extension, DYNC1LI2 gene expression is up-regulated [112], and phosphorylation of both DYNC1LI1 and DYNC1LI2 is increased [100]. Like DYNC1LI1, the chicken (G. gallus) DYNC1LI2 has also been termed DLC-A, as part of the DLC-A group of light chains [103].
Cytoplasmic dynein 2 light intermediate chain 1, DYNC2LI1.
DYNC2LI1 is a light intermediate chain that was identified in mammals by two groups and was originally designated D2LIC [14] and LIC3 [15]. DYNC2LI1 is the light intermediate chain that associates with DYNC2H1 in the cytoplasmic dynein 2 complex: Grissom and colleagues observed that DYNC2LI1 co-immunoprecipitated specifically with DYNC2H1 and co-localized with DYNC2H1 at the Golgi apparatus. Mikami and coworkers [15] found the 350-amino acid LIC3 polypeptide (AAD34055) had a 24% similarity to rat DYNC1LI2 but failed to observe Golgi localization. DYNC2LI1 has been identified in mouse, C. elegans, Drosophila, and Chlamydomonas [14,16]. A targeted deletion of Dync2li1 in mouse affects development, in particular ventral cell fates and axis establishment in the early embryo [113]. In Chlamydomonas, DYNC2LI1 (D1bLIC) is essential for retrograde IFT [71]. As mentioned above, DYNC2LI1 appears to bind exclusively with DYNC2H1; in agreement with this, we find DYNC2LI1 homologs in species that have DYNC2H1. The exclusive association of DYNC2H1 and DYNC2LI1 with one another, and not with any of the other cytoplasmic dynein subunits, emphasizes the distinct cellular identities and roles of these separate DYNC1H1 and DYNC2H1 dynein complexes.
Cytoplasmic Dynein Light Chain Gene Families
There are three known dynein light chain gene families that are components of cytoplasmic dynein 1: (1) the t-complex–associated family (DYNLT1, DYNLT3), (2) the Roadblock family (DYNLRB1, DYNLRB2), and (3) the LC8 family (DYNLL1, DYNLL2). The gene families are named according to their original discovery, through the effect of mutations in mouse (t-complex associated, Tctex1) and Drosophila (Roadblock), or according to the size of the protein in Chlamydomonas (LC8) as discussed below. We present each family by molecular weight, starting with the largest light chain protein gene family, the t-complex–associated family (~113 amino acids), through to the Roadblock family (~96 amino acids) and the smallest light chain proteins, the LC8 family (~89 amino acids). As described below, some of the light chains have cellular functions that are independent of their role in the cytoplasmic dynein 1 complex.
Cytoplasmic Dynein Light Chain Tctex1 Gene Family (DYNLT1, DYNLT3)
Figure 2D shows the phylogenetic relationships amongst the dynein light chain Tctex1-family protein sequences from various organisms. Our phylogeny shows distinct clades for DYNLT1-like and DYNLT3-like sequences. Tctex2-like sequences lie closer to the outgroup than they do to the DYNLT1 and DYNLT3 clades (not shown).
Cytoplasmic dynein light chain Tctex1, DYNLT1.
Tctex1 (t-complex testis-expressed) gene was originally identified within the mouse t-complex (a 30- to 40-Mb region of Mmu17) as a candidate for one of the “distorter” products responsible for the non-Mendelian transmission of variant t haplotypes [114]. Lader et al. [114] and O'Neill and Artzt [115] found evidence of four copies of Dynlt1 (Tctex1) in the mouse genome; we found that the current genomic sequence databases appear to contain only one such locus that maps to Mmu 17, although a processed pseudogene has also been described on Mmu6. Subsequently, DYNLT1 was found to be an integral component of cytoplasmic dynein [116], and has since also been identified within axonemal inner and outer arm dyneins [117,118]. DYNLT1 binds to the N-terminus of the intermediate chain DYNC1I [85]. Many studies have identified DYNLT1 as a binding partner for various cellular proteins, and it has been suggested that it may attach specific proteins or cellular components to cytoplasmic dynein; for example, DYNLT1, but not its homolog DYNLT3 (see below), binds to the C-terminal domain of rhodopsin and is required for the trafficking of this visual pigment within photoreceptors [119]. The two DYNLT1 polypeptides in the cytoplasmic dynein complex dimerize, and their dimer structure is similar to that of the DYNLL1, LC8, dimer [116,120–122]. The evidence suggests that the same Dynlt1 gene product is a component of both axonemal and cytoplasmic dyneins in mouse [117]. The binding site on DYNC1I for DYNLT1 has been mapped to a 19-amino acid region at the N-terminus [85].
The Schizosaccharomyces pombe DYNLT-like gene SPAC1805.08 (also referred to as Dlc1) is involved in movement of nuclear material during meiotic prophase and is expressed in astral microtubules and microtubule-anchoring sites on the cell cortex. The Dlc1 localization pattern is similar to that of cytoplasmic dynein heavy chain Dhc1 [123]. Dlc1 null mutants are viable but have irregular nuclear movement during meiosis and defects in sporulation, recombination, and karyogamy [123]. Genetic analyses in Drosophila, which appears to have only one member of the DYNLT family, suggest that DYNLT1 is not essential for cytoplasmic dynein function, as the null mutation is not lethal. However, the mutants do have sperm-motility defects, suggesting they do have an essential role in axonemal dynein [124,125]. In Chlamydomonas, Tctex1 is an axonemal inner arm dynein component [117], and recently a variant form has been identified in axonemal outer arm dynein (DiBella et al., in press).
Cytoplasmic dynein light chain, DYNLT3.
Closely related to DYNLT1 is DYNLT3, also known as rp3 because it was initially a candidate for causing X-linked retinitis pigmentosa type 3 [126]. However, the actual gene that is defective in this disease was later identified as a guanine nucleotide exchange factor that is unrelated to DYNLT3 [127]. Subsequently, King and colleagues found that DYNLT3 is a cytoplasmic dynein light chain that is differentially expressed in a cell- and tissue-specific manner [78,116]. Interestingly, while many proteins have been identified as binding partners for DYNLT1, none have been identified as binding exclusively to DYNLT3, though recently the Herpes simplex virus capsid protein VP26 has been shown to bind both DYNLT1 and DYNLT3 [128]. There is no evidence that DYNLT3 is a component of axonemal dyneins.
Axonemal dynein light chain, Tctex2.
To avoid confusion with Tctex2, an axonemal dynein subunit, the DYNLT2 designation is not used: a third human t-complex testis-expressed gene, originally characterized by Rappold and colleagues [129,130], was given the name Tctex2, and is also known as LC2, TCTE3, and Tcd3. Patel-King and colleagues demonstrated that it has 35% identity to the 19,000-Mr (relative mobility) axonemal outer arm dynein light chain (LC2) of Chlamydomonas [131], and that it is distantly related to cytoplasmic light chains DYNLT1 and DYNLT3 [116,120]. LC2 is essential for outer arm dynein assembly [132]. There is evidence that Tctex2 may interact substoichiometrically with cytoplasmic dynein, but there has not yet been a definitive demonstration that it is a cytoplasmic dynein subunit.
In mice, expression of Tctex2 is testis-specific, particularly in later spermatogenic stages, and isoforms are thought to be generated by alternative splicing [130]. As yet, isoforms of the human homolog have not been identified, and its expression is restricted to tissues containing cilia and flagella [133]. Mutations in Tctex2 have been implicated in the autosomal recessive disorder primary ciliary dyskinesia, which results in the impairment of ciliary and flagellar function, although these mutations are thought not to be the primary cause of the disorder [133].
Mouse Tctex2 lies within the Mmu17 t-complex in a central region containing the distorter/sterility locus Tcd3 [134]. Human Tctex2 maps to the long arm of Chromosome 6 [129] and, interestingly, is a neighbor of the two genes, TCP1 and TCP10, which are also homologs of mouse t-complex loci found adjacent to mouse Tctex2. This conservation of gene order suggests that the region of Chromosome 6q containing these genes is syntenic to the homologous central region of mouse Chromosome 17. In contrast, DYNLT1 and DYNLT3 are located on human Chromosome 6p and show synteny to the distal portion of the mouse t-complex, suggesting that the middle and distal portions of the mouse t-complex are syntenic to the long and short arms of human Chromosome 6, respectively [129].
Cytoplasmic Dynein Light Chain Roadblock Gene Family (DYNLRB1, DYNLRB2)
The first Roadblock gene was identified in Drosophila through mutational analyses, and from biochemical and sequence comparisons with the Chlamydomonas outer arm dynein LC7a light chain [135,136]. Drosophila has at least six Roadblock homologs, including bithoraxoid, which has been implicated in thoracic and abdominal parasegment development. These proteins belong to an ancient family that has been implicated in NTPase regulation in bacteria [137]. Mutations in the Roadblock genes result in the accumulation of axonal cargoes, mitotic defects, female sterility, and either larval or pupal lethality [135]. Roadblock mutations also affect neuroblast proliferation and result in reduced dendritic complexity, as well as in defects in axonal transport [138]. Mutational analysis in Chlamydomonas suggests that DYNLRB (LC7a) is involved in axonemal outer arm dynein assembly, and a related protein (LC7b) is associated with dynein regulatory elements [136,139].
Figure 2E shows the phylogenetic relationships amongst the dynein light chain Roadblock-family protein sequences from various organisms. The Roadblock sequences are remarkably well conserved between different organisms, with 96% of pair-wise sequence comparisons amongst all sequences shown in Figure 2E demonstrating an identity greater than 50% (data not shown). The high conservation of Roadblock-family sequences presumably arises from functional constraints on the proteins. We note that genes in mammals and in other species incorporate conserved and complete Roadblock sequences (known as Roadblock domains) within their coding regions [135,137]. However, these genes are not thought to be cytoplasmic dyneins; for example, MAPBPIP in human and mouse appears to function mainly in the endosome/lysosome pathway [140]. Both DYNLRB polypeptides are found in mammalian cytoplasmic dynein, but it is not yet known if just one, or both, are utilized in mammalian axonemal dyneins.
Cytoplasmic dynein light chain Roadblock1, DYNLRB1.
Database searches [135,141] (Figure 2E) revealed there are two Roadblock-related proteins in mammals, DYNLRB1 and DYNLRB2 (also termed DYNLC2A and DYNLC2B) [142]. Biochemical studies suggest that in mammals both Roadblocks exist as homo- and heterodimers that associate with cytoplasmic dynein [143] through specific binding sites on the intermediate chains, distinct from those for the DYNLL (LC8) and DYNLT (Tctex1) light chains [40]. Expression studies in humans have identified tissue-specific differences in the expression of the two human Roadblock-like genes, with strong expression of DYNLRB1 in heart, liver, and brain, and up-regulation in hepatocellular carcinoma tissues [142]. In a role that may be independent of its association with cytoplasmic dynein, TGFb phosphorylation of human DYNLRB1 (termed mLC7–1/km23 by Tang and colleagues [144]) results in the human DYNLRB1 binding to the TGFb receptor that mediates TGFb responses including JNK activation, c-JUN phosphorylation, and growth inhibition.
Cytoplasmic dynein light chain Roadblock 2, DYNLRB2.
DYNLRB2 was identified by EST database searches for sequences homologous to Chlamydomonas LC7a [135,141]; human DYNLRB2 was cloned in 2001 [142] and was found to be differentially expressed in various tissues, including hepatocellular carcinomas.
Cytoplasmic Dynein Light Chain LC8 Gene Family (DYNLL1, DYNLL2)
Cytoplasmic dynein light chain LC8 1, DYNLL1.
DYNLL (the light chain that has been known as LC8, as well as LC8a and PIN) is a component of many enzyme systems, and it has a long and somewhat confusing history. This protein was originally identified, using biochemical methods, as a light chain of the Chlamydomonas axonemal outer arm dynein [145,146]. The term LC8 derives from the observation that this component migrates at ~8 kDa in SDS-PAGE gels, and it is also the smallest of the eight light chains then known within this Chlamydomonas axonemal dynein. It was first cloned from Chlamydomonas, and closely related sequences were identified in mouse and nematode along with more distantly related proteins in higher plants [147,148]. Using biochemical and immunochemical methods, DYNLL was also identified as an integral component of brain cytoplasmic dynein [104]. Only recently, it has been realized that mammals have two closely related DYNLL genes, and that the protein products of both genes are components of cytoplasmic dynein [148,149]. Thus, most of the studies on the cellular roles of DYNLL do not distinguish between the two DYNLL polypeptides.
Another factor complicating efforts to elucidate the role of the DYNLL polypeptides in dynein function was the realization that large amounts of DYNLL1 in brain, and presumably cells in general, are not associated with the dynein complex [104]. In fact, the DYNLL polypeptides have other important functions unrelated to their role in axonemal and cytoplasmic dyneins. DYNLL1 is a subunit of the flagellar radial spokes which are involved in control of axonemal dynein motor function [150]. DYNLL1 is also a substrate of a p21-activating kinase, and its interaction with the kinase may be important for cell survival [151]. A DYNLL is an integral component of the actin-based motor myosin V [152]. Immunostaining shows that a DYNLL is concentrated in dendritic spines and growth cones, and it is proposed that this is due to its association with the actin-based motor myosin V [149]. DYNLL1 was identified within neuronal nitric oxide synthase (nNOS) [14] and named “PIN” for “protein inhibitor of nNOS” [153]. However, it is unclear whether it is actually an inhibitor of nNOS or is merely a component of the nNOS complex, as DYNLL1 appears to be required for the stability of various multimeric enzyme complexes. DYNLL1 has been found to interact with a wide variety of other cytoplasmic components, including the pro-apoptotic factor Bim [154], Drosophila swallow [155,156], and rabies virus P protein [157], and it may act to attach them to the dynein and/or myosin-V molecular motors. In addition, there are many other DYNLL-interacting proteins not mentioned here that have been identified using yeast two-hybrid screens and other methods.
There are two copies of DYNLL in the cytoplasmic dynein complex, and the crystal and NMR structures of the DYNLL dimer with bound peptide are known [104,158–160]. Both monomers contribute to the formation of two symmetrical grooves in the dimer that are the binding sites for two DYNC1I polypeptides reviewed in [161]. Adding DYNLL to an N-terminal polypeptide of DYNC1I in vitro increases the structural order of DYNC1I, suggesting that DYNLL is important for the assembly of a functional dynein complex [84].
Figure 2F shows the phylogenetic relationships amongst the dynein light chain LC8-family protein sequences from various organisms. Our phylogeny shows that the mammalian LC8 light chain family falls into two distinct clades containing DYNLL1- and DYNLL2-like genes. DYNLL is highly conserved from alga and humans, and homologs are required for sensory axon projection and other developmental events in Drosophila [162,163], nuclear migration in Aspergillus [164], and retrograde IFT in Chlamydomonas [72]. The phenotype of partial loss-of-function mutants in Drosophila revealed a wide array of pleiomorphic developmental defects; the total loss-of-function mutation was embryonic lethal [162]. The Drosophila dynein light chain 1 (Cdlc1, also known as ddlc1 and “cut-up” [ctp]) is ubiquitously expressed during development and in adult tissue, and is required for proper embryogenesis and cellular differentiation. Mutations in this gene result in female sterility, which may be due to the severely disordered cytoskeletons of ovarian and embryonic cells [162]. A high degree of sequence similarity (92%) exists between Drosophila Cdlc1 and the 8-kDa flagellar outer arm dynein light chain from Chlamydomonas, and with human and C. elegans light chain 1 (91%), suggesting this gene has been under strong selective pressure [162]. S. pombe has a single known DYNLL homolog, SPAC926.07c (also referred to as Dlc2); it is transcribed during the vegetative phase, induced at low level in the sexual phase, and is enriched at the nuclear periphery [123]. A Dlc2 null mutant has been described with marginally reduced recombination in meiosis, but no other reported phenotype [123].
During the course of homology searches for this paper, we noted that DYNLL1 has related sequences in several locations in the human genome (data not shown); none of these appear to be associated with expressed sequences and thus may be pseudogenes. There was also a discrepancy in the likely mapping position of DYNLL1, and therefore we carried out a sequence analysis of DYNLL1-related genomic loci and show that the cognate human locus lies on Hsa12q24.31 (data not shown), which agrees with the mouse mapping result of Dynll1 on Mmu5.
Cytoplasmic dynein light chain LC8 2, DYNLL2.
DYNLL2, also known as DYNLL2 and LC8b, is the second member of this light chain family. It was identified by micro-sequencing of polypeptides from purified brain cytoplasmic dynein [148] and a yeast two-hybrid screen [149]. Mammalian DYNLL1 and DYNLL2 have 93% identity, differing by only six amino acids out of 89. Indicative of the extraordinary conservation of these proteins, the amino acid sequences of both DYNLL1 and DYNLL2 from human, mouse, rat, pig, and cow are identical [148]. Human DYNLL2 was identified in a yeast two-hybrid screen using the guanylate kinase–associated protein (GKAP) as bait and may mediate the interaction between GKAP and actin- and microtubule-based motors, allowing GKAP and its associated proteins to be translocated as a cargo, although DYNLL1 also binds to GKAP [149]. DYNLL2 binds the pro-apoptotic factor Bmf, which binds Bcl2, neutralizing its antiapoptotic activity, a role comparable to that reported for the binding of Bim to DYNLL1 [165]. However, it has also been observed that Bim and Bmf have identical binding affinities for both DYNLL1 and DYNLL2 [166].
It has further been proposed that DYNLL1 binds specifically to the dynein intermediate chain DYNC1I, while DYNLL2 binds to the myosin-V heavy chain. However, DYNLL2 co-purifies with cytoplasmic dynein from various rat tissues [148], and DYNLL1- and DYNLL2-GST are equally effective in binding myosin V [149]. Furthermore, DYNLL1 and DYNLL2 bind with equal affinity to DYNC1I in pair-wise yeast two-hybrid studies (K. W. Lo and K. K. Pfister, unpublished data). It is not yet known if one, or both, of the DYNLL polypeptides are associated with axonemal dyneins; however, DYNLL1 is enriched in testes and lung—tissues that have large numbers of cilia or flagella [148].
Human and Mouse Cytoplasmic Dyneins: Nomenclature, Map Positions, and Sequences
To create Table 1, we cataloged, by literature searches, all known gene and protein names for the cytoplasmic dyneins in mouse and human. In addition, aliases were recorded from the single-query interface LocusLink (http://www.ncbi.nlm.nih.gov/LocusLink) and the Mouse Genome Informatics (MGI) website (http://www.informatics.jax.org). We also included aliases previously approved by the HUGO Gene Nomenclature Committee (http://www.gene.ucl.ac.uk/nomenclature) as well as aliases referenced in sequence submissions to the GenBank (http://www.ncbi.nlm.nih.gov/Genbank) and Entrez (http://www.ncbi.nlm.nih.gov/entrez) sequence databases [167].
Human and mouse orthologs in Table 1 are taken from the literature and databases. Human and mouse chromosomal locations were obtained from the literature and from the MGI and LocusLink databases. The OMIM numbers given for gene and disease loci in humans refers to the unique accession numbers in the On-line Mendelian Inheritance in Man database (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=OMIM). Nucleotide and protein sequences (prefix NM_ and NP_, respectively) are National Center for Biotechnology Information (NCBI) Reference Sequence (RefSeq, http://www.ncbi.nlm.nih.gov/RefSeq) and Swiss-Prot accession numbers (http://www.ebi.ac.uk/swissprot), respectively [167]. The NCBI RefSeq project provides a non-redundant and comprehensive collection of nucleotide and protein sequences drawn from the primary-sequence database GenBank. RefSeq collates and summarizes primary-sequence data to give a minimal tiling path for individual transcripts, using available cDNA and genomic sequence whilst removing mutations, sequencing errors, and cloning artifacts. Sequences are validated in silico by NCBI's Genome Annotation project to confirm that any genomic sequence incorporated into a RefSeq cDNA matches primary cDNA sequences in GenBank, and that the coding region really can be translated into the corresponding protein sequence. Accession numbers beginning with the prefix XM_ (mRNA) and XP_ (protein) are RefSeq sequences of transcripts and proteins that are annotated on NCBI genomic contigs; these may have incomplete cDNA-tiling-sequence data or contig sequences [168]. For dyneins with known isoforms, isoform-sequence accession numbers available within nucleotide and protein databases are given.
Included in the heavy chains that we found were “Cell Division Cycle 23, yeast homolog” (CDC23) and “Cell Division Cycle 22, yeast homolog” (CDC22), which are GenBank aliases for human and mouse dynein heavy chain 1, respectively. We found no evidence in the literature to support the “CDC” designation of these genes and their products in terms of either “Cell Division Cycle” or “Cytoplasmic Dynein Chain”. We compared mouse and human heavy chain 1 cDNA and protein sequences with mouse, human, and yeast CDC23 and CDC22 sequences and found no similarity to support this designation (data not shown). We concluded that the synonym CDC had most likely been attributed in error, and we contacted NCBI who, in agreement with our findings, removed the CDC designation from the sequences involved.
Human/Mouse Homology Searches
Homology searches of human cytoplasmic dynein subunit genes were conducted using position-specific iterative BLAST (PSI-BLAST) [169] at NCBI (http://www.ncbi.nlm.nih.gov/BLAST; Table 2). The PSI-BLAST program identifies families of related proteins using an iterative BLAST procedure [170]. In an initial search, a position-specific scoring matrix is constructed from a multiple sequence alignment of the highest scoring hits. Subsequent iterations using the position-specific scoring matrix are performed in a new BLAST query to refine the profile and find additional related sequences. We used nucleotide and protein sequences from each known human dynein gene to query the human and mouse non-redundant sequence databases at GenBank, using default parameters and the BLOSUM-62 substitution matrix, which has been shown to be the most effective substitution matrix to identify new members of a protein family [171]. Where dynein isoforms were present, the longest sequence was used to search the databases.
Phylogenetic Analysis
To establish gene family groupings, we investigated the phylogenetic relationships between dynein protein homologs in various organisms. Homologous sequences were identified by searching the GenBank non-redundant protein database, with the human protein using PSI-BLAST with default parameters and the BLOSUM-62 substitution matrix. Searches of pufferfish sequence Takifugu rubripes (commonly known as Fugu rubripes), for which little transcribed sequence exists although a usable genome assembly is present, were performed using the BLAST (TBLASTN) feature at the Ensembl Fugu Genome Browser (version 2.0; http://www.ensembl.org/Fugu_rubripes), searching with human protein sequence against a translated nucleotide database.
Protein sequences were aligned for comparison across their full lengths using the multiple sequence alignment program CLUSTALW [172] (http://www.ebi.ac.uk/clustalw) and applying the GONNET250 matrix as default. The GONNET250 is a widely used matrix for performing protein-sequence alignments, allowing 250 accepted point mutations per 100 amino acids, using scoring tables based on the PAM250 matrix [173].
Two different phylogenetic methods were used to analyse the dynein gene family alignments. Maximum-likelihood trees were inferred under the Jones, Taylor, and Thornton (JTT) empirical model of amino-acid substitution using PHYML version 2.4.3 [174], as was non-parametric bootstrapping using 100 resampled alignments for each gene family. Bayesian analyses were performed using MrBayes version 3.0B4 [175], using the default Bayesian priors on tree topologies and branch lengths. Two different sets of analyses were performed for each gene family, the first allowing the Markov-chain Monte-Carlo algorithm to move between the 11 different amino-acid substitution models available in MrBayes, and another specifying the JTT model. The first analysis allows the chain to take into account uncertainty in the substitution process. For all analyses performed here, the posterior probability of the JTT model was at least 99%, confirming that this model best describes the evolution of the dynein sequences—so only results from the fixed-JTT model analyses are shown here.
For each analysis, three chains of 1,000,000 generations each were run, sampling parameters every 100 generations and discarding the first 100,000 generations as a burn-in period. Running these multiple independent chains allowed visual confirmation that the chains had reached a stationary state by ensuring that all three chains were moving around a region of similar likelihood. For one of the gene families (cytoplasmic dynein heavy chain), the three chains had reached different likelihood values after 1,000,000 generations, suggesting failure to converge. Running another three independent chains resulted in five out of six chains agreeing on the likelihood values, suggesting that only one chain had not converged properly. In all cases, the phylogeny presented is the majority-rule consensus of the posterior sample of tree topologies from all three Markov chains, drawn using TreeView [176] with posterior clade probabilities and maximum-likelihood bootstrap values shown for each clade on these trees.
Searching for Function and Mutant Phenotypes
As well as literature searches, information on protein function was taken from the Gene Ontology database (http://www.geneontology.org), which provides data on function and processes associated with a search protein. Mutant-phenotype data were obtained from the literature and the following sources: Online Mendelian Inheritance in Man at NCBI for human (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=OMIM); MGI for mouse (http://www.informatics.jax.org); FlyBase for Drosophila (http://www.flybase.org); and WormBase for C. elegans (http://www.wormbase.org).
Conclusions
In this paper, we have provided an overview of the two cytoplasmic dynein complexes, cytoplasmic dynein 1 and cytoplasmic dynein 2, from a genetic perspective. We have highlighted the unique subunit compositions and cellular functions of the two cytoplasmic dyneins, and we have emphasized the unique role of cytoplasmic dynein 2 in IFT. We have described the different mammalian dynein gene families, and have shown the phylogenetic and functional relationships between members of individual families. We carried out initial database searches and clarified and corrected anomalous data. We have also discussed known functions and mutations of these proteins, and we have highlighted both their fundamental importance to the cell and the fact that much research remains to be carried out to define the roles of individual proteins.
Supporting Information
Table S1 Species Names, NCBI/GenBank Protein-Sequence Accession Numbers, and NCBI/GenBank Gene/Protein Names for Figures 2A–F
(53 KB DOC)
Click here for additional data file.
Accession Numbers
The Entrez Gene database (http://www.ncbi.nlm.nih.gov/entrez) accession numbers for the proteins discussed in this paper are Cdic (also referred to as cDic and Dic) (44160); che-3 (DYNC2H1 homolog) (172593); DYNC1I1 (1780); DYNC1I2 (1781); DYNC1LI1 (51143); DYNC1LI2, (1783); DYNC2H1, (79659); DYNC2LI1 (51626); DYNLL1 (LC8) (8655); DYNLL2 (also known as DYNLL2 and LC8b) (140735); DYNLRB1 (also termed DYNLC2A) (83658); DYNLRB2 (also termed DYNLC2B) (83657); DYNLT1 (Tctex1) (6993); MAPBPIP (28956); SPAC1805.08 (also referred to as Dlc1) (3361491).
The Entrez Gene database (http://www.ncbi.nlm.nih.gov/entrez) accession numbers for the genes discussed in this paper are Dhc64C (38580); dli-1 (178260); dyn1 (853928); Dync1h1 (13424); DYNC1H1 (1778) ;
Dync1i1 (13426); Dync1i2 (13427); Dync2li1 (213575); Dynlt1 (Tctex1) in the mouse genome (21648); DYNLT3 (6990); human Tctex2 (also known as LC2, TCTE3, and Tcd3) (6991); mouse Tctex2 (21647); xbx-1 (184080).
The Entrez Protein database (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=protein&cmd=search&term=) accession numbers for the proteins discussed in this paper are C. elegans LC8 sequence (49822); C. elegans light chain 1 (498422); Cdlc1 (525075); Chlamydomonas 19,000-Mr axonemal outer arm dynein light chain (LC2) (AAB58383); rat DYNC1I1 (062107); rat DYNC1LI2 (112288).
The OMIM (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=OMIM) accession numbers for the proteins discussed in this paper are autosomal recessive disorder primary ciliary dyskinesia (242650); Bim (603827); Bmf (606266); X-linked retinitis pigmentosa type 3 (300389).
The Ensembl (http://www.ensembl.org/Fugu_rubripes/textview) accession number for the Takifugu LC8 sequence is SINFRUP0000015498.
The SwissProt (http://ca.expasy.org/sprot/) accession numbers for the Chlamydomonas 8-kDa flagellar outer arm dynein light chain and the Chlamydomonas LC2 light chain used as the outgroup in Figure 2 are Q39580 and T08216, respectively.
PRS, HH, and EMCF are supported by the UK Medical Research Council, the Motor Neurone Disease Association, and the American Amyotrophic Lateral Sclerosis Association. KKP is supported by a grant from the National Institute of Neurological Disorders and Stroke, at the National Institutes of Health (NIH). SMK is supported by grants (GM51293 and GM63548) from the National Institutes of General Medical Sciences, NIH, and is an investigator of the Patrick and Catherine Weldon Donaghue Medical Research Foundation. JC is supported by the Biotechnology and Biological Sciences Research Council (BBSRC), grant 40/G18385. AR is supported by grants from the BBSRC and the Royal Society. We are most grateful to Lois Maltais of the Mouse Genomic Nomenclature Committee and to Mathew Wright of the Human Genome Organization Gene Nomenclature Committee for their help and support in preparing this manuscript. We thank Ray Young for supplying graphics.
Abbreviations
ATPadenosine triphosphate
BBSRCBiotechnology and Biological Sciences Research Council
IFTintraflagellar transport
JTTJones, Taylor, and Thornton
MGIMouse Genome Informatics
Mrrelative mobility
NCBINational Center for Biotechnology Information
NIHNational Institutes of Health
nNOSneuronal nitric oxide synthase
PSI-BLASTposition-specific iterative BLAST
RefSeqReference Sequence
==== Refs
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PLoS GenetPLoS GenetpgenplgeplosgenPLoS Genetics1553-73901553-7404Public Library of Science San Francisco, USA 1644005710.1371/journal.pgen.002000905-PLGE-RA-0283R2plge-02-01-08Research ArticleAllergy - ImmunologyDiabetes - Endocrinology - MetabolismEvolutionGenetics/Gene DiscoveryGenetics/Population GeneticsGenetics/Genome ProjectsGenetics/Genetics of DiseaseGenetics/Disease ModelsGenetics/Complex TraitsGenetics/Chromosome BiologyHomo (Human)Genetic Analysis of Completely Sequenced Disease-Associated MHC Haplotypes Identifies Shuffling of Segments in Recent Human History Complete MHC Sequence AnalysisTraherne James A 1Horton Roger 2Roberts Anne N 3Miretti Marcos M 2Hurles Matthew E 2Stewart C. Andrew 1Ashurst Jennifer L 2Atrazhev Alexey M 4Coggill Penny 2Palmer Sophie 2Almeida Jeff 2Sims Sarah 2Wilming Laurens G 2Rogers Jane 2de Jong Pieter J. 5Carrington Mary 6Elliott John F 4Sawcer Stephen 7Todd John A 3Trowsdale John 1Beck Stephan 2*1 Department of Pathology, Immunology Division, University of Cambridge, Cambridge, United Kingdom
2 Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge, United Kingdom
3 Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, University of Cambridge, Wellcome Trust/MRC Building, Addenbrooke's Hospital, Cambridge, United Kingdom
4 Alberta Diabetes Institute (ADI), Department of Medical Microbiology and Immunology, Division of Dermatology and Cutaneous Sciences, University of Alberta, Edmonton, Canada
5 Children's Hospital Oakland Research Institute, Oakland, California, United States of America
6 Basic Research Program, SAIC-Frederick, Inc., Laboratory of Genomic Diversity, National Cancer Institute, Frederick, Maryland, United States of America
7 Department of Clinical Neurosciences, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom
Roopenian Derry EditorThe Jackson Laboratory, United States of America* To whom correspondence should be addressed. E-mail: [email protected] 2006 27 1 2006 2 1 e914 9 2005 13 12 2005 © 2006 Traherne et al.2006This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.The major histocompatibility complex (MHC) is recognised as one of the most important genetic regions in relation to common human disease. Advancement in identification of MHC genes that confer susceptibility to disease requires greater knowledge of sequence variation across the complex. Highly duplicated and polymorphic regions of the human genome such as the MHC are, however, somewhat refractory to some whole-genome analysis methods. To address this issue, we are employing a bacterial artificial chromosome (BAC) cloning strategy to sequence entire MHC haplotypes from consanguineous cell lines as part of the MHC Haplotype Project. Here we present 4.25 Mb of the human haplotype QBL (HLA-A26-B18-Cw5-DR3-DQ2) and compare it with the MHC reference haplotype and with a second haplotype, COX (HLA-A1-B8-Cw7-DR3-DQ2), that shares the same HLA-DRB1, -DQA1, and -DQB1 alleles. We have defined the complete gene, splice variant, and sequence variation contents of all three haplotypes, comprising over 259 annotated loci and over 20,000 single nucleotide polymorphisms (SNPs). Certain coding sequences vary significantly between different haplotypes, making them candidates for functional and disease-association studies. Analysis of the two DR3 haplotypes allowed delineation of the shared sequence between two HLA class II–related haplotypes differing in disease associations and the identification of at least one of the sites that mediated the original recombination event. The levels of variation across the MHC were similar to those seen for other HLA-disparate haplotypes, except for a 158-kb segment that contained the HLA-DRB1, -DQA1, and -DQB1 genes and showed very limited polymorphism compatible with identity-by-descent and relatively recent common ancestry (<3,400 generations). These results indicate that the differential disease associations of these two DR3 haplotypes are due to sequence variation outside this central 158-kb segment, and that shuffling of ancestral blocks via recombination is a potential mechanism whereby certain DR–DQ allelic combinations, which presumably have favoured immunological functions, can spread across haplotypes and populations.
Synopsis
A group of genes involved in the human immune system are contained within a surprisingly short section of Chromosome 6 that has long been recognised as the most important genomic region in relation to disease susceptibility. Discerning the actual genes playing a role in disease has proved difficult mainly because the region contains numerous genes and is also the most genetically variable in the genome. Within this jungle of variation, the research reported here has identified and characterised a discrete segment shared by two individuals that is virtually devoid of variation—a polymorphism desert. The conservation of this segment amongst a background of extreme variation suggests both an ancient origin and genetic exchange in early human history. These observations are important in evolutionary terms as they reveal a potential mechanism whereby certain genetic segments associated with favourable immune functions have spread across human populations. Within medical terms this may also explain contrasting disease risks in people from different ethnic backgrounds. Public access to these data will help researchers find specific variants conferring disease susceptibility or resistance and, as in this report, rule out regions for conveying specificity to certain diseases.
Citation:Traherne JA, Horton R, Roberts AN, Miretti MM, Hurles ME, et al. (2006) Genetic analysis of completely sequenced disease-associated MHC haplotypes identifies shuffling of segments in recent human history. PLoS Genet 2(1): e9.
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Introduction
The classical major histocompatibility complex (MHC) containing the human leukocyte antigen (HLA) loci on human Chromosome 6p21.31 is a gene-dense region spanning nearly 4 Mb. The region plays an important role in disease resistance and susceptibility. About 30% of the approximately 150 constituent-expressed genes encode functioning immune-related molecules. The allelic and genetic structure of the MHC is complex. It harbours some of the most polymorphic genes in the genome, and sequences differ in size and gene composition partly as a result of non-allelic homologous recombination [1]. These extreme levels of polymorphism and dense genetic organisation, including highly reiterated sequences, have made particular parts of this biologically and medically important region less accessible to genome-wide analyses such as those employed by the International SNP and HapMap projects (International SNP Map Working Group 2001, [http://snp.cshl.org] [2]; The International HapMap Consortium 2003, [http://www.hapmap.org] [3]). In addition, polymorphism studies within the MHC have historically focused on small areas or have been anecdotal. Consequently, knowledge of sequence variation across the complex has been fragmentary, and conventional single nucleotide polymorphism (SNP) densities have not been sufficient to provide a contiguous map of allelic variation that captures the genetic diversity of the MHC, which is a limitation in the identification of MHC variants primarily associated with disease.
The MHC Haplotype Project (http://www.sanger.ac.uk/HGP/Chr6/MHC) was designed to address this problem by cloning and sequencing of bacterial artificial chromosome (BAC) library clones derived from unrelated consanguineous cell lines homozygous for certain HLA haplotypes thereby eliminating the possibility of heterozygosity [4]. This resource will provide ready access to the genomic sequences of eight different MHC haplotypes and their resulting SNP and deletion/insertion polymorphism (DIP) variations, their ancestral relationships, and the accurate annotation of all loci and their splice variants. To date, the study has contributed over 60% of the total number of recorded SNPs (dbSNP124) across the MHC and provided more than 50% of the available SNPs for the most-recent high-resolution haplotype map of the MHC that can be applied to association studies of MHC-linked diseases [5].
In total, autoimmune diseases affect approximately 5% of the population and include type 1 diabetes, rheumatoid arthritis, and multiple sclerosis. Linkage scans and association-mapping studies have identified the MHC as influencing most, if not all, autoimmune conditions [6–12] along with certain infectious diseases, including malaria and AIDS [13,14]. For some common autoimmune diseases, the MHC provides by far the largest genetic contribution by a single chromosome region. For example, the MHC accounts for at least 30% of the familial aggregation in type 1 diabetes and rheumatoid arthritis [15–17], with additional chromosome loci outside the MHC contributing smaller individual genetic effects. The highly polymorphic HLA class I and class II loci are believed to be the major determinants of MHC-associated disease, but in general the precise MHC variants influencing these conditions remain unknown despite intense study. Hampered by inadequate knowledge of sequence variation across the complete MHC, fine-mapping of the primary causal variants has been additionally confounded by the complexity of associations in which several MHC genes may be involved. Moreover, the extensive linkage disequilibrium (LD) between certain genes of the MHC, notably HLA-DRB1 and -DQB1, along with the high gene density of the region makes it difficult to rule out a contribution from other linked genes, such as those in class III and the extended class I regions.
We have previously reported the complete and contiguous sequence and annotation for two human MHC haplotypes, PGF (HLA-A3-B7-Cw7-DR15-DQ6) and COX (HLA-A1-B8-Cw7-DR3-DQ2), each spanning just less than 5 Mb [18] (see Table S1 for DNA typing profiles) and producing 16,013 SNPs. Here we describe 4.25 Mb of sequence and the variation content from a second DR3-DQ2 haplotype, QBL (HLA-A26-B18-Cw5-DR3-DQ2). Currently, these MHC haplotypes represent the only long-range single haplotype sequences in the human genome [18]. The cell lines were selected for study from the 10th International Histocompatibility Workshop panel [19]. The haplotypes chosen are disease-associated and common, with northern European frequencies on the order of 10%. The COX haplotype has been associated with susceptibility to a wide range of diseases, including type 1 diabetes, systemic lupus erythematosus, and myasthenia gravis [20]. The PGF haplotype provides protection against type 1 diabetes and predisposes to other diseases such as multiple sclerosis and systemic lupus erythematosus [21–24]. The QBL haplotype is positively associated with Graves' disease and type 1 diabetes [25].
The MHC lends itself to analysis of meiotic recombination because of the high density of variation. Comparison of two full-length DR3 haplotypes allowed demarcation of sequence shared between two HLA-related haplotypes that show differing disease associations. We assessed the LD structure of a 158-kb segment of shared sequence identified in the two DR3 haplotypes at the population level using high-density SNP-typing data for 180 Centre d'Etude du Polymorphisme Humain (CEPH) founder chromosomes. These findings emphasize the importance of fine-scale LD structure of the MHC and suggest that DR/DQ segment exchange between MHC haplotypes in relation to recombination hotspots may be responsible for contrasting disease risk related to non-DR/DQ loci and to different ethnic backgrounds.
Results
Sequence
Approximately 4.25 Mb of the QBL haplotype sequence, from RFP to KIFC1, spanning the MHC class I, class II, and class III regions, along with part of the extended class I region, was obtained from large-insert BAC clones by shotgun sequencing. There were five small gaps of the size range 26–159 kb in the QBL sequence, owing to incomplete clone coverage, which by comparison to PGF comprised a total of only about 317 kb (see Figure 1). A test was conducted to ensure that QBL BACs were derived from a single haplotype by comparing adequately overlapping reads from the shotgun sequencing strategy extending into adjacent BAC clones. The QBL BAC overlaps indicated homozygosity over the entire MHC for the QBL cell line.
Figure 1 Positional Distributions of Variations between PGF and QBL and COX and QBL
(A) Shows the distribution for PGF and QBL and (B) shows COX and QBL. MHC sequences were divided into 10-kb bins, and variations were calculated in each bin. Results are expressed as variations per 1 kb. Red and blue plots relate to SNP and DIP variations respectively. The sequence is interrupted by five gaps, shown as green vertical bars, where BACs encompassing these regions could not be identified from the clone library, which by comparison with PGF comprise a total of approximately 317 kb. The lengths and gene content of these gaps were as follows, from left to right: 159 kb including OR2U1P to OR12D2; 51 kb containing HCP5; 26 kb containing C6orf26, C6orf27, and the three exons of 3′ end of MSH5; 53 kb containing CREBL1, FKBPL, and six exons of the 5′ end of TNXB; and 27 kb containing HLA-DOB. These gaps do not represent large genomic deletions within the QBL haplotype since exonic sequence from selected genes within these regions were successfully amplified from QBL genomic DNA and sequenced to confirm their identity. The grey shaded area at the telomeric end of the map represents sequence for which overlap was not obtained and was therefore outside the area that was compared.
Boundaries of the class I, II, and III regions are shown. The positions of RFP and KIFC1 that define the ends of the MHC haplotype sequencing project are indicated. Landmark genes are labelled in blue. Regions 1 and 2 are the RCCX module and the HLA-DRB region, respectively. The HLA-DRB3 and HLA-DQB3 region, which shows little variation between COX and QBL haplotypes, is shaded in orange.
Annotation and Gene Content
The human-curated annotation of the QBL haplotype sequence revealed a total of 259 loci, including 149 coding, 27 transcribed loci, and 83 pseudogenes. All these loci are the same as those found in the PGF and COX haplotypes [18], after taking into account the gaps in the QBL sequence contig, and the conformations of the DR and RCCX (RP-C4A/B-CYP21-TNXB) regions (see below).
QBL and COX do not differ in respect to their HLA-DRB gene composition, and both haplotypes contain a single C4 gene, although of a different allele (see below). The intronless pseudogene, PPP1R2P1, has a full-length open reading frame in QBL, as found previously in PGF and COX [18], and does not possess the frameshift mutation present in the original reference MHC gene sequence [26,27]. Four other notable and potentially functional alterations to loci were observed within the class I region in an area shown to be important in susceptibility to psoriasis [28–30]: (1) The locus C6orf205 encoding a putative transmembrane protein [31,32] possesses a coding minisatellite in exon 2: PGF and COX both possess only 27 copies, whereas QBL possesses 31 copies of the 45-mer repeat sequence, thus extending the coding sequence (CDS) by 180 nucleotides and the translated CDS by 60 amino acids. (2) In QBL, PSORS1C1, a psoriasis candidate locus [33], has a deletion of one nucleotide in a polyC tract in exon 5 (at base position 118 in the PGF CDS) compared to COX and PGF. This produces a frameshift in the spliced mRNA transcript and a premature stop codon in the following exon, which would shorten the CDS by 266 base pairs (bp) (resultant CDS 192: 64 amino acids; compared to CDS 459: 152 amino acids in PGF and COX), resulting in a novel stretch of 24 amino acids in the terminal end of the protein product. (3) POU5F1 encodes a POU homeodomain–containing transcription factor. A SNP identified in PGF disrupted the Met start codon of the alternative splice isoform, resulting in a shorter open reading frame in PGF than in QBL and COX. (4) HCG22 (HLA complex group 22) encodes a novel transcript. The second exon in the QBL haplotype is approximately 174 bp shorter than in either PGF or COX; however, as no open reading frame for this transcript could be identified, the significance of this observation is not known. Other changes observed are consistent with known allelic polymorphisms (see Table S1).
The annotation of the haplotypes is available as a general resource through the Vertebrate Genome Annotation (VEGA) database (http://vega.sanger.ac.uk) with PGF as the reference haplotype. All annotation is to standards set by the Human Annotation Workshop (HAWK), providing accuracy greater than currently possible through in silico methods [34]. The annotation shown reflects the cDNA, EST (expressed sequence tag), and protein evidence available at the time of analysis, and it is therefore appreciated that this will change with future information. Splicing cDNA or EST evidence was required for annotation of all splice variants.
Sequence Variation
Table 1 summarises all variations observed between the PGF reference and QBL haplotypes. Across 4.25 Mb of sequence, this included a total of 17,695 variations, of which 15,345 were SNPs and the remainder (13%) were DIPs. There were 528 SNPs in coding regions. The mean SNP density was 2.12/kb within coding regions. Exonic UTR and intronic DNA displayed higher variation densities of 2.50/kb and 2.48/kb, respectively, and even higher variation densities were observed in intergenic non-repeat DNA, pseudogene sequences, and interspersed repeats (average 3.76/kb).
Table 1 Sequence Variations between QBL and PGF
Table 2 Codon Changes due to Coding SNPs between QBL and PGF
The coding SNPs between PGF and QBL were contained in 483 codons: 243 in the nine classical HLA loci and 240 in all other loci (Table 2). Categorization of the coding SNPs into types of codon changes introduced showed that the nine classical MHC genes had a non-synonymous:synonymous (N:S) ratio of approximately 3:1 (Table 2). These data are consistent with positive selection acting on these genes [35,36]. Of the non-synonymous amino acid changes, 43% were non-conservative, as defined by a negative score in a BLOSUM 62 matrix [37]. In contrast, the pooled set of non-classical MHC genes had a N:S ratio of 1:1. Amongst the non-synonymous changes, the pooled sets of both classical and non-classical MHC genes had similar proportions of non-conservative changes.
The coding DIPs observed between PGF and QBL (Table 1) altered the CDS of MICA and HLA-DQA1 as is consistent with known allelic polymorphisms (see Table S1), and C6orf205 and PSORS1C1 as described earlier. A plot of size distribution of all DIPs within the range of 1 bp to 36 bp showed a negative correlation between frequency and DIP size with a log-linear distribution (Figure S1).
Thirty-four large DIPs (96–5,157 bp) were identified between PGF and QBL haplotypes (Table S2), some of which have previously been identified (e.g., [38–42]); 22 were found by comparison of previously reported haplotypes PGF and COX [18]. Fifteen DIPs (of the 34) resulted from Alu element insertions. The majority of these belonged to the younger Alu Y subfamily, and more specifically five were of the Ya5 and Yb8 types, which emerged after the divergence of humans from African apes [43]. There were also differences between PGF and QBL in the presence/absence of more ancient sequences, such as AluS and repeats of the HERV (human endogenous retrovirus), LINE (long interspersed nuclear element), LTR (long tandem repeat), MER (mammalian interspersed repetitive element), and SVA (SINE-VNTR-Alu) families. These differences were concentrated between DRB1 and DQB1 in the class II region but were more evenly distributed across the class I region, consistent with an early divergence of the PGF and QBL haplotypes in the DR–DQ and class I regions relative to other regions of the MHC. These large DIPs account for at least 37 kb of sequence.
For comparative study of sequence diversity, we measured heterozygosity per nucleotide site (π). The π value between any two haplotypes throughout the genome has been estimated to lie between 5 × 10−4 and 9 × 10−4 and is known to vary by chromosomal region [2,44–46]. The level of SNP variation between COX and PGF (3.4 × 10−3), and QBL and PGF (3.6 × 10−3) is 4- to 7-fold higher than estimates for genome-wide heterozygosity. In comparing π for PGF versus COX or QBL, there was bias by selection of HLA-disparate haplotypes. The π value between QBL and COX (2.7 × 10−3), two DR3-DQ2 haplotypes, was therefore expected to be less than between totally disparate HLA haplotypes, but it nevertheless remained significantly higher than genome-wide estimates. Approximately 40% (6,230 of 15,345) of the SNPs and 57% (1,332 of 2,350) of the DIPs from QBL were distinct from those already identified in the comparison of PGF–COX. For all haplotype comparisons, however, the relatively high level of variation observed was mostly due to the peaks surrounding the classical HLA loci, since sequence diversity outside these areas was comparable to the genome-wide estimates (data not shown).
To display the variations between haplotypes, we plotted variation density against genomic position (Figure 1). As expected, the highest levels concentrated in peaks overlying the classical class I and class II loci, and are usually explained by balancing selection acting on the peptide-binding domains of the HLA loci [35,36] with “hitch-hiking” of neighbouring mutations [47]. Some smaller peaks were sited over genes other than classical HLA loci, consistent with independent selection for variation. An example of this concentrated variation was previously observed in the comparison between PGF and COX telomeric of HLA-C from CDSN to POU5F1 [18], and a similar peak was identified in the comparison of COX with QBL.
HLA-DR Region Sequence Variation
The HLA-DRB region is known to be extremely polymorphic both in terms of SNPs, and of the insertion and deletion of large fragments of genomic sequence [48], such that different haplotypes have missing or alternative arrangements of HLA-DRB genes and pseudogenes [49]. QBL and COX possessed the same HLA-DRB gene composition that was distinct from PGF, with both haplotypes sharing the loci arrangement DRB9, DRB3, DRB2, and DRB1. These two haplotypes therefore carry two functional DRB genes (DRB3 and DRB1) and two DRB pseudogenes (DRB9 and DRB2). The high degree of variation between PGF and COX/QBL in the DR/DQ region suggests that the HLA haplotypes embodied in PGF (DR15-DQ6) and COX/QBL (DR3-DQ2) are ancient and highly diverged relative to each other.
Interestingly, when part of the HLA-DRB region containing the genes HLA-DRB1, HLA-DQA1, and HLA-DQB1 and pseudogenes HLA-DRB2 and MTCO3P1 was compared between COX and QBL only 14 SNPs were found in 158 kb; approximately 1 per 10 kb (π = 8.2 × 10−5) (Figure 2). The coding sequences of all these genes were identical in the two haplotypes. By contrast, although this region is not entirely represented in the PGF haplotype, in the 128 kb of sequence also found in PGF, there were 3,754 SNPs when compared with QBL (π = 2.9 × 10−2), and 3,808 when compared with COX (π = 3.0 × 10−2). Using a range of plausible mutation rates from 1.3 to 2.1 × 10−8 substitutions per site per generation [50], based on the assumption that the human and chimpanzee lineages split 6 million years ago (and in absence of selection), we estimate that the time to the most recent common ancestor of two sequences that have accumulated only 14 SNPs within 158 kb is 2,100–3,400 generations.
Figure 2 Positional Distributions of Variations between COX and QBL in the HLA-DR Region
MHC sequences were divided into 10-kb bins, and variations were calculated in each bin. Results are expressed as variations per 1 kb. Red and blue plots relate to SNP and DIP variations respectively.
Within a stretch of approximately 160 kb between HLA-DRB3 and HLA-DQB3, only 14 SNPs and six small DIPs, comprising 1 bp, 6 bp, 10 bp (five copies of a dinucleotide repeat), and 54 bp (two copies of 27 mer), were contained. None of the variations located to coding sequence or the defined promoter regions of the HLA class II genes [86].
Four 1-bp DIPs, labelled in grey, were identified between DRB1 and DQA1 where LR-PCR products were used to close a small gap resulting from clone deficit. These DIPs were located in polyA/T tracts in which the probability of Taq slippage in PCR products is much higher than in in-vivo amplified plasmid DNA such that their authenticity was questionable and they were excluded from analyses (Figure S2 shows one alignment of sequence traces with differing polyT tracts).
The presence of the SNP desert and its boundaries was evaluated at the population level by assessing the diversity in the DR–DQ region in chromosomes sharing the DRB1*0301-DQA1*0501-DQB1*0201 (DR3-DQ2) haplotype common to COX and QBL. This analysis was carried out using high-density SNP-typing data for 140 phased CEPH founder chromosomes [5] with available HLA-typing information. For the 158-kb segment defined above, the mean sequence pairwise differences considering 12 chromosomes sharing the DR3-DQ2 segment is ten times lower than the divergence estimate based on the adjacent centromeric 158-kb fragment. We then investigated the diversity in these two regions on 26 chromosomes sharing the most common HLA class II haplotype in this sample: DRB1*1501-DQA1*0102-DQB1*0602 (DR15–DQ6). In this case, the divergence estimate for the DR–DQ region is 200 times lower when compared to the 158-kb segment immediately centromeric (Table 3). High identity can be predicted when SNP typing is carried out in a sub-sample of chromosomes bearing the same DRB1*1501–DQB1*0602 (DR15–DQ6) haplotype. Nevertheless, the abrupt transition from almost complete identity to increased diversity in fewer than 5 kb on the centromeric side of the DR–DQ 158-kb segment is remarkable. The boundary between these adjacent segments of contrasting diversity levels can be observed in the alignment of multiple haplotypes sharing DRB1*1501–DQB1*0602 (DR15–DQ6) (Figure 3). This 5-kb region separating these two DNA segments corresponds to a recombination hotspot identified by sperm-based genotyping [51,52]. As expected, the variation level was higher at more distant loci such as HLA-A and HLA-B-HLA-C (Table 3).
Table 3 Divergence Estimates for Different Regions in the MHC Observed on 26 Chromosomes Sharing the DRB1*1501-DQA1*0102-DQB1*0602 (DR15–DQ6) Haplotype
Figure 3 Haplotype Alignment of the Region Presenting Differing Variation Rates
The alignment covers the centromeric side of the DR–DQ 158-kb DNA segment (left half, low variation) and the adjacent DNA segment (increased variation). Coordinates refer to Chromosome 6 build NCBI35. Rows represent the allelic state for 26 single chromosomes with the same DRB1*1501-DQA1*0102-DQB1*0602 (DR15–DQ6) haplotype at successive SNPs which are represented by columns (A, red; C, blue; G, orange; and T, green). Identity is interrupted at a position perfectly matching with a recombination hotspot coordinate [5,53] represented as hotspot number 2 in Figure 4.
Figure 4 LD Structure around the HLA-DR Region
High-resolution view of the HLA-DR region, as represented by GOLDsurfer three-dimensional view of D′ values [81]. The position of the 158-kb segment shared by identical by descent between COX and QBL is shown by a dashed white line. High LD areas (red blocks) are separated by LD breaks. The first LD break (1) corresponds to a recombination hotspot mapped between NOTCH4 and C6orf10 in the class II–III boundary region. Another LD break (2) is visualized at another recombination hotspot centromeric of HLA-DQB1 at the boundary of the SNP desert between COX and QBL. This is followed centromerically by a further four LD breaks corresponding to recombination hotspots mapped at BRD2/HLA-DOA interval, within HLA-DMB, within TAP2 and HLA-DQB2/-DOB interval [5,51,53]. An asterisk (*) indicates a region of depleted SNP data, likely owing to substantial genotyping failure in an area with an extreme level of polymorphism.
In order to investigate potential analogies at different HLA loci, we analysed pairwise variation levels at the HLA-C-B region and at its adjacent centromeric segment in a sample of 22 chromosomes sharing the HLA-C*0702–HLA-B*0702 haplotype, the most common HLA-C-B haplotype in the sample. For the 201-kb segment containing the HLA-C-B genes, the mean number of pairwise differences between all pairs of haplotypes (5.39 × 10−6) was ten times lower than the value obtained for the adjacent 201-kb segment on its centromeric side (6.39 × 10−5). Comparable variation levels have been observed in both HLA-DRB-DQ and HLA-C-B blocks when strengthening homozygosity in each region by sub-sampling only chromosomes sharing the most frequent HLA haplotype. However, variation level in the 158-kb segment immediately centromeric to the DR–DQ block was more prominent. Also, the HLA-C-B block boundary was not as steep and narrow as in the class II block.
The 158-kb sequence, which is clearly identical by descent between COX and QBL, is abruptly interrupted at coordinate AL731683.12 73150 on the centromeric side in the COX sequence contig beyond which the SNP density rises to >1/kb. Therefore, we examined the LD profile in order to investigate the distribution of LD patterns in the region and search for potential LD breaks associated with the boundaries of the QBL–COX SNP desert segment. A high resolution LD map (1 SNP/1.8 kb) covering ~855 kb was constructed based on genotyping data from a panel of 180 CEPH founder chromosomes [5]. The centromeric end of the reduced variability segment perfectly coincides with an LD break observed between DQB1 and DQB3. This LD break corresponds to the recombination hotspot shaping the genealogy of the DR15–DQ6 haplotype mentioned above. Figure 4 shows a view of the LD structure (D′) of the region where this hotspot can be identified in context with additional recombination hotspots described in the MHC [53]. After a high LD region covering DQB1 (~44 kb), LD is interrupted towards the telomeric part of the DRB1/DQB1 segment involving a region including the DRB1 and DRB2 genes which, owing to lack of SNP-typing information, has been designated as a region of depleted SNP data in Figure 4. The lack of SNP-typing information is a result of significant genotyping failure most likely attributable to the highly polymorphic nature of this region. Consequently, the presence of LD breaks (recombination) on this edge of the referred DNA segment cannot be established.
The region showing the next lowest variation between COX and QBL is a 94 kb section of DNA between TAP1 and HLA-DMB in which only 15 SNPs are found and results in a 2-fold higher π value of 1.6 × 10−4 although not approaching the π value of the 158 kb segment.
The RCCX Region
Within the class III region, there is structural variation in DNA sequence resulting in the duplication of a module termed RCCX, which contains the complement component gene, C4, along with portions of adjacent pseudogenes [54]. C4 has been classified into two versions, C4A and C4B [55]. The two genes show differences in amino acid sequence in their corresponding protein products, which alters their binding properties [56].
The PGF haplotype possesses a bimodular distribution (Chromosome 6 build NCBI35 32056288..32089024, and 32089025..32121879) with two copies of C4 as C4B and C4A respectively [18], both of the “long” form which includes a HERVC4 insertion in intron 9. The COX haplotype, which has a monomodular conformation (AL662849.8 66699 to AL662828.5 6726) containing C4B, also differs in that the gene is of the “short” form without the HERVC4 insertion. Although QBL has a monomodular conformation (AL844853.23 134656 to AL929593.6 16047) similar to COX, the C4 gene present is “long”, and a C4A.
Comparison of the coding sequences of these four copies of the C4 gene not only confirms the published [54] differences between C4A and C4B in exon 26 and exon 28 but also suggests that there are a further two structural variations (at coding bases 2719, exon 21; and 3218 exon 25) (Table S3). A synonymous variation at coding base 2475 (exon 20) appears to be specific to COX C4B, whereas nonsynonymous variations at coding bases 3527 and 3856 (exons 28 and 29) appear to be specific to PGF C4A.
Discussion
We have sequenced 4.25 Mb of a common MHC haplotype that is associated with certain autoimmune diseases, including type 1 diabetes. The full variation content has been defined in relation to two complete MHC haplotypes that we have previously sequenced [18]. The cell lines used in this study were invaluable in obtaining homozygous DNA from the classical MHC and allowed the determination of a complete inventory of polymorphism and sequence across the whole MHC in the context of HLA-defined haplotypes, including gene, pseudogene, promoter, intergenic and complex repeat sequences.
Our approach of cloning and shotgun sequencing, instead of direct sequencing of PCR product [57], evaded the potential pitfalls inherently associated with PCR-amplification within the MHC region, such as mispriming in under-characterised highly polymorphic areas. In addition, many sequences are difficult to PCR for more general reasons, for example, the GC-rich 5' UTRs of genes. The validity of our experimental strategy is reflected by the completeness of the polymorphism map, from SNPs to large DIPs. The identification of significantly altered coding sequences in different haplotypes stresses the value of careful and thorough annotation. The results of these efforts will ensure that the MHC research community has comprehensive genomic information for medical research. The study design will serve as a model system for future sequencing projects of other complex, polymorphic immune gene clusters of the human genome that are associated with disease, such as the leukocyte receptor complex (LRC).
HLA haplotypes in QBL and COX cell lines share identical alleles at HLA-DRB1 (*0301) and -DQB1 (*0201) genes and, therefore, some commonality in their origin, composition and function. We were able to define this shared portion of DNA identical by descent to only a small 158-kb segment telomeric of HLA-DRB3 and centromeric of HLA-DQB3. When comparing these two cell lines, this segment presents exceptionally low divergence relative to other regions within the MHC. Outside this segment, the divergence between these haplotypes is as extensive as that we found previously between two HLA-disparate haplotypes: We identified about 15,000 SNPs, of which approximately 40% were novel to the newly sequenced haplotype. Approximately 2,000 DIPs were also identified. The nucleotide heterozygosity between the two haplotypes was 3-fold higher than typical genome-wide diversity. In contrast, the extreme conservation of the 158-kb segment points to a relatively recent common ancestor fewer than 3,400 generations ago.
A number of factors contribute to the variation within the MHC and could potentially be responsible for the existence of the shared 158-kb segment, including conventional and gene-conversion–mediated recombination [1,58]. We propose that this segment originated by conventional recombination, possibly involving recombination hotspots 1 and 2 (Figure 4), giving rise to an original region of about 450 kb. This is supported by extremely low sequence divergence (π = 8.47 × 10−7) within the 158-kb segment and is continued by lower than expected sequence divergence within the remaining approximately 290 kb up to the recombination hotspot between NOTCH4 and C6orf10. At both ends, the divergence collapses at LD breaks coinciding with confirmed recombination hotspot 2 [52] at the centromeric end and predicted hotspot 1 at the telomeric end. To our knowledge there has never been a gene conversion–mediated recombination event described involving more than 10 kb of sequence. According to the HLA allele frequencies, the MHC can be divided into only a few blocks that contain non-randomly associated alleles at different loci [59]. The HLA-DRB1*0301–DQB1*0201 (DR3–DQ2) block is present in a number of populations, including Caucasians (Whites of northern and western European ancestry), ethnic Africans and Filipinos [59–61], and is often associated with type 1 diabetes, coeliac disease, autoimmune thyroid disease, and multiple sclerosis incidence [25,60,62]. This shared DR3–DQ2 identical-by-descent segment or “frozen block” [63] is the most commonly observed DR/DQ haplotype in different European populations in which the ancestral MHC haplotypes A1-B8-Cw7-DR3-DQ2 (e.g., COX) and A30-B18-Cw7-DR3-DQ2 account for by far the largest proportion of its frequency. These extended haplotypes are generally believed to have arisen from their rapid expansion across Europe driven by the selection pressure for the function of a single locus or multiple functional loci of the haplotypes [64]. However, DR3–DQ2 has also been observed constituting other much less frequent extended haplotypes [65–67]. The wide distribution of the conserved block in Old World haplotypes deserves further investigation. Because this segment has not been split by recent recombination events, the small number of minor variants distributed over it presumably occurred by mutation. By scoring them in DR3–DQ2 blocks in different populations, we will be able to track an accurate clade structure that can be used for timing of association with different flanking regions in relation to population structure and disease association.
Our model is, therefore, consistent with the idea that a DNA segment derived from an ancestral haplotype has been transferred into a number of diverse and widely distributed haplotypes by recombination [63,68], and that certain recombinant haplotypes have subsequently expanded in frequency across European populations (see Figure 5). The data suggest that ancestral DR–DQ blocks have been shuffled into different MHC haplotypes. The expansion of the resultant novel haplotypes could relate either to selection for resistance to disease by offering an evolutionary advantage in terms of HLA class II functions and peptide binding specificities, for example, or to neutral genetic drift, perhaps in an ancestral population with a small effective population size. Although not proven, recent data support the long-held view that sequence variation within HLA genes is driven by resistance to infection [69,70]. The spread of the DR3–DQ2 ancestral segment by inter-haplotype exchange may also have been driven by selection. This interesting hypothesis might be tested in further studies by, for example, haplotype-based tests for positive selection [71]. It is not trivial, however, to explain the contrasting genealogy of ancestral haplotype segments in a chromosome. If ancestral DR/DQ haplotypes (i.e., DR3–DQ2) have exchanged the discrete segment of the MHC that appears identical by descent between COX and QBL so recently, it might be possible that similar exchange of different HLA sequences between haplotypes of this defined DR/DQ segment may be responsible for contrasting disease risk related to non-DR/DQ loci [25] and to different ethnic backgrounds [72]. An alternative explanation would require the action of purifying selection on this fragment keeping substitution rates low. However, this argument requires the majority of the DNA to be functional and therefore intolerant of substitutions. The differential contributions of selection and recombination in shaping the contrasting evolutionary history of ancestral haplotype segments containing classical HLA class II genes might be categorized in further studies expanding the population range and increasing SNP density.
Figure 5 Model of Haplotype Divergence over DR–DQ Region in Relation to Extended MHC Haplotypes
(A) Divergence of DR–DQ region over tens of millions of years [73].
(B) Transfer of divergent blocks into other haplotypes by recombination. This does not need a double crossover but could occur by single crossovers separated in time.
(C) Relative expansion of ancestral DR–DQ haplotypic segments that may result from either positive selection or neutral processes. Black vertical stripes represent occasional SNP mutations occurring within the MHC including within the ancestral haplotypic segments. Small crosses represent crossovers occurring more frequently outside the conserved DR–DQ blocks relative to inside these blocks. However, allele or gene conversion may take place by closely spaced double crossovers, resulting in diversification of the peptide-binding groove without flanking recombination [87,88]. This model was predicted by Gaudieri et al. (1997) [63] based on incomplete sequence analysis.
(D) Examples of contemporary MHC haplotypes containing ancestral DR–DQ segments (data provided by www.allelefrequencies.net).
The “modular” or block structure of the MHC is well known to the HLA community [59,63,68]. Whether the maintenance of polymorphic conserved and common blocks such as the DR–DQ segment is due to suppression of recombination or selection has never been satisfactorily resolved [73]. It has been argued that it is advantageous to maintain clusters of polymorphic genes whose products interact [1]. The DQA and DQB loci are good examples because these polymorphic genes encode a heterodimeric molecule with constraints on pairing of the α and β protein chains [74]. Similarly, different DR/DQ allelic pairs could be advantageous since they perform interrelated functions. These considerations may lie behind the characteristics of the MHC of ancient, highly diverged haplotypes that appear to be evolving independently except for sequence in the peptide-binding grooves and rare “block shuffling” as we indicate here. They also lie behind the difficulties in locating single gene contributions to disease in which multiple linked interacting genes are at work [25,75]. Our results point to a particular selective advantage of the 158-kb–segment allelic variation in the history of Europeans.
The data generated from the MHC haplotype project provide a major resource for the construction of informative and high-resolution genetic maps in a region that has been more refractory to certain whole-genome analysis methods than less complex regions of the genome. Characterisation of fine segmental LD structure is an essential part of disease mapping, because it provides guidance for the selection of markers [76]. To date, over 40,000 variations from the project have been submitted to dbSNP. Over 60% of the mapped variations in this region were novel submissions from this study. These maps will provide a guide to the fine-scale patterns of LD and recombination within the MHC and will aid methods used to identify optimal sets of tag SNPs that allow association studies to be conducted more efficiently [77]. These methods take advantage of high-resolution maps and can show increasing efficiency at higher marker density [78]. Eventual elucidation of the specific disease-predisposing variants will require detailed association analysis of all genetic differences in tag SNP–defined intervals in a large number of affected individuals and controls, along with functional analysis of associated variants, to verify a biological function consistent with the disease phenotype. The annotation of disease-associated MHC haplotypes in the context of complete information, encompassing all described splice variants of expressed genes and UTR sequences, will provide an initial basis for the subsequent experimental verification of candidate MHC loci and structural variants in disease and in gene expression analyses. The sequences of the remaining haplotypes will not only reveal further polymorphisms for genetic dissection of the MHC in disease, but also define the genealogical relationships between haplotypes. There appears to be differential associations with some immune-mediated diseases and the two B18-DR3 and B8-DR3 haplotype groups studied here. Our data indicate that the variability is probably not determined by sequence variation within the class II gene–containing 158-kb chromosome segment.
Taking together our finding of the conserved sequence block between the DR3-DQ2 COX and QBL sequences, and our observations of a similar level of sequence conservation in the DR–DQ region for DR15–DQ6 haplotypes, a recent inter-haplotype exchange of this discrete portion of the MHC is suggested. The DR–DQ segment is one of the most variable in the genome, yet it is apparently “fixed” in some haplotypes. The precise explanation for this interesting situation needs further investigation, particularly the relative contributions of recombination suppression, selection, and population expansion.
Materials and Methods
Cell lines.
The HLA-homozygous typing cell lines, QBL (DR3, Caucasian, Netherlands), COX (DR3, Caucasian, South Africa) and PGF (DR15, Caucasian, England) were selected from the 10th International Histocompatability Workshop panel [19]. The DNA-typing profiles from these cell lines can be found at http://www.ebi.ac.uk/imgt/hla. Following cultivation, DNA from these cell lines was typed to confirm identity and homozygosity (http://www.sanger.ac.uk/HGP/Chr6/MHC).
BAC clone library construction.
The approach used to construct and screen the PGF and COX BAC libraries, as well as library designations, has been described previously [18]. The QBL library was constructed by Eae-I partial digestion of high-molecular weight DNA, ligation to a 100-fold molar excess of Eae-I to Bst-XI linker-adaptors (which change all EaeI cut “sticky ends” into 3′ overhangs with the sequence CACA), and sorting for high molecular weight DNA by three consecutive passages over 0.5% agarose/TAE gels (gel dimensions 6.4 × 10.1 × 1.0 cm) run in a custom-made orthogonal pulse field mini-gel apparatus. The gel box was made from a Rubbermaid servin' saver 1.2 l plastic container (approx. 16 × 16 × 7 cm), which was raised 1.4 cm off the bench via four plastic “feet” (caps from 15-ml tubes attached with silicon), so that air blown from a small fan could continuously cool the undersurface of the apparatus. The alternating electric fields (45° to the right of direction of DNA travel, alternating with 45° left) were supplied by four 11.5 cm–long platinum electrodes mounted inside, along the base of each of the four sides of the container. Gels were run for 12 h at 70 V, with switch times varying uniformly from 2–14 s over the course of the run.
After each pulse-field run the desired part of the gel (DNA ≥ 120 kb) was excised and DNA recovered by electroelution within dialysis tubing; after the final electroelution, the resulting genomic DNA was ligated into Bst-XI + Sfi-I doubly-cut vector DNA. The vector used (pDNA-Arts.BAC1) is derived from pBeloBAC11; when cut with BstXI and SfiI a short insert containing the pUC18 origin of replication is liberated from within the polylinker, and the two vector ends are left having identical 3′ overhangs: TGTG. Prior to ligation with genomic DNA, the vector was separated from the short pUC origin insert by gel exclusion chromatography over sephacryl S1000 superfine (column dimensions 26 × 0.4 cm), and aliquots of prepared vector and size-selected genomic DNA were analyzed on 0.5% agarose/TAE gels in order to estimate how much of each DNA preparation to use in the ligation reaction (target ratio for genomic DNA mass:vector DNA mass was 20:1).
Ligation reactions were transformed into Escherichia coli strain DH10B via electroporation using frozen electro-competent cells purchased from Invitrogen (Carlsbad, California, United States). In total, 357 individual 384-well plates were robotically arrayed (137,088 individual clones) to yield the library designated “DAQB.” The library was gridded onto 17 separate 22 × 22 cm nylon filters (Genetix, New Milton, Hampshire, United Kingdom) and screened with a mixture of 164 different 32P-labelled overgo probes, which collectively span the entire MHC region. All positive clones were placed in a new array, which was used to generate 164 identical filters that were then probed with each of the individual overgo probes. This allowed placement of clones in rough order, and a tiling path was determined by a combination of BAC end sequencing and BAC fingerprinting.
BAC clones from CHORI-501 (PGF) and CHORI-502 (COX) libraries can be requested from BACPAC resources (http://www.chori.org/bacpac). BAC clones from the QBL library can be requested from [email protected].
Mapping, sequencing, gene annotation, and variation analysis.
Methods used for mapping, sequencing, gene annotation and variation analysis have been described previously [18]. For the variation analysis, all splice variants of each locus were included. Of several alignment procedures tested, cross-match gave the most accurate detection of haplotype variations (see [18]), and was, therefore, used for the SNP and DIP analysis in this study. We refer to haplotype differences (polymorphisms) without regard to function or frequency and purely as sequence differences. The ratio of non-synonymous (amino acid–altering) to synonymous (silent) substitutions (N:S) was used as an indicator of selection acting on genes, because synonymous alterations are unlikely to exert a selective advantage.
As previously applied in the comparison of the PGF and COX haplotypes [18], a test was conducted to ensure that QBL BACs were derived from a single haplotype by comparing adequately overlapping reads from the shotgun sequencing strategy extending into adjacent BAC clones.
A gap within the QBL HLA-DR region resulting from a clone deficit was closed by sequencing three LR-PCR products that spanned the approximately 20-kb gap. The LR-PCR primers were as follows: QBL-A sense 5′GTG AGG AGT GAT GGG TGA GA3′ with QBL-A antisense 5′GGA AAT AAG GAG GAG GGA AGG3′, QBL-B sense 5′TAC ATG GGT GTC CTT TCA GC3′ with QBL-B antisense 5′TCC TGG TCT CGC TCT TCT TC3′, QBL-C sense 5′TGG GCA AAA TCT TAC CAA CC3′ with QBL-C antisense 5′TCC TTG GGG CTC AGT TAG TG3′.
All sequences presented in this paper have been submitted to the EMBL/Genbank/DDBJ databases and allocated accession numbers (Table S4). For purposes of clarity, all BAC clones are referred to using their accession numbers. All variations from the study were submitted to dbSNP (http://www.ncbi.nlm.nih.gov/SNP) using the submitter handle SI_MHC_SNP. The annotated haplotype sequences can be accessed using the VEGA database (http://vega.sanger.ac.uk; [33]). The SNP data can viewed in the context of the relevant genome annotation through the GLOVAR genome browser (http://www.glovar.org), and in the context of genetic findings in type 1 diabetes in T1DBase (http://www.t1dbase.org).
Estimation of LD and divergence.
Genotyping data from 180 CEPH founder chromosomes [5] were used to estimate the strength of LD in an approximately 855-kb DNA segment within the MHC bounded by dbSNP rs2849013 (NCBI35 Chr6; 32,240,568) and rs7754316 (NCBI35 Chr6; 33,095,976). This fragment contains the DRB/DQB segment shared by QBL and COX cell lines and neighbouring regions extending over each side where recombination hotspots have been previously mapped. Estimates of pairwise-disequilibrium coefficient—D′ [79]—between SNPs were obtained employing the ldmax program in the GOLD package [80]. Long-range LD patterns—high LD and LD breaks—were visualised using GOLDsurfer program [81]. Detailed haplotype-block structure according to Gabriel et al. (2002) [82] criteria was also derived [83] in order to cross-validate interpretations of the LD landscape in the region investigated and correlate LD breaks with recombination hotspot data available from previous studies [5,51–53].
Sub-samples of 140 CEPH founder chromosomes with available HLA-typing data and phase-known SNP-typing information [5] were selected according to their HLA haplotypes to assess variation level at different MHC loci. The mean number of pairwise differences between all pairs of haplotypes in the sample (Arlequin software, [84]) was used as an estimate of relative divergence for each DNA segment in a sample of chromosomes sharing the DR3-DQ2 haplotypes (n=16) common to QBL and COX cell lines. Variation levels were compared between DNA segments extending over HLA-DRB1 / HLA-DQB1 genes (158kb), adjacent 160kb on its centromeric side, HLA-A (201kb) and HLA-C / HLA-B genes (201kb). Variation level in these segments was also contrasted in a sample of chromosomes homozygous for the most frequent HLA class II haplotype (DR15–DQ6, n = 26) and in another set homozygous for the most common HLA-C-B haplotype (HLA-C*0702–HLA-B*0702, n=22).
Dating of the ancestry of the shared DNA segment.
The number of mutations (N) expected between two contemporaneous sequences of a given length (L) is determined by the mutation rate per nucleotide per year (μ) and the length of time since they last shared a common ancestor (t). Mutations accumulate independently on the two lineages from the common ancestor and so the amount of evolutionary time separating the two sequences is twice the age of the common ancestor, hence:
In this scenario, we assumed that the mutation rate was the same on the two lineages. We took two estimates of base substitution rate (μ) from Nachman and Crowell [50], who compared human and chimpanzee divergence. These estimates represent reasonable bounds on the true mutation rate, given the knowledge of the likely speciation time of human and chimps (~6 million years ago) and conservative estimates of the ancestral population size of the common ancestor (between 10,000 and 100,000; [85]). These bounds are 2.1 × 10−8 and 1.3 × 10−8 per nucleotide per generation.
Supporting Information
Figure S1 Frequency Distribution of DIP Sizes between Haplotypes
(48 KB PPT)
Click here for additional data file.
Figure S2 Alignment of Sequences Showing Differing PolyT Tracts from a Single PCR Product
(130 KB JPG)
Click here for additional data file.
Table S1 Allele Designation of MHC Loci within MHC Haplotypes
(45 KB DOC)
Click here for additional data file.
Table S2 Genomic Locations of Major DIPs between PGF and QBL
(70 KB DOC)
Click here for additional data file.
Table S3 Comparison of the CDSs of Four Copies of the C4 Gene.
(53 KB DOC)
Click here for additional data file.
Table S4 List of Accession Numbers for QBL Clones
(39 KB DOC)
Click here for additional data file.
The authors thank J. G. R. Gilbert and S. J. Keenan for assistance with the VEGA database. We wish to thank all staff of the DNA Sequencing Division and the HAVANA group (http://www.sanger.ac.uk/HGP/havana) at the Wellcome Trust Sanger Institute (http://www.sanger.ac.uk). We are grateful to R. Ward for technical assistance. This work was supported by a joint grant (048880) from the Wellcome Trust to JAT, JT, SB, and SS, and a Wellcome Trust/Juvenile Diabetes Research Foundation grant to JAT.
This publication has been funded in part with federal funds from the National Cancer Institute, National Institutes of Health (NIH), under Contract No. NO1-CO-12400. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. This research was supported in part by the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research.
Author contributions. J. Rogers, P. J. de Jong, J. F. Elliott, S. Sawcer, J. A. Todd, J. Trowsdale, and S. Beck conceived and designed the experiments. J. A. Traherne, C. A. Stewart, A. M. Atrazhev, P. Coggill, S. Palmer, J. Almeida, S. Sims, and J. F. Elliott performed the experiments. J. A. Traherne, R. Horton, A. N. Roberts, M. M. Miretti, M. E. Hurles, C. A. Stewart, J. L. Ashurst, A. M. Atrazhev, P. Coggill, S. Palmer, J. Almeida, S. Sims, L. G. Wilming, J. Rogers, P. J. de Jong, J. F. Elliott, S. Sawcer, J. A. Todd, J. Trowsdale, and S. Beck analyzed the data. J. L. Ashurst, S. Palmer, J. Almeida, J. Rogers, P. J. de Jong, M. Carrington, J. F. Elliott, J. A. Todd, and S. Beck contributed reagents/materials/analysis tools. J. A. Traherne, R. Horton, A. N. Roberts, M. M. Miretti, M. E. Hurles, C. A. Stewart, P. Coggill, P. J. de Jong, J. F. Elliott, S. Sawcer, J. A. Todd, J. Trowsdale, and S. Beck wrote the paper.
Competing interests. The authors have declared that no competing interests exist.
Abbreviations
BACbacterial artificial chromosome
bpbase pairs
CDScoding sequence
CEPHCentre d'Etude du Polymorphisme Humain
dbSNPSingle Nucleotide Polymorphism database
DIPdeletion/insertion polymorphism
HLAhuman leukocyte antigen
LDlinkage disequilibrium
MHCmajor histocompatibility complex
SNPsingle nucleotide polymorphism
VEGAVertebrate Genome Annotation
==== Refs
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PLoS Comput BiolPLoS Comput. BiolpcbiplcbploscompPLoS Computational Biology1553-734X1553-7358Public Library of Science San Francisco, USA 1642492110.1371/journal.pcbi.002000405-PLCB-RA-0193R2plcb-02-01-03Research ArticleBioinformatics - Computational BiologyBiotechnologyDevelopmentMolecular Biology - Structural BiologyNeuroscienceSystems BiologyMus (Mouse)Homo (Human)Rattus (Rat)DogUnusual Intron Conservation near Tissue-Regulated Exons Found by Splicing Microarrays Regulated Exons Found by Splicing MicroarraysSugnet Charles W 1Srinivasan Karpagam 23Clark Tyson A 4O'Brien Georgeann 3Cline Melissa S 4¤aWang Hui 4Williams Alan 4Kulp David 4¤bBlume John E 4Haussler David 1Ares Manuel Jr.23*1 Department of Computer Science, Baskin School of Engineering, University of California Santa Cruz, Santa Cruz, California, United States of America
2 Department of Molecular, Cell, and Developmental Biology, Sinsheimer Labs, University of California Santa Cruz, Santa Cruz, California, United States of America
3 Hughes Undergraduate Research Laboratory, Thimann Laboratories, University of California Santa Cruz, Santa Cruz, California, United States of America
4 Affymetrix, Santa Clara, California, United States of America
Brenner Steven EditorUniversity of California Berkeley, United States of America* To whom correspondence should be addressed. E-mail: [email protected]¤a Current address: Pasteur Institute, Paris, France
¤b Current address: Department of Computer Science, University of Massachusetts, Amherst, Massachusetts, United States of America
1 2006 20 1 2006 14 12 2005 2 1 e44 8 2005 14 12 2005 © 2006 Sugnet et al.2006This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.Alternative splicing contributes to both gene regulation and protein diversity. To discover broad relationships between regulation of alternative splicing and sequence conservation, we applied a systems approach, using oligonucleotide microarrays designed to capture splicing information across the mouse genome. In a set of 22 adult tissues, we observe differential expression of RNA containing at least two alternative splice junctions for about 40% of the 6,216 alternative events we could detect. Statistical comparisons identify 171 cassette exons whose inclusion or skipping is different in brain relative to other tissues and another 28 exons whose splicing is different in muscle. A subset of these exons is associated with unusual blocks of intron sequence whose conservation in vertebrates rivals that of protein-coding exons. By focusing on sets of exons with similar regulatory patterns, we have identified new sequence motifs implicated in brain and muscle splicing regulation. Of note is a motif that is strikingly similar to the branchpoint consensus but is located downstream of the 5′ splice site of exons included in muscle. Analysis of three paralogous membrane-associated guanylate kinase genes reveals that each contains a paralogous tissue-regulated exon with a similar tissue inclusion pattern. While the intron sequences flanking these exons remain highly conserved among mammalian orthologs, the paralogous flanking intron sequences have diverged considerably, suggesting unusually complex evolution of the regulation of alternative splicing in multigene families.
Synopsis
Alternative splicing expands the protein-coding potential of genes and genomes. RNAs copied from a gene can be spliced differently to produce distinct proteins under regulatory influences that arise during development or upon environmental change. These authors present a global analysis of alternative splicing in the mouse, using microarray measurements of splicing from 22 adult tissues. The ability to measure thousands of splicing events across the genome in many tissues has allowed the capture of co-regulated sets of exons whose inclusion in mRNA occurs preferentially in a given set of tissues. An examination of the sequences associated with exons whose expression is regulated in brain or muscle as compared to other tissues reveals extreme conservation of intron sequences nearby the regulated exon. These conserved regions contain sequence motifs likely to contribute to the regulation of alternative splicing in brain and muscle cells. The availability of global gene expression data with splicing level resolution should spur the development of computational methods for detecting and predicting alternative splicing and its regulation. In addition, the authors make strong predictions for biological experiments leading to the identification of components and their mechanisms of action in the regulation of splicing during mammalian development.
Citation:Sugnet CW, Srinivasan K, Clark TA, O'Brien G, Cline MS, et al. (2006) Unusual intron conservation near tissue-regulated exons found by splicing microarrays. PLoS Comput Biol 2(1): e4.
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Introduction
Splicing is an essential process that constructs protein coding messenger RNA (mRNA) sequences using tiny segments of information buried in the much larger primary transcripts of the eukaryotic gene. Regulated alternative splicing can create different protein coding sequences under different biological circumstances, allowing the production of functionally related but distinct proteins (for review, see [1]). In addition, alternative splicing can mediate the repression of gene expression by stimulating the formation of transcripts subject to nonsense-mediated decay [2–5]. Splicing patterns seem distinct in the vertebrate nervous system compared to other tissues [6,7], and it is tempting to hypothesize that neural alternative splicing contributed to the rapid evolution of the vertebrate brain without large increases in gene number [8].
Biochemical analysis of alternative splicing has shown that numerous RNA binding proteins influence the use of specific splice sites to stimulate splicing events that lead to particular mRNA isoforms [1,9]. These RNA binding proteins may activate or repress the use of splice sites by binding to nearby sequences in the exon (exonic splicing enhancers [ESEs] or exonic splicing silencers [ESSs]) or in the intron (intronic splicing enhancers [ISEs] or intronic splicing silencers [ISSs] [1,9]). In many cases, multiple RNA binding proteins combine to create repressing and activating influences that produce patterns of splicing control [6,9]. Some proteins, such as SR proteins and the CELF proteins, have mostly activating roles, whereas others, such as hnRNP A1, PTB, and nPTB, have mostly repressing roles. Certain proteins can either activate or repress splicing in different contexts, depending on the position of their binding sites or the expression of other RNA binding proteins [10,11].
A complete catalog of the RNA sequences corresponding to the enhancers and silencers bound by splicing regulatory proteins would greatly aid the understanding of splicing regulatory networks. Thus far, there are only a handful of splicing regulators whose corresponding RNA binding motifs have been identified (for review, see [12]), whereas there may be many splicing regulators among the hundreds of RNA binding proteins encoded by the mouse genome. In addition, several related but distinct genes produce proteins that bind the same or overlapping sets of sequences; for example, Fox-1 and RBM9 each bind UGCAUG [13,14], and the branchpoint binding protein SF1 and the protein quaking (QK) each bind UACUAAC-like motifs [15–17]. Adding to this complexity is the tendency for the mRNAs of RNA binding proteins to be alternatively spliced, leading to multiple RNA binding protein isoforms with potentially different functions (e.g., [14,18–20]). Currently, the methods available for expanding the list of known regulators and their target sequences are limited, and the development of this catalog is in the early stage [12].
Much of the available genomic information on alternative splicing is derived by the alignment of large numbers of expressed sequence tags (ESTs) and messenger RNAs (high-quality cDNA sequences) to genome sequences (for many genomes, see [21]). The analysis of exons that appear to be constitutive (i.e., present in every example of a transcript from a given locus) or alternative (exons or parts of exons that are sometimes skipped) has led to the successful identification of many distinguishing features of alternatively spliced regions [22–28], even allowing their accurate prediction without cDNA evidence [26,29,30]. Although cDNA libraries have been invaluable for discovering general features of alternatively spliced exons, it is difficult to connect specific regulatory sequences to specific biological conditions with confidence due to variable and sometimes missing information about the source materials and methods of cDNA library construction. The relatively low number of transcripts present from any one gene also makes it difficult to estimate differences in expression levels using library representation as a measure. Thus, more direct methods are needed to associate alternative splicing events with underlying biological conditions.
The recent application of microarray technology to questions of splicing and splicing regulation promises to reveal parallel connections between many splicing events and specific biological or experimental conditions [31–41]. Analysis of experimental changes in splicing for many genes simultaneously should reveal biological conditions necessary for proper splicing regulation in a way that analysis of cDNA libraries cannot, and with breadth that cannot be achieved by analysis of a reporter construct or a few endogenous target genes. To demonstrate this, we constructed a DNA microarray designed to capture splicing information for about 6,200 alternative events in the mouse transcriptome, using a combination of splice junction and exon probes, and have hybridized RNA from 22 adult mouse tissues. We examine splicing in these tissues by asking three questions.
First we ask, Which RNA isoforms are present in a particular tissue sample? To answer this simple question, we used a new method based on comparing the intensity of the probes in a probe set to the distribution of intensities from all probes with similar G + C level. This is similar in spirit although different in approach to the present-absent calls from Affymetrix MAS 5.0 algorithms [42], as this microarray did not contain mismatch probes. Using RT-PCR, we show that this method has a true-positive rate of 85%.
Second we ask, Which RNA isoforms are differentially expressed across the tissues examined? For each RNA isoform, the intensities of the isoform-specific junction probes were examined across tissues using the Kruskal-Wallis statistical test. After correcting for multiple testing, about 40% of the 6,216 total alternative splicing events examined were found to have more than one RNA form that was differentially expressed, indicating widespread tissue differences in splicing over the tissues.
Third we ask, Which cassette exons are included differentially between brain (or muscle) and nonbrain (or nonmuscle) tissues? To answer this, we used a regression-based bootstrapping method, which also allows an estimate of the relative change in skipping and inclusion in the two sample groups. We analyzed the intron sequences associated with exon skipping events that are differentially regulated in brain or muscle relative to other tissues and found unusual patterns of sequence conservation that provide new information about tissue regulation of alternative splicing and its evolution.
Results
Broad Detection of Tissue-Regulated Alternative Splicing in Mouse Using Microarrays
The general idea of using DNA microarrays with combinations of splice junction and exon probes designed to capture splicing information has been presented previously [31,35] and applied in various forms by a number of groups to questions of alternative splicing [32–34,37,38,40]. The specific DNA microarray designed for these experiments uses the Affymetrix format [43,44] similar to the microarray used previously [35], except that most mismatch probes were not included. Briefly, for each gene there is a “common” probe set to determine whether the gene is expressed, as well as “isoform-specific” splice junction and exon probe sets able to distinguish alternative mRNA isoforms (Figure 1A). Each splice junction probe set contains six 25-mer DNA oligonucleotide probes tiled across the junction (see Materials and Methods).
Figure 1 Array Design and Analysis of Splicing-Sensitive Microarray Data
(A) Probe design and expression counts of alternative event-specific probe sets. Probe sets were designed to both the skipping splice junctions and include splice junctions, as well as the alternative exons when possible, ensuring that probe sets are specific to the exon skipped or included spliced isoforms. Probe sets for constitutive portions of the gene were used to measure overall expression of the locus.
(B) Scatterplot of skip probe set intensity versus include probe set intensity for Biaip exon 15 in brain (squares) and nonbrain (circles) tissue samples. Each data point is derived from one RNA sample and represents the skip-to-include ratio for that sample. The lines represent the robust regression coefficient (constrained to go through the origin) for each tissue group. The log2 difference in the slopes is −2.53, indicating 5.7-fold inclusion in brain relative to nonbrain tissues.
As the junction probes should be specific for the RNA form derived by the alternative splicing event that creates the junction, we first asked simply whether each particular junction is detected in different tissues. To do this, we first determined which genes were likely to be expressed above background, using the “common” probe set designed to detect gene expression (Figure 1A). For the alternative junction probe sets in the genes that were expressed, we then estimated the probability that the intensity measured by each junction probe was above background, using an empirical cumulative distribution function (CDF) (see Materials and Methods). Thus, to detect the expression of a particular alternatively spliced RNA from a particular gene, we demand that both the gene probe set and the junction-specific probe set be called expressed. Alternative splicing is then inferred if two alternative isoforms are both significantly expressed.
In the set of adult mouse tissues we studied, a large number and variety of alternative splicing events were detected using this first method (Table S1). Among the class of expressed genes for which we could detect at least one alternative event, we could observe a second alternative event in 18% to 30% of cases, depending on the type of splicing event. Alternatively skipped (cassette) exons were the largest class, with 376 exons. For the purposes of this study, we defined a cassette exon to be any exon that can be included or skipped in its entirety, regardless of other alternative splicing events that affect it (e.g., alternative 3′ or 5′ splice sites). RT-PCR validation experiments (Table S2) indicate that the true-positive rate (fraction of situations in which a splice junction predicted to be expressed by the microarray is detected by RT-PCR) is about 85% (217 RT-PCRs; a reaction is one primer pair with cDNA from one tissue, designed to detect two isoforms, or 434 independent splice junction tests [see Materials and Methods]), and the false-negative rate (fraction of times that a splice junction that could not be detected on the microarray is detected by RT-PCR) is about 47%, due to the relatively greater ability of RT-PCR to detect low levels of gene expression (data not shown).
Simple present or absent determinations of RNA forms containing particular splice junctions such as those shown in Table S1 are likely to miss changes in alternative splicing that do not involve large changes in overall transcript level. To improve our detection of smaller-scale changes in alternative splicing, we asked which alternative RNA forms are differentially expressed across tissues. To determine this, we used the Kruskal-Wallis test to determine whether the intensity measured for probes specific for each junction in different samples was likely to come from the same or different distributions. Alternative splicing is again inferred if two alternative RNA forms (differing at alternative junctions) are both significantly differentially expressed (Table 1).
Table 1 Differential Expression of Alternative Splicing Event Isoforms
For most classes of alternative splicing events, nearly 5-fold more alternative events were identified by the Kruskal-Wallis test than by simple presence-absence tests, as expected. By this analysis, about 40% of the alternative RNA forms we could detect are differentially expressed across tissues. Using the PCR tests for those genes in Table S2 for which at least five tissues were tested, we find that the Kruskal-Wallis test gives an 89% true-positive rate (25 of 28 isoforms predicted to be differentially expressed versus being detected or not detected by nonquantitative RT-PCR across at least five tissues) with a 7% false-positive rate (2 of 28).
These validation data are consistent with other studies using microarrays to detect alternative splicing [32,37]. During validation, evidence for new isoforms not identified in the EST/mRNA data was obtained in about 20% of cases (data not shown), indicating that much alternative splicing remains undiscovered, as other studies have also noted [32]. Since our microarray design relies on initial evidence for alternative splicing from EST/mRNA data, we would not expect our current analysis to detect such events except during validation by RT-PCR. We conclude that, although limited in sensitivity, the data and our analysis are specific and likely provide a conservative representation of splicing events across the adult mouse transcriptome.
Detection of Strongly Regulated Alternative Splicing in Brain and Muscle Tissues
A critical challenge is to distinguish differences in alternative splicing from changes in transcript level, as the overall transcription of individual genes varies greatly across tissues. To focus on brain- and muscle-enriched alternative splicing independent of changes in transcript level, we devised a simple method to identify pairs of alternative splice junctions whose expression relative to each other differs greatly between two subsets of tissues in our dataset. A measure of alternative splicing is the ratio of skipped isoform to included isoform. We wanted to test whether this ratio is different between two groups of tissues (e.g., brain and nonbrain). A natural measure of the overall ratio is the slope of the line created by plotting the skip probe set intensities versus the include probe set intensities for many samples from a tissue group. If there is a consistent alternative splicing pattern within the group, these values should fall on a line with a slope given by the skip/include ratio (Figure 1B). We then test for differences between the two groups of tissues by bootstrapping multiple rounds of robust regressions for each group and comparing the slopes of the regression lines for the two groups of tissues (Figure 1B; see Materials and Methods). The difference in the slope between the tissue groups is a measure of the difference in the average ratio of skipping to inclusion for the indicated exon in the two different groups of tissues over a wide range of transcription levels. In the example shown, exon 15 of the membrane-associated guanylate kinase (MAGUK) gene Baiap1 (MAGI-1) is preferentially included more than 4-fold in brain tissues compared to the nonbrain tissues in which the gene is expressed (Figure 1B).
By searching this way through all the cassette exons within expressed genes (as defined by intensity measured by “common” probe sets), we identified 171 exons that appear differentially regulated in brain tissue compared to nonbrain tissue (Table S3). Of these, 91 are preferentially skipped in brain, whereas 80 are preferentially included in brain. To focus our studies on exons whose regulation is most extreme between the two groups of tissues, we further examined the set where the log2 difference between regression slopes was greater than 2. This criterion resulted in a set of 36 brain-included and 36 brain-skipped cassette exons whose skip/include ratios were more than 4-fold different on the average in brain relative to nonbrain tissues. Details concerning the genes associated with these exons, as well as a set of muscle-regulated exons derived by comparison of heart and skeletal muscle samples with nonmuscle tissues, are found in Tables S3 and S4. Having identified sets of exons with similar levels and patterns of splicing regulation, we compared the nature of their nearby intron sequences to those of a large control set of constitutive exons.
Evolutionary Conservation of Intron Sequences Adjacent to Tissue-Regulated Exons
A number of studies have noted extended regions of high conservation of intron sequences surrounding ISE and ISS elements found adjacent to regulated exons (e.g., nPTB [45], FGFR1, FGFR2 [46,47]). In addition, EST/mRNA-based studies have noted that alternative exons and their nearby intron regions are generally conserved in different organisms, suggesting the presence of cis-acting regulatory elements [22–28]. To examine sequence conservation associated with our brain- or muscle-regulated alternative exons and their nearby introns, we used the program phyloHMM. phyloHMM uses sequence alignments and phylogenetic trees to calculate the posterior probability that an observed alignment results from a conserved rather than a neutral model of evolution [48,49]. Conservation can be compared to the positions of exons using tracks displayed by the University of California Santa Cruz Genome Browser [21]. The brain-included exon 15 of Baiap1 (MAGI-1) is shown as an example (Figure 2A, see also Figure 1B). In addition to conservation of the exon, it is clear that intron sequences adjacent to the exon are highly conserved. Most of the alternative exons identified as brain- or muscle-regulated are associated with conserved adjacent intron sequences (Figure 2B and 2C).
Figure 2 Conservation of Cassette Exons Preferentially Included in Brain
(A) Extreme conservation in flanking intronic sequences of Baiap1 cassette exon seen in University of California Santa Cruz genome browser.
(B) Median conservation probability at each base 100 base pairs upstream (left) and downstream (right) of the exon for 36 brain-included exons (gray circles), 36 brain-skipped exons (hollow gray squares), about 1,000 skipped mouse exons conserved and alternatively spliced in both human and mouse (gray triangles), and about 47,000 constitutive mouse exons (black circles). These last two sets of exons are from an EST/mRNA-based study [24].
(C) Histograms of the median probability of conservation per 100 base pairs upstream and downstream of the brain preferentially included (light gray), constitutive exons (black), and overlapping regions (dark gray).
We systematically analyzed this conservation in our set of brain-regulated exons compared to constitutive exons in two ways. First, we asked about the probability of conservation at each nucleotide position (Figure 2B) as distance from the exon increases upstream (left) or downstream (right). Both the brain-included (gray circles) and the brain-skipped (gray squares) exons are significantly more likely to be associated with conserved intron sequences than about 47,000 constitutive mouse exons (black circles, [24]). The conservation levels for about 1,000 unselected skipped exons from mouse that are also skipped in the human transcriptome (gray triangles, [24]) are lower than those of the extremely regulated brain exons. This result suggests that extraction of splicing events by tissue-regulated pattern and magnitude of regulation using arrays also extracts associated sequence conservation patterns. Intron nucleotides near brain-included exons are even more conserved than those that are preferentially skipped (Figure 2B). The probability of conservation generally decreases with distance from the exon although not smoothly. There appear to be “bumps” centered about 50 nucleotides downstream of the 5′ splice site and about 70 nucleotides upstream of the 3′ splice site, suggesting that many brain-included and brain-skipped exons are more likely to have more conserved nucleotides in these regions (Figure 2B). In general, the 3′ splice site region upstream of the regulated exon is more extensively conserved for a longer distance than the region downstream of the exon adjacent to the 5′ splice site (Figure 2B).
Second, we asked about the median probability of conservation of the entire set of 100 nucleotides immediately upstream (Figure 2C, left) or downstream (right) for the brain-included exons and plotted the distribution of their median conservation probabilities (gray bars) compared to the constitutive exons (Figure 2C, black bars). The histograms show that about 30% of the exons in the brain-included set have upstream intron sequences (3′ splice site regions) whose median probability of conservation exceeds 0.9 (left) compared to less than 1% of constitutive exons. About 20% are similarly conserved for the downstream (5′ splice site) region (right). In some cases, the conservation of the intronic regions exceeds that of the exon. Other studies have noted this trend using alternative exons observed in cDNA libraries [24–26]. Our results using microarrays now allow extraction of tissue regulation-associated intron conservation for further analysis (see below).
We were concerned that conservation levels similar to protein coding sequence might indicate that many exons in our set had nearby splice sites that could be used to create larger, protein-coding alternative versions of the exon through splicing. If true, the high level of conservation could be explained by protein coding rather than a splicing-related function, despite the absence of evidence for such splicing in the EST/mRNA data. To test this, we used the program QRNA [50] to analyze the sequences. QRNA examines pairwise alignments of orthologous sequences from different organisms (in this case, mouse and human), notes the pattern of sequence divergence, and evaluates three models of evolutionary constraint: protein coding, RNA structure, and “other.” Using the conserved elements that overlap the brain-included exon as chosen by phyloHMM [48], usually including the exons themselves, QRNA predicts that its RNA structure evolution model fits the data best in 43% of the regions, whereas the protein coding model fits only 19% of these regions. In contrast, for 500 conserved regions that overlap constitutive exons, which usually include only protein-coding sequence, QRNA predicts the RNA structure model for only 16% and chooses the protein-coding model for 69% of the regions. Elimination of the protein-coding model by QRNA for more than 80% of the regions overlapping our regulated set is a strong indication that the conserved sequences associated with regulated exons are unlikely to represent cryptic protein-coding sequence. We conclude that the conservation of intronic sequence is likely due to its function in splicing regulation.
Searching for Regulatory Motifs in the Conserved Regions near the Tissue-Regulated Alternative Exons
Our sets of exons are defined by similar regulatory patterns obtained from splicing-sensitive microarray data. In contrast to alternative exons culled from unselected EST/mRNA collections in which all regulatory signals are superimposed, the sequence composition of our brain- or muscle-enriched exons may allow identification of ISE and ISS sequences particular to the regulatory events that mediate alternative splicing in these tissues. We wanted to ask whether the conserved regions contain cis-acting elements known to be important for splicing regulation. To do this, we examined the frequency of several known splicing regulator RNA-binding motifs in the intronic sequences near the 171 brain-regulated exons to those of a control set of about 47,000 mouse exons that appear constitutively spliced in the mRNA/EST data [24]. To estimate these frequencies, we used the consensus motifs that represent the core elements of more complex recognition sequences. For the regulators PTB and nPTB, we used CUCUCU [51]. For hnRNP H/F, we used GGGGG [52]. The consensus sequence used for the Fox-1 family of proteins A2BP1 and RBM9 was GCAUG [53], and for Nova, it was UCAUY [54]. For hnRNP A1, the consensus motif of UAGGG was used [55]. The approach of using these simple representative sequences for frequency determination estimates is conservative, and it may miss related sequence examples of the motif that nonetheless are functional.
When we compared the frequency of these consensus sequences near our selected sets of exons to the frequency to those of the constitutive exons (Figure 3), we found that both the Nova and the Fox-1 consensus motifs were enriched significantly in the 150 nucleotides of intron sequence downstream of brain-included exons (P = 3.9 × 10−7 and 5.3 × 10−6, respectively), whereas no such enrichment was observed downstream of the brain-skipped exons (Figure 3). We observed an enrichment of Fox-1 sites upstream of exons skipped in muscle, as did Jin et al. [53]; however, too few observations were made to estimate the statistical significance of this finding. These data support the idea that these two splicing factors contribute to the inclusion of nearby exons in the brain from positions downstream of the regulated exon. Nova is expressed only in neural tissues and can activate or repress exon inclusion [10,56–58], in some cases positively from downstream positions [56,58]. Sequences containing the Fox-1 motif have also been shown to activate inclusion of exons from this position [53,59–62], although proteins that bind it are not restricted to brain [13,14,53,60,61].
Figure 3 Counts of RNA Binding Motifs in Intron Sequences adjacent to Brain-Regulated Exons
The 100 base pairs upstream and downstream regions for constitutive exons (control), preferentially brain-included (brain include), and preferentially brain-skipped exons (brain skip) were evaluated for the presence of sequences related to binding sites for known splicing regulators. Sequences used as the consensus binding sites were Nova: UCAUU or UCAUC; Fox-1: GCAUG; PTB/nPTB: CUCUCU; hnRNP-H/F: GGGGG; and hnRNP A1: UAGGG.
In contrast, the PTB/nPTB consensus sequence is significantly depleted (P = 4.7 × 10−3) from the region downstream of the brain-included exons (Figure 3). Consensus hnRNP F/H binding sites are significantly depleted (P = 2.6 × 10−3) from the region upstream of the brain-included exons (Figure 3). This suggests that the absence of PTB/nPTB binding to the region downstream of the exon, or absence of hnRNP F/H binding to the region upstream of the exon, may be important for proper regulation of some brain-included exons. hnRNP A1 binding sites did not appear significantly enriched or depleted in either region near the brain-included exons (P > 0.05). Although we have restricted our search to the 150 nucleotides proximal to the upstream and downstream sides of the regulated exons, we find significant enrichment and depletion of intronic sequence motifs known to influence alternative splicing. We conclude that selection of alternative splicing events on the basis of microarray data results in the identification of new candidates for exons regulated in the brain by RNA binding proteins with Nova and Fox-1 type specificities.
In addition to examining the frequency of the consensus sequence of known RNA binding factors, we examined the tissue-regulated exons for novel motifs. To identify new motifs near tissue-regulated exons, we used the Improbizer motif-finding program written by Jim Kent [63]. This program identifies sequence motifs present in a set of sequences compared to a background sequence set. As a background sequence set, we used the upstream or downstream regions of a set of about 47,000 exons showing no alternative splicing in mouse [24]. Improbizer identified the Nova motif in the intron sequence downstream from the brain-included exons, consistent with the increased counts of the Nova core sequence (Figure 3). Two additional interesting motifs were found by Improbizer (Figure 4), as well as by MEME [64,65], although the precise weight matrices of the motifs differ slightly (data not shown). A motif with consensus sequence UGYUUUC (Y = C or U) was identified in the 150 nucleotides upstream of brain-included exons (Figure 4A). Although pyrimidine rich, this motif is found above a background of constitutive exons and thus may not be a typical feature of polypyrimidine tracts generally associated with the 3′ splice site. To estimate the probability of finding this motif by chance, the input sequences were randomly permuted 1,000 times, and the Improbizer program was run for each randomized set. The UGYUUUC motif found in the natural sequences had a higher score than any motif found in the 1,000 randomized control runs. We further examined the counts of all 4-mers in the 150 nucleotides upstream using a binomial test to look for differences in the proportion of 4-mers compared to the control set. Five 4-mers had significant P-values after using the Bonferroni correction to account for multiple testing with the significance level of 0.05/256 = 1.95 × 10−4. The 4-mers found to be enriched were CUCC, CUCU, CUUU, UCCU, and UGCU. Two of the five significant 4-mers are subsequences of the UGYUUUC motif found by Improbizer, further indicating that this motif is not likely to have occurred by chance. None of the above 4-mers was enriched in the 150 nucleotides upstream of the exons preferentially skipped in brain. This motif could either repress exon inclusion in non-brain tissues or activate inclusion in brain tissues. No splicing factors are known to bind this sequence at this time.
Figure 4 New Motifs in the Tissue-Regulated Exons
(A) A new motif UGYUUUC found upstream of brain-included exons. The logo is shown above, and the graph of the frequency of this motif in different regions of the brain-enriched exons is below.
(B) A sequence similar to the recognition sites for SF1 and QK proteins is enriched near the 5′ splice site of the muscle-included exons. The logo is shown above, and the graph of the frequency of the core of this motif in different regions of the muscle-enriched exons is below.
(C) Locations of multiple copies of conserved sequences matching the motif in (B) found in a conserved “bump” downstream of a muscle-included (and brain-skipped) exon 16 in the Vldlr gene.
A motif with striking similarity to the branchpoint consensus sequence UACUAAC is found in the intron region downstream of the 5′ splice site of heart and skeletal muscle–included exons (Figure 4B). While the small number of sequences makes testing the statistical significance of finding this motif using Improbizer difficult, the biological importance of this sequence motif has been thoroughly demonstrated in several contexts (see Discussion). To investigate this further, we performed an analysis similar to that of the other known motifs (Figure 3) and measured the frequency of the core of the branchpoint motif (Figure 4B, CUAAC) near muscle exons compared to constitutive exons. We found that CUAAC is enriched downstream of the 5′ splice site in muscle exons but not upstream of the exons compared to constitutive exons. Near some muscle-included exons such as Vldlr exon 16, there are multiple copies of this motif contained within regions that are highly conserved among mammals (Figure 4C). We propose that a protein recognizes this motif and activates inclusion of nearby exons in heart and skeletal muscle. After identifying this motif in the muscle-included exons, we revisited the brain exons and determined the frequency of the core CUAAC sequence in the nearby regions. The frequency of CUAAC is enriched downstream of the brain-included exons as well, whereas exons skipped in brain showed no significant enrichment. This suggests that CUAAC motifs downstream of the 5′ splice site may activate exon inclusion in both muscle and brain cells.
Paralogous Brain-Included Exons in Three Members of the MAGUK Family
Four of the 22 members of the MAGUK family present in the mouse genome have exons that appear to be differentially included in brain tissues. The guanylate kinases are important in the transport, anchoring, and signaling of synaptic receptors and ion channels (for review, see [66]). The kinase domain no longer functions, and it appears that the MAGUK family has evolved to act as a scaffold to bind other proteins. The four MAGUK family members found to have brain-included exons are called Cask, Dlgh1, Baiap1 (human ortholog is Magi-1), and 4732496O19Rik (human ortholog is Magi-3). The latter two are paralogs apparently resulting from gene duplication, and their regulated alternative exons are also paralogous (Figure 5). Another paralog called Acvrinp1 (human ortholog is Magi-2) also contains a paralogous exon that is alternatively spliced in humans. Although there is no mouse cDNA representing the skipping event, RT-PCR analysis confirmed that the Acvrinp1 exon is also alternatively spliced in mouse, being included in the brain but skipped in nonbrain tissues (Figure 5C). In all three genes, the alternative exon lies just downstream of a C-terminal PDZ domain and is not predicted to influence nonsense-mediated decay, suggesting that the protein encoded by these alternative exons may influence PDZ-mediated protein-protein interactions in the brain (for review, see [67]).
Figure 5 Tissue-Regulated Splicing Controls the C-Terminal Sequences of Mouse MAGI Proteins
(A) Microarray intensity (top) and RT-PCR results (bottom) for the alternative exon in Baiap1.
(B) Microarray intensity (top) and RT-PCR results (bottom) for the alternative exon in 653047C02Rik.
(C) RT-PCR results (bottom) for the alternative exon in Avricp1 (this gene was not present on the array).
(D) Diagram of the alternative splicing and coding patterns at the 3′ end of the mouse MAGI genes.
All three of the MAGI gene alternative exons have nearby intronic sequences that are highly conserved with their respective orthologous regions in other organisms (Figure 6A). The 89-nucleotide exon 15 of mouse Baiap1 lies in a highly conserved region (Figure 6A, see also Figure 2A), and there is EST evidence supporting its alternative splicing in species as distant as chicken. The tissue patterns of alternative splicing for these paralogous exons are similar, as detected by the microarray data and confirmed by RT-PCR (Figure 5 and data not shown). The intron sequences downstream from the 5′ splice site are highly conserved in the orthologs (Figure 6A), but comparison of the paralogous intron sequences shows that they have diverged considerably in overall sequence (Figure 6B). The same conservation pattern holds for the exons and the region upstream of the exons. This appears to conflict with the expectation that alternative exons of common descent and regulatory pattern should possess common regulatory elements. This expectation holds for orthologs despite occupying different genomes but apparently not for paralogs sharing similar regulatory profiles within the same genome.
Figure 6 Multiple Alignment of the Flanking Intron Sequences Downstream of the Alternative Exons from Baiap1 (MAGI-1), Acvrinp1 (MAGI-2), and 4732496O19Rik (MAGI-3) and the Orthologous Sequences from Rat, Human, Dog, and Chicken
While the orthologous sequences have high conservation between them (A), the paralogous sequences have diverged considerably (B). The 5′ splice sites are at the left. Genome sequences similar to TACTAAC are between gray bars. In the region downstream of the 5′ splice site these may act as regulatory binding sites for SF1, quaking (QK), or some other factor. Fox-1 sites are shown between black bars.
Discussion
Identification of Brain and Muscle-Regulated Exons Using Microarrays
A significant challenge of using DNA microarrays to study alternative splicing is separating changes in overall gene-derived transcript level from changes in alternative splicing. An additional difficulty is that splicing cannot be assessed in tissues in which the gene is not expressed, a situation that creates missing data, which in turn can confound many statistical similarity metrics. Numerous groups are now using microarrays to study the regulation of splicing, and all have had to develop methods to account for transcriptional effects [31–41]. Examining the fold change of isoform-specific probes, normalized by the change in probes common to all transcripts, has worked well in treatment and control experimental designs [31,39,41]. More sophisticated model-based methods that either directly estimate isoform concentration [35,38] or find loci that do not fit a constitutive exon model, thus identifying candidate alternative exons [32,68], have been applied to tissue panels to detect the occurrence of alternative splicing differences between the tissues. Other methods have used pairwise anticorrelation to identify cases where a constitutive exon model is inappropriate [37]. By comparing splicing in grouped sets of tissue samples (e.g., brain versus nonbrain), we were able to use a relatively simple regression-based statistical test to identify regulated alternative splicing (Figure 1B). This test isolates our parameter of interest, the relative use of pairs of alternative junctions, and identifies a significant difference in this parameter between two sample populations. Using this method, we discovered hundreds of tissue-regulated alternative splicing events (Tables S3 and S4).
The identification of a splicing event as tissue-regulated does not necessarily imply that all cells within a tissue share the same splicing pattern. Many tissues are heterogeneous populations of distinct cell types, and dramatic examples of differences in splicing in individual cells within a tissue are known (for review, see [69]). Our method appears sufficient to capture splicing events despite this heterogeneity. However, it is possible that many instances of cell type–specific splicing are missed because the superposition of the splicing patterns from the tissue generates a mixed signal. Such cell type–specific splicing events could be identified using purified cell populations from the same tissue.
Extreme Conservation near Alternative Exons
Large blocks of conserved sequence are found in the introns both upstream and downstream of the alternative exons identified in our microarray data (Figure 2), consistent with earlier computational studies based on EST/mRNA data [24–26]. The high levels of conservation do not appear to be due to cryptic protein-coding function as determined by QRNA, even though the conserved regions analyzed often included a portion of the coding exon. It is possible that these highly conserved regions contain multiple RNA binding protein motifs that act in concert to regulate the splicing of the alternative exon. A selective pressure on the type, number, and order of these RNA binding proteins could explain the large blocks of conserved sequence seen near these exons (for examples and additional references, see [45–47]). It is hard to visualize how such large blocks of conservation are required given the flexible way that RNA protein binding sites can function in other contexts. It seems likely that the secondary RNA structures of these regions play some role in the binding of proteins that influence alternative splicing or may play some direct role themselves by an as-yet-unknown mechanism. It is difficult to make convincing secondary structure models of these conserved regions since they lack the phylogenetic variation necessary to support such models. Much future work remains to be done to determine the functional elements that regulate these alternative exons.
Conserved Intron Sequences near Regulated Exons Are Enriched for Known Motifs
Nova has previously been shown to regulate alternative splicing in the brain [10,56–58,70], and the enrichment of Nova sites in the brain-included exons extends the potential list of Nova splicing targets. Our data show that the 150 nucleotides downstream of the brain-included exons has a significant increase in Nova-1 consensus motif sites, whereas we could not detect enrichment in the regions immediately upstream of brain-included exons. These results suggest that most often Nova stimulates exon inclusion in the brain from positions in the150 nucleotides downstream of the exon (Figure 3).
Similarly, the consensus motif for Fox-1 proteins is enriched downstream but not upstream of the brain-included exons, suggesting that like Nova-1, Fox-1 proteins contribute most often to activation of exons from positions to the 5′ splice site side (Figure 3). At least one other mouse protein, RBM9, has an RRM family RNA recognition motif essentially identical to that of vertebrate Fox-1 [13,14,53] and is therefore likely to recognize the UGCAUG sequence. Unlike Nova proteins, the expression of Fox-1 proteins is not normally restricted to brain [13,14,53]; thus, the Fox-1 motif is not strictly brain associated and is known to regulate nonneural splicing events [13,14,23,53,59–62,71,72]. Previous EST/mRNA-based studies [23,25,59] have noted an enrichment of the Fox-1 motif near brain-included alternative exons but not the enrichment of the Nova motif. This illustrates the utility of empirical data that accurately enriched our exon set for strong regulation.
Discovery of New Motifs Associated with Regulated Exons
An unexpected finding is that a sequence motif similar to the yeast branchpoint consensus sequence UACUAAC is found enriched downstream of exons included in heart and skeletal muscle (Figure 4). Although the set of muscle-included exons was too small for robust statistical evaluation of the Improbizer results, this motif is known to be biologically important (see below). In addition, the frequency of occurrence of the core of the motif CUAAC is significantly enriched in this region (Figure 4B). Several proteins have been shown to bind sequences related to this motif. The best understood in molecular terms is SF1, a KH-domain family member that binds the pre-mRNA branchpoint during intron recognition [17,73,74] and is not known to bind and regulate exon inclusion from downstream sites. An NMR structure of the SF1 KH-domain bound to UACUAAC RNA provides a detailed picture of the contacts that lead to sequence specificity [17]. Recent work has revisited the question of whether SF1 is required for splicing of every mammalian intron [75,76], opening the possibility that it might have functions beyond those as a general splicing factor. SF1 is broadly expressed, so if it has a special role in regulating muscle exon inclusion it will be interesting to know how it might carry out both a general and a regulatory function in the same cells. Strikingly, muscle-specific exons appear to be depleted of CUAAC sequences upstream of the intron, suggesting they may have weak SF1-branchpoint interactions.
Other metazoan proteins with similar KH domains that bind nearly identical sequences include Caenorhabditis elegans GLD-1 and the vertebrate QK known to regulate cytoplasmic mRNA stability through sites in the 3′ UTR of their target mRNAs [15,16,77]. QK is expressed in brain, heart, and muscle [20], and one QK isoform (QK-I5) enters the nucleus, where it may regulate splicing [19,78]. Experiments to test the role of QK in splicing regulation clearly show it can influence splicing [18]; however, these studies predated the identification of the QK binding site [15]. Thus, it is unclear whether QK splicing regulation [18] is directly mediated through the UACUAAC-like QK binding site. In addition, the mouse genome contains at least two additional genes (KHDRBS2 and KHDRBS3) predicted to contain KH domains with binding specificities that could be similar to SF1 and QK, although little is known of their expression or localization. Further work will be necessary to determine if and how this sequence influences exon inclusion in heart and skeletal muscle. Although many studies of heart- and muscle-specific splicing have identified motifs and factors using directed analysis of one or a few substrates, none has identified this motif.
We have also identified a novel UGYUUUC motif that is enriched upstream of the exons most strongly included in brain (Figure 4A). Thus far, no RNA binding protein is reported to recognize UGYUUUC. It is interesting to note that whereas exons preferentially included in brain are near intron sequences enriched for known ISE motifs, those preferentially skipped in the brain are not. It is likely that many exons that are preferentially skipped in brain may be preferentially included in other tissues, and their tissue-specific activation signatures are superimposed and thus lost by the heterogeneous grouping of “not brain.” It is important to note that our analysis does not guarantee that every important motif has been identified. We have selected the new motifs we discuss as contrasting examples: one novel with no known biological explanation, and the other with well-demonstrated biological functions in other contexts.
Conservation in Orthologs and Divergence in Paralogs: Why Maintain Similar Splicing Patterns Using Distinct Sequences?
Nature has provided an interesting case study with the paralogous alternative exons of the MAGUK proteins (Figures 5 and 6). The paralogous alternative exons in the MAGUK genes suggest either that there are many combinations of distinct RNA binding sites that can mediate a similar tissue-specific splicing pattern or that other levels of regulation besides primary RNA sequence exist. The striking difference in the primary sequence of the exons, and nearby intronic sequence, coupled with the apparent similarity in splicing profiles conflicts with the parsimonious hypothesis that alternative exons resulting from a gene duplication event should have similar RNA binding motif profiles regulating them. It is possible that the regulation of the exon occurs through some more distal site as other studies have shown for both Nova [56] and Fox-1 [53,60] or could even reside within the exons themselves. Yet the high level of conservation in orthologous species suggests that the primary sequence of these exons, and nearby introns, is important for the function of these genes. It is also possible that there are subtle differences in the regulation of the paralogous exons that we cannot distinguish looking at the heterogeneous cell populations (e.g., neurons and glia) that make up a tissue extract. Even if there are differences within regulation between subpopulations of cells in brain, it seems plausible to expect that at least the mechanism of skipping in other tissues might be common among them. Taken together, the large blocks of extremely conserved intronic sequence, much larger than typical RNA binding motifs (Figure 2), the QRNA prediction that these blocks are not protein coding, and the paralogous alternative exons with common regulation from divergent sequence (Figures 5 and 6) all suggest that there are other levels of evolutionary constraint on these regions in addition to RNA binding motifs that influence alternative splicing. In addition to the possible direct effect of the primary sequence motifs we have noted, the folded structure of these RNA regions could be important, even though we cannot now determine the nature of these folds. Alternatively, relatively small contributions of the primary sequence to splicing efficiency could make very large contributions to evolutionary fitness and reproduction of vertebrates that could be difficult to assay in the laboratory. It will be important to understand the functional basis for this unusual conservation. A recent study has suggested that evolutionary forces act to produce either multiply duplicated gene families poor in alternative splicing or single genes with complex alternative splicing [8]; however, the MAGUK gene family appears to have taken an intermediate path.
Materials and Methods
Design of microarray to monitor alternative splicing in the mouse.
Oligonucleotide microarrays to study mouse alternative splicing were designed as part of this collaboration and constructed by Affymetrix (Santa Clara, California, United States). All cDNA and EST information used for developing the mouse splicing sensitive chips were aligned to the February 2002 genome freeze of the mouse genome. Briefly, mouse cDNA sequences were aligned to the genome using psLayout, an early version of BLAT [79]. Orientation of cDNA was determined using EST read directions, coding sequence annotations, and poly(A) signals. Alignment discontinuities between cDNA and genome bordered in the genome by GT–AG, AT–AC, or GC–AG were interpreted as introns. The program AltMerge [80] was run on the aligned cDNAs to create a gene model that describes the different paths through the gene created by alternative splicing, for each gene.
DNA oligonucleotide probes are synthesized on a glass surface by photolithographic methods [43,44]. Probes used in this work were 25 nucleotides long and restricted to 711 × 711 adjacent positions. Each microarray has 5.05 × 106 probes. Probes are grouped conceptually into “probe sets” of six to ten probes designed to measure the same transcript feature. Three types of probe sets were created for each gene model. One set, the “gene probe set,” consists of eight to ten probes placed in the regions of the gene found in all mRNA isoforms and is meant to measure the overall transcript level from the gene. There are gene probe sets for 15,000 RefSeq genes in addition to those for alternative splicing events in genes not identified in RefSeq. A second kind of probe set is the “splice junction probe set,” which consists of six probes that step across a splice junction, centered at positions −3, −2, −1, +1, +2, and +3 relative to the junction. The third kind of probe set is the “exon probe set,” which consists of variable number of probes for distinct alternative exon regions in the gene model. After design and manufacture using the February 2002 assembly of the mouse genome, the probes were reanalyzed and remapped to the May 2004 assembly. The microarray has probes sufficient for the detection of more than 6,000 alternative splicing events. All probes are “perfect match” probes and there are no “mismatch” probes. Thus, we used alternative data analysis strategies to subtract background nonspecific hybridization signals from the true hybridization signals (described below). A small set of known alternatively spliced genes was annotated by hand, and probe sets designed for these genes included mismatch and perfect match probes.
Animal care and maintenance.
Male and female 8-wk-old C57/BLJ6 were obtained when necessary from Simonsen Labs (Gilroy, California, United States). Animals were provided with unlimited access to food and water and were housed on site for at least 2 wk before use.
Tissue dissection.
Tissues were dissected from individual age- and weight-matched mice. Animals were deeply anesthetized with an overdose of Nembutal by intraperitoneal injection prior to any dissection. Brain tissues used were cerebellum, cortex, olfactory bulb, pineal gland, hindbrain, median eminence, thalamus, and cerebellar hemispheres. Nonbrain tissues used were spinal cord, heart, lung, liver, spleen, kidney, testis, ovary, mammary gland, salivary gland, esophagus, stomach, small intestine, colon, and skeletal muscle. Males were used for all tissues, except that estrus cycle–matched females were used for mammary gland and ovary. Tissues from different individuals were not pooled, with the exception of olfactory bulb, pineal gland, testis, and ovary. Tissues were frozen in liquid nitrogen, and RNA was extracted immediately or stored after freezing at −80 °C until RNA extraction.
RNA extraction.
RNA from cortex, cerebellum, olfactory bulb, thalamus, hindbrain, pineal gland, median eminence, cerebellar hemispheres, lung, liver, spleen, testis, colon, kidney, small intestine, ovaries, mammary gland, and salivary gland was extracted using the Invitrogen Micro-to-Midi Total RNA Purification System (catalog 12183–018; Carlsbad, California, United States). RNA from skeletal muscle, heart, and esophagus was extracted using the Qiagen RNEasy kit for Fibrous Tissue (catalog 74704; Valencia, California, United States). All RNA samples were treated with DNase I for 30 min at 37 °C, extracted twice with phenol/chloroform and once with chloroform, and ethanol precipitated. RNA samples were run on an Agilent Bioanalyzer (Agilent Technologies, Palo Alto, California, United States) to determine RNA integrity as measured by the ratio of 28S to 18S rRNA. RNA concentration was measured using either a regular spectrophotometer or a Nanodrop short–path length spectrophotometer.
Labeling protocol.
Tissue RNA from at least three individual animals (or three separate groups of animals in the case where tissues needed to be pooled to obtain sufficient RNA) was used to make target for hybridization to triplicate oligonucleotide microarrays. RNA was primed with random hexamers and reverse transcribed. After the reaction was completed, RNA was removed from the reaction by alkaline hydrolysis and the cDNA was purified using Qiagen PCR Quick Purification Kit. A typical reaction started with 5 to 6 μg of total RNA usually yielded about 3 μg of cDNA. The cDNA was then fragmented using DNase I in an empirically controlled reaction that yields DNA fragments of 50 to 200 bases. This fragmented cDNA was then end labeled using terminal deoxynucleotidyl transferase and DNA-Labeling-Reagent-1a (DLR-1a), which is a biotinylated dideoxynucleoside triphosphate. Labeled targets were mixed with Affymetrix Eukaryotic mix (biotin-labeled oligos for which control probes exist on the chip as internal controls), and heated at 99 °C for 5 min before hybridizing to microarray. Targets were hybridized to chips in 7% DMSO solution for 16 h overnight at 50 °C. Microarrays were washed and processed with anti-biotin antibodies and streptavidin-phycoerythrin according to the standard Affymetrix protocol. After scanning the chips and aligning the grids to the scanned image, intensity values were extracted using the software associated with the Affymetrix scanner.
Normalization and analysis of intensity data.
Normalization and analysis of intensity values from the DNA microarrays were done using a quantile normalization [81], and probe set summaries were derived using the Robust Multi-chip Analysis (RMA) procedure [82,83] with two modifications. The first modification was to remove all probes with 17 or more continuous bases that match to any other mouse transcript, in order to minimize cross-hybridization issues. The second modification was to use the mode of the probe intensity values of similar GC content probes for the background estimate of a particular probe. For example, if a probe has a GC count of 16, then the mode of the intensity of all the probes with a GC count of 16 was used as a background estimate.
Global detection and comparison of alternative splicing.
The DNA microarrays used in this study lack mismatch probes, preventing use of the standard Affymetrix MAS 5.0 protocols for calling the absence or presence of a target complementary to a particular probe set in the sample [42]. We first removed from consideration all probes with 17 or more continuous bases that spuriously match to any other mouse transcript, in order to minimize cross-hybridization issues. Next the remaining probe intensities for an array were used to construct an empirical CDF. The empirically derived CDF was used to calculate an empirical P-value that a particular probe's intensity arose from the background of all probes. This corresponds to using the relative rank of a probe's intensity as a P-value for the probability that the probe's intensity is due to background. Probes were stratified by G + C count as probes with a higher G + C count are known to generally have more nonspecific binding due to the thermodynamically more favorable G + C base pairing. For each probe set, the median P-value of the set of individual probes in the probe set was used as the P-value for that probe set. The probe set P-values of expression for biological replicates were combined using Fisher's χ2 method [84] to derive a probability of expression in a particular sample. To detect the expression of a particular alternatively spliced RNA from a particular gene, we first determine that the gene is expressed. For each expressed gene, we then determine whether RNA containing either of two or more alternative splice junctions is detectable using the isoform-specific probes according to the method described above. If probe sets for two or more alternative isoforms are called “present” in any sample of the data, this is taken as evidence for alternative splicing. As the probe selection process is constrained at splice junctions and we are using only perfect match probes, rather than both perfect match and mismatch probes, these estimates may not be as robust as those derived from the MAS 5.0 algorithm.
Estimating overall differential expression of alternatively spliced RNAs across tissues.
For each alternative splicing event, the junction probe sets that would hybridize to individual isoforms were identified and the probes they contained were used for the Kruskal-Wallis test [85]. Individual probe intensity values were normalized as described above, and probe intensities from the replicates from each tissue were grouped together. The Kruskal-Wallis test as implemented by the R function kruskal.test was used to test the hypothesis that the probe intensities come from identical populations. If the resulting P-value was small enough, the null hypothesis was rejected and the alternative hypothesis that the probes were differentially expressed across tissues was accepted. To determine an appropriate value for the 0.01 significance level, we first ran 12,740 Kruskal-Wallis tests on randomly selected probe sets and found the α value associated with the 1% quantile of randomly selected probes to be 1.975486 × 10−3. To account for multiple testing, a Bonferroni-corrected α value of 1.975486 × 10−3/1.2740 × 104 = 1.550617 × 10−7 was used as a P-value cutoff for significance. If more than one alternative RNA form was differentially expressed, this was taken as evidence of alternative splicing across the tissues. These data are presented in Table 1.
Capturing strong differential tissue regulation of exon inclusion and skipping.
Regression coefficients between the include and the skip probe set intensities for brain and nonbrain (or heart and skeletal muscle versus nonmuscle) tissues were compared to determine if the level of inclusion or skipping of each exon is different between the two populations of tissues. To avoid assuming normality, a bootstrap method was used to determine the significance of the difference between the regression coefficient for two tissue populations using the method described by Wilcox [85]. Briefly, the number of times that the regression coefficient for one population is greater than the other is tabulated for all of the bootstrap samples and is used to calculate the significance of the difference between the two populations. Formally, we wish to test the null hypothesis that the skip/include ratios are the same in the brain and nonbrain groups, H0 : βbrain = βnonbrain. We resample the data with replacement D times. For each resampling d, Id = 1 if βbrain − βnonbrain > 0 and Id = 0 if βbrain − βnonbrain = 0. Thus, P(H0) = 2 * min[(Σd
I/D), 1 − (Σd
I/D)]. We reject the null hypothesis H0 if P(H0) < 10−4. To implement this test, we took 10,000 bootstrap samples from the two populations (D = 10,000), and a robust regression coefficient was calculated for each one using iterated re-weighted least squares as implemented by the rlm function in R [86]. Thus, if one group of tissues T1 uses the include isoform more than another group of tissues T2, then a subsample of T1 should also use the include isoform more than a subsample of T2. Looking at 10,000 sets of such subsamples strengthens the argument that the differences in skip/include ratios in the two groups are not due to chance. All regression calculations were required to go through the origin (zero intensity), since without transcription there can be no included or skipped transcripts. To help ensure that the genes analyzed had independent evidence for expression before analyzing their splice junction signals, only samples in which the gene probe set was called present (see previous section) with a P-value of ≤0.75 were used in the regression calculation. Furthermore, a minimum of eight hybridizations in which the gene was considered expressed for each tissue group was required before a splicing event could be considered for analysis. The difference between regression slopes can be used to estimate the magnitude of the fold difference in splicing between the two groups of tissues. Only events for which all bootstrap regression tests consistently called one slope greater than another (P < 0.0001) were used in subsequent analysis.
Motif finding and calculation of motif enrichment.
To identify new motifs we used Improbizer [63]. As described in the supplement to [63], Improbizer searches for motifs in DNA or RNA sequences that occur with improbable frequency using an algorithm adapted from MEME. The algorithm works with position-weight matrices (PWMs). At each position in a motif, the PWM contains a probability for each of the four nucleotides. The algorithm (1) constructs an initial PWM based on the first 6-mer in the input sequence set, (2) finds the best placement of the PWM on each sequence in the input, (3) constructs a new PWM by taking a weighted average of the PWMs found in step 2 and assesses whether adding or subtracting a column from either end of the PWM will increase the score, (4) repeats steps 2 through 4 until there is no change in the PWM, (5) then constructs a new initial PWM based on the next 6-mer in the input, and (6) repeats steps 2 through 6 over a user-specified subset of the input (by default all of the first 20 input sequences), (7) reports the best scoring PWM, (8) probabilistically erases the occurrences of the best scoring PWM from each sequence, and (9) iterates steps 1 through 8 until it has found as many PWMs as the user has requested. A key to the algorithm is the scoring of PWM placements. The score is an odds score: P
observed/PWM/P
observed/background model where the numerator is the product of the probabilities of the nucleotides at a particular position according to the PWM, and the denominator is the probability according to a simple Markov chain constructed by examining frequencies of nucleotide occurrences throughout a large background (nonenriched for the motifs of interest) sequence set. Further details and the source code are available from W. J. Kent ([email protected]). There is also a Web site (http://www.soe.ucsc.edu/~kent/improbizer/improbizer.html) with an online version of the algorithm.
As a background sequence set, we used about 47,000 exons that show no alternative splicing in mouse [24]. For motif frequency determination, the counts of consensus motifs were calculated for the 150 nucleotides upstream and downstream from the exon of interest. Counts were compared to those of the about 47,000 constitutive exons using Pearson's χ2 test statistic, as implemented by the R function prop.test, to determine the likelihood (P-value) that the motif counts of interest could have come from the same distribution found in the constitutive exons.
RT-PCR validation of microarray predictions.
cDNA was generated from about 2 μg of total RNA using the TaqMan Reverse Transcription Reagents kit (Applied Biosciences, Foster City, California, United States) using a mixture of oligo-dT and random hexamers, following the manufacturer's instructions. For PCR, about 50 to 100 ng of cDNA was used as a template with primer pairs designed to amplify the region containing the skipped exon. Reactions used Taq Polymerase (Promega, Madison, Wisconsin, United States) and were run for 25 to 35 cycles at annealing temperatures appropriate for the primer pairs used. PCR products were run out on 2% agarose gels and stained with ethidium bromide.
Supporting Information
Table S1 Presence Absence Determination of Alternative RNA Forms
(22 KB PDF)
Click here for additional data file.
Table S2 RT-PCR Testing of Array Predictions
(22 KB PDF)
Click here for additional data file.
Table S3 Brain-Regulated Exons
(23 KB PDF)
Click here for additional data file.
Table S4 Muscle-Regulated Exons
(15 KB PDF)
Click here for additional data file.
Accession Numbers
Information about the microarray data in this study has been deposited at GEO (http://www.ncbi.nlm.nih.gov/geo) under the accession number GSE3063. A Web site with additional information is available at http://ribonode.ucsc.edu/Sugnet_etal_05/Suppl.
We are indebted to Doug Black, Miriam Meisler, and Alison McInnes for critical reading of the manuscript and to Lily Shiue for help in and advice on processing microarrays. This work was supported by National Institutes of Health (NIH) grant GM 040478 and by a grant from Howard Hughes Medical Institute (HHMI) for the Hughes Undergraduate Research Laboratory (to MA). CWS was supported by an HHMI predoctoral fellowship. The University of California Santa Cruz Microarray Facility is supported by NIH R24 grant to Doug Black (University of California Los Angeles), Xiang-Dong Fu (University of California San Diego), and MA, as well as by a National Human Genome Research Institute center grant to DH and a grant from the Packard Foundation. DH is an investigator of the HHMI. MA is a professor of the HHMI.
Author contributions. CWS, KS, TAC, DK, and MA conceived and designed the experiments. KS, TAC, GO, and MA performed the experiments. CWS, KS, TAC, GO, DH, and MA analyzed the data. CWS, KS, TAC, MSC, HW, AW, DK, JEB, DH, and MA contributed reagents/materials/analysis tools. CWS and MA wrote the paper.
Competing interests. The authors have declared that no competing interests exist.
A previous version of this article appeared as an Early Online Release on December 14, 2005 (DOI: 10.1371/journal.pcbi.0020004.eor).
Abbreviations
CDFcumulative distribution function
ESEexonic splicing enhancer
ESSexonic splicing silencer
ESTexpressed sequence tag
ISEintronic splicing enhancer
ISSintronic splicing silencers
MAGUKmembrane-associated guanylate kinase
PWMposition-weight matrix
QKprotein quaking
==== Refs
References
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Siepel A Haussler D 2004 Phylogenetic estimation of context-dependent substitution rates by maximum likelihood Mol Biol Evol 21 468 488 14660683
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Chou MY Underwood JG Nikolic J Luu MH Black DL 2000 Multisite RNA binding and release of polypyrimidine tract binding protein during the regulation of c-src neural-specific splicing Mol Cell 5 949 957 10911989
Markovtsov V Nikolic JM Goldman JA Turck CW Chou MY 2000 Cooperative assembly of an hnRNP complex induced by a tissue-specific homolog of polypyrimidine tract binding protein Mol Cell Biol 20 7463 7479 11003644
Jin Y Suzuki H Maegawa S Endo H Sugano S 2003 A vertebrate RNA-binding protein Fox-1 regulates tissue-specific splicing via the pentanucleotide GCAUG EMBO J 22 905 912 12574126
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Lim LP Sharp PA 1998 Alternative splicing of the fibronectin EIIIB exon depends on specific TGCATG repeats Mol Cell Biol 18 3900 3906 9632774
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Huh GS Hynes RO 1994 Regulation of alternative pre-mRNA splicing by a novel repeated hexanucleotide element Genes Dev 8 1561 1574 7958840
Ao W Gaudet J Kent WJ Muttumu S Mango SE 2004 Environmentally induced foregut remodeling by PHA-4/FoxA and DAF-12/NHR Science 305 1743 1746 15375261
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0112-a0101115595176EnvironewsScience SelectionsPOPs in Polar Bears: Organochlorines Affect Bone Density Tenenbaum David J. 12 2004 112 17 A1011 A1011 2004Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Both organochlorine chemicals—including a series of solvents and pesticides that have been banned in many parts of the world—and their metabolites have been linked to bone loss in a variety of species. Now, Christian Sonne of the Danish National Environmental Research Institute and colleagues have found a similar association in a new species [EHP 112:1711–1716]. Their work shows a link between organochlorine exposure and reduced bone mineral density among polar bears in East Greenland.
Organochlorines are among the chemicals known as persistent organic pollutants (POPs), which resist breakdown, store easily in fat, and bioaccumulate through the food chain. Organochlorines have been accumulating in the Arctic for decades, thanks to northward atmospheric transport. Due to the quirks of atmospheric transport, polar bears from East Greenland, Svalbard, and the Kara Sea have higher body burdens of organochlorines than polar bears from the rest of the Arctic.
In controlled studies of laboratory mammals, organochlorines including the pesticide DDT and the group of industrial chemicals known as polychlorinated biphenyls (PCBs) have caused changes in bone composition including reduced bone mineral density. Organochlorines have also been implicated in other bone diseases including periodontitis, a disorder of the gums and bones around the teeth.
The Danish researchers examined samples from 139 East Greenland polar bear skulls collected between 1892 and 2002. Samples collected in the period 1966–2002 were considered “post-pollution”—that is, they were collected after high concentrations of POPs began appearing in polar bear fat. Those collected in the period 1892–1932 were considered “pre-pollution.”
The researchers measured bone mineral density with dual X-ray absorptiometry—the same test used to detect osteoporosis in humans. They also examined organochlorine body burden in a subset of 58 samples collected between 1999 and 2002 for links with bone mineral density.
Among younger bears and adult males, bone mineral density was significantly reduced in post-pollution samples. The pattern was not seen among adult females, possibly due to an age-associated decline of estrogen, or because they were pregnant or nursing pups (both conditions mobilize bone calcium for the benefit of the offspring, and reduce bone density). Bone formation and resorption are governed by estrogen and androgen hormones, which help regulate both osteoblasts (cells that form bone cells) and osteoclasts (cells that break down bone cells).
In the 58 skulls collected between 1999 and 2002, exposure to total PCB compounds and to chlordane (a now-banned insecticide) both correlated with low bone mineral density among younger bears. In adult males, concentrations of dieldrin (another banned insecticide) and total DDT residues also correlated with low density. The researchers conclude that disruption of bone mineral composition correlates with the presence of PCBs, DDT residues, and other POPs among polar bears from East Greenland.
Bear-bones data.
New research shows a link between Arctic organochlorine pollution and decreased bone mineral density in East Greenland polar bears.
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Environ Health Perspect. 2004 Dec; 112(17):A1011
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0112-a1010aEnvironewsScience SelectionsSwimmer’s Lung?: Indoor Pools and Respiratory Effects Spivey Angela 12 2004 112 17 A1010 A1010 2004Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Bronchiolar epithelium cells known as Clara cells are thought to help defend airways against damage; Clara cell protein (CC16) is a lung-specific protein thought to protect the respiratory tract from inflammation. Studies have shown a positive association between ozone exposure and increased concentrations of CC16 in blood serum, leading researchers to suggest that ozone exposure damages the lung epithelium, causing it to “leak” proteins such as CC16. Now Birgitta Json Lagerkvist of Umeå University in Sweden and colleagues present findings that complicate that idea [EHP 112:1768–1771].
Lagerkvist and colleagues report that children who regularly visited indoor swimming pools actually showed decreased concentrations of CC16 both before and after sessions of exercise outdoors in ambient levels of ozone. The researchers theorize that repeated exposure to disinfection by-products around indoor swimming pools may damage Clara cells, decreasing production of CC16 and possibly masking any effect that ozone exerts on CC16 levels.
This report is part of a set of studies examining possible changes in CC16 serum levels in relation to ambient ozone exposure as well as exposure to other environmental agents such as the chlorine around swimming pools and its by-products. In addition to the current study, which examined children in the summer, three other studies have examined children in winter, adults in summer, and adults in winter.
Subjects were screened using blood samples, lung function tests, and questionnaires. The final 57 subjects were 33 boys and 24 girls aged 10–11 years who had no asthma, pollen allergy, or initial decreased lung function. One subset of the 34 children had visited an indoor pool for at least one hour per month for six months or longer; the remaining 23 children had not.
The children exercised lightly outdoors for two hours on the Umeå University campus, where the ambient ozone was a moderate 77–116 micrograms per cubic meter. Lung function testing and blood sampling were conducted both before and after exposure.
The researchers saw no impairment of lung function in any of the children, and in looking at the entire study group, they did not find any statistically significant relationships between ozone exposure and CC16 concentrations. Among the children who did not visit swimming pools, there was a marginally significant tendency toward such a correlation. However, the children who regularly visited chlorinated indoor swimming pools showed significantly lower CC16 serum concentrations both before and after ozone exposure, compared to those who didn’t visit pools.
The authors say this may indicate that repeated exposure to disinfection by-products around indoor swimming pools damages Clara cell function. This theory is supported by previous studies, including one by report coauthor Alfred Bernard that showed an association between regular pool attendance among people taking swimming lessons and reduced CC16 concentrations. The researchers speculate that if regular indoor pool attendance does decrease CC16 production, then this effect may mask any increased leakage of CC16 caused by ozone. They further suggest that such masking may have happened in this study, indicated by the slight tendency to show a correlation between ozone exposure and increased CC16 concentrations only in the children who did not visit swimming pools.
They conclude that Clara cell damage associated with indoor pool use may diminish the anti-inflammatory effect of CC16 in the lung. Another previous study by Bernard showed increased incidence of asthma among children who regularly visited indoor pools, and the authors of the current report call for further investigation of the possibility that exposure to disinfection byproducts around indoor pools can play a role in inducing asthma through impaired Clara cell function.
Bathers beware?
Repeated exposure to disinfection by-products around indoor swimming pools may damage cells that produce protective lung-specific proteins.
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Environ Health Perspect. 2004 Dec; 112(17):A1010a
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0112-a1010bEnvironewsScience SelectionsRoe, Interrupted: Estrogen Exposure Impairs Fish Fertility Spivey Angela 12 2004 112 17 A1010 A1011 2004Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Major research efforts have shown that endocrine disruptors—environmental chemicals that can interfere with the endocrine system—may affect reproduction of wildlife and even humans. Studies in fish, for example, have shown that endocrine disruptors can reduce sperm count, induce both male and female gonadal tissue or intermediate sexual characteristics in the same individual, and induce female-specific proteins in males. But little evidence to date has elucidated the effect of such changes on fertility. This month, Jon Nash of the Katholieke Universiteit Leuven in Belgium and colleagues report that long-term exposure to low concentrations of a synthetic estrogen may severely undermine the breeding success of wildlife populations, chiefly by producing sexually compromised males who disrupt breeding dynamics [EHP 112:1725–1733].
Using zebrafish because of their short generation time, the researchers measured effects of exposure over three generations. They began with 720 fish divided into 60 groups of 12. The team recreated natural conditions in the aquaria to optimize fish breeding, and eggs were collected each day.
After a baseline assessment of egg numbers and egg viability (a cumulative statistic of unfertilized eggs and embryo mortality), the researchers exposed different groups to environmentally relevant concentrations of various estrogens: 5.0 nanograms per liter (ng/L) of the endogenous estrogen estradiol or either 0.5, 5.0, or 50.0 ng/L ethynylestradiol, a potent synthetic estrogen used in oral contraceptives. A control group received no exposure.
Except for the highest concentration of ethynylestradiol, none of the estrogen treatments affected egg numbers or egg viability in the baseline generation. Nor did any of the treatments affect survival of the eggs spawned by this generation.
But after 210 days (a full zebrafish lifetime) of exposure to the middle dose of 5.0 ng/L ethynylestradiol, the second generation of fish showed reduced fertility. None of the male fish in the second generation had normal testes, and they did not produce expressible sperm, although the females were fertile. None of this generation’s progeny survived beyond 14 hours postfertilization. In almost 12,000 eggs spawned, none were viable.
When two healthy, nonexposed males were added to the populations that had experienced reproductive failure, embryos began surviving. But the embryos’ rate of survival was still significantly less than in the control group. After close observation of the spawning in these tanks, the researchers found that the infertile males showed normal reproductive behavior, chasing the spawning females and competing with the fertile males for access. The researchers suggest that the reduced fertilization was caused at least in part by the compromised males interfering with the fertilization capability of the healthy males.
The researchers say their data show that development of the testes is more sensitive to disruption by ethynylestradiol than is reproductive behavior. Yet the relatively higher threshold of sensitivity of behavioral disruption may in fact produce stronger population-level consequences, as infertile males have a greater ability to interfere with breeding dynamics. They conclude that more information about the effects of endocrine disruptors on the interactions between fish in a spawning group is needed before the population-level effects of endocrine disruption can be understood.
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Environ Health Perspect. 2004 Dec; 112(17):A1010b-A1011
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0011015687037EnvironewsInnovationsAquatic Alchemy Frazer Lance 2 2005 113 2 A110 A114 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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The average Earthbound American, estimates the U.S. Environmental Protection Agency (EPA) uses nearly 24 gallons of water daily for personal uses such as drinking, toilet flushing, hygiene, and cleaning. International Space Station (ISS) crew members, on the other hand, are limited to little more than half a gallon per day. Yet, with that little bit of water weighing about 5 pounds, and the cost of lifting something into low Earth orbit can top $10,000 per pound, it would make far better economic sense to provide astronauts with water on-site.
Aerospace engineers are busy working on a modern-day form of alchemy, finding new ways to find water in space without having to lug it along from Earth. And while many of the technologies are being developed with an eye toward the stars, certain aspects may be suitable for use here at home on planet Earth.
Pulling Water from the Air
Every time an astronaut exhales, washes up, or urinates, water is involved. In an effort to minimize the amount of fluid lifted into orbit, the National Aeronautics and Space Administration (NASA) is seeking ways to recapture that water, clean it, and store it for reuse.
The ISS currently provides clean water through the use of a water recovery system that reclaims wastewater such as used oral hygiene water, urine, and cabin humidity condensate. But the space environment offers unique challenges to the provision of water. For example, water must contain no dissolved gases, as gas doesn’t separate well at zero gravity, either in tanks or in the body. Therefore, the water must be purified to a level exceeding Environmental Protection Agency standards for Earth drinking water. The WRS consists of a urine processor assembly (essentially a still that boils off water, leaving a thick waste layer behind) and a water processor assembly developed by researchers at NASA’s Marshall Space Flight Center and Hamilton Sundstrand Space Systems International.
According to Dave Parker, program manager for Hamilton Sundstrand’s water processor program, the water processor assembly is a multistage system that uses filters to remove particulate matter and salts. The water is then run through a catalytic oxidizer to remove low-molecular-weight organic molecules such as alcohols. This part of the process takes place at a temperature of 275°F, and under pressure so it doesn’t flash to steam, Parker explains. The system then removes any byproducts and remaining dissolved gasses, and next goes through an ion exchange process to remove the oxidation products.
This process, according to Parker, can produce about 1.5 gallons of treated water per hour, and uses approximately 700 watts of power. To reduce maintenance times and the volume of consumables that must be delivered from Earth, the system has been designed with an 80- to 90-day change-out schedule for particulate filters, and a 60- to 70-day schedule for chemical filters, with no more than 12 hours of maintenance time required per year.
The water processor assembly is designed to provide limited amounts of highly purified water with minimal energy consumption and maintenance, but Parker believes the system could be scaled up for Earth usage. “We’re producing as nearly pure potable water as you’re likely to find anywhere,” he says. “The question you’d have to answer on Earth is whether you need water of that purity.” Parker suggests the system could be used in military applications, to protect crews against chemical or biological attack, or aboard naval vessels. It could also work in hospitals, where high-quality water is important. “We’re also working with the Army to design a system to create potable water from diesel exhaust,” he says.
There are also substances often encountered in terrestrial water that you’ll never find aboard the space station, such as arsenic, mercury, and heavy metals, but Parker says the water processor assembly could be adjusted to deal with these substances in a limited-application water stream. “While you could plug this system into a municipal water system, I suspect that the economics wouldn’t work. The system operates to higher standards, and avoids things that municipalities traditionally employ, such as the addition of chlorine to sterilize water. NASA doesn’t allow any chlorine aboard the ISS, so we use heat instead. That wouldn’t be economical in a multimillion-gallon-throughput municipal system.”
Further Out, Longer Stay
With NASA looking in more detail at a manned Mars mission—which would involve 40 million miles and 3–5 years—work has begun on a fully regenerative water recycling system, one that can provide a crew with adequate water for drinking and hygienic needs for up to three years without recharging. Enter the Vapor Phase Catalytic Ammonia Removal (VPCAR) system.
Michael Flynn, the project’s principal investigator at NASA’s Ames Research Center, says VPCAR has been designed to mimic the natural hydrologic cycle. “On Earth, you open the tap, drink water, produce waste, treat the waste, and discharge it back into the ocean,” he explains. “The sun heats the water, which evaporates and forms clouds. Those clouds are exposed to ultraviolet radiation, which destroys organic contaminants, and then rain falls to begin the cycle again. We’ve integrated all of these processes into a single small machine. We take in waste water, vaporize it, oxidize organic contaminants, re-condense it, and the water is ready for use.”
In general, says Flynn, nonregenerative technologies (like the water processor assembly), are dominated by adsorptive technologies such as activated carbon, which boast low power consumption. “You make the trade-off of having a shuttle fly up every ninety days to resupply expendables like filters because the system uses relatively little power,” he says. On the other hand, there will be no resupply opportunity on a manned Mars mission, so it’s desirable to spend more on power than on resupply. For example, Flynn says, the nonregenerative systems aboard the ISS only use an estimated 123 watt hours/kg, compared to what he says is around 300 watt hours/kg for the fully regenerative VPCAR.
VPCAR works by sending the waste stream across a wiped-film rotating disk evaporator, which removes inorganic salts and nonvolatile large-molecular-weight organic contaminants. Lightweight organic molecules and ammonia, which are volatilized in the evaporator, are oxidized by a high-temperature catalytic oxidation reactor, converting these organics into carbon dioxide, water, and nitrous oxide. This high-temperature process also helps destroy any biologically active organisms in the waste stream.
Full characterization studies of VPCAR have been completed, and the system, Flynn says, meets all NASA specifications. The next step will be a test aboard training aircraft, followed by full-scale flight-testing during a proposed lunar mission.
VPCAR, Flynn admits, would have limited applications on Earth, although some aspects of it are being considered, including the evaporative portion of the system, which has been examined by the U.S. South Pole research station, which wants to purify its sewage such that it could be used to make an ice layer for the research station’s runway. “We also have some rural Alaskan tanneries looking at using the system to recycle their waste, and some oil companies are looking at it as a technology to separate oil and water,” Flynn says. “VPCAR is also being looked at as an alternative method of distilling salt water into fresh.”
Further and Longer: Bearing ARMS
While some researchers are trying to make sure there are no bacteria in the water, others are going out of their way to welcome them. Tony Rector is a bioprocess engineer with Dynamac Corporation at Kennedy Space Center, where he and colleagues are working on a project called ARMS—the Aerobic Rotational Membrane System.
ARMS consists of a clear Plexiglas reactor vessel, filled with 115 tubes (dubbed “membranes”) that are home to a community of bacteria. Oxygen moves from the inside of each membrane to its outside surface, where bacteria are present in colonies called biofilms. Contaminated water flows past these biofilms, where the bacteria can use the oxygen to transform undesirable compounds found in the wastewater to less harmful compounds. Biological treatment reactors using membranes aren’t new technology, but this system is innovative in that the membranes rotate, exposing more of the bacteria to more of the infused oxygen and the contaminants that provide their nourishment.
“Biological systems like these can achieve high treatment efficiencies with low mass and energy requirements,” says Rector. “By rotating the membranes, we can enhance mass transfer, making the system as efficient as possible.”
As living organisms, bacteria are subject to many of the same stresses that will impact human astronauts, and if the bio-community goes down due to some shock (for example, an unexpected radiation dose or loss of heat or oxygen) it can be days before the community can be resuscitated. “Part of our long-term plan is to subject these organisms to a variety of shocks, and see how they react and recover from such events,” says Rector. “We’d also like more information on their ideal living conditions, what bacterial species are most tolerant and resilient, and other information of that sort.” Rector’s research group will also be examining different biochemical processes in a variety of bacterial species, including nitrification bacteria and hydrogen-oxidizing bacteria. Potential candidate bacteria will have to be evaluated for their tolerance for pharmaceuticals, hormones, antibiotics, and other substances that may be excreted by the astronauts using the system.
Because of the potential sensitivity of the living organisms, and because bacteria are unable to process all of the materials in a typical waste stream, Rector envisions using ARMS as a first phase in an overall water treatment process in space, followed by a chemical/mechanical process. “Our goal is to reduce the contaminant loading on these processes and therefore reduce the size and energy requirements for larger-scale physical and chemical systems,” Rector says.
“Bioregenerative research, focusing on water recovery, has been conducted at NASA research centers for many years. While we are in the initial stages of the ARMS project, we are very encouraged by its current performance,” says Rector. “Depending on mission scenarios and timelines, it could be five or six years before we can complete our investigation of the biological and mechanical component aspects of this system. I think there’s a lot of potential, but there’s still a lot of research to be done.”
ARMS, he says, could well find maritime applications, perhaps aboard cruise ships, which must thoroughly treat their waste before discharge. “This system will find its best use in a small environment,” he says.
“Best use in a small environment” seems to be a key descriptor of all of these processes. None of the individuals involved in research on the three systems envisions them as replacements for large-scale municipal water treatment facilities, but all agree they should work—and work well—in a confined environment, where it’s necessary to have very clean water, and all that’s available as feedstock is what Flynn describes as “the nastiest stuff imaginable.” The military, medical facilities, any place with a need for high-quality water supplies where conventional sources are unreliable—all are potential down-to-Earth targets for NASA’s spaceborne water systems.
Sources. Shiklomanov IA, State Hydrological Institute, United Nations Educational, Scientific, and Cultural Organisation. 1999. World Resources 2000–2001—People and Ecosystems: The Fraying Web of Life. Washington, D.C.: World Resources Institute; Harrison P, Pearce F. AAAS Atlas of Population 2001. Berkeley, CA: American Association for the Advancement of Science, University of California Press.
Precious cargo. The International Space Station and (inset) the water recovery system developed by Hamilton Sundstrand that’s aboard.
Space drinks. (left) The photo shows two ARMS reactors which are currently being evaluated. It shows the teststand for the ARMS systems, which includes the reactor vessel and sensors. (right) VPCAR being tested in the lab.
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Suggested Reading
Carrasquillo RL Cloud D Kundrotas RE 2004. Status of the node 3 regenerative ECLSS water recovery and oxygen generation systems. SAE Technical Paper 2004-01-2384. Presented at: 34th International Conference on Environmental Systems, 19–22 July 2004, Colorado Springs, CO. Warrendale, PA: Society of Automotive Engineers International.
Dusenbury JS 2003. Military land-based water purification and distribution program. Presented at: Maintaining Hydration: Issues, Guidelines, and Delivery, 10–11 December 2003, Boston, MA. Available: ftp://ftp.rta.nato.int/PubFullText/RTO/MP/RTO-MP-HFM-086/MP-HFM-086-11.pdf [accessed 11 January 2005].
Flynn MT 2004. The development of the vapor phase catalytic ammonia removal (VPCAR) engineering development unit. SAE Technical Paper 2004-01-2495. Presented at: 34th International Conference on Environmental Systems, 19–22 July 2004, Colorado Springs, CO. Warrendale, PA: Society of Automotive Engineers International.
Rector T Garland J Strayer RF Levine L Roberts M Hummerick M 2004. Design and preliminary evaluation of a novel gravity independent rotating biological membrane reactor. SAE Technical Paper 2004-01-2463. Presented at: 34th International Conference on Environmental Systems, 19–22 July 2004, Colorado Springs, CO. Warrendale, PA: Society of Automotive Engineers International.
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Environ Health Perspect. 2005 Feb; 113(2):A110-A114
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0061016140611EnvironewsInnovationsBREAKING THE CODE: Predicting Where Disease Will Strike Lougheed Tim 9 2005 113 9 A610 A613 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Health authorities today enjoy an embarrassment of riches when it comes to data on disease incidence, thanks to the rapidly increasing use of electronic record keeping at hospitals, clinics, and related businesses such as pharmacies. Where significant numbers of paper files once required days or weeks to process for survey purposes, much larger volumes of material on patient conditions can today be assembled in a matter of hours. This growing abundance of information has raised expectations about the prospect of identifying local outbreaks of disease at ever earlier stages so that such outbreaks may be addressed and contained as soon as possible—expectations that are perhaps more easily raised than met, given statistical realities.
One of the most recent strategies for harnessing these data is offering some promising results, however. A paper that appeared in the March 2005 edition of PLoS Medicine outlines a new methodology that requires only that researchers know the actual number of disease cases occurring in a given area over a given period of time. The resulting statistical model makes only the most limited number of assumptions surrounding the emergence of a disease, while attempting to compensate for naturally occurring temporal and geographical variations in disease reporting.
This offers greater detail than one-dimensional statistical methods, which track disease outbreaks in either purely temporal or purely geographic terms. Above all, the new model eliminates the need for information about the local population and its relative risk for disease—for example, whether a neighborhood contains a higher-than-average proportion of groups, such as infants or the elderly, who may be prone to specific ailments.
A Window on Disease
Underlying the work presented in PLoS Medicine is the principle of the scan statistic, which offers a probability of an excessive number of case reports appearing within a narrowly defined space and time, as compared with a probability determined from information collected in a larger region or over a longer period. Scan statistics aren’t new, but the PLoS Medicine paper adds a twist: the calculation of probabilities for disease outbreak within various samples of space and time. In the case of purely geographic surveillance, sudden highly localized outbreaks may be hidden in the data that have been aggregated for a region, whereas such events are more likely to be revealed once a temporal dimension has been incorporated. Space–time permutation scan statistics could therefore become a preferred way of representing the occurrence of diseases such as cancer, where the number of actual and expected reports are counted within particular “windows.”
These windows can be visualized as a set of thousands or even millions of overlapping “cylinders” within the geographic area in question, each varying in the amount of territory and length of time it covers. Harvard University Department of Ambulatory Care and Prevention associate professor Martin Kulldorff, one of the paper’s authors, uses the cylinder as a way of visualizing the sampling of data in three dimensions, with the x and y axes representing the geographic area being surveyed and a z axis representing time. As time passes, subsequent samples are added atop previous ones, and the cylinder thus grows in height. Mathematically, each cylinder uses a likelihood function to compare its expected and observed numbers of cases, making it possible to single out locations and days in which the latter number was unexpectedly high.
The new model was tested in conjunction with the New York City Department of Health and Mental Hygiene, which has been among the leading agencies collecting the data necessary to gauge the spread of disease. In the late 1990s, the city launched a dedicated program of syndromic surveillance, tracking ambulance reports, emergency room visits, and pharmacy sales—all with the aim of spotting anomalous clusters of cases that could signal a disease outbreak.
Kulldorff says the researchers took several steps to manage the computational burden of this exercise. The circular cylinder base was in turn one of several combinations of 183 New York City zip codes with a radius of zero to 5 kilometers. Each of the cylinders the researchers defined was seven “days” high (the team reasoned that if an outbreak has existed for more than a week, it will likely have already been picked up by clinicians or laboratories).
But while the geographic area covered by each cylinder remained the same, the specific days changed. For example, over the course of a month running from day 1 to day 30, the first statistical analysis would take place on a cylinder with a height defined by data from day 1 to day 7, the next would take place on a cylinder with a height defined by data from day 2 to day 8, and so on. This moving window makes it possible to look for changes taking place in a strictly defined time and space.
Thus, the team could, for example, catch a disease outbreak that began emerging on day 7, something that health authorities might not otherwise have identified for many more days. This early signal would prompt officials to check out the situation sooner and perhaps contain any outbreaks more successfully. To keep such signals in perspective, the statistical analyses also refer to the previous 30 days, so that any longer-term trends or variations could be compared with what has been seen in the seven-day window.
Evaluating the Approach
The evaluation of this approach began by focusing on historical reports of diarrhea taken from emergency department data collected daily by the Department of Health and Mental Hygiene between November 2001 and November 2002. Files categorized individual cases according to nonspecific conditions or symptoms, such as “diarrhea” or “flu-like,” and included the zip code of patients’ homes and where they were treated.
Four of the five most statistically significant groupings of data produced by the model correlated to citywide outbreaks of rotavirus, norovirus, and influenza during the study period, meaning this information would have given early warning of these outbreaks had it been available at the time. And while the system did generate some signals with no correlating outbreak, these were relatively few.
One problem the team had to address was the fact that people work during specific times of the week, and that clinics or pharmacies may operate only between set hours. This creates variations that could create false signals, such as high sales on Sunday for drugs from a pharmacy that is open when most others in the area are closed. Such distortions were avoided by taking each day of the week into account when calculating the random probabilities of disease outbreaks. If data appeared to show a clustering of disease on a Sunday, for example, the probability of that finding was assessed relative to other Sundays, rather than to any given day of the week.
More problematic for many systems is inconsistent reporting of data across locations and days. “These are challenges in the field as a whole,” says Kulldorff, noting that many sophisticated electronic systems experience reporting lags. “One of those challenges is the timeliness of the report, in the sense that sometimes there are partly missing data. You maybe only get ninety percent of the data, and the rest doesn’t come in until a few days later.” Nevertheless, he regards these initial results from the space–time permutation scan statistic to be promising enough to warrant much more intensive study.
Kulldorff would like to hone the model by evaluating its performance with other sources of information. “It’s not clear what data sets are actually the best to use for infectious disease outbreaks—emergency department visits, ambulatory care visits, laboratory test results, and so on,” he says. Nor is it clear that the model should be limited to tracking natural disease outbreaks. He suggests that it could be successfully applied to other public health phenomena, such as the spread of antibiotic-resistant bacteria, as well as in completely distinct fields such as criminology, ecology, or engineering.
Software Solution
In order to promote these broader applications, the model has been incorporated into SaTScan™, software Kulldorff has been developing since before he began working with the Department of Health and Mental Hygiene a few years ago. The program can be downloaded for free at http://www.satscan.org.
SaTScan’s purpose is to identify anomalous clusters of data that can be related to disease—that is, aggregations that are statistically distinct from the regular variations of health information that are being assembled from day to day in a particular area. Such anomalies can take a temporal form (cropping up in a short period) or a geographic form (occurring in a small region).
One of Kulldorff’s departmental colleagues at Harvard has reviewed the use of SaTScan for assessing disease outbreaks, including ones that could be the result of deliberate actions. Katherine Yih was part of a team working with the National Bioterrorism Syndromic Surveillance Demonstration Program, an initiative mounted by the Centers for Disease Control and Prevention (CDC) in collaboration with health care organizations covering more than 20 million people across various states. The goal of this program is to use data from health plans and practice groups to detect localized outbreaks and facilitate rapid public health follow-up.
Program researchers examined ambulatory patient records—reflecting medical care that was provided in a clinical setting rather than a hospital—which were organized according to zip codes as in the New York City project. The records were assembled on a 24-hour cycle; each night the system would look for clusters of illness that might signal an outbreak of statistical significance, so that an alert could be issued to officials in the affected area.
In a paper published in the 24 September 2004 issue of the CDC’s Morbidity and Mortality Weekly Report, Yih and her colleagues reported detecting unusual respiratory illness clusters in Colorado, Texas, and Massachusetts that were associated with severe influenza outbreaks. They are now turning their attention to evaluating the system’s ability to detect naturally occurring outbreaks of gastrointestinal illness, using data taken from known outbreaks that were identified by the Minnesota Department of Health. They are also carrying out simulations to determine if the system would be sensitive to acts of bioterrorism.
Yih endorses the approach, although she notes a few limitations. “The data are collected for other purposes,” she says. For instance, some of her most recent work draws on information from a Minnesota-based group that uses a triage nurse to provide health care information by telephone. The priorities of organizations operating such services start with trying to relate a patient’s self-reported symptoms to a readily identifiable disease group, rather than just recording the symptoms by themselves. That makes sense in terms of providing immediate advice to a caller, but it means researchers like Yih may have a much harder time going through the organization’s recorded data to find the symptoms that were being reported.
“The appeal of it is that you don’t have to request that a bunch of people at these health care organizations spend time entering data into your nice survey format or data format. They enter the data in the course of their routine patient care,” she says. By the same token, she adds, these data may not take a form or be timely enough to be optimal for identifying disease outbreaks. Nor is it always certain that the data capture a proportion of the population sufficient to justify an alert. For example, as little as 5–15% of the people in a metropolitan area might be covered by a health plan that may be supplying the data.
Kulldorff and his colleagues cite a complementary issue that affects their own disease monitoring strategy, which detects specific outbreaks at a highly localized level rather than simultaneous outbreaks taking place over a broad surveillance area. If an infectious agent is transmitted on the subway, for example, the people affected will not necessarily live anywhere near one another, nor are they likely to wind up in the same emergency room.
Moreover, their method counts on a disease having symptoms severe enough to send someone to the emergency room, making it harder to detect small outbreaks of less serious ailments. For this reason, they do not offer this approach as an exclusive choice. Instead, they suggest that efficient and comprehensive surveillance should be based on the use of different detection systems, each with its own strengths and weaknesses.
Yih recommends ongoing evaluations of SaTScan and similar systems to determine whether the expenditure of resources for disease data monitoring programs—as well as resources that public health officials would put into following up on the warning signals generated by those programs—would be justified. Ideally those signals should make life easier for public officials, ensuring that their efforts will be all the more effective.
Such assurance is the real service that systems such as scan statistics can offer to these officials, says Emory University biostatistician Lance Waller, who has critiqued the methodology behind disease surveillance models. “These databases that were collected separately and sat on their own computers before, can now be put together,” he says. “That’s a new thing for public health workers—to be able to get multiple health records from multiple hospitals, pharmacy sales from drug stores. These kinds of methods are good tools in a toolbox.”
New York City seems to think the system is a good thing. Since 2003, the scanning software has been applied daily using the city’s surveillance system, monitoring respiratory symptoms, fever, flu, and diarrhea reported by emergency department records from 38 hospitals in the city. Soon the system was routinely picking up patterns that were eluding frontline clinicians, including an outbreak of highly contagious norovirus.
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Suggested Reading
Kleinman KP Abrams AM Kulldorff M Platt R 2005 A model-adjusted space–time scan statistic with an application to syndromic surveillance Epidemiol Infect 133 3 409 419 15962547
Kulldorff M Heffernan R Hartman J Assunção R Mostashari F 2005 A space–time permutation scan statistic for disease outbreak detection PLoS Med 2 3 e59 15719066
National Bioterrorism Syndromic Surveillance Demonstration Project homepage , http://btsurveillance.org/
SaTScan homepage , http://www.satscan.org/
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Environ Health Perspect. 2005 Sep; 113(9):A610-A613
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7629ehp0114-00000116393649Commentaries & ReviewsA Case Study of Tire Crumb Use on Playgrounds: Risk Analysis and Communication When Major Clinical Knowledge Gaps Exist Anderson Mark E. 12Kirkland Katherine H. 3Guidotti Tee L. 4Rose Cecile 51 Department of Community Health Services, Denver Health, Denver, Colorado, USA2 Department of Pediatrics, University of Colorado Health Science Center, Denver, Colorado, USA3 Association of Occupational and Environmental Clinics, Washington, DC, USA4 Department of Environmental and Occupational Health, Mid-Atlantic Center for Child Health and the Environment, School of Public Health and Health Sciences, George Washington University Medical Center, Washington, DC5 Departments of Medicine/Preventive Medicine and Biometrics, National Jewish Medical and Research Center, Denver, Colorado, USAAddress correspondence to M.E. Anderson, 777 Bannock St., Mail Code 1911, Denver, CO 80204 USA. Telephone: (303) 436-4098. Fax: (303) 436-3056. E-mail:
[email protected] authors declare they have no competing financial interests.
1 2006 17 8 2005 114 1 1 3 30 9 2004 17 8 2005 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Physicians and public health professionals working with the U.S. Environmental Protection Agency’s Region 8 Pediatric Environmental Health Specialty Unit (PEHSU) received several telephone calls requesting information regarding the safety of recycled tire crumb as a playground surface constituent placed below children’s play structures. There were no reported symptoms or adverse health effects in exposed children. The literature available on the safety and risk of exposure to crumb rubber constituents was limited and revealed no information quantifying exposures associated with product use. Callers were informed by the PEHSU that no evidence existed suggesting harm from intended use of the product, but gaps in knowledge about the product were identified and communicated. Here the case of crumb rubber on playgrounds is used as a model to present an approach to similar environmental medicine questions. From defining the question, to surveying traditional and nontraditional resources for information, synthesis of findings, and risk communication, the case provides a model to approach similar questions.
childrenenvironmentPEHSUplaygroundrecycled tirerisk communicationtire crumb
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Case Presentation
The U.S. Environmental Protection Agency’s (EPA) Rocky Mountain region (Region 8) Pediatric Environmental Health Specialty Unit (PEHSU) received three telephone inquiries from parents concerned about health risks to children exposed to a recycled tire crumb product used as a soil additive, or “amendment,” on school playgrounds. A school district in the region had applied the product, which is made from recycled automobile tires, under outdoor play structures as an alternative to sand or wood chips. The crumbled tire amendment had the appearance of very small ball bearings or large, round grains of sand. One of the parents reported finding fibers in her child’s hair each day after school, and noted similar fibers in the lint collector of the family’s clothes dryer. The callers also reported seeing as the children played a visible haze in the air above the playground that used the tire crumb. No odor was reported with this haze, nor was the incident further characterized by time of day, moisture, or dust. The callers were aware of no illnesses among school children associated with exposure to the product, and specifically knew of no new or worsened respiratory illnesses such as asthma. None of the children of these concerned parents had reported skin, eye, lung, or mucous membrane irritation symptoms. One of the callers had attempted extensively to investigate the product and confessed to difficulty in her dealings with the manufacturer.
Discussion
The callers’ question regarding the safety of tire crumb for use on children’s playgrounds is appropriate, and the case highlights many of the difficulties that care providers and health advisors encounter in daily practice. Knowledge gaps are more typical than is established science, especially when children are the exposed population. This case report exemplifies the pathway that care providers can follow from the concerned parent’s question to risk communication regarding the exposure. Important steps along the pathway include defining the question, searching the literature for published information, searching for information from nontraditional sources such as the manufacturer, looking to relevant governmental agencies for information, synthesizing the information gathered, and finally, offering a summary of the information with recommendations to the parent.
The approach to the callers’ question begins by defining the question. The stated concern relates to use of a loose, crumbled product made from used tires. Children playing on tire crumb could potentially be exposed by ingestion of the product directly, by ingestion of surface water runoff through the product, by inhalation of dust, or by skin contact with the material or surface water runoff. An alternative crumb rubber product embeds the loose product into a resin to form a tile, which is placed over a hard surface and locked together with other tiles. Nonplayground uses include as an asphalt additive in road building and as an aggregate in concrete. Tire crumb contributes to the strength of concrete, and the product is reportedly lighter in weight than typical concrete (Pierce and Blackwell 2003). The Region 8 Agency for Toxic Substance Disease Registry (ATSDR) has done specific work suggesting that smokestack emissions produced by burning tire crumb to generate electricity are comparable with that of coal, with some minor differences (Willis et al. 2003). Conceivably, these industrial exposures should not be applicable to children. The many references to use of tire crumb were irrelevant to our central question regarding safety with its use on playground surfaces.
To examine further the known risks to children from exposure to the playground product, we turned to traditional published scientific literature and the network of PEHSUs in the United States. One study, done by investigators working in Alberta (Birkholz et al. 2003), examined the human and ecosystem hazard presented by tire crumb using in vitro mutagenicity assays. The associated hazard analysis suggested that the risk associated with playground use was very low. Toxicity to all of the aquatic organisms tested was observed in the fresh aqueous extract, but activity disappeared with aging of the tire crumb for 3 months in place on the playground. The investigators concluded that the use of tire crumb in playgrounds results in minimal hazard to children and the receiving environment, assuming intended use of the product, such as exclusive outdoor use and the presence of no solvents other than water. Regarding our central question of potential harm to children, the published literature contained some information about the product, including an in vitro toxicity model, but traditional published resources and a network of environmental health experts could not establish the product’s safety in use with children.
We then turned to additional resources for information: manufacturers and governmental resources such as the U.S. EPA and the ATSDR. Several states have published information regarding recycled tires and tire disposal (Arizona Department of Environmental Quality 2002; Moulton-Patterson et al. 2003; Texas Commission on Environmental Quality 2003). The Consumer Product Safety Commission had no information available on the crumb rubber product. However, after surveying these resources, we knew little more about the crumb rubber product in its playground application. A last search for information used the Google online search engine (http://www.google.com). This yielded multiple industry, federal, state, and local websites with information on the use of recycled tires and tire disposal. These resources failed to address our central question regarding children’s contact with the crumb rubber product on playgrounds.
Some literature was informative regarding use of the product and potential dangers. The crumb rubber on playgrounds case may typify environmental medicine cases where published information is available but not necessarily relevant. Research suggests that work with heated asphalt containing recycled tire crumb may expose workers to carcinogenic polycyclic aromatic hydrocarbons including benz[a]anthracene, chrysene, and their methylated derivatives (Watts et al. 1998). The tire crumb product has been studied for safety in its intended use as a playground surface amendment using the methods of risk assessment, genotoxicity assays, and ecotoxicity assays. The investigators concluded that it probably would not represent an exposure hazard for children or risk to the environment (Birkholz et al. 2003). An epidemiologic study to validate the findings on human risk has not been feasible.
Compiling the information gathered into a statement of potential risk requires a balanced presentation of benefits and problems involved with a given product. As for the tire crumb product, several potential advantages exist in its use as a playground surface amendment. Although most discarded tires are placed into landfills to degrade slowly, economic use of tire crumb diverts old tires from landfills and piles where they present serious hazards. Stockpiled tires in landfills can contribute to fires that are difficult to extinguish, releasing combustion products (e.g., benzene, other volatile organic hydrocarbons, and dioxins) into the air. The hollow structure of a tire creates a breeding space for human disease vectors. Advantages are that the crumb product is lightweight and cost-effective according to school district users. The manufacturer claims that the product has a superior degree of cushioning against falls, the main purpose of its use below play structures. Direct application is simple and cheap, much like that of sand: Simply shovel it into place.
Despite potential advantages in terms of injury prevention and waste recycling, the use of recycled tire crumb products on playgrounds has had little health investigation. The major unresolved concern is the potential for latex allergy with short-term dermal exposure. Latex is a known airway and dermal sensitizer, but the vulcanized chemistry of tire manufacture should destroy these allergens. Latex allergens have been identified as components of urban air, and the potential risk of tire crumb must be distinguished from the potential risk of “tire dust,” which has been considered a possible hazard in urban air pollution. A study of particulate air pollutants in Denver, Colorado, found black respirable particulates that were identified as airborne tire fragments (Williams et al. 1995). Williams et al. (1995) suggest that these respirable “tire dust” particles may contribute to the pathogenesis of lung diseases related to air pollution. Whether respirable particles are created during regular use of the tire crumb product requires further investigation, given that a high amount of energy is required to create smaller crumb rubber particles. Reports of haze while children played on the applied product may or may not be related.
The process of risk communication should include limited use of vernacular and, most important, an open offer to maintain communication. Success should not be measured in “closing the case” but in gaining the confidence of the callers so that information continues to flow in a meaningful and productive manner. This is important not only for care providers who may encounter these difficult questions, but also for governmental and nongovernmental entities to maintain productive communication with concerned callers. In our crumb rubber case, the callers expressed frustration in communications with the manufacturer. If true, this represents an error on the part of the manufacturer because this only creates enmity, may fuel further questioning, and does not promote a meaningful process of inquiry.
The pathway from question to risk communication is lengthy, time consuming, and is not practical for a typical provider to follow, unless specific expertise or interest exists. The crumb rubber case involved input from the providers working with the Region 8 PEHSU, the national PEHSU network, Region 8 EPA, and ATSDR, the Consumer Product Safety Commission, and the manufacturer. Searching the literature was time-consuming and did not yield an answer to the question. Although time-consuming and sometimes not fruitful, the process is an important one. Any initiatives that join together professionals with specific expertise and care providers who encounter the initial questions are valuable in that they allow a quick inquiry and should be used extensively. The case demonstrates the wisdom of the PEHSU network.
Conclusion
Environmental health professionals commonly encounter questions regarding the safety of a particular product or substance. Although the literature may yield some answers, knowledge gaps are common, and networks such as the national PEHSUs can be invaluable resources in the search for information. We present here a case involving use of crumb rubber tire as a surface amendment on playgrounds as a model case where the published literature did not contain the needed answers. We demonstrate a process, also shown in Figure 1, from defining and researching the question to risk communication with concerned callers or parents.
No published information is available specifically regarding exposure to crumb rubber constituents from use of the product on playgrounds. The research by Birkholz et al. (2003) addressed many of the potential concerns but did not include evaluation of actual playground use. The product has several obvious advantages including the useful recycled use of a product that is otherwise discarded and improved mechanical safety under playground equipment. Some published information was available discussing use of the product in asphalt installation, for example. Risks may exist in working with the product, but the question regarding hazards posed to children playing on the amended playgrounds is left unanswered. Clearly, more investigation is needed, and efforts such as those by the California Integrated Waste Management Board to look at the use of tire crumb and the potential for release of respirable particles are timely and welcome.
Communication with callers or parents regarding the information and study on the product and the development of a message regarding potential risk is the final step in the investigation process. The risk message should include a straightforward statement regarding what is known and offer an open channel of communication regarding continued concern with the product. The role for entities such as the regional PEHSUs is critical to this process because it requires time and the special expertise that may not be directly available to typical care providers. Providers encountering children who may be suffering illness related to exposure to products such as tire crumb should involve their environmental health colleagues and federal, state, and local resources extensively.
We acknowledge the many professionals working in the national Pediatric Environmental Health Specialty Unit program along with their government partners.
Figure 1 The process of risk communication. Crafting and communicating a message regarding risk with a specific exposure begins by defining the exposure, searching traditional and nontraditional resources, weighing the gathered information, crafting the message, and maintaining an open channel of communication around the potential exposure.
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References
Arizona Department of Environmental Quality 2002. Waste Tire Report. Phoenix, AZ:Arizona Department of Environmental Quality.
Birkholz D Belton K Guidotti T 2003 Toxicological evaluation for the hazard assessment of tire crumb for use in public playgrounds J Air Waste Manag Assoc 53 903 907 12880077
Moulton-Patterson L Medina J Jones SR Paparian M Peace C Washington C 2003. Five Year Plan for the Waste Tire Recycling Management Plan 620-03-007. Sacramento, CA:State of California Integrated Waste Management Board.
Pierce C Blackwell M 2003 Potential of scrap tire rubber as lightweight aggregate in flowable fill Waste Manag 23 197 208 12737962
Texas Commission on Environmental Quality 2003. Using Scrap Tires and Crumb Rubber in Highway Construction in Texas. SFR-069/03. Houston:Texas Commission on Environmental Quality.
Watts R Wallingford K Williams R House D Lewtas J 1998 Airborne exposures to PAH and PM2.5 particles for road paving workers applying conventional asphalt and crumb rubber modified asphalt J Expo Anal Environ Epidemiol 8 213 229 9577752
Williams P Buhr M Weber R Volz M Koepke J Selner J 1995 Latex allergen in respirable particulate air pollution J All Clin Immunol 95 88 95
Willis BC Poulet C Kowalski PJ 2003. Health Consultation, CEMEX, Inc. Technical Report. Atlanta, GA:Agency for Toxic Substance Disease Registry.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8194ehp0114-00000416393650Commentaries & ReviewsRecent Applications of DNA Microarray Technology to Toxicology and Ecotoxicology Lettieri Teresa Laboratory of Molecular Ecotoxicology, Inland and Marine Water Unit, Institute for Environment and Sustainability, Joint Research Centre of the European Commission, Ispra, ItalyAddress correspondence to T. Lettieri, Laboratory of Molecular Ecotoxicology, Inland and Marine Water Unit, Institute for Environment and Sustainability, Joint Research Centre of the European Commission, 21020, Ispra (VA), Italy. Telephone: 39-0332-789868. Fax: 39-0332-785212. E-mail:
[email protected] author declares she has no competing financial interests.
1 2006 9 8 2005 114 1 4 9 11 4 2005 9 8 2005 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Gene expression is a unique way of characterizing how cells and organisms adapt to changes in the external environment. The measurements of gene expression levels upon exposure to a chemical can be used both to provide information about the mechanism of action of the toxicant and to form a sort of “genetic signature” for the identification of toxic products. The development of high-quality, commercially available gene arrays has allowed this technology to become a standard tool in molecular toxicology. Several national and international initiatives have provided the proof-of-principle tests for the application of gene expression for the study of the toxicity of new and existing chemical compounds. In the last few years the field has progressed from evaluating the potential of the technology to illustrating the practical use of gene expression profiling in toxicology. The application of gene expression profiling to ecotoxicology is at an earlier stage, mainly because of the the many variables involved in analyzing the status of natural populations. Nevertheless, significant studies have been carried out on the response to environmental stressors both in model and in nonmodel organisms. It can be easily predicted that the development of stressor-specific signatures in gene expression profiling in ecotoxicology will have a major impact on the ecotoxicology field in the near future. International collaborations could play an important role in accelerating the application of genomic approaches in ecotoxicology.
ecotoxicologygene expression profilegenetic signaturemicroarraytoxicogenomicstoxicology
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Gene expression is a sensitive indicator of toxicant exposure, disease state, and cellular metabolism and thus represents a unique way of characterizing how cells and organisms adapt to changes in the external environment. The measurement of gene expression levels upon exposure to a chemical can both provide information about the mechanism of action of toxicants and form a sort of “genetic signature” from the pattern of gene expression changes it elicits both in vitro (Burczynski et al. 2000; Waring et al. 2001) and in vivo (Hamadeh et al. 2002). The development of such gene expression signatures would allow fast screening of unknown or suspected toxicants on the basis of their similarity to known toxicants.
The possibility of analyzing the effect of chemicals and environmental stressors on a large number of genes in a single experiment has led to the development of the field of toxicogenomics. Proponents of toxicogenomics aim to apply both mRNA and protein expression technology to study chemical effects in biological systems (Afshari et al. 1999; Lovett 2000; Olden and Guthrie 2001).
The availability of the complete human genome and of the genome of several other organisms (NCBI 2005b) allows the application of microarray technology to several model organisms (from bacteria, to yeast, to fish) and mammalian cell lines.
In this review I evaluate the potential of microarray technology for ecotoxicology. I briefly review recent applications of DNA microarray to toxicology and analyze how the field of ecotoxicology could benefit from the experience already gained from toxicology.
I describe examples of the contribution of the technique in addressing important ecotoxicology problems as well as problems and limitations associated with the technique. Finally, I suggest future paths for more extensive application of microarray to ecotoxicology.
This is not a comprehensive review of the current state of the art in DNA microarray technology; several exhaustive reviews are available on both the practical aspects of DNA microarrays and the analysis of data (Knudsen 2004; Schena 1999, 2003; Schulze and Downward 2001).
Overview of Gene Expression Analyses
The field of DNA microarray has evolved from Ed Southern’s key insight (Southern 1975) 25 years ago showing that labeled nucleic acid molecules could be used to interrogate nucleic acid molecules attached to a solid support. The resulting Southern blot is considered to be the first DNA array (Southern 2000). It was only a small step to improve the technique to filter-based screening of clone libraries, which introduced a one-to-one correspondence between clone and hybridization signal (Grunstein and Hogness 1975). The next advance was the use of gridded libraries stored in microtiter plates and stamped onto filters in fixed positions. With this system, each clone could be uniquely identified and information about it accumulated. Several groups explored expression analysis by hybridizing mRNA to cDNA libraries gridded on nylon filters. The subsequent explosion of array technologies was sparked by two key innovations. The first was the use of nonporous solid support, such as glass, which has facilitated the miniaturization of the array and the development of fluorescence-hybridization detection (Lockhart et al. 1996; Schena et al. 1995, 1996). The second critical innovation was the development of methods for high-density spatial synthesis of oligonucleotides, which allows the analysis of thousands of genes at the same time. Recently, a significant technical achievement was obtained by producing arrays with more than 250,000 oligonucleotides probes or 10,000 different cDNAs per square centimeter (Lipshutz et al. 1999). DNA microarrays are fabricated by high-speed robots, generally onto glass. Because the DNA cannot bind directly to the glass, the surface is first treated with silane to covalently attach reactive amine, aldehyde, or epoxies groups that allow stable attachment of DNA, proteins, and other molecules.
The nucleic acid microarrays use short oligonucleotides [15–25 nucleotides (nt)], long oligonucleotides (50–120 nt), and PCR-amplified cDNAs (100–3,000 bp) as array elements. The short oligonucleotides are used primarily for the detection of single-nucleotide polymorphisms (SNPs). Because this application requires the discrimination of only one mismatch, the presence of a short oligonucleotide maximizes the destabilization caused by mis-pairing (Lockhart et al. 1996). Conversely, the PCR-amplified cDNAs produce strong signals and high specificity (DeRisi et al. 1996). The cDNA elements are readily obtained from cDNA libraries and are typically used for organisms for which only a limited part of the whole genome information is available. The long nucleotides offer strong hybridization signal, good specificity, unambiguous sample identification, and affordability (Hughes et al. 2000; Kane et al. 2000; Schena et al. 1998). All these advancements have allowed gene arrays to become a standard tool in molecular toxicology. With this technology, cells or tissues are exposed to toxicants, and then gene expression is measured by collecting mRNA, converting mRNA to labeled cDNA, hybridizing it to the DNA array, staining it with an appropriate dye, and visualizing the hybridized genes using a fluorometer (DeRisi et al. 1996; Lashkari et al. 1997; Schena et al. 1995) (Figure 1). The raw data are analyzed using bioinformatics software and databases. The aim is to obtain meaningful biological information such as patterns of relative induction/repression levels of gene expression, participation in biochemical pathways, and (in the most favorable cases) “genetic signatures.”
Recent Applications of DNA Microarrays to Toxicology
The field of toxicogenomics has progressed rapidly since the application of DNA chips to toxicology was proposed in the late 1990s (Afshari et al. 1999). Publications have evolved from evaluating the potential of the technology (Burchiel et al. 2001; Fielden and Zacharewski 2001; Nuwaysir et al. 1999; Simmons and Portier 2002; Smith 2001; Tennant 2002; Ulrich and Friend 2002) to illustrating the practical use of gene expression profiling in toxicology (Bartosiewicz et al. 2001; Bulera et al. 2001; Hamadeh et al. 2002; Waring et al. 2001).
Waring et al. (2001) analyzed the hepatic effects of a new chemical substance that inhibits the expression of cellular adhesion proteins. They treated rats for 3 days and then performed the microarray analyses on RNA from livers of treated animals. The comparison of the gene expression profile with a database of profiles of known hepatotoxins indicated that hepatic toxicity of the new chemical is mediated by the aryl hydrocarbon nuclear receptor. Hamadeh et al. (2002) analyzed the patterns of gene expression in liver tissue taken from rats exposed to different chemicals. Their analysis revealed similarities in gene expression profiles between animals treated with different chemicals belonging to the same class of compounds (peroxisome proliferators). In contrast, animals treated with a different class of compounds (enzyme inducers) showed a very distinctive gene expression profile.
To expand the use of microarray technology in toxicology, several national and international initiatives have been developed to better standardize and harmonize the technology. One of the early concerns about the use of DNA microarray in toxicology has been how to properly compare experiments that use a wide variety of commercial and proprietary platforms, protocols, and analysis methods. In the United States, the National Institute of Environmental Health Sciences (NIEHS) has created the National Center for Toxicogenomics (NCT) to provide a reference system of genomewide gene expression data and to develop a knowledge base of chemical effects in biological systems (Tennant 2002). The NCT has conducted some proof-of-principle experiments to establish signature profiles of known toxicants and to link the pattern of altered gene expression to specific parameters of conventional indices of toxicity (Hamadeh et al. 2002). These studies show that it is possible to identify a signature of expressed gene patterns after exposure to a given toxicant (Tennant 2002).
The Health and Environmental Sciences Institute (HESI) of the International Life Sciences Institute (ILSI) has coordinated an international study involving more than 30 pharmaceutical companies and governmental and academic institutions to evaluate the harmonization of gene expression data and analyses (Pennie et al. 2004). In the ILSI Application of Genomics to Mechanism-Based Risk Assessment project, common pools of RNA were analyzed in more than 30 different laboratories using both similar and different technical platforms. Overviews of the design and objectives of the experimental program and more technical articles have been published in the mini-monograph Application of Genomics to Mechanism-Based Risk Assessment (Environmental Health Perspectives 2004). Amin et al. (2004) identified gene markers of renal toxicity, and Thompson et al. (2004), markers of cisplatin nephrotoxicity. Two research groups performed an interlaboratory evaluation of clofibrate-induced gene expression changes in rat liver (Baker et al. 2004) and of rat hepatic gene expression changes induced by methapyrilene (Waring et al. 2004). In addition three research groups have published overviews on the interlaboratory collaborations to evaluate the effects of nephrotoxicants (Kramer et al. 2004), genotoxic chemicals (Newton et al. 2004), and hepatotoxicants (Ulrich et al. 2004) on gene expression.
The experimental programs have shown that a) patterns of gene expression relating to biological pathways are robust enough to allow insight into mechanisms of toxicity, b) gene expression data can provide meaningful information on the physical location of the toxicity, c) dose-dependent changes can be observed, and d) concerns about oversensitivity of the technology may be unfounded (Pennie et al. 2004).
Very recently, DNA microarrays have been used to develop a much deeper insight into the mechanism of chemical toxicity at the molecular level. Andrew et al. (2003) used cDNA microarrays to compare the effects of arsenic, nickel, chromium, and cadmium on the expression of 1,200 human genes in human bronchial BEAS-2B cells. Cells were exposed both to low doses of the different metals and to a cytotoxic dose of sodium arsenite. Metal exposure modified only a small subset of the 1,200 genes, and each metal modified the expression of a largely unique set of genes; thus, these results could provide the basis for the development of metal-specific biomarkers. Exposure to low concentrations of sodium arsenite modified the expression of genes involved in transcription factors, inflammatory cytokines, kinases, and DNA repair. High doses of sodium arsenite gave a very different profile, modifying the expression levels of genes codifying for heat-shock proteins and other genes involved in stress-response pathways. The researchers suggested that this change in gene expression profiles represents a switch from a survival-based biological response at the lower dose to a cell-death–inducing apoptotic response at the higher dose.
Gene expression profiling has been used to show that the specific genes repressed or induced upon exposure to a toxic stress vary depending on the cell type and the type of toxicants to which the cells were exposed (Troester et al. 2004). The researchers cultured separate breast cancer cell lines known to have distinct responses to two chemotherapeutic drugs: doxorubicin (DOX), and 5-fluorouracil (5-FU). Different cell lines (two basal-like and two luminal epithelial) were treated with toxic concentrations of DOX and 5-FU, and then mRNA was extracted and analyzed. Gene expression profiling identified those genes that had been up- or down-regulated and showed a characteristic pattern of gene expression in response to DOX and 5-FU in each cell type. Detailed analyses identified a subset of 100 genes that could be used to differentiate between DOX-treated and 5-FU–treated samples.
Ezendam et al. (2004) fed Brown Norway rats with two different concentrations (low and high doses) of hexachlorobenzene (HCB) for 4 weeks, and then mRNA from several tissues was collected and analyzed. The most significant changes in gene expression, relative to the control group, occurred in spleen, followed by liver, kidney, and mesenteric lymph nodes. The gene expression profiling confirmed known effects of HCB on the immune system and induction of enzymes involved in drug metabolism and reproduction. In addition they found new up-regulated genes encoding proinflammatory cytokines, antioxidants, acute-phase proteins, complements, chemokines, and cell adhesion molecules.
A recent article clearly highlights one of the problems with using DNA microarrays. To analyze the effect of sampling differences on transcriptional profiling, investigators treated male Fischer 344 rats with toxic and nontoxic doses of acetaminophen and took liver samples of their left and median lobes (Irwin et al. 2005). Transcript profiling using microarrays showed clear differences between the left and median lobes of liver, both at low doses and at doses that cause hepatotoxicity. The two lobes of liver showed clear differences both in the pattern of gene expression and in the total number of repressed or enhanced genes.
Public Databases for DNA Microarray Experiments
Because of the various methodologies for arraying genes and assessing mRNA expression levels, and different bioinformatics tools for the management and analyses of the data, investigators quickly realized the need to establish standards for recording and reporting microarray-based gene expression data. To this end, the Minimum Information about a Microarray Experiment (MIAME) guidelines (Brazma et al. 2001) have been developed at the European Bioinformatics Institute (EBI). This standard describes the minimum information required to ensure that microarray data can be easily interpreted and that results derived from its analysis can be independently verified.
Several public repositories of microarray gene expression data have been developed to store the results of array experiments: Array-Express (Brazma et al. 2003) in Europe, Gene Expression Omnibus (GEO) in the United States (Edgar et al. 2002), and the Center for Information Biology Gene Expression Database (CIBEX) (Ikeo et al. 2003) in Japan. Several journals already require an accession number (indicating that a data set has been submitted to one of these public databases) before publication, and there are increasing calls for mandatory submission of microarray data to a public database before publication (Ball et al. 2004).
Several initiatives aim to extend the scope of public databases of microarray data to incorporate toxicology and biologic end points. These toxicogenomics databases are being developed with the goal of creating a knowledge base to support genomic applications in hazard identification (Mattes et al. 2004). Two international consortia are developing public toxicogenomics databases with extensive cross-links to existing biological information and annotation: Tox-MIAMExpress is being developed at EBI, and the Chemical Effects in Biological Systems (CEBS) database (Waters et al. 2003) is being developed at NCT (Table 1). The CEBS knowledge base is designed to meet the information needs of “systems toxicology” involving the study of perturbation by chemicals and stressors, monitoring changes in molecular expression and conventional toxicologic parameters, and iteratively integrating biological response data to describe the functioning organism. If successfully implemented with the appropriate depth of data content, such databases could serve as robust resources for advanced queries.
Publicly available software tools have been developed to help in the interpretation and analyses of DNA microarray data. ArrayTrack (Tong et al. 2004), developed at the National Center for Toxicological Research (NCTR) of the Food and Drug Administration, is public microarray data management and analysis software. It provides data management, visualization tools, and functional information about genes, proteins, and pathways drawn from various public biological databases for facilitating data interpretation.
Recent Applications of DNA Microarrays to Ecotoxicology
One challenge facing ecotoxicology is to understand the mechanism of action of toxicants on living organisms (Snape et al. 2004). Such knowledge would help to develop predictive simulation models of toxic effects, to link molecular biomarkers with population-level effects, and then to anticipate ecologic risk assessment issues for new chemicals. Gene expression profiles represent the primary level of integration between environmental factors and the genome, providing the basis for protein synthesis, which ultimately guides the response of organisms to external changes. Thus, the analysis of gene expression changes is a powerful tool both to diagnose major stressors in natural populations and to analyze the mechanisms of such stress responses.
Using gene expression profiles in ecotoxicology requires careful planning of experimental protocols that should take into proper account possible sources of variations in gene expression in natural populations. In fact, differences in gene expression due to sex, genotype, age, and intrinsic genetic variability can be quite substantial (Jin et al. 2001; Oleksiak et al. 2002; Ranz et al. 2003; Townsend et al. 2003).
DNA microarray technology has been applied extensively to the analyses of natural and anthropogenic factors in yeast for which whole-genome chips have been available for a few years (Causton et al. 2001; Gasch et al. 2000; Momose and Iwahashi 2001). Causton et al. (2001) analyzed how the whole genome of yeast is remodeled in response to environmental stressors such as temperature, pH, oxidation, and nutrients. The stress response was dependent on the level of the stress and showed an additive effect for multiple stressors. Similar results were found when using different stressors such as temperature shock, amino acid starvation, nitrogen source depletion (Gasch et al. 2000), and cadmium (Momose and Iwahashi 2001). The same approach has been used to characterize the alteration of gene expression in yeast induced by the pesticide thiuram (Kitagawa et al. 2002). The results obtained for stress response in yeast likely will provide a reference frame for similar experiments with more complex organisms.
Custom-made microarrays have been used to understand responses to endocrine modulators in zebrafish (Hoyt et al. 2003). Zebrafish embryos were exposed in vitro to the environmental contaminant 4-nonylphenol (4NP). The gene expression profiling (using a custom-made microarray with 230 genes) identified a set of 9 genes associated with the function of estrogen response that is indicative of embryo exposure to 4NP even at low concentrations. A similar approach has been used to study the gene expression profiling in response to environmental stressors in the typical plant model organism Arabidopsis thaliana. Using a cDNA microarray containing about 7,000 genes, Seki et al. (2002) determined the expression profiles under drought, cold, and high-salinity conditions. Their analysis revealed a subset of 53, 277, and 194 genes that were differentially expressed > 5-fold after cold, drought, and high-salinity treatments, respectively. A set of 22 stress-inducible genes was found to respond to all three stress conditions. In a similar study the oxidative stress caused by high ozone concentrations has also been analyzed in A. thaliana (Ludwikow et al. 2004) with DNA microarray. A review of the applications of DNA microarrays for expression profiling under stress conditions in A. thaliana has been recently published by Seki et al. (2004) of the Riken Genomic Sciences Center in Kanagawa, Japan.
The application of gene expression profiles is not limited to model organisms for which the complete (or almost complete genome) is available. Several strategies are available to apply a genomic approach to species for which only a limited amount of genomic information is available. One possibility is heterologous hybridization. In fact, because of the length of the probes, cDNA microarrays can be used in heterologous hybridizations across strains and closely related species as long as sequence divergence is limited for a given gene (Rise et al. 2004b). For example, Hittel and Storey (2001) have used this approach to study the molecular basis of traits, such as hibernation, not present in model species. More recently, Renn et al. (2004) have used heterologous hybridization to study gene expression profiling across a wide range of different species of African cichlid fish.
Another possible approach consists of identifying stress-induced genes using special techniques based on PCR, such as differential display PCR, suppressive subtractive hybridization PCR, and representational difference analyses. The application of these techniques to ecotoxicology has been reviewed recently by Snell et al. (2003).
Gracey et al. (2001) used cDNA microarrays to identify hypoxia-induced genes in a nonmodel fish for which sequence data were unavailable. Their analysis revealed that although some changes in gene expression mirror the changes that occur in mammals, novel genes are differentially expressed in fish and tissue-specific patterns of gene expression occur during hypoxia.
Larkin et al. (2003) described an expression profiling model system for endocrine-disrupting compounds that mimic estrogens. The research group created a gene array by cloning 30 genes from sheepshead minnows. The genes had been previously identified by differential display reverse transcriptase PCR, a method that screens thousands of RNA messages to identify genes that are turned on or off by specific treatments. They treated the fish with a constant concentration of weak and strong environmental estrogens and then determined which genes were differentially expressed in the livers of treated and control fish. They found a group of genes that were up-regulated by all the tested compounds, while other genes showed differential expression only in response to a specific compound. Exposure to different concentrations of the strong estrogen 17α-ethynyl estradiol revealed that changes in gene expression levels are dose sensitive and that exposure thresholds vary for different genes. A similar approach has been used to identify alterations in gene expression due to exposure to androgen hormones in largemouth bass fishes (Blum et al. 2004).
Williams et al. (2003) used a cDNA microarray-based approach to analyze the expression level changes of recognized biomarkers in a relevant fish species, European flounder (Platichthys flesus). They arrayed 160 genes, of which 110 were already documented in the literature as biomarkers of toxic response in fishes and mammals. Five adult males and five adult feral females P. flesus were caught from the Tyne (polluted) and the Alde (unpolluted) estuaries in the United Kingdom. Gene expression analysis revealed that 11genes were expressed differently in males between the Tyne and Alde. Such differences were not statistically significant in females, probably because of interindividual variations. Vitellogenin levels differed radically among the female fish, suggesting that their reproductive cycles were at different stages.
Despite the lack of extensive genomic data, invertebrates are the subject of increased interest. Because of their characteristics, estuarine amphipods typically are used to assess the ecologic risk associated with contaminated sediments. Perkins and Lotufo (2003) isolated several genes from Leptocheirus plumulosus and developed a quantitative assay to measure the effects of water exposure to 2,4,6-trinitrotoluene and phenanthrene on gene expression. They found that expression of the genes for actin and a retrotransposone element, hopper, were dependent on the exposure and tissue concentrations of those chemicals. Diener et al. (2004) have optimized a protocol for differential display PCR to investigate gene expression in Daphnia magna. Their protocol requires submicrogram amounts of total RNA and fewer than 10 animals and thus could provide a significant technical improvement for gene expression analyses of toxicant exposure.
Several efforts are focusing on the detection of pathogen infection in different animal species. Panicker et al. (2004) developed a gene array for detection of pathogenic Vibrio species, which are natural inhabitants of warm coastal waters and shellfish. Recently, microarray analysis has also been applied successfully to identify molecular markers of pathogen infection in salmon (Rise et al. 2004a). This analysis identified transcripts induced and repressed by the pathogen, thus providing insights into the host response to the infection and into the mechanisms used by the pathogen to evade such response.
Limitations of DNA Microarrays in Ecotoxicology
The enormous potential that lies in the successful incorporation of genomic data into ecotoxicology faces several challenges. The major challenge is the difficult task of taking into account intrinsic sources of variability in gene expression levels due to different physiologic states, age, sex, and genetic polymorphisms in natural populations. Somewhat related is the additional problem of properly interpreting array data in the presence of such large intrinsic variations and then relating changes in gene expression to significant ecotoxicologic parameters (i.e., at the population level) such as survival, growth, and reproduction. A second major limitation is the high cost associated with the technology itself. These costs render repeat measures very expensive, and thus often only limited experimental data are available.
The expression of certain genes can vary considerably even under tightly controlled experimental conditions. Fay et al. (2004) observed that around 400 genes were differentially expressed when analyzing nine different strains of Saccharomyces cerevisiae. To minimize the effect of genetic polymorphisms on gene expression levels, investigators usually detemine the toxic properties of chemicals using inbred strains of mice and rats or well-characterized strains of yeast. In natural populations of non-model organisms, two approaches can be used to determine the “normal” gene expression patterns (Neumann and Galvez 2002). Pooling RNA samples from a large number of individuals in the control group will provide a measure of the mean gene expression response. This approach has the advantage of requiring a low number of microarrays, thereby reducing the overall cost of the experiment, but it does not provide any information about the distribution of responses in the natural population. The other, more expensive approach consists of measuring gene expression patterns for each individual in the control population. This approach makes it possible to obtain both the mean expression pattern and its distribution.
An additional problem is the limited availability of DNA arrays for nonmodel organisms (Table 2). Even if several techniques are available to identify subsets of genes that respond to environmental stressors, the lack of whole-genome arrays does not allow use of the full potential of microarrays. From this point of view, it is reassuring that the number of fully sequenced genomes is expanding very fast. For example, the recent sequencing of the diatom algae Thalassiosira pseudonana (Armbrust et al. 2004) has added to the list a very important organism for ecotoxicology studies.
One of the best ways to advance the field is for investigators to focus on more precise objectives (Snell et al. 2003): identify conserved genes that are up-regulated in response to toxicant exposure, determine how these gene expression profiles can be used to diagnose stressors, and identify those genes that are most informative to incorporate into stress gene arrays.
Conclusions
The application of gene expression analysis to toxicology is now a mature science. The field has rapidly progressed from the proof-of-principle phase to actual applications, and gene expression profiling is now being used in screening for toxicity of new and existing chemical compounds. It can be predicted with confidence that in the future, gene expression data will also be incorporated in the regulatory arena as soon as the relevant agencies establish the regulatory framework. The national and international collaborations (e.g., HESI and NCT) that have tested the capabilities and interlaboratory reproducibility of gene expression data have played an important role in this rapid progress.
The application of this technology to ecotoxicology is at an earlier stage compared with that of toxicology, mainly because of the more complex problem and the many variables involved in analyzing the status of natural populations in a real ecosystem. Investigators have obtained good results using DNA microarrays in ecotoxicology both with model and with nonmodel organisms. In particular, stressor-specific microarrays have now been developed, and more will likely be available in the near future.
International collaborations will play an important role in accelerating the pace of discoveries and the application of gene chip technology to urgent problems in ecotoxicology. The experience gained from the ILSI genomic project (Pennie et al. 2004) clearly shows the advantages of interlaboratory comparison tests in terms of validation of the technology. Such international collaborations will help to spread best laboratory practices and expertise and should speed up the adoption of these new techniques by research laboratories and by the regulatory agencies.
Despite all the complications described in this article, development of stressor-specific signatures in gene expression profiling in ecotoxicology will have a major impact on the field.
I thank U. Cullinan for help with the English revision of the manuscript.
Figure 1 Gene expression analyses by microarray. (A) One-color expression analysis uses a single fluorescent label and two arrays to generate expression profiles for two cell or tissue samples (test and reference samples). Activated and repressed genes are obtained by superimposing images obtained by the two arrays. (B) Two-color expression analysis uses two different fluorescent labels and a single array to generate expression profiles for the test and reference samples. Activated and repressed genes are obtained by superimposing images generated in different channels on a single array. In both cases the monochrome images from the scanner are imported into software in which the images are pseudocolored and merged. Data are viewed as a normalized ratio in which significant deviation from 1 (no change) indicates increased (> 1) or decreased (< 1) level of gene expression relative to the reference sample.
Table 1 List of cited databases and repository services.
Acronym Full name Website and reference
ArrayExpress ArrayExpress at EBI www.ebi.ac.uk/arrayexpress (EBI 2005a)
GEO Gene Expression Omnibus www.ncbi.nlm.nih.gov/geo (NCBI 2005a)
CIBEX Center for Information Biology
Gene Expression Database cibex.nig.ac.jp (National Institute of Genetics 2005)
Tox-MIAMExpress Toxicogenomics MIAMExpress www.ebi.ac.uk/tox-miamexpress (EBI 2005b)
CEBS Chemical Effects in Biological Systems cebs.niehs.nih.gov (NIEHS 2005)
ArrayTrack NCTR’s Center for Toxicoinformatics-ArrayTrack www.fda.gov/nctr/science/centers/toxicoinformatics/ArrayTrack (NCTR 2005)
Table 2 List of commercially available gene chips.
Organism Company Organism Company
Escherichia coli Affymetrix Bos taurus Affymetrix
Sigma-Genosys Canis familiaris Affymetrix
Takara Mus musculus Affymetrix
Bacillus subtilis Affymetrix Agilent
Sigma-Genosys Sigma-Genosysb
Pseudomonas aeruginosa Affymetrix SuperArrayb
Helicobacter pylori Sigma-Genosys Rattus norvegicus Affymetrix
Mycobacterium tuberculosis Sigma-Genosys Agilent
Staphylococcus aureus Affymetrix SuperArray
Synechocystis sp. Takara Takarac
Saccharomyces cerevisiae Affymetrix
Agilent Homo sapiens Affymetrix
Agilent
Magnaporthe grisea Agilent Genotypic
Plasmodium falciparum Affymetrix Sigma-Genosysb
Anopheles gambiae Affymetrix SuperArrayb
Caenorhabditis elegans Affymetrix Arabidopsis thaliana Affymetrix
Drosophila melanogaster Affymetrix Agilent
Micropterus salmoides EcoArraya Takara
Pimephales promelas EcoArraya Glycine max L. Affymetrix
Danio rerio Affymetrix Oryza sativa Agilent
Agilent Hordeum vulgare L. Affymetrix
Xenopus laevis Affymetrix Vitis vinifera Affymetrix
Company addresses are as follows: Affymetrix: Santa Clara, California, USA; Agilent: Palo Alto, California, USA; EcoArray: Alachua, Florida, USA; Genotypic: Bangalore, India; Sigma-Genosys: The Woodlands, Texas, USA; SuperArray: Frederick, Maryland, USA; Takara: Otsu Shiga, Japan.
a The bass and fathead minnow chips contain many genes important for toxicology response, including vitellogenin and several cytochrome P450s, among others.
b The cDNA or oligo microarrays have been designed to profile the expression of multiple genes involved in a specific biological pathway, or genes with similar functions or structural features. Mouse and human cDNA microarrays are also available for toxicology and pharmacology applications. This type of array is designed to determine the expression profile of genes responsible for metabolism of endogenous and exogenous compounds.
c This is a glass slide array immobilized with approximately 390 cDNA fragments of rat genes related to the stress and toxicity responses.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7940ehp0114-00001016393651Commentaries & ReviewsA Case for Revisiting the Safety of Pesticides: A Closer Look at Neurodevelopment Colborn Theo 121 University of Florida, Gainesville, Florida, USA2 TEDX (The Endocrine Disruption Exchange) Inc., Paonia, Colorado, USAAddress correspondence to T. Colborn, PO Box 1253, Paonia, CO 81428, USA. Telephone: (970) 527-6548. E-mail:
[email protected] author is employed by The Endocrine Disruption Exchange, Inc., a nonprofit organization whose goal is to reduce exposure to substances that interfere with development and function.
1 2006 7 9 2005 114 1 10 17 17 1 2005 7 9 2005 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. The quality and quantity of the data about the risk posed to humans by individual pesticides vary considerably. Unlike obvious birth defects, most developmental effects cannot be seen at birth or even later in life. Instead, brain and nervous system disturbances are expressed in terms of how an individual behaves and functions, which can vary considerably from birth through adulthood. In this article I challenge the protective value of current pesticide risk assessment strategies in light of the vast numbers of pesticides on the market and the vast number of possible target tissues and end points that often differ depending upon timing of exposure. Using the insecticide chlorpyrifos as a model, I reinforce the need for a new approach to determine the safety of all pesticide classes. Because of the uncertainty that will continue to exist about the safety of pesticides, it is apparent that a new regulatory approach to protect human health is needed.
adverse effectsbehaviorchlorpyrifosfetal developmenthuman functionneurodevelopmentpesticidestoxicity
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The U.S. Environmental Protection Agency’s (EPA) Office of Pesticide Programs (OPP) estimated that 891 pesticide active ingredients were registered in 1997 (Aspelin and Grube 1999) and that 888 million pounds of pesticide active ingredients were used in the United States in 2001 (Kiely et al. 2004). Few of these chemicals are applied alone but rather are applied in formulations using different combinations of several pesticide active ingredients (MeisterPRO 2004). It is not uncommon for many classes of pesticides, such as insecticides, herbicides, and fungicides, to be used on the same crop (National Agricultural Statistics Service 2005). In the case of insecticides, an adjuvant is often added to the formulations to enhance the intensity of the lethal effect. In the case of herbicides, due to the increasing incidence of plant tolerance to a specific pesticide, some formulations now have as many as three active ingredients (MeisterPRO 2004). Each active ingredient has a specific mode of action for controlling a pest, and each active ingredient has its own possible side effects on the wildlife and humans exposed to it. It is impossible to determine the cumulative risk posed to wildlife and humans as the result of releasing vast amounts of pesticide mixtures into the environment.
The quality and quantity of the data about the risk posed to humans by individual pesticides vary considerably. In some instances there are numerous studies about the health effects of a particular pesticide in humans and laboratory animals, and for others there are very few. In general, the longer the active ingredient has been on the market, the greater the number of citations in the peer-reviewed literature. Data are sparse when linking pesticides with neurodevelopmental effects other than for the insecticides chlorpyrifos (CPF), parathion, and 1,1,1-trichloro-2,2-bis(p-chlorophenyl)ethane (DDT).
Unlike obvious structural defects, most neurodevelopmental effects cannot be seen at birth or even later in life. Instead, adverse effects on the nervous system are expressed in terms of how an individual behaves or functions. Behavior and function vary considerably from birth through adulthood. Functional deficits are not “on” and “off” conditions but instead range from inconsequential through very mild to very severe to totally debilitating. Consequently, it is difficult to quantify neuro-developmental impairment. Some of the end points used in the laboratory to detect functional impairment of the brain and nervous system are measured at the gene, cell, biochemical, and/or physiologic levels and often require high-tech instrumentation to quantify. At the human level, a battery of tests is continuing to evolve to measure with increasing sensitivity psychomotor, psychologic, clinical, and psychiatric symptoms to better quantify functional impairment.
In this article I have two principal purposes in discussing the inherent risks of using pesticides, the limitations of testing techniques, and the intrinsic incompleteness of all scientific evidence: a) to encourage the use of the open literature about the neurodevelopmental effects of all classes of pesticides when setting the criteria for determining their safety and b) to encourage a more rigorous regulatory approach to protect human and environmental health in the absence of complete scientific certainty. I begin by presenting unequivocal evidence of pesticide exposure to numerous classes of pesticides during development. This is followed by a section on human epidemiology where only weak data are available linking neuro-developmental impairment with pesticides. Next, I present a case study of how CPF cryptically interferes with brain development one stage after another. This is followed with selected laboratory studies demonstrating that other insecticides as well as other pesticide classes target prenatal brain development similar to CPF and share similar and sometimes diverse impacts on the construction and function of the brain. As the data reveal, not only insecticides but other classes of pesticides, such as herbicides and fungicides, can also interfere with neurodevelopment. In the “Discussion” I challenge the protective value of current pesticide risk assessment strategies in light of the vast numbers of pesticide products on the market with untold numbers of targets and mechanisms of action that can cause neurodevelopmental damage.
Evidence of Exposure to Pesticides
Improvements in analytical laboratory equipment and testing procedures have made it easier to detect pesticides and their metabolites at very low concentrations in almost all human tissue. From routinely detecting parts per million (milligrams per kilogram) and more recently to as low as parts per trillion (picograms per kilogram), some laboratories are now able to measure concentrations down to parts per quintillion (femtograms per kilogram). The development of noninvasive sampling methods, such as testing for pesticides and their metabolites in urine, has made it possible to monitor pesticide exposure in infants and children. It is fairly safe to say that every child conceived today in the Northern hemisphere is exposed to pesticides from conception throughout gestation and lactation regardless of where it is born. The herbicide 2,4-dichlorophenoxyacetic acid (2,4–D) was found in approximately 50% of semen samples provided by 97 Ontario, Canada, farmers (Arbuckle et al. 1999). The herbicides atrazine, metolachlor, alachlor, and 2,4–D and the insecticides diazinon and the CPF analyte 3,5,6-trichloro-2-pyridinol (TCP) were found in semen of men in central Missouri and in urban Minneapolis, Minnesota (Swan et al. 2003); the insecticides chlordane, dichloro-diphenyldichloroethylene (DDE), heptachlor epoxide, and hexachlorobenzene (HCB) were found in ovarian follicular fluid from women undergoing in vitro fertilization in Halifax, Hamilton, and Vancouver, Canada (Jarrell et al. 1993); hexachlorocyclohexane and p,p′-DDE were found in amniotic fluid of women undergoing routine amniocentesis in Los Angeles, California (Foster et al. 2000); and nonpersistent pesticides were found in the amniotic fluid of women referred for amniocentesis in the agricultural San Joaquin Valley, California (Bradman et al. 2003). Pesticides were also found in maternal blood, placental, and umbilical cord blood from women experiencing normal births and stillbirths in India (Saxena et al. 1983), from urban and rural mothers during Caesarian section in the Atoya River basin, Nicaragua (Dorea et al. 2001), and from mothers delivering normal and subnormal weight babies (Siddiqui et al. 2003). In addition, pesticides were found in the breast milk of mothers who delivered by Caesarian section in Nicaragua (Dorea et al. 2001), native Alaskan mothers living an indigenous lifestyle (Simonetti et al. 2001), and women living in southwest Greece (Schinas et al. 2000). A median of 8.26 μg/mL CPF (range, 0.40–458.04 μg/mL) was discovered in the meconium of newborns in Manila, Philippines (Ostrea et al. 2002). Six organo-phosphate (OP) pesticide metabolites were found in the meconium of 20 newborns in New York City (Whyatt and Barr 2001). The babies’ first bowel movements held concentrations 10–100 times higher than their cord blood. One metabolite, diethylthiophosphate, was found in all 20 samples; another, diethyl-phosphate, was found in 19 of 20 samples. Both are metabolites of diazinon, CPF, and several other OP insecticides.
An eastern Washington State research team surveyed OP metabolites in the urine of 210 farmworkers and their children and in dust from their homes and vehicles (Coronado et al. 2004). They segregated farm chores into several classes: harvesting and picking, thinning, loading, transplanting, and pruning. Azinphosmethyl, an OP, was more often found in dust in thinners’ homes (92.1% vs. 72.7%) and vehicles (92.6% vs. 76.5%) than in those of workers who did no thinning. Thinners’ children had higher concentrations of OP metabolites in their urine, and the metabolites were found more frequently in the children (91.9% detectable in urine), compared to the adults (81.3% detectable; p = 0.002).
In Seattle, Washington, investigators measured five OP metabolites in 24-hr urine samples of preschool children (2–5 years of age) who were raised on either a predominantly organic (n = 18) or predominantly conventional diet (n = 21) (Curl et al. 2003). Pesticide use was also recorded for each home. Median total dimethylphosphate metabolites (0.06 μmol/L) were significantly higher than median total diethyl alkylphosphates (0.02 μmol/L; p = 0.0001) in the urine. Those children on a conventional diet had levels of dimethylphosphate metabolites six times higher than those of children on an organic diet (medians = 0.17 and 0.03 μmol/L, respectively; p = 0.0003). Median concentrations of both metabolites were almost an order of magnitude higher in the conventionally fed children (0.34 μmol/L vs. 0.04 μmol/L). There were no age differences in the children in the two groups. Home use of pesticides varied, with seven conventional-diet families using OPs versus three organic-diet families using OPs. Although the study group was small and there were difficulties collecting urine samples, this research provides the first empirical data comparing urinary levels of pesticides in youngsters consuming predominantly organic versus conventional diets.
Human Epidemiology
Determining a link between fetal exposure to a specific chemical and long-term expression of a change in health poses a monumental challenge when designing epidemiologic studies. For example, one human epidemiologic study uncovered weak but statistically significant associations between neurodevelopmental impairment as a result of exposure to two pesticides during gestation. In a large study of live births (n = 1,532), including 536 children fathered by pesticide applicators, Garry et al. (2002) discovered that “adverse neurologic and neurobehavioral developmental effects clustered among the children born to applicators of the fumigant phosphine [odds ratio (OR) = 2.48; 95% confidence interval (CI), 1.2–5.1].” They also discovered an OR for the herbicide glyphosate (Roundup) of 3.6 (95% CI, 1.3–9.6). Among the children in the phosphine group (n = 290), two were diagnosed with autism, which is high compared with the prevalence nationwide, and five were diagnosed with attention deficit disorder/attention deficit hyperactivity disorder (ADD/ADHD). It took years of close interaction with the families in this study to be able to track their pesticide exposure without having to resort to recall and to follow the children’s functional development (Garry VF, personal communication). The investigators were cautious about their findings and asked for confirmation.
Another study suggests that CPF might have an effect on head circumference related to the activity of paraoxonase (PON1), an enzyme that can detoxify CPF before it can inhibit acetylcholinesterase (Berkowitz et al. 2004). Babies with a small reduction in head circumference were from mothers whose TCP concentrations were above the detection limit, and their PON1 activity was in the lowest tertile (p = 0.014). Mothers and their infants (n = 404) were recruited from East Harlem and other sections of New York City.
In a more recent study, Young et al. (2005) looked at the relationship between maternal OP urine metabolites and infant neuro-development. They employed a battery of tests using the Brazelton Neonatal Behavioral Assessment Scale for habituation, orientation, motor performance, range of state, regulation of state, autonomic stability, and reflex in 381 infants younger than 62 days of age. Young et al. (2005) found a significant association between increasing total concentrations of maternal urine OP metabolites representing “approximately 80% of OPs used in the Salinas Valley” and increasing numbers of abnormal reflexes in the infants from days 3 to 62. The median age for testing the infants was day 3. Mothers’ urine was tested at 14 and 26 weeks during gestation and at day 7 post-partum. The median urine levels of dialkyl phosphate (DAP), dimethyl phosphate, and diethyl phosphate, respectively, were 132, 97, and 21 mol/L during gestation and 222, 160, and 27 nmol/L after delivery. DAP represents the total of diethyl and dimethyl phosphate metabolites. The dimethyl metabolites could reflect exposure to malathion, oxydemetonmethyl, dimethoate, naled, and methidathion, and the diethyl metabolites could reflect exposure to diazinon, CPF, and disulfoton used in the Salinas Valley. It is important to keep in mind that the OPs are readily metabolized, and exposure can vary considerably and most often is transient and unpredictable. The authors noted that there were large within-person variations in urine levels in this study.
A Case Study: The Cryptic Neurodevelopmental Effects of CPF
The insecticide CPF is an OP pesticide that has been on the market since 1965 to control insects in agriculture, gardens, building construction, and households. In 2002 the use of CPF was restricted to only agricultural applications, and all domestic use was to be completely phased out by 1 January 2005. The metabolites of CPF have been widely reported in human tissue. In a study based on data from the Centers for Disease Control and Prevention’s (CDC 2001) first National Report on Human Exposure to Environmental Chemicals,
Hill et al. (1995) found the CPF analyte TCP in 82% of urine samples (n = 1,000) from a broad sample of the U.S. population between the ages of 20 and 59 years from all regions of the country. The CDC’s Second National Report on Human Exposure to Environmental Chemicals (CDC 2003) states that the levels of TCP were similar to levels presented in the first National Report on Human Exposure to Environmental Chemicals (CDC 2001) but gave no statistics concerning the extent of exposure across the population. Like the other OP insecticides, CPF inhibits the enzyme acetylcholinesterase, which destroys acetylcholine, the neuro-transmitter that activates cholinergic neurons. These are an important group of nerve cells that control signals in the peripheral nervous system and in the brain and spinal cord. If acetylcholine is not inactivated immediately by the activity of acetylcholinesterase, it over-stimulates the neurons, and tremors, convulsions and death can follow.
As scientists probed deeper into the activity of CPF, a wealth of information surfaced from laboratory studies about its effects on the development and function of the brain and nervous system in embryos, fetuses, and young animals. Although many of the studies were performed on rats and there are differences in the ontogeny of specific parts of the brain between rats and humans, the development of the rat brain through postnatal day (PND) 21 provides a model for the development of the human brain through to birth.
A series of reports starting in 1991 confirmed that CPF is a cholinesterase inhibitor and that neonatal rats were more sensitive than adults when exposed to a single maximum tolerated dose (Pope and Chakraborti 1992; Pope et al. 1991, 1992). These studies also confirmed that the fetus recovers quicker than the adult from cholinesterase inhibition, suggesting that the fetus would be protected from CPF if all the adverse effects were due to cholinesterase inhibition alone. Lassiter et al. (1998), however, wrote that although the fetus could recover faster between repeated doses of CPF, this was only an “illusion that the fetal compartment is less affected than the maternal compartment.” Realizing that something other than cholinesterase inhibition was affecting the fetus, a team from Duke University led by Theodore Slotkin gradually began to demonstrate that other mechanisms of action of CPF alter prenatal development of the brain and behavior and that the embryo and fetus are sensitive to cholinesterase inhibition at doses that would not be toxic to an adult (Qiao et al. 2003; Slotkin 2004). These studies provided information about how the brain develops and functions and also provided a chronology of how CPF interferes at successional stages of brain development (Qiao et al. 2002). This team also demonstrated that CPF-oxon, the active metabolite of CPF, is the compound that causes cholinesterase inhibition and that the actual neuroteratogen is CPF (see Slotkin 2004 for a step-by-step description of how their CPF research progressed).
Slotkin and colleagues demonstrated that as the brain and nervous system are constructed and programmed, there are numerous points in time and at sites where CPF could interfere. CPF attacks the neurons that appear in the earliest stage of brain and central nervous system (CNS) development (Qiao et al. 2004). Neurons process information and are the signaling or transmitting elements in the nervous system. Damage to neurons at this early stage may not be expressed until years later. For example, a brief subtoxic dose of CPF [1 or 5 mg/kg body weight (bw)/day] during neurulation can cause behavioral alterations during adolescence and adulthood (Icenogle et al. 2004). And, although some early symptoms of CPF exposure disappear during certain stages of development, different neurologic symptoms can appear later in life (Qiao et al. 2002, 2003, 2004).
Glial cells that appear later than neurons during early development were shown to be more vulnerable than neurons to CPF (Qiao et al. 2002; Roy et al. 2004). There are more than twice as many glial cells (> 200 billion) in the body than neurons. Glial cells come in many varieties; they are supportive cells critical for normal development and function and serve as a “scaffold” for migration of cells during tissue construction [see Barone et al. (2000) on brain development]. Glial cells also provide nutrition to the neurons and provide a link with the immune system, responding to damage by acting as scavengers of pathogens and neuronal debris. CPF preferentially targets the glial cells among the cells it attacks (Garcia et al. 2002).
Slotkin and colleagues repeatedly demonstrated that CPF toxicity is not limited to cholinesterase inhibition alone but can act by other mechanisms. For example, in vitro and in vivo studies at three levels of development from DNA to the cell and the whole animal revealed that CPF is far more toxic than previously thought because of this wider range of activity (Crumpton et al. 2000). CPF impairs the binding to DNA of nuclear transcription factors (AP-1 and Sp1) that modulate cell replication and differentiation. When undifferentiated and differentiated neurons were exposed to CPF, the response of some transcription factors varied. Although the activity of one set of cells might not be affected, the activity of another set of cells might be significantly reduced. An independent study at Johns Hopkins University (Schuh et al. 2002) confirmed the ability of CPF to alter the activity of another nuclear transcription factor in cortical neurons, the Ca2+/cAMP response element binding protein (CREB), which is critical for cell survival and differentiation during development and is critical for memory. CPF increased the activated level of CREB at 0.01 nM, well below the level at which cholinesterase inhibition is expressed and below the typical level of human exposure. Schuh et al. (2002) also demonstrated that CPF-oxon did not cause the alteration, supporting the conclusion of Crumpton et al. (2000) that CPF is more than a cholinesterase inhibitor. Crumpton et al. (2000) also demonstrated that the CPF effects on the development of the forebrain in the rat, which reaches its peak stage of development during gestation, were not as severe as the effects on the cerebellum, which reaches its peak 2 weeks after birth. The cerebellar changes in the later stages of development, however, could not have been the result of cholinesterase inhibition because the cerebellum is not innervated with cholinergic receptors like the forebrain is (Crumpton et al. 2000).
Much of the research undertaken by Slotkin and colleagues demonstrated that models of adult toxicity do not extrapolate to fetuses and would not predict the vulnerability of the embryo to TCP and CPF (Aldridge et al. 2004, 2005a). The ever-changing state of the embryo makes it a more sensitive model for toxicity and a better predictor of long-term, delayed effects. Slotkin and colleagues have demonstrated that the embryo and fetus reveal innumerable mechanisms of action of toxicity that could not be detected in an adult animal. For example, in a series of in vitro studies, a 25% increase in reactive oxygen species (ROS) was found 10 min after undifferentiated glial C6 cells were exposed to CPF (Garcia SJ et al. 2001). During some stages of development, selected regions of the brain are vulnerable to CPF by interference with the G-protein in the adenylyl cyclase (AC) cascade by disrupting nuclear transcription DNA binding (Meyer et al. 2003; Slotkin 1999). CPF caused abnormal tissue/cell development in cultured rat embryos through vacuolation of the cytoplasm (Roy et al. 1998). CPF, CPF-oxon, and TCP inhibit DNA synthesis in PC12 cells (typical neuronal cells) and C6 cells (typical glial cells), having a greater effect on the glial cells, with the exception of the TCP (Qiao et al. 2001). Qiao et al. (2001) also showed that CPF is a stronger DNA synthesis inhibitor than CPF-oxon, although it is a weaker cholinesterase inhibitor. Confirming again that certain regions of the developing brain were more susceptible than others, Qiao et al. (2001) found that CPF and TCP suppress DNA synthesis in the epithelium of the forebrain and inhibit neural cell replication. These studies also revealed that serum binding proteins can be protective of DNA antimitotic activity, but because fetuses and newborns have lower concentrations of serum proteins than adults, they could be more vulnerable.
In a series of whole-animal studies looking at damage in rats from the embryo to the adult, Slotkin and colleagues demonstrated again that assays using adult animals cannot predict the long-term delayed effects in the offspring. For example, within hours after 9.5-day-old embryos were exposed to CPF, they showed clear signs of damage that was restricted to the primordial brain (Roy et al. 1998). Upon histologic examination, Roy et al. (1998) found apoptosis and altered mitotic figures, along with gross disruption of the architecture of the developing brain, all in the absence of any gross morphologic defects in the other parts of the embryo. As these animals matured, CPF damage was demonstrable in a wide variety of brain regions. The most vulnerable target was the hippocampus, with the damage expressed both as deficits in nerve activity and as corresponding behavioral abnormalities (Icenogle et al. 2004). Dosing an adult animal similarly would not have provoked these effects of fetal origin.
The complexity of the toxicity of CPF became more apparent as sex-related differences began to appear in in vivo assays. The sex-related changes occur when CPF exposure takes place during gestation days (GD) 17–20 (late gestation) and PND1–4 and again at PND11–14. The timing of this exposure in the rat is comparable to human brain development during the perinatal and neonatal period (Aldridge et al. 2004; Meyer et al. 2004a; Slotkin et al. 2001). Late prenatal exposure to CPF has also been shown to cause long-term sex-specific changes in cognitive performance (Levin et al. 2002). Adolescent and adult females were more vulnerable to CPF, based on their number of errors during working- and reference-memory tasks. Levin et al. (2002) also found profound differences between animals exposed to 1 mg/kg and 5 mg/kg CPF, reflecting a U-shaped dose curve. The lowest dose was the most potent in this case, although the highest dose caused the most inhibition of fetal brain cholinesterase. The non-monotonic dose–response curve discovered in the assay, combined with the fact that the results were not dependent on cholinesterase inhibition, raises questions about indirect effects of CPF and its metabolites on the endocrine system via the brain. However, as Slotkin (personal communication) pointed out, hormesis cannot be ruled out until further research proves otherwise. In light of their findings, Levin et al. (2002) noted the need for childhood and adolescent maturation studies and for the development of more sex-selected end points.
At a concentration somewhat higher than human exposure, 50 μg/mL CPF in vitro induces the release of norepinephrine from rat brain synaptosomes (Dam et al. 1999). Studies using whole animals confirmed that the release of norepinephrine inhibits synaptogenesis, a condition that persists to adulthood and is sex specific, long after exposure ceases and cholinesterase activity is restored (Levin et al. 2002). Aldridge et al. (2004) showed that CPF administered during GD9–12 up-regulated serotonin (5-hydroxytryptamine; 5-HT) receptors (5-HT-1 and 5-HT-2) and interfered with the 5-HT protein transporter from the neural tube stage through to adulthood. But during GD17–20, CPF initiated larger effects in regions with greater numbers of 5-HT nerve terminals, which were found more in males. This response continued through PND1–4. In contrast, the 5-HT protein transporter was downregulated in females (Aldridge et al. 2004). Aldridge et al. (2005a,b) performed studies demonstrating abnormalities of 5-HT–related behaviors in developing rats exposed to CPF. The research that preceded this report mapped out the ontogeny of serotonin receptors in the brainstem and forebrain (Aldridge et al. 2003). The authors pointed out that serotonin disruption has been linked to appetitive and affective disorders, and the biologic significance of these findings needs to be clarified. These disorders have been the focus of increasing research attention in recent years as the result of the increasing use of prescription and and illicit mind-altering drugs.
Other Pesticide Products That Interfere with Neurodevelopment
There are numerous opportunities during gestation where insecticides and products from several other chemical classes can alter the purpose of a cell, tissue, organ, or system function in the brain or CNS, much like the discoveries presented for CPF.
Herbicides.
Over the past 15 years, an Argentinian research team has produced a series of reports on 2,4-D that is comparable to the research on CPF. This team discovered that exposure during lactation to the herbicide 2,4-DBE (the butyl ester of 2,4-D) can alter brain production of 5-HT and its metabolite, 5-hydroxyindoleacetic acid (5-HIAA), in adulthood (Bortolozzi et al. 2001; Evangelista de Duffard et al. 1990; Garcia G et al. 2001). Concentrations of both dopamine and serotonin changed transiently if the animals were exposed only through birth (69 mg/kg bw/day from GD6 to birth; 15 days) and permanently if delivered to the offspring through breastfeeding as well from GD6 to weaning (30 days). Duffard et al. (1996) and Rosso et al. (2000) found that 2,4-D interfered with myelination in the brain as the result of lactational exposure. This caused changes in behavior patterns that included apathy, reduced social interaction, repetitive movements, tremors, and immobility in pups exposed to 2,4-D (Bortolozzi et al. 1999; Evangelista de Duffard et al. 1995). They also discovered that the serotoninergic and dopaminergic effects occurred during postnatal brain development, similar to the effects of CPF. Bortolozzi et al. (1999) and Evangelista de Duffard et al. (1995) also found 2,4-D in breast milk of 2,4-D–fed mothers and in the stomach content, brain, and kidney of 4-day-old pups (Sturtz et al. 2000).
Insecticides.
Cassidy et al. (1994) reported that the lowest dose of chlordane used in their studies (100, 500, 5,000 ng/g/day both prenatally and postnatally) caused a dose-dependent reduction in testosterone levels in females in adulthood. The lowest dose they used was 10 times lower than the U.S. EPA’s lowest observed adverse effect level (LOAEL) for neurologic effects (1,000 ng/g) and 50 times lower than the U.S. EPA’s LOAEL for developmental effects (5,000 ng/g) of chlordane (Cassidy et al. 1994). Females exhibited improved spatial abilities and auditory startle-evoked responses more similar to male responses, and slight increases in body weight. Changes in male mating behavior included shortening of latency to intromission and increased intromissions. The authors speculated that pesticides structurally similar to chlordane cause masculinization of function and behavior in both sexes because the pesticides mimic the sex steroids or change their plasma levels through other enzyme systems. The two lower doses in this study prompted greater change than the highest dose for auditory startle response, mating behavior, and body weight.
Methoxychlor (MXC), an insecticide whose toxicity depends on its conversion to several metabolites, was considered to be an estrogen for many years and only recently was discovered to have antiestrogenic and androgenic properties as well. To measure neuro-developmental impacts, Palanza et al. (2002) fed pregnant CD-1 mice environmentally relevant doses of MXC (0.02, 0.2, and 2.0 μg/g mother bw/day) from GD11 to GD17 and examined them on postpartum days 2–15. Mothers fed the lowest dose spent less time nursing than the controls, possibly reflecting the inverted U-shaped dose–response curve expressed by endocrine disruptors. At late adolescence the pups exhibited a reduction in novelty seeking (both the environment and objects), with a difference between males and females (Palanza et al. 1999). Male sexual aggression was reduced at puberty but returned to normal in adulthood. The reduction in aggressive behavior in the periadolescent male CD-1 mouse as a result of MXC exposure (20 μg/kg/day) occurred at a dose 100 times lower than the dose at which the Agency for Toxic Substances and Disease Registry (ATSDR 2002) deemed would cause no harm to humans in 1994. The ATSDR recently withdrew this minimum risk level in light of new evidence on MXC.
Dopaminergic neurons in the substantia nigra project to and release dopamine to the corpus striatum of the brain. This section of the brain integrates neuromuscular and behavioral information and is involved in the control of locomotor activity, exploration, and novelty-induced behavior. It also influences social–sexual interactions such as aggression and maternal behavior. The loss of dopamine function in the neurons connecting the corpus striatum with the midbrain of humans is the cause of Parkinson disease. Male offspring of mice exposed to 20 μg/kg/day MXC had fewer dopaminelike receptors in their corpus striatum and were less active than control females (vom Saal et al. 2003). Females exposed to the same concentrations showed a malelike profile in reactivity to novelty. Similar changes in males and females were seen in mice exposed to o,p′-DDT in the same study. In an unrelated study, Lamberson et al. (2001) discovered increased locomotor behavior in offspring of Sprague-Dawley rats administered 0.5 mg/kg/day MXC throughout gestation.
Prenatal exposure to aldrin also causes delayed neurologic impairment that extends through to adulthood. Castro et al. (1992) administered 1 mg/kg aldrin subcutaneously to female rats daily from conception to birth and tested their pups on PND1–2 and again on PND90. On PND90, the animals showed loss of locomotor control and behavioral change(s). Aldrin was not measurable in the animals at the time they were tested.
Paraoxon is the oxidized metabolite of parathion and a potent OP cholinesterase inhibitor. Chronic paraoxon exposure (0.1, 0.15, or 0.2 mg/kg subcutaneously) during a stage of rapid cholinergic brain development from PND8 to PND20 in male Wistar rats led to reduced dendritic spine density in the hippocampus without obvious toxic cholinergic signs in any of the animals (Santos et al. 2004). Some animals in the two highest dose groups died in the early days of the study. All doses caused retarded perinatal growth, and brain cholinesterase activity was reduced 60% by PND21.
Johansson et al. (1995) showed that a single exposure to a pesticide before or shortly after birth can sensitize the offspring to low doses of other pesticides later in life, even though there are no immediate changes in the structure and function of the nervous system at the time of exposure. Only as the exposed individual matures do irreversible alterations in structure and function become evident. The researchers exposed mice to one dose of DDT (0.5 mg/kg bw orally) on PND10 and then at 5 months of age exposed them to bioallethrin (0.7 mg/kg bw) (Johansson et al. 1995) or paraoxon (0.7 or 1.4 mg/kg bw) for 7 days (Johansson et al. 1996). When tested 2 months later, at 7 months of age, the offspring exhibited changes in spontaneous behavior and cholinergic muscarinic receptor density in the cerebral cortex, which led to impairment in learning and memory (Eriksson and Talts 2000). Again, the neurodevelopmental damage was not seen immediately, but instead took 2 months to be expressed. PND10 in the mouse is equivalent to the end of the second trimester in the human. It is during this stage, from the third trimester of pregnancy through 2 years of age in humans, when the neurotransmitter system in the CNS goes through a growth spurt (Eriksson 1997). Throughout these studies the animals showed no clinical signs of toxic symptoms, and the doses used for adult treatment in these studies had no immediate effect on the adult. The dose of DDT used in this study is in the range that human infants might be exposed to during lactation today (Smith 1999). Even though the functional and structural outcomes in the above studies are similar, it should be remembered that they were caused by different mechanisms. For example, bioallethrin causes harm by prolonging sodium channel openings, whereas paraoxon inhibits acetylcholinesterase activity; but they both caused similar neuronal changes, which raises questions about the combined effects of pesticide mixtures on development. These studies support the premise that the differences in susceptibility of adults to pesticides may not be genetic, but rather that susceptibility to pesticides can be acquired by low-dose pesticide exposure earlier in life.
Insecticide and acaricide.
Rat pups displayed deficits in learning and retention of memory after exposure to the organochlorine insecticide and acaricide endosulfan (6 mg/kg bw) on PND2–25 (Lakshmana and Raju 1994). The concentrations of the neurotransmitters, noradrenalin, dopamine, and serotonin in the olfactory bulb, hippocampus, visual cortex, brainstem, and cerebellum either increased or decreased depending on the days of examination, PND10 and PND25. The authors ruled out acetylcholinesterase inhibition as the cause of the alterations in the production of the neurotransmitters because they found no differences in acetylcholine activity in any of the regions of the brain used in the study. They suggested that endosulfan directly led to a “re-altering” of the construction of those parts of the brain. By PND25, as the differentiation and organization of the observed tissues proceeded in the presence of endosulfan, the rats’ performance became significantly compromised.
Fungicides.
Gray and Ostby (1998) provided an excellent overview of how prenatal exposure to a fungicide can alter sexual behavior and function in adulthood, even though growth and viability are not compromised. The neurobehavioral alterations quantified in the studies they reviewed include activity level, aggression, mounting frequency, and completed intromissions. In a study using the fungicide vinclozolin, Gray et al. (1994) reported that 100% of the exposed males failed to attain intromission, although there was no reduction in mounting behavior. In subsequent studies, newborn male and female rats were injected on PND2 and PND3 with 200 mg/kg vinclozolin and observed for social behavior on PND36 and PND37 (Hotchkiss et al. 2002). Both males and females exhibited changes in play behavior. Females became involved in increased rough-and-tumble play, a behavior imprinted by male hormones in the brain during early development. Conversely, the males’ rough-and-tumble play was reduced, and they behaved more like unexposed females. Because only one dose was used, this study does not indicate the lowest dose needed to initiate these changes. More recently, on PND34 Colbert et al. (2005) found significantly increased nape contact, pounce, pin, and wrestle play behavior in male offspring of females exposed to 6 and 12 mg/kg bw/day vinclozolin from GD14 to PND3. At a maternal dose of 1.5 mg/kg bw/day vinclozolin, there was a significant increase in penile dysfunction in adulthood. Future studies should include more than one dose, preferably over several orders of magnitude, to take into account the susceptibility and sensitivity of the developing animal.
Discussion
There is a great deal of uncertainty about the neurodevelopmental effects of pesticides among the human studies presented here. Exposure has become too complex because of the hundreds of pesticide active ingredients on the market, confounded by background exposure to industrial chemicals that share similar effects. In addition, functional changes are expressed over a continuum, making it difficult to document the damage which often is expressed as more than one lesion and at different intervals or stages of development. The pesticides discussed here, with the exception of DDT, are still widely used in the United States despite these data. Although this information is available, the U.S. EPA has rarely used the open literature in its risk assessments, generally using only data submitted by manufacturers. Industry continues to use traditional toxicologic protocols that test for cancer, reproductive outcome, mutations, and neurotoxicity, all crude end points in light of what is known today about functional end points. In using manufacturer data, the U.S. EPA misses almost all delayed developmental, morphologic, and functional damage of fetal origin and, in the case of CPF and all OPs, continues to rely primarily on blood cholinesterase inhibition data in risk assessments (Zheng et al. 2000). The U.S. EPA should accept nonguideline, open literature to determine the toxicity of a chemical. For example, Brucker-Davis (1998) published a comprehensive review of the open literature in which she found 63 pesticides that interfere with the thyroid system—a system known for more than a century to control brain development, intelligence, and behavior. Yet, to date, the U.S. EPA has never taken action on a pesticide because of its interference with the thyroid system.
It would be difficult to find another pesticide in use today that has been as systematically studied as CPF. The amazing litany of diverse mechanisms discovered in the series of CPF studies raises serious questions about the safety of not only CPF and the other OPs but all pesticides in use today. Most astounding is the fact that a large part of CPF’s toxicity is not the result of cholinesterase inhibition, but of other newly discovered mechanisms that alter the development and function of a number of regions of the brain and CNS. These findings send a warning that even though an OP pesticide like CPF may have a very high EC50 (concentration that produces 50% of the maximum possible effective response) for acute toxicity as a result of cholinesterase inhibition, it may have other toxic strategies that are far more egregious than cholinesterase inhibition. This raises a question about the value of using EC50 values if they do not represent the most sensitive end point. Qiao et al. (2003) warn that “developmental neurotoxicity consequent to fetal or childhood CPF exposure may occur in settings in which immediate symptoms of intoxification are absent.” They also point out that in the case of CPF, damage is not always global (referring to the entire brain) but may only interfere in specific regions of the brain during development, which could increase the difficulty of detecting the damage. S.J. Garcia et al. (2001) state that “measurement just of cholinesterase activity is a questionable approach in assigning an appropriate index of safety.”
The knowledge gained from a decade of the CPF/brain studies by Slotkin and colleagues and the 2,4-D/brain studies by Evangelista de Duffard and co-workers not only demonstrates the insidious nature of CPF and 2,4-D exposure, but it also demonstrates the weaknesses in current standard practices for determining the safety of a pesticide or any other synthetic chemical. These discoveries demonstrate that a much larger battery of tests must be used when determining the safety of commercial pesticides. Even a U.S. EPA analysis of developmental neurotoxicity studies stated that the U.S. EPA’s current developmental neurotoxicologic testing protocol is “not a sensitive indicator of toxicity to the offspring” and urged the U.S. EPA “to further consider if it will use literature data” (Makris et al. 1998). In this case, “literature data” refers to all of the peer-reviewed reports concerning the pesticide impacts on neuro-development that heretofore have not been used for risk assessment by the agency. In the case of CPF and 2,4-D, it appears that those who reviewed the data failed to understand its significance or had other reasons to ignore it. The U.S. EPA needs to convene a panel of independent experts to review these studies for applicability to determine if and how they can be used for registration.
Laboratory studies have clearly revealed neurologic damage after exposure to specific pesticides and in some studies at concentrations equivalent to ambient exposure. Even so, the animal testing for regulatory purposes that takes place today does not attempt to detect adverse health effects at the concentrations at which humans are exposed. Instead, the highest concentrations of chemicals tested are those that can be used without killing the animals or reducing the test mother’s weight and her reproductive ability. In most animal studies the pesticides are administered at high oral or subcutaneous doses orally, not reflecting that, for most humans and wildlife, exposure could in many instances be dermal or via inhalation and, in many cases, over a long period of time at low doses. The U.S. EPA currently requires chronic toxicity studies, but it is locked into using high doses to elicit effects and has not overcome the difficulty of detecting effects from chronic or ambient exposure or low doses. In addition, the human pharmacokinetics of pesticide exposure can either enhance or reduce the health impacts depending on individual variations. In some cases the major or minor metabolites are more toxic than the parent compound, which is listed as the active ingredient.
In a recent study, Bowers et al. (2004) found a different profile of developmental neurotoxicity between polychlorinated biphenyls (PCBs; such as Aroclor 1254) alone and with a mixture of organochlorine pesticides. Very low doses of the chemicals together delayed ear opening, affected geotaxis, and reduced grip strength. Ultimately, mortality, growth, thyroid function, and neurobehavioral development were affected. It is safe to say that there are very few people in the developed world today who are not carrying PCBs in their bodies. If animal testing continues to be used for determining the safety of pesticides, at least one group of the test animals should be exposed to PCBs before testing the pesticides for their ability to cause unpredictable interactive effects such as those described above.
It should be pointed out that the same signaling systems (AC cAMP) involved in the sex-selective changes in brain development have also been shown to alter heart and liver function in adulthood (Meyer et al. 2004a, 2004b). The AC system is ubiquitous throughout the body. In the future, the most efficient, comprehensive assays will take advantage of the fact that most chemicals have more than one effect in one system. Cross-disciplinary teams will be required to design these assays so that every organ system is carefully screened for damage. And most important, this will reduce by thousands the numbers of animals needed for testing. However, improved neurodevelopmental tests with laboratory animals will not fulfill their greatest potential if they are not backed up by better batteries of tests to detect functional disabilities in children. Such new, sophisticated quantitative tests are now available and are being updated regularly. These tests go beyond diagnostic testing to “performance evaluation” and are designed to detect the subtle effects of chronic, low-dose exposure (Davidson et al. 2000).
In conclusion, an entirely new approach to determine the safety of pesticides is needed. It is evident that contemporary acute and chronic toxicity studies are not protective of future generations. The range of doses used in future studies must be more realistic, based on levels found in the environment and human tissue. In this new approach, functional neurologic and behavioral end points should have high priority, as well as the results published in the open literature. In every instance, the impacts of transgenerational exposure on all organ systems must be meticulously inventoried through two generations on all contemporary-use pesticides and new pesticide coming on the market. To protect human health, however, a new regulatory approach is also needed that takes into consideration this vast new knowledge about the neurodevelopmental effects of pesticides, not allowing the uncertainty that accompanies scientific research to serve as an impediment to protective actions.
I thank the three anonymous reviewers for their comments.
This study was supported by The Starry Night Foundation, The Organic Center, the New York Community Trust, the Mitchell Kapor Foundation, and the Winslow Foundation.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8253ehp0114-00001816393652ResearchMortality among Workers Exposed to Polychlorinated Biphenyls (PCBs) in an Electrical Capacitor Manufacturing Plant in Indiana: An Update Ruder Avima M. Hein Misty J. Nilsen Nancy Waters Martha A. Laber Patricia Davis-King Karen Prince Mary M. Whelan Elizabeth National Institute for Occupational Safety and Health, Cincinnati, Ohio, USAAddress correspondence to A. Ruder, National Institute for Occupational Safety and Health, Mailstop R-16, 4676 Columbia Parkway, Cincinnati, OH 45226 USA. Telephone: (513) 841-4440. Fax: (513) 841-4486. E-mail:
[email protected] authors declare they have no competing financial interests.
1 2006 1 9 2005 114 1 18 23 26 4 2005 1 9 2005 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. An Indiana capacitor-manufacturing cohort (n = 3,569) was exposed to polychlorinated biphenyls (PCBs) from 1957 to 1977. The original study of mortality through 1984 found excess melanoma and brain cancer; other studies of PCB-exposed individuals have found excess non-Hodgkin lymphoma and rectal, liver, biliary tract, and gallbladder cancer. Mortality was updated through 1998. Analyses have included standardized mortality ratios (SMRs) and 95% confidence intervals (CIs) using rates for Indiana and the United States, standardized rate ratios (SRRs), and Poisson regression rate ratios (RRs). Estimated cumulative exposure calculations used a new job–exposure matrix. Mortality overall was reduced (547 deaths; SMR, 0.81; 95% CI, 0.7–0.9). Non-Hodgkin lymphoma mortality was elevated (9 deaths; SMR, 1.23; 95% CI, 0.6–2.3). Melanoma remained in excess (9 deaths; SMR, 2.43; 95% CI, 1.1–4.6), especially in the lowest tertile of estimated cumulative exposure (5 deaths; SMR, 3.72; 95% CI, 1.2–8.7). Seven of the 12 brain cancer deaths (SMR, 1.91; 95% CI, 1.0–3.3) occurred after the original study. Brain cancer mortality increased with exposure (in the highest tertile, 5 deaths; SMR, 2.71; 95% CI, 0.9–6.3); the SRR dose–response trend was significant (p = 0.016). Among those working ≥90 days, both melanoma (8 deaths; SMR, 2.66; 95% CI, 1.1–5.2) and brain cancer (11 deaths; SMR, 2.12; 95% CI, 1.1–3.8) were elevated, especially for women: melanoma, 3 deaths (SMR, 5.99; 95% CI, 1.2–17.5); brain cancer, 3 deaths (SMR, 2.87; 95% CI, 0.6–8.4). These findings of excess melanoma and brain cancer mortality confirm results of the original study. Melanoma mortality was not associated with estimated cumulative exposure. Brain cancer mortality did not demonstrate a clear dose–response relationship with estimated cumulative exposure.
cancercohort studyexposure assessmentoccupational exposurepolychlorinated biphenyls
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Polychlorinated biphenyls (PCBs) are synthetic chemicals that were produced commercially in the United States from 1929 to 1977 and used widely in the electrical industry because of their high stability, dielectric properties, and resistance to oxidation (U.S. EPA and Environment Canada 2004). They were also used in plasticizers, adhesives, and hydraulic fluids (Silberhorn et al. 1990; Smith and Brown 1987). PCBs have long half-lives, correlated with the degree of chlorination, and persist in humans and in the environment (Brown and Lawton 2001; Hansen 1998). Increasing concern in the 1970s about potential health and environmental risks led to a 1977 ban on PCB production and distribution in the United States.
The International Agency for Research on Cancer (IARC) classified PCBs as probable human carcinogens (2A) with sufficient evidence of carcinogenicity in animals but limited evidence from human studies (IARC 1987). The U.S. Environmental Protection Agency (EPA) also classified PCBs as probable human carcinogens [Integrated Risk Information System (IRIS) 1996]. The National Toxicology Program (NTP) has classified several PCB mixtures as “reasonably anticipated to be human carcinogens” since 1981 (NTP 2005).
The human carcinogenicity of PCBs remains an important issue. Almost 30 years after production was banned, PCBs are still a potential occupational exposure. According to the U.S. EPA and Environment Canada (2004), at least 44% (87,000) of PCB transformers and 10% (143,000) of PCB capacitors were disposed of between 1994 and 2000. However, as many as 113,000 PCB transformers and 1.33 million PCB capacitors may still be in use. Those who repair and maintain capacitors and transformers containing PCBs and those in the reclamation industry responsible for disassembly of PCB-containing capacitors and transformers have the highest potential for exposure.
This update of a cohort mortality study among workers exposed to PCBs in an electrical capacitor manufacturing plant in Indiana was undertaken because the carcinogenicity of PCBs in humans is unresolved, and because on initial follow-up, both potential latency and statistical power were limited. The primary purpose was to investigate further the increased risks for brain cancer and malignant melanoma originally observed in the cohort followed through 1984 (Sinks et al. 1992). Other a priori hypotheses were that PCB exposure would affect all-cause mortality, all cancer mortality, and, specifically, rectal, liver, biliary tract, and gallbladder cancer and non-Hodgkin lymphoma, for which other studies indicated increased risks (Brown 1987; Brown and Jones 1981; Rothman et al. 1997). We updated mortality through 1998, adding 14 years of follow-up.
Materials and Methods
Capacitors were manufactured at an Indiana facility, using PCBs as a dielectric fluid from fall 1957 until spring 1977, when PCBs were replaced with isopropyl biphenyl (Jones 1977). Two dielectric fluid formulations were used, Aroclor 1242 through 1971 and Aroclor 1016 from 1971 to 1977 (Jones 1977). In both formulations, dichlorobiphenyls to tetrachlorobiphenyls predominated, but Aroclor 1242 contained 5.5% pentachlorinated and hexachlorinated biphenyls versus 0.4% pentachlorinated biphenyls for Aroclor 1016 (Albro and Parker 1979; Hutzinger et al. 1985). Aroclor composition could vary from batch to batch (de Voogt and Brinkman 1989; Kimbrough 1995).
Capacitor production began by winding foil and film into bales in a dust-free room with minimal exposure to PCBs, placing bales in metal capacitor boxes, and welding boxes shut. Capacitors were impregnated with dielectric fluid in a heated vacuum chamber. Large capacitors requiring gallons of dielectric fluid were filled manually through ports on the top (reportedly resulting in spillage and extensive dermal contact with dielectric fluid). The ports of the filled, warm, wet capacitors were soldered shut, dielectric fluid was washed off the outside, and capacitors were sent to quality control for testing. No regular industrial hygiene monitoring was done at the facility. All operations were under one roof with partitions between operations. The administrative offices and a few specific processes were isolated by walls (Jones 1977).
In spring 1977, the National Institute for Occupational Safety and Health (NIOSH) collected 9 skin smear samples, 16 area samples, and 40 personal air samples to evaluate exposures to PCBs and other chemicals. Low levels of xylene (mean, 1.8 ppm) and toluene (mean, 2.7 ppm) were found for painters, and appreciable levels of 1,1,1-trichloroethane (7–339 ppm) were found in the degreaser area and trichloroethylene (62–290 ppm) in the plating and welding areas (Jones 1977).
Exposure assessment for the original mortality study (Sinks et al. 1992) was based on duration of employment in jobs judged to have high direct PCB exposure (impregnating, sealing, and testing capacitors), based on personal and area air sampling. About 10% of the work force was estimated to have had such exposure (Sinks et al. 1992).
Exposure assessment for this update was based on a newly created semiquantitative job–exposure matrix (JEM) (Nilsen et al. 2004). All unique jobs (n = 884) were categorized based on PCB exposure intensity and frequency, qualitatively ranked for both inhalation and dermal exposure. For inhalation exposure intensity, air concentration data permitted assignment of exposure units (parts per million), but for dermal exposure intensity, the lack of historical dermal exposure measurements resulted in a unitless measure of exposure. For each job category, the product of intensity and frequency (fraction of day exposed) was calculated. The inhalation and dermal JEMs were modified for an earlier and a later era (the former with estimated 20% higher exposure). Because dermal exposures account for a significant proportion of total PCB exposure (Fischbein et al. 1982), a combination JEM averaging inhalation and dermal (1:1) scores was used to estimate cumulative PCB exposure. Cumulative exposure was expressed in unit-days of exposure (but the “unit” was not defined).
The cohort includes 3,569 of the 3,643 workers ever employed at the facility (74 were ineligible). For the original study (Sinks et al. 1992), vital status was ascertained through 1984. For the update, we submitted names of cohort members to the National Death Index (NDI 2005) for determination of vital status through 31 December 1998, and obtained death certificates or NDI-Plus causes of death (CODs). NDI-Plus searches retrieve COD codes as well as date of death. Death certificate data were coded by a nosologist. Because the NDI does not include deaths before 1979, any worker lost to follow-up before 1979 was classified “vital status unknown” and considered alive until the date last observed (usually the date last employed). Death was coded to the revision of the International Classification of Diseases, 9th Revision [ICD-9; World Health Organization (WHO) 1979] in effect at the time of death. This study was approved by the NIOSH Human Subjects Review Board. As a records study, it was exempted from informed consent requirements.
Statistical analysis.
The standardized mortality ratio (SMR) is the ratio of observed to expected deaths. Sex/race/age/calendar period reference rate files based on mortality in the Indiana and U.S. populations include 99 CODs, each encompassing a number of ICD codes, and cover the period beginning in 1960. State rates control for local conditions that may have no association with occupational exposures. In addition to the gradient of disease with latitude seen for infectious diseases (Guernier et al. 2004) and some cancers (Nomura and Kolonel 1991; Schwartz 1992), regional differences can affect other CODs (Mansfield et al. 1999; Pickle et al. 1997). We present Indiana-based SMRs, except as noted.
Our analyses used the NIOSH PC Life Table Analysis System (Cassinelli et al. 1997; NIOSH 2001; Steenland et al. 1990, 1998; Waxweiler et al. 1983). The statistical significance of the SMR was determined by a two-tailed test based on the Poisson distribution. The program calculated the 95% confidence interval (CI) for each SMR estimate.
Race- and sex-specific person-years at risk (PYAR) were accumulated for each eligible worker across 5-year age and calendar year intervals, beginning on 1 January 1960 or the qualified date of first exposure, whichever was later, and ending with the date of death, the date last known alive, or 31 December 1998, the study end date. Cohort members known to be alive after 1 January 1979 and not identified as deceased were assumed to be alive on 31 December 1998. Latency began at the date of first exposure and ended with the date of death, the date last known alive, or on 31 December 1998. For analyses we used the Indiana and U.S. 99-COD rate files. Using a multiple-COD analysis (MCOD) and U.S. rates (Steenland et al. 1992), we investigated possible excesses in nonmalignant chronic diseases. A separate analysis was restricted to individuals who worked at least 90 days (n = 2,789) a) to facilitate comparison with analyses of New York and Massachusetts cohorts of capacitor manufacturing workers exposed to PCBs (Brown 1987; Brown and Jones 1981; Kimbrough et al. 1999, 2003) and b) because there appear to be lifestyle and mortality differences between short-term and long-term workers (Kolstad and Olsen 1999).
We calculated estimated cumulative PCB exposure for each worker, based on job titles, job codes, and era(s) of employment. Cumulative exposure ranged from 10 to 1,218,590 unit-days of PCB exposure (median, 16,860 unit-days) (Nilsen et al. 2004). Cut-points at 11,000 and 90,000 unit-days of exposure defined tertiles with approximately equal numbers of deaths. Standardized rates were calculated for each cumulative exposure tertile using the sum of all PYAR for each sex/race/age/calendar time stratum as the weight for the specific stratum. Standardized rate ratios (SRRs) were calculated for each higher exposure tertile relative to the lowest tertile. Based on a Taylor series approximation of the variance, 95% CIs were calculated and a test for a linear trend was performed based on a weighted regression of the standardized rates (Rothman 1989). We used multivariate Poisson regression modeling and SAS 9 software (SAS Institute 2004) to adjust for sex, age, calendar year, and latency, and to calculate rate ratios (RRs) for higher exposure tertiles relative to the lowest tertile. We repeated the analysis excluding 117 workers with potential exposure to solvents (xylene, toluene, 1,1,1-trichloroethane, and trichloroethylene).
We used original department and operation codes from plant records and a map of exposure zones developed for the original study (Sinks et al. 1992) to re-create exposure zone assignments. The mortality analysis was repeated using exposure zones.
Results
Table 1 shows the cohort stratified by race, sex, and vital status. About one-third of the cohort (1,176 workers, 33%) worked between 1 day and 6 months, one-third (1,133 workers, 32%) 6 months to 3 years, and one-third (1,260 workers, 35%) > 3 years. Nearly all (97%) in the highest tertile of estimated cumulative exposure worked > 3 years, and nearly all (93%) in the lowest exposure tertile worked < 3 years.
About 6% of men’s work-years and 1.2% of women’s were in the highest exposure jobs (salvage and repair; fill, solder, impregnate; leak tester). Men had a mean cumulative exposure of 82,503 unit-days; the mean for women was 47,824 unit-days; 23% of men’s and 16% of women’s PYAR fell into the highest exposure tertile. For 221 workers who provided blood specimens in 1977 (Smith et al. 1982), estimated cumulative exposure and serum PCB level were significantly correlated (Spearman correlation, r = 0.37, p < 0.0001); serum PCB level and duration of exposure were not well correlated (Spearman correlation, r = 0.10, p = 0.15).
Observed deaths, corresponding SMRs using Indiana rates, and the SMR CIs are presented in Table 2 for the three exposure tertiles and overall. Mortality overall was reduced (547 deaths; SMR, 0.81; 95% CI, 0.7–0.9). In race- and sex-specific analyses (not shown), the overall statistics for white males and females were 453 deaths (SMR, 0.82; 95% CI, 0.7–0.9) and 84 deaths (SMR, 0.74; 95% CI, 0.6–0.9), respectively. Two deaths occurred among 19 nonwhite female employees and eight among 11 nonwhite male employees.
No excess deaths due to malignant neoplasms overall were observed (171 deaths; SMR, 0.90; 95% CI, 0.8–1.0). In the MCOD analysis, 268 deaths had cancer as the underlying or contributing cause (MCOD U.S. SMR, 1.00; 95% CI, 0.9–1.1).
Among the a priori cancers of interest, melanoma was in statistically significant excess (9 deaths; SMR, 2.43; 95% CI, 1.1–4.6). In the original analysis (Sinks et al. 1992), there were 8 skin cancer deaths (all melanomas) (U.S. SMR, 4.1; 95% CI, 1.8–8.0). Seven brain cancer deaths occurred after the previous report (present update: 12 deaths; SMR, 1.91; 95% CI, 1.0–3.3; original study: 5 deaths, U.S. SMR, 1.8; 95% CI, 0.6–4.2). The 12 brain cancers included 8 gliomas and 4 carcinomas. Review of the death certificates indicated that 2 of the carcinomas (both in men) could have been metastases. In a sensitivity analysis to determine risk omitting those 2 deaths, brain cancer SMR decreased (10 deaths; SMR, 1.59; 95% CI, 0.8–2.9).
Non-Hodgkin lymphoma mortality, not reported separately in the original study (Sinks et al. 1992), was increased but not statistically significant (9 deaths; SMR, 1.23; 95% CI, 0.6–2.3). No other subcategory of hematopoietic cancers (Hodgkin disease, leukemia and aleukemia, or myeloma) showed excess deaths (results not shown). Other cancers of a priori interest (rectal and biliary passages, liver, and gallbladder) were not in excess.
As is typical of a working population, the cohort overall had no statistically significant increased SMRs for diseases other than cancer and generally decreased SMRs for heart diseases, especially ischemic heart disease (149 deaths; SMR, 0.84; 95% CI, 0.7–1.0). Cardiomyopathy mortality was elevated (13 deaths; SMR, 1.67; 95% CI, 0.9–2.9), with a significant excess in the lowest exposure tertile (7 deaths; SMR, 2.79; 95% CI, 1.1–5.7). There were decreased risks for deaths from other circulatory system diseases (27 deaths; SMR, 0.56; 95% CI, 0.4–0.8); digestive system diseases (14 deaths; SMR, 0.51; 95% CI, 0.3–0.9), including cirrhosis of the liver (6 deaths; SMR, 0.43; 95% CI, 0.2–0.9); and homicide (3 deaths; SMR, 0.46; 95% CI, 0.1–1.3).
In analyses restricted to 2,789 employees who worked at least 90 days, mortality overall was reduced (445 deaths; SMR, 0.79; 95% CI, 0.7–0.9), as was cancer overall (136 deaths; SMR, 0.85; 95% CI, 0.7–1.0). Both melanoma (8 deaths; SMR, 2.66; 95% CI, 1.1–5.2) and brain cancer (11 deaths; SMR, 2.12; 95% CI, 1.1–3.8) were in excess, especially among women: 3 melanoma deaths (SMR, 5.99; 95% CI, 1.2–17.5) and 3 brain cancer deaths (SMR, 2.87; 95% CI, 0.6–8.4). Eliminating 1 male brain cancer death that could have been a metastasis would change the overall SMR to 1.93 (95% CI, 0.9–3.6) and the SMR among men to 1.69 (95% CI, 0.7–3.5). The non-Hodgkin lymphoma mortality increase was not statistically significant (8 deaths; SMR, 1.31; 95% CI, 0.6–2.6), but the rate was higher among women (3 deaths; SMR, 2.42; 95% CI, 0.5–7.1). As in the cohort overall, mortality from heart disease, digestive system disease, and homicide was reduced (results not shown). Cardiomyopathy mortality remained elevated (10 deaths; SMR, 1.55; 95% CI, 0.7–2.8) and occurred exclusively in men (SMR, 1.82; 95% CI, 0.9–3.3).
Table 2 provides Indiana-based SMRs for the three exposure tertiles for all CODs. For both overall mortality and all cancer, there was no significant trend with increasing estimated cumulative exposure (Table 3). Table 3 presents SRRs and RRs (adjusted for sex, age, calendar year, and latency) for melanoma and brain cancer. Melanoma was in excess in the lowest tertile (5 deaths; SMR, 3.72; 95% CI, 1.2–8.7), but the trend test was not significant. Brain cancer mortality increased with exposure (5 deaths in the highest tertile; SMR, 2.71; 95% CI, 0.9–6.3); 3 of the 5 deaths were among women. SMRs, SRRs, and RRs increased with increasing exposure, but only the SRR dose–response trend was statistically significant (p = 0.016). In the sensitivity analysis, when we excluded 2 brain cancer deaths that could have been metastases, results by tertile changed [lowest tertile: 2 deaths; SMR, 0.92 (95% CI, 0.1–3.3); SRR 1.0; middle tertile: 4 deaths; SMR, 1.79 (95% CI, 0.5–4.6); SRR 1.64 (95% CI, 0.3–9.0); highest tertile: 4 deaths; SMR, 2.17 (95% CI, 0.6–5.6); SRR 1.96 (95% CI, 0.4–11.1)]. The SRR dose–response trend remained statistically significant (p = 0.01).
We reran the analysis excluding those (n = 117) with potential solvent exposure. No melanoma or brain cancer deaths occurred among the solvent-exposed workers. Results for all deaths and all cancer deaths did not change significantly (data not shown).
We repeated the estimated cumulative exposure analysis using the exposure zones developed for the original study. Results (data not shown) were similar to those using the JEM: brain cancer was associated with higher levels of exposure, but no dose–response relationship between estimated cumulative PCB exposure and melanoma was found. Analysis of the 153 deaths among the 1,139 workers who ever worked in the highest exposure zone identified in the original study (Sinks et al. 1992) demonstrated no increase in risk with time worked for all deaths or cancer deaths (data not shown). Comparing the original study exposure model with the two JEMs gave Spearman correlations of r = 0.6 (p < 0.0001) for the inhalation JEM and r = 0.4 (p < 0.0001) for the dermal JEM.
For cancer overall, stratifying by latency (time from first exposure to death) did not affect mortality [< 10 years: 11 deaths; SMR 0.80 (95% CI, 0.4–1.4); 10 to < 20 years: 37 deaths, SMR 1.03 (95% CI, 0.7–1.4); ≥20 years: 122 deaths, SMR 0.88 (95% CI, 0.7–1.0)]. Brain cancer mortality was elevated in each latency period [< 10 years: 2 deaths, SMR 2.44 (95% CI, 0.3–8.8); 10 to < 20 years: 4 deaths, SMR 2.63 (95% CI, 0.7–6.7); ≥ 20 years: 6 deaths, SMR 1.53 (95% CI, 0.6–3.3)]. Melanoma mortality was elevated among workers with shorter but not longer latency [< 10 years: 2 deaths, SMR 4.71 (95% CI, 0.6–17.0); 10 to < 20 years: 5 deaths, SMR 5.03 (95% CI, 1.6–11.7); ≥ 20 years: 2 deaths, SMR 0.88 (95% CI, 0.1–3.2)].
Discussion
Studies of mortality in cohorts occupationally exposed to PCBs present inconsistent findings. Nine cohorts of electrical capacitor and transformer manufacturers have been studied in, to date, 17 reports in the literature or in unpublished documents [see Supplemental Material (http://ehp.niehs.nih.gov/docs/2005/8253/supplement.pdf)]. In some cases SMRs were elevated for one sex but not the other. Excess deaths from particular cancers or other diseases have been reported, but there has been little consistency from cohort to cohort, or even within cohorts across studies.
Brown (1987) found excess liver cancer among the high-exposed group (1,607 workers) within a Massachusetts capacitor manufacturing cohort (4 deaths; SMR, 3.3; 95% CI, 0.9–9.3), with all four deaths occurring in women (SMR, 4.4; 95% CI, 1.2–12.3). Taylor et al. (1988) expanded the New York cohort to all those working 90 days or more (6,292 workers) and found a slight excess of digestive system cancer (44 deaths; SMR, 1.3; 95% CI, 1.0–1.8), whereas Kimbrough et al. (1999, 2003), studying a reexpanded New York cohort (n = 7,075) reported no cancer SMR excesses.
Three studies of the same Italian factory (Bertazzi et al. 1982, 1987; Tironi et al. 1996) reported elevated SMRs for lymphatic and hematopoietic cancers, and digestive system cancers in men. Swedish male capacitor manufacturing workers (Gustavsson et al. 1986; Gustavsson and Hogstedt 1997) had no increased mortality risk, whereas Canadian transformer manufacturing workers had excess pancreatic cancer, especially in the transformer assembly department (4 deaths; SMR, 9.8; 95% CI, 2.6–25) (Yassi et al. 1994). Transformer manufacturing workers in Massachusetts exposed to PCBs had an odds ratio of 3.3 (95% CI 1.1–9.3) for lymphoma, compared with co-workers not PCB-exposed (Greenland et al. 1994). In an Illinois capacitor manufacturing facility, Mallin et al. (2004) found excess gastrointestinal cancer, especially among those working 5 or more years between 1952 and 1977 [men, 3 stomach cancer deaths (SMR, 3.09; 95% CI, 0.6–9.0); women, 9 intestinal cancer deaths (SMR, 2.25; 95% CI, 1.0–4.3) and 4 liver cancer deaths (SMR, 5.57; 95% CI, 1.5–14.3)]. The original report on our cohort found three skin cancer (all melanoma) deaths (SMR, 7.0; 95% CI, 1.4–23) and three brain cancer deaths (SMR, 4.8; 95% CI, 1.0–16) among those employed at least 10 years (Sinks et al. 1992).
Some of these cohort studies are uninformative due to small sample size, insufficient latency, or problems in study design. Differences in findings between cohorts could be due to differences in materials or work practices; each plant potentially had a unique pattern of exposures. Although air concentrations are expressed in standard units that permit comparisons across plants, dermal exposure measurements are not, and dermal exposure is a significant route for PCBs (Lees et al. 1987; Safe 1984; Smith et al. 1982; Wolff et al. 1982). Different results in studies of the same cohort could be due to variations in study eligibility criteria, in choice of comparison groups, or in how results were presented.
Several studies have reported significantly higher serum or adipose tissue levels of PCBs in cancer cases than in controls (Aronson et al. 2000; Charlier et al. 2004; Howsam et al. 2004; Rothman et al. 1997). Others have seen no association of serum PCB and cancer risk (Dorgan et al. 1999; Gammon et al. 2002; Rusiecki et al. 2004; Ward et al. 2000). It should be noted that in all these studies, whether blood was collected prospectively in the 1970s when PCB use was widespread or retrospectively 20–30 years later when mean serum levels had decreased by 80–90% (Schecter et al. 2005), serum levels among the environmentally exposed would be much lower than among the occupationally exposed. For example, archived 1974 serum samples of Maryland residents had mean levels of 7.56 and 6.55 ng PCBs/mL for non-Hodgkin lymphoma cases and controls, respectively (Rothman et al. 1997), whereas workers from the Indiana plant had mean 1977 levels of 546 and 111 ng PCBs/mL serum for the most and least exposed workers, respectively (Smith et al. 1982).
As in many cohort mortality studies, limited data were available to construct the JEM: individual work histories, detailed job descriptions for hourly jobs, and 56 measurements collected at the plant in 1977. The JEM used proximity to the ovens, as did the zone classification used in the original study (Sinks et al. 1992), but also incorporated job descriptions, plant layouts, workers’ mobility, exposure intensity and frequency, inhalation and dermal exposure to PCBs, and exposure to other chemicals. Analyses replicating the exposure zones from the original study yielded results similar to those using the JEM (brain cancer was associated with higher levels of exposure; there was no dose–response relationship between estimated cumulative PCB exposure and melanoma). Mortality did not increase with time worked in the highest exposure zone. The zone exposure model and inhalation and dermal JEMs were significantly correlated. An independent measure of cumulative exposure is body burden. Serum collected from 221 Indiana workers in 1977 was analyzed for PCBs (Smith et al. 1982). Cumulative exposure estimated with the JEM and serum PCB levels were significantly correlated.
Our findings confirm those of the original study (Sinks et al. 1992) of excess melanoma and brain cancer mortality. Although no other study of capacitor manufacturing workers found elevated SMRs for these sites, a study of transformer manufacturing observed an elevated standardized incidence ratio (SIR) for brain cancer among males ever in PCB-exposed jobs (4 diagnoses; SIR, 4.4; 95% CI, 1.2–12) (Liss 1989). Excess melanoma was reported (Bahn et al. 1976) for PCB-exposed employees of a New Jersey petrochemical plant (2 cases; SIR, 50; 95% CI 5.6–217). Nine employees of Norwegian hydroelectric power plants ever exposed to PCBs had melanoma (SIR, 1.8; 95% CI, 0.8–3.6), with risk concentrated among those who also had > 15 μT years of magnetic field exposure versus fewer years (9 cases; SIR, 2.7; 95% CI, 1.2–5.2) or > 30 exposure-years to electrical discharges versus fewer years (7 cases; SIR, 2.8; 95% CI, 1.1–6.0) (Tynes et al. 1994). Loomis et al. (1997) found excess melanoma mortality in a large cohort of electric utility workers, with increasing risk for increasing cumulative exposure, as well as increased brain cancer risk among workers in the two intermediate (but not the highest) quartiles of exposure.
We found higher SMRs among women for both melanoma and brain cancer. Men, however, generally had jobs with higher exposure as well as higher cumulative PCB exposure. Some interaction between estrogenic PCBs and hormones may contribute to the higher risk for women (Gore et al. 2002; Soontornchat et al. 1994). Because women have a higher percentage of adipose tissue, PCBs may be stored in their bodies longer (Brown 1994).
Our sensitivity analysis of the brain cancer deaths (8 gliomas and 4 carcinomas), excluding 2 carcinomas that could have been metastasis, still found the highest SMRs and SRRs among workers in the highest tertile of estimated cumulative exposure. An analysis excluding 117 workers with potential solvent exposure at the plant did not affect melanoma or brain cancer mortality. Stratifying by latency (time from first exposure to death) did not affect mortality for cancer overall and for brain cancer. Melanoma mortality was elevated among workers with shorter but not longer latency.
As in most cohort studies, we had no information on risk factors such as family history or genetic susceptibility; lifestyle choices, such as sun exposure, that could affect mortality; or on previous or subsequent employment. This last limitation is significant for the two-thirds of the cohort who worked < 3 years in the plant. (It should be noted that 97% of workers in the highest tertile, with the highest brain cancer risk, worked ≥3 years.)
Increased mortality among short-term workers has been reported, particularly for CODs associated with disorders that might affect employment or with an unhealthy lifestyle. Kolstad and Olsen (1999) found that shorter durations of employment were associated with more preemployment hospitalizations for alcohol use, accidents, and the effects of violence. We have no lifestyle or hospitalization information for our cohort. However, when short-term workers were excluded from the analysis, the already low SMRs for cirrhosis of the liver and homicide (Table 2) dropped even farther, whereas the SMRs for melanoma and brain cancer increased.
In conclusion, we found evidence of an association between employment at this plant and melanoma and brain cancer mortality. We used a JEM that incorporated both inhalation and dermal exposure potentials to estimate cumulative exposure. However, melanoma mortality was not associated with estimated cumulative PCB exposure, and for brain cancer, the association between mortality and estimated PCB cumulative exposure did not demonstrate a clear dose–response relationship.
The cancer incidence study we are conducting on this cohort (and the New York and Massachusetts NIOSH cohorts) may provide some additional insight.
Supplementary Material
Supplementary Material Supplemental Material is available online at http://ehp.niehs.nih.gov/docs/2005/8253/supplement.pdf
We thank T. Schnorr, L. Pinkerton, T. Sinks, and the reviewers for their valuable comments. We also thank C. Gersic and V. Drake for assistance in data preparation and L. Schoolfield for assistance in retrieving relevant literature.
This study was entirely funded by National Institute for Occupational Safety and Health (NIOSH) base operating funds. The findings and conclusions in this report are those of the authors and do not necessarily represent the views of NIOSH.
Table 1 NIOSH Indiana capacitor cohort as of 31 December 1998.
Characteristic No., %
Total workers 3,643
Excluded from analysisa 74
Race, sex, and vital status
No. (no. of deaths, % dead in stratumb)
White females 833 (84, 10%)
Nonwhite females 19 (2, 11%)
White males 2,706 (453, 17%)
Nonwhite males 11 (8, 73%)
Total analyzed 3,569 (547, 15%)
Age at first employment (years)
Median 24
Mean ± SD 27 ± 8.2
Duration of employment (years)
Median 1.3
Mean ± SD 3.9 ± 5.3
Estimated cumulative exposure (unit-days)c 22% worked < 90 days 43% worked 90 days to < 3 years 35% worked ≥3 years
Lowest tertile (0 to < 11,000) 753, 97% 634, 42% 105, 8%
Middle tertile 21, 3% 867, 57% 371, 30%
Highest tertile (≥90,000) 0 22, 1% 775, 62%
PYAR 108,930
a Forty-one workers were missing employment dates, 1 stopped working before PCBs were used, 1 worked < 1 day, 26 were missing date of birth, and 5 were lost to follow-up before 1960.
b Subjects coded as “alive” include 104 persons with vital status unknown (considered alive until the date lost to follow-up).
c Estimated cumulative exposure could not be calculated for 21 workers with periods of unknown exposure level.
Table 2 Mortality in the NIOSH Indiana capacitor cohort for selected causes, by exposure tertile and overall, based on Indiana state rates for 1960–1998.
Lowest tertilea Middle tertile
Highest tertile
Overall b
Underlying CODc (ICD-9 codes) nd SMR 95% CI n SMR 95% CI n SMR 95% CI n SMR 95% CI
All cancers (140–208) 56 0.94 (0.7–1.2) 62 0.93 (0.7–1.2) 52 0.83 (0.6–1.1) 171 0.90 (0.8–1.0)
Buccal and pharyngeal (140–149) 2 1.97 (0.2–7.1) 0 1 0.88 (0.0–4.9) 3 0.90 (0.2–2.6)
Digestive system (150–159) 14 1.13 (0.6–1.9) 14 0.99 (0.5–1.7) 11 0.80 (0.4–1.4) 39 0.96 (0.7–1.3)
Esophagus (150) 2 1.47 (0.2–5.3) 2 1.27 (0.2–4.6) 3 1.95 (0.4–5.7) 7 1.55 (0.6–3.2)
Stomach (151) 3 2.40 (0.5–7.0) 0 2 1.47 (0.2–5.3) 5 1.23 (0.4–2.9)
Intestine (except rectum) (152–153) 7 1.43 (0.6–3.0) 4 0.72 (0.2–1.8) 4 0.74 (0.2–1.9) 15 0.94 (0.5–1.5)
Rectum (154) 0 1 0.97 (0.0–5.4) 0 1 0.34 (0.0–1.9)
Biliary passages, liver, and gallbladder (155, 156) 0 2 1.46 (0.2–5.3) 0 2 0.51 (0.1–1.8)
Pancreas (157) 2 0.77 (0.1–2.8) 5 1.69 (0.5–3.9) 2 0.70 (0.1–2.5) 9 1.06 (0.5–2.0)
Respiratory system (160–165) 16 0.77 (0.4–1.3) 24 0.98 (0.6–1.5) 19 0.79 (0.5–1.2) 59 0.85 (0.6–1.1)
Trachea, bronchus, and lung (162) 16 0.80 (0.5–1.3) 24 1.02 (0.7–1.5) 19 0.82 (0.5–1.3) 59 0.88 (0.7–1.1)
Breast (174–175) 4 1.04 (0.3–2.7) 3 0.92 (0.2–2.7) 0 8 0.83 (0.4–1.6)
Male genital organs (185–186) 1 0.43 (0.0–2.4) 2 0.69 (0.1–2.5) 1 0.32 (0.0–1.8) 4 0.47 (0.1–1.2)
Prostate (185) 1 0.48 (0.0–2.7) 2 0.76 (0.1–2.7) 1 0.33 (0.0–1.8) 4 0.51 (0.1–1.3)
Urinary organs (188–189) 1 0.39 (0.0–2.2) 1 0.34 (0.0–1.9) 2 0.71 (0.1–2.6) 4 0.48 (0.1–1.2)
Kidney (189.0–189.2) 0 1 0.55 (0.0–3.0) 1 0.59 (0.0–3.3) 2 0.38 (0.0–1.4)
Bladder and other urinary organs (188, 189.3–189.9) 1 1.11 (0.0–6.2) 0 1 0.87 (0.0–4.9) 2 0.63 (0.1–2.3)
Other and unspecified sites (170–173, 187, 190–199) 13 1.54 (0.8–2.6) 8 0.88 (0.4–1.7) 13 1.63 (0.9–2.8) 34 1.32 (0.9–1.9)
Melanoma (172) 5 3.72* (1.2–8.7) 2 1.51 (0.2–5.4) 2 1.97 (0.2–7.1) 9 2.43* (1.1–4.6)
Brain and nervous system (191–192) 3 1.38 (0.3–4.0) 4 1.79 (0.5–4.6) 5 2.71 (0.9–6.3) 12 1.91 (1.0–3.3)
Other and unspecified sites (187, 194–199) 5 1.25 (0.4–2.9) 1 0.22 (0.0–1.2) 5 1.17 (0.4–2.7) 11 0.86 (0.4–1.5)
Lymphatic and hematopoietic (200–208) 5 0.82 (0.3–1.9) 10 1.53 (0.7–2.8) 5 0.87 (0.3–2.0) 20 1.08 (0.7–1.7)
Non-Hodgkin lymphoma (200, 202) 1 0.42 (0.0–2.3) 5 1.93 (0.6–4.5) 3 1.30 (0.3–3.8) 9 1.23 (0.6–2.3)
Diabetes mellitus (250) 2 0.42 (0.1–1.5) 3 0.58 (0.1–1.7) 5 1.03 (0.3–2.4) 10 0.67 (0.3–1.2)
Blood and blood-forming diseases (281–289) 0 1 1.40 (0.0–7.8) 0 1 0.49 (0.0–2.7)
Alcoholism and mental disorders (290–319) 1 0.54 (0.0–3.0) 2 1.03 (0.1–3.7) 0 3 0.54 (0.1–1.6)
Nervous system diseases (320–337, 340–389) 0 3 0.82 (0.2–2.4) 2 0.60 (0.1–2.2) 5 0.47 (0.2–1.1)
Diseases of the heart (390–398, 402, 404, 410–414, 420–429) 48 0.76 (0.6–1.0) 64 0.85 (0.7–1.1) 67 0.90 (0.7–1.1) 179 0.83* (0.7–1.0)
Ischemic heart disease (410–414, 429.2) 37 0.72* (0.5–1.0) 58 0.94 (0.7–1.2) 54 0.88 (0.7–1.1) 149 0.84* (0.7–1.0)
Cardiomyopathy (425) 7 2.79* (1.1–5.7) 3 1.10 (0.2–3.2) 3 1.20 (0.2–3.5) 13 1.67 (0.9–2.9)
Other circulatory system (401, 403, 405, 415–417, 430–438, 440–459) 10 0.70 (0.3–1.3) 8 0.48* (0.2–0.9) 8 0.48* (0.2–1.0) 27 0.56** (0.4–0.8)
Respiratory system (460–466, 470–478, 480–487, 490–519) 10 0.78 (0.4–1.4) 14 0.93 (0.5–1.6) 12 0.77 (0.4–1.3) 37 0.85 (0.6–1.2)
Digestive system (520–537, 540–543, 550–553, 555–558, 560, 562–579) 8 0.90 (0.4–1.8) 3 0.31* (0.1–0.9) 3 0.35 (0.1–1.0) 14 0.51** (0.3–0.9)
Cirrhosis of liver (571) 5 1.10 (0.4–2.6) 0 1 0.23 (0.0–1.3) 6 0.43* (0.2–0.9)
Genitourinary system (580–608, 610, 611, 614–629) 2 0.80 (0.1–2.9) 0 1 0.37 (0.0–2.1) 3 0.37 (0.1–1.1)
Skin and subcutaneous tissue (680–686, 690–709) 0 1 5.94 (0.2–33.1) 0 1 2.09 (0.1–11.6)
Symptoms and ill-defined conditions (780–796, 798, 799) 2 1.24 (0.2–4.5) 1 0.64 (0.0–3.6) 0 3 0.69 (0.1–2.0)
Accidents (E800–E848, E850–E888, E890–E949) 19 0.85 (0.5–1.3) 12 0.59 (0.3–1.0) 14 1.18 (0.6–2.0) 45 0.82 (0.6–1.1)
Suicide (E950–E959) 6 0.72 (0.3–1.6) 6 0.77 (0.3–1.7) 7 1.41 (0.6–2.9) 19 0.90 (0.5–1.4)
Homicide (E960–E978) 2 0.71 (0.1–2.6) 1 0.42 (0.0–2.3) 0 3 0.46 (0.1–1.3)
HIV related (042–044) 3 3.16 (0.7–9.2) 0 0 3 1.42 (0.3–4.1)
Other causes (residual codes) 4 0.77 (0.2–2.0) 3 0.59 (0.1–1.7) 6 1.47 (0.5–3.2) 13 0.90 (0.5–1.5)
CODs not obtained 7 1 2 10
All causes 180 0.84* (0.7–1.0) 185 0.78** (0.7–0.9) 179 0.83* (0.7–1.0) 547c 0.81** (0.7–0.9)
a Lowest tertile defined by cumulative exposure < 11,000 unit-days and highest tertile by cumulative exposure ≥ 90,000 unit-days.
b Total is greater than sum of tertiles because cumulative exposure could not be estimated for 21 workers, including 3 deceased workers with periods of employment lacking exposure data.
c Categories omitted because no deaths occurred include female genital organ cancers (ICD-9 codes 179–184), benign neoplasms (210–239), tuberculosis (010–018), and musculoskeletal diseases (710–721, 730).
d Observed number of deaths.
* p < 0.05.
** p < 0.01.
Table 3 NIOSH Indiana capacitor cohort: mortality from selected CODs according to estimated cumulative exposure to PCBs.
Cumulative exposure (unit-days)
< 11,000
11,000–89,999
≥90,000
Underlying COD n Ratio (95% CI) n Ratio (95% CI) n Ratio (95% CI) Trend p-value
All causes SMRa 180 0.84 (0.7–1.0)* 185 0.78 (0.7–0.9)** 179 0.83 (0.7–1.0)* 0.84
All cancers SMR 56 0.94 (0.7–1.2) 62 0.93 (0.7–1.2) 52 0.83 (0.6–1.1) 0.48
Melanoma SMR 5 3.72 (1.2–8.7)* 2 1.51 (0.2–5.4) 2 1.97 (0.2–7.1) 0.62
SRR 1 0.38 (0.1–2.0) 0.58 (0.1–3.5) 0.72
RRb 1 0.43 (0.1–2.3) 0.59 (0.1–3.2) 0.71
Brain cancer SMR 3 1.38 (0.3–4.1) 4 1.79 (0.5–4.6) 5 2.71 (0.9–6.3) 0.34
SRR 1 1.01 (0.2–4.6) 1.48 (0.3–6.4) 0.016
RR 1 1.29 (0.3–5.8) 1.95 (0.4–8.5) 0.37
a SMR using Indiana rates.
b Obtained via Poisson regression, adjusted for sex, age (< 50, ≥50 years), calendar year (before 1980, after 1980), and latency (< 10, 10–19, ≥20 years).
* p < 0.05.
** p < 0.01.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8273ehp0114-00002416393653ResearchRapidly Measured Indicators of Recreational Water Quality Are Predictive of Swimming-Associated Gastrointestinal Illness Wade Timothy J. 1Calderon Rebecca L. 1Sams Elizabeth 1Beach Michael 2Brenner Kristen P. 3Williams Ann H. 1Dufour Alfred P. 31 National Health and Environmental Effects Research Laboratory, Human Studies Division, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA2 Parasitic Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA3 National Exposure Research Laboratory, U.S. Environmental Protection Agency, Cincinnati, Ohio, USAAddress correspondence to R.L. Calderon, U.S. EPA Human Studies Division, MD 58A, Research Triangle Park, NC 27711 USA. Telephone: (919) 966-0617. Fax: (919) 966-6212. E-mail:
[email protected] authors declare they have no competing financial interests.
1 2006 1 9 2005 114 1 24 28 2 5 2005 1 9 2005 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Standard methods to measure recreational water quality require at least 24 hr to obtain results, making it impossible to assess the quality of water within a single day. Methods to measure recreational water quality in ≤ 2 hr have been developed. Application of rapid methods could give considerably more accurate and timely assessments of recreational water quality. We conducted a prospective study of beachgoers at two Great Lakes beaches to examine the association between recreational water quality, obtained using rapid methods, and gastrointestinal (GI) illness after swimming. Beachgoers were asked about swimming and other beach activities and 10–12 days later were asked about the occurrence of GI symptoms. We tested water samples for Enterococcus and Bacteroides species using the quantitative polymerase chain reaction (PCR) method. We observed significant trends between increased GI illness and Enterococcus at the Lake Michigan beach and a positive trend for Enterococcus at the Lake Erie beach. The association remained significant for Enterococcus when the two beaches were combined. We observed a positive trend for Bacteroides at the Lake Erie beach, but no trend was observed at the Lake Michigan beach. Enterococcus samples collected at 0800 hr were predictive of GI illness that day. The association between Enterococcus and illness strengthened as time spent swimming in the water increased. This is the first study to show that water quality measured by rapid methods can predict swimming-associated health effects.
bathing beachescohort studiesdiarrheagastrointestinal diseasesGreat Lakes Regionrecreational waterswimmingwater quality
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Swimming in coastal waters is a favored pastime in the United States. In a survey of > 75,000 households, 42% of respondents ≥ 16 years of age, equivalent to approximately 89 million individuals, reported swimming in recreational waters annually (National Survey on Recreation and the Environment 2000–2002). Such waters are often contaminated by human sewage as a result of discharges or overflows [U.S. Environmental Protection Agency (EPA) 2001]. Swimming in fecally contaminated recreational waters has consistently been associated with gastrointestinal (GI) illness (Pruss 1998; Wade et al. 2003). The incidence of illness attributable to recreational water exposure appears to be increasing. The Centers for Disease Control and Prevention (CDC) reported 21 recreational water outbreaks in 2000, more than any single previous year since systematic surveillance began (Lee et al. 2002). The Natural Resources Defense Council (Dorfman 2005) reported that there were more beach closings and advisories in 2000 than in any previous year; 85% of these closings and advisories were due to bacteria levels that exceeded standards.
Because of the great diversity of pathogenic microorganisms transmitted by contaminated water and the difficulty and cost of directly measuring all microbial pathogens in environmental samples, organisms that may indicate the presence of sewage and fecal contamination (indicator organisms) are often used for monitoring and regulation of recreational and drinking waters. Indicator organisms are common inhabitants of the intestinal tract of warm-blooded animals. They are found in fecal material at high concentrations and are easier to measure in the environment than are pathogens. Although indicator organisms do not cause illness under normal conditions, they represent a measure of fecal contamination. Human sewage is a source of fecal contamination and also is known to contain pathogenic micro-organisms (Griffin et al. 2003; Jones 2001; Madore et al. 1987). Direct and indirect exposure to sewage has been associated with illness (Alexander et al. 1992; El-Sharkawi and Hassan 1982; Fleisher et al. 1996; Khuder et al. 1998; Mac Kenzie et al. 1994; Yamamoto et al. 2000).
Current recreational water-quality guidelines are based on studies conducted in the 1970s and 1980s (Cabelli et al. 1975, 1979, 1982; Dufour 1984). The currently recommended bacterial indicators are based on microbiological methods that involve culturing fecal indicator bacteria, such as Enterococcus spp. or Escherichia coli, and counting the colony-forming units. One shortcoming of these methods is that the bacteria require at least 24 hr to grow visible colonies, making it impossible for beach managers to assess the quality of water on the day of sample collection.
Because microbial water quality can change rapidly (Boehm et al. 2002), guidelines based on indicator organisms that require 24 hr to develop are likely to result in both unnecessary beach closings and the exposure of swimmers to poor-quality water. A recent study estimated that up to 40% of beach closures are in error (Kim and Grant 2004).
In 2000, Congress passed an amendment to the Clean Water Act, the Beaches Environmental Assessment and Coastal Health (BEACH) Act (2000). Among other provisions, the BEACH Act required the U.S. EPA to conduct research to provide the support of new criteria for recreational waters. Methods have been developed to measure microorganisms more rapidly. A modified version of polymerase chain reaction (PCR), quantitative TaqMan PCR (QPCR; Applied Biosystems, Foster City, CA), has been developed to quantify indicator bacteria in recreational waters (Santo Domingo et al. 2003) in ≤ 2 hr. Because these methods provide a faster assessment of water quality, they have the potential to significantly reduce illnesses resulting from exposure to recreational waters and also to reduce errors in beach closings or public notifications.
In 2003, we conducted the first in a series of studies designed to evaluate the ability of QPCR to predict health effects of recreational-water exposure. Secondary goals were to evaluate specific study design and analytical methods, such as methods for averaging indicator values, assignment of exposure measures to swimmers, and swimming definitions.
Materials and Methods
We conducted a prospective cohort study of beachgoers at two beaches in the Great Lakes region. One beach was located in the Indiana Dunes National Lakeshore, in Indiana, on Lake Michigan (beach A), and the second was located near Cleveland, Ohio, on Lake Erie (beach B). The study consisted of a health survey of beachgoers and water-quality evaluation.
The beaches were selected specifically because they were affected by discharges from waste treatment plants. The sources of fecal contamination affecting beach A are waste-water treatment plant effluents from at least four communities that collectively contribute about 16 million gallons per day to small streams. The streams are tributaries of Burns Ditch, which empties into Lake Michigan approximately 2 miles east of the beach. Beach B is a short distance west of metropolitan Cleveland, Ohio. The beach is potentially affected by sewage treatment plant discharges into Lake Erie to the east and west. An outfall about 7 miles to the west discharges 6.5 million gallons of wastewater per day. Within 5 miles to the east of the beach, two other wastewater treatment plants discharge about 40 million gallons of treated sewage per day.
The study design, questionnaires, and materials were reviewed and approved by an Institutional Review Board for the CDC. All participants provided verbal informed consent before enrollment. We complied with all applicable ethical requirements, in accordance with all federal regulations for the protection of human subjects, in conducting this study.
Beachgoer health surveys.
The health survey was administered in three parts: enrollment, beach interview, and telephone interview. Interviewers approached beachgoers on weekends and holidays during the summer. Beachgoers who agreed to participate provided verbal informed consent and returned to complete the beach interview as they left the beach. An adult (≥ 18 years of age) answered questions for other household members. The beach interview included questions about demographics, swimming and other beach activities, consumption of raw or undercooked meat or runny eggs, chronic illnesses, allergies, acute health symptoms in the past 48 hr, contact with sick persons in the past 48 hr, other swimming in the past 48 hr, and contact with animals in the past 48 hr. The telephone interview was conducted 10–12 days after the beach visit, and an adult ≥ 18 years of age answered questions for other household members who visited the beach. The telephone interview consisted of questions about health symptoms experienced since the beach visit, and other swimming- or water-related activities, contact with animals, and consumption of high-risk foods since the beach visit. Bilingual (English–Spanish) interviewers were available. Interviews were conducted at beach A between 1 June 2003 through 3 August 2003 and at beach B between 2 August 2003 and 14 September 2003.
Although respiratory, ear, eye, and skin rash symptoms were also evaluated, we present results only for GI illness. GI illness was defined as any of the following: diarrhea (three or more loose stools in a 24-hr period), vomiting, nausea and stomachache, and nausea or stomachache that affect regular activity (inability to perform regular daily activities). This definition of GI illness is consistent with definitions used in recent studies (Colford et al. 2002; Payment et al. 1991, 1997).
Water sample collection and analysis.
Water samples were collected on each study day. Three times a day (0800 hr, 1100 hr, and 1500 hr), two water samples were collected at beach A along each of three transects perpendicular to the shoreline, one in waist-high water (1 m deep) and one in shin-high water (0.3 m deep). A representation of the sampling locations and additional details of the sampling protocol have been described previously (Haugland et al. 2005). Transects were located ≥ 60 m apart to include the area used by most beachgoers. Water samples were collected at beach A on weekends and holidays during the period from 31 May 2003 through 3 August 2003. Samples also were collected three times a day at nine beach B locations. Because jetties divided the beach and prevented free circulation of water, additional samples were collected to characterize the beach (Haugland et al. 2005). Samples were kept on ice at 1–4°C during the time before analysis.
A detailed description of sample preparation and QPCR analysis for Enterococcus spp. has been described elsewhere (Haugland et al. 2005). Primers and probes for the Bacteroides analyses were conducted as described by Dick and Field (2004), and analyses were conducted using conditions described by Haugland et al. (2005). Additional details regarding the estimation of cell equivalents have also been described (Applied Biosystems 1997). In brief, we used QPCR to detect and quantify Enterococcus and Bacteroides in water samples based on the collection of these organisms on membrane filters, extraction of their total DNA, and PCR amplification (i.e., a process whereby the quantity of DNA is doubled in each cycle of amplification) of a genus-specific DNA sequence using the TaqMan PCR product detection system. The reactions were performed in a specially designed state-of-the-art thermal cycling instrument (SMART Cycler TD System, Cepheid, Sunnyvale, CA) that automates the detection and quantitative measurement of the fluorescent signals produced by probe degradation during each cycle of amplification. Cell equivalents were estimated by comparing the cycle threshold to standard samples containing a known quantity of the target organism cells. If no threshold was achieved after 45 cycles, the sample was considered below the limit of detection. Because a separate set of calibrator reactions was conducted for each test sample, the limit of detection (LOD) can vary from sample to sample. This process has been described previously (Applied Biosystems 1997; Haugland et al. 2005). Water samples were filtered at the local laboratory (Great Lakes Scientific, Inc., Stevensville, MI, and Cuyahoga County Sanitary Engineering Division, Cleveland, OH), and filters were shipped on dry ice to the contract laboratory (EMSL Analytical Inc. Laboratory, Westmont, NJ) for QPCR analysis. Results for the QPCR analyses are expressed as QPCR cell equivalents (QPCRCE) per 100-mL volume.
Data analysis.
We created two variables to represent exposure to indicator organisms: an average of all measures collected by day, and an average of measures specific to day and reported swimming location. The base 10 log (log10) of the geometric mean (the mean of the log10 of the count) was used for averaging results. Measures below the LOD were assigned values using maximum likelihood, assuming a log-normal distribution (El-Shaarawi and Viveros 1997). Quantile–quantile plots confirmed the approximate log-normal distribution of the water-quality measures, which are often approximately log-normally distributed (El-Shaarawi 1989; El-Shaarawi and Viveros 1997; Noble et al. 2003). We defined swimming in three ways: “any contact” included anyone reporting contact with water; and “body immersion” and “head immersion” included swimmers who reported a minimum of immersing their body or head, respectively.
We used logistic regression to model the effect of swimming and water quality on illness. Models included continuous measures of water quality as predictor variables and a 0/1 indicator of illness as the outcome. We used nested interaction terms to allow contrasts among swimmers and between swimmers and nonswimmers. To evaluate the overall risk associated with swimming, we excluded the water-quality measures from the models. We determined odds ratios (ORs) by taking the exponent of the regression coefficients from the logistic regression models. We estimated adjusted predicted probabilities from logistic regression models, holding covariates constant at their mean.
Variables that were related to GI illness or swimming in tabulations, or were suspected by investigators to correlate with GI illness, were considered for regression models. As a result, we evaluated the following variables in initial models: age; sex; race; allergies; swimming within 48 hr before the beach visit or between the beach visit and telephone interview; contact with animals; contact with persons with GI illness; consumption of raw meat, fish, or undercooked eggs; presence of chronic GI illness, skin conditions, or asthma; frequency of beach visits; and use of nose plugs. We excluded from the analysis beachgoers who reported any GI symptoms within 48 hr of the beach visit.
We selected final regression models using backward deletion as described by Rothman and Greenland (1998). Initially, all covariates were included in the model. Covariates were then removed in an iterative fashion until removal of any remaining covariates resulted in > 5% change in the exposure–illness relationship.
We used SAS, version 8.0 (SAS Institute, Cary, NC), S-plus, version 6.1 (Insightful Corp. 2002), and Stata, version 8.2 (Stata Corp., College Station, TX) for data analysis.
Results
We interviewed beachgoers at beach A from 1 June 2003 through 3 August 2003 on weekends and holidays, for a total of 20 days. We interviewed beachgoers at beach B from 2 August 2003 through 14 September 2003, for a total of 13 days. At beach B, no interviews were conducted because of bad weather on 17 August and 1 September. There were 5,796 household interview attempts at both beaches. The household interviewing response rate (completed/attempted) through the completion of the telephone interview was 56%. Data were available for a total of 3,221 households (5,717 individuals), 1,639 households (2,840 individuals) at the Lake Erie beach (beach B), and 1,582 households (2,877 individuals) at the Lake Michigan beach (beach A). After excluding subjects with GI illness at baseline, data were available for 5,667 individuals.
Water quality.
QPCRCE results for the measurements of indicator organisms on study days are shown in Table 1. The QPCRCE for Bacteroides was considerably higher than that for Enterococcus, although there were more results below the LOD for Bacteroides. At beach A, 28% of Bacteroides samples were below the LOD, and at beach B, 21% of Bacteroides samples were below the LOD. Enterococcus QPCRCE at beach A was slightly higher than at beach B (p = 0.06). There was no difference in Bacteroides QPCRCE between beach A and beach B.
Swimming and GI illness.
The incidence of GI illness among swimmers and nonswim-mers is shown in Table 2. At beach A, the incidence of GI illness was 10% among swimmers, compared to 5% among nonswimmers. At beach B, the incidence among swimmers ranged from 12% for those with any contact with water and to 14% for those who immersed their head, compared to 10% in nonswimmers. Fewer beachgoers reported swimming at beach B than at beach A: at beach A, 75% of respondents reported contact with water, whereas only about 50% reported contact with water at beach B. GI illness was associated with swimming at both beaches. At beach A, those with any contact with water were almost twice as likely to have GI illness compared with nonswimmers [adjusted OR (AOR) = 1.96; 95% confidence interval (CI), 1.33–2.90]. Those immersing their body and head were at slightly higher risk (for body immersion: AOR = 2.26; 95% CI, 1.51–3.39; for head immersion: AOR = 2.14; 95% CI, 1.41–3.27). The risk of GI illness associated with swimming was slightly less at beach B (for head immersion: AOR = 1.50; 95% CI, 1.06–2.13).
At both beaches, swimmers were younger, more likely to be male, more likely to eat food or consume beverages at the beach, and more likely to report allergies. At beach A, swimmers were more likely to have consumed raw or undercooked meat within 48 hr of the beach visit, more likely to have had contact with known or unknown animals, and slightly less likely to report chronic GI illness (1.2% vs. 2.2%). At beach B, nonswimmers were more likely to have GI symptoms at baseline (3.4% vs. 1.7%) and more likely to report asthma.
Water quality and GI illness.
Table 3 shows the associations between QPCRCE and the risk of GI illness for each beach and both beaches combined. In these models, contrasts were created to show ORs of a unit increase in exposure among swimmers. At both beaches, we observed a trend between increasing mean log10 QPCRCE of Enterococcus and risk of GI illness.
We observed a slightly stronger association with GI illness for the overall daily average of Enterococcus QPCRCE than for averages specific to a beachgoer’s reported swimming location. At beach A, a log10 increase in the daily average of Enterococcus QPCRCE was associated with a 1.43 (95% CI, 1.08–1.90) increase in the odds of GI illness for those immersing their bodies. At beach B, estimates for trends between GI illness and Enterococcus QPCRCE daily averages were also elevated but slightly lower.
Bacteroides QPCRCE was positively associated with illness at beach B, but trends were of borderline statistical significance (p < 0.1). Again, we found little difference between the overall daily average and averages based on a beachgoer’s reported swimming location. No association was observed between Bacteroides QPCRCE and GI illness at beach A.
Trends tended to be stronger when we defined swimming as body or head immersion than when we defined swimming as any contact with water. Defining swimming as head immersion at beach B resulted in a weaker trend than did body immersion or any contact with water, but at this beach only 18% of respondents reported immersing their head.
We included an indicator for beach in the models that combined the results for both beaches. No trend between GI illness and Bacteroides QPCRCE was observed when both beaches are combined because of the lack of an observed trend at beach A. Trends between illness and daily averages of Enterococcus QPCRCE were statistically significant (p = 0.005). A log10 increase in Enterococcus QPCRCE was associated with a 1.37 (95% CI, 1.10–1.71) increase in the odds of GI illness. A likelihood ratio test comparing the saturated model with the restricted model indicated that the interaction between beach and daily averaged water-quality measure was not statistically significant (p = 0.48). The beach effect was statistically significant (AOR = 0.64; 95% CI, 0.52–0.73, beach B vs. beach A), reflecting the lower overall incidence of GI illness at beach A.
Figure 1 illustrates the predicted probabilities for GI illness as a function of the log10 QPCRCE Enterococcus measures for swimmers immersing their bodies at both beaches combined.
We examined the 0800 hr samples separately to see if water samples tested in the morning were predictive of GI illness among swimmers that day. As shown in Table 3, Enterococcus QPCRCE measured at 0800 hr was associated with GI illness that day. Although the trends are not as strong as the daily or location-specific averages, Enterococcus QPCRCE measured at 0800 hr was predictive of GI illness that day, with a log10 increase associated with an approximately 1.2 increase in the odds of GI illness.
The trend between increasing Enterococcus QPCRCE with illness was stronger among swimmers who spent more time in the water (Table 4). A log10 increase in Enterococcus QPCRCE and GI illness among those spending > 2 hr in the water was associated with a nearly 3-fold increase in the odds of GI illness (AOR = 2.89; 95% CI, 1.55–5.40).
Discussion
This is the first study to demonstrate the ability of rapid indicator methods to predict health effects. The results showed that Enterococcus measured by QPCR can predict GI illness after swimming in fecally contaminated fresh water. The results also demonstrate that samples collected each morning could allow beach managers to assess the microbiological safety of the beach before most beachgoers are exposed. Incorporation of rapid measurements such as these into a regulatory framework has the potential to improve beach management decisions and protect swimmers’ health.
Swimmers at the two Great Lake beaches had a higher incidence of GI illness than did nonswimmers. Among swimmers at beach A, risk of illness increased as daily averages of Enterococcus QPCRCE increased. Among swimmers at beach B, daily averages of Enterococcus QPCRCE were also positively associated with GI illness, although the 95% CI of the OR included 1.0. This power to detect a significant effect at beach B may have been limited because of fewer swimmers at this beach. Combining beaches produced significant trends with both daily averages and averages of samples collected at 0800 hr only. The association between Enterococcus QPCRCE and GI illness strengthened as the time spent in water increased, possibly reflecting an increased risk of illness resulting from increased exposure to fecal contamination among those spending longer periods in the water.
Using QPCRCE averages specific to a beachgoer’s reported swimming location did not improve the relationship between illness and water quality. This may be because swimmers swam in several locations and did not restrict their swimming along one transect. Also, recall or reporting errors in swimming location would lead to misclassification. As a result, the daily averages that combined results at each location, time, and water depth may have been a better characterization of the exposure of an average swimmer.
Results for Bacteroides QPCRCE were less promising, and interpretation of the results is limited because a relatively high proportion of samples were below the LOD. Although a borderline trend was noted at beach B, where fewer samples were below the LOD, no trend was observed at beach A. Imputing the censored values using one-half the LOD did not improve the relationship, nor did eliminating the censored data points. Efforts are being made to improve the sensitivity of the Bacteroides assay with the hope of improving its reliability as a predictor of illness. One of the advantages of the QPCR method is the ability to archive samples, and if improvements are made to the assay, they will be retested.
The two beaches differed with respect to swimming, demographic characteristics, and baseline illness. At beach B, more respondents were > 35 years of age (59% vs. 39%) and white (90% vs. 73%) than at beach A. A higher proportion of nonswimmers at beach B reported illness than at beach A (10% vs. 5%). Differences in the study populations may have been responsible for the higher overall risk in illness among swimmers compared with non-swimmers at beach A.
We observed no striking difference in the trend between illness and water quality for the different types of swimming definitions. With the exception of Enterococcus at the Lake Erie beach (beach B), trends tended to be stronger when swimming was defined as body immersion and head immersion compared with any contact with water. This is consistent with the hypothesis that more active types of swimming would result in greater exposure to fecally contaminated water.
Because trends were evaluated among swimmers, it is unlikely that the observed associations could be attributed to unmeasured confounding factors. It is unlikely that swimmers associated themselves with different water quality with respect to characteristics that could affect GI illness. Adjusting for covariates tended to strengthen the trend and association between illness and water quality.
Although we selected beaches affected by human fecal contamination, we do not know whether fecal contamination from other bathers was an important contributor to the overall level of fecal contamination. Although there was no significant difference in Enterococcus QPCRCE by collection time, the average QPCRCE increased slightly throughout the day, suggesting that swimmers may have contributed some fecal contamination.
Because QPCR relies on DNA to quantify organisms, viable organisms are not necessary for measurement. As a result, indicators measured by QPCR may differ in their sensitivity to some environmental conditions. For example, we did not see a reduction in QPCRCE over the course of the day, an effect that has been observed for culture-based indicator organisms resulting from die-off caused by ultraviolet radiation (Whitman et al. 2004). There is a need for additional studies to better understand how indicators measured by QPCR are affected by physical and environmental factors in recreational waters.
Because this is the first and only study to evaluate the ability of rapid water-quality indicators to predict GI illness, additional studies will be required to evaluate the generalizability of these findings. Additional studies and analyses will help determine whether these preliminary findings are consistent and robust enough from a regulatory perspective to recommend a rapid indicator for recreational water quality, and to evaluate the conditions under which such indicators can successfully be applied. Ultimately, the use of faster indicators of recreational water quality will result in the ability to make decisions about recreational water quality on the day of sample collection. This, in turn, could lower GI illnesses in communities, especially in those dependent on beach-related tourism.
We thank R. Haugland, L. Wymer, J. Hansel, K. DeLaTorre, K. Patrizi, R. Clickner, and the NEEAR team; R. Whitman; C. Hoffman; Indiana Dunes National Park; Cleveland Metroparks; and the Cuyahoga County Board of Health for their assistance.
This study was funded by the U.S. EPA and managed by Westat Corp. (contract 68-D-02-062).
The information in this document has been subjected to review by the National Health and Environmental Effects Research Laboratory and approved for publication. Approval does not signify that the contents reflect the views of the agency.
Figure 1 Predicted probabilities of GI illness as a function of Enterococcus QPCRCE, predicted from the logistic regression model, adjusted for age and beach.
Table 1 Summary statistics for log10 indicator organisms, measured by QPCR.
Enterococcusa Bacteroidesb
Beach A Beach B Beach A Beach B
No. of days 20 13 20 13
No. of samples 329 350 329 350
QPCRCE/100 mL
Mean 2.04 1.90 3.08 3.02
Median 2.07 2.05 3.34 3.63
SD 0.97 1.03 1.12 1.56
Minimum/maximum –1.53/4.20 –1.75/4.17 0.97/5.37 –0.23/5.57
No. (%) < LOD 9 (2.74) 11 (3.14) 91 (27.66) 74 (21.14)
a p = 0.06 for difference in log QPCRCE (t-test).
b p = 0.55 for difference in log QPCRCE (t-test).
Table 2 GI illness among swimmers and nonswimmers.
No. (% of total) No. reporting GI illness (% of exposed) AOR (95% CI)
Beach A
No contact with water 722 (25) 36 (5.0)
Any contact with water 2,154 (75) 208 (9.7) 1.96 (1.33–2.90)*
Body immersion 1,667 (58) 169 (10) 2.26 (1.51–3.39)*
Head immersion 1,210 (42) 117 (9.7) 2.14 (1.41–3.27)*
Total respondents 2,876a
Beach B
No contact with water 1,535 (54) 147 (10)
Any contact with water 1,305 (46) 159 (12) 1.27 (0.97–1.67)**
Body immersion 757 (27) 101 (13) 1.45 (1.06–1.98)*
Head immersion 524 (18) 71 (14) 1.50 (1.06–2.13)*
Total respondents 2,840
Both beaches
No contact with water 183 (8)
Any contact with water 367 (11) 1.45 (1.17–1.80)*
Body immersion 270 (11) 1.63 (1.29–2.07)*
Head immersion 188 (11) 1.61 (1.25–2.07)*
a One missing value.
* p < 0.05.
** p < 0.1.
Table 3. AORs (95% CIs) for a 1 log10 increase in Enterococcus QPCRCE and GI illness.a
Enterococcus QPCRCE
Bacteroides QPCRCE
Exposure Average by day Average by day and location of swimming Average 0800-hr sample Average by day Average by day and location of swimming Average 0800-hr sample
Beach A
Any contact 1.36* (1.05–1.76) 1.32* (1.04–1.67) — 0.89 (0.75–1.06) 0.87 (0.74–1.02) —
Body immersion 1.43* (1.08–1.90) 1.34* (1.03–1.74) — 0.89 (0.74–1.07) 0.87 (0.73–1.04) —
Head immersion 1.49* (1.07–2.08) 1.41* (1.04–1.90) — 0.84 (0.67–1.05) 0.87 (0.37–1.17) —
Beach B
Any contact 1.25 (0.93–1.67) 1.20 (0.87–1.66) — 1.15 (0.94–1.41) 1.12** (0.97–1.31) —
Body immersion 1.38** (0.94–2.01) 1.27 (0.90–1.81) — 1.24** (0.96–1.60) 1.17** (0.97–1.40) —
Head immersion 1.17 (0.76–1.82) 1.15 (0.77–1.71) — 1.28** (0.95–1.73) 1.20* (0.97–1.49) —
Beaches A and B combined
Any contact 1.30* (1.08–1.57) 1.25* (1.05–1.49) 1.18* (1.03–1.34) 0.99 (0.87–1.13) 1.01 (0.90–1.12) 0.95 (0.86–1.05)
Body immersion 1.37* (1.10–1.71) 1.26 (1.04–1.53) 1.21* (1.04–1.40) 1.00 (0.86–1.16) 1.01 (0.89–1.14) 0.94 (0.84–1.06)
Head immersion 1.35* (1.05–1.75) 1.29* (1.03–1.63) 1.21* (1.02–1.44) 0.99 (0.83–1.17) 1.00 (0.86–1.15) 0.92 (0.80–1.06)
a ORs estimated from multivariate logistic regression of GI illness on the log (base 10) indicator measure.
* p < 0.05.
** p < 0.1.
Table 4 AORs (95% CIs) for a 1 log10 increase in the daily average of Enterococcus QPCRCE and GI illness among swimmers by time spent in water, beaches A and B combined.a
Time spent in water (min) No. AOR per 1 log10 increase (95% CI)
≥ 15 2,477 1.45 (1.14–1.85)*
≥ 30 1,572 1.48 (1.12–1.96)*
≥ 60 735 1.84 (1.25–2.72)*
≥ 120 289 2.89 (1.55–5.40)*
a Body immersed in water.
* p < 0.05.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8335ehp0114-00002916393654ResearchFine Particulate Air Pollution and Mortality in Nine California Counties: Results from CALFINE Ostro Bart 1Broadwin Rachel 1Green Shelley 1Feng Wen-Ying 2Lipsett Michael 31 California Office of Environmental Health Hazard Assessment, Oakland, California, USA2 University of California Davis, Davis, California, USA3 University of California San Francisco, San Francisco, California, USAAddress correspondence to B. Ostro, Air Pollution Epidemiology Section, California Office of Environmental Health Hazard Assessment, 1515 Clay St., 16th Floor, Oakland, CA 94612 USA. Telephone: (510) 622-3157. Fax: (510) 622-3210. E-mail:
[email protected] authors declare they have no competing financial interests.
1 2006 1 9 2005 114 1 29 33 18 5 2005 1 9 2005 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Many epidemiologic studies provide evidence of an association between daily counts of mortality and ambient particulate matter < 10 μm in diameter (PM10). Relatively few studies, however, have investigated the relationship of mortality with fine particles [PM < 2.5 μm in diameter (PM2.5)], especially in a multicity setting. We examined associations between PM2.5 and daily mortality in nine heavily populated California counties using data from 1999 through 2002. We considered daily counts of all-cause mortality and several cause-specific subcategories (respiratory, cardiovascular, ischemic heart disease, and diabetes). We also examined these associations among several subpopulations, including the elderly (> 65 years of age), males, females, non-high school graduates, whites, and Hispanics. We used Poisson multiple regression models incorporating natural or penalized splines to control for covariates that could affect daily counts of mortality, including time, seasonality, temperature, humidity, and day of the week. We used meta-analyses using random-effects models to pool the observations in all nine counties. The analysis revealed associations of PM2.5 levels with several mortality categories. Specifically, a 10-μg/m3 change in 2-day average PM2.5 concentration corresponded to a 0.6% (95% confidence interval, 0.2–1.0%) increase in all-cause mortality, with similar or greater effect estimates for several other subpopulations and mortality subcategories, including respiratory disease, cardiovascular disease, diabetes, age > 65 years, females, deaths out of the hospital, and non-high school graduates. Results were generally insensitive to model specification and the type of spline model used. This analysis adds to the growing body of evidence linking PM2.5 with daily mortality.
air pollutionCaliforniafine particlesmortalityparticulate matterPM2.5
==== Body
Over the last decade, studies conducted over five continents have demonstrated associations between daily exposure to particulate matter (PM) < 10 μm in aerodynamic diameter (PM10) and premature mortality [U.S. Environmental Protection Agency (EPA) 2004]. The U.S. EPA promulgated ambient air quality standards for fine particles [those < 2.5 μm in diameter (PM2.5)] in 1997 and is currently considering revisions to these standards; however, relatively few studies have examined relationships of this pollutant class with mortality (Burnett et al. 2003; Schwartz 2003; U.S. EPA 2004). In addition, most studies to date have been conducted in the eastern United States, Canada, and Western Europe. Relatively few studies have been conducted in California, where particle sources, chemistry, size distribution, and temporal patterns of exposure are quite different. Specifically, existing evidence suggests that, in California, a) nitrates comprise a larger fraction of PM2.5 than they do in other regions, and b) mobile sources represent the predominant source of PM2.5, whereas a mix of mobile and stationary sources predominate elsewhere (Blanchard 2003). Moreover, in the Los Angeles air basin, peak PM2.5 exposures occur in both winter and nonwinter months.
In 1999, the U.S. EPA and the California Air Resources Board (CARB) embarked on a program to collect daily data on PM2.5 in many cities throughout California. We have obtained and linked daily readings of PM2.5 with mortality in nine heavily populated counties in California. The ability to explore hypotheses of association with adverse health in multiple cities has several distinct advantages. It enhances the power of the statistical analysis and reduces the likelihood of spurious results or publication bias that might result from the analysis of a single city (Anderson et al. 2005). In this article, we report the results of our analysis of the relationship between mortality and fine particles in California (CALFINE).
Materials and Methods
Mortality data.
Data on daily mortality were obtained for all California residents from the California Department of Health Services (CDHS), Health Data and Statistics Branch, for the period 1 January 1999 through 31 December 2002 (CDHS 1999–2002). Our study was limited to deaths occurring in nine California counties (cities where the monitors were located are in parentheses): Contra Costa (Concord), Fresno (Fresno), Kern (Bakersfield), Los Angeles (Los Angeles, North Long Beach, Azusa), Orange (Anaheim), Riverside (Riverside), Sacramento (Sacramento), San Diego (San Diego, Escondido, El Cajon), and Santa Clara (two in San Jose). Data were limited to deaths occurring in the decedents’ county of residence. Daily counts of total deaths (minus accidents and homicides) were aggregated. Using the International Classification of Diseases, 10th Revision (ICD-10) (World Health Organization 1993), total daily counts of deaths from respiratory disease (ICD-10 codes J00–J98), cardiovascular disease (ICD-10 codes I00–I99), ischemic heart disease (ICD-10 codes I20–I25), and diabetes (ICD-10 codes E10–E14) were also calculated.
We also calculated daily, all-cause mortality counts for the following subpopulations and mortality categories: a) age > 65 years, b) males, c) females, d) white non-Hispanic, e) black non-Hispanic, f) Hispanic, g) in hospital, h) out of hospital, i) less than high school education, and j) high school graduate.
Pollutant and meteorologic data.
We obtained pollution data for the 4-year period 1999 through 2002 from multiple sources. Daily average PM2.5 data were obtained from the U.S. EPA’s Aerometric Information Retrieval System (AIRS) database. PM2.5 monitors were filter-based, ambient air samplers (model RAAS2.5-300; Andersen Instruments, Inc., Smyrna, GA).
This sequential sampler is designated as a federal reference method sampler for collection of PM2.5. There was only one monitor collecting daily PM2.5 data in each of the nine counties, except for Los Angeles, San Diego, and Santa Clara counties, which had three, three, and two monitors, respectively. Data from the nine counties represent nearly all locations of monitors in California that measured PM2.5 on a daily basis for large parts of 1999–2002. A substantial number of days were missing data, which varied by county and appeared to be fairly random, with a few exceptions. Specifically, in 1999 several of the counties had no data from January through March, and from March through December, Los Angeles and Riverside counties had data only every third day.
Data on gaseous pollutants, including carbon monoxide, nitrogen dioxide, and ozone, were obtained from the CARB air quality database for all nine counties. Most of the monitors for gases were part of the State and Local Air Monitoring Stations (SLAMS) network. All gases were reported as 24-hr averages, except ozone, which was reported as both an 8-hr average (1000–1800 hr) and as a 1-hr maximum.
For counties with multiple monitors, the daily average was calculated using all available data. To account for missing data among some of the monitors, we used a process similar to that described by Wong et al. (2001). The average was developed by a) calculating the mean for each monitor, b) subtracting the mean concentration of each monitor from the nonmissing daily values, c) calculating the mean of the available adjusted data, and d) adding back the grand mean of the data.
To allow adjustment for the effect of weather on mortality, we collected daily average temperature and humidity data at weather stations in each of the nine counties. Hourly temperature data were obtained from AIRS for all sites except Contra Costa and Santa Clara counties, for which data were obtained from the Bay Area Air Quality Management District and from Golden Gate Weather Services, respectively. All daily mortality, pollutant, and meteorologic data were converted into a SAS database (SAS Institute Inc., Cary, NC) and merged by date. This resulted in 4 years (1,461 days) of daily time-series data.
Methods.
Counts of daily mortality are nonnegative discrete integers representing rare events; such data typically follow a Poisson distribution. Therefore, the analysis relied on Poisson regression, conditional on the explanatory variables. In the basic analytic approach, we used similar model specifications for each city, including smoothing spline functions for time trend and weather. We examined both penalized and natural spline models. The penalized spline model is a flexible, nonparametric approach using cubic splines and a term that penalizes the curvature of the smoothing function (Wood 2000). The “roughness penalty” controls the trade-off between a precise fit of the data and a smoothed function. The model then minimizes the sum of the squared deviations plus the penalty function to determine the amount of smoothing in the fit. The natural spline model is a parametric approach that fits piecewise polynomial functions joined at knots, which are typically placed evenly throughout the distribution of the variable of concern, such as time. The function is constrained to be continuous at each knot (Ruppert et al. 2003). The model also places two additional knots at the ends of the data, with the function constrained to be linear beyond these points. The number of knots used determines the overall smoothness of the fit. Previous analysis has indicated that different spline models generate relatively similar results (Health Effects Institute 2003). However, depending on the underlying data and model specifications, different splines might produce varying degrees of bias and efficiency in the regression estimates.
For the initial analysis of all-cause, cardiovascular, respiratory, and above-age-65 mortality, a penalized spline regression was used with R (R Development Core Team 2004). We incorporated a smoothed spline function of time, which can accommodate nonlinear and nonmonotonic patterns between time and mortality, offering a flexible modeling tool (Hastie and Tibshirani 1990). In addition, the smooth of time diminishes short-term fluctuations in the data, thereby helping to reduce the degree of serial correlation. Based on previous findings reported in the literature (e.g., Samet et al. 2000), the basic model included a smoothing spline for time with 7 degrees of freedom (df) per year of data. This number of degrees of freedom controls well for seasonal patterns in mortality and reduces and often eliminates autocorrelation. Visual inspection of the data indicated a spike in mortality in several of the cities in southern and central California during a 3-week period starting 17 December 1999. During this period, the actual number of cases exceeded the smoothed estimate. Therefore, for all of the regression models, we added a second smooth of time with 3 knots for this 3-week period.
Other covariates, such as day of the week and smoothing splines of 1-day lags of average temperature and humidity (each with 3 df), were also included in the model because they may be associated with daily mortality and are likely to vary over time in concert with air pollution levels. Previous studies have reported stronger associations of mortality with PM lagged 1 or 2 days or with cumulative exposures over several days. Therefore, in our primary analysis of PM2.5, we examined two different a priori lag structures: a 2-day average of lags 0 and 1 (lag 01) and a single-day lag of 2 days (lag 2). The county-specific results were then combined in a meta-analysis using a random effects model in Stata (StataCorp 2003). The meta-analysis focused primarily on all-cause mortality and on cardiovascular, respiratory, and elderly (> 65 years of age) mortality, because these categories have been the focus of previous time-series studies (Health Effects Institute 2003).
We also conducted several sensitivity analyses. First, we examined these same four outcomes using a similar specification, but with a natural spline model. For each county, we used lag 01 for PM2.5 and 4, 8, and 12 df/year for the smooth of time. Second, using lag 01 and penalized spline models with 7 df for the smooth of time, we examined other mortality groupings and classifications, including those for males, females, whites, blacks, Hispanics, high school and non-high school graduates, deaths occurring in and out of hospitals, ischemic heart disease, and diabetes. Finally, we examined the impact on the estimated coefficient of PM2.5 when gaseous pollutants were added to the penalized spline model (i.e., in two-pollutant models specified with PM2.5 and each of the gaseous pollutants).
All final results were calculated using R (version 1.9), and the results are presented as the percent change in daily mortality per 10 μg/m3 PM2.5. The percent change per 10 μg/m3 is simply the β-coefficient (times 1,000) from the Poisson regression.
Results
Tables 1 and 2 provide the descriptive statistics for population, air quality, mortality, and meteorologic data from the nine counties. The populations in 2000 ranged from 661,645 in Kern County to 9,519,338 in Los Angeles County; the total in these nine counties accounted for 65% of California’s population in 2000. Mean daily mortality varied from 146 in Los Angeles County to 11 in Kern County. Mean daily PM2.5 levels ranged from 14 μg/m3 in Sacramento and Contra Costa Counties to 29 μg/m3 in Riverside County, exceeding the U.S. EPA annual average PM2.5 standard of 15 μg/m3 in six of the nine counties. Temporally, among the cities, PM2.5 was highly correlated with both nitrogen dioxide (mean r = 0.56; range, 0.38–0.66) and carbon monoxide (mean r = 0.60; range, 0.37–0.83), but only moderately and often inversely correlated with both 1-hr ozone levels (mean r = −0.14; range, −0.39 to 0.17) and 8-hr ozone levels (mean, −0.22; range, −0.47 to 0.12).
Table 3 summarizes the basic results for the meta-analyses for four mortality categories using penalized splines with two different lag structures. The results suggest associations between PM2.5 and all-cause, cardiovascular, respiratory, and elderly mortality. Point estimates of risk were particularly elevated for respiratory-specific mortality. Also, cumulative exposures of 2 days generated larger pooled effect estimates than did the single-day lags that were examined. Diagnostics indicated that autocorrelation was present over the entire data series for many of the counties when a simple smooth of time was used. The autocorrelation was eliminated, however, when the second smooth of time was included for the 3-week period starting 17 December 1999.
Table 4 summarizes the results for the meta-analyses for four mortality categories when similar models were used with lag 01 for PM2.5 and natural splines for the smoothers of temperature and humidity and three alternative smoothers of time. The results generally support, but are slightly lower than, those observed using penalized splines (Table 3), indicating associations with all-cause, respiratory, and elderly mortality and more modest associations with cardiovascular mortality. In addition, greater degrees of freedom for time trend tended to lower the effect estimates.
Table 5 summarizes the meta-analytic results for PM2.5 for different mortality categories and subpopulations using a penalized spline model and lag 01. The results suggest somewhat stronger associations of daily PM2.5 concentrations with mortality for diabetics, females, and whites. The association for deaths occurring outside of hospitals was demonstrated with greater precision than for those occurring inside hospitals. In addition, the point estimate for mortality among those who had not graduated from high school was more than twice that of those who had, with an association that was of marginal statistical significance (p < 0.10). Finally, in multipollutant models (using lag 01), the estimated PM2.5 coefficient was attenuated when the highly correlated pollutants—nitrogen dioxide and carbon monoxide—were added to the model but was not affected by the inclusion of either 1-hr or 8-hr ozone. However, for mortality among those > 65 years of age, the inclusion of any of the gaseous pollutants to the model did not affect the PM2.5 coefficient (data not shown).
Discussion
In this time-series analysis in nine California counties, short-term exposures to PM2.5 were associated with increased daily mortality. These results appear to be relatively insensitive to the use of natural versus penalized spline model and the degrees of freedom in the smoothing functions for time, although both of these factors alter the effect estimates. Specifically, PM2.5 was associated with all-cause, cardiovascular, and respiratory mortality, as well as with deaths in persons > 65 years of age. PM2.5–mortality associations were particularly elevated among females, whites, persons who did not graduate from high school, diabetics, and those who died out of hospital.
Several earlier studies that examined associations between daily mortality and either PM10 or PM2.5 were reanalyzed for the Health Effects Institute (Health Effects Institute 2003). The reanalyses were conducted after the generalized additive models had been found to produce biased effect estimates and standard errors when default convergence criteria were used in S-Plus (Dominici et al. 2003). Regarding PM2.5, Schwartz et al. (1996) found statistically significant increases in mortality in their reanalysis of the Six Cities study using both natural spline [1.29% per 10 μg/m3 PM2.5; 95% confidence interval (CI), 0.88–1.70] and penalized spline (1.13%; 95% CI, 0.70–1.56) models with 4 df/year for time. Burnett et al. (2003) reexamined nonaccidental mortality from 1986 to 1996 in eight Canadian cities, using natural spline models with 2 df/year for time, and reported a 1.10% increase in mortality (95% CI, 0.35–1.85) per 10 μg/m3 of PM2.5. A reanalysis of another Canadian study found a nonsignificant increase in mortality (0.46% per 10 μg/m3 PM2.5) in Montreal from 1984 to 1993 (Goldberg and Burnett 2003). In a reanalysis of a time-series study in Santa Clara, California, Fairley (2003) reported a 2.75% increase (95% CI, 0.61–4.89) in nonaccidental mortality per 10 μg/m3 PM2.5 using a natural spline model with 9 df/year. The reanalyses of data from Detroit (Ito 2003) and Los Angeles (Moolgavkar 2003) using natural spline models demonstrated positive but nonsignificant increases in mortality of 0.79 and 0.55%, respectively, per 10 μg/m3 PM2.5. Finally, in a study in Mexico City, Mexico, PM2.5 was associated with a 1.4% (95% CI, 0.2–2.5) increase in daily mortality per 10 μg/m3 (Borja-Aburto et al. 1998).
Our effect estimate of about 0.6% per 10 μg/m3 PM2.5 for all-cause mortality is in the lower end of the range of these previous estimates. There are several possible explanations for the lower effect estimates. First, large exposure measurement errors were likely, owing to the use of one to three monitors to represent exposure in these counties, some of which extend over thousands of square miles. Therefore, assuming such measurement errors were nondifferential with respect to the populations at risk, the effect estimates would likely be biased downward. Second, the composition of PM2.5 in California, which in several of these counties is dominated by nitrates, may be less toxic, particularly to the cardiovascular system (Schlesinger and Cassee 2003). However, this hypothesis contrasts with the findings of one of the few studies to explicitly examine the effects of nitrates, which were associated with significant increases of mortality in Santa Clara County (Fairley 2003). Third, California residents may be less susceptible to the cardiovascular effects of air pollution, possibly due to differences in exercise and dietary patterns, or to active and passive smoking rates that are lower than national averages. Fourth, there may be geographic confounding related to some unknown and therefore unmeasured spatially varying factors. Finally, this could be a chance finding. The likely potential importance of measurement error, geographic confounding, and chance is suggested by the large variability in effect estimates among the nine counties. Such heterogeneity has also been reported in the analysis of the 90 largest U.S. cities (Samet et al. 2000). There is no obvious explanation for the different PM2.5–mortality associations in each county. This merits further study.
Of additional interest is the strength of the association of PM2.5 with respiratory mortality relative to that for cardiovascular mortality. Many previous studies [reviewed by Ostro et al. (1999)] report stronger effects for cardiovascular mortality, which may be due to a) the greater prevalence of circulatory disease (and therefore increased statistical power) and b) the likely attribution of cause of death as cardiovascular when there is uncertainty or when there is an underlying respiratory condition. It is often more difficult to detect associations between air pollution and respiratory deaths because the latter generally represent a small fraction of total mortality and are more likely to be ascribed to cardiovascular causes than vice versa. However, it is clear that PM2.5 and other PM metrics are associated with daily mortality from respiratory causes. For example, Penttinen et al. (2004), Zanobetti et al. (2003), Braga et al. (2001), and Ostro et al. (1999) all report stronger associations of PM with respiratory than with cardiovascular mortality. De Leon et al. (2003) reported that those with an underlying respiratory condition were more susceptible to the impacts of air pollution on nonrespiratory (e.g., circulatory or cancer-related) mortality. Associations have also been reported between PM2.5 and respiratory morbidity, including hospitalizations and emergency department visits for respiratory disease (Delfino et al. 1997; Ito 2003; Peel et al. 2005).
Our analysis also suggests that diabetics and those with less than a high school education may be at increased risk from exposure to PM2.5. Several previous time-series studies have reported that diabetics may be at increased risk from exposure to PM (Goldberg et al. 2001; Zanobetti and Schwartz 2002). Pope et al. (2002) reported that educational attainment was an important effect modifier in the association between long-term exposure to PM2.5 and survival. However, susceptibility to PM pollution is not likely to be affected by education per se, but rather by factors that might be associated with education, such as nutritional status, access to health care, occupation, psychosocial stress, and residential proximity to heavy traffic. On the other hand, most time-series studies to date have not reported a significant effect modification by socioeconomic status (Samet et al. 2000; Schwartz 2000). We also found, as have others, a better model fit for PM2.5 for deaths occurring out of hospital (Schwartz 2000). We found that when copollutants highly correlated with PM2.5 were included in the model, they tended to attenuate the magnitude and significance of its coefficient, except for mortality for those > 65 years of age. The latter finding suggests that, at least for deaths occurring in the elderly, gaseous copollutants do not confound the PM2.5–mortality associations. The gaseous pollutants, however, are spatially heterogeneous and may involve significant exposure misclassification. The separate effects of the gaseous pollutants on mortality will be the focus of subsequent analyses.
Overall, this large, multicounty analysis provides evidence of significant associations of PM2.5 with daily mortality among nearly two-thirds of California’s population.
We thank F. Forastiere and M. Stafoggia for their technical assistance.
The opinions expressed in this article are solely those of the authors and do not represent the policy or position of the State of California or the California Environmental Protection Agency.
Table 1 Descriptive statistics for air pollutants and mortality in nine California counties, 1999–2002.
County 2000 populationa Days with data for PM2.5, temperature, and RH (n) Mean daily PM2.5 (μg/m3)b Mean daily temperature (°F)b Mean daily RH (%)b Mean daily all-cause mortalityb
Contra Costa 949 698 14 (1–77) 60 (34–91) 64 (10–96) 16 (4–32)
Fresno 799 1,024 23 (1–160) 65 (35–94) 55 (18–96) 13 (3–28)
Kern 662 1,186 22 (1–155) 65 (36–95) 56 (13–100) 11 (2–25)
Los Angeles 9,519 1,221 21 (4–85) 64 (46–89) 57 (15–88) 146 (99–242)
Orange 2,846 682 21 (4–114) 63 (46–84) 67 (6–95) 40 (20–75)
Riverside 1,545 976 29 (2–120) 65 (43–90) 58 (6–100) 28 (9–63)
Sacramento 1,223 1,214 14 (1–108) 61 (36–91) 66 (13–100) 22 (7–45)
Santa Clara 1,683 717 15 (2–74) 59 (40–89) 69 (22–96) 22 (9–44)
San Diego 2,814 1,333 16 (0–66) 61 (43–84) 74 (16–100) 49 (26–87)
RH, relative humidity.
a In thousands.
b Values in parentheses indicate minimum–maximum.
Table 2 Mean daily deaths by mortality category in nine California counties, 1999–2002.
Mortality category Contra Costa Fresno Kern Los Angeles Orange Riverside Sacramento Santa Clara San Diego
Age > 65 years 12.2 10.0 8.1 108.6 31.4 22.2 16.1 16.5 38.7
Male 7.2 6.4 5.4 70.7 18.3 13.8 10.4 10.3 23.7
Female 8.4 6.8 5.6 75.6 21.5 14.2 11.3 11.4 25.5
White non-Hispanic 12.3 9.3 8.6 86.0 32.7 23.1 16.8 15.5 39.6
Black non-Hispanic 1.5 0.8 0.6 20.6 0.4 1.3 2.0 0.5 2.1
Hispanic 0.9 2.4 1.5 26.7 3.6 3.0 1.3 2.5 4.9
In-hospital death 6.4 6.2 5.6 79.8 17.4 11.5 9.7 9.9 18.6
Out-of-hospital death 9.2 7.0 5.4 66.5 22.3 16.5 12.1 11.7 30.6
High school graduate 12.3 7.9 6.6 99.0 30.8 20.4 15.9 15.9 37.6
Not high school graduate 3.1 5.1 4.1 40.7 8.2 6.8 5.3 5.4 10.2
Diabetes 0.4 0.5 0.3 5.1 1.1 0.6 0.6 0.6 1.2
Cardiovascular disease 6.5 5.7 4.9 67.0 17.7 13.0 9.2 9.1 20.4
Ischemic heart disease 3.3 3.2 3.1 42.6 10.8 8.0 5.3 4.8 11.4
Respiratory disease 1.7 1.5 1.4 15.0 4.3 3.2 2.6 2.4 5.7
Table 3 Percent change in daily mortality categories and 95% CIs per 10-μg/m3 increment in PM2.5 using penalized splines and alternative lags [percent change (95% CI)].
County, laga All-cause mortality Cardiovascular mortality Respiratory mortality Mortality > 65 years of age
Contra Costa
2 0.8 (−1.0 to 2.6) 0.6 (−2.1 to 3.3) 0.4 (−5.1 to 6.0) 0.5 (−1.5 to 2.5)
01 0.4 (−1.9 to 2.7) −0.6 (−4.1 to 2.9) 6.9 (0.1 to 13.8) 0.2 (−2.4 to 2.8)
Fresno
2 0.3 (−0.8 to 1.4) 0.5 (−1.1 to 2.2) 1.2 (−1.8 to 4.2) 0.8 (−0.5 to 2.0)
01 0.2 (−1.1 to 1.5) −0.1 (−2.1 to 1.9) 2.0 (−1.6 to 5.6) 0.4 (−1.1 to 1.9)
Kern
2 −0.4 (−1.5 to 0.7) 0.8 (−0.6 to 2.3) −1.2 (−3.9 to 1.5) −0.1 (−1.4 to 1.1)
01 −0.3 (−1.5 to 0.9) 1.3 (−0.4 to 3.0) −1.2 (−4.3 to 1.9) −0.1 (−1.5 to 1.3)
Los Angeles
2 −0.1 (−0.5 to 0.4) 0.1 (−0.6 to 0.8) 1.2 (−0.2 to 2.6) −0.3 (−0.8 to 0.3)
01 0.6 (0.1 to 1.1) 0.4 (−0.3 to 1.2) 2.1 (0.6 to 3.6) 0.5 (−0.1 to 1.1)
Orange
2 1.7 (0.6 to 2.9) 0.8 (−0.9 to 2.6) 5.7 (2.4 to 9.0) 1.2 (−0.1 to 2.5)
01 2.3 (1.0 to 3.6) 1.8 (−0.2 to 3.8) 7.6 (3.7 to 11.5) 2.3 (0.9 to 3.8)
Riverside
2 −0.2 (−1.1 to 0.6) 0.0 (−1.2 to 1.2) −0.5 (−2.7 to 1.7) −0.3 (−1.3 to 0.6)
01 0.2 (−0.9 to 1.2) −0.1 (−1.6 to 1.3) −0.4 (−3.1 to 2.3) 0.1 (−1.0 to 1.3)
Sacramento
2 0.8 (−0.4 to 2.0) 1.1 (−0.7 to 2.8) 3.5 (0.3 to 6.7) 0.9 (−0.4 to 2.3)
01 0.5 (−1.0 to 1.9) 0.9 (−1.2 to 3.0) 4.0 (−1.6 to 6.4) 1.1 (−0.6 to 2.8)
Santa Clara
2 0.0 (−1.1 to 1.1) −0.2 (−1.8 to 1.4) 1.7 (−1.6 to 5.0) −0.2 (−1.4 to 1.0)
01 1.1 (−0.1 to 2.3) 1.1 (−0.6 to 2.9) 1.7 (−1.9 to 5.3) 1.2 (−0.1 to 2.6)
San Diego
2 0.7 (−0.8 to 2.2) 1.0 (−1.3 to 3.2) 1.4 (−2.8 to 5.6) 1.4 (−0.3 to 3.0)
01 0.8 (−1.0 to 2.6) 0.3 (−2.2 to 2.9) 4.0 (−1.0 to 9.0) 1.2 (−0.8 to 3.2)
Pooled results
2 0.2 (−0.2 to 0.7) 0.3 (−0.1 to 0.7) 1.3 (0.1 to 2.6) 0.2 (−0.2 to 0.7)
01 0.6 (0.2 to 1.0) 0.6 (0.0 to 1.1) 2.2 (0.6 to 3.9) 0.7 (0.2 to 1.1)
CI, confidence interval.
a Lag 01, average of 0- and 1-day lags of PM2.5; lag 2, 2-day lag of PM2.5. Model also includes day of week, spline smoothers of temperature and humidity, and two spline smoothers for time. Pooled results based on meta-analysis using a random-effects model.
Table 4 Pooled estimates of percent changes in daily mortality categories and 95% CIs per 10-μg/m3 increment in PM2.5 using natural splines.
Mortality category df/year % Change (95% CI)
All cause 4 0.5 (−0.1 to 1.1)
8 0.4 (−0.1 to 0.9)
12 0.3 (−0.1 to 0.7)
Cardiovascular 4 0.4 (−0.2 to 0.9)
8 0.1 (−0.5 to 0.6)
12 0.0 (−0.6 to 0.6)
Respiratory 4 2.1 (0.2 to 4.1)
8 1.6 (−0.5 to 3.6)
12 1.3 (−0.3 to 2.9)
Older than 65 years 4 0.7 (0.0 to 1.3)
8 0.4 (−0.1 to 0.9)
12 0.3 (−0.1 to 0.8)
Model includes average of 0- and 1-day lags of PM2.5, day of week, spline smoothers of temperature and humidity, and two spline smoothers of time. Pooled results based on meta-analysis using a random-effects model.
Table 5 Pooled estimates of percent changes in daily mortality categories and 95% CIs per 10-μg/m3 increment in PM2.5 using penalized splines.
Mortality category % Change (95% CI)
All-cause 0.6 (0.2 to 1.0)
Cardiovascular 0.6 (0.0 to 1.1)
Respiratory 2.2 (0.6 to 3.9)
Age > 65 years 0.7 (0.2 to 1.1)
Ischemic heart disease 0.3 (−0.5 to 1.0)
Diabetes 2.4 (0.6 to 4.2)
Males 0.5 (−0.2 to 1.2)
Females 0.8 (0.3 to 1.3)
Whites 0.8 (0.2 to 1.3)
Blacks 0.1 (−0.9 to 1.2)
Hispanics 0.8 (−0.1 to 1.6)
In hospital 0.6 (−0.1 to 1.3)
Out of hospital 0.6 (0.1 to 1.1)
High school graduates 0.4 (0.0 to 0.8)
Non-high school graduates 0.9 (−0.1 to 1.9)
Model includes average of 0- and 1-day lags of PM2.5, day of week, spline smoothers of temperature and humidity, and two spline smoothers of time. Pooled results based on meta-analysis using a random-effects model.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8286ehp0114-00003416393655ResearchPerinatal Environmental Tobacco Smoke Exposure in Rhesus Monkeys: Critical Periods and Regional Selectivity for Effects on Brain Cell Development and Lipid Peroxidation Slotkin Theodore A. 1Pinkerton Kent E. 2Seidler Frederic J. 11 Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, North Carolina, USA2 Center for Health and the Environment, and California National Primate Research Center, University of California, Davis, California, USAAddress correspondence to T.A. Slotkin, Box 3813 DUMC, Duke University Medical Center, Durham, NC 27710 USA. Telephone: (919) 681-8015. Fax: (919) 684-8197. E-mail:
[email protected] authors declare they have no competing financial interests.
1 2006 7 9 2005 114 1 34 39 5 5 2005 7 9 2005 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Perinatal environmental tobacco smoke (ETS) exposure in humans elicits neurobehavioral deficits. We exposed rhesus monkeys to ETS during gestation and through 13 months postnatally, or postnatally only (6–13 months). At the conclusion of exposure, we examined cerebrocortical regions and the midbrain for cell damage markers and lipid peroxidation. For perinatal ETS, two archetypal patterns were seen in the various regions, one characterized by cell loss (reduced DNA concentration) and corresponding increases in cell size (increased protein/DNA ratio), and a second pattern suggesting replacement of larger neuronal cells with smaller and more numerous glia (increased DNA concentration, decreased protein/DNA ratio). The membrane/total protein ratio, a biomarker of neurite formation, also indicated potential damage to neuronal projections, accompanied by reactive sprouting. When ETS exposure was restricted to the postnatal period, the effects were similar in regional selectivity, direction, and magnitude. These patterns resemble the effects of prenatal nicotine exposure in rodent and primate models. Surprisingly, perinatal ETS exposure reduced the level of lipid peroxidation as assessed by the concentration of thiobarbituric acid reactive species, whereas postnatal ETS did not. The heart, a tissue that, like the brain, has high oxygen demand, displayed a similar but earlier decrease (2–3 months) in lipid peroxidation in the perinatal exposure model, whereas values were reduced at 13 months with the postnatal exposure paradigm. Our results provide a mechanistic connection between perinatal ETS exposure and neurobehavioral anomalies, reinforce the role of nicotine in these effects, and buttress the importance of restricting or eliminating ETS exposure in young children.
β-adrenergic receptorbrain developmentenvironmental tobacco smokeheart developmentlipid peroxidationmuscarinic acetylcholine receptornicotine
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Environmental tobacco smoke (ETS) exposure is now recognized as a health risk for pregnant women and children (Dunn and Zeise 1997; Witschi et al. 1997), and it is increasingly evident that ETS affects the developing brain and cardiovascular system (Eskenazi and Trupin 1995; Hutchison et al. 1998; Makin et al. 1991). The consequences of fetal or early neonatal ETS exposure mimic those of active maternal smoking, albeit with a lesser magnitude (Makin et al. 1991), and for both active smoking and ETS, the dose–effect relationships correlate well with the levels of nicotine and its metabolites (Fried et al. 1995). ETS generally achieves fetal nicotine metabolite concentrations similar to those seen with light, active maternal smoking (Eliopoulos et al. 1996; Jauniaux et al. 1999; Ostrea et al. 1994), and young children exposed to ETS typically display levels exceeding those seen in older children (Fried et al. 1995; Kohler et al. 1999).
Animal studies demonstrate conclusively that nicotine damages the developing brain by altering the formation, survival, and differentiation of brain cells, eliciting deficits in structure, synaptic function, and behavioral performance (Levin and Slotkin 1998; Slotkin 1998, 2004; Walker et al. 1999). This provides a mechanistic link between maternal smoking during pregnancy and adverse neurobehavioral consequences in the offspring (Fried et al. 1992, 1998, 2003; Wakschlag et al. 2002; Weitzman et al. 2002). However, much less is known about the mechanisms underlying comparable effects of ETS. In a recent pair of studies, we found that perinatal ETS exposure in rhesus monkeys elicits alterations in cell signaling in the developing brain akin to those identified for nicotine administration in rodents, including the up-regulation of nicotinic cholinergic receptors, a characteristic of chronic nicotine-induced neuronal stimulation (Slotkin et al. 2000, 2002). These findings were important for two reasons: first, they provided the first evidence that ETS supplies sufficient nicotine to the developing brain to evoke inappropriate activation of the pathways that lead to altered cell development, and second, they demonstrated these effects in primates. The latter point is particularly important: the rat and mouse are altricial species, so brain development at birth corresponds to fetal stages of human development (Rodier 1988), and thus the concentrations or temporal factors for nicotine or ETS may not reflect those experienced in typical human exposure scenarios.
The present study, again using rhesus monkeys, was undertaken for four distinct purposes. First, we examined the relative importance of continuous perinatal ETS exposure compared with later exposure, determinations that are essential to identify the critical periods in which the developing brain is vulnerable to adverse effects of ETS. Our earlier work in rats indicated an extended period of vulnerability, lasting well into the postnatal period, and therefore in the present study we examined perinatal exposure up to 13 months of age, compared with ETS administered only during later postnatal stages, from 6 through 13 months. Second, we examined a variety of cortical brain regions and the midbrain, areas that, based on the known effects of nicotine, are likely to be compromised by developmental ETS exposure (Levin and Slotkin 1998; Slotkin 1998, 2004). Third, within each region, we characterized the neural cell damage caused by the different ETS regimens, using strategies adapted from prior rodent studies of nicotine or ETS (Gospe et al. 1996; Levin and Slotkin 1998; Slotkin 1998, 2004). Each neural cell contains only a single nucleus (Winick and Noble 1965), so the DNA concentration (DNA per unit tissue weight) reflects the cell packing density (Bell et al. 1987; Slotkin et al. 1984; Winick and Noble 1965). We also characterized the complement of cell proteins that reflect indices of cell type and size. The brain contains numerous glia, which are considerably smaller than neurons and thus possess less total protein per cell and a higher surface-to-volume ratio, which can be assessed by the proportions of total protein/DNA and membrane/total protein. At the same time, as neurons specialize, they enlarge and develop axonal and neuritic projections, which increases both the total protein/DNA ratio and membrane/total protein ratio in parallel (Qiao et al. 2003, 2004; Slotkin et al. 2005). These indices thus provide insight into the architectural alterations underlying the neurochemical effects of ETS. For example, the typical response to neuronal injury, neuronal replacement by smaller and more numerous glia (O’Callaghan 1988, 1993), produces an increase in cell packing density, a decrease in total protein/DNA, and an increase in membrane/total protein. In contrast, neuronal loss accompanied by perikaryal swelling, another archetypical injury response (Roy et al. 2005), elicits a decrease in cell packing density, an increase in total protein/DNA, and a decrease in the membrane/total protein ratio. A third pattern, damage to neuritic projections, produces a decrement in the membrane/total protein ratio in the nerve terminal region but an increase in areas where reactive sprouting takes place (Kostrzewa and Jacobowitz 1974; Navarro et al. 1988).
As a fourth objective, we made determinations of lipid peroxidation. Nicotine induces free radical generation and contributes a major proportion of the net oxidative stress imposed by tobacco use (Bhagwat et al. 1998; Newman et al. 2002; Qiao et al. 2005; Yildiz et al. 1999). At the same time, many other products in tobacco smoke similarly have the potential to produce oxidative damage (Huang et al. 2005), and oxidative stress contributes to the effects of many neurotoxicants (Gitto et al. 2002; Gupta 2004; Ohtsuka and Suzuki 2000; Olanow and Arendash 1994). To evaluate the role of oxidative damage in the effects of ETS in our primate model of brain development, we assessed the concentration of thiobarbituric acid–reactive species (TBARS) (Guan et al. 2003), contrasting the effects in brain regions with those in the heart. Both the brain and heart are highly vulnerable because of their high oxygen consumption, but the brain is especially sensitive for two reasons: first, neural cell membrane lipids are high in oxidizable polyunsaturated fatty acids (Gupta 2004); second, the developing brain has an increased metabolic demand associated with its perinatal growth spurt, during which it has lower reserves of protective enzymes and antioxidants (James et al. 2005) and is deficient in glia, which ordinarily protect neurons from oxidative molecules (Tanaka et al. 1999). In addition, we conducted studies to characterize the temporal appearance of alterations in cardiac TBARS as well as the potential neurotransmitter-receptor–driven mechanisms that underlie developmental vulnerability or protection from oxidative stress.
Materials and Methods
Materials.
We purchased standardized 1R4F research cigarettes from the University of Kentucky (Louisville, KY). [3H]AFDX384 (specific activity, 133 Ci/mmol) and [125I]iodo-pindolol (specific activity, 2,200 Ci/mmol) were obtained from PerkinElmer Life Sciences (Boston, MA). All other chemicals were purchased from Sigma Chemical Co. (St. Louis, MO).
Animal treatments.
All studies were carried out in accordance with the declaration of Helsinki and with the Guide for the Care and Use of Laboratory Animals as adopted and promulgated by the National Institutes of Health (National Research Council 1996). We obtained 15 pregnant rhesus macaque monkeys from the California National Primate Research Center breeding colony and assigned them to three different treatment groups: animals to be exposed to filtered air, those to receive both prenatal and postnatal ETS exposure, and those to receive postnatal exposure only. The estimated gestational age for each dam was established by sonography performed before gestation day (GD) 40. Animals were selected based on a history of successful vaginal delivery and previous infant rearing experience, with estimated delivery dates separated by approximately 1 week per animal to facilitate experimental procedures. In addition to the present study of indices of brain development, these animals were used for evaluations of ETS effects on perinatal lung development, immune function, airway hyperresponsiveness through autonomic regulation, endothelial markers of mitochondrial DNA damage relevant to cardiovascular disease, and other determinations involving bone marrow, kidneys, eyes, heart, aorta, gastrointestinal tract, and reproductive organs.
To deliver ETS, we used two inhalation chambers, each with an air capacity of 3.5 m3, with each housing two monkeys. Aged and diluted sidestream smoke was used as a surrogate for ETS. Standardized 1R4F research cigarettes were smoked simultaneously with a single puff volume of 35 mL per cigarette and a duration of 2 sec, once per minute. Sidestream smoke from the smoldering end of each cigarette was collected and aged, and then diluted with filtered air to achieve a final particulate concentration of 1 mg/m3. Airflow through the system was set for 30 changes per hour, and samples were collected daily to determine the concentrations of total suspended particulates, nicotine (average, 162 μg/m3), and carbon monoxide (average, 4.3 ppm). These concentrations represent the high end of field measurements reported for household ETS but are within the range of what a child would experience if the caretaker is a smoker; the cloud of ETS generated around a smoker contains particulates up to 2 mg/m3, twice the exposure used here (Jenkins et al. 2000; U.S. Environmental Protection Agency 1992). Exposure to ETS occurred for 6 hr/day, 5 days/week, beginning at about GD50; pregnant animals in the control and postnatal exposure groups received filtered air in the same apparatus on the same schedule. All dams were allowed to give birth spontaneously, and then ETS or filtered air exposures were continued through 13 months postnatally, with the chamber containing both the mother and infant until removal of the mother at weaning (5 months of age). The group with ETS exposure limited to the postnatal period was switched from filtered air to ETS at 6 months of age and continued through 13 months. At 13 months, the offspring were anesthetized with ketamine (10 mg/kg intramuscular) and euthanized with pentobarbital (80 mg/kg intravenous). The heart was dissected and brain samples were taken from the three regions of the cerebral cortex (frontal, temporal, and occipital cortex) as well as the midbrain, using anatomical landmarks to ensure sampling of the same area from each monkey. Tissues were flash-frozen and stored at –80°C until assayed.
Each group contained both male and female offspring: four males and one female in the control group, three males and two females in the group receiving continuous ETS exposure, and three males and two females in the group receiving only postnatal ETS exposure.
In an additional set of monkeys (five males and three females in the controls, three males and five females in the ETS group), we evaluated cardiac effects elicited by continuous perinatal ETS exposure at an earlier time point (postnatal days 70–80).
Biomarkers of neural cell development.
Tissues were thawed in 19 volumes of ice-cold 10 mM sodium–potassium phosphate buffer (pH 7.4) and homogenized with a Polytron (Brinkmann Instruments, Westbury, NY). DNA was assessed with a modified (Trauth et al. 2000) fluorescent dye-binding method (Labarca and Piagen 1980). Aliquots were diluted in 50 mM sodium phosphate, 2 M NaCl, 2 mM EDTA (pH 7.4), and sonicated briefly (Virsonic Cell Disrupter, Virtis, Gardiner, NY). Hoechst 33258 was added to a final concentration of 1 μg/mL. Samples were then read in a spectrofluorometer using an excitation wavelength of 356 nm and an emission wavelength of 458 nm and were quantitated using standards of purified DNA. The total concentration of tissue proteins was assayed from the original homogenate spectro-photometrically with bicinchoninic acid (Smith et al. 1985); in addition, we assessed the concentration of membrane proteins from the membrane preparations used for radio-ligand binding. For calculation of the ratio of membrane/total protein, the membrane protein value was averaged across the different membrane preparations.
Thiobarbituric acid reactive species.
Lipid peroxidation was evaluated by assessment of TBARS using established techniques (Ohkawa et al. 1979). Triplicate aliquots of the same homogenate used for determination of DNA and proteins were added to an equal volume of 10% trichloroacetic acid, followed by addition of 1 volume of thiobarbituric acid reagent: 0.75% 2-thiobarbituric acid dissolved in 1 M NaOH, followed by addition of acetic acid to a final concentration of 20%. Samples were incubated for 1 hr at 95–100°C, cooled to ambient temperature, and sedimented at 3,500 × g for 10 min. The pellet was discarded, the supernatant solution was resedimented, and the absorbance of the final supernatant solution was determined at 532 nm. Standard curves were constructed with known concentrations of malondialdehyde that had been run through the same reaction. Values were determined relative to total protein.
Receptor binding assays.
Cardiac receptor binding capabilities were determined by methods described previously (McMillian et al. 1983; Slotkin et al. 1987a; Song et al. 1997; Zahalka et al. 1993). Aliquots of the original tissue homogenate were sedimented at 40,000 × g for 15 min and were then prepared in two different ways, one for β-adrenergic receptor (βAR) binding and the other for m2-acetylcholine receptor (m2AChR) binding. For βARs, the membrane pellets were resuspended and resedimented in a buffer consisting of 125 mM sucrose, 6 mM MgCl2, 50 mM Tris-HCl (pH 7.5), whereas for m2AChR binding, we maintained the same sodium-phosphate buffer used for the original homogenization. To evaluate βAR binding, aliquots of membrane preparation were incubated with [125I]iodopindolol (final concentration, 67 pM), in 145 mM NaCl, 2 mM MgCl2, 1 mM Na ascorbate, 20 mM Tris (pH 7.5), for 20 min at room temperature in a total volume of 250 μL. Displacement of nonspecific binding was evaluated with 100 μM d,l-isoproterenol. Binding to m2AChRs was evaluated with 1 nM [3H]AFDX384 incubated for 60 min at room temperature in 10 mM sodium phosphate (pH 7.4), and nonspecific binding was evaluated with 1 μM atropine.
Data analysis.
Data are presented as means and SEs. The effects of ETS exposure were first evaluated by global analysis of variance (ANOVA; data log-transformed because of heterogeneous variance) incorporating the three different treatments (control, continuous perinatal ETS, postnatal ETS 6–13 months), the various regions, and the repeated measures representing the biomarkers of neural cell development: DNA concentration, total protein/DNA ratio, and membrane/total protein ratio. Because this initial test indicated a significant difference of treatment effects according to the type of measurement, we used lower order ANOVAs (treatment, region) to assess the effects separately for each measure. Finally, where the lower order test indicated an interaction of treatment with region, separate post hoc analyses (Fisher’s protected least significant difference) were undertaken to determine the effects of ETS exposure on each individual region; in the absence of an interaction, only the main effect of ETS was reported. Similarly, TBARS were assessed initially with a two-factor ANOVA (treatment, region), and cardiac receptor binding studies were first evaluated by ANOVA incorporating treatment and receptor type (βAR, m2AChR). Significance for all tests was assumed at p < 0.05.
Results
Prepartum ultrasonography performed at GD40, GD90, GD120, and GD150 revealed no significant differences in fetal growth between those exposed to ETS or those exposed to filtered air. Similarly, the ETS group showed normal weights and other somatic indices of gestational age at birth, and there were no effects on growth through 13 months postnatal age (not shown).
At 13 months of age, there were no differences in body weights among the three groups (control, 2.3 ± 0.1 kg; continuous ETS, 2.2 ± 0.2 kg; ETS 6–13 months, 2.3 ± 0.2 kg), nor were there differences in general health or activity. Nevertheless, ANOVA across the three biomarkers of neural cell development indicated highly significant differences among the three groups (p < 0.0008) that depended both upon the specific measure and brain region (treatment × measure × region, p < 0.0002). Accordingly, we subdivided the assessments into the three different developmental indices and reevaluated the main treatment effects and regional specificity. The DNA concentration, an index of cell packing density, showed regionally selective changes elicited by ETS exposure (Figure 1A). Although values were unaffected in the frontal cortex, both the occipital cortex and midbrain displayed significant increases after either continuous ETS exposure or ETS exposure restricted to the postnatal 6–13 month period. In contrast, values tended to be reduced in the temporal cortex, achieving statistical significance with the postnatal exposure group.
Both indices of cell size also displayed ETS-induced differences. For the total protein/DNA ratio, the values were reciprocally related to the change in DNA concentration. Accordingly, reductions were seen in the occipital cortex and midbrain, whereas an increase was obtained in the temporal cortex (Figure 1B). The membrane/total protein ratio showed overall increases that were not regionally selective but that were statistically significant both for continuous ETS exposure and for the group receiving only postnatal exposure (Figure 1C).
In contrast to the similarity of effects of continuous perinatal ETS exposure and postnatal exposure on neural cell development bio-markers, there were radically different effects on TBARS (Figure 2). The continuous ETS group showed marked reductions in TBARS in the frontal cortex and temporal cortex, without significant effects in the other regions or in the heart. In contrast, when ETS exposure occurred postnatally from 6–13 months, there were no significant differences in TBARS. The absence of effects in the heart, a tissue that, like the brain, has high oxygen demand, could imply that only the brain is targeted by ETS exposure, or alternatively that the heart may show similar effects but with a different temporal relationship. To distinguish these two possibilities, we performed an additional study with continuous ETS exposure, but conducting the evaluations earlier, on postnatal days 70–80. Under these circumstances, we obtained the same robust decrease in TBARS in the heart that we observed later in the brain (control, 1.71 ± 0.4 nmol/mg protein; ETS, 0.88 ± 0.11 nmol/mg protein; p < 0.008).
Earlier studies in rodents, using either nicotine or ETS exposure, indicated down-regulation and/or desensitization of cardiac autonomic receptors whose activity influences oxidative demand (Joseph et al. 2002; Navarro et al. 1990; Remondino et al. 2003; Slotkin et al. 1999, 2001). Accordingly, we assessed effects on both cardiac βAR and m2AChR binding with both the continuous perinatal exposure and postnatal exposure models (Figure 3). Although continuous exposure had no significant effect, both receptor types were down-regulated in the group where ETS exposure was restricted to the postnatal period of 6–13 months.
Discussion
Perinatal or postnatal ETS exposure elicited two characteristic patterns of neural cellular effects, both of which resemble earlier findings for effects of prenatal nicotine exposure in rodents (Levin and Slotkin 1998; Roy et al. 1998, 2002; Roy and Sabherwal 1994, 1998; Slotkin 1998, 2004; Slotkin et al. 1987b). In the occipital cortex and midbrain, there were smaller cells (reduced total protein/DNA ratio) and a corresponding increase in cell packing density (DNA concentration), features that are likely to reflect neuronal damage and “reactive gliosis,” that is, replacement with smaller, glial cells (O’Callaghan 1988, 1993; Roy et al. 1998, 2002; Roy and Sabherwal 1994, 1998). In contrast, in the temporal cortex, we found a reduction in the total number of cells (reduced DNA) with hypertrophy of the remaining cells (increased total protein/DNA ratio), changes indicative of cell loss with perikaryal swelling (Roy et al. 2005). Superimposed on these two patterns, we also found an overall increase in the membrane/total protein ratio, which is compatible either with smaller cells (higher surface-to-volume ratio) or with increased neuritic sprouting. Given the disparate underlying regional patterns for the other two markers, the first explanation is likely to be true for the occipital cortex and midbrain, whereas the latter is more probable for the temporal cortex: reactive sprouting is typical after damage to developing nerve terminals or projections (Kostrzewa and Jacobowitz 1974) and, again, has been found for the effects of prenatal nicotine exposure in rodents (Navarro et al. 1988). These neurochemical inferences point to the need for detailed, quantitative morphologic investigations of ETS effects on primate development paralleling those done for nicotine in rodent models (Roy et al. 1998, 2002; Roy and Sabherwal 1994, 1998), and the present results provide the necessary guidance as to which regions should be evaluated and what types of changes are likely to be found.
In addition to regional selectivity, there were two other notable features of ETS-induced alterations in neurochemistry. First, although the changes were statistically significant, not surprisingly, the effects were smaller in magnitude than those associated with direct nicotine administration (Levin and Slotkin 1998; Slotkin 1998, 2004). Given that ETS delivers higher levels of oxidative free radicals than does just the administration of nicotine (Huang et al. 2005), our results imply that the role of nicotine in adverse neurobehavioral outcomes is primary; indeed, as discussed below, nicotine-induced damage may actually limit the contributions of oxidative injury. Nevertheless, as seen here for different cortical regions, heterogeneity of the effects is likely to reduce measured differences by diluting highly affected nuclei or neuron types with larger amounts of unaffected subregions. Consequently, biochemical examinations of even broader regional groupings may give false negative results because of opposing changes in different subregions (Gospe et al. 1996). Even here, with subregional dissection into the frontal, temporal, and occipital cortex, we are still incorporating heterogeneous layers and nuclei, which means that significant, small changes imply much larger focal effects that are likely to be identified by quantitative morphology. Indeed, with prenatal nicotine exposure in rats, we have already shown distinct targeting of different types of neurons even within a single layer of the somatosensory cortex or in specific zones of the hippocampus (Roy and Sabherwal 1994, 1998; Roy et al. 2002).
The second unexpected feature of the effects of ETS was that both continuous and postnatal exposure produced neurochemical changes that were similar in regional selectivity and magnitude, despite the obvious, major differences in exposure period and duration. Translated to human ETS exposure, this finding points out the importance of reducing the exposure of young children to tobacco smoke in the home or in child care settings. However, our results also pose a conundrum: how can continuous perinatal exposure give the same net effect as exposure restricted to the postnatal period of 6–13 months of age? It is highly unlikely that damage to the developing brain occurs only with postnatal exposure, given the known effects of prenatal nicotine on brain development (Levin and Slotkin 1998; Slotkin 1998, 2004; Slotkin et al. 2005). Alternatively, the effects of continuous perinatal exposure may be greater than those of postnatal ETS, with the differences masked by the limits of resolution imposed by regional heterogeneity; in that case, detailed morphologic studies will again reveal the disparities between the two exposure paradigms. However, our results for lipid peroxidation also point to the possibility that some factors operate to constrain the degree of these specific types of cellular damage. Surprisingly, perinatal ETS exposure reduced TBARS in cortical subregions, rather than evoking the expected increase, thus suggesting an enhancement of antioxidant defenses in the exposed offspring. This result is in keeping with a recent study of human maternal and cord blood, which similarly found an increase in antioxidant molecules with active smoking during pregnancy and smaller changes in the same direction with ETS exposure (Fayol et al. 2005). Here, we found evidence that prenatal ETS exposure programs antioxidant responses that limit the additional effects of postnatal ETS: TBARS were reduced with the perinatal exposure model but not with postnatal exposure, despite the fact that both groups received equivalent ETS for the 7 months preceding the tissue sampling at 13 months of age. It is likely that programming of defense mechanisms is still going on in the neonatal period, albeit at a much lower level than with prenatal exposure, because we did not find an elevation in TBARS in the postnatal ETS group. In contrast, nicotine administered by itself to older animals produces an increase in TBARS in a variety of brain regions, even at nicotine doses simulating ETS exposure (Qiao et al. 2005), whereas much higher doses in the fetus do not (Slotkin et al. 2005). Although we did not evaluate which specific mechanisms contribute to the net reductions in TBARS, it is important to note that some of the factors may actually not be beneficial. During development, a mild degree of oxidative stress is required for the appropriate timing of neuronal cell differentiation (Katoh et al. 1997), so oxidative stress from ETS exposure and the adaptive changes in defense mechanisms are both likely to preempt this natural signal. Furthermore, a number of the known, neurotoxic effects of nicotine on brain development are themselves liable to reduce oxidative damage. Nicotine actually protects developing neurons from the effects of other oxidative molecules (Guan et al. 2003; Qiao et al. 2005). In addition, the developmental neurotoxicity of nicotine produces changes that promote resistance to oxidative stress, including marked reductions in synaptic development and activity, and the replacement of damaged neurons with glia, cells that possess major antioxidant pathways (Levin and Slotkin 1998; Roy et al. 2002; Slotkin 1998, 2004; Slotkin et al. 2005; Tanaka et al. 1999). Specifically, prenatal nicotine exposure grossly reduces neonatal activity of nerve pathways using catecholamine neuro-transmitters (Levin and Slotkin 1998; Slotkin 1998, 2004), which are strongly oxidative (Olanow and Arendash 1994). Accordingly, the primary neurotoxic effects of nicotine may limit the apparent contribution of oxidative damage to the net neurobehavioral effects of ETS, so looking at lipid peroxidation alone may be misleading without considering the whole picture.
Unlike the effects of ETS on TBARS in the brain, we did not find significant reductions in the heart after perinatal ETS exposure, nor did postnatal ETS produce an effect. These results indicate either that the heart displays a different critical period for the programming of antioxidant defenses, or alternatively that the timetable for appearance and disappearance of the effect might be different. In fact, when we examined lipid peroxidation at an earlier time point 2–3 months after birth, we were able to demonstrate a significant reduction in cardiac TBARS in the perinatal exposure group, implying that the effects were present but disappeared by the later sampling at 13 months of age. Similarly, then, brain regions that did not display a significant decrease at 13 months may not in fact be spared from the effects but may simply show a more rapid return to normal oxidative status. The temporal dichotomy is a reflection of the fact that TBARS measurements take a momentary “snapshot” of lipid peroxidation rather than representing long-term damage, whereas neural cell biomarkers provide a much longer integrative time frame.
The results in the heart also provide confirmation that the protection from oxidative stress comprises alterations that actually reflect functional loss, evidenced by the reductions in βARs and m2AChRs. Cardiac βAR overstimulation evokes oxidative stress, leading to myocyte apoptosis (Remondino et al. 2003), whereas βARs protect neurons (Sarker et al. 2000) and show no down-regulation by developmental ETS exposure (Slotkin et al. 2005). In turn, cardiac m2AChRs may be reduced as a compensation to maintain the balance of autonomic input or, alternatively, may be specifically down-regulated because of their similar involvement in oxidative stress (Joseph et al. 2002). Indeed, in rats with ETS exposure, the degree of cardiac m2AChR down-regulation exceeds that of βARs (Slotkin et al. 2001). Again, there may be a specific role for nicotine in these potentially maladaptive responses: by itself, prenatal nicotine exposure leads to decrements in cardiac βAR function (Navarro et al. 1990).
In summary, our findings show that perinatal or postnatal ETS exposure in primates elicits changes in brain cell development akin to those found for either prenatal nicotine exposure or perinatal ETS exposure in rodents (Gospe et al. 1996; Levin and Slotkin 1998; Navarro et al. 1988; Slotkin 1998, 2004) as well as for prenatal nicotine in monkeys (Slotkin et al. 2005). This reinforces a mechanistic connection between nicotine as a specific contributor to the adverse neurobehavioral effects of developmental ETS exposure and supports the use of nicotine metabolite measurements in fetuses and children as an appropriate predictor of outcome (Eliopoulos et al. 1996; Fried et al. 1995; Jauniaux et al. 1999; Kohler et al. 1999; Ostrea et al. 1994). Equally significant, we found that postnatal ETS produces effects very similar to those achieved with continuous prenatal and postnatal exposure, buttressing the importance of restricting or eliminating exposure in young children. Finally, although ETS exposure also elicits signs of chronic oxidative stress, demonstration of a specific role of this mechanism in brain damage remains elusive, confounded by adaptive mechanisms and perhaps most of all by the underlying damage caused by nicotine. Indeed, for prenatal exposure, attempts to offset oxidative damage by dietary supplementation with antioxidants may actually worsen nicotine-related neurodevelopmental damage by secondary pharmacokinetic effects that increase nicotine concentrations in the fetal compartment (Slotkin et al. 2005), indicating the danger of focusing on oxidative damage as a primary mechanism rather than on the net neurotoxic outcome of all ETS components.
We thank M.M. Cousins, C.A. Oliver, and C.A. Tate for technical assistance.
Research was supported by grants from the Philip Morris External Research Program and the National Institutes of Health (ES011634, ES05707, and RR00169).
Figure 1 Effects of ETS exposure on biomarkers of neural cell development: (A) DNA concentration (ANOVA: treatment, p < 0.01; treatment × region, p < 0.0001). (B) Total protein/DNA ratio (ANOVA: treatment × region, p < 0.004). (C) Membrane/total protein ratio (ANOVA: treatment, p < 0.008; there was no treatment × region interaction); the main effect of each ETS treatment in (C) is as follows: continuous ETS, p < 0.005; ETS 6–13 months, p < 0.008.
*Individual values for which the ETS groups differ from the corresponding control. These were not evaluated in (C) because of the absense of a treatment × region interaction.
Figure 2 Effects of ETS exposure on TBARS in brain regions and heart (note different scales). ANOVA across all treatments and tissues: treatment, p < 0.0001; treatment × tissue, p < 0.004. Lower order ANOVAs for each tissue are shown within the figure.
*Individual values for which the ETS groups differ from the corresponding control.
Figure 3 Effects of ETS exposure on cardiac βAR and m2AChR binding (note different scales); ANOVA: treatment, p < 0.004. There was no interaction of treatment × receptor type; the only main treatment effect is ETS 6–13 months (p < 0.003). There were no significant differences in the concentration of membrane proteins.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8130ehp0114-00004016393656ResearchReproductive Disruption in Wild Longear Sunfish (Lepomis megalotis) Exposed to Kraft Mill Effluent Fentress Jennifer A. 1*Steele Stacy L. 1*Bart Henry L. Jr.23Cheek Ann Oliver 11 Department of Biological Sciences, Southeastern Louisiana University, Hammond, Louisiana, USA2 Department of Ecology, Evolution, and Organismal Biology, and 3 Tulane University Museum of Natural History, Tulane University, New Orleans, Louisiana, USAAddress correspondence to A.O. Cheek, Division of Environmental and Occupational Health Sciences, University of Texas Health Science Center at Houston School of Public Health, 1200 Hermann Pressler Dr., RAS610, Houston, TX 77030 USA. Telephone: (713) 500-9231. Fax: (713) 500-9249. E-mail:
[email protected]*These authors contributed equally to this work.
The authors declare they have no competing financial interests.
1 2006 7 9 2005 114 1 40 45 18 3 2005 7 9 2005 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Worldwide, wild fish living in rivers receiving municipal and industrial discharges may experience endocrine disruption as a result of exposure to anthropogenic pollutants. The purpose of this study was to evaluate the hormonal status of wild fish in a U.S. river receiving unbleached kraft and recycled pulp mill effluent (Pearl River at Bogalusa, LA). We evaluated two alternative hypotheses: the effluent contained constituents that suppressed male and female reproduction, or it contained an androgenic substance that masculinized females. To evaluate the likelihood of fish exposure to effluent, we marked 697 longear sunfish (Lepomis megalotis) over a 2-year period; 83% of recaptured fish were found at the site of initial capture, and only one fish migrated from an effluent-receiving site to a reference site. We can reasonably assume that fish captured from an effluent-receiving site are residents, not transitory migrants. To diagnose endocrine disruption, we measured sex steroid hormone [17β-estradiol (E2), testosterone (T), and 11-ketotestosterone (11KT)] and vitellogenin (VTG) concentrations in male and female longear sunfish captured at two sites upstream and two sites downstream of the effluent outfall. Kraft pulp mill effluent did not affect male reproductive physiology but did suppress female T and VTG levels when effluent constituted ≥ 1% of river flow. Masculinization was not observed. Longear sunfish in the Pearl River experience moderate reproductive suppression in response to unbleached kraft and recycled pulp mill effluent.
11-ketotestosteroneendocrine disruption17β-estradiolpaper mill effluentteleosttestosteronevitellogenin
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Biologists around the world have accumulated substantial evidence of endocrine disruption in wild fish, particularly in waters receiving sewage treatment plant or kraft mill effluents (KME) (Jobling et al. 1998; McMaster et al. 1996). Wild fish in KME-receiving waters in Canada, Scandinavia, and the United States experience several kinds of reproductive perturbations: reproductive suppression in both sexes (Dube and MacLatchy 2000; Karels et al. 2001; McMaster et al. 1996; Sepulveda et al. 2002), masculinization of females (Bortone and Davis 1994), or male-skewed sex ratios in exposed fry (Larsson and Forlin 2002).
Reproductive suppression includes effects such as delayed sexual maturity, reduced gonad size, suppressed steroid hormone and vitellogenin (VTG) levels, and impaired pituitary hormone release (Karels et al. 2001; McMaster et al. 1996; Sepulveda et al. 2001). In some species, these physiologic effects are accompanied by a limited ability to spawn (lake whitefish, Coregonus clupeaformis, and perch, Perca fluviatilis) or by reduced fry number and size (largemouth bass, Micropterus salmoides), whereas other species are able to spawn normally (longnose sucker, Catostomus catostomus; white sucker, Catostomus commersoni; and roach, Rutilus rutilus) (Karels et al. 2001; Lister and Van Der Kraak 2001; Sepulveda et al. 2003).
Masculinization can include induction of male secondary sex characteristics in females or male-skewed sex ratio in offspring (Howell et al. 1980; Larsson and Forlin 2002). In masculinized mosquitofish (Gambusia affinis), females grew a sperm delivery organ, the gonopodium, and displayed malelike courtship behavior toward other females, even though gonadal sex was unchanged and they continued to produce offspring (Howell et al. 1980). A different form of masculinization, male-skewed sex ratio, occurs in the eelpout (Zoarces viviparus), a live-bearing marine fish. Females captured in the effluent plume from an elemental chlorine-free bleached pulp mill had broods that were 55–65% male. After a mill shutdown that coincidentally occurred during early eelpout gestation, broods developed with the same 50:50 sex ratio observed at reference sites (Larsson and Forlin 2002).
KME can cause estrogenic effects such as abnormal VTG production in males, but these effects have never been observed in wild populations, only in sexually immature fish exposed under experimental conditions: caged in the effluent-receiving stream for short periods (Mellanen et al. 1999; Soimasuo et al. 1998), exposed in outdoor mesocosms under flow-through conditions (Van Den Heuval and Ellis 2002), or exposed in the laboratory under static renewal conditions (Tremblay and Van Der Kraak 1999).
The purpose of this study was to determine the extent of endocrine disruption in wild fish in a U.S. river receiving unbleached KME. Our objectives were to assess individual site fidelity in order to estimate the likelihood of long-term effluent exposure and to measure sex hormone and VTG levels in males and females upstream and downstream of the mill. We evaluated two alternative hypotheses: the KME contained constituents that suppressed male and female reproduction, or it contained androgenic constituents that masculinized females. We found that the effluent altered female reproductive physiology, suppressing female testosterone (T) and VTG levels.
Materials and Methods
Site selection.
The Pearl River originates in northeastern Mississippi and flows into the Gulf of Mexico. Approximately 43% of the river basin is forested land, 10% is marsh and/or swamp, and 27% is agricultural (Mississippi Department of Environmental Quality 2000). Although the basin has little urban or industrial development, several industrial sites are part of the Lower Pearl watershed, including the Bogalusa Mill in Bogalusa, Louisiana [Louisiana Department of Environmental Quality (LDEQ) 2005].
The Bogalusa Mill is an integrated pulp and paper mill that began operation in 1918 and currently manufactures linerboard paper using the unbleached kraft and semichemical processes. The mill pulps 1,814 metric tons/day of softwood and up to 907 metric tons/day of recycled waste paper. Treated process wastewater, sanitary wastewater, and storm water from the processing site are transported via a 3.2-km-long, 1.4-m-diameter pipe to a 92-m-diameter primary clarifier. Clarifier overflow enters a 25.5-ha aerated stabilization basin (ASB), where it is retained for 3.3 days. Water from the ASB enters the Pearl River via a submerged 1.8-m-diameter pipe. The mean daily discharge of effluent during this study (October 2000 through October 2002) was 0.88 ± 0.08 m3/sec. We calculated effluent concentration in the river from monthly discharge monitoring reports filed by the Bogalusa Mill with the LDEQ and river flow data from the U.S. Geological Survey (Table 1; LDEQ 2002, USGS 2003). No characterization of resin acids or phytosterols in the effluent is available for this mill.
We captured and sampled fish at two sites upstream and two sites downstream of the effluent discharge (Figure 1). Sites were chosen based on sunfish (Lepomis spp.) abundance. Upstream sites were 2 km (upstream 1, US1) and 5 km (upstream 2, US2) upstream of the discharge site and downstream sites were 1.9 km (downstream 1, DS1) and 2.2 km (downstream 2, DS2) downstream. The downstream sites were close to one another to avoid large differences in effluent dilution. US2 and DS2 (sandbar sites) included shallow sandbars at the mouths of creeks and had slower flow rates relative to the US1 and DS1 sites (streamside sites).
Although we sampled duplicate upstream and downstream sites, these sites are in the same river and must be considered pseudo-replicates. The alternative approach of sampling reference and KME-receiving sites in another river would not provide true replication because the processing, wood furnish, water quality, water flow, and habitat are unique for every mill (McMaster et al. 1996). In our analyses, we compared the US1 and DS1 sites with each other and separately compare the US2 and DS2 sites. Habitat was more similar within each upstream–downstream pair, and US2 and DS2 were sampled less frequently because of the logistical challenges of collecting fish at four sites in a single day.
Species.
We focused on longear sunfish (Lepomis megalotis), the most abundant species of centrarchid in the Pearl River (Gunning and Suttkus 1990). Longear sunfish are fairly sedentary: 80–90% of fish move < 50 m from the site of initial capture (Berra and Gunning 1972; Gunning and Shoop 1963).
Male and female longear sunfish spawn multiple times between April and August, although the exact number of spawns per individual is unknown. As in many sunfish species, male reproductive strategies depend upon body size: large males construct nests in the substrate, court females, and defend eggs. Smaller males are less likely to defend a nest but will streak spawn, darting in to release sperm while a nesting male and female are spawning. Streaking is an opportunistic strategy practiced by all sizes of males, however. Females visit the nests, which may be solitary or grouped in colonies of 2–15 nests, and are more likely to approach and spawn with large males (Dupuis and Keenleyside 1988; Jennings and Phillips 1992).
Sampling.
All fish used in this study were treated humanely in accordance with a protocol approved by the Southeastern Louisiana University Committee on Use of Humans and Animals in Research. Longear sunfish were captured via electrofishing from October 2000 through October 2002. Fish were sampled twice monthly during the spawning season (within 3 days of the full and new moons) at the US1 and DS1 sites (eight collections from 7 May through 28 September 2001, nine collections from 4 April through 20 September 2002) and approximately once monthly during the nonspawning season. The US2 and DS2 sites were sampled once monthly during the spawning season (four collections in 2001, six collections in 2002). Four to 14 individuals of each sex were sampled at each site on each date. Because of small sample size, collections from several dates during the 2001 spawning season were combined on the basis of Julian date and similarity of water quality. Data are plotted versus the median date of the combined sample periods. All other collections were made on the exact dates shown.
Captured fish were anesthetized (1:100,000 MS-222; Sigma-Aldrich, St. Louis, MO), bled (within 5 ± 0.003 min), measured, and marked with an individual-specific pattern of colored elastomer (Northwest Marine Technology, Shaw Island, WA). Blood was drawn from the caudal vein (27 gauge needle, 1 mL syringe with 6 mg/mL ammonium heparin in 0.9% NaCl) and mixed with 0.84 TIU/mL aprotinin, and then held on ice until centrifugation at 14,000 rpm for 5 min at 4°C. Plasma was aspirated and stored at –80°C. During the breeding season, gametes were expressed to determine sex. From late August 2001 through April 2002, fish were sacrificed for macroscopic examination of the gonads.
Steroid analysis.
We analyzed 17β-estradiol (E2), T, and 11-ketotestosterone (11KT) by acetylcholinesterase-based competitive enzyme-linked immunosorbent assay (ELISA) performed according to manufacturer’s instructions (Cayman Chemical, Ann Arbor, MI). Before analysis, duplicate plasma aliquots (T and 11KT, 2 μL; E2, 15–25 μL) were triple extracted with anhydrous diethyl ether, evaporated to dryness, and reconstituted in assay buffer (1 M sodium phosphate, pH 7.4, 1% bovine serum albumin, 4 M NaCl, 10 mM EDTA, 0.1% sodium azide).
Parallel dilution of endogenous steroid in longear sunfish plasma relative to steroid standards was demonstrated for all steroids. Recovery of known steroid concentrations was 92% for T, 76.2% for E2, and 73% for 11KT. Intraassay variation was 13.2% ± 1.6 for T, 13.4% ± 2.2 for E2, and 8.1% ± 0.8 for 11KT. Interassay variation was 21.8% for T (n = 37 assays), 26.7% for E2 (n = 35 assays), and 19.9% for 11KT (n = 32 assays).
VTG ELISA.
We analyzed VTG using a heterologous competitive ELISA developed for bluegill, Lepomis macrochirus (Cheek et al. 2004). Western blotting verified that anti-bluegill VTG antiserum recognized a single major polypeptide in plasma from E2-injected juvenile and vitellogenic female longear sunfish. Antisera were diluted 1:120,000, and longear sunfish plasma was diluted 1:9,000–1:6,000. Plasma from vitellogenic female longear sunfish diluted in parallel with the purified bluegill VTG standard curve. The average recovery of purified bluegill VTG in longear sunfish plasma was 84%; intraassay variation was 10.7%, and interassay variation was 17.4% (n = 37 assays).
Statistics.
We analyzed site-specific differences in water quality using the Friedman test. Hormone values were log(10Y + 1) transformed (Sokal and Rohlf 1981). Two levels of analysis were performed. First, a two-way analysis of covariance for the main effects of date and site with size as a covariate was performed on the US1 and DS1 combined data set (2001–2002) to demonstrate seasonal patterns in reproductive physiology and evaluate site-specific changes. Size was included as a covariate because male androgen levels can increase with body size in species with alternative male reproductive strategies (Brantley et al. 1993; Kindler et al. 1989). If the interactions between size and main effects were not significant, the interaction terms were removed. If hormone or VTG levels did not vary significantly with size, the covariate was removed and a two-way analysis of variance for the main effects of site and date was performed. Second, because effluent concentration was twice as high during summer 2002 (Table 1), the data set was divided into 2001 and 2002 spawning seasons and the same analyses were performed.
Results
Water quality.
Although temperature profiles were similar among sites, temperatures differed significantly between sites (χ2 = 11.6, p = 0.02) with DS2 > DS1 > US1 > outfall > US2. This rank ordering is an artifact of sampling order: sites were sampled from upstream to downstream beginning at the farthest upstream location, US2, in the early morning when surface water temperature was coolest. Dissolved oxygen levels did not vary significantly between sites (χ2 = 8.86, p = 0.06), and all sites were always ≥ 69% saturation. Conductivity was slightly but significantly higher at downstream sites (χ2 = 29.036, p < 0.0001), showing an effluent signature.
Effluent dilution.
Mean daily discharge of paper mill effluent was similar between spawning seasons (2001, 0.81 m3/sec; 2002, 0.94 m3/sec), but river flow was greater during the 2001 spawning season (234.2 m3/sec) than during the 2002 season (149.3 m3/sec; Table 1). Consequently, effluent concentration was higher during the 2002 spawning season than during the 2001 spawning season (0.95% vs. 0.45%; Table 1).
Site fidelity.
A total of 697 longear sunfish were marked between October 2000 and August 2002. Of these, 18 (2.6%) were recaptured. Most recaptures occurred at the site of initial capture (83%). No marked fish migrated from upstream (reference) to downstream (effluent receiving), but one moved from downstream to upstream, one migrated from one downstream site to the other, and one moved from one upstream site to the other.
Reproductive physiology.
Males.
Male androgen levels increased significantly with body size (T: Fsize = 46.4, p < 0.0001; 11KT: Fsize = 56.01, p < 0.0001) and varied significantly over time (11KT: Fdate = 4.10, p < 0.0001; T: Fdate = 2.4, p = 0.0049) but were similar between sites (Figure 2). Separate analysis of the two spawning seasons gave the same results. Intensive sampling during 2002 showed that T and 11KT peaked twice during the spawning season (T: Fdate = 2.77, p = 0.01; 11KT: Fdate = 4.28, p = 0.0002).
E2 and VTG levels were similar among all sizes of males but varied significantly over time (Figure 3; E2: Fdate = 3.31, p < 0.0001; VTG: Fdate = 3.93, p < 0.0001). Separate analysis of the two spawning seasons showed that a single significant increase in E2 occurred during late May 2002 at DS1 (Fsite = 54.62, p < 0.0001; Fdate = 27.95, p < 0.0001; Fsite × date = 13.62, p < 0.0001). Peak E2 production coincided with peak T and 11KT production at both sites. In contrast, male VTG concentrations were significantly higher during the nonspawning season, October through March, with a marked increase occurring in September at both sites (Figure 3; 2001: Fdate = 6.05, p = 0.001; 2002: Fdate = 4.16, p = 0.0003).
Females.
All sizes of females had similar VTG levels, and VTG changed significantly over time (combined 2001 and 2002 data, Fdate = 5.80, p < 0.0001), with the highest concentrations during the spawning season (Figure 4A). The effects of site and date differed between years. In 2001, average VTG concentration was statistically similar between sites. In contrast, during 2002, VTG increased sharply between early March and late April at the upstream site and remained elevated through late July. At the effluent-receiving site, VTG also increased sharply in March and April but then declined steadily throughout the summer (Figure 4A; Fdate = 6.3, p < 0.0001; Fsite × date = 3.28, p = 0.003).
Female E2 and T increased significantly with body size and varied significantly over time. The highest hormone concentrations occurred during the spawning season (Figure 4B, C; E2: Fdate = 11.82, p < 0.0001; T: Fdate = 7.10, p < 0.0001) and were similar between sites (2001: Fdate = 8.83, p < 0.0001; 2002: Fdate = 4.78, p = 0.0001). Female T concentrations varied significantly throughout the spawning season in both years (2001: Fdate = 3.21, p = 0.03; 2002: Fdate = 4.58, p = 0.0002). Average T concentration was significantly lower at the effluent-receiving site in the combined data set (2001 and 2002) and in 2002 (Figure 4C; combined 2001 and 2002: Fsite = 5.2, p = 0.02; 2002: Fsite = 7.97, p = 0.0062).
Female 11KT was unrelated to body size and did not differ between sites but did change throughout the year (Figure 5; Fdate = 6.14, p < 0.0001). Unlike E2, T, and VTG, female 11KT values were elevated during the non-spawning season, October through March.
At the sandbar sites, US2 and DS2, the relationships of male and female hormone and VTG concentrations to sampling date and body size were similar to those observed at the streamside sites (US1 and DS1; data not shown). Although data from the sandbar sites generally corroborate data from the streamside sites, far fewer individuals were sampled at the sandbar sites (n = 42 females and 57 males upstream, n = 38 females and 51 males at the effluent receiving site) compared with the streamside sites (n = 104 females and 105 males upstream and 102 females and 110 males at the effluent receiving site). Also, the second effluent-receiving site (DS2) was 0.3 km farther downstream, creating the possibility of greater effluent dilution compared with DS1.
Discussion
Establishing potential causal relationships between contaminant sources and effects in free-living animals is always difficult because animals are exposed to multiple environmental stressors. One approach is to measure the contaminants of interest in the environment and in animal tissues and then suggest causal relationships based on the concentrations measured. The limitation of this approach is that only a single possible cause of the effect has been identified and quantified (Adams et al. 1996).
We used an approach that evaluates the likelihood of exposure to an industrial effluent based on fish movement patterns. We marked 697 longear sunfish over a 2-year period and recaptured 2.6% of marked fish. Our recapture rate in a large, fast-flowing river is low compared with recapture rates in small streams (Berra and Gunning 1972; Smithson and Johnston 1999); however, the percentage of recaptured fish remaining at the site of initial capture is almost identical. In our study, 83% of recaptured fish were found at the site of initial capture. Previous investigations also indicated limited adult migration, 70–90% of recaptured longear sunfish were found at the site of initial capture (Berra and Gunning 1972; Gunning and Shoop 1963; Smithson and Johnston 1999). Because adults exhibit strong site fidelity in the Pearl River, we can reasonably assume that longear sunfish captured at effluent receiving sites are residents, not transitory migrants.
Unbleached KME from the Bogalusa Mill appears to have little effect on male longear sunfish reproductive physiology. Androgen levels did not vary between sites, nor did E2 or VTG levels. Regardless of effluent exposure, androgen concentrations increased with male body size and varied significantly throughout the year and the spawning season. More frequent sampling during the 2002 spawning season clearly documented multiple T and 11KT peaks (Figure 2). High plasma androgen levels are probably associated with the initiation of spawning bouts as they are in bluegill, a closely related species with similar reproductive strategies (Kindler et al. 1989).
Although KME exposure had no effect, male E2 and VTG showed significant seasonal variation (Figure 3). Mean plasma E2 was 0.11 ng/mL in males, a value approximately 10-fold lower than the overall mean in females (1.56 ng/mL). Male VTG concentrations increased sharply in late September and were highest during the nonspawning period (Figure 3). The average fall/winter VTG concentration in males (342 μg/mL) was equivalent to the average concentration in females during the same period (454 μg/mL) but was 10-fold lower than the spawning female average (4,400 μg/mL).
Much recent work has promulgated the paradigm that normal male fish produce little E2 and undetectable amounts of VTG. When investigators detect VTG in reference or control males, the findings are often labeled “unexpected” and are sometimes attributed to a prior, unknown exposure to an estrogenic substance (Denslow et al. 2004). We suggest that low circulating levels of E2 and VTG are part of normal male fish physiology.
Male E2 levels have been measured infrequently in wild fish, but seasonal differences have been documented in perch, roach (Karels et al. 1998, 2001), cutthroat trout (Fukada et al. 2001), plainfin midshipman (Sisneros et al. 2004), and longear sunfish (the present study). E2 concentrations were highest before the single annual spawning event in male roach, cutthroat trout, and perch (Fukada et al. 2001; Karels et al. 1998, 2001). In males that spawn multiple times during an extended reproductive season, E2 levels may be low and constant throughout the year as in male midshipman (Sisneros et al. 2004) or may cycle during the spawning season as in male longear sunfish (the present study). Given the important role of E2 in mammalian spermatogenesis (Couse and Korach 1999), the presence of estrogen receptor (ER)-α in the fish testis (Bouma and Nagler 2001), and the seasonality of circulating E2 in a variety of wild male fish, low but detectable E2 in males from reference or control populations should be considered normal.
Likewise, low concentrations of VTG in unexposed male fish are probably normal. Seasonal variation in VTG occurs in wild male cutthroat trout (Fukada et al. 2001), roach (Karels et al. 2001), and longear sunfish (the present study) collected from relatively pristine locations. Male VTG concentrations vary between species, ranging from 5–200 ng/mL in cutthroat trout, roach, and mummichog to several hundred micrograms per milliliter in largemouth bass, longear sunfish, and cunner (Tautogolabrus adspersus) (the present study; Hiramatsu et al. 2005).
The function of VTG in male fish is an intriguing question. Perhaps it is produced as a physiologic artifact in response to low levels of endogenous E2 (Hiramatsu et al. 2005). We suggest that VTG could serve an osmo-regulatory role. Ion loss across the body surface, particularly the gills, is a continual osmotic challenge for freshwater and estuarine fish. Calcium is one of the major ions actively transported across the gill epithelium into the bloodstream of freshwater fishes (Perry 1997). VTG strongly binds calcium (Ng and Idler 1983) and could serve as a plasma calcium reservoir. Consistent with this hypothesis, VTG increased in male longear sunfish in the fall, a time when river flow increased and conductivity (ion concentration) decreased. Decreased ion concentration creates a more pronounced osmotic gradient between the fish and its environment. An osmotic function would also be consistent with Kirby et al.’s (2004) observation that VTG concentrations increase in male flounder (Platichthys flesus) from fall through spring—a period when they migrate from the coastal shelf into estuaries.
Unbleached KME suppressed vitellogenesis in female longear sunfish, but the effect occurred only in 2002 when effluent constituted ≥ 1% of river flow for 3 months (May through July) and averaged 0.95% of flow from May through September (Table 1). No alteration of female T, E2, or VTG occurred during 2001 when effluent constituted 0.45% of flow from May through September and never exceeded 1% (Table 1).
What are the consequences of suppressed VTG production? Females may release fewer eggs per spawning bout or may spawn fewer times during the reproductive season. Based on the number of sampling periods with elevated T concentrations, females at the effluent receiving site appear to have spawned fewer times in both years—twice upstream versus once downstream in 2001 and three times upstream versus twice downstream in 2002 (Figure 4).
Oddly, VTG suppression in KME-exposed female longear sunfish occurred without concurrent E2 suppression. Given that E2 specifically stimulates vitellogenesis in the liver and that T is the precursor for ovarian E2 production, one would predict that suppressed VTG would be accompanied by suppressed E2 and possibly T. Instead, E2 was unaffected, although T and VTG were significantly reduced. Apparently, sufficient T exists to allow unaltered E2 production. Why is VTG suppressed? Perhaps T up-regulates the number of ER in the liver, indirectly enhancing sensitivity to E2 stimulation. With suppressed T concentrations, perhaps ER sensitivity is reduced, resulting in slightly depressed VTG production. A few reports of in vivo T treatment enhancing estrogen-binding capacity in rodents lend some support to this idea (Cano et al. 1986; Ho and Yu 1993; Jaubert et al. 1995).
Although female T was suppressed at the KME-receiving site, female 11KT was not different between sites. The function of 11KT in female fish is unknown (Lokman et al. 2002), but in longear sunfish, the female and male 11KT profiles coincided, suggesting that 11KT may play a role in female spawning behavior. Average female 11KT (1.47 ng/mL) was 5-fold lower than average male 11KT. In contrast, average female T concentration during the spawning season (3.98 ng/mL) exceeded male T (1.99 ng/mL). T concentrations in vitellogenic females often equal or exceed T concentrations in spermiating males of many fish species (Lokman et al. 2002), possibly because of the critical role of T as a precursor for E2.
Reproductive suppression occurs in fish downstream from paper mills regardless of bleaching or secondary treatment processes used (Dube and MacLatchy 2000; Karels et al. 2001; McMaster et al. 1996; Sepulveda et al. 2002). Significant hormonal effects occur when mill effluents constitute ≥ 1% (volume) of the receiving environment (Dube and MacLatchy 2000; Sepulveda et al. 2002). The Bogalusa Mill is an elemental chlorine-free mill with secondary treatment, but longear sunfish in the receiving stream experience reproductive suppression, particularly when effluent flow equals or exceeds 1% of river flow. Mill processes are unlikely to explain this suppression, nor is climate or habitat: reproductive suppression occurs in both cold (Canada and Finland) and warm (Florida and Louisiana, USA) climates and in fresh and saltwater (Dube and MacLatchy 2000; Karels et al. 2001; Sepulveda et al. 2002). Organic compounds in the wood itself are the most likely cause of reproductive suppression (Dube and MacLatchy 2001). Hewitt et al. (2002) showed that compounds derived from both hardwoods and softwoods caused similar suppressive effects.
Conclusions
Longear sunfish in the Pearl River experience moderate reproductive suppression in response to unbleached KME. Males appear to be unaffected, but females experience suppressed vitellogenesis when KME constitutes ≥ 1% of river flow. No alterations in male or female androgen status or secondary sex characteristics were observed, indicating that unbleached KME from the Bogalusa Mill does not masculinize sunfish. Modulating effluent flow in response to seasonal and annual changes in river flow could maintain effluent concentrations below 1% in receiving water and minimize reproductive impacts on sunfish.
We thank V. Todaro for captaining the electro-fishing boat and T. Lorenz, N. Anderson, E. Spalding, M. Mask, B. Henry, and W. Wood for field assistance.
This work was funded by a Southeastern Louisiana University (SLU) Office of Student Creativity and Research grant (S.L.S.), SLU faculty development grants (A.O.C.), and Louisiana Board of Regents Support Fund grant LEQSF (2000-02)-RD-A-29 (A.O.C.).
Figure 1 Site locations along the Pearl River near Bogalusa, Louisiana. Outfall, site of wastewater discharge into the Pearl River (river flows south). Map was adapted from the USGS Bogalusa East, LA-MS map, USGS entity ID MPTLA0072PP01, 1997.
Figure 2 Male longear sunfish sampled from the Pearl River near Bogalusa, Louisiana. (A) 11KT. (B) T. Numbers below the x-axis indicate sample sizes for each site on each date. Both androgens increased with body size and varied significantly over time, but did not differ between sites. Values are mean ± SEM.
Figure 3 Male longear sunfish sampled from the Pearl River near Bogalusa, Louisiana. (A) VTG. (B) E2. Neither VTG nor E2 varied with body size or between sites, but both parameters showed significant seasonal variation. Sample sizes for each site on each date are shown in Figure 2B. Values are mean ± SEM.
Figure 4 Female longear sunfish sampled from the Pearl River near Bogalusa, Louisiana. (A) VTG. (B) E2. (C) T (note logarithmic scale). All three parameters varied significantly over time. Numbers below the x-axis indicate sample sizes for each site on each date. E2 was similar between sites; VTG was significantly lower at the downstream site in 2002 when effluent concentration exceeded 1% of river flow; and T was significantly lower in females sampled at the downstream site. Values are mean ± SEM.
Figure 5 Female longear sunfish sampled from the Pearl River near Bogalusa, Louisiana. 11KT varied significantly over time, but not between sites. Sample sizes for each site on each date are shown in Figure 4C. Values are mean ± SEM.
Table 1 Mean daily flow rate of the Pearl River at Bogalusa, Louisiana, and mean daily wastewater discharge from the Bogalusa Mill aeration basin.
Month Pearl Rivera (m3/sec) Bogalusa millb (m3/sec) Effluent (% river flow)
Oct 2000 38 0.93 2.44
Nov 2000 87 0.93 1.07
Dec 2000 83 1.06 1.28
Jan 2001 360 0.84 0.23
Feb 2001 277 0.92 0.33
Mar 2001 1,026 0.90 0.09
Apr 2001 344 0.82 0.24
May 2001 88 0.83 0.95
Jun 2001 192 0.87 0.45
Jul 2001 138 0.81 0.58
Aug 2001 296 0.82 0.28
Sep 2001 346 0.73 0.21
Oct 2001 190 0.75 0.39
Nov 2001 82 0.82 1.00
Dec 2001 451 0.80 0.18
Jan 2002 315 NR
Feb 2002 523 0.79 0.15
Mar 2002 364 0.90 0.25
Apr 2002 428 0.95 0.22
May 2002 71 0.89 1.25
Jun 2002 65 0.98 1.51
Jul 2002 74 0.92 1.23
Aug 2002 128 0.89 0.69
Sep 2002 131 1.01 0.77
Oct 2002 401 0.92 0.23
NR, no report filed with the LDEQ.
a Measured at site USGS 02489500, 30°47′35″ N, 89°49′15″ W, Pearl River near Bogalusa, Louisiana. Data from USGS (2003).
b As reported in monthly discharge monitoring reports to the LDEQ (2002).
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8405ehp0114-00004616393657ResearchExposure, Postexposure, and Density-Mediated Effects of Atrazine on Amphibians: Breaking Down Net Effects into Their Parts Rohr Jason R. 12Sager Tyler 3Sesterhenn Timothy M. 3Palmer Brent D. 31 Penn State Institutes of the Environment, and 2 Department of Entomology, Penn State University, University Park, Pennsylvania, USA3 Department of Biology, University of Kentucky, Lexington, Kentucky, USAAddress correspondence to J.R. Rohr, Department of Entomology, 501 ASI Building, Penn State University, University Park, PA 16802 USA. Telephone: (814) 865-4603. Fax: (814) 865-3048. E-mail:
[email protected] authors declare they have no competing financial interests.
1 2006 7 9 2005 114 1 46 50 14 6 2005 7 9 2005 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Most toxicology studies focus on effects of contaminants during exposure. This is disconcerting because subsequent survival may be affected. For instance, contaminant-induced mortality can be later ameliorated by reduced competition among the survivors, a concept we refer to as “density-mediated compensation.” Alternatively, it can be exacerbated by toxicant effects that persist or appear after exposure, a phenomenon we term “carryover effects.” We developed a laboratory framework for testing the contribution of exposure, density-mediated, and carryover effects to net survival, by exposing embryos and larvae of the streamside salamander (Ambystoma barbouri) to atrazine (0, 4, 40, 400 ppb; 3 ppb is the U.S. drinking water maximum) and quantifying survival during and 14 months after exposure. Atrazine is the most commonly used herbicide in the United States and a documented endocrine disruptor. We show that atrazine-induced mortality during exposure was ameliorated by density-dependent survival after exposure, but complete density-mediated compensation was precluded by significant carryover effects of atrazine. Consequently, salamanders exposed to ≥4 ppb of atrazine had significantly lower survival than did control animals 14 months postexposure. The greatest change in survival occurred at low exposure concentrations. These nonlinear, long-term, postexposure effects of atrazine have similarities to effects of early development exposure to other endocrine disruptors. Together with evidence of low levels of atrazine impairing amphibian gonadal development, the results here raise concerns about the role of atrazine in amphibian declines and highlight the importance of considering persistent, postexposure effects when evaluating the impact of xenobiotics on environmental health.
amphibian declinesatrazinedensity dependencedevelopmentendocrine disruptionnonlinear dose responsepesticidepostexposure effectssalamander
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A standard approach for evaluating the threat that a xenobiotic poses to environmental health is to test whether environmentally realistic concentrations have adverse effects on organisms that may be harbingers of possible risk to humans or ecosystem services. Amphibians have long been considered one of these bioindicators of environmental stress, especially for xenobiotics, because many amphibians experience aquatic and terrestrial stressors, play vital roles in communities, are sensitive to contaminants, complete their life cycles near fields where pesticides are applied, and have vulnerable embryo and larval stages whose development coincides with pesticide applications (Blaustein and Kiesecker 2002; Hayes et al. 2003; Rohr and Madison 2003; Rohr et al. 2004; Westerman et al. 2003). Most toxicologic studies, however, examine the effects of xenobiotics only during exposure (Figure 1) (Jensen and Forbes 2001; Ng and Keough 2003), and amphibian studies certainly are not an exception (Rohr and Palmer 2005). This is a concern because the effects of a contaminant can be unchanged, exacerbated, or ameliorated once contaminant exposure has ceased. For instance, if a stressor, such as a toxicant, induces juvenile mortality, the survivors may experience substantially less mortality than controls after exposure because of reduced competition for resources (Figure 1) (e.g., Moe et al. 2001). We refer to this concept as density-mediated compensation, a concept with a long history of study in the ecologic and wildlife literature. Alternatively, the effects of a pollutant may persist or appear after exposure (e.g., Hernandez-Avila et al. 1996; Quistad et al. 2003; Rice 1996), a phenomenon we refer to as carryover effects of the contaminant (Figure 1).
Ideally, decisions regarding environmental health should be made with an understanding of the effects that xenobiotics have on population growth rates (Forbes and Calow 2002; Schmidt 2004); however, this can be challenging for amphibians because they have relatively long generation times (often ≥ 3 years) and are frequently not amenable to life-cycle completion under tractable conditions. Moreover, the sheer number of registered chemicals (tens of thousands) and species to be tested and the complexities of population growth rate analyses may make this approach too time-consuming considering the urgent need to identify causal factors in the global decline of amphibians (Houlahan et al. 2000; Stuart et al. 2004). Researchers have suggested that a more practical and efficient alternative may be to study the effects of contaminants across life stages using amphibians reared under semi-natural conditions where postexposure effects and density-dependent regulation are permitted (Rohr et al. 2004; Sih et al. 2004). The proxy for population-level effects would be the net effect of the contaminant at some point near reproductive age, where the net effect is defined as the sum of exposure effects, carryover effects, and density-mediated compensation (Figure 1). We used this approach to investigate the net effects of the herbicide atrazine (0, 4, 40, and 400 ppb) on the streamside salamander, Ambystoma barbouri.
We focused on atrazine for several reasons. Atrazine is the most widely used pesticide in the United States, and possibly the world, and is one of the most common contaminants in groundwaters and surface waters [U.S. Department of Agriculture (USDA) 2002; U.S. Environmental Protection Agency (U.S. EPA) 1994]. Thus, many amphibians are likely exposed to atrazine. Atrazine concentrations seldom exceed 20 ppb for long but are likely to remain low (parts per billion) for extended time periods, especially considering that reported half-lives in fresh water exceed 100 days (de Noyelles et al. 1980, 1989; Klaassen and Kadoum 1979). Near agricultural areas, atrazine levels have been reported as high as 500 ppb in ponds (de Noyelles et al. 1982), 200 ppb in streams (U.S. Geological Survey 2000), and 40 ppb in rain (Nations and Hallberg 1992).
Recent studies have raised concerns regarding the effects of atrazine on amphibian fitness. The endocrine-disrupting properties of atrazine have been implicated in exposures between 25 and 0.1 ppb inducing gonadal abnormalities and hermaphroditism in leopard frogs (Rana pipiens) and African clawed frogs (Xenopus laevis) (Carr et al. 2003; Hayes et al. 2002a, 2002b, 2003; Tavera-Mendoza et al. 2002a, 2002b). Exposure to 3 ppb has increased tadpole mortality (Storrs and Kiesecker 2004) and susceptibility to infection (Kiesecker 2002).
The level of acute toxicity of a compound may not be as important as the persistence of its toxic effects (Rohr and Palmer 2005). Embryo and larval salamanders exposed to environmentally realistic atrazine concentrations have been reported to have an increased desiccation risk 8 months after exposure had ceased (Rohr and Palmer 2005), indicating that the carryover effects of atrazine on amphibian survival should be explored. The purpose of the present study was to determine the contributions of exposure, carryover, and density-mediated effects to the net survival of streamside salamanders 14 months after atrazine exposure.
Materials and Methods
Postmetamorphic A. barbouri used in this study are from a static renewal experiment conducted by Rohr et al. (2004). Thirty-eight A. barbouri egg clutches were collected from Fossil Creek (Jessamine County, KY) in February 2003 and were mixed to homogenize genetic variation. Forty embryos were randomly placed in each of 48 aquaria containing 9.5 L of constantly aerated, charcoal-filtered, dechlorinated municipal tap water. Aquaria were maintained at 15°C on 12:12-hr photo-period, and their water was changed weekly. On day 16 of the experiment, after 99% of the embryos had hatched, we arbitrarily removed larvae from the aquaria to ensure that all had 24 larvae. This was done to obtain tissue samples for another study, to lower larval densities, and to reduce the starting variation in the number of larvae among aquaria.
We employed a 4 × 2 ×2 complete factorial design in which each aquarium was randomly assigned one of four ecologically relevant atrazine concentrations (0, 4, 40, or 400 ppb; 98% pure; Chemservice, West Chester, PA), one of two food-level treatments (low or high), and one of two hydroperiod treatments (presence or absence of a water level reduction). Atrazine was predissolved in acetone; all treatments contained 0.004% acetone, and concentrations were confirmed by flame ionization detection gas chromatography. Similar solvent concentrations were shown to have no effect on A. barbouri behavior, life history, or survival (Rohr et al. 2003a), and so a negative control was not included in this experiment. Low-food aquaria received 2.24 g (± 0.005 g) of live black worms (Lumbriculus variegates) twice a week, and larvae in the high-food aquaria were fed black worms ad libitum. In half the aquaria, the volume of water was incrementally reduced during water changes to simulate summer drying of water bodies, but atrazine concentration remained the same. Water volume did not drop below 2 cm. Mortality was quantified every other day throughout the exposure period, and animals were exposed to treatments until metamorphosis (mean ± SD, 59.9 ± 4.52 days; range, 47–70 days; for effects on embryos, larval growth, behavior, and timing of metamorphosis, see Rohr et al. 2004). In summary, there were three replicate aquaria of each treatment combination and a total of 12 aquaria exposed to each of the four atrazine concentrations.
After metamorphosis, A. barbouri from each aquarium were placed into terraria (18.5 cm in diameter, 7.5 cm high) so that the 48 aquarium replicates were preserved. Thus, density at metamorphosis carried over into the terrestrial stage, simulating what occurs in nature and allowing us to quantify any density-mediated compensation. Each terrarium contained approximately 6 cm of homogeneously mixed, organic top soil. Every week, we fed the salamanders vitamin- and mineral-enriched crickets ad libitum and added water to the terraria (if needed) to ensure that each had sufficient moisture. Terraria were maintained at room temperature on a 12:12-hr photoperiod. We recorded survival on 13 July 2004, a mean of 421 days (SD = ± 4.52; range, 410–433 days) since metamorphosis and last atrazine exposure. Survival was not recorded between metamorphosis and 13 July 2004 because postmetamorphic A. barbouri burrow into the substrate and digging them out greatly disturbs both the salamanders and the terraria. When possible, salamanders were treated humanely and with regard for alleviation of suffering.
We used the general linear model to test for the main and interaction effects of atrazine (continuous predictor and log10-transformed), food level, and dry down on angularly transformed percent survival at metamorphosis (during atrazine exposure), after metamorphosis (carryover effect plus density-mediated compensation), and on 13 July 2004 (net atrazine effect; Figure 1). To test for carryover effects, we controlled for starting density in the postexposure phase (by incorporating survival to metamorphosis as a covariate in the statistical model) when examining postexposure survival. Because density-mediated compensation is driven by the different starting densities in the postexposure period, controlling for starting density removes density-mediated compensation, leaving any carryover effects of the contaminant (plus error; Figure 1). Where the main effect of atrazine was significant, we examined all pairwise comparisons among concentrations using Fisher’s least significant difference (LSD) tests.
To compare the magnitude (slope of regression) of atrazine’s exposure, post-exposure, carryover, and net effects, we examined all pairwise comparisons of these four effects using the general linear model, treating pairs of effects as repeated measures variables (i.e., we compared these effects within aquaria/terraria). An interaction between effect type and atrazine would indicate that mortality associated with atrazine depended upon the type of effect considered. The α-value for these tests (α = 0.0083) was modified for the number of comparisons using a Bonferroni adjustment. Comparisons with the carryover effect were conducted using the standardized residuals of survival at metamorphosis regressed against survival after metamorphosis.
The mean and variance of survival during the exposure and postexposure periods differed substantially (because of their different durations), making it difficult to visually evaluate the magnitude of atrazine effects across life stages. To facilitate visually comparing the exposure, postexposure, carryover, and net effects of atrazine, we standardized these effects using Z-scores so that the overall mean and SD in each phase were 0 and 1, respectively. Standardization does not affect statistical results.
Results
The effect of atrazine on survival was independent of food level and dry down treatments for exposure, postexposure, carryover, and net effects (interactions including atrazine: p > 0.11). Thus, in this article, we focus strictly on the main effects of atrazine.
During the exposure period, atrazine concentration was positively associated with mortality (F1,40 = 13.80, p < 0.001), with survival in control aquaria differing from that in aquaria containing 40 ppb (LSD, p = 0.027) and 400 ppb atrazine (LSD, p < 0.001; 0 vs. 4 ppb, p = 0.094; Figure 2A). Exposure concentration was also positively associated with mortality for the carryover effect (F1,39 = 10.71, p = 0.002). Survival of salamanders not exposed to atrazine was, once again, greater than the survival of animals exposed to 40 ppb (LSD, p = 0.039) and 400 ppb (LSD, p = 0.015; 0 vs. 4 ppb, p = 0.056; Figure 2B). The carryover effect analysis revealed that starting density in the postexposure phase was positively associated with postexposure mortality (F1,39 = 8.76, p = 0.005), indicating that the heavy mortality imposed by atrazine during the exposure period was ameliorated after exposure by density-mediated compensation (Figure 2B). Although the carryover effect was greater than density-mediated compensation, this compensation was substantial enough to offset a sizable portion of the carryover effect, resulting in a nonsignificant relationship between exposure concentration and net postexposure survival (F1,40 = 3.55, p = 0.067; Figure 2C).
Although density-mediated compensation neutralized the bulk of the atrazine-related carryover effect, it was not substantial enough to fully counteract the mortality associated with both the exposure and carryover effects. Consequently, exposure concentration was related positively to the net mortality across both the exposure and postexposure stages or mortality at the end of the study (F1,40 = 8.91, p = 0.005; Figure 2D). A. barbouri that were not exposed to atrazine had greater net survival than did A. barbouri exposed to 4 ppb (LSD, p < 0.013), 40 ppb (LSD, p < 0.007), or 400 ppb atrazine (LSD, p < 0.001; Figure 2D).
Pairwise comparisons of exposure, post-exposure, carryover, and net effects of atrazine on A. barbouri mortality revealed that the post-exposure effect (density-mediated + carryover effects) was significantly weaker than both the carryover (F1,40 = 17.58, p < 0.001) and net effects (F1,40 = 15.47, p < 0.001; Figure 2). No other comparisons were significant (p > 0.255).
Discussion
We have shown that larval streamside salamander mortality attributed to atrazine exposure can be ameliorated by density-dependent processes after exposure, but that complete density-mediated compensation may be precluded by the long-term, postexposure effects of atrazine. Consequently, relative to control animals, A. barbouri previously exposed to concentrations of atrazine as low as 4 ppb had significantly lower survival 421 days after exposure, a result that was apparent only when considering the accumulation of both exposure and carryover effects. Atrazine at 4 ppb is only 1 ppb greater than the maximum allowable level in U.S. drinking water (U.S. EPA 2002) and a concentration to which amphibians may be chronically exposed (Hayes et al. 2003). In the process of measuring the effects of atrazine on A. barbouri survival, we developed a laboratory framework for quantifying exposure, carryover, density-mediated, and net effects of stressors on survival that may serve as a more practical alternative to the rigorous demands and complexities of population growth rate analyses.
Although we do not know when the post-exposure mortality occurred, we saw no evidence that there was substantial mortality soon after the salamanders were transferred to their terraria. Further, in a separate study on this species with similar exposure and rearing conditions, atrazine had no significant effect on larval survival, yet exposure was associated with hyperactivity and increased desiccation risk 8 months after exposure, with no detectable recovery from atrazine exposure (Rohr and Palmer 2005). These data suggest that exposure to atrazine early in development may have permanent effects on these salamanders. This conclusion is supported by our finding that the effect of atrazine during exposure did not differ from the magnitude of its carryover effect, once again suggesting that there was no recovery from atrazine exposure. Atrazine has been shown to disrupt neuroendocrine processes in amphibians (Hayes et al. 2002b, 2003; Larson et al. 1998), and this certainly is a possible mechanism for the observed long-term effects on behavior and survival.
Various studies have reported nonlinear relationships between atrazine concentration and amphibian responses, and many of these relationships have been nonmonotonic, with the largest change in response occurring at low exposure levels (Hayes et al. 2002b, 2003; Larson et al. 1998; Storrs and Kiesecker 2004). Endocrine disruptors commonly induce non-monotonic dose–response curves (Welshons et al. 2003), and thus the endocrine-disrupting potential of atrazine has been suggested as the cause of the documented nontraditional non-monotonic dose responses (Hayes et al. 2002b, 2003; Larson et al. 1998; Storrs and Kiesecker 2004). The relationship between atrazine concentration and survival in this study was nonlinear (logarithmic) but monotonic. However, we cannot rule out the possibility of a non-monotonic relationship because detection depends upon selecting the right concentrations to reveal any nonmonotonicity. The responses to atrazine recorded in A. barbouri have many consistencies with endocrine disruption. The largest adverse change in survival occurred at low exposure concentrations. Further, exposure to endocrine-disrupting compounds during critical developmental stages often induce irreversible effects (Bigsby et al. 1999), not unlike the long-term, postexposure effects observed here and in a previous study (Rohr and Palmer 2005). Virtually every response to atrazine we have quantified in this species using these concentrations has had the greatest response change at low concentrations (Rohr et al. 2004; Rohr and Palmer 2005; Rohr et al. unpublished data), suggesting that A. barbouri may be sensitive to marginal atrazine inputs into aquatic systems.
The deleterious, long-term effects of larval atrazine exposure suggest that encounters with contaminants after metamorphosis may not be necessary for contaminants to harm postmetamorphic amphibians, a life stage that often disproportionately affects population dynamics (Biek et al. 2002, Schmidt et al. 2005, Vonesh and De la Cruz 2002). This is important because exposure to substantial concentrations of contaminants is probably more likely before metamorphosis, because most amphibian embryos and larvae are strictly aquatic and cannot readily escape water bodies where many contaminants accumulate and concentrate.
Although the data in this laboratory study suggest that atrazine may have adverse effects on A. barbouri, we caution against extending this interpretation to populations in nature or to other amphibian species for the following reasons: a) causes of postmetamorphic mortality in the laboratory may not match those in the field; b) there is documented annual variation in the effects of atrazine on amphibians (Rohr et al. 2004); c) the strength of density-dependent processes may be different in the wild and for other species (Hellriegel 2000); d) species can differ in their susceptibility to contaminants (Bridges and Semlitsch 2000; Forbes and Calow 2002); and e) effects of contaminants can depend upon community structure (Relyea and Mills 2001; Rohr and Crumrine 2005), which was not incorporated into this study. Further, density-mediated compensation can be delayed by a generation or more; this, however, seems unlikely for atrazine because it is applied as a preemergent and thus enters ponds and wetlands in agriculture landscapes annually at relatively consistent times and quantities (Hayes et al. 2003, Huber 1993, Solomon et al. 1996). Consequently, larvae of many amphibian species are likely to be exposed to atrazine each spring, reducing the chances of cross-generational compensation. Nevertheless, the possibility of cross-generational compensation suggests that combining experiments that span life stages with demographic models might be particularly insightful for evaluating the population-level effects of environmental stress. Despite the aforementioned admonishments, this study, and evidence that exposure of amphibians to low levels of atrazine can increase mortality (Storrs and Kiesecker 2004), impair reproductive development (Hayes et al. 2002a, 2002b, 2003; Tavera-Mendoza et al. 2002a, 2002b), and adversely interact with natural stressors (Boone and James 2003; Kiesecker 2002; Rohr and Palmer 2005), must raise concerns about the role of this widespread, persistent, and mobile herbicide in the international decline of amphibians. Certainly, more research on the effects of atrazine on amphibians is necessary.
In addition to concerns for amphibians, the persistent effects of atrazine should be of general concern because some of the most catastrophic effects of contaminants on wildlife and human populations have been associated with lasting, postexposure effects, such as the enduring effects of organochlorine insecticides [e.g., dichlorodiphenyltrichloroethane (DDT)] and the delayed neurotoxicity of organophosphorus pesticides and various metals (e.g., Hernandez-Avila et al. 1996; Quistad et al. 2003; Rice 1996). Despite these historical cases, studies focusing on carryover effects of contaminants remain surprisingly rare (Jensen and Forbes 2001, Ng and Keough 2003). This is especially disconcerting when one considers that short-term effects are typically inferior to long-term effects for explaining population-level changes and are more likely to give the erroneous impression that certain xenobiotics are innocuous or harmful. Although it can be challenging to quantify long-term effects of stressors across temporal scales (Rohr et al. 2003b), it will likely be necessary to fully understand the impacts of xenobiotics on environmental health.
We thank B. Stiff, I. Struewing, S. Palmer, J. Niedzwiecki, and T. McCarthy for assistance in animal rearing; and M. Boone, P. Crowley, S. Perkins, T. Raffel, R. Relyea, B. Schmidt, and anonymous reviewers for improving the manuscript.
This study was supported by the U.S. Environmental Protection Agency (STAR grant R829086 to B.D.P.), the National Science Foundation (grant 0516227 to J.R.R.), and the Kentucky Academy of Sciences (grant to J.R.R.).
Figure 1 Heuristic model for the contribution of exposure, carryover, and density-mediated effects of a stres-sor to a stressor’s net effect on survival.
Figure 2 Effects of embryo and larval A. barbouri exposure to atrazine on survival through development, where the net effect 421 days after atrazine exposure (D) is broken into exposure (A), carryover (B), and carryover plus density-mediated effects (C). The exposure effect was the effect before metamorphosis; the carryover and density-mediated effects occurred after metamorphosis, and the net effect is the effect across both life stages. In (A), (C), and (D), values are the standardized weighted means. In (B), values are the standardized least-squares means, which are the weighted means adjusted for starting postmetamorphic density, a covariate incorporated into the statistical model. Standardizing means (zero mean and unit variance) facilitates comparing the effect of atrazine across life stages (see text for details). Error bars indicate SEs, and solid lines are best-fit lines; n = 12 for each treatment group. p-Values for the relationship between atrazine concentration and mortality are as follows: (A), p < 0.001; (B), p < 0.002; (C), p < 0.067; (D), p < 0.005. Different lowercase letters within each panel reflect significant differences (p < 0.05) among atrazine concentrations according to a Fisher’s LSD multiple comparison test. In (B), the effect of density-mediated compensation was inferred by subtracting the best-fit line for the carryover effect (B) from the best-fit line for the carryover effect plus density-mediated compensation (C).
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Westerman AG Wigginton AJ Price DJ Linder G Birge WJ 2003. Integrating amphibians into ecological risk assessment strategies. In: Amphibian Decline: An Integrated Analysis of Multiple Stressor Effects (Linder G, Krest SK, Sparling DW, eds). Pensacola, FL:Society of Environmental Toxicology and Chemistry (SETAC ), 283–313.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7962ehp0114-00005116393658ResearchInhalation of Ultrafine Particles Alters Blood Leukocyte Expression of Adhesion Molecules in Humans Frampton Mark W. 12Stewart Judith C. 1Oberdörster Günter 2Morrow Paul E. 2Chalupa David 1Pietropaoli Anthony P. 1Frasier Lauren M. 1Speers Donna M. 1Cox Christopher 3Huang Li-Shan 4Utell Mark J. 121 Department of Medicine, and2 Department of Environmental Medicine, University of Rochester School of Medicine, Rochester, New York, USA3 Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA4 Department of Biostatistics, University of Rochester School of Medicine, Rochester, New York, USAAddress correspondence to M.W. Frampton, University of Rochester Medical Center, 601 Elmwood Ave., Box 692, Rochester, NY 14642-8692 USA. Telephone: (585) 275-4861. Fax: (585) 273-1114. E-mail:
[email protected] authors declare they have no competing financial interests.
1 2006 20 9 2005 114 1 51 58 25 1 2005 20 9 2005 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Ultrafine particles (UFPs; aerodynamic diameter < 100 nm) may contribute to the respiratory and cardiovascular morbidity and mortality associated with particulate air pollution. We tested the hypothesis that inhalation of carbon UFPs has vascular effects in healthy and asthmatic subjects, detectable as alterations in blood leukocyte expression of adhesion molecules. Healthy subjects inhaled filtered air and freshly generated elemental carbon particles (count median diameter ~ 25 nm, geometric standard deviation ~ 1.6), for 2 hr, in three separate protocols: 10 μg/m3 at rest, 10 and 25 μg/m3 with exercise, and 50 μg/m3 with exercise. In a fourth protocol, subjects with asthma inhaled air and 10 μg/m3 UFPs with exercise. Peripheral venous blood was obtained before and at intervals after exposure, and leukocyte expression of surface markers was quantitated using multiparameter flow cytometry. In healthy subjects, particle exposure with exercise reduced expression of adhesion molecules CD54 and CD18 on monocytes and CD18 and CD49d on granulocytes. There were also concentration-related reductions in blood monocytes, basophils, and eosinophils and increased lymphocyte expression of the activation marker CD25. In subjects with asthma, exposure with exercise to 10 μg/m3 UFPs reduced expression of CD11b on monocytes and eosinophils and CD54 on granulocytes. Particle exposure also reduced the percentage of CD4+ T cells, basophils, and eosinophils. Inhalation of elemental carbon UFPs alters peripheral blood leukocyte distribution and expression of adhesion molecules, in a pattern consistent with increased retention of leukocytes in the pulmonary vascular bed.
blood leukocyteshumanmonocytesultrafine particles
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Exposure to particulate matter (PM) air pollution is associated with increased respiratory and cardiovascular morbidity and mortality (Peters et al. 2000, 2001a; Pope et al. 2004). Plausible mechanisms explaining the cardiovascular effects of particle exposure have not been clearly defined (Utell et al. 2002). However, recent studies provide evidence that PM exposure is associated with systemic inflammation and changes in vascular function that have been implicated in the pathophysiology of cardiovascular disease, providing clues to possible mechanisms. PM exposure has been associated with increased systolic blood pressure (Ibald-Mulli et al. 2001), plasma viscosity (Peters et al. 1997a), C-reactive protein (Peters et al. 2001b), fibrinogen (Pekkanen et al. 2000), and release of leukocytes from the bone marrow (Mukae et al. 2001; Tan et al. 2000). Increases in ambient concentrations of PM were associated with increased blood leukocyte and platelet counts, as well as fibrinogen (Schwartz 2001). Brook et al. (2002) found evidence for systemic vasoconstriction in resting human subjects exposed to concentrated ambient air particles and ozone.
Ultrafine particles (UFPs), defined as particles with a diameter < 100 nm, have been hypothesized as contributors to cardiovascular effects of PM (Seaton et al. 1995) because, compared with fine particles at similar mass concentrations, they have greater pulmonary deposition efficiency (Chalupa et al. 2004; Daigle et al. 2003), induce more pulmonary inflammation (Li et al. 1999; Oberdörster et al. 1995), have enhanced oxidant capacity (Brown et al. 2001; Li et al. 2003), have a higher propensity to penetrate the epithelium and reach interstitial sites (Stearns et al. 1994), and may even enter the systemic circulation in humans (Nemmar et al. 2002; Oberdörster et al. 2002).
Relatively few epidemiologic studies have examined the health effects of UFP exposure because most ambient air monitoring measures particle mass, and there is relatively poor correlation between particle mass (dominated by fine particles) and particle number (dominated by UFPs). However, a recent study in Erfurt, Germany, found associations between ambient UFPs and mortality (Wichmann et al. 2000). In a study of patients with stable coronary artery disease (Pekkanen et al. 2002), investigators performed repeated exercise tests concurrent with monitoring of ambient particle mass and number counts. Significant independent effects were found for both fine particles and UFPs on the degree of ST-segment depression on the electrocardiogram during exercise.
Asthma, a disease characterized by airway inflammation, confers an increased risk for PM health effects (Atkinson et al. 2001; Lipsett et al. 1997; Tolbert et al. 2000). There is evidence for activation of lung leukocytes and pulmonary vascular endothelium in subjects with asthma, particularly during exacerbations (Ohkawara et al. 1995). Activation of T-lymphocytes with production of “type 2” inflammatory cytokines drives the recruitment and retention of eosinophils in the airway, which contribute to the chronic epithelial injury characteristic of this disease (Corrigan and Kay 1990; Wilson et al. 1992). Treatment with inhaled corticosteroids reduces expression of activation markers CD25 and human leukocyte antigen (HLA)-DR in lung lymphocytes and also reduces HLA-DR expression in blood lymphocytes (Wilson et al. 1994). In asthma, blood CD4+ T cells express increased mRNA for interleukin (IL)-4, IL-5, and granulocyte macrophage colony stimulating factor, and IL-5 mRNA expression correlates with asthma severity and eosinophilia (Corrigan et al. 1995). Allergen challenge in subjects with asthma causes a reduction in blood CD4+ T cells (Walker et al. 1992) and an increase in airway CD4+ cells (Virchow et al. 1995). UFP exposure may worsen asthma by further shifting lymphocyte responses to the type 2 phenotype, by further activating resident lymphocytes, by increasing the likelihood that lymphocytes will encounter antigen, and/or by increasing penetration of allergen through an injured epithelium.
We have initiated controlled exposure studies with carbon UFPs in humans, as a surrogate for environmental UFPs, demonstrating that UFPs have a high pulmonary deposition efficiency in healthy subjects (Daigle et al. 2003), which is further increased in subjects with asthma (Chalupa et al. 2004). Exposure to 50 μg/m3 carbon UFPs caused a reduction in the pulmonary diffusing capacity for carbon monoxide (Pietropaoli et al. 2004b) associated with reductions in the systemic vascular response to increased flow (Pietropaoli et al. 2004a), without significant effects on symptoms, airway inflammation, lung function, or markers of blood coagulation (Pietropaoli et al. 2004c). We hypothesized that inhalation of UFPs alters vascular function, detectable as alterations in blood leukocyte distribution, activation, and expression of adhesion molecules. We further hypothesized that people with asthma, who have airway and systemic inflammation at baseline as well as enhanced UFP deposition, have enhanced susceptibility to these vascular effects. In this article we present detailed analyses of venous blood leukocytes from subjects participating in four separate studies involving carbon UFP exposure: three protocols with varying exposure concentrations in healthy subjects, and one protocol with asthmatic subjects. Some data in this article have been presented previously in abstract form (Frampton et al. 2004).
Materials and Methods
Subjects.
Written, informed consent was obtained from all subjects, and the studies were approved by the Research Subjects Review Board of the University of Rochester. Fifty-six never-smoking subjects 18–40 years of age (40 healthy and 16 with asthma) participated and were paid a stipend. Subjects were not studied within 6 weeks of a respiratory infection. Healthy subjects were required to have normal spirometry, a normal 12-lead electrocardiogram, and no history of chronic respiratory disease.
Inclusion criteria for subjects with asthma have been reported previously (Chalupa et al. 2004). These criteria included a consistent clinical history, and either a significant bronchodilator response or airway hyper-responsiveness to methacholine. The severity was consistent with mild intermittent to moderate persistent asthma (National Institutes of Health 1997). Subjects with forced expiratory volume in 1 sec (FEV1) < 70% of predicted at baseline screening, or with > 20% reduction in FEV1 after the screening exercise, were excluded.
Study design.
Each study used a crossover design in which each subject was exposed to filtered air and to UFPs, so that each subject served as his or her own control. Within each study, the order of air/UFP exposure was randomized, and the randomization was blocked by order of presentation and sex, so that equal numbers of men and women inhaled air first or UFPs first. Exposures were blinded to both subjects and investigators.
Table 1 provides details of each study protocol. The first, UPREST, involved 12 (six female) subjects exposed at rest to approximately 10 μg/m3 UFPs or filtered air for 2 hr. The second study protocol, UPDOSE, involved 12 subjects (six female) with three 2-hr exposures with exercise for each subject: approximately 10 μg/m3 UFPs, approximately 25 μg/m3 UFPs, and filtered air. Subjects exercised on a bicycle ergometer for 15 min of each half hour at an intensity adjusted to increase the minute ventilation to approximately 20 L/min/m2 body surface area. For safety reasons, the order of exposure was randomized in a restricted fashion, so that each subject received the 10-μg/m3 exposure before the 25-μg/m3. The third protocol, UP50, involved 16 healthy subjects (eight female) exposed to approximately 50 μg/m3 UFPs and air for 2 hr, with intermittent exercise as in the UPDOSE protocol. The final protocol, UPASTHMA, involved 16 subjects with asthma (eight female) exposed to approximately 10 μg/m3 UFPs and air for 2 hr, with intermittent exercise as in the UPDOSE protocol. All exposures were separated by at least 2 weeks.
Exposures to either filtered air or UFPs were administered by mouthpiece (with nose clip) for 2 hr, interrupted by a 10-min break after the first hour. Before and at 0, 3.5, and 21 hr after exposure, blood pressure, heart rate, and oxygen saturation by pulse oximetry were measured, and blood was drawn from an antecubital vein. For UP50 and UPASTHMA, measurements were also obtained 45 hr after exposure.
Exposure system.
The rationale and design of the exposure facility have been described in detail elsewhere (Chalupa et al. 2002). Briefly, particles [count median diameter ~ 25 nm, geometric standard deviation ~ 1.6] were generated in an argon atmosphere using an electric spark discharge between two graphite electrodes, and then deionized and diluted with filtered air to the desired concentration. Particle number, mass, and size distribution were monitored on both the inspiratory and expiratory sides of the subject. Electronic integration of a pneumotachograph signal provided tidal volume, respiratory frequency, and minute ventilation measurements. Air for the control exposures, and for dilution of the particles, was passed through charcoal and high-efficiency particle filters and was essentially free of particles (0–10 particles/cm3).
Blood leukocyte immunofluorescence analysis.
Fresh heparinized whole blood was stained with three monoclonal antibodies: the marker of interest (Table 2) conjugated to fluorescein isothiocyanate, CD14 conjugated to phycoerythrin, and CD45 conjugated to pericidin chlorophyll protein. This permitted determination of the relative expression of adhesion molecules and other markers separately on polymorphonuclear leukocytes (PMNs), eosinophils, lymphocytes, and monocytes. The appropriate isotype control antibodies were run with each experiment to assist in appropriate gate setting. The adhesion markers shown in Table 2 were measured in each of the study protocols, except for CD18, which was measured in UP50 and UPASTHMA only.
Red blood cells were lysed and cells were analyzed on a FACScan flow cytometer (BD Bioscience, San Jose, CA) equipped with a 15-mW argon ion laser emitting at 488 nm. Ten thousand events were collected from each sample in list mode. Standardized fluorescent microbeads (Quantium 24P and 25P; Bangs Laboratories, Fishers, IN) were run with each experiment to convert mean channel numbers to molecules of equivalent soluble fluorochrome (MESF) (Gavras et al. 1994). This provided a correction for minor day-to-day instrument variations in fluorescence detection.
Total and differential blood leukocyte and platelet counts were performed in the clinical laboratories of Strong Memorial Hospital, using an automated analyzer (Celldyne 4000; Abott Laboratories, Santa Clara, CA).
Data handling and statistical methods.
Data were entered on a desktop computer using Microsoft Excel and analyzed using SAS (SAS Institute Inc., Cary, NC).
UPREST, UPASTHMA, and UP50 used a standard, two-period crossover design in which each subject received both particles and air. Equal numbers of males and females were included. The order of presentation was randomized separately for each sex, with half of each group of subjects receiving each of the two possible orders. UPDOSE used a three-period crossover design in which each subject received air and both 10-μg/m3 and 25-μg/m3 concentrations of particles. There were then three possible exposure sequences, depending on where in the sequence the air exposure was placed. Equal numbers of subjects were randomly assigned to each sequence.
Repeated-measures analysis of variance (ANOVA) was used (Wallenstein and Fisher 1977), with order of presentation as a between-subjects factor, with exposure and time as within-subject factors. The analysis included tests for period and carryover effects, although the latter were expected to be minimal because of the nature of the exposures and the length of the washout period. In cases where carryover effects were significant, first-period data were examined separately (Jones and Kenward 1989). Each ANOVA included an examination of residuals as a check on the required assumptions of normally distributed errors with constant variance. If these assumptions were not satisfied, data transformations (e.g., square-root transformation for cell counts) were considered. A p-value of 0.05 was required for statistical significance. Data are shown as mean ± SE, unless otherwise indicated.
Results
Exposure data and subject characteristics.
Table 3 shows the exposure parameters and subject characteristics for each protocol. Most of the subjects with asthma were atopic (15 of 16), and most (11 of 16) were not on inhaled steroids, long-acting bronchodilators, or leukotriene inhibitors. All subjects completed every exposure; men and women did not differ in the achieved minute ventilation, adjusted for body surface area. There were no significant effects of UFP exposure on ventilatory parameters or pulmonary function; these results, and UFP deposition, have been published previously (Daigle et al. 2003).
The UPREST protocol, with exposures at rest to 10 μg/m3 UFPs, showed no convincing differences between particle and air exposure for leukocyte expression of adhesion molecules or total and differential leukocyte counts. There were rare statistically significant comparisons, but the significance levels were modest, and the data did not suggest a consistent biologic response. Overall, exposure to 10 μg/m3 UFPs at rest had no significant effects on blood leukocytes.
Findings from the three studies involving exercise are described below.
Blood leukocyte expression of adhesion molecules.
In these studies, quantitative surface expression of molecules that mediate leukocyte-endothelial interactions served as an indirect indicator of exposure effects on pulmonary vascular endothelial function. The use of flow cytometry with calibrated fluorescent beads allowed quantitation of small changes in surface marker density. Adhesion molecule expression for monocytes and PMNs in the three protocols involving exercise is shown in Tables 4–6.
UPDOSE.
UFP exposure caused a concentration-related reduction in monocyte expression of CD54 [intercellular adhesion molecule-1 (ICAM-1) (exposure effect, p = 0.0012); Figure 1]. Expression increased after exposure to filtered air and decreased with 25 μg/m3 UFPs, with differences resolving by 21 hr after exposure. Expression of CD62L showed a significant exposure–sex interaction (p = 0.0006; data not shown), with expression increasing in females but decreasing in males relative to air exposure. However, these findings lacked a clear concentration response.
UP50.
Exposure to 50 μg/m3 UFPs also reduced expression of CD54 on monocytes (Figure 2A,B), but to a greater extent in males (exposure–sex interaction, p = 0.025). The percentage of monocytes expressing CD54 was also reduced (p = 0.035; data not shown). UFP exposure persistently blunted the air-related increase in CD18 expression on monocytes (p = 0.0002; Figure 2C). Expression of CD18 was also reduced on PMNs (Figure 2D), and ANOVA indicated significantly increased CD11a expression on PMNs (exposure–time interaction, p = 0.037; data not shown).
UPASTHMA.
As expected, we found baseline differences between healthy and asthmatic subjects in leukocyte expression of adhesion molecules; these data are shown in Table 7. For example, monocyte expression of CD11b, CD54, and CD62L was higher in subjects with asthma than in healthy subjects.
In subjects with asthma, exposure to 10 μg/m3 UFPs reduced expression of CD11b on blood monocytes (p = 0.029; Figure 3A) and also reduced expression on eosinophils (p = 0.015; Figure 3B). Expression of CD62L on PMNs increased in males but not females (exposure–sex interaction, p = 0.011; Figure 3C,D). Expression of CD54 on PMNs decreased, with the greatest difference from control at 45 hr after exposure (exposure–time interaction, p = 0.031; data not shown).
Lymphocyte subsets and activation.
There was evidence for increased activated T cells after UFP exposure in healthy subjects. In UPDOSE, CD25 expression on CD3+ T cells increased in females, but not in males, early after exposure to 25 μg/m3 UFPs (exposure–sex interaction, p = 0.002; Figure 4A,B). In UP50, exposure to 50 μg/m3 increased CD25 expression on T cells 0 hr after exposure (p = 0.001 by paired t-test at 0 hr after exposure; p = 0.085 by ANOVA; Figure 4C). There were no other changes in lymphocyte subsets in the healthy subjects.
In UPASTHMA, CD4+ T cells decreased immediately after exposure to UFPs, compared with air (exposure–time interaction, p = 0.021; Figure 2D). There were no significant effects on other lymphocyte subsets or CD25 expression. However, the percentage of T-lymphocytes expressing the activation marker CD25 was higher in asthmatic subjects than in healthy subjects before exposure (UPASTHMA, 33.0 ± 3.3%, vs. UPDOSE, 27.0 ± 2.5%; p = 0.04).
Overall, the data suggest that UFP exposure induces activation (healthy subjects) or sequestration (subjects with asthma) of T-lymphocytes.
Blood leukocyte and platelet counts.
In each of the protocols involving exercise (UPDOSE, UP50, and UPASTHMA), consistent postexposure increases were seen in the total leukocyte count and the percentage of PMNs, with decreases in the percentage of eosinophils and monocytes. In the UPDOSE protocol, ANOVA showed a significant exposure–sex interaction for an effect on the percentage of blood monocytes (p = 0.0015). As shown in Figure 5A,B, in females monocytes decreased after exposure to 25 μg/m3 and did not return to baseline at 21 hr after exposure. This observation was confirmed when monocyte numbers were analyzed by flow cytometry, using light scatter and CD14 expression (overall effect of UFPs, p = 0.035; exposure–sex interaction, p = 0.002). A significant decrease in blood basophils in females was also seen with both UFP concentrations (exposure–sex interaction, p = 0.015; data not shown).
Exposure to 50 μg/m3 UFPs caused small reductions in the percentage of eosinophils, with a larger effect in females (Figure 5C,D). There were no significant effects on the percentage of blood monocytes, PMNs, or basophils in this protocol.
In subjects with asthma exposed to 10 μg/m3 UFPs, basophils decreased in both men and women at 0 and 3.5 hr after exposure to UFPs, compared with air exposure (exposure–time interaction, p = 0.02; data not shown). The percentage of blood eosinophils as determined by flow cytometry decreased 0 and 3.5 hr after exposure, with greater reductions after UFP exposure than after air (p = 0.049).
UFP exposure did not change platelet counts in any of the exposure protocols.
These data suggest that exposure to UFPs with exercise causes small changes in blood leukocyte differential counts in both healthy and asthmatic subjects.
Discussion
The objective of these studies was to determine whether inhalation of carbon UFPs has vascular effects in healthy subjects, and in subjects with asthma. We postulated that changes in blood leukocyte phenotype and expression of adhesion molecules would serve as a “window” on vascular inflammatory effects after inhalation challenge. Although the specific findings differed among the protocols, all three protocols with exercise showed UFP-associated reductions in expression of adhesion molecules on leukocytes, mainly ICAM-1 (CD54) and the β2 integrins CD11b and CD18. There were significant differences between healthy and asthmatic subjects in leukocyte expression of adhesion molecules, when measured before exposure (Table 7). For example, blood monocytes from subjects with asthma showed decreased expression of CD11a and increased expression of CD11b, CD49d, and CD54 relative to healthy subjects. This may reflect relative activation or priming of circulating leukocytes as a consequence of airway inflammation. In subjects with asthma, inhalation of UFPs reduced expression of CD11b on monocytes and eosinophils (Figure 3) and reduced CD54 expression on PMNs (Table 6).
In addition, the data suggested subtle reductions relative to air exposure in the percentage of blood monocytes, eosinophils, and basophils. There was evidence for activation of CD4+ T-lymphocytes in healthy subjects and transient reductions in CD4+ T-cell numbers in asthmatic subjects. Sex interactions were seen for some of these changes. A summary of these findings is shown in Table 8.
The findings provide evidence that inhalation of elemental carbon UFPs, with intermittent exercise, causes phenotypic alterations in blood leukocytes at concentrations as low as approximately 10 μg/m3 or approximately 2 × 106 particles/cm3. However, the overall nature and direction of the changes do not suggest increased systemic inflammation. This is consistent with the lack of evidence for airway or systemic inflammation that we have reported previously for these studies (Pietropaoli et al. 2004a, 2004c).
The reductions in leukocyte subsets and adhesion molecule expression seen in these studies suggest the possibility of leukocyte sequestration or margination in response to UFP exposure. The relative reductions in monocyte, basophil, and eosinophil percentages may result from slightly prolonged transit time through the pulmonary circulation after exposure to UFPs, possibly as a consequence of pulmonary vasoconstriction. The reductions in expression of the adhesion molecules CD54, CD11b, and CD18 are consistent with this hypothesis. Blood leukocytes normally marginate in the lung, requiring several seconds to transit the pulmonary circulation (Doerschuk 2003). PMNs are larger than pulmonary capillaries and must deform in order to transit. The integrins CD11a and CD11b are expressed as dimers with CD18 and mediate blood leukocyte recruitment to areas of inflammation and injury via specific receptors on vascular endothelial cells. Activation of monocytes and PMNs increases expression of CD11b and CD18 and decreases cell deformability through actin polymerization (Anderson et al. 2001), slowing transit time. Exercise increases pulmonary blood flow and decreases leukocyte transit time through the pulmonary circulation, leading to mobilization of the pulmonary leukocyte pool into the systemic vascular pool. Van Eeden et al. (1999) have shown that maximal exercise increases the blood leukocyte count and also increases expression of CD11b on peripheral blood PMNs, suggesting that cells expressing higher levels of CD11b preferentially marginate in the pulmonary circulation and are “flushed out” with exercise. Thus, our data are consistent with, but do not prove, increased retention of leukocytes expressing higher levels of adhesion molecules in the pulmonary vascular bed in response to UFP exposure.
Pulmonary vasoconstriction in response to UFP exposure would be expected to delay leukocyte transit through the lung. We have reported (Pietropaoli et al. 2004b) that, in the UP50 protocol, UFP exposure caused reductions in the diffusing capacity for carbon monoxide, without effects on the forced vital capacity, consistent with reduced vascular perfusion or reduced ventilation/perfusion matching. We also reported preliminary findings (Pietropaoli et al. 2004a) of subtle but significant effects on systemic flow-mediated vascular dilatation, and a decrease in blood nitrate levels, suggesting the vascular effects may result from decreased nitric oxide availability. Batalha et al. (2002) have shown pulmonary vaso-constriction in rats exposed to concentrated ambient fine particles.
Alternative mechanisms for reductions in leukocyte and their surface markers include a) direct effects of UFPs on blood leukocytes, reducing surface marker expression through shedding, redistribution, or internalization; b) indirect effects of mediators released by vascular endothelium, such as nitric oxide, which has anti-inflammatory properties (Lefer 1997), reduces endothelial expression of adhesion molecules via inhibition of nuclear factor κB activation, and reduces monocyte adhesion to endothelium (De Caterina et al. 1995); c) adsorption of soluble cytokines, such as transforming growth factor-β, onto the surface of the particles, reducing inflammatory effects (Kim et al. 2003); d) recruitment of immature leukocytes from the bone marrow in response to UFP inhalation, as has been suggested in previous studies of fine particle exposure (Tan et al. 2000); and e) selective toxicity of UFPs for activated blood leukocytes, inducing apoptosis of specific cell subsets.
The two protocols with exercise in healthy subjects showed increased expression of CD25 on blood T-lymphocytes, and subjects with asthma showed a transient reduction in CD4+ lymphocytes after UFP exposure. CD25 is the α-chain of the IL-2 receptor; IL-2 promotes lymphocyte proliferation. We found that lymphocyte CD25 expression was higher in subjects with asthma than in healthy subjects, confirming previous observations that people with asthma have a higher percentage of circulating activated T-lymphocytes (Corrigan and Kay 1990), which may explain why UFP exposure did not increase it further in these subjects. The rapid and transient nature of the reduction in CD4+ T cells suggests redistribution or margination of cells, as postulated above for other blood leukocytes.
The changes in response to carbon UFP exposure reported in these studies were generally small and would not be expected to adversely affect healthy and mildly asthmatic subjects similar to those studied. However, ambient UFPs contain reactive organic species and transition metals that may induce greater effects than those we observed. People with severely compromised cardiovascular status may experience adverse effects from even small changes in vascular homeostasis. Furthermore, prolonged, repeated exposures may hasten the progression of atherosclerosis, as has been suggested in an epidemiology study of fine particle exposure (Künzli et al. 2005).
The UFP number concentrations used in these studies are higher than UFP background concentrations but are relevant to episodic levels seen in specific situations. UFPs are always present in ambient air, with background urban levels in the range of 40,000–50,000 particles/cm3 or estimated mass concentrations of 3–4 μg/m3 (Peters et al. 1997b). Episodic increases have been documented to 300,000 particles/cm3, or estimated to approximately 50 μg/m3 UFPs as an hourly average (Brand et al. 1991, 1992). Particle numbers inside a vehicle on a major highway reached 107 particles/cm3 (Kittelson et al. 2001), comparable with the highest concentrations used in our studies.
Although not specifically powered to detect sex differences, these studies were designed to include an analysis of sex interactions with the effects of UFP exposure. In the UPDOSE protocol, females showed greater decreases in blood monocytes (Figure 5A) and basophils and greater increases lymphocyte CD25 expression (Figure 4A) compared with males. Females also showed decreased eosinophils in the UP50 protocol (Figure 5C). In UPASTHMA, expression of L-selectin (CD62L) on PMNs was increased in males (Figure 3B). It is possible that males and females differ in their cardiovascular responses to particle exposure. There are known sex differences in leukocyte function and cardiovascular responses, based in part on hormonal influences. For example, females have a higher percentage of CD4+ T cells and a higher CD4+:CD8+ ratio than do males. Stimulated blood monocytes from females produce more prostaglandin E2 (Leslie and Dubey 1994) and less tumor necrosis factor-α and IL-6 (Schwarz et al. 2000) than those from males. There are also sex differences in endothelial function and antioxidant defenses that may affect vascular response to inhaled challenge. However, we do not feel that these studies have convincingly established or excluded significant sex differences in responses to carbon UFPs.
There are limitations to this study. First, our particles were laboratory-generated elemental carbon, without significant organic species, metals, oxides, nitrates, or sulfates. The findings of these studies may not be representative of exposure to ambient particles, which are a mix of ultrafine, fine, and coarse particles, with reactive organic species, metals, and oxidants in addition to elemental carbon. These and other chemical species may enhance pulmonary and vascular effects. Second, each protocol involved a fairly large number of measurements, and some statistically significant changes may have been chance related. Our approach was to consider results that showed consistency within and across protocols and to discount findings of isolated statistical significance that were not supported by other data. The observations of UFP effects on leukocyte distribution and surface marker expression meet those criteria.
Conclusions
Overall, the findings from these studies provide evidence that inhalation of carbon UFPs, with exercise, reduces peripheral blood monocytes, eosinophils, and basophils and reduces expression of some adhesion molecules on monocytes and PMNs. When considered in light of other evidence, the leukocyte changes may be a consequence of endothelial activation or vasoconstriction in the pulmonary and/or systemic circulation.
This work was supported by contract 98-19 from the Health Effects Institute (HEI); U.S. Environmental Protection Agency (EPA) assistance agreements R826781-01 and R827354-01; grants RO1 ES011853, RR00044, and ES01247 from the National Institutes of Health; and grant 4913-ERTER-ES-99 from the New York State Energy Research and Development Authority.
Some of the research described in this article was conducted under contract to the HEI, an organization jointly funded by the U.S. EPA (assistance agreement X-812059) and automotive manufacturers. The contents of this article do not necessarily reflect the views of the HEI, nor do they necessarily reflect the policies of the U.S. EPA or of automotive manufacturers.
Figure 1 Changes in monocyte expression of CD54 (ICAM-1), UPDOSE protocol. In this and following figures, data are shown as mean ± SE changes from baseline. Nominal UFP exposure concentrations are shown in μg/m3. Exposure, p = 0.012.
Figure 2 Changes in leukocyte expression of adhesion molecules, UP50 protocol. (A) Monocyte expression of CD54, females. UFP × sex, p = 0.025. (B) Monocyte expression of CD54, males. UFP × sex, p = 0.025. (C) Monocyte expression of CD18. UFP, p = 0.0002. (D) PMN expression of CD18. UFP, p = 0.023.
Figure 3 Changes in leukocyte expression of adhesion molecules, UPASTHMA protocol. (A) Monocyte expression of CD11b. UFP, p = 0.029. (B) Eosinophil expression of CD11b. UFP, p = 0.015. (C) PMN expression of CD62L, females. UFP × sex, p = 0.011. (D) PMN expression of CD62L, males. UFP × sex, p = 0.011.
Figure 4 Changes in blood T-lymphocyte subsets. (A) UPDOSE protocol, percentage of CD25+ cells within the T-cell (CD3+) gate, females. UFP × sex, p = 0.0024. (B) UPDOSE protocol, CD3+CD25+ T cells, males. UFP × sex, p = 0.0024. (C) UP50 protocol, CD3+CD25+ T cells, all subjects. UFP × time, p = 0.085. (D) UPASTHMA protocol, CD4+ T cells, all subjects. UFP × time, p = 0.021.
Figure 5 Changes in percentage of blood leukocytes with exposure to UFPs. (A) UPDOSE protocol, monocytes, females. UFP × sex, p = 0.0015. (B) UPDOSE protocol, monocytes, males. UFP × sex, p = 0.0015. (C) UP50 protocol, eosinophils, females. UFP × time × sex, p = 0.01. (D) UP50 protocol, eosinophils, males. UFP × time × sex, p = 0.01.
Table 1 Study design (mean ± SD).
UPREST UPDOSE UP50 UPASTHMA
No. of subjects 12 12 16 16
Subject age (years) 30.1 ± 8.9 26.9 ± 5.8 26.9 ± 6.5 23.0 ± 2.7
FEV1 (% predicted) 103.8 ± 8.0 106.3 ± 16.6 102.8 ± 9.5 97.6 ± 5.0
Nominala particle mass (μg/m3) 0, 10 0, 10, 25 0, 50 0, 10
Rest/exercise Rest Intermittent exercise Intermittent exercise Intermittent exercise
a The target mass concentration of UFPs for each protocol.
Table 2 Leukocyte markers measured in each protocol.
Cluster designation Name Source (clone) Description
CD3 BD Biosciencea (SK7) Marker of T-lymphocytes
CD4 BD Bioscience (SK3) Marker of T-helper lymphocytes
CD8 BD Bioscience (SK1) Marker of T-cytotoxic lymphocytes
CD11a Leukocyte function antigen-1 GenTrakb (38) or Coulterc (25.3.1) Part of β2 integrin adhesion molecule complex
CD11b Mac-1 Ancelld (ICRF44) Subunit of complement receptor 3, part of β2 integrin adhesion molecule complex
CD18e Pharmigena (6.7) or BD Bioscience (L130) Part of β2 adhesion molecule complex with CD11a and CD11b
CD25 Tac BD Bioscience (2A3) Epitope of IL-2 receptor, activation marker on lymphocytes
CD49d Very late antigen-α4 Serotecf (44H6) Part of β1 integrin adhesion molecule complex
CD54 Intercellular adhesion molecule-1 Southern Biotechnologyg (15.2) Adhesion molecule
CD62L L-selectin Coulter (DREG56) or Pharmigen (DREG56) Adhesion molecule
a San Jose, CA.
b Plymouth Meeting, PA.
c Miami, FL.
d Bayport, MN.
e Measured in UP50 and UPASTHMA only.
f Raleigh, NC.
g Birmingham, AL.
Table 3 Exposure parameters (mean ± SD).
UPREST UPDOSE UPDOSE UP50 UPASTHMA
Nominal particle mass (μg/m3) 10 10 25 50 10
Measured particle mass (μg/m3) 10.00 ± 2.14 13.87 ± 4.02 28.46 ± 5.13 49.97 ± 3.88 11.08 ± 3.11
Particle number (× 106 particles/cm3) 1.88 ± 0.09 2.04 ± 0.07 6.96 ± 0.10 10.79 ± 1.66 2.20 ± 0.10
CMD (nm) 27.3 ± 2.5 25.2 ± 1.7 26.5 ± 1.5 27.9 ± 2.2 23.1 ± 1.6
GSD 1.62 ± 0.02 1.60 ± 0.02 1.60 ± 0.02 1.65 ± 0.02 1.64 ± 0.01
Abbreviations: CMD, count median diameter; GSD, geometric standard deviation.
Table 4 Adhesion molecule expression on monocytes and PMNs, UPDOSE protocol (mean ± SE, MESF).
Exposure (μg/m3) Baseline 0 hr 3.5 hr 21 hr ANOVA
Monocytes
CD11a Air 64,429 ± 2,072 62,483 ± 2,140 62,571 ± 1,689 65,682 ± 2,435
UFP 10 63,818 ± 4,109 59,900 ± 2,493 59,190 ± 3,063 65,249 ± 2,518
UFP 25 62,835 ± 2,644 56,207 ± 5,436 59,635 ± 2,404 63,008 ± 2,126
CD11b Air 19,034 ± 986 19,497 ± 997 21,076 ± 1,653 20,901 ± 1,912
UFP 10 17,632 ± 990 17,287 ± 1,171 18,335 ± 1,501 19,391 ± 1,185
UFP 25 19,056 ± 1,214 17,769 ± 922 22,059 ± 4,697 22,669 ± 3,357
CD49d Air 14,222 ± 1,000 13,562 ± 854 13,717 ± 880 13,989 ± 964
UFP 10 13,634 ± 1,029 12,587 ± 694 12,946 ± 706 13,059 ± 797
UFP 25 13,590 ± 839 12,779 ± 574 12,372 ± 683 13,542 ± 935
CD54 Air 12,188 ± 319 13,096 ± 519 13,908 ± 645 13,307 ± 823 Exposure
UFP 10 12,541 ± 469 12,470 ± 583 12,855 ± 592 13,110 ± 781 p = 0.001
UFP 25 13,717 ± 686 12,591 ± 584 13,533 ± 856 14,482 ± 991
CD62L Air 43,970 ± 3,212 34,937 ± 3,519 37,600 ± 3,391 37,399 ± 3,716 Exposure × sex
UFP 10 38,953 ± 3,465 30,281 ± 2,510 32,409 ± 1,719 36,356 ± 3,207 p = 0.006
UFP 25 41,357 ± 4,453 33,134 ± 2,940 34,676 ± 3,234 39,168 ± 4,196
PMNs
CD11a Air 28,637 ± 1,073 28,613 ± 1,228 28,793 ± 1,183 28,867 ± 1,503
UFP 10 29,124 ± 1,073 26,216 ± 1,160 26,260 ± 985 27,620 ± 923
UFP 25 28,444 ± 1,397 27,939 ± 1,151 27,817 ± 1,137 27,157 ± 1,411
CD11b Air 18,467 ± 1,117 18,837 ± 1,223 21,427 ± 3,186 21,189 ± 2,383
UFP 10 16,728 ± 907 15,997 ± 1,175 16,049 ± 1,112 21,169 ± 2,394
UFP 25 19,778 ± 2,671 15,671 ± 1,179 20,461 ± 3,457 18,653 ± 1,760
CD49d Air 7,422 ± 593 6,572 ± 542 6,404 ± 498 6,098 ± 686 Exposure × sex
UFP 10 7,007 ± 561 6,172 ± 559 6,173 ± 423 6,340 ± 650 p = 0.007
UFP 25 6,681 ± 465 6,031 ± 442 5,677 ± 446 5,925 ± 470
CD54 Air 4,792 ± 279 4,500 ± 280 4,586 ± 246 4,457 ± 243
UFP 10 4,953 ± 271 4,292 ± 242 4,608 ± 424 4,435 ± 213
UFP 25 4,771 ± 321 4,084 ± 216 4,122 ± 215 4,417 ± 230
CD62L Air 66,179 ± 3,910 59,419 ± 4,413 64,867 ± 4,303 59,671 ± 5,970
UFP 10 60,976 ± 4,340 57,202 ± 4,515 56,621 ± 4,636 60,626 ± 4,180
UFP 25 66,145 ± 4,231 60,044 ± 5,434 59,625 ± 4,296 61,184 ± 4,054
Table 5 Adhesion molecule expression on monocytes and PMNs, UP50 protocol (mean ± SE, MESF).
Exposure Baseline 0 hr 3.5 hr 21 hr 45 hr ANOVA
Monocytes
CD11a Air 65,882 ± 3,277 66,463 ± 2,934 65,658 ± 2,963 69,888 ± 2,853 71,292 ± 2,885
UFP 69,090 ± 3,146 68,680 ± 2,935 66,222 ± 2,696 69,813 ± 2,835 71,773 ± 3,132
CD11b Air 16,840 ± 899 20,104 ± 905 19,938 ± 835 18,728 ± 1,092 18,364 ± 993
UFP 18,365 ± 1,153 19,733 ± 1,206 18,531 ± 952 18,389 ± 932 18,369 ± 815
CD18 Air 62,675 ± 2,948 68,897 ± 2,942 67,872 ± 2,780 68,661 ± 2,749 68,963 ± 3,187 Exposure
UFP 67,246 ± 2,751 67,175 ± 2,582 66,277 ± 2,488 67,307 ± 2,768 68,754 ± 3,052 p = 0.0002
CD49d Air 16,334 ± 939 16,588 ± 859 17,371 ± 954 16,951 ± 9,571 17,126 ± 1,079
UFP 16,643 ± 938 16,445 ± 874 17,182 ± 965 17,282 ± 909 17,484 ± 1,167
CD54 Air 9,637 ± 1,431 10,654 ± 1,668 11,198 ± 1,728 9,969 ± 1,639 9,827 ± 1,687 Exposure × sex
UFP 10,526 ± 1,715 11,095 ± 1,782 10,889 ± 1,871 10,352 ± 1,791 10,339 ± 1,811 p = 0.025
CD62L Air 58,551 ± 3,188 50,197 ± 3,410 48,580 ± 3,027 9,699 ± 1,557 59,189 ± 2,271
UFP 57,666 ± 3,519 49,307 ± 3,261 50,241 ± 2,848 56,880 ± 3,515 58,283 ± 3,020
PMNs
CD11a Air 30,921 ± 851 30,934 ± 862 31,339 ± 960 31,683 ± 944 31,712 ± 937 Exposure × time
UFP 31,569 ± 1,014 32,158 ± 1,055 31,652 ± 912 31,751 ± 927 32,130 ± 921 p = 0.037
CD11b Air 16,406 ± 628 18,053 ± 934 17,262 ± 678 17,355 ± 869 17,525 ± 848
UFP 16,678 ± 830 19,155 ± 1,953 17,076 ± 777 18,014 ± 713 17,545 ± 694
CD18 Air 34,919 ± 1,335 36,961 ± 1,352 36,486 ± 1,286 35,907 ± 1,226 35,868 ± 1,450 Exposure
UFP 36,010 ± 1,032 37,687 ± 1,810 36,255 ± 1,060 35,316 ± 983 35,682 ± 1,087 p = 0.023
CD49d Air 6,455 ± 412 6,345 ± 264 6,399 ± 279 6,145 ± 204 6,070 ± 203
UFP 6,186 ± 335 6,252 ± 330 6,362 ± 340 6,284 ± 305 6,114 ± 258
CD54 Air 8,182 ± 584 8,339 ± 484 8,973 ± 552 8,114 ± 415 8,072 ± 383
UFP 8,524 ± 427 9,071 ± 545 8,668 ± 458 8,501 ± 402 8,446 ± 389
CD62L Air 87,437 ± 4,510 88,596 ± 3,485 88,617 ± 4,056 87,244 ± 3,362 89,489 ± 2,648
UFP 92,053 ± 4,760 89,783 ± 4,262 90,736 ± 4,227 89,363 ± 3,898 94,055 ± 4,598
Table 6 Adhesion molecule expression on monocytes and PMNs, UPASTHMA protocol (mean ± SE, MESF).
Exposure Baseline 0 hr 3.5 hr 21 hr 45 hr ANOVA
Monocytes
CD11a Air 21,179 ± 4,120 20,442 ± 3,989 19,336 ± 4,042 21,126 ± 5,569 21,407 ± 5,550
UFP 32,102 ± 7,076 30,277 ± 6,791 29,592 ± 6,630 30,468 ± 6,809 29,751 ± 6,640
CD11b Air 25,022 ± 2,822 31,626 ± 5,969 26,553 ± 3,319 26,345 ± 3,456 27,703 ± 3,228 Exposure
UFP 26,958 ± 4,112 25,452 ± 4,611 25,742 ± 4,241 24,498 ± 4,199 25,814 ± 3,502 p = 0.029
CD18 Air 85,586 ± 6,773 87,234 ± 8,882 82,899 ± 6,465 82,697 ± 7,370 85,455 ± 7,819
UFP 84,999 ± 7,252 81,131 ± 7,931 81,297 ± 9,950 82,028 ± 6,767 77,346 ± 7,334
CD49d Air 17,172 ± 731 16,739 ± 925 16,013 ± 616 16,627 ± 837 16,856 ± 771
UFP 18,378 ± 865 16,967 ± 873 17,138 ± 919 17,715 ± 877 17,327 ± 879
CD54 Air 19,102 ± 1,386 19,432 ± 1,430 18,285 ± 1,248 19,043 ± 1,410 19,281 ± 1,319
UFP 20,673 ± 2,009 20,438 ± 2,088 19,861 ± 1,934 20,014 ± 1,853 19,284 ± 1,491
CD62L Air 45,571 ± 2,571 39,446 ± 2,652 41,214 ± 2,703 45,100 ± 2,847 44,329 ± 2,870
UFP 51,939 ± 5,305 43,483 ± 4,955 42,198 ± 3,954 46,105 ± 4,023 45,608 ± 4,271
PMNs
CD11a Air 10,540 ± 1,775 10,010 ± 1,771 10,107 ± 1,837 10,986 ± 2,830 11,199 ± 2,953
UFP 14,562 ± 2,749 14,161 ± 2,679 13,790 ± 2,780 13,765 ± 2,727 13,710 ± 2,652
CD11b Air 24,078 ± 2,783 26,353 ± 3,578 25,211 ± 2,533 25,199 ± 2,072 30,893 ± 4,350
UFP 23,819 ± 2,343 22,792 ± 3,224 25,376 ± 2,984 22,085 ± 2,479 22,781 ± 1,886
CD18 Air 48,861 ± 3,054 47,564 ± 3,026 45,449 ± 2,457 45,303 ± 2,719 50,312 ± 5,429
UFP 46,982 ± 2,925 44,465 ± 2,676 43,512 ± 3,174 44,599 ± 2,862 43,470 ± 3,006
CD49d Air 5,342 ± 211 5,122 ± 228 5,090 ± 162 4,805 ± 248 4,923 ± 185
UFP 5,499 ± 315 4,964 ± 212 4,887 ± 210 4,783 ± 234 4,950 ± 241
CD54 Air 5,631 ± 230 5,348 ± 236 5,234 ± 222 5,433 ± 277 5,635 ± 239 Exposure × time
UFP 6,262 ± 451 5,759 ± 453 5,604 ± 458 5,535 ± 399 5,660 ± 398 p = 0.031
CD62L Air 78,859 ± 3,812 69,825 ± 3,978 71,796 ± 3,691 72,829 ± 4,711 72,429 ± 4,184 Exposure × sex
UFP 79,315 ± 6,332 75,646 ± 6,405 70,468 ± 4,961 74,971 ± 5,500 74,541 ± 5,925 p = 0.011
Table 7 Blood leukocyte marker expression at baseline that differed between asthmatic and healthy subjects (mean ± SE, MESF).
Healthya Asthma p-Value
Lymphocytes
CD11a 41,710 ± 1,844 14,575 ± 4,161 < 0.001
CD11b 1,460 ± 67 1,784 ± 107 0.017
CD49d 8,168 ± 335 10,486 ± 324 < 0.001
CD54 2,381 ± 69 2,964 ± 155 0.003
Monocytes
CD11a 64,155 ± 4,041 26,220 ± 5,260 < 0.001
CD11b 17,944 ± 915 25,047 ± 2,751 0.025
CD49d 13,556 ± 915 17,089 ± 642 0.005
CD54 12,314 ± 401 17,942 ± 1,065 < 0.001
PMNs
CD11a 28,358 ± 904 12,753 ± 2,276 < 0.001
CD11b 16,868 ± 1,055 24,178 ± 2,705 0.021
CD49d 7,189 ± 545 5,292 ± 282 0.007
CD62L 63,591 ± 4,614 80,656 ± 5,954 0.032
a Includes subjects from UPREST and UPDOSE (n = 24). Source of some immunofluorescence markers differed for UP50, resulting in changes in baseline values, so these healthy subjects were not included.
Table 8 Summary of UFP exposure effects.
Protocol Adhesion molecules Lymphocyte subsets and activation Leukocyte counts
UPREST (n = 12) No convincing effects (see text) No effects No effects
UPDOSE (n = 12) Decreased monocyte CD54
Decreased PMN CD49d (males) Increased CD25+ T cells (females) Decreased monocytes and basophils (females)
UP50 (n = 16) Decreased monocyte CD18 and CD54 (males) Increased CD25+ T cells Decreased eosinophils
Decreased PMN CD18 and increased CD11a
UPASTHMA (n = 16) Decreased monocyte CD11b
Decreased PMN CD54 and increased CD62L (males)
Decreased eosinophil CD11b Decreased CD4+ T cells and basophils Decreased eosinophils
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8410ehp0114-00005916393659ResearchComparison of Indoor Mercury Vapor in Common Areas of Residential Buildings with Outdoor Levels in a Community Where Mercury Is Used for Cultural Purposes Garetano Gary 12Gochfeld Michael 34Stern Alan H. 251 Hudson Regional Health Commission, Secaucus, New Jersey, USA2 Department of Environmental and Occupational Health, University of Medicine and Dentistry of New Jersey–School of Public Health, Piscataway, New Jersey, USA3 Department of Environmental and Occupational Medicine, University of Medicine and Dentistry of New Jersey–Robert Wood Johnson Medical School, Piscataway, New Jersey, USA4 Environmental and Occupational Health Sciences Institute, Piscataway, New Jersey, USA5 New Jersey Department of Environmental Protection, Division of Science, Research, and Technology, Trenton, New Jersey, USAAddress correspondence to G. Garetano, Hudson Regional Health Commission, 595 County Ave., Secaucus, NJ 07094 USA. Telephone: (201) 223-1133. Fax: (201) 223-0122. E-mail:
[email protected] authors declare they have no competing financial interests.
1 2006 20 9 2005 114 1 59 62 18 6 2005 20 9 2005 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Elemental mercury has been imbued with magical properties for millennia, and various cultures use elemental mercury in a variety of superstitious and cultural practices, raising health concerns for users and residents in buildings where it is used. As a first step in assessing this phenomenon, we compared mercury vapor concentration in common areas of residential buildings versus outdoor air, in two New Jersey cities where mercury is available and is used in cultural practices. We measured mercury using a portable atomic absorption spectrometer capable of quantitative measurement from 2 ng/m3 mercury vapor. We evaluated the interior hallways in 34 multifamily buildings and the vestibule in an additional 33 buildings. Outdoor mercury vapor averaged 5 ng/m3; indoor mercury was significantly higher (mean 25 ng/m3; p < 0.001); 21% of buildings had mean mercury vapor concentration in hallways that exceeded the 95th percentile of outdoor mercury vapor concentration (17 ng/m3), whereas 35% of buildings had a maximum mercury vapor concentration that exceeded the 95th percentile of outdoor mercury concentration. The highest indoor average mercury vapor concentration was 299 ng/m3, and the maximum point concentration was 2,022 ng/m3. In some instances, we were able to locate the source, but we could not specifically attribute the elevated levels of mercury vapor to cultural use or other specific mercury releases. However, these findings provide sufficient evidence of indoor mercury source(s) to warrant further investigation.
cultural use of mercuryelemental mercuryindoor air qualitymercurymercury exposuremercury vaporSanteriavoodoo
==== Body
Mercury is one of two elements that are liquid at ambient temperature. It is 13 times heavier than water, and its unique properties have led to a wide variety of uses in industry and elsewhere. Elemental mercury is still widely used in dentistry and a variety of hospital applications (Haas et al. 2003). It is also found in a number of technologic applications such as thermometers, barometers, thermostats, switches, gas meters, and especially fluorescent lights that may be found in residential buildings. In the past, organic mercury compounds were widely used as preservatives in household paints, and mercury antiseptics are still in use.
The unique properties of elemental mercury or quicksilver have led people to attribute magical and spiritual powers to it through the ages. Mercury was viewed as an essential component of the alchemical triad of mercury, sulfur, and air and has been associated with the Hindu god Shiva (Little 1997). Mercury amalgam religious icons remain available today (Garetano G, unpublished data). Elemental mercury is also used in the spiritual practices associated with Santeria, voodoo, Espiritismo, Palo Mayumbo, and other Afro-Caribbean syncretic religions [Riley et al. 2001; U.S. Environmental Protection Agency (EPA) 2002]. Additional uses of elemental mercury in a superstitious manner have been reported (Wendroff 1990). These practices include sprinkling elemental mercury in the home, in cars, or around babies and carrying capsules of mercury as amulets to bring good luck or love (Johnson 1999; U.S. EPA 2002). These activities do not appear to be components of ceremonial use associated with spiritual traditions, nor are they condoned or recommended by serious practitioners of those traditions (Stern et al. 2003). We label these uses of mercury, separate from the ceremonial use in spiritual traditions, as cultural uses. In communities where cultural uses of mercury are believed to be prevalent, the availability of mercury in specialty shops called botanicas has been well documented (Riley et al. 2001; Wendroff 1990; Zayas and Ozuah 1996).
Both the technologic applications and cultural uses of mercury provide the opportunity for it to be an indoor air pollutant in residential settings. Elemental mercury evaporates at a rate of 7 μg/cm2/hr at 20°C (Andren and Nriagu 1979). Up to 80% of inhaled mercury is absorbed and readily crosses the blood–brain barrier (Cherian et al. 1978; Clarkson 2002). The primary health concern associated with inhaled mercury vapor is its neurotoxicity, and infants are considered particularly vulnerable. The Agency for Toxic Substances and Disease Registry (ATSDR) and the U.S. EPA, respectively, have established a minimal risk level (MRL) of 300 ng/m3 and a reference concentration (RfC) of 200 ng/m3 for elemental mercury vapor in residential quarters (ATSDR 1999; U.S. EPA 1995). The release of elemental mercury in a household may pose some health risk for those who are exposed. For example, broken clinical thermometers typically contain only 600–675 mg elemental mercury but can generate mercury vapor concentrations an order of magnitude above both the U.S. EPA RfC and the ATSDR MRL (Carpi and Chen 2001; Muhlendahl 1990; Riley et al. 2001; Smart 1986). Health effects in children have been documented from such exposures (Moreno-Ramírez et al. 2004).
By comparison, elemental mercury for cultural use is commonly distributed in gelatin capsules containing approximately 9 g elemental mercury (Riley et al. 2001; Wendroff 1990), which, when released, can result in high concentrations of vapor (Riley et al. 2001; U.S. EPA 1993). At least one case of significant human exposure to elemental mercury requiring medical intervention as a result of cultural practices has been reported (Forman et al. 2000).
Once spilled, sprinkled, or left in an open container, elemental mercury may release vapor for prolonged periods. Significant levels of mercury vapor have been found in buildings decades after spillage, resulting in the significant exposure of subsequent building occupants without their knowledge (Centers for Disease Control and Prevention 1996; Orloff et al. 1997).
Other than those investigations conducted in response to known spills, data regarding mercury vapor concentration in residential buildings are scant. Carpi and Chen (2001) surveyed 12 residential and commercial sites in the New York metropolitan area without prior knowledge of mercury contamination. Eleven of these locations were found to have mercury vapor concentrations significantly elevated over outdoor concentrations. Prior breakage of clinical fever thermometers was subsequently identified as the probable mercury source in two of the locations.
Given the lack of documentation of mercury vapor in residential buildings in general or of a disproportionate elevation of mercury vapor in buildings in communities where it is used culturally, we chose to conduct a survey of residential dwellings in a community in which elemental mercury is readily available to assess the prevalence of mercury use or spillage.
We hypothesized that elevated levels of mercury vapor would be found in residential buildings in communities that engage in cultural uses of mercury. We further hypothesized that these elevated levels can serve as a signal of significant cultural use in addition to unintentional breakage and spillage from other sources. In this article we address the first hypothesis. We address the second hypothesis in a subsequent study to be published separately.
Materials and Methods
Rationale for this study design.
Riley et al. (2001) described a high level of apprehension and distrust of authorities or any outsider from a different culture. As a result of these cultural barriers, direct investigation of the residences of persons possibly using mercury for cultural purposes without first establishing a cause for concern was deemed inappropriate. Therefore, as a first step in characterizing the extent of this phenomenon, we chose to monitor mercury vapor within interior hallways of residential buildings, rather than directly measuring mercury vapor in residences, under the assumption that intentional and unintentional releases of mercury within the building would be reflected in elevated concentrations in common areas compared with the respective outdoor concentrations. Measurement of mercury vapor in common areas does not provide a direct estimate of exposure, but by comparing these measurements with respective outdoor levels and by comparing measurements across buildings, we can assess the prevalence of elevated indoor mercury concentrations. This information can inform decisions about appropriate public health strategies and can guide future surveys.
Site selection.
The information on cultural uses of mercury suggests that such uses are most common among certain Latino-Caribbean populations. The geographic area selected for inquiry was based on our prior knowledge of both the predominant Latino population and the presence of botanicas that typically sell mercury (Riley et al. 2001; Stern et al. 2003). The study was conducted in the New Jersey municipalities of Union City and West New York, comprising a total area of approximately 2.4 mi2 (6.2 km2), with 82.3 and 78.7% Latino population, respectively. Multifamily buildings were chosen for accessibility of common areas as well as for the potential for efficient screening. A primary criterion was that the buildings surveyed be within 0.5 miles (0.8 km) of a botanica. On the initial sampling date, a building meeting this criterion was selected on referral from a local health official, and all accessible buildings for approximately a two-block radius were evaluated. On subsequent sampling dates the same procedure was followed in other areas of the community meeting the same criteria. Additionally, three botanicas and one former botanica encountered during the residential building surveys were also visited.
Mercury vapor monitoring.
We measured real-time mercury vapor concentration in air using an atomic absorption spectrometer (model 915+; Ohio Lumex Co. Inc., Twinsburg, OH). The instrument has a sensitivity of 2 ng/m3 of mercury in air and has been successfully used for measuring mercury in ambient air (Ohio Lumex 2000; Zdravko and Mashyanov 2000). In previous studies, residential structures identified as having elevated mercury concentration with such direct reading instruments were also found to have elevated mercury vapor concentration with 8-hr sampling and subsequent laboratory analysis (Singhvi et al. 2001).
The instrument was factory calibrated according to the manufacturer’s specification and was within its factory calibration schedule. The spectrometer warmup, operation, and calibration followed the manufacturer’s instructions. Internal calibration uses a builtin mercury cell and was performed in the field before and on completion of sampling in typical field conditions. During internal calibration, measured mercury concentration varied from the predicted concentration by < 10% on each date. We validated precision by evaluating the relative deviation of triplicate measurements at each sampling location. The overall relative deviation for the 286 triplicate sample sets that were equal to or exceeding the manufacturers’ stated detection limit of 2 ng/m3 mercury vapor was 7.9%. Once the instrument was warmed up and calibrated, it was operated continuously. All measurements were recorded at a height of approximately 1 m above the floor unless otherwise indicated. Each data point is the average of three discrete 10-sec measurements at a given sampling location. The instrument also displayed mercury concentration continuously in a real-time sampling mode. This allowed evaluation of spatial variation and trends in mercury vapor concentration. Potential sources were localized where possible.
Site visits were conducted on 6 days in June and August 2002. Although only one visit was planned for each site, repeat visits were made to two buildings because of the high mercury vapor concentration encountered. Mercury vapor was monitored in the vestibule and the interior hallways on each floor of the buildings. These interior hallways contain the entrances to residential apartments. About half the buildings surveyed had open access to both locations. A total of 227 locations in 67 buildings were surveyed. On average, five hallway locations were assessed in those buildings that were fully accessible. All buildings were visited once except the two buildings with the highest readings. Mercury vapor measurements were recorded in 37 outdoor locations in proximity to the buildings evaluated. Outdoor readings near neighboring buildings showed low variation. Within the three botanicas and one former botanica, mercury vapor was monitored in the retail portion of the store.
Additional data.
In addition to mercury vapor measurements, the following data were also collected for each building: number of residential units, number of floors, presence of a central heating ventilation and air conditioning system (HVAC), and the presence of open windows.
Data analysis.
We calculated the mean mercury vapor concentration for each floor of a building by averaging all data points for that floor. We computed the average mercury concentration for a building by averaging the mean concentration for each floor. The maximum mercury vapor concentration reported for a building is the maximum data point from any hallway location within the building. Statistical analysis was conducted using SPSS software (SPSS Inc., Chicago, IL). Specific tests are indicated in the results section as applicable.
Results
Site access and characteristics.
Sixty-seven buildings were visited, of which approximately half were fully accessible. Only vestibules were accessible in the remainder. All buildings in which the interior halls were was accessed (n = 34) were multistory (mean, 4 floors) with a total of 497 residential units (mean, 14 units). Buildings in which only the vestibule was accessible tended to be slightly smaller (mean, 12 units), although this difference was not significant (p = 0.18). Based on familiarity with the area, including community history, overall appearance, and census characteristics, all buildings are believed to be > 50 years old, although records were not uniformly available. None of the buildings had HVAC systems that influenced the areas evaluated. Ventilation within the hallways was primarily influenced by windows and doors to residential apartments; 12 of 34 (35%) buildings had open hallway windows during the time of the visit.
Mercury vapor concentration.
The data were log-normally distributed; thus, arithmetic and geometric mean values, as well as percentiles, are reported. Because of relatively limited sample size and non-normal distributions, we compared mercury values using the Mann-Whitney U-test as well as by t-test on log-transformed data, unless otherwise indicated.
Outdoor mercury vapor concentrations had a mean value of 5 ng/m3 with an 80th percentile of 12 ng/m3 and a 95th percentile of 17 ng/m3. Our findings are consistent with outdoor levels measured elsewhere ranging from several nanograms per cubic meter to 20 ng/m3, with higher concentrations associated with urban/industrial areas and ambient mercury outside a mercury storage facility in Hillsborough, New Jersey, ranging from 2 to 8 ng/m3 (ATSDR 1999; Gochfeld M, unpublished data; New Jersey Department of Environmental Protection 2001).
The geometric and arithmetic mean mercury concentrations in building hallways were 10 ng/m3 and 25 ng/m3, respectively. In building vestibules, the geometric and arithmetic means were 7 ng/m3 and 11 ng/m3, respectively. The mercury vapor concentration in interior hallways was significantly greater than that found outdoors (p < 0.001) and in building vestibules (p < 0.05). Mercury vapor in vestibules was also greater than that found outdoors (p < 0.001). All three locations were found to differ significantly (p < 0.001) when compared simultaneously using the Kruskal-Wallis nonparametric one-way analysis of variance test. Indoor and outdoor mercury vapor concentrations are summarized in Tables 1 and 2.
Spatial variability.
We were able to localize potential sources of mercury contamination in seven buildings as evidenced by increasing mercury concentration as the “source area” was approached. At two sites, the probable source of mercury vapor emission was tracked to areas on the floor surface, one near a building entrance, the second on a stairway to a roof exit. In the remaining five buildings, mercury vapor concentration increased as certain individual or groups of apartment entrances were approached. No visible contamination was noted in any of the cases, and the actual source of vapor remained unknown.
We noted order of magnitude differences in mercury concentration between locations in buildings with high mercury concentration. For example, mercury vapor concentration ranged from 35 ng/m3 to 2,022 ng/m3 in the building with the highest concentration. Similar findings were noted elsewhere. The difference between mercury concentration on the building level (floor) on which the maximal value was noted and the remainder of the building was significantly higher in four of the buildings (p < 0.04).
Temporal variability.
Although our intent was to survey buildings once, two buildings had maximum hallway mercury vapor concentrations of 2,022 ng/m3 and 774 ng/m3, which exceeded both the ATSDR MRL (300 ng/m3) and U.S. EPA RfC (200 ng/m3). Local public health officials were notified, and repeat visits were made to each building. The building with the highest concentration was visited on five dates. Both the average and maximum mercury vapor concentrations of the building were significantly different on repeat visits (Kruskal-Wallis test, p < 0.04). Outdoor temperature ranged from 17 to 31°C, and hallway windows were open, providing passive ventilation, on all dates. The building hallways were not cooled, and indoor temperature was similar to that outdoors. Unexpectedly, mercury vapor concentration did not vary as a result of temperature changes (p > 0.7), and contrary to expectation, higher mercury vapor concentrations were noted on cooler days. By the final visit, maximum mercury vapor concentrations in each building (109 and 19 ng/m3, respectively) were significantly reduced (p < 0.01) compared with the initial visit. In both buildings, mean and maximum mercury concentrations fell below MRL and RfC. Despite the reduction in vapor concentration, the area of maximum concentration remained consistent.
Discussion
Our findings provide a valuable first look at the differences between indoor mercury concentrations and those outdoors in an area with known cultural use of mercury. Although our data are not intended as estimates of residential exposure to mercury vapor, they do indicate that, compared with outdoor levels, such exposures are likely in a significant proportion of multifamily residential buildings in an area with known cultural uses of mercury. This study did not include comparison with indoor mercury concentrations in a comparable area that can serve as a control for cultural use of mercury. Therefore, these data cannot distinguish between those elevations in mercury concentration resulting from cultural uses and those resulting from unintentional releases of mercury (e.g., broken thermometers or fluorescent lightbulbs, spilled gas meter seals). We are currently engaged in a follow-up study to investigate these questions.
There are relatively few reports of “background” mercury concentration in indoor air in residential buildings or “noncontaminated” environments to which our results can be compared. Our finding of mercury vapor in greater concentrations indoors compared with outdoors is consistent with the findings of Carpi and Chen (2001), who investigated mercury in residences without prior knowledge of mercury use or release.
Carpi and Chen (2001), using a direct reading instrument, were able to identify specific points inside several of the apartments they investigated that appeared to be the source of mercury emissions. Likewise, we were able to localize potential mercury sources in several buildings with elevated mercury concentrations. We clearly observed an increasing gradient in mercury vapor concentration as a potential source was approached. Although the exact source was not identified, the potential source of mercury vapor seemed to be residential apartments in five of the buildings with elevated mercury vapor concentration. Our finding that > 20% of buildings we studied had average and 35% had maximum mercury vapor concentrations that exceed the 95th percentile of outdoor concentrations is significant and leads to the conclusion that sources of contamination are present and prevalent indoors in this community. These findings are consistent with the hypothesis of cultural use of mercury, but not definitive. The elevated mercury vapor concentration found in botanicas is also consistent with its availability for cultural use.
These measurements were not made in areas that directly reflect exposure, nor, for the most part, do they measure concentration at the emission source. Therefore, these measurements could underestimate mercury concentration at the point of long-term exposure. Our surveys were subject to the variability in environmental conditions that occurs in occupied residential buildings and possibly the variability in patterns and methods of cultural mercury use. In most buildings surveyed, including those with the highest mercury vapor concentration, windows were open. This may partially explain the variability in mercury concentration and the lack of association with temperature we found in the sites with repeated visits. Although spot measurements of mercury vapor concentration in buildings may not reflect long-term average mercury concentration, we believe that the signals of elevated mercury concentration provided by spot measurements are relevant as a screening tool in identifying the presence of mercury release regardless of its source. For this approach to be more effective as a tool for screening for exposures of concern, models need to be developed that can reasonably predict the transit of mercury vapor from a source “behind closed doors” to other rooms or areas of a building under conditions that simulate occupancy.
Whether exposure to elevated mercury vapor arises from intentional cultural uses or from unintentional breakage and spillage of mercury-containing equipment, these exposures pose the potential for adverse health effects and should be addressed. However, the nature and scope of the public health problem will be significantly different for each of these cases. Each will require a different public health outreach and intervention strategy. It is therefore essential that future investigations clarify the relative contribution of each cause. We are currently continuing research to this end.
Given the findings of Carpi and Chen (2001) and this investigation, we feel some broader evaluations to establish reference ranges of mercury concentrations in the indoor residential environment are warranted. Such a reference range would include mercury contamination resulting from historical accidental breakage of mercury-containing equipment. Such contamination may be widespread and would likely be independent of cultural factors. Based on reports on the manner in which mercury may be used for cultural purposes, and our present findings, we also recommend expanded screenings in areas where mercury may be used for cultural purposes with the inclusion of suitable control locations. Although cultural obstacles may be present that may impede a direct approach to assessing human exposure to mercury vapor as a result of cultural practices and its relevance to public health, we believe further evaluations in the field will ultimately shed light on this elusive issue.
We thank D. Riley, C.A. Newby, and T.O. Leal for their assistance with related portions of this project; and J. Burger (Rutgers University) for use of the Lumex analyzer. J. Klotz, J. Zhang, and M. Robson provided valuable input during the preparation of the manuscript.
The New Jersey Department of Environmental Protection provided grant support for the project.
Table 1 Comparison of mercury vapor concentration (ng/m3) within building hallways and outdoors.
Location No. Arithmetic mean ± SD Geometric mean (SD)
Outdoors 37 5 ± 5 4 (2)
Building vestibule 57 11 ± 12 7 (2)
Mean in building hallways 34 25 ± 53 10 (4)
Maximum in building hallways 34 102 ± 364 17 (4)
Mann-Whitney U-test, p < 0.001.
Table 2 Distribution of mercury vapor concentration (ng/m3) within building hallways and outdoors.
Percentile
Location 25th 50th 75th 90th 95th
Outdoors 3 4 6 12 17
Building vestibules 4 7 13 22 36
Mean of building hallways 6 11 16 66 155
Maximum within hallways 9 14 25 106 1,086
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References
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Carpi A Chen YF 2001 Gaseous elemental mercury as an indoor air pollutant Environ Sci Technol 35 4170 4173 11718328
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Cherian MG Hursh JG Clarkson TW 1978 Radioactive mercury distribution in biological fluids and excretion in human subjects after inhalation of mercury vapor Arch Environ Health 33 190 214
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Stern AH Gochfeld M Riley D Newby A Leal T Garetano G 2003. Research Project Summary: Cultural Uses of Mercury in New Jersey. Trenton, NJ:New Jersey Department of Environmental Protection. Available: http://www.state.nj.us/dep/dsr/research/mercury-cultural.pdf [accessed 27 December 2004].
U.S. EPA 1993. RM2 Assessment Document for Cultural Uses of Mercury. Washington, DC:U.S. Environmental Protection Agency.
U.S. EPA 1995. Mercury, elemental. CASRN 7439-97-6. Integrated Risk Information System. Washington, DC:U.S. Environmental Protection Agency. Available: http://www.epa.gov/iris/subst/0370.htm [accessed 28 November 2004].
U.S. EPA 2002. Ritualistic Uses of Mercury Task Force Report. EPA/540-R-01-005. Washington, DC:U.S. Environmental Protection Agency.
Wendroff A 1990 Domestic mercury pollution [Letter] Nature 347 623 2215693
Zayas LH Ozuah PO 1996 Mercury use in espiritismo: a survey of botanicas Am J Public Health 86 111 112 8561228
Zdravko S Mashyanov NR 2000 Mercury measurements in ambient air near natural gas processing facilities Fresenius J Anal Chem 366 429 432 11220333
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8397ehp0114-00006316393660ResearchUse of the Land Snail Helix aspersa as Sentinel Organism for Monitoring Ecotoxicologic Effects of Urban Pollution: An Integrated Approach Regoli Francesco Gorbi Stefania Fattorini Daniele Tedesco Sara Notti Alessandra Machella Nicola Bocchetti Raffaella Benedetti Maura Piva Francesco Istituto di Biologia e Genetica, Università Politecnica delle Marche, Ancona, ItalyAddress correspondence to F. Regoli, Istituto di Biologia e Genetica, Università Politecnica delle Marche, Via Ranieri Monte d’Ago, 60100 Ancona, Italy. Telephone: 39 071 2204613. Fax: 39 071 2204609. E-mail:
[email protected] authors declare they have no competing financial interests.
1 2006 20 9 2005 114 1 63 69 9 6 2005 20 9 2005 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Atmospheric pollution from vehicular traffic is a matter of growing interest, often leading to temporary restrictions in urban areas. Although guidelines indicate limits for several parameters, the real toxicologic impacts remain largely unexplored in field conditions. In this study our aim was to validate an ecotoxicologic approach to evaluate both bioaccumulation and toxicologic effects caused by airborne pollutants. Specimens of the land snail Helix aspersa were caged in five sites in the urban area of Ancona, Italy. After 4 weeks, trace metals (cadmium, chromium, copper, iron, manganese, nickel, lead, and zinc) and polycyclic aromatic hydrocarbons (PAHs) were measured and these data integrated with the analyses of molecular and biochemical responses. Such biomarkers reflected the induction of detoxification pathways or the onset of cellular toxicity caused by pollutants. Biomarkers that correlated with contaminant accumulation included levels of metallothioneins, activity of biotransformation enzymes (ethoxyresorufin O-deethylase, ethoxycoumarin O-deethylase), and peroxisomal proliferation. More general responses were investigated as oxidative stress variations, including efficiency of antioxidant defenses (catalase, glutathione reductase, glutathione S-transferases, glutathione peroxidases, and total glutathione) and total oxyradical scavenging capacity toward peroxyl and hydroxyl radicals, onset of cellular damages (i.e., lysosomal destabilization), and loss of DNA integrity. Results revealed a marked accumulation of metals and PAHs in digestive tissues of organisms maintained in more traffic-congested sites. The contemporary appearance of several alterations confirmed the cellular reactivity of these chemicals with toxicologic effects of potential concern for human health. The overall results of this exploratory study suggest the utility of H. aspersa as a sentinel organism for biomonitoring the biologic impact of atmospheric pollution in urban areas.
atmospheric pollutantsbioindicatorsbiomarkersDNA integritylysosomesmetallothioneinsoxidative stressperoxisomespolycyclic aromatic hydrocarbonstrace metals
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Increased vehicular traffic and emissions are major contributors to air pollution and a matter of growing importance in many city centers [World Health Organization (WHO) 2000]. Human and ecotoxicologic risks range from asthmatic, respiratory, and cardiovascular problems to long-term effects caused by carcinogenic and mutagenic properties of many chemicals associated in complex mixtures, with overall biologic effects difficult to predict (Maynard 2004).
Normative limits and international guidelines indicate the maximum levels for a number of individual pollutants in air samples. Severe traffic restrictions were imposed recently in many Italian cities after values for particulate matter ≤ 10 μm in aerodynamic diameter (PM10) were exceeded, resulting in a public discussion on such political decisions. Municipalities often rely on automatic monitoring stations for their air quality programs, which is useful for defining both short- and long-term variations. However, this approach is somewhat limited. The relatively elevated costs for installation and maintenance sometimes preclude the detailed monitoring of large urban areas. In addition, automatic stations generally analyze only a set of parameters (e.g., PM10, ozone, benzene, carbonic monoxide, sulfur oxides), whereas other factors primarily involved in risk disease, such as PM2.5 (PM ≤ 2.5 μm in aerodynamic diameter), polycyclic aromatic hydrocarbons (PAHs), or metals are not detected. Most important, instrumental analyses do not account for interactions among different chemicals co-occurring in complex mixtures, and the association of these data with the onset of deleterious biologic effects are debated and controversial (Gamble 1998; Mainard 2004).
In contrast to automatic monitoring techniques, the study of bioindicator organisms can reveal the biologic impact of pollution over a geographical and temporal scale, depending on the selected species and approach. Mosses and lichens have been recognized as suitable biomonitors or bio-accumulators for air pollution (Bargagli 1998; Bargagli et al. 2002; Cislaghi and Nimis 1997; Wolterbeek 2000), and terrestrial invertebrates are used for monitoring air and soils (Dallinger 1994).
Among terrestrial invertebrates, the gastropods Helix spp. have the capability to accumulate different classes of chemicals and serve as pertinent species for monitoring trace metals, agrochemicals, urban pollution, and electromagnetic exposure (Beeby and Richmond 2002, 2003; Berger and Dallinger 1993; Gomot de Vaufleury and Pihan 2000; Regoli et al. 2005; Snyman et al. 2000; Viard et al. 2004). Other biologic effects have also been described, including growth inhibition, impairment of reproductive capacity, and induction of metallothioneins (MT); specific proteins are involved in metal homeostasis and detoxification (Dallinger 1996; Gomot-de Vaufleury and Kerhoas 2000). Pollutants accumulated through different routes are transported by blood cells to the digestive gland, which also represents the main target organ for metabolic and detoxification processes (Beeby and Richmond 2002; Regoli et al. 2005).
The main objective of the present study was to develop an integrated ecotoxicologic approach with the land snail Helix aspersa for monitoring both accumulation and toxicologic effects caused by urban pollutants, including vehicular exhausts and other chemicals such as those associated with tire manufacturing, which can be transported by PM from the road surface. The use of sentinel species is of particular interest to assess biologic reactivity of such complex mixtures that are difficult to characterize on a chemical basis. Analyses of individual analytes in abiotic matrices do not necessarily relate to their bioavailability and do not evaluate synergistic or cumulative effects caused by various classes of chemicals. In this study, organisms were caged in different sites within the city of Ancona, Italy, and analyzed after 4 weeks for the trace metals and PAHs chosen as model chemicals potentially associated with urban pollution. We evaluated the biologic significance of these data using the assessment of a wide panel of molecular–biochemical alterations (biomarkers) reflecting both the induction of specific metabolic/detoxification pathways and the early onset of cellular damages caused by different classes of pollutants or chemical mixtures. Among specific responses, induction of MT, cytochrome P450, and peroxisomal proliferation were selected for metals and organic aromatic pollutants. Although the biotransformation pathway of cytochrome P450 is often not consistent in invertebrates (Livingstone et al. 2000), there is some evidence of its involvement in the metabolism of xenobiotics in gastropods (Ismert et al. 2002). Proliferation of peroxisomes has also been documented as a toxicologic effect of several chemicals in both vertebrate and invertebrate models (Cancio and Cajaraville 2000; Lock et al. 1989). A general pathway of toxicity for several pollutants is mediated by the enhancement of intracellular reactive oxygen species (ROS), which often modulate the occurrence of cell damage (Regoli et al. 2002, 2003). In the present study, we measured variations of antioxidant defenses as biomarkers of contaminant-mediated pro-oxidant challenge. The overall susceptibility to oxidative stress conditions was also assessed by the total oxyradical scavenging capacity (TOSC) assay, which quantifies the capability to neutralize specific ROS such as peroxyl radicals (ROO•) and hydroxyl radicals (HO•) (Gorbi and Regoli 2003). To further investigate pollutant-mediated oxidative toxicity, we estimated lysosomal membrane stability and loss of DNA integrity as typical targets of environmental contaminants, which act through direct mechanisms or enhanced oxyradical formation (Moore et al. 2004; Regoli 2000; Regoli et al. 2004).
The use of these cellular biomarkers is also of potential interest for assessing the impact of air pollution on human health. A large proportion of PM originates from mobile sources and includes both aromatic hydrocarbons and trace metals (Shi et al. 2001). Several epidemiologic and laboratory investigations support the evidence that these chemicals induce inflammatory responses through enhanced formation of ROS and other cellular mechanisms modulated by antioxidant variations and oxidative injuries (Sioutas et al. 2005). Recent studies also revealed a higher incidence of genotoxic damages in traffic police and populations exposed to moderate levels of PAHs in urban areas (Kyrtopoulos et al. 2001; Maffei et al. 2005).
We expected the overall results of this study on H. aspersa to provide useful indications on the biologic reactivity and toxicologic effects of atmospheric pollutants in field conditions, to assess the validity of Helix spp. as a model for human disease outcomes, and investigate the possibility of integrating a multimarker ecotoxicologic approach in air quality programs in urban areas.
Materials and Methods
Experimental design.
This study was carried out in the city of Ancona in central Italy, where five locations were chosen for caging experiments. An extraurban area was the reference (site 1); the other stations were selected according to characteristics of daily vehicular traffic. At site 2, a relatively small and one-way street, snails were caged 200 m past a traffic light and thus were exposed to moving cars. More elevated and slower traffic flows characterized site 3, close to the entrance of the university complex, and to a greater extent, sites 4 and 5, located before a traffic light on a large road and in proximity to a tunnel, respectively. Based on estimates carried out during the peak time, the Office for Public Works and Traffic of Ancona Municipality indicated traffic intensities of 25,900 vehicles/hr for the whole urban area; between 4,000 and 3,000 vehicles/hr at sites 4 and 5; between 1,200 and 1,800 vehicles/hr at site 3; between 600 and 1,200 vehicles/hr at site 2; and < 600 vehicles/hr at site 1 (Piano Generale del Traffico Urbano 2005). Cars were the dominant vehicles in all the sites, although an elevated number of mopeds (some hundreds) also passed through site 3. No other sources of pollutants were noted at the investigated sites.
Gastropods H. aspersa (4–6 g total weight) were purchased from a local farm, divided into groups of 50 specimens, and settled in plastic cages (50 × 40 × 20 cm) excluding a direct contact with soil. At least two cages were deployed in May 2004 within approximately 1 m from the road margin at each location. Daily, transplanted snails were fed carrots and moistened to prevent the occurrence of a dormancy state. After 4 weeks of exposure, the mortality rate appeared < 10% in all the sites, and the snails were recovered and sacrificed. Digestive glands were rapidly dissected out, frozen in liquid nitrogen, and stored −80°C. Hematocytes were withdrawn from the hemocel cavity and immediately processed for assessment of lysosomal membrane stability and DNA integrity. Animals were treated humanely and with regard for alleviation of suffering.
Chemical analyses.
We measured trace metals and PAHs in composite pools of digestive glands dissected from 20 snails (five samples, each constituted by tissues of four specimens). For trace metals, tissues were dried at 60°C until they reached a constant weight, and approximately 0.5 g dried samples were digested under pressure with 5 mL nitric acid and 1 mL hydrogen peroxide in a microwave digestor system (Microwave Laboratory System; Milestone, Shelton, CT, USA). Quality assurance and quality control were tested by processing blank samples and standard reference material (SRM; mussel tissue SRM 2977; National Institute of Standards and Technology, Gaithersburg, MD, USA). Metals (cadmium, chromium, copper, iron, manganese, nickel, lead, and zinc) were analyzed by atomic absorption spectrophotometry with electrothermal atomization (SpectrAA 300 Zeeman, Varian, Mulgrave, VIC, Australia) and flame atomization (Varian SpectrAA 220FS, Varian) and expressed as micrograms per gram dry weight. When necessary, we applied the standard addition technique for resolution of matrix effects, and a palladium solution (1 mg/mL, 10% nitric acid, 10% citric acid) was added as chemical matrix modifier. The concentrations obtained for the SRM were always within the 95% confidence interval of certified values.
For PAHs, about 1 g digestive tissues (wet weight) were extracted in 5 mL 0.5 M potassium hydroxide in methanol with a microwave (150 W for 10 min). Samples were centrifuged at 1,000 × g for 5 min. Methanolic solutions were concentrated in a SpeedVac (RC1009; Jouan, Nantes, France) and purified with solid-phase extraction (Octadecyl C18, 500 mg × 6 mL, Bakerbond; Mallinckrodt Baker, Phillipsburg, NJ, USA). A final volume of 1 mL was recovered with acetonitrile, and HPLC analyses were carried out using a water-acetonitrile gradient and fluorimetric detection. Individual PAHs were identified by the retention time of appropriate pure standard solutions, and the quality assurance/quality control were tested by processing blank and references samples (mussel tissues SRM 2977, NIST). The concentrations obtained for the SRM were always within the 95% confidence interval of certified value. The water content in tissues was determined during preparation of samples for metal analysis and used to normalize PAH concentration (micrograms per gram) to dry weight.
Biochemical analyses.
These determinations were carried out in composite pools of digestive glands dissected from 20 snails (five samples, each constituted by tissues of four specimens). For the analysis of MT, samples were homogenized [1:3 weight/volume (wt/vol)] in 20 mM Tris-HCl buffer (pH 8.6), 0.5 M sucrose, 0.006 mM phenylmethylsulfonyl fluoride (PMSF) and 0.01 % β-mercaptoethanol. After acidic ethanol/chloroform fractionation of the tissue homogenate, MT were quantified by a spectrophotometric assay using reduced glutathione (GSH) as standard (Viarengo et al. 1997).
We measured ethoxyresorufin O-deethylase (EROD) and ethoxycoumarin O-deethylase (ECOD) activities after homogenization (1:5 wt/vol) in 0.1 M K-phosphate buffer (pH 7.5), 0.15 M KCl, and 1 mM EDTA. After centrifugation at 12,000 × g for 15 min (Regoli et al. 2003), 250 μM β-nicotinamide adenine dinucleotide (NADPH) was added to S9 aliquots in 0.1 M K-phosphate buffer (pH 7.4) containing 7-ethoxyresorufin (4 μM in dimethyl sulfoxide) or 7-ethoxycoumarin (50 μM in ethanol). Reactions were stopped after 5 min, and blank values were subtracted. Fluorescence samples were quantified by a calibration curve with resorufin or 7-hydroxy-coumarin standards, using 535 or 380 nm (excitation wavelength) and 585 or 460 nm (emission wavelength), respectively.
We analyzed peroxisomal proliferation by the activity of acyl-coenzyme A oxidase (AOX) in samples homogenized (1:5 wt/vol) in 1 mM NaHCO3, 1 mM EDTA, 0.1% ethanol, and 0.01% Triton X-100 and then centrifuged at 500 × g for 15 min at 4°C. AOX was spectrophotometrically measured in supernatants according to Small et al. (1985). The H2O2 production was followed at 502 nm by the oxidation of dichlorofluorescein-diacetate catalyzed by an exogenous horseradish peroxidase (HRP). A final volume of 1 mL contained 0.5 M K-phosphate buffer (pH 7.4), 2.2 mM dichlorofluorescein-diacetate (DFA-DA), 40 μM sodium azide, 0.01 % Triton X-100, and 1.2 U/mL HRP; 30 μM palmytoil-CoA was added as substrate for AOX after a pre-incubation of 5 min in the dark.
Enzymatic antioxidants were measured in samples homogenized (1:5 wt/vol) in 100 mM Tris-HCl buffer (pH 8.0), 0.1 mM PMSF, 0.008 trypsin inhibitor units/mL aprotinin, 1 μg/mL leupeptin, 0.5 μg/mL pepstatin, and 0.6% NaCl and centrifuged at 100,000 × g for 1 hr at 4°C to obtain cytosolic fractions. Spectrophotometric measurements were carried out as described elsewhere (Regoli et al. 2004). Catalase was quantified by the decrease in absorbance at 240 nm due to H2O2 consumption. Glutathione reductase (GR) activity was followed by the oxidation of NADPH at 340 nm during the reduction of oxidized glutathione (GSSG). Glutathione peroxidases (GPx) were measured at 340 nm in a coupled enzyme system where cumene hydroperoxide is used as substrate for the sum of Se-dependent and Se-independent forms and NADPH is consumed by GR to convert the formed GSSG to its reduced form. Glutathione S-transferases (GST) were determined at 340 nm using 1-chloro-2,4-dinitrobenzene as substrate. Total glutathione was analyzed after homogenization (1:5 wt/vol) of tissues in 5% sulfosalicilic acid with 4 mM EDTA. Samples were maintained for 45 min on ice and centrifuged at 37,000 × g for 45 min. The resulting supernatants were assayed by following the GR-catalyzed reaction of GSH with 5,5′-dithiobis-2-nitrobenzoic acid and comparing this rate with a standard GSH curve.
TOSC was measured in samples homogenized as described above for the enzymatic antioxidants, without adding PMSF to the buffer. The TOSC assay quantifies the capability of cellular antioxidants to inhibit the oxidation of 0.2 mM α-keto-γ-methiolbutyric acid to ethylene gas in the presence of different forms of oxyradicals artificially generated at a constant rate (Regoli and Winston 1999; Winston et al. 1998). ROO• and HO• were generated by the thermal homolysis of 20 mM 2-2′-azo-bis-(2-methylpropionamidine)-dihydrochloride and from an Fe ascorbate Fenton reaction (Regoli and Winston 1999), respectively. TOSC values were quantified from the following equation:
where ∫SA and ∫CA are the areas integrated under the kinetic curves for sample (SA) and (CA) reactions, respectively (Winston et al. 1998). TOSC values were normalized to content of proteins, measured in both S9 and cytosolic fractions with the Lowry method and bovine serum albumin as standard.
Neutral red retention time assay.
Lysosomal membrane stability was measured in freely circulating hematocytes by the neutral red retention time (NRRT) assay, which quantifies the capability of these organelles to retain the vital dye (Regoli 2000; Snyman et al. 2000). Hemolymph was withdrawn from the visceral hemocel of 10 individual snails and incubated on a microscope slide with a neutral red working solution as previously described (Regoli et al. 2005). Hematocytes were observed under a light microscope at 2-min intervals, and only the most abundant cell type, namely, the smaller hyaline and agranular hematocytes with pseudopodia, were considered. The NRRT was calculated as the time at which ≥ 50% of the counted cells presented reddish cytosols after the leakage of the dye from lysosomes.
Single-cell gel electrophoresis.
We performed the comet assay on hematocytes freshly collected from 10 snails; the cells were diluted in Ca2+- and Mg2+-free buffers (20 mM HEPES, 120 mM NaCl, 5 mM KCl, 10 mM EDTA), and spun at 1,000 rpm for 1 min at 4°C. Detailed procedures for sample preparation and comet assay conditions have been described elsewhere (Regoli et al. 2005). After electrophoresis, slides were stained with SYBR green 1X (Molecular Probes, Leiden, The Netherlands) and observed under a fluorescence microscope (200× magnification, Eclipse E-600, Nikon, Kawasaki, Japan). At least 100 randomly selected cells from each slide and two replicates per sample were counted and classified in five classes of damage according to the length of DNA migration and the relative proportion of head/tail fluorescence (Collins 2002), as follows:
Class 1: intact DNA without migrated fragments
Class 2: dense nucleus with slight migration and a small tail
Class 3: tails have separated from the nucleus, with a weaker fluorescence
Class 4: clear tails that may reach full length
Class 5: nucleus appears small and completely separated from the tail.
Comet results are given as percentage distribution of cells within the various classes. We summarized these data in a synthetic index of total damage (TD) calculated according to the following equation:
where n1, n2, n3, n4, and n5 indicate the percentage of cells within each of five classes of damage. Thus, TD ranges between 100 and 500, corresponding to the totality of cells in class 1 or class 5, the lowest and highest level, respectively, of DNA damage.
Statistical analyses.
We performed statistical analyses using Statistica Software (version 6.0; Stat Soft, Tulsa, OK, USA). Chemical and biochemical parameters in snails from different sites were compared by one-way analysis of variance (ANOVA). The homogeneity of variance was analyzed by Cochran C, and post hoc tests (Newman-Keuls) were used to discriminate between means of values. The non-parametric Kruskal-Wallis test was applied to the results of the comet assay for comparing the distribution of cells within five classes of damage.
We used multivariate statistical analysis [principal component analysis (PCA)] to investigate correlations between the different variables.
Results
Metal concentrations in the digestive gland of H. aspersa caged in May 2004 in different urban sites are reported in Table 1. A marked increase in Cr, Cu, Fe, Pb, Mn, Ni, and Zn was evident at site 5 and, with a few differences, at site 4. Compared with reference, the accumulation of metals was still significant at site 3 and at site 2, to a lesser extent, with values (especially for Pb and Cu) considerably lower than in other sites.
Higher concentrations of PAHs were also measured in caged snails with low molecular weight (lmw) hydrocarbons (i.e., naphthalene and fluorene) always prevailing over high molecular weight (hmw) congeners (i.e., fluoranthene, pyrene, benzo[a]anthracene, benzo-[b]fluoranthene, benzo[k]fluoranthene). Values of total PAHs increased from sites 2 and 3 to sites 4 and 5 (Table 1), but organisms at site 3 showed an elevated accumulation of pyrene and fluorene. Preliminary results from chemical data revealed sites 5, 4, and 3 as the most impacted, with the following approximate order of bioavailable pollutants for various urban areas: site 5 ≥ site 4 > site 3 >> site 2 ≥ site 1.
Variations in biochemical and cellular bio-markers are summarized in Table 2. Results on MT confirmed those on metals bio-accumulation, with levels significantly increasing from site 2 to the more traffic-congested sites (4 and 5). The activity of AOX revealed peroxisomal proliferation in organisms from sites 4 and 5 to a greater extent than in those at site 3. Cytochrome P450 assessed as EROD did not exhibit any change, whereas ECOD increased in specimens from sites 3 and 5.
Antioxidant responses showed different patterns of variations (Table 2). Catalase and GR were induced at sites 3, 4, and 5. Snails caged at site 5 exhibited significantly lower values for GST and a trend toward higher activities for GPx. The TOSC assay demonstrated an increased antioxidant efficiency in snails exposed in more traffic-congested sites (Table 2), with higher TOSC values toward both ROO• and HO•.
The lysosomal membrane stability was not compromised in snails from sites 1 and 3, whereas a significant destabilization was measured at sites 2, 4, and 5 (Table 2). The pattern of DNA damage was revealed by the comet assay, with a clear increase of percentage distribution of cells in classes 4 and 5 for snails caged at sites 3, 4, and 5 (Figure 1). These results were confirmed by the values of TD significantly higher in snails at more-impacted sites (Table 2).
From the PCA analysis, the first two axes explained 82% of the variance (Table 3). The factor loading showed that within the first axis, concentrations of Cu, Cr, Fe, Mn, Ni, Pb, Zn, naphthalene, fluorene, anthracene, pyrene, total lmw PAHs, total hmw PAHs, and total PAHs were positively correlated with the levels of MT; activity of AOX, ECOD, catalase, GR, TOSC toward ROO• and HO•; and onset of total DNA damage, whereas the same parameters were negatively associated with NRRT of lysosomes. In axis 2, positive associations were obtained for Cd, GPx, and levels of total glutathione and negative for Fe, phenanthrene, AOX, and GST.
The ordination plot (Figure 2) confirmed the marked separation of sites 1 and 2 from sites 3, 4, and 5 on the basis of chemical residues and biologic parameters associated with axis 1. Snails at site 3 were further differentiated from those at sites 4 and 5 by the higher concentrations of Fe and phenanthrene, the more elevated activities of AOX and GST, and the reduced values for GPx and total glutathione (despite the fact that these latter parameters did not significantly change according to ANOVA).
Discussion
These results demonstrate the possibility of an ecotoxicologic approach for assessing the biologic impact and risks from airborne and vehicular pollutants in urban areas. The use of caged snails might represent an improvement to actual monitoring techniques because the method is relatively cheap, easy to perform, and allows an active translocation procedure to investigate selected sites even in the absence of native organisms. In addition, the biologic significance of the results presented here is important both in terms of accumulated chemicals and appearance of toxicologic responses. Bioindicator organisms provide a time-integrated assessment of environmental quality reflecting the exposure over a 4-week translocation period, and thus are less affected by daily or even hourly fluctuations of chemical parameters.
Because we aimed to validate a protocol rather to monitor the urban area of Ancona, we selected a limited number of sites on the basis of vehicular traffic characteristics, and only one seasonal period was investigated. Overall results revealed marked effects in snails caged at various locations, that is, in the different accumulation of metals, which confirmed H. aspersa as a suitable bioindicator for these environmental pollutants (Beeby and Richmond 2002; Dallinger 1994). The digestive gland was the main target organ, with concentrations generally 5- to 10-fold higher than those measured in foot and lung (not shown). The uptake of contaminants in digestive tissues was not surprising (Beeby and Richmond 2003; Gomot de Vaufleury and Pihan 2000) and suggested that deposition and ingestion through PM was the main exposure route for such contaminants. The range of intersample variability for considered analytes was within expected values, based on other studies of chemical accumulation in terrestrial and marine invertebrates (Beeby and Richmond 2002; Berger and Dallinger 1993; Dallinger 1994; Gomot de Vaufleury and Pihan 2000; Regoli et al. 2004). Analysis of a minimum of five samples (each including tissues of at least four organisms) can thus be recommended to minimize erroneous interpretation of data. Levels of metals in snails caged at site 1 were typical for unpolluted reference organisms (Beeby and Richmond 2002; Dallinger 1994), whereas several elements were strongly accumulated in more traffic-congested sites (e.g., Pb, Cu, Zn, Cr, Fe, Mn, and Ni). Worthy to note is the variation of Pb, with concentrations increasing from < 2 μg/g up to 80 μg/g in snails caged close to the tunnel. Despite the use of unleaded gasoline, obligatory in Italy since January 2001 and expected to improve atmospheric pollution (Viard et al. 2004), our results indicated that this metal might still represent an important contaminant in urban areas. A persistent role of soil particles as an additional exposure route for Pb in snail tissues could be hypothesized, considering that this element can remain in soils for several years after the conversion of a country to unleaded gasoline. The marked accumulation of metals was also reflected by more elevated content of PAHs in snails caged at sites 4 and 5 and, for pyrolitic combustion-derived congeners, also in organisms exposed at site 3.
One of the main objectives of this study was to demonstrate the suitability of a wide battery of cellular biomarkers for assessing the earliest responses to atmospheric pollutants and the onset of toxicologic alterations which might be of concern also for human health. The overall results from multivariate analysis confirmed the possibility to discriminate the most impacted sites (sites 3, 4, and 5), where accumulation of chemical residues in digestive gland of snails correlated with a large number of biologic alterations. The significant induction of MT in snails with higher concentrations of metals demonstrated that these elements were accumulated in a biologically active form. Two distinct MT isoforms have been characterized previously in the digestive gland of Helix pomatia, the Cu-MT principally involved in homeostasis of Cu, and the Cd-MT inducible by exposure to metals (Dallinger et al. 2004). Our data did not discriminate between the isoforms but further supported these proteins as an excellent biomarker of metals contamination in different field conditions (Dallinger 1996).
Among biologic effects caused by aromatic xenobiotics, proliferation of peroxisomes in mammalian systems appears to have a role in hepatic carcinogenesis (Lake 1995). Metabolism of peroxisomes and mechanism of responses are largely unknown in invertebrates, with limited data available only for some marine species (Cancio and Cajaraville 2000). This study provided the first characterization of AOX in H. aspersa, showing basal activities comparable to the bivalve Mytilus galloprovincialis (Cancio and Cajaraville 2000). The significant induction of AOX in more polluted sites would also indicate the responsiveness of peroxisomes to atmospheric pollutants. Future investigations at the molecular level should be carried out to clarify if any mechanistic relationship exists between accumulation of fluorene, phenanthrene, anthracene, and pyrene and the contemporary induction of both peroxisomal proliferation and GST as indicated by PCA analysis in snails from site 3. It is unknown why snails at site 3 showed higher levels of some contaminants and different biologic responses compared with sites 4 and 5. The possible influence of moped traffic can be only speculated.
Biotransformation of PAHs by cytochrome P450 is controversial in terrestrial invertebrates. Some evidence of benzo[a]pyrene metabolism has been shown in the earthworms Lumbricus terrestris and Eisenia fetida, where some isoforms were induced but others did not respond to PAHs (Lee 1998). In land snails, the digestive gland of H. aspersa exhibited a low (1.3- to 1.5-fold) but significant induction of either EROD or ECOD activity after exposure to a naphthalene-saturated atmosphere (Ismert et al. 2002). Our results confirmed the presence of cytochrome P450 activities in these gastropods but low levels of EROD, and the highly variable responses for ECOD did not support a clear role of biotransformation enzymes in metabolism of xenobiotics, nor their suitability as biomarkers for monitoring programs with H. aspersa.
Airborne pollutants cause a significant perturbation of the redox status, as indicated by the wide spectrum of oxidative parameters characterized in H. aspersa. Among these, catalase showed elevated basal activities approximately 5- to 10-fold greater than those typical of marine mollusks (Regoli et al. 2004; Regoli and Principato 1995), thus indicating an efficient protection toward H2O2, a potent oxidant and the main precursor of HO•(Regoli et al. 2004). Nonetheless, the significant increase of both catalase and GR reflects a varied pro-oxidant challenge in snails caged in more traffic-congested sites 3, 4, 5. Catalase has already been reported to be sensitive to chemical pollutants in aquatic bioindicators (Livingstone 2001; Regoli et al. 2003; Regoli and Principato 1995). Translocation experiments also demonstrated the possibility of biphasic variations, where initial counteracting responses might be followed by inhibition at longer exposure periods (Regoli et al. 2004; Regoli and Principato 1995). On the other hand, variations of GR modulate responsiveness of glutathione metabolism in invertebrates by increasing the capability to reconvert oxidized GSSG to the functionally active GSH (Regoli et al. 2002, 2003, 2004; Ringwood et al. 2004). Although laboratory exposures to naphthalene did not affect the activity of GR in H. aspersa (Ismert et al. 2002), our results confirmed the possibility of using this enzyme as a sensitive biomarker in field conditions.
Snails caged at site 5 exhibited a significant inhibition of GST, a multigene family that catalyzes both detoxification of organic compounds and antioxidant reactions through the reduction of hydroperoxides. Similarly to catalase, H. aspersa also showed elevated basal levels for these enzymes at least an order of magnitude above those commonly measured in the digestive gland of M. galloprovincialis (Regoli et al. 2004; Regoli and Principato 1995). Such elevated GST activities might explain the limited and fluctuating variations observed in various sites. However, contradictory results have often been described in field conditions, with increases, decreases, and transitory changes in these enzymes according to the intensity and duration of exposure (Regoli et al. 2003).
The effects on individual antioxidants were useful as sensitive warning signals of oxidative perturbation in most impacted sites (especially sites 3, 4, 5), but also confirmed complex interactions and responses that are not easy to predict. The overall biologic significance of these variations was better assessed by the measurement of TOSC, which summarizes in quantitative terms the susceptibility of a tissue to oxidative stress (Gorbi and Regoli 2003; Regoli et al. 2002; Regoli and Winston 1999). In the present study, increased TOSC values toward ROO• and HO• were measured in organisms caged at more traffic-congested locations (sites 3, 4, and 5), indicating that the higher pro-oxidant pressure and specific alterations of certain antioxidants (such as catalase, GR, GST) were reflected in a more integrated imbalance of oxyradical metabolism. A varied capability to neutralize ROS is of great value in assessing the biologic impact of pollutants because this alteration predicts the onset of other cellular damages in several animal models and in humans (Gorbi and Regoli 2003).
Our results confirmed the lysosomal membrane as a typical cellular target of chemical toxicity. Both lipophilic xenobiotics and metals alter the efficiency of membrane-bound proton pumps, increasing membrane permeability and eventually resulting in the loss of acid hydrolases into cytosol (Moore et al. 2004; Moore and Simpson 1992; Regoli 2000). These effects can be mediated by direct binding to the lysosomal membrane and indirectly by the enhanced formation of oxyradicals (Regoli 2000). The lysosomal compartment is highly developed in mollusks (Moore et al. 2004), and a significant reduction of NRRT was observed in snails caged in all urban areas, with the exception of site 3. Because of their elevated sensitivity, lysosomal biomarkers were confirmed as suitable tools for early detection of biologic disturbance, but they did not discriminate between sites with increasing levels of environmental pollutants. Normal values of NRRT measured in H. aspersa (approximately 30 min) were much lower than those typical for other invertebrates (90–120 min in M. galloprovincialis) (Regoli et al. 2004), but have been described as typical for this species (Snyman et al. 2000).
The evident accumulation of metals and PAHs and the general alterations of oxyradical metabolism were also reflected by genotoxic effects in snails exposed in more traffic-congested sites. Aromatic hydrocarbons have the potential to enhance oxyradical formation in invertebrates through redox cycling and impairment of cellular antioxidant systems (Livingstone 2001; Regoli et al. 2003). Similarly, an oxidative pathway for DNA damage has been documented for trace metals that can catalyze Fenton-like reactions, interact with –SH groups, and increase intracellular pro-oxidant conditions (Livingstone 2001; Machella et al. 2004; Regoli and Principato 1995; Regoli et al. 2004). The possibility of detecting loss of DNA integrity at locations 3, 4, and 5 is certainly useful for a better assessment of toxicologic risks associated with atmospheric pollutants.
In the present study, the ecotoxicologic approach appears to be a valuable tool for monitoring air quality in urban areas. The snail H. aspersa was an efficient bioindicator that accumulated bioavailable contaminants and allowed the integration of these data with toxicologic responses. Results obtained in the urban area of Ancona indicate that vehicular traffic plays a prominent role in the perturbed responses of H. aspersa, suggesting the potential use of land snails in larger monitoring networks. These snails might also be used to evaluate the efficacy of mitigation decisions or temporary or long-term variations of atmospheric pollutants. This pilot study suggests that important ramifications need to be explored. It is unknown whether the response of sentinel species to urban pollutants can be influenced by natural fluctuations in biologic features (e.g., metabolic status and reproductive cycle), by the seasonality of environmental factors (e.g., traffic intensities and emissions, temperature, or raining regimes), or the local characteristics of different urban areas.
The ecotoxicologic approach described here might also have a relevance for the impact of atmospheric pollutants on both ecosystems and human health. Snails are representative primary consumers in terrestrial food webs and can thus be important indicators of the potential transfer of pollutants to higher trophic levels (Gomot de Vaufleury and Pihan 2000). Deleterious health effects caused by airborne chemicals have been widely documented in humans (Maynard 2004), although only a few studies have attempted to relate human disease incidence with biomonitoring outcomes (Cislaghi and Nimis 1997; Wappelhorst et al. 2000). Both homologies and differences in toxicologic responses can be expected between snails and human models. Oxidative mechanisms and pollutant-mediated ROS generation are well recognized in humans and have been related to epidemiologic evidence and deleterious health effects caused by vehicular traffic in several urban areas (Sioutas et al. 2005). Other cellular pathways, such as peroxisomal proliferation and biotransformation of PAHs by cytochrome P450, are more responsive in humans, enhancing the carcinogenic properties of aromatic chemicals. Although in our study snails were presumably exposed to particles of respirable size, the link between observed responses and human health link would be strengthened by some direct analyses of air pollutants and a better assessment of exposure profiles in individuals with different lifestyles (specific jobs, time spent in or near vehicles, location of working and living places, etc.). At present, it is difficult to address the transferability of our results obtained in gastropods to expected thresholds/effects in humans or ecologic populations. However, this uncertainty should stimulate the development of multi-disciplinary programs integrating emission control and analytical monitoring of air samples, use of sentinel species, laboratory investigations and toxicity tests, and ecologic and epidemiologic studies.
We thank I. Alessandrini (Office for Public Works and Traffic, Municipality of Ancona) for his collaboration and for providing data on traffic intensities.
Figure 1 Loss of DNA integrity in snails caged in urban sites, expressed as the percentage distribution of cells within the five classes of DNA damage (mean ± SD; n = 5/group).
Figure 2 PCA results and separation of sites (S1, S2, S3, S4, and S5) on the basis of chemical residues and biologic parameters associated with axis 1 and axis 2 (see also Table 3).
aCu, Pb, Cr, Ni, Mn, Zn, naphthalene, fluorene, anthracene, pyrene, lmw PAHs, hmw PAHs, total PAHs, MT, AOX, ECOD, catalase, GR, TOSC-ROO•, TOSC-HO•, DNA TD.
Table 1 Concentrations (mean ± SD) of trace metals and PAHs (μg/g dry weight) in the digestive gland of snails caged in various urban sites (n = 5/group).
Contaminant p-Value Site 1 Site 2 Site 3 Site 4 Site 5
Metals
Cd NS 5.61 ± 0.93 8.60 ± 2.04 5.60 ± 1.58 7.29 ± 2.12 8.93 ± 2.22
Cr p < 0.001 0.35 ± 0.05 0.57 ± 0.03 1.30 ± 0.68* 1.25 ± 0.63* 1.65 ± 0.66*
Cu p < 0.0005 8.65 ± 1.34 17.4 ± 2.12* 21.2 ± 1.91* 80.8 ± 22.9** 75.0 ± 26.3**
Fe p < 0.0001 87.5 ± 8.04 150 ± 25.5* 2,016 ± 886# 959 ± 478** 555 ± 273**
Pb p < 0.0001 1.62 ± 0.35 5.06 ± 0.88* 3.12 ± 0.92* 15.2 ± 5.22** 80.5 ± 39.5#
Mn p < 0.005 146 ± 17.5 223 ± 31.1* 467 ± 197** 517 ± 209** 409 ± 166**
Ni p < 0.001 0.39 ± 0.08 1.23 ± 0.19* 1.56 ± 0.19* 0.98 ± 0.36* 2.64 ± 1.32*
Zn p < 0.005 126 ± 16.1 183 ± 30.4* 297 ± 91.0** 502 ± 187# 514 ± 199#
PAHs
Naphthalene p < 0.005 260 ± 73.0 344 ± 19.7* 389 ± 70.0* 497 ± 204** 502 ± 41.8**
Acenaphthene ND ND ND ND ND
Fluorene p < 0.01 35.3 ± 11.3 49.9 ± 18.7 64.7 ± 2.13* 56.8 ± 19.0* 63.3 ± 5.38*
Phenanthrene NS 8.70 ± 3.44 12.4 ± 3.68 15.4 ± 0.71 11.0 ± 3.60 11.9 ± 2.70
Anthracene p < 0.01 0.57 ± 0.26 2.12 ± 0.36* 4.56 ± 1.66* 2.77 ± 1.67* 5.02 ± 0.45*
Fluoranthene p < 0.005 0.59 ± 0.35 0.38 ± 0.31 1.82 ± 1.82 17.8 ± 11.2** 4.24 ± 3.56*
Pyrene p < 0.001 7.39 ± 3.44 7.37 ± 3.88 19.5 ± 3.24* 10.9 ± 9.38* 19.4 ± 9.46*
Benzo[a]anthracene ND ND ND 1.08 ± 0.64 3.13 ± 0.44
Chrysene ND ND ND ND ND
Benzo[b]fluoranthene ND ND ND 2.12 2.66 ± 2.64
Benzo[k]fluoranthene ND ND ND 0.66 ± 0.86 1.20 ± 0.40
Benzo[a]pyrene 0.85 ± 0.13 ND ND ND ND
Dibenzo[a,h]anthracene ND ND ND ND ND
Benzo[g,h,i ]perylene ND ND ND ND ND
Total lmw PAHs p < 0.005 305 ± 82.9 409 ± 33.9* 473 ± 70.1* 568 ± 226** 582 ± 46.6**
Total hmw PAHs p < 0.001 8.54 ± 2.77 7.76 ± 4.19 20.7 ± 4.81* 32.6 ± 4.32* 28.2 ± 8.41*
Total PAHs p < 0.005 314 ± 85.1 417 ± 32.7* 494 ± 72.5* 601 ± 109** 610 ± 53.0**
Abbreviations: ND, not detectable; NS, not significant.
* p < 0.05
** p < 0.001
# p < 0.0001 indicate significant variations and differences between groups of means (post hoc comparison).
Table 2 Biochemical and cellular biomarkers in the digestive gland of H. aspersa (mean ± SD; n = 5/group).
Biomarker p-Value Site 1 Site 2 Site 3 Site 4 Site 5
MT [eq.(G)SH nmol/mg protein] p < 0.001 3.41 ± 2.14 9.33 ± 3.35* 12.1 ± 1.58* 13.7 ± 3.98** 15.4 ± 5.31**
AOX (nmol/min/mg protein) p < 0.05 0.11 ± 0.05 0.10 ± 0.05 0.48 ± 0.08** 0.26 ± 0.06* 0.26 ± 0.07*
EROD activity (pmol/min/mg protein) NS 0.62 ± 0.09 0.33 ± 0.10 0.52 ± 0.10 0.38 ± 0.20 0.66 ± 0.13
ECOD activity (pmol/min/mg protein) p < 0.05 1,432 ± 101 1,279 ± 219 2,440 ± 781* 1,682 ± 285 2,163 ± 395*
Catalase (μmol/min/mg protein) p < 0.0005 321 ± 48.7 323 ± 46.2 700 ± 144* 545 ± 51.3* 697 ± 257*
GR (nmol/min/mg protein) p < 0.001 16.1 ± 3.54 12.7 ± 1.27 22.4 ± 5.08* 29.0 ± 7.77* 26.1 ± 5.69*
GST (nmol/min/mg protein) p < 0.05 1,892 ± 219 1,629 ± 555 2,214 ± 363 1,540 ± 371 1,219 ± 125*
GPx (nmol/min/mg protein) NS 13.1 ± 5.66 12.9 ± 2.51 7.68 ± 4.35 15.3 ± 2.95 19.3 ± 9.17
Total glutathione (μmol/g tissue) NS 1.63 ± 0.25 1.38 ± 0.42 1.05 ± 0.33 1.60 ± 0.16 1.94 ± 0.49
TOSC (ROO•; U/mg protein) p < 0.01 981 ± 96.1 826 ± 102 1,588 ± 61.7* 1,330 ± 147* 1,437 ± 105*
TOSC (HO•; U/mg protein) p < 0.01 1,071 ± 132 953 ± 259 1,544 ± 189* 1,206 ± 132* 1,470 ± 219*
NRRT (min) p < 0.0005 27.4 ± 0.66 11.6 ± 7.03* 20.4 ± 8.64 9.97 ± 7.28* 10.8 ± 7.62*
DNA TD (arbitrary units) p < 0.005 128 ± 15.6 111 ± 5.28 197 ± 74.9* 184 ± 44.9* 215 ± 64.85*
Abbreviations: eq, equivalents; NS, not significant.
* p < 0.05 and
** p < 0.001 indicate significant variations and differences between groups of means (post hoc comparison).
Table 3 Eigenvalues, percentage, and total variance of factors obtained from PCA analysis of chemical and biologic parameters of the land snail H. aspersa.
Axis Eigen-value Percent variance Cumulative variance Contaminant/biomarker Axis 1 (PC1) Axis 2 (PC2) Axis 3 (PC3) Axis 4 (PC4)
Cu 0.790705† 0.539160 0.165378 0.238207
PC1 17.70858 59.02861 59.0286 Pb 0.715212† 0.500997 −0.542411 −0.110842
Cd 0.313324 0.752549† −0.005360 −0.579197
PC2 6.91878 23.06260 82.0912 Cr 0.997280† −0.008272 −0.070683 −0.019186
Ni 0.784127† 0.126048 −0.399784 −0.457635
PC3 3.02699 10.08994 92.1812 Mn 0.900092† −0.161097 0.377186 0.147014
Fe 0.595581 −0.764651† 0.238275 0.061795
PC4 2.34565 7.81885 100.00 Zn 0.917575† 0.341097 0.113295 0.169924
Naphthalene 0.922264† 0.331423 0.198704 0.010255
Fluorene 0.920068† −0.215378 0.108228 −0.308827
Phenanthrene 0.516094 −0.722838† 0.174026 −0.561636
Anthracene 0.913881† −0.204078 −0.170257 −0.306899
Fluoranthene 0.475363 0.353075 0.626410 0.506930
Pyrene 0.846901† −0.382454 −0.355005 −0.102269
Total lmw PAHs 0.946469† 0.257876 0.190068 −0.039624
Total hmw PAHs 0.920453† 0.156682 0.158835 0.320918
Total PAHs 0.949531† 0.250606 0.188443 −0.008797
MT 0.948667† 0.145167 0.175937 −0.219096
AOX 0.693021† −0.716261† 0.065546 0.048953
EROD activity 0.158404 −0.108723 −0.921801† 0.336705
ECOD activity 0.786172† −0.549701 −0.281625 0.021227
Catalase 0.939616† −0.299041 −0.163484 0.031132
GR 0.867874† 0.105793 0.137845 0.465404
GST −0.354490 −0.917779† 0.134982 0.117469
GPx 0.309859 0.901686† −0.284869 0.098988
Total glutathione 0.171919 0.840484† −0.437953 0.268751
TOSC (ROO•) 0.865488† −0.434499 −0.099349 0.228628
TOSC (HO•) 0.821282† −0.464366 −0.320765 0.083491
Lysosomal NRRT −0.555389 −0.556256 −0.400064 0.471244
DNA TD 0.947211† −0.181370 −0.170131 0.202365
Abbreviations: PC, principal component; PC1, axis 1; PC2, axis 2; PC3, axis 3; PC4, axis 4. Factor loadings are given for each parameter.
† Values ≥ 0.7.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8143ehp0114-00007016393661ResearchEffects of Organochlorine Contaminants on Loggerhead Sea Turtle Immunity: Comparison of a Correlative Field Study and In Vitro Exposure Experiments Keller Jennifer M. 12McClellan-Green Patricia D. 13Kucklick John R. 2Keil Deborah E. 4*Peden-Adams Margie M. 45671 Nicholas School of the Environment and Earth Sciences, Coastal Systems Science and Policy, and Integrated Toxicology Program, Duke University, Beaufort, North Carolina, USA2 National Institute of Standards and Technology, Hollings Marine Laboratory, Charleston, South Carolina, USA3 Department of Environmental and Molecular Toxicology, and Center for Marine Science and Technologies, North Carolina State University, Morehead City, North Carolina, USA4 Marine Biomedicine and Environmental Science Center, and 5 Department of Pediatrics, Medical University of South Carolina, Charleston, South Carolina, USA6 Grice Marine Laboratory, College of Charleston, Charleston, South Carolina, USA7 Mystic Aquarium and Institute for Exploration, Mystic, Connecticut, USAAddress correspondence to J.M. Keller, National Institute of Standards and Technology, Hollings Marine Laboratory, 331 Ft. Johnson Rd., Charleston, SC 29412 USA. Telephone: (843) 762-8863. Fax: (843) 762-8742. E-mail:
[email protected] address: University of Nevada-Las Vegas, Clinical Laboratory Sciences Program, Las Vegas, NV, USA.The authors declare they have no competing financial interests.
1 2006 21 9 2005 114 1 70 76 23 3 2005 21 9 2005 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Several laboratory and field studies indicate that organochlorine contaminants (OCs), such as poly-chlorinated biphenyls (PCBs) and pesticides, modulate immune responses in rodents, wildlife, and humans. In the present study we examined the effects of OCs on immunity in free-ranging loggerhead sea turtles (Caretta caretta). Mitogen-induced lymphocyte proliferation responses, lysozyme activity, and OC concentrations were measured from blood samples. Mitogens chosen in the lymphocyte proliferation assay were phytohemagglutinin (PHA) and concanavalin A (ConA) for T-lymphocyte stimulation, and lipopolysaccharide (LPS) and phorbol 12,13-dibutyrate (PDB) for B-lymphocyte stimulation. Lysozyme activity was significantly and negatively correlated with whole-blood concentrations of 4,4′-dichlorodiphenyldichloroethylene (4,4′-DDE) and the sum of chlordanes. Lymphocyte proliferation responses stimulated by PHA, LPS, and PDB were significantly and positively correlated with concentrations of the sum of PCBs measured in whole blood. LPS- and PDB-induced proliferation were also significantly and positively correlated with 4,4′-DDE blood concentrations. These correlative observations in free-ranging turtles suggest that current, chronic exposure to OCs may suppress innate immunity and enhance certain lymphocyte functions of loggerhead sea turtles. To further test this hypothesis, lymphocyte proliferation was measured after in vitro exposure of peripheral blood leukocytes from 16 turtles to Aroclor 1254 (0–13.5 μg/mL) or 4,4′-DDE (0–13.4 μg/mL). Both contaminants increased PHA- and PDB-induced proliferation at concentrations below those that affected cell viability. Moreover, the concentrations that enhanced PDB-induced proliferation in vitro were similar to concentrations measured in turtles with the highest proliferative responses. The similarities between the in vitro experiments and the correlative field study suggest that OC exposure modulates immunity in loggerhead turtles.
DDTimmunotoxicityorganochlorine contaminantsorganochlorine pesticidesPCBspersistent organic pollutantspolychlorinated biphenylsreptile
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Environmental contaminants, such as organochlorine contaminants (OCs), have been shown to affect the immune functions of animals exposed in the laboratory (Harper et al. 1993; Ross et al. 1996; Segre et al. 2002; Silkworth et al. 1984; Smialowicz et al. 1989; Smits et al. 2002; Wu et al. 1999). These experiments have substantiated relationships observed between OC concentrations and immunomodulation in free-ranging wildlife (Grasman and Fox 2001; Lahvis et al. 1995; reviewed by Keller et al. 2000). OCs, such as polychlorinated biphenyls (PCBs), 4,4′-dichlorodiphenyldichloroethylene (4,4′-DDE), chlordanes, and other pesticides, have been recently documented in blood and adipose tissue of loggerhead sea turtles (Caretta caretta) from North Carolina (Keller et al. 2004a). Although the concentrations were low relative to other wildlife species that feed at higher trophic levels, the concentrations significantly correlated with several health indicators, including white blood cell counts and some plasma chemistry measurements (Keller et al. 2004c). The effects of environmental contaminants on functional aspects of sea turtle immunity, however, have not yet been addressed in any published study.
Sea turtles face many impacts from human activity, including hunting, fisheries interactions, loss of nesting habitat due to coastal development, and anthropogenic chemical contamination. Of these threats, the effects of environmental contaminants on sea turtle health are the least understood, and few studies have addressed this potential impact (Aguirre et al. 1994; Day 2003; Heesemann et al. 2004; Keller et al. 2004c; Lutcavage et al. 1995; Peden-Adams et al. 2002, 2003; Podreka et al. 1998). All species of sea turtles found in U.S. waters are protected by the U.S. Endangered Species Act under either an endangered or threatened status (Pritchard 1997). Specifically, the loggerhead sea turtle (Caretta caretta) is protected as a threatened species, and although some populations are recovering, others may still be declining (Turtle Expert Working Group 2000). It is therefore important to understand the risk that contaminants pose to the general health and immunologic function of loggerhead sea turtles because these effects could affect the survival of their populations.
In this study we examined how OCs influence loggerhead sea turtle immune responses using the mitogen-induced lymphocyte proliferation assay and plasma lysozyme activity. The lymphocyte proliferation assay has been optimized for loggerhead and green sea turtles (Chelonia mydas) (Keller et al. 2005; McKinney and Bentley 1985; Work et al. 2000). Lysozyme activity, a measure of innate immunity, has not previously been reported for any sea turtle species. Because lymphocyte proliferation and lysozyme activity have been shown to be altered by OC exposure in other species (Burton et al. 2002; Grasman and Fox 2001; Lahvis et al. 1995; Ross et al. 1996; Segre et al. 2002; Smits et al. 2002; Wu et al. 1999), we hypothesized that OC exposure may also modulate these immune functions in loggerhead sea turtles. If shown to be affected by OCs, these immune measurements would offer a relatively simple biomarker that requires only a nonlethal blood sample.
Materials and Methods
Sampling.
All turtles used in this study were treated humanely in accordance with protocols approved by Duke University Institutional Animal Care and Use Committee (protocols A351-99-07-2, A351-99-07-3, and A206-01-07) and with required federal and state permits (U.S. Fish and Wildlife Service permits PRT-676379 and TE-676379-2, National Marine Fisheries Service permit 1245, and North Carolina Wildlife Resources Commission permits 00ST70 and 01ST45). Forty-eight free-ranging juvenile loggerhead sea turtles with straight carapace lengths (SCLs; measured from the nuchal notch to the most posterior marginal notch) between 45.7 and 77.3 cm were captured as by-catch from a pound net fishery located in Core Sound, North Carolina, in July and August 2000 and in July 2001. Most turtles appeared healthy upon visual examination, and body condition indices and plasma chemistry values were measured and reported elsewhere (Keller et al. 2004c). Lymphocyte proliferation was measured in only the 2001 samples, whereas lysozyme activity was measured in both years. Blood was collected within 10 min of capture from the dorsocervical sinus using double-ended Vacutainer needles directly into Vacutainer blood collection tubes containing sodium heparin (Becton Dickinson, Franklin Lakes, NJ) and kept cool until processing. One blood tube from each turtle was frozen at −20°C for contaminant analysis. Plasma from another blood tube collected from 45 of the turtles was frozen at −80°C for lysozyme activity measurements. An additional blood tube from 24 of the turtles captured only in July 2001 was processed for lymphocyte proliferation. Turtles were tagged, measured, weighed, and released near their capture location. Body condition was calculated as weight (kilograms) divided by the cube of SCL (centimeters) and multiplied by 100,000 [body condition = weight/(SCL3) × 100,000] as described by Bjorndal et al. (2000). Sex of the turtles was determined by measuring plasma testosterone concentrations (Owens 1997). An additional 16 juvenile turtles (SCL ranged from 52.8 to 72.3 cm) were captured in offshore waters of South Carolina, Georgia, and northeastern Florida during June 2003 (n = 8) and June 2004 (n = 8). These turtles were randomly captured in trawl nets without turtle excluder devices at randomly selected stations using a trawl tow time of 30 min. Blood samples were collected and processed in the same manner as described above for use in in vitro exposure experiments.
Contaminant analysis.
Concentrations of OCs, including 55 PCB congeners and 24 pesticides, were determined in whole blood of the North Carolina turtles and are reported elsewhere (Keller et al. 2004a). Briefly, samples were amended with internal standards and extracted with organic solvents. After lipid content was determined gravimetrically, biologic molecules of large molecular weight were removed from the extracts using alumina columns. PCBs were separated from the pesticides by polarity into two fractions using an aminopropylsilane column. Compounds were quantified using gas chromatography with electron capture and mass spectrometry detection. Analytical blanks and standard reference materials from the National Institute of Standards and Technology were analyzed with each batch of samples. The blood lipid content did not correlate to blood OC concentrations (Keller JM, unpublished data); therefore, the blood concentrations were calculated based on the wet mass of blood extracted (nanograms per gram wet mass). OC concentrations that were below the detection limit were estimated at half the detection limit for correlations. The detection limits were calculated as the amount (nanograms) of compound in the most dilute calibration standard solution yielding a signal significantly different from the noise, divided by the grams of tissue extracted.
Lysozyme activity.
We measured lysozyme activity using slight modifications of a standard turbidity assay as previously described by Demers and Bayne (1997). A 1 mg/mL stock solution of hen egg lysozyme (HEL; Sigma, St. Louis, MO) was prepared in 0.1 M phosphate buffer (pH 5.9), and aliquots were frozen until use. A solution of Micrococcus lysodeikticus (Sigma) was prepared fresh daily by dissolving 50 mg of the lyophilized cells in 100 mL 0.1 M phosphate buffer (pH 5.9). HEL was serially diluted in phosphate buffer to produce a standard curve of 40, 20, 10, 5, 2.5, 1.25, 0.6, 0.3, and 0 μg/μL. Aliquots of each concentration (25 μL/well) were added to a 96-well plate in triplicate. For each sample, 25 μL of test plasma was added in quadruplicate to the plate. The solution of M. lysodeikticus (175 μL/well) was quickly added to three sample wells and to each of the standard wells. The fourth well containing plasma received 175 μL phosphate buffer and served as a blank. Plates were assessed for absorbance at 450 nm with a spectrophotometer (SpectraCount; Packard, Meridian, CT) immediately (T0) and again after 5 min (T5). Absorbance unit (AU) values at T5 were subtracted from AU values at T0 to determine the change in absorbance. The AU value for the blank sample well was subtracted from the average of the triplicate sample wells to compensate for any hemolysis in the samples. The resultant AU value was converted to HEL concentration (micrograms per microliter) via linear regression of the standard curve.
Mitogen-induced lymphocyte proliferation.
Lymphocyte proliferation assay for correlations with OCs.
Lymphocyte proliferation responses have been reported elsewhere (Keller et al. 2005). Rather than a density gradient method, peripheral blood leukocytes (PBLs) were collected from the buffy layer within 36 hr of blood collection using a slow-spin technique (42 × g for 25 min) as described in detail by Keller et al. (2005). No density gradient method is available to obtain a pure isolation of loggerhead lymphocytes (Harms et al. 2000). Cells were rinsed once with cell culture media composed of RPMI 1640 media (Mediatech, Inc., Herndon, VA) supplemented with final concentrations of 5% fetal bovine serum (FBS; Hyclone, Logan, UT), 1% (vol/vol) 100× solution of nonessential amino acids (Gibco, Grand Island, NY), 1 mM sodium pyruvate (Gibco), 10 mM HEPES (Mediatech), 50 IU/mL penicillin, and 50 μg/mL streptomycin (Mediatech) and initially brought to pH 6.9.
Viable PBLs were counted by trypan blue exclusion using light microscopy. Although this technique cannot distinguish sea turtle lymphocytes from thrombocytes or other small PBLs (Work et al. 1998), thrombocytes are known to aggregate. The aggregating cells were not counted in order to decrease the chance of counting these nontarget cells. There is no evidence in the literature that thrombocytes would proliferate in the presence of a mitogen; furthermore the use of a stimulation index (SI) should account for any potential, although unexpected, background proliferation of any other PBL type.
We split the cell suspension into two tubes and diluted in two different media compositions; media 1, as described above, or media 2, which differed only by the FBS manufacturer (BioWhittaker, Walkersville, MD). Cells were plated at 1.8 × 105 cells/well into 96-well plates. We used phytohemagglutinin P (PHA) and concanavalin A (ConA) as T-lymphocyte mitogens, and lipopolysaccharide (LPS) and phorbol 12,13-dibutyrate (PDB) as B-lymphocyte mitogens. PDB has previously been shown to stimulate avian B lymphocytes (Scott and Savage 1996). ConA from Jack bean type IV-S (C5275, Sigma) and LPS from Escherichia coli serotype 0111:B4 (L2630, Sigma) were diluted in media 1. PHA (Amersham Pharmacia Biotech Inc., Pascataway, NJ), ConA type IV from jack bean (Canavalia ensiformis) (C2010, Sigma), and LPS from E. coli serotype 0127:B8 (L3129, Sigma) were diluted in media 2. PDB (Sigma) was tested in both media types. Cells were tested in triplicate for each unstimulated control (containing only media 1 or 2, respectively) and each mitogen concentration with final volumes at 200 μL/well for mitogens in media 1 and 100 μL/well for mitogens in media 2. The plates were incubated at 30°C with 5% CO2. Final concentrations of mitogens in culture wells and culture conditions are listed in Table 1 and in each figure.
We tested proliferation by adding 0.5 μCi 3H-thymidine (ICN Biomedical, Irvine, CA) in a volume of 100 μL to each well after a 4-day incubation (96 hr) or a 5-day incubation (120 hr) with mitogens. Plates were further incubated for an additional 16 hr and then harvested onto Unifilter plates (Packard, Meridian, CT) using a Packard Filtermate 96-well plate harvester. The plates were allowed to dry, and 25 μL Microscint 20 (Packard) was added to each well. The samples were analyzed using a Packard Top Count-NXT scintillation counter. Unstimulated wells produced counts per minute (cpm) that ranged from 20 cpm to 1,162 cpm. Only three samples produced < 300 cpm. The SI was calculated as the counts per minute of mitogen-stimulated cells divided by the counts per minute of unstimulated control (media only) cells. Not all mitogen concentrations and stimulation durations were tested on all available samples because the samples collected during the 2001 sampling season were also used to optimize the lymphocyte proliferation assay. Decisions regarding optimal conditions were not yet determined, and thus the sample sizes vary among the different culture conditions (Table 1).
Lymphocyte proliferation assay for in vitro exposure experiments.
PBLs from 16 loggerhead turtles (8 captured in 2003 and 8 captured in 2004) were collected as described above. Cells and mitogens were diluted in a third media composition identical to media 1 described above except for the FBS manufacturer (Gemini, Calabasas, CA). This change in FBS source was shown in paired samples in 2003 not to alter the lymphocyte proliferation response (Keller et al. 2005). Cells were plated at 1.8 × 105 cells/well in a final volume of 200 μL/well. Final mitogen concentrations in the culture wells were 0.2 μg/mL PDB (Sigma) or 5 μg/mL PHA (Amersham Pharmacia Biotech in 2003; L9132 from Sigma in 2004). Paired sample experiments with these two PHA sources indicated no difference in the proliferation response (Peden-Adams MM, unpublished data).
In 2003, solutions of 100 μg/mL Aroclor 1254 and 4,4′-DDE (both from ChemService, West Chester, PA) in methanol were evaporated to dryness under a stream of ultra-high-purity nitrogen and dissolved into dimethyl sulfoxide (DMSO). In 2004, neat Aroclor 1254 (Supelco, Bellefonte, PA) and 4,4′-DDE (Aldrich Chemical Co., Milwaukee, WI) were weighed and diluted in sterile DMSO to make stock solutions. The stocks were further diluted in media, and 5 μL/well of these substocks was added after plating the cells. The final concentration of DMSO in each culture well, including the DMSO control wells, was standardized to 0.03% (vol/vol). Non-DMSO controls, DMSO controls, and each contaminant concentration were tested in triplicate with and without mitogen stimulation for each turtle. The concentrations of Aroclor 1254 (a technical mixture of PCBs) in the culture wells were 0, 0.1, 1.0, 2.5, 5.0, 10, 48, 100, 498, 993, 7,500, and 13,500 ng/mL, and the concentrations of 4,4′-DDE in the culture wells were 0, 0.05, 0.1, 0.25, 0.5, 0.75, 1.0, 5.0, 48, 500, 992, 7,500, and 13,400 ng/mL. PBLs from individual turtles were exposed to all concentrations, resulting in a dose–response relationship for each individual animal. Cells were incubated at 30°C with 5% CO2 for 5 days, at which time 3H-thymidine was added and proliferation was measured 16 hr later, as described above. Cell viability was determined by trypan blue exclusion of cells from an additional nonstimulated replicate of each contaminant concentration on day 5 of the exposure.
The blood OC concentrations were not measured from these 16 turtles, but the PBLs were rinsed free of plasma where most of the OCs distribute in loggerhead sea turtle blood (Keller et al. 2004b). Because prior exposure may affect a turtle’s cellular response to the in vitro exposure, the data were handled to reduce interindividual variability. The response of each turtle at each contaminant concentration was calculated as a percentage of its non-DMSO control response. For example, the exposed SI for 5 ng/mL 4,4′-DDE was calculated as counts per minute of cells exposed to PHA plus 5 ng/mL 4,4′-DDE divided by counts per minute of cells exposed to only 5 ng/mL 4,4′-DDE. This SI value was then normalized to the turtle’s non-DMSO SI (counts per minute of cells exposed to PHA divided by counts per minute of cells exposed to only media).
Statistics.
We used nonparametric Spearman rank correlations because contaminant concentrations did not fit a normal distribution before or after transformation. We used these correlations to examine the relationship of lymphocyte proliferation and lysozyme activity with OC concentrations determined in the blood (nanograms per gram wet mass). The proportional responses of the in vitro exposure experiments were not normally distributed, so the data were log-transformed. Analysis of variance (ANOVA) with a Dunnet’s multiple comparison test was used to compare the responses at each contaminant concentration to the DMSO control. All statistical analyses were performed using JMP 4.0.2 (SAS Institute Inc., Cary, NC).
Results
Organochlorine concentrations.
OC concentrations in samples from turtles captured in 2000 and 2001 have been reported elsewhere on both a wet-mass and a lipid-normalized basis (Keller et al. 2004a). For the purpose of comparing immune function and OC concentrations, only samples from turtles assessed for lymphocyte proliferation in July 2001 are described here (n = 27). The mean (± SE) concentrations of ∑PCBs (the sum of PCBs), 4,4′-DDE, and ∑chlordanes (the sum of chlordanes) were 6.25 ± 1.14 ng/g wet mass, 0.721 ± 0.152 ng/g wet mass, and 0.253 ± 0.048 ng/g wet mass, respectively. The concentrations of these compounds in this group of turtles were not significantly different from turtles sampled in 2000 (Mann-Whitney t-test; all p-values > 0.05).
Plasma lysozyme activity.
We measured lysozyme activity in plasma samples from 45 animals that were assessed for OC concentrations. Lysozyme activity was 6.58 ± 0.58 μg HEL/μL (mean ± SE) with a range of 1.94–25.2 μg HEL/μL. The Spearman correlation coefficients, rS (p-values) for lysozyme versus ∑PCBs, 4,4′-DDE, and ∑chlordanes were −0.269 (0.074), −0.310 (0.038), and −0.368 (0.013), respectively. All slopes were negative, and correlations with 4,4′-DDE and ∑chlordanes were statistically significant at α = 0.05 (Figure 1). Lysozyme activity did not differ between gender and did not correlate to body condition or plasma testosterone levels.
Lymphocyte proliferation: correlations with blood OC concentrations.
The mean lymphocyte proliferation responses, reported elsewhere (Keller et al. 2005), are tabulated along with the correlative results between lymphocyte proliferation and OC concentrations in Table 1. Proliferation in this data set did not differ between sexes and did not correlate with testosterone concentrations (data not shown). Body condition correlated with lymphocyte proliferation only in media type 1 when stimulated with 0.2 μg/mL PDB for 5 days (rS = 0.528; p = 0.012).
Some proliferative responses were significantly correlated with blood OC concentrations. Lymphocyte proliferation stimulated by 4 days of exposure to 10 μg/mL LPS was significantly correlated with blood concentrations of ∑PCBs, 4,4′-DDE, and ∑chlordanes. PHA-induced proliferation (5 μg/mL PHA) correlated with blood concentrations of ∑PCBs. Proliferation stimulated by 0.8 μg/mL PDB was also positively correlated with ∑PCB and 4,4′-DDE concentrations. ConA stimulation, however, did not correlate with any contaminant. All statistically significant correlations between OC concentrations and lymphocyte proliferation had positive slopes, indicating that turtles with higher contaminant levels exhibited elevated lymphocyte proliferation responses (Figures 2 and 3). Correlations were also examined between lymphocyte proliferation and OC concentrations calculated on a lipid-normalized basis (data not shown). These correlations were very similar to those shown in Table 1.
Lymphocyte proliferation: in vitro exposure experiments.
PBLs from 16 turtles were exposed to increasing concentrations of Aroclor 1254 (0–13.5 μg/mL) or 4,4′-DDE (0–13.4 μg/mL). Cell viability was measured for 8 of these turtles after 5 days of contaminant exposure, examining only cells not stimulated with the mitogen (data not shown). Viability was unaffected by concentrations of ≤ 1,000 ng/mL of either contaminant. Both contaminants significantly decreased cell viability at concentrations of ≥ 7,500 ng/mL. Therefore, lymphocyte proliferation responses at these higher concentrations are not reported.
The effects of in vitro exposure to Aroclor 1254 on lymphocyte proliferation responses are shown in Figure 4. All of the tested concentrations of Aroclor 1254 generally increased PHA-induced proliferation, albeit not significantly, compared with the response in the DMSO control (Figure 4A). In vitro exposure to 5 ng/mL Aroclor 1254 significantly increased PDB-induced proliferation, whereas 498 ng/mL Aroclor 1254 significantly decreased this response (Figure 4B).
In vitro exposure to 4,4′-DDE significantly enhanced PHA- and PDB-induced proliferation (Figure 5). Increasing concentrations of 4,4′-DDE administered to the culture wells produced an increasing trend in PHA-induced proliferation (Figure 5A). Statistically significant increases from the DMSO control were observed at 48 ng/mL and 992 ng/mL 4,4′-DDE. Only one concentration of 4,4′-DDE (0.5 ng/mL) caused a statistically significant effect on PDB-induced proliferation (Figure 5B). Additionally, it should be noted that B-lymphocyte proliferation appeared to be more sensitive than did T-lymphocyte proliferation. Statistically significant effects on PDB-induced proliferation were observed at lower concentrations of both contaminants compared with PHA-induced proliferation (Figures 4 and 5).
Discussion
The field of wildlife immunotoxicology is relatively new, and studies examining reptiles have been initiated only in the last 10 years (reviewed by Keller et al., 2006). The present study is the first published study to demonstrate that OCs may modulate immune function in sea turtles. Because the OC concentrations found in loggerhead turtles are lower than those observed to alter immunity in other species, we did not expect to observe significant correlations. The fact that even moderate correlations were observed with immune function suggests that loggerhead sea turtles may be sensitive to the immunomodulatory effects of OCs.
One concern regarding correlative studies is interaction with other factors, such as sex, body condition, or hormone concentrations. The latter may be of significant importance when dealing with endocrine-active compounds such as OCs. Other studies assessing loggerhead turtles from South Carolina, Georgia, and Florida have reported that testosterone levels in juvenile female loggerhead turtles weakly and negatively correlate with PDB-induced lymphocyte proliferation (n = 127), but body condition and sex are not related to any of the proliferation responses (Keller et al. 2005). Previous analyses of the present data set showed that OC concentrations were not significantly different between sexes, and they did not correlate with body condition (Keller et al. 2004a, 2004c). OC concentrations did not correlate with testosterone levels in this data set, either (n = 48; p > 0.05). Estrogen levels were not measured because plasma estrogen levels in juvenile sea turtles are typically below detection limits (Owens 1997). We observed no relationships with body condition, sex, or testosterone in the present data set for lymphocyte proliferation or lysozyme activity, except for one significant, positive correlation between body condition and proliferation stimulated with 0.2 μg/mL PDB. However, this particular mitogen and media type did not correlate with plasma OC concentrations. The overall lack of interaction of these additional variables adds further strength to the argument that OC concentrations may be modulating immune measurements and that the observed effects are not related to a covarying, confounding factor.
Another recognized limitation of this study is the use of multiple individual correlations. Using this statistical approach, we expected 5% of correlations to be significant by chance alone, which is 3 of the 45 correlations in Table 1. Half of those would be expected to have positive slopes, whereas the other half would be negative. Because we observed 6 significant correlations and all had positive slopes, we conclude that these correlations were not likely due to chance alone. This correlative study, designed as a pilot study to investigate possible contaminant effects, led to a hypothesis that OCs may enhance lymphocyte proliferation. This hypothesis was subsequently tested in the in vitro component of this study.
All of the statistically significant correlations between OC concentrations and lymphocyte proliferation were positive, suggesting immunoenhancement (Table 1). Although traditional thought would have expected immune suppression (Harper et al. 1993; Silkworth et al. 1984), enhanced lymphocyte proliferation and PHA skin responses have been observed after OC exposure in several controlled laboratory studies with wildlife species (Peden-Adams 1999; Segre et al. 2002; Smits et al. 2002; Wu et al. 1999). Environmental studies with free-ranging wildlife and epidemiologic studies with humans have also shown significant, positive relationships between lymphocyte proliferation and OC exposure (Croisant and Grasman 2002; Lü and Wu 1985; Peden-Adams et al. 1996; Rooney et al. 2003). The immunoenhancement noted in these previous laboratory and environmental studies support the conclusions that OCs may enhance certain responses of loggerhead turtle immune cells and that lymphocyte proliferation may indeed be a useful biomarker of exposure to OCs. It should be noted, however, that enhancement of immune responses is not necessarily a healthy outcome, because immunoenhancement can lead to autoimmune diseases and hypersensitivity (Burns et al. 1996). Any alteration of immune function, even enhancement, can be considered an adverse effect.
Admittedly, results from correlative field studies are largely circumstantial, and no causal relationship can be identified with certainty. Because intentional, experimental exposure of protected sea turtles to contaminants is not feasible, we used in vitro experiments to further investigate the immunoenhancement suggested by the correlations observed between lymphocyte proliferation and actual environmental exposure to OCs in the pilot study. Although typical dose–response curves were not always observed in the in vitro experiments, similarities between these experiments and the correlative field study were seen not only in the concentrations that produced significant results but also, more consistently, in the direction of the response. Both study components demonstrated immunoenhancement instead of suppression. Interestingly, in vitro exposure to both PCBs and 4,4′-DDE significantly enhanced PDB-induced proliferation at concentrations that are found in loggerhead sea turtle blood (vertical lines in Figures 4B and 5B). Specifically, in vitro exposure to 5 ng/mL Aroclor 1254 significantly increased PDB-induced proliferation (Figure 4B), and the two turtles with the strongest PDB-induced proliferation responses in the correlative field study had blood ∑PCB concentrations similar to this (4.32 and 8.40 ng/g wet mass). Likewise, the one concentration of 4,4′-DDE (0.5 ng/mL) that increased PDB-induced proliferation responses in vitro (Figure 5B) is similar to the blood 4,4′-DDE concentrations measured in the top five responding turtles (0.603–1.13 ng/g wet mass) in the field study. Although these comparisons are not identical in methods (different PDB concentrations), these results, together with the general enhancement observed in Figures 4A and 5A, support the hypothesis that environmentally relevant concentrations of OCs may enhance certain loggerhead sea turtle immune responses.
Previous studies have examined similar in vitro exposures of mammalian lymphocytes to PCBs (De Guise et al. 1998; Smithwick et al. 2003; Snyder and Valle 1991). Only one of these studies, however, tested PCB concentrations in the same range as used in the present study (Snyder and Valle 1991). Snyder and Valle (1991) also observed immunoenhancement of lymphocyte proliferation using rat splenocytes exposed to 0.01 μg/mL Aroclor 1254. The other two studies demonstrated immunosuppression, but higher exposure concentrations were used (15 μg/mL of three PCB congeners combined, De Guise et al. 1998; 10 μg/mL Aroclor 1242, Smithwick et al. 2003). These concentrations are near the high end of the concentration range used in the present study that resulted in decreased cell viability. These comparisons suggest that stimulation of lymphocyte proliferation may occur at low levels of exposure and that sea turtle lymphocytes may be more sensitive to the cytotoxic effects of OCs than are mammalian cells.
To our knowledge, no previous study examining sea turtle immunity has measured innate (nonspecific) immune functions. Circulating lysozyme is a marker of pro-inflammatory responses, has antibacterial functions, and is a measure of innate immunity (Burton et al. 2002; Weeks et al. 1992). In mammals and fish, lysozyme is secreted by neutrophils (cellular equivalents of reptilian heterophils) upon entry of foreign bacteria and lyses gram-positive bacterial cells by degrading the cell wall (Balfry and Iwama 2004; Ito et al. 1997). In fish, PCBs are known to exhibit varied effects on lysozyme activity (Burton et al. 2002; Hutchinson et al. 2003). In the present study, we observed significant negative correlations between lysozyme and both 4,4′-DDE and ∑chlordanes. These findings suggest that OCs in the blood may suppress lysozyme production or activity in sea turtles. More recently, we have also assessed lysozyme activity in a separate set of juvenile loggerhead turtles captured from near shore waters of South Carolina, Georgia, and Florida and obtained similar results (Peden-Adams MM, Keller JM, unpublished data). In that study, blood ∑PCB, 4,4′-DDE, and ∑chlordane concentrations correlated with lysozyme activity (rS = −0.496, −0.505, and −0.381, respectively, with p = 0.005, 0.004, and 0.038, respectively) measured in 30 turtles captured in July 2001. Interestingly, in that study and the present one, 4,4′-DDE exhibited very similar, statistically significant correlations. ∑DDT concentrations in human breast milk have been previously noted to correlate with lysozyme in milk (Saleh et al. 1998). Although the mechanism of decreased lysozyme is not understood, the data suggest that heterophil function is suppressed in turtles as exposure to OCs increases. Future studies should assess phagocytosis and respiratory burst to further elucidate effects of OCs on innate immune functions in sea turtles.
In a parallel study with the same sample set of loggerhead sea turtles, the ratio of heterophils to lymphocytes was significantly and positively correlated with adipose concentrations of mirex and dioxin-like PCBs (Keller et al. 2004c). An elevation in this ratio is a common response to many stressors in birds, mammals, and sea turtles (Aguirre et al. 1995; Grasman et al. 1996; Gross and Siegel 1983; Maxwell and Robertson 1998). The fact that concentrations of various OC classes in the loggerhead sea turtles were significantly correlated to four immune parameters (increased T-cell proliferation, increased B-cell proliferation, decreased lysozyme activity, and increased heterophil:lymphocyte ratio) provides additional evidence that turtles with elevated OC exposure exhibit immunomodulation.
Conclusion
Certain lymphocyte proliferation responses in loggerhead sea turtles are positively correlated with OC concentrations measured in blood, even though the concentrations in sea turtles are generally much lower than in fish-eating wildlife. In vitro exposure experiments using relevant concentrations of both PCBs and 4,4′-DDE support the correlative field observations. Another measure of immune function, lysozyme activity, is also significantly correlated with concentrations of two major classes of OCs in the blood. These results are similar to the findings of many other wildlife studies and suggest that the sea turtle immune system is modulated by environmentally relevant concentrations of OCs. Future studies could use these relatively simple immune function assays as biomonitoring tools. They should also develop and optimize additional assays, such as natural killer cell activity, in order to more completely assess the effects of environmental contaminants on the immune system of sea turtles.
We thank S. Epperly, J. Braun-McNeill, and L. Avens, National Marine Fisheries Service (NMFS), and the pound net fisherman for their help in obtaining the North Carolina turtles. We thank M. Arendt, P. Maier, A. Segars, and J.D. Whitaker, South Carolina Department of Natural Resources (SC DNR), Marine Resources Division, for collecting the turtle samples from South Carolina, Georgia, and Florida. We also thank J. EuDaly, A. Johnson, L. Heesemann, and M. Lee for their help with the immune measurements; L. Schwacke and T. Hulsey for statistical advice; M. Lee and D. Owens for testosterone and sex determination; and R. Day, P. Becker, S. Wise, M. Schantz, and K. Grasman for their critical review of the manuscript.
This study was funded in part by the Morris Animal Foundation (P.D.M.-G.), Disney Wildlife Conservation Fund (P.D.M.-G.), Oak Foundation (J.M.K.), Duke University Marine Biomedical Center (P.D.M.-G. and J.M.K.), and NMFS (SC DNR turtle project). This work constitutes scientific contribution number 156 from the Sea Research Foundation Inc.
Certain commercial equipment or instruments are identified in the article to adequately specify the experimental procedures. Such identification does not imply recommendations or endorsement by the National Institute of Standards and Technology, nor does it imply that the equipment or instruments are the best available for the purpose.
Figure 1 Scatterplots of plasma lysozyme activity versus concentrations of 4,4′-DDE (A; rS = −0.310, p = 0.038) and ∑chlordanes (B; rS = −0.368, p = 0.013) measured in the blood of loggerhead sea turtles. Linear trend lines demonstrate the negative relationships determined using Spearman rank correlations.
Figure 2 Scatterplots of ∑PCB blood concentrations versus loggerhead sea turtle lymphocyte proliferation (SI) responses stimulated for 5 days with 5 μg/mL PHA (A; rS = 0.596, p = 0.012) and 0.8 μg/mL PDB (B; rS = 0.564, p = 0.018). SI = cpm of mitogen-stimulated cells/cpm of unstimulated cells. Linear trend lines demonstrate the positive relationships determined using Spearman rank correlations.
Figure 3 Scatterplots of 4,4′-DDE blood concentrations versus loggerhead sea turtle lymphocyte proliferation (SI) responses stimulated for 5 days with 5 μg/mL PHA (A; rS = 0.431; p = 0.084) and 0.8 μg/mL PDB (B; rS = 0.507; p = 0.038). SI = cpm of mitogen-stimulated cells/cpm of unstimulated cells. Linear trend lines demonstrate the positive relationships determined using Spearman rank correlations.
Figure 4 The effect of a 5-day in vitro exposure to Aroclor 1254 on loggerhead sea turtle lymphocyte proliferation (SI) responses stimulated by 5 μg/mL PHA (A) and 0.2 μg/mL PDB (B). Data are shown as mean ± SE of the percentage of the SI measured in the control (no DMSO or Aroclor 1254) for each turtle. Sample sizes are 8 or 16 depending on the treatment group. The x-axis crosses the y-axis at the percentage of the control value for the wells receiving only DMSO. The mean ± SE SI for the DMSO controls in the PHA and PDB experiments was 96.6 ± 15.0 and 172 ± 39, respectively. Vertical dashed lines indicate the range of ∑PCB concentrations measured in the blood of 17 loggerhead sea turtles used in the correlative field study.
*Significantly different from the DMSO control (ANOVA with log-transformed data, Dunnet’s multiple comparison test; p < 0.05).
Figure 5 The effect of a 5-day in vitro exposure to 4,4′-DDE on loggerhead sea turtle lymphocyte proliferation (SI) responses stimulated by 5 μg/mL PHA (A) and 0.2 μg/mL PDB (B). Data are shown as mean ± SE of the percentage of the SI measured in the control (no DMSO or 4,4′-DDE) for each turtle. Sample sizes are 8 or 16 depending on the treatment group. The x-axis crosses the y-axis at the percentage of the control value for the wells receiving only DMSO. The mean ± SE SI for the DMSO controls in the PHA and PDB experiments was 96.6 ± 15.0 and 172 ± 39, respectively. Vertical dashed lines indicate the range of 4,4′-DDE concentrations measured in the blood of 17 loggerhead sea turtles used in the correlative field study.
*Significantly different from the DMSO control (ANOVA with log-transformed data, Dunnet’s multiple comparison test; p < 0.05).
Table 1 Mitogen-induced lymphocyte proliferation of loggerhead sea turtles and Spearman rank correlations between lymphocyte proliferation responses and OC concentration measured in whole blood.
Mitogen
Spearman correlation coefficient [rS (p-value)] between lymphocyte proliferation and OCs in whole blood
Medium typea Typeb Concentration (μg/mL) Dayc Mean SI (SE)d Sample size ∑PCBs 4,4′-DDE ∑Chlordanes
1 ConA (C5275) 20 4 3.94 (0.95) 19 −0.132 (0.591) 0.035 (0.989) −0.065 (0.792)
ConA (C5275) 20 5 2.47 (0.52) 24 −0.073 (0.735) −0.020 (0.926) −0.084 (0.698)
LPS (L2630) 10 4 3.41 (0.52) 19 0.528 (0.020)* 0.495 (0.031)* 0.484 (0.036)*
LPS (L2630) 10 5 3.01 (0.47) 24 0.249 (0.241) 0.170 (0.426) 0.157 (0.463)
PDB (P1269) 0.2 4 4.52 (1.23) 19 0.074 (0.764) −0.063 (0.797) 0.035 (0.887)
PDB (P1269) 0.2 5 2.84 (0.55) 24 0.113 (0.600) 0.121 (0.574) 0.159 (0.459)
2 PHA 5 5 114 (55) 17 0.596 (0.012)* 0.431 (0.084) 0.434 (0.082)
PHA 10 5 29.1 (13.5) 17 0.020 (0.941) 0.020 (0.941) 0.003 (0.993)
ConA (C2010) 10 5 1.89 (0.37) 10 0.370 (0.293) 0.370 (0.293) 0.406 (0.244)
ConA (C2010) 20 5 3.56 (0.70) 24 −0.111 (0.605) −0.118 (0.582) −0.151 (0.480)
LPS (L3129) 2.5 5 2.01 (0.39) 17 0.385 (0.127) 0.201 (0.439) 0.243 (0.348)
LPS (L3129) 5 5 1.40 (0.25) 17 0.186 (0.474) 0.103 (0.694) 0.226 (0.384)
PDB (P1269) 0.2 5 7.12 (2.88) 10 0.006 (0.987) 0.006 (0.987) 0.079 (0.829)
PDB (P1269) 0.4 5 6.08 (1.73) 17 0.015 (0.955) −0.005 (0.985) 0.012 (0.963)
PDB (P1269) 0.8 5 4.10 (1.28) 17 0.564 (0.018)* 0.507 (0.038)* 0.476 (0.054)
a Media types are described in “Materials and Methods” and differ only by the manufacturer of FBS.
b Catalog numbers for mitogens purchased from Sigma are shown in parentheses; PHA was purchased from Amersham Pharmacia Biotech.
c Duration of mitogen exposure indicating when 3H-thymidine was added (96 hr or 120 hr, respectively).
d SI = cpm of mitogen-stimulated cells/cpm of unstimulated cells. Data from Keller et al. (2005). *p < 0.05.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8255ehp0114-00007716393662ResearchEvidence of Spatially Extensive Resistance to PCBs in an Anadromous Fish of the Hudson River Yuan Zhanpeng 1Courtenay Simon 2Chambers R. Christopher 3Wirgin Isaac 11 Department of Environmental Medicine, New York University School of Medicine, Tuxedo, New York, USA2 Department of Fisheries and Oceans, Gulf Region, Canadian Rivers Institute/Department of Biology, University of New Brunswick, Fredericton, New Brunswick, Canada3 Howard Marine Sciences Laboratory, Northeast Fisheries Science Center, National Oceanic and Atmospheric Administration Fisheries Service, Highlands, New Jersey, USAAddress correspondence to I. Wirgin, Department of Environmental Medicine, New York University School of Medicine, 57 Old Forge Rd., Tuxedo, NY 10987 USA. Telephone: (845) 731-3548. Fax: (845) 351-5472. E-mail:
[email protected] authors declare they have no competing financial interests.
1 2006 21 9 2005 114 1 77 84 26 4 2005 21 9 2005 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Populations of organisms that are chronically exposed to high levels of chemical contaminants may not suffer the same sublethal or lethal effects as naive populations, a phenomenon called resistance. Atlantic tomcod (Microgadus tomcod) from the Hudson River, New York, are exposed to high concentrations of polycyclic aromatic hydrocarbons (PAHs) and bioaccumulate polychlorinated biphenyls (PCBs), polychlorinated dibenzo-p-dioxins (PCDDs), and polychlorinated dibenzofurans (PCDFs). They have developed resistance to PCBs and PCDDs but not to PAHs. Resistance is largely heritable and manifests at early-life-stage toxic end points and in inducibility of cytochrome P4501A (CYP1A) mRNA expression. Because CYP1A induction is activated by the aryl hydrocarbon receptor (AHR) pathway, as are most toxic responses to these compounds, we sought to determine the geographic extent of resistance to CYP1A mRNA induction by PCBs in the Hudson River tomcod population. Samples of young-of-the-year tomcod were collected from seven locales in the Hudson River, extending from the Battery at river mile 1 (RM 1) to RM 90, and from the Miramichi River, New Brunswick, Canada. Laboratory-reared offspring of tomcod adults from Newark Bay, in the western portion of the Hudson River estuary, were also used in this study. Fish were partially depurated in clean water and intraperitoneally injected with 10 ppm coplanar PCB-77, 10 ppm benzo[a]pyrene (BaP), or corn oil vehicle, and levels of CYP1A mRNA were determined. CYP1A was significantly inducible by treatment with BaP in tomcod from the Miramichi River, from laboratory-spawned offspring of Newark Bay origin, and from all Hudson River sites spanning 90 miles of river. In contrast, only tomcod from the Miramichi River displayed significantly induced CYP1A mRNA expression when treated with PCB-77. Our results suggest that the population of tomcod from throughout the Hudson River estuary has developed resistance to CYP1A inducibility and probably other toxicities mediated by the AHR pathway. Tomcod from the Hudson River may represent the most geographically expansive population of vertebrates with resistance to chemical pollutants that has been characterized.
AHRAtlantic tomcodCYP1Aevolutionary changegenetic adaptationHudson RiverPCBsresistance
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Resistance to toxicants has been reported in populations of invertebrates (Klerks and Levinton 1989) and fish (reviewed by Wirgin and Waldman 2004) from highly polluted aquatic ecosystems. For example, populations of Atlantic killifish, Fundulus heteroclitus, from three highly contaminated estuaries along the Atlantic Coast of the United States exhibit dramatic resistance to aromatic hydrocarbon (AH) compounds, including polychlorinated biphenyls (PCBs) (Bello 1999; Elskus et al. 1999; Nacci et al. 1999), 2,3,7,8-tetra-chlorodibenzo-p-dioxin (TCDD) (Prince and Cooper 1995a, 1995b), and creosote-containing polycyclic aromatic hydrocarbons (PAHs) (Meyer et al. 2002; Van Veld and Westbrook 1995). Resistance to AH contaminants in these populations has been observed at the molecular, biochemical, and organism levels, as evidenced by significantly reduced inducibility of cytochrome P4501A (CYP1A) and reduced sensitivities of early life stages to toxicities elicited by exposures to these chemicals. In these cases of resistance, sensitivities to chemicals are usually not completely abolished but are reduced by one or two orders of magnitude relative to responsive populations.
Resistance can result from genetic adaptations from chronic exposures of populations to contaminants, in which case resistance will usually persist for many generations after remediation of the environment. If an ecosystem harbors a single panmictic population of the affected species, the entire population may develop resistance. If individuals persist in a fragmented mosaic of populations with limited gene flow among subgroups, then some subpopulations may show little or at least varying degrees of resistance to the toxicant. Alternatively, resistance can result from physiologic acclimation(s) without an underlying genetic basis, in which case resistance will be quickly lost after cessation of exposures to contaminants. Physiologic acclimation should be expressed only by highly exposed individuals and may not be detected in all members of even the local subpopulation. To date, studies on killifish indicate that resistance is largely a genetically based phenotype (Bello et al. 2001; Meyer and Di Giulio 2002, 2003; Nacci et al. 1999), although evidence exists in one affected population of a nongenetic, physiologic role in the resistance phenotypes (Meyer et al. 2002). Studies that have attempted to elucidate the mechanistic basis of resistance in these populations have focused on components of the aryl hydrocarbon receptor (AHR) pathway (reviewed by Wirgin and Waldman 2004), which activates transcription of CYP1A mRNA and mediates most overt toxicities to AH compounds in mammals (Ma 2001) and fishes (Hahn 1998).
Irrespective of its mechanistic basis, resistance of natural populations to toxicants is believed to involve trade-offs associated with constraints imposed by a limited energy budget. Evidence of such trade-offs includes increased sensitivities to other stressors or reduced performance in the absence of contaminants (McKenzie 1996; Shaw 1999). Effects of resistance may also be evident at the community level. Contaminants may be transferred up the food chain because resistant populations survive to be consumed by predators. If contaminant transfer occurs, it is likely to result in ever-greater levels of contaminants at higher trophic levels if the resistant population is prey consumed by an array of consumers. The abundance and trophic importance of a resistant population are therefore likely to be key factors in determining the magnitude of bioaccumulation at higher trophic levels. Furthermore, for chemicals such as polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), and PCBs, which are highly refractory to metabolism in fishes, trophic transfer of contaminants by resistant populations may be particularly profound. As a result, the geographic extent of resistance in a population is important in predicting its consequences at the population, community, and ecosystem levels.
Most evidence of resistance in aquatic ecosystems has been from populations with limited geographic distributions. The spatial restriction of resistant populations studied to date is likely a function of the distribution of the contaminants that historically elicited a toxic response and the relatively low mobility of affected populations. For example, a resistant population of the oligochaete worm Limnodrilus hoffmeisteri was restricted to one cove of the Hudson River where unusually high levels of metals were detected in the sediments (Klerks and Levinton 1989). Similarly, resistance in populations of killifish in New Bedford Harbor, Massachusetts (Nacci et al. 2002a) and the Elizabeth River, Virginia (Ownby et al. 2002) was limited to within several kilometers of sites known for high concentrations of PCBs and PAHs, respectively, at those locales. In these examples, the limited spatial distribution of resistance is consistent with the limited mobility (Lotrich 1975) of the focal species and, hence, negligible levels of gene flow among populations (Mulvey et al. 2002, 2003).
Contamination of the Hudson River estuary.
More than 200 miles of the Hudson River is a U.S. federal Superfund site because of the release of PCBs for more than four decades from two manufacturing facilities at river mile (RM) 195 and RM 197 (Farley and Thomann 1998). PCBs from these upriver sources were transported downriver and resulted in an inverse concentration gradient between levels of contaminants in Hudson River sediments, as well as tissue burdens in fishes, and distance downstream. Major secondary peaks of PCBs in sediments and fishes have been observed in the vicinity of New York City and are believed to originate at local municipal and industrial sources. The Passaic River, one of two tributaries of Newark Bay in the western portion of the lower Hudson River estuary, is also designated as a federal Superfund site because of high levels of contamination with TCDD that originated from the Diamond Alkali herbicide manufacturing facility situated on its banks. TCDD has been transported downstream and has contaminated much of Newark Bay (Bopp et al. 1991). Finally, levels of PAHs in the sediments of the lower Hudson River estuary were the second highest of any U.S. estuary surveyed in the mid-1980s [National Oceanic and Atmospheric Administration (NOAA) 1988].
Life history characteristics of Atlantic tomcod.
Atlantic tomcod, Microgadus tomcod, is a common anadromous species in many estuaries along the Atlantic Coast of North America from the Hudson River to Labrador, Canada (Bigelow and Schroeder 1953). Within the Hudson River estuary, juvenile and adult tomcod are seasonally distributed over 145 miles of Hudson River from the Battery at RM 1 to Albany at RM 145 (Klauda et al. 1988) and in Newark Bay and its tributaries. Estimated population sizes for adult tomcod in the Hudson River range from 0.8 to 3.0 × 106 fish (Draft Environmental Impact Statement 1999), with a generation time of approximately 1.1 years (Mattson M, personal communication).
Throughout its range, tomcod spawn in midwinter. The winter spawning of tomcod is unique among the ichthyofauna of the lower Hudson River estuary. In addition to a protracted benthic embryonic period due to annual minimum water temperatures experienced by embryos, the timing of spawning results in young life stages of tomcod being a key prey item for major resource species and other ecologically important species within the lower estuary in spring and early summer (Dew and Hecht 1994). Key consumers of tomcod, among others, include bluefish (Pomatomus saltatrix), striped bass (Morone saxatilis), weakfish (Cynoscion regalis), summer flounder (Paralichthys dentatus), American eel (Anguilla rostrata), and white catfish (Ictalurus catus). Examination of the gut content of these consumer species indicates that tomcod is their dominant prey when these species are associated with the bottom habitat of the Hudson River during the spring and summer months (Chambers and Witting, in press).
The entire life cycle of tomcod occurs within or in close proximity to their natal estuaries. Adults are highly migratory within the confines of their natal estuaries, with a dominant seasonal relocation in autumn to upstream locations for spawning (Klauda et al. 1988). Because of the mobility of adults, and because of their more diverse diets—including other fishes—the exposure experience of adult tomcod is likely to be more a reflection of estuary-wide contaminant levels than would be true for younger tomcod life stages. Tomcod are associated with the river bottom in all life stages except larval life and thus may be exposed to lipophilic AHs via direct contact with sediments, ingestion of sediments, and consumption of benthic prey. Juvenile and adult tomcod from the Hudson River have unusually high levels of hepatic lipids that may further serve to bioaccumulate unusually high levels of lipophilic contaminants (Cormier et al. 1989).
Contamination of tomcod from the Hudson River.
PAHs cannot be directly measured in fish livers because of their rapid metabolism (Krahn et al. 1986). Surrogate measures of PAH exposure have been developed, and these revealed high levels of PAH metabolites in bile and hepatic DNA adducts (also a signature of PAH exposure) in adult tomcod from the Hudson River (Wirgin et al. 1994). Furthermore, elevated levels of persistent hepatic PCBs and PCDDs/PCDFs were detected in adult tomcod from the Hudson River (Courtenay et al. 1999), in their unfertilized eggs (Roy et al. 2001), and in juvenile tomcod (Yuan et al. 2001). Further studies quantified congener-specific levels of hepatic PCBs and PCDDs/PCDFs in adult and juvenile tomcod at 20 locations in the Hudson River estuary extending from the Battery at RM 0 to RM 107, in the Hackensack River (the second of two tributaries of Newark Bay), and in Newark Bay. These studies showed major differences among collection sites in liver burdens and homologue/congener composition of PCBs (Fernandez et al. 2004).
Perturbations in tomcod from the Hudson River.
Tomcod from the Hudson River exhibited one of the highest prevalences of tumors ever observed in a natural population. In the early 1980s, ≥ 40% of 1-year-old and 90% of 2-year-old Hudson River tomcod exhibited hepatocellular carcinomas (Dey et al. 1993). Tumors were absent (Couillard et al. 1999) or had < 5% prevalences in tomcod from cleaner rivers (Cormier and Racine 1990). Concurrently, the tomcod population in the Hudson River exhibited a truncated age-class structure compared with those from elsewhere. However, the prevalence of gross hepatic lesions has decreased (Young J, personal communication), and the longevity in the population has increased in recent years (New York State Department of Environmental Conservation 2003).
CYP1A function and its use as a bio-marker in tomcod.
Levels of CYP1A mRNA, protein, and encoded enzyme are widely used in fishes as biomarkers of exposure to AHs and their early biologic effect (Wirgin and Theodorakis 2002). CYP1A expression in fishes is inducible by exposure to PCDDs/PCDFs, coplanar PCBs, and some PAHs (Stegeman and Hahn 1994) and is usually a sensitive, dose-responsive, reversible, and specific biomarker to these contaminants and may be predictive of some higher level toxic effects. Usually, PCDDs/PCDFs are the most potent inducers of CYP1A, and PAHs the least. Levels of CYP1A expression in fish populations have been correlated with higher level biologic effects such as DNA damage (Wirgin et al. 1994), prevalence of hepatic neoplasms (Stein et al. 1994), teratogenicity, and increased early-life-stage mortality (Wright and Tillitt 1999).
Environmentally exposed adult tomcod collected from the Hudson River and immediately sacrificed exhibited significantly higher expression of hepatic CYP1A mRNA expression than did tomcod from four cleaner rivers (Kreamer et al. 1991; Wirgin et al. 1994). In controlled laboratory studies, however, CYP1A mRNA was not inducible in tomcod from the Hudson River that were extensively depurated and then treated with PCB-77 (0.1–10 mg/kg fish) or TCDD (100 ng/kg fish) (Wirgin et al. 1992). In contrast, CYP1A was highly inducible in Hudson River tomcod treated with benzo[a]pyrene (BaP) or β-naphthoflavone (β-NF). Furthermore, CYP1A was highly inducible with PCB-77, TCDD, BaP, and β-NF in tomcod from the cleaner Miramichi River (New Brunswick, Canada). In later, more intensive studies, significant CYP1A mRNA inducibility occurred in tomcod from the Hudson River at concentrations of halogenated aromatic hydrocarbons (HAHs) two orders of magnitude higher than in tomcod from relatively pristine sites. Furthermore, the reduced sensitivity of Hudson River tomcod occurred in all tissues and at all life stages (Yuan et al. 2005). Additionally, studies showed that reduced inducibility of CYP1A mRNA by PCB-77 was in part heritable to the F1 larvae (Roy et al. 2001) and more so to F2 embryos (Wirgin II, unpublished data).
In this study, we sought to determine the geographic distribution of resistance in the Hudson River population of tomcod. Because almost 200 miles of the Hudson River are highly contaminated with PCBs and in the absence of any information of the reproductive fragmentation of the Hudson River population, we hypothesized that tomcod from throughout its 145-mile distribution in the Hudson River would exhibit significantly reduced inducibility of CYP1A compared with those from cleaner locales. From a management perspective, this information is important in assessing the potential risks of contamination of tomcod to the Hudson River ecosystem.
Materials and Methods
Source of juvenile tomcod.
CYP1A inducibility and constitutive expression in fishes vary with life stage, sex, and reproductive status with respect to the spawning season (Elskus et al. 1992). Less variation occurs in CYP1A inducibility among juvenile fish than for adults, including tomcod (Williams et al. 1998). Because the spatial distribution of juveniles is more localized than that of adults, CYP1A mRNA expression in juveniles is more likely to reflect the fine-scale distribution of bioavailable contaminants.
Because tomcod in the Hudson River mature at 1 year of age, all juveniles are young-of-the year fish. We used three sources for juvenile tomcod in this study. One source was the Hudson River, where tomcod were collected by trawling during the summer months of 2000 and 2003. Fish were collected from seven sites in the Hudson River estuary (RM 1, RM 11, RM 18, RM 37, RM 43, RM 80, and RM 90) (Figure 1) and transported to the laboratory, where they were maintained separately by site in 100-gallon aquaria filled with 20 ppt seawater at 12°C for at least 21 days before experimentation. A second source of juvenile tomcod was the Miramichi River, where tomcod were collected with beach seines in June 2004. Hepatic levels of PCBs and PCDDs/PCDFs in juvenile tomcod from the collection sites or nearby sites for both the Hudson River and the Miramichi River are presented in Table 1. For the third source of juvenile tomcod, we used offspring of wild-caught adults collected from Newark Bay, New Jersey, by otter trawl in December 2000. Total TCDD toxic equivalent quotients (TEQs) in adult tomcod collected in 1998 from Newark Bay are also presented in Table 1. The adults were sent to the Howard Marine Sciences Laboratory (NOAA Fisheries Service, Highlands, NJ, USA) where, when ripened, they were stripped of gametes to be used for fertilization. Embryos and larvae were grown under controlled conditions in the laboratory, and juveniles were maintained until use in this study.
Experimental design.
All fish were weighed before experimentation, and then each source of fish was treated in one of three ways. One group from each source was injected intraperitoneally (ip) with 10 ppm PCB-77. A second group received an ip injection of 10 ppm BaP. A third group received an ip injection of corn oil vehicle. Treated fish were maintained in the laboratory and then sacrificed after either 7 days (PCB-77), 2 days (BaP), or both 2 and 7 days (corn oil). In previous extensive kinetic experiments, we determined that maximum levels of induction of CYP1A mRNA in tomcod occurred at these times after treatment with these chemicals (Courtenay et al. 1999). PCB-77 is very persistent in tomcod livers, whereas PAHs such as BaP are rapidly metabolized and cleared. Livers of treated fish were dissected and frozen at –80°C until processed.
Preparation of RNA samples.
Total RNA was isolated from approximately 50 mg of liver tissue per specimen. Tissues were homogenized in RNAzol or Ultraspec reagent (both from BIOTECX Laboratories Inc., Houston, TX, USA) in 1.5 mL microcentrifuge tubes, and RNAs were isolated as recommended by the manufacturer and modified as described by Courtenay et al. (1999). RNA pellets were washed twice in 75% ethanol, resuspended in 50 μL of diethylpyrocarbonate (DEPC)-treated water, and stored at –80°C until further processing. Concentrations and purities of RNA samples were determined by ultraviolet (UV) spectrophotometry at 260 and 280 nm.
All RNA samples were further analyzed for integrity using denaturing Northern gels. Two micrograms of each RNA was denatured and loaded into 1% agarose gels cast in 1× MOPS buffer containing 1% formaldehyde (vol/vol), stained in ethidium bromide solution, and photographed under UV illumination to evaluate the integrity of the 28S and 18S ribosomal RNA bands (Courtenay et al. 1999). Degraded RNAs were discarded and RNAs from these tissues were re-isolated.
CYP1A mRNA quantification by slot blot hybridization.
Two micrograms of RNA samples were denatured and applied onto Nytran Nylon Plus membranes (Schleicher & Schuell, Keene, NH, USA) using a 72-well slot blot apparatus (Schleicher & Schuell) as described by Courtenay et al. (1999).
Full-length CYP1A cDNA isolated from a β-NF–treated Hudson River tomcod (Roy et al. 1995) (GenBank accession no. L41886; Genbank 1996) and full-length rat 18S rRNA cDNA (Chan et al. 1984) were labeled with 32P using Random Priming Kits (Roche Diagnostics Corp., Indianapolis, IN, USA) according to the the manufacturer’s instructions; we used these to probe the blots.
Membranes were prepared, prehybridized, and hybridized overnight at 65°C as described by Courtenay et al. (1999). After hybridizations, the membranes were washed three times in 6× saline-sodium phosphate-EDTA (SSPE)/0.1% at room temperature for 5 min each and twice in 1× SSPE/0.1% sodium dodecyl sulfate (SDS) at 65°C for a total of 1 hr. CYP1A mRNA levels were quantified from phosphor imaging screens using a Storm 860 scanner and ImageQuant for Macintosh software (version 1.0; Molecular Dynamics, Sunnyvale, CA, USA). CYP1A probes were then stripped off membranes by twice immersing them in boiling 0.1× standard sodium citrate/0.5% SDS while shaking. The membranes were then prehybridized and hybridized with 18S rRNA probes and quantified as above.
The length of time for which membranes were phosphor imaged for both CYP1A and 18S rRNA varied and therefore resulted in differing absolute optical density (OD) units among blots.
Statistical analysis.
We conducted three sets of statistical analyses. All used CYP1A mRNA concentrations (relative OD units) as proxies for a toxic response. First, we evaluated juvenile tomcod from three Hudson River sites (RM 11, RM 18, and RM 37) for spatial differences in CYP1A mRNA concentrations. Second, we compared juvenile Hudson River tomcod with juvenile tomcod from the Miramichi River. Third, juvenile tomcod from four Hudson River sites (RM 1, RM 43, RM 80, and RM 90) were compared with laboratory-reared tomcod juveniles from Newark Bay in the western Hudson River estuary.
For the second analyses, the CYP1A mRNA levels of the Miramichi River juveniles injected with corn oil vehicle did not differ between times of sacrifice (i.e., 2 vs. 7 days), so these two groups were pooled for comparison with other treatments. Similarly, Hudson River tomcod from the three sites (RM 11, RM 18, RM 37) did not differ in their responses to each of the three ip treatments, so data from the three sites were pooled before comparison with responses from vehicle controls and from Miramichi River tomcod.
In all cases, CYP1A mRNA data were normalized to respective 18S rRNA concentrations, and the results were log-transformed to improve distribution normality. CYP1A data were compared by analysis of variance (ANOVA) followed by the Tukey multiple range test. Means and 95% confidence intervals (CIs) were back-transformed to original units for presentation.
Results
Site differences in Hudson River tomcod CYP1A levels.
Juvenile tomcod from three Hudson River sites, RM 11, RM 18, and RM 37, that were depurated and injected ip with 10 ppm of BaP, a potent PAH-type inducer of CYP1A, did not differ from one another but did exhibit significantly higher CYP1A mRNA concentrations than did vehicle-treated controls collected from the same sites (one-way ANOVA F2,67 = 55.288, p < 0.001; Tukey test p < 0.001) (Figure 2). By contrast, juvenile Hudson River tomcod from all three sites that were injected ip with 10 ppm PCB-77 showed no induction above controls. CYP1A mRNA concentrations were significantly higher in chemically treated tomcod from RM 11 than in those from RM 37 (two-way ANOVA F2,61 = 4.447, p = 0.016; Tukey test p = 0.011) for both BaP and PCB-77 treatments (nonsignificant interaction term in two-way ANOVA).
Differences in CYP1A levels between Hudson River and Miramichi River tomcod.
A subset of Hudson River juveniles from the three sites (RM 11, RM 18, and RM 37; three to five fish per treatment per site) were compared with Miramichi River tomcod for CYP1A mRNA response to ip injection with 10 ppm BaP and 10 ppm PCB-77 (Figure 3). Fish from the two populations responded differently to one or both chemical treatments (two-way ANOVA, population × treatment interaction: F2,63 = 9.081, p < 0.001). Separate comparison of each chemical with the control indicated that the population difference in response was to PCB-77 (interaction term: F1,44 = 11.249, p = 0.002). Consistent with the previous results, the Hudson River tomcod were relatively insensitive to PCB-77 compared with the response elicited in Miramichi River tomcod. Both populations responded similarly to BaP (interaction term: F1,44 = 0.689, p = 0.411).
Differences in CYP1A levels between wild and laboratory-reared Hudson River tomcod.
Juvenile tomcod sampled from various Hudson River locations (RM 1, RM 43, RM 80, and RM 90) and laboratory-reared offspring of approximately the same age from the western Hudson River estuary all responded significantly and similarly to ip injection of 10 ppm BaP when compared with laboratory-reared corn-oil control fish (one-way ANOVA F5,28 = 11.694, p < 0.001; Tukey test, p ≤ 0.006 for all groups compared with controls) (Figure 4). Furthermore, there were no significant differences among BaP-injected groups in magnitude of induction (p > 0.4, Tukey test). By contrast, significant induction over controls was not observed in any of the field-sampled or laboratory-reared tomcod injected ip with PCB-77 (p > 0.5, Tukey test). CYP1A mRNA concentrations were similarly low among all PCB-77–injected groups (p > 0.5, Tukey test).
Discussion
Juvenile Atlantic tomcod from a wide range of locations (90 miles) in the Hudson River appear to be resistant to induction of CYP1A mRNA by coplanar PCB congener 77. Additionally, resistance was observed in the laboratory-reared offspring of parents collected in Newark Bay in the western estuary. In contrast, CYP1A mRNA was significantly inducible by treatment with BaP in tomcod from all Hudson River sites and in laboratory-reared fish. The distribution of juvenile tomcod in the Hudson River in some years extends from the river’s mouth to Albany, New York, at RM 145 and includes the western estuary. Thus, juvenile tomcod from a large proportion of its range in the Hudson River estuary exhibit resistance to CYP1A inducibility and perhaps other toxicities mediated by the AHR pathway. There was no significant difference among collection sites in CYP1A expression levels of fish treated with PCB-77, suggesting that the degree of resistance was the same in fish collected from all Hudson River locales. In contrast, juvenile tomcod from the Miramichi River exhibited significant induction of CYP1A mRNA after treatment with either BaP or PCB-77.
Almost 200 miles of Hudson River is a federal Superfund site because of PCB contamination of sediments and biota (Wirgin et al. 2005). PAHs also contaminate much of the lower estuary at levels that are among the highest of any U.S. estuary (NOAA 1988). The Passaic River is also a Superfund site because of TCDD contamination, and much of Newark Bay and the Hackensack River are highly polluted with TCDD, PCBs, and PAHs. Tomcod from throughout the estuary are exposed to and bioaccumulate these contaminants, and the resistance of this population is consistent with an estuary-wide exposure to elevated levels of these contaminants. There is considerable variation, however, in tissue burdens of HAH contaminants in juvenile tomcod collected from multiple locales within the estuary. For example, significant spatial variation in PCB levels and congener patterns have been observed among juvenile tomcod. Three statistical clusters were identified in juveniles collected from RM 1 to RM 17, from RM 37 to RM 77, and from RM 80 to RM 107 (Fernandez et al. 2004). Despite their differing exposure histories, groups of juvenile tomcod from all three clusters exhibit resistance to PCB treatment.
Resistance of tomcod to HAHs over such a large portion of its distribution is consistent with its life history and exposure history in the Hudson River estuary. There are no known barriers to gene flow within the main stem Hudson River, although it is possible that tomcod in Newark Bay and its tributaries in the western estuary are largely isolated from those in the main stem of the Hudson River. Some support for the idea of reproductive isolation of tomcod in the western estuary comes from the different ratio of total PCDDs to PCDFs in tomcod from Newark Bay/Hackensack River compared with those in the main stem of the Hudson River (Courtenay et al. 1999). However, results from limited tagging studies have reported recaptures of tomcod in Newark Bay that were tagged in the Hudson River (Mattson M, personal communication), suggesting population movements between these sites. No genetic studies have empirically tested whether there are any spatial or temporal population subdivisions of tomcod within the estuary. It is known, however, that tomcod spawn over ≥ 50 miles of the main stem of the river extending from at least the Tappan Zee (RM 25) to Poughkeepsie, New York (RM 75) (Klauda et al. 1988). Spawning of tomcod almost certainly occurs in the Hackensack River, as evidenced by the seasonal abundance of young juveniles in early May (Wirgin II, Chambers RC, unpublished data). The likelihood of spawning in the Passaic River is unknown. Although tomcod eggs are demersal, yolk-sac larvae and older feeding larvae regularly ascend the water column and would be carried downstream. By late spring, juveniles are found throughout the lower estuary to waters adjacent to New York City. Considerable mixing of tomcod larvae from different spawning locales likely occurs during this process. It is likely that tomcod from throughout the Hudson River constitute a single panmictic population, and thus, the allelic variants underlying the resistant phenotypes are likely to be homogeneously distributed throughout the Hudson River population.
How geographically extensive is resistance in tomcod from the Hudson River compared with that reported in other species? Ownby et al. (2002) evaluated the extent of resistance to contaminated sediments in the F1 descendants of killifish collected from four sites on the Elizabeth River with varying levels of PAHs in their sediments and from a nearby reference locale, the York River, Virginia. They reported that the extent of resistance, as measured by the frequency of development of cardiovascular abnormalities [which is probably an AHR-mediated response (Dong et al. 2004; Teraoka et al. 2003)], differed significantly among sites on the Elizabeth River and between all Elizabeth River sites and the York River. This suggests that varying degrees of resistance had developed in killifish with differing exposure histories in the Elizabeth River. Nacci et al. (2002a) evaluated the degree of resistance in one and sometimes two generations of killifish whose parents were collected from 14 sites along the northeastern Atlantic coast of the United States from New Bedford Harbor to Tuckerton, New Jersey. The extent of resistance in offspring from these sites was compared with the surficial sediment concentration of total PCBs at each of the collection sites. They found that PCB-126 LC50 values (concentration lethal to 50%) of embryos and larvae differed significantly—in excess of 25,000-fold—among sites. Levels of resistance among collection sites as measured by tolerance to PCB-126 or survival were significantly correlated with sediment total PCBs (r2 = 0.968). Significant resistance was restricted to the offspring of fish collected within approximately 5 km of the PCB “hotspot” in upper New Bedford Harbor and from highly contaminated Newark Bay. Sediment concentrations of total PCBs at sites from which their descendents exhibited resistance exceeded 541 ng total PCBs/g dry sediment. Thus, the degree of resistance in killifish varied among sites and closely mirrored sediment concentrations of the agent thought to induce resistance.
We believe that the Hudson River hosts the most geographically extensive resistant population of any vertebrate reported in the literature. We found no variation in the degree of resistance to PCB-77 in fish collected over a 90-mile length of river despite the large variation in hepatic burdens of coplanar PCBs and PCDDs/PCDFs in juvenile tomcod from this area (Fernandez et al. 2004), including many of the same or nearby Hudson River sites from which tomcod were collected and treated for this study. Similarly aged juvenile tomcod that were the F1 descendants of parents collected from the western estuary in Newark Bay showed no evidence of gene induction by PCB-77, despite their being highly sensitive to induction by BaP. These laboratory-reared juveniles exhibited resistance despite their low tissue burdens of coplanar PCBs and PCDDs/PCDFs. These results suggest that resistance in Hudson River tomcod not only is prevalent throughout the estuary but also may be heritable to at least the F1 generation.
The difference in resistance patterns between tomcod and killifish almost certainly reflects their different propensity to move and their life history characteristics—limited mobility in killifish compared with estuary-wide movements in tomcod. It is likely that tomcod from the Hudson River estuary are reproductively isolated from the most proximal reproducing populations in Shinnecock Bay, New York, and in Long Island Sound, although this has not been empirically tested with population-genetic approaches. If this is so, after environmental remediation, the source of “sensitive” alleles will need to be variants from within the Hudson River population rather than migrants from elsewhere. We have already demonstrated that variation in CYP1A expression levels is high in environmentally exposed and chemically treated adult tomcod from the Hudson River (Courtenay et al. 1994), suggesting that its population contains individuals with differing CYP1A inducibility genotypes and therefore could serve as a source of sensitive fish to repopulate the river after its remediation.
In this study, tomcod from throughout the estuary exhibited significant CYP1A mRNA inducibility by BaP but not PCB-77. This result is consistent with dose–response studies previously conducted with adult tomcod collected from a single Hudson River locale, Garrison, New York, which showed significant inducibility of hepatic CYP1A by BaP and β-NF but not by a variety of coplanar PCBs or TCDD (Courtenay et al. 1999; Yuan et al. 2005). In contrast, killifish from the creosote-contaminated Elizabeth River were resistant to CYP1A induction by both PAHs (Meyer et al. 2002) and coplanar PCB-126 (Meyer and Di Giulio 2003), despite the absence of PCB contamination of that ecosystem. Killifish from PCB-contaminated New Bedford Harbor also exhibited resistance to both PAH and PCB induction of CYP1A (Nacci et al. 1999, 2002a), but less so for β-NF (a PAH) than for TCDD (Bello et al. 2001).
CYP1A transcription and most toxic responses to PCBs, PCDDs/PCDFs, and PAHs are believed to be mediated by the AHR pathway (Ma 2001). The persistence of halogenated AHs such as PCBs and PCDDs/PCDFs in fish tissues far exceeds that of PAHs. Hepatic PAHs are metabolized within hours (Varanasi and Stein 1991), whereas PCBs and PCDDs/PCDFs are often recalcitrant to metabolism and highly persistent, with half-lives in fish measured in weeks and months (Muir et al. 1992). As a result, the toxicities from exposures to halogenated AHs and nonhalogenated AHs are very different. Because of their reactivity, metabolites of PAHs are genotoxic by adducting to DNA and they are also acutely toxic, whereas the effects of HAHs are more chronic. Therefore, the selective pressure on tomcod populations from exposure to the two classes of AH toxicants may differ significantly. It has been demonstrated in marine fish species that metabolically refractory and persistent PCB-77 generates high levels of reactive oxygen species (ROS) within the active site of CYP1A and uncouples the catalytic cycle of CYP1A (Schlezinger et al. 1999). Because tomcod from the Hudson River bioaccumulate high levels of hepatic PCBs and PCDDs/PCDFs, it can be envisioned that down-regulation of CYP1A activities would result in lowered ROS and reduced cellular damage and therefore prove selectively advantageous. In this scenario, two distinct mechanisms of activation of CYP1A transcription would exist, one AHR dependent and a second that is AHR independent. In fact, some evidence of an AHR-independent pathway of PAH-induced toxicity has been demonstrated in rodent models (Bhat and Bresnick 1997; Dertinger et al. 2000).
Inhibition of CYP1A induction in its own right may have important consequences for the resistant population, but is it predictive of decreased sensitivity of Hudson River tomcod to toxicities at higher levels of biologic organization? In killifish from New Bedford Harbor (Nacci et al. 1999), Newark Bay (Prince and Cooper 1995a), and the Elizabeth River (Meyer et al. 2002; Ownby et al. 2002), reduced CYP1A inducibility co-occurs with decreased sensitivities to early-life-stage toxicities from exposure to AH compounds. In extensive dose–response studies with an environmentally relevant PCB mixture, TCDD (Wirgin and Chambers, in press), and individual PCB congeners (Wirgin II, Chambers RC, unpublished data), we observed significant differences between tomcod embryos from the Hudson River, Miramichi River, and Shinnecock Bay in sensitivities to morphologic malformations, hatching success, and behavioral deficits in emerging larvae. Early life stages of tomcod from the Hudson River were unaffected at these end points, whereas those from other populations were highly sensitive. Thus, reduced CYP1A mRNA inducibility in tomcod from the Hudson River is almost certainly predictive of reduced sensitivities at higher level toxic end points to coplanar PCBs and TCDD exposures. We have yet to compare sensitivities of the populations to PAH exposures.
Evolutionary costs of resistance to the Hudson River population of tomcod have yet to be identified, although the effects of coexposures to metals and BaP or PCB-77 are often different than those from either contaminant alone (Sorrentino et al. 2004, 2005). Offspring of killifish from the PAH-contaminated Elizabeth River were more sensitive to acute phototoxicity, ambient UV light, and low oxygen conditions than were those from reference locales and exhibited reduced growth and survivorship in the absence of contaminants (Meyer and Di Giulio 2003).
Regardless of the trade-off and the cost of tolerance to Hudson River tomcod, it is almost certain that resistant Hudson River tomcod are important sources of persistent PCBs and PCDDs/PCDFs at higher trophic levels within this ecosystem. The wide distribution and high abundance of juvenile tomcod throughout the tidal estuary in April through June, combined with its role as prey for many, diverse, and common fishes of the estuary, place tomcod at a critical node in the Hudson River food web. Not only do the spatial distributions of consumers of tomcod span the freshwater, estuarine, and marine portions of the estuary, but also these fishes differ in their residency within the estuary and hence their potential for translocating contaminants throughout and beyond the Hudson River ecosystem.
In summary, juvenile and adult tomcod from many Hudson River sites are highly contaminated with, yet resistant to, PCBs and PCDDs/PCDFs. The broad-scale resistance exhibited by tomcod from throughout the Hudson River estuary suggests a combination of high chronic exposure and a mobile pan-mictic population. This resistance is likely to have evolved relatively recently (the 1900s) and remains persistent despite cessation of most point source releases of PCBs and PCDDs/PCDFs. The full extent of community and ecosystem consequences of resistance in the Hudson River tomcod population is yet to be fully evaluated.
This study was supported by the Superfund Basic Research Program (grant ES10344), a National Institute of Environmental Health Sciences Center grant (ES00260), the Hudson River Foundation, and the Northeast Fisheries Science Center of the National Oceanic and Atmospheric Administration Fisheries Service.
Figure 1 Map of the Hudson River estuary indicating sites from which tomcod were collected for this study and others cited in the text. (A) is an enlargement of the boxed area in (B).
Figure 2 CYP1A mRNA expression levels (mean and 95% CIs, expressed in OD units) in juvenile tomcod sampled from three sites in the Hudson River, depurated in the laboratory for 21 days, and then injected ip with 10 ppm BaP, corn oil vehicle, or 10 ppm PCB-77. Data are back-transformed from log-transformed data used for ANOVA comparisons. Numbers above bars represent sample size.
Figure 3 CYP1A mRNA expression levels (mean and 95% CIs, expressed in OD units) in juvenile tomcod from the Hudson River (HR) and Miramichi River (MR) injected ip with 10 ppm BaP, corn oil vehicle, or 10 ppm PCB-77. Data are back-transformed from log-transformed data used for ANOVA comparisons. Numbers above bars represent sample size.
Figure 4 CYP1A mRNA expression levels (mean and 95% CIs, expressed in OD units) in juvenile Hudson River tomcod injected ip with 10 ppm BaP, corn oil vehicle (in Lab group only), or 10 ppm PCB-77. Lab indicates Newark Bay tomcod spawned in the laboratory; other fish were sampled from different RMs of the Hudson River. Data are back-transformed from log-transformed data used for ANOVA comparisons; n = 4–5/group, except for Lab control, where n = 11.
Table 1 Hepatic burdens of coplanar PCBs, PCDDs, and PCDFs in juvenile Atlantic tomcod sampled from seven geographically distant sites in the Hudson River (HR) in 1998 and 2000, one site from the Miramichi River (MR) in 1998, and adult tomcod collected in Newark Bay (NB) in 1998.
PCBs
PCDDs
PCDFs
Total TEQs
Source 1998 2000 1998 2000 1998 2000 1998 2000
HR (RM)
1 5 18 31 62 13 42 52 121
10 4 14 12 33
17 13 7 35 239 31 175 85 420
37 15 11 16 17 27 11 68 38
43 10 5 4 19
80 10 14 13 37
91 9 7 12 28
MR 0.30 0.4 0.19 0.96
NB 15 671 50 741
All concentrations are given in units of TCDD TEQs: for PCBs, sum of TCDD TEQs of four coplanar PCB congeners (International Union of Pure and Applied Chemistry numbers, 77, 81, 126, and 169); for PCDDs, sum of TCDD TEQs from PCDD congeners; for PCDFs, sum of TCDD TEQs from PCDF congeners; for total TEQs, total TCDD TEQs from PCB, PCDD, and PCDF congeners. These are a subset of the data expressed as wet and dry weight concentrations by Fernandez et al. (2004).
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8085ehp0114-00008516393663ResearchIn Vitro Immune Toxicity of Depleted Uranium: Effects on Murine Macrophages, CD4+ T Cells, and Gene Expression Profiles Wan Bin 12Fleming James T. 13Schultz Terry W. 124Sayler Gary S. 1231 Center for Environmental Biotechnology, 2 Department of Ecology and Evolutionary Biology, 3 Department of Microbiology, and 4 Department of Comparative Medicine, University of Tennessee, Knoxville, Tennessee, USAAddress correspondence to G.S. Sayler, Center for Environmental Biotechnology, University of Tennessee, Knoxville, 676 Dabney Hall, Knoxville, TN 37996-1605 USA. Telephone: (865) 974-8080. Fax: (865) 974-8086. E-mail:
[email protected] authors declare they have no competing financial interests.
1 2006 17 8 2005 114 1 85 91 2 3 2005 17 8 2005 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Depleted uranium (DU) is a by-product of the uranium enrichment process and shares chemical properties with natural and enriched uranium. To investigate the toxic effects of environmental DU exposure on the immune system, we examined the influences of DU (in the form of uranyl nitrate) on viability and immune function as well as cytokine gene expression in murine peritoneal macrophages and splenic CD4+ T cells. Macrophages and CD4+ T cells were exposed to various concentrations of DU, and cell death via apoptosis and necrosis was analyzed using annexin-V/propidium iodide assay. DU cytotoxicity in both cell types was concentration dependent, with macrophage apoptosis and necrosis occurring within 24 hr at 100 μM DU exposure, whereas CD4+ T cells underwent cell death at 500 μM DU exposure. Noncytotoxic concentrations for macrophages and CD4+ T cells were determined as 50 and 100 μM, respectively. Lymphoproliferation analysis indicated that macrophage accessory cell function was altered with 200 μM DU after exposure times as short as 2 hr. Microarray and real-time reverse-transcriptase polymerase chain reaction analyses revealed that DU alters gene expression patterns in both cell types. The most differentially expressed genes were related to signal transduction, such as c-jun, NF-κ Bp65, neurotrophic factors (e.g., Mdk), chemokine and chemokine receptors (e.g., TECK/CCL25), and interleukins such as IL-10 and IL-5, indicating a possible involvement of DU in cancer development, autoimmune diseases, and T helper 2 polarization of T cells. The results are a first step in identifying molecular targets for the toxicity of DU and the elucidation of the molecular mechanisms for the immune modulation ability of DU.
apoptosisCD4+ T cellcytokine gene expressiondepleted uraniummacrophage functionnecrosis
==== Body
Depleted uranium (DU) is a by-product of the enrichment process of natural uranium (Priest 2001). The release of uranium into the environment presents a threat to human and ecologic health in many parts of world (Hass et al. 1998; Murray et al. 2002). DU shares chemical properties with natural or enriched uranium, but the major hazard rendered by DU results from its heavy metal toxicity rather than from radiologic toxicity (Fisenne and Welford 1986; Priest 2001). The adverse health effects of DU compounds are partially dependent on its chemical form. Uranium compounds in +2 to +4 valence states are essentially insoluble. However, in vivo soluble uranium is always hexavalent, regardless of the oxidation state of uranium compound taken up (Edison 1994). It is this form (+6) that is of toxicologic importance. Because of their high affinity for phosphate, carboxyl, and hydroxyl groups, uranyl compounds readily combine with proteins and nucleotides to form stable complexes (Moss 1985).
Serum uranium forms a variety of nondiffusible complexes such as uranium–albumin compounds and diffusible ones such as ionic uranyl hydrogen carbonate complex (Moss 1985). Although the most characteristic response to DU exposure either short or long term is renal dysfunction (Domingo 1995; Leggett 1989; Zamora et al. 1998), uranium is also localized within the central nervous system, testes, lymph nodes, and spleen, suggesting the potential for uranium to cause health problems at these sites (Domingo 2001; Pellmar et al. 1999; Wrenn et al. 1985). Uranium-induced pathological changes in the testes and thyroid glands have been documented (Malenchenko et al. 1978).
In vitro studies have examined the effects of DU on a variety of cell types. For example, Chinese hamster ovary cells exposed to DU exhibit lower cell viability, depressed cell cycle kinetics, and increased sister chromatid exchanges, micronuclei, and chromosomal aberrations after DU exposure (Lin et al. 1993). Kidney cells release lactate dehydrogenase upon uranium exposure (Furuya et al. 1997), whereas human osteoblast cells are transformed to a neoplastic phenotype after in vitro DU exposure (Miller et al. 1998).
More important to this investigation, some studies indicated that immune cells are also involved in DU toxicity. Macrophages can actively internalize the uranium, with the subsequent occurrence of cell apoptosis (Kalinich and McClain 2001; Kalinich et al. 2002). Other evidence suggests the involvement of cytokine gene expression in DU toxicity, and the changes of some of these genes are associated with immune responses. For example, recent studies demonstrated that DU induces abnormal expression and release of tumor necrosis factor (TNF) and interleukin (IL)-6 in macrophages (Gazin et al. 2004: Zhou et al. 1998).
During the Gulf War, tons of DU weapons were fired, and DU shrapnel was permanently embedded in the bodies of many soldiers (sometimes removing shrapnel is fatal). In addition inhalation of DU combustion particles on the battlefield is also a major source of exposure to high concentrations of DU. It was hypothesized that Gulf War syndrome may be explained as a systemic shift in cytokine balance from a T helper (Th) 1 profile toward a Th2 profile because the syndrome is clinically similar to autoimmune diseases (Rook and Zumla 1997; Skowera et al. 2004). In this study we hypothesized that DU exposure may compromise the immune system function by inducing immune cell apoptosis and modulating immune cell cytokine gene expression, which may be predictive of DU immunotoxicity. This hypothesis is consistent with the findings of Li et al. (2001), Pallardy et al. (1999), and Rodenburg et al. (2000), which showed that cell death through apoptosis or necrosis may cause serious adverse effects such as immunosuppression or lead to an altered immune response. More specifically, because of the macrophage’s phagocytosis activity and ubiquitous presence throughout the body, it is also important to assess the effect DU may have on macrophage function as accessory cells to T-lymphocyte activation/proliferation. Cytokine gene expression profiling of DU-exposed immune cells should contribute to the understanding of the molecular mechanisms of DU toxic effects on the immune system. To test the above hypotheses, we exposed macrophages and primary CD4+ T cells to DU (in the form of uranyl nitrate) and examined for evidence of apoptosis and altered macrophage function in promoting lymphocyte proliferation. Macrophages and T cells were also exposed to DU at noncytotoxic concentrations, and the effect of DU-modulated cytokine gene expression was examined. The results of these experiments suggest a possible role for DU in carcinogenesis and autoimmune diseases.
Materials and Methods
Chemicals.
Uranyl nitrate [UO2(NO3)2 · 6H2O], with a specific activity of approximately 0.2 μCi/mg, and sodium nitrate (NaNO3) were purchased from Mallinckrodt Specialty Chemicals Co. (Phillipsburg, NJ) and both were dissolved in water. Lipopolysacchride and concanavalin A (ConA) were from Sigma (St. Louis, MO) and were dissolved in DMSO. [α-33P]-Deoxyadenosine 5′-triphosphate was purchased from ICN Radiochemicals (Costa Mesa, CA).
Animals.
BALB/c and DO11.10 T-cell receptor (TCR)–transgenic mice were originally obtained from The Jackson Laboratory (Bar Harbor, ME) and bred and housed under pathogen-free conditions in the animal care facility at the University of Tennessee, Knoxville, according to the animal protocol procedures approved by the Committee on the Care of Laboratory Animal Resources. Mice 6–8 weeks of age were used for cell preparation.
Cell preparations.
Collected peritoneal elicited macrophages were collected and pooled from three to four Balb/c mice injected intraperitoneally with thioglycollate (TG) broth (3% wt/vol; 1 mL/mouse; Difco Laboratories, Livonia, MI) 4 days before cell collection. Cells were plated onto 25 cm2 Corning cell culture flasks or polystyrene six-well flat-bottom microtiter plates and were incubated at 37°C, 5% CO2/95% air, and 95% humidity for 4 hr to allow the macrophages to adhere to the surfaces. The surfaces were washed twice with warm PBS to remove all nonadherent cells, and the macrophage layer was cultured overnight in complete RPMI-1640 (cRPMI-1640) containing 10% low-endotoxin, heat-deactivated fetal bovine serum (Sigma, Copenhagen, Denmark), 10–5 M 2-mercaptoethanol, L-glutamine (20 mM), and penicillin and streptomycin (100 U/mL each). The resulting macrophage purity was > 95%, determined by CD11b staining analysis. Cells were then washed twice, stained with trypan blue exclusive dye, and counted. The cells were exposed to uranyl nitrate at various concentrations in 25-cm2 flasks (for RNA isolation) or six-well plates (for flow cytometry analysis).
Splenic CD4+ T cells were negatively isolated using the magnetic activated cell sorting method according to the manufacturer’s protocols (Miltenyi Biotec, Auburn, CA). In brief, pooled splenic cells from three to four DO11.10 mice were stained with a cocktail of biotin-conjugated monoclonal antibodies against CD8a(Ly-2) (rat IgG2a), CD11b (Mac-1) (rat IgG2b), CD45R (B220) (rat IgG2a), DX5 (rat IgM), and Ter-119 (rat IgG2b). The mixture was then incubated with Anti-Biotin MicroBeads (Miltenyi Biotec) and the cell suspension was passed through an LS magnetic separation column (Miltenyi Biotec). The major cell composition of elute is CD4+ T cells (> 95%). After washing, the cell density was adjusted to 1 × 106 cells/mL cRPMI-1640 media and the DU exposure was performed in anti-CD3–coated 96-well plates, followed by RNA isolation or flow cytometry analysis. Fresh cells from new living animals were purified each time the assays were repeated; three individual experiments were performed.
Cell staining and flow cytometry analysis of cell death.
For both macrophages and primary CD4+ T cells, cell death analysis using flow cytometry was performed in triplicate. We handled and treated cells according to the protocol provided with the Annexin-V–fluorescein (A-V–FITC) Apoptosis Detection Kit (Sigma, Copenhagen, Denmark). Cells (1 × 106 cells/mL) were exposed to uranyl nitrate at 0–200 μM (macrophages) or 0–500 μM (CD4+ T cells). The cells were harvested and washed once with PBS and resuspended in 1 × binding buffer. A 500-μL aliquot of the cell suspension was stained with 5 μL of A-V–FITC and 10 μL of propidium iodide (PI) in a 12 × 75 mm test tube for 10 min at room temperature, protected from light. We then analyzed cells using the flow cytometry FACScan (BD Biosciences, San Jose, CA) by counting 30,000 events. The data files were saved automatically by CellQuest software (BD Biosciences), and WinMDI (version 2.8; The Scripps Institute, Flow Cytometry Core Facility, La Jolla, CA), was used to perform quadrant analysis. A two-tailed t-test was performed to determine the significant difference between treatment and control experiments using Excel (Microsoft, Redmond, WA).
Lymphoproliferation assay.
We used a T-lymphocyte proliferation assay to estimate the macrophage function as accessory cells. The exposure follows the model described by Krocova et al. (2000): elicited peritoneal macrophages were placed into the wells of 96-well microtiter plates (Corning Inc., Corning, NY), allowed to adhere for 4 hr, and then exposed to cRPMI-1640 media containing DU (10, 50, 100, 200, 500, and 1,000 μM) or an equal amount of medium with no DU added. After 2 hr incubation, the cells were washed, and then the purified CD4+ T cell suspension (1 × 106 cells/mL with 5 μg/mL ConA) was added to each well containing treated adherent macrophages. Simultaneously, we set up non–T-cell controls by replacing the T cells with same amount of culture medium. The plate was incubated at 37°C, 5% CO2/95% air, and 95% humidity for 48 hr. Then, we added 10 μL of MTT [3-(4,5-dimethylthiazolyl-2)-2,5-diphenyltetrazolium bromide] solution (5 mg/mL) to each well, and allowed the plate to incubate for an additional 4 hr. At the end of the incubation, we added 100 μL of acidic isopropanol (0.04 M HCl in absolute isopropanol) and mixed to dissolve the converted dye formazan. The absorbance data were recorded by a spectrophotometer (Bio-Tek Instruments, Inc., Winooski, VT) at 562 nm.
Mouse cytokine cDNA microarray analysis.
The Panorama mouse cytokine gene array (Sigma, St. Louis, MO) consisting of 514 different cytokine-related cDNAs printed onto charged nylon membranes was used to analyze gene expression profile. A detailed description and a list of genes included on the array can be found on the Sigma-Genosys website (Sigma 2004). Briefly, we exposed macrophages and CD4+ T cells to 50 and 100 μM DU, respectively, for 24 hr; after treatment, we extracted total RNA from each sample using Trizol reagent and treated the RNA with RNase-free DNase I (Gibco-BRL Life Technologies Inc., Grand Island, NY). Using mouse cytokine gene cDNA-labeling primers (Sigma-Genosys, St. Louis, MO), 2 μg RNA were reverse transcribed to generate a [α-33P]-dATP–labeled cDNA probe. We removed unincorporated nucleotides from the probe using NucTrap probe purification columns (Stratagene, La Jolla, CA). EDTA was added to bring the final concentration to 10 mM, and the probe was heat denatured at 95°C for 5 min. Arrays were hybridized with probes in ULTRArray hybridization buffer (Ambion, Inc., Austin, TX) overnight at 55°C. The arrays were then washed extensively at 50°C under both low-and high-stringency conditions [2 × saline sodium citrate (SSC), 0.5% SDS for low-stringency wash solution, 0.5 × SSC, 0.5% SDS for high-stringency wash solution] for 2 × 30 min. The membranes were air dried, sealed in a clear plastic bag, and exposed to low-energy storage phosphoimage screens (Kodak, Rochester, NY). The images were scanned at 50-μm resolution on a Storm 840 PhosphorImager (Molecular Dynamics, Inc., Sunnyvale, CA). The image files were analyzed using ArrayVision software (version 6.0; Imaging Research, St. Catharines, Ontario, Canada), and the numerical output was exported in Microsoft Excel format to ArrayStat (version 1.0; Imaging Research Inc.) for statistical analysis. Microarray data obtained here are available at Gene Expression Omnibus (GEO 2005; accession no. GSE2333).
Real-time reverse-transcriptase PCR analysis.
Real-time reverse-transcriptase polymerase chain reaction (RT-PCR) was used to verify gene expression of microarray analysis. The assay was performed in triplicate in a DNA Engine Opticon system (MJ Research Inc., Waltham, MA) using SYBR Green I as the detection format (Qiagen, Inc, Valencia, CA). First, we converted total RNA to first-strand cDNA using reverse transcription, and then performed real-time RT-PCR analysis using the SYBR Green PCR kit (Qiagen). The PCR primers are listed in Table 1. After PCR, we performed melting curve analysis and visualized the PCR products using gel electrophoresis to assess the specificity of PCR amplification reactions. For both the reference gene [glyceralde-hyde-3-phosphate dehydrogenase (GAPDH)] and test gene, we constructed standard curves and determined the slope to calculate the PCR efficiency according to Pfaffl (2001; Pfaffl et al. 2002). We calculated differences in gene expression between treatment and control using PCR efficiencies and threshold cycle numbers (Ct values), which were normalized against GAPDH. The formulation used for calculating the ratio of gene expression between control and treatment groups is described by Pfaffl (2001; Pfaffl et al. 2002) as
[1] where ratio(S:C) is the expression ratio of DU-treated sample over control; Etarget gene is the PCR efficiency of target gene; Ereference gene is the PCR efficiency of reference gene; ΔCt-target gene (control–sample) is the difference of target gene Ct values between control (C) and DU-treated samples (T); and ΔCt-reference gene (control–sample) is the difference of reference gene Ct values between control and DU-treated samples.
Statistics.
Data were expressed as the mean ± SD, and a Student t-test was used to compare the differences between treatment and control groups, with the significance level set at p < 0.05. For microarray data analysis, blots were normalized to the mean values of the entire array with background subtraction. Three sets of data were generated from three biologic replicate experiments; if there were only one valid observation for a gene within a condition, that datum was disregarded for analysis. The outliers were detected by examining standardized residuals automatically using ArrayStat software. A curve-fit random error estimate method was employed for a proportional model with offset. The data were transformed logarithmically, and a Z-test for two independent conditions was performed according to Benjamini and Hochberg (1995). Differentially expressed genes were identified on the basis of the significance level (p < 0.05 or effective p < 0.05/number of analyzed genes).
Results
Macrophage cell death.
We determined induction of apoptosis in macrophages using flow cytometry analysis of cells labeled with A-V–FITC. A-V protein can bind, by a calcium-dependent process, to phosphatidylserine (PS) presented on the surface of cells undergoing apoptosis (Bertho et al. 2000). PS is normally sequestered on the inner leaflet of the plasma membrane. However, during apoptosis, membrane phospholipid asymmetry is lost and PS is exposed to the outer leaflet, where it can interact with A-V. Cells that stain positively for A-V and negatively for PI are considered cells in an early stage of apoptosis, whereas those that stain positively for both indicators either are in late apoptotic stage or are undergoing necrotic cell death.
The treatment of macrophages with DU as uranyl nitrate resulted in apoptotic cell death. As shown in Figure 1A, treatment with 20 and 50 μM DU for 24 hr did not cause an apparent increase in A-V and PI staining. However, 100 and 200 μM DU treatment led to a significant increase of both A-V and PI staining (p < 0.05), with the percentages of A-V binding at 12.3 and 30.5%, respectively, and the percentage of PI positive cells at 12.4 and 49.2%, respectively. The results indicated that both cell apoptosis and necrosis increase with an increase in concentration. DU at 50 μM was determined as noncytotoxic to macrophages.
Using light microscopy and atomic force microscopy, we investigated the morphologic changes in macrophages treated with 100 μM DU for 24 hr (Figure 2). We treated the macrophages with 0 or 100 μM DU for 24 hr and fixed the cells by soaking them in 2% formaldehyde (prepared in propanol), followed by incubation in 0.1% Triton-X 100 for 15 min. We then air dried the cells at room temperature in preparation for imaging by atomic force microscopy. Alternatively, cells after DU exposure were directly imaged by light microscopy. When adherent macrophages undergo necrosis or cellular disintegration, adherent cells will disassociate and float in the medium. Figure 2 shows that the apoptotic cells are still adherent to the surface and show the rough shape of the cell membrane and no apparent nuclear structure (Figure 2D). In the atomic force microscopy images, the darkness represents the height of sample surface over the vessel surface. Note that in Figure 2C, which is a normal cell, the thicker area (brightest) is the nucleus of the cell, but in Figure 2D, there is no such area, which is indicative of loss of nuclear structure. In Figure 2D, the small particle-like areas indicate apoptotic bodies.
CD4+ T-cell death analysis.
The treatment of CD4+ T cells with DU (as uranyl nitrate) resulted in apoptotic as well as necrotic cell death. As shown in Figure 1B, treatment with 1, 10, and 100 μM DU for 24 hr did not result in significant increase of either A-V staining or PI staining, indicating that these treatments did not induce cell apoptosis or necrosis. However, there was a significant increase of apoptotic and necrotic cells after treatment with 500 μM DU (p < 0.05), 64.5 and 15.3%, respectively, whereas the apoptotic and necrotic cell percentages of negative controls were 3.5 and 1.5%, respectively. As expected, the positive controls had a much higher percentage of necrotic cells, whereas the percentages of necrotic and apoptotic cells in the 1 mM NaNO3 control group were not different from those of negative control, indicating that apoptosis and necrosis in DU treatment were not attributable to the NO3– ion but were due to the uranyl ion. DU at 100 μM was determined as noncytotoxic to CD4+ T cells during 24-hr exposure.
Lymphoproliferation assay.
Concanavalin A (ConA) was used to activate T cells. The likely mechanism is that ConA indirectly cross-links the TCR and sends the activation signals. Activation of T cells is also dependent on the presence of non-T cells that function as accessory cells, which provide additional and essential costimulatory signals for T-cell proliferation (Coligan 1991). Exposure to DU at concentrations > 200 μM significantly enhanced the functionality of macrophages, as shown by increased T-cell proliferation under the induction of ConA (Figure 3, solid bars). Lower concentrations of DU < 100 μM did not change the T-cell proliferation. However, 200, 500, and 1,000 μM DU treatment of macrophages significantly (p < 0.05) enhanced T-cell proliferation in a concentration-dependent manner, with the optical density measurements in MTT assay increasing from 0.56 (control) to 0.76, 0.86, and 0.87, respectively. In addition, 1 mM NaNO3 treatment did not influence the measurement, excluding the contribution of nitrate ions (NO3–) to the toxic effect on macrophages. In this study, pretreatment of macrophages with various concentrations of DU for 2 hr did not cause significant loss of cells in 96-well plates, as shown by open bars in Figure 3. Therefore, it is safe to preclude the possibility that the alteration of T-cell proliferation is caused by variations in macrophage cell numbers.
DU influence on gene expression.
Gene expression in macrophages and CD4+ T cells under noncytotoxic DU exposure was analyzed by cDNA microarray. The results were from three biologic replicates. Table 2 lists 29 (6% of all analyzed genes) genes whose expressions were significantly (p < 0.05) changed in macrophages upon 50 μM DU exposure. Of these 29 genes, 24 (5%) were up-regulated, and 5 (1%) were down-regulated. Although a variety of gene groups are affected, the groups with multiple affected genes include signal transduction–related, cytokine- and IL-related, and apoptosis-related groups. Other genes with altered expression such as LTBP-2 and Mdk are neurotrophic factors or involved in binding protein, respectively.
The differentially expressed genes in CD4+ T cells under noncytotoxic (100 μM) DU exposure are listed in Table 3. Although many of the same gene groups are represented, the specific genes, except for Mdk listed in Tables 2 and 3 are different. Specifically, chemokine-related genes are up-regulated in CD4+ T cells but not in macrophages. Moreover, IL-5 is related to T-cell functionality.
Real-time RT-PCR analysis.
We used real-time RT-PCR analysis as a confirmative method for the genes determined to be differentially expressed by microarray analysis and performed the analysis for selected genes in both cell types. Table 4 shows some of the RT-PCR results, along with the corresponding microarray results. RT-PCR analysis showed that under DU exposure, expression of genes such as Mdk, c-jun, and IL-10 was enhanced in macrophages, and Mdk and IL-5 in CD4+ T cells. The ratios of all genes except for IL-10 were in accordance with those determined by microarray analysis. We performed the assay in triplicate. Generally, this quantitative RT-PCR assay confirmed the microarray results.
Discussion
Uranium environmental contamination from mining, processing, and military industries has heightened concern of the possible environmental and health effects of DU exposure. DU can enter the body by ingestion, inhalation, contamination of wounds, and embedded shrapnel (McClain et al. 2001). At the cellular level, accumulation of DU has been observed in various macrophage cell lines (Gazin et al. 2004; Kalinich et al. 2002), and one of the first issues to address is whether DU induces macrophage death and at what level this toxic effect occurs. In this study we used flow cytometry analysis of A-V/PI binding to study apoptosis and necrosis. The results showed that apoptosis and necrosis occurred after 24 hr with DU (as uranyl nitrate) treatments ≥ 100 μM. These results are similar to those observed in a previous study that used uranyl chloride and a macrophage cell line, J774 (Kalinich et al. 2002) and were also comparable with other reported experiments using human osteoblast cells (Miller et al. 1998). Apoptosis and, to a lesser extent, necrosis occurred simultaneously after 24 hr when T cells were exposed to concentrations as high as 500 μM DU. Below 500 μM, apoptosis and necrosis were not observed. CD4+ T cells are more resistant than macrophages, which may be because macrophages can actively engulf DU particles (Kalinich et al. 2002), but CD4+ T cells do not. Compared with other heavy metals, DU is much less toxic to CD4+ T cells than mercury, whereas lead and vanadium have approximately the same toxicity as DU (Shen et al. 2001).
Once the toxicity of DU to immune cells was determined, the issue arose as to how DU affects the function of immune cells. In the presence of ConA, in vitro T-cell activation requires accessory cells for co-stimulatory signals (Pollard and Landberg 2001). In the present study we assessed the ability of DU-exposed macrophages to function as accessory cells by measuring the CD4+ T-cell proliferation. The results indicated that higher concentrations (200 to ~ 1,000 μM) of DU were able to alter macrophage functionality in vitro in a concentration-dependent manner, which led to significant T-cell proliferation. This response is similar but occurs at higher concentrations compared with other heavy metals such as lead, which induces lymphocyte proliferation at a concentration range of approximately 12–120 μM (Krocova et al. 2000). The results in this study demonstrated that a short-term, high-concentration DU exposure was able to perturb rapidly the interaction between macrophages and T cells, and immune function.
Previous studies on the toxic effects of heavy metals, including uranium, indicated the involvement of cytokine regulation in immunomodulatory activities (Gazin et al. 2002; Krocova et al. 2000). However, these studies focused on the expression of only a few cytokine genes such as interleukins, NF-κB, or TNF-α. Global gene expression analysis in kidney tissue after DU exposure suggested that genes involved in multiple biologic functions, including signal transduction, may be altered by uranium exposure (Taulan et al. 2004). We further asked what effects DU might have on the immune system if the exposure scenario were nonlethal and long term and how it might relate to cytokine gene expression.
In this present study we used a mouse cytokine gene array, and as expected, genes related to signal transduction pathways were significantly modulated by DU exposure (Tables 2, 3). In DU-exposed macrophages the most highly expressed gene was NF-κ B p65 (Table 2). Miller et al. (2004) demonstrated that DU (5–50 μg/mL or 18.5–185 μM) had profound influences on multiple signaling pathways in HepG2 cells; interestingly, the genes they identified also related to the NF-κ B pathway, as indicated in our research. The important role of NF-κ B in uranium toxic effects has been reported previously by Gazin et al. (2002). Our results provide direct evidence showing that DU is able to activate NF-κ B by increasing the expression of the p65 subunit. The NF-κ B family of transcription factors not only are key regulators of genes involved in immune and inflammatory reactions (Li et al. 2001; Tak and Firestein 2001) but also are involved in many aspects of cell growth, differentiation, and proliferation via the induction of certain growth and transcription factors (e.g., c-myc, ras, p53). The co-induction of NF-κ B, MMP-13, and c-myc indicated in our microarray results is consistent with previous work by Tak and Firestein (2001).
NF-κ B can mediate both inflammatory and antiinflammatory responses by regulating genes encoding either proinflammatory or antiinflammatory activities (e.g., IL-10) (Baldwin 2001; Bierhaus and Nawroth 2003; Xu and Shu 2002). In our microarray analysis, the latter was indicated by the up-regulation of IL-10 gene in macrophages upon DU exposure (Table 2). Activation of NF-κ B requires degradation of I-κ B (nucear factor of kappa light chain gene enhancer in B-cells inhibitor, β ) with the help of I-κ B kinases, the activity of which depends on binding with NF-κ B–inducing kinase (NIK) (Chen et al. 2001; Wooten 1999). The activation of NIK as shown in this study (Table 2) supports the conclusion that the NF-κ B signaling pathway was adopted by macrophages under the DU exposure. DU may induce NIK activity leading to the up-regulation of NF-κ B (by increasing the p65 subunit level), which further activates expression of a variety of cytokine genes, such as c-myc and MMP-13, as indicated in our array data. This hypothesis is supported by the study of Miller et al. (1998) that demonstrated DU-induced tumorigenic activity in osteoblast cells.
It is interesting to note that the expression of the neurotrophic factor Mdk was highly induced in both primary macrophages and CD4+ T cells (Tables 2, 3) after DU exposure. Mdk gene expression is restricted to only a few types of cells such as kidney and epithelial cells (Garver et al. 1993; Hu et al. 2002); it is very unusual that this gene was regulated by DU in immune cells. To our knowledge [main references were Tully et al. (2000) and Yamada and Koizumi (2002)], the up-regulation of Mdk by heavy metal exposure has not been previously reported, which may indicate a common mechanism of DU immunotoxicity to both macrophages and T cells and may provide a biologic marker for DU exposure. Because Mdk levels often increase in the early stage of cancer progression, it has been suggested as a tumor marker (Muramatsu 2002). High induction of Mdk expression in this study presents further evidence for the possible involvement of DU in carcinogenesis, as reported by Miller et al. (1998).
A DU-induced Th1–Th2 shift has been long postulated to play a role in the development of Gulf War syndrome (Rook and Zumla 1997; Skowera et al. 2004). The complex balance between Th1 and Th2 cells can be disturbed by a variety of factors, including heavy metals; a shift to a Th2 phenotype has been correlated with the development of allergic responses and some autoimmune diseases (Harber et al. 2000; Mosmann and Sad 1996). As part of our efforts, cytokine gene expression was studied in CD4 cells to investigate the DU-induced Th2 shift hypothesis.
Our array data showed an approximately 2-fold induction of IL-5 expression in CD4+ T cells and 1.7-fold induction of IL-10 in macrophages upon DU exposure (Tables 2, 3). We postulate that the reason changes in TNF and IL-6 were not detected, as reported by Krocova et al. (2000), is because of differences in exposure conditions and cell type (we used mouse primary peritoneal macrophages vs. rat alveolar macrophages and lung fibroblasts). However, the conclusions were similar because IL-10 and IL-5 were found to be up-regulated, and both genes are of the Th2 type. IL-5 is a signature cytokine of Th2 cells, which also produce cytokines such as IL-4 and IL-10 (Cousins et al. 2002; Mazzarella et al. 2000). IL-10 can also create a microenvironment to facilitate Th2 cell development (Malefyt et al. 1991; Mosmann and Sad 1996). Therefore, up-regulation of IL-5 and IL-10 expression in our study indicates a Th2 differentiation tendency after DU exposure. This is direct evidence, at the transcriptional level, for a DU-induced Th2 shift. Th2 domination of T-helper cell population differentiation is often found in association with strong antibody (e.g., autoimmune diseases) and allergic responses (Harber et al. 2000; Mosmann and Sad 1996). Interestingly, elevated blood IL-10 concentrations have been detected in symptomatic Gulf War veterans who were potentially exposed to DU under battlefield conditions (Skowera et al. 2004; Zhang et al. 1999). The data in our study show that DU may contribute to an increase in IL-10 levels through its action on macrophages. Additionally, the induction of IL-5 expression in CD4+ T cells, and possibly IL-10 in macrophages suggests an important role for DU in promoting Th2 shifting.
Conclusions
In summary we have demonstrated DU-induced apoptosis and necrosis in both peritoneal macrophages and splenic CD4+ T cells in a cell-specific and concentration-dependent manner. Short-term DU exposure (> 200 μM) to macrophages interferes with the interplay between macrophages and CD4+ T cells, resulting in an enhanced T-cell proliferation response. At lower (noncytotoxic) concentrations, DU has the potential to influence immune function by modulating cytokine gene expression mainly involved in signal transductions, interleukin production, chemokine and chemokine receptors, and neurotrophic factors. Array analyses have successfully identified differentially regulated genes implicating DU in carcinogenesis and the development of autoimmune diseases. The up-regulation of IL-5 and IL-10 genes in CD4+ T cells and macrophages, respectively, strongly suggests a DU-induced Th2 shift during naive T-cell differentiation. Considering the substantial sequence homology between the mouse and human genome and the conserved expression patterns of orthologs reflecting common physiologic functions in these two organisms (Su et al. 2002), the alteration in immune functions and cytokine gene expression in murine immune cells demonstrated in this study identify putative molecular targets for the toxic actions of DU and suggest molecular mechanisms for the development of DU-related diseases in humans.
We acknowledge the assistance of H. Zaghouani, B. Rouse, U. Kumaraguru, J. Young, and personnel of the University of Tennessee animal facilities.
This work was supported by the Center for Environmental Biotechnology, Research Center of Excellence, by the Waste Management Research and Education Institute, and by the Office of the Vice Chancellor for Research, University of Tennessee. Partial support for this project was provided by National Science Foundation grant BES-0116610.
Figure 1 Cell apoptosis and necrosis under DU exposure. (A) Macrophages were treated with 0 (control), 20, 50, 100, or 200 μM DU for 24 hr. (B) CD4+ T cells were treated with 0 (negative control), 1, 10, 100, or 500 μM DU, 1 mM NaNO3, or 1 μg/mL staurosporine (positive control) for 24 hr. Data are presented as percentage of cells in the apoptotic or necrotic state and are the means of triplicate experiments. Error bars represent SD.
*Difference from negative control is statistically significant (p < 0.05).
Figure 2 Representative bright-field and atomic force photomicrographs of macrophages with or without DU exposure. The cells were cultured in medium without or with 100 μM DU (as uranyl nitrate) for 24 hr and then processed for microscopy. Bright-field images (40 × magnification) of control (A) and DU-treated cells (B); the rough membrane structure of DU-treated cells was shown in B. Atomic force microscopy images of single control (C) and DU-treated cells (D), with or without nucleus area, respectively. The apoptotic body is presented in D as smaller separated bodies. The arrows in B and D indicate cells undergoing apoptosis after 24 hr of 100 μM DU exposure. Arrow in C indicates the nucleus feature of a normal cell.
Figure 3 Effect of DU on the accessory cell function of peritoneal adherent macrophages. The effect of DU on lymphocyte (CD4+ T cell) proliferation was determined by MTT assay, which indirectly reflects the accessory cell function of macrophages in promoting lymphocyte proliferation. Results are expressed in optical density values read at 562 nm wavelength as mean and SD of triplicate analyses.
*Difference from control is statistically significant (p < 0.05).
Table 1 Primer sequences used in quantitative RT-PCR for differentially expressed genes
Target Sequences Amplicon size (bp)
IL-10 F: 5′ -CAT GGG TCT TGG GAA GAG AA-3′
R: 5′-CAT TCC CAG AGG AAT TGC AT-3′ 194
Mdk F: 5′-ACC GAG GCT TCT TCC TTC TC-3′
R: 5′-GGC TCC AAA TTC CTT CTT CC-3′ 230
BMP-11 F: 5′-TTC ATG GAG CTT CGA GTC CT-3′
R: 5′-AGC ATG TTG ATT GGG GAC AT-3′ 299
c-jun F: 5-TGA GAA CTT GAC TGG TTG CG-3′
R: 5′-AAA GTC CAT CGT TCT GGT CG-3′ 222
Stat-1 F: 5′-TGG TGA AAT TGC AAG AGC TG-3′
R: 5′-TGT GTG CGT ACC CAA GAT GT-3′ 119
Tlr6 F: 5′-ACA CAA TCG GTT GCA AAA CA-3′
R: 5′-GGA AAG TCA GCT TCG TCA GG-3′ 128
IL-5 F: 5′-GTC CCT ACT CAT AAA AAT CAC CA-3′
R: 5′-GAA TAG CAT TTC CAC AGT ACC C-3′ 105
GAPDHa F: 5′-TGA TGA CAT CAA GAA GGT GGT GAA G-3′
R: 5′-TCC T TG GAG GCC ATG TAG GCC AT-3′ 240
Abbreviations: F, forward primer; R, reverse primer.
a Sequences are from Lee et al. (2000).
Table 2 Differentially regulated genes in DU-exposed peritoneal adherent macrophages as determined by array analysis.
Gene abbreviationa Gene symbola Accession no.b Gene description Gene group Z-Testc Ratiod 95% CIe
Up-regulated gene expression
NF-kB p65 Rela NM_009045 avian reticuloendotheliosis viral (v-rel) oncogene homolog A Signal transduction 5.8 × 10–8 3.9 6.3–2.4
LTBP-2 Ltbp2 AF004874 latent TGF-beta binding protein-2 Binding protein 1.8 × 10–6 3.2 5.3–2.0
WNT-8b Wnt8b NM_011720 wingless related MMTV integration site 8b Developmental factors 9.3 × 10–6 3.2 5.4–1.9
Mdk Mdk NM_010784 midkine Neurotrophic group 1.5 × 10–5 3.1 5.2–1.9
c-jun Jun NM_010591 Jun oncogene Signal transduction 7.1 × 10–5 3.2 5.8–1.8
Map3k14 Nik—pending NM_016896 Nfkb inducing kinase Apoptosis related 1.5 × 10–3 2.4 4.0–1.4
BDNF Bdnf NM_007540 brain derived neurotrophic factor Neurotrophic group 2.4 × 10–3 1.7 2.5–1.2
SOCS-1 Cish1 NM_009896 cytokine inducible SH2-containing protein 1 Signal transduction 2.8 × 10–3 2.4 4.2–1.4
c-myc Myc NM_010849 myelocytomatosis oncogene Signal transduction 7.2 × 10–3 2.2 3.8–1.2
NSG1/p21 Nsg1 NM_010942 neuron specific gene family member 1 Signal transduction 7.8 × 10–3 2.0 3.3–1.2
IL-10 Il10 NM_010548 interleukin 10 Interleukin 8.2 × 10–3 1.7 2.5–1.2
Stat 1 Stat1 NM_009283 signal transducer and activator of transcription 1 Signal transduction 9.2 × 10–3 2.0 3.4–1.2
IL-12 Rb1 Il12rb1 NM_008353 interleukin 12 receptor, beta 1 Interleukin receptor 1.1 × 10–2 1.6 2.4–1.1
CIS3 Cish3 NM_007707 cytokine inducible SH2-containing protein 3 Signal transduction 1.1 × 10–2 1.8 2.8–1.1
BMP-9 Bmp9 AF188286 bone morphogenetic protein 9 TGF-beta family 1.4 × 10–2 1.8 2.7–1.1
SMAD7 Madh7 NM_008543 MAD homolog 7 (Drosophila) Signal transduction 1.9 × 10–2 1.6 2.5–1.1
Tlr2 Tlr2 AF185284 toll-like receptor 2 Cell surface protein 1.9 × 10–2 1.8 2.8–1.1
CT-1 Ctf1 NM_007795 cardiotrophin 1 Cytokine and receptors 1.9 × 10–2 1.9 3.4–1.1
EphA3 Epha3 M68513 mouse eph-related receptor tyrosine kinase (Mek4) Eph family 2.1 × 10–2 1.6 2.3–1.1
MMP-13 Mmp13 NM_008607 matrix metalloproteinase 13 Protease or related factor 2.2 × 10–2 1.9 3.3–1.1
BMP-11 Gdf11 AF092734 growth/differentiation factor 11 TGF-beta family 3.2 × 10–2 1.8 3.1–1.0
GDNF Gdnf D49921 glial cell line-derived neurotrophic factor (GDNF) Neurotrophic group 3.5 × 10–2 1.6 2.4–1.0
Itgb7 Itgb7 M95632 integrin beta-7 subunit Intergrin 2.9 × 10–2 1.7 2.9–1.0
MMP-8 Mmp8 NM_008611 matrix metalloproteinase 8 Protease or related factor 4.9 × 10–2 1.6 2.5–1.0
Down-regulated gene expression
Tlr6 Tlr6 NM_011604 toll-like receptor 6 Cell surface protein 2.7 × 10–2 0.6 0.9–0.4
Cox-2 Ptgs2 NM_011198 prostaglandin-endoperoxide synthase 2 Apoptosis related 2.3 × 10–2 0.6 0.9–0.4
VCAM-1 Vcam1 NM_011693 vascular cell adhesion molecule 1 Adhesion molecule 1.2 × 10–2 0.5 0.9–0.3
FLIPL Cash NM_009805 caspase homolog Apoptosis related 1.1 × 10–2 0.6 0.9–0.4
SARP-1 Sdf5 NM_009144 stromal cell derived factor 5 Apoptosis related 7.3 × 10–4 0.6 0.8–0.4
Results are from three individual experiments.
a From Sigma-Genosys (http://www.sigmaaldrich.com/catalog/search/ProductDetail/GENOSYS/G2041).
b From GenBank (http://www.ncbi.nlm.nih.gov/Genbank/).
c p-Values in the two-tailed Z-tests for the comparison between control and DU treatments.
d The ratio expression values for the average expression values of each gene between DU and control, that is, ratio = intensity value from DU-treated cells divided by that from control cells.
e Confidence intervals (CIs) determined for ratios, p < 0.05.
Table 3 Differentially regulated genes in DU-exposed CD4+ T cells as determined by array analysis.
Gene abbreviationa Gene symbola Accession no.b Gene description Gene group Z-Testc Ratiod 95% CIe
Up-regulated gene expression
TECK/CCL25 Scya25 NM_009138 small inducible cytokine A25 Chemokine 1.5 × 10–14 4.2 6.2–2.9
Mdk Mdk NM_010784 midkine Neurotrophic group 2.9 × 10–7 3.0 4.7–1.9
IL-5 Il5 NM_010558 interleukin 5 Interleukin 9.5 × 10–4 1.9 2.9–1.3
VEGF-A Vegf NM_009505 vascular endothelial growth factor Angiogenic factor 2.5 × 10–5 1.8 2.2–1.4
Down-regulated gene expression
EphA3 Epha3 M68513 eph-related receptor tyrosine kinase (Mek4) Eph family 4.0 × 10–8 0.5 0.6–0.4
CCR-4 Cmkbr4 NM_009916 chemokine (C-C) receptor 4 Chemokine receptor 1.8 × 10–5 0.6 0.8–0.5
LIF Lif NM_008501 leukemia inhibitory factor Cytokine and receptors 3.0 × 10–5 0.6 0.8–0.4
GDF-7 Gdf7 U08339 BALB/c putative growth factor GDF7 (Gdf7) gene TGF-beta superfamily 2.4 × 10–4 0.5 0.7–0.3
EBF Ebf NM_007897 early B-cell factor Signal transduction 3.7 × 10–4 0.5 0.7–0.4
CD27/TNFRSF7 Tnfrsf7 L24495 CD27 antigen TNF superfamily 6.4 × 10–4 0.6 0.8–0.4
SLIT-3 Slit3 AF088902 SLIT1 protein Developmental factor 9.2 × 10–4 0.7 0.8–0.5
CX3CL1 Scyd1 NM_009142 small inducible cytokine subfamily D, 1 Chemokine 1.2 × 10–3 0.4 0.7–0.2
SMAD1 Madh1 NM_008539 MAD homolog 1 (Drosophila) Signal transduction 1.3 × 10–3 0.6 0.8–0.5
A1 Bcl2a1a L16462 hemopoietic-specific early response protein Apoptosis related 1.5 × 10–3 0.6 0.8–0.4
Results are from three individual experiments.
a From Sigma-Genosys (http://www.sigmaaldrich.com/catalog/search/ProductDetail/GENOSYS/G2041).
b From GenBank (http://www.ncbi.nlm.nih.gov/Genbank/).
c p-Values in the two-tailed Z-tests for the comparison between control and DU treatments.
d The ratio expression values for the average expression values of each gene between DU and control, that is, ratio = intensity value from DU-treated cells divided by that from control cells.
e 95% Confidence intervals (CIs) determined for ratios, p < 0.05.
Table 4 Comparison of the gene expression ratios in macrophages determined by microarray and quantitative RT-PCR analysis.
Gene abbreviation Array ratios RT-PCR ratioc
Mdka 3.1 3.2
c-juna 3.2 1.9
BMP-11a 1.8 1.6
Stat-1a 2.0 2.2
IL-10a 1.7 6.9
Tlr6a 0.6 0.6
Mdkb 3.0 1.9
IL-5b 1.9 3.2
a The gene was differentially expressed in macrophages.
b The gene was differentially expressed in CD4+ T cells.
c Results from triplicate s (n = 3).
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7915ehp0114-00009216393664ResearchGene Expression Analysis of the Hepatotoxicant Methapyrilene in Primary Rat Hepatocytes: An Interlaboratory Study Beekman Johanna M. 1Boess Franziska 2Hildebrand Heinrich 3Kalkuhl Arno 4Suter Laura 21 Schering AG, Berlin, Germany2 F. Hoffmann-La Roche Ltd., Basel, Switzerland3 Bayer HealthCare AG, Wuppertal, Germany4 Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, GermanyAddress correspondence to J.M. Beekman, Schering AG, Global Pharmacogenomics, Muellerstrasse 170-178, 13342 Berlin, Germany. Telephone: 49-30-468-12554. Fax: 49-30-468-11323. E-mail:
[email protected] authors declare they have no competing financial interests.
1 2006 12 8 2005 114 1 92 99 11 1 2005 11 8 2005 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Genomics technologies are used in several disciplines, including toxicology. However, these technologies are relatively new, and their applications require further investigations. When investigators apply these technologies to in vitro experiments, two major issues need to be clarified: a) can in vitro toxicity studies, in combination with genomics analyses, be used to predict the toxicity of a compound; and b) are the generated toxicogenomics data reproducible between laboratories? These questions were addressed by an interlaboratory study with laboratories of four pharmaceutical companies. We evaluated gene expression patterns from cultured rat primary hepatocytes after a 24-hr incubation with methapyrilene (MP). Extensive data analysis showed that comparison of genomics data from different sources is complex because both experimental and statistical variability are important confounding factors. However, appropriate statistical tools allowed us to use gene expression profiles to distinguish high-dose–treated cells from vehicle-treated cells. Moreover, we correctly identified MP in an independently generated in vitro database, underlining that in vitro toxicogenomics could be a predictive tool for toxicity. From a mechanistic point of view, despite the observed site-to-site variability, there was good concordance regarding the affected biologic processes. Several subsets of regulated genes were obtained by analyzing the data sets with one method or using different statistical analysis methods. The identified genes are involved in cellular processes that are associated to the exposure of primary hepatocytes to MP. Whether they are specific for MP and are cause or consequence of the toxicity requires further investigations.
hepatotoxicityinterlaboratory studymethapyrilenemicroarrayrat hepatocytestoxicogenomics
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In the last decade, genomic technologies have become gradually integrated into several phases of drug development. In the field of toxicology, drug safety laboratories have begun to use these technologies to assist research to conduct toxicity evaluations on as many potential lead compounds as feasible and to gain a better understanding of the mechanisms of toxicities. For investigators to be successful in the selection of compounds most likely to succeed during preclinical development, the methods they use should have a medium throughput, a short turnaround time, a good predictivity, and be reproducible.
In vitro systems are being used in toxicology studies to determine several kinds of toxicities. Mouse lymphoma cells, primary rat hepatocytes, and human lymphocytes are among the mammalian cell systems used to determine mutagenicity (Kilbey et al. 1984). Primary rat or human hepatocytes are used to determine cytotoxicity as well as metabolism of compounds or their ability to induce cytochrome P450 genes (Gómez-Lechón et al. 1988; Paillard et al. 1999). However, only a few laboratories have investigated whether in vitro systems can be used in the toxicogenomics evaluation of development compounds. Harries et al. (2001) used the human liver HepG2 cell line to investigate gene expression changes of two hepatotoxins. The results strongly suggested that different mechanisms of hepatotoxicity may be associated with specific markers of gene expression. Waring et al. (2001) showed that gene expression profiles for compounds with similar mechanisms of toxicity tested in vitro on primary rat hepatocytes formed clusters, suggesting a similar effect on transcription. Conversely, Boess et al. (2003) characterized several hepatic in vitro systems on the basis of gene expression profiling and concluded that the results were poorly comparable with the in vivo outcome, depending on the cell culture system used. It is therefore essential to obtain more knowledge on the in vitro system used to achieve better understanding and interpretation of genomics data.
As genomics technologies have been introduced more and more in toxicology, the International Life Sciences Institute Health and Environmental Sciences Institute (ILSI/HESI) has formed a consortium with more than 30 pharmaceutical companies to address the issues of reliability and reproducibility of these assays (Robinson et al. 2003). Within the ILSI/HESI consortium, the hepatotoxicity working group evaluated the two hepatotoxicants methapyrilene (MP) and clofibrate by gene expression analysis of rat livers (Baker et al. 2004; Chu et al. 2004; Hamadeh et al. 2002; Pennie et al. 2004; Ulrich et al. 2004; Waring et al. 2004). The results of these studies showed that the transferability of microarray technologies between laboratories posed serious protocol-related issues that could be solved only with appropriate and sophisticated statistical tools (Waring et al. 2004).
In the present study, a toxicogenomics experiment using primary rat hepatocytes was performed in the laboratories of four pharmaceutical companies: Bayer HealthCare AG (BA), Boehringer Ingelheim Pharma GmbH & Co. KG (BI), F. Hoffmann-La Roche Ltd. (RO), and Schering AG (SAG). The cell cultures were exposed to two concentrations of MP, an H1 histamine receptor antagonist (Noguchi et al. 1992) that is known to cause periportal cell necrosis (Steinmetz et al. 1988) and liver tumors in rats (Liijnski et al. 1980; Mirsalis 1987). The study was designed to assess the biologic and experimental variability of the in vitro systems of the laboratories, to compare their statistical analysis strategies, and to determine whether an in vitro toxicogenomics experiment, performed in different laboratories from cell culture to data analysis, would identify a toxic compound with the same reliability.
To reduce the experimental variability, a cell culture protocol with a standardization of the main parameters such as culture medium was used. However, many steps, including perfusion and RNA isolation, followed the individual in-house protocols. Each laboratory performed Affymetrix gene expression analysis on the RG-U34A chip and analyzed the data according to its own methods/software.
Materials and Methods
Test article and formulation.
Methapyrilene hydrochloride (CAS no. 135-23-9, lot no. 037F0929) was obtained from Sigma Chemical Corp. (St. Louis, MO, USA). MP was formulated in dimethyl sulfoxide (DMSO).
Primary rat hepatocytes.
Primary rat hepatocytes were isolated from 10- to 12-week-old male Han:WIST rats (200–300 g body weight; SAG: Tierzucht Schoenwalde GmbH, Schoenwalde, Germany; BA: Harlan Winkelmann, Borchen, Germany; BI: Charles River Deutsch-land GmbH, Sulzfeld, Germany; RO: RCC Ltd., Itingen, Schweiz) by a two-step collagenase liver perfusion method (Seglen 1972). After perfusion the liver was excised and the cells were resuspended in William’s E medium (WME) without phenol red and filtered. Dead cells were removed by a Percoll (Sigma) centrifugation step (Percoll density, 1.06 g/mL, 50 g, 10 min; only at RO and SAG). Primary hepatocyte viability was assessed by trypan blue exclusion and ranged between 72 and 92% (Table 1).
Cells were cultured in six-well plates coated with collagen (Menal GmbH, Herbolzheim, Germany) at a density of 106 cells/well in 2 mL WME supplemented with 10% fetal calf serum (Invitrogen Technologies, Paisley, UK), glutamine (2 mM), hydrocortisone (54 ng/mL), glucagon (7 ng/mL), insulin (5 μg/mL), penicillin (100 U/mL), streptomycin (100 mg/mL), and gentamicin (10 μg/mL) at 37°C in an atmosphere of 5% CO2/95% air. After an attachment period of 3 hr, the medium was replaced by 2 mL serum-free WME, with the same supplements.
Treatment conditions.
To determine the concentration of MP that causes a toxic response in hepatocytes, each laboratory performed two-dose finding studies. After an overnight preculture period of 16–18 hr, the cells were treated with MP, 0–300 μM (BA and RO), and 0–1,000 μM (BI and SAG) in 0.2% DMSO (final concentration) or vehicle (0.2% DMSO, final concentration). The same procedure was performed for the main study, using the two selected concentrations.
Biochemistry.
Cytotoxicity was determined as lactate dehydrogenase (LDH) release into the cell culture medium. LDH activity was determined spectrophotometrically with commercially available test kits (Table 1). Enzyme activity in the medium was determined and expressed as percentage of LDH activty present in the medium of vehicle-treated cells.
RNA isolation.
Cells were harvested at 24 hr after treatment either in Qiagen lysis buffer (RNeasy mini kits; Qiagen, Hilden, Germany) without (BA and SAG) or with proteinase K (BI) or in RNAzol/Bio101 (RO) (RNAzol: Tel-Test, Inc., Friendswood, TX, USA; Bio101: Buena Vista, CA, USA). Total RNA was isolated using Qiagen RNeasy columns. The quality of the RNA was determined using the Agilent Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA). Amounts of RNA were determined with RiboGreen (Molecular Probes, Leiden, the Netherlands) or by OD260/OD280 determination.
DNA microarray analysis.
Processing of RNA and microarray experiments were carried out basically as recommended by Affymetrix (Affymetrix, Inc., High Wycombe, UK) (Lockhart et al. 1996), with some user-specific variations (Table 1). Labeled in vitro transcripts (10–20 μg) for each RNA sample were hybridized on the RG-U34A array. A starting amount of 5–20 μg total RNA was used for the synthesis of double-stranded cDNA with a commercially available kit (Superscript Choice System; Invitrogen Life Technologies) in the presence of a T7-(dT)24 DNA oligonucleotide primer. The cDNA was purified by phenol/chloroform/isoamyl alcohol extraction and ethanol precipitation or using the Affymetrix cleanup columns. The purified cDNA was then transcribed in vitro (Enzo Diagnostics, Inc., Farmingdale, NY, USA; Ambion, Inc., Austin, TX, USA) in the presence of biotinylated ribonucleotides to form biotin-labeled cRNA. The labeled cRNA was purified on an affinity resin (RNeasy, Qiagen, or Affymetrix cleanup), quantified, and fragmented. Labeled cRNA (10–20 μg) was hybridized for approximately 16 hr at 45°C onto the RG-U34A array. The arrays were washed and stained with streptavidin-R-phycoerythrin (SAPE, Molecular Probes, CA, USA), and the signal was amplified using a biotinylated goat anti-streptavidin antibody (Vector Laboratories, Burlingame, CA, USA) followed by a final staining with SAPE. Arrays were stained using the GeneChip Fluidics Workstation 400 (Affymetrix). The arrays were then scanned using a confocal laser scanner (GeneArray Scanner 2500; Hewlett Packard, Palo Alto, CA, USA, or Agilent Technologies) resulting in an image file (*.DAT file). Using the Affymetrix software, *.CEL files were calculated from the image files.
Data analysis.
The *.DAT and *.CEL files were distributed among the participants. The data were condensed and normalized (Table 1). The individual analysis strategy of the raw data is described below.
Investigators at BA identified the genes that are regulated to a statistically significant extent by performing a t-test (Welch’s modification; Welch 1938) between the control group and each of the treatment groups using Expressionist software (GeneData, Basel, Switzerland). A p-value of 0.01 was chosen in conjunction with a 1.5-fold change cutoff.
Investigators at BI, in addition to the values derived from Microarray Analysis Software (MAS, version 5.0; Affymetrix), performed analysis calculations using the Statistical Analysis System (SAS) software (version 6.12; SAS Institute, Cary, NC, USA). To extract differential expressed genes, the following cutoff criteria were defined. The extracted genes must have a p-value of 0.05 (one-sided) according to the Mann-Whitney U-test. In addition, each probe set (gene) with a fold change value of at least 1.2 was selected. This approach was used as a first filter (and not considered statistically significant). The generated data can then be analyzed by using in-house marker genes [selected in earlier studies of a licensed database (DB)] or in-depth analysis of single selected genes.
Investigators at RO compared treated and control groups and statistical analyses were performed with in-house developed software. Gene expression changes are measured by the Affymetrix software as fluorescence intensities with a given signal (numerical value) and a qualifier or call (present, absent, marginal). If probe sets are detected as expressed, the call is set to 1; if the probe set is absent, this value is set to 0, and if marginal to 0.5. To allow comparability between microarrays, the signal is scaled using the mean intensity of all probe sets on a chip. The numerical values for several replicates are condensed by using the mean and the SD. Differences in expression levels are expressed as change factors (CHGF), which report the change in expression (signal) between two experimental conditions (baseline = control and treated). If an increase is seen, CHGF is calculated as [(signal treated/signal control) − 1]; for a decrease it is [− (signal control/signal treated) + 1]. Thus, the data are symmetrically distributed around 0; a 2-fold increase gives a CHGF of 1, whereas a 50% reduction gives a CHGF of −1. Statistical analysis was based on analysis of variance and Student’s t-test. Gene probes considered “expressed” in 50% of the samples (call ≥ 0.5) and showing fold changes > 1.25 or < −1.25 with a significance value of at least 0.1 (paired t-test) in one of the individual data sets were selected.
Investigators at SAG, compared treated and control groups, and statistical analyses were performed with Expressionist software. To extract differentially expressed genes, a t-test was used. Genes with a p-value < 0.01 and a fold change > 1.5 were extracted from every participant’s experiment set of three.
Comparison with an in vitro toxicogenomics database.
The data sets processed by RO were compared with the Roche proprietary in vitro toxicogenomics DB consisting of 17 compounds that had been tested previously in at least two concentrations. These compounds were tested following Roche-specific cell culture protocols, which were similar but not identical to the protocol described here. Among them was a previous experiment with MP on rat primary hepatocytes at two concentrations (MP_DB; 100 and 300 μM). The comparisons are based on the individual gene expression ratios (fold changes).
Results
Biochemistry.
In a pilot study the four different laboratories performed a cell culture experiment by incubating primary rat hepatocytes with several concentrations of MP (0–1,000 μM) and analyzing liver enzyme (LDH) release into the medium 24 hr after treatment. Of the four companies, three showed a slight but significant increase of LDH release into the medium at a concentration of 100 μM MP, whereas at a lower dose (20 μM) there was no enhanced LDH leakage compared with untreated cultures (Figure 1A). On the basis of this result, investigators chose a high dose of 100 μM and a low dose of 20 μM for the toxicogenomics experiments.
As anticipated from the results of the pilot experiments, a tendency toward increased LDH release was seen after 24-hr treatment with 100 μM MP during the toxicogenomics experiment (Figure 1B). However, in agreement with the pilot experiment (Figure 1A), this was not seen in all companies. It is important to note that the absolute values of LDH release in the vehicle controls varied considerably between the individual repeats within as well as between the companies, depending on the respective batch of freshly isolated hepatocytes and the different methodologies used to measure the LDH. Therefore, the results were expressed as percentage of LDH release in vehicle-treated cells.
Gene expression—comparisons across users.
In the toxicogenomics experiment rat primary hepatocytes were incubated with 0, 20, or 100 μM of MP for 24 hr and analyzed for gene expression responses using Affymetrix GeneChips. The raw data (*.CEL and *.DAT files) were exchanged among the participants of this study for individual analysis.
Analysis of all data sets with one method.
All data sets were analyzed following the analysis strategy from SAG. First, to obtain a general overview of similarities among experimental data sets, a one-dimensional hierarchical clustering (Figure 2) was performed on all data sets. This analysis shows that the data sets cluster together according to their origin. The differences in the gene expression responses are greater between different laboratories than between treated and control hepatocytes.
In the next round of analyses, SAG identified differentially regulated probe sets for each of the participating laboratories (t-test with p < 0.01 plus fold change > 1.5). This approach eliminates the variability caused by different analysis strategies and reveals the variability due to hepatocyte culture and chip processing protocols. In all studies a substantial increase in regulated probe sets is seen when the MP dose is increased (data not shown). The data set generated from the BI study appeared to have significantly more differentially regulated probe sets at the low dose compared with the other laboratories, whereas the data set of SAG showed the fewest changed probe sets at the high dose. The union of all differentially expressed probe sets results in a number of 744. The overlapping number of probe sets detected as regulated in the experiments of all four users was only five and in at least three of four experiments was 46 (data not shown). The highest concordance between two companies, defined as percentage of “own” genes shared with another company, was 34% (data not shown). When using all 744 probe sets detected as regulated in a principal component analysis (PCA), a distinct separation can be achieved between the untreated samples and those treated with the high-dose MP (Figure 3A). This is in good agreement with the biochemistry data, which showed that slight cytotoxicity was observed at the highest dose of MP, at least by most of the companies. The low-dose samples do not separate well from the untreated for all laboratories. This low dose was chosen as a dose that would not show toxicity based on LDH release. The data show that PC1 (accounting for 15.4% of the variance) drives the treatment-related differences as indicated by the arrows, whereas PC2 (accounting for 8.9% of the variance) shows a separation of the individual laboratories.
The same group of probe sets was used in an unsupervised clustering method, hierarchical clustering. The dendrogram (Figure 3B) shows a clustering of the low-dose samples with their untreated counterparts as well as a clustering of the high-dose samples. The only exception is one of the low-dose samples of BI that clusters together with the high-dose sample of the same experiment.
Analysis of one data set with different methods.
The four laboratories used very different analysis approaches with different main objectives (described in “Materials and Methods” and Table 2). To evaluate the differences of the resulting gene lists generated by the analysis method, the four participating laboratories analyzed one data set (*.DAT or *.CEL files provided by BI) according to their own standard methods. The methods used basically selected genes according to p-values from a given statistical test and fold changes (Table 2). RO and BI used a relatively low stringency to select a high number of differentially regulated genes, which then can be compared with their gene expression DB to search for similarities with known toxic compounds. BA and SAG used methods with a higher stringency to obtain gene lists with a low number of false positives. The resulting genes are then annotated and assigned to pathways to determine their biologic significance with respect to the mechanism of toxicity of the investigated compound. Table 2 lists the number of genes found with each method, and Figure 4 displays a Venn diagram depicting the number of genes shared between the different analysis methods. As expected, the different analysis strategies have an immense impact on the number of genes that are defined as differentially regulated. A total of 111 genes were detected with all four methods, whereas three of four methods detected an additional 194 genes (i.e., at least three of four methods detected 305 genes).
Analysis of each data set with individual methods.
Each laboratory analyzed its own data set using the specific methods as described in “Materials and Methods.” The resulting lists of differentially expressed genes are given in Table 3. Again, as expected, more stringent criteria used by BA and SAG detected only 126 and 185 probe sets as changed, respectively; whereas BI and RO obtained 2,486 and 1,085 probe sets, respectively. Comparison of the gene lists resulting from these analyses shows that BA and SAG share 45% or more of their changed probe sets with BI and RO but only 9–16% with each other. The Venn diagram in Figure 5 shows the relation between the different gene lists. Fourteen genes were detected as regulated by all companies, and an additional 103 genes by three of four companies. The identity of the regulated genes as well as the affected cellular pathways and their biologic significance were determined (Table 4). The probe sets consistently detected by all involved users are associated with detoxification, mitochondrial function, energy production, cell stress, and many general housekeeping processes.
Comparison with a gene expression database.
The gene expression profiles of the high-and low-dose MP from the experiments performed in the individual companies (*.DAT files) and analyzed with the strategy of RO were compared with the Roche in vitro toxicogenomics DB. At the time of analysis, this proprietary DB contained 47 data sets from 17 different hepatotoxic compounds. The comparison revealed that the high-dose data of each company, except those of SAG, fitted best to the Roche MP data, which were generated in a previous, independent experiment (Table 5). The high dose of SAG and the low doses of all companies were more difficult to predict. When the data sets of this study were incorporated in the DB, the MP data from each company always fitt best to the data from this experiment of the other companies. In most cases, this was also true for the low-dose experiments (Table 5).
Discussion
The aim of this multisite experiment was to obtain an estimate of lab-to-lab variability for in vitro gene expression analysis and to determine whether an in vitro toxicogenomics experiment performed in different laboratories from cell culture to data analysis would identify a toxic compound with the same reliability. The toxicogenomics in vitro approach shows the known advantages of other in vitro test systems, namely, the reduction of the number of animals used for biologic assays as well as the time involved and the cost of the assays. For this investigation, we selected the well-known nongenotoxic hepatocarcinogen MP, which had earlier been chosen as a model hepatotoxin within the ILSI/HESI consortium. To comply with minimal statistical requirements (Lee et al. 2000), each experiment was performed in triplicate using three different batches of primary rat hepatocytes. The number of replicates required to achieve the necessary statistical power was not addressed in this work. Although the main cell culture conditions were standardized, slight differences were already observed when comparing the cytotoxicity of various concentrations of MP during the pilot studies performed to define suitable concentrations. Although increased LDH release was observed with concentrations of 100 μM MP and above in three of the four companies, no increased LDH leakage was observed by BA with concentrations up to 300 μM in a pilot experiment (Figure 1A). The reason for this was not investigated further, and concentrations that caused only marginal or no LDH release were chosen for the main experiment (20 and 100 μM).
Analysis of the gene expression data with one-dimensional hierarchical clustering using the whole set of genes available on the RG-U34A GeneChip revealed that the differences between laboratories were greater than the differences between treatment groups. This was not surprising, as it has already been observed in an interlaboratory analysis reported by Waring et al. (2004). However, when focusing on the statistically significant gene expression changes from the data sets of all laboratories (genes were obtained by using the statistical methods of laboratory SAG: t-test, p < 0.01, fold change > 1.5), the clustering results reflected the experimental design, allowing the high-concentration samples to be separated from the controls and low-dose samples (Figure 3B). In addition the hepatocyte cultures of BA and BI appeared to be more sensitive to MP treatment than those of RO and SAG because PCA showed the separation of the low dose from the untreated for BA and BI. This might be because RO and SAG perform a Percoll gradient to separate the live hepatocytes from dead cells. This also removes other cell types from the preparation and might affect the sensitivity of the test system. Thus, using a suitable statistical method, the effect of the treatment supersedes the experimental variability. Differences on the experimental systems such as cell preparation (Percoll purification step) were also detected. In addition to the statistical methods applied by SAG, RO used its own analysis method and cutoff values from all data sets to compare each of them with a reference in vitro toxicogenomics DB. This proprietary DB contained 17 known toxic compounds tested on rat hepatocytes, including an independent exposure to MP under slightly different experimental conditions. For three of the data sets (BA, BI, RO), the gene expression profiles allowed the correct identification of MP as the best match in the DB, independently of the site where the experiment was performed.
Next, we investigated the influence of the use of different data analysis strategies to identify altered genes on the same data set. The individual analysis methods are described in Table 2, including differences in the definition of cutoff values for parameters such as fold change or p-value. The arbitrary choice of these cutoff values is not trivial and greatly influences the outcome of the analysis. On the one hand, stringent cutoff values lead to a smaller false-positive rate and a high false-negative rate (or low power). This approach can be recommended if each single gene will be interpreted and discussed regarding safety assessment. However, important signals might be missed because relatively small changes in expression may be of high biologic and toxicologic relevance. On the other hand, less stringent filtering criteria cause a high number of false positives but ensure that no relevant genes will remain undetected. In our case, BA and SAG used stringent statistical approaches (t-test with p-value < 0.01, fold change > 1.5 fold), whereas BI and RO used smaller fold changes as cutoff criteria (1.2-fold or 1.25-fold, respectively). As expected, BA and SAG detected fewer regulated genes than did BI and RO (Figure 4, Table 2). For BI the obtained gene list was used as a first-pass filter for the comparison with in-house defined marker genes or for hypothesis generation with a subsequent in-depth analysis of selected genes. When all companies analyzed their own data with their own methods, only 14 probe sets were considered deregulated by all the users in all experiments, and an additional 103 were detected by three of the four laboratories (Figure 5). This demonstrates that an additional layer of complexity and a source of differing interpretation originate from different statistical analysis methods.
The gene changes observed after 24 hr of incubation with the test compound might not be ideal to elucidate the primary events (cause) that trigger the hepatotoxicity of MP. However, the elucidation of downstream gene expression changes, indicative of general cellular dysfunction as a consequence of MP toxicity is valuable as a possible predictor for hepatotoxicity. The identity of the genes that were found changed in at least three of four laboratories (117 genes) represent biologically relevant processes that are obviously affected by MP. Several genes involved in amino acid and nucleotide metabolism were down-regulated. Also, the expression of genes that play a role in the cell cycle and/or apoptosis was changed by MP. Among them, the mitogen-activated protein kinase 6 and ornithine decarboxylase antizyme inhibitor were up-regulated, whereas ectonucleotide pyrophosphatase/phosphodiesterase 2 and insulin growth factor–binding protein were down-regulated. These signals appear contradictory because those genes promoting cell proliferation are not regulated in the same direction. However, the detected changes were generally consistent across users, increasing the confidence in the findings. Another affected pathway involved genes related to the glutathione homeostasis. Ratra et al. (2000) showed that the levels of reduced glutathione are increased to 140% of the control after administration of MP to male Han:Wistar rats. In agreement with this, our experiments show that MP had a substantial effect on genes involved in glutathione metabolism (5-oxopro-linase) and glutathione conjugation (glutathione S-transferase 3 and Yb). Also, other genes involved in detoxification, such as l-gulono-gammalactone oxidase and sulfotranferase family 1A were down-regulated. MP also seems to have an effect on the energy balance of the liver. Many genes in the glycolysis pathway and several genes involved in mitochondrial function were down-regulated by the treatment. This finding is also in agreement with previous results obtained in vivo and in vitro. It has been described that MP leads to a significant increase in mitochondria of periportal hepatocytes in rats (Reznik-Schuller and Lijinski 1981). Also, MP caused mitochondrial dysfunction, as detected by mitochondrial swelling, significant losses of ATP, and loss of mitochondrial calcium homeostasis in cultured hepatocytes (Ratra et al. 1998). In addition to the metabolic and energy impairment responses, MP elicits a stress response in the hepatocytes. Reactive oxygen producing systems are repressed, and stress-response genes are up-regulated. This is indicative of the oxidative stress produced by MP (Ratra et al. 1998) and was also described using gene expression profiles of livers of rats treated with MP (Waring et al. 2004). We observed the up-regulation of the ribosome associated membrane protein 4, which belongs to a family consisting of several ribosome associated membrane protein sequences that are known to stabilize membrane proteins in response to stress (Yamaguchi et al. 1999). Also, the myeloid differentiation primary response gene 116 (Gadd34), whose overexpression promotes apoptosis (Hollander et al. 2003), was detected as induced. The Gadd family is known to be up-regulated upon cellular stress and was strongly up-regulated by MP after in vivo exposure (Waring et al. 2004). Because we analyzed the toxicity of MP in isolation, we cannot determine which of these gene changes are specific to MP or might be regulated by other compounds. Also, most of the gene-by-gene changes described occurred at the high concentration, concomitant with slight cytotoxicity. However, some of the differentially expressed genes were also detected at the low dose by some laboratories. It was clear from the clustering data that both RO and SAG could not separate the low dose from the untreated samples. Gene expression data from BA and BI, however, showed that > 25% of the genes were already detectable at the low concentration (Table 4). These two laboratories did not perform a Percoll purification step during the hepatocyte isolation procedure. This interesting finding led us to the hypothesis that in the presence of additional cell types not eliminated by a Percoll purification step (e.g., Kupffer cells or damaged hepatocytes), gene expression changes occur already at concentrations that do not show an effect on the viability of the cells. Further experiments with controlled cell compositions should be performed to clarify this point and define the best-suited in vitro system in terms of sensitivity.
Our results show that several factors from experimental conditions to statistical data analysis contribute to the interlaboratory variability observed for gene expression results. Our data and other published results (Harries et al. 2001; Waring et al. 2001) show that in vitro assays coupled with microarray analysis are useful for detection of hepatotoxicity and mechanistic elucidation of cellular events related to it. This applies best when the experimental and analytical variability is reduced to a minimum, which cannot always be ensured. However, we were able to show that using suitable statistical analysis tools, we could, despite the experimental variability, uncover the commonalities among the experiments. We demonstrated that using a subset of deregulated genes for the analysis, the effects of a high concentration of MP on the cells supersede the interlaboratory variability and that this variability does not mask clear treatment-dependent effects. This finding agrees with a similar analysis performed in vivo (Waring et al. 2004) and also held true when we compared the data obtained at several sites with one in vitro toxicogenomics DB. The encouraging outcome of the comparison with an independent DB is pivotal and indicates that gene expression profiles have the potential to be used as a diagnostic tool for toxicology. However, it is also clear from the presented results that the differences between laboratories make the gene-by-gene comparison of gene expression data from different sources very difficult. This task can be undertaken only with sound statistical tools that allow a relevant subset of genes to be selected.
From a mechanistic point of view, it is important to note that there was good concordance among all users regarding the affected biologic processes, as shown in Table 4. Most of the consistently regulated genes play a role in detoxification/metabolism, processes of growth and death control, immune response, stress, and transport. This indicates that the interpretation of the data from different sources leads to similar conclusions in terms of toxicity and underlying mechanisms despite the differences in number and identity of genes and in the intensity of the regulation.
In summary, our data show that both experimental and statistical variability are important sources of different outcomes between laboratories. To minimize the experimental variation, it is advisable to perform the cell culture and microarray experiments whenever possible at the same experimental site. This is not always possible because often experimental protocols need to be transferable. In these cases, suitable and robust statistical analyses help overcome the differences. Also, we showed that cellular mechanisms involved in MP toxicity can be consistently detected, as illustrated by the gene expression changes listed in Table 4. In addition the positive outcome of the comparison with an in vitro DB underlines that microarray analyses of in vitro systems are robust and can be predictive of toxicity. Whether the involved cellular pathways are specific for MP and are causal to the toxicity in vitro and/or in vivo requires further investigations.
We thank S. Koehl (Boehringer Ingelheim), C. Kneilmann (Boehringer Ingelheim), G. Wasinska-Kempka (Bayer), G. Gehrmann (Schering), M. Jarzombek (Schering), N. Schaub (Hoffmann-La Roche), and E. Durr (Hoffmann-La Roche) for performing the rat hepatocyte experiments. Our special thanks go to M. Thiel (Bayer) and S. Patkovic (Schering) for their skillful assistance with the micro-array analyses.
Figure 1 LDH release in the culture medium. (A) Pilot study. (B) Main study (n = 3). Inset in A shows the increase in LDH release at 100 μm MP.
Figure 2 One-dimensional hierarchical clustering of all experiments using all genes of the RG-U34A GeneChip. Distance metric used: positive correlation.
Figure 3 (A) PCA of all experiments using the union of genes regulated by MP according to the method of SAG (744 probe sets). Distance metric used: covariance matrix. (B) One-dimensional hierarchical clustering of all experiments using the union of genes regulated by MP according to the method of SAG (744 probe sets). Distance metric used: positive correlation.
Figure 4 Venn diagram depicting the differentially expressed genes of the BI experiments determined by the four different analysis strategies.
Figure 5 Venn diagram depicting the differentially expressed genes of each company’s experiments determined by its own analysis strategy.
Table 1 Sample preparation methods and data analysis tools used by the contributing companies.
Analysis site Viability (%)a Cell purification LDH assay RNA extraction IVT Data condensation/normalization Data analysis tools/software
BA 85, 90, 89 None Hitachi 717/Roche RNeasy Enzo-Affymetrix MAS 5.0 Expressionist
BI 81, 74, 73 None Hitachi 917/Roche RNeasy (+ Prot. K) Enzo-Affymetrix MAS 5.0 SAS
RO 87, 92, 90 Percoll ADVIA 1650/LDH P-L Bayer RNAzol/Bio 101 Ambion MAS 5.0 In-house software
SAG 84, 84, 72 Percoll Hitachi/SYS1 Roche RNeasy Enzo-Affymetrix In-house software Expressionist
Abbreviations: Enzo-Affymetrix, Enzo Diagnostics Inc. and Affymetrix, Inc.; IVT, in vitro transcription; Prot. K, proteinase K.
a Cell viability of the hepatocyte preparations in the main study (n = 3).
Table 2 Number of genes regulated by MP of the BI experiments calculated by the four different laboratories according to their applied method.
Treatment BA BI RO SAG
Low-dose 75 1,296 687 84
High-dose 211 1,914 1,011 289
Union 256 2,486 1,286 332
Method MAS 5.0
Welch’s t-test
p < 0.01 MAS 5.0
Mann-Whitney U-test
p < 0.05 MAS 5.0
Paired t-test,
p < 0.1 In-house software
t-test
p < 0.01
Cutoff > 1.5-fold change > 1.2-fold change > 1.25-fold change > 1.5-fold change
Table 3 Intersections between the genes regulated by MP per laboratory calculated by its own methods (values in brackets are percentage of that company’s genes shared with the respective other companies).
Company BA (%) BI (%) RO (%) SAG (%)
BA 185 138 (6) 84 (8) 16 (13)
BI 138 (75) 2,486 579 (53) 82 (65)
RO 84 (45) 579 (23) 1,085 74 (59)
SAG 16 (9) 82 (3) 74 (7) 126
Table 4 Genes regulated by a low or high dose of MP detected by at least three of the four laboratories.
BA
BI
RO
SAG
Affymetrix probe set IDa Gene descriptiona 20 μM 100 μM 20 μM 100 μM 20 μM 100 μM 20 μM 100 μM Direction of change
Amino acid metabolism
AB003400_at d-amino acid oxidase −1.29 −2.13b −1.96b −3.35b −1.22 −1.89b −1.06 −1.52 Down
AF038870_at betaine-homocysteine methyltransferase −2.28b −5.02b −4.41b −7.21b −1.23 −2.04b 1.20 −1.62 Down
D17370_at CTL target antigen −1.27 −2.32b −1.50b −2.58b 1.17 −1.43b 1.04 −1.57 Down
D87839_g_at 4-aminobutyrate aminotransferase −2.22b −4.81b −2.34b −8.16b −1.54b −5.00b −1.09 −2.64b Down
J02827_g_at branched chain alpha-ketoacid dehydrogenase subunit E1 alpha −1.54 −1.45 −1.46b −1.59b −1.18 −1.35b 1.03 −1.80b Down
M88347_s_at cystathionine beta synthase −1.50 −2.44b −1.60b −2.81b −1.23 −2.44b −1.29 −1.29 Down
U68168_at kynureninase (L-kynurenine hydrolase) −2.12b −5.46b −2.36b −6.22b −1.28b −4.17b −1.08 −2.30b Down
Cell-cycle/apoptosis
AB002086_at p47 protein 1.21 1.35 1.37b 1.56b 1.26b 1.60b 1.23 1.66b Up
AF020618_g_at myeloid differentiation primary response gene 116 2.05b 4.71b 1.82b 5.25b 1.21 3.88b −1.06 2.55b Up
D28560_at ectonucleotide pyrophosphatase/phosphodiesterase 2 −1.22 −2.01b −1.65b −3.24b −1.22 −2.04b −1.15 −2.28 Down
D28560_g_at ectonucleotide pyrophosphatase/phosphodiesterase 2 −1.67b −2.43b −3.11b −9.83b −1.28b −2.27b −1.24 −2.11 Down
rc_AI043631_s_at ornithine decarboxylase antizyme inhibitor 1.67b 2.75b 2.72b 4.25b 1.29 2.90b −1.04 2.26 Up
S46785_at insulin-like growth factor binding protein complex acid-labile subunit 1.03 −1.10 −1.41b −1.69b −1.20 −1.79b 1.67b 1.00 Down
Detoxification
D14564cds_s_at L-gulono-gamma-lactone oxidase (BLAST) −1.32 −1.49 −2.05b −2.50b −1.20 −2.17b −1.04 −1.78b Down
J03914cds_s_at glutathione S-transferase Yb subunit gene −1.39 −1.92b −1.43b −1.93b −1.14 −1.41b −1.01 −1.66b Down
L19998_at sulfotransferase family 1A, phenol-preferring, member 1 −1.93 −4.14 −5.86b −9.35b −1.59b −8.33b −1.21 −6.39b Down
L19998_g_at sulfotransferase family 1A, phenol-preferring, member 1 −1.71 −3.36 −5.97b −6.94b −1.59b −6.25b −1.17 −5.14b Down
M23601_at monoamine oxidase B −1.45 −2.40b −2.07b −4.65b −1.06 −2.44b −1.04 −2.08b Down
rc_AA892234_at ESTs, highly similar to microsomal GST 3 −1.46 −2.26b −1.55b −3.54b −1.33b −2.33b −1.02 −2.18 Down
U70825_at 5-oxoprolinase (ATP-hydrolyzing) −1.47 −2.30b −1.95b −5.46b −1.23 −3.33b −1.14 −2.05 Down
Glycolysis and gluconeogenesis
AF062740_at pyruvate dehydrogenase phosphatase isoenzyme 1 1.30 1. 69b −1.42b 1.37b −1.04 1.31b 1.32 1.39 Up
J05446_at glycogen synthase 2 (liver) −2.00b −3.33b −2.35b −5.92b −1.19 −2.86b 1.05 −2.11 Down
M12919mRNA#2_at aldolase A 1.25 1.55 1.30b 1.84b −1.01 1.76b 1.11 1.72b Up
M83298_at protein phosphatase 2, regulatory subunit B, α isoform 1.37 1.78b 1.61b 2.18b 1.09 1.46b −1.07 1.32 Up
M86240_at fructose-1,6-bisphosphatase 1 −2.03b −2.55b −2.14b −3.15b −1.24 −2.70b −1.02 −2.32b Down
rc_AA892395_s_at aldolase B −2.12 −3.59 −1.73b −4.84b −1.09 −2.78b 1.01 −2.37b Down
rc_AA945442_at glucokinase regulatory protein −1.67b −2.06b −1.38b −2.02b −1.30 −1.61b −1.18 −1.88b Down
S79213_at phosphatase inhibitor-2 1.57b 2.11b 2.00b 2.48b 1.20 1.39b −1.09 1.30 Up
U32314_g_at pyruvate carboxylase −1.55b −1.62b −2.28b −4.61b −1.06 −1.52b −1.08 −1.30 Down
X02291exon_s_at aldolase B (BLAST) −1.58 −2.24 −1.39b −3.00b −1.08 −2.17b −1.06 −2.03b Down
X53428cds_s_at glycogen synthase kinase 3 beta 1.52b 1.82b 2.12b 2.78b 1.08 2.41b 1.01 2.60 Up
X73653_at glycogen synthase kinase 3 beta 1.32 1.67b 1.99 2.42 1.03 1.87b 1.09 2.72b Up
Immune response
AF029240_g_at BM1k MHC class Ib antigen, strain SHR −1.61 −1.54 −1.63b −2.42b −1.20 −1.82b −1.18 −2.09b Down
L12025_at tumor-associated glycoprotein pE4 2.15b 3.43b 2.59b 5.30b 1.19 3.03b −1.02 1.81 Up
U47031_at purinergic receptor P2X, ligand-gated ion channel −1.14 −1.13 −1.21b −1.36b −1.14 −1.56b −1.09 −1.62b Down
Mitochondrial function
AF062740_at pyruvate dehydrogenase phosphatase isoenzyme 1 1.30 1.69b −1.42 1.37b −1.04 1.31b 1.32 1.39 Up
D00569_g_at 2,4-dienoyl CoA reductase 1, mitochondrial −1.39 −1.63b −1.11 −2.07b −1.18 −1.64b −1.05 −1.42 Down
D30740_at 14-3-3 protein mRNA for mitochondrial import stimulation factor (MSF) S1 subunit 1.32 1.51b 1.56b 1.78b 1.17 1.29b 1.08 1.30 Up
J05029_s_at acyl coenzyme A dehydrogenase, long chain −1.02 −1.25 −1.43b −1.98b −1.09 −1.79b −1.08 −1.54b Down
J05030_at acyl coenzyme A dehydrogenase, short chain −1.27 −1.54b −1.62b −1.63b −1.10 −1.82b 1.01 −1.58 Down
M23601_at monoamine oxidase B −1.45 −2.40b −2.07b −4.65b −1.06 −2.44b 1.04 −2.08b Down
M33648_at mitochondrial 3-hydroxy-3-methylglutaryl-CoA synthase −4.16b −11.38b −2.92b −13.51b −1.30 −6.25b −1.06 −2.93b Down
M33648_g_at mitochondrial 3-hydroxy-3-methylglutaryl-CoA synthase −2.40b −7.85b −2.46b −10.44b −1.25b −4.55b 1.00 −2.02 Down
rc_AA817846_at ESTs, highly similar to BDH_RAT d-beta-hydroxybutyrate dehydrogenase −1.95 −3.25 −3.32b −8.04b −1.41 −5.00b −1.07 −2.58b Down
rc_AI176422_at ESTs, highly similar to S41115 probable flavoprotein-ubiquinone oxidoreductase −1.22 −1.53 −1.61b −2.36b −1.19 −1.30b −1.03 −1.79b Down
U32314_g_at pyruvate carboxylase −1.55 −1.62 −2.28b −4.61b −1.06 −1.52b −1.09 −1.30 Down
Y12635_at ATPase, H+ transporting, lysosomal, isoform 2 1.40 2.43b 1.51b 2.74b 1.15 2.18b 1.04 1.81 Up
Nucleotide metabolism
D28560_at ectonucleotide pyrophosphatase/phosphodiesterase 2 −1.22 −2.01b −1.65b −3.24b −1.22 −2.04b −1.15 −2.28 Down
D28560_g_at ectonucleotide pyrophosphatase/phosphodiesterase 2 −1.67b −2.43b −3.11b −9.83b −1.28b −2.27b −1.24 −2.11 Down
M97662_at ureidopropionase, beta −1.93 −3.08 −2.99b −4.56b −1.43b −3.13b −1.10 −4.08b Down
rc_AA799402_at ESTs, weakly similar to S18140 hypoxanthine phosphoribosyl-transferase −1.10 −1.93 −1.79b −1.67b −1.27 −1.27b −1.11 −1.59b Down
Protein metabolism
AF100470_g_at ribosome associated membrane protein 4 1.11 1.30 1.29b 1.41b 1.18 1.61b 1.07 1.57b Up
L38482_g_at serine protease gene 1.11 1.29 1.20b 1.09 1.11 1.40b 1.09 1.78b Up
M96633_at mitochondrial intermediate peptidase −1.48 −2.45b −1.74b −3.47b −1.23 −2.22b 1.08 −1.77 Down
rc_AA892831_s_at ESTs, highly similar to JC6524 26S proteasome regulatory complex chain p44.5 1.12 1.28 1.44b 1.32b 1.09 1.50b 1.05 1.84b Up
X70900_at hepsin −1.59b −2.45b −1.69b −2.56b −1.23 −1.96b 1.02 −2.56b Down
Signal transduction
AF036537_g_at homocysteine respondent protein HCYP2 1.56 1.67 1.83b 1.85b 1.38b 2.18b −1.16 1.95b Up
AF076619_at growth factor receptor bound protein 14 −1.11 −1.67b −1.09 −2.23b −1.08 −1.79b −1.03 −1.35 Down
L14323_at phospholipase C-beta1 −1.27 −2.03b −1.55b −3.34b 1.06 1.67b −1.05 −1.33 Down
M64301_g_at mitogen-activated protein kinase 6 1.26 2.17b 1.06 2.51b −1.06 1.62b −1.04 1.27 Up
M83298_at protein phosphatase 2 (formerly 2A), regulatory subunitB (PR 52), alpha isoform 1.37 1.78b 1.61b 2.18b 1.09 1.46b −1.07 1.32 Up
rc_AA891580_at ESTs, highly similar to cylindromatosis (turban tumor syndrome); cylindromatosis 1 1.27 1.61b 1.90b 2.02b 1.27b 1.32 1.00 1.30 Up
rc_AI070721_s_at glial cell line derived neurotrophic factor family receptor α1 −2.49 −2.68 −1.19 −2.15b −1.32 −2.77b −1.07 −1.95b Down
rc_AI171630_s_at p38 mitogen activated protein kinase −1.21 −1.70b −1.75b −2.37b −1.23 −1.64b −1.10 −1.29 Down
Stress response
M23601_at monoamine oxidase B −1.45 −2.40b −2.07b −4.65b −1.06 −2.44b −1.04 −2.08b Down
M86389cds_s_at heat shock 27 kDa protein 1.65 2.15 3.11b 2.24b 1.16 2.22b 1.25 2.87b Up
rc_AA891286_at thioredoxin reductase 1 1.45 1.78b 1.77b 1.85b 1.22 1.66b 1.12 1.40 Up
rc_AI171630_s_at p38 mitogen activated protein kinase −1.21 −1.70b −1.75b −2.37b −1.23 −1.64b −1.10 −1.29 Down
rc_AI179610_at heme oxygenase 1.34 3.37b 1.87 3.45 1.18 2.99b 1.12 2.74b Up
Transcription
AB012230_at NF1-B1 −1.20 −1.86b −1.03 −2.11b −1.32b −1.32 1.00 1.00 Down
AF003926_at nuclear receptor subfamily 2, group F, member 6 −1.24 −1.69b −1.03 −1.58b −1.10 −1.41b −1.04 −1.41 Down
AF016387_g_at retinoid X receptor gamma −1.31 −2.15b −1.39b −2.56b −1.10 −1.69b 1.00 1.00 Down
Transport
AB015433_s_at solute carrier family 3, member 2 1.36 1.89b 1.96b 3.30b 1.18 1.77b 1.07 1.89 Up
U72741_g_at lectin, galactose binding, soluble 9 (galectin-9) −1.37 −1.46 −2.28b −3.44b −1.23 −1.49b −1.06 −1.98b Down
Z36944cds_at putative chloride channel (similar to Mm Clcn4-2) −1.88b −2.50b −1.57b −1.93b −1.12 −2.13b −1.09 −1.37 Down
a From Affymetrix, Inc. (http://www.affymetrix.com).
b Significant fold changes.
Table 5 Comparisons with the Roche in vitro toxicogenomics database.
Similarity index
BA
BI
RO
SAG
Data set Dose (μM) Mechanism High Low High Low High Low High Low
MP_BA_high 100 Direct reaction N/A 28.55a 28.44a 24.23a 24.06a 4.72 17.78 0.31
MP_BA_low 20 Direct reaction 28.55a N/A 12.66 18.46 11.78 4.95 15.00 0.35
MP_BI_high 100 Direct reaction 28.24a 12.66 N/A 33.44a 26.02a 3.12 13.97 0.00
MP_BI_low 20 Direct reaction 24.23 18.46a 33.44a N/A 21.67 5.51 21.14a 0.84
MP_RO_high 100 Direct reaction 24.06 11.78 26.02 21.67 N/A 4.95 20.29a 1.10
MP_RO_low 20 Direct reaction 4.72 4.95 3.12 5.51 6.69 N/A 6.69 1.06
MP_SAG_high 100 Direct reaction 17.78 15.00 13.97 21.14 20.29 6.69a N/A 3.77a
MP_SAG_low 20 Direct reaction 0.31 0.35 0.00 0.84 1.10 1.06 3.77 N/A
MP_DB_100 100 Direct reaction 22.16b 12.81b 20.95b 19.50b 21.82b 6.69ab 15.71b 0.94
MP_DB_300 300 Direct reaction 20.08b 8.47 23.21b 12.25 17.51b 3.08 7.60 0.41
Other_cmp N/A Direct reaction 2.82 3.74 2.80 4.88 2.57 4.16 4.25 2.83ab
Other_cmp N/A Direct reaction 11.98 8.45 12.65 13.39 11.87 4.35b 11.21 1.76b
Other_cmp N/A Direct reaction 17.93 11.84b 16.3 14.35b 14.33 3.84 10.92 1.14
Other_cmp N/A Perox. prolif. < 0 < 0 < 0 < 0 < 0 < 0 0.85 0.00
Other_cmp N/A Perox. prolif. < 0 < 0 < 0 < 0 < 0 0.27 0.72 < 0
Abbreviations: N/A, not applicable; Other_cmp: other compound in DB; Perox. prolif., peroxisome proliferators.
a Top two of comparison including data sets of this study.
b Top two of comparison without data sets of this study.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8149ehp0114-00010016393665ResearchEstrogen-Like Properties of Fluorotelomer Alcohols as Revealed by MCF-7 Breast Cancer Cell Proliferation Maras Marleen 1Vanparys Caroline 1Muylle Frederik 1Robbens Johan 1Berger Urs 2Barber Jonathan L. 3Blust Ronny 1De Coen Wim 11 Laboratory for Ecophysiology, Biochemistry, and Toxicology, University of Antwerp, Antwerp, Belgium2 Norwegian Institute for Air Research, Polar Environmental Centre, Tromsø, Norway3 Environmental Science Department, Lancaster University, Lancaster, United KingdomAddress correspondence to M. Maras, University of Antwerp, Laboratory of Ecophysiology, Biochemistry, and Toxicology, Groenenborgerlaan 171, B-2020 Antwerp, Belgium. Telephone: 32-32-653-479. Fax: 32-32-653-497. E-mail:
[email protected] authors declare they have no competing financial interests.
1 2006 1 9 2005 114 1 100 105 25 3 2005 1 9 2005 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. We investigated estrogen-like properties of five perfluorinated compounds using a combination of three in vitro assays. By means of an E-screen assay, we detected the proliferation-promoting capacity of the fluorotelomer alcohols 1H,1H,2H,2H-perfluorooctan-1-ol (6:2 FTOH) and 1H,1H,2H,2H-perfluoro-decan-1-ol (8:2 FTOH). The more widely environmentally distributed compounds perfluoro-1-octane sulfonate, perfluorooctanoic acid, and perfluorononanoic acid did not seem to possess this hormone-dependent proliferation capacity. We investigated cell cycle dynamics using flow cytometric analyses of the DNA content of the nuclei of MCF-7 breast cancer cells. Exposure to both fluorotelomer alcohols stimulated resting MCF-7 cells to reenter the synthesis phase (S-phase) of the cell cycle. After only 24 hr of treatment, we observed significant increases in the percentage of cells in the S-phase. In order to further investigate the resemblance of the newly detected xenoestrogens to the reference compound 17β-estradiol (E2), gene expression of a number of estrogen-responsive genes was analyzed by real-time polymerase chain reaction. With E2, as well as 4-nonylphenol and the fluorotelomer alcohols, we observed up-regulation of trefoil factor 1, progesterone receptor, and PDZK1 and down-regulation of ERBB2 gene expression. We observed small but relevant up-regulation of the estrogen receptor as a consequence of exposures to 6:2 FTOH or 8:2 FTOH. The latter finding suggests an alternative mode of action of the fluorotelomer alcohols compared with that of E2. This study clearly underlines the need for future in vivo testing for specific endocrine-related end points.
cell cycleE-screenfluorotelomer alcoholsreal-time PCRxenoestrogen
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Over past decades, a whole range of fluorinated chemicals have been synthesized and used as wetting agents, lubricants, corrosion inhibitors, insecticides, cosmetics, fire retardants, paper coatings, and surfactants (Renner 2001, 2003). The high stability of the carbon–fluorine bond and the inert characteristics of most of these compounds are regarded as attractive properties during the manufacture of plastics, electronics, textiles, or construction materials. For a long time, these fluorinated chemicals were considered metabolically inert and nontoxic (Sargent and Seffl 1970). However, environmental monitoring has shown that degradation to persistent molecules does happen on a large scale, as deduced from the worldwide distribution of compounds such as perfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA), perfluorohexanesulfonate, and perfluorooctanesulfonamide (Dimitrov et al. 2004; Dinglasan et al. 2004; Giesy et al. 2001; Hoff et al. 2003a, 2003b; Martin et al. 2004). Martin et al. (2004), for instance, describe fluorotelomer alcohols as potential sources of perfluorinated acids in regions as remote as the Arctic. Although the fluorotelomer alcohols are known as volatile chemicals that are capable of long-range atmospheric transport, biologic transformation seems to be the major degradation pathway causing deposition of mentioned perfluorinated acids. In addition, during past years many of the perfluorinated compounds have been found to possess undesirable toxic characteristics. As reviewed by Lau et al. (2004), perfluoroalkyl acids and their derivatives can cause developmental toxicity. Exposures of rats to PFOA may cause significant lags of weight gain of the offspring and a statistically significant increase in mortality in both male and female pups. PFOS exposure may provoke weight loss, hepatotoxicity, and reduction of serum cholesterol and thyroid hormones. PFOS apparently is also able to affect the neuroendocrine system (Austin et al. 2003). Female rats injected with PFOS have a disturbed estrous cyclicity and increased serum corticosterone levels with decreasing serum leptin levels. Increased norepinephrine concentrations were found in the paraventricular nucleus of the hypothalamus. The fact that perfluorinated chemicals may disturb the endocrine system is worrying and deserves further investigation. It is generally known that a well-functioning endocrine system depends on a delicate balance of hormones and hormone receptors that interact to provoke complex cellular signaling. Different environmental pollutants act as hormone mimics, binding to specific hormone receptors or indirectly interfering with hormone signaling. The consequence may be irreversible damage to the reproductive system, especially when living organisms are exposed during the embryonic stages of life (Degen and Bolt 2000; Rosselli et al. 2000). Behavioral changes are another well-known adverse effect of disturbance caused by endocrine-disruptive chemicals (Schantz and Widholm 2001). Although disturbance of the thyroid system seems to be provoked by specific perfluorinated chemicals such as PFOS, their potential for estrogen-like properties has not been reported until now. In the present study, we evaluated the capacity of perfluorinated compounds to reinduce cell proliferation of growth-arrested MCF-7 breast cancer cells. Using a combination of the E-screen assay, cell cycle analysis, and gene expression analysis of estrogen-responsive bio-marker genes, we demonstrate the estrogen-like properties of the fluorotelomer alcohols 1H,1H,2H,2H-perfluorooctan-1-ol (6:2 FTOH) and 1H,1H,2H,2H-perfluorodecan-1-ol (8:2 FTOH) in vitro.
Materials and Methods
Chemicals.
PFOS (perfluoro-1-octane sulfonate, tetramethylammonium salt; 98%), 17β-estradiol (E2), 4-nonylphenol (4-NP), and PFOA (pentadecafluorooctanoic acid; 96%) were purchased from Sigma-Aldrich (Steinheim, Germany), and perfluorononanoic acid (PFNA; 97%) was provided by Avocado Research Chemicals (Lancashire, UK). We purchased 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) from LGC Promochem (Middlesex, UK) and Dulbecco’s minimal essential medium (DMEM) and fetal bovine serum (FBS) from Gibco BRL Life Technologies (Paisley, Scotland). 1H,1H,2H,2H-Perfluorooctan-1-ol (6:2 FTOH) and 1H,1H,2H,2H-perfluorodecan-1-ol (8:2 FTOH) were purchased from Interchim (Montlucon, France). We assessed the purity of the fluo-rotelomer alcohol standards by gas chromatography coupled to full-scan mass spectrometry in the electron impact (EI), negative chemical ionization (NCI), and positive chemical ionization (PCI) modes. No impurities could be detected in either EI or PCI mode. In NCI mode, several signals were observed. Retention times and full-scan mass spectra of these signals revealed that they were very closely related to the main signal. They were tentatively elucidated as branched isomers of the FTOHs.
Cell culture.
MCF-7 human Caucasian breast adenocarcinoma cells (no. 86012803; European Collection of Cell Cultures, Salisbury, UK) were cultured in 25-cm2 Nunc cell culture flasks (Nunc, Roskilde, Denmark) in standard growth medium (DMEM; Gibco BRL; supplemented with 2 mM glutamine, 1% nonessential amino acids, 5% heat-inactivated FBS, and phenol red as an indicator of pH). Cells were maintained in a 37°C incubator under a 5% CO2 atmosphere over a maximum of 30 passages. Cells were grown to 80–90% confluency before splitting them into one-fifths.
E-screen assay.
We tested the proliferation-inducing capacity of chemicals using the E-screen assay according to Payne et al. (2000), with minor modifications to the protocol. In short, cells were seeded in black 96-well microtiter plates with clear, flat bottoms (Nunc) at a density of 2,000 cells/well. Cells attached overnight, after which standard growth medium was replaced with phenol red–free medium containing 5% charcoal/dextran-stripped FBS (CSFBS). A previous wash step with phosphate-buffered saline (PBS) assured removal of all estrogenic compounds. Cells were then incubated for 72 hr to make them estrogen responsive. Exposures to estrogenic compounds or xenoestrogens were started by adding chemicals to the cells from so-called chemical plates. In the latter plates, 2-fold dilution series of the chemicals were prepared, followed by transferring 20–180 μL in the wells of the cell plates. To guarantee minimal interference with cell physiologic responses, the concentration of the solvent (DMSO) did not exceed 0.1%. Plates were covered with gas-permeable sealing tape and incubated at 37°C for 6 days. Proliferation of cells was assessed using the CyQuant assay (Molecular Probes, Invitrogen, Merelbeke, Belgium) (Jones et al. 2001).
Cell cycle analysis.
We seeded MCF-7 cells in 25-cm2 Nunc cell culture flasks in standard growth medium at a density of 300,000 cells/flask. Cells attached overnight, after which the growth medium was replaced by phenol red–free DMEM containing CSFBS. After incubation in estrogen-free medium for 72 hr, cells were exposed to E2 or test compounds at concentrations corresponding to the highest observed effect during the E-screen assay. After 24 hr, cells were harvested by trypsinization and washed twice in PBS. Next, cell nuclei were isolated and stained with propidium iodide (PI) for 1 hr as described by Vindelov et al. (1983). We performed flow cytometric analysis of cell cycle distribution and apoptosis with an LSRII flow cytometer with a 488-nm argon-ion laser (Becton Dickinson, San Jose, CA, USA). PI fluorescence was collected at bandpass 575/26 nm(FL2, red fluorescence channel) in the linear mode. For each measurement, data from 10,000 single cell events were collected, whereas cell aggregates and doublets were gated out in the two-parameter histograms of pulse height to pulse width of PI fluorescence. We analyzed cell cycle histograms using ModFit LT 3.0 software (Variety Software House, Topsham, ME, USA).
Gene expression analysis by reverse-transcription polymerase chain reaction (PCR).
MCF-7 cells were seeded and grown in estrogen-free medium in a manner analogous to that described above for flow cytometric analyses. After 48 hr exposure to E2 or test compounds, total RNA was extracted from the cell using the RNeasy kit (Qiagen GmbH, Hilden, Germany) according to manufacturer’s instructions. RNA quantity and quality were evaluated using a NanoDrop spectrophotometer (Nanodrop Technologies, Wilmington, DE, USA). First-strand cDNA was synthesized using the Fermentas first-strand cDNA synthesis kit (MBI Fermentas Life Sciences, St. Leon-Rot, Germany). In brief, 1 μg total RNA was incubated with 0.5 μg oligo(dT)18 primer and incubated at 70°C for 5 min to denature RNA. Next, 20 U recombinant ribonuclease inhibitor and 1 mM dNTP mix were added to the RNA in the following reaction buffer: 50 mM Tris-HCl, pH 8.3; 50 mM KCl; 4 mM MgCl2; and 10 mM dithiothreitol. cDNA synthesis was started by adding 40 U M-MuLV (Moloney murine leukemia virus) reverse transcriptase at 37°C for 1 hr. Reaction was stopped by inactivation of the reverse transcriptase at 70°C for 10 min. The final volume (20 μL) was adjusted to 100 μL.
We designed highly purified salt-free OliGold primers (Eurogentec, Seraing, Belgium) for the internal control gene hypoxanthine phosphoribosyltransferase 1 (HPRT1) and for target genes estrogen receptor-α (ESR1), progesterone receptor (PGR), PDZ domain containing 1 (PDZK1), and erb-b2 erythroblastic leukemia viral oncogene homolog 2 (ERBB2) using Roche Lightcycler software (Roche Diagnostics Belgium, Vilvoorde, Belgium). The sequences of the primers were as follows: for HPRT1 (GenBank accession no. NM-000194; GenBank 2005), 5′-TGACACTGGCAAAACAATGCA-3′ and 5′-GGTCCTTTTCACCAGCAAGCT-3′; for ESR1 (GenBank accession no. NM-000125), 5′-CCATGGAATAGCTAGT-3′ and 5′-CAGTGGCCTAAATCAA-3′; for PGR (GenBank accession no. NM-000926). 5′-TGGTCCTTGGAGGTCG-3′ and 5′-GCCTCTCGCCTAGTTG-3′; for PDZK1 (GenBank accession no. AF012281), 5′-AACCATGACTCGCACA-3′ and 5′-AGCCGTCTGCAATAGC-3′; for ERBB2 (nuclear receptor ERBB2; GenBank accession no. AF094517), 5′-GACACCTACGGCAGAG-3′ and 5′-GTGGCATCCACTGGAC-3′. The sequences of the primers for trefoil factor 1 (TFF1; pS2; GenBank accession no. X00474) as derived from Bièche et al. (2001) were 5′-CATCGACGTCCCTCCAGAAGAG-3′ and 5′-CTCTGGGACTAATCACCGTGCTG-3′.
For the Lightcycler reaction, we prepared a master mix of the following components to the indicated end concentration: 9 μL water, 1 μL forward primer (0.5 μM), 1 μL reverse primer (0.5 μM), and 4 μL Lightcycler Fast Start DNA Master SYBR Green I reagent mixture (Roche). The Lightcycler glass capillaries were filled with 15 μL master mix, and 5 μL cDNA (50 ng reverse-transcribed total RNA) was added as the PCR template. We used the following experimental protocol: denaturation program (95°C for 5 min), amplification program (95°C for 5 sec, 58°C for 5 sec, 72°C for 13 sec, with a single fluorescence measurement at the end of DNA synthesis), melting curve program (55–95°C with a heating rate of 0.1°C/sec and a continuous fluorescence measurement), and finally a cooling step to 40°C. Expression changes of specific target genes were deduced from shifts of the crossing points for the target genes in exposed versus non-exposed cells and normalized by comparison with the internal control gene HPRT1. The “crossing point” is the point at which the fluorescence rises appreciably above the background fluorescence. The relative expression ratio of the gene under study normalized by the internal control HPRT1 gene was calculated using REST software (version 2; Pfaffl et al. 2002). We used the pairwise fixed reallocation randomization test included in the REST software to test the significance of the derived results. Each chemical treatment was performed in triplicate, which provided three replicate samples for the reverse-transcriptase (RT)-PCR analyses. To check amplification of the correct PCR products, we performed analysis by 1.5% agarose gel electrophoresis.
Results
Stimulation of MCF-7 cell proliferation.
For this study, we adapted the E-screen assay to 96-well microtiter plate format in order to test broad concentration ranges of compounds for their proliferation-inducing capacity in growth-arrested breast cancer cells. In accordance with Payne et al. (2000), we incubated the MCF-7 cells for 72 hr in estrogen-free growth medium to induce the estrogen responsive.
Figure 1 shows the proliferative effect (PE) as a result of exposure to a concentration range of the test compounds. PE (expressed as fold/control) is calculated as the ratio between the cell yield obtained with the test chemical and that with the hormone-free control. Table 1 presents the relative proliferative effect (RPE), which corresponds to the ratio of the maximal cell yield achieved by the minimum dose of xenobiotic relative to the reference compound (1 nM E2) and multiplied by 100. Table 1 also shows the relative proliferative potency (RPP), which corresponds to the ratio of the dose of E2 and that of xenobiotic needed to achieve a maximal PE. As demonstrated in Figure 1 and in Table 1, 6:2 FTOH and 8:2 FTOH behave like xenoestrogens in vitro. These compounds clearly induce cell proliferation at 10 μM, the concentration at which many other xenoestrogens are also active (Soto et al. 1995). Neither exposures to PFOS or PFOA within the concentration ranges applied nor the negative control TCDD led to increased cell proliferation.
Effects of perfluorinated compounds on cell cycle distribution.
We performed further evaluation of the fluorinated compounds by flow cytometric analyses of the cell cycle. Although growth-arrested MCF-7 cells are predominantly in the G0/G1-phase of the cell cycle, addition of (xeno-)estrogens makes cells proliferate again, shown by the marked increase in the percentage of cells in the S-phase after 24 hr of exposure.
In Figure 2, the histograms of DNA content show increases in S-phase as the result of exposures to the fluorotelomer alcohols and 4-NP. In Table 2, results are expressed as percentages of cells in the different phases of the cell cycle. The increases of cell numbers in the S-phase range from 6% (solvent control) to almost 35% (1 nM E2 and 4-NP), approximately 31% (30 μM, 6:2 FTOH), and approximately 29% (10 μM, 8:2 FTOH). PFOS, PFOA, or PFNA, at concentrations ≤ 50 μM, did not affect proliferation. As shown in Table 3, fluorotelomer alcohols stimulate MCF-7 cells at concentrations ranging between 10−6 and 10−5 M. At 10−7 M, the stimulatory effect is lost. These results corroborate the findings from the E-screen assay and demonstrate the reinduction of cell proliferation of growth-arrested MCF-7 cells within a much shorter exposure period.
Expression alterations of estrogen-responsive genes.
Reverse-transcription PCR was performed to analyze the expression of specific estrogen-responsive biomarker genes after 48 hr of exposure. As presented in Figure 3, significantly high up-regulation of TFF1 mRNA was observed with E2 (7×), 4-NP (3.8×), 6:2 FTOH (6.2×), and 8:2 FTOH (2.4×). A small up-regulation was also observed with PFOA (1.4×), and a small but significant down-regulation was observed upon exposure to PFOS (1.7×). Exposure to E2 induced very high PGR mRNA levels (30×), whereas significant up-regulations were seen with 4-NP (4.5×), 6:2 FTOH (10.4×), and 8:2 FTOH (2.4×). ESR1 was down-regulated upon exposures to E2 (1.33×) or 4-NP (2.1×). With 6:2 FTOH and 8:2 FTOH, however, small but significant up-regulations of ESR1 were observed (2.2× up with both compounds). PFOS exposure resulted in a significant small down-regulation (3.8×). We studied the expression levels of two additional estrogen-responsive genes in order to further reveal the similarity of the telomeric alcohols to E2. An up-regulation of PDZK1 expression was observed with E2 (41×), with 4-NP (13.2×) as well as with both perfluorinated telomeric alcohols (5.4× with 6:2 FTOH and 2.4× with 8:2 FTOH). Significant down-regulation of ERBB2 was observed with E2 (4.5×) and 4-NP (4.4×), whereas less pronounced but nonetheless significant down-regulations were also observed with 6:2 (2.4×) FTOH and 8:2 FTOH (2.4×). A small down-regulation of ERBB2 was observed with PFOA (1.5×).
Discussion
A range of fluorinated chemicals synthesized during the past few years are promising for various industrial applications (Lehmler 2005). However, because of the persistent nature of these chemicals, monitoring their environmental fate and their ecotoxicologic characteristics is especially warranted (Van de Vijver et al. 2003, 2004). Chemicals that are difficult to degrade biologically may bioaccumulate and may affect the health of humans and biota. Disturbance of the endocrine system is one example of the toxic effects that need careful follow-up. Endocrine disruptors may mimic hormones or interfere indirectly with hormonal pathways. Damage caused by these compounds may, in the long term, lead to drastic effects such as decreased reproduction, or perhaps more subtle effects, such as a disturbance to the developmental system resulting in behavioral effects (e.g., learning disorders). Until now, studies that investigated endocrine-disrupting capacities of fluorinated compounds have been difficult to find. In a review describing developmental toxicity of perfluoroalkyl acids, Lau et al. (2004) highlighted the disturbances of the thyroid gland caused by such compounds (e.g., PFOS causing hypothyroxinemia). Because thyroid hormones are known to regulate brain development, these findings merit further research.
During the present study, the estrogen-like capacities of the fluorinated compounds 6:2 FTOH, 8:2 FTOH, PFOS, PFOA, and PFNA were studied in vitro. We used a combination of three different in vitro assays to demonstrate these findings. First, we used the E-screen assay, a commonly used high-throughput test to detect estrogen-like compounds in environmental samples. Using this assay, we found that 6:2 FTOH and 8:2 FTOH behave like xenoestrogens in vitro. These compounds clearly induce cell proliferation at 10 μM, the concentration at which many other xenoestrogens are also active (Soto et al. 1995). To complement and corroborate our E-screen results, we studied cell cycle dynamics using flow cytometry. Cells in estrogen-free growth medium do not enter the S-phase easily (Villalobos et al. 1995). Although > 80% of the MCF-7 cells were in growth arrest (G0/G1-phase of the cell cycle), the addition of xenoestrogens stimulated cells to synthesize new DNA in preparation of cell division, as revealed by the significant increase of the number of cells in the S-phase of the cell cycle. These increases were clearly observable after 24 hr exposure to the telomeric alcohols. Upon comparison of the E-screen assay with flow cytometric analyses, we found similar effective concentrations of E2, 4-NP, and the fluorotelomer alcohols. However, the estrogen-like compounds only induced 2- to 3-fold increases of cell numbers during the E-screen assay, whereas during flow cytometric analyses, we observed up to 5-fold increases of cells in S-phase. Because the exposure periods of E-screen (6 days) and flow cytometric analyses (24 hr) are very different, we also studied cell cycle dynamics after longer exposure periods. After 48 hr, we observed a significant drop of the percentage of cells in S-phase (results not shown). Apparently, cells that are boosted to reenter the cell cycle by a 24-hr xenoestrogen exposure rapidly return to a more modest proliferation rate after 48 hr. One possible explanation is based on the fact that MCF-7 cells express the estrogen receptor as well as the progesterone receptor. Cross-talk exists between nuclear receptors. For instance, progesterone receptor A may act as a repressor of transcriptional activities of different other members of the nuclear receptor family, among them the estrogen receptor (Kraus et al. 1995, 1997). A variation in parameters, such as the ratio of progesterone receptor A to progesterone receptor B, may be the consequence of exposures to (xeno-)estrogens, and apparently this altered ratio may dramatically affect estrogen receptor signaling activities. Another important issue to investigate further is the fact that nuclear receptor levels differ in different breast cancer cell lines and even within different clones of a cell line, which may explain why xenoestrogens provoke different cell proliferation responses with different MCF-7 cell lines (Coser et al. 2003; Villalobos et al. 1995).
In order to unravel the mode of action of estrogens and xenoestrogens, gene expression analysis of selected estrogen-responsive genes was performed (Frasor et al. 2003; Inoue et al. 2002). The expression changes of a small number of estradiol-responsive genes such as TFF1, PGR, ESR1, PDZK1, and ERBB2 were studied using reverse-transcription PCR. TFF1 is generally accepted as one of the most reliable estrogen-responsive biomarker genes for in vitro MCF-7 breast cancer cells (Jorgensen et al. 2000; Olsen et al. 2003; Wang and Lou 2004). This factor, also known as pS2, belongs to a family of “trefoil peptides” probably involved in the regulation of cell proliferation. PDZK1 is another frequently reported estrogen-responsive gene (Ghosh et al. 2000; Yoshida et al. 2004). Proteins containing the PDZ domain are involved in organizing cell membrane proteins and are also involved in linking transmembrane proteins to the actin cytoskeleton (Yang et al. 1998). The induction of PDZK1 by E2 is suggested to play a crucial role in membrane alterations that happen upon estrogen treatment such as formation of microvilli. ERBB2 is a transmembrane tyrosine kinase receptor playing a role in mammary oncogenesis. This receptor is up-regulated in MCF-7 cells grown in estradiol-free medium and is down-regulated again upon addition of E2 (Martin et al. 2004; Vendrell et al. 2004). The estrogen-responsive genes TFF1, PGR, PDZK1, and ERBB2 were commonly responsive to E2 as well as to the xenoestrogen 4-NP and the tested fluorotelomer alcohols. However, although a common up- or down-regulation is observed, the degree of response of the different genes may differ markedly, probably as a consequence of structural differences of the xenoestrogens (Terasaka et al. 2004). These differences may be responsible for and reflect the modes of action. Such differences were also found during the present study. For instance, although 4-NP appears to be a weaker inducer of TFF1 and PGR than 6:2 FTOH, it seems to be a stronger inducer of PDZK1. To discriminate between different xenoestrogens with different modes of action, many more estrogen-responsive genes should be studied. Microarray analyses are used to characterize and classify known and newly detected xenoestrogens according to their different modes of action. The MCF-7 cell line may be an attractive model for this kind of study, due to possible cross-talk between the different hormone receptors of this cell line (Lange et al. 2005). Although we observed a down-regulation of ESR1 expression by E2 and 4-NP, the fluorotelomer alcohols used in the present study caused a significant, approximately 2-fold up-regulation of this receptor. This finding suggests an alternative mode of action, different from that of the reference compound E2. Up-regulation of the estrogen receptor by presumed xenoestrogens is not unusual, as previously demonstrated for endosulfan, toxaphene, and dieldrin (Soto et al. 1995).
These results warrant further work toward in vivo testing for specific endocrine-disruptive end points. Our results have been generated with an in vitro system using a single cell line, confirming the estrogen-like properties at different molecular levels. However, at present, it is not at all clear whether fluorotelomer alcohols are causing endocrine disruption under realistic environmental exposure conditions. Information concerning in vivo studies is just becoming available. A one-generation reproductive toxicity study with rats suggests no harmful effect on reproduction (Mylchreest et al. 2005). These authors did not observe any test-substance–related effects on estrous cycle parameters or sperm morphology, motility, or epididymal sperm counts in the first parental generation. Mylchreest et al. (2005) detected no clear estrogen-like properties in this rat in vivo study. In another long-term rat exposure study (90 days) using a mixture of fluorotelomer alcohols (at doses ≥ 100 mg/kg/day), Ladics et al. (2005) found a persistent elevation of liver weights and thyroid follicular hypertrophy. One possible explanation for the observed discrepancy between our in vitro results and the few in vivo data might be related to differences in fluorotelomer metabolism between the breast cancer cell line and the in vivo exposure condition. These compounds may be converted in rats to other fluorinated molecules, such as PFOA, and hence, fluorotelomer alcohol exposures result in PFOA-like in vivo effects (Berger U, personal communication; Lehmler 2004). Clearly, these possible contradictions between in vitro screening assays and in vivo data merit further study. At present, it is questionable whether the fluorotelomer alcohols used in the present study might act as endocrine-disrupting xenoestrogens on various organisms that might have different metabolizing capacities. Organisms or individuals with a low fluorotelomer-metabolizing activity might be at risk.
Regarding environmental exposure conditions, few data are available at present. Although fluorotelomer alcohols have been detected in the atmosphere at concentrations up to 135 pg/m3 (Martin et al. 2004), there are presently no records of these compounds in surface water, sediment, or wildlife.
In conclusion, we characterized fluorotelomer alcohols as xenoestrogens in vitro. The structural similarities of these compounds and 4-NP, the reference xenoestrogen, offer a possible explanation why these new compounds may act as ligands for the estrogen receptor (Katzenellenbogen 1995). para-Alkylphenols have been shown to bind fully to the estrogen receptors in a dose-dependent manner, and the interaction of alkylphenols with the receptor became stronger with an increase in the number of alkyl carbons (Tabira et al. 1999). In the present study, 6:2 FTOH was characterized as a stronger xenoestrogen than 8:2 FTOH. It is very likely that the chain length of the alkyl group is the responsible factor. The characterization of fluorotelomer alcohols as in vitro xenoestrogens demonstrates the need to carefully monitor their environmental distribution and to further investigate the effects of perfluorinated compounds on biota.
This study received support from the Instituut voor de Aanmoediging van Innovatie door Wetenschap en Technologie through the Generisch basisonderzoek aan de universiteiten project (IWT 020176) titled “Development of Environmental Diagnostics Based on Toxicogenomics and Bio-informatics.” This study was further supported by the Fonds voor Wetenschappelijk Onderzoek (G.0355.02N and G.0466.03N) and by the PERFORCE project (European Union, NEST-508967).
Figure 1 Analysis of estrogenicity of E2 (A), 4-NP (B), 6:2 FTOH (C), 8:2 FTOH (D), PFOS (E), and PFOA (F ) by the E-screen assay in MCF-7 cells. 0.1% DMSO was the solvent control. Results are expressed as mean ± SD of three replicates for each data point.
Figure 2 Histograms of DNA content showing the effects of perfluorinated compounds on cell cycle distribution. (A) 0.1% DMSO (solvent control). Cells were cultured in DMEM plus 5% CSFBS for 72 hr before exposing them to estrogenic compounds (B, 1 nM E2; C, 10 μM 4-NP; D, 30 μM 6:2 FTOH; and E, 10 μM 8:2 FTOH) and non-estrogenic perfluorinated compounds (F, 50 μM PFOS; G, 50 μM PFNA) for 24 hr. 4-NP (C) was the positive control, and 10 nM TCDD (H) was the negative control.
Figure 3 Effect of perfluorinated chemicals on mRNA expression of estrogen-responsive genes in MCF-7 cells were treated with 0.1% DMSO, 1 nM E2, 10 μM 4-NP, 30 μM 6:2 FTOH, 10 μM 8:2 FTOH, 50 μM PFOS, 50 μM PFNA, 50 μM PFOA, or 10 nM TCDD. After exposure to the test compounds for 48 hr, mRNA levels of TFF1 (A), PGR (B), ESR1 (C), PDZK1 (D), and ERBB2 (E) were measured by real-time PCR and normalized using HPRT1 as an internal control. Results are means from three replicate measurements and are expressed as fold relative to 0.1% DMSO; error bars indicate SD.
*p < 0.05. **p ≤ 0.001.
Table 1 Estrogenic effect of perfluorinated compounds according to the E-screen assay.
Compound Concentration RPE RPP
E2 1 nM 100 1
4-NP 10 μM 100 10−4
6:2 FTOH 10 μM 50 10−4
8:2 FTOH 10 μM 46 10−4
Table 2 Results of cell cycle analyses of MCF-7 cells exposed to different perfluorinated compounds given as the percentage of cells by phase.
Treatment G1/G0-phase S-phase G2/M-phase
0.1% DMSO 90.43 ± 1.01 6.00 ± 1.00 3.57 ± 1.64
E2 (1 nM) 63.14 ± 1.61 34.84 ± 2.48 2.03 ± 0.88
4-NP (10 μM) 63.71 ± 1.86 34.64 ± 0.47 1.64 ± 1.41
6:2 FTOH (30 μM) 66.98 ± 4.09 30.83 ± 3.23 2.19 ± 0.87
8:2 FTOH (10 μM) 68.53 ± 1.48 29.36 ± 1.78 2.11 ± 0.49
PFOS (50 μM) 85.63 ± 0.94 10.49 ± 0.71 3.87 ± 0.73
PFNA (50 μM) 85.53 ± 1.64 9.89 ± 1.53 4.57 ± 0.42
PFOA (50 μM) 83.57 ± 1.04 9.17 ± 0.57 6.83 ± 0.59
TCDD (10 nM) 87.46 ± 0.30 7.96 ± 0.37 4.58 ± 0.45
Values are mean ± SD of three measurements per treatment. During all measurements, coefficient of variation values of the G0/G1 peak were < 3.6 (n = 3).
Table 3 Results of cell cycle analyses of MCF-7 cells exposed to concentrations of fluorotelomer alcohols given as the percentage of cells by phase.
Treatment G1/G0-phase S-phase G2/M-phase
0.1% DMSO 90.43 ± 1.01 6.00 ± 1.00 3.57 ± 1.64
6:2 FTOH (30 μM) 66.98 ± 4.09 30.83 ± 3.23 2.19 ± 0.87
6:2 FTOH (3 μM) 80.14 ± 3.04 15.63 ± 3.07 4.22 ± 0.34
6:2 FTOH (0.3 μM) 88.22 ± 0.48 7.89 ± 0.53 3.89 ± 0.38
8:2 FTOH (10 μM) 68.53 ± 1.48 29.36 ± 1.78 2.11 ± 0.49
8:2 FTOH (1 μM) 82.70 ± 1.04 12.72 ± 0.84 4.58 ± 0.35
8:2 FTOH (0.1 μM) 87.04 ± 0.47 8.41 ± 0.04 4.56 ± 0.43
Values are mean ± SD of three measurements per treatment. During all measurements, coefficient of variation values of the G0/G1 peak were < 3.6 (n = 3).
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8451ehp0114-00010616393666ResearchThe Estrogenic Effect of Bisphenol A Disrupts Pancreatic β-Cell Function In Vivo and Induces Insulin Resistance Alonso-Magdalena Paloma 1Morimoto Sumiko 12Ripoll Cristina 1Fuentes Esther 1Nadal Angel 11 Instituto de Bioingeniería, Universidad Miguel Hernández de Elche, Alicante, Spain2 Departamento de Biología de la Reproducción, Instituto Nacional de Ciencias Médicas y Nutrición “Salvador Zubirán,” México City, MéxicoAddress correspondence to A. Nadal, Instituto de Bioingeniería, Universidad Miguel Hernández de Elche, Carretera Alicante-Valencia Km 87, Sant Joan d’Alacant, 03550 Alicante, Spain. Telephone: 34-96-5919535. Fax: 34-96-5919547. E-mail:
[email protected] authors declare they have no competing financial interests.
1 2006 20 9 2005 114 1 106 112 30 6 2005 19 9 2005 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. The function of the pancreatic β-cell is the storage and release of insulin, the main hormone involved in blood glucose homeostasis. The results in this article show that the widespread environmental contaminant bisphenol-A (BPA) imitates 17β-estradiol (E2) effects in vivo on blood glucose homeostasis through genomic and nongenomic pathways. The exposure of adult mice to a single low dose (10 μg/kg) of either E2 or BPA induces a rapid decrease in glycemia that correlates with a rise of plasma insulin. Longer exposures to E2 and BPA induce an increase in pancreatic β-cell insulin content in an estrogen-receptor–dependent manner. This effect is visible after 2 days of treatment and starting at doses as low as 10 μg/kg/day. After 4 days of treatment with either E2 or BPA, these mice developed chronic hyperinsulinemia, and their glucose and insulin tolerance tests were altered. These experiments unveil the link between environmental estrogens and insulin resistance. Therefore, either abnormal levels of endogenous estrogens or environmental estrogen exposure enhances the risk of developing type 2 diabetes mellitus, hypertension, and dyslipidemia.
bisphenol Adiabetesendocrine disruptorsestradiolestrogen receptorinsulinislet of Langerhansnongenomicxenoestrogens
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Insulin resistance is a crucial constituent of the metabolic syndrome, and its presence predicts type 2 diabetes and atherosclerotic cardiovascular disease (DeFronzo and Ferrannini 1991). In addition to insulin resistance, type 2 diabetes mellitus is also characterized by a progressive β-cell dysfunction. In most patients, both symptoms are present several years before the onset of hyperglycemia.
The incidence of diabetes has increased in the last decades, and at present it is reaching epidemic levels (177 million persons with diabetes in the world; World Health Organization 2005). The cornerstones of its development are related to modern lifestyle, principally, a lack of exercise and an unhealthy diet. Other pathologies whose incidence rose dramatically after World War II, such as cancer, reproductive impairment, and neurodegenerative diseases, are probably related to the increase of endocrine-disrupting chemicals (EDCs) in the environment (Colborn et al. 1996). However, an experimental link between EDCs and diabetes has not yet been established, although a connection at the epidemiologic level in humans has been recently proposed for dioxin, an environmental contaminant that acts through other than estrogen receptors (ERs) as an endocrine disruptor (Bertazzi et al. 2001; Rene and Bunce 2002).
A large number of EDCs act by mimicking the action of the sex hormone 17β-estradiol (E2) (Colborn et al. 1993). In most cases they bind to the classic ERs, ER-α and ER-β (McLachlan 2001; Newbold 2004), but they can also act through novel estrogen targets (Nadal et al. 2005). At physiologic levels, E2 is thought to be involved in maintaining normal insulin sensitivity and to be beneficial for β-cell function (Livingstone and Collison 2002; Louet et al. 2004). However, abnormal levels of E2 may promote insulin resistance (Livingstone and Collison 2002), similar to what occurs in normal puberty and pregnancy (Amiel et al. 1991; Hollingsworth 1983). Therefore, the exposure to an exogenous chemical acting as the natural hormone E2, but at an inappropriate concentration and during an improper time window, may enhance the risk of developing insulin resistance.
In spite of the many clinical studies that link sex steroids and actions of insulin, few studies have investigated the molecular basis of the interaction between E2, the pancreatic β-cell function, blood glucose homeostasis, and the development of diabetes. Pancreatic β-cells contain both types of ERs, ER-α and ER-β (Nadal et al. 2000). Although their functions are still greatly undetermined, ER-α and ER-β are involved in important aspects of the β-cell physiology (Nadal et al. 2004; Sutter-Dub 2002). These include protection against β-cell death caused by cytokines (Contreras et al. 2002) and, after a prolonged application, a beneficial effect on diabetes in mice expressing human islet amyloid peptide (Geisler et al. 2002). The involvement of ERs in lipid and glucose metabolism has been demonstrated in ER-α knockout mice that display increased adiposity, insulin resistance, and glucose intolerance (Heine et al. 2000). In addition, β-cells have the nonclassical membrane ER (ncmER) that triggers rapid effects (Nadal et al. 1998, 2000; Quesada et al. 2002). Recently, a similar receptor has been found in Drosophila (Srivastava et al. 2005). E2 rapidly potentiates β-cell signaling systems and insulin release via this ncmER, an effect that is mimicked by EDCs, including bisphenol A (BPA) (Nadal et al. 2004). BPA is one of the most common chemicals that behaves as an endocrine disruptor. It was the first synthetic estrogen without a steroid structure (Dodds and Lawson 1936), but because of its properties as a cross-linking chemical, BPA was widely chosen by the chemical industry to produce plastic polymers, mainly poly-carbonates. Nowadays, it is used in the manufacture of barrier coatings for the inner surfaces of food and beverage cans. High concentrations of BPA have been detected in food and water extracted from autoclaved cans (Brotons et al. 1995). BPA is one of the highest-volume chemicals produced in the world, and its exposure is widespread: it has been found in 95% of the urine samples from people in the United States and to a similar extent is found in human blood, as well (Ikezuki et al. 2002; vom Saal and Hughes 2005). The European Commission’s Scientific Committee on Food (ECSCF 2002) reported a tolerable daily intake (TDI) of 10 μg/kg/day. However, the U.S. Environmental Protection Agency (U.S. EPA) considers 50 μg/kg/day the reference dose based on the lowest observed adverse effect level (LOAEL) of 50 mg/kg/day, according to studies performed in the 1980s (vom Saal and Hughes 2005). In vivo studies using much lower doses of BPA than the LOAEL have shown that it affects sexual maturation (Howdeshell et al. 1999), induces a decrease in daily sperm count and fertility (vom Saal et al. 1998), disrupts chromosome alignment (Hunt et al. 2003), and affects synaptogenesis (McLusky et al. 2005). In spite of this evidence, there is an ongoing debate with the chemical industry that is still skeptical about the risk of low doses of BPA (Bisphenol A Global Industry Group 2005).
In this article we show that the exposure of adult mice to BPA, at doses about 1,000-fold less than the LOAEL established by the U.S. EPA, alters blood glucose homeostasis in vivo. It rapidly increases plasma insulin, altering blood glucose concentration through a nonclassical estrogen pathway. Longer exposure increases β-cell insulin content, an effect that involves classic ERs. These longer exposures generate chronic hyperinsulinemia in the fed state and peripheral insulin resistance.
Materials and Methods
Materials.
We obtained ICI182,780 (ICI) from Tocris Cookson Ltd. (Avonmouth, UK), tocopherol-stripped corn oil from MP Biomedicals, LLC (Solon, OH, USA), and soluble insulin from Humulina Regular (Lilly, Madrid, Spain). Other substances were obtained from Sigma (Madrid, Spain).
Animals.
We used Swiss albino OF1 male mice (8–10 weeks of age) throughout this study. All animals were kept under standard housing conditions and were treated humanely and with regard for alleviation of suffering. An internal animal care and use committee reviewed and approved the method used.
Treatment.
Stimuli (E2 and BPA) were dissolved in tocopherol-stripped corn oil and administered subcutaneously at various concentrations. The amount of vehicle was kept constant at 100 μL. In long-term experiments, animals were injected twice per day, at 0900 hr and 2000 hr, with 50 μg/kg or 5 μg/kg of test compound. Two injections of 50 μg/kg/day during 4 days gave a plasma concentration of E2 similar to that found in late pregnancy (Song et al. 2001). In ICI experiments, animals were injected intraperitoneally with a single dose of 500 μg/kg/day for 3–4 days, always at 0800 hr.
Glycemia determination.
We determined glucose in blood obtained from the tail vein using an Accu-check compact glucometer (Roche Diagnostic GmbH, Mannheim, Germany).
Insulin secretion and content.
To measure plasma insulin, mice were anesthetized with 50 mg/kg body weight sodium pentobarbital. Blood (~ 1 mL) was obtained by cardiac puncture with a syringe containing 24 mM EDTA. We determined the levels of plasma insulin by enzyme-linked immunosorbent assay (ELISA) using a mouse insulin assay kit from Mercodia AB (Uppsala, Sweden).
To measure insulin release from isolated islets, mice were killed by cervical dislocation, and pancreatic islets of Langerhans were isolated by collagenase digestion as described previously (Morimoto et al. 2001). Islets were washed twice with a buffer solution containing 20 mM HEPES, 115 mM NaCl, 5 mM NaHCO3, 5 mM KCl, 2.6 mM CaCl2, 1.2 mM KH2PO4, 1.2 mM MgSO4, 3 mM d-glucose, and 1% bovine serum albumin (pH 7.4). Groups of 10 islets were then incubated in 1 mL of this buffer in the presence of 3, 7, and 16 mM glucose. After 1 hr, the medium was collected, and insulin was measured in duplicate samples by radioimmunoassay using a Coat-a-Count kit (DPC, Los Angeles, CA, USA).
The insulin content was determined in islets isolated as described above. Islets were grouped in batches of 10 and incubated overnight in an ethanol/HCl buffer at 4°C. At the end of the incubation period, the buffer was removed and studied for insulin content using radioimmunoassay with a Coat-a-Count kit (DPC). Protein determination was performed by the Bradford dye method.
Immunocytochemistry and insulin content.
Islets isolated as above were dispersed into single cells as previously described (Nadal et al. 1998). Briefly, islets were disaggregated into single cells with trypsin. Cells were then centrifuged and resuspended in Hank’s modified medium supplemented with 200 U/mL penicillin, 0.2 mg/mL streptomycin, 5 mM glucose, and 1% fatty acid–free albumin, pH 7.4. They were then plated on 24-well tissue culture plates. After 30 min, cells were washed with phosphate-buffered saline (PBS) and fixed with Bouin’s solution for 5 min. Then, they were dehydrated with 30, 50, and 70% ethanol, 3 min each, and then washed with PBS. After washing, these cells were first incubated with a monoclonal anti-insulin antibody (1:200 dilution; Sigma, Madrid, Spain) for 2 hr and then for 1 hr with a secondary antibody, anti-mouse IgG-conjugated fluorescein isothiocyanate (IgG-FITC; 1:200 dilution; Sigma), both at room temperature. Cells were washed with PBS overnight. Images were acquired with a confocal microscope Zeiss Pascal 5 using a Zeiss 20× objective (numerical aperture = 0.5) and analyzed using LSM Zeiss software (Zeiss, Jena, Germany). We measured immunofluorescence intensity in random fields. The results were expressed as average pixel intensity and normalized with respect to vehicle-treated animals. Pixel intensity was normalized with respect to the mean value of the pixel intensity of vehicle-treated animals at the corresponding days.
Glucose and insulin tolerance tests.
For glucose tolerance tests, animals were fasted overnight for 12 hr, and blood samples were obtained from the tail vein. Animals were then injected intraperitoneally with 2 g/kg body weight of glucose, and blood samples were taken at the indicated intervals.
For insulin tolerance tests, fed animals were used. Animals were injected intra-peritoneally with 0.75 IU/kg body weight of soluble insulin. Blood glucose was measured in each sample using an Accu-check compact glucometer (Roche).
Statistical analysis.
Data are expressed as mean ± SE. Pairwise comparisons were made using Student’s t-test. A probability level < 0.05 was considered statistically significant.
Results
E2 and BPA rapidly alter glycemia and insulinemia.
In vitro, both E2 and BPA alter the function of pancreatic β-cells through the ncmER (Nadal et al. 1998, 2000; Quesada et al. 2002; Ropero et al. 2002). In vivo, the administration of 10 μg/kg E2 to adult male mice resulted in a significant decrease of glycemia measured at 30, 60, and 120 min after the injection, compared to the increase of blood glucose produced by the fatty acids contained in the vehicle injection (tocopherol-stripped corn oil) (Figure 1A). The administration of 1, 10, and 100 μg/kg E2 evoked a clear dose-dependent decrease in the rise of glycemia 30 min after the E2 injection (Figure 1B). This effect is mimicked by equal doses of the environmental estrogen BPA (Figure 1C). Thirty minutes after injection, this decrease in blood glucose is parallel to an increase in plasma insulin (Figure 1D) of 3.20 ± 0.45-fold for E2-treated mice and 2.76 ± 0.5-fold for BPA-treated mice. Rapid effects elicited by E2 and BPA in islets of Langerhans in vitro are initiated after they bind at the ncmER that is insensitive to the pure anti-estrogen ICI (Nadal et al. 2004). This anti-estrogen was described as blocking classic ER-mediated actions in vivo (Johnson et al. 2003; Perez-Martin et al. 2003). To evaluate whether the E2 and BPA actions described above require an ER with a sensitivity to the pure antiestrogen similar to a classical ER, we undertook experiments using mice treated with 500 μg/kg/day ICI administered intraperitoneally for 3 days (Johnson et al. 2003). As shown in Figure 2A, ICI had no effect on the E2 and BPA-dependent blood glucose decrease nor on the E2- and BPA-dependent increase of plasma insulin (Figure 2B).
Therefore, both E2 and BPA rapidly change glycemia most likely by inducing an hypersecretion of insulin through a non-classical ER-mediated mechanism that may involve the ncmER previously described in these cells (Nadal et al. 2000, 2004).
E2 and BPA increase β-cell insulin content.
Adult male mice were injected twice a day with the vehicle, E2 or BPA for 4 days. Afterward, insulin content was measured in individual cells by immunocytochemistry. Figure 3A shows confocal images of β-cells obtained from mice treated 4 days with vehicle, 100 μg/kg/day E2, or 100 μg/kg/day BPA. Figure 3B illustrates a three-dimensional reconstruction of images in Figure 3A showing a pixel intensity scale from 0 to 256 pixels. The images and the three-dimensional graphs illustrate that β-cells from animals treated with E2 and BPA presented higher staining than those treated with the vehicle and thus higher insulin content in every cell. After 4 days of treatment, the insulin increase was already manifested at doses of 10 μg/kg/day of either E2 or BPA (Figure 3C). Nonetheless, this effect was small and 100 μg/kg/day was needed to produce a potent increase in insulin content (Figure 3C). A treatment of 100 μg/kg/day E2 for 4 days gives an E2 plasma concentration similar to that found in late pregnancy (Song et al. 2001). Therefore, the chronic action of E2 and BPA was manifested at higher concentrations than was required for the acute effect. On the basis of this response, we use 100 μg/kg/day as the paradigmatic concentration in long-term experiments.
Time-course experiments demonstrated that the onset of the increase in insulin content occurs after 24–48 hr of treatment with either E2 or BPA (Figure 3D). The experiment in Figure 3D shows that most single β-cells increased their insulin content. The experiment in Figure 3D shows that after 4 days of treatment, the insulin content is higher in E2 and BPA-treated mice. The radioimmunoassay analysis of insulin content performed 4 days after the treatment with 100 μg/kg/day of either E2 or BPA produced similar results as those presented with immunocytochemistry (Figure 3E). These results demonstrate that the insulin content was increased by both the natural hormone and the environmental estrogen to a similar extent.
To study the involvement of classical ERs in the regulation of E2- and BPA-induced insulin expression, we used mice treated with the antiestrogen ICI, as described for the experiment presented in Figure 2. Animals treated with 100 μg/kg/day E2 for 4 days presented higher levels of insulin immunoreactivity than the control (Figure 4A). In animals treated with ICI, E2 had no effect (Figure 4A). The action of the pure antiestrogen is manifested to the same extent with BPA, as demonstrated by immunocytochemistry (Figure 4B) and radioimmunoassay (Figure 4C). The ability of the pure anti-estrogen to completely block the effect of both E2 and BPA indicates that the action performed on insulin content is mediated by a classical ER.
E2 and BPA administration provokes hyperinsulinemia and insulin resistance.
An increment in the insulin content of every β-cell within an islet causes it to release a higher amount of insulin every time it is stimulated (Marban et al. 1989). This occurs, for instance, during pregnancy, when islets adapt to deal with peripheral insulin resistance (Costrini and Kalkhoff 1971; Faure et al. 1985). In our system (Figure 5A), we show that, at high glucose concentrations (16 mM), islets from mice treated with 100 μg/kg/day E2 for 4 days secrete 1.54 ± 0.12-fold more insulin than those from mice treated with the vehicle, and the same occurs in islets from mice treated with BPA. In the latter case, differences are significant even at lower glucose concentrations (7 mM). This effect has been recently described in vitro (Adachi et al. 2005). Therefore, if E2- and BPA-treated mice have higher insulin content, they should release more insulin than untreated mice, and accordingly, these animals should be hyperinsulinemic in the fed state. This is shown in Figure 5B: 4 days after the E2 treatment, fed mice had blood insulin levels 1.7-fold higher than those of vehicle-treated mice. Blood glucose levels were 186 ± 5 mg/dL in vehicle-treated mice and 170 ± 6 mg/dL in E2-treated mice (p = 0.065, not significant). Plasma insulin levels for the group treated with BPA were 1.5-fold higher than those for the vehicle-treated mice; however, their blood glucose levels were 153 ± 4 mg/dL for the vehicle (n = 5) and 154 ± 7 mg/dL for BPA (n = 7; p = 0.93). This experiment indicates that, in the E2-treated animals, there was mild insulin resistance, because there are 1.7-fold higher circulating insulin levels but a decrease of blood glucose, although it is not significant. This effect is remarkably manifested with BPA; in this case, plasma insulin levels are 1.53-fold higher and blood glucose concentration does not vary, a clear symptom of insulin resistance.
To prove that glucose tolerance is altered, we performed an intraperitoneal glucose tolerance test in fasted (12 hr) mice. Contrary to vehicle-treated mice, blood glucose increased to a higher level in E2-treated (Figure 6A) and BPA-treated mice (Figure 6B). The higher increase in blood glucose manifested 15 and 30 min after a glucose challenge. We found that the area under the curve (milligrams per deciliter-minute) was 162 ± 6 (vehicle, n = 24), 181 ± 5 (E2, n = 16, p = 0.025 vs. vehicle), and 190 ± 8 (BPA, n = 8, p = 0.028 vs. vehicle). These results indicate that an impaired glucose tolerance was present in E2-treated mice and in those treated with BPA. When an insulin tolerance test (intraperitoneal injection of 0.75 IU/kg body weight soluble insulin) was performed in fed mice, a significantly reduced hypoglycemic response was observed in both E2- and BPA-treated mice (Figure 6C). This insulin intolerance also manifested in animals given an oral dose of 100 μg/kg/day BPA for 4 days. In this case, the BPA was dissolved in tocopherol-stripped corn oil and delivered through a pipette placed into the animal’s mouth (Howdeshell et al. 1999) (Figure 6D). All these results indicate that E2-treated animals and, in a remarkably similar manner, BPA-treated animals develop insulin resistance without any changes in glycemia or weight (35–40 g, with no significant differences between the diverse treatments applied).
Discussion
The results presented in this article demonstrate a link between estrogenic endocrine disruptors and insulin resistance. We have shown that BPA mimics E2 effects on blood glucose homeostasis through two different pathways. A nonclassical pathway produces a rapid increase in plasma insulin and a decrease in blood glucose. This is unaffected by the anti-estrogen ICI and is most likely initiated by the ncmER already described in islet cells (Alonso-Magdalena et al. 2005; Nadal et al. 2000; Quesada et al. 2002; Ropero et al. 2002). Nonetheless, other estrogen and xenoestrogen actions initiated at the plasma membrane are mediated via classical ERs (Losel et al. 2003; Wozniak et al. 2005). The activation of this alternative pathway occurs at low doses of both E2 and BPA, becoming significant at 10 μg/kg. When animals were treated with either the natural hormone or BPA for a longer period of time, there was an increase in the pancreatic insulin content. This effect was completely blocked by ICI, suggesting that a classical ER is involved. This implies that the action described here for E2 and BPA can be extrapolated to other estrogenic EDCs. Any EDC having an estrogenic effect through classical ERs can be a candidate to induce insulin overexpression. This chronic treatment induced insulin resistance.
Type 2 diabetes mellitus is characterized by insulin resistance, which results in lower levels of blood glucose uptake into target tissues. Consequently, blood glucose levels increase and more insulin is released, producing hyperinsulinemia, which manifests early in type 2 diabetes. In addition, several studies have demonstrated that the hypersecretion of insulin is a primary defect of type 2 diabetes and that insulin resistance develops secondarily to the chronic hyperinsulinemia (Charollais et al. 2000; DeFronzo 1997; Devedjian et al. 2000; McGurry 1992). Indeed, the persistence of chronic physiologic euglycemic hyperinsulinemia for 3–5 days can induce severe insulin resistance in healthy subjects with normal glucose tolerance (Del Prato et al. 1994). Furthermore, patients with insulinoma display a correlation between hyperinsulinemia and insulin resistance (Pontiroli et al. 1992; Skrha et al. 1996). When insulin is overexpressed, a chronic hyperinsulinemia manifests, as shown in transgenic mice that overexpressed the insulin gene (Marban et al. 1989). Remarkably, these transgenic mice developed insulin resistance. Here we have shown that 4 days of treatment with either E2 or BPA induces an increase in β-cell insulin content; these mice were hyper-insulinemic and presented altered glucose and insulin tolerance tests. These alterations may result from a direct effect of E2 and BPA on β-cell insulin content, or a compensatory response resulting from the insulin resistance noted in these animals, or both.
We have demonstrated that after 4 days of treatment, isolated islets respond more vigorously to glucose (Figure 5A), likely because their insulin content is higher (Marban et al. 1989). This phenomenon may be responsible for the chronic hyperinsulinemia that manifested in the fed state (Figure 5B). In addition, the altered glucose and insulin tolerance test results were consistent with the fact that these mice had developed insulin resistance. Therefore, our results suggest that the sustained hyperinsulinemia produced by E2 and BPA affects peripheral tissues, producing insulin resistance, most likely by down-regulation of insulin receptor number and function. However, the extent of the insulin resistance with this treatment is not enough to induce hyperglycemia in the fasted state.
The assumption described above does not rule out a direct effect of both E2 and BPA on peripheral tissue. BPA produces down-regulation of glucose transporters in adipocytes (Sakurai et al. 2004), an action that may induce insulin resistance. Moreover, BPA combined with insulin favors the conversion of fibroblasts to adipocytes (Masuno et al. 2002), enhancing the risk of obesity, a metabolic disorder that has been related to endocrine disruptors in the last years (Heindel 2003; Mead 2004). Hence, the direct effect of BPA on peripheral tissue might also be of importance to developing insulin resistance.
At present, there is a debate about determining the safe levels of BPA exposure and whether there is a need for a new risk assessment (vom Saal and Hughes 2005). The U.S. EPA considers 50 μg/kg/day as the reference dose based on a LOAEL of 50 mg/kg/day, according to studies performed in the 1980s (vom Saal and Hughes 2005). The ECSCF (2002) reported a TDI of 10 μg/kg/day. The results presented here show a rapid nongenomic effect at a dose of 10 μg/kg/day, five times lower than the reference dose established by the U.S. EPA and equal to the TDI reported by the ECSCF. This dose of BPA results in levels of parent (unconjugated) BPA in blood of 3–4 nM after 30 min and maintained 24 hr later (Zalko et al. 2003), below the level reported in blood from human fetuses at parturition (Schonfelder et al. 2002). This low dose of BPA produces a 2.5-fold increase in plasma insulin and a 20% decrease in blood glucose levels, 30 min after its application. Remarkably, a low dose of 10 μg/kg/day was able to slightly change insulin content as well. A higher dose of 100 μg/kg/day dramatically increased pancreatic insulin content after only 4 days of exposure. Moreover, this treatment, delivering BPA either via injection or through oral intake, induced insulin resistance. This dose is only twice the reference dose recommended by the U.S. EPA and 10 times higher than the TDI recommended by the ECSCF.
The present study demonstrates a connection between BPA and insulin resistance at doses much lower than the LOAEL used up to now (50 mg/kg/day), and therefore it is strong evidence supporting a review of the risk assessment concerning BPA.
We thank B. Fernández for excellent technical assistance and I. Quesada, A. Gomis, and A.B. Ropero for critical reading of the manuscript.
This study was supported by the Spanish Ministry of Education and Science (grant BFI2002-01469 and BFU2005-01052) and Instituto de Salud Carlos III (grants RCMN C03/08 and 03/0178). P.A.M. has a fellowship from the Ministry of Education and Science. S.M. was a postdoctoral fellow from CONACYT, Mexico.
Figure 1 Rapid change in blood glucose levels with E2 and BPA compared with tocopherol-free corn oil (vehicle). (A) Measurement of blood glucose concentration in animals fasted for 12 hr, injected with 100 μL vehicle or 10 μg/kg body weight E2 (n = 6–14 mice); *p < 0.05. (B) Increment of glycemia 30 min after the injection of vehicle or E2 (n = 7–16); *p < 0.05 compared with vehicle. (C) Increment of glycemia 30 min after the injection of vehicle or BPA (n = 4–10); *p < 0.05 compared with vehicle. (D) Circulating plasma insulin in fasted (12 hr) animals 30 min after the injection of vehicle, 10 μg/kg E2 or 10 μg/kg BPA (n = 8–16); *p = 0.024, and **p = 0.004 compared with vehicle. Error bars indicate SE.
Figure 2 Increment of glycemia (A) and plasma insulin (B) 30 min after the injection of vehicle, 10 μg/kg E2, or 10 μg/kg BPA in animals with or without treatment with ICI (500 μg/kg/day) for 3 days.
In (A), n = 4–12; *p < 0.002 compared with ICI or vehicle. In (B), n = 4–7; *p < 0.035 compared with vehicle.
Figure 3 Insulin content in β-cells from E2- and BPA-treated mice. (A) Immunofluorescent staining of insulin in cells from mice treated with vehicle, 100 μg/kg/day E2, or 100 μg/kg/day BPA for 4 days. Bar = 50 μm; blue indicates low fluorescence intensity, and red indicates high intensity. (B) Three-dimensional graphs of cells in (A), showing the pixel intensity [0 (low) to 256 pixels (high)]. (C) Quantification of insulin content using confocal microscopy of β-cells from mice treated with vehicle, E2, or BPA for 4 days at either 10 or 100 μg/kg/day, shown as normalized pixel intensity. Each point represents the mean of at least 1,000 cells from three mice; *p < 0.003 compared with vehicle. (D) Time course indicating E2 and BPA action in β-cells. Each point represents the mean of at least 1,000 single cells obtained from two mice; *p < 10–5 compared with vehicle. (E) Insulin content of islets obtained from mice treated with vehicle, 100 μg/kg/day E2 (n = 6; *p = 0.014), or 100 μg/kg/day BPA for 4 days (n = 6; **p = 0.04). All error bars indicate SE.
Figure 4 Insulin content in β-cells from E2- and BPA-treated mice with and without treatment with the pure antiestrogen ICI. (A) Immunofluorescent staining for insulin in mice treated with vehicle, 100 μg/kg/day E2, or E2 plus 500 μg/kg/day ICI for 4 days. Bar = 50 μm; blue indicates low fluorescence, and red indicates high fluorescence. (B) Quantification of insulin content using confocal microscopy of β-cells from mice treated with vehicle, 100 μg/kg/day E2 or BPA, or 100 μg/kg/day E2 or BPA plus 500 μg/kg/day ICI. Each point represents the mean of at least 2,000 individual cells from four mice; *p < 10–10. (C) Insulin content obtained by radioimmunoassay (n = 6); *p < 0.05, comparing E2 with E2+ICI and BPA with BPA+ICI. All error bars indicate SE.
Figure 5 Insulin secretion in vitro and in vivo. (A) Glucose-induced insulin secretion from isolated islets at 3, 7, and 16 mM glucose, from mice treated with vehicle, 100 μg/kg/day E2, or 100 μg/kg/day BPA for 4 days (n = 4–6 animals per group); *p < 0.005, compared with vehicle. (B) Normalized plasma insulin with respect to the plasma concentration in mice treated with vehicle, 100 μg/kg/day E2 or 100 μg/kg/day BPA for 4 days (n = 5–10 mice per group); *p < 0.0075 compared with vehicle. In the E2 group, circulating insulin levels were 1.53 ± 0.25 ng/mL for vehicle-treated mice and 2.58 ± 0.42 ng/mL for E2-treated mice (n = 5; p = 0.038); in the BPA group, circulating insulin levels were 1.02 ± 0.14 ng/mL for the vehicle-treated mice and 1.56 ± 0.11 ng/mL for those treated with BPA (n = 7; p = 0.005). All error bars indicate SE. Both vehicle groups were combined; the data are normalized with respect to the vehicle value from each group.
Figure 6 E2 and BPA alter glucose tolerance and induce insulin resistance. (A) Glucose tolerance test in mice treated with vehicle or 100 μg/kg/day E2 for 4 days; (n = 16); *p = 0.02, and **p = 0.003. (B) Same experiment as in (A) but with animals treated with vehicle or 100 μg/kg/day BPA (n = 8); *p = 0.017, and **p = 0.009. (C) Insulin tolerance test in awake, fed mice previously treated with vehicle, 100 μg/kg/day E2, or 100 μg/kg/day BPA (n = 9); *p < 0.04, **p = 0.007, and ***p = 0.0002 compared with vehicle. (D) Experiment as in (C) but using an oral intake of either vehicle or 100 μg/kg/day BPA (n = 5); *p = 0.026, and **p = 0.0012. All error bars indicate SE.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8035ehp0114-00011316393667ResearchEnvironmental MedicineSerum Cadmium Levels in Pancreatic Cancer Patients from the East Nile Delta Region of Egypt Kriegel Alison M. 1Soliman Amr S. 2Zhang Qing 3El-Ghawalby Nabih 4Ezzat Farouk 4Soultan Ahmed 4Abdel-Wahab Mohamed 4Fathy Omar 4Ebidi Gamal 4Bassiouni Nadia 4Hamilton Stanley R. 5Abbruzzese James L. 6Lacey Michelle R. 7Blake Diane A. 11 Department of Biochemistry, Tulane University Health Sciences Center, New Orleans, Louisiana, USA2 Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, Michigan, USA3 Department of Epidemiology, University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA4 Gastrointestinal Surgery Center, Mansoura University, Mansoura, Egypt5 Division of Pathology and Laboratory Medicine, and6 Department of Gastrointestinal Medical Oncology, University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA7 Department of Mathematics, Tulane University, New Orleans, Louisiana, USAAddress correspondence to A.S. Soliman, Department of Epidemiology, School of Public Health, University of Michigan, 109 Observatory Rd., Rm. 2550, Ann Arbor, MI 48109 USA. Telephone: (734) 764-5469. Fax: (734) 764-3192. E-mail:
[email protected] authors declare they have no competing financial interests.
1 2006 25 8 2005 114 1 113 119 21 2 2005 25 8 2005 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. The northeast Nile Delta region exhibits a high incidence of early-onset pancreatic cancer. It is well documented that this region has one of the highest levels of pollution in Egypt. Epidemiologic studies have suggested that cadmium, a prevalent pollutant in the northeast Nile Delta region, plays a role in the development of pancreatic cancer.
Objective: We aimed to assess serum cadmium levels as markers of exposure in pancreatic cancer patients and noncancer comparison subjects from the same region in Egypt.
Design and Participants: We assessed serum cadmium levels of 31 newly diagnosed pancreatic cancer patients and 52 hospital comparison subjects from Mansoura, Egypt.
Evaluation/Measurements: Serum cadmium levels were measured using a novel immunoassay procedure.
Results: We found a significant difference between the mean serum cadmium levels in patients versus comparison subjects (mean ± SD, 11.1 ± 7.7 ng/mL vs. 7.1 ± 5.0 ng/mL, respectively; p = 0.012) but not in age, sex, residence, occupation, or smoking status. The odds ratio (OR) for pancreatic cancer risk was significant for serum cadmium level [OR = 1.12; 95% confidence interval (CI), 1.04–1.23; p = 0.0089] and farming (OR = 3.25; 95% CI, 1.03–11.64; p = 0.0475) but not for age, sex, residence, or smoking status.
Conclusions: The results from this pilot study suggest that pancreatic cancer in the East Nile Delta region is significantly associated with high levels of serum cadmium and farming.
Relevance to Clinical Practice/Public Health: Future studies should further investigate the etiologic relationship between cadmium exposure and pancreatic carcinogenesis in cadmium-exposed populations.
cadmiumEast Nile Delta regionenvironmental exposureimmunoassaysoccupational exposurepancreatic cancerpollution
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Pancreatic cancer is one of the most deadly forms of cancer. Although it is the fourth leading cause of cancer deaths in the United States, it accounts for only 2% of all newly diagnosed cancers each year. Five-year survival rates for patients diagnosed with pancreatic cancer in the United States average only 4.4% (American Cancer Society 2005). In developing countries, pancreatic cancer appears to be extremely rare. Studies in Egypt, Algeria, and Iraq suggest a low incidence of the disease in the Middle East (Al-Bahrani et al. 1982; Mokhtar 1991; Parkin 1986; Parkin et al. 1997; Sherif and Ibrahim 1987). Risk factors for this disease have generally been grouped into four categories: a) cigarette smoking (Doll et al. 1994; Howe et al. 1991); b) chronic pancreatitis (largely from alcohol consumption) (Lowenfels et al. 1993) and genetic predisposition (Brand and Lynch 2000; Flanders and Foulkes 1996; Goggins et al. 1996; Lowenfels et al. 1997); c) diabetes mellitus and macro- or micronutrients (Bueno de Mesquita et al. 1992; Everhart and Wright 1995); and d) occupational and environmental contamination from exposure to pesticides and fertilizers (Anderson et al. 1996; Fontham and Correa 1989; Hoppin et al. 2000), manufacturing paints and pigments (Norell et al. 1986; Raymond and Bouchardy 1990; Sheffet et al. 1982), metalworking (Mallin et al. 1986; Maruchi et al. 1979; Park and Mirer 1996; Rotimi et al. 1993; Silverstein et al. 1988; Sparks and Wegman 1980), and soldering (Ji et al. 1999).
The incidence of pancreatic cancer in Egypt has been previously studied with particular attention to Dakahlia Province (Soliman et al. 2002). This primarily rural province is the largest of all the provinces in the East Nile Delta region. The population in this region exhibits an unusually high rate of early-onset pancreatic cancer. Although most cases in the United States occur in patients older than 65 years of age (Lowenfels and Maisonneuve 1999), the incidence rates for patients younger than 65 years in the East Nile Delta region were more than twice as high as those observed in Americans in the same age group and significantly higher than those seen in other parts of Egypt (Soliman et al. 2002). The reason for the incidence of early-onset pancreatic cancer in the East Nile Delta region is unclear. There is currently no good evidence that the first three risk factors named above would preferentially affect residents of the East Nile Delta region relative to the rest of Egypt. It is, however, well documented that this region has one of the highest levels of pollution in Egypt.
Nile River water is seriously contaminated with heavy metals, pesticides, and hydrocarbons as a result of increasing discharge of untreated industrial wastes and agricultural irrigation wastewater (Badawy et al. 1995). Several reports on Dakahlia Province show high levels of heavy metal and organocholorine pesticides in the soil and water in this region (Abdel-Haleem et al. 2001; Dekov et al. 1997; Reinhardt et al. 2001; Siegel et al. 1994). High concentrations of heavy metals, including cadmium, are among the pollutants in the water. Plants and fish grown in this water are also contaminated with heavy metals (Abdel-Sabour 2001; Abou-Arab and Abou Donia 2000), which can in turn accumulate in humans and animals that feed on these contaminated foods (Osfor et al. 1998). The serum cadmium levels of residents of Dakahlia Province are almost 10-fold higher than those of residents from cadmium-polluted areas in Cairo and 32 times higher than reference levels for healthy populations in the United States (Hossny et al. 2001; Osfor et al. 1998; Soliman et al. 2002). Cadmium is a known human carcinogen (Boffetta 1993) and has recently been implicated as a cause of pancreatic cancer (Schwartz and Reis 2000). Two main risk factors for pancreatic cancer, age and cigarette smoking, are also associated with cadmium exposure. Cadmium accumulates in the body over time because there are no specific mechanisms for its removal. The half-life of this metal in the body ranges from 10 to 30 years, with an average of 15 years (Jin et al. 1998). In addition, cigarette smoking is a significant source of cadmium. One cigarette contains 1–2 μg cadmium (Goyer 1996), and inhaled cadmium is absorbed much more efficiently than is ingested cadmium (Friberg 1984). Measurement of cadmium in the pancreas of autopsy patients showed significantly higher levels in smokers than in nonsmokers (Elinder et al. 1976). Urinary cadmium levels, commonly used as an indication of life-long exposure, are also significantly higher in smokers versus nonsmokers (Schwartz et al. 2003).
In this study, we assessed serum cadmium levels of newly diagnosed histologically confirmed pancreatic cancer patients with hospital comparison subjects from the same region in Egypt, using a novel immunoassay procedure. We have also examined the contributions of age, residence, smoking status, and profession to overall risk for pancreatic cancer. Although the sample size in this study is small, it is our hope that these initial data will act as a springboard for larger, more in-depth studies that will analyze the relationship between cadmium and pancreatic cancer in a more detailed fashion.
Materials and Methods
Materials.
The 2A81G5 monoclonal antibody, which recognizes Cd(II)–EDTA complexes, was prepared from a hybridoma generated in the Blake laboratory (Blake et al. 1996). A Cd(II)–EDTA–horseradish peroxidase (HRP) conjugate was prepared as described by Darwish and Blake (2002, 2001). We purchased a pooled human serum sample from Intergen (Milford, MA, USA); this serum sample was analyzed by graphite-furnace atomic absorption spectroscopy (AAS) and shown to be free of endogenous cadmium (data not shown). ELISA high-binding 96-well plates were a product of Corning-Costar (Cambridge, MA, USA). We obtained AAS standard cadmium (1,000 mg/L in 2% nitric acid) from Perkin-Elmer (Norwalk, CT, USA).
Patient selection.
Between September 2001 and February 2002, 31 newly diagnosed patients with adenocarcinoma of the pancreas from the Gastrointestinal Surgery Center of Mansoura University, Dakahlia Province in Egypt were recruited to participate in this study. Pancreatic cancer was confirmed by reviewing the histopathologic slides of all patients both at Mansoura University and at M.D. Anderson Cancer Center. No patients with chronic pancreatitis were included in this study. Histopathologic examination of the tissues of the pancreatic cancer patients included in the study revealed no signs of chronic pancreatitis. All patients were Egyptian citizens, permanent residents of Dakahlia Province, and recruited before receiving chemotherapy or radiotherapy. There were no restrictions based on age, sex, or tumor stage. In addition, 52 comparison subjects were recruited from the earnose-throat and ophthalmology departments at Mansoura University General Hospital on the same medical campus of Mansoura University. The comparison subjects were chosen by systematic random sampling from inpatients admitted for surgeries related to nonchronic illnesses. After the nature of the study had been fully explained, informed oral and/or written consent was obtained from each person in the study.We used an interviewer-administered questionnaire, which included questions about lifetime occupational, residential, and smoking histories. Information was also collected about family history of pancreatic cancer.
Sample collection.
For each patient and comparison subject, 10 mL of blood was collected into a sterile nonheparinized Vacutainer tube. The sample was allowed to clot for 5–10 min and then centrifuged for 10 min at 13,000 rpm. At least 2 mL of clear serum from each sample was transferred to glass tubes, which were labeled with patient name and identification number, clinic name, and collection time and date. Specimens were frozen at –20°C until they were transferred (within 3 weeks) to M.D. Anderson Cancer Center. Samples were hand-carried in dry ice during transport from Egypt to Houston to New Orleans, Louisiana, where they were stored at –80°C. The study was approved by the human subject committees of the Universities of Texas, Tulane, and Michigan in the United States and Mansoura University in Egypt.
Modification of the one-step immunoassay for cadmium in serum.
The method described by Soliman et al. (2002) required relatively high quantities of the anti-cadmium monoclonal antibody, 2A81G5. Therefore, we modified the original method by using a goat anti-mouse antibody to capture and concentrate the 2A81G5 antibody on the microwell plate, as shown in Figure 1. Preliminary experiments demonstrated that when goat anti-mouse IgG, coated at a concentration of 2.0 μg/mL, was used to capture mouse monoclonal antibody, 5-fold less 2A81G5 was required for the subsequent cadmium assay (data not shown).
Pooled human serum versus pooled Egyptian comparison serum.
We calculated standard curves in two separate sample matrices using the modified one-step immunoassay for cadmium. We compared a pooled human serum sample from Intergen Co. (Norcross, GA, USA) with a mixture of all the comparison samples from Egypt in a competition assay. Twenty-five microliters of each of the 52 Egyptian comparison samples were combined together and mixed well. Various concentrations of atomic absorption grade cadmium were spiked into the Egyptian pooled serum as well as Intergen’s pooled serum (Norcross, GA, USA). We then performed an immunoassay and compared inhibition curves.
Determination of cadmium in human serum samples.
We diluted goat anti-mouse IgG1 (heavy chain specific; Jackson Immuno-Research Laboratories, West Grove, PA, USA) into HEPES-buffered saline (HBS; 137 mM NaCl, 3 mM KCl, and 10 mM HEPES, pH 7.4) to a concentration of 2 μg/mL. The diluted antibody (50 μL) was introduced to each well of a 96-well high-binding micro-well plate and incubated for 2 hr at 37°C. The plates were washed three times with phosphate-buffered saline containing 0.05% Tween-20. The wells were blocked with 3% bovine serum albumin in HBS at 37°C for 1 hr, followed by a wash step. The anti-Cd(II)–EDTA antibody, 2A81G5, was diluted to 0.5 μg/mL in HBS. A 50 μL aliquot of the diluted antibody was added to each well and incubated at room temperature for 1 hr, followed by a wash step.
Egyptian serum samples were thawed at room temperature and mixed well. A 75 μL aliquot of serum was added to 75 μL of 5% HNO3 and mixed well. The mixture was incubated at room temperature for 5 min and then spun at 15,000 rpm for 5 min. Acidification of the sample allows the release of cadmium from metallothionein and other protein ligands. A 90 μL aliquot of the supernatant was removed and conditioned with 10 μL of a 10X concentrated buffer (1.37 M NaCl, 30 mM KCl, 50 mM EDTA, 100 mM HEPES). The presence of EDTA allows the formation of Cd(II)–EDTA complexes. The sample was neutralized with 10 M KOH. Neutralized serum was added to an equal volume of 0.1 μg/mL Cd(II)–EDTA–HRP and mixed well. The mixture (50 μL/well) was added to three separate wells. After 1 hr incubation at room temperature, the plate was washed. 3,3′,5,5′-Tetramethylbenzidine peroxidase substrate (50 μL/well; Kirkegaard-Perry Laboratories, Gaithersburg, MD, USA) was added to each well, and color formation was stopped after 10 min with an equal volume of 1 N HCl. The absorbance of each well was measured in a dual-wavelength mode (450–650 nm) using a Vmax Kinetic Microplate Reader (Molecular Devices Corporation, Sunnyvale, CA, USA). For preparation of a standard curve, pooled human serum from Intergen was spiked with various concentrations of atomic-absorption–grade cadmium standard and treated in a manner identical to that used for the serum samples noted above.
Data analysis for the immunoassay.
We calculated standard curves on each immunoassay plate in replicates of four. We determined the mean value and SD of the absorbance provided by each cadmium concentration. These mean absorbance values (y) were plotted on a curve versus the cadmium concentrations (x) and used to fit the equation y = a0 – (a1 × x)/(a2 + x), where y is the experimental absorbance, x is cadmium concentration in the standard or sample, a0 is the absorbance in the absence of cadmium, a1 is the difference between the absorbance in the absence of cadmium and at a saturating concentration of cadmium, and a2 is the Cd(II) concentration that produces a 50% inhibition of signal (IC50). To account for the variability in the data, confidence bounds for each standard Cd(II) concentration were computed. The upper and lower curves were fit from the mean values ± 2 SD, respectively. The limit of detection (LOD) was determined with the use of the standard curves. The absorbance value for zero cadmium (y-intercept) for the lower curve (mean – 2 SD) was determined and then applied to the upper curve (mean + 2 SD) to find the cadmium concentration designated as the LOD. A visual depiction of how the LOD was determined is provided in Figure 2.
Each Egyptian serum sample was analyzed in triplicate in a particular assay. The mean absorbance value and SD were determined for each sample. We used SDs to help eliminate nonprecise values. Mean absorbance values for each serum sample were applied to the mean standard curve performed on the same plate to determine the cadmium concentration for that sample. When the sample was analyzed multiple times, the cadmium concentrations were averaged. Each original serum sample was diluted by a factor of 4.57 during the immunoassay procedure. To obtain the actual cadmium concentration of the serum samples, the assay cadmium concentration was multiplied by this number.
Statistical methods.
For those samples that fell below the LOD (18 of 52), we used the method described by Helsel (1990) to assign a value for the cadmium concentration. In this method, one assumes that the actual cadmium concentration in these samples is described by a Gaussian distribution whose lower bound is zero and whose upper bound is the LOD of the immunoassay. The median cadmium concentration in these samples would be 50% of the LOD, and this value was therefore assigned to each nondetect sample. This is a conservative method of handling immunoassay data; the actual differences between the cases and the comparisons might be even greater than reported herein. The logarithm of serum cadmium level was used as the continuous exposure because the distribution of serum cadmium level was skewed. We used the Student’s t-test to test the differences among means of the logarithms of serum cadmium level. Because both transformed and untransformed analytical methods yielded comparable conclusions, we present the analysis based on the untransformed data for better comparison with other studies.
We also aimed to find a subset of predictors for pancreatic cancer disease status. The main response variable was disease status and potential predictors included age, sex, serum cadmium level, smoking, and farming. We analyzed the longest occupation reported by study subjects as farming and nonfarming. Farming jobs were defined as being a farmer or a housewife who lived in a rural area. Nonfarming occupations included administrators, barbers, businessmen, teachers, students, and women who lived in urban areas but reported their job title as “housewife.” Nonfarming also included industrial workers, such as painters and welders. We did not include residence in the final model because of its high correlation with occupation. Residence alone was not significant in a logistic regression model that did not include occupation. In this analysis, a study subject was considered a smoker if he or she reported ever smoking 100 cigarettes.
Multiple logistic regression models were fitted based on the variables chosen from the univariate analysis. The logistic regression models were designed to estimate the probability of having pancreatic cancer, whereas incorporating the covariates of age, sex, serum cadmium levels, occupation, and smoking. p-Values < 0.05 are reported as significant. There is no collinearity among the predictors. The Hosmer and Lemeshow goodness-of-fit test was used to test the model fitting. All the statistical tests were two sided. Data were analyzed using the Statistical Analysis System package (version 8.2; SAS Institute Inc., Cary, NC, USA).
Results
One-step immunoassay for cadmium.
In this study we used a previously published immunoassay procedure to quantify cadmium in serum samples. The assay was selective for cadmium; other metal ions, including manganese, cobalt, copper, zinc, magnesium, mercury, calcium, nickel, iron, and lead, did not significantly interfere with the assay even when tested at concentrations considerably higher than those present in human serum (Darwish and Blake 2002). The original one-step immunoassay for cadmium designed by Darwish and Blake (2002) required 125 ng of 2A81G5 antibody to be coated in each micro-well. With the large number of Egyptian serum samples to be assayed, an unacceptable quantity of antibody would have been required for these experiments. We therefore developed a modification of the original method that reduced the amount of 2A81G5 necessary for the capture of the Cd(II)–EDTA complexes by 5-fold. An overall schematic of the modified one-step assay is shown in Figure 1. In human serum, cadmium usually exists as a complex with metallothionein and other proteins. In order to determine the total level of cadmium in a serum sample, cadmium must be extracted from these proteins, a process most effectively achieved by acidification of the sample. Addition of a conditioning buffer containing EDTA allowed the formation of Cd(II)–EDTA complexes. This unlabeled Cd(II)–EDTA competed with a known concentration of enzyme-labeled Cd(II)–EDTA to bind to the 2A81G5 antibody immobilized on the microwell plate. After a wash step to remove unbound Cd(II)–EDTA and Cd(II)–EDTA–HRP, a chromogenic peroxidase substrate was added and HRP activity was detected. A standard curve was prepared by adding varying concentrations of atomic absorption grade cadmium to a pooled human serum sample that was treated in an identical manner as the patient samples. This standard curve was then used to determine the concentration of cadmium in each sample based on the observed spectrophotometric absorbance.
Pooled human serum used preparation of the standard curve.
In previous studies (Darwish and Blake 2002), a pooled human serum sample obtained from Intergen was used as the sample medium for preparing the standard curves on each of the immunoassay plates. In the present study, however, we were concerned that this pooled serum sample, from U.S. donors, might not be an appropriate medium for analysis of Egyptian donors. A large pooled serum sample from Egyptian donors was not available, so we prepared a small pooled sample by combining an equal volume (25 μL) of all the Egyptian noncancer comparison samples available from this study (a total of 52). The two pooled serum samples (from Intergen and the Egyptian comparisons) were spiked with various concentrations of cadmium and the subsequent inhibition curves were compared. As shown in Figure 3, the two curves were nearly identical. IC50 values were very similar, 16.74 ng/mL and 17.44 ng/mL for Intergen and Egyptian samples, respectively. These data indicate that the Egyptian samples did not contain any endogenous components that might affect immunoassay performance and that the pooled human serum from Intergen was an appropriate medium for all subsequent standard curves.
Standard curve and determination of LODs in the immunoassay.
A typical standard curve, obtained by adding known concentrations of cadmium and EDTA to a pooled human serum sample, is shown in Figure 2. As the concentration of unlabeled cadmium increased, less of the enzyme-labeled Cd(II)–EDTA was able to bind to the 2A81G5 immobilized in the microwell; absorbance in the assay decreased as the unlabeled cadmium concentration increased. Four replicates of each standard concentration were analyzed. The average values of each four standard replicates were plotted on the graph and fitted using the equation described in “Materials and Methods” (Figure 2). Variability (2 × SD) was determined and graphed as described in “Materials and Methods” (Figure 2). These upper and lower limit curves were used to determine the LOD of the immunoassay, as shown in Figure 2. The LODs were determined for each standard curve and ranged between 0.4 and 1.0 ng/mL cadmium for the pancreatic cancer patients and between 0.3 and 1.0 ng/mL cadmium for the comparison subjects. These ranges show the consistency of standard curves performed from day to day and the high sensitivity of the assay. Each sample was assayed at least one time in triplicate. Many of the samples were assayed on multiple days, also in triplicate. The average absorbance value was detected and then applied to the mean standard curve like the one shown in Figure 2 to obtain a serum cadmium concentration.
Characteristics of the study population.
There were no significant differences between the pancreatic cancer patients and the comparison subjects in terms of age, sex, residence, smoking status, or occupation, as shown in Table 1. Across the study population, age ranged from 21 to 75 years, with a median age of 52 in the cases and 49 in the comparison subjects. There were slightly more males than females in each group. Most study subjects (52% and 71% in cases and comparison subjects, respectively) were urban dwellers, and almost two-thirds were nonsmokers. Less than half of the cases had professional backgrounds, compared with two-thirds of the comparison subjects. There was, however, a significant difference in mean serum cadmium levels between the cases and comparisons (p = 0.012). In patients with pancreatic cancer, the mean cadmium level was 11.1 ± 7.7 ng/mL, whereas in the comparisons the mean ± SD was 7.1 ± 5.0 ng/mL. Although pancreatic cancer patients who reported histories of smoking showed higher but not significant differences in serum cadmium levels than those of comparison subjects who reported histories of smoking (11.8 ± 9.3 ng/mL and 7.1 ± 4.7 ng/mL, p = 0.14, in patients and comparison subjects, respectively), there were statistically higher serum cadmium levels in nonsmoker patients than in non-smoker comparison subjects (10.7 ± 6.9 ng/mL and 7.1 ± 5.3 ng/mL, p = 0.038, in patients and comparisons, respectively.
A comparison of the different ranges of cadmium levels determined in the serum samples of the case and comparison populations is shown in Figure 4. Most comparison subjects exhibited serum cadmium levels between the LOD and < 10 ng/mL, with only one > 20 ng/mL Cd(II). The pancreatic cancer patients showed an overall trend toward higher serum cadmium concentrations. Most cases demonstrated serum cadmium levels between 10 and 20 ng/mL, and 4 of 31 exhibited cadmium levels > 20 ng/mL.
Variation in cadmium levels in the study population.
Within the study population as a whole, there were no significant differences in serum cadmium levels as a function of sex, residence (rural or urban), or smoking status, as shown in Table 2. There was a trend toward significance on the basis of occupation (p = 0.0827) with farmers showing the highest levels (mean ± SD, 13.34 ± 8.29 ng/mL), followed by industrial workers (8.68 ± 5.46 ng/mL) and professionals (7.84 ± 6.14 ng/mL).
Risk factors for pancreatic cancer.
The association between pancreatic cancer risk and subject age, residence (urban or rural), smoking status, and occupation (farming or non-farming) is indicated by the odds ratios (ORs) shown in Table 3. Both serum cadmium levels and farming were independently associated with increased risk of pancreatic cancer. The OR for serum cadmium levels was 1.12 [95% confidence interval (CI), 1.04–1.23; p = 0.0089]. Farming was associated with increased risk for pancreatic cancer OR = 3.25 (95% CI, 1.03–11.64) (Table 3).
Discussion
In this pilot study, we tested the hypothesis that pancreatic cancer patients in this Egyptian population were exposed to higher levels of cadmium than were noncancer subjects. Serum cadmium levels were used as a marker for cadmium exposure. It is difficult to find a perfect dose estimator for cadmium. Urinary cadmium levels are often used; however, studies with animals and humans have shown that renal damage may lead to higher than normal cadmium excretion (Friberg et al. 1985; Nordberg and Piscator 1972). Jarup et al. (1997) have shown that blood cadmium can provide a better dose estimate than urinary cadmium concentrations, especially when tubular proteinuria is present. During high cadmium exposures, the cadmium in the blood increases relatively rapidly until, after some months, it reaches a concentration that corresponds to the intensity of exposure. If the exposure stops, the blood cadmium decreases with an initial half-time of 2–3 months (Elinder et al 1994; Jarup et al. 1983). Cadmium accumulated in the body, however, will continue to influence blood levels. Even after exposure ceases, the concentration in the blood never returns to preexposure levels. Thus blood cadmium has been proposed as one of the more accurate estimators of accumulated body burden (Alfven et al. 2002). Only about 10% of whole-blood cadmium is circulating in serum, but serum levels appear to correlate with blood levels (Lauwerys et al. 1994). The use of serum rather than whole blood in this study allowed us much greater flexibility in collecting, storing, and transporting patient samples.
In the patient population studied here, serum cadmium levels and farming were strong independent risk factors for pancreatic cancer, whereas age, sex, smoking, and residence were not. The highly significant association of pancreatic cancer with serum cadmium level is also consistent with other epidemiologic evidence. The strongest suspicion of an association between cadmium exposure and pancreatic cancer has been reported in Louisiana (Blot et al. 1978; Lemus et al. 1996; Tchounwou et al. 1996). Industrial activity along the Mississippi River has led to an accumulation of contaminants in southern Louisiana (Mielke et al. 2000). Seafood and rice are popular food items in the local diet. Both rice and fish harvested from cadmium-polluted areas may contain high levels of cadmium (Ikeda 1992; Modigosky et al. 1991). A case–control study in Louisiana showed a significantly increased risk for pancreatic cancer associated with rice consumption among Cajuns, with a dose–response relationship (Falk et al. 1988). Louisiana residents are also exposed to cadmium by inhalation. Lemus et al. (1996) have shown that 64 of 315 samples (20.3%) of indoor and outdoor air from 53 households in Louisiana exceeded the U.S. Environmental Protection Agency’s permissible levels for cadmium (Lemus et al. 1996). It is interesting to note the association between pancreatic cancer risk and farming in this study (OR = 3.25, p = 0.0475).
Although smoking has been identified as a strong risk factor for pancreatic cancer in previous studies (Doll et al. 1994; Howe et al. 1991), it was not significant in this study. There are at least two possible explanations for this finding. First, most subjects (approximately two-thirds) in both the case and comparison groups were nonsmokers. Second, cadmium is a by-product of cigarette smoke, and adjusting for serum cadmium levels in this study may have controlled for the effect of smoking. There was also no significant association between risk of pancreatic cancer and residence. Although rural residents might be expected to show increased risk because of exposure to pesticides and fertilizers (Anderson et al. 1996; Fontham and Correa 1989), exposure of urban industrial workers employed in manufacturing (Norell et al. 1986; Sheffet et al. 1982), metalworking (Maruchi et al. 1979; Rotimi et al. 1993; Silverstein et al. 1988), and soldering (Ji et al. 1999) may be equally significant. There may also be other important differences in cadmium exposure (e.g., diet and air quality) that distinguish urban from rural populations. Reports from urban areas in Egypt have shown high levels of cadmium in organ meats that are frequently consumed by the local population (Abou-Arab and Abou Donia 2000). A recent study on markers of environmental pollution in Egypt has also shown significantly higher levels of oxidative damage in urban populations, compared with rural populations (Soliman et al. 2004). The higher risk of pancreatic cancer associated with farming in this study may reflect the intense exposure to cadmium and other farming-related occupations in this population. Thus, two major risk factors may have overshadowed the relationship between smoking and cadmium levels in this study. The first was the exceptionally high levels of environmental pollution in our study region. The second included the high dietary intake of rice and fish grown in polluted soils and water of the study region. The National Food Consumption Study in Egypt has shown that residents of our study region of Dakahlia Province consume more fish and rice than does any other group in Egypt (Galal and Khorshid 1995). Unfortunately, neither detailed environmental exposures nor dietary intake was measured in the present study.
Studies with rodents showed the ability of the pancreas to accumulate high concentrations of cadmium. Mice injected with 4 mg/kg CdCl2 expressed a 2.5-fold increase in DNA synthesis in the pancreas, which Andrews et al. (1990) and Hellman (1986) interpreted as a sign of increased metallothionein synthesis. Rats injected with 4 and 8 mg/kg cadmium demonstrated 9.8- and 17.9-fold increases, respectively, in pancreatic metallothionein levels (Wormser and Calp 1988). Cadmium interferes with the use of essential metals such as calcium, zinc, selenium, and iron. Deficiencies of these essential metals, in conjunction with protein and vitamin deficiencies, exaggerate cadmium toxicity by increasing absorption through the gut and enhancing retention in different organs (Waalkes 1986). Studies on adult Wistar rats showed that zinc deficiency markedly increases cadmium accumulation in various organs (Waalkes 1986). Studies from our research region (Galal and Khorshid 1995) showed that 46% of women have a calcium intake < 50% of the required daily allowances (RDA); the consumption of B12, a marker of protein intake, is also < 50% of the RDA in 33% of women in the region. Nutritional deficiencies in our study region may be a major factor in cadmium accumulation in body organs of the local population.
Cadmium is a potent carcinogen in rodents. Injecting rats with CdCl2 resulted in a significant increase (from 2.2% to 8.5%) in the incidence of pancreatic islet cell tumors (Poirier et al. 1983). In experiments designed to determine the carcinogenic effects of repeated exposure to CdCl2, Fischer and Wistar rats showed a very high incidence of dose-related pancreatic metaplasia, as reflected by pancreatic hepatocyte formation (Konishi et al. 1990). These pancreatic hepatocytes are thought to arise from the ductal and interstitial cells of the pancreas, which resemble oval cells of the liver (Rao et al. 1986). Cadmium may activate oncogenes such as c-myc, mdm2, and cellular tumor antigen p53; inhibit tumor suppressor genes such as wild-type p53 and p27; and consequently promote the proliferation of initiated cells (Fang et al. 2002).
The present study has several important strengths. The Dakahlia region has documented high levels of pollution and is therefore a unique setting for studying environmental risk factors and cancer occurrence. The stability of population without migration in Dakahliam maximizes the chances of life-long environmental exposure (Aly and Shields 1996). The high incidence of young pancreatic cancer patients (younger than 50 years of age) provides a unique model system for studying the effect of early environmental exposures on pancreatic carcinogenesis (Soliman et al. 2002). Finally, the wide range of occupational and lifestyle factors in this region provide help to investigate the occupational and epidemiologic risk factors associated with pancreatic cancer. The relatively small sample size of this rare cancer may limit the generalization of results.
In summary, this pilot study has shown a statistically significant association between pancreatic cancer and serum cadmium levels and farming. Future studies should expand on this pilot investigation by studying a larger number of pancreatic cancer patients and by collecting extensive information on the lifetime occupational, residential, and environmental exposures and dietary influences in order to clarify the role of cadmium in pancreatic cancer etiology in this population. Several molecular mechanisms have been identified by which cadmium may influence pancreatic cells (Chai et al. 1999; Merali and Singhal 1976; Shimoda et al 2001; Waalkes et al. 1992), and a better understanding of these processes is under study in our laboratories. It will also be important to examine genetic susceptibility and markers of genetic damage caused by environmental exposures to clarify the role of such exposures in pancreatic carcinogenesis.
We thank D. Garabrant (University of Michigan School of Public Health) for his comments during the data analysis.
This work was supported by Eli Lilly Research; the Topfer Research fund from the M.D. Anderson Cancer Center; National Cancer Institute grants CA K07 090241 and R03 CA099513-01; and University of Michigan Cancer Center Support grant 5 P30 CA46592 to A.S.S. Stipend support for A.M.K. was provided by the Office of Science (Biological and Environmental Research), U.S. Department of Energy (grant DE-FG02-98ER62704 to D.A.B.).
Figure 1 Modified one-step assay for Cd(II) in human serum. (A) Cadmium was first displaced from metallothionein and other proteins under acidic conditions. (B) An EDTA-containing buffer was added to the serum to form Cd(II)–EDTA complexes, which are recognized by the 2A81G5 antibody. (C) Microwell plates were coated and blocked as described in “Materials and Methods.” A serum sample was mixed 1:1 with enzyme-labeled Cd(II)–EDTA and subsequently added to the coated wells. The enzyme-labeled Cd(II)–EDTA competes with the Cd(II)–EDTA complexes from the serum sample for immobilized antibody binding sites. (D) Peroxidase substrate was added to the wells and absorbance was read at 450–650 nm.
Figure 2 Typical standard curve using the one-step assay for Cd(II) in pooled human serum. See “Materials and Methods” for details. Data shown were the means of four replicates with a best-fit line (solid). Dashed lines on either side of the main curve represent the best-fit line of the mean values ± 2 SD.
Figure 3 Pooled human serum versus pooled Egyptian comparison serum. Binding curves were calculated with cadmium spiked into the pooled human serum from Intergen or into a sample of pooled Egyptian comparison serum. IC50 values were very similar, 16.74 ng/mL and 17.44 ng/mL for Intergen and Egyptian samples, respectively. Values reported are mean ± SD (n ≥ 6).
Figure 4 Distribution of samples and serum Cd(II) concentrations in comparison subjects versus pancreatic cancer patients. The values reported are the percentage of the total samples for that population.
Table 1 Characteristics of the study population [n (%)].
Characteristic Case (n = 31) Comparison (n = 52) p-Value
Age (years)
< 60 21 (67.7) 41 (78.8) 0.3023
≥ 60 10 (32.3) 11 (21.2)
Sex
Male 19 (61.3) 30 (57.7)
Female 12 (38.7) 22 (42.3) 0.8198
Residence
Rural 15 (48.4) 15 (28.8) 0.0989
Urban 16 (51.6) 37 (71.2)
Smoking
Yes 11 (35.5) 21 (40.4)
No 20 (64.5) 31 (59.6) 0.8161
Occupation
Farmer 7 (22.6) 3 (5.8) 0.0805
Housewife 10 (32.3) 19 (36.5)
Industrial 8 (25.8) 11 (21.2)
Professional 6 (19.4) 19 (36.5)
Farming-related occupation
Yes 11 (35.5) 8 (15.4)
No 20 (64.5) 44 (84.6) 0.0570
Serum cadmium level (ng/mL)
Total
Mean ± SD 11.1 ± 7.7 7.1 ± 5.0 0.0120
Median (range) 11.0 (27.9) 5.7 (24.2)
Smokers
Mean ± SD 11.8 ± 9.3 7.1 ± 4.7 0.1386
Median (range) 13.7 (27.0) 6.4 (16.5)
Nonsmokers
Mean ± SD 10.7 ± 6.9 7.1 ± 5.3 0.0380
Median (range) 11.0 (27.0) 5.5 (24.2)
p-Values for quantitative variables were calculated using Student’s t-test; p-values for proportions and qualitative variables were calculated using the chi-square test.
Table 2 Serum cadmium concentration (ng/mL) in the combined patients and comparison sample.
No. Mean ± SD Median (range) p-Value
Sex
Male 49 9.21 ± 7.09 7.77 (27.88)
Female 34 7.72 ± 5.21 6.40 (19.19) 0.2966
Residence
Rural 30 8.52 ± 6.31 7.31 (20.11) 0.9281
Urban 53 8.65 ± 6.51 6.86 (27.42)
Smoking
Yes 32 8.75 ± 6.86 7.08 (27.42)
No 51 8.5 ± 6.16 6.86 (26.96) 0.8634
Occupation
Farmer 10 13.34 ± 8.29 11.88 (1.83–28.79) 0.0827
Housewife 29 7.56 ± 6.05 5.94 (0.91–27.88)
Industrial 19 8.68 ± 5.46 7.77 (0.91–16.45)
Professional 25 7.84 ± 6.14 5.03 (1.37–25.14)
p-Values for quantitative variables were calculated using Student’s t-test; p-values for proportions and qualitative variables were calculated using the chi-square test.
Table 3 ORs of risk factors for pancreatic cancer.
Case n = 31) Comparison n = 52) OR (95% CI) p-Value
Serum cadmium level (ng/mL, mean ± SD) 11.1 ± 7.7 7.1 ± 5.0 1.12 (1.04–1.23) 0.0089
Age [years, n (%)]
< 60 21 (67.7) 41 (78.8) 1 0.1677
≥ 60 10 (32.3) 11 (21.2) 2.25 (0.71–7.26)
Sex [n (%)]
Male 19 (61.3) 30 (57.7) 2.06 (0.51–8.61) 0.311
Female 12 (38.7) 22 (42.3) 1
Smoking [n (%)]
Yes 11 (35.5) 21 (40.4) 0.54 (0.13–2.19) 0.3859
No 20 (64.5) 31 (59.6) 1
Farming-related occupation [n (%)]
Yes 11 (35.5) 8 (15.4) 3.25 (1.03–11.64) 0.0475
No 20 (64.5) 44 (84.6) 1
ORs were calculated using logistic regression analysis. p-Values for quantitative variables were calculated using Student’s t-test; p-values for proportions and qualitative variables were calculated using the chi-square test.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8371ehp0114-00012016393668ResearchEnvironmental MedicineIncreased Risk of Paroxysmal Atrial Fibrillation Episodes Associated with Acute Increases in Ambient Air Pollution Rich David Q. 12Mittleman Murray A. 23Link Mark S. 4Schwartz Joel 125Luttmann-Gibson Heike 1Catalano Paul J. 67Speizer Frank E. 15Gold Diane R. 15Dockery Douglas W. 1251 Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA2 Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA3 Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA4 New England Medical Center, Tufts University, Boston, Massachusetts, USA5 Channing Laboratory, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA6 Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA7 Department of Biostatistical Science, Dana-Farber Cancer Institute, Boston, Massachusetts, USAAddress correspondence to D.W. Dockery, Harvard School of Public Health, Department of Environmental Health, Landmark Suite 415 West; 401 Park Dr., Boston, MA 02115 USA. Telephone: (617) 384-8741. Fax: (617) 384-8745. E-mail:
[email protected] authors declare they have no competing financial interests.
1 2006 20 9 2005 114 1 120 123 1 6 2005 19 9 2005 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Objectives: We reported previously that 24-hr moving average ambient air pollution concentrations were positively associated with ventricular arrhythmias detected by implantable cardioverter defibrillators (ICDs). ICDs also detect paroxysmal atrial fibrillation episodes (PAF) that result in rapid ventricular rates. In this same cohort of ICD patients, we assessed the association between ambient air pollution and episodes of PAF.
Design: We performed a case–crossover study.
Participants: Patients who lived in the Boston, Massachusetts, metropolitan area and who had ICDs implanted between June 1995 and December 1999 (n = 203) were followed until July 2002.
Evaluations/Measurements: We used conditional logistic regression to explore the association between community air pollution and 91 electrophysiologist-confirmed episodes of PAF among 29 subjects.
Results: We found a statistically significant positive association between episodes of PAF and increased ozone concentration (22 ppb) in the hour before the arrhythmia (odds ratio = 2.08; 95% confidence interval = 1.22, 3.54; p = 0.001). The risk estimate for a longer (24-hr) moving average was smaller, thus suggesting an immediate effect. Positive but not statistically significant risks were associated with fine particles, nitrogen dioxide, and black carbon.
Conclusions: Increased ambient O3 pollution was associated with increased risk of episodes of rapid ventricular response due to PAF, thereby suggesting that community air pollution may be a precipitant of these events.
air pollutionarrhythmiasfibrillationepidemiologycase—crossoverozone
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In previous studies, we reported statistically significant associations between ambient air pollution and cardiac arrhythmias in patients with implantable cardioverter defibrillator (ICD) devices (Dockery et al. 2005a, 2005b; Peters et al. 2000b; Rich et al. 2005). A pilot study of 100 patients in Boston, Massachusetts, found significantly increased risk of ICD discharges associated with nitrogen dioxide and black carbon among patients with repeated events (Peters et al. 2000b). In a larger study of approximately 200 Boston-area ICD patients, we found a nonstatistically significant increased risk of ventricular arrhythmias (confirmed by an electrophysiologist) associated with 2-day mean NO2, particulate matter < 2.5 μm in aerodynamic diameter (PM2.5), black carbon, carbon monoxide, ozone, and sulfur dioxide (Dockery et al. 2005a, 2005b). In a case–crossover analysis of these data, which allowed us to match the time of onset of these arrhythmias with ambient air pollution concentrations, we found stronger, statistically significant associations of ventricular arrhythmias with mean PM2.5 and O3 in the 24 hr before the arrhythmia (Rich et al. 2005).
Although ICDs are designed to detect and treat life-threatening ventricular arrhythmias, supraventricular arrhythmias may also be detected. Many of these supraventricular arrhythmias may be atrial fibrillation, which is the most common sustained arrhythmia in clinical practice (Go et al. 2001) and a risk factor for stroke (Prystowsky et al. 1996) and premature mortality (Kanel et al. 1983). We used a case–crossover design to examine the association of ICD-detected paroxysmal atrial fibrillation and hourly measurements of community air pollution concentrations.
Materials and Methods
Study population.
Two hundred three patients who had a third-generation Guidant ICD (Cardiac Pacemakers, Inc., Minneapolis, MN) implanted at the Tufts–New England Medical Center between 1 June 1995 and 31 December 1999, were followed until their last clinic visit before 15 July 2002. Patients who lived within 40 km (25 mi) of the air pollution monitoring station at the Harvard School of Public Health were included for analysis. The Guidant ICDs record intracardiac electrograms and were the most common ICD implanted at Tufts–New England Medical Center during the study period. Each patient’s first 14 days after implantation and any events that occurred during inpatient hospital visits were excluded. Further description of this population has been published previously (Dockery et al. 2005a, 2005b).
Outcome and clinical data.
For each ICD-recorded episode of tachyarrhythmia, the date, time, beat-to-beat intervals, and intracardiac electrogram before, during, and after episodes were recovered from the ICD. In a small number of cases in which the patient experienced a large number of ICD-detected episodes since the previous clinic visit, early electrograms in the ICD memory, but none of the other episode-specific data, may have been overwritten. ICD settings including ventricular tachycardia rate cutoffs (i.e., detection rates) were also abstracted from the ICD records. Ventricular tachycardia rate cutoffs were set by the treating electrophysiologist based on the clinical features of the patients.
All of the ICD-detected episodes were reviewed and characterized by an electrophysiologist (M.S.L) blinded to air pollution levels. Details of this arrhythmia classification have been published previously (Dockery et al. 2005b). Briefly, patients who presented with atrial fibrillation at all clinic follow-ups were classified as in permanent atrial fibrillation, and they were excluded from this analysis. Episodes of paroxysmal atrial fibrillation (PAF) were defined by a ventricular rate between 120 and 200 beats per minute, irregularity of the beat-to-beat intervals, no change in QRS morphology (except for a small number of cases with no ventricular electrogram), and lack of conversion following ventricular therapies (except when therapy was not applied). If a dual-chamber device had been implanted and an atrial electrogram was available, the atrial electrogram was also used to characterize ICD-recorded episodes. This analysis was restricted to PAF episodes that occurred at least 60 min after the previous event. Residence ZIP code, date of birth, race/ethnicity, clinic visit dates, and medications prescribed (beta-blockers, digoxin, and other antiarrhythmics) were abstracted from patients’ records.
The Harvard School of Public Health Human Subjects Committee and the Tufts–New England Medical Center Institutional Review Board approved this record review study.
Air pollution.
The air pollution measurements have been described previously (Dockery et al. 2005a, 2005b; Rich et al. 2005). Briefly, ambient concentrations of O3, NO2, SO2, and CO were measured hourly by the Massachusetts Department of Environmental Protection at four to six sites in the Boston metropolitan area during the entire follow-up period. We calculated the hourly average air pollution concentration across all available monitoring stations, accounting for differences in the annual mean and daily standardized deviations of each monitor (Schwartz 2000). PM2.5 was measured hourly in South Boston (~ 5 km east of the Harvard School of Public Health) from 1 April 1995 to 20 January 1998, and at the Harvard School of Public Health from 16 March 1999 to 31 July 2002. Black carbon was measured hourly in South Boston from 1 April 1995 to 29 March 1997, and at the Harvard School of Public Health from 15 October 1999 to 31 July 2002.
Acute effect of pollutants.
We analyzed the association of ambient air pollution concentrations and episodes of PAF using a case–crossover design (Maclure 1991). These methods have been used previously to study triggers of acute cardiovascular events (Albert et al. 2000; D’Ippoliti et al. 2003; Hallqvist et al. 2000; Mittleman et al. 1995; Peters et al. 2000a; Rich et al. 2005). In this design, each subject contributes information as a case during the event periods and as a matched control during nonevent times. Because cases and their matched controls are derived from the same person and a conditional analysis is conducted, non-time-varying potential confounders such as underlying medical condition and long-term smoking history are controlled by design. Variables that may be related to both air pollution and the occurrence of PAF that fluctuate over time (e.g., meteorologic conditions) are possible confounders.
We defined case periods by the detection time of each confirmed episode of PAF, rounded to the nearest hour. We matched control periods on weekday and hour of the day within the same calendar month (Lumley and Levy 2000). We calculated average pollution concentrations and weather conditions during the hour and during the 24 hours before the case and control time periods for this analysis.
Conditional logistic regression models, including the mean pollutant concentration in the hour of the arrhythmia (lag hour 0) and natural splines [3 degrees of freedom (df)] for the mean temperature, dew point, and barometric pressure in the 24 hr before the arrhythmia, were run separately for each pollutant (PM2.5, black carbon, NO2, CO, SO2 and O3). Different individuals may have different cardiac responses to pollution, based on their clinical history and genetic characteristics. Therefore, we included a frailty term (Therneau and Grambsch 2000) for each subject (akin to a random intercept) in all the above models. Odds ratios (ORs), 95% confidence intervals (CIs), and p-values for statistical significance testing are presented for an interquartile range increase in each pollutant. We considered associations with longer exposures before the PAF episode using the mean of the pollutant in the previous 24 hr (lag hours 0–23).
To assess the sensitivity of our results to the influence of outliers, we reran analyses, trimming the highest 5% and lowest 5% of air pollution concentrations. For O3, which has a strong seasonal pattern, we examined whether the association between PAF and O3 concentration was limited to the 6 months with the highest mean ambient temperature (May–October) by adding an O3/warm month interaction term to the conditional logistic regression model. We assessed the linearity of the PAF and O3 association by replacing the linear air pollution term with a penalized spline (3 df) in the conditional logistic regression model. We plotted the covariate adjusted log OR for the risk of PAF in the spline and linear models versus 1-hr O3 concentration.
We used SAS (version 9.1; SAS Institute Inc., Cary, NC) software to construct all datasets and to calculate descriptive statistics. We used S-Plus 6.2 (Insightful Inc., Seattle, WA) software for all modeling.
Results
There were 203 ICD patients enrolled in the study who lived within 40 km of the Harvard School of Public Health with a mean (± SD) follow-up time of 3.1 ± 1.8 years (maximum = 7.0 years). Ninety-five patients had a total of 1,574 recorded ICD events, 933 of which were separated by > 1 hr. Ninety-one (9.8 %) of these events, among 29 subjects, were confirmed episodes of PAF. Because PM2.5 and black carbon were not measured during the entire study period, analyses of PM2.5 included at most 52 episodes of PAF from 22 subjects, and analyses of black carbon included at most 46 episodes of PAF from 18 subjects.
The 29 subjects with PAF episodes were primarily male (79%) and white (79%), and they ranged in age from 45 to 78 years (mean, 65 years). At their first clinic follow-up visit, 69% of subjects were listed as being prescribed beta-blockers, 57% digoxin, and 24% other antiarrhythmics (i.e., amiodarone, quinidine, sotalol, or mexilitine). Two subjects (7%) were not prescribed any of these medications. The most common diagnoses at implantation were coronary artery disease (76%) and idiopathic cardiomyopathy (22%). Before ICD implantation, 55% of subjects had left ventricular ejection fractions < 35%. Subjects’ ICDs were programmed with ventricular tachycardia detection rates (i.e., ventricular rate threshold above which the electrogram and date/time for a tachyarrhythmia would be recorded) that had a 10th to 90th percentile range of 140 to 200 beats/min (median = 175).
Of the 29 subjects who experienced at least one episode of PAF, 15 (52%) experienced > 1 event, while 2 (7%) experienced ≥ 10. Twenty (69%) also experienced a ventricular arrhythmia during follow-up. Episodes of PAF were more frequent in the late morning (0900–1100 hr), with a smaller evening peak (1800–2000 hr).
The distributions of air pollution concentrations and meteorologic characteristics in Boston during the study period, averaged hourly and daily, are summarized in Table 1. The highest average PM2.5 and black carbon concentrations were observed early in the morning (0600–0800 hr), highest NO2 in the early morning (0600–0800 hr) and early evening (1600–2100 hr), and highest O3 at midday (1200–1400 hr). Further detail has been provided previously (Dockery et al. 2005b).
We found a statistically significant increased risk of PAF associated with mean O3 concentration in the concurrent hour (lag hour 0; Table 2). The estimated relative odds for the 24-hr moving average concentration was positive (OR > 1), but not statistically significant. We did not find statistically significant associations with any other pollutant in the concurrent hour, but associations were positive for PM2.5 and NO2. Risk estimates for 24-hr average PM2.5, NO2, and black carbon were positive, but none was statistically significant. Risk estimates for 24-hr average CO and SO2 were protective (OR < 1), but neither was statistically significant (Table 2).
For O3 in the concurrent hour, there was little change in risk of PAF when we excluded the top 5% and bottom 5% of concentrations (OR = 2.15, 95% CI = 1.04–4.44, p = 0.04). The association between PAF and O3 in the concurrent hour in the cold months (OR = 2.21; 95% CI = 0.98–4.98; p = 0.06) was comparable to that in the warm months (OR = 1.98; 95% CI = 1.05–3.73; p = 0.04), with no significant interaction (p = 0.84).
Figure 1 shows the covariate adjusted log OR for the risk of PAF versus 1-hr O3 concentration modeled using first a linear term and then a penalized spline (3 df). We found no evidence of a deviation from linearity (nonlinear term, p = 0.63).
Discussion
In a study designed to assess the association of ambient air pollution with ventricular arrhythmias among ICD patients, 91 of the ICD-detected episodes were identified by electrophysiologist review as PAF. Although these episodes of PAF were likely an underrepresentation of all those PAF episodes experienced by these patients, they provided a unique opportunity to assess associations between air pollution and episodes of PAF. We found a statistically significant 2-fold increase in risk of PAF episodes associated with each 22-ppb increase in mean ambient O3 concentration in the concurrent hour. We found no evidence that this association was nonlinear.
An earlier study reported a 10.5% increase in supraventricular ectopy (~ 3.5 beats/hr increase in supraventricular ectopy compared to the population mean rate of supraventricular ectopy) associated with each 7-μg/m3 increase in ambient PM10 (particulate matter < 10 μm in aerodynamic diameter) concentration in a panel of chronic obstructive pulmonary disease patients (Brauer et al. 2001). They reported smaller increases in supraventricular ectopy associated with outdoor and personal PM2.5 and sulfates.
Our findings identify ambient air pollution as a potential precipitant of supraventricular arrhythmias. Atrial fibrillation is the most common supraventricular arrhythmia. At least 2.3 million adults in the United States have some form of atrial fibrillation (Go et al. 2001), and this number is likely an underestimate because many people with this condition are asymptomatic (Chugh et al. 2001). The incidence of atrial fibrillation doubles with each decade of adult life (Falk 2001). Although atrial fibrillation is not usually considered a lethal rhythm, it is associated with premature mortality and increased risk for hospitalization and stroke (Wolf et al. 1998; Benjamin et al. 1998). If not on antithrombotic therapy, people with atrial fibrillation have a 5-fold increased risk of stroke (Ryder and Benjamin 1999). Therefore, even a modest risk of atrial fibrillation associated with acute exposure to elevated ambient air pollution in the general population would have a substantial attributable risk.
In prior analyses in this cohort of ICD patients, we found significantly increased risk of ventricular arrhythmias associated with mean PM2.5 and O3 concentrations in the 24 hr before the episode (Rich et al. 2005), and marginally significant increased risk (p < 0.10) associated with mean black carbon and NO2 concentrations over the previous 2 days (Dockery et al. 2005a, 2005b). The findings of positive associations between episodes of PAF and O3 concentration (1-hr) is consistent with these observations, although the timing (1 hr vs. 24 hr or 1 day) suggests a more rapid response to air pollution with PAF.
O3 is an acute lung irritant that has been associated with acute myocardial infarction (Ruidavets et al. 2005), decreased lung function, exacerbation of asthma or other respiratory conditions, increased hospitalizations, and premature mortality (Thurston and Ito 1999). O3 is a highly reactive oxidant formed by photochemical reactions in the atmosphere. O3 concentrations are highest on warm sunny days, and highest during the afternoon hours. However, we found a statistically significant association with O3 after adjustment for temperature, and we found no evidence that the O3 associations were restricted to the six warmest months.
We also found positive associations with PM2.5, NO2, and black carbon, but the CIs were wide and the risk estimates were not statistically significant. The number of PAF episodes with matching O3 and NO2 concentrations was small (n = 90), and they were even smaller for PM2.5 (n = 52) and black carbon (n = 46), which resulted in reduced power to detect any associations. Thus, this small number of confirmed PAF episodes dictates caution in our interpretation of specific associations. Although we have highlighted the association with O3 in the concurrent hour, it would be premature to attribute the increased risk of PAF to O3 alone. We suggest that community air pollution may be associated with the incidence of PAF. Confirmation of this association and examination of associations with specific pollutants requires a larger number of confirmed PAF episodes.
A problem in studying incidence of PAF is the definition of time of onset of new episodes. Although the ICD device provides a detection time for each episode of PAF, this is the time that the ventricular rate (responding to the atrial stimulus) exceeded the patient’s specific programmed criteria for a tachyarrhythmia. The PAF episode may have started earlier than the time recorded by the ICD. This situation would lead to mismatching of air pollution concentrations to case and control time periods. However, this exposure misclassification would be nondifferential with respect to case/control status. Therefore, it would have resulted in a bias toward the null, underestimates of risk, and wide CIs.
Episodes of PAF also may have been misclassified. However, any outcome misclassification, if present, was likely independent of air pollution levels and nondifferential. This misclassification would have produced wider CIs, a bias toward the null, and underestimates of risk.
Our analysis was limited to a subset of all PAF episodes that these subjects experienced. PAF episodes with ventricular response rates that remained below the ICD’s preset detection criteria for the duration of the arrhythmia would not have been recorded. These under-detected episodes likely represented a substantial fraction of the PAF episodes experienced by these patients. However, we used the case–crossover method, where each person serves as his or her own control, and event times are contrasted with matched control times. Such misclassification would have resulted in a loss of power, but no bias in our risk estimates. Whether our finding of an association between transient ambient air pollution concentrations and PAF is limited to this particular subset of PAF episodes, however, is unknown. New studies using devices programmed to detect a wider range of PAF episodes with more precise data on the timing of arrhythmia initiation are required to confirm and quantify this association further.
The Health Effects Institute (grant 98-14) and the National Institute of Environmental Health Sciences (NIEHS; grants ES-09825 and ES00002) funded this study. Particulate air pollution measurements were supported in part by the Environmental Protection Agency (grant R827353). D.R. received support from an NIEHS Training Grant (5T32 ES007069).
Figure 1 Log OR of PAF by 1-hr O3 concentration modeled as a linear term and using a penalized spline with 3 df. Vertical lines on abscissa indicate the O3 concentrations of observed events.
Table 1 Boston air pollution profile, August 1995 to June 2002.
Percentile
Parameter No. of hours or days 25th 50th 75th Maximum
PM2.5 (μg/m3)a
Hourly 48,592 5.6 9.2 15.0 84.1
Daily 2079 6.7 9.8 14.5 53.2
Black carbon (μg/m3)b
Hourly 36,789 0.44 0.77 1.35 23.93
Daily 1555 0.58 0.94 1.41 7.32
NO2 (ppb)
Hourly 60,555 15.8 21.7 29.0 78.8
Daily 2526 18.1 22.4 27.3 61.8
SO2 (ppb)
Hourly 60,620 2.6 4.3 7.5 71.6
Daily 2526 3.2 4.8 7.3 31.4
CO (ppm)
Hourly 60,091 0.46 0.73 1.04 5.83
Daily 2526 0.52 0.78 1.03 2.48
O3 (ppb)
Hourly 60,210 11.3 22.2 33.0 119.5
Daily 2524 15.2 22.6 30.9 77.5
Temperature (°C)
Hourly 60,449 3 11 18 36
Daily 2526 4 11 18 32
Dew point (°C)
Hourly 60,356 −3 6 13 25
Daily 2526 −2 5 13 23
Barometric pressure (mmHg)
Hourly 60,379 758 762 766 784
Daily 2525 758 762 766 781
Air pollution was measured hourly; total possible hours = 60,624; total possible days = 2,526.
a Concentrations missing from 21 January 1998 to 15 March 1999.
b Concentrations missing from 30 March 1997 to 15 October 1999.
Table 2 ORs for PAF associated with an interquartile range increase in the mean of pollutant lag hours.
Mean of pollutant Interquartile range Lags No. of subjects No. of PAF episodes OR (95% CI) p-Value
PM2.5 (μg/m3) 9.4 0 22 52 1.41 (0.82–2.42) 0.22
7.8 0–23 22 47 1.13 (0.63–2.03) 0.68
Black carbon (μg/m3) 0.91 0 18 46 0.81 (0.42–1.56) 0.53
0.83 0–23 18 46 1.46 (0.67–3.17) 0.34
NO2 (ppb) 13.2 0 28 90 1.21 (0.80–1.83) 0.37
9.2 0–23 27 89 1.18 (0.79–1.76) 0.43
CO (ppm) 0.58 0 28 90 0.87 (0.56–1.37) 0.55
0.51 0–23 28 90 0.71 (0.39–1.28) 0.25
SO2 (ppb) 4.9 0 28 90 1.02 (0.81–1.28) 0.87
4.1 0–23 28 90 0.99 (0.71–1.39) 0.97
O3 (ppb) 21.7 0 28 90 2.08 (1.22–3.54) 0.007
15.8 0–23 28 89 1.60 (0.89–2.89) 0.12
==== Refs
References
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Benjamin EJ Wolf PA D’Agostino RB Silbershatz H Kannel WB Levy D 1998 Impact of atrial fibrillation on the risk of death: the Framingham Heart Study Circ 98 10 946 952
Brauer M Ebelt ST Fisher TV Brumm J Petkau AJ Vedal S 2001 Exposure of chronic obstructive pulmonary disease patients to particles: respiratory and cardiovascular health effects J Exp Anal Environ Epidemiol 11 490 500
Chugh SS Blackshear JL Shen WK Hammill SC Gersh BJ 2001 Epidemiology and natural history of atrial fibrillation: clinical implications JACC 37 2 371 378 11216949
D’Ippoliti D Forastiere F Ancona C Agabiti N Fusco D Michelozzi P 2003 Air pollution and myocardial infarction in Rome: a case–crossover analysis Epidemiol 14 5 528 535
Dockery DW Luttmann-Gibson H Rich DQ Link MS Mittleman MA Gold DR 2005a Association of particulate air pollution with arrhythmias recorded by implantable cardioverter defibrillators Environ Health Perspect 113 670 674 15929887
Dockery DW Luttmann-Gibson H Rich DQ Link MS Schwartz JD Gold DR 2005b. Particulate Air Pollution and Nonfatal Cardiac Events, Part II. Association of Air Pollution with Confirmed Arrhythmias Recorded by Implanted Defibrillators. HEI Research Report 124. Boston, MA:Health Effects Institute.
Falk RH 2001 Atrial fibrillation New Engl J Med 344 14 1067 1078 11287978
Go AS Reed GL Hylek EM Phillips KA Liu L Henault LE 2001 Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the AnTicoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Study JAMA 285 18 2370 2375 11343485
Hallqvist J Moller J Ahlbom A Diderichsen F Reuterwall C de Faire U 2000 Does heavy physical exertion trigger myocardial infarction? A case-crossover analysis nested in a population-based case-referent study Am J Epidemiol 151 5 459 467 10707914
Kanel WB Abbott RD Savage DD McNamara PM 1983 Coronary heart disease and atrial fibrillation: the Framingham Study Am Heart J 106 2 389 396 6869222
Lumley T Levy D 2000 Bias in the case-crossover design: implications for studies of air pollution Environmetrics 11 689 704
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Prystowsky EN Benson Jr DW Fuster V Hart RG Kay GN Myerburg RJ 1996 Management of patients with atrial fibrillation: a statement for healthcare professionals from the subcommittee on electrocardiography and electrophysiology, American Heart Association Circulation 93 6 1262 1277 8653857
Rich DQ Schwartz J Mittleman MA Link M Luttmann-Gibson H Catalano PJ 2005 Association of ambient air pollution and ICD-detected ventricular arrhythmias in Boston, MA Am J Epidemiol 161 1123 1132 15937021
Ruidavets J-B Cournot MC Cassadou S Giroux M Meybeck M Ferrieres J 2005 Ozone air pollution is associated with acute myocardial infarction Circulation 111 563 569 15699276
Ryder KM Benjamin EJ 1999 Epidemiology and significance of atrial fibrillation Am J Cardiol 84 9A 131R 138R
Schwartz J 2000 The distributed lag between air pollution and daily deaths Epidemiology 11 320 326 10784251
Therneau TM Grambsch PM 2000. Frailty models. In: Modeling Survival Data: Extending the Cox Model (Statistics for Biology and Health). New York:Springer, 231–260.
Thurston GD Ito K 1999. Epidemiologic studies of ozone exposure effects. In: Air Pollution and Health (Holgate ST, Samet JM, Koren HS, Maynard RL, eds). San Diego, CA:Academic Press, 486–510.
Wolf PA Mitchell JB Baker CS Kannel WB D’Agostino RB 1998 Impact of atrial fibrillation on mortality, stroke, and medical costs Arch Int Med 158 3 229 234 9472202
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8030ehp0114-00012416393669ResearchChildren's HealthWater Manganese Exposure and Children’s Intellectual Function in Araihazar, Bangladesh Wasserman Gail A. 12Liu Xinhua 23Parvez Faruque 4Ahsan Habibul 5Levy Diane 3Factor-Litvak Pam 5Kline Jennie 256van Geen Alexander 7Slavkovich Vesna 4LoIacono Nancy J. 4Cheng Zhongqi 7Zheng Yan 78Graziano Joseph H. 41 Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York, New York, USA2 New York State Psychiatric Institute, New York, New York, USA3 Department of Biostatistics,4 Department of Environmental Health Sciences, and5 Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, USA6 Gertrude H. Sergievsky Center, Columbia University, New York, New York, USA7 Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York, USA8 Queens College, City University of New York, New York, New York, USAAddress correspondence to G.A. Wasserman, New York State Psychiatric Institute, 1051 Riverside Dr., Unit 78, New York, NY 10032 USA. Telephone: (212) 543-5296. Fax: (212) 543-1000. E-mail:
[email protected] authors declare they have no competing financial interests.
1 2006 9 8 2005 114 1 124 129 17 2 2005 9 8 2005 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Exposure to manganese via inhalation has long been known to elicit neurotoxicity in adults, but little is known about possible consequences of exposure via drinking water. In this study, we report results of a cross-sectional investigation of intellectual function in 142 10-year-old children in Araihazar, Bangladesh, who had been consuming tube-well water with an average concentration of 793 μg Mn/L and 3 μg arsenic/L. Children and mothers came to our field clinic, where children received a medical examination in which weight, height, and head circumference were measured. Children’s intellectual function was assessed on tests drawn from the Wechsler Intelligence Scale for Children, version III, by summing weighted items across domains to create Verbal, Performance, and Full-Scale raw scores. Children provided urine specimens for measuring urinary As and creatinine and were asked to provide blood samples for measuring blood lead, As, Mn, and hemoglobin concentrations. After adjustment for sociodemographic covariates, water Mn was associated with reduced Full-Scale, Performance, and Verbal raw scores, in a dose–response fashion; the low level of As in water had no effect. In the United States, roughly 6% of domestic household wells have Mn concentrations that exceed 300 μg Mn/L, the current U.S. Environmental Protection Agency lifetime health advisory level. We conclude that in both Bangladesh and the United States, some children are at risk for Mn-induced neurotoxicity.
childrenIQmanganese
==== Body
Manganese, a transition metal, is an essential nutrient in humans and animals. Like many other essential metals, excessive exposure has been associated with adverse health effects, in this case, neurotoxicity. Mn deficiency is rare in humans because the element is ubiquitous in common foods. Typically, dietary Mn intake greatly exceeds that from drinking water; exposure from water is usually small. Nevertheless, the World Health Organization (WHO) has established a health-based water Mn (WMn) standard of 500 μg/L (WHO 2002). Similarly, the U.S. Environmental Protection Agency (EPA) recently issued a drinking water health advisory for Mn that yielded a lifetime health advisory value of 300 μg/L in drinking water (U.S. EPA 2004).
Although the evidence linking inhalation exposure to neurotoxicity is compelling, evidence that links Mn exposure from drinking water to adverse neurologic effects is unconvincing. Occupational inhalation exposures in adults (Gorell et al. 1999; Mena et al. 1967; Takeda 2003) have repeatedly been associated with neuromotor consequences—specifically, akinetic-rigid Parkinsonism—largely due to toxic effects on dopaminergic neurons of the basal ganglia. Very little research has examined the consequences of excessive Mn exposure on neurologic or developmental functioning in children.
Last year, from a cross-sectional study of 201 10-year-old children in Bangladesh, we concluded that water arsenic (WAs) exposure was adversely associated with intellectual function (Wasserman et al. 2004). In the same study, we observed a moderate and statistically significant positive association between WAs and WMn (Spearman r = 0.39, p < 0.0001). Roughly 75% of the wells in our study region contained As in excess of the maximum contaminant level of 10 μg/L (van Geen et al. 2003), and 80% had levels of Mn that exceeded the WHO standard of 500 μg/L (Cheng et al. 2004). Among those 201 children, well WMn was adversely associated with children’s intellectual functioning; however, the magnitude of the association was reduced by approximately half (and was no longer statistically significant) when we adjusted for WAs. It was thus unclear whether or not WMn had an independent association with intellectual functioning. To facilitate interpretation, we sought an additional sample of children with low WAs.
Thus, to test the hypothesis that Mn might have an independent adverse effect on cognitive function, we returned to our Bangladesh study region. Our original As study included 54 children using wells with low As concentrations, that is, ≤ 10 μg/L. In our subsequent fieldwork, we recruited 88 additional children drinking from wells comparably low in As. Here we report on associations between WMn and intellectual function in the combined sample of 142 children.
Materials and Methods
Overview
Our present project is part of a larger ongoing multidisciplinary study by health, earth, and social scientists working collaboratively in Araihazar, Bangladesh. The study was approved by the Bangladesh Medical Research Council and the Columbia University Medical Center institutional review boards. We have previously described the region and the larger cohort study of adults, whose children are the focus of this investigation (Wasserman et al. 2004). As in most of rural Bangladesh, people in Araihazar live in houses with floors made from mud or cement, with roofs and walls constructed from concrete, tin, or straw. Members of extended families live in clusters of individual houses (a bari), surrounded by family farmland. Each bari has one or more tube wells, usually owned by a senior family member. This region is not particularly poor by Bangladeshi standards. Before conducting this study, we secured review and approval from institutional review boards at Columbia and in Bangladesh and obtained written informed consent from parents as well as child assent.
Subjects
Details regarding the enrollment of the original 54 children are available in a previously published study (Wasserman et al. 2004). Briefly, in 2002, of the 11,749 adults enrolled in our cohort study, we selected 400 of their children at random (using 400 different wells) between 9.5 and 10.5 years of age; we ultimately assessed 201 children, 54 of whom were drinking from a well with WAs concentration ≤ 10 μg/L and who are included in these analyses. In 2004, we identified an additional 407 children who met three inclusion criteria: a) Their well had ≤ 10 μg As/L, b) their estimated age from earlier parental interviews was 9.5–10.5 years, and c) neither they nor their siblings had participated in our previous study. We limited the sample to children who were currently attending school. Of the first 199 families visited between May and November 2004, 88 met the inclusion criteria and consented to participate. Of the remaining 108 children, 76 were not of the desired age, 8 did not attend school, 11 had relocated, 12 refused to participate, and 1 was physically disabled.
Procedure
Children and their mothers came to our field clinic, where the children participated in assessments described below and received a medical examination by a study physician. Weight, height, and head circumference were measured. In addition, children provided spot urine specimens for the measurement of urinary As (UAs) and urinary creatinine (UCr) and were asked to provide a blood sample for the measurement of blood Mn (BMn), blood As (BAs), blood lead (BPb), and hemoglobin (Hgb) concentrations. Of the 142 children assessed, 95 agreed to provide blood samples. Because our original intent was to measure only BPb and BAs, Mn-free needles were not used. The anticoagulant had negligible concentrations of Mn, Pb, and As. Blood and urine samples were frozen at –20°C and transported on dry ice to New York. Information on family demographics (e.g., parental education, occupation, housing type) was obtained from the baseline interview of parents during their enrollment in the cohort study. Information on the primary source of drinking water was obtained from the child’s mother. Parents were asked their age, education, and occupation; whether their home included a television; and the birth order of their children. As an additional surrogate for social class, the type of roofing on the well-owner’s home was recorded as thatched, tin, or cement and subsequently ranked on a scale (thatched, lowest; cement, highest). Children were given a toy as a sign of appreciation for their participation; all families participating in the larger cohort study continue to receive primary medical care at our own field clinic.
Measures
Water analyses.
Water samples were collected at the onset of the cohort study as part of a survey of all wells in the study region. Field sample collection and laboratory analysis procedures are described elsewhere in detail (Cheng et al. 2004; van Geen et al. 2003, 2005). In brief, samples were collected in 60-mL acid-cleaned polyethylene bottles, and 1 mL 7 N high-purity HCl was added for preservation before being shipped to Columbia University’s Lamont-Doherty Earth Observatory for analysis. Initially, some samples were analyzed for As only by graphite-furnace atomic absorption spectrometry. For the present study, all samples were reanalyzed by high-resolution inductively coupled plasma mass spectrometry (HR ICP-MS). The analytical detection limit of the method is 0.1 μg/L; the standard deviation of a single measurement is conservatively estimated at 4 μg/L (van Geen et al. 2005). Mn concentrations were also determined by HR ICP-MS. The detection limit of the method is also 0.1 μg/L, and its precision was 2% (Cheng et al. 2004).
Urinary measurements.
UAs concentrations were assayed by graphite-furnace atomic absorption at the Mailman School of Public Health at Columbia University, using a Perkin-Elmer Analyst 600 system (PerkinElmer, Shelton, CT) as described previously (Nixon et al. 1991). Our laboratory participates in a quality-control program coordinated by P. Weber at the Québec Toxicology Center (Sainte-Foy, Québec, Canada). During the course of this study, intraclass correlation coefficients between our laboratory’s values and samples calibrated at Weber’s laboratory were 0.99. UAs levels were also adjusted for UCr concentrations, which were analyzed by a colorimetric Sigma Diagnostics Kit (Sigma, St. Louis, MO). Blood Hgb levels were determined by standard methods.
ICP-MS blood measurements.
Venous whole blood samples were analyzed for BPb, BMn, and BAs concentrations in the Trace Metal Core Laboratory at the Mailman School of Public Health, which used a Perkin-Elmer Elan DRC II ICP-MS equipped with an AS 93+ autosampler. ICP-MS-DRC methods for metals in whole blood were developed according to published procedures (Pruszkowski et al. 1998; Stroh 1988), with modifications for blood sample preparation as suggested by the Laboratory for ICP-MS Comparison Program (Institut National de Santé Publique du Québec). A 3-mL EDTA Vacutainer of whole blood was thawed, thoroughly mixed, and then diluted 50 times with the following diluent: 1% HNO3, 0.2% Triton-X-100, 0.5% NH4OH. The sample was then centrifuged for 10 min at 3,500 rpm, and the supernatant used for analysis. One multielement standard solution was used for instrument calibration. The metal concentrations of that solution were chosen to cover the expected ranges of analyte concentrations in the blood samples: 5, 25, and 50 μg/L. Special attention was given to correction for matrix-induced interferences. Matrix suppression is compensated very well by the selection of suitable internal standards (IS), which are matched to masses and, if possible, to ionization properties of the analytes. For As, we used iridium (Ir); for Pb and Mn, we used lutetium (Lu) and gallium (Ga), respectively. A stock IS spiking solution was prepared that ultimately delivered to each tube 10 ng of Lu and Ir, and 100 ng Ga. After calibrating the instrument, we ran quality control samples, that is, blood samples with known analyte concentrations obtained from the Laboratory for ICP-MS Comparison Program.
Quality-control blood samples were purchased to cover the range of concentrations of analytes of interest and were run during the course of each day. Over a period of 1 month, during which all of these study samples were analyzed, the intraprecision coefficients of variation for BPb, BMn, and BAs were 1.5, 4.0, and 4.3%, respectively. In late 2004, when these samples were run, we also joined the Québec Multi-Elements External Quality Assessment Scheme run by the Laboratory for ICP-MS Comparison Program. Three times per year, that lab sends blood urine and serum samples with known concentrations of 23 elements. Only one blood sample was received and analyzed during the course of this study, but our reported concentrations for BPb, BMn, and BAs were well within the expected target ranges.
Children’s intellectual function.
The Wechsler Intelligence Scale for Children, version III (WISC-III) (Wechsler 1991), suitable for children ≥ 6 years of age, consists of five (or six, depending on administration) verbal subtests that together provide a Verbal IQ score, and a similar number of performance subtests that together provide a Performance IQ. Neither the WISC-III (Wechsler 1991) nor any other recently well-standardized child IQ test has been adapted or standardized for use in Bangladesh.
In Araihazar, living conditions differ dramatically from those in the Western settings where this test was developed, which necessitated adaptations of the test for use in this culture. For example, a typical Araihazar home consists of a single room, often with a dirt floor. Most families use biomass fuel (leaves, hay, dung) for cooking. Electricity is available in most homes, typically consisting of one or two bulbs used for lighting. Many common Western household items, such as telephones and bathtubs, are rare.
We have previously described our adaptations of the WISC-III for this population (Wasserman et al. 2004). In short, we used six subtests that seemed the most culturally adaptable to this cultural context. Of the WISC-III Verbal subtests, we used Similarities and Digit Span; of the Performance subtests, we used Picture Completion, Coding, Block Design, and Mazes. As noted previously (Wasserman et al. 2004), we summed items across Verbal, Performance, and Full-Scale domains to create Verbal, Performance, and Full-Scale raw scores and also transformed these into measures of estimated Verbal, Performance, and Full-Scale IQ using procedures presented in the test manual (Wechsler 1991), despite the obvious limitations in application to this population.
Maternal intelligence was assessed with Raven’s Standard Progressive Matrices, a non-verbal test relatively free of cultural influences (Raven et al. 1983).
Translation and Training
All tests and interviews were translated (and back-translated) between Bangla (Bengali) and English. As noted above, items deemed to be culturally inappropriate were altered or omitted. Materials were piloted to ensure maternal and child comprehension. Subsequently, two interviewers were trained by a competent tester (G.A.W.) and continued with supervised practice sessions for 2 weeks. All written test responses were rechecked when data were sent to Columbia University for entry.
Statistical Analyses
Outcomes.
Because of concerns regarding the application of U.S. standardization of the WISC-III to Bangladeshi children, we first conducted analyses that predicted Verbal, Performance, and Full-Scale raw scores. Because the psychometric properties of IQ scores are more familiar to readers, we also applied the same analytical models to the prediction of estimated Verbal IQ, Performance IQ, and Full-Scale IQ.
Covariate adjustment and missing data.
We adjusted our models for the same set of covariates described in our previous As study (Wasserman et al. 2004): maternal education (categorized as none, 1–5 years, and 6–13 years) and intelligence, house type (thatched roof or poorer, corrugated tin, concrete construction), family ownership of a television, and child height and head circumference. For one girl without height data, we substituted the mean height for other participating girls.
Analytical model.
Analyses first estimated differences in the three measures of intellectual function, based only on the sociodemographic maternal factors, using linear regression models. We then estimated the incremental association of exposures (WMn, UAs, and WAs) singly and in combination, measured continuously. We repeated our analyses, categorizing children into groups based on quartiles of WMn to illustrate dose–response relationships. To examine further the dose–response relationship between WMn and intellectual function, we subsequently stratified children into four approximately equal-sized groups, based on well WMn. Because results based on quartiles of exposure were similar to those based on cut-points used in policy statements, we present data arrayed by the cut points, which correspond to various policy guidelines: Group 1 (reference), Mn < 200 μg/L (n = 38; 27%); group 2, 200 ≥ Mn < 500 μg/L (n = 45; 32%); group 3, 500 ≥ Mn < 1,000 μg/L (n = 31; 22%); group 4, Mn ≥ 1,000 μg/L (n = 28; 20%).
We next repeated these analyses for the subset of 95 children who provided blood samples for BMn, BPb, and BAs, measured continuously. The following variables were log-transformed to normalize their distributions: BPb, BAs, BMn, UAs, UCr, WMn, and WAs. For the most part, analyses are based on n = 142 children; however, those considering BPb, BAs, and BMn are based on n = 95 children.
Results
Sample Characteristics
Table 1 presents descriptive information for all demographic, water, and biochemical variables. Average child age was 10 years; approximately half of the sample were male; roughly one-third of children had regular access to a television, and > 70% lived in a house with a tin roof. On average, mothers and fathers reported 3.1 and 3.9 years of education, respectively. Children providing blood samples did not differ on any measure of exposure or intellectual function or on sociodemographic characteristics from those not providing blood samples (data not shown).
Exposure Characteristics
The mean WMn concentration was 795 μg/L, with a very wide range, from 4 to 3,908 μg/L. By design, the range of WAs concentrations was narrow (0.1–10 μg/L), with a mean of 3.0 μg/L. Table 2 presents a matrix of Spearman correlation coefficients among water, urine, and blood metal measurements. Despite the restricted range of As exposure in the present sample, correlations among measurements of As in water, urine, and blood were all significantly positive. WMn was also positively correlated with WAs and BAs, but not so highly correlated as to preclude examination of their independent effects on child intelligence. WMn was not associated with BMn.
To obtain rough estimates of Mn intake from drinking water, we first calculated the mean WMn concentration for each quartile of WMn. The means for the four quartiles were 103, 440, 801, and 1,923 μg Mn/L. Based on a recent U.S. Institute of Medicine (IOM) report (IOM Food and Nutrition Board 2004), we estimated daily water intake for 10-year-old boys and girls to be 2.4 and 2.1 L/day, respectively. For the four quartiles, the product of WMn concentration times daily water intake yielded estimates of daily Mn consumption (from water only) of 0.25, 1.06, 1.92, and 4.37 mg/day for boys and 0.21, 0.93, 1.68, and 3.82 mg/day for girls.
Relationship between Covariates and Intellectual Function
Linear regression analyses, predicting test raw scores from the sociodemographic features retained in the final “core” model, revealed better scores among children who a) had more educated mothers; b) lived in more adequate dwellings; c) had access to television; d) were taller; and e) had a larger head circumference (data not shown). Collectively, these factors explained 25.0, 24.1, and 17.7% of the variances in Full-Scale, Performance, and Verbal raw scores, respectively.
Relationship between Well WMn and Intellectual Function
As Table 3 shows, before adjustment for sociodemographic factors, WMn was significantly associated with Full-Scale, Performance, and Verbal raw scores (B-values = –5.20, –4.43, and –0.80; p-values < 0.001, 0.001, and 0.02, respectively), explaining 10, 10, and 4%, respectively, of the variances in scores. After adjusting for sociodemographic factors, WMn concentration remained significantly and negatively associated with all three scores (B-values = –4.35, –3.76, and –0.63; p-values < 0.001, 0.001, and 0.05, respectively) and also explained incremental portions of the variances in scores (6.3, 6.9, and 2.3%, respectively). The addition of WMn to the core regression models produced negligible changes in the associations between core model variables and intellectual function raw scores. All results were similar when “IQ” outcomes were substituted for raw scores (data not shown).
Controlling for As Exposure
The addition of WAs to these regression models failed to change the pattern of associations between intellectual function and sociodemographic variables, or between intellectual function and WMn. Not surprisingly, given that we sampled only children with low levels of WAs, WAs was not significantly associated with intellectual function. Similarly, associations between WMn and intellectual function scores were unchanged when we adjusted for both UAs and UCr. Neither UAs nor UCr was associated with children’s intellectual function (data not shown).
Dose–Response Relationships between Well WMn and Intellectual Function
To examine the dose–response relationship between WMn and intellectual function, we subsequently stratified children into four approximately equal-sized groups, based on well WMn.
Before adjustment.
Unadjusted for other contributors, children in group 1, compared with those in the other three groups with higher WMn, had higher Full-Scale scores: groups 2 and 4 were significantly different from group 1 (B-values = –11.93 and –23.80; p-values < 0.05 and 0.0001, respectively), whereas the finding for group 3 was in the same direction but did not achieve significance (B = –8.92, p = 0.09). Similarly, compared with children in group 1, children in groups 2, 3, and 4 had lower Performance scores (B-values = –10.42, –7.97, and –20.39; p-values < 0.05, 0.07, and 0.0001, respectively). Finally, compared with children in group 1, those in group 4 also had significantly poorer Verbal scores (B = –3.76, p < 0.005); children in groups 2 and 3 had poorer Verbal scores than did those in group 1, but not significantly so.
After adjustment.
Figure 1 illustrates the adjusted Full-Scale, Performance, and Verbal raw scores by WMn group. After adjustment for other factors, children in groups 1 and 4 were significantly different for Full-Scale, Performance, and Verbal scores (B-values = –21.28, –18.43, and –3.19; p-values < 0.0001, 0.0001, and 0.02, respectively). Compared with group 1, children in groups 2 and 3 had lower, albeit not significantly so, Full-Scale (B-values = –8.57 and –7.90, respectively; p-values < 0.10) and Performance scores (B-values = –7.79 and –7.34, respectively; p-values ≤ 0.07). Verbal score comparisons between children in groups 2 and 3 and those in group 1 were in the expected direction but did not approach significance.
Relations considering BPb, BAs, BMn, and intellectual function.
For the 95 children with blood samples, we examined the relations of BPb, BAs, and BMn to intellectual function, again adjusting for the same demographic features. When all three blood measures were added to the core model, only BPb was related to intellectual function (data not shown). In subsequent analyses that simultaneously considered WMn, WAs, and BPb, the adverse associations between WMn and Full-Scale and Performance scores persisted after adjustments (B-values = –4.56 and –3.82; p-values < 0.01).
Discussion
This study indicates that exposure to Mn in drinking water is associated with neurotoxic effects in children. In our previous cross-sectional study of 10-year-old children in Araihazar, which reported an adverse association between WAs and child intellectual function, the mean WAs concentration was 118 μg/L, and the mean WMn concentration was 1,386 μg/L. In that study, before adjustment for WAs, WMn was adversely associated with children’s intellectual function, but the association did not persist once WAs was added to the regression model. The present study, however, was specifically designed to examine possible effects of WMn in the absence of confounding by WAs. WAs was controlled by limiting the sample to children drinking from wells with As ≤ 10 μg/L (with a mean of 3 μg/L), whereas WMn was free to vary. The lower mean Mn concentration of well water compared with the previous study (793 vs. 1,386 μg/L) reflects the fact that a significant fraction of the wells selected on the basis of their low As content tap older and deeper Bangladesh aquifers that are generally lower in Mn. This is the case not only in our study area but throughout the country (British Geological Survey and Department of Public Health and Engineering 2001; Cheng et al., 2004). Although the concentrations of the two elements were correlated (Spearman r = 0.36), exposure to As, as measured by water, urine, and blood concentrations, was essentially negligible, and none of the measures of As exposure was associated with any measure of intellectual function. In neither study have we detected a significant interaction between As and Mn exposure in relation to intellectual function; however, we lack adequate statistical power to definitively address this possibility.
The neurotoxicity of Mn in adults with occupational inhalation exposure is well established (Agency for Toxic Substances and Disease Registry 2001; Cook et al. 1974; Roels et al. 1999). The syndrome known as “manganism” is characterized by a Parkinson-like condition with weakness, anorexia, apathy, slowed speech, emotionless facial expression, and slow movement of the limbs. In contrast, findings from studies of environmental exposures to Mn are limited (Hudnell 1999; Mergler 1999; Mergler and Baldwin 1997). An epidemiologic investigation in Greece examined possible correlations between long-term (> 10 years) Mn exposure from drinking water and neurologic effects in a random sample of an elderly population (Kondakis et al. 1985); WMn concentrations ranged from 4 to 2,300 μg/L. Composite neurologic scores (including weakness/fatigue, gait disturbances, tremors) differed significantly between highly exposed and control populations. Two available studies of environmental Mn exposure in children have focused on motor functioning (He et al. 1994, as cited in Mergler 1999; Takser et al. 2003).
Among Parisian children followed from birth through their preschool years (Takser et al. 2003), after adjustment for sex and maternal education, cord BMn levels were negatively associated with scores on three scales derived by the authors from the McCarthy Scales (McCarthy 1972): attention, nonverbal memory, and hand skill. Among children 11–13 years of age, a comparison (unadjusted) of those from an area with high levels of Mn sewage irrigation with those from a control area revealed lower scores on tests of short-term memory, manual dexterity, and visuoperceptive speed in exposed children (He et al. 1994, as cited in Mergler 1999).
Mn is an essential element that is required by enzymes such as Mn superoxide dismutase and pyruvate carboxylase and serves to activate certain kinases, transferases, and other enzymes (WHO 2002). Substantial amounts are obtained in the diet, and deficiency is extremely rare. The first reported case of deficiency occurred in a man fed a chemically defined diet (as part of a study of vitamin K requirements) in which Mn was inadvertently left out (Doisy 1973). The IOM has determined the total adequate intake values for Mn for boys and girls, 9–13 years of age (the age group of interest to the present study) to be 1.9 and 1.6 mg/day, respectively (IOM Food and Nutrition Board 2002). One would therefore expect the shape of the dose–response relationship between WMn and cognitive function to be complex and dependent on dietary intake. We made no attempt to estimate dietary intake in this study of 10-year-old children and therefore cannot comment as to whether the WHO and U.S. EPA drinking water standards of 500 μg/L and 300 μg/L, respectively, are protective of the health of children.
Based on a comprehensive review of the literature, a risk assessment carried out by the IOM (IOM Food and Nutrition Board 2002) has generated age-dependent estimates of the tolerable upper intake level (UL), which is defined as the highest daily total dose of Mn that is likely to pose no risk of adverse health effects in almost all people. The UL for children 9–13 years of age was estimated to be 6 mg/day. In the present study, our estimates of water-borne Mn intake (by quartile of WMn) were 0.25, 1.06, 1.92, and 4.27 mg/day for boys, and 0.21, 0.93, 1.68, and 3.82 mg/day for girls. Obviously, none of these values exceed the UL of 6 mg/day, although additional dietary exposure could have pushed the total daily dose above that value. However, both the valence state and the bioavailability of Mn in food (oxidized Mn) and water (reduced Mn) differ, and these factors may contribute to the observed neurotoxicity of Mn from drinking water. The bioavailability of dietary Mn is very low, with estimates ranging from roughly 1 to 5% (IOM Food and Nutrition Board 2002); absorption is increased in the presence of iron deficiency (Finley 1999) and impaired by calcium supplementation (Freeland-Graves and Lin 1991). In contrast, it has been estimated that compared with that for food, the bioavailability of Mn from drinking water is 1.4 times greater in nonfasted subjects, and two times greater in fasted subjects (Ruoff 1995, cited in U.S. EPA 1999). Thus, dose for dose, water-borne Mn is likely more toxic than dietary Mn.
We found no evidence of a relationship between BMn and any measure of child intellectual function. This is not surprising because the use of blood levels as a means of evaluating occupational exposure to Mn has also been disappointing (National Research Council 1973). Our failure to use Mn-free needles may have introduced noise into the measurement of BMn and may explain the absence of an observed association between BMn and child intellectual functioning. As mentioned above, others (Takser et al. 2003) have observed a relationship between cord BMn and McCarthy scores at age 3. Because Mn is transported in the blood on transferrin, we speculate that serum Mn levels might be a better biomarker of exposure. Indeed, several reports indicate that serum or plasma Mn concentrations vary with dietary Mn intake (Davis and Greger 1992; Freeland-Graves and Turnlund 1996).
Limitations.
We cannot comfortably make a statement about IQ points lost in relationship to WMn exposure, because we cannot apply U.S. standardization norms to generate IQ scores in the present study population. As we have previously pointed out, the lack of measures of intelligence, standardized for use in Bangladesh, hampers our ability to draw inferences about IQ points lost at given levels of exposure. Although we have followed sound procedures for adapting a widely used instrument to this very different cultural setting, and although we have avoided, for the most part, drawing conclusions about IQ, the measures used here are not measures of IQ, and the absence of standardized measures remains a limitation.
Our use of raw scores avoids pitfalls that would result from using nonstandardized procedures, but the removal of culturally bound items and subscales diverges from common practice. Nevertheless, the fact that other predictors of child intellectual function, such as maternal education and child height and head circumference, were significantly related to intellectual raw scores in the expected directions gives us confidence in the validity of the observed associations with Mn.
To date, we have studied only 10-year-old children, and we do not know if the observed deficits can be detected earlier in life. It is interesting to note that although breast milk contains between 3 and 10 μg Mn/L (Agency for Toxic Substances and Disease Registry 2001), infant formulas (Collipp et al. 1983) have been reported to contain as much as 50 to 300 μg/L. Our findings, coupled with the absence of reports of Mn deficiency in young children, led us to conclude that the possible consequences in children of excess exposure to Mn from water, diet, and gasoline additives (Kaiser 2003) deserve further attention.
We did not measure Mn in food or in air and thus could not estimate total Mn exposure. On the other hand, the impact of the absence of these exposure inputs would actually bias our findings toward the null. The fact that we observe a relationship between WMn and child intellectual function in the absence of estimates of food and air Mn exposure is therefore even more compelling. Moreover, because our original intent was to measure only BPb and BAs, Mn-free needles were not used.
Implications.
Our study findings led us to ask whether Mn exposure from drinking water might be a concern in the United States. Since 1991, the National Water-Quality Assessment Program (NAWQA) of the U.S. Geological Survey (USGS 2005) has systematically assessed the quality of source water for > 60% of the nation’s drinking water. Study areas were selected to represent a variety of important hydrologic and ecologic resources; critical sources of contaminants from agricultural, urban, and natural sources; and a high percentage of population served by municipal water supply and irrigated agriculture. Figure 2 and Table 4 illustrate the distribution of domestic household wells with Mn concentration > 300 μg/L obtained by the USGS NAWQA program. Based on the results of the present report, and the USGS finding that roughly 6% of domestic wells contain more than 300 μg/L, we believe that some U.S. children may be at risk for Mn-induced neurotoxicity.
Correction
Some of the values were incorrect in the section “Dose–Response Relationships between Well WMn and Intellectual Function” and in Table 1 in the original manuscript published online; they have been corrected here.
We thank our Bangladeshi field staff and the people of Araihazar. In particular, we thank M.A.B. Siddique, B. Begum, and R. Sultana.
This work was supported by National Institute of Environmental Health Sciences grants P42 ES 10349 and P30 ES 09089, the Mailman School of Public Health, the Lamont-Doherty Earth Observatory, and the Earth Institute at Columbia University.
Figure 1 Adjusted and unadjusted scores by four groups of WMn for Full-Scale, Performance, and Verbal raw scores. In each case, adjustments were made for maternal education and intelligence, type of housing, child height and head circumference, and access to TV. Error bars indicate SEM.
Figure 2 Distribution of the domestic household wells tested for Mn concentration based on data obtained by NAWQA of the USGS and downloaded at USGS (2005).
Table 1 Characteristics of study participants.a
Variable Mean ± SD
Maleb 70 (49.3)
TV accessb 52 (36.6)
House typeb
Thatched roof or poorer 20 (14.1)
Corrugated tin 101 (71.1)
Concrete construction 21 (14.8)
Father’s occupationb
Other/missing 19 (13.4)
Laborer/farmer 24 (16.9)
Factory/other paid job 49 (34.5)
Business 50 (35.2)
Child age 10.0 ± 0.4
Full-Scale IQ 64.5 ± 11.6
Verbal IQ 70.8 ± 12.2
Performance IQ 63.9 ± 11.9
Full-Scale raw score 71.2 ± 22.9
Verbal raw score 15.9 ± 5.4
Performance raw score 55.4 ± 18.9
Height (cm) 126.5 ± 6.7
Weight (kg) 22.4 ± 3.7
Body mass index 13.9 ± 1.3
Head circumference (cm) 49.5 ± 1.5
Mother’s education (years) 3.1 ± 3.5
Father’s education (years) 3.9 ± 3.8
Mother’s age (years) 33.8 ± 6.3
Mother’s Raven score 14.1 ± 3.1
WMn (μg/L) 795 ± 755
WAs (μg/L) 3.0 ± 2.6
UAs (μg/L) 57.5 ± 67.6
UCr (mg/dL) 45.4 ± 30.2
UAs (μg/g creatinine) 133.0 ± 86.8
Hgbc (g/dL) 12.6 ± 1.1
BPbc (μg/dL) 12.0 ± 3.7
BMnc (μg/L) 12.8 ± 3.2
BAsc (μg/L) 4.3 ± 1.9
a Except where noted, sample size is 142.
b Values reflect n (%).
c n = 95.
Table 2 Unadjusted associations (Spearman correlation coefficients) among measures of exposure to As, Pb, and Mn.
UAs (μg/g creatinine) WMn (μg/L) BAs (μg/L) BMn (μg/L) BPb (μg/dL)
WAs 0.27** 0.36# 0.30** –0.05 –0.06
UAs (μg/g creatinine) 0.16 0.51# –0.03 –0.06
WMn 0.23* –0.04 –0.13
BAs 0.02 –0.11
BMn 0.13
For comparisons between water and urinary concentrations, n = 142. Correlations involving whole blood metal concentrations are for the subset of 95 children who gave blood samples.
* p < 0.05,
** p < 0.01,
# p < 0.001.
Table 3 Predicting Verbal, Performance, and Full-Scale raw scores from WMn before and after covariate adjustment: unstandardized regression B-coefficient.
Variable Full-Scale Performance Verbal
Before adjustment
WMn (μg/L) –5.20# –4.43# –0.80*
After adjustment
Maternal education (years)
None –6.09 –2.95 –2.72*
1–5 –1.26 –0.37 –0.59
5–13a — — —
Maternal intelligence 0.43 0.38 0.07
House type
Thatched roof or poorer –5.73 –6.02 0.21
Corrugated tin –0.60 0.63 0.06
Concretea
TV access 2.32 1.98 0.48
Height (cm) 0.79** 0.62** 0.16*
Head circumference (cm) 3.48** 3.01** 0.50
WMn (μg/L) –4.35# –3.76# –0.63*
Total R2 (%) 31.29 31.01 20.11
R2, total variance explained.
a Reference group.
* p < 0.05,
** p < 0.01,
# p < 0.001.
Table 4 Mn in U.S. domestic groundwater wells (n = 2,624).
Mn (μg/dL) Frequency Percent Cumulative percent
< 200 2,386 90.9 90.9
201–300 81 3.1 94.0
301–500 71 2.7 96.7
501–1,000 56 2.1 98.9
> 1,000 30 1.1 100.0
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8319ehp0114-00013016393670ResearchChildren's HealthBlood Lead Concentrations in Children and Method of Water Fluoridation in the United States, 1988–1994 Macek Mark D. 12Matte Thomas D. 3Sinks Thomas 34Malvitz Dolores M. 21 Department of Health Promotion and Policy, Baltimore College of Dental Surgery, Dental School, University of Maryland, Baltimore, Maryland, USA2 Division of Oral Health, National Center for Chronic Disease Prevention and Health Promotion,3 National Center for Environmental Health, and4 Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention, Atlanta, Georgia, USAAddress correspondence to M.D. Macek, Baltimore College of Dental Surgery, Dental School, University of Maryland, 666 West Baltimore St., Room 3-E-02, Baltimore, MD 21201-1586 USA. Telephone: (410) 706-4218. Fax: (410) 706-3028. E-mail:
[email protected] authors declare they have no competing financial interests.
1 2006 17 8 2005 114 1 130 134 13 5 2005 17 8 2005 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Some have hypothesized that community water containing sodium silicofluoride and hydrofluosilicic acid may increase blood lead (PbB) concentrations in children by leaching of lead from water conduits and by increasing absorption of lead from water. Our analysis aimed to evaluate the relation between water fluoridation method and PbB concentrations in children. We used PbB concentration data (n = 9,477) from the Third National Health and Nutrition Examination Survey (1988–1994) for children 1–16 years of age, merged with water fluoridation data from the 1992 Fluoridation Census. The main outcome measure was geometric mean PbB concentration, and covariates included age, sex, race/ethnicity, poverty status, urbanicity, and length of time living in residence. Geometric mean PbB concentrations for each water fluoridation method were 2.40 μg/dL (sodium silicofluoride), 2.34 μg/dL (hydrofluosilicic acid), 1.78 μg/dL (sodium fluoride), 2.24 μg/dL (natural fluoride and no fluoride), and 2.14 μg/dL (unknown/mixed status). In multiple linear and logistic regression, there was a statistical interaction between water fluoridation method and year in which dwelling was built. Controlling for covariates, water fluoridation method was significant only in the models that included dwellings built before 1946 and dwellings of unknown age. Across stratum-specific models for dwellings of known age, neither hydrofluosilicic acid nor sodium silicofluoride were associated with higher geometric mean PbB concentrations or prevalence values. Given these findings, our analyses, though not definitive, do not support concerns that silicofluorides in community water systems cause higher PbB concentrations in children. Current evidence does not provide a basis for changing water fluoridation practices, which have a clear public health benefit.
adolescentschildrenfluoridationnutrition surveysleadUnited States
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The ability of fluoride to prevent dental caries has been well documented across various populations and study conditions (Booth et al. 1992; Brunelle and Carlos 1990; Burt et al. 1986; Clark et al. 1995; Eklund et al. 1987; Gilchrist et al. 2001; Newbrun 1989; Rugg-Gunn et al. 1988). Three primary mechanisms of action have been identified (Burt and Eklund 1999): a) promotion of remineralization and inhibition of demineralization of early lesions; b) inhibition of bacterial metabolism; and c) reduction of enamel solubility in acid, bestowed prior to tooth eruption. In 2000, the Centers for Disease Control and Prevention (CDC) estimated that 162.1 million Americans were receiving fluoridated water, which is 57.6% of the total population and includes 65.8% of those on public water systems (Apanian et al. 2002). In the United States, several agents are used to fluoridate community water supplies, including silicofluoride compounds (sodium silicofluoride and hydrofluosilicic acid) and sodium fluoride.
The adverse health effects of lead have been described in detail. In children, elevated concentrations of lead are associated with impairment of cognitive development and adverse behavioral changes [Agency for Toxic Substances and Disease Registry (ATSDR) 1999; Johnston and Goldstein 1998]. For children age 6 years or younger, elevated blood lead (PbB) concentrations are defined as those ≥ 10 μg/dL (CDC 1991). The home environment remains an important setting for lead exposure, especially for children living in older dwellings. Heavily leaded paints were used before 1950, but lead compounds continued to be added to some paints until the Consumer Product Safety Commission (CPSC) banned the practice in 1978 (CPSC 1977). Before the 1930s, lead was used to produce pipes for drinking water systems in the United States; although copper replaced lead in pipe production after the 1930s, lead was still used as solder until the U.S. Environmental Protection Agency (EPA) banned leaded solder and pipes in 1986 (U.S. EPA 1986). As a result of the historic patterns of lead use in housing, the oldest dwellings contain more leaded paint and lead-contaminated dust (Jacobs et al. 2002), and children who live in these homes are more likely to have elevated PbB concentrations (Pirkle et al. 1998).
Two studies have reported ecologic associations between use of silicofluoride compounds in community water systems and elevated PbB concentrations among children in Massachusetts and New York (Masters and Coplan 1999; Masters et al. 2000). In the Massachusetts study (Masters and Coplan 1999), the authors stated that children who lived in communities with old housing were at increased risk for elevated PbB concentrations. In the New York study (Masters et al. 2000), the authors concluded that the highest likelihood of elevated PbB concentrations occurred when children were exposed to both water treated with silicofluorides and another risk factor known to be associated with high blood lead, such as old housing. These studies had some important limitations, however, including the lack of data on covariates at the individual level, unclear sampling methods, and use of highly skewed, untransformed PbB concentration data in analysis-of-variance models. In this analysis, we tested possible associations between water fluoridation method and PbB concentrations in U.S. children using a representative sample and addressing some of the limitations of earlier studies.
Materials and Methods
Study population.
PbB concentration data and covariates for children aged 1–16 years were obtained from the Third National Health and Nutrition Examination Survey (NHANES III), a cross-sectional survey of the civilian, noninstitutionalized population of the United States. NHANES III was administered by the National Center for Health Statistics (NCHS) between 1988 and 1994, with participants sampled according to a complex, multistage probability sampling method. Young children, older adults, non-Hispanic blacks, and Mexican Americans were oversampled so that population estimates for these population groups would be statistically reliable. Detailed descriptions of the NHANES III methodology have been published elsewhere (NCHS 1994, 1996).
There were 13,944 children 1–16 years of age eligible for inclusion in NHANES III, of whom 9,477 had a known PbB concentration measurement. There was no significant difference in fluoridation status between children with a known PbB concentration and those with an unknown or missing PbB concentration. The overall response rate for this analysis was 68.0%. The final sample represented 52.2 million U.S. children.
Assignment of water fluoridation exposure.
Between 1975 and 1992, the CDC periodically collected water fluoridation status information from states and published this information in a series of monographs called the Fluoridation Census. For the 1992 Fluoridation Census, the CDC sent a printout of water fluoridation status data from the 1989 Fluoridation Census to each state. A responsible person in the health or water departments was asked to update, edit, and verify the information. Edits were made to reflect installations of new water systems, systems that had stopped fluoridation, and changes in population. In addition, states were asked to report a) each fluoridated water system and the communities each system served; b) the status of fluoridation (“adjusted” to provide optimal levels; “consecutive,” i.e., water systems that purchased fluoridated water from another system; or “natural”); c) the system from which water was purchased (if another system served as the primary source); d) the date on which fluoridation started; and e) the chemical used for fluoridation (if adjusted to provide optimal levels or purchased from another source). The final 1992 Fluoridation Census document represented information returned from state respondents to the CDC (1993).
Information regarding the locations from which NHANES III selected its sample participants is not made available to the public because of concerns about the confidentiality of survey results and other risks of disclosure. To create an analytic file for this analysis, NCHS used the 1992 Fluoridation Census to assign a water fluoridation method value to each child in NHANES III, based on the child’s county of residence. NCHS forwarded the analytic file to us without county-level data. NCHS maintains a copy of the combined data file and provides access to this file through the NCHS Research Data Center.
NCHS classified the water fluoridation method into one of six categories (sodium silicofluoride, hydrofluosilicic acid, sodium fluoride, natural fluoride, no fluoride, and unknown/mixed status) according to the following algorithm: a) If at least 90% of a NHANES III county received a single type of fluoride or no fluoride, then the county was assigned that water fluoridation method category; b) if < 90% of a NHANES III county received a single type of fluoride or no fluoride, or if > 10% of a NHANES III county received an unidentified type of fluoride, then the county was classified as “unknown/mixed status.”
We were unable to assign a water fluoridation method to children who were not served by a public water system, so we included these children in the “unknown/mixed status” category. Furthermore, we were unable to account for changes in the type of fluoride used by water systems over time. Given that water systems do not routinely change type of fluoride used, misclassification due to changes over time would probably not have influenced our findings.
Blood lead measurement.
Blood was collected from individual survey participants ≥ 1 year of age via venipuncture during the phlebotomy component of NHANES III. Blood specimens were analyzed for lead at the NHANES Laboratory, Division of Environmental Health Laboratory Sciences, National Center for Environmental Health, CDC, using graphite furnace absorption spectrophotometry and previously described methods (Gunter et al. 1996).
Covariates.
Other independent variables associated with PbB concentrations were obtained from NHANES III, including age (1–5 years, 6–16 years), sex, race/ethnicity (non-Hispanic white, non-Hispanic black, Mexican American, other), poverty status [<100% of the federal poverty level (FPL), < 100% FPL, unknown], urbanicity (urban = population ≥ 250,000 persons, suburban/rural = population < 250,000 persons), duration of residence (lifetime, less than lifetime, unknown), and year in which dwelling was built (before 1946before 1946–1973–1974 to present, unknown).
Statistical analysis.
We used SUDAAN statistical software for personal computers (Research Triangle Institute 2000) to estimate PbB concentrations and to estimate multiple linear and logistic regression coefficients for change in PbB concentration, controlling for covariates. SUDAAN accounted for the complex sampling design of NHANES III when deriving standard errors (SEs) and confidence intervals (CIs). The α-value for statistical significance was set at 0.05 for all analyses.
Because PbB concentrations have a highly positively skewed distribution, we used log-transformed PbB concentration data in all linear regression analyses, and used antilog transformations to convert mean log PbB concentration values to geometric mean (GM) PbB concentration values and to convert regression coefficients estimating changes in mean log PbB concentration to estimated ratios of GM PbB concentrations. Estimates with a corresponding SE equivalent to ≥ 30% of the estimate were identified as statistically unreliable and should be interpreted with caution.
If silicofluoride compounds in water were truly able to leach lead from drinking water conduits and/or increase absorption of ingested lead, one would expect that sodium silicofluoride and hydrofluosilicic acid would be associated with higher PbB concentrations in older dwellings, because older dwellings are more likely to have lead pipes or copper plumbing with lead solder (Berkowitz 1995) than are newer dwellings. To evaluate this hypothesis we also tested whether the year in which the dwelling was built interacted with water fluoridation method in its association with PbB concentrations.
We used crude Wald-F-test statistics to assess whether bivariate linear regression associations (selected characteristics versus mean log PbB concentrations) were significant. To assess whether interaction terms should be included in the multivariable models, we assessed the statistical significance of each interaction term (in the presence of its component main effect variable) using adjusted Wald-F statistics. When significant interactions were found, we conducted stratified analyses to measure stratum-specific associations between water fluoridation method and mean log PbB concentrations.
For comparison, we also modeled the adjusted odds of an elevated PbB concentration for each water fluoridation method across year-during-which-dwelling-was-built strata. We used a liberal 5-μg/dL cut-off to define elevated PbB concentration because the prevalence of an elevated PbB concentration using the standard 10-μg/dL cut-off (CDC 1991) was so low (3.3%).
For the linear and logistic regression analyses, the reference category for water fluoridation method was no fluoride. To compare one stratum-specific PbB concentration to the reference category, we calculated ratios of stratum-specific GMs divided by the reference GM for the no-fluoride category. This ratio showed whether the GM for that category of water fluoridation method was higher or lower than the GM for the reference. Odds ratios (ORs) derived from logistic regression also compared PbB concentration prevalence values for one category of water fluoridation method to the reference no-fluoride category.
Results
From the NHANES III data (Table 1), we estimate that approximately one-third of American children 1–16 years of age were lifetime residents of their current dwelling, and about one-fifth in houses built before 1946. Approximately one in four lived in a county having hydrofluosilicic acid in its community water supply, and somewhat less than one-fifth lived in a county with no fluoride in its community water supply.
Overall, the GM PbB concentration for the population was 2.19 μg/dL (Table 2). As reported in earlier analyses of NHANES III data (Brody et al. 1994; Pirkle et al. 1994), younger age, male sex, minority race/ethnicity, and poverty status were each associated with higher GM PbB concentrations in children. Our analysis also showed that duration of residence was significantly associated with GM PbB concentration (p < 0.01), as was year in which dwelling was built (p < 0.01). GM PbB concentration was not associated with urbanicity (p = 0.14).
Despite a nonsignificant association between water fluoridation method and GM PbB concentration at the bivariate level (p = 0.88), the statistical interaction between fluoridation and year in which dwelling was built was associated with PbB concentration at the multivariable level (adjusted Wald-F = 9.3; p < 0.01). Consequently, the association between fluoridation and PbB concentration is shown stratified by year in which dwelling was built (Table 3). According to the stratum-specific models, fluoridation was significantly associated with PbB concentration only for the “before 1946” (adjusted Wald-F = 2.8; p = 0.03) and “unknown” (adjusted Wald-F = 2.8; p = 0.03) strata. In the before-1946 model, however, none of the individual fluoridation categories (including the silicofluorides compounds) was significantly higher than the reference no-fluoride category. In the unknown-year model, the hydrofluosilicic acid category was significantly different than the no-fluoride category: the GM PbB concentration for hydrofluosilicic acid was 45% higher. This significant association between hydrofluosilicic acid and GM PbB concentration seen in the unknown-year stratum was not observed in the other strata. In addition, there was no trend toward increasing GM ratios for the silicofluoride categories with increasing dwelling age.
Having a statistically significant interaction term while also having no statistically significant stratum-specific associations between fluoridation and GM PbB concentration was somewhat unexpected. To further investigate the association between fluoridation and PbB concentration, we conducted multiple logistic regression analysis, stratified by dwelling age.
Overall, 14.4% of the population had a PbB concentration ≥ 5 μg/dL (compared with 3.3% for the standard 10-μg/dL cut-off). Again, water fluoridation method was significantly associated with PbB concentration only for the before-1946 (adjusted Wald-F = 5.0; p < 0.01) and unknown (adjusted Wald-F = 9.5; p < 0.01) strata (Table 4). In the before-1946 model, however, neither silicofluoride category was significantly higher than the reference no-fluoride category. In the unknown-year model, both unknown/mixed status and hydrofluosilicic acid categories were significantly higher than the no-fluoride category; however, the significant association between hydrofluosilicic acid and PbB concentration seen in the unknown-year stratum was not observed in the other strata. Consistent with the linear regression findings, there was no trend toward increasing PbB concentration ORs for the silicofluoride categories with increasing dwelling age.
Discussion
It has been hypothesized that silicofluoride compounds might enhance lead leaching from drinking water conduits and increase lead absorption from drinking water (Masters and Coplan 1999; Masters et al. 2000). If this hypothesis were true, one would expect to see an increasingly greater effect for the silicofluoride groups as one compared multivariable models for older dwellings with those for newer ones. Our analysis showed that, overall, the PbB concentrations of children living in counties receiving silicofluorides (sodium silicofluoride and hydrofluosilicic acid) did not differ significantly from the PbB concentrations of children living in counties without fluoridated water. When examined by year in which dwelling was built, our findings were inconsistent with our hypothesis. Among children living in dwellings of known age, silicofluorides were not associated with higher GM PbB concentrations. Specifically, with increasing dwelling age, there was no trend for an increase in the point estimates for the ratio of GM PbB concentrations, and there was no trend for an increase in adjusted odds of elevated PbB concentrations among those exposed to hydrofluosilicic acid or sodium silicofluoride, compared with no fluoride. Among children living in dwellings of unknown age, hydrofluosilicic acid was associated with a higher GM PbB concentration and an elevated PbB concentration, but sodium silicofluoride was not. Given these findings, our analysis, while not definitive, does not support concerns that silicofluorides in community water systems cause higher PbB concentrations in children.
Our investigation has several limitations. The first is the potential for exposure misclassification from use of an ecologic, county-level measure of fluoridation method. Although misclassification is always a potential threat to epidemiological studies, there is no reason to believe that misclassification in this analysis was systematic or nonrandom, and there is little reason to believe that it might have produced the observed association between silicofluorides and PbB concentrations in pre-1946 dwellings and in dwellings of unknown age. On the other hand, if a true association existed between silicofluorides and PbB concentrations, overall random misclassification could have attenuated the association.
A second limitation is the potential for confounding. We controlled at the individual level for specific risk factors for lead exposure, such as race/ethnicity, poverty status, and year in which dwelling was built. Because these variables are only proxies for actual lead exposure, we cannot exclude the possibility of residual confounding of the relation between water fluoridation method and PbB concentrations. For example, NHANES did not measure the lead content of drinking water consumed by study participants. This limitation also precluded our ability to examine more directly a potential interaction between lead in drinking water and water fluoridation method that would be expected if the hypothesized enhancement of lead uptake were correct. In addition, we did not control for community-level factors, such as density of older housing, which might be an independent risk factor for lead exposure. Finally, we were unable to control for factors that might influence the solubility of lead in pipes, including pH, temperature, and water hardness.
A third limitation is the restricted ability to reject the alternative hypothesis of relatively small but potentially important differences in PbB concentrations across water fluoridation method categories. For example, among dwellings built before 1946, the upper 95% confidence limit of the estimated GM PbB concentration ratio for hydrofluosilicic acid compared to no fluoride is consistent with a value as large as 1.7. Although no association between water fluoridation method and PbB concentrations was observed among children living in dwellings of known age, it is possible that larger samples might have identified additional, significant differences.
Conclusions
Our analysis does not offer support for the hypothesis that silicofluorides in community water systems increase PbB concentrations in children. On the other hand, given the limitations of our data, our analyses cannot refute a possible link between water fluoridation method and lead uptake in children, particularly among those who live in older dwellings. Although other ecologic studies might allow another opportunity to test the relation between water fluoridation method and PbB concentrations in U.S. children, such analyses are likely to have similar limitations. Ultimately, the hypothesis that one or more fluoride compounds is associated with enhanced lead leaching or increased lead absorption is best addressed via systematic study of lead concentrations in drinking water, experimental chemical investigations, and studies of animal toxicology. Efforts to decrease exposure to lead among children by targeting prevention efforts at high-risk communities and/or populations as well as efforts to prevent dental caries via the use of fluoridated drinking water should continue unless a causal impact of certain fluoridation methods on PbB concentration is demonstrated by additional research.
We gratefully acknowledge the technical assistance and guidance of L. Barker, A. Dannenberg, R. Hirsch, and J. Madans.
This investigation was supported by the Centers for Disease Control and Prevention (CDC), which funded the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. The findings and conclusions in this report are those of the authors and do not necessarily represent views of the CDC.
Table 1 Sample characteristics for U.S. children 1–16 years of age, by selected characteristics, 1988–1994, with estimates for the U.S. population.a
Characteristic Sample size (n = 9,477) Estimateb (%)
Age (years)
1–5 4,624 29.6
6–16 4,853 70.4
Sex
Male 4,692 51.7
Female 4,785 48.3
Race/ethnicity
Non-Hispanic white 2,551 65.1
Non-Hispanic black 3,119 15.5
Mexican American 3,338 9.2
Other 469 10.2
Poverty status
≥ 100% FPL 5,108 70.4
< 100% FPL 3,612 24.5
Unknown 757 5.1
Urbanicityc
Urban 7,373 71.9
Suburban/rural 2,104 28.1
Duration at residence
Lifetime 3,377 31.5
Less than lifetime 3,928 49.4
Unknown 2,172 19.1
Year in which dwelling was built
Before 1946 1,560 19.8
1946–1973 3,818 35.2
1974 to present 2,769 35.1
Unknown year 1,330 9.9
Water fluoridation method
Unknown/mixed status 2,303 30.0
Sodium silicofluoride 1,021 10.2
Hydrofluosilicic acid 2,149 25.9
Sodium fluoride 346 7.3
Natural fluoride 1,127 8.0
No fluoride 2,531 18.6
a From the Third National Health and Nutrition Examination Survey (1988–1994) and 1992 Fluoridation Census.
b Weighted to reflect the civilian noninstitutionalized population of the United States. Persons with unknown blood lead levels were excluded from analysis.
c Urban, population ≥ 250,000; surburban/rural, population < 250,000.
Table 2 Weighted geometric mean (μg/dL) PbB concentrations for U.S. children 1–16 years of age, by selected characteristics, 1988–1994 (n = 9,477).a
Characteristic GM (95% CI)b Crude Wald-F p-value
Overall 2.19 (2.00–2.39) —
Age (years) < 0.01
1–5 3.09 (2.82–3.38)
6–16 1.91 (1.74–2.09)
Sex < 0.01
Male 2.40 (2.19–2.63)
Female 2.00 (1.82–2.18)
Race/ethnicity < 0.01
Non-Hispanic white 1.95 (1.78–2.13)
Non-Hispanic black 3.31 (3.03–3.62)
Mexican American 2.57 (2.35–2.81)
Other 2.24 (1.96–2.56)
Poverty status < 0.01
≥ 100% FPL 1.91 (1.74–2.09)
< 100% FPL 3.24 (2.96–3.54)
Unknown 2.63 (2.20–3.15)
Urbanicityc 0.14
Urban 2.29 (2.09–2.51)
Suburban/rural 2.00 (1.67–2.39)
Duration at residence < 0.01
Lifetime 2.34 (2.14–2.57)
Less than lifetime 2.00 (1.82–2.18)
Unknown 2.57 (2.24–2.94)
Year in which dwelling was built < 0.01
Before 1946 2.95 (2.58–3.38)
1946–1973 2.19 (2.00–2.39)
1974 to present 1.74 (1.59–1.90)
Unknown year 2.75 (2.41–3.15)
Water fluoridation method 0.88
Unknown/mixed status 2.14 (1.87–2.45)
Sodium silicofluoride 2.40 (2.00–2.87)
Hydrofluosilicic acid 2.34 (2.05–2.68)
Sodium fluoride 1.78d (1.08–2.92)
Natural fluoride 2.24 (1.79–2.81)
No fluoride 2.24 (2.04–2.45)
a From the Third National Health and Nutrition Examination Survey (1988–1994) and 1992 Fluoridation Census.
b Weighted to reflect the civilian noninstitutionalized population of the United States. Persons with unknown blood lead levels were excluded from analysis.
c Urban, population ≥ 250,000; surburban/rural, population < 250,000.
d Does not meet the standard for statistical reliability.
Table 3 Geometric mean PbB concentrations and ratios for U.S. children 1–16 years of age, by water fluoridation method and year in which dwelling was built, 1988–1994 (n = 9,477).a
Before 1946
1946–1973
1974–present
Unknown
Water fluoridation methodb No. GM Ratioc (95% CI) No. GM Ratio (95% CI) No. GM Ratio (95% CI) No. GM Ratio (95% CI)
Unknown/mixed status 473 2.57 0.93 (0.68–1.29) 837 2.04 0.93 (0.79–1.15) 674 1.66 1.02 (0.79–1.26) 319 2.57 1.07 (0.81–1.41)
Sodium silicofluoride 141 2.51 0.91 (0.63–1.32) 420 2.19 1.00 (0.76–1.32) 289 1.74 1.07 (0.85–1.35) 171 3.02 1.26 (0.95–1.66)
Hydrofluosilicic acid 448 3.55 1.29 (0.93–1.78) 839 2.09 0.95 (0.79–1.15) 605 1.86 1.15 (0.91–1.45) 257 3.48 1.45 (1.15–1.82)
Sodium fluoride 78 3.09 1.12 (0.74–1.70) 127 1.62 0.74 (0.59–0.93) 81 1.35d 0.83 (0.52–1.32) 60 2.09 0.87 (0.49–1.55)
Natural fluoride 113 2.40 0.87 (0.63–1.20) 419 2.63 1.20 (0.95–1.51) 413 1.70 1.05 (0.74–1.41) 182 2.40 1.00 (0.79–1.26)
No fluoride 307 2.75 Reference 1,176 2.19 Reference 707 1.62 Reference 341 2.40 Reference
Adjusted Wald-F p-value 0.03 0.10 0.08 0.03
a From the Third National Health and Nutrition Examination Survey (1988–1994) and 1992 Fluoridation Census.
b Weighted to reflect the civilian noninstitutionalized population of the United States; persons with unknown blood lead levels were excluded from analysis.
c Ratio of the geometric mean for each category of water fluoridation method to the geometric mean for the no-fluoride category; analysis controlled for age, sex, race/ethnicity, poverty status, urbanicity, and duration of residence.
d Does not meet the standard for statistical reliability.
Table 4 Prevalence and adjusted odds of an elevated PbB concentration at the 5-μg/dL cut-off for U.S. children 1–16 years of age, by water fluoridation method and year in which dwelling was built, 1988–1994 (n = 9,477).a
Before 1946
1946–1973
1974–present
Unknown
Water fluoridation methodb No. Percentc OR (95% CI)d No. Percent OR (95% CI) No. Percent OR (95% CI) No. Percent OR (95% CI)
Unknown/mixed status 473 24.7 0.9 (0.4–1.9) 837 11.4 1.1 (0.4–2.7) 674 8.3 1.2 (0.5–3.2) 319 21.9 3.8 (2.0–7.0)
Sodium silicofluoride 141 20.7e 0.9 (0.3–2.8) 420 16.8 0.8 (0.3–2.5) 289 6.5e 1.0 (0.4–2.5) 171 30.1 2.8 (0.8–9.8)
Hydrofluosilicic acid 448 30.1 1.2 (0.6–2.5) 839 14.7 1.4 (0.7–2.9) 605 5.4 1.7 (0.6–4.3) 257 24.7 5.3 (2.7–10.5)
Sodium fluoride 78 20.9 0.8 (0.3–1.7) 127 7.6e 1.5 (0.4–5.3) 81 6.0e 0.6 (0.1–4.6) 60 6.6e 1.0 (0.3–3.6)
Natural fluoride 113 19.4 0.3 (0.1–0.6) 419 17.3 1.5 (0.7–3.2) 413 7.3e 1.1 (0.3–3.8) 182 16.6 1.0 (0.4–2.2)
No fluoride 307 26.4 Reference 1176 16.0 Reference 707 6.4 Reference 341 18.4 Reference
Adjusted Wald-F p-value < 0.01 0.76 0.76 < 0.01
a From the Third National Health and Nutrition Examination Survey (1988–1994) and 1992 Fluoridation Census.
b Weighted to reflect the civilian noninstitutionalized population of the United States; persons with unknown blood lead levels were excluded from analysis.
c Percentage of the population with an elevated blood lead concentration (≥ 5 μg/dL).
d Adjusted OR of an elevated blood lead concentration at the 5-μg/dL cut-off, controlling for age, sex, race/ethnicity, poverty status, urbanicity, and duration of residence.
e Does not meet the standard for statistical reliability.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7981ehp0114-00013516393671ResearchChildren's HealthHome Endotoxin Exposure and Wheeze in Infants: Correction for Bias Due to Exposure Measurement Error Horick Nora 1Weller Edie 1Milton Donald K. 23Gold Diane R. 23Li Ruifeng 4Spiegelman Donna 141 Department of Biostatistics, and2 Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA3 Channing Laboratory, Harvard Medical School, Boston, Massachusetts, USA4 Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USAAddress correspondence to D. Spiegelman, Department of Epidemiology, Harvard School of Public Health, 677 Huntington Ave., Boston, MA 02115 USA. Telephone: (617) 432-0205. Fax: (617) 566-7805. E-mail:
[email protected] authors declare they have no competing financial interests.
1 2006 12 8 2005 114 1 135 140 31 1 2005 11 8 2005 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Exposure to elevated levels of endotoxin in family-room dust was previously observed to be significantly associated with increased wheeze in the first year of life among a cohort of 404 children in the Boston, Massachusetts, metropolitan area. However, it is likely that family-room dust endotoxin was a surrogate for airborne endotoxin exposure. Therefore, a related substudy characterized the relationship between levels of airborne household endotoxin and the level of endotoxin present in house dust, in addition to identifying other significant predictors of airborne endotoxin in the home. We now reexamine the relationship between endotoxin exposure and wheeze under the assumption that the level of airborne endotoxin in the home is the exposure of interest and that the amount of endotoxin in household dust is a surrogate for this exposure. We applied a measurement error correction technique, using all available data to estimate the effect of endotoxin exposure in terms of airborne concentration and accounting for the measurement error induced by using house-dust endotoxin as a surrogate measure in the portion of the data in which airborne endotoxin could not be directly measured. After adjusting for confounding by lower respiratory infection status and race/ethnicity, endotoxin exposure was found to be significantly associated with a nearly 6-fold increase in prevalence of wheeze for a one interquartile range increase in airborne endotoxin (95% confidence interval, 1.2–26) among the 360 children in households with dust endotoxin levels between the 5th and 95th percentiles.
asthmaendotoxinmeasurement errorregression calibrationwheeze
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Bacterial endotoxin is a lipopolysaccharide found in the outer cell membrane of gram-negative bacteria (GNB). Among its many known biologic activities, endotoxin is a cause of airway inflammation when inhaled. Exposure to endotoxin is associated with increased risk of nonatopic wheeze and with reduced prevalence of inhalant allergy, eczema, and atopic wheezing. Given the pervasive presence of GNB in household dust and air, everyone is exposed to at least low levels of environmental endotoxin.
In the past decade, studies have linked endotoxin in house dust with increased severity of asthma in both adults (Michel et al. 1991, 1996) and children (Rizzo et al. 1997). Recently, Park et al. (2001a) demonstrated that, after controlling for cockroach allergen, lower respiratory illness, smoking during pregnancy, lower birth weight, maternal asthma, presence of dog, and race/ethnicity, exposure to an elevated level of endotoxin in settled family-room house dust [≥100 endotoxin units (EU)/mg] during the first year of life is associated with a marginally significant increased risk of “any wheeze” [relative risk (RR) = 1.33; 95% confidence interval (CI), 1.00–1.76] and a significantly increased risk of “repeated wheeze” (RR = 1.56; 95% CI, 1.03–2.38). This study used endotoxin measurements obtained from 404 living-room floor-dust samples to quantify the presence of endotoxin, and dichotomized individual exposure into high and low categories using the sample median as the cutoff.
To what extent, however, does the amount of endotoxin present in settled family-room house dust reflect individual exposure? Although ingestion of dust endotoxin is a possible route of exposure for infants and toddlers, it is likely that the relevant exposure for irritant airway symptoms is inhaled (airborne) endotoxin. Therefore, it may be argued that the amount of endotoxin present in a household’s air rather than in the settled dust better characterizes its inhabitants’ exposure to endotoxin. If this is the case, then studies of airway irritant symptoms such as the one conducted by Park et al. (2001a) have used a surrogate measure to quantify the relationship between exposure and disease, while employing statistical methods that assume the exposure is perfectly measured. It should be noted that because endotoxin levels in air are determined by many factors in addition to the amounts of endotoxin present in settled dust, direct measurement of airborne endotoxin may give a truer indication of exposure than do dust measurements, even though dust measurements may integrate exposure over longer periods of time.
Our aim in the present analysis is to reexamine the relationship between exposure to endotoxin and wheeze in the first year of life, accounting for the measurement error associated with using house-dust endotoxin measurements as surrogates for true exposure. To this end, using dust endotoxin and wheeze outcome measurements from a large longitudinal study and airborne endotoxin measurements from a validation substudy, we performed a measurement error correction analysis according to the regression calibration method described by Rosner et al. (1989). With this method, the bias incurred by using a surrogate exposure metric, which controls random error, is removed; the resulting effect estimate is given in terms of the hypothesized “true” exposure metric, and the variance of this estimate reflects the fact that exposure is estimated among those without airborne endotoxin measurements.
Materials and Methods
The Epidemiology of Home Allergens and Asthma study is an ongoing longitudinal study conducted among 499 families in the Boston, Massachusetts, metropolitan area. This study includes children born between September 1994 and June 1996 in Brigham and Women’s Hospital and having at least one parent with a history of doctor-diagnosed allergy and/or asthma. The first visit to the homes of participating families occurred 2–3 months after birth of the index child, at which time samples of dust from the living-room floor were collected in a standardized fashion (Park et al. 2001a) and analyzed using the kinetic Limulus assay with resistant-parallel-line estimation (KLARE) (Milton et al. 1992, 1997). There were 404 living-room samples with sufficient dust to perform an endotoxin assay. In addition to the home visits when dust collection took place, caregivers were contacted by telephone on a monthly basis and asked about their child’s respiratory health, including their wheezing. Information from those monthly phone calls made during the index child’s first year was summarized to reflect whether each child experienced “any wheeze,” corresponding to one or more wheezing events in the first year. Thus, the main study potentially could consist of those 404 families who contributed both wheeze outcomes and living-room floor endotoxin measurements.
Living-room airborne endotoxin measurements were collected among a 23% subset (n = 93) of these 404 main study families and assayed for endotoxin using the KLARE method as previously described (Park et al. 2001b). Samples were collected on 0.4-μm polycarbonate filters at 2 L/min for an average of 1.5 days. In 36 homes, airborne measurements were obtained concurrently with dust measurements collected at the time of the first home visit, whereas in the remaining 57 homes, airborne samples were collected during a subsequent visit that occurred 6–8 months after the birth of the index child. The airborne measurements obtained during the latter home visits do not accurately reflect the child’s exposure to endotoxin before assessment of the outcome and thus cannot be used for exposure–response modeling. Therefore, only those 36 families who contributed airborne samples during the first home visit are considered members of the internal validation study, and the 57 families contributing airborne samples at the 6- to 8-month visit comprise the external validation study. Five homes contributed airborne samples on two occasions, the latter of which was not considered in this analysis, bringing the total number of homes in the validation study to 88. After excluding participants missing data on key model covariates, the final validation study sample size was 82.
Statistical methods.
Assay of floor-dust samples is a common approach in assessing allergen exposure in environmental studies. However, the degree to which such measures accurately reflect pulmonary endotoxin exposure is not entirely clear. When considering airways irritation, the amount of endotoxin suspended in the air, rather than the amount present in settled family-room dust, may represent a more valid gauge of endotoxin exposure, because the route of exposure is respiratory. Therefore, we identified airborne endotoxin as the true exposure and house-dust endotoxin as the surrogate exposure in the present analysis, recognizing that repeated air sampling over the course of the year and personal rather than area sampling could have provided a still more accurate measure of exposure than that considered in this study. As long as the variation in air endotoxin samples varies randomly around individual’s true long-term average exposure, as we believe to be the case here, the methods applied in this article will be valid (Spiegelman et al. 1997; Wacholder et al. 1993).
Rosner et al. (1989) proposed a regression calibration method for correcting odds ratio and corresponding CI estimates for systematic and random measurement error using a logistic regression model. InAppendix 1, we briefly show the applicability of this technique for obtaining measurement-error–corrected estimates of prevalence and risk ratios, which we directly estimate here, because it is well known that for a common event such as wheeze, the odds ratio overestimates the risk and prevalence ratios (Skov et al. 1998; Wacholder 1986; Zocchetti et al. 1997). The derivation given inAppendix 1 also applies to RR estimators obtained by Poisson regression with the robust variance, which can be used validly and, often, with little loss of efficiency when there are numerical difficulties fitting the log-binomial model (Spiegelman and Hertzmark 2005; Zou 2004).
Rosner’s method requires a main study/ validation study design, in which outcome and continuous surrogate exposure measurements are obtained in n1 main study participants, and true and surrogate exposures are measured in n2 validation study participants. The RR regression model fit in the main study expresses the (log-transformed) probability of the binary outcome as a linear function of the surrogate exposure and other covariates assumed to be measured without error, and is estimated using the main study data. The resulting estimate of risk (
) represents the (log) RR of outcome associated with a one-unit increase in the surrogate exposure level, uncorrected for measurement error. When the log-binomial model fails to provide the maximum likelihood estimates because of convergence or other numerical problems, one may obtain a generalized estimating equation (GEE) estimator by fitting a robust log-Poisson model (Zou 2004). Constructed using data from the validation study, the measurement error model expresses the true exposure as a linear function of the surrogate exposure and covariates, and the estimated coefficient (
) is the slope of true against surrogate measures. In the simplest case, when only outcome, true, and surrogate exposures are considered, the corrected estimate of risk is given by
, and the variance of the corrected estimate is given by
Rosner et al. (1990) developed computational details for models with multiple covariates measured with and without error. In the multivariate version of this method, all covariates included in the primary regression model are included in the measurement error model. Otherwise, the measurement-error–corrected RR estimates will be biased (Rosner et al. 1990). Methods for validating prediction models are not applicable in this context.
From among the list of possible determinants of wheeze considered by Park et al. (2001a) in this main study population, history of lower respiratory infection and race/ethnicity were identified as statistically significant independent risk factors. Park et al. (2001b) also examined the relationship between airborne and house-dust endotoxin and developed a prediction model for airborne endotoxin using internal cross-validation by minimizing the predicted residual error sum of squares (PRESS statistic) (Allen 1971; Svendsgaard et al. 1997). The final model identified current and former presence of a dog in the home, use of a dehumidifier, the total amount of fine dust, and the presence of a concrete floor and water damage as playing key roles in the relationship between airborne and settled-dust endotoxin (Park et al. 2001b).
To apply the regression calibration method of Rosner et al. (1989), we first constructed a main study model that relates the log-transformed probability of experiencing any/repeated wheeze to terms for dust endotoxin level (log10-transformed), and covariates using the significant independent predictors identified by Park et al. (2001a, 2001b), as described above. To meet the requirements for this regression calibration method, we also included in the main study model all covariates in the measurement error model. We next constructed a measurement error model that relates the airborne endotoxin level (log10-transformed) to the family-room dust endotoxin level (log10-transformed), and the covariates identified by Park et al. (2001a, 2001b), as described above. In the same way as for the main model, to apply the Rosner et al. (1989) method, we included the covariates from the main study model in the measurement error model.
In fitting the RR model in the main study and measurement error model in the validation study, when no information was available for a binary covariate, missingness indicators were included. However, for some covariates there were too few missing values to allow for estimation of a missingness indicator; thus, only homes with complete covariate information for these variables were considered eligible for the present study.
The main study model was fit among the main study participants to obtain
, as well as parameter estimates for the other covariates in the model, and the measurement error model was fit in the validation data to obtain
and other parameter estimates.
Before correcting
for bias due to measurement error through regression calibration, it is necessary to verify the assumptions required for valid application of this methodology: that a) the measurement error model is linear and homoscedastic, b) that the main study model is linear on the log prevalence scale, c) that house-dust endotoxin is a proper surrogate for the true exposure, and d) that the measurement error is not severe.
To assess the linearity of the main study and measurement error models, we compared linear models with more flexible restricted cubic spline models that allow for nonlinearity in the relationship between dust endotoxin level and outcome (Durrleman and Simon 1989). Linearity of the relationship between airborne and dust endotoxin in the measurement error model was confirmed, because none of a wide range of nonlinear terms from a very general model was selected at the p = 0.05 level in a stepwise selection procedure. However, there was evidence against a linear relationship between dust endotoxin and the risk of wheeze in the main study (p = 0.005, test for nonlinearity). Examination of plots of the fitted spline models suggested linearity might hold on the subset of homes with nonextreme dust endotoxin levels. On refitting the spline models among the observations with dust endotoxin levels between the 5th and 95th percentiles, linearity was confirmed because none of a wide range of nonlinear terms from a 10-knot restricted cubic spline model was selected at the p = 0.05 level in a stepwise selection procedure. Thus, only the 442 homes with dust endotoxin measurements above the 5th and below the 95th percentiles were included in the present analysis, leaving n1 = 360 in the main study and n2 = 82 in the validation study.
Homoscedasticity of the measurement error was verified by computing the correlation between the fitted values and the squared residuals. After removal of two influential points, as well as the observations with incomplete covariate information, the estimated Pearson’s correlation coefficient between the fitted values and squared residuals was low (r = 0.16), indicating that the assumption of homoscedasticity of the measurement error model is reasonable here.
The third assumption requires that once airborne endotoxin exposure is taken into account, house-dust endotoxin has no further independent association with wheeze. We assessed this assumption by comparing two models fit to data from those homes with airborne endotoxin measured concurrently with house-dust endotoxin obtained at the first home visit. The first modeled the relationship between wheeze and airborne endotoxin, and the second included an additional term for house-dust endotoxin. Comparison of these two models did not suggest that the surrogacy assumption was violated—that is, adding dust endotoxin to the model did not produce a significant increase in explanatory power after adjusting for race and lower respiratory infection (p = 0.46). Power was low in the data for assessing the consistency of the data with this assumption, because the internal validation study contained only 34 participants with known outcome, of whom nine were cases.
The fourth assumption was examined by computing the multiple correlation coefficient for the measurement error model, as the
. Here, this was 0.67, well within the range of a moderate error scenario needed for valid application of the regression calibration method.
Software to execute these techniques is readily available in the form of SAS macros (SAS Institute Inc., Cary, NC) that can be downloaded from the website of the senior author of this article (www.hsph.harvard.edu/facres/spglmn.html).
Results
Table 1 displays the prevalence of wheeze, information regarding the distribution of endotoxin measurements, and other covariate information. At least one episode of wheeze during the first year of life was reported for 42% of the main study participants included in the analysis. The mean and standard deviation of dust endotoxin levels were very similar in the main and validation study participants, although the range was wider in the validation study. The distribution of other factors is reasonably similar between the two study populations. Figure 1 shows the scatter plot of airborne on dust endotoxin in the validation study data. The estimated correlation coefficient between the true and surrogate exposures on the log scale was 0.29. Estimated coefficients from the measurement error model in the validation study are given in Table 2. The multiple correlation coefficient of the true exposure with the surrogate exposure and other covariates (
) for the fitted measurement error model was 0.67, reflecting considerable ability to estimate true exposure given the surrogate and other variables.
Results of the uncorrected and measurement-error–corrected analyses, in the subset of homes with dust endotoxin measurements above the 5th and below the 95th percentiles, are presented in Table 3. Note that the uncorrected and corrected estimates of RR must be reported on different scales. The uncorrected estimate represents the relative increase in risk associated with an increase over the inter-quartile range in dust endotoxin exposure [0.34 log10(EU/mg)], whereas the corrected RR is for an interquartile range increase in air-borne endotoxin exposure of 0.39 log10 (EU/m3). The univariate analysis revealed substantial deattenuation of risk after correction for measurement error. The uncorrected estimate of RR was 1.33 for a one interquartile range increase in airborne endotoxin (95% CI, 1.11–1.60), but after correcting for measurement error, the estimate of RR was 3.11 for a one interquartile range increase in airborne endotoxin (95% CI, 1.04–9.28). In the multivariate analysis, there was a larger effect of measurement error correction. The uncorrected RR was 1.45 (95% CI, 1.20–1.76), and RR increased to 5.56 (95 CI, 1.19–26.0) after correction for measurement error.
The increase in the width of the CIs associated with the corrected estimates of RR points to three facts: First, with a binary response, the variance increases with the value of the point estimate—as the risk ratio increases, so does its variance. Second, the corrected estimator has an additional component of variation, owing to the uncertainty in the values of the parameters
and other coefficients in the measurement error model. Finally, there is an increase in the underlying variability of the estimator due intrinsically to measurement error itself. This is evident in the first term of the function for the variance of the corrected estimate, in which (in the univariate case), the variance of
is divided by
To assess whether an individual’s history of lower respiratory infection acts as an intermediate variable in the relationship between endotoxin exposure and risk of developing wheeze, a measurement error correction analysis was performed on a multivariate model from which the lower respiratory infection covariate was omitted. If this covariate were an intermediate variable rather than a confounder, one would expect the estimate of RR from a model that does not include lower respiratory infection status to increase compared with the estimate that arises from a model including this covariate (Lin et al. 1997). However, because the estimated RR from the analysis in which lower respiratory infection is excluded was less than the estimate obtained in the analysis that controlled for lower respiratory infection, it appeared that lower respiratory infection is more likely to be a confounding variable rather than an intermediate variable and should therefore be adjusted for in multivariate analysis.
Discussion
This analysis suggests that the prevalence of “any wheeze’“ in the first year of life increases 6-fold for every 0.4 log10(EU/m3) increase in airborne endotoxin exposure. When house-dust measurements are used to quantify endotoxin exposure, findings have been much more modest. Park et al. (2001a), using a dichotomous exposure variable, reported a 33% increase in “any wheeze” for children exposed to “high” levels of endotoxin in house dust compared with children in “low” exposure households. In the present study, using a continuous exposure variable, the uncorrected risk ratio is 1.45, suggesting a 45% increase in any wheeze per 0.34 log10(EU/mg) increase in endotoxin measured in house dust. Clearly, correction for measurement error has a large impact on the point estimate of the effect of increased exposure to endotoxin, underscoring the importance of this substance in inducing or exacerbating wheezing episodes among infants.
One implication of finding a much stronger association of wheeze with airborne endotoxin than with dust endotoxin in the family room is that it is reasonable to assume that airborne endotoxin measured over 1.5 days is a more direct measure of exposure than is dust endotoxin. Dust endotoxin, used to gauge exposure in most epidemiologic studies of domestic exposure and asthma risk, may be a direct exposure measure if the sample is taken from bedding and if the major route of exposure is via large particles inhaled from the bedding surfaces. Alternatively, dust in the family room is necessarily a surrogate for “true” exposure, either from bedding or via an airborne route. A long-term or often-repeated personal breathing zone air sample would be a more precise measure of inhaled endotoxin than anything that is feasible in a large community-based study. For the present analyses, we have assumed that airborne endotoxin is the “true” exposure and that average airborne exposure in the first months of life can be validly estimated by a single measurement collected for an average of 1.5 days. We previously reported, from a small convenience sample of homes in metropolitan Boston, that both floor-dust and air measurements have high within-home variation relative to between-home variation (Park et al. 2000). However, recent analysis of dust endotoxin from the much larger birth cohort described here shows much more favorable ratios of within-home to between-home variance (Abraham et al. 2005). Unfortunately, we do not have repeated measurements of airborne endotoxin in this study. But these data show that single measures of airborne endotoxin taken over 1.5 days were able to identify important differences in exposure between homes.
Another implication of these results is that low-level exposures to endotoxin may have a stronger modulating effect on airway inflammation in young children than previously appreciated. Few birth cohort studies have examined airborne endotoxin in homes. This analysis suggests that airborne measurements may be important in identifying the true magnitude of effects of microbial stimuli to the innate immune system and should be considered in future studies of domestic exposure to endotoxin, peptidoglycan, and other pathogen-associated molecular patterns implicated by the hygiene hypotheses of allergy and asthma pathogenesis (Eder and von Mutius 2004; van Strien et al. 2004).
Several key assumptions need to be met for valid application of regression calibration. First, the measurement error model assumed and fit in the validation study is assumed to apply to the main study as well. Additionally, the measurement error model must be linear and homoscedastic, the main study model must be linear on the log prevalence scale, and the usual exposure measure must contain no further information about the distribution of the outcome when data on the gold standard are available. Each of these assumptions but the first is empirically verifiable (see “Results”). As always in regression analyses when continuous covariates are included, care must be taken to ensure that outliers, or sparse data at the extremes of exposure, are not overly influential. In the study presented here, linearity of the exposure–response relationship may be the most difficult of these assumptions to meet and could be verified only in data between the 5th and 95th percentiles. Results of this study should not be extrapolated beyond the range of the data included in this analysis. Studies of airborne endotoxin exposure in early life that include families with more data falling at high exposures, such as studies in farming communities, are needed to determine whether the dose–response curve remains linear and if the present results are applicable above the 95th percentile of exposure in the present data.
In the present example, we were not able to examine repeated wheeze because, as previously described by Park et al. (2001a), there is a J-shaped relationship between the RR of repeated wheeze and the surrogate exposure. Because of the complex and opposite relationships between endotoxin exposure and atopic and nonatopic wheeze in older children (Braun-Fahrlander et al. 2002; Eduard et al. 2004), regression calibration may not be useful in analyzing endotoxin exposure and the combination of the two outcomes simply identified as “wheeze.” However, it may be useful in examining the relationship of endotoxin and atopic wheeze or endotoxin and nonatopic wheeze, if these outcomes are shown to have a linear exposure–response relationship. Among infants, we cannot distinguish between atopic and nonatopic wheeze, and these analyses of repeated wheeze were not attempted here.
Because of missing exposure and/or covariate data, 12% of both the main study and validation study participants were not included in the analysis. We assume that this moderate amount of missingness was jointly unrelated to exposure and outcome after controlling for observed covariates, and jointly unrelated to the values of the parameter estimates of the measurement error model after controlling for observed covariates. Then selection bias would have little if any impact on the results of this analysis.
The regression calibration approach to measurement error correction has several features that make its use attractive to environmental health researchers. Provided that a set of reasonable assumptions are met, this technique yields an approximately unbiased estimate of the effect of exposure on disease, with associated standard error estimates that fully account for the true uncertainty inherent in estimating health effects from error-prone exposure data. All available data are used, and the corrected estimate of effect is in units of the exposure of interest, rather than the surrogate. Data from the main and validation studies are combined to produce a unified set of results that are easily interpreted.
A limitation of regression calibration is that it cannot increase the underlying power of a given study design. It can improve the validity of the point estimate by removing bias, but the fact that measurement error is present and that its magnitude must be estimated from the validation study limit the power of the analysis. The significance level of hypothesis tests in most common models will not change (Tosteson and Tsiatis 1988), but confidence limits will broaden as point estimates move away from the null. However, this drawback can be overcome by planning to correct for bias due to exposure measurement error at the design stage of a study. Studies can be augmented to include a validation substudy in a cost-efficient manner (Holcroft and Spiegelman 1999; Spiegelman and Gray 1991).
In summary, the analysis of airborne endotoxin presented here confirms earlier findings that endotoxin exposure in early life has important health implications. It supports the hypothesis that inhalation is the relevant route of exposure. This analysis suggests that although uses of surrogate exposure measures such as dust endotoxin are effective means to identify a role of endotoxin in childhood asthma, it also suggests that the magnitude of endotoxin’s effect may be underestimated by such studies. This may be of secondary importance when hypothesis testing is the only goal, but it is important when the goal is to assess the relative impact of various exposures, or to provide a basis for control strategies and regulations, as is typically the case in environmental epidemiology.
We thank K. McGaffigan and D. Sredl for assistance with data management and analysis, and the research assistants who collected questionnaire information and dust and air samples and who assayed samples. We especially thank the caregivers of the children who participated in the study.
This work was supported by National Institute for Environmental Health Sciences (NIEHS) grant R01 ES-07036, National Institute of Allergy and Infectious Diseases/NIEHS grant R01 AI/EHS-35786, and NIEHS Center grant 2P30ES00002 and by a gift from BioWhittaker (Walkersville, MD).
Appendix 1. Derivation of the regression calibration estimator for a relative risk (log-binomial) model
Assume Pr (Y=1|X, U) = eβ0 +
Xβ1 +
Uβ2, where Y is a binary outcome, X is the true exposure variable, Z is a surrogate for X, and U is a vector of covariates assumed perfectly measured. Note that the relative risk equals eβ1, where Δ is an increment in X of biological or public health significance. Further, assume that μX|Z,U = E(X|Z,U ) = α0 + Zα1 + Uα2 and Var(X|Z, U) = α2X|Z,U.
Integrate directly.
If the assumptions about the measurement error model are appropriate, Pr(Y=1|Z,U) = ∫x
exp(β0 + Xβ1 + Uβ2) fX|Z,U(X|Z,U ) dx, where fX
|Z,U is the normal density function of X given (Z,U) with mean μX|Z,U and variance σ2X|Z,U. After some algebra and completing the square, Pr(Y=1|Z,U) = exp(β0 + 1/2β12 σ2X|Z,U + μX |Z,U β1 + Uβ2). If the measurement error model fits the data, then model for the probability of the disease outcome given Z and U is given by Pr (Y=1|Z,U) = exp(α0 + Zα1 + Uα2), where α0 = β0 + 1/2β12σ2X
|Z,U + γ0β1, α1 = β1γ1 and α2 = β1γ2 + β2.
Taylor series expansion.
An approximation of Pr (Y=1|Z,U) can be found without the normality assumption for f
X
|Z,U by substituting the second-order Taylor series expansion of Pr (Y=1|X,U) about μX
|Z,U in Pr (Y=1|Z,U ) = ∫ Pr (Y=1|X,U) fX|Z,U (X |Z,U )dx. After taking the expectation of the expansion, the linear term is equal to zero. The following approximation is found:
If β12 σ2X
|Z,U is close to zero, the third term disappears and Pr (Y=1|Z,U ) is approximated by exp[β0 + β1μX
|Z,U + Uβ2].
Regardless of which derivation (and which assumptions) are used, if estimates of α′ =(α0, α1, α2′) are obtained from the model of the probability of disease in relation to Z and U and estimates of γ′ = (γ0, γ1, γ2′) are obtained from the measurement error model of X on Z and U, an estimated exposure effect corrected for measurement error is computed by
. The measurement error-corrected effects of the perfectly measured covariates are estimated by
. The measurement error-corrected intercept is estimated by
when β12σ2X|Z,U ≡ 0.
The variance of these estimates is derived from the multivariate delta method.
Figure 1 Scatter plot of airborne endotoxin versus dust endotoxin in validation study (n2 = 82); r = 0.29.
Table 1 Basic characteristics of the study populations (%).a
Characteristic Main study (n1 = 360) Validation study (n2 = 82)
Any wheeze (≥ 1 episode) 42 26
Lower respiratory illness (≥ 1 episode) 28 21
Race/ethnicity
White 78 81
Black 11 13
Hispanic 6 0
Asian 4 4
Other 1 2
Presence of dog
Current 17 20
Former 21 23
Use of dehumidifier 20 17
Presence of concrete floor 7 5
Presence of water damage 36 41
Dust endotoxin (EU/mg) [mean (minimum–maximum)] 79.6 (26.2–241.6) 93.1 (27.7–1249.0)
Airborne endotoxin (EU/m3) [mean (minimum–maximum)] — 0.81 (0.23–5.87)
Total fine dust (g) [mean (minimum–maximum)] 1601.6 (258.0–11467.0) 1329.1 (477.0–6075.0)
a Values are percentages unless noted otherwise.
Table 2 Measurement error model for log10(airborne endotoxin) [log10(EU/m3)] (n2 = 82).
Variable p-Value
Log10(dust endotoxin) [log10(EU/mg)] 0.25 0.09 < 0.01
Log10(total fine dust) [log10(g)] 0.21 0.11 0.05
Lower respiratory infection 0.07 0.06 0.30
Race/ethnicity
Black 0.04 0.08 0.64
Asian/othera 0.18 0.11 0.11
Presence of dog
Current 0.22 0.07 < 0.01
Former 0.14 0.07 0.03
Use of dehumidifier −0.11 0.08 0.15
Presence of concrete floor
Living room 0.28 0.12 0.03
Dining room and kitchen 0.28 0.15 0.06
Presence of water damage 0.11 0.05 0.04
a There are no Hispanics in the validation study.
Table 3 Association between endotoxin exposure and wheeze (n1 = 360, n2 = 82).
Uncorrected
Corrected
Model (p-value) a (95% CI) (p-value) b (95% CI)
Univariate 0.84 (< 0.01) 1.33 (1.11–1.60) 2.91 (0.04) 3.11 (1.04–9.28)
Multivariatec 0.89 (< 0.01) 1.35 (1.11–1.65) 3.63 (0.05) 4.12 (1.01–16.83)
Multivariated 1.09 (< 0.01) 1.45 (1.20–1.76) 4.40 (0.03) 5.56 (1.19–26.03)
a Estimated RR reflects an increase of one interquartile range [0.34 log10(EU/mg)] in dust endotoxin exposure.
b Estimated RR reflects an increase of one interquartile range [0.39 log10(EU/m3)] in airborne endotoxin exposure.
c Adjusted for race, presence of dog in home, former (not current) dog in home, use of dehumidifier, total mass of dust sample collected (in log scale), presence of concrete floor, missingness indicator for presence of concrete floor, and presence of water damage in the measurement error model.
d Further adjusted for lower respiratory illness, in addition to covariates of the previous multivariate model.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7881ehp0114-00014116393672ResearchChildren's HealthPrevalence and Implementation of IAQ Programs in U.S. Schools Moglia Dena 1Smith Alisa 1MacIntosh David L. 2Somers Jennifer L. 21 U.S. Environmental Protection Agency, Office of Air/Indoor Environments Division, Washington, DC, USA2 Environmental Health and Engineering Inc., Newton, Massachusetts, USAAddress correspondence to D. Moglia, U.S. Environmental Protection Agency, Environmental Health and Engineering Inc., Office of Air/Indoor Environments Division, 1200 Pennsylvania Ave. NW (MC 6609J), Washington, DC 20460 USA. Telephone: (202) 343-9221. Fax: (202) 343-2393. E-mail:
[email protected] authors declare they have no competing financial interests.
1 2006 21 7 2005 114 1 141 146 21 12 2004 21 7 2005 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. In this study, we determined the extent to which U.S. schools are implementing indoor air quality (IAQ) programs. We administered a questionnaire on IAQ programs and practices to a representative sample of schools. Participants were asked to provide information on the use, administration, implementation, challenges, and benefits of the IAQ program in their school. We developed an IAQ Practice Index to determine the level of activity directed toward IAQ in schools. The index was computed based on responses to specific survey questions and was normalized to a range of 0 to 100. Each question was weighted qualitatively according to its contribution to strong IAQ management practices. Forty-two percent of schools in the United States have an IAQ management program, and there has been sustained growth from 1998 through 2002 in the number of schools that have such programs. Nearly half of those schools use the U.S. Environmental Protection Agency’s IAQ Tools for Schools program. The IAQ Practice Index scores varied widely for schools with an IAQ management program, suggesting that having a program is not equivalent to implementing effective IAQ policies and procedures. Respondents indicated that their IAQ programs led to improved workplace satisfaction, fewer asthma attacks, fewer visits to the school nurse, and lower absenteeism. When actively supported by the school administration, an IAQ program appears to be a valuable factor in improving the learning environment for U.S. schoolchildren.
airasthmachildren’s healthenvironmentschoolssurvey
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Schools are locations where children spend a large amount of their time, second only to time spent indoors at home. According to the U.S. Department of Education (DoE) National Center for Education Statistics (NCES) report Condition of America’s Public School Facilities: 1999 (Lewis et al. 2000), about one-quarter of U.S. schools need extensive repair or replacement of one or more buildings. Nearly 11 million students attend these schools. Approximately 40% of schools report at least one unsatisfactory environmental condition such as poor ventilation, heating or lighting problems, or poor physical security (Lewis et al. 2000). Improper building operations and deferred maintenance contribute to poor indoor environmental conditions, affecting the levels of mold, mildew, dust, animal dander, radon, secondhand smoke, asbestos, and formaldehyde in schools (U.S. General Accounting Office 1995). These pollutants can affect indoor air quality (IAQ) and trigger various health symptoms, from headaches to allergies and asthma attacks (Samet and Spengler 1991).
The toll of these health conditions on education in America is large. Asthma alone accounts for 14 million missed school days each year (Centers for Disease Control and Prevention 2002). Asthma prevalence has been on a steep rise since 1980. Although many cases of asthma probably go undiagnosed, health officials estimate that 6.1 million children in the United States have asthma. Allergies are estimated to be the cause of an additional 2 million lost school days annually. Current evidence indicates that viral infections predispose children to asthma attacks and allergic responses (Papadopoulos and Johnston 2001). This is important, given that schoolchildren are estimated to experience 7–10 colds each year (Johnston and Holgate 1996) and that improved IAQ and ventilation may reduce the airborne transmission of viruses (Myatt et al. 2004). The effective management of IAQ in schools can reduce students’ exposure to the air pollutants that trigger allergies and asthma attacks, potentially improving students’ ability to learn.
There is strong research relating certain IAQ management practices with IAQ in buildings. For example, if you increase outdoor ventilation, all else being equal, indoor pollutant concentrations will decrease. This is the basis for national ventilation standards. Removing or managing sources of contaminants correspondingly decreases pollutant levels. The same can be said for air cleaning or filtration. Indeed, there are health and comfort relationships with such practices [see U.S. Environmental Protection Agency (EPA) 2004 for more information].
To gain information about the number of schools that have implemented IAQ management programs in our nation’s schools, the Office of Radiation and Indoor Air’s Indoor Environments Division of the U.S. EPA created the IAQ Practices in Schools Survey (U.S. EPA 2001). The IAQ Practices in Schools Survey examines the extent to which public and private schools nationwide have taken action to improve IAQ and implement an IAQ program.
Materials and Methods
The IAQ Practices in Schools Survey included a representative sample in 2002 of all public and private schools that were operating in the United States during the 1999–2000 school year. The primary objective was to obtain a sample size sufficient to provide a reliable estimate of the fraction of schools throughout the United States that have implemented IAQ management practices, such as those recommended in the U.S. EPA’s Indoor Air Quality Tools for Schools (IAQ TfS) Action Kit program. A secondary objective was to obtain a sample with sufficient power to detect meaningful variation in IAQ management practices among schools.
Data used to identify the IAQ Practices in Schools Survey study population were obtained from the DoE’s NCES school registry, which features two separate databases, one for public schools and another for private schools. Both databases are updated on an annual basis. At the time that the questionnaire was developed, quality-assured public and private school data for the 1999–2000 school year were available from the Common Core of Data website (NCES 2002).
The eligibility criteria we established for public and private school data required that schools have a state postal code from one of the 50 states or the District of Columbia, were open during the reporting year of 1999–2000, and reported student enrollment > 0. Fundamental attributes of the public and private school data for 1999–2000 are summarized in Table 1. A total of 118,785 schools met the eligibility criteria for the combined data set. Public and private schools accounted for 75 and 25% of the eligible schools, respectively. Six percent of the public schools were omitted from the combined data set for failing to meet one or more of the eligibility criteria. All of the private schools met the eligibility criteria.
We employed a random sampling strategy, stratified by U.S. EPA region and school type, to distribute the IAQ Practices in Schools Survey (see Table 2 for a breakdown of states by U.S. EPA region). Sampling frequency was determined by the number of students in grades pre-kindergarten through 12. The percentage of schools sampled matched the percentage of schools in each of the 10 U.S. EPA regions stratified by public and private school system. Based on this strategy, 2,004 schools out of 118,785 eligible facilities were sampled. Sampling 2,004 schools, with an expected 50% return rate, yielded an anticipated sample size of 1,000 surveys. The sample of 1,000 schools was based on budget limitations. Precision and power analyses were conducted on the basis of 1,000 completed surveys. The sampling strategy had sufficient statistical power to detect a difference between different levels of a single variable, such as public and private schools, if one existed. However, this sampling strategy had insufficient power to detect a statistically significant difference between the 20 smaller strata of U.S. EPA region and school type. For this reason, conclusive analysis is limited to testing for differences on groups defined by different levels of a single variable, such as school type, rural versus urban school location, or school grade level. Differences found between the U.S. EPA regions and additional strata are discussed because they generate important hypotheses that will guide future research.
The IAQ Practices in Schools Survey was sent to 1,519 public and 485 private schools that met the eligibility criteria. Schools that did not return the survey within 3 weeks after distribution were called by telephone and prompted to complete and return the questionnaire. A total of 809 surveys were returned. The error rates for sample sizes of 1,000 and 809 are both within a 3% margin. Consequently, we determined that the sample size does not change the assumptions made in the sampling plan.
The four-page IAQ Practices in Schools Survey contains 11 multipart questions. Question 1 asks whether respondents use IAQ TfS, another IAQ management program, or none at all. Question 2 asks how long the plan has been in effect, using multiple-choice answers. Questions 3–7 ask respondents to rate multiple characteristics of their schools’ programs on a scale of 0 (none, not at all) to 5 (very much, excellent), as follows:
3. Please rate the quality and effectiveness of your school’s IAQ management plan (rate each item).
A person is designated as IAQ coordinator/manager and has authority to carry out the IAQ management plan.
The building and heating, ventilating, and air conditioning (HVAC) system receive regular maintenance to ensure that all systems are consistently functioning as designed.
IAQ is a priority consideration for repairs and upgrades of the school building and HVAC system.
The HVAC system consistently provides adequate control of temperature, humidity, and outdoor air ventilation to all occupied spaces.
Art classes, industrial art classes, and science laboratories choose products and/or incorporate specific methods, e.g., exhaust ventilation, to minimize exposures of all students and staff to pollutants produced from these activities.
Housekeeping (custodial) services maintain clean conditions in all areas.
Cleaning products and methods are chosen to minimize exposure of students and staff to pollutants produced by housekeeping products.
An IAQ walkthrough inspection and periodic checkups are used to monitor IAQ conditions and practices in the school(s).
4. Pest control in the school(s) is accomplished using the following (rate each item).
Traps are used to monitor pest populations.
Threshold targets are established for pest populations.
Traps are used to kill and control pests.
Hygienic conditions are strictly maintained to prevent infestations.
Leaks, spills, condensation, and other moisture sources are strictly controlled.
Pesticides are applied on a regular basis.
5. Your school administration supports the IAQ program.
6. School personnel actively participate in the IAQ program (rate participation by each group).
Teachers
Administrative staff
Custodial staff
Food service staff
Health officers/school nurses
Facilities and maintenance staff
Other (specify)
7. Please rate the extent to which the following have been barriers to implementing IAQ practices in your school(s).
Potential liability
Costs
Lack of resources
Lack of knowledge
Competing priorities
School administration
School board Question 8 asks for the outdoor air ventilation rate in respondents’ schools, using multiple-choice options.
Question 9 asks which of the various IAQ TfS checklists have been distributed, specifying that respondents should check all that apply. Question 10 asks what percentage of those checklists have been completed and returned, using multiple-choice options. (This question helps gauge the extent to which the entire school team participates in IAQ management.) Question 11 asks respondents to offer their opinions as to whether their IAQ program has led to lower absenteeism, better test scores, increased productivity, fewer asthma episodes, improved workplace satisfaction, or fewer visits to the health officer or school nurse. Respondents are instructed to check all that apply for question 11.
We designed an IAQ Practice Index as a way to quantify the extent of each school’s IAQ management practices. It is a tool to facilitate the use and interpretation of the survey results. An IAQ Practice Index score for each respondent was computed from responses to questions 3, 4, 5, 6, and 8. Questions 1 and 2 were excluded from the index because they are not quantitative questions; however, the way in which these questions relate to the IAQ Practice Index is addressed in the analysis. Questions 7 and 11 were excluded because they do not provide an absolute measure of implementation. Rather, they offer insight into factors that the investigators expected to correlate with the strength of IAQ management practices. Questions 9 and 10 were excluded because they relate specifically to IAQ TfS, whereas the index is intended to measure management practices regardless of the program employed.
We assigned values to index questions based on our professional assessment of the qualitative importance of each question’s contribution to good IAQ management practices. Table 3 depicts the IAQ Practice Index scoring methodology.
We determined that responses to the five IAQ Practice Index questions must be at least 80% complete for a survey to be included in the index. This completeness criterion was established based on the fact that non-responses were given scores of zero when computing the index. Thus, incomplete surveys would almost necessarily receive a lower IAQ index score than would complete surveys. We chose this approach, rather than computing the index from only those questions on any given survey that received complete responses, because we felt that basing the index on only a subset of IAQ metrics would yield invalid results. There is a fairly strong correlation (Spearman r = 0.45) between completeness and index scores across all 809 schools. However, the correlation becomes quite weak (Spearman r = 0.18) for only schools with completeness > 80%. Thus, applying the 80% completeness criterion affords characterization of IAQ management practices across schools with minimal potential bias from incomplete questionnaires.
In addition, completeness dropped off precipitously < 80%. This means that the study would have had to include substantially incomplete questionnaires to gain even a modest increase in sample size for the IAQ index. For example, to add even an additional 100 schools to the sample size (a 17% increase over the 587 schools), the study would have had to include completeness percentages as low as 55%. We determined that the limited amount of information provided from schools with completeness < 80% could not be considered a valid indicator of their IAQ management practices.
Results
Quality assurance.
A total of 809 completed questionnaires were returned for an overall survey response rate of 40%. There was no evidence of systematic error or selection bias associated with the response rate. The distribution of returned and targeted questionnaires was similar with respect to the stratification criteria of geographic region and public/private schools. Academic resource, demographic, and socioeconomic characteristics of schools that returned the questionnaire were approximately equal to those of schools that did not return it. IAQ management practices were independent of the amount of follow-up effort required to elicit return of a questionnaire.
Seventy-two percent (586 of 809) of the returned questionnaires met our completeness criterion of 80% for inclusion in the IAQ Practice Index calculation. A total of 2,004 surveys were mailed to schools. Thus, the questionnaire completion rate used to calculate the index was 29% (586 of 2,004 questionnaires).
Prevalence of IAQ programs.
Forty-two percent of the 809 schools that responded to the questionnaire had an IAQ management program, and 20% used U.S. EPA’s IAQ TfS program. Thirty-six percent of schools with an IAQ management program had an IAQ plan in place for > 5 years, 22.5% of schools for 2–4 years, 19.6% of schools for 1–2 years, and 13% of schools for < 1 year.
IAQ programs do not appear to be distributed evenly between public and private schools. The survey results indicate that nearly 50% of public schools across the nation have a program to manage IAQ. However, only 20% of private schools appear to have an IAQ program.
The percentage of schools in each U.S. EPA region with an IAQ management program is presented in Figure 1. The portion of schools using U.S. EPA’s IAQ TfS is distinguished from schools that use a different IAQ management program. The plot shows that at least 40% of the schools in U.S. EPA regions of the eastern United States have an IAQ management program, whereas < 40% of the schools in the U.S. EPA regions of the western United States have an IAQ management program.
With regard to IAQ TfS, distribution and use of program checklists are important indicators of IAQ program implementation. The administration, ventilation, building maintenance, and walkthrough checklists were distributed to staff in more than half of the schools that use IAQ TfS. The waste management checklist was the least frequently distributed checklist. Approximately one-fifth of schools that use IAQ TfS reported that 57.3% of the IAQ checklists had been completed and returned. In comparison, the remaining four-fifths of schools that reported not using a management plan or using a plan other than IAQ TfS reported that 7.8% and 12.8% of the IAQ checklists had been completed and returned, respectively.
Nearly three-quarters (73.2%) of the schools with an IAQ management program report receiving substantial support for the program from their school administration (based on a rating of 4 or 5 for question 5). Among schools with active IAQ management programs, facilities, maintenance, and custodial staff were active participants in nearly 80% of the programs (based on responses to question 6). Food service, health care, and administrative staff were also active participants in the school’s IAQ programs. This indicates a strong measure of engagement among all members of a school community, which is an important aspect of a well-functioning IAQ management program.
IAQ Practice Index.
The IAQ Practice Index ranges from a minimum possible score of 0 to a maximum of 100. The survey results revealed that the quality and effectiveness of IAQ management programs varied widely, from 20.6 to 100, as measured by the index. Given our expertise in IAQ management, we determined that a score of 70 would be used as a baseline, indicating a well-functioning IAQ program consistent with U.S. EPA guidance. Of the schools with an IAQ management program, 57% had a score > 70. A comparison of schools that have an IAQ program to those without an IAQ program on five parameters affecting IAQ policies and procedures is presented in Figure 2.
The mean IAQ Practice Index across U.S. EPA regions ranged from 64.4 in Region 4 (Southeast) to 77.1 in Region 10 (Upper Northwest). Mean IAQ Practice Indices varied significantly (p = 0.0307) among U.S. EPA regions according to the results of a one-way generalized linear model, although the differences across regions are < 15 index units (see Figure 3 for further regional comparisons). More observations from schools in Regions 7 and 8 are needed to explore spatial variability of IAQ management programs in schools more fully (see Table 2 for further regional statistics).
The mean IAQ Practice Index did not differ significantly (p = 0.7746) between the 287 public schools (mean = 70.8) and 31 private schools (mean = 71.7) that met the completeness criterion for scoring and calculation of the index.
Questionnaire respondents were asked their opinion on whether their IAQ program led to any associated benefits. Improved work-place satisfaction was the most frequently reported benefit of an IAQ program among schools that have an IAQ program. Improved health status of students, as indicated by fewer asthma episodes, fewer visits to the school nurse, and lower absenteeism, was reported by 28–33% of schools that have an IAQ program. Cost, lack of resources or knowledge, and competing priorities were the most frequently reported barriers to implementation of an IAQ program among the schools that do not have a program.
Discussion
The IAQ Practices in Schools Survey was the first national assessment of IAQ management programs in U.S. schools. The survey yielded unique information about management of factors that influence IAQ in schools and provided a basis for evaluation of the status and trends of school IAQ management programs.
The principal limitations of the survey are associated with the mechanism chosen to administer the questionnaire, certain details of the questionnaire format and wording, and the potential for self-selection bias.
The survey consisted of a self-administered questionnaire that was addressed to the “school official.” School representatives with 350 different job titles completed and returned the survey, although principals represented the bulk of respondents at 33.6%. The next most frequently reported job title represented only 6.4% of the respondents. School officials with different job titles and responsibilities may have different amounts of information about IAQ management programs in schools and also may hold different perspectives about the importance and role of IAQ management programs in schools. We analyzed the survey responses and found no evidence that principals responded differently to the questionnaire than other categories of respondents. This suggests that if any bias was present based on the respondent’s position within the school, it is unlikely to have had an impact on the analysis.
Another result of the self-administered feature of the survey is that respondents had limited ability to resolve questions about the intent and meaning of instructions, queries, and answers included in the questionnaire. The distribution of responses to certain questions is evidence of apparent confusion on the part of some respondents. For example, 404 schools reported that they do not use an IAQ management program (question 1), yet 20% of those same schools reported that their IAQ management plan had been in use for < 1 year to > 5 years. The distinction between an IAQ management program and IAQ management plan may not have been clear to all of the respondents.
The internally inconsistent responses are in part likely the result of potentially ambiguous instructions and questions in selected portions of the questionnaire. For example, questions 3, 5, 6, 7, and 11 are queries about various aspects of a school’s IAQ management program. One might anticipate that only schools with an IAQ management program would respond to those questions. Indeed, some respondents who checked “None” for question 1 (i.e., they do not have an IAQ program) did not complete the remainder of the questionnaire. However, many schools without an IAQ management program did respond to these questions with an answer other than “None.” Responses to these questions by schools without an IAQ management program are difficult to interpret. In future surveys, the instructions on the questionnaire will stipulate whether responses to certain questions are conditional on responses to preceding questions.
The wording of select questions also may have been a source of confusion for schools rating their IAQ management practices. For example, question 7 asks the school to rate the extent to which potential liability, costs, lack of resources, and other factors have been barriers to implementing IAQ practices. There are two possible interpretations of the rating scale: A factor could be construed as a “poor” or otherwise weak attribute (rating of 0 or 1) of the school’s IAQ program, or its significance as a barrier could be construed as “a lot” or “very much” (rating of 4 or 5). The bimodal distribution of the relationship between the IAQ Practice Index and responses to question 7 supports the idea that interpretation of the rating scale for this question differed among schools.
Finally, the IAQ Practices in Schools Survey identified a wealth of data on IAQ management in schools. However, the relationship between implementation of IAQ management practices and actual IAQ in schools cannot be addressed by questionnaire. The prospect of obtaining quantitative measures of IAQ in conjunction with detailed information on IAQ management practices is a consideration for future programmatic efforts.
The survey results indicate that many public schools in the United States have adopted IAQ management programs. Fifty percent of U.S. public schools have some sort of IAQ program. With only a 20% adoption rate, private schools, on the other hand, have room for improvement with respect to their IAQ management practices. The reasons for this disparity will be an interesting topic of future research. The northwestern United States has schools most involved in their IAQ management programs, scoring the highest average IAQ Practice Index in the country, with the mid-Atlantic region scoring a close second. The Great Lakes region has the highest percentage of schools with an IAQ program. Areas of the country where IAQ has received less attention include the Southeast, which had the lowest IAQ Practice Index, and the midwestern states, which have both a low IAQ Practice Index score and the fewest schools with an IAQ management program.
The breadth of questions covered in the questionnaire provides a way to quantify the quality of overall management practices for schools that have an IAQ management program. The central tendency of the IAQ Practice Index indicates the typical level of activity directed toward IAQ in schools, whereas the dispersion of the IAQ Practice Index describes the variability in activity of IAQ programs across schools. The quality and effectiveness of IAQ management programs, as measured by the IAQ Practice Index, varied substantially among schools. Assuming that the index accurately reflects the extent of IAQ program implementation, this finding implies that the use of an IAQ management program is not equivalent to implementing effective policies and procedures that proactively and effectively manage IAQ issues. We believe that additional outreach efforts may effectively improve the IAQ of schools in the United States, but further research would be required to support this assumption.
The sample size provided sufficient statistical power to identify relationships between IAQ management practices (measured by the IAQ Practice Index) and factors such as administrative support and authority to implement the program. Notably, the IAQ Practice Index was positively correlated with the reported level of administration support for a school’s IAQ management program and the designation of a manager or coordinator to implement the program. Thus, if a school’s administration was reported to support the school IAQ management plan and there is a designated IAQ manager/coordinator, the school was more likely to have an effective, higher-quality IAQ management program. The relationships between the IAQ Practice Index and selected questions were evaluated for questionnaires that were at least 80% complete. The analyses were repeated using completeness criteria of 90% and 100%, and there were no appreciable changes in the results. Thus, the findings are robust with respect to the choice of 80% completeness criterion.
A total of 809 completed questionnaires were returned, for a survey response rate of 40%. The survey was designed with a 50% response rate. Because of the lower than anticipated response rate, we conducted two sets of analyses to address the potential for substantive bias in the survey results.
In the first set of analyses, we examined the completed questionnaires for indications that the follow-up effort required for a school to return the questionnaire is associated with IAQ management practices. The assumption is that less follow-up effort indicates greater interest in IAQ and that interest in IAQ is associated with IAQ management practices. If response time is not associated with IAQ management practices, then one reason for concern about the potential for self-selection to bias the survey results would be eliminated. We measured follow-up effort by the number of telephone calls made to the school before the questionnaire was returned. The percentage of schools with an IAQ management program and the IAQ Practice Index varied little among levels of follow-up effort. In addition, the number of follow-up telephone calls was not correlated with the percentage of schools with an IAQ management program (Spearman r = 0.33, p = 0.2238), completeness (Spearman r = 0.37, p = 0.4685), or IAQ index (Spearman r = 0.09, p = 0.8717). These results suggest that IAQ practices in schools that did not return the questionnaire and presumably required more intensive follow-up are not substantively different from schools that did return the questionnaire. These data suggest that the survey results are not influenced by self-selection bias.
The second set of analyses consisted of two phases. First, characteristics of schools that returned the questionnaire were compared with characteristics of schools that did not return the questionnaire. The assumption is that certain school characteristics that were not included in the survey design such as stratification variables may be associated with IAQ management practices. In that case, differences in such characteristics between responder and nonresponder schools could indicate bias in the survey results. Second, for characteristics that differed substantially between responder and nonresponder schools, we assessed the relationship between those characteristics and IAQ management practices to evaluate the magnitude of potential bias.
Academic resource, demographic, and socioeconomic characteristics of schools that returned the questionnaire were approximately equivalent to those of schools that did not return the questionnaire, as shown in Table 4. In addition, IAQ management practices as measured by the IAQ Practice Index did not vary with respect to socioeconomic or demographic attributes of schools or their student populations, including Title I (federal program for economically disadvantaged children who reside in areas with a high concentration of low-income families) status, grade level, enrollment, rural/nonrural location, median household income, or percentage of students eligible for free or reduced-price lunch programs. These results indicate that schools in less affluent areas are as likely to have an IAQ management program as are schools in other areas and suggest that school size and financial resources are not important determinants of a school’s ability or willingness to implement an IAQ management program. In part, this may reflect the fact that many IAQ practices are low-cost activities.
Of the 124,288 public and private schools in the NCES databases, 30,645 (24.7%) are in rural locations. Thus, the survey results over-represent rural schools by approximately 17%. This overrepresentation is of consequence only if IAQ management practices differ between rural and nonrural schools. Mean completeness and IAQ index agreed well for rural (82% and 51.3, respectively) and nonrural schools (81% and 54.7) that returned a questionnaire. Based on these findings, there does not appear to be a significant difference in IAQ management practices between rural and urban schools. The percentage of rural schools with an IAQ program was 37% (87 of 238), whereas 45% (255 of 571) of nonrural schools had a program. Overall, 42.3% [95% confidence interval (CI), 38.9–45.7%] of schools that returned a questionnaire had an IAQ program. We devised an adjusted estimate by weighting the proportion of the rural and nonrural schools with an IAQ program by the national distribution of rural and nonrural schools. After this adjustment, 42.7% of schools nationwide are estimated to have an IAQ program. The adjusted estimate is nearly equal to the original estimate and is within the 95% CI derived from the raw survey data.
A particularly encouraging result is that 50% of the public schools surveyed reported use of an IAQ management program. The information on the number of years that an IAQ management program has been in place suggests that there has been a sustained increase in the use of IAQ management programs over time. The survey results indicate that schools are paying an increasing amount of attention to IAQ.
Conclusion
An estimated 42.3% of schools in the United States have an IAQ program, and the use of IAQ management programs in schools has increased from 1998 through 2002. Variation in the IAQ Practice Index indicates that having an IAQ management plan is not equivalent to implementation of effective IAQ policies and procedures. When actively supported by the school administration, an IAQ program is reported to be a valuable factor in improving the learning environment for U.S. schoolchildren.
We thank S. Wright with the Cadmus Group Inc. for assistance in preparing the manuscript.
Figure 1 Percentages of schools using the U.S. EPA’s IAQ TfS program or another IAQ management program across the U.S. EPA’s 10 geographic regions in the United States.
Figure 2 Percentages of schools using an IAQ program and not using an IAQ program: five parameters affecting IAQ policies and procedures.
Figure 3 IAQ Practice Index (mean ± SE) across the U.S. EPA’s 10 geographic regions for those schools reporting use of an IAQ management program and completing at least 80% of the survey.
Table 1 Attributes of the NCES public and private school data sets [no. (%)] for the 1999–2000 school year.
Attribute Public schools Private schools
Schools in database 95,289 (100) 28,939 (100)
School not in 50 states or D.C. 1,849 (2) 0 (0)
Closed school 2,034 (2) 0 (0)
School did not report enrollment 533 (1) 0 (0)
School enrollment = 0 1,059 (1) 0 (0)
Schools not meeting eligibility criteria 5,443 (6) 0 (0)
Schools meeting eligibility criteria 89,846 (94) 28,939 (100)
Table 2 IAQ Practice Indices across the 10 U.S. EPA regions for schools with an IAQ management program.a
Region States Frequency IAQ Practice Indexb Schools with index ≥ 70 (%)
1 Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont 25 71.8 64
2 New Jersey, New York, Puerto Rico,c U.S. Virgin Islandsc 28 71.9 57
3 Delaware, Maryland, Pennsylvania, Virginia, West Virginia, District of Columbia 21 77.0 71
4 Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee 46 64.4 41
5 Illinois, Indiana, Michigan, Minnesota, Ohio, Wisconsin 87 73.0 60
6 Arkansas, Louisiana, New Mexico, Oklahoma, Texas 40 70.7 60
7 Iowa, Kansas, Missouri, Nebraska 15 66.7 53
8 Colorado, Montana, North Dakota, South Dakota, Utah, Wyoming 13 66.3 54
9 Arizona, California, Hawaii, Nevada, Pacific Islands,c Tribal Nations subject to U.S. law 27 70.1 56
10 Alaska, Idaho, Oregon, Washington 16 77.1 81
a The total number of schools that report having an IAQ management plan with a questionnaire completion rate of > 80% is 318.
b Mean IAQ Practice Indices varied significantly (p = 0.0307) among U.S. EPA regions.
c Schools that were not in the 50 states or the District of Columbia were excluded from this study.
Table 3 IAQ Practice Index scoring methodology.
Question Value Scoring/ranking methodology
Question 3 30 Determine the average score for subquestions a–h and divide by 5 (the range of possible answers). Multiply this total by the assigned weight, 30.
Question 4 10 Create a filter for this question. If a respondent answers subquestion f with a response of 0 or 1, then total the average score of subquestions a–e and divide by 5. Multiply the total by the assigned weight, 10. However, if a respondent answers subquestion f with a response of 2–5, then divide the total average score of subquestions a–e in half; divide by 5; and multiply by the assigned weight, 10.
Question 5 25 Divide the score by 5 and multiply the total by the assigned weight, 25.
Question 6 25 Determine the average score for subquestions a–g, divide by 5, and multiply by the assigned weight, 25.
Question 8 10 The following values have been assigned to each subquestion:a) No particular setting = 0b) < 5 cfm per occupant = 0c) 5–10 cfm per occupant = 3d) 11–14 cfm per occupant = 7e) ≥ 15 cfm per occupant = 10
cfm, cubic feet per minute.
Table 4 Selected characteristics of schools that did (n = 809) and did not (n = 1,195) return the IAQ Practices in Schools Survey.
Returned questionnaire
Did not return questionnaire
Interquartile range
Interquartile range
Characteristic Median Q1a Q3b Median Q1a Q3b
Student:teacher ratio 15.4 12.6 18.1 15.5 12.9 18.8
Enrollment (no. of students) 392 200 645 376 144 637
Median household income ($)c 38,676 32,171 50,729 39,156 31,292 51,336
Free lunch program (%)d 23 10 41 27 12 52
Reduced-price lunch program (%)e 7 4 11 7 4 11
a First quartile (Q1) is the 25th percentile of distribution.
b Third quartile (Q3) is the 75th percentile of distribution.
c Median household income for ZIP code of school.
d Percentage of school students eligible for the free lunch program.
e Percentage of school students eligible for the reduced-price lunch program.
==== Refs
References
Centers for Disease Control and Prevention 2002 Surveillance summaries: surveillance for asthma 1980–1999 MMWR Morb Mortal Wkly Rep 51 5 9 11831432
Johnston S Holgate S 1996. Epidemiology of viral respiratory infections. In: Viral and Other Infections of the Human Respiratory Tract (Myint S, Taylor-Robinson D, eds). London:Chapman and Hall, 1–38.
Lewis L Snow K Farris E Smerdon B Cronen S Kaplan J 2000. Condition of America’s Public School Facilities: 1999. NCES 2000-032. Washington, DC:U.S. Department of Education, National Center for Education Statistics.
Myatt TA Johnston SL Zuo Z Wand M Kebadze T Rudnick S 2004 Detection of airborne rhinovirus and its relation to outdoor air supply in office environments Am J Respir Crit Care Med 169 1187 1190 14754759
NCES 2005. Common Core of Data. National Center for Education Statistics, U.S. Department of Education. Available: http://www.nces.ed.gov/ccd [accessed 4 July 2005].
Papadopoulos NG Johnston SL 2001 The role of viruses in the induction and progression of asthma Curr Allergy Asthma Rep 1 2 144 152 11899297
Samet JM Spengler JD eds. 1991. Indoor Air Pollution, a Health Perspective. Baltimore, MD:Johns Hopkins University Press.
U.S. EPA 2004. Scientific Findings of Health and Productivity in Support of Indoor Environmental Quality Management of Buildings. Washington, DC:U.S. Environmental Protection Agency.
U.S. EPA 1995. Indoor Air Quality Tools for Schools Action Kit. Washington, DC:U.S. Environmental Protection Agency. Available: http://www.epa.gov/iaq/schools/ [accessed 4 July 2005].
U.S. EPA 2001. Survey of Indoor Air Quality Practices in Schools. ICR No. 1885.02, OMB Control No. 2060-0436. Washington, DC:U.S. Environmental Protection Agency.
U.S. General Accounting Office 1995. School Facilities: Condition of America’s Schools. GAO/HEHS 95-61. Washington, DC:U.S. General Accounting Office.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a0001216393636PerspectivesEditorialGuest Editorial: Environmental Health and Hurricane Katrina Falk Henry Baldwin Grant Coordinating Center for Environmental Health and Injury Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, E-mail:
[email protected] Falk serves as the Director of the Coordinating Center for Environmental Health and Injury Prevention (CCEHIP), which is one of four coordinating centers at the Centers for Disease Control and Prevention (CDC). CCEHIP includes the National Center for Environmental Health/Agency for Toxic Substances and Disease Registry (NCEH/ATSDR) and the National Center for Injury Prevention and Control (NCIPC).
Grant Baldwin serves as the acting Special Assistant to the Director at the CCEHIP. He has been at the CDC since 1996, serving in a variety of capacities within the NCEH/ATSDR.
The authors declare they have no competing financial interests.
1 2006 114 1 A12 A13 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
==== Body
Hurricane Katrina caused enormous physical destruction, environmental degradation, and human misery (Travis 2005). Full remediation will take years, and many decisions that are fundamental to the restoration and rejuvenation of the Gulf Coast are yet to be made. The challenges for New Orleans, Louisiana, are particularly complex.
Criticism of the disaster response and preparedness effort was swift and intense (Anonymous 2005; Coghlan and Mullins 2005). Who will forget the searing photographs of stranded and desperate New Orleanians in the days after the city was flooded? Lessons abound and will undoubtedly inform future disaster planning at all levels for many years (Nieburg et al. 2005).
Compounding the devastation in New Orleans was the near total disruption of the public health and medical infrastructure. Although federal disaster preparedness plans include provisions of surge capacity through Disaster Medical Assistance Teams (DMATS) and other resources, extraordinary and often improvised measures were needed in New Orleans and in numerous shelters and points of refuge to cope with the scale of the displaced population. State and local governments, the U.S. Public Health Service within the Department of Health and Human Services (HHS), other federal agencies, academic institutions, private and nonprofit organizations, and an outpouring from the lay public contributed enormously to the immediate postevent response. The acts of heroism and dedication among the public health and medical communities during and immediately after hurricane Katrina are numerous (Berggren 2005; Raggi and Raggi 2005; Schwartz 2005).
An early report from a Centers for Disease Control and Prevention (CDC)/U.S. Environmental Protection Agency (EPA) team provided an initial overview of the environmental health issues in New Orleans in relation to safe rehabitation of the area (CDC/U.S. EPA 2005). Issues related to housing, debris removal, toxic chemicals, sewage treatment, safe drinking water, and occupational health headed the list. Although essential infrastructure and supporting services have been restored in some areas of New Orleans, this is not yet the situation in many of the hardest hit areas of the city.
Individually, these are all very complex and difficult problems. The housing stock is being systematically evaluated for structural integrity and viability, and a significant number will not be viable. The decision-making process for the housing stock will be affected by the financial impact of rebuilding requirements, such as the need to be above the flood plain, and by city-wide plans for low-lying neighborhoods.
Mold is ubiquitous and is present in almost unprecedented quantities in New Orleans (CDC Mold Workgroup 2005). This poses a critical health risk to returnees and particularly to sensitive populations. City government and health authorities responded by providing guidance and a wide array of educational materials to returning homeowners and physicians (CDC 2005; U.S. EPA 2005). Despite the emphasis on prevention and surveillance, concerns about mold-related symptoms have surfaced (“Katrina Cough”) and need sustained attention to assure that unexpected problems are identified and revised prevention messages are disseminated as quickly as possible.
The scope of the debris removal is so large that it has forced officials to consider the use of incineration and other volume-reduction strategies, such as grinding, as well as creating new landfills or reopening existing landfills. Environmental health authorities have emphasized best practices for preventing exposure to hazardous substances in sediment while conducting extensive sampling and focusing on localized hot spots of potentially significant exposure (Pardue et al. 2005; U.S. EPA 2005). Environmental groups have highlighted the hot spots and expressed concern about prolonged, close contact with sediments.
The drinking-water and sewage systems are also not fully operational. The damage to the central plants and to the extensive distribution and collection systems for the respective drinking-water and sewage systems are being systematically repaired but will take many more months for completion. Moreover, upgrades to these older systems need to be considered.
Successful recovery will require the breadth of vision and wisdom to link multiple environmental health solutions to the many broad decisions being made by governments, communities, industry, and other key stakeholders. The simultaneous restoration of all of these individual environmental health services presents a unique situation and governmental challenge. Clear and coordinated communication and outreach across stakeholders will play an important role in the recovery.
Disruption in medical care, loss of relatives and friends, stress and mental health concerns, ad hoc living arrangements, separation from home and community, financial ruin, and many other factors contribute to the difficulty in resuming “normal life” (Voelker 2005). Those with individual resources and professional skills will be more able to either return and rebuild or to integrate elsewhere, in spite of the challenges. The greatest concern will be for those with limited means and ability to start over on their own; who will have been in constant flux from shelters to hotels to trailer parks; and who will be facing an uncertain future. The individuals in the lowest-lying and most flood-prone and vulnerable areas are often those with the greatest difficulties in restoring, rebuilding, and resuming their lives; they will also have the greatest need for governmental and other assistance. Hurricane Katrina has so visibly reinforced the impact and need for addressing health disparities (Atkins and Moy 2005).
A fundamental question facing New Orleans, and influencing many other decisions, is how to make the city safe from future hurricanes. There is now intense focus on the levee system for New Orleans, and a number of groups have recently raised concerns about design flaws and other lapses in this system (Kintisch 2005; Seed et al. 2005). Can a better system be designed? What degree of protection will be provided (for a Category 3, 4, or 5 hurricane)? Can the low-lying parts of New Orleans be adequately protected? Are the financial resources and management wherewithal available to accomplish this in a timely manner? If not, what next? If yes, will these solutions work for the long term, given the predictions of sea level change by global climate theories? Or, more directly, will other major construction projects also be necessary to increase silt and sediment deposits for restoration of coastal wetlands and marshes (Stokstad 2005)? All of these are very difficult, but necessary, questions to answer.
There is a strong need and desire to maintain the essential character of New Orleans and other devastated Gulf Coast areas as they are restored and rebuilt. At the same time many environmental health proponents will also see the unique opportunities for applying the principles of smart growth to the rebuilt environment and wisely balancing out the old and the new.
This unprecedented disaster poses many difficult environmental health challenges. The skills and talents of many in our professional community will contribute to the recovery of New Orleans and the Gulf Coast region.
==== Refs
References
Anonymous 2005 Katrina reveals fatal weaknesses in US public health [Editorial] Lancet 366 867 16153993
Atkins D Moy E 2005 Left behind: the legacy of hurricane Katrina BMJ 331 916 918 16239669
Berggren R 2005 Hurricane Katrina. Unexpected necessities—inside Charity Hospital N Engl J Med 353 1550 1553 16221776
CDC 2005. Mold After a Disaster. Available: http://www.bt.cdc.gov/disasters/mold/ [accessed 5 December 2005].
CDC Mold Workgroup 2005 . Mold: Prevention Strategies and Possible Health Effects in the Aftermath of Hurricanes Katrina and Rita. Atlanta, GA:Centers for Disease Control and Prevention. Available: http://www.bt.cdc.gov/disasters/mold/report/ [accessed 9 December 2005].
CDC/U.S. EPA 2005. Environmental Health Needs & Habitability Assessment. Joint Taskforce. Atlanta, GA:Centers for Disease Control and Prevention and U.S. Environmental Protection Agency. Available: http://www.bt.cdc.gov/disasters/hurricanes/katrina/envassessment.asp [accessed 9 December 2005].
Coghlan A Mullins J 2005 The day their luck ran out New Sci 187 2156 8 9 16276666
Kintisch E 2005 Hurricane Katrina. Levees came up short, researchers tell Congress Science 310 953 955 16284149
Nieburg P Waldman RJ Krumm DM 2005 Hurricane Katrina. Evacuated populations—lessons from foreign refugee crises N Engl J Med 353 1547 1549 16221774
Pardue J Moe W McInnis D Thibodeaux L Valsaraj K Maciasz E 2005 Chemical and microbiological parameters in New Orleans floodwater following Hurricane Katrina Environ Sci Technol 39 8591 8599 16323752
Raggi P Raggi J 2005 Doctoring through Katrina: dedicated to all the people who suffered during a natural disaster Arch Intern Med 165 2458 2459 16314540
Schwartz D 2005 The NIEHS responds to Hurricane Katrina Environ Health Perspect 113 A722 16263489
Seed RB Nicholson PG Dalrymple RA Battjes J Bea RG Boutwell G 2005. Preliminary Report on the Performance of the New Orleans Levee Systems in Hurricane Katrina on August 29, 2005. Report No. UCB/CITRIS - 05/01. Berkley, CA:University of California at Berkley and American Society of Civil Engineers.
Stokstad E 2005 After Katrina. Louisiana’s wetlands struggle for survival Science 310 1264 1266 16311311
Travis J 2005 Hurricane Katrina. Scientists’ fears come true as hurricane floods New Orleans Science 309 1656 1659 16150980
U.S. EPA (U.S. Environmental Protection Agency) 2005. Hurricane Response 2005. Available: http://www.epa.gov/katrina/ [accessed 5 December 2005].
Voelker R 2005 Katrina’s impact on mental health likely to last years JAMA 294 1599 1600 16204652
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a0001316393637PerspectivesEditorialNote from the Editor: Looking Forward Burkhart James G. Acting Editor-in-Chief, EHP, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, E-mail:
[email protected] 2006 114 1 A13 A13 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
==== Body
With this issue EHP bids a warm goodbye to Tom Goehl, our Editor-in-Chief since October 2001. Tom is a true altruist, always believing that the role of EHP is to impact the human condition by providing a forum for scientific information to be used by researchers, policy makers, and individuals to improve human health around the world. His devotion, drive, and integrity in working toward this goal are unmatched. I have no doubt that Tom will continue to be a dedicated contributor to global environmental health, though he claims it may be from a secluded camping spot far, far away. Farewell and safe journeys!
The January 2006 cover introduces a new look for the face of EHP. Over the last few years we have received many compliments on the cover photography and graphic art appearing with each new issue. These images have always been designed to convey a visual link to an environmental health topic in each issue. The new covers will continue in this tradition with some modifications we hope will enhance usability, mainly by providing in the left margin a way for the reader to quickly scan major news and research topics in the issue. The cover titles will also be more descriptive. The Table of Contents will now appear solely inside the journal, leaving the back cover available for announcements or advertisements. As always, we welcome any comments and suggestions you may have.
The staff of EHP wishes each of you the very best in the coming year and success in meeting the challenges that will inevitably come our way. As a member of a dynamic global environmental health community, EHP will continually strive to provide you with the best information for improving human and global health.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a0001416393638PerspectivesDirector's PerspectiveEnvironmental Genomics: An Opportunity for the NIEHS Schwartz David A. MDDirector, NIEHS and NTP E-mail:
[email protected] 2006 114 1 A14 A14 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
==== Body
As I continue to consider new research opportunities for the NIEHS, my desire to support research in environmental genomics grows. While the accomplishments and available tools in genetics and genomics certainly enhance my enthusiasm for this field of research, my attraction to environmental genomics stems from my belief that environmental exposures can be used to understand the role of transcriptional regulation and genetic variation in the development and progression of common yet complex human diseases.
A growing body of research helps to illustrate the opportunities and challenges that lie before us. The influence of environmental exposures on transcriptional regulation of genes is clearly highlighted by the field of epigenetics. Michael Skinner at Washington State University and colleagues recently demonstrated the potential transgenerational adverse effects of intrauterine exposure to endocrine-disrupting pesticides on male fertility (Anway et al. 2005). Findings from Randy Jirtle’s laboratory at Duke University indicate that exposure through maternal diet to common methylating agents found in vegetables and vitamin supplements can have profound effects on gene expression in offspring that continue to be inherited in subsequent generations (Waterland and Jirtle 2003). Moreover, since monozygotic twins diverge in the concordance of methylation as a function of age (Fraga et al. 2005), it is abundantly clear that methylation is a dynamic process.
These findings underscore the role that intrauterine exposures could potentially have on common complex diseases that involve developmentally vulnerable organ systems. Such research also indicates that environmental exposures may serve as biological clues to understanding the regulation of gene expression and the role that transcriptional regulation may have on the risk of developing disease, as well as point to novel therapeutic interventions.
Environmental exposures can also be used to simplify complex biological processes to both discover unique biological mechanisms and narrow the pathophysiologic phenotype of complex human diseases. For instance, the discovery of the aryl hydrocarbon receptor (AhR) occurred as a direct result of the known toxicity of dioxin and polycyclic aromatic hydrocarbons. Not only did this discovery demonstrate the biological role of the AhR in mediating the toxicity to these agents, it also revealed the role of the AhR in homeostatic and basic pathophysiologic processes. Most importantly, however, the identification of the AhR led to the ultimate discovery of the PAS (PER-ARNT-SIMS) superfamily of receptors that mediate response to various forms of environmental stress such as hypoxemia and circadian rhythm, and control basic physiologic activities such as vascular development, learning, and neurogenesis (Kewley et al. 2004; Nebert et al. 2004).
Likewise, understanding of environmental exposures can simplify complex disease processes by narrowing the pathophysiologic phenotype to elucidate the genetics and biology that underlie a particular condition. For example, diseases such as asthma arise from dozens of etiologic agents. Since asthma caused or exacerbated by dust mites, endotoxin, or ozone involves different genes and different biological mechanisms, the disease can be better studied by focusing the investigation on a specific etiologic type of asthma.
Given that an extensive number of animal genomes have been sequenced and have demonstrated the evolutionary conservation of biology and genetic structure, comparative genomics will be an important tool for identifying the genes that control response to specific environmental agents, which in turn will accelerate our discoveries in environmental health sciences. For instance, the discovery of the importance of the toll-like receptors in innate immunity in mammals occurred as a direct result of the observation that a defective receptor in flies caused them to be much more susceptible to Aspergillus fumigatus (Lemaitre et al. 1996; Medzhitov et al. 1997). The ease with which we can observe and apply knowledge across model systems must be exploited so that we can efficiently understand the biological and clinical importance of environmentally responsive genes.
A clear challenge to the field of environmental health sciences will be to make the best use of environmental genomics to inform our understanding of the interaction between environmental exposures and genes in the development and progression of human diseases.
To facilitate progress in environmental genomics, we need to train young investigators in the discipline and support scientific programs that focus on biological and clinical problems that can most directly be solved by employing these novel conceptual and methodological approaches. However, to truly have an impact on human health, we need to extend these approaches to understanding chronic complex human diseases including cardiac disease, cancer, diabetes, chronic lung disease, and cerebrovascular disease. These diseases account for substantial morbidity and mortality worldwide, yet avoidable environmental exposures and reversible behaviors play a critical role in their development (Willett 2002). A clear challenge to the field of environmental health sciences will be to make the best use of environmental genomics to inform our understanding of the interaction between environmental exposures and genes in the development and progression of human diseases, and ultimately to translate this knowledge into effective prevention, intervention, and treatment strategies.
==== Refs
References
Anway MD Cupp AS Uzumcu M Skinner MK 2005 Epigenetic transgenerational actions of endocrine disruptors and male fertility Science 308 1466 1469 15933200
Fraga MF Ballestar E Paz MF Ropero S Setien F Ballestar ML 2005 Epigenetic differences arise during the lifetime of monozygotic twins Proc Natl Acad Sci USA 102 10604 10609 16009939
Kewley RJ Whitelaw ML Chapman-Smith A 2004 The mammalian basic helix-loop-helix/PAS family of transcriptional regulators Int J Biochem Cell Biol 36 2 189 204 14643885
Lemaitre B Nicolas E Michaut L Reichhart JM Hoffmann JA 1996 The dorsoventral regulatory gene cassette spatzle/Toll/cactus controls the potent antifungal response in Drosophila adults Cell 86 973 983 8808632
Medzhitov R Preston-Hurlburt P Janeway CA 1997 A human homologue of the Drosophila Toll protein signals activation of adaptive immunity Nature 388 394 397 9237759
Nebert DW Dalton TP Okey AB Gonzalez FJ 2004 Role of aryl hydrocarbon receptor-mediated induction of the CYP1 enzymes in environmental toxicity and cancer J Biol Chem 279 23847 23850 15028720
Waterland RA Jirtle RL 2003 Transposable elements: targets for early nutritional effects on epigenetic gene regulation Mol Cell Biol 23 5293 5300 12861015
Willett WC 2002 Balancing life-style and genomics research for disease prevention Science 296 695 698 11976443
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a0001716393640PerspectivesCorrespondenceThe Human Population: Accepting Species Limits Salmony Steven Earl Disability Determination Services, Raleigh, North Carolina, E-mail:
[email protected] author declares he has no competing financial interests.
1 2006 114 1 A17 A18 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
==== Body
In “The Population Equation: Balancing What We Need with What We Have,” Dahl (2005) presented generally accepted thought and consensually validated data regarding the human population, even though he did not include an adequate scientific theory of absolute human population numbers. Dahl also appeared to confirm the wide agreement among scientists that it is difficult to make theoretical advances or conduct human population research because humankind is seen as essentially different from other species and the human world is viewed as being composed of many intricately connected things that interact in extremely complex ways. Therefore, the population dynamics of Homo sapiens are effectively relegated to the preternatural realm and are believed to include a number of factors that are so complicated and enormous as to be unsuitable for empirical research or else unknowable.
A theory of human population numbers that could objectively explain the increase and decrease of the human population would be useful. Perhaps correlation data from Hopfenberg and Pimentel (2001) and the recent mathematical formulation of this biologic phenomenon by Hopfenberg (2003) provide a basis for an apparently unexpected theoretical perspective. According to the empirical research (Hopfenberg 2003), human population growth is a rapidly cycling positive feedback loop in which food availability drives population growth and this growth in human numbers gives rise to the mistaken impression that food production needs to be increased even more.
The data of Hopfenberg (2003) and Hopfenberg and Pimentel (2001) indicate that the world’s human population—all segments of it—grows by approximately 2% per year, including more people with brown eyes and more with blue eyes; more tall people and more short people; and more people who grow up well fed and more who grow up hungry. We may or may not be reducing hunger by increasing food production; however, we are most certainly producing more and more hungry people. The evidence suggests that the remarkably successful efforts of humankind to increase food production to feed a growing population results in even greater increase in population numbers.
Hopfenberg and Pimentel (2001) pointed out that the perceived need to increase food production to feed a growing population is a misperception, a denial of the physical reality of the space–time dimension. If people are starving at a given moment in time, increasing food production cannot help them. Are these starving people supposed to be waiting for sowing, growing, and reaping to be completed? Are they supposed to wait for surpluses to reach them? Without food they would die. In such circumstances, increasing food production for people who are starving is like tossing parachutes to people who have already fallen out of the airplane—the produced food arrives too late. However, this does not mean human starvation is inevitable.
If this view of the human population is somehow correct, then human population dynamics are not biologically different in essence from the population dynamics of other species (Hopfenberg and Pimentel 2001). We do not find hoards of starving roaches, birds, squirrels, alligators, or chimpanzees in the absence of food as we do in many civilized human communities today, because these nonhuman species are not annually increasing their own production of food. Among tribal peoples in remote original habitats, we do not find people starving. Like nonhuman species, “primitive” human beings live within the carrying capacity of their environment. History is replete with examples of early humans and other ancestors not increasing their food production annually, but rather living successfully off the land for thousands of years as hunters and gatherers of food. Before the agricultural revolution and the production of more food than was needed for immediate survival, human numbers supposedly could not grow beyond their environment’s physical capacity to sustain them because human population growth or decline is primarily a function of food availability (Hopfenberg 2003; Hopfenberg and Pimentel 2001).
Given its current scale and rate of growth, the human population worldwide has identifiable, potentially destructive ecological consequences. From this theoretical perspective, recent global human population growth can be understood as a primary causative factor of a range of phenomena including biodiversity loss and environmental degradation.
==== Refs
References
Dahl R 2005 The population equation: balancing what we need and what we have Environ Health Perspect 113 A598 Available: http://ehp.niehs.nih.gov/members/2005/113-9/ehp0113-a00598.pdf [accessed 3 September 2005].16140609
Hopfenberg R 2003 Human carrying capacity is determined by food availability Popul Environ 25 2 109 117
Hopfenberg R Pimentel D 2001 Human population numbers as a function of food supply Environ Dev Sustain 3 1 1 15
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a0001816393641PerspectivesCorrespondenceSources of Blood Lead in Children Brown Mary Jean Lead Poisoning Prevention Branch, Centers for Disease Control and Prevention, Atlanta, Georgia, E-mail:
[email protected] David E. U. S. Department of Housing and Urban Development, Washington, DCThe authors declare they have no competing financial interests.
1 2006 114 1 A18 A19 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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In their article on seasonality and children’s blood lead (BPb) levels, Laidlaw et al. (2005) stated that “lead-contaminated soil in and of itself may be the primary driving mechanism of child BPb poisoning in the urban environment.” We believe that the data presented by Laidlaw et al. (2005) do not support this conclusion and that they misrepresent the many other studies of childhood lead poisoning, which support a more comprehensive, validated approach.
To support their “soil-only” hypothesis, Laidlaw et al. (2005) made three primary arguments: a) soil lead represents a large and available reservoir of environmental lead; b) resuspension of lead from contaminated soil followed by inhalation of airborne particulate matter < 10 μm in diameter (PM10) and dust deposition on interior surfaces is the major source of lead exposure to children; and c) the major source of lead contaminated soil is fallout from the past use of tetraethyl lead in gasoline.
Laidlaw et al. (2005) did not cite the compelling body of scientific evidence demonstrating that deteriorated lead-based paint and the contaminated dust and soil it generates is highly correlated with BPb levels in children. These have been reviewed at length elsewhere (National Academy of Sciences 1993; Jacobs 1995; President’s Task Force on Environmental Health Risks and Safety Risks to Children 2000). Indeed, Laidlaw et al. failed to recognize the enlightened statutory definition of the term “lead-based paint hazard,” which includes not only deteriorated lead-based paint but also interior settled house dust and bare soil. Together, these constitute the principal exposure sources and pathways for most (but not all) children today (Residential Lead-Based Paint Hazard Reducation Act of 1992—Title X 1992). Furthermore, documented evidence shows that soil lead levels are highest in soil at the house drip line and greatly decrease farther away from the house, regardless of whether or not the house is in a rural area or city (Jacobs 1995).
Laidlaw et al. (2005) ignored confounding due to the coexistence of old, poorly maintained lead-painted housing and traffic congestion in urban areas. They failed to develop any rationale to exclude lead paint as a prominent source of lead exposure and should have included a measure of it in their models. Furthermore, they did not support their assumption that PM10 data can be used as a surrogate for airborne lead particulate. Laidlaw et al. should have used the more direct measures of airborne lead particulate levels, which are available from the U.S. Environmental Protection Agency’s (EPA) National Ambient Air Quality program (U.S. EPA 2004), rather than the convoluted indirect measures of particulate matter < 10 μm in diameter (PM10), soil moisture, and other variables.
Studies of the effectiveness of soil removal in urban residential areas without addressing deteriorated lead paint have demonstrated that the “soil-only” approach being recommended by Laidlaw et al. (2005) is of limited value (U.S. EPA 1996). Even in Superfund sites where old mining and smelter wastes have resulted in very high soil lead levels, efforts that do not also address deteriorated lead paint often are disappointing. Furthermore, in the largest and most recent study of lead-based paint hazard control (which addressed lead paint hazards in > 3,000 homes in a dozen jurisdictions), house dust lead levels remained below preintervention levels for at least 3 years following the intervention (National Center for Healthy Housing and University of Cincinnati 2004). In a smaller follow-up study, dust lead levels remained between 11% and 75% lower than baseline levels for 6 years following lead-based paint hazard intervention (Wilson J, Pivetz T, Ashley P, Jacobs D, Strauss W, Menkedick J, et al., unpublished data). If the contention of Laidlaw et al. (2005) is correct (i.e., that urban soil lead is being resuspended and deposited inside homes), dust lead levels should have increased after intervention in these studies. In fact, they did not. This directly contradicts the authors’ conclusions.
Finally, Laidlaw et al. (2005) erroneously cited a pooled analysis (Lanphear et al. 1998), which they believe supports their view that soil and dust lead are the most significant predictors of children’s BPb. In fact, the model used in that study also included paint lead and paint condition as variables. If the dust and soil lead terms are forced out of the model, paint lead becomes the most significant predictor, which is consistent with the now well-known pathway of paint to settled house dust and bare soil, to children’s hands, to ingestion through hand-to-mouth contact. The pooled analysis (co-authored by D.E.J.) cannot be used to justify Laidlaw et al.’s “soil-only” approach.
The latest figures from the National Health and Nutrition Examination Survey indicate that the enormous disparity in the prevalence of BPb levels > 10 μg/dL once seen between African-American and white children has diminished greatly [Centers for Disease Control and Prevention (CDC) 2005]. Overall, the number of children in the United States with excessive BPb levels has declined from 890,000 in 1991–1994 to 310,000 in 1999–2002. Much of this is the result of federal, state, and local efforts to create a reservoir of lead-safe housing in communities at greatest risk. This success is tempered by recent evidence that a safe BPb level for children has not been demonstrated. The lack of a safe threshold reinforces the realization that to prevent the adverse health effects caused by lead exposure, we must exercise the wisdom to recognize and address the many sources of lead in children’s environments. The reality is too complicated and the cost of failure too devastating to reduce this to a one-source solution.
==== Refs
References
CDC (Centers for Disease Control and Prevention) 2005 Blood lead levels—United States, 1999–2002 MMWR Morb Mortal Wkly Rep 54 513 516 15917736
Jacobs DE 1995. Lead paint as a major source of childhood lead poisoning: a review of the evidence. In: Lead in Paint, Soil and Dust: Health Risks, Exposure Studies, Control Measures and Quality Assurance (Beard ME, Allen Iske SD, eds). ASTM STP 1226. Philadelphia:American Society for Testing and Materials.
Laidlaw MAS Mielke HW Filippelli GM Johnson DL Gonzales CR 2005 Seasonality and children’s blood lead levels: developing a predictive model using climatic variables and blood lead data from Indianapolis, Indiana, Syracuse, New York, and New Orleans, Louisiana (USA) Environ Health Perspect 113 793 800 10.1289/ehp.7759 15929906
Lanphear BP Matte TD Rogers J Clickner RP Dietz B Bornschein RL 1998 The contribution of lead-contaminated house dust and residential soil to children’s blood lead levels: a pooled analysis of 12 epidemiological studies Environ Res 79 51 68 9756680
National Academy of Sciences 1993. Measuring Lead Exposure in Infants, Children, and Other Sensitive Populations. Washington DC:National Academy Press.
National Center for Healthy Housing and University of Cincinnati 2004. Evaluation of the HUD Lead-Based Paint Hazard Control Grant Program. Final Report. Washington, DC: U.S. Department of Housing and Urban Development. Aviailable: http://www.centerforhealthyhousing.org/HUD_National_Evaluation_Final_Report.pdf [accessed 21 June 2005].
President’s Task Force on Environmental Health Risks and Safety Risks to Children 2000. Eliminating Childhood Lead Poisoning: A Federal Strategy Targeting Lead Paint Hazards. Washington, DC: U.S. Department of Housing and Urban Development and U.S. Environmental Protection Agency. Available: http://www.hud.gov/offices/lead/reports/fedstrategy.cfm [accessed 5 December 2005].
Residential Lead-Based Paint Hazard Reducation Act of 1992—Title X 1992. Public Law 102-550. Available: http://www.hud.gov/utilities/intercept.cfm?/offices/lead/regs/leatilex.pdf [accessed 5 December 2005].
U.S. EPA 1996. Urban Soil Lead Abatement Demonstration Project: Vol I, EPA Integrated Report. EPA 600/P-93/001aF. Washington DC:U.S. Environmental Protection Agency.
U.S. EPA 2004. The Particle Pollution Report: Current Understanding of Air Quality and Emissions through 2003. Washington DC:U.S. Environmental Protection Agency. EPA 454-R-04-002. Available: http://www.epa.gov/airtrends/pm.html [accessed 28 July 2005].
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a00020PerspectivesCorrespondenceAnogenital Distance and Phthalate Exposure: Swan et al. Respond Swan Shanna H. University of Rochester, Rochester, New York, E-mail:
[email protected] Katharina University of Copenhagen, Copenhagen, DenmarkKruse Robin Stewart Sara University of Missouri-Columbia, Columbia, MissouriRedmon Bruce Ternand Christine University of Minnesota Medical School, Minneapolis, MinnesotaSullivan Shannon University of Iowa, Iowa City, IowaThe authors declare they have no competing financial interests.
1 2006 114 1 A20 A21 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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In their letter, McEwen and Renner raise several points that we would like to discuss.
First, because all infants in our study (Swan et al. 2005) appeared normal, McEwen and Renner infer that there is no evidence of an adverse effect. However, the absence of evidence of an effect in infancy does not preclude serious adverse effects in later life. For example, the genital cancers that were identified in young women, on average 19 years after their prenatal exposure to the drug diethylstilbestrol, were seen in females who had appeared to be completely normal until that time (Herbst et al. 1971). In this case, unlike that example, we do have some evidence of anatomical changes in young boys. Although anogenital distance (AGD) has rarely been used as a measure of androgen action in humans, our data suggest that shortened AGD reflects reduced androgen action in utero; AGD was correlated with the degree of testicular descent and penile volume, and children with smaller AGD tended to have smaller scrotums; these are all signs of reduced androgen action.
McEwen and Renner state that the range of AGD reported in our study (Swan et al. 2005) is likely to be representative of normal study subjects. In fact, this information is not yet available because this is the first population-based study that utilized this measurement. AGD has, however, been used in the diagnosis of medical conditions such as congenital adrenal hyperplasia, in which AGD in females is increased by excess androgen exposure (Callegari et al. 1987). AGD is also known to be sexually dimorphic in humans as well as rodents (Salazar-Martinez et al. 2004).
McEwen and Renner point out that one previous study [n = 42; (Salazar-Martinez et al. 2004)] used an alternative measure of AGD in human infants. However, as we indicated in our article (Swan et al. 2005), this alternative definition is less precise than the one we used and does not correspond to the measure of anogenital distance most frequently used in toxicologic studies of rodents. Our use of this measure of AGD emphasizes the correspondence between traditional toxicology studies and our study.
In our study (Swan et al. 2005) we did not have data that would allow us to consider parental phenotype (e.g. parental height or father’s AGD), as McEwen and Renner suggest should be done. If AGD was affected by parental stature (through infant body size), this association should be controlled for by adjusting for body size. Moreover, in order for a phenotypic variable to explain the observed association, it too would have to be related to maternal phthalate levels. This, too, would be an interesting finding.
McEwen and Renner question the use of normalizing AGD by dividing by weight (AGI) at examination. We examined several alternative measures of body size and, as discussed in our article (Swan et al. 2005), AGI provided the best fit to the data (independent of phthalates). Vanderbergh and Huggett (1995) found the same to be true in rodents. The fact that there was some variation of AGI with age is to be expected; not all 1-year-olds have the same length, either.
McEwen and Renner point out potential sources of “exposure misclassification” which, we agree, may have been present (and we stated so) (Swan et al. 2005). However, unless these sources of measurement error were related to AGD, their presence would lead to underestimates of the strength of the associations we presented.
We examined a number of potential confounders, such as maternal smoking and alcohol consumption; the prevalence of both was quite low (Swan et al. 2005). None affected results appreciably. Of course, the phantom “unmeasured confounder” always lurks in the wings of any observational study, can never be ruled out, and is a favorite of critics of epidemiologic studies. Any constructive suggestions for alternatives to observational studies would be appreciated; the only alternative we know of, randomizing pregnant women to receive phthalates (or not), hardly seems ethical.
Rodent studies test only one phthalate at a time. As we demonstrated (Swan et al. 2005), women were exposed to measurable levels of multiple phthalates, many known to be reproductively toxic. Until we have data on the toxicology of this complex mixture, we do not have the information to draw conclusions about the relative toxicity of these compounds in rodents versus humans. Furthermore, although doses in rodent studies of specific phthalates are high, effects have been demonstrated at lower doses used in recent studies (Lehmann et al.). Unfortunately no toxicologic study has yet examined effects of phthalates at environmental levels. Because we did find a significant association with phthalates at such levels, we can only conclude that environmental levels, however low, are associated with somatic alterations in humans.
Our study (Swan et al. 2005) is relatively small and must be replicated; subsequent studies will undoubtedly eliminate many of the sources of potential exposure and outcome misclassification. Nonetheless, in this first study of its kind, we set out to test the hypothesis, suggested by a large toxicologic literature (Gray et al. 2000), that prenatal phthalate exposure is associated with several measures in humans that reflect the antiandrogenic action of these chemicals. Using similar outcome measures to those utilized in these toxicologic studies, that is what we found.
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References
Callegari C Everett S Ross M Brasel JA 1987 Anogenital ratio: measure of fetal virilization in premature and full-term newborn infants J Pediatr 111 240 243 3612396
Gray LE Jr Ostby J Furr J Price M Veeramachaneni DNR Parks L 2000 Perinatal exposure to the phthalates DEHP, BBP, and DINP, but not DEP, DMP, or DOTP, alters sexual differentiation of the male rat Toxicol Sci 58 350 365 11099647
Herbst AL Ulfelder H Poskanzer DC 1971 Adenocarcinoma of the vagina: association of maternal stilbestrol therapy with tumor appearance in young women N Engl J Med 284 878 881 5549830
Lehmann KP Phillips S Sar M Foster PM Gaido KW 2004 Dose-dependent alterations in gene expression and testosterone synthesis in the fetal testes of male rats exposed to di (n -butyl) phthalate Toxicol Sci 81 1 60 68 15141095
Salazar-Martinez E Romano-Riquer P Yanez-Marquez E Longnecker MP Hernandez-Avila M 2004 Anogenital distance in human male and female newborns: a descriptive, cross-sectional study Environ Health 3 8 10.1186/1476-069X-3-8.15363098
Swan SH Main KM Liu F Stewart SL Kruse RL Calafat AM 2005 Decrease in anogenital distance among male infants with prenatal phthalate exposure Environ Health Perspect 113 1056 1061 10.1289/ehp.8100 [Online 27 May 2005]. 16079079
Vandenbergh JG Huggett CL 1995 The anogenital distance index, a predictor of the intrauterine position effects on reproduction in female house mice Lab Anim Sci 45 567 573 8569159
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a00021PerspectivesErrataErrata 1 2006 114 1 A21 A21 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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In the October articles “Children’s Centers Study Kids and Chemicals” [
Environ Health Perspect 113:A664–A668 (2005)] and “Are EDCs Blurring Issues of Gender?” [
Environ Health Perspect 113:A670–A677 (2005)], photographs and their captions erroneously imply that plastic drink bottles contain ortho-phthalates. Plastic drink bottles sold in the United States are made from polyethylene terephthalate and do not contain ortho-phthalates. Also, at the end of the EDCs article, references are made to plastic wrap and Saran Wrap. For clarification, neither plastic wrap nor Saran Wrap contains ortho-phthalates. EHP regrets these errors.
EHP regrets the incorrect and unintentional inference in “Paving Paradise: The Peril of Impervious Surfaces” [
Environ Health Perspect 113:A456–A462 (2005)] that coal tar pitch is used in the actual hot-mix asphalt used to pave roads. Coal tar pitch is instead used in many sealcoat formulations used atop asphalt pavement. Findings published in the 1 August 2005 issue of Environmental Science & Technology suggest, in fact, that coal tar-based parking lot sealant may be a major contributor to stream loads of polycyclic aromatic hydrocarbons, including many known carcinogens.
In Figure 1 of the article by Chen et al. [
Environ Health Perspect 113:1723–1729 (2005)], the legend should have read (A) PM10; (B) PM2.5, instead of (A) PM2.5; (B) PM10.
In Figure 1 of the article by Tsan et al. [
Environ Health Perspect 113:1784–1786 (2005)], the double bond between HN and boron was incorrect. The corrected figure appears below.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a0002416393643EnvironewsForumFood Safety: Allergen Labeling Takes Effect Dahl Richard 1 2006 114 1 A24 A24 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Since 1994 food manufacturers have been required to list all the ingredients on their products’ labels. A new law now takes this obligation a step further, requiring manufacturers to notify consumers in “plain language” of certain allergens contained in their products. This is good news for the estimated 11 million Americans who have food allergies. But some question whether the new labels might be too much of a good thing.
The Food Allergen Labeling and Consumer Protection Act of 2004, or FALCPA, applies to foods labeled on or after 1 January 2006. It mandates that the nutritional labels on food packages plainly identify any of eight specified food allergen sources— milk, eggs, fish, crustacean shellfish, tree nuts, peanuts, wheat, and soybeans— that are present in the product. Together, these eight food categories account for about 90% of all food allergies. The law stipulates that the warning label be placed near the ingredient list.
Stephen L. Taylor, who heads the Food Processing Center at the University of Nebraska–Lincoln, lauds the “plain language” requirement as an overdue development. “In the past, you’ve seen terms like ‘casein’ and ‘whey,’” he says. “Consumers often had to learn the hard way that those terms are synonymous with ‘milk.’”
But while the new law makes the presence of certain allergens in food products more understandable, Taylor also contends that the act is too strict in requiring that allergens be listed if they are present in the faintest traces. For example, he says, the law requires the listing of not only ingredients but also processing aids that may include allergens, such as soybean lecithin, which is used by baking companies as a stick-release agent for pans.
“My view is that in this particular application the exposure to soybean allergens is extremely low, but with the new labeling requirements you’re going to be advising all soy-allergic individuals not to eat the vast majority of bakery products,” Taylor says. “And I don’t think that’s particularly in their best interests.”
The law makes clear that decisions about allergen labeling for food products will be an ongoing process. It requires that the Secretary of Health and Human Services provide a report to Congress in February 2006 that’s to include information about unintentional contamination of foods with allergens stemming from equipment that is used for multiple food processes. In addition, the U.S. Food and Drug Administration has created the Threshold Working Group to examine approaches that could be used to establish thresholds below which manufacturers would not be required to list food allergens.
Anne Muñoz-Furlong, founder and chief executive officer of the Food Allergy & Anaphylaxis Network (FAAN), a nonprofit educational organization, considers the law an important step. “With food allergies, there’s no cure,” she explains. “[Allergic] individuals depend on other people, whether in a restaurant or the food industry, to provide accurate information so they can make the right choices.”
According to figures from FAAN, each year some 30,000 Americans require emergency room treatment for allergic reactions to food, and 150 to 200 people die from such reactions. Furthermore, the number of people with food allergies is increasing around the world.
Of particular concern to many food allergists is the sharp increase of food allergies in children. According to A. Wesley Burks, a professor of pediatrics at Duke University Medical Center, peanut allergies have doubled over the last decade among children under the age of five.
Nobody really knows why allergies are on the rise. One theory holds that improved hygiene leaves the human immune system with less to do, Muñoz-Furlong says, so it identifies a particular food as dangerous and responds by attacking it.
Muñoz-Furlong believes that the next step in the development of allergen labeling should be to create binding guidelines for what is currently the voluntary use of “precautionary labeling,” which warns of the possibility that an allergen might be present as the result of shared production processes. As for the longer-term issue of how to establish threshold levels, Muñoz-Furlong says that most of the parents of food-allergic children she’s talked to believe the answer is simple: “They want zero. They don’t want to risk that their child might be in that small percentage of the population that’s below the threshold.”
Plain talk about allergens.
New labeling requirements should make it easier for allergic consumers to tell if a food is safe for them to eat. Next up? Some suggest codifying the now-voluntary use of precautionary labeling (large photo).
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a00027EnvironewsForumEHPnet: CDC: Environmental Concerns After Hurricane Katrina NIEHS: Natural Disaster Response Dooley Erin E. 1 2006 114 1 A27 A27 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Since Hurricane Katrina struck the U.S. Gulf Coast on 29 August 2005, Americans have sought reliable information on how to safely reenter flood-damaged environments. The U.S. Department of Health and Human Services (DHHS) has been at the forefront of the effort to assist those affected by this disaster. Two DHHS agencies, the Centers for Disease Control and Prevention (CDC) and the NIEHS, have developed websites offering information on dealing with post-hurricane conditions.
The CDC page, located at http://www.bt.cdc.gov/disasters/hurricanes/environmental.asp, gives visitors access to information from both the CDC and the U.S. Environmental Protection Agency (EPA). The site contains a 38-page report, released on September 17, summarizing an environmental health needs and habitability assessment of the city of New Orleans conducted by these two agencies. The report provides conclusions about the habitability of the city as well as recommendations on how best to go about allowing citizens to repopulate the city. There is also a health consultation on the Murphy Oil Company spill, which released 25,110 barrels of mixed crude oil into the area around Meraux and Chalmette, Louisiana.
The site also includes several documents to guide residents as they resume life along the Gulf Coast. There is basic information on cleaning up mold, disinfecting wells, protecting oneself from debris smoke, avoiding carbon monoxide, dealing with animal and insect hazards, and managing chemicals released during flooding. The mold cleanup section also links to other information sources, some of which are available in Spanish and Vietnamese (many Vietnamese have settled along the Gulf Coast since the 1950s). For response and cleanup workers there are links to federal guidelines and recommendations on personal protective equipment, cleaning HVAC systems, and handling and burning hurricane debris.
The NIEHS Natural Disaster Response page is located at http://www-apps.niehs.nih.gov/katrina/. The page features geographic information system (GIS) maps that the NIEHS and its academic partners created that identify chemical plants, refineries, Superfund sites, and other potential sources of contamination. It also contains satellite images of the areas affected by the hurricanes. In the future, the section will feature a functional set of GIS layers that will let visitors customize their own maps. These images can help decision makers and others in identifying sources and routes of contaminants, analyzing the potential for future exposures, assessing human exposures in the immediate aftermath of the hurricanes, and predicting long-term health impacts linked with these exposures.
The Questions and Answers page brings together resources from several federal agencies to answer frequently asked questions about mold, sewage, and seafood consumption. This page also contains information on the NIH Katrina Call Center, available at 1-866-887-2842, which provides round-the-clock medical consultation by telephone to anyone affected by Hurricane Katrina.
The NIEHS Program Resources section of the page has links to four programs that the NIEHS had in place long before the disaster struck, which are now being called into action. One of these, the Worker Education and Training Program, offers a PowerPoint presentation for cleanup workers titled Protecting Yourself While Helping Others, developed jointly by the NIEHS and other federal agencies to guide those responding to the storms of 2005. This presentation is also available in Spanish and Vietnamese. Visitors can also find safety posters for responders, guidelines for the protection and training of mold cleanup workers, and other checklists, safety plans, and materials.
As a service to NIH- and NIEHS-funded researchers at flooded universities, this site provides links to information for grantees affected by Hurricane Katrina, including notices from the NIH Guide.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a0002816393644EnvironewsNIEHS NewsNIEHS Responds to Katrina Twombly Renée 1 2006 114 1 A28 A29 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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NIEHS director David Schwartz knows firsthand what the country’s worst natural disaster looks like. Within days of Hurricane Katrina’s winds and waves, he led an advance medical team of 50 physicians, nurses, and health care workers from the NIH, the NIEHS, and Duke University Medical Center to Mississippi to respond to the disaster. There he found “nothing short of what one would expect in a war zone,” as he wrote on the NIEHS website when he returned two weeks later. The extent of destruction was “overwhelming, with cars upturned, tractor trailers scattered like matchsticks, homes completely leveled, buildings destroyed.”
Schwartz was just one of many NIEHS specialists who were, and in some cases still are, part of the largest disaster response mobilization in U.S. history. The institute’s response to Katrina involved quick, extensive planning and organization within the NIEHS and across a span of sister agencies, such as the NIH, the Environmental Protection Agency (EPA), the Occupational Safety and Health Administration (OSHA), the Centers for Disease Control and Prevention (CDC), the Department of Defense, the Food and Drug Administration, the U.S. Department of Agriculture (USDA), and the Department of Homeland Security and Federal Emergency Management Agency (FEMA).
“Katrina was an environmental health catastrophe, and [Hurricane Rita a month later] just added to the damage,” says Allen Dearry, the NIEHS associate director for research coordination, planning, and translation, who has acted as the institute’s response coordinator. “The institute’s expertise is connecting environmental exposure to human health, and there are bigger questions as the result of this natural disaster than we have encountered before.”
Immediate Response on Many Fronts
The NIEHS went into action shortly after Katrina hit. On August 31, the day after the New Orleans levees broke, Joseph “Chip” Hughes and the team he directs at the NIEHS Worker Education and Training Program (WETP) developed a PowerPoint safety awareness training primer for first responders and posted it on the NIEHS website. The group had produced 11 versions of the primer by October 27, updated as the scope of the disaster unfolded to include information on such health threats as trench foot, waterborne diseases, and mold. The primer—available in English, Spanish, and Vietnamese (since there are many Vietnamese in the Gulf Coast region)—has been downloaded at least 1,600 times, and more than 35,000 printed copies have been distributed. The WETP team has also delivered hands-on hazards training to federal employees and federally employed contractors in the field in Mississippi, Louisiana, Alabama, and Texas.
Just as human health was at risk, so was that of the animals left stranded by the hurricane. Starting September 7, William Stokes, director of the National Toxicology Program (NTP) Interagency Center for the Evaluation of Alternative Toxicological Methods, who also serves as the chief veterinary officer of the U.S. Public Health Service, headed the federal effort to assist with the rescue and shelter of those animals. Stokes led an initial team of 10 veterinarians and a public health nurse whose number quickly doubled to meet the overwhelming needs of two emergency animal shelters, one located on the Louisiana State University Baton Rouge campus, and one at a livestock exposition center in Gonzales. The shelters’ residents included carriage horses from New Orleans, a pet alligator, an eight-foot-long python, pot-bellied pigs, birds, turtles, and a variety of other pets. A total of 35 Public Health Service veterinarians and countless volunteers examined and treated more than 5,000 creatures, inserted identifying m i c r o c h i p s , t o o k photographs, and moved many of the animals out to other shelters to await their owners.
“In addition to keeping all of these animals healthy, our goal was to ensure that as many as possible were returned to their owners in order to avoid further stress from the pet loss on top of all their other losses,” says Stokes. He adds that in the future, he hopes evacuation policies will allow for animals to accompany their owners.
Meeting of the Minds
As the extent of the disaster unfolded, the NIEHS continued to send out experts to assist other federal agencies. Mary Wolfe, director of the NTP Office of Liaison and Scientific Review, was sent to CDC headquarters in Atlanta for five days in mid-September to help assimilate field data from teams along the Gulf Coast who were assessing emerging health threats. Sam Arbes, an epidemiologist in the NIEHS Laboratory of Respiratory Biology who studies the health effects of mold, went to Baton Rouge with a CDC team to prepare a document that helped local and state officials assess environmental damage and public health issues as they planned for reentry of residents and restoration. The document addressed public health issues associated with drinking water, sewage disposal, roads and transportation, toxic exposures, housing, and schools, among other things.
NIEHS-funded environmental health sciences centers also swung into action. Immediately after the hurricane, Schwartz asked the center directors to work collaboratively to define the research questions that would surround the effects of the hurricane and the recovery of the population. Five working groups within the centers program addressed issues of worker surveillance and health, water quality and microbes, water quality and chemical contamination, mold and respiratory consequences, and outreach and education for the affected populations. The groups have since provided Schwartz with a critical assessment of the research questions that could be addressed.
Some action has begun. Staff from the centers’ Community Outreach and Education Programs have banded together to create educational and outreach materials about the hazards that the populace may find in their homes [see “COEPs Contribute to Hurricane Relief,” next article]. Centers will also be conducting pre-and postdeployment blood sampling and analysis of New York City firefighters deployed to help the relief efforts in New Orleans. And key experts from the centers have been invited by groups such as the American Red Cross to consult on environmental problems in the region that arose from the storms. They have done some sampling of water, molds, and sediment in the region.
Back home, institute staff developed an NIEHS Natural Disaster Response website to disseminate information to workers and residents about conditions in the Gulf Coast [see the EHPnet article, p. A27 this issue]. Dearry acted as a liaison with call centers set up by the NIH and the CDC, providing information on human and environmental health issues to pass along to callers. The call centers initially took calls just from health care providers, state and local environmental and health agencies, clinics, and other providers, but were soon opened to calls from the public as well.
Long-Term Study of Environmental Health Risks
Some of the NIEHS disaster response efforts are unique programs that will help identify the environmental hazards produced by Katrina as well as provide long-term insights into the link between environmental toxicants and health outcomes. For example, the NIEHS website features a geographic information system (GIS) database that is designed to help expedite cleanup efforts, but which can be continually developed and updated as a tool to track environmental health.
Led by William Suk, director of both the NIEHS Center for Risk and Integrated Sciences and the Superfund Basic Research Program, the GIS overlays maps and high-resolution aerial photography of Texas, Louisiana, and Mississippi with a wealth of demographic, hydrographic, infrastructure, and industrial/agricultural data from publicly available sources. With the assistance of NIEHS academic partners at Duke University and the University of California, San Diego, supercomputing center, the interactive maps pinpoint the location of Superfund sites (four in New Orleans alone), scores of Toxics Release Inventory–reporting sites (those that release toxic contaminants), and the hundreds of oil and gas rigs, gas stations, chemical industries, refineries, and crude petroleum and natural gas operations in the Gulf Coast region.
Information now being collected on water and air sampling in the area will be added as a way to model the movement of contaminants and identify sources of human exposure. For example, one-quarter of the areas sampled by the EPA in New Orleans by late September showed benzene levels that were more than twice the NTP intermediate safety level. And there were hundreds of reported oil and toxicant spills—including gas that may have seeped from an estimated 350,000 swamped cars—as well as drowned industrial and toxic waste dumps. Suk and his team of institute scientists and academic partners are working 14 to 20 hours a day to pull in data from federal agencies such as the EPA, the CDC, and OSHA in order to create what he calls a “national model that can track environmental health, both for the short-term use of responders and cleanup crews and long-term assessment of health consequences.” The model is available on the NIEHS Natural Disaster Response website.
Among the resources they are tapping are the Centers for Oceans and Human Health, supported jointly by the NIEHS and the National Science Foundation. The four centers have been sampling and analyzing floodwaters from New Orleans, and received $150,000 in National Science Foundation “rapid response” funding to collaboratively investigate the health of Lake Pontchartrain, into which 100 billion liters of New Orleans floodwater has been pumped. Researchers at these centers will sample and document the presence, abundance, and fate of waterborne pathogens such as Escherichia coli and Vibrio vulnificus (which produces a cholera-like infection and is already responsible for deaths in the area) as well as heavy metals and other toxicants in the pollution plume entering Lake Pontchartrain and beyond. They will also monitor the development of harmful algal blooms that could result from matter pumped into the lake. The information will then be linked to the GIS database.
Frederick Tyson, who administers the Centers for Oceans and Human Health program, says, “We have galvanized the talents we have to give us important answers to a public health crisis that is happening right now and that will impact public health in that region.” Suk adds that Katrina has offered “an experiment that no one wanted but which we now have in place to study real problems that will allow us to gain a better understanding of environmental health risks.”
At the ready.
NIEHS staff came soon after Katrina hit to help at a 500-bed field hospital in a Meridian, Mississippi, hangar.
Saving man’s best friends.
Bill Stokes and a team of vets and volunteers helped stranded pets.
Rebuilding safely.
The NIEHS WETP has developed a primer to guide construction and cleanup workers in rebuilding the Gulf Coast in a safe manner.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a0003016408360EnvironewsNIEHS NewsBeyond the Bench: COEPs Contribute to Hurricane Relief Tillett Tanya 1 2006 114 1 A30 A31 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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The conditions in Louisiana and Mississippi following Hurricanes Katrina and Rita reminded us all of our commonality in the human experience and moved many to help. Among those moved to help were the staff at the Community Outreach and Education Programs (COEPs) of NIEHS Centers across the country. Responding to communities in need is one of the primary functions of the COEPs, so providing outreach to those areas on the Gulf Coast impacted by the hurricanes seemed a natural step to take.
“When our director volunteered our COEP [to lead efforts], we remarked that if COEPs had never existed, they would have had to be invented on August 28,” says Pamela Diamond, director of the NIEHS Center COEP at University of Texas Medical Branch (UTMB) in Galveston. Adds Robin Fuchs-Young, director of the COEP of the Center for Research on Environmental Disease at the University of Texas, M.D. Anderson Cancer Center (UTMDACC), “All of us saw what was happening on television and felt compelled on a human level to help in whatever way we could.”
A Helping Hand
Says Diamond, “Most of the community outreach directors and staff across the country knew one another and trusted one another, and we could quickly organize a response. It was quite a pickup operation—cell phone calls, e-mails in the middle of the night. During our own evacuation due to Hurricane Rita, we sat on [Fuchs-Young’s] back porch, planning supply deliveries, editing public service announcements [PSAs], and identifying scientists in distant states to provide reliable information and data for flyers.”
Two teams from the UTMB COEP were dispatched in early October with different objectives. One team, led by Diamond, connected with shelters in rural LaFourche Parish and delivered humanitarian supplies including first aid equipment, diapers, and drinking water. The other team covered a wider range including Calcasieu, Jefferson, Orleans, Terrebonne, and LaFourche Parishes, as well as Baton Rouge and New Iberia and Port Arthur, Texas, to contact community-based environmental organizations whose operations had been disrupted by the hurricanes. These groups were asked how the events had disrupted their normal functions, what environmental damage they observed, what they saw as the greatest environmental threats facing residents on reentering impacted areas, and how they could unite their skills and networks with scientific and clinical expertise. These interviews were compiled in a DVD format and are being sent to the directors of each COEP and interested personnel at the national level.
The UTMB COEP is also collaborating with the Louisiana Environmental Action Network in funding the preparation and delivery of re-entry hazard protection kits for residents involved in recovery operations. These kits focus on mold and toxic residue hazards and—along with information prepared by the NIEHS, the Centers for Disease Control and Prevention, and the Federal Emergency Management Agency—aim to mitigate citizen exposures.
Education for the Re-entry Process
The COEPs also recognize that the devastated areas will need resources to help them deal with the long-term environmental aftermath of the hurricanes. Soon after Katrina hit, reports indicated high levels of arsenic and lead in the floodwaters and severe mold contamination. The programs joined forces to provide long-term outreach, and divided into areas of strongest expertise to develop fact sheets offering clear, useful information for citizens in the affected areas.
“The strong desire to return families to their homes and to rebuild neighborhoods needs to be balanced with care to do things right,” says Ruth Woods, program administrator of the Center for Child Environmental Health Risks Research and the Pacific Northwest Center for Human Health and Ocean Studies, both at the University of Washington (UW). “Environmental cleanup needs to be a high priority so that people are not made ill from [environmental exposures].”
The COEPs from UW, the Kresge Center for Environmental Health at Harvard University (in conjunction with Columbia University), the University of Iowa Environmental Health Sciences Research Center (EHSRC), and the Wayne State University Environmental Health Sciences Center in Molecular and Cellular Toxicology with Human Applications have developed fact sheets addressing various elements of returning home safely. Topics include lead and arsenic contamination from floodwaters, mold hazards, and safe cleanup procedures.
Some of the fact sheet material is based on Katrina-specific studies. Peter Thorne, director of the University of Iowa COEP, says members of his group have collected air and surface samples from water-damaged homes in New Orleans. One study showed that the mean airborne endotoxin concentration was 200-fold higher than in nonflooded homes, and levels of airborne mold spores were so high that N95 respirators—devices with a filter efficiency of 95%–are inadequate protection. Thorne says the fact sheets his working group created describe mold hazards and instruct residents on precautions necessary for safe re-entry and cleanup.
To date, the COEPs have distributed more than 67,000 flyers to local leaders in the storm-damaged area. “We are hoping that other . . . COEPs have information on the same or other topics that can be developed into flyers,” says Lisa Pietrantoni, project coordinator for the Wayne State COEP.
What was particularly gratifying about the flyer effort was how clearly the flyers were needed. “I often encountered someone in a shelter who told me they had mold re-entry flyers,” says Diamond. “When we looked at them, they were the flyers that had been created at UW or Wayne State, . . . copied by shelter workers, and passed down the line.”
The COEPs are also using PSAs to get safety information out to residents. The program at the University of New Mexico Center for Environmental Health Sciences produced six PSAs on topics such as safe cleanup methods, water safety, and toxics, and is working with American Forum, a nonprofit media company, to disseminate them to over 3,000 radio, television, and print media outlets in the Gulf Coast area. The UTMDACC COEP is developing PSAs for especially susceptible groups of people, including immunocompromised patients. Still more PSAs may be developed to target specific regional issues and incorporate data that emerge from environmental health studies being conducted. Spanish-language PSAs might also target workers doing the repairs and rebuilding.
More to Be Accomplished
At the NIEHS Core Centers Annual Meeting held this fall at the Vanderbilt University Center in Molecular Toxicology, COEP staff discussed their outreach efforts and looked ahead to some next steps, such as community forums, town hall sessions, and continued data collection. They concluded that there is still much environmental health aid these towns and cities will need.
One potential partnership that could help the COEPs offer some long-term solutions is the Katrina Environmental Research and Restoration Network (KERRN), a vision conceived by John McLachlan, director of the Center for Bioenvironmental Research at Tulane and Xavier Universities in New Orleans. According to McLachlan, KERRN is “a network of researchers sharing data and ideas, crossing disciplinary, geographical, and institutional boundaries, providing models to respond to and recover from major environmental disasters.” The network, funded by a grant from the National Science Foundation, could be a great help for the residents in the affected area. As Fuchs-Young notes, “Folks in the Gulf Coast want science and data. They want to know what’s going to happen to their water supply and wetlands, and what will be the effect of flooded toxic waste dumps on their lives and livelihoods.”
The communities located throughout the Gulf Coast have a long road ahead of them. There is no question in the minds of most that they can and should rebuild; many have lived in this area for generations, and don’t want to change their way of life. But environmental health experts caution that much care must be taken because of the health threat that contaminants like mold can pose. States Thorne, “There remains extensive remediation work [in the Gulf Coast area] that will expose residents and contractors to mold hazards. The potential for allergy, asthma, and lung infections is high due to the enormous concentrations encountered. It is critically important that residents of Louisiana and Mississippi are protected from these exposures.”
Pitching in.
Center staff stepped in at several points, including taking water samples (left, at the 17th Street Canal) and helping area victims sign up for assistance and humanitarian aid (above, at the LaRose community shelter).
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a00031EnvironewsNIEHS NewsHeadliners: Immune Response: Lead Disrupts T Cell Function Tillett Tanya 1 2006 114 1 A31 A31 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Farrer DG, Hueber SM, McCabe MJ Jr. 2005. Lead enhances CD4+ T cell proliferation indirectly by targeting antigen presenting cells and modulating antigen-specific interactions. Toxicol Appl Pharmacol 207:125–137.
Although lead has been banned from use in products like house paint, gasoline, and water pipe solder in the United States, it is still present in older housing, and is used in products in other countries. Besides its widely studied neurotoxicity, lead is also a well-known immunotoxicant, though little is known about its mechanism of action. Now NIEHS grantee Michael McCabe and colleagues at the University of Rochester have discovered how lead may work to disturb T cell function in the body.
Previous studies have suggested that lead’s immunotoxic effects may occur at exposures even lower than those required for neurotoxicity to occur; thus, suboptimal immune function may affect people who do not even realize they have been exposed to lead. Older adults and lactating, pregnant, and postmenopausal women are at greater risk for lead exposure as lead stored in the bones is released back into the blood and soft tissues. Children are also at heightened risk for lead exposure because they engage in more hand-to-mouth activity and absorb a larger proportion of ingested lead across the intestinal epithelium than do adults.
The Rochester researchers used flow cytometry to analyze T cell division in cell cultures derived from lead-treated mice. T cells help regulate the body’s immune system by attacking bacteria, viruses, foreign tissue, and tumor cells. At day 4 of treatment, the frequency of proliferating T cells was much greater in treated than in nontreated cultures. Lead appeared to target a type of cell known as antigen presenting cells, and its effect was based on specific peptide-major histocompatibility complex conjugate. The results suggest that lead may pose even more long-term health threats than originally thought.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a0003216393645EnvironewsFocusIn Katrina’s Wake Manuel John 1 2006 114 1 A32 A39 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Hurricane Katrina has been called the most devastating natural environmental calamity in U.S. history. Visitors to the scene say the destruction is worse than anyone can imagine. Scientists also say that some perceived health threats have been overblown and others understated. Months after Katrina roared into the Gulf Coast, the environmental health implications of the storm are still being assessed.
Katrina presented residents of the Gulf Coast with a bewildering array of environmental health hazards. Aside from standing floodwater, hazards included a lack of potable water, sewage treatment, and electricity; chemical spills; swarms of insects (with anecodotal accounts of vermin and hungry domestic dogs); food contamination; disrupted transportation; mountains of debris; buildings damaged and destroyed; rampant mold growth; tainted fish and shellfish populations; and many potential sources of hazardous waste. Some impacts, such as deaths from drowning and injuries from cleaning up debris, have been relatively easy to determine. Others, such as post-traumatic stress disorder from the loss of homes and loved ones, may never be fully quantified.
In the weeks following the storm, federal agencies such as the NIEHS, the Centers for Disease Control and Prevention (CDC), and the Environmental Protection Agency (EPA), as well as state environmental and public health agencies, sent scientists to the region to begin assessing the environmental and human health impact of the disaster. Much of what they found was presented on October 20 at a meeting of the National Academies Institute of Medicine’s Roundtable on Environmental Health Sciences, Research, and Medicine (commonly known as the EHSRT), supported by the NIEHS, the CDC, the EPA, Exxon-Mobile Corporation, the American Chemistry Council, and the Brita Water Research Institute. Still more information continues to emerge today. And much simply remains to be seen.
Katrina Hits
Katrina, rated as a Category 4 hurricane on the Saffir-Simpson scale, made landfall near New Orleans on 29 August 2005. Wind damage extended as far as 150 miles inland. Heavy rain battered the area, and the storm surge—measuring as high as 30 feet and sweeping several miles inland—breached several levees intended to protect New Orleans from the waters of Lake Pontchartrain. Water poured through the breaks in the days following the storm, covering approximately 80% of the city with water as deep as three meters. The American Red Cross estimates that more than 354,000 homes along the Gulf Coast were destroyed or damaged beyond repair by Katrina and, a month later, Hurricane Rita. Hundreds of small manufacturers or businesses using chemicals or fuels also were impacted.
Flooding, wind, and waves caused major damage to buildings and infrastructure whose integrity is key to the environmental health of the local citizenry. The EPA estimated that more than 200 sewage treatment plants in Louisiana, Mississippi, and Alabama were affected, with almost all the plants around New Orleans knocked out of action. Loss of power meant lift stations (which pump sewage uphill) could not work, causing sewage to overflow into houses and streets.
The region struck by Katrina and Rita is home to a large number of oil refineries and chemical plants. Prior to Katrina, the EPA had identified nearly 400 sites in the affected area as possibly needing cleanup because of their potential impact on human health. Following the storm, the U.S. Coast Guard reported numerous oil spills from refineries and tank farms in South Louisiana. A story in the September 30 Boston Globe reported that Katrina damaged 140 oil and gas platforms in the Gulf of Mexico, 43 seriously, including some that floated away or sank.
Across the Gulf Coast, more than 1.5 million people evacuated as the storm approached. More than 100,000 stayed behind in New Orleans, unwilling or unable to leave. As New Orleans flooded, thousands waded through chest-deep floodwaters to reach shelters or higher ground. Thousands more remained trapped in homes, hospitals, and nursing homes. Conditions in shelters rapidly became unsanitary. Many people were exposed to the elements for five days or more, living with little or no food, drinking water, or medicine. As of December 5, the death toll was reported at 1,071 in Louisiana, 228 in Mississippi, 14 in Florida, 2 in Alabama, and 2 in Georgia.
First Response
Numerous federal, state, and local agencies, as well as private individuals and relief groups, swung into action in the wake of the storm. Troops from the U.S. Army, Coast Guard, and National Guard as well as state and local officials and private citizens rescued those they could. The Federal Emergency Management Agency (FEMA) was assigned the lead in disaster relief planning and administration, including provision of emergency food and shelter and contracting for debris removal. The Department of Health and Human Services (DHHS) declared a public health emergency in the Gulf states and directed the CDC to take appropriate action. The CDC deployed more than 600 professionals into the disaster zone, including specialists in public health nursing, occupational safety and health, laboratory science, medicine, epidemiology, sanitation, environmental health, disease surveillance, public information, and health risk communication.
The CDC also joined with the EPA to set up a joint task force to conduct an environmental health needs and habitability assessment to identify critical public health issues for the reinhabitation of New Orleans. This city was unique among the areas hit in that it was the only one left with standing water. Major urban areas in Mississippi and Alabama, while devastated, did not remain flooded.
In advance of the storm’s arrival, the EPA had predeployed teams to the area, with the mission of guiding debris disposal, assisting in the restoration of drinking and wastewater treatment systems, and containing hazardous waste spills. Immediately after the storm, these teams used their 60 watercraft to help search-and-rescue efforts, rescuing about 800 people, according to EPA administrator Stephen Johnson. Five days after the storm, the EPA began testing floodwaters in New Orleans for biological and chemical contamination.
In coordination with the Louisiana Department of Environmental Quality (LDEQ), the EPA analyzed floodwaters for more than 100 hazardous pollutants such as volatile and semivolatile organic compounds, metals, pesticides, herbicides, and polychlorinated biphenyls. They also tested for biological agents such as Escherichia coli. Their testing revealed “greatly elevated” levels of E. coli, as much as ten times higher than EPA’s recommended levels for contact. According to the EPA, agency scientists found levels of lead and arsenic at some sites in excess of drinking water standards—a potential threat given the possibility of hand-to-mouth exposure. The EPA posted these and other findings on its Hurricane Response 2005 website (http://www.epa.gov/katrina/), created after the storm.
Shortly after the hurricane struck, the U.S. Coast Guard began working with the EPA, the Louisiana state government, and private industries to identify and recover spilled oil along the coast. The team identified 6 major, 4 medium, and 134 minor spills totaling 8 million gallons. One of the most notorious spills occurred at the Murphy Oil Company plant, which dumped more than 25,000 barrels of oil into the streets of Chalmette and Meraux, Louisiana. As of December 7, the Coast Guard reported the recovery of 3.8 million gallons, with another 1.7 million evaporated, 2.4 million dispersed, and 100,000 onshore.
Meanwhile, the NIEHS was joining with Duke University Medical Center, the NIH, and the CDC to provide assistance with relief and recovery operations along the Gulf Coast, as well as working at home to establish a web-site on environmental health issues related to Katrina [for more information, see “NIEHS Responds to Katrina,” p. A28 this issue].
Floodwater Hazards
Kevin Stephens is director of the New Orleans Department of Health. He was in charge of interpreting the EPA data and advising citizens and responders about the health hazards presented by the floodwaters. “I struggled every day to determine what [the data] meant and what to tell our health workers and the public,” he says. “What does ‘not an immediate health hazard’ mean when you have people wading through the water? What does ‘not in excess of drinking water standards’ mean? Is it a danger if you get your hands wet and touch your mouth?” Journalists claimed the floodwaters were a “toxic gumbo” of dangerous chemicals and microbes, raising fears that any contact was a health threaten.
These concerns prompted a team of scientists led by John Pardue, director of the Louisiana Water Resources Research Institute at Louisiana State University (LSU), to conduct its own study of the New Orleans floodwaters. The report, published 15 November 2005 in Environmental Science & Technology, stated categorically that, contrary to claims in the media, the floodwater was not a “toxic soup.”
“Chemical oxygen demand and fecal coliform bacteria were elevated in surface flood-water, but typical of stormwater runoff in the region,” the report said. “Lead, arsenic, and in some cases chromium exceeded drinking water standards, but with the exception of some elevated lead concentrations were generally typical of stormwater.” The LSU study also found only low concentrations (less than 1%) of benzene, toluene, and ethylbenzene even in places where there was a visible oil sheen. “Collectively, these data indicate that Katrina floodwater is similar to normal stormwater runoff, but with somewhat elevated lead and VOC concentrations,” the report concluded.
However, the LSU study was limited to two areas within the city of New Orleans, and the authors warned that conditions could be different elsewhere, particularly in Lake Pontchartrain, where floodwaters were being pumped. LSU and the University of Colorado are currently conducting studies of Lake Pontchartrain looking for a wide range of pathogens. The Colorado team is measuring aerosols created by pumping floodwater into the lake, while the LSU team is analyzing the lake water itself.
More Water Hazards
Still other threats were posed by water. As of December 9, the EPA reported that 99% of the waste treatment and water supply systems were back online, but some had been out of operation for weeks. At the October 20 EHSRT, Howard Frumkin, director of the National Center for Environmental Health and Agency for Toxic Substances and Disease Registry (NCEH/ATSDR), said that despite the percentage of sewage treatment plants already online at that point, the danger wasn’t over. “We have no guarantees that sewage being flushed is getting to treatment plants,” he said. “Raw sewage is going into the Mississippi River.”
Though most water supply systems may be functioning again, the safety of distribution lines that were flooded can’t yet be ensured either. “There are possible changes in pipe ecology due to the intrusion of contaminants,” said Frumkin. “And we have additional concerns for homes on wells.” Louisiana officials speaking at the roundtable said there are dozens of community water systems and tens of thousands of private wells that need to be tested for contamination.
Standing water poses a different threat, serving as a breeding ground for bacteria and mosquitoes. Even prior to Katrina, Louisiana had the highest number of reported cases of West Nile virus (66) of any state in the union, according to the CDC. West Nile virus can be transmitted to humans via mosquito bites, and the warm, wet weather following the storm was ideal for breeding of mosquitoes. The U.S. Air Force sprayed areas of standing water with pesticides to kill mosquito larvae. The CDC reported on its Update on CDC’s Response to Hurricanes website that postspraying surveillance at ten sites found a 91% reduction in total mosquito density compared to prespraying surveillance results [for more information on this website, see the EHPnet article, p. A27 this issue].
The Gulf Coast is also known for the presence of the bacterium Vibrio vulnificus. This relative of the pathogen that causes cholera thrives in brackish waters in warmer times of the year. Humans may become infected by eating contaminated seafood or through open wounds exposed to water. While not harmful to individuals in good health, it can be fatal to those with liver damage. Health officials at the roundtable reported counting 22 cases of illness induced by V. vulnificus following the storm, including 5 deaths.
In late September, the EPA launched the Ocean Survey Vessel Bold to conduct water quality testing in the river channels and nearshore waters of the Mississippi Delta. The agency monitored 20 areas to determine whether fecal pollution from flooded communities had spread into these waters. All 20 monitoring stations showed that, at the time, the water was safe for primary contact, including swimming. The EPA said on its website, however, that the data “should not be used to assess the safety of consuming raw or undercooked molluscan shellfish.”
In the wake of the storm, Louisiana, Mississippi, and Alabama closed their shellfishing waters until testing could be done. On December 8, the three states issued a joint press release saying that fish and shellfish samples collected and analyzed since the hurricanes “show no reason for concern about the consumption of Gulf seafood.” Louisiana and Alabama subsequently reopened their waters, while Mississippi’s oyster reefs remain closed pending additional studies.
Toxicants in Sediment and Air
Health officials also anticipated a threat from contaminated sediment in the days and weeks following the storm. As floodwaters were pumped out of inundated areas, a dark sludge was found coating buildings, land, and pavement. E. coli was detected at elevated levels in many sediment samples taken from around New Orleans, implying the presence of fecal bacteria. The EPA has no standards for determining human health risks from E. coli in sediment, but warned people to limit exposure, and if exposed, to wash skin with soap and water.
The EPA was concerned, too, about the region’s Superfund sites, which include former dump sites of pesticides and dioxins. The EPA identified 54 Superfund sites in the affected area. Officials worried that at least some of these sites might have been compromised, releasing toxic chemicals into the land or water. Johnson reported at the EHSRT that as of October 20, the EPA had visually inspected all of the sites and sampled many. As of December 5, the EPA’s posted test results for these sites indicated that none were compromised in a way that would present a human health hazard.
Elsewhere, as late as November 20, chemical testing of sediment samples in Louisiana’s Orleans and St. Bernard Parishes indicated the continued presence of petroleum. However, the EPA’s website states that exposures of emergency responders at these levels are not expected to cause adverse health effects as long as the proper personal protective equipment is worn, such as gloves and safety glasses. Volatile and semivolatile organic compounds, pesticides, and metals including aluminum were found, but at levels below what the ATSDR and CDC consider to be immediately hazardous to human health. However, the site continues, “EPA and ATSDR/CDC continue to recommend that residents avoid all contact with sediment deposited by floodwater, where possible, due to potential concerns associated with long-term skin contact.”
The Natural Resources Defense Council (NRDC) and a host of local environmental groups paint a darker picture of the contamination situation. In a December 1 press release, the NRDC stated that tests it had conducted revealed “dangerously high levels” of industrial chemicals and heavy metals in the sediment covering much of New Orleans. For example, tests found arsenic levels in some neighborhoods that exceeded EPA safety limits by a factor of 30.
“We found arsenic and other cancer-causing contaminants in sediment all across the entire city,” said Monique Hardin, co-director of the New Orleans–based Advocates for Human Rights, at an NRDC press briefing. “We also found hot spots where there were some nasty surprises, such as banned pesticides.” The groups urged the EPA to begin cleaning up or removing contaminated topsoil across the city and to conduct further testing in certain neighborhoods.
The NRDC also challenged the EPA’s assertion that the flooded Superfund sites posed no threat. The December 1 press release stated that NRDC’s own assessment of one of these sites, the New Orleans Agricultural Street Landfill Superfund Site, showed “visible leachate emerging from the site and spreading across the street and onto a local senior center’s property. Sediment testing at this site found contamination as much as 20 times higher than the EPA soil cleanup standards for four [polycyclic aromatic hydrocarbons].”
LDEQ toxicologist Tom Harris responded in press reports that the NRDC’s findings were fundamentally flawed because arsenic levels are naturally above the EPA’s residential standard in Louisiana and elsewhere. “I have never personally seen soil samples come back below the residential screening level for arsenic,” Harris told PlanetArk World Environmental News on December 5. “It’s a naturally occurring [element] you can find everywhere.” The state of Louisiana and the EPA continue to perform testing of sediment to determine when to give an all-clear to residents with respect to exposure to sediment.
The EPA has also addressed concerns about air quality in the Gulf region. According to Johnson, most of the agency’s stationary air quality monitors were knocked out by Katrina. The EPA reinstalled the stationary monitors and employed their Airborne Spectral Photometrics Environmental Collection Technology to undertake airborne monitoring. The EPA also employed two Trace Atmospheric Gas Analyzer buses, self-contained mobile laboratories capable of continuous real-time sampling and analysis.
Air samples were tested for volatile priority pollutants such as benzene, toluene, and xylene, which are commonly found in gasoline, as well as other industrial solvents. The screening results indicated that chemical concentrations in most areas were below the ATSDR health guidelines of concern. The EPA stated on its website, “The low level of volatile pollutants is not surprising as contaminants may be bound in sediment. Monitoring data directly around Murphy Oil spill reveal some slightly elevated levels of benzene and toluene that are associated with petroleum release. Long-term exposure (a year or longer) at the levels measured would be required for health effects to be a concern.”
Air may also play a role in an illness known as “shelter cough,” or “Katrina cough.” Shelter cough is presumed to be an allergic reaction to some particulate matter in the air, according to Stephens. However, despite the presence of shelter cough and earlier concerns about a wave of infectious diseases in the wake of Katrina, acute respiratory illness have made up only 8.7% of diagnoses between August 29 and September 24, according to the October 7 Morbidity and Mortality Weekly Report. “We have no evidence of infectious disease outbreaks,” Stephens said at the EHSRT.
A Mountain of Debris
The amount of debris generated by Katrina is by all accounts staggering. FEMA estimates there are 39.9 million cubic yards of debris in Mississippi alone. Mark Williams, administrator of solid waste policy, planning, and grants at the Mississippi Department of Environmental Quality (MDEQ), says that state has enough space for the initial removal of debris to staging areas, but not for long-term deposition in landfills.
Jimmy Guidry, medical director of Louisiana’s Department of Health and Hospitals, says Louisiana, too, lacks sufficient landfill space for all the debris: “We have more than three hundred thousand refrigerators that need to be disposed of. All these have freon in them.” Guidry said at the roundtable that the Louisiana Department of Environmental Quality has approved dozens of temporary debris disposal sites, which will have to be carefully monitored.
Appliances can be recycled for metal content. Televisions and household computers pose a different problem. A single computer monitor contains 4.5 pounds of lead, and computer processing units contain trace metals that can leach out of unlined landfills.
As much as one-third of the debris is vegetative matter that can be burned or chipped for compost. The rest must be recycled or landfilled. Williams says burning of vegetative debris has been allowed in Mississippi for some months and is now largely complete. He adds, “EPA in conjunction with MDEQ has done some monitoring in the area [of controlled burns], which has indicated some elevated levels of formaldehyde and acrolein in certain areas.” In the interest of minimizing air pollution, the EPA and MDEQ allowed only clean vegetative debris to be burned and strongly encouraged the use of air curtain destructors and other combustion units in the early stages of cleanup.
Williams says another daunting challenge was disposing of thousand of tons of food—chicken, fish, and beef—rotting in warehouses on the docks. Officials from Mississippi’s Natural Resources Conservation Service said more than 6 million dead animals—poultry and livestock—had to be removed from farms in the affected area. Now officials are dealing with wastes in homes, including such items as propane tanks, household pesticides, and asbestos from roofing, insulation, and other home sources. The waste is taken to staging areas where hazardous waste is pulled out for disposal by the EPA. As of October 31, the EPA had collected an estimated 1 million pounds of household hazardous waste in Louisiana (the agency did not report on collections in other states).
Injury Protection
One of the major concerns officials have with regard to the handling and disposal of debris is the safety of workers. “We have a large number of workers coming to the Gulf seeking employment, and many of them are not properly trained and protected,” says Max Kiefer, assistant director of emergency preparedness and response for the National Institute for Occupational Safety and Health (NIOSH). High-risk occupations include debris removal, levee rebuilding, residential refurbishment, and infrastructure rebuilding.
NIOSH is trying to keep workers apprised of health hazards. “We have assessed exposure to silica and metals during levee rebuilding, debris removal, and tasks involving the sediment,” Kiefer said at the roundtable. “We also worried that people were wearing protective gear that may induce heat stress. After assessing certain tasks, we were able to downgrade our gear recommendations in light of that. Psychological stress on responders has been significant. But by far the biggest issue has been injuries—lacerations, falls, and trips.” NIOSH is providing guidance for responders and providers on the CDC hurricane response website.
Private citizens also face significant risk of injury during cleanup. Officials talk of a “second wave” of injury following a natural disaster as citizens undertake to remove debris and repair buildings themselves. Will Service, the industrial hygiene coordinator with the North Carolina Office of Public Health Preparedness and Response, worked in a mobile hospital in Waveland, Mississippi, in the days following the storm. “We saw a lot of injuries from things like chain saws used during cleanup,” Service says. “People are tired, their thinking isn’t clear. They’re doing things they don’t normally do.”
Illnesses and injuries associated with Katrina are being tracked by the CDC, with updates posted regularly on its website. Confirming what public health officials warned about a second wave of injuries, the most common diagnosis (26.2%) in reporting hospitals and clinics from September 8 to October 4 was injury. The major cause of injury was falling, followed closely by vehicle crash–related injuries (likely related to missing or nonfunctioning traffic signs and signals). Cutting and piercing injuries ranked third.
Coming Home to Hazards
Mold growth in houses damaged by Katrina is of enormous concern to health and housing officials. Estimates of the number of homes suffering water damage range in the hundreds of thousands. Claudette Reichel, an LSU professor of education and housing specialist, says that virtually every home that sustained flood damage will experience mold growth. “Houses that people were not allowed back into for weeks will all have mold, and that mold will have had time to multiply, spread, and get really thick,” she says. Says Frumkin, “The magnitude of mold exposure in the Gulf region will in many instances greatly exceed anything we have seen before, adding to the concern and uncertainty regarding health effects.”
How or even whether mold causes human health problems is disputed by public health professionals, but most acknowledge a connection. “It is a very difficult science, because there is no clear-cut dose–response threshold,” Reichel says. “It is highly dependent upon the type of mold, whether the mold is producing a mycotoxin, the susceptibility of the patient, and the amount of exposure.”
The CDC states that people who are sensitive to mold may experience stuffy nose, irritated eyes, or wheezing. People allergic to mold may have difficulty in breathing. People with weak immune systems may develop lung infections.
Health and housing officials advise homeowners and renters to throw out any furnishings, insulation, and bedding that may have gotten wet, to clean walls and floors with soap and water, to ventilate, and then to close up and dehumidify the home.
The CDC also reported a spike in post-Katrina carbon monoxide poisoning in the Gulf Coast in the October 7 Morbidity and Mortality Weekly Report. From August 29 to September 24, a total of 51 cases of carbon monoxide poisoning, including 5 deaths, were reported in Alabama, Louisiana, and Mississippi. After the hurricanes, many residents used gasoline-powered portable generators to provide electricity to their homes and businesses. These devices produce carbon monoxide, which can build up to fatal levels if run inside a living space or garage.
A number of other health issues loom as residents begin returning to New Orleans, where health care services aren’t widely available, sewer and water services are still spotty, and structural inspections aren’t complete. Residents have asked city officials for a health assessment to address their concerns about oil spills, mold contamination, and the possible long-term health effects related to mold and chemical exposures. “We are developing an assessment tool for this purpose, and we anticipate that it will be developed for the beginning of [2006],” says Stephens.
Many health care professionals worry that mental health may be the most serious long-term health issue resulting from Katrina. Hundreds of thousands of people across the Gulf region have had their homes destroyed. Thousands are still living in shelters. Many have no jobs, no health insurance, and no job prospects. “We are seeing a lot of symptoms of post-traumatic stress disorder,” says Marty Allen, a psychologist with the Mississippi Department of Mental Health. “The trauma was not just the day of the storm. People are still being traumatized by living in tents, not having jobs, and having to walk for miles just to get food and water.”
Lessons Learned?
What lessons have been learned from Katrina with respect to environmental health? Debate about how to protect Gulf Coast citizens from hurricanes and storm surge was ongoing before the storm and will continue with renewed intensity.
In Mississippi, Governor Haley Barbour enlisted the Chicago-based Congress for New Urbanism to come up with recommendations for rebuilding the Gulf Coast. The Congress sponsored a week-long Mississippi Renewal Forum in October attended by some of the nation’s leading architects, engineers, and urban planners. Working with local leaders, the teams produced reports for 11 coastal towns impacted by the storm. Recommendations include improving the connectivity between towns by moving the CSX freight line north and transforming the abandoned right-of-way into a boulevard for cars and transit, connecting the Gulf region towns with high-speed rail, realigning and revising U.S. 90 to become a pedestrian-friendly “beach boulevard,” and creating a Gulf Coast bikeway.
A similar process is under way in Louisiana under the auspices of the Louisiana Recovery Authority created by Governor Kathleen Blanco. The authority is developing short-, medium-, and long-range plans to guide the rebuilding of Louisiana in the wake of the hurricanes. At the authority’s request, the American Association of Architects, in collaboration with the American Planning Association, presented the Louisiana Recovery and Rebuilding Conference on November 10–12. The authority has developed a 100-day plan that includes completion of an environmental evaluation of damages caused by the hurricanes and development of recommendations for how to proceed with reconstruction.
Discussion will center on how to protect New Orleans from further flooding and whether certain low-lying parts of the city should even be reoccupied. Such decisions will be made in the months and years to come. Meanwhile, environmental and public health officials have drawn some conclusions about how to better respond to events like Katrina.
Officials at the EHSRT agreed that communication in advance and in the wake of natural and man-made disasters is key. Fears and rumors of disease ran rampant in the days following Katrina. Citizens, the media, and even public health officials did not know which factors presented a genuine health threat and which did not. Federal agencies conducted testing and provided data, but people often did not know how to interpret those data with respect to the kinds of exposures they were encountering.
“The public health community must be actively involved and articulate key health issues,” said Kellogg Schwab, an assistant professor at Johns Hopkins Bloomberg School of Public Health. “We must keep the message simple and focused. We must develop effective strategies to provide targeted timely results. We must provide concise and accurate public health information and advice.”
Officials also agreed that responders must be properly trained and deployed, provided with proper protective gear and an effective communications system (land lines and cell phones were inoperative in much of the area for weeks after Katrina). Health officials must be able to assess the particular kinds of exposures that people have been subjected to and respond accordingly.
“Your response strategy for exposure varies with each event,” said Paul Lioy, deputy director of the Environmental and Occupational Health Sciences Institute at Rutgers University. “The World Trade Center [collapse] was an instantaneous acute air exposure event like we’d never experienced. Katrina for the most part involved an acute water exposure event, but the exposure was over a longer period of time.”
Lioy pointed out the need for a national review of the kind of standards and guidelines necessary to ensure that the correct information is given out to the public about immediate hazards versus long-term exposures and risks. “Comparison to general drinking water or ambient air quality standards are not sufficient for guiding the public or public officials during an acute exposure event,” he said.
Most of all, roundtable participants agreed, Katrina represents a chance for officials across all levels of government to do things better—evacuation planning, urban design, communication, environmental monitoring, and involvement of citizenry, particularly minority and low-income residents. John McLachlan, director of the Tulane/Xavier Center for Bioenvironmental Research, said that preparing for disasters like Katrina requires the involvement of virtually every academic discipline. To that end, Tulane and Xavier are creating a Katrina Environmental Research and Restoration Network (KERRN) of researchers who share data and ideas across disciplinary, geographical, and institutional lines. Paraphrasing one of his colleagues, McLachlan stated, “This is the mother of all multidisciplinary problems.”
Comprehending the catastrophe.
(above) Phyllis Howley, 70, sits on what’s left of the porch of her son’s New Orleans home. (left) The beach in Biloxi, Mississippi, four days after Katrina.
Hazards in wading?
Initial reports labeled the floodwaters through which many New Orleans residents were forced to wade a “toxic gumbo.” Later testing of stormwaters found elevated levels of fewer contaminants than feared, but sampling was limited and the water may yet present long-term problems.
Vehicle slaughter.
Vehicles destroyed in the storm surge of Hurricane Katrina (left) are being stockpiled north of Gulfport, Mississippi (right). The thousands of automobiles are just the tip of the iceberg of waste that communities must deal with as a result of the hurricane.
A slicker picker-upper.
Absorbent pads are used to clean up surface oil at the Bass Enterprises South Facility in Cox Bay, Louisiana, where Katrina caused the release of an estimated 3.8 million gallons of oil. Oil spills may have long-lasting effects on water supplies and surrounding ecologies.
Waves of destruction.
(above) A motorcyclist rides past a mountain of trash, wallboard, and furniture removed from homes damaged by Katrina. (inset) Thousands of damaged refrigerators await safe disposal at a landfill near New Orleans. The freon in these appliances will need to be handled carefully.
Opportunistic attacker.
The warm, damp conditions left in homes following Katrina provided the perfect medium for the growth of mold. Because mold can be extremely toxic and hard to eradicate, many homes may not be salvageable.
Chemical calamity.
A worker tests hazardous household liquids at the Fort Jackson “orphan” tank and drum staging area in Louisiana.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a0004016393646EnvironewsSpheres of InfluenceLouisiana’s Wetlands: A Lesson in Nature Appreciation Tibbetts John 1 2006 114 1 A40 A43 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Hurricane Katrina’s disastrous flooding of the Gulf Coast confirmed three decades of warnings by scientists. Most of New Orleans is below sea level, and South Louisiana’s coastal wetlands, which once helped buffer the city from giant storms, have been disappearing at a spectacularly swift pace. Now some researchers are calling for restoration of wetlands and barrier islands to help protect New Orleans the next time a hurricane strikes.
An average of 34 square miles of South Louisiana land, mostly marsh, has disappeared each year for the past five decades, according to the U.S. Geological Survey (USGS). As much as 80% of the nation’s coastal wetland loss in this time occurred in Louisiana. From 1932 to 2000, the state lost 1,900 square miles of land to the Gulf of Mexico.
By 2050, if nothing is done to stop this process, the state could lose another 700 square miles, and one-third of 1930s coastal Louisiana will have vanished. Importantly, New Orleans and surrounding areas will become ever more vulnerable to future storms. “New Orleans can’t be restored unless we also address coastal and wetland restoration too,” says Craig E. Colten, a geographer at Louisiana State University (LSU).
A River and a City
The vast watershed of the Mississippi River ranges from Montana in the west to New York state in the east. Spring rains send sediment-rich runoff into the river and its tributaries. For thousands of years, the Big Muddy has flowed down to the Gulf of Mexico, where great floods periodically burst over the riverbanks, allowing huge quantities of silt to settle and nourish wetlands. The land naturally sinks, or subsides, as loose sediments from the Mississippi River settle and compact.
The river slows as it reaches the gulf because of the tides pushing upstream; as it slows down, it spreads out and delivers much of its sediment load into deltaic deposits. The Mississippi Delta was fed by these influxes of mud, creating 5 million acres of South Louisiana before the twentieth century. Every millennium or so, the Mississippi River would change direction at its gulf outlet, meandering from east to west and back again. As a result, the river created six different delta “lobes” on which the entire coastline of South Louisiana was formed.
In 1718, French settlers founded New Orleans on a natural ridge of high land on a bend of the Mississippi River, with Lake Pontchartrain (which is actually an inlet of the Gulf of Mexico) to the north and coastal wetlands to the east, west, and south. But flooding was a problem. By 1812, the settlers had built levees on the east bank to Baton Rouge, 130 miles upstream, and on the west bank as far as Pointe Coupée, 165 miles upstream.
Over the next two centuries, the city drained surrounding wetlands to prevent disease and encourage development. The city eliminated swamps following mosquito-borne yellow fever epidemics that killed 40,000 residents between 1817 and 1905. As the city grew, the only lands available for development were low-lying areas north toward Lake Pontchartrain. At the turn of the twentieth century, the city created an integrated public works department, which was responsible for draining the wetlands.
“It was the draining of the lower areas that allowed suburbanization to occur,” says Colten. But the lowlands, originally just inches above sea level, steadily sank. “When you drain these areas, you suck the water out of the peaty soils, which begin to compress, or subside,” he says. “That’s why these areas have continued to subside.”
New Orleans also continually built higher and stronger levees to contain river flooding. In 1928, Congress authorized major levee improvements, and the U.S. Army Corps of Engineers began shoring up the flood control system, including levees, along the entire lower Mississippi and in New Orleans. By the 1950s, LSU geology professor James P. Morgan had begun to document dramatic rates of land loss in Louisiana’s coastal zone, which stretches 300 miles from the Texas border to the Mississippi state line and 50 miles inland.
The River Today
Today, South Louisiana is one of most intensively engineered places in the nation. Vast quantities of water are diverted or rerouted through a lacework of navigation corridors held in place by 2,000 miles of earthen, rock, and concrete levees. Walled off from the floodplains, the river can no longer provide enough silt to the delta to keep up with natural subsidence and sea level rise. About two-dozen dams also hold sediment back from the river and its tributaries. “We have tamed the river for the almost exclusive benefit of navigation,” says David R. Conrad, a senior water resources specialist with the National Wildlife Federation.
The construction of high levees did end the spring floods along the lower Mississippi, but at an environmental cost, eventually eliminating many of the wetlands, floodplains, and barrier islands of the delta. “When you lose wetlands and flood-plains, you lose their natural services including storage capacity during floods, and when you lose coastal wetlands, you lose wave and storm protections,” says Sandra Postel, director of the Global Water Policy Project, a nonprofit organization based in Amherst, Massachusetts. “Katrina in South Louisiana was an example of what happens when you disturb the natural infrastructure.”
In November 2005, the National Academies released a report, Drawing Louisiana’s New Map: Addressing Land Loss in Coastal Louisiana. The report notes that building and maintaining levees and dams along the Mississippi River was a “more or less ubiquitous” cause of wetland loss. Another geographically widespread cause was voracious grazing by nutria, a nonnative species, which destroyed wetland vegetation.
But the report also points out that there were other causes “superimposed on these broad influences,” particularly including activities by the oil and gas industry. Peaking during the 1960s through the 1980s, oil and gas companies dredged canals for exploration. There are currently 10 major navigation canals and 9,300 miles of pipelines in coastal Louisiana serving about 50,000 oil and gas production facilities. These canals, which are perpendicular to the coast, have created new open water areas, drowning wetlands and allowing salt-water intrusion into freshwater ecosystems. The result—land loss hot spots. “There is also evidence,” the report says, “that extraction of large volumes of oil and gas has exacerbated the problems of inundation and saltwater intrusion”—that is, withdrawing oil and gas along geologic faults seems to exacerbate subsidence in coastal Louisiana.
The Mississippi Delta is also home to South Louisiana’s port complex, which lines both banks of the Mississippi River for 172 miles as well as points offshore, including the Port of New Orleans, the Port of South Louisiana, the Port of Baton Rouge, and the Louisiana Offshore Oil Port in the Gulf. Because of its size and location, adjacent to oil and gas refineries and drilling platforms, this port complex is one the most important in the United States. Louisiana’s coastline produces one-fifth of the country’s oil and one-quarter of its natural gas. Through South Louisiana’s ports the bulk commodities of U.S. agriculture—corn, wheat, and soybeans—are sent around the world, and the bulk commodities needed for American industry—steel and concrete, for instance—come into the country.
The Mississippi River Gulf Outlet, a little-used 40-year-old shipping channel connecting the Gulf of Mexico to the Mississippi River, is believed to have served as a funnel for Katrina’s storm surge. The navigation channel and the eastern levee of the Mississippi River seem to have directed high water into the Breton Sound estuary southeast of New Orleans, according to Greg Steyer, a USGS wetland scientist. From there, the surge poured into Lake Pontchartrain and an industrial canal, where it overwhelmed levees, contributing to flooding in St. Bernard Parish and the Lower Ninth Ward of New Orleans. Like the oil and gas canals, the outlet also allows saltwater intrusion and tidal action into freshwater ecosystems, killing vegetation and turning the marsh into a stretch of open muddy water.
The Gulf of Mexico is also subject to the general sea level rise being observed worldwide, with potential ramifications for the Gulf Coast. Over the past century, the warming climate has pushed up mean sea level four to eight inches worldwide, and computer models suggest that this rise will probably accelerate, according to a 2001 report of the U.S. Global Change Research Program, Climate Change Impacts on the United States: The Potential Consequences of Climate Variability and Change. By 2100, global sea level is projected to rise an additional 19 inches along most of the U.S. coastline.
Death of the Wetlands
This combination of factors has killed wetlands in South Louisiana from the inside out. “Some of the inner marshes have actually eroded faster than some of the extreme coastal areas,” says Gary Fine, manager of the Natural Resources Conservation Service’s Golden Meadow Plant Materials Center in Galliano, Louisiana. In the delta, sediment deposits from tidal creeks and rivers build up the banks, creating modest natural ridges. Land elevations fall toward the center of coastal marshes, freshwater swamps, and bald cypress forests. Starved of new sediments and flooded by tides, the inner areas become constantly submerged. “Especially in the salt marshes,” Fine explains, “the plants start dying in the center due to rising water and decreasing sediments, and then the loss expands outward to the edges.” As a result, South Louisiana has become a patchwork of open water and remnant wetlands.
“By 2050, the city will be closer to and more exposed to the Gulf of Mexico,” noted authors of a restoration proposal, Coast 2050: Toward a Sustainable Coastal Louisiana. Hurricane Katrina itself pushed the city closer to the coast. The hurricane, making landfall in lower Plaquemines Parish, had a storm surge of almost 30 feet, which caused extensive erosion at the coastal edge. For example, Katrina almost wiped out the Chandeleur Islands, a 40-mile-long series of uninhabited barrier islands southeast of New Orleans. “The sand and marsh are gone,” says Asbury Sallenger, an oceanographer with the USGS Center for Coastal and Watershed Studies in St. Petersburg, Florida. “Before Katrina, the islands were five meters high; now there’s a less than half a meter left.”
Gregory W. Stone, a coastal geologist at LSU, says that if the current trend of wetland loss and barrier island erosion continues, it will worsen the effects of future hurricane surges in South Louisiana. “Storm surge and storm waves will increase if we lose more wetlands and our barrier coast,” he says. “Wetlands and barrier islands are the first line of defense. That means areas such as New Orleans would become more vulnerable to inundation.”
Further land loss would also endanger oil and gas facilities, the huge port complex, and the gulf’s valuable fishing industry. South Louisiana’s wetlands are critical nursery areas for commercially important marine species, including shrimp, blue crabs, oysters, redfish, and menhaden. Land loss in South Louisiana, says Stone, “is not a local problem—it’s a national problem.”
Restoration Plans
In an effort to rebuild the state’s natural infrastructure, Congress passed the 1990 Coastal Wetlands Planning, Protection, and Restoration Act, sponsored by Senator John Breaux (D–LA). The Breaux Act provides about $50 million each year for wetlands restoration projects in Louisiana. The Breaux Act has provided funding for 118 restoration projects, and 75 projects have already been built. But most of these projects are relatively small in scale.
In 1996, the state of Louisiana and a group of federal agencies joined with parish officials and the public to create a consensus document. The result, after 65 public meetings over 18 months, was Coast 2050, which outlined strategies and measures needed to restore the state’s wetlands and barrier islands.
Coast 2050 proposed that the Mississippi River be re-engineered to imitate natural processes. That is, some portion of the river’s flow should be re-diverted via pipelines or canals to flush into the delta so that South Louisiana’s sinking ecosystems could be built up. “Coast 2050 essentially calls for putting holes in the straitjacketed Mississippi River,” says Conrad. “This process could be one of the most interesting and expensive and important environmental engineering processes ever. It is a huge opportunity to put things back together if we have the will.”
These water diversions would feed freshwater marshes and control saltwater intrusion from being pushed upriver by the rising sea level. The Caernarvon Freshwater Diversion Project, funded in the mid-1980s, could be one model for this approach. The diversion consists of a $26-million opening in the river levee built by the Army Corps about 24 miles south of New Orleans. A concrete culvert diverts water into a canal that feeds marshes behind Breton Sound, which had been losing land. This diversion has been shown to increase marsh and freshwater plant acreage.
Coast 2050 also recommended that federal agencies dredge soils and ancient sand-bars to create new marshlands; plug up the Mississippi River Gulf Outlet; and shore up barrier islands that are the first line of defense against approaching hurricanes. However, the cost cited in the report for all these projects seemed too huge to consider: $14 billion (by comparison, estimates for rebuilding after the 2005 hurricane season have been placed as high as $200 billion).
Kerry St. Pé, director of the Barataria-Terrebonne National Estuary Program, says there’s no time to waste. Freshwater diversions alone are not enough to solve the land loss problem, he adds. Dredge material should be pumped immediately via pipes from navigation channels in the delta, including the Mississippi River, to shore up hot spots of wetland loss. “We need the sediment now,” he says. The Corps of Engineers already dredges 40–45 million cubic yards of sediment from the delta’s numerous navigation channels each year, he says, and the material is discharged off the end of the continental shelf because that’s the least expensive method of disposal. “We could use that sediment to build wetlands,” says St. Pé.
From 2000 through 2003, the Corps of Engineers and the state of Louisiana collaborated on a feasibility study for a $17-billion coastal restoration plan lasting 30 years. Yet this study, based on Coast 2050, also seemed far too expensive at the time. “It never went up to Congress because it exceeded what potentially could be funded,” says Steyer. “We were asked to focus it on more of the near term, over ten years, addressing what are the critical projects that could be done.”
In November 2004, state and federal agencies proposed a near-term effort, the Louisiana Coastal Area Ecosystem Restoration Study. The findings from this study led to the 2005 Water Resources Development Act, which calls for Congress to spend $1.9 billion over 10 years on restoration efforts in the delta; the bill is still being worked out in Congress. The act—intended to be a first, smaller step toward a 30-year $17-billion plan—follows the strategies of Coast 2050, says Steyer.
However, Oliver Houck, who directs the environment program at Tulane University Law School, says that nothing less than letting the river go its own way will solve the land loss problem. “Coast 2050 is history,” he says. “Katrina upped the ante so much. What has to be done now is to let the Mississippi River take its natural course and allow the full bed load of the river to rebuild the marsh.” He adds, “The problem with Coast 2050 and other restoration plans is that they fail to halt wetland destruction in the same areas they are trying to restore. New canals, deeper canals, expanded ports are all on the table. No way that works.”
Indeed, if water control projects were destroyed and the Mississippi were allowed to take its natural course, it would inevitably become captured by the Atchafalaya River, which empties off the south-central coast of Louisiana. The combined flow and increased sediment load would help build up the most land-starved region of Louisiana’s coast. But if the Mississippi River were set free, one of today’s most important shipping channels would become water-starved from Baton Rouge to the gulf outlet.
So how would giant oceangoing ships reach the ports of South Louisiana? Houck recommends cutting an entirely new shipping channel from the gulf to the port complex of South Louisiana. Where would this channel be located? “That’s up to the engineers,” Houck says.
A Muddy Future
No matter how it’s done, there is a new urgency to address the land loss problem. Senator Mary Landrieu (D–LA) has proposed a Hurricane Katrina Disaster Relief and Economic Recovery Act, cosponsored by Senator David Vitter (R–LA). This proposal would provide $250 billion for hurricane reconstruction, including $40 billion in ecosystem restoration and levee improvements. Some feel, though, that this proposal actually hurt Louisiana’s chances for restoration monies by appearing to reach for too much to fund a grab bag of projects. “Major restoration funding remains in doubt,” says Houck, “as indeed does the mega-question: how to restore.” At press time the bill had not made any progress.
It has taken a major hurricane to show the nation that it’s necessary to rebuild the wetlands and barrier islands of Louisiana. Although stakeholders have generally agreed on a plan to rehabilitate these resources, major funding has not been available. To restore New Orleans to health after Hurricane Katrina, though, it seems clear that the nation must find a way to fund the largest ecological rehabilitation project in U.S. history, a comprehensive effort to rebuild South Louisiana’s disappearing landscape.
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Environ Health Perspect. 2006 Jan; 114(1):A40-A43
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a0004416393647EnvironewsInnovationsRaising the Bar for Levees Lougheed Tim 1 2006 114 1 A44 A47 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Human beings have likely been battling rising waters since the dawn of organized agriculture. Farmers around the world have traditionally been drawn to the rich soils of floodplains, which are generally well worth the trouble occasionally caused by surrounding waterways. Densely populated urban areas subsequently grew up around many of these same places, attracted by additional assets such as access to fishing and easy navigation. These settlements often require substantial and ongoing engineering efforts to secure the physical safety of the community. While the fundamental principles and challenges of holding back water have not changed, the tools we can bring to the task continue to become more sophisticated.
As events in the Gulf Coast recently demonstrated, efforts to hold back the sea are sometimes doomed to failure. Engineers are debating how and even whether the levee system around the New Orleans area should be rebuilt. But the options today are much greater than when the Mississippi River levees were first built.
Levees built today may look the same as they always have but can incorporate design, construction, and maintenance innovations that are finding their way into civil engineering. Some of these features smack of high technology, such as elaborate sensors to detect stresses and strains within the structure, so as to provide a warning of critical pressures that could signal serious damage or collapse. Similarly, impermeable lining materials known as geomembranes can be laid down underneath the structure before it is built, so that the seepage of water through the ground cannot erode foundations.
Above all, engineers continue to improve their understanding of water flows, taking advantage of ever more detailed computer modeling techniques to describe the implications of barrier design to experts in the field, political or legal authorities who may be responsible for those barriers, and members of the public.
Lessons from the Dutch
Perhaps no country has a more vested interest in levee safety than the Netherlands, which has occasionally paid a high price for sustaining major population centers well below the level of the stormy North Sea. In the winter of 1953, the sea breached a system of dikes that had been in place since the Middle Ages, causing floods that killed nearly 2,000 people. This catastrophe galvanized the nation’s political and social commitment to mounting and maintaining a sophisticated system of barriers that has set the standard for the rest of the world.
From the 1950s to the 1980s, major dams were constructed to hem in hundreds of miles of the country’s vulnerable coastline, knit together with earthen embankments and massive sluice gates over the delta stretching across the mouths of the Rhine, Maas, Waal, and Schelde Rivers, which all drain into the North Sea. The scale of this project—dubbed the Delta Works—is highlighted by the Oosterschelde storm surge barrier, which was completed in 1986. Designed to protect the ecological integrity of the surrounding estuary, the structure features 62 openings for tides to flow back and forth.
Engineers had never before attempted to erect sea defenses on this scale, and the Dutch became pioneers in the field. The five-mile-wide opening at the Oosterschelde, for example, called for 65 separate concrete piers more than 100 feet in height, which were built in place to an accuracy on the order of a few inches. Such precision was ensured by setting them on gigantic steel mesh “mattresses” filled with sand and gravel, which would prevent erosion that could shift the piers out of position.
In 1997 an even more ambitious undertaking was completed in the country’s southwest, where the Maeslant flood barrier includes two hollow arched doors, each about 1,000 feet long and 70 feet high, which float in side channels when not in use. They are rotated into their protective posture by steel ball joints 35 feet in diameter. Once the gates meet in the middle, they fill with water and sink onto a concrete pad, effectively blocking any storm surge.
These engineering marvels are based on earlier measurements of river floods and storm surges, baseline data that go back only to the early twentieth century. “That’s all [the data] we have to extrapolate to a situation of one in ten thousand years,” says flood management engineer Jos Dijkman, referring to the need to design infrastructure to cope with millennial-scale events such as the most extreme flooding. “Such an extrapolation is by definition uncertain, and you can go into all sorts of statistical methods and techniques to fine-tune that prediction.” Dijkman works for Delft Hydraulics, a Dutch company that has positioned itself as a leader in water management strategies.
Ground Control
Dijkman says the country’s engineering community has been moving away from a dependence on solid, immutable defenses. Designers have increasingly been looking to the natural landscape to mitigate the impact of flooding on developed areas, freeing up regions such as marshlands to take on excess water temporarily and so lessen a tendency to continue raising the height of levees as an exclusive means of enhancing protection. An example of this policy goes by the name “Room for the Rhine,” which combines engineering principles with research into the factors affecting the health of floodplains, such as the relationship between vegetation and water quality. In places where the setting back of a dike has not been possible, the Dutch also reserve “green” rivers, areas between dikes where water flows only during floods.
“For the old-fashioned way of building a gigantic floodway, you don’t necessarily have to know the [wetlands] system in all the details” says Dijkman. “If you want to develop a wetland that will absorb the energy of flood surge, you’d better know in detail what the processes are that drive the formation of these wetlands.”
Following the devastating flood of 1953, Dutch engineers also began to develop a new generation of tough, synthetic textiles that could be used to anchor earthen levees from below, preventing movement of the soil and even the penetration of water. A domestic manufacturer, Nicolon BV, emerged as one of the leaders in this field, eventually setting up an American operation in Georgia to serve the U.S. market. In 1991, Nicolon joined forces with North Carolina–based Mirafi, which had been experimenting with even more sophisticated geosynthetic fabrics since the late 1960s.
This technology was used to refurbish and upgrade parts of the New Orleans levee system as recently as the summer of 2005. On that occasion, the U.S. Army Corps of Engineers used a 900-foot section to compare the effectiveness of three Mirafi products—an impermeable geosynthetic textile and two types of a more loosely woven material known as geogrid. Strain-monitoring gauges were installed as part of this work. Although the geogrids lent slightly greater stability to the soil, the geotextiles perfomed nearly as well and saved nearly $340,000 (46%) over the cost of the geogrid.
Feedback from Fiber Optics
Sheer physical mass will never be sufficient to protect against waters that would flood. Aftab Mufti, president of the Intelligent Sensing for Innovative Structures (ISIS) Canada Research Network, compares the situation of today’s levee builders with one faced by a previous generation of aircraft designers. Prior to World War II, planes were built and flown without much attention to the specifics of performance, so that revisions to details such as wing span or tail height were being carried out constantly, based on in-service flight reports. But the push for high-performance military aircraft accelerated the emergence of a design philosophy that was premised primarily on theory and modeling, rather than simply building something and seeing if it would fly.
Today’s aerospace engineers would be loathe to put something in the air that had not been modeled extensively on computers and in wind tunnels, using flight data obtained using avionics, so that the final working product differs little from the prototype. Mufti regards civil engineers as being ready to make the same leap in their field, after many generations of building structures that are far less modeled and monitored than they could be. He says the civil engineering discipline will have to develop “civionics” as the aerospace engineering has developed avionics to be able to monitor the health of civil engineering structures.
More specifically, Mufti endorses the use of electronic and fiber-optic sensors to assess changes in the geometry and forces within a built structure, such as a bridge, a dam, or a levee. These sensors can take advantage of time domain reflectometry (TDR), in which light signals sent through a fiber-optic cable (set, for example, into the soil of an embankment) with any interruption reflect movement that can be readily located. Over time, Mufti says, these readings can provide invaluable insight into how well a structure is holding up.
“What you get out of this is data which you can use to improve your designs in the future,” he says, adding that these data can likewise be applied to future construction regulations. “Our codes at the moment are approximate, therefore conservative. We work in the laboratory and do the testing and monitoring of the structures and materials in the laboratory. Now what we’re finding is that structures and materials behave and age in real life quite differently than what we are seeing in the laboratory.”
Among the leading firms collecting such TDR data is Kane GeoTech, based in Stockton, California, which has carried out much of its work on the levee systems in the floodplain around Sacramento. The most likely model for use in New Orleans is a system deployed since 2002 by Kane GeoTech to measure pore pressures and seepage beneath a levee in the Sacramento River Delta. Vibrating wire piezometers measure water levels in the adjoining river, as well as pressures underneath the levee structure, correcting the latter against parallel measurements of barometric pressure above. These data are collected every hour, and can be downloaded by an inspector to a handheld computer from onsite monitoring stations.
Kane GeoTech has also installed a slightly more sophisticated system for railroad tracks that run along coastal cliffs for trains operated by the North County Transit District in San Diego. Here pulses are sent along cables every four minutes, and any spikes in the signal that would indicate ground movement are sent to a central office, which can immediately dispatch personnel to check out the situation.
Kane GeoTech representatives have suggested that similar TDR sensor cables could be installed in damaged New Orleans levees as they are being rebuilt, thereby minimizing the cost of introducing a similar monitoring system to this area. Given the communications technology that is now available, this instrumentation could well include modems that would transmit the resulting data over the Internet.
Innovation of Another Sort
One thing that’s certain is that Hurricane Katrina exposed the limitations of the traditional approach to levee building, as was obvious to a national panel of experts investigating firsthand how the storm surge after the hurricane caused the New Orleans structures to fail. The panel noted several instances where simple improvements could be made. For instance, a great deal of damage occurred when water overtopping the levees created waterfalls that tumbled over the normally dry sides of these structures. These steady cascades created “scour holes” that weakened levee foundations. This problem could be mitigated by placing concrete protective aprons at points where such waterfalls could occur.
Panelist Tom Zimmie, acting chairman of the civil and environmental engineering department of Rensselaer Polytechnic Institute, acknowledges that solutions to these problems may prove to be more expensive than even the most ambitious rebuilding effort will accommodate. But he argues that the scale of the project would make even the most modest improvements well worthwhile. “You’re talking about millions and millions of cubic yards of dirt,” he says. “There’s three hundred fifty miles of levees; a lot of them have to be patched up. A small innovation, a small saving, is a big deal.”
Dijkman notes, however, that building and monitoring infrastructure is not sufficient to fully protect against flooding. “A legal framework that requires regular reporting to the government about both the quality of the infrastructure and possible changes in storm conditions ensures that politicians are informed about any deficiencies,” he says. “They can then use that information to appropriate funds to help the flood defenses meet their original objectives.”
Dutch law not only specifies protection levels for flood-prone areas, but also requires levee managers to inspect their levees every five years, taking into account updated storm conditions. Dijkman suggests, “It could be worth considering such legislation in the United States. This could avoid any gap between the information available in the engineering and science community and the political arena.”
Hope for renewal.
Use of innovative construction and maintenance technologies may allow engineers to rebuild the New Orleans levee system (shown here flooding the Ninth Ward on 30 August 2005) stronger than before.
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Suggested Reading
[No author.] 2005. Case study: levee project comparison of geogrid and geosynthetic products. Available: http://www.mirafi.com/mirafactssummer2005/levee.htm
Mileti DS 1999. Disasters by Design: A Reassessment of Natural Hazards in the United States. Washington, DC: Joseph Henry Press.
Rogers JD Should I trust that levee? Available: http://web.umr.edu/~rogersda/flood_hazards/
Silva W Klijn F Dijkman J 2001. Room for the Rhine Branches in The Netherlands: What the Research Has Taught Us. Lelystad, Netherlands: Institute for Inland Water Management and Waste Water Treatment; Delft: Delft Hydraulics.
Smits AJM Nienhuis PH Leuven RSEW eds. 2000. New Approaches to River Management. Leiden, Netherlands: Backhuys Publishers.
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Environ Health Perspect. 2006 Jan; 114(1):A44-A47
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a00049EnvironewsScience SelectionsHypothesis Decay?: Blood Lead–Fluoridation Link Not Confirmed Tibbetts John 1 2006 114 1 A49 A50 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Numerous studies of various populations have shown that adding fluoride to drinking water prevents dental decay. However, a 1999 study in Massachusetts and a 2000 study in New York reported associations between the use of silicofluoride compounds in community water systems and elevated blood lead (PbB) concentrations in children. Now a team of researchers has tested the hypothesis generated by the Massachusetts and New York studies against findings from two other studies and found no cause for concern [EHP 114:130–134].
As of 2000, the Centers for Disease Control and Prevention estimated that more than 162 million Americans were receiving fluoridated water. In the United States, several agents are used for fluoridation, including silicofluoride compounds (sodium silicofluoride and hydrofluosilicic acid) and sodium fluoride. Researchers with the Massachusetts and New York studies hypothesized that the silicofluoride compounds in tap water might enhance lead leaching from pipes and increase lead absorption from the water itself. Elevated PbB concentrations in children are associated with a host of cognitive, developmental, and behavioral impairments so serious that lead-based paint was banned in the United States in 1978 and lead water pipe solder was banned in the 1980s.
The current research group evaluated the relationship between water fluoridation method and PbB concentrations in children by conducting a large-scale statistical analysis of two other preexisting studies: the 1992 Fluoridation Census and the Third National Health and Nutrition Examination Survey (NHANES III). In analyzing data from NHANES III and the 1992 Fluoridation Census, the team improved on prior analyses by log-transforming raw PbB concentration and by including information on possible confounding factors missing from the Massachusetts and New York studies. These included poverty status, urbanicity, duration of residence, and year in which the dwelling was built.
The NHANES III sample was comprehensive, representing more than 52 million U.S. children. This survey also oversampled young children, older adults, non-Hispanic blacks, and Mexican Americans to ensure that population estimates for these groups would be statistically reliable.
The team found that, overall, the PbB concentrations of children who lived in counties receiving silicofluorides did not differ significantly from the PbB concentrations of children living in counties without fluoridated water. This was true even when researchers controlled for the year in which children’s homes were built. Given these findings, the team states there is no support for concerns that silicofluorides in community water systems cause higher PbB concentrations in children.
However, the investigators acknowledge that their analysis has limitations. For example, NHANES III did not measure the lead content of drinking water consumed by study participants. Also, the team did not control for factors such as density of older housing, and they were unable to control for the solubility of lead in pipes affected by different temperatures and water hardness.
Because of these limitations, the investigators cannot completely rule out a link between water fluoridation method and lead uptake in children, particularly in children living in older dwellings. They speculate that other studies, possibly those including chemical investigation and animal toxicology, could yield additional valuable data. They conclude that efforts to prevent dental decay via the use of fluoridated drinking water should continue unless a causal effect of specific fluoridation methods on PbB concentration is demonstrated by additional research.
Refreshing news.
Although some questions remain, a new data analysis fails to confirm fears that fluoridation of drinking water results in higher blood lead along with stronger teeth.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a00050EnvironewsScience SelectionsManganese in Drinking Water: Higher Doses May Hamper Intellectual Function Sharma Dinesh C. 1 2006 114 1 A50 A50 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Manganese is an essential nutrient for humans, but its excessive consumption can cause adverse health impacts. Past studies have linked inhalation of excessive manganese to neurotoxicity in adults. Now a group of U.S. researchers suggests that ingesting high doses of manganese in drinking water can hamper intellectual function in children [ EHP 114:124–129]. These effects were seen most strongly on scales that measure performance aspects of intellectual function.
The same group had earlier observed a negative impact of water arsenic on intellectual function among children in Araihazar, Bangladesh. Though the manganese concentration in the water these children drank was much higher than its arsenic content, the independent impact of manganese on intellectual function could not be verified. The present study included 142 10-year-old children (including 54 children from the earlier study) who consumed well water with average concentrations of 793 micrograms per liter (μg/L) manganese and 3 μg/L arsenic.
The children’s intellectual function was assessed on six tests (similarities, digit span, picture completion, coding, block design, and mazes) drawn from the Wechsler Intelligence Scale for Children, Version III. Results were summed to create Verbal, Performance, and Full-Scale raw scores. These tests were chosen as they could be applied to Bangladesh’s rural context with minimal alteration. The results showed that manganese concentration had a significant negative dose–response association with all three raw scores.
The researchers found that children in exposure groups 1 (manganese lower than 200 μg/L) and 4 (manganese higher than 1,000 μg/L) differed significantly from one another for Verbal, Performance, and Full-Scale raw scores. Compared to group 1, children in exposure groups 2 (manganese between 200 μg/L and 500 μg/L) and 3 (manganese between 500 μg/L and 1,000 μg/L) had lower Full-Scale and Performance scores, but the differences were not statistically significant. Verbal scores of the children in groups 2 and 3 also did not differ significantly from those in group 1. Due to the lack of measures of intelligence standardized for use in Bangladesh, the team could not calculate IQ points lost.
Though the children’s waterborne manganese intake was lower than the highest safe daily dose (6 milligrams per day) estimated by the U.S. Institute of Medicine, the authors write that additional dietary exposure could have pushed the total daily dose above this value. Moreover, bioavailability of manganese from food is very low, while it is high from drinking water. This could have contributed to neurotoxicity seen in children drinking water with higher amounts of manganese.
The authors point out that their findings are relevant in the United States as well. Data collected by the U.S. Geological Survey have shown that about 6% of domestic wells contain manganese concentrations higher than 300 μg/L. Based on these data and their study results in Bangladesh, the researchers suggest that some U.S. children may be at risk for manganese-induced neurotoxicity.
Toxics, toxics everywhere . . .
Many studies have looked at the health effects of arsenic in Bangladeshi well water. New data now show that manganese in the water may also cause adverse effects.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a00051AnnouncementsNIEHS Extramural UpdateTiffany G. Bredfeldt, University of Arizona: Recipient of the 2005 Karen Wetterhahn Memorial Award 1 2006 114 1 A51 A51 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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The Superfund Basic Research Program (SBRP) is pleased to announce that Ms. Tiffany G. Bredfeldt of the University of Arizona is the recipient of the eighth annual Karen Wetterhahn Memorial Award. The award will presented to Ms. Bredfeldt on 13 January 2006 at the SBRP annual meeting in New York, New York.
The SBRP presents this annual award to an outstanding scholar to pay tribute to the life and scientific accomplishments of Karen E. Wetterhahn, former director of the SBRP at Dartmouth College. Dr. Wetterhahn died in 1997 as the result of an accidental exposure to dimethylmercury. An acknowledged international expert on the effects of heavy metals on biologic systems, Dr. Wetterhahn was a leader in conducting research on how metals initiate cancer and other metal-induced human diseases at the molecular level. She fostered links among biology, chemistry, environmental studies, engineering, and medical science, insisting that “the life sciences are interdisciplinary.”
Ms. Bredfeldt is a magna cum laude graduate of the University of Arkansas, where she earned a B.S. in microbiology and minored in Spanish. She is in the fifth year of a Ph.D. program at the University of Arizona, where, under the guidance of Dr. A. Jay Gandolfi, she is working to identify which species of arsenic have the potential to malignantly transform human cells. In research that Dr. Gandolfi characterizes as “breakthrough,” Ms. Bredfeldt demonstrated that a) at environmentally relevant levels of arsenic exposure, human bladder cells can generate monomethylarsonous acid (MMAIII), an arsenic metabolite that is 20 times more toxic than inorganic arsenic, and b) MMAIII can transform human bladder cells into a new cancer cell line. This appears to be the first observation of MMAIII-induced cellular transformation of any human cell line—a truly exciting finding. Ms. Bredfeldt’s observations strongly support the notion that arsenic metabolites may be functioning as the ultimate toxicants in arsenic-induced pathologies due to their heightened toxicity compared with inorganic arsenic.
The NIEHS congratulates Ms. Bredfeldt on her research accomplishments and wishes her continued success in her scientific career.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a00052AnnouncementsFellowships, Grants, & AwardsFellowships, Grants, & Awards 1 2006 114 1 A52 A57 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Environmental Health Sciences Core Center Grants
The National Institute of Environmental Health Sciences (NIEHS) invites applications from qualified institutions for support of the Environmental Health Sciences Core Centers. These centers are designed to build infrastructure in the fields of environmental health sciences and environmental medicine. By facilitating the use of shared research resources that serve the research in the mission areas of the NIEHS, investigators who are associated with EHS Core Centers will be poised to lead the field in new and important directions. The mission of NIEHS is to improve human health by increasing understanding of how environmental exposures impact biological processes and systems that result in disease.
A P30 Core Center grant is an institutional award, made in the name of a principal investigator, to support centralized resources and facilities shared by investigators with existing research projects. It is awarded competitively, initially for up to four years, and may be renewed for periods of up to five years. By providing a center structure and core resources, this support is intended to enhance the productivity of traditional research grants at the institution, focus investigators on environmental science issues relevant to clinical medicine and public health, and thereby improve the health of communities and the nation. A core center grant helps to integrate and promote research in existing projects and provides an administrative framework within one or several central themes; however, no funds are provided for direct support of research projects, except for pilot projects, recruitment of select new investigators, and research program development.
In contrast to past EHS Core Centers, the next generation of core centers is expected to bring their efforts to bear to a greater degree on translating environmental health research and related basic science results to public health and clinical arenas. One of the new goals of this program will be to develop opportunities and resources that support the integration of basic science with clinical research on patients and their care and public health research studying highly exposed population in the United States and around the globe. The core center thus is charged with creating effective teams at the institution(s) it serves to enhance existing programs in environmental health research and to build capacity in new and emerging areas which support or enhance these new directions in environmental health. The emphasis should be on fostering scientific excellence by providing resources unlikely to be attained by individual investigators, promoting collaborations among basic biomedical and clinical researchers, reaching out to innovative investigators in complementary fields, and facilitating cutting-edge research that addresses public health issues in a timely manner.
In addition to direct research support services, the center should provide career development for future research leaders. This can include training and mentoring to junior faculty in environmental health sciences, promoting interactions with established investigators in related disciplines, and helping young scientists and clinician-scientists to build foundations for careers in NIEHS-sponsored programs. Investigators and trainees are encouraged to interact with NIEHS program officials with the goal of promoting grants-manship and eventual funding by NIEHS.
Overall, NIEHS expects that an EHS Core Center will: 1) provide intellectual leadership and innovation in environmental health sciences, environmental medicine, and public health; 2) stimulate integration of basic, applied, and clinical research in order to better understand the impact of environmental exposures on human disease; 3) facilitate and develop new multidisciplinary and interdisciplinary research strategies to advance the field; 4) incorporate novel technologies and methods into EHS research; 5) serve as a source of information and expertise to the surrounding communities and stakeholders in order to further scientific collaborations and dissemination of research results; 6) provide career development for future research leaders. Membership in the center should help build identity in the fields of environmental health sciences, environmental medicine, and public health through mentoring, training, and interactions with programs developed by NIEHS.
The EHS Core Center must be an identifiable organizational unit within a single university, medical center, or a consortium of cooperating institutions with a university affiliation. The EHS Core Center grant mechanism provides core support to foster integration, coordination, and interdisciplinary interaction and cooperation among a group of established investigators conducting high-quality research clearly related to the effects of environmental factors on human health. The NIEHS uses this mechanism to integrate and build upon existing programs and institutional resources such as university-wide facilities and services that encourage and enhance research on environmentally induced disorders.
An EHS Core Center provides an administrative structure and an environment to strengthen and increase productivity and generate new ideas through organized interdisciplinary collaborative efforts. Its goal is to enhance the capabilities of existing programs in environmental health sciences, to assist with building the capacity for environmental health studies at institutions with less developed programs, and to support the development of future directions needed for the field to mature. Therefore, the EHS Core Center grant provides an added dimension that includes capability and potential for net accomplishment which will be greater than that possible by the support of individual projects. The EHS Core Center grant provides support for core resources and facilities to be used by center investigators.
This support includes administrative and facilities personnel, equipment, supplies, and services. In addition, it provides limited funds for pilot projects, training, career development, and, optionally, community outreach. The EHS Core Center grant does not provide direct funding for ongoing research projects which are expected to be supported through other mechanisms, mainly individual research grants and program projects awarded by the NIH. Stipends and tuition for trainees, with certain exceptions for named new investigators and newly recruited investigators as described below, are not provided by the EHS Core Center grant.
To qualify for an EHS Core Center the applicant institution must already have an identity in environmental health sciences as defined as a substantial base of ongoing, independently supported, peer-reviewed research projects related to the study of environmental health sciences or environmental medicine, a substantial portion of which should be supported by NIEHS. This currently funded research base provides the major support for a group of investigators who would benefit from shared resources. The research base must exist prior to the submission of an application and will be a critical element considered during the peer review process. Focus, relevance, interrelationships, quality, productivity, and, to some extent, quantity, are all considerations in judging the adequacy of the research base. 1) The center director is the designated leader of the EHS Core Center and provides scientific and administrative leadership for the total program. The center director is required to commit a minimum of 20% effort to the center. 2) An administrative core oversees organizational, budgeting, and reporting aspects, and provides the leadership for scientific and programmatic activities of the EHS Core Center. 3) Facility Cores are shared facilities that serve to enhance or make more cost effective the services, techniques, or instrumentation used by the investigators within the EHS Core Center. Cores should extend, support, and contribute to the work of the center members. A center should have a minimum of two facility cores, including an Integrative Health Sciences Facility Core.
Facility cores are the major function of the EHS Core Center. Facility cores must have at least three users and are designed to furnish groups of center investigators with techniques, services, or instrumentation that will enhance the research in progress, consolidate manpower effort, and contribute to cost effectiveness. A new requirement for the core centers program is that an Integrative Medicine Facility Core is required (see below). This core is intended to facilitate clinical investigations—either patient-oriented or population-based research that would enhance translation of basic research findings into practical impacts for patients and communities. Services available through this core would provide the opportunity for center members to obtain clinical samples and patient data needed for their research. These services could also be directed at studies of the natural history and prognosis of disease in patient populations.
A Community Outreach and Education Core (COEC) is optional and not required. However, inclusion of a COEC allows the center to make an additional request of up to $100,000 annual direct costs. 4) A pilot projects program is required and is considered to be an integral part of the support provided. This program provides modest support for new initiatives or feasibility projects for either new investigators or for established mid-level investigators who are moving into research areas of direct interest to the EHS Core Centers. Up to 25% of the budget can be allocated to the pilot projects program.
As of 1 September 2005, the following changes are implemented for new and competing applications to the Environmental Heath Sciences Core Centers: 1) NIEHS intends to merge the NIEHS Core Centers and Marine Freshwater Biology Centers programs beginning September 1, 2005. The new combined program will be called the Environmental Health Sciences Core Centers Program (EHS Core Centers). 2) Site visits will no longer be conducted as part of the review process. Program staff may decide to visit selected applicants to gain further information on which to base funding decisions. 3) The program endeavors to focus investigators to a greater extent on clinical applications, translation, and multidisciplinary research that will speed research findings to clinical practice and environmental medicine. 4) In order to provide increased flexibility in organization and structure of the EHS Core Center, the director may develop a dynamic structure which meets the on-going intellectual needs of the center. This structure can change as the intellectual needs change to accommodate new opportunities for collaboration. Research cores are no longer required as organizational units in the center. The proposed center organization must include the required components outlined above, but beyond those requirements no additional structure is imposed by NIEHS. 5) An Integrative Health Sciences Facilities Core is required as one of the Center Facility Cores. 6) Community Outreach and Education programs which focus on partnering with stakeholders in order to disseminate EHS Center research results are optional. Centers that choose to apply for a Community Outreach and Education Core are eligible for additional $100,000 direct costs. Kindergarten–Grade 12 curriculum development and implementation is no longer allowed as a COEC activity. 7) Page limits apply to the application (see Section IV, Part 6 Other Submission Requirements of this RFA). Applicants can download preformatted tables to facilitate completion of the application from the NIEHS website at http://www.niehs.nih.gov/centers/appguide.htm.
A vision and set of goals must be developed and described in the application. The center director must provide the leadership in a written strategy for how the center will implement this vision and future directions during the project period. The plan will outline the existing skills, technologies, and scientific research base, and other resources at an institution. This plan should describe how the core center will enhance ongoing projects, assist in the introduction of outstanding new projects, and promote collaborations, advances in technology, and progress in environmental health sciences. The center director must detail expected scientific outcomes, including a description of the clinical application and/or impact of these outcomes on public health and environmental medicine. An organizational chart should be included to illustrate the structure, interactions, and leaders of the core center. The application must define, in this section, the eligibility criteria for center membership and note which individuals play key leadership roles in the center.
This plan must address the following critical elements: 1) Theme: Provide the central theme(s) of the EHS Core Center and the likely supported research, resources, and relevance to environmental medicine. The theme may be broad or focused, depending upon the goals of the core center. 2) Goals and directions: Describe current and future directions for the core center in the forthcoming project period. How will the research supported by the EHS Center impact the understanding of environmental health sciences and, ultimately, public health? Describe the short-, mid- and long-term goals and measures of success. What are the likely advances expected in the field of environmental health, and how can these advances be applied to clinical medicine and public health? Describe any basic science work that has successfully been translated to the bedside or community or plans to enhance that translation in the next project period. What expected, widely applicable research tools and scientific advances will emerge from the center’s emphasis? Document how the center will organize and lead the team towards these advances. Identify levels of risk for these goals, potential roadblocks to achieving them, and how the center might respond to these challenges. Competing continuation applications must also describe the accomplishments of the center in the preceding project period and how it intends to build upon its successes. These accomplishments should be presented in three areas: basic science, clinical research, and public health science. The impact of center-based science should be discussed in detail. 3) Integration of investigators of multiple skills and talents: Outline steps the center will take to promote multidisciplinary studies and collaborations, especially among basic scientists and clinical researchers. What types of initiatives will stimulate the teams and attract high-caliber professionals? To what degree will high-risk/high-payoff research that may require long-term support be implemented? 4) Building research capacity: Provide details on the special talents and resources that will be drawn to and built upon at the center. How will these talents be harnessed and used to promote new collaborations and produce multidimensional teams to address more complex questions? Include a plan for bringing investigators into the center from within and outside EHS. Describe academic and research partnerships that will be pursued by the center to advance its goals and missions. 5) Provide a plan to determine the need for services and instrumentation of the center. Address the steps that will ensure that the core center proceeds at the cutting edge of technology and concepts. It is expected that facility cores needs may change with time. Include information on the process of reevaluation of needs and implementation of changes.
The EHS Core Center grant mechanism fosters interdisciplinary cooperation among established investigators conducting high-quality research in environmental health science. Therefore, existence of a strong research capability in environmental health sciences is fundamental to establishment of a new, or continuation of an existing, EHS Core Center. To qualify for EHS Core Center support, an institution must demonstrate this research capability so as to have a clearly identifiable, major scientific focus in environmental health research. Consequently, an existing program of excellence in biomedical research in the field of environmental health science is a basic prerequisite for establishment of an EHS Core Center. Furthermore, a center must be able to capitalize upon these research capabilities and resources to advance significantly our understanding of its chosen scientific focus.
At the time of submission of a new or competing continuation application, any institution or consortium wishing to qualify for the EHS Core Center grant must have a minimum of five active NIEHS-supported research grants from four distinct principal investigators. Acceptable grant support includes R01, R21, R37, P01, P42, P50, Cooperative Agreements (U-grants) or Research Career Development Awards (K-grants) with a minimum of two years of funding remaining, not including administrative extensions, either with or without additional funds. Each multicomponent (e.g., P01, P50, or U01) award will count as one qualifying research project. A subproject of a multicomponent award (e.g., P01) that is subcontracted to the applicant institution can be counted only once towards the research base.
Research grant support from NIH and sources other than PHS should be listed and will be considered in the determination of its suitability of focus on environmental health sciences if the research is 1) related to human health in areas where there is evidence for the involvement of environmental factors in disease etiology or phenotypic expression; 2) of outstanding quality; and 3) funded by an entity using peer or internal review of rigor comparable to that of PHS. NIEHS will have the final decision in determining whether the applicant center institution has the critical mass of direct costs, grants, and investigators. Prior to submission of an application, the proposed center director must consult with institute staff regarding the adequacy of the research base.
Applicants must detail grants and funding sources in this section by completing, for example, Table A: Grant Support (http://www.niehs.nih.gov/centers/appguide.htm) and by describing environmental health sciences research at the applicant institution(s) that emphasize the focus, interactions, relationships, and scientific excellence of the projects and investigators and the impact on advancing scientific knowledge relevant to environmental health issues. Include in the appendix a brief abstract of approximately one-half of a page for each project. All tables mentioned below may be accessed at this website.
Competing continuation applications need to describe how the existing center facilitated a leading role in environmental health at its home and associated institutions and should document the outcomes and impact of the core center on research efforts during the preceding funding period. This should include a summary of research highlights which were accomplished as a result of center infrastructure and support, how facilities were made available to the maximum number of qualified investigators, the changes in resources that might have been made to accommodate altered user needs and/or increased demand, a composite list of publications, examples of subsequent funding for new directions highlighting collaborations fostered by the center, and career advances and training outcomes. To assist in preparing the application, Table D1: Publications Resulting from Center Involvement, and, if appropriate, Table D2: Publications Resulting from Pilot Program Funding Applicants, which have been preformatted to facilitate completion, can be downloaded from the NIEHS website. Measures of accomplishment also include pilot projects that led to NIH or other peer-reviewed research applications; new or improved tools, discoveries, or patented inventions (and documentation of the wide use of such tools); training and recruiting of new investigators who have advanced in their careers in environmental health; and, where applicable, outreach to affected communities and appropriate educational outcomes.
Each applicant institution will specify an experienced and respected center director with authority to oversee the organization and operation of the center and to provide scientific and administrative leadership for the total program. The center director should devote at least 20% total effort to the center. A deputy center director must also be designated to serve in the absence of the director, with other responsibilities described. The background and scientific and administrative expertise of the center director and the deputy director should be described fully in the application. For competing applications, an assessment of past performance is required.
Emphasis on career development for environmental health scientists is strongly encouraged. The application should address plans that will promote training of new investigators and bring new expertise into the area of environmental health sciences. Specify the plans to cross-train researchers in current techniques that are absent from the EHS Core Center or individual research programs. Training and cross-training may include collaborations that will introduce a focus on human subjects and tissues into laboratory-based studies. These aspects of the program should be designed to prepare new investigators for an independent career in environmental health sciences.
The following activities are consistent with this aspect of the EHS Core Center: New investigator: Temporary salary support (up to 75%) and laboratory set-up costs can initially be provided in the application for a named new investigator in a specified area of research. The investigator can be a worker in the basic sciences, clinical research, or public health disciplines relevant to environmental health. This investigator is eligible to compete for support for up to two years through the pilot project program. Subsequently, recruited individuals are to be named by the center director and submitted for approval to the center’s internal or external advisory board, as appropriate.
Newly recruited center investigators: The EHS Core Center grant may provide partial salary support (up to 50%) for investigators newly recruited from outside the center. This mechanism is intended to develop research programs by providing support for younger investigators who are at the beginning stages of their research careers, to add needed expertise to the center structure, or to bring new methods and technologies into the environmental health sciences arena that enhance the center’s activities. Likewise, former graduate and postdoctoral students of center members should not be considered for support unless it can be satisfactorily demonstrated that they have established independent research careers.
Funds awarded under this section may be used for salary, technical support, and equipment. The remaining salary support for the newly recruited center investigator must be derived from other than center funds. For each investigator, the duration of support as a newly recruited center investigator will be limited to no more than two years. Specific individuals to be awarded newly recruited center investigator support need not be identified in the application, but the amount budgeted for this purpose should be declared, and, to the extent possible, the types of individuals sought and their expected roles in the center described. Competing continuation applications should include a discussion of how these funds were used in the previous project period in terms of who was recruited and how these individuals benefit the center programs.
Career development activities in clinical research: The EHS Core Centers mechanism requires clinical and basic scientists with a broad range of skills to work together on a unified theme. Therefore, it presents a rich environment for young clinical investigators to be exposed to and develop additional research skills. Mid-level clinical investigators and scientists in other fields may also be attracted by opportunities in the center to focus their attention on issues in environmental health sciences and environmental medicine. Financial support can be provided for training and mentoring of physician scientists to study environmental health issues that are relevant to the public health arena and clinical practice. In addition environmental health scientists can be supported to engage in activities which increase their understanding of clinical medicine. The objective of this activity would be to assist new investigators in progressing to more senior status and eventual NIEHS funding by enhancing their research skills and knowledge of the grants process. These activities can be constituted as an independent facility core, or as part of the administrative core.
The career development activities should be directed by an investigator with strong mentoring credentials who will devote a defined percent effort (5% suggested). To facilitate mentoring and multidisciplinary developmental activities, active involvement by senior investigators within the core center is strongly encouraged in an effort to match mentors with candidates. The plan for career development activities will be evaluated in terms of potential effectiveness in developing the skills and research capabilities of new clinical investigators as reflected in the following required elements of the application: 1) a discussion of how mentoring and the professional development of the investigators will be achieved, including their progression to a more independent status; 2) a plan for monitoring the progress of the career development of selected investigators; 3) examples of planned scientific enrichment activities for selected investigators, including training experiences, mini-sabbaticals, special lectures, visiting scientist symposia, seminars, workshops, and short courses both at the parent institution or off-site.
To increase diversity in the student and faculty populations and the participation of individuals currently underrepresented in the biomedical, clinical, behavioral, and social sciences, applicants are encouraged to designate new and newly recruited investigators from the following groups: women; underrepresented racial and ethnic groups; individuals with disabilities; and individuals from socially, culturally, economically, or educationally disadvantaged backgrounds that have inhibited their ability to pursue a career in health-related research.
Direct costs for career development activities should not exceed $50,000. Assisting new investigators in attaining independent status should be an objective of the core activities. Sponsored participants should be encouraged to apply for NIEHS-sponsored career development awards, patient-oriented research grants, or other types of independent support. Contact with NIEHS program staff is encouraged at an early stage in submission of new applications.
The institutional commitment at the applicant institution will be a major consideration in ensuring the goals of the core center. The parent institution should recognize the EHS Core Center as a formal organizational component and provide documented evidence of space dedicated to the needs of center, protected time to devote to center activities, staff recruitment, dedicated equipment, or other financial support for the proposed center. The parent institution should provide assurance of its commitment to continuing support of the EHS Core Center in the event of a change in directorship and a well-defined plan for this eventuality should be in place.
The organization and structure of the EHS Core Center should reflect the goals of the center, encourage collaboration, develop and implement center-wide initiatives, and promote the use of shared resources and pilot project funds. The structure can change as needed based on new scientific opportunities and partnerships. The application should include a description of the organization and structure of the center and illustrate all components in an organizational chart.
It is expected that organization of the administrative core will provide a supportive structure sufficient to ensure accomplishment of the following: 1) coordination and integration of center components and activities; 2) assessment of productivity, effectiveness, and appropriateness of center activities and determination of center membership assessment of scientific opportunities and areas for collaboration among center members; 3) organization of center activities, such as retreats, invitation of consultants, meetings, and focus groups; 4) organization of the internal and external advisory groups; 5) record keeping of meeting minutes and measures of success including: use of EHS Core Center facilities, publications, pilot project awards, and new grant applications resulting from preliminary data enabled by the center; 6) interactions with other centers, the NIEHS, and other appropriate individuals, groups, or organizations.
The administrative structure must include an Internal Advisory Committee (IAC) and an External Advisory Committee (EAC). Further details for constitution of these committees are available in the complete set of Guidelines for Environmental Health Sciences Core Center Grants. Competing continuation applications must document the functions and effectiveness of the external and internal advisory committees.
To assist in preparing the application Table B: center members, which has been preformatted to facilitate completion, can be downloaded from the NIEHS website.
The major function of the center grant is to support facility cores which are designed to furnish groups of center investigators with techniques, services, or instrumentation that will enhance the research in progress, consolidate manpower effort, and contribute to cost effectiveness. At least three investigators with independently funded projects and demonstrated need for such a core service form the minimum required research base to establish a core facility. Additionally, the minimum of three funded investigator users does not in itself provide sufficient justification for establishment of a facility core. The center must have at least two facility cores. A new requirement is the Integrative Health Sciences Facility Core, which is described, below. To assist in preparing the application, Table C: Facility Core Use, which has been preformatted to facilitate completion, is provided and can be downloaded from the NIEHS website. Separate tables, such as Table C, are to be provided for each facility core.
Facility cores should draw on center research needs, including but not limited to animal use and transgenics, imaging, tissue culture, pathology support, statistical support, oligonucleotide synthesis, analytical chemistry, proteomics, bioinformatics, exposure assessment, and handling of human tissue specimens. Establishment and continued support for facility cores by an EHS Core Center application must be justified on the basis of use by independently funded center investigators. The utilization of facility cores by pilot projects is encouraged. Use of core facilities by projects funded by research and development contracts will be evaluated on an individual basis. In general, use of core facilities by contracts must be paid in full from the contract funds, not from the EHS Core Center grant funds.
Facility cores for the EHS Core Center should be unique and are not to duplicate services or facilities that already exist at the parent or collaborating institutions. University-wide facility cores providing services in areas relevant to environmental health research have become more widely available at many research centers. EHS Core Centers should utilize existing facility cores where appropriate and describe in the application how members of the EHS Core Center would receive priority access, favorable cost arrangements, and training on unique technologies. If facilities within a university-wide facility are not sufficient to meet the needs of the EHS Core Center, then the applicant is to provide information on the existing facilities and on how the center and greater university facility plan to partner. Proposed center facility cores that appear to replicate services already available at the applicant institution will not be allowed without extensive justification.
The application must provide the total operating budget for each facility core together with the percentage of support requested from the center grant. User logs or similar information used to complete the online form should be maintained and made available on request to the NIEHS in order to validate the extent of use and degree of sharing. In the case of new proposed centers or new facility cores within an existing center, similar information regarding anticipated use of the cores should be provided. Define the use or expected use of the facility core by center members and/or projects in terms of low, medium, or high (on a scale of 1–3).
Each facility core must have a designated leader who will be responsible for core activities. The application should explain the organization and proposed mode of operation of each core. It should include a plan for prioritizing investigator use of the core as well as a definition of qualified proposed and potential users. This definition need not be too narrow, since limited use of a core might be an enticement to established investigators in other fields to lend their expertise to the field of environmental health. The use of the facility core for training purposes is encouraged, and, if so planned, a description of the extent of and approach to this training should be included.
Although facility cores are meant to provide services for center members, they also play an important role in developing new methodologies, adapting instrumentation for center needs, and educating center members of the value and utility of services and methods. Limited funds can be designated to support these aspects of the facility cores and discussion of how these activities will be performed should be included in the application.
The Integrative Health Sciences Facility Core is required and should be designed to facilitate the translation of basic research findings into clinical or public health applications. This core provides new and critical resources and will be a vital component of the progression of environmental health sciences from the bench to the bedside and affected communities. It is expected that the concepts and goals of environmental medicine will be integrated into the range of activities that the greater core center undertakes.
This core is to be designed to support collaborative efforts among basic scientists, clinical researchers, and/or public health practitioners by 1) providing services and access to instrumentation and technologies that foster integration of basic science, public health research, including epidemiology and intervention studies, and patient-oriented clinical research; 2) supporting research to improve early detection, prevention, and/or therapy for environmentally-related disorders; 3) support partnerships between researchers and community-based organizations which impact on conduct of clinical and public health research.
Among its functions, the Integrative Health Sciences Facility Core may provide services which provide access to well-characterized patients and control subjects for research projects. These can include study subject recruitment and retention activities, and follow-up by mail, phone, or in person to gather needed data for research projects. Clinical services may include clinical laboratory or other assessments, pathology services, collection, processing and long-term storage of human tissue samples, blood, urine, or other biospecimens, and preparation of questionnaires or other assessment tools. The IHSFC can facilitate and support partnerships between study populations or communities, health care providers, or others. Description of services, equipment, and other activities of this core need to be well documented. Procedures for collecting, storing, and distributing biological samples, should be included in the application. Partnerships with other units at the institution which support these types of activities (e.g., General Clinical Research Centers) are encouraged and letters of support should be included in the application. As for all facility cores, the application should include a description of the types of research projects and/or clinical trials that use or plan to use the core. Include specific examples and the likely benefits to other research activities.
For the purposes of the EHS Core Centers, clinical research is as defined by NIH. This definition can be found in the Guidelines for Environmental Health Sciences Core Center Grants.
Inclusion of a Pilot Projects Program is required and is an integral part of the EHS Core Centers. A plan to support pilot studies for basic or clinical biomedical, epidemiological, educational, or behavioral research should be included and budgeted in the application. The description of a plan to solicit, review, and administer pilot grants must be included in the administrative core and a separate budget, including the total request for pilots, must be submitted. Criteria for review of pilot studies must be developed and included in the application. Up to 25% of the direct cost budget for each year should be allocated to the Center Pilot Projects Program to support short-term projects to explore the feasibility of new areas of study which leads to collection of sufficient data to pursue support through other funding mechanisms. Include a clear description of the process designed to award and evaluate progress in pilot projects. Investigators are encouraged to consult with NIEHS program staff for submission of new NIH applications based on pilot project-supported data.
Competing continuation applications should provide documentation of the existing pilot projects program. Include the process for application review and award and the measures of success, such as publications, subsequent funding, and career advancement of the sponsored individuals. A competing continuation application should include: historical overview of the Pilot Project Program during the last program period; a description of the management of the program; and a listing of all pilot projects which were supported during the last project period. To assist in preparing the application, Table E1: Pilot Projects Outcomes and Table E2: Grant Details for Pilot Projects, which have been preformatted to facilitate completion, are provided and can be downloaded from the NIEHS website.
Pilot projects are primarily intended to 1) provide initial support for new investigators to establish new lines of research; 2) allow exploration of possible innovative new directions representing a significant departure from ongoing funded research for established investigators in environmental health sciences; ideas of particular importance in environmental health sciences are paramount; 3) stimulate investigators from other areas of endeavor to apply their expertise to environmental health research and environmental medicine; 4) foster opportunities that meet goals set out in the EHS Core Center Plan. Pilot projects should strive to fill in gaps in research areas relevant to the scientific focus of the core center.
NIEHS Core Centers have the option to develop and sustain community outreach and education activities. The objective of the Community Outreach and Education Core (COEC) is the translation of research information into knowledge for various professional and public stakeholders. Therefore, each center that chooses to develop a COEC must demonstrate that the objectives, activities, and products are aligned and integrated with the research strengths and focus of their center.
Programs developed by COEC will lead the field of environmental health outreach and education at the local and national level. To this end, the goals of the COEC are to 1) develop partnerships with stakeholders to translate and disseminate EHS Core Center science; 2) work with community-based organizations, disease advocacy groups, and other local, state, or regional partners to enhance the dialogue on environmental health issues in their regions; 3) develop and implement appropriate outreach and educational programs to increase awareness and understanding of environmental health research being conducted at the EHS Core Centers; 4) evaluate outreach models, disseminate results at local and national levels, and promote models for national implementation.
To meet these goals, it is essential for COECs to state clear and measurable objectives; possess appropriate expertise to fulfill its stated objectives; identify specific environmental problems; demonstrate alignment to research strength and focus of the center; identify existing and future partners; prioritize short-, mid-, and long-term activities to be implemented; list and describe expected products; state anticipated impacts and their significance for environmental public health; and define evaluation tools to measure the impact of core activities.
For the purposes of the EHS Core Center Program, there are three target audiences of interest: community, policy makers, and public health and/or health care professionals. COECs may select more than one target audience, but are required to choose only one.
Target Audience: Community.
Types of activities: The COEC may include one or more of the following: 1) convene public environmental health awareness forums or workshops in the community with defined goals and measurable outcomes; 2) host and organize disease prevention and intervention programs, especially those that are community-based; 3) create informational programs that address environmental health concerns or issues in the community, e.g., radio or television shows; 4) evaluate local outreach models, disseminate findings, and promote dissemination of models for national implementation; 5) establish environmental health research programs for high school students that nurture their interest in science and public health.
Target Audience: Public Health and/or Health Care Professionals.
Types of activities: The COEC may include one or more of the following: 1) develop and implement educational programs in environmental health science for health care providers. COECs may wish to develop and offer continuing education workshops; 2) create tools and resources for health care providers that can be used and disseminated nationally; 3) create and nurture national networks of public environmental health outreach specialists to have a national impact. COECs should build upon the outreach and education expertise around the United States.
Target Audience: Public Health Decision Makers.
Types of activities: The COEC may include one or more of the following: 1) provide advice and information to stakeholders who participate in designing and implementing health policy; 2) create materials based on research that can be used to educate decision makers about environmental health issues; 3) organize meetings addressing defined environmental health issues; 4) participate in committees at the local and national levels in order to translate the scientific findings of the center into public health and regulatory programs.
Should a core center choose to support a COEC? 1) The COEC is required to establish a stakeholder advisory board to strengthen the bidirectional interaction between the core center and partners. The purpose of this advisory group is to ensure center understanding of community and other stakeholder needs, as well as to ensure more effective dissemination of center research in appropriate venues. The center should develop a specific plan and set of integrated activities for COEC, particularly with respect to the center’s defined community and target audience. COEC must be a logical outgrowth of the scientific focus of the center and exhibit the potential for mutual benefit due to interactions with center investigators. 2) COECs must possess the appropriate expertise for the identified target audience and outlined activities. It is important that COECs be directed by staff trained in public health, outreach and education, and other relevant disciplines at a master’s or doctoral level. 3) Collaborations among COECs in EHS Core Centers are desirable. Support of collaborations can be from NIEHS/NIH or other agencies and foundations. 4) COECs are encouraged to collaborate with NIEHS staff within the Division of Extramural Research and the Office of Communication and Public Liaison in developing printed and audiovisual educational materials. These outreach activities must be identified as programs supported by the NIEHS Core Center. All COEC-produced materials must be submitted to the Community Outreach Resource Center. 5) Support for appropriate staff positions, travel, equipment, and supplies for this activity is allowed. 6) Please note COEC is not intended to include human subject research, epidemiology, clinical trials, clinical services delivery, or community-based research. However, COEC may be useful as a means of establishing a relationship with a community-based organization that could form the foundation of a research grant application. In such cases, appropriate COEC proposals may be considered for pilot project funding. The program should not go beyond public and community education concerning environmental disease risk and/or hazard exposure recognition, because the COEC is not intended to give medical, legal, political, social, or economic advice. 7) K–12 curriculum development is no longer allowed as a COEC activity.
This funding opportunity will use the P30 award mechanism. As an applicant, you will be solely responsible for planning, directing, and executing the proposed project.
This funding opportunity uses the just-in-time budget concepts. It also uses the nonmodular budget format described in the PHS 398 application instructions (see http://grants.nih.gov/grants/funding/phs398/phs398.html). A detailed categorical budget for the Initial Budget Period and the Entire Proposed Period of Support is to be submitted with the application.
The PHS 398 application instructions are available at http://grants.nih.gov/grants/funding/phs398/phs398.html in an interactive format. Applicants must use the currently approved version of the PHS 398. For further assistance contact GrantsInfo, 301-435-0714 (telecommunications for the hearing impaired: TTY 301-451-0088) or by e-mail: [email protected].
Applications must be prepared using the most current PHS 398 research grant application instructions and forms. Applications must have a D&B Data Universal Numbering System (DUNS) number as the universal identifier when applying for Federal grants or cooperative agreements. The D&B number can be obtained by calling 866-705-5711 or through the website at http://www.dnb.com/us/. The D&B number should be entered on line 11 of the face page of the PHS 398 form.
The deadline for receipt of letters of intent is 15 February 2006, with 15 March 2006 the deadline for receipt of applications. The complete version if the RFA is available at http://grants.nih.gov/grants/guide/rfa-files/RFA-ES-05-008.html
Contact: Leslie Reinlib, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, P.O. Box 12233 (EC-21), 27709 USA, 919-541-4998, fax: 919-316-4606, e-mail: [email protected]. Reference: RFA-ES-05-008
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a0016a16393639PerspectivesCorrespondenceBisphenol A and Risk Assessment Politch Joseph A. Department of Obstetrics and Gynecology, Boston University School of Medicine, Boston, Massachusetts, E-mail:
[email protected] author is a consultant for the Weinberg Group in Washington, DC.
1 2006 114 1 A16 A16 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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In a recent article, vom Saal and Hughes (2005) proposed that a new risk assessment on bisphenol A (BPA) is needed because of the availability of extensive new literature, including “recent epidemiologic evidence that BPA is related to disease in women.” Specifically, the only research that vom Saal and Hughes cited as evidence relating BPA to disease is a study by Takeuchi et al. (2004), which they describe as a case–control study that reports that ovarian disease in women is related to blood levels of BPA.
Vom Saal and Hughes (2005) have misrepresented the Takeuchi study (Takeuchi et al. 2004): It is not a case–control study, and it does not demonstrate that BPA is specifically associated with ovarian disease.
Takeuchi et al. (2004) conducted a small cross-sectional descriptive study that assessed 73 women with respect to serum BPA, hormone concentrations, and their clinical condition at a single point in time. Women were categorized clinically as normal (either obese or nonobese), or as having hyper-prolactinemia, hypothalamic amenorrhea, or polycystic ovary syndrome (PCOS) (again, either obese or nonobese). The six groups in the study each contained as many as 19 subjects (nonobese normal group) and as few as 6 subjects (PCOS obese group). The authors reported that serum BPA was higher in women with PCOS (both obese and not obese) and obese normal women than normal women who were not obese. There were also significant positive correlations between serum BPA and various androgens. Takeuchi et al. (2004) concluded that there is a strong relationship between serum BPA and androgen, and they noted that there are a number of possible explanations for this relationship.
Takeuchi et al. (2004) appropriately acknowledged that their study was a hypothesis-generating study and they did not attempt to draw conclusions about causal relationships.
Vom Saal and Hughes (2005) overstated the importance of this low-level epidemiologic evidence by referring to it as a case–control study. A case–control study is a more rigorous epidemiologic study in which a group of cases (i.e., with the disease of interest) is compared to a group of controls (i.e., without the disease of interest) with respect to exposures that occurred before the development of disease. Rather, Takeuchi et al. (2004) conducted a cross-sectional study in which both exposure and disease were assessed at a single point in time. When both exposure and outcome are assessed at a single point in time, it is not possible to determine whether the exposure preceded the clinical condition or whether the clinical condition affected the individual’s level of exposure. A cross-sectional study cannot test hypotheses; at most, it can merely examine correlations. Furthermore, cross-sectional studies cannot control for confounding factors that may obscure the true relationship between exposure and disease (Hennekens and Buring 1987).
Vom Saal and Hughes (2005) overlooked the intended primary focus of the paper by Takeuchi et al. (2004), which is that there is a relationship between serum BPA and androgen levels. The exact nature of this relationship is not known at this time and Takeuchi et al. (2004) speculate that BPA may stimulate androgen production, or, more likely, androgen may suppress the metabolism of BPA. Consequently, women who have clinical conditions that are associated with elevated androgen (e.g., PCOS or obesity) may have elevated levels of BPA as a result of their elevated androgen. The cross-sectional study by Takeuchi et al. cannot shed light on the time course of events and, therefore, cannot address causal relationships among any of the variables studied in these women.
In addition, a number of recent studies have reported that several of the ELISA kits available for measurement of serum BPA [the analytic method used by Takeuchi et al. (2004)] overestimate BPA concentrations and exhibit considerable cross-reactivity, calling into question the validity of results generated by such methods (Fukata and Mori 2004; Fukata et al. 2003; Kawaguchi et al. 2003). Furthermore, it is well known that BPA is metabolized and eliminated rapidly (Volkel et al. 2002), so serum levels provide only a snapshot of BPA exposure within the last day. It is not meaningful to correlate an acute exposure (serum BPA at one time-point) with a chronic disease that took years to develop. Chronic exposure to BPA would have to be demonstrated and not assumed.
The Takeuchi et al. (2004) study suggests a hypothesis that could be further examined in an appropriately controlled analytic study. It should not be portrayed as recent epidemiologic evidence that demonstrates an association between blood levels of BPA and clinical disease in women.
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References
Fukata H Mori C 2004 Considerations in quantifying endocrine disrupting chemicals especially those in human samples Japan Society of Endocrine Disrupters Research Newsletter 6 3
Fukata H Teraoka M Takada H Todaka E Mori C 2003. Measurement of bisphenol A by HPLC and ELISA in serum and urine [Abstract]. In: Proceedings of the 6th Annual Meeting of Japan Society of Endocrine Disrupters Research, 2–3 December 2003, Sendai, Miyagi, Japan. Tsukuba, Ibaragi, Japan:Japan Society of Endocrine Disrupters Research, B-1-2.
Kawaguchi M Ito R Funakoshi Y Nakata H Yoshimura M Inoue K Nakazawa H 2003. Estimation of analytical methods for measurement of BPA in human samples [Abstract]. In: Proceedings of the 6th Annual Meeting of Japan Society of Endocrine Disrupters Research, 2–3 December 2003, Sendai, Miyagi, Japan. Tsukuba, Ibaragi, Japan:Japan Society of Endocrine Disrupters Research, PA-28.
Hennekens CH Buring JE 1987. Epidemiology in Medicine. Boston:Little, Brown and Company.
Takeuchi T Tustsumi O Ikezuki Y Takai Y Taketani Y 2004 Positive relationship between androgen and the endocrine disruptor, bisphenol A, in normal women and women with ovarian dysfunction Endocr J 51 165 169 15118266
Volkel W Colnot T Csanady GA Filser JG Dekant W 2002 Metabolism and kinetics of bisphenol A in humans at low doses following oral administration Chem Res Toxicol 15 1281 1287 12387626
vom Saal FS Hughes C 2005 An extensive new literature concerning low-dose effects of bisphenol A shows the need for a new risk assessment Environ Health Perspect 113 926 933 10.1289/ehp.7713 16079060
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a0016b16393639PerspectivesCorrespondenceBisphenol A: vom Saal and Hughes Respond vom Saal Frederick S. Division of Biological Sciences, University of Missouri, Columbia, Missouri, E-mail:
[email protected] Claude Department of Biology, East Carolina University, Greenville, North CarolinaThe authors declare they have no competing financial interests.
1 2006 114 1 A16 A17 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Our commentary describing the extensive new literature reporting low-dose effects of bisphenol A (BPA) in experimental animals (vom Saal and Hughes 2005) was written in response to a report from the Harvard Center for Risk Analysis (HCRA) by Gray et al. (2004), who concluded that “the weight of the evidence for low-dose effects [of BPA] is very weak.” The HCRA report was funded by the American Plastics Council and involved a selective review of only 19 of a much larger number of studies that could have been reviewed. In our commentary we showed that a comprehensive review of the now extensive literature concerning studies in experimental animals that used doses of BPA within the range of human exposure led to exactly the opposite conclusion from that reached in the HCRA report (Gray et al. 2004), which was released 2.5 years after it was written.
At this time there are only two published epidemiologic studies showing a relationship between blood levels of BPA and diseases in humans. In his letter, Politch focuses his attention on a single study by Takeuchi et al. (2004) that describes a relationship between BPA in blood and polycystic ovary disease (PCOS) in Japanese women. In a second recently published article, Sugiura-Ogasawara et al. (2005) reported a relationship between blood levels of BPA and recurrent miscarriage in Japanese women. Politch seeks to deflect attention from the central issue of our review by focusing only on the study by Takeuchi et al. (2004) and stating that such studies “cannot address causal relationships” and suggesting that “appropriately controlled” human studies are required. We are certain that readers of Environmental Health Perspectives (EHP) realize that these are criticisms that can be directed at all epidemiologic studies, which can never achieve the control required in laboratory experiments. Additionally, there is always some risk in arguing the methodologic details of a peer-reviewed publication in one field of scientific research (epidemiology) when the commentator’s core expertise (biopsychology) lies elsewhere. Most importantly, based on his criticism of the levels of BPA reported in the blood of women by Takeuchi et al. (2004), Politch appears to be unaware of the large literature concerning the levels of BPA in human blood, urine, and tissues from studies conducted in different regions of the world reporting virtually identical mean and/or median values. For example, in a recent study at the Centers for Disease Control and Prevention, Calafat et al. (2005) found BPA in 95% of the human urine samples they assayed—in the same range reported in human blood in other studies (e.g., Schonfelder et al. 2002; Tan and Mohd 2003). All of this published literature is listed in a document available on the University of Missouri Endocrine Disruptor web site (Endocrine Disruptors Group 2005).
One point-of-view expressed by Politch that we strongly support is the proposition that human studies linking developmental exposure with adult disease are also required, based on the extensive evidence that the developing fetus and neonate are the most vulnerable to endocrine disruption. We hope that the planned National Children’s Study will address this issue and begin to characterize which exposures are and are not consequential for human health. In the absence of such a study, which will take decades to complete, we rely on experimental studies in animals to make decisions regarding the potential hazards posed by chemicals.
Our comment that the epidemiologic evidence “adds to our concern” about the potential hazards posed to humans by BPA hardly qualifies as justification for the criticism that we “overstated the importance” of this or any other single study. Our concern about the potential hazards of BPA to humans is justified by the fact that the limited epidemiologic studies do follow and generally support findings from over 125 experiments with laboratory animals showing that low doses of BPA cause adverse effects on a wide range of outcomes. We also pointed out in our article (vom Saal and Hughes 2005) that 100% of the studies showing significant effects of BPA in laboratory animals were funded by government agencies, and 100% of the studies funded by chemical corporations conclude that the same low doses of BPA do not cause significant effects. What is crucial in relation to the critique by Politch is that the two epidemiologic studies relating BPA in blood to diseases in women are consistent with the findings from studies of the hazards of BPA in animals at doses that lead to blood levels in animals within and below those detected in human blood.
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References
Calafat AM Kuklenyik Z Reidy JA Caudill SP Ekong J Needham LL 2005 Urinary concentrations of bisphenol A and 4-nonyl phenol in a human reference population Environ Health Perspect 113 391 395 15811827
Endocrine Disruptors Group 2005. Bisphenol A References. Columbia, MO:Curators of the University of Missouri. Available: http://endocrinedisruptors.missouri.edu/vomsaal/vomsaal.html [accessed 30 November 2005].
Gray GM Cohen JT Cunha G Hughes C McConnell EE Rhomberg L 2004 Weight of the evidence evaluation of low-dose reproductive and developmental effects of bisphenol A Human Ecol Risk Assess 10 875 921
Schonfelder G Wittfoht W Hopp H Talsness CE Paul M Chahoud I 2002 Parent bisphenol A accumulation in human maternal-fetal-placental unit Environ Health Perspect 110 A703 A707 12417499
Sugiura-Ogasawara M Ozaki Y Sonta S Makino T Suzumori K 2005 Exposure to bisphenol A is associated with recurrent miscarriage Hum Reprod 20 2325 2329 15947000
Takeuchi T Tsutsumi O Ikezuki Y Takai Y Taketani Y 2004 Positive relationship between androgen and the endocrine disruptor, bisphenol A, in normal women and women with ovarian dysfunction Endocr J 51 165 169 15118266
Tan BLL Mohd MA 2003 Analysis of selected pesticides and alkylphenols in human cord blood by gas chromatograph-mass spectrometer Talanta 61 385 391 18969198
vom Saal FS Hughes C 2005 An extensive new literature concerning low-dose effects of bisphenol A shows the need for a new risk assessment Environ Health Perspect 113 926 933 16079060
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a0019a16393642PerspectivesCorrespondenceBlood Lead in Children: Laidlaw et al. Respond Laidlaw Mark A. S. School of Population Health, University of Western Australia, Crawley, Western Australia, E-mail:
[email protected] Howard W. Gonzalez Christopher R. College of Pharmacy, Xavier University of Louisiana, New Orleans, LouisianaFilippelli Gabriel M. Department of Geology, Indiana University–Purdue University, Indianapolis, IndianaJohnson David L. State University of New York, College of Environmental Science and Forestry, Syracuse, New YorkThe authors declare they have no competing financial interest.
1 2006 114 1 A19 A19 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Our article (Laidlaw et al. 2005) is about seasonality of blood lead (BPb) and developing a predictive model using climatic variables. It is a new and unique finding about lead, marked particularly by the fact that it identifies diffuse soil lead as a significant component of lead sources in urban children. In the article, we argued that meteorologic factors can be robustly applied as a predictor for seasonal variations in children’s BPb, which can be used as a potential tool for health care clinicians in the fight to eliminate lead poisoning in youth. We did not intend to review the literature on lead sources; a vast literature already exists of such studies. Suggesting that we propose the “soil-only hypothesis” completely misconstrues our work.
Brown and Jacobs fail to appreciate our argument that lead accumulation in soil is from a combination of lead-based paint, leaded-gasoline, and many other sources, but is clearly not from lead-based paint alone. They fail to understand the fact that soil normally contains very small amounts of lead, and research from many cities has shown that there has been an excessive accumulation of lead in inner-cities (Filippelli et al. 2005; Mielke 2005). We acknowledge that multiple exposure routes for lead exist for children and likely influence the observed seasonality trends in BPb levels (Laidlaw et al. 2005). We seek to assist with scientific understanding of how and why inner-city children are commonly excessively exposed to lead, and we seek a solution to that problem.
We are concerned that people working at agencies that should champion the reduction of lead exposure do not appreciate the fact that multiple sources of lead have accumulated in urban environments and that all major sources and reservoirs need full attention if we expect to meet the goals of Healthy People 2010 (2005). Our work suggests that, to fully address the childhood lead exposure problem in the United States, a paradigm shift is required that includes all major reservoirs of active lead dust.
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References
Filippelli GM Laidlaw M Raftis R Latimer JC 2005 Urban lead poisoning and medical geology: an unfinished story GSA Today 15 4 11 0.1130/1052-5173(2005)015< 4ULPAMG>2.0.CO;2.
Healthy People 2010 2005. Healthy People 2010: What Are Its Goals? Available: http://www.healthypeople.gov/About/goals.htm/ [accessed 30 November 2005].
Mielke HW 2005 Lead’s toxic urban legacy and children’s health GeoTimes (May) 22 26
Laidlaw MAS Mielke HW Filippelli GM Johnson DL Gonzales CR 2005 Seasonality and children’s blood lead levels: developing a predictive model using climatic variables and blood lead data from Indianapolis, Indiana, Syracuse, New York, and New Orleans, Louisiana (USA) Environ Health Perspect 113 793 800 10.1289/ehp.7759.15929906
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a0019b16393642PerspectivesCorrespondenceValidity of Anogenital Distance as a Marker of in Utero Phthalate Exposure McEwen Gerald N. Jr.Cosmetic, Toiletry and Fragrance Association, Washington, DC, E-mail:
[email protected] Gerald Colipa, The European Cosmetic Toiletry and Perfumery Association, Brussels, BelgiumThe authors are employed by advocacy groups that represent the interests of the cosmetic, toiletry, and fragrance industry.
1 2006 114 1 A19 A20 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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In their article in the August issue of EHP, Swan et al. (2005) purport to show that anogenital distance (AGD) in male infants is correlated with maternal phthalate exposure during pregnancy. The AGD has been shown to decrease in male newborn rats following maternal exposure to antiandrogens (Gray et al. 2001). However, little is known regarding AGD in humans, or about effects on AGD, if any, following in utero hormonal exposure. Furthermore, significant limitations in the study undermine the validity of the correlation reported by these authors. Some major considerations include the following.
All male infants evaluated in the study appeared normal (Swan et al. 2005). Therefore, there is no evidence for potential adverse effect in the test population. Because little is known about AGD in human infants and its variation, no conclusion can be drawn whether the reported values are normal or abnormal. The range of AGD values seen among study subjects likely represents typical biologic variation that would be expected to occur among normal study subjects.
The only available historical data on AGD in male human infants (Salazar-Martinez et al. 2004) used a different parameter for male infants (distance from anus to the base of the scrotum), which did not show a similar correlation with maternal phthalate exposure.
Swan et al. (2005) failed to take into account the phenotypes of the parents as a variable that would influence the AGD of the study subjects, much as other human anatomical parameters (i.e., height) would have a genetic component.
The study subjects varied widely in age, height, and weight. To compensate for this variability, Swan et al. (2005) defined a new parameter, which they termed the “anogenital index” (AGI), by dividing AGD by body weight. In the absence of validation, the significance of the AGI is not known, and variation cannot be assumed to be related to hormonal exposure. Swan et al. suggested that the AGI is proportional to the normal genital development of male infants, but they provided no supporting evidence. Also, much scatter can be seen in the plot of “AGI by boy’s age” (Figure 1; Swan et al. 2005). Salazar-Martinez et al. (2004) found that, in male infants,AGD correlated best with length, not weight.
Per definition, the AGD represents a one-dimensional parameter of the human anatomy. In analogy to similar anatomic parameters (e.g., length of limbs, hands, or feet), the AGD is likely to be proportional to body length and not to body weight. Therefore, Swan et al.’s use of the (body weight-related) AGI in the study has little biologic plausibility and appears to be arbitrary.
Swan et al. (2005) did not normalize maternal phthalate urinary concentrations for urine volume. This leaves open the possibility that higher urinary phthalate concentrations in individuals may have been due to lower urinary volume rather that higher phthalate exposure, and casts doubt on the maternal exposure classification categories. Phthalate levels were based on only a single sample per individual, and fetal development at the time of urine sampling was not reported.
Numerous maternal factors (alcohol consumption, medication, profession, body mass) may affect fetal development. Although it is unknown what factors, if any, would influence AGD in human infants, in the absence of these data, confounding factors cannot be excluded.
The levels of phthalates Swan et al. (2005) reported in maternal urine samples are extremely low, and the corresponding exposures are many orders of magnitude lower than the exposures at which selected phthalates have been found to have adverse reproductive effects in rodents. For example, assuming excretion of 2 L of urine/day, the reported concentration of butyl benzyl phthalate corresponds to an exposure of approximately 60 μg/day, or 1 μg/kg/day for a woman weighing 60 kg. Butyl benzyl phthalate has been shown to have only slight, hormone-like effects in rats at doses of ≥ 100 mg/kg/day (Nagao et al. 2000), or ~ 100,000-fold higher than the levels seen by Swan et al. (2005). In the case of the metabolite monoethyl phthalate, the exposure level for the corresponding parent compound diethyl phthalate was on the order of 1,000,000-fold lower than a level found to have no adverse reproductive effects in rats (4,000 mg/kg/day, the highest dose tested) (Scientific Committee on Cosmetic Products and Non-food Products 2002). It is biologically and toxicologically inconceivable that such low levels of human exposure would produce the significant structural differences claimed by Swan et al. (2005).
In summary, the relevance of AGD as an end point of interest in humans is entirely speculative, and the correlation reported by Swan et al. (2005) is lacking in biologic plausibility and remains unproven.
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References
Gray LE Ostby J Furr J Wolf CJ Lambright C Parks L 2001 Effects of environmental antiandrogens on reproductive development in experimental animals Hum Reprod Update 7 248 264 11392371
Nagao T Ohta R Marumo H Shindo T Yoshimura S Ono H 2000 Effect of butyl benzyl phthalate in Sprague-Dawley rats after gavage administration: a two-generation reproductive study Reprod Toxicol 14 513 532 11099877
Salazar-Martinez E Romano-Riquer P Yanez-Marquez E Longnecker MP Hernandez-Avila M 2004 Anogenital distance in human male and female newborns: a descriptive, cross-sectional study Environ Health 3 8 13 15363098
Scientific Committee on Cosmetic Products and Non-food Products 2002. Scientific Committee on Cosmetic Products and Non-food Products Intended for Consumers Concerning Diethyl Phthalate. SCCNFGP/ 0411/01, Final. Available: http://europa.eu.int/comm/health/ph_risk/committees/sccp/documents/out168_en.pdf [accessed 7 December 2005].
Swan SH Main KM Liu F Stewart SL Kruse RL Calafat AM 2005 Decrease in anogenital distance among male infants with prenatal phthalate exposure Environ Health Perspect 113 1056 1061 10.1289/ehp.8100 16079079
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a0025a16408359EnvironewsForumChildren’s Health: Breastfeeding: Nature’s MRE Barrett Julia R. 1 2006 114 1 A25 A25 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Low breastfeeding rates and inadequate emergency planning left many infants dehydrated and hungry in the wake of Hurricane Katrina. Health and educational organizations responded rapidly with breastfeeding information and assistance. Through direct contact with mothers and emergency responders, the groups strove to implement long-standing international guidelines for feeding infants in emergencies.
Breastfeeding provides optimal nutrition, protection against infection, and a safe, reliable food source for babies—attributes that are critical in emergencies. International health organizations including the World Health Organization (WHO) and the United Nations Children’s Fund (UNICEF) promote breastfeeding as the best way to feed infants in a crisis. Although formula is an adequate substitute when a child does not receive breast milk, it must be available with a supply of clean water and containers, and instructions for feed preparation must also be available. Yet potable water, formula itself, and even mixing containers may be impossible to acquire in an emergency.
The WHO and UNICEF have long had guidelines that strongly favor breastfeeding in crises. Current guidelines stem in part from the March 1999 Kosovo crisis in which war forced thousands of Kosovar Albanians into refugee camps. Andrew Seal, a lecturer in international nutrition at the London-based Institute of Child Health and coauthor of a 1999 report based on the Kosovo experience, says, “I think the guidelines are quite good, but it’s like any other specific technical sector—it depends on having people within the organization who have the interest and awareness to champion that particular cause when there are one thousand and one other things to be thinking about.”
Breastfeeding should begin at birth, but a full milk supply can be established even several days after birth. If a nonbreastfed infant is less than six months old, a mother may be able to relactate; beyond that, it is sometimes possible to induce lactation for a partial milk supply. Health organizations dispute the common beliefs that stress “dries up” a mother’s milk and that malnourished mothers cannot produce milk, but emphasize that optimal breastfeeding requires a supportive environment.
Guidelines issued by the American Academy of Pediatrics in 2005 emphasize that children younger than six months old require no other food or fluids beyond breast milk and recommend that breastfeeding continue after solid foods are introduced for at least the first year of life or longer if mother and child wish to continue. The WHO and UNICEF recommend breastfeeding for at least two years.
One significant problem in the Gulf Coast crisis was a lack of breastfeeding knowledge in the affected population. “We sent . . . board-certified lactation consultants into the shelters to start working directly with the mothers who wanted our help,” says Katy Lebbing, herself an international board-certified lactation consultant with La Leche League International, an organization that supports and promotes breastfeeding. But few women were already breastfeeding. “Not only did we have to help people with breastfeeding, but we also had to educate people about breastfeeding,” she says.
Getting breastfeeding support and information to people in crisis is problematic, though. Says Seal, “We need integrated interventions that acknowledge the reality of a mother’s established feeding decisions.”
Indeed, one reality is that breastfeeding rates are extremely low in many areas, including Louisiana and Mississippi, which have some of the lowest breastfeeding rates in the nation, according to the Centers for Disease Control and Prevention. Nevertheless, Lebbing hopes that breastfeeding promotion efforts after Katrina planted a seed. “Natural disasters and other types of disasters happen,” she says. “The best choice is to breastfeed because you don’t have to worry about your baby’s milk supply.”
Comfort food.
Breastfeeding, as in this refugee camp in Thailand’s Mae Hong Son Province, is best for infants in emergency situations.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a0025b16408359EnvironewsForumThe Beat Dooley Erin E. 1 2006 114 1 A25 A27 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Liver Library
Johnson & Johnson’s pharmaceutical research and development division has contributed a library of expression profiles for 100 paradigm compounds, primarily hepatotoxicants, to the Chemical Effects in Biological Systems (CEBS) knowledge base based at the National Center for Toxicogenomics, a part of the NIEHS. CEBS users can select arrays corresponding to one or more compounds from the library and use knowledge base tools to identify genes with significantly changed transcript levels. Lists of altered genes can then by annotated with current annotation provided by CEBS or projected onto biological pathways from groups like BioCarta, KEGG, and the Gene Ontology Consortium. CEBS is accessed at http://cebs.niehs.nih.gov/.
Action for Indoor Air
At its 4 September 2005 congress, the International Academy of Indoor Air Sciences called on the governments, institutions, and corporations of the world to invest more in reducing indoor air pollution. According to the academy, indoor air pollution in developing countries can exceed international health-based guidelines by 20 times or more, and the use of coal contaminated with arsenic and fluorine is poisoning millions in China. The World Health Organization estimates that indoor solid fuel burning causes about 1.6 million premature deaths annually, mainly among women and children. These problems are easily solved, however. Low-cost interventions including education, improved cooking devices and fuels, better stove placement and ventilation, and a focus on reducing children’s exposures have been shown to successfully reduce the health effects of indoor air pollution.
Nanodatabase Unveiled
The International Council on Nanotechnology and Rice University’s Center for Biological and Environmental Nanotechnology unveiled the world’s first database of scientific findings on nanotechnology on 19 August 2005. Available at http://icon.rice.edu/research.cfm, the database was created by Rice University researchers, the chemical industry, and the Department of Energy, and will be updated and enhanced over the next year. The database is searchable by author, year, keyword, type of particle, and type of experiment. Currently the database houses only abstracts and summaries of papers from peer-reviewed scientific journals, but policy reports and commentaries on key papers in the field will be added in the future.
Arsenic in U.S. Rice
Researchers from Scotland’s University of Aberdeen reported in the 1 August 2005 issue of Environmental Science & Technology that U.S.-grown rice contains an average of 1.4 to 5.0 times more arsenic than from Europe, India, Bangladesh. Most U.S. rice is grown in fields that once grew cotton, which depends on arsenic-based chemicals to kill boll weevils and remove its leaves before harvesting. Because of the form that arsenic takes in the rice may not pose a threat; arsenic found in drinking water is estimated to be five times more toxic. However, one of the few epidemiological studies on eating a subsistence diet of arsenic-contaminated rice has linked it with an increase in bladder cancer.
Managing Chemicals Together
Representatives of the world’s governments, intergovernmental groups, and other stakeholders met in Vienna in September 2005 to finalize the Strategic Approach to International Chemicals Management (SAICM). SAICM is a framework for global policy on chemical hazards and will ensure that by 2020 chemicals are manufactured and used in ways that minimize impacts on the environment and human health—a goal outlined at the 2002 World Summit on Sustainable Development. SAICM also promotes capacity building, technology transfer, and improved chemicals management, allowing better implementation of international treaties on chemicals such as the Basel Convention on the Transboundary Movement of Hazardous and Other Wastes. Three core documents from the Vienna meeting are expected to be adopted at a February 2006 conference in Dubai.
Green Plan for Rebuilding NOLA
In the October 2005 issue of Environmental Building News (EBN), executive editor Alex Wilson outlines a 10-point plan for rebuilding New Orleans. The plan, developed with EBN’s editorial board and other sustainable planning and design experts, calls first for the formation of a Sustainable New Orleans planning task force. Coast and floodplain restoration is cited as the first priority. The plan also calls for salvaging and warehousing building materials, rebuilding a stronger levee system that is integrated into a perimeter park, mandating green building of both housing and commercial structures, creating more sustainable Gulf Coast fisheries, cleaning up the new brownfields using the greenest means, and partnering with industry to clean up factories in the region.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a0026aEnvironewsForumInfectious Disease: Meaner MRSAs Potera Carol 1 2006 114 1 A26 A26 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Most methicillin-resistant Staphylococcus aureus (MRSA) infections are contracted in hospitals and other health care facilities. Antibiotic use, patients’ weakened immune systems, close contact among people, and open wounds all make hospitals prime breeding grounds for these bugs. But community-acquired MRSA strains, which attack healthy individuals with seemingly normal immune systems, are becoming more prevalent. A recent comparison of representative strains of hospital- and community-acquired MRSAs now suggests that the latter are more virulent and that they excel at escaping destruction by white blood cells.
Infectious disease experts suspected that community-acquired strains can overcome a healthy immune system because they operate differently than those acquired from hospital or health care settings. Microbiologist Frank DeLeo of the National Institute of Allergy and Infectious Diseases’ Rocky Mountain Laboratories led a multi-institutional team of researchers in comparing the two types. In studies described in the 15 September 2005 issue of The Journal of Immunology, they evaluated the potency of three community-acquired MRSA strains (MW2, LAC, and MnCop) and two hospital-acquired strains (MRSA252 and COL).
Healthy adult mice were injected with each strain. All the mice infected with community-acquired strains became ill, and several died. None of the mice infected with the hospital-acquired strains died, and only one mouse became ill. Then the MRSA strains were mixed with human neutrophils (white blood cells), the body’s first line of defense against bacterial invasion, which kill bacteria by producing hydrogen peroxide and other toxic oxygen metabolites. After half an hour, the community-acquired strains survived neutrophil destruction better than the hospital-acquired ones. After six hours, the community-acquired strains had begun rupturing the neutrophils and were actually growing.
Next the researchers used micro-arrays to uncover genes that differed during interaction with neutrophils. Not surprisingly, genes that encode virulence factors, toxin production, and stress responses were induced in all the MRSA strains. However, about two dozen genes that encode surface or secreted proteins of unknown function were upregulated only in the community-acquired strains. Gene knockout experiments are under way to identify whether these genes contribute to neutrophil killing. The researchers are also exploring how the community-acquired strains withstand neutrophils’ toxic compounds.
The findings suggest that community- and hospital-acquired MRSA strains differ broadly in their biology and genetics. Will this new information help physicians on the front lines who are fighting MRSA infections? “[The findings] do not have immediate therapeutic implications, but maybe down the line therapies will be developed based on such findings,” says Henry Chambers, an infectious disease physician at the University of California, San Francisco, School of Medicine.
One bad bug.
Community-acquired methicillin-resistant S. aureus (in blue) overcomes the immune system by destroying neutrophils, thus breaching the body’s first line of defense.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a0026bEnvironewsForumInnovative Technologies: X-Rays Get in Synch Stemp-Morlock Graeme 1 2006 114 1 A26 A26 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Synchrotrons may have been designed with high-energy physics in mind, but now biologists are starting to see the light too. Jeffrey Gillow, a researcher at Brookhaven National Laboratory, has been making use of the X-ray microscope at the National Synchrotron Light Source (NSLS) in New York to see extremely fine details of bacteria biochemistry in a technique known as X-ray spectromicroscopy.
Gillow’s team, funded by the Department of Energy Office of Science, uses “soft” X-rays (up to 800 electronvolts, a relatively small amount of energy) to study the chemical structure of organic compounds. “It’s great because you get more than just a detailed picture,” says Gillow. “You also get chemical information about your sample.”
Gillow uses the synchrotron to precisely tune the energy of the X-rays, knocking carbon electrons out of their orbitals. The resulting disturbance changes the bonds of molecules, and the researchers can read the spectra to see which elements were bonded to which.
The precise nature of the X-ray microscope allows Gillow to see exacting chemical detail within bacteria. Recently, his team used the 30-nanometer resolution of the NSLS X-ray microscope to observe an immature spore develop within a Clostridium sp. bacterium, something far too minute and hidden within its host for any conventional electron microscope. These findings were published in the June 2005 issue of the Journal of Electron Spectroscopy and Related Phenomena.
Another strength of X-ray spectromicroscopy is that samples require only minimal preparation. Says Gillow, “There is no staining necessary. Basically you just put the sample on the window and away you go.” Without staining or heat fixing, the bacterium maintains its naturally occurring biochemical composition.
However, X-ray spectromicroscopy does require that experiments be conducted in close proximity to a synchrotron. And even though there are currently 40 of these very expensive machines in the world, only a few have the capabilities to conduct this type of research. Further, no live specimens can be studied due to the extraordinary amount of radiation they receive.
Regardless, X-ray spectromicroscopy offers environmental scientists chemical detail and unaltered observations like never before, which is key to understanding the complex biochemical reactions that bacteria undergo in the environment. For example, groups interested in bioremediation can now see on a molecular scale how bacteria alter the chemistry of metals and radionuclides and remove them from soils and waters.
A better understanding of subcellular microorganism chemistry, specifically sporulation, might also help authorities neutralize bioterrorism threats before they become a problem. “Finding ways to interrupt sporulation could stop bioterrorism attacks,” says Gillow. “But I doubt you will ever see a synchrotron at an airport scanning your luggage.”
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a0048aEnvironewsScience SelectionsBeach Bug Bingo: Toward Better Prediction of Swimming-Related Health Effects Bazilchuk Nancy 1 2006 114 1 A48 A48 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Swimming is a popular pastime in the United States. The 2000–2002 National Survey on Recreation and the Environment reported that each year an estimated 89 million Americans swim in recreational waters including lakes, oceans, streams, rivers, and ponds. But swimming waters may also be contaminated by human sewage from treatment plants and runoff, raising the risk of gastrointestinal (GI) illness in swimmers. The recommended test for measuring contamination requires culturing fecal indicator bacteria, which means that beach managers must wait 24 hours for results. This built-in delay is problematic, potentially exposing swimmers to unhealthy water quality and sometimes resulting in unnecessary beach closures. Now a team of federal researchers has shown that a rapid method for measuring water quality can accurately predict swimming-related health effects [EHP 114:24–28].
The researchers conducted health surveys of beachgoers at two public beaches, one on Lake Michigan and one on Lake Erie, and compared them with thrice-daily water quality measurements along transects at the beaches. They evaluated water quality using a modified version of the polymerase chain reaction method (QPCR) to quantify indicator bacteria in water samples. The advantage of this method is that it can provide results in two hours or less. The researchers chose Enterococci and Bacteroides as their indicator organisms.
Survey participants were interviewed as they left the beach; follow-up interviews were conducted by telephone 10 to 12 days after the beach visit. When researchers compared results of the water quality tests to participant reports of GI and other illnesses, they found a significant trend between increased reports of GI illnesses and Enterococci at the Lake Michigan beach and a positive, though statistically insignificant, trend for Enterococci at the Lake Erie beach. Bacteroides did not prove to be as powerful in predicting illness, with an insignificant positive trend found only at the Lake Erie beach and no trend at the Lake Michigan beach.
When results from the two beaches were combined, the trend for Enterococci and GI illness remained statistically significant, a finding that held true even when samples collected at 8:00 a.m. were compared to daily averages. Beach managers could thus test early-morning samples to assess water quality and, if necessary, close beaches before the majority of swimmers were exposed.
In spite of the promising nature of the findings, the authors caution that much research remains to be done before the results can be generalized. One of the key remaining questions relates to the method itself: QPCR relies solely on the presence of DNA to quantify organisms, so pathogens are detected even if they are dead and thus harmless. QPCR may therefore suggest a problem with the water when in fact there is none. The authors say additional studies should help determine if the approach is robust enough to be used in water quality regulations.
Wave of the future?
If validated, a modified polymerase chain reaction method may become useful for earlier identification of hazardous beach water conditions.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a0048bEnvironewsScience SelectionsExploring the Roots of Diabetes: Bisphenol A May Promote Insulin Resistance Washam Cynthia 1 2006 114 1 A48 A49 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Poor diet and lack of exercise are known contributors to the epidemic of type 2 diabetes spreading around the world. Now researchers have implicated another possible culprit in the rise of the disease [EHP 114:106–112]. A team of Spanish and Mexican researchers reports discovering that the endocrine-disrupting chemical bisphenol A (BPA) causes insulin resistance in mice similar to that seen just before the onset of type 2 diabetes.
Type 2 diabetes occurs when insulin receptors throughout the body fail; this is known as insulin resistance. Complications of diabetes include heart disease, kidney failure, blindness, and nerve damage. The World Health Organization estimates that at least 154 million people around the world have type 2 diabetes, and predicts that number will more than double within 25 years.
Endocrine disruptors mimic the natural sex hormone 17β-estradiol (E2), which is involved in the development of sexual traits. Scientists have known for years that BPA and other endocrine disruptors can diminish sperm production, accelerate the onset of puberty, and damage sexual organs. But they had not studied a link between the chemicals and glucose metabolism, even though increases in E2 are known to cause insulin resistance.
The researchers chose to study BPA because its use is so widespread. Since the 1950s, it has been used in plastics for water bottles and jugs, baby bottles, toys, and the linings of food and beverage cans. People ingest BPA that leaches from containers into foods and drinks. Studies in the United States showed that BPA appeared in the blood and urine of 95% of people tested.
The researchers tested BPA’s effect on glucose regulation by measuring glucose and insulin levels in adult male mice treated with BPA injections, then comparing them with levels in mice treated with E2 and a control group treated with corn oil. BPA caused oversecretion of insulin in mice at a dose of 10 micrograms per kilogram body weight per day (μg/kg/day) via a rapid mechanism, taking only 15 to 30 minutes. Treatment over a course of four days with 100 μg/kg/day induced the insulin resistance that precedes type 2 diabetes. E2 had the same effects at the same doses. Glucose metabolism remained stable in the control rats.
These results are novel because the mechanism reported is the lesser known of the two major pathways used by estrogens and other steroids. It involves signaling rapidly initiated from the plasma membrane rather than the nuclear transcription pathway depicted in most textbooks.
The BPA dose high enough to cause insulin resistance in mice was in the same range as the 50 μg/kg/day reference dose established by the U.S. Environmental Protection Agency, which is based on a lowest-observed-adverse-effect level of 50 milligrams per kilogram per day. The researchers see the newly discovered link between BPA and insulin resistance as one more reason the agency should at least consider lowering the lowest-observed-adverse-effect level. They further suspect that because other endocrine disruptors mimic E2, they too may hinder glucose metabolism.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a0064a16393648AnnouncementsBook ReviewHandbook of Urban Health: Populations, Methods, and Practice Kjellstrom Tord Tord Kjellstrom trained in medicine and engineering at universities in Stockholm, Sweden. He has more than 30 years experience in environmental and occupational health, primarily as an academic teacher and researcher in Sweden, New Zealand, and Australia. In addition, he was an environmental epidemiologist for 12 years at the World Health Organization. He is currently Visiting Professor at the Swedish National Institute of Public Health and Visiting Fellow at the National Centre for Epidemiology and Population Health, Australian National University.1 2006 114 1 A64 A64 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Edited by Sandro Galea and David Vlahov
New York:Springer, 2005. 599 pp. ISBN- 0-387-23994-4, $89.95
The world is undergoing major urbanization. Within 25 years, more than half the world’s population will be living in urban areas, and in this period approximately 1 million people will be added to city populations each week. Urban health is thus significant for population health, and this handbook provides a timely review of the issues involved.
This book takes a broad view of urban health, emphasizing urban social factors important to population health. The editors successfully bridge urban health inquiry and public health practice by combining descriptions of issues in urban health, methods used in urban health studies, and examples from practitioners. The authors of the 29 chapters come from different professional backgrounds, primarily in North America, but those from developing countries add a global flavor.
Part 1, “Populations,” includes 11 chapters on health aspects of different socioeconomic groups in cities and different age groups, describing time trends and geographic differences. Special issues for minority groups are reviewed, and each chapter provides a wealth of up-to-date research references.
In Part 2, “Methods,” 10 chapters present methods in anthropology, epidemiology, demography, sociology, environmental health, and economic analysis. Each chapter provides relatively detailed descriptions, some including detailed mathematical formulas for analyzing data. Although this handbook can present only part of the knowledge required to be fully conversant with any of the methods, these chapters give a good overview of methods available in urban health. One limitation lies in the examples used to describe the methods. For example, the environmental health chapter deals almost exclusively with urban air pollution while other important urban health hazards, such as community noise, are barely mentioned.
Part 3, “Practice,” includes excellent examples of the broad approach to urban health used in the Healthy Cities movement as well as more focused examples from a local health department level. Legal issues and suggestions for teaching urban health are also presented. The editors note that success in interventions that target proximal determinants of health depends on more upstream laws and regulations. Promoting health in cities requires an appreciation of the multiple levels of determinants that shape population health, and this handbook is a good starting point for such appreciation.
In handbooks that cover a multitude of fields and examples, some issues are not given the space that they may deserve. Here, apart from the limited range of environmental health issues presented, the book rarely mentions injuries and their more proximal determinants. Living and working in cities almost invariably require daily transport, and the risks involved are surely urban health issues. Neither is much attention given to workplace factors and health, even though cities serve as magnets for both people and different types of modern workplaces. A variety of occupational health hazards, including psychosocial factors as well as traditional chemical and physical hazards, are prominent determinants of population health in urban areas.
Nevertheless, the editors intended the handbook to form one step toward the systematic study of urban health. They have succeeded by giving readers a thorough view of the social factors involved. The handbook is an excellent resource for students, researchers, teachers, and practitioners in urban health. The aspects of urban health given less prominence in this volume may be a suitable focus of a companion volume at a later stage. Further study of urban health situations and determinants is required to meet the challenges of global urbanization during this century. This handbook is a valuable contribution.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0114-a0064b16393648AnnouncementsNew BooksNew Books 1 2006 114 1 A64 A64 2006Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Agricultural Biodiversity and Biotechnology in Economic Development
Joseph Cooper, Leslie Marie Lipper, David Zilberman
New York:Springer, 2005. 480 pp. ISBN: 0-387-25407-2, $99
Air Quality in Airplane Cabins and Similar Enclosed Spaces, Vol. 4: Air Pollution, Part H
Martin B. Hocking, Diana Hocking, eds.
New York:Springer, 2005. 410 pp. ISBN: 3-540-25019-0, $179
Algorithms in Bioinformatics
Rita Casadio, Gene Myers, eds.
New York:Springer, 2005. 436 pp. ISBN: 3-540-29008-7, $78
Appraising Sustainable Development: Water Management and Environmental Challenges
Asit K. Biswas, Cecilia Tortajada, eds.
New York:Oxford University Press, 2005. 242 pp. ISBN: 0-19-566891-X, $34.50
Bioethics: An Introduction for the Biosciences
Ben Mepham
New York:Oxford University Press, 2005. 408 pp. ISBN: 0-19-926715-4, $39.95
Bioinformatics and Computational Biology Solutions Using R and Bioconductor
R. Gentleman, V. Carey, W. Huber, R. Irizarry, S. Dudoit, eds.
New York:Springer, 2005. 452 pp. ISBN: 0-387-25146-4, $89.95
Data Mining and Knowledge Discovery Handbook
Oded Maimon, Lior Rokach
New York:Springer, 2005. 1,383 pp. ISBN: 0-387-24435-2, $199
DNA Methylation, Epigenetics and Metastasis
Manel Esteller, ed.
New York:Springer, 2005. 310 pp. ISBN: 1-4020-3641-8, $149
Environmental Issues in Latin America and the Caribbean
Aldemaro Romero, Sarah E. West, eds.
New York:Springer, 2005. 299 pp. ISBN: 1-4020-3773-2, $129
Genes on the Menu: Facts for Knowledge-Based Decisions
Paul Pechan, Gert de Vries
New York:Springer, 2005. 217 pp. ISBN: 3-540-20178-5, $39.95
Globalization and Urban Development
Harry W. Richardson, Chang-Hee C. Bae, eds.
New York:Springer, 2005. 321 pp. ISBN: 3-540-22362-2, $99
Greenhouse Warming and Nuclear Hazards: A Series of Essays and Research Papers
Peter Fong
Hackensack, NJ:World Scientific Publishing, 2005. 276 pp. ISBN: 981-256-422-5, $48
Oceans and Health: Pathogens in the Marine Environment
Shimshon S. Belkin, Rita R. Colwell, eds.
New York:Springer, 2005. 464 pp. ISBN: 0-387-23708-9, $89.95
Proteomics in Cancer Research
Daniel C. Liebler, ed.
Hoboken, NJ:John Wiley & Sons, 2005. 201 pp. ISBN: 0-471-44476-6, $135
Small-scale Freshwater Toxicity Investigations: Vol. 1—Toxicity Test Methods
Christian Blaise, Jean-François Férard, eds.
New York:Springer, 2005. 551 pp. ISBN: 1-4020-3119-X, $129
Sustainability and Cities: Concept and Assessment
Ooi Giok Ling
Hackensack, NJ:World Scientific Publishing, 2005. 252 pp. ISBN: 981-256-163-3, $48
The Obesity Epidemic: Science, Morality and Ideaology
Michael Gard, Jan Wright
New York:Routledge, 2005. 218 pp. ISBN: 0-415-31895-5, $125
The Toxicology of Fishes
Richard T. Di Giulio, David E. Hinton, eds.
New York:Routledge, 2005. 752 pp. ISBN: 0-415-24868-X, $138.48
Toxicology of Organophosphate and Carbamate Compounds
Ramesh C. Gupta, ed.
Burlington, MA:Elsevier, 2005. 768 pp. ISBN: 0-12-088523-9, $139.95
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Harm Reduct JHarm Reduction Journal1477-7517BioMed Central London 1477-7517-2-251628394410.1186/1477-7517-2-25ResearchHealth problems and help-seeking activities of methadone maintenance clients at Auckland Methadone Service (AMS): potential for community pharmacy service expansion? Sheridan Janie [email protected] Amanda [email protected] Carina [email protected] The School of Pharmacy, University of Auckland, Private Bag 92019, Auckland, New Zealand2 Clinical Research Resource Centre, Waitemata District Health Board, Auckland, New Zealand3 Auckland Methadone Service, Waitemata District Health Board, Auckland, New Zealand2005 13 11 2005 2 25 25 14 9 2005 13 11 2005 Copyright © 2005 Sheridan et al; licensee BioMed Central Ltd.2005Sheridan et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
In general the health of methadone clients has been found to be poorer than that of the general population. In New Zealand specialist drug services are not funded to provide primary healthcare services. Many health conditions could potentially be managed by community pharmacists who have frequent contact with this client group. This study sought to explore the health problems suffered by methadone clients, who they sought help from, and the potential for greater involvement of pharmacists.
Methods
Self-completion questionnaire of methadone maintenance clients managed in specialist care in Auckland, New Zealand.
Results
The most common health problem experienced by these clients in the past three months was sweating (70.0%), and more than half of the respondents also reported experiencing headache, fatigue and depression. The least frequently experienced conditions were hay fever (12.9%) and abscesses (12.1%). Respondents indicated that the top three choices from whom they would seek help were GP (56.7%), the client's partner (31.6%) and community pharmacists (27.9%). Barriers to seeking help from pharmacists included issues around cost, perceptions of pharmacist knowledge and skills, privacy and confidentiality.
Conclusion
Methadone clients in this study indicated that they suffered a number of general health problems, and in many cases were likely to seek help from a GP or their own partner, before seeking help from pharmacists. However, for over one quarter of respondents the pharmacist was in the top three from whom they would seek advice. Any barriers towards consulting pharmacists, in the main seem to be resolvable.
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Background
In general the health of drug users has been found to be poorer than that of the general population. Ryan and White [1] noted that the health status of clients entering a public methadone programme in Australia was generally poorer on measures of physical and psychological health than that of the general population. A New Zealand study of self-assessed health status (using SF36) found this was significantly worse in methadone patients than the New Zealand population mean score, and 75% of the sample had engaged in help-seeking activities around general health issues for themselves [2]. With regard to psychological health, Brienza et al found that 42% of methadone patients met criteria for major depression [3].
Research focusing on more specific aspects of methadone client health has pointed to significant poor health and morbidity. In a study of nutrition, methadone clients were found to prefer sweet foods and to consume in excess of the daily recommended proportion of mono and disaccharides [4]. A similar finding was noted in Australia where women in methadone treatment had a high sugar and low fibre intake [5], and Best and colleagues noted that among methadone patients in their study, 3% reported no "eating events" in the previous three days and 27% had eaten no cooked meals [6]. Other health problems which particularly affect methadone clients include sweating and constipation [7], sleep disturbances [8,9] and dental problems [10-12]. Lack of treatment for such problems may result in increased health-related harm to drug users.
In New Zealand, community pharmacists have long been involved in the provision of harm reduction services to drug misusers – either through their role in the provision of needle exchange [13,14] or through the dispensing of methadone prescriptions for the management of opiate dependence. In 2003 it was estimated that around 1 in 5 community pharmacies provided a needle exchange service (personal communication – Charles Henderson) and approximately a half of community pharmacists work in pharmacies which dispensed methadone prescriptions (personal communication – Linda Bryant). These figures are similar to those found in England and Wales [15] and represent a major contribution of the profession to providing support and healthcare to drug users.
The provision of these services brings community pharmacists into frequent contact with injecting drug users and opiate dependent patients [16], thus creating potential opportunities for further intervention around a wide range of issues. For example, many needle exchange pharmacies provide written and verbal information on testing for blood borne viruses, accessing treatment, avoiding overdose and safer injecting [17]. Such interventions are obvious in relation to the client group. However, it may be argued that community pharmacists having successfully taken on such roles, have tended to focus on the drug user in terms of their drug use and route of use, and have possibly paid less attention to the general health issues of drug users. Such health problems may be the same kind of health problems suffered by the general population (e.g. coughs, colds, headache etc) or may be directly related to their drug use. There is however, the issue of stigmatisation of drug users, in which legitimate requests for over-the-counter remedies for problems such as pain, coughs and colds may be treated with suspicion, as many of these products contain substances liable to abuse [18,19].
This present study has sought to explore some of these issues in a group of methadone clients at Auckland Methadone Service (AMS), New Zealand. AMS provides a methadone maintenance, methadone reduction and methadone detoxification service to opiate dependent clients in a catchment area of almost 1.2 million [20-22]. At the time of conducting the study (Mid January to mid April 2005), AMS was funded to provide services for 989 methadone clients. Around three quarters are managed within the service having their methadone prescribed to them by AMS medical officers and seeing a case manager at least once every three months at a Community Alcohol and Drug service (CADS) clinic of their choice, the remaining quarter managed in primary care having their methadone prescribed by authorised general practitioners and routinely accessing AMS for further consultation. GP managed clients have been stabilised within the service and are considered to be more stable with regard to drug use and behaviour than those managed within AMS.
AMS is not funded to provide general healthcare to clients, so those managed within the service who do not have a GP to consult about their health may need to use other health professionals or lay people for help with such problems. In addition, in New Zealand, GP care incurs a cost to the patient at the point of access and may be seen as a barrier to accessing treatment; treatment through AMS is free of charge.
Another health professional trained to provide advice, treatment and referral is the community pharmacist. Consulting the pharmacist is free of charge and no appointment is needed. However, if medicines are required they must either be purchased from the pharmacy or obtained on prescription, both of which will incur a cost. In theory, with methadone clients seeing pharmacists several times a week to collect their prescribed methadone there are numerous opportunities for clients to consult pharmacists about their health, or for pharmacists to proactively enquire about health issues known to be of particular problem for methadone clients.
There are a number of potential barriers to methadone clients seeking healthcare from community pharmacists. Research into drug users' views of accessing methadone and needle exchange services through pharmacies have described several barriers. These include the attitudes of the pharmacist and staff and lack of privacy [23-25]. In addition, many pharmacists report not wishing to provide these services [15,16] and thus maybe unlikely to be willing to provide primary healthcare services to drug users.
In New Zealand, no data exist about the incidence of general health problems suffered by methadone patients, nor have the potential barriers to accessing advice and treatment through community pharmacies been explored. In order to better understand some of the issues around the general health problems of methadone clients, whom they access for help and treatment, and barriers to utilising community pharmacists for help and treatment, a study was designed with the following aims:
• To review the general health problems that AMS clients managed within the specialist services suffered in a 3 month period;
• To document the incidence of chronic medical conditions;
• To explore who AMS clients turn to for advice;
• To explore whether clients utilise community pharmacists for this type of advice;
• To explore barriers to a greater role for community pharmacists.
Methods
Study design
The study employed a cross sectional design, using a self-completion questionnaire.
Sample
All AMS clients managed within specialist services, but not those managed through authorised GP care, and who had appointments within the next three months were included in the study. AMS is not funded to provide general healthcare to methadone clients, thus those managed by GPs were not included because they already had access to healthcare via their GP-managed methadone treatment and thus may have biased the results.
Sampling procedure
AMS clients managed through the CADS services were identified through the AMS database, and a list of AMS clients attending each CADS clinic on a "once every three months" basis was provided to the admissions officers (AOs) at each of the five CADS clinics. As a client presented for their appointment, he or she was asked whether they would be willing to complete a brief questionnaire either whilst waiting for their appointment or take it home and complete it later, returning it in a prepaid envelope provided for its return. Once a client had been approached the AO ticked off the client from the list, ensuring clients were only approached once.
This process took place between mid January and mid April 2005, thus capturing a 3-month time period in which, theoretically, each client would be seen by the service. In reality, a number of clients did not make or attend appointments, and the denominator for this study was therefore the number of clients attending appointments during this time period.
Questionnaire design
A self-completion quantitative questionnaire was developed to explore perceived health status and treatment-seeking behaviour for individuals. Although other instruments are available that measure heath and wellbeing such as SF36 [26], the Quality of Wellbeing Scale [27] and Sickness Impact Profile Scale [28], they tend to focus on global health issues and are not designed with opiate users in mind. The health sections of the Opiate Treatment Index [29] contain a number of questions on health. However, the instrument is designed to explore health in a four-week period in a clinical setting, and is not designed for self-completion. In this study, the aim was to explore incidence of general health issues such as minor ailments which may or may not be specific to drug use, as a way of gauging potential for a community pharmacist role. From this perspective a three month time period was used, (four weeks being considered to short a period to capture the incidence of rarer health problems), and a selection of general health items was chosen that could be responded to by community pharmacists, and for which there are over-the-counter remedies available. Other more drug-specific items were included after discussion with the AMS clinical team, who wished to utilise the opportunity provided by the study to collect data on the clients. The questionnaire was reviewed by consumer advisors, AMS medical officers and case managers. Questionnaires were pre-piloted amongst staff and consumer advisors at CADS, and feedback indicated it was easy to complete, taking 5–10 minutes.
Questionnaires were anonymous – no client identifiers were required. Health problems surveyed include general health problems and symptoms suffered in the previous three months (e.g. cold, indigestion, nausea, diarrhoea, sweating, constipation, toothache, loss of appetite, abscesses, swollen hands and feet/fluid retention), psycho-neurological problems (headaches, difficulty sleeping, fatigue, depression), respiratory problems (hay fever cough, dry mouth), dry and itching eyes, and skin rashes. The existence of chronic health problems was also explored. Help- and treatment-seeking behaviour, barriers to help-seeking, and substance use were also surveyed.
Data were entered into SPSS version 12.1, and analysed using general frequencies. Further analysis was conducted using appropriate parametric and non-parametric statistical tests.
Results
During the study period 715 appointments with AMS clients were scheduled, and a total of 556 clients (77.7%) were seen at appointments. Of these 556, 335 (60.3%) were male, 424 (76.3%) were European/Pakeha, 65 (11.7) Maori, 8 (1.4%) Pacific, 8 (1.4%) Asian and 51 (9.2%) other. Of these, 361 clients (65%) (57.9% male) were 'offered' a questionnaire, according to the records kept by AOs at each CADS clinic. Of these, 231 (64%) completed and returned a questionnaire (representing 42% who had appointments). Just over half (50.2%) were male (data missing on 14 cases), and the majority self-identified solely as European/Pakeha (72.7%). Maori (including those identifying as Maori and Pakeha) clients comprised 15.5%, Pacific Islanders 1.8%, Asian 0.9% and others 2.2% of the respondents (data missing on 16 cases). Just over half of the respondents (52.9%) stated they had a GP with whom they could comfortably discuss health problems (data missing on 23 cases).
Respondents had used the following substances once a week or more frequently in the previous three months: morphine 14.5%, cannabis 40.5%, stimulants (e.g. methamphetamine) 11.5%, ecstasy 2.2%, alcohol 23.3% and cigarettes 74.4% (data missing on 8 cases).
Table 1 provides data on self-reported chronic health problems. The most common condition that the respondents reported suffering from was hepatitis C (51.6%). About a quarter of the respondents (24.0%) reported mental health problems, 23.1% reported suffering from chronic pain, and 21.3% suffered from migraine. Only 2.3% of the respondents had diabetes and none of them reported being HIV positive.
Table 1 Self-reported medical conditions* (N = 221)
N (%)
Any self reported medical condition from list below 186 84.2
Hepatitis C 114 51.6
Mental health problems 53 24.0
Chronic pain 51 23.1
Migraine 47 21.3
Asthma 40 18.1
Hayfever or other allergies 36 16.3
Eczema/dermatitis 20 9.0
High blood pressure 18 8.1
Arthritis 12 5.4
Hepatitis B 12 5.4
Diabetes 5 2.3
HIV 0 0
*identified by ticking a box. No tick assumed to be not suffered from the condition
Clients were asked to identify health problems suffered in the last three months from a list provided (see Table 2 for details), by ticking a box for 'yes'. A blank box was assumed to be a negative response. The most common health problem experienced by these clients in the past three months was sweating (70.0%). More than half of the respondents also reported experiencing headache, fatigue and depression. The least frequently experienced conditions were hay fever (12.9%) and abscesses (12.1%). Respondents had suffered from a mean of 6.76 conditions (sd = 4.12; median = 6; mode = 4; range = 0–19).
Table 2 Health problems suffered in last 3 months* (N = 231)
N %
No health problems 3 1.3
Cold 56 24.2
Hayfever 28 12.1
Headaches 130 56.3
Indigestion 54 23.4
Cough 56 24.2
Nausea 80 34.6
Diarrhoea 39 16.9
Sweating 162 70.1
Skin rashes 49 21.2
Dry/itchy eye 36 15.6
Dry mouth 104 44.2
Constipation 105 45.5
Sleep problems 135 58.4
Toothache 108 46.8
Fatigue 117 50.6
Depression 117 50.6
Swollen hands/feet 66 28.6
Loss of appetite 90 30.9
Abscesses 32 13.9
* identified by ticking a box. No tick assumed to be not suffered from the problem
Clients were asked to tick up to three individuals or groups from a list provided, that they were most likely to ask for help when suffering from any health-related problems mentioned in Table 2, by ticking a box for 'yes'. A blank box was assumed to be a negative response. The most popular choices were the GP (56.7%), the client's partner (31.6%) and community pharmacists (27.9%). None of the respondents identified Maori health workers nor Hapu, Iwi health kaimahi among their first three choices. Five respondents stated that they would not seek help from anyone apart from themselves (see Table 3).
Table 3 Individuals or groups most likely to be consulted with regard to health problems in Table 2. (N = 215)
Individual or group *Most likely to consult (can tick up to 3) Who else would consult (can tick as many as required)
N % N %
GP (doctor) 122 56.7 84 36.4
Partner 68 31.6 42 18.2
Pharmacist in chemist shop 60 27.9 95 41.1
Family/whanau1 member 47 21.9 62 26.8
AMS Case manager 45 20.9 93 40.3
Friend 43 20.0 76 32.9
Chemist shop staff 15 7.0 37 16.0
Needle exchange staff 1 0.5 17 7.4
Maori health worker 0 0.0 5 2.2
Hapu, Iwi health kaimahi2 0 0.0 2 0.9
Other (includes Alcohol and drug hotline, CADS doctor, dentist, fellow methadone programme client, (mental health) nurse, therapist, naturopath, cleric). 12 5.6 11 4.8
*(NB respondents could tick up to three from a list. Those ticking more than three were excluded from this analysis).
1Whanau is a Maori word which means "the extended family which includes the nuclear family, and aunts, uncles and cousins".
2Hapu, Iwi health kaimahi – a community worker based in a local Maori community
Similarly, the respondents were then asked to identify who else they would consider asking for advice/help (see Table 3). Collating responses to 'who would you be most likely to consult' and 'who else would you consult', 93% of respondents indicated GPs, 69% community pharmacists and 61% AMS case managers. Around half indicated lay people such as family, friends or partner.
Respondents were asked to indicate, from a list provided, why they would not contact a community pharmacist for help, by ticking a box for 'yes'. A blank box was assumed to be a negative response (respondents could choose up to 3 reasons) – see Table 4. The most common reason for not seeking help from a community pharmacist was financial. The second and third most common reasons for not asking for help were that people preferred to wait for the problem to get better, and that they believed pharmacists did not understand their problems. In addition, 15.3% of people would not ask a pharmacist for help as they felt lack of privacy was an issue, and 12.4% of them had concerns about confidentiality.
Table 4 Reasons for AMS clients not seeking treatment or advice from a community pharmacist when suffering from health problems identified in Table 2 (N = 208)
N %
Cannot afford it 88 42.1
Prefer to wait until the problem gets better 77 36.8
Believing pharmacist lacks understanding about client health problems 38 18.2
Not private enough 32 15.3
Concerns about confidentiality 26 12.4
Prefer to seek treatment and advice from someone else 25 12.0
Time consuming 13 6.2
Have medicine at home 9 4.3
(NB respondents could tick up to three from a list. Those ticking more than three were excluded from this analysis).
(Data missing on 1 case)
Respondents were also asked to indicate how many times they had seen a GP, pharmacist, case manager, dentist, AMS doctor or needle exchange staff for help in the previous three months. Overall the number of consultations with health professionals was very low. Data were not normally distributed with some individuals being high users (e.g. 10–15 consultations in last three months), and the majority having no consultations. With the exception of GPs (median = 1), the median for all other health professionals was 0 (data missing on 14 cases).
Discussion
This study represents the first published study which has specifically explored New Zealand methadone clients' primary healthcare needs and from whom they access treatment. The results indicate that this is a group that suffers a range of health problems similar to those encountered in the general population. Problems considered to be associated with drug use (and opiate use in particular) were reported by a large proportion of users – e.g. dental problems, insomnia, depression and constipation. Respondents also reported a high mean number of individual health problems, indicating there is a need for provision of primary healthcare to this group. Care in interpreting these results may however be needed for items such as depression, which are based on self-report and may not have actually been diagnosed.
The most likely individual to be contacted for support with healthcare was the GP, followed by a person's partner. Indeed friends and family also featured quite strongly, possibly indicating a high reliance on the lay health network over health professionals. Reasons for this need to be explored further, but results from this study indicate that for pharmacists at least, cost is a major barrier – being cited by two fifths of respondents and 'waiting until things get better' being second and a possibly proxy for cost. In the UK, the National Health Service has introduced a 'minor ailments scheme' in which community pharmacists are able to 'prescribe' to individuals in certain financially disadvantaged groups (though not specifically drug users) 'over-the-counter' (OTC) treatments free of charge [30,31]. Such a scheme might be considered for methadone clients. However, further study needs to be undertaken into the cost of non-treatment.
Other barriers to accessing help from pharmacists included lack of privacy and concerns about confidentiality. These results echo those found in studies of drug using clients in Scotland [23-25]. The issue of privacy may be addressed by the introduction of private areas and in Scotland funding has been provided to pharmacists to do this as an asset for all patients. The issue of confidentiality needs to be addressed through client education – the pharmacists' code of ethics specifically precludes breach of confidentiality except in issues where a duty of care may supersede a code of confidentiality. Nonetheless, pharmacy staff may need to be specifically educated with regard to this.
A number of respondents had concerns about whether community pharmacists were knowledgeable enough to provide such healthcare. In general with regard to minor ailments this should not be an issue. However, there are drug-related contextual issues that may need to be covered in tailored, professional development programmes such as the relationship between drug use and dental problems, managing pain with OTC products and when to refer and the need for this requires further exploration. Another approach to overcoming this barrier is to provide education for methadone clients on the role of the pharmacist and their ability to manage such health problems.
Respondents reported a high rate of use of other substances such as cannabis, tobacco and alcohol. With regard to smoking, American studies have shown that many methadone clients want to quit and are favourable towards the use of nicotine-replacement-therapies (NRT) [32]. Community pharmacists have been shown to be effective in helping smokers to quit [33] and with appropriate collaboration with drug services may have an important role to play.
A number of limitations should be taken into account in interpreting and extrapolating results from this study. The response rate of those who were 'offered' a questionnaire was 65%, which is high for a survey that was unable to employ a non-responder follow-up methodology. However, there are issues with regard to those who were not 'offered' a questionnaire – i.e. did they actually decline to take part, or were they simply missed during a busy clinic day. Furthermore, despite a requirement for clients to be seen once every 3 months, only 556/715 attended appointments. It maybe that those not attending are very stable on treatment; however, there may also be some who did not attend because they were unwell, and as such this potential bias needs to be taken into account. Thus, while a 65% response rate from those given a questionnaire was high, generalisations to the whole of the methadone clients in the study should be made with caution. The sample of respondents also under-represents males and slightly over-represents Maori when compared with data for all attendees during the study time period.
In designing the study it was initially envisaged that only those 'minor ailments' that might be attended to by community pharmacists would be included in the study. However, feedback from AMS staff indicated that they would like a number of other conditions or symptoms included such as depression, loss of appetite and abscesses – issues much less likely to be managed in community pharmacy. When asking respondents whom they would be most likely to contact about any of these health issues, and with high proportions reporting they suffered from depression for example, the results are thus likely to be skewed away from the community pharmacist. Ideally, respondents would have been asked whom they would have consulted for each condition, but this would have made the questionnaire too complex and may possibly have compromised a good response rate.
Conclusion
Methadone clients in this study indicated that they suffered a number of general health problems, but in many cases were likely to seek help from a GP or their own partner, before seeking help from pharmacists. They also indicated that they might simply wait for problems to resolve. However, a significant proportion cited the community pharmacist as a person whom they would be most likely to contact, and the barriers towards consulting pharmacists in the main, are resolvable. In particular issues around cost, pharmacist training and reorientation of premises to allow for improved privacy need to be taken into consideration when attempting to expand a harm reduction role for pharmacists with this client group. Furthermore, there seems to be a need to address clients' beliefs about the community pharmacist's role in the provision of healthcare.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
JS designed the study and data collection instruments, supervised the researchers, analysed the data and drafted the manuscript. AW facilitated the process of data collection, supervised the researchers and contributed to the writing of the manuscript. CW helped with the design of the questionnaires, facilitated data collection and contributed to the writing of the manuscript. All authors read and approved the final manuscript.
Acknowledgements
The authors would like to acknowledge the hard work of Dora Hu in the data collection and Bhavesh Makan for his help with data entry and data cleaning. We would also like to acknowledge the support of the admissions officers at the CADS units, AMS case managers, medical officers and consumer advisors, and all the clients who completed the survey. Funding was provided by the University of Auckland Faculty of Medical and Health Sciences Summer Studentship Programme to support Dora Hu.
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Global HealthGlobalization and Health1744-8603BioMed Central London 1744-8603-1-171633668510.1186/1744-8603-1-17Short ReportTRIPS, the Doha Declaration and increasing access to medicines: policy options for Ghana Cohen JC [email protected] M [email protected] K [email protected] LC [email protected] G [email protected] Assistant Professor, Leslie Dan Faculty of Pharmacy, University of Toronto; Director, Comparative Program on Health and Society, Munk Centre for International Studies, University of Toronto2 Programme Manager, Ghana National Drug Programme, Ministry of Health, Republic of Ghana3 Senior Clinical Officer, Family Health International4 Ph.D. student, Leslie Dan Faculty of Pharmaceutical Sciences, University of Toronto5 Law student, Faculty of Law, University of Toronto2005 9 12 2005 1 17 17 5 7 2005 9 12 2005 Copyright © 2005 Cohen et al; licensee BioMed Central Ltd.2005Cohen et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
There are acute disparities in pharmaceutical access between developing and industrialized countries. Developing countries make up approximately 80% of the world's population but only represent approximately 20% of global pharmaceutical consumption. Among the many barriers to drug access are the potential consequences of the Trade Related Aspects of Intellectual Property Rights (TRIPS) Agreement. Many developing countries have recently modified their patent laws to conform to the TRIPS standards, given the 2005 deadline for developing countries. Safeguards to protect public health have been incorporated into the TRIPS Agreement; however, in practice governments may be reluctant to exercise such rights given concern about the international trade and political ramifications. The Doha Declaration and the recent Decision on the Implementation of Paragraph 6 of the Doha Declaration on the TRIPS Agreement and Public Health may provide more freedom for developing countries in using these safeguards. This paper focuses on Ghana, a developing country that recently changed its patent laws to conform to TRIPS standards. We examine Ghana's patent law changes in the context of the Doha Declaration and assess their meaning for access to drugs of its population. We discuss new and existing barriers, as well as possible solutions, to provide policy-makers with lessons learned from the Ghanaian experience.
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Introduction
The disparity in pharmaceutical access between developed and developing countries is stark. Developing countries make up approximately 80% of the world's population but only represent approximately 20% of global pharmaceutical consumption[1]. Market failures, government failures and income differences account for this persisting inequity[2]. Specifically, high drug costs, weak or corrupt institutions, contributing to less than effective pharmaceutical purchasing and distribution systems, and the potential consequences of the Trade Related Aspects of Intellectual Property (TRIPS) Agreement all constrain drug access.
Many developing countries have recently modified their patent laws to conform to TRIPS standards, raising the urgency of TRIPS' potentially detrimental impact on drug supply and access. However, recent developments such as the Decision on the Implementation of Paragraph 6 of the Doha Declaration on the TRIPS Agreement and Public Health may offer developing countries more freedom to use TRIPS safeguards to address public health needs.
This article focuses on Ghana, a developing country that recently changed its patent laws to conform to TRIPS standards. While Ghana has made strides in improving public health, the country has urgent and serious health needs that cannot be met by the existing system. Improving pharmaceutical access is one of the core challenges facing the Government. As such, there is a menu of choices available for possible use. These include "Paragraph 6", compulsory licensing, parallel importing and attracting investment for the local production of essential medicines to combat HIV/AIDS, malaria and tuberculosis. In this paper, we discuss a selection of pharmaceutical policy choices available to governments that may lead to improved access to medicines. We do this specifically through the case of Ghana. As a test case, Ghana represents the dilemma faced by many developing countries -"make or buy." That is to say, should a government invest more in local production or continue to import medicines? In short, we examine Ghana's patent law changes in the context of the Doha Declaration and assess their meaning for access to drugs of its population. New and existing barriers will be discussed and options for addressing them proposed, to provide policy-makers with lessons learned from the Ghanaian experience.
This paper is based on research conducted for the UK Department for International Development (DfID)[3]. Our study implicitly focuses on the basic pharmaceutical public policy issues that currently face decision-makers in Ghana: 1) how to improve pharmaceutical coverage for the majority of Ghanaians and most importantly for the poor; 2) how to make treatment and drugs more affordable; and 3) how to ensure the government gets maximum value from its pharmaceutical budget. We collected data through: a review of "hard" documents (recent academic literature, policy documents, recent country studies, laws and regulations relating to drug access issues in Ghana and internationally), interviews with key stakeholders and informants to identify major gaps in drug access in Ghana, and inputs from the Access to Medicines Ghana Initiative Advisory Group.
We organize our paper as follows: first, we describe Ghana and its pharmaceutical system; second, we discuss the TRIPS Agreement and the Doha Declaration; third, we discuss Ghana's 1992 Patent Law and 2003 Patent Act; fourth, we discuss Ghana's new Patent Act in the context of the Doha Declaration and of pharmaceutical access in Ghana, followed by conclusions.
The State of Pharmaceuticals in Ghana
Ghana has made significant improvements in its overall health status over the past few decades, with life expectancy reaching 57 years in 2002 and infant mortality declining to 56 per 1000 live births. Despite these improvements, there are significant health issues facing the country: approximately 3.6% of the population is infected with HIV/AIDS, malaria accounts for 40% of outpatient visits and 25% of mortality under the age of five and the annual risk of tuberculosis infection is approximately 1–2%[4]. High mortality rates, frequent epidemics, unequal access to health services, and uneven health outcomes throughout the country are also major problems.
Pharmaceuticals are available in many health facilities across the country; however, access is largely limited due to financial barriers for most of the population, particularly the poor. According to the World Bank, Ghana had a per capita income of US$380 (2004), which is about one-fifth below the average of US$490 for sub-Saharan Africa. Moreover, a recent study indicates that 40 percent of Ghana's population earns less than minimum wage with this proportion increasing in rural areas[5]. As a result, the poverty level makes it difficult for patients to purchase drugs. For example, HIV/AIDS patients receiving one-month of anti-retroviral therapy paid for by the Global Fund are still required to pay 10% of the costs of medicines, at approximately 50,000 cedis (over $US 5). In real terms, it would require a person who is earning the minimum wage more than 5 working days to cover the co-payment. In a small random sample of interviews with patients at the HIV/AIDS Clinic in St. Martin's Hospital, Agomanya, we observed that many patients were not working at all and had to borrow money from family members to cover this co-payment.
Until recently, Ghana's public health and pharmaceutical system operated under the "cash & carry" (C&C) model, which assumes that drug co-payments can help finance and, therefore, improve the delivery of primary health care services. This system involved a series of self-financing revolving drug funds (RDF), which cascaded down each institutional level, marking up the basic purchase price for drug products to obtain revenue to re-supply the products. Additively, these mark-ups could also increase the price of a drug well beyond the reach of most Ghanaians. The government provided exemptions for co-payments for specific categories, including TB patients, psychiatric patients, children under five years of age, the indigent, pregnant women and the elderly. Identifying who is truly indigent is difficult to do in Ghana because poverty is viewed from a socio-cultural point of view as "shameful" and many poor people are reluctant to admit it. Essentially, this system did not achieve its intent to provide widespread affordable access to medicines for the population.
The Government of Ghana declared its intention to abolish the C&C system and passed a National Health Insurance Bill in 2003, which recently has been implemented. Several districts have also introduced health insurance in their localities. These insurance measures may serve to improve access to medicines; however, their effectiveness is yet to be observed. Importantly, the draft minimum package for pharmaceuticals includes medications for outpatient and inpatient services. Antiretrovirals are covered under different arrangements using the Global Fund to fight AIDS, Tuberculosis and Malaria. The Ministry of Health is reviewing exemption policies and the proposed National Health Insurance drug list to ensure consistency with the Essential Drug List. Despite the difficulties noted above, the Insurance system hopes to use local structures to identify who is "poor" to ensure these categories can access care. A national pricing policy, informed by a comprehensive examination of pharmaceutical pricing models internationally, can also facilitate better financial access of the population to medicines.
Pharmaceutical mark-ups are another policy issue that needs reform. The international research-based and generic pharmaceutical industries provide discounted medicines to Ghana, however once products arrive in Ghana, mark-ups between 11% to 275% wipe out many price advantages[3]. Tax and tariff rates vary but are applicable to all medicines, except public sector procurement done according to the Essential Drug List. In the private sector, depending on the local agent or manufacturer, the cost of antiretrovirals can exceed 32.5% more than the discounted price obtained through the Accelerated Access Initiative (AAI). In some cases, the private health facility adds further margins to increase the cost. In order to effectively address mark-ups, national tax, tariff and mark-up policies need to be reviewed to determine what policy changes could facilitate more affordable prices. Government can regulate wholesale and retail mark-ups on essential medicines. Policies regarding the private sector management chain and public sector supply management chain need examination and adjustment to make medicines more affordable for patients.
Ghana has potential to supply more of its medicine needs; local manufacturing accounts for 20% of the pharmaceutical market share. There are about 30 pharmaceutical manufacturing facilities in Ghana and about 17–18 produce all year round. Most raw materials needed for local manufacture are imported and subject to duties, taxes and tariffs, which erode the potential cost advantage that local manufacturing can provide. Currently, manufacturers pay 12.5% VAT (Value Added Tax), 0.5% EDIF (Economic Development Investment Fund), 0.5% ECOWAS levy, handling and inspection charges, GCNet charges (0.004% of cost and freight)[3]. However, Schedule 1A of the 34 materials of Active Ingredients of Essential Medicines are exempt from tax. Manufacturers apply a mark up that can range from 10 to 40%. Wholesalers add a further 10 to 20% when selling to the retailer. Then, the retailer adds another margin of 20 to 50%. All of these margins obviously increase the price of the drug for the patient, thereby contributing to the financial barrier to medicines. To help local industry, the government restricts the importation of 17 pharmaceutical products (e.g. paracetamol, chloroquine). Local production is beneficial given that it also provides employment for the population; however the barrier of limited human and technological capacity must first be overcome.
The TRIPS Agreement and the Doha Declaration
By way of brief background, the TRIPS Agreement provides minimum standards for intellectual property law and procedures and remedies that should be available so rights holders can enforce their rights effectively[6]. The default principle concerning patents is that they should be available for any invention, whether product or process, in all fields of technology without discrimination[6]. With respect to pharmaceutical patents, the minimum TRIPS obligations include 20 years of patent protection from the inventor's filing date (Article 33), patent rights free of discrimination against the origin of invention or production (Article 27.1), and exclusive marketing rights for the entire patent duration (Article 28)[7]. Transitional periods are granted before TRIPS requirements for patent protection must be met; the deadline for least-developing country members was ultimately extended to 2016 (Articles 65 and 66)[6].
The TRIPS Agreement also outlines provisions around patent rights for member states. For example, Articles 8.2, 31(k) and 40 offer flexibility to member countries to prevent or remedy anti-competitive practice[8]. Article 30 facilitates an early working provision, allowing the limited use of an invention without the patent holder's authorization[9]. Generic companies can use this provision to obtain product approval, facilitating immediate entry into the market after patent expiration. Article 31 permits a government to issue a compulsory license to a third party without the patent holder's consent, if justified in the public interest[7]. Compulsory licensing allows governments to pursue the local production of medicines as one strategy to improve access of the population to essential medicines. Parallel importing, legally pursuant to TRIPS Article 8.1 and Article 6, is the import and resale in a state without the consent of a patent holder, of a patented product in another market. Its rationale is to allow governments and others to "price shop" internationally for pharmaceutical products, based on the underlying principle that the patent holder has been rewarded through the first sale and thus has "exhausted" rights. Compulsory licensing and parallel importation are the focus of this paper.
In practice, governments may realistically be reluctant to exercise TRIPS provisions given some concern about political and economic ramifications, particularly in the area of trade sanctions. The Doha Declaration, issued by the WTO in November 2001, partially aimed to address this concern. It states " [the] TRIPS Agreement does not and should not prevent members from taking measures to protect public health... [and it] should be interpreted and implemented in a manner supportive of WTO members' right to protect public health and, in particular, to promote access to medicines for all" [emphasis added][10]. Furthermore, the Declaration specifically reaffirms member countries' rights to determine the grounds on which compulsory licenses may be issued, to determine what constitutes a national emergency or circumstance of extreme urgency, and to determine their own regime for the exhaustion of intellectual property rights.
The WTO has, however, not explicitly defined the legal status of the Declaration. An unofficial explanation of the Declaration, available on the WTO website, states that the Declaration provides "important guidance" to individual members and WTO dispute settlement bodies in the interpretation of TRIPS[11]. Correa identifies the Declaration as a "strong political statement" and "a 'subsequent agreement' between the parties regarding the interpretation of a treaty or the application of its provisions, under Article 31.3(a) of the Vienna Convention on the Law of the Treaties"[12]. Ultimately, the functional application of the Declaration is to interpret TRIPS[13], however as Reichman and Hasenzahl note[14], the exact legal status of the Declaration will not be clear until its practical application has been observed through future WTO panels and the Appellate Body.
The Declaration was partially an effort to interpret Article 31(f) of the TRIPS Agreement, which states that compulsory licensing shall be "predominantly for the supply of the domestic market." Given that the majority of developing countries lack the domestic capacity or technical expertise to manufacture on-patent pharmaceuticals, the interpretation of this terminology is crucial for ensuring access to medicine for the poor in many developing countries. On August 30, 2003, the WTO issued a temporary solution, the "Decision on the Implementation of Paragraph 6 of the Doha Declaration on TRIPS Agreement and Public Health"[15]. The Decision temporarily waives Article 31(f), permitting countries with local manufacturing capacity to issue compulsory licenses to produce and export drugs to countries without adequate manufacturing capacity, in return for a pledge from countries not to use the Decision "...to pursue industrial or commercial policy objectives"[16]. Eligible importing countries include least-developed countries or a country that can demonstrate insufficient or no manufacturing capacity for the purpose of meeting its needs. No country, at the time of writing, has yet notified the WTO of its intention to operate as an importer under this decision. Countries may in fact be reluctant to do so as a result of economic and political pressure.
Ghana's Patent Law
The Patent Law of 1992 provided for the protection of patents in Ghana until recently. This law provided that all inventions, products or processes which were "new, involve an inventive step and are industrially applicable," were patentable (Sec.2)[17]. Pharmaceuticals were considered patentable inventions and patent duration was 10 years (Sec.31(1)). The law permitted compulsory licensing in cases of no or insufficient local working of the patented invention (Sec.45(1)), based on the interdependence of patents (Sec.46), and for products or processes declared to be of vital importance to defence, economic or public health interests (Sec.47). Section 30 considered the patent holder's right exhausted only when he put his patented product on the Ghanaian market, rendering parallel importation impossible. To meet all TRIPS obligations and take advantage of its safeguards, the Ghanaian government reviewed the Patent Law of 1992 and passed a bill to replace it in early 2003.
The changes introduced in the 2003 Patent Act removed some legal tools that may have helped improve access to medicines[18]. Under Section 7 of the 1992 Patent Law, the Ghanaian government had the authority to temporarily exclude inventions or discoveries, such as pharmaceuticals, from patentability "... in the interests of national security, economy, health or any other national concern." The 2003 Patent Act removed this exception. Arguably, the government of Ghana could have excluded specific pharmaceutical products from patentability as a temporary means to address urgent public health concerns. Temporary excludability is particularly useful when procedural requirements to compulsory licensing cannot be met[9]. However, as Correa explains, a literal interpretation of Article 27.1 does not allow the exclusion of pharmaceuticals[9]. He notes that under TRIPS Article 27.2 ordre public and Article 8.1 "...pharmaceuticals might conceivably be excluded from patentability, but neither appear sufficient to justify this exclusion except in limited circumstances,"[9]. In any case, the option of using temporary excludability appears unviable at the present time.
Section 13(2) addresses the royalty rate for compulsory licenses: "... [t]he exploitation of the invention...shall be subject to the payment to the owner of an adequate remuneration, taking into account the economic value of the Minister's decision as determined in the decision..."[18]. Adequate remuneration is undefined, allowing for the negotiation of prices, which could have either positive or negative effects on price control and access to medicines. The Ghanaian government has since developed administrative guidelines, proposing the creation of a committee that would determine the level of compensation to be given to a patent holder. It proposed that remuneration for drugs used to treat HIV/AIDS, TB and malaria not exceed 1% of the retail price. In Canada, a country with extensive history of compulsory licensing, a royalty rate of 4% was used[19]. Furthermore, Canada's recent Jean Chrétien Pledge to Africa Act may offer useful guidance, as the royalty rate varies from 4 to 0.02% depending upon the importing country's standing on the UN Human Development Index[20].
Section 12(1) of the 2003 Patent Act incorporated Article 33 of TRIPS, doubling the period of patent protection to 20 years. The result will be a delay in the entry of generic competition, and since generic competition tends to lower drug prices, a reduction in overall cost-savings is likely[21]. This provision does not offer any flexibility to member countries; therefore the ensuing barriers will require circumvention via other TRIPS safeguards or policy alternatives. As noted earlier, the Ghana's major disease burden includes malaria, TB and HIV/AIDS. While these diseases can be treated with off-patent medications, extended patent life will be problematic in situations where no other therapeutic options are available[22]. Specifically, evidence of resistance to traditional antimalarial therapy (e.g., chloroquine) exists and patients who develop resistance to anti-retroviral medications or experience treatment failure will need access to new, patented medicines in the future. The increase in duration of patent protection impedes Ghana's autonomy over defining their population's therapeutic needs.
Changes were also introduced in the 2003 Patent Act that may promote access to medicines. Section 11(4a) of the 2003 Patent Act allows the international exhaustion of intellectual property rights. This legalizes the parallel importation of lower-priced pharmaceuticals from other countries into Ghana, which will be discussed in detail below. Compulsory licenses can now be issued in circumstances of anti-competitive practice, which allows Ghana to remedy abusive practices and excessive prices, potentially increasing the availability of affordable medicines[9]. Potential also exists to use TRIPS' anti-competitive provisions, accompanied by appropriate national competition policy, to promote the development of the local pharmaceutical industry[8]. The successful suit by the Aids Law Project (ALP) against two major pharmaceutical companies with the South African Competition Commission in 2002 illustrates this potential. Other positive changes include new procedures in granting compulsory licenses: a waiver to seek a voluntary license in "cases of national emergency or other circumstances of extreme urgency," (Section 13(10)) and ministerial authorization instead of the previous lengthy and resource-intensive requirement of legislative authorization (Sec.13(1)).
The 2003 Patent Act widens the provision for issuing a non-voluntary license under local working requirements. Local working requires the manufacturing of a patented product to a minimum degree within the country, potentially stimulating growth of the local pharmaceutical industry. Specifically, the 2003 Patent Act allows non-voluntary licenses in situations where "...the patented invention is not exploited or is insufficiently exploited by working the invention locally, or by importation in the country," (Sec.14(1)) [18]. Whereas the 1992 Patent Act listed four relatively specific instances, where the compulsory license could be invoked if the invention was not being worked, the 2003 Patent Act makes this more open-ended. The limits of this new clause will likely be drawn by the TRIPS agreement and legislative intent during drafting the Patent Bill in Ghanaian courts; its exact interpretation is still unclear. Given the USTR complaint against Brazil regarding its local working requirement, however, the current political feasibility of including and invoking such a clause is tenuous[19].
How will these developments impact access to medicines in Ghana?
Compulsory Licensing
Ghana's 2003 Patent Act, TRIPS and the Doha Declaration offer Ghana sufficient legal ground to use compulsory licensing to address its public health concerns. Compulsory licensing can be used for either local pharmaceutical production or importation, however the latter may be more feasible in the short-term. This will be discussed below. Paragraph 5b of the Declaration explicitly reaffirms the right of countries to "...grant compulsory licenses and the freedom to determine the grounds upon which such licenses are granted." As Correa notes, Paragraph 5b merely states the obvious: Article 31 of TRIPS only requires certain conditions for the granting of compulsory licenses, "but it does not limit the grounds on which such licenses can be granted"[12]. Paragraph 5(c) further facilitates compulsory licensing through the recognition that "public health crises ... can represent a national emergency or other circumstance of extreme urgency" and clarifies that members need not declare a "fully-fledged national emergency"[23].
From a political perspective, the feasibility of using compulsory licensing to address public health concerns has also become more favourable. One event suggesting this is the abandonment of a pharmaceutical industry lawsuit seeking to remove South Africa's amendment in its Medicines and Related Substances Act, which would permit compulsory licensing and parallel importation. The suit was abandoned due to international pressure and the resolve of the South African government. Other events include Brazil's successful use of the threat of compulsory licensing in negotiations to obtain significant discounts of 40–65% on patented antiretrovirals from Roche and Merck and a public statement by Boehringer-Engelheim in 2003 that it will not interfere in the issuance of compulsory licenses and will respect the Doha Declaration[24]. The World Health Organization (WHO) also explicitly supports developing countries in the use of TRIPS safeguards to promote access to medicines[25]. Given these developments, political and legal repercussions from other powerful countries are less likely. For a country to make effective use of compulsory licensing a number of other potential barriers must be addressed and certain requirements must be met.
Effective implementation of compulsory licensing requires the adequate know-how and administrative infrastructure, however many developing countries, including Ghana, do not have this requisite capacity[26]. Article 67 of the TRIPS Agreement requires developed countries to provide technical assistance, "on request and on mutually agreed terms and conditions", to developing and least-developed countries to help address such gaps. Developing countries like Ghana should use this provision and approach developed countries and international organizations for support.
The usefulness of compulsory licensing, for local production or as a negotiating tool, largely depends on whether the appropriate technological and production capacity exists and whether appropriate human resources are available. The experience of Brazil provides an illustrative example on this point. Cohen and Lybecker argue that Brazil's success is based on three main factors: first, the threat posed by Brazil is credible in that it has a viable local industry; second, Brazil's market is "...one of the largest in Latin America and among the top ten globally;" third, Brazil initiated its threats with the strong support of the international community[24]. As we noted earlier, the international community continues to be supportive of developing countries' use of compulsory licensing to address public health needs. In comparison to Brazil, however, Ghana's pharmaceutical market is small which may make the process economically unfeasible. The viability of Ghana's local industry is also questionable as approximately 30 pharmaceutical manufacturing facilities exist but only 17–18 produce throughout the entire year. Careful cost-benefit analysis of the value of domestic production versus the importation of pharmaceuticals is necessary to determine whether Ghana can benefit from compulsory licensing for local production, as it is commonly more economical to import medicines than to produce them locally.
Parallel Importation
Parallel imports are of particular importance in meeting public health needs since the pharmaceutical industry generally sets differential prices globally for the same medicine. Thus, parallel importation of a patented medicine from a country where it is sold at a lower price will enable more patients in the importing country to gain access to cheaper drugs (whether international exhaustion applies to medicines produced under compulsory licensing, however, is still a live debate; see Abbott 2002). Paragraph 5(d) of the Declaration explicitly reaffirms members' freedom to determine their own regimes for the exhaustion of intellectual property rights without challenge. Ghana's 2003 Patent Act finally facilitates this by incorporating this provision; it allows parallel importation only under the condition that the product to be imported is already "put on the market in any country by the owner of the patent or with the owner's consent," (Sec.11(4a)).
Parallel importing introduces more challenges. Administrative capacity issues exist with parallel importation as with compulsory licensing. In Ghana, import permits for companies engaged in the parallel importation of drugs are difficult to obtain at this time, which may pose another barrier. Parallel importation increases the opportunity for the influx of sub-standard products and thus attendant recall problems. Some critics argue that parallel importing acts as a disincentive to differential pricing by research-based pharmaceutical companies due to a risk of diversion of low-cost products to lucrative, developed country markets; however, as Outterson notes "...empirical evidence to date does not indicate a sizable arbitrage market in ARVs from low income countries into the high income countries"[20]. Furthermore, there have been no reported cases of diverted drugs from Ghana to other markets. The European Commission's (EC) May 2003 Regulation to facilitate differential pricing may provide another option while lessening some of the industry's re-exportation concerns. The EC regulation provides anti-diversion measures against specific pharmaceutical products and requires manufacturers to reduce their essential medicines export prices to developing countries by "75% off the average 'ex factory' price in OECD countries, or at the cost of production plus 15%"[27]. If parallel importation is to be useful to Ghana, administrative, institutional and managerial capacity must be developed for effective implementation, to prevent the unlawful importation and exportation of products and to ensure quality control.
Importation Pursuant to Paragraph 6
If Ghana decides against using its' local industry for the production of generic medicines, Paragraph 6 may offer another potential solution. While some critics have viewed this provision favourably, others have criticized it as too administratively complex for developing countries. Correa explains that the implementation of the Paragraph 6 Decision requires a number of steps, among which include: 1) in most cases, compulsory licenses issued by importing and exporting countries, 2) the importing country's establishment of insufficient or no local manufacturing capacity in the specified pharmaceutical sector, 3) importer notification to the WTO of its intention to use the system detailing product(s) requested and quantities (accompanied by confirmation of insufficient manufacturing capacity and that a compulsory license is or will be granted), and 4) notification of the exporting country's compulsory license to the WTO and the conditions attached[28]. Paragraph 4 of the Decision requires the importing country to "take reasonable measures within their means ... to prevent re-exportation"; this requires countries to implement anti-diversion measures including special marking and labelling of the product(s)[15]. An obstacle might be introduced with strict data protections laws, as exporters must either obtain authorization from the patent holder to obtain efficacy and toxicity data or when denied, perform its own clinical studies to collect this data; this can increase the exporter's costs, therefore increasing the drug cost in the importing country[28]. Additional obstacles and delays may depend on the importing country's national laws on product registration and the exporting company's capacity to manufacture the specified product[28]. Lastly, the Decision limits the exporting country's compulsory license to a "single-supply basis," implying that this entire process must repeat for each new request[28].
Clearly, administrative barriers may hinder some developing countries from using this; however, Article 67 may be used to obtain external assistance to overcome these gaps. Some critics have also claimed that the Decision will not benefit smaller-sized markets, providing minimal incentive to exporting manufacturers. Paragraph 6 of the Decision may help alleviate this problem. It waives the obligations of TRIPS article 31(f) "to the extent necessary to enable a pharmaceutical product produced or imported under a compulsory license in that member to be exported" to other developing or LDCs that are party to the same regional trade agreement and share the health problem in question. Theoretically, this allows a country like Ghana to harness economies of scale and generate more incentive for generic manufacturers to export. However, such a regional trade area must have been formed in conformity with the provisions of Article XXIV of GATT[15].
To date, only Canada, Norway and the Netherlands have passed legislation to allow export of pharmaceuticals under this provision[29,30]. To implement Paragraph 6, developing countries will have to address barriers introduced by these exporting countries. For instance, the recently passed Canadian bill contains a restrictive list of medicines that can be produced for the purposes of Paragraph 6. Norway's legislative counterpart is less restrictive. Whether exporting countries will be able to satisfy the global demand for affordable, generic drugs will be observed.
It is also unclear whether a developing country like Ghana can use Paragraph 6 and if so, under what circumstances. LDCs are automatically eligible, but since Ghana is a developing country, it is required to examine its local manufacturing capacity and establish that "...excluding any capacity owned or controlled by the patent owner, it is currently insufficient for the purposes of meeting its needs"[15]. Ghana has been classified as a country that can reproduce drugs as long as it imports the active ingredients, therefore a cost-benefit analysis is necessary to determine whether or not local manufacture is both technically and economically viable[10]. Whether the lack of economic viability would be considered as "insufficient local manufacturing capacity" in the event of a dispute is questionable. Correa argues that if low-priced medicines cannot be produced because "...meaningful economies of scale have not been reached..." then one of the main objectives of the Doha Declaration, "to promote access to medicines for all," cannot be reached[12]. However, reports indicate that the US informed the Philippines and other countries that it does not consider "economic efficiency" as valid ground for the use of Paragraph 6[31,32]. It is also important to note that the General Council Chairperson's Statement on 30 August 2003, includes a mechanism that allows any member to challenge another member's "interpretation and implementation" of the Decision "with a view to taking appropriate action"[16]. The legal implications of the Chairperson's Statement must still be observed; it is unclear whether a developing country like Ghana will be able to use Paragraph 6 without legal challenge or political and/or economic consequences.
Strengthen local industry capacity
The TRIPS Agreement has several provisions, which deal explicitly with the issue of technology transfer. For instance, article 7 states, "The protection and enforcement of intellectual property rights should contribute to technological innovation and to transfer and disseminate technology, to the mutual advantage of producers and users of technological knowledge and in a manner conducive to social and economic welfare and to a balance of rights and obligations" [emphasis added]. Despite such provisions, little technology transfer to developing countries has taken place[33]. To strengthen local industry, developing countries like Ghana should still pursue initiatives to absorb new technology. Public-private partnerships (PPPs) may be one mechanism to achieve this. Generally, PPPs require private sector companies to provide the technology and expertise while public sector partners provide development funding and help ensure access to the medications. PPPs can facilitate technology transfer to developing and least-developed countries while creating opportunities to initiate research into developing country diseases[23].
Pooled Procurement
Pooled procurement is a cost-containment strategy that can assist developing countries in financing of the drug needs of their populations, as it is the one area of the drug supply management cycle that can offer the greatest amount of cost savings. For example, the Eastern Caribbean Drug Service (ECDS) used pooled procurement to lower pharmaceutical expenditure by an average of 44%[34]. The recently established West Africa Manufacturers Association has put in place mechanisms to take advantage of the economics of scale in the pooled procurement of both raw materials and finished products. In (Economic Community of West African States), political realities have to be addressed given that Francophone West-African countries are already involved in pooled procurement procedures. Thus, if Ghana participates, it would have to comply with established procedures. Ghana must carefully examine the costs and benefits of different procurement policies to determine which ones are most viable and cost-effective. Effective implementation, to be sure, demands institutional capacity, financial management systems, quality assurance and transparency.
Voluntary Differential Pricing Arrangements
Ghana can also pursue the procurement of affordable drugs through voluntary differential pricing arrangements. These arrangements can operate through supplier's charity, desire for favourable public relations, or other criteria not immediately or apparently related to market forces. Currently, limited implementation of differential pricing outside of anti-retroviral therapies occurs in Ghana. As noted earlier, the research-based pharmaceutical industry often cites the risk of re-exportation of these drugs to developed country markets as a barrier to scaling up these initiatives, despite the lack of sufficient evidence. To mitigate these concerns, pharmaceuticals firms often require that recipients of their drugs sign a supply agreement that indicates that the recipient will take measures to ensure the security of the drug supply they receive. To further lessen this risk, companies have special packaging and labelling of drugs provided under special programmes like the Accelerated Access Initiative. Developing countries like Ghana can comply with these measures with assistance from firms and established programs to further encourage differential pricing arrangements.
Conclusion
In this paper, we discuss several possibilities for working within the TRIPS regime to gain better access of the population to medicines. These options include compulsory licensing, parallel importing, technology transfer, local production and voluntary differential pricing. We put forward some favoured policy choices for Ghana. First, we encourage Ghana and its Access to Medicines (ATM) Advisory Committee to consider local production. Local manufacturing can be an effective option if human and technological capacity is scaled up. However, we emphasize that this option should only be pursued if it makes economic sense. As a start, an objective cost-benefit analysis should be done to determine whether it makes economic sense for Ghana to pursue local production. Among the alternatives available to strengthen local industry include more aggressive technology transfer.
Next, we encourage the use of compulsory licensing. If Ghana decides to pursue compulsory licensing, it must then address administrative and knowledge barriers. This can be achieved through obtaining support from developed countries and/or international organizations on the effective implementation of compulsory licensing. There is great potential for Ghana particularly given the Government's commitment to build up its knowledge base in this area. In September 2004, members of the Ghanaian Access to Medicines (ATM) Advisory Committee visited Canada to learn about Canada's past experience with compulsory licensing and what measures could be applied to Ghana. If, however, Ghana determines that it is more technically or economically feasible to pursue importation, Paragraph 6 may provide an option. Ghana will first have to establish insufficient manufacturing capacity in the pharmaceutical sector in question, and then consider what political or economic repercussions may follow. More concrete alternatives for importation include parallel importation or the voluntary tiered-pricing arrangement proposed by the European Commission. Importantly, it is critical to monitor any public policy reform to assess whether or not they are achieving attendant outcomes and adjust accordingly. This will require baseline assessments and regular review at intervals.
The opportunities presented above can only be effective in addressing access to medicines in Ghana if other existing barriers are simultaneously addressed. First and foremost, the development and implementation of an effective exemption policy for the poor without co-payments is vital. Policies can vary such as implementing a national pricing policy that control prices on the supply side by regulating actual drug costs or the demand side, through reimbursement schemes such as reference-based pricing or generic substitution policies. Furthermore, reduction of mark-ups in the public sector may generate competition and drive private sector prices down. A hard but necessary policy reform is needed in the area of national tax, tariff and mark-ups to determine what changes could facilitate more affordable prices for the population.
Is the Ghana case generalisable for other African countries? We hope that as a minimum this case adds to the debate in other African countries about public policies they should pursue to improve access to medicines. Some policies may be more applicable than others depending on economic and political realities. There is not a "one-size-fits-all" policy menu that should be applied. Governments need to make informed policy choices when it comes to improving access to medicines and assess which measures are most needed and viable for their particular country.
Acknowledgements
The Department for International Development (DfID), UK provided funds for the initial policy option analysis for increasing access to medicines in Ghana.
The authors declare that they have no competing interests. However, for the record, please note that Jillian Clare Cohen, Martha Gyansa-Lutterodt and Kwasi Torpey were DfID consultants in Ghana in 2003.
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BMC Public HealthBMC Public Health1471-2458BioMed Central London 1471-2458-5-1281633665610.1186/1471-2458-5-128Research ArticleSkin infection, housing and social circumstances in children living in remote Indigenous communities: testing conceptual and methodological approaches Bailie Ross S [email protected] Matthew R [email protected] Elizabeth [email protected] Stephen [email protected] David [email protected] Gary [email protected] Steven [email protected] Menzies School of Health Research and Institute of Advanced Studies, Charles Darwin University, Darwin, Australia2 Flinders University Northern Territory Clinical School, Darwin, Australia3 School for Social and Policy Research, Institute of Advanced Studies, Charles Darwin University, Darwin, Australia4 Northern Territory Department of Health and Community Services, Darwin, Australia2005 8 12 2005 5 128 128 9 7 2005 8 12 2005 Copyright © 2005 Bailie et al; licensee BioMed Central Ltd.2005Bailie et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Poor housing conditions in remote Indigenous communities in Australia are a major underlying factor in poor child health, including high rates of skin infections. The aim of this study is to test approaches to data collection, analysis and feedback for a follow-up study of the impact of housing conditions on child health.
Methods
Participation was negotiated in three communities with community councils and individual participants. Data were collected by survey of dwelling condition, interviews, and audit health centre records of children aged under seven years. Community feedback comprised immediate report of items requiring urgent repair followed by a summary descriptive report. Multivariate models were developed to calculate adjusted incidence rate ratios (IRR) for skin infections and their association with aspects of household infrastructure.
Results
There was a high level of participation in all communities. Health centre records were inadequate for audit in one community. The records of 138 children were available for development of multivariate analytic models. Rates of skin infection in dwellings that lacked functioning facilities for removing faeces or which had concrete floors may be up to twice as high as for other dwellings, and the latter association appears to be exacerbated by crowding. Younger children living in older dwellings may also be at approximately two-fold higher risk. A number of socioeconomic and socio-demographic variables also appear to be directly associated with high rates of skin infections.
Conclusion
The methods used in the pilot study were generally feasible, and the analytic approach provides meaningful results. The study provides some evidence that new and modern housing is contributing to a reduction in skin infections in Aboriginal children in remote communities, particularly when this housing leads to a reduction in crowding and the effective removal of human waste.
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Background
Bacterial skin infections are a common and important cause of morbidity in disadvantaged populations. In Indigenous communities in the Northern Territory the prevalence has been reported at between 10 and 70% [1-4]. Pyoderma is important not only because of its local effects as a skin infection, but more importantly because the primary pathogen underlying skin infection in Aboriginal children is a Group A Streptococcus (GAS) [5]. GAS infections of the skin are believed to be an important factor in acute post-streptococcal glomerulonephritis (APSGN) and acute rheumatic fever (ARF) [2,4,6,7]. Rates of ARF and consequent rheumatic heart disease (RHD) in Aboriginal children living in these communities are reported to be among the highest in the world [8-10]. GAS is also believed to contribute to the high rates of chronic renal failure in these communities [11]. Pyoderma is believed to be the major source of invasive GAS disease. Scabies infestation is believed to underlie between 50–70% of cases of pyoderma [1], and has a reported prevalence among children of around 50% [1,12].
The underlying determinants of these high rates of pyoderma are reported to include crowding [13,16], inadequate water supply [17,18], heat and humidity [19,20], poor education and poor hygiene [19,21-23]. The interdependence of these and other socio-economic factors has led to difficulty in assessing the relative importance of such factors [13,17]. This difficulty applies to diverse aspects of health in these communities and not only to skin health in children.
An important aspect of the environment of these children that is potentially amenable to relatively immediate intervention is the quantity and quality of housing. A major objective of housing programs in Indigenous communities is the improvement of health. Work on defining components of household infrastructure important to the conduct of a set of 'Healthy Living Practices' (HLPs) [24,25] has been influential in the development of housing programs in remote communities. Components of household infrastructure relevant to the HLPs, including those important to the prevention of skin infections, are in poor condition in many remote Indigenous Australian communities [26-28]. However, there is a lack of empirical data that can be used to inform how housing design can achieve the most significant gains in health, and to understand other factors that may moderate or mediate potential health improvements. Understanding these other factors, and developing programs to address them, should contribute to ensuring housing improvements flow through to improved health. A simple conceptual representation categorises these factors as (1) infrastructure, (2) household composition and social process and (3) condition of household environment (Figure 1).
Figure 1 Conceptual Framework relating household composition and processes, infrastructure condition, hygiene condition and childhood skin infections.
This paper reports on the findings of a pilot study conducted in the lead up to a before and after controlled study of the impact of provision of better quality housing on the health of children in eleven remote Aboriginal communities. The objectives of the pilot study were to test data collection procedures, to refine specification of variables, to test the conceptual framework for the study and to refine the approach to the analysis and community feedback procedures (community feedback is not discussed in this paper). The objectives of the analysis were to examine the association between skin infections in Aboriginal children living in remote communities and the functional status of basic items of household infrastructure, and to examine the extent to which these associations are mediated by other household and carer characteristics.
Methods
Study setting and agreement to participate
Three remote communities were approached to participate in the study. Communities were selected to ensure some variation in size, development and geographic spread. The three communities typified the very poor environmental conditions prevalent in remote Indigenous communities in Australia. Participation was initially negotiated with community councils, and individual consent was subsequently provided by individual participants. Two communities were located in the Top End of the Northern Territory and the third was located in Central Australia. The study was approved by the Top End and Central Australian Human Research Ethics Committees and by the associated Indigenous health research sub-committees.
Survey Processes
Three data collection processes were used: 1) a survey of dwelling condition; 2) interviews with the main householder of each dwelling and with the main carer of each child aged under seven years; and 3) an audit of health centre records. All dwellings in each community were included in the housing survey. Data on dwelling condition and the availability of cleaning materials were obtained through an inspection of the dwelling by an environmental health officer or housing officer. The functional state of each item was scored on a previously described five point ordinal scale using standardised survey forms and protocols developed for housing program management purposes and refined through evaluations of the survey process and survey data for three consecutive annual surveys [26,29]. Over 90 infrastructure items in and around the dwelling were examined in the survey, and where appropriate, physically tested. Items that were present and required no more than minor repairs were defined as 'functional'. Information on items requiring immediate or urgent repair were reported to the community housing office at the time of the survey and a report on the general state of community housing was provided following more detailed analysis of the survey data.
Children were eligible for inclusion in the study if they were less than seven years of age and had spent at least six of the previous 12 months living in the community. For all dwellings with at least one eligible child data on socioeconomic and demographic variables were collected through structured face-to-face interviews with the main householder of each dwelling and with the main carer of each eligible child. Where the main householder or carer was not available the interview was conducted with a secondary householder or carer. The definition of study variables is included in Table 1. Research personnel included a research officer with wide experience as a remote area nurse and health service manager with the assistance of an Aboriginal man with experience in Aboriginal community housing management. They were accompanied and assisted by a community resident employed for this purpose through the community council.
Table 1 Definition and categories of variables
Variable Description and categories
Outcome variable
Skin infection incidence rate (person-year) For each child aged under seven years, data on presentations to the health centre for skin infections (scabies and/or bacterial infection) were collected through an audit of health centre records for the one year period preceding the survey. Any record in the clinical notes indicating a diagnosis of scabies, impetigo or infected skin sores was counted as an episode, except if the record was within seven days of an earlier diagnosis of the same condition.
Primary explanatory variables
Housing condition, materials and design
Facilities for washing children not functioning 1. For child <1 year: (i) bathroom basin, hot tap, cold tap, bench, door, electrical and general structure are all functioning, or (ii) kitchen sink, hot tap and cold tap all functioning.
2. For child aged 1 to <3 years: (i) laundry trough, hot tap, cold tap, shelf, electricity, floor drainage and general structure are all functioning, or (ii) the bathroom shower head, hot tap, cold tap, drainage, bench, electrical and general structure are all functioning.
3. For child aged 3 to less than 7 years: bathroom shower head, hot tap, cold tap, drainage, bench, door, electrical and general structure are all functioning. Facilities not functioning versus facilities functioning (reference).
Facilities for washing clothes and bedding not functioning 1. Laundry trough, hot tap, cold tap, shelf, electricity, floor drainage and general structure all functioning.
Facilities not functioning versus facilities functioning (reference).
Facilities for removing human faeces not functioning 1. If child <1 year: toilet pan, cistern, water supply, drainage, bathroom basin and hot and cold taps are all functioning.
2. If child aged 1 to <3 years: child toilet equipment (e.g. potty – small plastic toilet), toilet pan, cistern, water supply, drainage, electricity, general structure, bathroom basin and hot and cold taps are all functioning.
3. If child aged 3 to less than 7 years: toilet door, electricity, general structure, toilet pan, cistern, water supply, drainage, bathroom basin, hot tap and cold tap are all functioning.
Facilities not functioning versus facilities functioning (reference).
Combined HLPs Failed at least one of the three healthy living practices previously defined
Concrete/other floor material (no tiles) Observed by surveyor. The floor type for the main living areas (kitchen and living room) of dwellings were categorised according to (1) concrete, (2) tiles, (3) other, (4)concrete and tiles, (5) concrete and other, and (6) tiles and other. The variable was then dichotomised to reflect the contrast between dwellings that have concrete/other flooring versus those that have tiles (reference).
Dwelling built pre 1980 As reported by community housing office or experienced government housing program staff. Older dwellings (pre 1980) versus newer dwellings (reference).
Secondary explanatory variables
Community A Community A versus Community B (reference).
Hygiene condition of dwelling environment and immediate surrounds
Internal contaminants present Presence of obvious organic contaminants observed by surveyor in the dwelling (e.g. faecal contamination (disposable nappies), food scraps, etc.) versus no contaminants (reference).
External contaminants present Presence of obvious organic contaminants observed by surveyor on the sealed surrounds (i.e. veranda) of the dwelling versus no contaminants (reference).
Household cleaning software
Household cleaning equipment missing Observed by surveyor. At least one of the broom, mop or bucket absent (as observed by the surveyor) versus dwellings with all items present (reference).
No soap in dwelling Observed by surveyor. No soap around the bathroom, kitchen or laundry sinks versus soap present (reference).
Child and carer socio-demographic factors
Child age less than 3 years Reported by carer and date of birth verified against health centre audit (health centre records used as true age). Child less than 3 years versus child 3 to less than 7 years (reference).
Child sex (male) Reported by carer. Child sex male versus female (reference).
Carer age Reported by carer. Categorised into three dummy variables: carer less than 20 years, carer 20 to 34 years (reference), and carer 35 plus years.
Low family income Family income per week as reported by the primary carer. Low family income (less than median family income ($935) for the two communities) versus greater than median family income (reference).
Carer highest education Highest formal qualification as reported by the carer. Categorised to three dummy variables. Grades 11 and 12 (reference), grades 9 and 10, and grade 8 and below.
Carer unemployed Reported by carer. Employed refers to any employment including CDEP (a type of 'work-for-the-dole' scheme). Carer unemployed versus carer employed (reference).
Household composition
Four or more children aged less than 7 years in the dwelling Number of children less than 7 years usually resident in the dwelling (at least 6 out of 12 months) as reported by householder and primary carer. Four or more children less than 7 years in dwelling versus less than 4 children (reference).
Three carers' in dwelling Reported by householder and carer. Only the main carer of each child was counted (i.e. variable refers to number of carers of different children). Three carers in dwelling versus one or two carers' in dwelling (reference).
Crowding: Residents per bedroom Number of residents (includes visitors) reported by householder and carer. Number of bedrooms based on dwelling survey. Categorised into three dummy variables. Two persons or less per bedroom (reference), greater than two to four or less persons per bedroom, and greater than 4 persons per bedroom.
Child mobility (high) Reported by carer whether child sleeps at another dwelling at least one night in ten (or 10%). Child mobility high versus low mobility (reference).
Presence of a visitor in the dwelling Reported by householder and carer. A visitor was defined as anyone who is not a usual resident who is currently staying at the dwelling for at least one week. Visitor(s) present versus no visitors (reference).
For each child, data on presentations to the health centre for skin infections and other specific conditions were collected through an audit of health centre records for the one year period preceding the survey. In community C the recording of presentations in health centre records of children with common childhood conditions was inadequate for audit purposes. This community was therefore excluded from analysis of the occurrence of skin infection.
Data analysis
Our analysis of the relationship between child skin infections and household composition and process, condition of household environment, and dwelling infrastructure follows the conceptual model presented in Figure 1. Composite variables reflecting the functionality of the infrastructure required for each of the three healthy living practices [24-26,28] that were hypothesised to be of primary importance to skin infections (wash children, wash clothes, removal of faeces) and a combined HLP variable were constructed (Table 1). Decisions regarding the infrastructure required for each healthy living practice for children of different ages were based on discussions with Indigenous and non-Indigenous people with wide experience of maternal and child health and housing programs in remote Indigenous communities.
Counts and percentages were calculated for all explanatory variables by community and differences were assessed using Fisher's exact test. Skin infection incidence rates (person-year) were tested for over dispersion to determine whether Poisson or negative binomial regression was used to model the counts of presentations with a skin infection diagnosis. All regression models were adjusted for clustering of children within dwellings using the Huber-White sandwich variance estimator [30]. Univariate incidence rate ratios were calculated for all explanatory variables.
The multivariate analysis examines how different aspects of the housing in which children lived related to rates of skin infection while controlling for the effects of other household composition and processes (see figure 1 and table 1). Dwelling infrastructure variables included the three HLP variables, the combined HLP variable, dwelling age (built pre/post 1980), and the floor type (tiles or concrete/other). A main effects multivariate regression model using stepwise backward elimination with a probability of 0.1 for exclusion of variables was developed. After main effects models were established, clinically or conceptually justifiable interaction terms (first order effects) were entered starting with the most significant interaction term. Only interactions where the cross tabulation had at least 3 observations per cell were tested to avoid spurious incidence rate ratios or a lack of convergence in the iterative regression estimation process. If the term added to the explanatory power of the model, it was included in the final model. When final models were determined, contrasts for each interaction term(s) were tested to assess those that were significantly associated with skin infection incidence rates.
Four models were developed (Table 4) to tease out significant associations of different aspects of household infrastructure with the incidence of skin infections, the factors that mediate or moderate these associations and identify other factors that are directly associated with skin infections. Model 1 included all the explanatory variables from table 1 (excluding combined HLP due to multi-collinearity). Model 2 used the combined HLP variable instead of the three separate HLP variables. Model 3 substitutes the dwelling age and flooring material variables for the HLP variables. Model 4 includes the three separate HLP variables and no other infrastructure variables. All statistical analyses were carried out using Stata version 8.2.
Results
Complete housing survey and interview data were obtained for 161 of an estimated 212 (76%) children in the eligible age range from the three communities. For the remaining estimated 51 children, the household residents declined to participate, were known to be away for an extended period or were not available for interview on at least three repeat visits. The analysis of skin infection data included the records of 138 children living with 80 carers in 69 dwellings in two communities. Levels of crowding and the functional state of household infrastructure varied between communities (Table 2).
Table 2 Summary statistics for each study community: Primary explanatory variables (at the dwelling level), and population and housing variables
Community A Community B Community C Total
Population and housing
Estimated number of children aged < 7 years 135 54 23 212
Number of children < 7 years surveyed 100 38 23 161
Estimated response rate 74.1 70.4 100.0 75.9
Number of carers§ 54 26 15 95
Number of residents¶ 473 175 102 750
Number of usual residents 420 157 87 664
Number of dwellings 46 23 13 82
Number of bedrooms 132 58 36 226
Mean no. of bedrooms per dwelling (SD) 2.9 (0.5) 2.5 (0.8) 2.8 (0.8) 2.8 (0.7)
Mean no. of residents per bedroom (SD) 3.6 (1.5) 3.4 (1.6) 2.7 (1.4) 3.4 (1.5)
Primary explanatory variables
Number of dwellings (%) n (%) n (%) n (%) n (%)
Facilities not functioning to:
Wash children 15 (32.6) 1 (4.3) 7 (53.8) 23 (28.0)
Wash clothes 21 (45.7) 5 (21.7) 5 (38.5) 31 (37.8)
Remove faeces 21 (45.7) 7 (30.4) 12 (92.3) 40 (48.8)
Combined healthy living practices 29 (63.0) 10 (43.5) 12 (92.3) 51 (62.2)
Concrete/other floor material 19 (41.3) 1 (4.3) 11 (91.7) 31 (37.8)
Dwelling built pre 1980 8 (17.4) 8 (34.8) 5 (38.5) 21 (25.6)
§ Carers' of children less than 7 years
¶ Residents refers to usual residents plus visitors (see Table 1).
Almost 40% (54) of children had no health centre presentations for skin infection over the study period, while over a third (47) had two or more presentations and 10% (14) had five or more (Table 3). Explanatory factors showing considerable variation between the two communities included the availability of household cleaning equipment, availability of soap, family income, carers' employment status, carers' education and crowding.
Table 3 Summary statistics for explanatory variables and univariate incidence rate ratios (IRRs) for presentation with skin infections: Confidence intervals adjusted for clustering of children in dwellings.
Community A (NA = 100) Community B (NB = 38) All children (N = 138) Univariate incidence rate ratios
n (%) n (%) n (%) IRR (95% CI)
No skin infections 41 (41.0) 13 (34.1) 54 (39.1) na
2 or more skin infections 37 (37.0) 10 (26.3) 47 (34.0) na
5 or more skin infections 12 (12.0) 2 (5.3) 14 (10.0) na
Total number of skin infections 162 50 212 na
Median skin infections per child (range) 1 (0 – 8) 1 (0 – 9) 1 (0 – 9) na
Skin incidence rate (person-years) 1.84 1.47 1.74 na
Primary explanatory variables
Dwelling facilities required to:
- wash children not functioning 35 (35.0) 3 (7.9) ** 38 (27.5) 1.23 (0.75 – 1.99)
- wash clothes not functioning 51 (51.0) 10 (26.3) * 61 (44.2) 1.40 (0.83 – 2.36)
- remove human faeces not functioning 42 (42.0) 8 (21.1) * 50 (36.2) 1.68 (0.99 – 2.87)
Combined HLPs 64 (64.0) 15 (39.5) * 79 (57.2) 1.96 (1.25 – 3.07)
Concrete/other floor material (no tiles) 53 (53.0) 2 (5.3) ** 98 (71.0) 1.26 (0.74 – 2.14)
Dwelling built pre 1980 15 (15.0) 14 (36.8) ** 29 (21.0) 1.23 (0.68 – 2.25)
Secondary explanatory variables
Community A - - 100 (72.5) 1.27 (0.74 – 2.20)
Community B (reference category) - - 38 (27.5) 1.00
Health software
Household cleaning equipment missing 79 (79.0) 6 (15.8) ** 87 (63.0) 1.59 (0.90 – 2.80)
No soap in bathroom 57 (57.0) 5 (13.2) ** 62 (44.9) 1.23 (0.72 – 2.09)
Contaminants
Internal contaminants present 79 (79.0) 35 (92.1) 114 (82.6) 0.57 (0.28 – 1.14)
External contaminants present 82 (82.0) 36 (94.7) 118 (85.5) 1.66 (0.83 – 3.31)
Child and carer socio-demographic factors
Child less than 3 years 49 (49.0) 19 (50.0) 68 (49.3) 1.95 (1.40 – 2.72)
Child sex (male) 52 (52.0) 17 (44.7) 69 (50.0) 0.86 (0.52 – 1.43)
Child mobility (high) 7 (7.0) 5 (13.2) 12 (8.7) 0.78 (0.42 – 1.47)
Carer age < 20 years 13 (13.0) 3 (7.9) 16 (11.6) 3.20 (2.18 – 4.71)
Carer 20 years to LT 35 years 77 (77.0) 29 (76.3) 106 (76.8) 1.00
Carer 35 years or more 10 (10.0) 6 (15.8) 16 (11.6) 1.79 (1.06 – 3.04)
Low family income (≤ $935 or median) 58 (58.0) 12 (31.6) ** 70 (51.1) 2.10 (1.30 – 3.40)
Carer unemployed 76 (76.0) 9 (23.7) ** 85 (61.6) 1.25 (0.78 – 1.99)
Carer highest level of education
Years 11 or 12 11 (11.0) 6 (15.8) 17 (12.3) 1.00
Years 9 or 10 23 (23.0) 21 (55.3) ** 44 (31.9) 1.26 (0.74 – 2.16)
Year 8 or below 66 (66.0) 11 (29.0) ** 77 (55.8) 1.92 (1.19 – 3.12)
Household composition variables
Four or more children < 7 yrs in dwelling 29 (29.0) 8 (21.1) 37 (26.8) 1.54 (0.84 – 2.82)
Three carers in dwelling 12 (12.0) 4 (10.5) 16 (11.6) 2.26 (1.24 – 4.13)
Visitors present in dwelling 20 (20.0) 12 (31.6) 31 (22.6) 0.79 (0.49 – 1.28)
Residents per bedroom
Lowest (1.33–2.00) 9 (9.0) 10 (26.3) * 19 (13.8) 1.00
Middle (2.33–4.00) 57 (57.0) 16 (42.1) 73 (52.9) 1.51 (0.59 – 3.86)
Highest (4.33–8.33) 34 (34.0) 12 (31.6) 46 (33.3) 1.11 (0.44 – 2.79)
Difference between two communities: * = p ≤ 0.05; ** = p ≤ 0.01.
Table 4 Multivariate adjusted incidence rate ratios for skin infections using a negative binomial model adjusted for clustering of children in dwellings.
Model 1 Model 2 Model 3 Model 4
IRR (95% CI) IRR (95% CI) IRR (95% CI) IRR (95% CI)
Main effects
Facilities for removing human faeces not functioning 1.28 (0.88–1.85) na na 2.03 (1.34–3.06)
Combined HLPs na 1.37 (0.92–2.03) na na
Dwelling built pre 19801,2,3 0.76 (0.36–1.61) 0.64 (0.32–1.30) 1.16 (0.51–2.60) na
Concrete/other floor material1 2.27 (0.67–7.67) a a na
Household cleaning equipment missing3 a a 1.82 (1.18–2.81) a
Internal contaminants present a a a 0.67 (0.45–1.00)
External contaminants present a a a 2.27 (1.28–4.03)
Child age
Less than 3 years1,2,3 1.57 (1.06–2.33) 1.46 (0.98–2.18) 1.54 (1.04–2.29) 1.63 (1.17–2.28)
3 to less than 7 years 1.00 1.00 1.00 1.00
Carer age
Less than 20 years 2.29 (1.60–3.26) 2.36 (1.54–3.63) 2.19 (1.45–3.29) a
20 to 34 years 1.00 1.00 1.00 a
35 years or more 2.41 (1.57–3.71) 2.30 (1.56–3.39) 2.38 (1.64–3.44) a
Three carers in dwelling4 a a a 0.63 (0.21–1.90)
4 or more children under 7 years 2.55 (1.70–3.81) 1.95 (1.26–3.02) 1.89 (1.23–2.92) a
Child mobile 1.39 (0.88–2.20) 1.46 (0.90–2.36) 1.57 (0.98–2.50) a
Residence per bedroom
Lowest (≤ 2 per bedroom) 1.00 1.00 1.00 a
Middle (2 to ≤ 4 per bedroom)1 2.22 (0.89–5.52) 1.35 (0.72–2.52) 1.25 (0.70–2.26) a
Highest (4 to 8.33 per bedroom)1 1.61 (0.61–4.21) 1.81 (0.90–3.63) 1.54 (0.81–2.93) a
Carers education
Grade 11 and 12 a a a 1.00
Grade 9 and 10 a a a 0.92 (0.53–1.61)
Grade 8 and below a a a 1.70 (1.02–2.84)
Low family income (≤ $935) 2.21 (1.53–3.20) 1.84 (1.25–2.69) 1.78 (1.24–2.57) 1.62 (1.10–2.37)
Carer unemployed4 0.73 (0.52–1.03) a a 0.53 (0.35–0.82)
Interactions
Child less than 3 years × dwelling built pre-1980 3.04 (1.52–6.08) 3.63 (1.74–7.57) 4.14 (1.95–8.80) na
Cleaning equipment missing × dwelling built pre-1980 a a 0.36 (0.17–0.74) na
Concrete/other floor material × middle level crowding 0.35 (0.09–1.30) a a na
Concrete/other floor material × highest level crowding 1.11 (0.29–4.26) a a na
Carer unemployed × three or more carer's in dwelling a a a 6.19 (1.93–19.84)
Log pseudo-likelihood -196.298 -201.777 -199.553 -203.606
Model: Pseudo R2 15.1% 12.7% 12.7% 11.9%
1 Significant interaction term in model 1
2 Significant interaction term in model 2
3 Significant interaction term in model 3
4 Significant interaction term in model 4
a Variable dropped during backward stepwise elimination (p > 0.10) or non-significant interaction term na Variable not included in backward stepwise elimination process
In the univariate analysis all of the HLP variables showed a positive association with presentations for skin infections in study children, although only combined HLP reached statistical significance (Table 3). Other explanatory variables showing significant positive univariate associations with skin infection incidence rates include child less than 3 years, younger carers and older carers, low family income, carers education year 8 or below and having 3 carers in the dwelling.
The multivariate analysis suggests the variables with the strongest and most consistent association with incidence of skin infections are those reflecting household composition and social process. These include carer's age less than 20 or over 35, four or more children under the age of seven in the dwelling, and low family income (Table 4). Each of these variables was significant in at least three of the four models. High child mobility also tended to be associated with an increased risk of skin infection. All models contained significant interaction terms and contrasts for these are presented in Table 5.
Table 5 Significant contrasts for interaction terms in models 1 to 4: Incidence rate ratios (95% confidence intervals).
Model/Significant contrasts for interaction terms Incidence rate ratio (95% CI)
Model 1
Young children (<3 years) & old dwelling (pre-1980) versus young children & newer dwellings 2.30 (1.57 – 3.38)
Young children (<3 years) & old dwelling (pre-1980) versus old children & newer dwellings 3.62 (2.32 – 5.64)
Young children (<3 years) & old dwelling (pre-1980) versus old children & old dwellings 4.79 (2.59 – 8.86)
Young children (<3 years) & newer dwelling (post-1980) versus old children & newer dwellings 1.57 (1.06 – 2.33)
Young children (<3 years) & newer dwelling (post-1980) versus old children & old dwellings 2.08 (0.98 – 4.42)
High level of crowding & concrete/other floor versus high crowding & tiled floor 2.52 (1.38 – 4.59)
High level of crowding & concrete/other floor versus middle crowding & tiled floor 11.56 (1.50 – 89.3)
High level of crowding & concrete/other floor versus low crowding & tiled floor 4.05 (1.50 – 10.9)
Model 2
Young children (<3 years) & old dwelling (pre-1980) versus young children & newer dwellings 2.33 (1.54 – 3.53)
Young children (<3 years) & old dwelling (pre-1980) versus old children & newer dwellings 3.41 (2.24 – 5.21)
Young children (<3 years) & old dwelling (pre-1980) versus old children & old dwellings 5.30 (2.83 – 9.94)
Young children (<3 years) & newer dwelling (post-1980) versus old children & newer dwellings 1.46 (0.98 – 2.18)
Young children (<3 years) & newer dwelling (post-1980) versus old children & old dwellings 2.27 (1.12 – 4.61)
Model 3
Young children (<3 years) & old dwelling (pre-1980) versus young children & newer dwellings 4.79 (2.48 – 9.28)
Young children (<3 years) & old dwelling (pre-1980) versus old children & newer dwellings 7.40 (3.52 – 15.6)
Young children (<3 years) & old dwelling (pre-1980) versus old children & old dwellings 6.39 (3.30 – 12.4)
Young children (<3 years) & newer dwelling (post-1980) versus old children & newer dwellings 1.54 (1.04 – 2.29)
Cleaning equip§ missing & newer dwelling (post-1980) versus equipment missing & old dwelling 2.43 (1.16 – 5.10)
Cleaning equip§ missing & newer dwelling (post-1980) versus equip§ not missing & newer dwelling 1.82 (1.18 – 2.81)
Model 4
Carer unemployed & 3 carers in dwelling versus carer unemployed & 1 or 2 carers in dwelling 3.90 (2.45 – 6.21)
Carer unemployed & 3 carers in dwelling versus carer employed & 1 or 2 carers in dwelling 2.09 (1.31 – 3.32)
Carer unemployed & 3 carers in dwelling versus carer employed & 3 carers in dwelling 3.31 (1.07 – 10.2)
Carer unemployed & 1 or 2 carers in dwelling versus carer employed & 1 or 2 carers in dwelling 0.54 (0.35 – 0.82)
§ equipment
The infrastructure variables that remain in model 1 include the presence of facilities to remove faeces, the age of the dwelling and the flooring material (Table 4). The association with dwelling age is modified by child age, with young children living in older dwellings appearing to be at high risk (Table 5). The association with flooring material is modified by crowding, with children living in dwellings with high levels of crowding and un-tiled floors apparently at high risk (Table 5).
Of the three HLP variables, having the facilities to remove faeces appears most important (Table 4). In model 2, where the separate HLP variables are excluded from the model to allow for inclusion of combined HLP, the combined variable was forced to remain in the model, but did not reach statistical significance.
Model 3 is similar to model 2 with the exception that missing household cleaning equipment replaces combined HLP. As with models 1 and 2, the association of this variable with the incidence of skin infections in children was modified by the age of the dwelling, and the association with missing cleaning equipment was stronger for children living in newer, rather than older dwellings (Table 5).
In model 4 where flooring material is excluded, the HLP variable reflecting facilities to remove faeces shows a statistically significant association with the incidence of skin infections. This model also indicates the potential importance of a number of other variables that dropped out of the first three models. The presence of organic contaminants in the immediate dwelling surrounds shows a direct association with skin infections, as does lower educational status of the carer (Table 4). This model also indicates that carer's employment status is modified by the number of carers in the dwelling. Children of unemployed carers in dwellings with three or more carers appear at high risk of skin infections (Table 5). Carers' education level also remained in this model, replacing carers' age from models 1 to 3.
Discussion
The study demonstrated high levels of willingness to participate by community residents, with the main reason for non-participation being extended absence from the community. Very few residents declined to participate in housing surveys or interviews. The quality of child health records in two of the three communities proved adequate for the purposes of the study. The analysis of the data from the two communities for which skin infection data were available indicates that a number of the housing infrastructure variables defined and measured in this study are associated with the occurrence of skin infections in children. The positive independent association between some measures of quality of household infrastructure and skin infections in children is consistent with the general understanding of the importance of housing to health [31-34]. The multivariate analysis provides some insight into the pathways whereby housing conditions may increase risk of skin infections in the remote Indigenous community environment, and is generally supportive of the simple conceptual framework used in the study.
The infrastructure variables most strongly (IRR approaching or more than 2) and consistently (the same or a related variable retained in more than one of the four models) associated with skin infections were the functional state of facilities to remove faeces and the age of the dwelling, although the association with dwelling age was strongly modified by child age. Type of flooring appears to have an important independent association with the occurrence of skin infections. This is evident in the large IRR for contrasts for interaction terms, (Table 5) particularly in crowded dwellings. While older dwellings (built pre 1980) appear to pose a higher risk, it is not clear from this study what characteristics of older dwellings are responsible for this increased risk. The HLP variables that reflect the functional status of facilities for washing clothes and washing children both tended to be associated with higher rates of skin infection in the univariate analysis, but neither of these variables remained in any of the multivariate models.
The variables categorised under the general heading of household composition and social process tended to show the strongest and most consistent associations with increased risk of skin infections. Of particular note and consistent with other studies of child health are the risks posed by crowding of young children and their carers, younger carers and older carers, higher child mobility, lower educational attainment, and lower family income. While this supports the importance of a number of socio-economic variables that have direct associations with the occurrence of skin infections and which may modify the effect of housing infrastructure [35,36], it also suggests more specific insights into the social conditions that may contribute to increased health risks for children in these communities.
Because of the cross sectional nature of the data, this study, as with much housing research internationally, has limited capacity to infer causation [31,37-39]. Other limitations relate to sample size and definition and measurement of variables. A more detailed analysis of risk for specific age groups of children could be important because the risks posed by different aspects of household infrastructure may vary according to child age and developmental stage. Our sample size limited the extent to which this was possible. The sample size also limited the potential to explore the extent to which the risk factors for scabies and bacterial skin infections may differ, or the extent to which the findings are driven by a small number of individuals with multiple infections. However, this latter situation is unlikely as almost 60% of children had at least one skin infection and only 14 (10%) had five or more. The inadequacy of the health records in one of the three communities reduced the potential sample size, although this community was the smallest of the three and the reduction in sample size was therefore relatively minor. The generalisability of the study findings may also be limited by the focus on two selected communities. However, the significant differences between communities across a range of explanatory variables (Tables 2 and 3) indicate these factors may be amenable to intervention.
The multivariate models utilise a large number of variables, including interaction terms, on a relatively small dataset. As such, caution is advised when interpreting these results as the model may overfit the data. However, a key aim of this study was to develop an analytic approach to this complex dataset that will be used to analyse data from the main study. The results from each of the four models are reasonably consistent and suggest insights into the association between housing condition and the number of skin infections that will be further explored with a larger dataset.
The extent to which the composite variables for the functional state of facilities for healthy living practices reflect the practical reality in households is likely to be limited. This is most evident in the rules used to define facilities required to conduct HLPs for washing children and removal of faeces for different age groups (Table 1). Furthermore, the difficulties of assessment of household composition in remote Indigenous communities in Australia is well documented [40-43], and the data collected in this study can at best be a partial reflection of a complex reality.
The analysis of skin health data is clearly only possible where adequate clinic records are available. The credibility of results from these analyses can also be undermined by systematic bias in the recording of health information for children of different social circumstances. The potential for such bias to influence study findings would be less in a follow-up study where children act as their own controls when analysing the change in infection rate before and after a housing intervention.
The absence of effect of the HLP variables reflecting facilities to wash children and to wash clothes warrants a closer examination of their construction and measurement. While the absence of these variables from any of the multivariate models may indicate that these factors are relatively weak determinants of skin infections, it is also possible that our definition and measurement of these variables has been inadequate. The inconsistency of the association of skin infection with contamination of the household environment is also expected to be at least partly a result of the difficulties of assessing contamination in an unobtrusive and sensitive way. The expected importance of contamination (internal and external) as an explanatory factor requires the development of accurate and appropriate approaches to measurement in order to refine the general explanatory power of this type of research. In addition, from a practical intervention perspective, the likely potential for reducing risk by minimizing contamination requires the development of appropriate skills in talking about this issue (i.e. hygiene) in a remote community context and research is one channel through which this development may be achieved.
In addition to refining the definition and measurement of variables included in this pilot study, the identification and inclusion of other important factors may enhance the potential value of future studies. For example, the apparent risk posed by older dwellings warrants closer examination through definition and measurement of variables that may explain this risk. Possible explanations include the type and quality of materials used in construction, the general condition of the structure (and the potential of structural materials to harbour infective materials); and features of housing design that may have changed over the years [32,44]. The information that may be obtained through the measurement and analysis of such variables may be particularly important to informing approaches to renovation of older dwellings as opposed to the construction of new dwellings. Similarly, identification and measurement of other important factors related to household composition and social processes may contribute to the understanding of how interventions to address these factors may enhance the health gains that may be achieved through infrastructure projects.
The study findings should be treated as appropriate to an exploratory pilot study. Confirmation of these findings in larger studies across a larger number of communities will be useful. Nevertheless, the findings do provide some guidance in the development of public health and preventive programs that aim to reduce the occurrence of skin infections in remote Indigenous communities. Key messages are that the provision of new and modern housing appears to be contributing to a reduction in skin infections, particularly where the housing programs lead to a reduction in crowding and the effective removal of human waste (i.e. having a functioning toilet). However, the capacity of infrastructure projects to improve health is likely to be limited in the absence of interventions that effectively address social and economic conditions. This conclusion is generally consistent both with an ecological understanding of the determinants of child health and with international experience [35]. The risk of skin infection associated with crowding of young children and their carers, with younger and older carers, low family income and with high child mobility is important in the development of criteria for housing allocation, social and health support programs and clinical awareness.
Conclusion
The methods used in this pilot study were generally feasible, and the analytic approach provides for a meaningful interpretation that is consistent with contemporary international understandings of the impact of the social and physical environment on child health. Refinement of methods from the experience of this pilot study is expected to provide for a deeper level of understanding from a current larger follow-up study of the impact of housing improvements on child health. In general terms, the key areas for refinement of methods were: 1) development of a more comprehensive conceptual model that includes influences such as psycho-social measures for carers and householders, community status of the householder and measures of the condition of the wider community environment; and 2) development of more structured survey tools that incorporated a range of questions from widely used standard survey tools.
Competing interests
Two of the authors (RB, MS) have conducted housing related evaluations under contract to the Northern Territory (NT) government. SG is an employee of the NT government. Financial support for this project was provided by the Indigenous Housing Authority of the NT.
Authors' contributions
RB was responsible for the overall conception design, project management and drafting of the paper. MS made a major contribution to the development of the analytic approach, conducted the analyses, and contributed to the drafting of the paper. EM made a major contribution to project design, conducting the fieldwork, and contributed to the statistical analysis and drafting of the paper. SH contributed to the development of the analytic approach and to conducting the analysis. DB, GR and SG contributed to the conceptualisation and design of the study, and interpretation of the study findings. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The support and participation of the community councils and community residents was essential to the successful conduct of this project, as was the support of the staff of the NT Departments of Health and Community Services and Community Development, Sport and Cultural Affairs. Harold Ulamari made a major contribution to conduct of fieldwork. The project was supported by funding from the Indigenous Housing Authority of the NT and by NHMRC project grant number 236205. We are grateful for the advice of the Housing Improvement and Child Health Project Reference Group and for comments on an earlier draft of this paper from Xavier Schobben, Geoff Barker and Nick Scarvelis.
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BMC NephrolBMC Nephrology1471-2369BioMed Central London 1471-2369-6-151635430110.1186/1471-2369-6-15Research ArticleExperimental glomerulonephritis induced by hydrocarbon exposure: A systematic review Ravnskov Uffe [email protected] Magle Stora Kyrkogata 9, S-22350 Lund, Sweden2005 14 12 2005 6 15 15 18 5 2005 14 12 2005 Copyright © 2005 Ravnskov; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Much epidemiological evidence suggests that hydrocarbon exposure may induce glomerulonephritis and worsen its course in many patients. The mechanisms are unknown, however, no specific microscopic pattern has been identified, and it has also been argued that hydrocarbon exposure causes tubular damage mainly. Studying experimental animals may best answer these questions, and as no systematic review of glomerulonephritis produced experimentally by hydrocarbon exposure has been performed previously, I found it relevant to search for and analyse such studies.
Methods
Animal experiments having mimicked human glomerulonephritis by hydrocarbon exposure were sought on Medline and Toxnet
Results
Twenty-six experiments using thirteen different hydrocarbons were identified. Several human subtypes were observed including IgA nephritis, mesangial, proliferative and extracapillary glomerulonephritis, focal and focal-segmental sclerosis, minimal change nephropathy, anti-GBM and anti-TBM nephritis, and glomerulonephritis associated with peiarteritis nodosa. Glomerular proteinuria was seen in 10/12 experiments that included urine analyses, and renal failure in 5/8 experiments that included measurements of glomerular function. All experiments resulted in various degrees of tubular damage as well. In most studies, where the animals were examined at different times during or after the exposure, the renal microscopic and functional changes were seen immediately, whereas deposits of complement and immunoglobulins appeared late in the course, if at all.
Conclusion
These experiments are in accord with epidemiological evidence that hydrocarbon exposure may cause glomerulonephritis and worsen renal function. Probable mechanisms include an induction of autologous antibodies and a disturbance of normal immunological functions. Also, tubular damage may increase postglomerular resistance, resulting in a glomerular deposition of macromolecules. In most models a causal role of glomerular immune complex formation was unlikely, but may rather have been a secondary phenomenon. As most glomerulonephritis subgroups were seen and as some of the hydrocarbons produced more than one subgroup, the microscopic findings in a patient cannot be used as a clue to the causation of his disease. By the same reason, the lack of a specific histological pattern in patients with glomerulonephritis assumed to have been caused by hydrocarbon exposure is not contradictive.
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Background
There is much observational evidence that exposure to organic solvents, paints, glues, fuels, motor exhausts and other environmental hydrocarbon contaminants may induce glomerulonephritis and also worsen renal function in a large number of patients [1-3]. Indeed, this hypothesis satisfies all except one of Hill's criteria for causality [3]. In spite of that the significance of hydrocarbon exposure has not been generally acknowledged and most current textbooks mention little if anything about this issue. Arguments often used by sceptics are that no probable mechanisms are known, that kidneys from animals exposed to hydrocarbons have shown tubular damage mainly, and that no specific glomerular pattern of hydrocarbon-associated glomerulonephritis has been identified in human beings. Glomerulonephritis has indeed been produced in a few experiments by exposing animals to hydrocarbons [3]. They are little known, and as no review of this subject has been published previously I found it relevant to perform a systematic search for such studies and found twenty-six.
Methods
Using Medline and Toxnet I sought experiments that had produced glomerulonephritis by exposing animals to hydrocarbons. The search strategy included the formula (glomerulonephritis OR glomerulopathy) AND experiment* AND (hydrocarbon* OR solvent* OR X) where X was substituted by a large number of various hydrocarbons with putative toxicity and commonly used in the industry or elsewhere. Relevant papers were also sought in the reference lists of the studies. Papers that mentioned glomerular changes of any kind in the abstract were required as were papers without an abstract. All papers in the Western European languages were considered and included if appropriate.
Results
Twenty-six experiments were identified, where the authors had noted microscopical changes in the kidneys of the animals similar to those seen in human glomerulonephritis after having exposed them to various hydrocarbons [4-28]. One experiment was reported in two papers [4,5], two groups used two different hydrocarbon [7,23]; totally 13 different hydrocarbons were used in 26 experiments. In two experiments [9,15] the animals were exposed to a single dose of the hydrocarbon, in the rest they were exposed intermittently. In 15 experiments, mainly the more recent ones [[6,9,11-14,16-18,22,24-27], 35], unexposed control animals or animals exposed to neutral substances were included. In all of them the renal changes, if any, were mild and did not exceed those seen in normal, aging rats. In ten experiments, the kidneys were examined by light microscopy (LM) only, in the rest by immunofluorescence microscopy (IM), and/or scanning or transmission electron microscopy (EM) also. Glomerular proteinuria was found in 10/12 experiments that included an examination of the urine. Evidence of renal insufficiency was found in 5/8 experiments that included a determination of renal function. In 19 studies the tubulointerstitial tissue were described also and in all of them varying degrees of damage were noted, in particular the two experiments that produced anti-TBM nephritis[19,20]. Findings similar to most of the human subgroups were seen, including IgA nephritis [18], mesangial [13,16], crescentic [6,14], proliferative [6,20] and focal segmental proliferative [19,20] glomerulonephritis, focal [18], focal segmental [17,22,27] and total [11,26] glomerular sclerosis, minimal change nephropathy [11,20,25], anti-TBM nephritis [19] and glomerulonephritis associated with periarteritis nodosa [9]. A summary of the findings is given in Additional file 1.
Discussion
As shown in this review glomerular changes similar to human glomerulonephritis have been found in 26 expeiments by exposing animals to hydrocarbons, a strong support to the view, that such exposure may induce glomerulonephritis in human beings. The histological documentation in the early studies was cursory, but the more recent experiments, where the authors had examined the renal tissue by EM and/or IM also, have mimicked most of the human subgroups successfully. Few models of experimental glomerulonephritis using manipulation of the immune system have resulted in more harm to the kidneys than trace or transient proteinuria, unless they have included the use of Freund's adjuvant [29], the main ingredient of which is a mixture of hydrocarbon oils. In contrast, most of the experiments reviewed here, that included laboratory measurements, resulted in severe proteinuria and renal failure.
Up to five different subgroups of glomerulonephritis were seen after exposure to the same hydrocarbon. In some of the models it was obvious that they represented various stages of the disease. Genetic differences may also play a role, as many different animals were used. The most likely explanation is probably that the hydrocarbons used may not have been pure. Volatile hydrocarbons are purified by distillation, but as raw oil is composed of thousands of different hydrocarbons, many of which have a similar boiling point it is difficult to obtain a pure hydrocarbon. Therefore it is not possible to know whether it is the hydrocarbon, the name of which is printed on the bottle, that has resulted in a certain subgroup, or whether it has been caused by one of the impurities.
One of the arguments against the idea that hydrocarbon exposure may cause glomerulonephritis has been that a specific glomerular pattern has never been identified, but as almost all subgroups were found in these experiments this argument is obviously untenable.
The experiments offer several explanations regarding the mechanisms behind hydrocarbon-induced glomerulonephritis. Each of them may be sufficient by itself, although a complex co-operation seems more likely.
By combining with renal proteins hydrocarbons may act as haptens and induce autoimmunity against kidney cells. This mechanism was proposed by Nakajima et al in experimental DNCB nephritis [20] and may also have been operating in the experiment by Hartmann et al. [9].
A causal role of glomerular immune complex formation was unlikely in most of the studies because glomerular deposits of immunoglobulin and complement appeared late in the course, if at all and may therefore have been secondary phenomena without pathogenetic importance. This is in agreement with many clinical observations, where deposits of Ig and C in the glomeruli have been seen without evidence of renal disease [29]. Also, no study has ever found an association between degree of immune complex deposition or degree of glomerular damage, and the clinical course or outcome [29,30].
Hydrocarbons may influence T-cell functions leading for instance to an extrarenal production of cytokines with harmful effects on the glomerular epithelial and/or endothelial cells as in human minimal change nephropathy. This mechanism was likely in the experiment with maleic vinyl ether anhydride (MVE-2), diacetylbenzidine and carbon tetrachloride, as both podocyte fusion and proteinuria was seen immediately after the exposure [11,15,25-27] whereas immune deposits, if any, were seen much later [26]. In favour of an effect directed against extrarenal T-cells were the findings that pre-treatment with irradiation [25,26] or methylprednisolone [25] abrogated proteinuria, and also that radiolabeled MVE-2 was found to be located mainly to the reticuloendothelial system, not to the kidneys themselves [31].
The effects of hydrocarbons on the immune system are probably more complicated. Many hydrocarbons are able to suppress a wide range of immunological functions including the lymphoproliferative response to T-cell mitogens, memory cell and macrophage function, delayed hypersensitivity, serum immunoglobulin synthesis and susceptibility to infectious challenges, both in experimental animals and in man [32].
All experiments resulted in various degrees of tubular damage. Such damage may lead to an obstruction of the tubular flow, an increased postglomerular resistance and eventually a decreased glomerular filtration rate [33]. Together with an increased glomerular permeability these effects may result in a secondary trapping of circulating macromolecules. That proximal tubular damage may play a role in the causation of glomerulonephritis is suggestive also from the fact that most nephritogenic chemicals, for instance mercury, gold, silicium and lithium, are tubulotoxic as well, and that a large number of studies have found that the renal function and the course in glomerulonephritis is strongly predicted by the degree of tubulointerstitial damage, but not by the degree of glomerular damage [29].
The finding in one of the experiments that buffalo rats were susceptible to exposure, but not mice [7], and in another that female rats were more susceptible than male rats [6] confirms the experience from clinical studies that a genetic or sexual predisposition is necessary. This finding may also explain why many similar experiments have failed to produce glomerulonephritis.
The animal experiments are in accordance with the results from many cross-sectional and case-control studies. Thus, in 14 studies of workers exposed regularly to hydrocarbons significantly more had urinary findings typical of early glomerulonephritis than had age and sex-matched control individuals. The findings included a pathological urinary sediment, increased excretion of albumin and/or transferrin and/or glomerular antigens, and in three of them renal failure was also more prevalent [3]. And in a meta-analysis of 18 case-control studies odds ratio for exposure was 1.1 or lower in four studies that included early glomerulonephritis only, whereas in all studies that included patients with failure the ratio was 1.7 or, more often, much higher, in particular in studies that included patients with end-stage renal failure only [3].
Conclusion
The successful imitation of human glomerulonephritis achieved in 26 experiments by exposing animals to various hydrocarbons is a further confirmation, suggested by numerous observational studies, that such exposure may induce glomerulonephritis in predisposed individuals. To convert this knowledge to benefit for the patients demands more research, however. Most important would be prospective studies of the effect of a discontinuation of the exposure. If the course in patients with renal failure could be reversed in this way, as was the case in a few retrospective studies and in a small prospective one [3], it would be the final proof that hydrocarbon exposure is causal in glomerulonephritis. At the same time it may become a major improvement in the prevention of terminal renal failure because hydrocarbon exposure is prevalent in patients with renal failure; in the four case-control studies that included patients with end-stage renal failure only, more than 50 % reported about significant exposure. As the microscopic findings obviously cannot be used as a marker for hydrocarbon exposure, such studies demand a thorough questioning of all patients with glomerulonephritis for possible environmental hazards, preferably in co-operation with experts in occupational medicine.
Competing interests
The author(s) declare that they have no competing interests.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Supplementary Material
Additional File 1
Table 1. Summary of methods, microscopic changes of the kidneys, and laboratory parameters of renal function in 23 experiments where animals were exposed to hydrocarbons. The author of this review has classified the glomerulonephritides. The tubular and interstitial changes are mentioned cursorily only.
Click here for file
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==== Front
BMC Oral HealthBMC Oral Health1472-6831BioMed Central London 1472-6831-5-91633666110.1186/1472-6831-5-9Case ReportMandibular facial talon cusp: Case report Oredugba Folakemi A [email protected] Department of Child Dental Health, College of Medicine University of Lagos PMB 12003 Idi-Araba Lagos, Nigeria2005 8 12 2005 5 9 9 20 5 2005 8 12 2005 Copyright © 2005 Oredugba; licensee BioMed Central Ltd.2005Oredugba; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Talon cusp is a supernumerary structure projecting from the dento-enamel junction to a variable distance towards the incisal edge of an anterior tooth. Studies have shown that it consists of enamel, dentine and a variable amount of pulp tissue. Hyperactivity of the enamel organ during morphodifferentiation has been attributed to its formation. Most previous reports have been made concerning the occurrence of this structure on primary and permanent teeth and mostly on the palatal aspect. Only few have been reported on the facial aspect of the teeth. When it occurs, the effects are mainly aesthetic and functional and so early detection and treatment is essential in its management to avoid complications.
Case presentation
An unusual case of talon cusp on the facial aspect of a mandibular central incisor is reported. Its presence resulted in attrition of the opposing tooth. Reduction of the cusp and topical application of fluoride gel was initiated.
Conclusion
The management and treatment outcome of talon cusp depends on the size, presenting complications and patient cooperation.
==== Body
Background
This unusual dental anomaly showing an accessory cusp-like structure projecting from the cingulum to the cutting edge was first described by Mitchell in 1892 [1]. It was thereafter named a Talon cusp by Mellor and Ripa [2] due to its resemblance to an eagle's talon. Since then, this odontogenic anomaly has been given several descriptions, such as, prominent accessory cusp-like structure [3], exaggerated cingula [4] additional cusp [5], cusp-like hyperplasia [6], accessory cusp [7] and supernumerary cusp [8]. It has been defined as a supernumerary accessory talon-shaped cusp projecting from the lingual or facial surface of the crown of a tooth and extending for at least half the distance from the cemento-enamel junction to the incisal edge [7]. There is a wide variation in the size and shape of this anomaly. Due to this variation, and in order to have a diagnostic criteria, it has been classified into 3 types by Hattab et al [9]:
Type1: Talon – refers to a morphologically well-delineated additional cusp that prominently projects from the palatal (or facial) surface of a primary or permanent anterior tooth and extends at least half the distance from the cemento-enamel junction to the incisal edge.
Type 2: Semi talon – refers to an additional cusp of a millimeter or more extending less than half the distance from the cemento-enamel junction to the incisal edge. It may blend with the palatal surface or stand away from the rest of the crown.
Type 3: Trace talon – an enlarged or prominent cingula and their variations, i.e. conical, bifid or tubercle-like.
Radiographically, it may appear typically as a v-shaped radiopaque structure, as in true talon or semi- talon, or be tubercle-like, as in trace talon, originating from the cervical third of the root. The radiopaque v-shaped structure is superimposed over the normal image of the crown of the tooth. The point of the 'V' is inverted in mandibular cases. This appearance varies with the shape and size of the cusp, and the angle at which the radiograph is taken.
It is composed of enamel, dentine and a varying amount of pulp tissue [10,11]. The extent of pulp extension into the cusp is however difficult to determine because of its superimposition over the main pulp chamber [12]. While some indicated that talon cusps contain pulp tissue [2,10,13], some found no evidence of pulp extension into the cusp [14,15]. However, it has been suggested that large talon cusps, especially those that stand away from the tooth crown are more likely to contain pulp tissue [9,12]. A review of the literature showed that over the last two decades, increasing reports have been made of the occurrence of the condition. The reported prevalence outside Africa is between 0.06% in Mexicans [16] and 7.7% in a northern Indian population [17]. It has also been found to be relatively common in the Chinese [5,6] and Arab [9], and predominantly in the male population [18]. These wide variations in prevalence could be due to individual differences in definitions of observation, from enlarged cingula to semi- or true talons [17]. If data is taken from those who reported for treatment only, a high prevalence might be observed. Patients may seek treatment when there is a problem, usually with large cusps. No prevalence data has been found in the literature for Africans.
Aetiology
The exact aetiology is not known, but it is suggested to be a combination of genetic and environmental factors [9,19,20]. It is thought to arise during the morphodifferentiation stage of tooth development, as a result of outfolding of the enamel organ or hyperproductivity of the dental lamina [9,21]. It is suggested that disturbances during morphodifferentiation such as altered endocrine function might affect the shape and size of the tooth without impairing the function of ameloblasts and odontoblasts [22]. There is also a suggestion of a strong genetic influence in its formation as evidenced by its occurrence in close family members [18,20,23-25]. Talon cusp may occur in isolation or with other dental anomalies such as mesiodens [3], odontome, unerupted or impacted teeth [13,26], peg-shaped maxillary incisor [26], dens invaginatus [26-28], cleft lip and distorted nasal alae [29], bilateral gemination [18,30], fusion [31,32], supernumerary teeth and enamel clefts [33,34]. It has also been associated with some systemic conditions such as Mohr syndrome (oro-facial-digital II) [35], Sturge-Weber syndrome (encephalo-trigeminal angiomatosis) [6], Rubinstein-Taybi syndrome [36], incontinentia pigmenti achromians [37] and Ellis-van Creveld syndrome [38].
Presentation
It is more common in the permanent dentition (75%) than in the primary dentition, while 92% affect the maxillary teeth [8,9]. The maxillary lateral incisor is the most frequently affected in the permanent dentition while the maxillary central incisor is the most affected in the primary dentition [8]. Most times it occurs unilaterally but bilateral cases, including multiple talon cusps have also been reported [3,6,9,24,25,33,39]. In a particular case, talon cusps have occurred on both maxillary and mandibular teeth in the same patient [11]. Rarely, two talon cusps may occur on a single tooth. Abbot reported a labial and a palatal talon on a maxillary right central incisor [40], while another report from Nigeria presented two palatal talons on a maxillary left central incisor [39].
Complications and management
The complications of talon cusp are diagnostic, functional, aesthetic and pathological [3,41]. A large talon cusp is unaesthetic and presents clinical problems. It may present diagnostic problems if it is unerupted and resembles a compound odontome or a supernumerary tooth and so leads to unnecessary surgical procedure. Functional complications include occlusal interference, trauma to the lip and tongue, speech problems and displacement of teeth. The deep grooves which join the cusp to the tooth may also act as stagnation areas for plaque and debris, become carious and cause subsequent periapical pathology [2,3,41].
Management will depend on individual presentation and complications. Small talon cusps are asymptomatic and need no treatment [24,33]. Where there are deep developmental grooves, simple prophylactic measures such as fissure sealing and composite resin restoration can be carried out [2,13,42-44]. An essential step, especially in case of occlusal interference, is to reduce the bulk of the cusp gradually and periodically and application of topical fluoride such as Duraphat ® or Acidulated Phosphate Fluoride (APF) gel to reduce sensitivity and stimulate reparative dentine formation for pulp protection [45], or outright total reduction of the cusp and calcium hydroxide pulpotomy [46]. It may also become necessary sometimes, to fully reduce the cusp, extirpate the pulp and carry out root canal therapy [19]. Orthodontic correction may become necessary when there is tooth displacement or malalignment of affected or opposing teeth [14,47].
This is a report of an unusual case of talon cusp which presented on the facial aspect of a mandibular central incisor.
Case presentation
A healthy looking 29 year old Nigerian male presented at the dental outpatient clinic of the Lagos University Teaching Hospital for the purpose of a dental check-up. It was his first visit to the dentist. He did not present any significant medical history. Oral examination showed a fair oral hygiene, no carious lesion, and all the permanent teeth were present. The mandibular left central incisor was displaced lingually with an accessory cusp on the facial aspect which had an attrition facet on the incisal edge. The cusp projected from the cemento – enamel junction and curved towards the incisal edge of the incisor (Figure 1). There was also attrition of the incisal half of the palatal aspect of the maxillary left central incisor. There was a negative family history of such dental anomaly from the patient and there was no associated systemic disorder. A periapical radiograph revealed an inverted V-shaped radiopaque structure on the mandibular left central incisor (Figure 2). The extent of pulp tissue into the cusp could not be determined on the radiograph. A diagnosis of type 1 talon cusp was made. The condition and the planned periodic and gradual reduction of the cusp with topical fluoride application and Composite resin facing was explained to the patient. Orthodontic alignment of the displaced central incisor was also planned. With his consent, after oral prophylaxis, a minimal reduction of the talon cusp was carried out using a diamond bur in a high-speed water-cooled handpiece. Acidulated Phosphate Fluoride (APF) gel was applied to the surface of the reduced cusp and the maxillary left central incisor. The patient however failed to turn up for further treatment. It was assumed that as the patient was not initially concerned with the aesthetic effect of the cusp, the outcome was not important to him.
Figure 1 Intra-oral photograph showing the facial talon cusp and lingual displacement of the mandibular left central incisor.
Figure 2 Peri-apical radiograph of the mandibular left central incisor showing the inverted V-shaped talon cusp.
Discussion
Reports of mandibular talon cusps are rare in literature. Only ten had been reported, including the present case, with only one on a primary incisor [Table 1]. [11,15,41,48-52]. It is agreed that it is more common in maxillary teeth. Facial talons are also rare: only six cases having been reported before this case [7,37,40,49,52]. It is even more rare in mandibular teeth.
Table 1 Reported cases of mandibular talon cusps
Author Tooth type Tooth surface
Goel et al, 1976 [15] Mandibular R 1 Lingual
Mader, 1982 [41] Mandibular R 1 Lingual
Falomo, 1985 [48] Mandibular R 2 Lingual
McNamara et al, 1997 [49] Mandibular R 1 Facial
Hegde and Kumar, 1999 [50] Mandibular L b
Mandibular L 1 Lingual
Lingual
Nadkarni et al, 2002 [51] Mandibular R 1 Lingual
Dash et al, 2004 [11] Mandibular R 1 Lingual
Llena-Puy and Navarro, 2005 [52] Mandibular L 2 Facial
Oredugba (Present report) Mandibular L 1 Facial
There was no associated systemic or local condition in this patient as is the case in most previous reports. Most cases occur in isolation of other conditions [53]. The patient in this report also did not give a history of its occurrence in any member of his family. Of all the cases reported from Nigeria, only two females were affected. This finding supports earlier reports of a higher prevalence of the condition in males. Mays reported a statistically significant bias in favour of males [54].
The present case is a type 1 talon. Although such large cusps which stand away from the tooth had been shown to contain an extension of the pulp, superimposition of the image of the cusp over the main tooth made it difficult to determine the extent of pulp tissue in the anomalous cusp. The constant attrition on the cusp may also mean that there may be reparative dentine which would have taken up part of the pulp space in the cusp.
The presence of a talon cusp is not always an indication for dental treatment unless it is associated with problems such as compromised aesthetics, occlusal interference, tooth displacement, caries, periodontal problems or irritation of the soft tissues during speech or mastication [3,7,42]. Occlusal interference can damage the periodontium, cause infra-occlusion of the opposing tooth and also temporo-mandibular joint pain [25,55]. Severe attrition or fracture of the enamel surface can cause exposure of the dentine-pulp complex and consequently, pulp necrosis [56-58]. In this case, the cusp was prominent and sharply defined and projected from the cervical region to the incisal edge of the tooth. This resulted in occlusal interference, which caused the attrition of the tip of the cusp and the opposing maxillary incisor, and displacement of the mandibular central incisor. The patient was however less concerned due to the painless complications. This explained the lack of compliance with appointment. It is necessary to evaluate and treat talon cusps soon after eruption to prevent these complications.
Conclusion
The management and treatment outcome of talon cusp depends on the size, presenting complications and patient cooperation.
Competing interests
The author(s) declare that they have no competing interests.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
Written consent was obtained from the patient for publication of this study.
==== Refs
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