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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7046ehp0112-00116515289161ResearchArticlesHair Mercury Levels in U.S. Children and Women of Childbearing Age: Reference Range Data from NHANES 1999–2000 McDowell Margaret A. 1Dillon Charles F. 1Osterloh John 2Bolger P. Michael 3Pellizzari Edo 4Fernando Reshan 4de Oca Ruben Montes 5Schober Susan E. 1Sinks Thomas 2Jones Robert L. 2Mahaffey Kathryn R. 61National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, MD, USA2National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, Georgia, USA3Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, College Park, Maryland, USA4Research Triangle Institute, Research Triangle Park, NC, USA5The Orkand Corporation, Falls Church, Virginia, USA6The Office of Science Coordination and Policy, Office of Prevention, Pesticides, and Toxic Substances, U.S. Environmental Protection Agency, Washington, DC, USAAddress all correspondence to M.A. McDowell, National Center for Health Statistics, 3311 Toledo Rd., Room 4335, Hyattsville, MD 20782 USA. Telephone: (301) 458-4368. Fax: (301) 458-4028. E-mail:
[email protected] to sites of non-CDC organizations on the Internet are provided as a service to readers and do not constitute or imply endorsement of these organizations or their programs by the CDC or the U.S. Department of Health and Human Services. The CDC is not responsible for the content of pages found at these sites.
The authors declare they have no competing financial interests.
8 2004 27 5 2004 112 11 1165 1171 19 2 2004 27 5 2004 Publication 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 methyl mercury, a risk factor for neurodevelopmental toxicity, was assessed in U.S. children 1–5 years of age (n = 838) and women 16–49 years of age (n = 1,726) using hair mercury analysis during the 1999–2000 National Health and Nutrition Examination Survey (NHANES). The data are nationally representative and are based on analysis of cross-sectional data for the non-institutionalized, U.S. household population. The survey consisted of interviews conducted in participants’ homes and standardized health examinations conducted in mobile examination centers. Distributions of total hair mercury levels expressed as micrograms per gram hair Hg and the association of hair Hg levels with sociodemographic characteristics and fish consumption are reported. Geometric mean (standard error of the geometric mean) hair mercury was 0.12 μg/g (0.01 μg/g) in children, and 0.20 μg/g (0.02 μg/g) in women. Among frequent fish consumers, geometric mean hair mercury levels were 3-fold higher for women (0.38 vs. 0.11 μg/g) and 2-fold higher for children (0.16 vs. 0.08 μg/g) compared with nonconsumers. The NHANES 1999–2000 data provide population-based data on hair mercury concentrations for women and children in the United States. Hair mercury levels were associated with age and fish consumption frequency.
bloodchilddietfemalehairmercuryNHANESpreschool
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Mercury is a naturally occurring heavy metal whose presence in the environment is widespread and persistent [Agency for Toxic Substances and Disease Registry (ATSDR) 1999; National Research Council (NRC) 2000]. Hg occurs in metallic or elemental, inorganic, and organic forms (ATSDR 1999). When elemental Hg is emitted as a combustion by-product of fossil fuels, it becomes methylated in the environment and accumulates in animal tissues, including fish. Methyl mercury (MeHg) in the aquatic food chain contributes to higher tissue Hg levels among fish consumers (Boening 2000). Total Hg in the hair of fish eaters correlates with Hg in the target tissue, the brain (Cernichiari et al. 1995).
The mammalian nervous system is highly vulnerable to MeHg (Castoldi et al. 2001). Exposure to high levels of MeHg during the last two trimesters of pregnancy produces documented neurodevelopmental problems, including language, attention, and memory problems [Marsh et al. 1987; NRC 2000; U.S. Environmental Protection Agency (EPA) 1997]. Accidental poisoning incidents in Japan (Harada 1995) and Iraq (Amin-Zaki et al. 1974) demonstrated the pronounced neurologic injuries that result from high-level MeHg exposures, particularly in children who were exposed in utero.
More recent prospective epidemiologic data from New Zealand, the Faroe Islands, and the Seychelles assessed developmental effects of lower level MeHg exposure in fish-consuming populations resulting from maternal and fetal exposures to MeHg (Cernichiari et al. 1995; Grandjean et al. 1999). The U.S. EPA MeHg exposure reference dose (RfD) of 0.1 μg/kg body weight/day was based on data from the Faroes and New Zealand, with supporting analyses from all three major prospective cohort studies (Rice et al. 2003). The assessments of Hg exposure for the U.S. population have included regional biomonitoring studies conducted by state and federal agencies (Pellizzari et al. 1999) and assessments of population subgroups, including sport fisher-men and their families (Burge and Evans 1994; Kosatsky et al. 2000), pregnant women in selected geographic areas (Björnberg et al. 2003; Stern et al. 2001), high-end fish consumers (Hightower and Moore 2003), and American Indian and Alaskan Native groups (Rothschild and Duffy 2001).
Total blood and hair Hg are indicators of MeHg exposure in fish consumers and others who are not exposed to inorganic and elemental Hg occupationally or incidentally (Carrington and Bolger 2002; Mahaffey 2000; NRC 2000). Exposure to MeHg increases with fish consumption [International Programme on Chemical Safety (IPCS) 1990; Yamaguchi et al. 1975]. Once consumed, 90% of MeHg is absorbed in the human gut (Miettinen 1973). Approximately 95% of measurable Hg in blood is the methylated form (Sherlock et al. 1984). After absorption, MeHg is distributed throughout the body within hours (Clarkson 1997). Peak MeHg blood levels in human subjects fed fish containing known concentrations of MeHg occurred within 4–14 hr of ingestion (Kershaw et al. 1980). The mean ± SE half-life of blood MeHg reported from a study with 20 adults whose diet included halibut with measured MeHg was 50 ± 1 days (range, 42–70 days) (Sherlock et al. 1984).
Hair Hg concentration is the preferred biomarker for evaluating Hg exposure for extended periods of time such as periods of weeks or months (NRC 2000). Hair incorporates Hg present in circulating blood during hair formation in the hair follicle (Clarkson 1983). Hair growth and analysis studies assessed hair growth rates and the relationship between MeHg intake and hair levels (Clarkson 1992; Grandjean et al. 1994). Hair growth averages 1–1.5 cm per month and provides a time record of previous Hg exposure depending on the length of the hair (Suzuki et al. 1984). Approximately 80% of hair Hg is MeHg (Cernichiari et al. 1995; Phelps et al. 1980). Total Hg and MeHg levels in hair are linearly related (Pellizzari et al. 1999), with total Hg concentrations in hair thought to average 150- to 200-fold higher than Hg concentrations in blood (Gill et al. 2002).
The Centers for Disease Control and Prevention (CDC), the U.S. EPA, U.S. Food and Drug Administration, Department of Energy, and the National Oceanographic and Atmospheric Administration funded a comprehensive Hg assessment component as part of the 1999–2001 National Health and Nutrition Examination Survey (NHANES). The NHANES Hg assessment component targeted children 1–5 years of age and women 16–49 years of age. Preliminary blood and hair Hg results from NHANES 1999 were reported in 2001 (CDC 2001). A detailed analysis of NHANES 1999–2000 blood Hg data was published in 2003 (Schober et al. 2003). Blood Hg levels were examined by race/ethnicity, maternal education level, and fish consumption frequency. Blood Hg levels were low overall in children and adults, but approximately 8% of women had levels > 5.8 μg/L, a level that corresponds to the U.S. EPA’s RfD. At the time, geometric mean (GM) total hair Hg values were not computed for the NHANES 1999 data. This report on hair Hg describes hair Hg levels in U.S. children and women of childbearing age by race/ethnicity, fish consumption frequency level, pregnancy status, and education level (females 20–49 years of age) and examines the relationship between total blood and hair Hg.
Materials and Methods
Survey description.
The NHANES survey is conducted to provide continuous health and nutrition data on the U.S. population for all ages [National Center for Health Statistics (NCHS) 2002b]. Standardized interviews and examination methods are administered by trained staff, including physicians, dentists, health technologists, interviewers, and laboratory technicians. Health examinations are conducted in mobile examination centers that travel to 15 geographic sites per year.
Sample design.
The NHANES survey design is a stratified, multistage, national probability sample of the civilian, noninstitutionalized U.S. population; regional estimates are not produced (NCHS 2002). One or more participants within the eligible households sampled are included in the survey. Annual samples are nationally representative and include approximately 6,000 interviewed and 5,000 examined persons. NHANES 1999–2001 included expanded samples of Mexican Americans, non-Hispanic blacks, pregnant females, adolescents 12–19 years of age, and adults ≥ 60 years of age.
Data collection for the Hg assessment component.
The NHANES 1999–2000 Hg assessment component included a dietary interview and blood, hair, and urinary Hg assessments. Venipuncture blood collection was conducted on persons ≥ 1 year of age, hair collection on children 1–5 years of age and women 16–49 years of age, and urine collection on persons ≥ 6 years of age. The NHANES dietary assessment component included a 24-hr dietary recall interview on all examined persons and fish and shellfish consumption frequency questions on persons ≥ 1 year of age. All survey examination protocols, questionnaires, and reporting criteria were approved by the NCHS institutional review board; total hair Hg values of ≥ 15 μg/g or total blood Hg values of ≥200 μg/L were reported to participants. Signed informed consent was obtained for all survey participants. Participants or their guardians provided consent for participants < 18 years of age. Examined persons received remuneration for their participation in the survey ranging from $30 to $100, depending on their age and examination content; transportation and child care expenses were also compensated.
Hair specimen collection.
A minimum of 5–10 mg of hair was required for the hair analysis assay. Approximately 100 strands of hair (~ 50 mg) were gathered and cut from the occipital region of the scalp (approximately the diameter of a pencil eraser). A 1.5 × 2 in. Post-it (adhesive paper square; 3M, St. Paul, MN) was placed over the end of the hair strands closest to the scalp; the paper was marked with an arrow indicating the end of hair closest to the scalp. A plastic clip was placed over the paper to secure the hair sample. The samples were placed in a resealable plastic bag and shipped to the Research Triangle Institute for analysis. Respondents or proxies for children were asked if the respondent’s hair had been given a permanent or been treated with a hair dye or a hair relaxer product within the last month. The analysis compared total hair Hg in persons with treated hair (any of the products listed) to those with untreated hair.
Hair Hg analysis.
Hair segments of 0.4 in. (1.0 cm) closest to the scalp, approximately 1 month’s growth, were analyzed for total Hg concentration using a cold vapor atomic fluorescence spectroscopy method (Pellizzari et al. 1999). The method involves digestion of the analyte from hair samples using a 30:70 mixture of sulfuric and nitric acids and subsequent analysis by cold vapor atomic fluorescence spectrometry. The analyte is identified by the presence of fluorescence signal from a Hg-specific detector. During NHANES 1999–2000, hair Hg was analyzed in batches of 20–40 samples, and quantification of the analyte was carried out using batch-specific standard calibration curves. Linearity greater than 0.99 was confirmed for each curve using eight aqueous calibration standards (0, 5, 10, 30, 50, 80, and 85 pg/mL Hg) as previously described (Pellizzari et al. 1999). Daily quality control (QC) procedures included analysis in triplicate of a known human hair reference standard certified at 4.42 μg/g Hg (Pellizzari et al. 1999). QC standard checks were performed initially and after every 10th sample, and replicate measurements were performed (duplicate sample preparation with duplicate analysis of each preparation).
Percent recovery of the Hg analyte was monitored by analyzing hair samples spiked with a known Hg reference standard before the digestion process. Mean percent recovery of Hg (± SD) in the spiked samples was 96.2 ± 12.1%]. The precision of analysis was assessed from duplicate extracts (i.e., reanalysis of the same extract at a later time) and from duplicate hair sample analyses (i.e., a second aliquot of hair processed through the entire analysis process). The mean precision (± SD) for duplicate extract and sample analyses was 5.4 ± 8.7% and 11.7 ± 14.4%), respectively. The limit of detection (LOD) for total hair Hg varied by analytic batch because of the laboratory’s batch-specific standardization methodology. Method detection limits ranged from 0.0006 to 0.06 μg/g. Whenever the values for hair Hg were below a batch LOD, a fill value equal to the batch-specific LOD divided by the square root of 2 was used (Taylor 1987).
Fish and shellfish consumption frequency questionnaire.
Fish and shellfish consumption frequency during the 30-day period before hair collection was reported. The fish and shellfish frequency questions were administered by bilingual interviewers in English or Spanish. A proxy respondent reported for children < 6 years of age and proxy-assisted interviews were conducted with children 6–11 years of age (NCHS 2002a). No information was obtained about portion sizes, recipes, or preparation methods. Fish frequency data were grouped into three categories: no fish consumed, fish consumed one or two times, and fish consumed three or more times during the past 30 days. Shellfish consumption was coded as either no shellfish consumed or shellfish consumed one or more times during the past 30 days. Weighted GM total hair Hg levels and percentiles were computed for each fish and shellfish consumption frequency category.
Covariates.
Race and ethnicity were categorized as non-Hispanic white, non-Hispanic black, and Mexican American based on self-reported information provided by the survey participants. The sample size for the other race/ethnicity group that includes Asians and American Indians was too small to present separate estimates; the total population estimates include data for the other race/ethnicity group composed of 103 children and 195 women. Age in years at the time of the household interview was used. Pregnancy status was self-reported during private interviews administered to women ≥ 12 years of age at the mobile examination center. Pregnancy status was confirmed with a urinary pregnancy test in females ≥ 18 years of age in NHANES 1999 and in females ≥ 8 years of age in NHANES 2000.
Statistical methods.
Weighted estimates were produced using NHANES mobile examination center–examined sample weight values. These NHANES 1999–2000 sample weights adjust for the differential probabilities of selection and nonresponse in the survey sample (NCHS 2002b). The sample weights were poststratified to the 1990 U.S. Census total population estimates. A second non-response adjustment was applied during the hair Hg analysis to adjust for the higher, non-random hair collection nonresponse rates among African-American and Mexican-American boys (Lohr 1999).
Distributions, extreme hair Hg values, influential observations based on an analysis of the survey sample weights and hair Hg data, laboratory QC data, and correlation analyses were examined initially (Korn and Graubard 1999). After the initial analyses demonstrated that the total hair Hg data were non-normally distributed, a logarithmic transformation of total hair Hg was applied to normalize the distribution data. Outliers and influential points were detected using box plots, normal probability plots, and residual analysis, including studentized residuals for hair Hg and log of hair Hg, weighted and unweighted. Percentiles, means, and GMs were calculated to describe the distributions of hair Hg levels in children and women; arithmetic and GMs were included for comparison with other published reports. Standard errors (SEs) of the GMs and means and their confidence intervals (CIs) were computed using SUDAAN (Survey Data Analysis) weighted delete-1 jackknife (Research Triangle Institute 2001). Tests of statistical significance used = 0.05. Weighted Pearson partial correlations were used for bivariate comparisons and to identify predictors for the multiple regression models. Correlations of total log blood and log hair Hg were performed using Pearson’s method. Total hair to total blood Hg ratios were computed for children and women. The natural logarithm of hair Hg was the dependent variable for all of the regression analyses. Multiple regression was performed using SUDAAN. Tests of differences among groups used regressions with groups defined by dummy variables described in multivariate analysis, and detection of predictors through regression (Srivastava and Sen 1990).
Results
Response rates.
A total of 12,160 persons were selected to participate in NHANES 1999–2000; 9,282 completed the household interview and health examination components (76.3%). Of the 1,250 children 1–5 years of age selected for the survey, 1,013 (81%) were interviewed and examined. Hair specimens were obtained from 838 examined children (83% of examined children); hair collection response rates were lower among non-Hispanic black and Mexican-American males because of insufficient hair specimen collection. Hair samples were obtained from 44 and 82% of non-Hispanic black and Mexican-American males respectively, compared with 90% of non-Hispanic white males. This response was not random, and sample weight adjustments were applied during final data analysis. A total of 2,314 women 16–49 years of age were selected to participate; of these, 1,819 were interviewed and examined (79%). Hair specimens were obtained from 1,726 women (95% of the examined women). Fish and shellfish consumption data were obtained for 93% of children (n = 785) and 96% of women (n = 1,660) with hair Hg data. Detectable total hair Hg levels were measured in 84 and 89% of hair specimens obtained for children and women, respectively.
Outliers and influential points.
Three data points were considered outliers and influential. The values for hair Hg were 109.8, 415.2, and 849.0 μg/g for three Mexican-American participants who were 3, 1, and 37 years of age, respectively. Repeat hair Hg analyses confirmed the high hair Hg values, and laboratory QC specimens for the batches in which the specimens were analyzed were within specification. Blood Hg data were examined for the hair Hg outlier subset; total and inorganic blood Hg levels for all three persons were elevated, suggesting significant exposure to organic and inorganic Hg sources. Appropriate follow-up notification measures were undertaken to inform survey participants of these findings. Multivariate analyses with the outlier data did not alter the results appreciably. GMs, means, and medians increased slightly when the outlier values were included. For the purpose of reporting national reference values for total hair Hg, the hair Hg outlier values were excluded from further analysis.
GMs and percentiles.
GMs, means, and percentiles of hair Hg levels for children and women, respectively, are presented in Tables 1 and 2. For children, data are presented by race/ethnicity, fish consumption, shellfish consumption, and hair treatment group (Table 1). For women, results are presented by race/ethnicity, age, fish consumption, shellfish consumption, hair treatment, pregnancy status, and education group (Table 2).
Race/ethnicity.
Among children, the overall GM total hair Hg was 0.12 μg/g. Non-Hispanic black and Mexican-American children had higher hair Hg levels than non-Hispanic white children. Boys had higher (non-significant) hair Hg levels than girls in all race/ethnicity groups (not shown). For women, non-Hispanic white females had significantly higher GM hair Hg levels than did non-Hispanic blacks and Mexican Americans.
Hair treatment.
The GM hair Hg level of children who received hair treatments during the past month did not differ from that of the untreated group. Thirty-seven percent of women reported using a hair treatment; the GMs total hair Hg levels of the treated and untreated hair groups were the same (0.20 μg/g).
Fish consumption.
GM hair Hg increased with increasing frequency of fish consumption for children and adults (Tables 1 and 2). The GM hair Hg level of children consuming fish three or more times during the past month was twice as high as for nonconsuming children (0.16 vs. 0.08 μg/g). Frequent fish consumers 30–49 years of age had the highest GM hair Hg levels of the adult groups examined (0.41 μg/g).
Pregnancy status.
GM hair Hg levels of pregnant women (n = 292) did not differ from that in nonpregnant women. Analysis by race/ethnicity among the pregnant sub-sample was limited to comparisons of GM and median hair Hg values because of small sample sizes; race/ethnicity differences were not statistically significant among the pregnant women. Non-Hispanic white and Mexican-American women had higher hair Hg levels than did non-Hispanic black women. GM hair Hg levels among the pregnant, frequent fish consumers (n = 79) did not differ from that among nonpregnant frequent fish consumers (0.56 vs. 0.37 μg/g, respectively).
Regression analysis.
Separate multiple linear regression models were developed for children and women (Tables 3 and 4). The reference group for the regression models was the non-Hispanic white group. Log-transformed hair Hg was regressed on race/ethnicity, fish consumption, shellfish consumption, and hair treatment for children (Table 3). Among children, differences were observed by race/ethnicity and fish consumption status. Compared with non-Hispanic whites, non-Hispanic blacks and Mexican Americans had higher total hair Hg levels. The least-squares (LS) means and their SEs were computed using the regression model y = log (hair Hg). LS means for non-Hispanic blacks and Mexican Americans were 0.19 and 0.16 μg/g, respectively, compared with 0.09 μg/g for non-Hispanic whites. Children who consumed fish one or more times during the previous month had higher total hair Hg levels than did children who did not consume fish. LS means for children (Table 3) who consumed fish one to two and three or more times per month were the same (0.14 μg/g) and can be compared with those of children who did not consume fish during the previous month (0.08 μg/g).
A separate regression model was developed for women 16–49 years of age (Table 4). Pregnancy status, age group, race/ethnicity, fish consumption, shellfish consumption, and hair treatment were included in the model. Hair Hg levels differed by race/ethnicity, and fish consumption and shellfish consumption frequencies. Log hair Hg levels of non-Hispanic whites and Mexican Americans were similar. Non-Hispanic whites had higher hair Hg levels than did non-Hispanic blacks. There was a positive relationship between fish consumption frequency and log hair Hg; log hair Hg levels of persons who consumed fish three or more times had higher total hair Hg levels than did women who consumed zero or one to two servings of fish during the past 30 days. When age groups were compared, hair Hg levels among women 40–49 years of age were higher than the reference group (women 16–19 years of age); the 20- to 29-and 30- to 39-year-old groups were not different from the reference group. LS means and 95% CIs were computed using the multiple regression model (Table 4). A separate analysis (not shown) was completed on a sub-sample of women 20–49 years of age to test the effects of education level as a surrogate marker for socioeconomic status. Education level was not associated with total hair Hg levels and did not alter the relationship between hair Hg and the other covariates.
Hair-to-blood correlations and ratios.
Weighted Pearson correlations between log blood and log hair Hg were 0.67 for children and 0.79 for women, respectively (both correlations were p < .0001). Hair-to-blood Hg ratios were computed using total hair Hg (ng/g hair) and blood Hg (μg/L) data. The SUDAAN Proc Ratio procedure for correlated variables was used to compute the ratios (Research Triangle Institute 2001). Weighted delete-1 jackknife was used; the numerator and denominators were weighted sums for nonmissing values. The total sample mean ± SE hair-to-blood Hg ratios were 342 ± 20 for children 1–5 years of age and 234 ± 15 for females 16–49 years of age.
Discussion
The NHANES 1999–2000 total hair Hg data provide national hair Hg reference data for U.S. children 1–5 years and women 16–49 years of age, including three major race/ethnicity subgroups. These baseline data will be used to monitor hair Hg levels in the U.S. population over time. These results were compared with reports across studies that used different biologic matrices to estimate Hg exposure.
The total hair Hg levels of NHANES children and women were generally lower than the levels reported in other studies of U.S. and international populations. GM hair Hg level in fish-consuming children 7 years of age in the Faroes was 2.99 μg/g (Grandjean et al. 1999) compared with a GM (SE) value of 0.16 (0.02) μg/g among frequent fish consumers in the NHANES sample 1–5 years of age. The mean ± SD and median maternal hair Hg levels of women in the Seychelles where frequent fish consumption occurs were 6.85 ± 4.5 μg/g and 5.94 μg/g, respectively (Cernichiari et al. 1995). The median total hair Hg level of women in the Faroes birth cohort study was 4.5 μg/g; 12% had levels > 10 μg/g (Grandjean et al. 1992). Recent hair Hg data for Japanese adult females residing in five districts showed an overall total hair Hg GM of 1.43 μg/g (range, 1.23–2.50 μg/g; Yasutake et al. 2003). The GM (SE) and median values for frequent fish consumers among women of childbearing age in NHANES were 0.77 (0.09) μg/g and 0.33 μg/g, respectively. The arithmetic mean hair Hg level reported in a probability-based sample of U.S. Great Lakes region residents ≥ 21 years of age was 0.29 μg/g (Pellizzari et al. 1999), compared with an arithmetic mean ± SE hair Hg value of 0.47 ± 0.06 μg/g for NHANES females 16–49 years of age.
We report that the GM total hair Hg among pregnant women was 0.21 μg/g and did not differ from hair Hg levels of nonpregnant women (GM = 0.20 μg/g). The mean ± SE hair Hg level reported in a study of 189 New Jersey pregnant women was 0.53 ± 0.07 μg/g (range, < 0.2–9.1 μg/g; Stern et al. 2001); the NHANES pregnant females had an arithmetic mean ± SE hair Hg of 0.43 ± 0.089 μg/g. Median hair Hg levels for 127 pregnant Swedish women were 0.35 mg/kg (range, 0.07–1.5 mg/kg; Björnberg et al. 2003). Prenatal assessments of women from the Seychelles women reported mean ± SD total hair Hg of 6.85 ± 4.5 ppm and a median value of 5.94 ppm (Myers et al. 2003).
These hair Hg values provide an estimate of exposure over an approximate 1 month period, as recent exposure is not yet incorporated into the hair growth outside of the scalp. The steady-state hair-to-blood MeHg concentration ratio reported by the World Health Organization for adults was approximately 250:1, compared with a total hair-to-blood Hg ratio of 234 for NHANES females (IPCS 1990). Values for NHANES children were higher (ratio value of 342 overall), and this may reflect the higher percentages of children with blood Hg levels below the LOD. The hair-to-blood ratios were highly variable, in part, because hair and blood measurements are not comparable regarding the time period of exposure. Additionally, Hg exposure in this study is low for most respondents, and these results may not be comparable with correlations and ratios predicted in groups with higher levels of Hg exposure.
The advantages of hair Hg assessment include the fact that hair collection is noninvasive, and good response rates can be achieved in population subgroups that are difficult to obtain blood specimens from, such as children. For example, during NHANES 1999–2000, blood Hg data were collected on 56% of selected children, whereas hair collection was completed on 67% of selected children. Additionally, hair is a time record marker of MeHg exposure in individuals and can be used to estimate Hg exposure over extended periods of time such as fetal exposure during gestation (Cernichiari et al. 1995).
Several considerations for interpreting the NHANES 1999–2000 hair Hg results are provided. The NHANES 1999–2000 sample, although nationally representative, does not permit estimation of MeHg exposure in population groups with potentially high dietary exposure such as subsistence fishers, residents in specific geographic regions of the United States, sport fishers, and members of racial and ethnic population subgroups (e.g., Asians and Pacific Islanders). These subgroups may consume more fish than the general U.S. population and have higher MeHg exposure. Seafood consumption among Asian Americans and Pacific Islanders (AAPI) in King County, Washington, averaged 117 g/day (Sechena et al. 2003), compared with mean intakes of 10–14 g/day for the total U.S. population ≥ 20 years of age (U.S. Department of Agriculture 1999). Significant variation was observed in consumption rates and food preferences of the 10 AAPI groups. Second, three extreme hair Hg values were analyzed and confirmed in NHANES and were discarded in developing the distributions because it was not possible to determine the contributing factors that resulted in these values. This underscores the complexity of Hg assessment and exposure in populations. Finally, the NHANES 1999–2000 sample design was composed of a small number of primary sampling units (PSUs; 26 unique PSUs total). This feature limits regional or geographic area comparisons and statistical comparisons between population subgroups.
The NHANES data may be useful for assessing the prevalence of health risks in the U.S. population when the associated risks of low-level Hg content are better defined and may be used to support diet and health research, policy, and monitoring activities. Diet remains the primary contributor to MeHg exposure in populations. More than 50% of NHANES participants consumed fish during a 30-day reference period. Annual seafood consumption projections for the U.S. population indicate that 75–93% of adult women and 58–72% of children 2–5 years of age consume seafood (Carrington and Bolger 2002).
Hair Hg analysis in national samples of U.S. children and women of childbearing age provide a useful biomarker for long-term Hg exposure. Acceptance of the hair collection procedure was high among survey participants and excellent method precision was achieved, allowing for of detection of hair Hg in approximately 84 and 89% of children and women, respectively. Total hair Hg is associated with age, race/ethnicity, and fish consumption frequency. Among women of childbearing age, total hair Hg levels of pregnant and non-pregnant women were the same.
Table 1 GM and selected percentile (95% CI) total hair Hg (μg/g): U.S. children 1–5 years of age, NHANES 1999–2000.
Percentile
Sample description No. GM Mean 10th 25th 50th 75th 90th 95th
Total 838 0.12 0.22 0.03 0.06 0.11 0.21 0.41 0.64
(0.10–0.13) (0.18–0.25) (0.01–0.05) (0.05–0.07) (0.10–0.13) (0.15–0.27) (0.33–0.49) (0.52–0.76)
Race and ethnicity
Non-Hispanic white 238 0.09 (0.08–0.11) 0.18 (0.12–0.23) 0.03a (0.02–0.03) 0.05 (0.04–0.06) 0.09 (0.07–0.10) 0.17 (0.14–0.20) 0.31 (0.12–0.51) 0.60 (0.23–0.96)
Non-Hispanic black 161 0.19 (0.14–0.24) 0.32 (0.20–0.43) 0.06a (0.06–0.07) 0.10 (0.07–0.13) 0.19 (0.11–0.27) 0.33 (0.22–0.44) 0.58 (0.15–1.00) 0.81 (0.44–1.19)
Mexican American 336 0.15 (0.12–0.17) 0.22 (0.18–0.25) 0.05 (0.04–0.06) 0.09 (0.08–0.10) 0.15 (0.12–0.17) 0.27 (0.20–0.34) 0.42 (0.35–0.50) 0.56 (0.40–0.73)
Fish consumption frequency in past 30 days
0 354 0.08 (0.07–0.10) 0.13 (0.11–0.14) 0.03a (0.02–0.03) 0.05 (0.04–0.06) 0.08 (0.07–0.09) 0.14 (0.11–0.18) 0.26 (0.21–0.32) 0.38 (0.35–0.40)
1 or 2 times 221 0.14 (0.11–0.16) 0.21 (0.17–0.24) 0.05 (0.04–0.06) 0.07 (0.05–0.09) 0.12 (0.10–0.15) 0.22 (0.14–0.30) 0.39 (0.34–0.44) 0.60 (0.24–0.97)
≥ 3 times 208 0.16 (0.11–0.21) 0.40 (0.24–0.55) 0.04 (0.01–0.06) 0.06 (0.03–0.09) 0.14 (0.09–0.19) 0.30 (0.24–0.36) 0.91 (0.40–1.42) 2.00 (0.39–3.62)
Shellfish consumption frequency in past 30 days
0 587 0.11 (0.09–0.12) 0.21 (0.17–0.24) 0.03 (0.02–0.04) 0.05 (0.04–0.07) 0.10 (0.08–0.12) 0.18 (0.12–0.24) 0.38 (0.28–0.47) 0.64 (0.55–0.73)
≥ 1 time 195 0.15 (0.11–0.19) 0.27 (0.17–0.36) 0.05 (0.04–0.06) 0.07 (0.02–0.12) 0.14 (0.10–0.18) 0.28 (0.22–0.33) 0.40 (0.26–0.55) 0.66 (0.24–1.08)
Recent hair color or treatment
No 798 0.12 (0.10–0.13) 0.22 (0.18–0.25) 0.03 (0.01–0.05) 0.06a (0.05–0.06) 0.11 (0.10–0.13) 0.21 (0.16–0.27) 0.41 (0.31–0.51) 0.66 (0.52–0.79)
Yes 39 0.11 (0.08–0.14) 0.14 (0.12–0.15) 0.04 (–0.00–0.08)b 0.08 (0.05–0.10) 0.10 (0.07–0.13) 0.13 (0.10–0.16) 0.37 (–0.22–0.97)b 0.44 (0.35–0.53)
a Bound of CI and percentile are equal because of round-off error.
b Jackknife estimate not stable.
Table 2 GM and selected percentile (95% CI) total hair Hg (μg/g): U.S. females 16–49 years of age, NHANES 1999–2000.
Percentile
Sample description No. GM Mean 10th 25th 50th 75th 90th 95th
Total 1,726 0.20 (0.16–0.24) 0.47 (0.35–0.58) 0.04 (0.03–0.05) 0.09 (0.07–0.11) 0.19 (0.15–0.23) 0.42 (0.29–0.55) 1.11 (0.54–1.68) 1.73 (1.44–2.02)
Race and ethnicity
Non-Hispanic white 582 0.20 (0.16–0.24) 0.42 (0.32–0.51) 0.04 (0.02–0.05) 0.09 (0.07–0.11) 0.19 (0.14–0.24) 0.45 (0.21–0.70) 1.17 (0.38–1.96) 1.84 (0.82–2.86)
Non-Hispanic black 356 0.14 (0.11–0.17) 0.48 (0.04–0.91) 0.04 (0.02–0.05) 0.07 (0.05–0.08) 0.13 (0.10–0.16) 0.27 (0.23–0.30) 0.50 (0.35–0.65) 0.88 (0.26–1.50)
Mexican American 593 0.18 (0.15–0.21) 0.28 (0.24–0.31) 0.06 (0.03–0.09) 0.10 (0.07–0.13) 0.18 (0.15–0.20) 0.32 (0.24–0.40) 0.50 (0.46–0.54) 0.78 (0.51–1.05)
Age group (years) 516 0.13a (0.13–0.20) 0.29 (0.21–0.36) 0.03a (0.02–0.03) 0.06a (0.05–0.06) 0.12 (0.09–0.15) 0.27 (0.23–0.31) 0.52 (0.29–0.76) 0.87 (0.51–1.22)
20–29 449 0.18a (0.18–0.28) 0.39 (0.29–0.48) 0.03 (0.02–0.05) 0.08 (0.07–0.10) 0.18 (0.11–0.24) 0.35 (0.23–0.48) 0.81 (0.65–0.98) 1.43 (1.23–1.64)
30–39 408 0.21 (0.13–0.20) 0.57 (0.33–0.80) 0.04 (0.03–0.05) 0.09 (0.08–0.10) 0.19 (0.12–0.26) 0.40 (0.31–0.48) 1.12 (0.58–1.66) 2.04 (1.55–2.53)
40–49 353 0.26 (0.18–0.28) 0.49 (0.37–0.60) 0.06 (0.04–0.09) 0.11 (0.06–0.16) 0.24 (0.18–0.29) 0.55 (0.40–0.69) 1.39 (1.06–1.72) 1.71 (1.06–2.36)
Fish consumption frequency in past 30 days
0 639 0.11 (0.08–0.13) 0.25 (0.11–0.38) 0.02 (0.01–0.04) 0.05 (0.03–0.07) 0.10 (0.07–0.14) 0.20 (0.16–0.24) 0.40 (0.25–0.56) 0.55 (0.36–0.74)
1 or 2 times 573 0.20 (0.16–0.25) 0.36 (0.28–0.43) 0.05 (0.04–0.06) 0.10 (0.07–0.13) 0.19 (0.13–0.25) 0.39 (0.33–0.45) 0.79 (0.76–0.81) 1.26 (0.74–1.78)
≥ 3 times 447 0.38 (0.28–0.48) 0.77 (0.59–0.94) 0.09 (0.07–0.10) 0.17 (0.09–0.24) 0.34 (0.24–0.45) 0.81 (0.33–1.30) 1.75 (0.75–2.76) 2.75 (1.99–3.50)
Shellfish consumption frequency in past 30 days
0 878 0.13 (0.10–0.15) 0.26 (0.20–0.31) 0.03 (0.02–0.04) 0.06 (0.04–0.08) 0.12 (0.10–0.14) 0.25 (0.19–0.32) 0.53 (0.43–0.62) 0.81 (0.24–1.38)
≥ 1 time 782 0.31 (0.25–0.36) 0.64 (0.50–0.77) 0.08 (0.06–0.10) 0.14 (0.12–0.17) 0.28 (0.24–0.32) 0.58 (0.46–0.70) 1.50 (1.29–1.70) 2.22 (1.62–2.83)
Pregnancy status
Not pregnant 1,429 0.20 (0.16–0.24) 0.47 (0.35–0.58) 0.04 (0.03–0.05) 0.09 (0.07–0.11) 0.19 (0.15–0.23) 0.42 (0.28–0.57) 1.11 (0.50–1.73) 1.71 (1.18–2.25)
Pregnant 292 0.21 (0.15–0.27) 0.43 (0.27–0.58) 0.05 (0.04–0.07) 0.09 (0.08–0.10) 0.17 (0.06–0.28) 0.43 (0.32–0.53) 1.04 (–1.09–3.19)b 1.84 (0.03–3.65)
Recent hair color or treatment
No 1,089 0.20 (0.16–0.25) 0.43 (0.33–0.52) 0.04 (0.02–0.06) 0.09 (0.07–0.11) 0.19 (0.16–0.23) 0.43 (0.27–0.58) 1.12 (0.57–1.67) 1.70 (0.90–2.49)
Yes 637 0.20 (0.16–0.25) 0.52 (0.34–0.69) 0.04a (0.04–0.05) 0.09 (0.07–0.11) 0.18 (0.16–0.21) 0.41 (0.32–0.51) 1.10 (–0.01–2.21)b 1.94 (0.74–3.14)
Education (women ≥ 20 years of age)
< High school 342 0.19 (0.15–0.24) 0.46 (0.28–0.63) 0.05 (0.01–0.08) 0.09 (0.07–0.11) 0.17 (0.13–0.21) 0.34 (0.24–0.43) 0.80 (0.00–1.60) 1.73 (0.95–2.51)
High school 285 0.17 (0.13–0.21) 0.28 (0.22–0.33) 0.04 (0.02–0.06) 0.09 (0.07–0.12) 0.16 (0.11–0.21) 0.31 (0.17–0.44) 0.61 (0.23–0.99) 1.09 (0.60–1.57)
> High school 580 0.24 (0.19–0.30) 0.58 (0.42–0.73) 0.04 (0.03–0.06) 0.10 (0.08–0.12) 0.24 (0.19–0.28) 0.52 (0.32–0.72) 1.41 (0.99–1.82) 2.11 (1.60–2.63)
a Bound of CI and percentile are equal because of round-off error.
b Jackknife estimate not stable.
Table 3 Regression model (R2 = 0.15) and LS means for y = log(hair Hg), U.S. children 1–5 years of age, NHANES 1999–2000.
Characteristics b (p-Value) SE Exp (LS means) (μg/g) (95% CI)
Intercept −2.64 (< 0.05) 0.09 0.11 (0.10–0.13)
Race/ethnicity
Non-Hispanic white — 0.09 (0.08–0.11)
Non-Hispanic black 0.69 (< 0.05) 0.15 0.19 (0.14–0.25)
Mexican American 0.51 (< 0.05) 0.10 0.16 (0.14–0.18)
Fish consumption frequency in past 30 days
0 — 0.08 (0.07–0.10)
1 or 2 times 0.57 (< 0.05) 0.11 0.14 (0.12–0.17)
≥ 3 times 0.57 (< 0.05) 0.18 0.14 (0.11–0.19)
Shellfish consumption frequency in past 30 days
0 — 0.11 (0.10–0.13)
≥ 1 time −0.04 (0.74) 0.13 0.11 (0.08–0.14)
Recent hair color or treatment
No — 0.11 (0.10–0.13)
Yes −0.19 (0.30) 0.18 0.09 (0.06–0.13)
—, Reference level.
Table 4 Regression model (R2 = 0.27) and LS means for y = log(hair Hg), U.S. females 16–49 years of age, NHANES 1999–2000.
Characteristics b (p-Value) SE Exp (LS means) (μg/g) (95% CI)
Intercept −2.64 ( < 0.05) 0.11 0.19 (0.16–0.22)
Race/ethnicity
Non-Hispanic white — 0.20 (0.16–0.24)
Non-Hispanic black −0.32 (< 0.05) 0.12 0.14 (0.12–0.17)
Mexican American 0.07 (0.44) 0.10 0.21 (0.18–0.25)
Fish consumption frequency in past 30 days
0 — 0.11 (0.09–0.13)
1 or 2 times 0.55 (< 0.05) 0.10 0.19 (0.6–0.23)
≥3 times 1.05 (< 0.05) 0.14 0.32 (0.24–0.41)
Shellfish consumption frequency in past 30 days
0 — 0.14 (0.12–0.17)
≥1 time 0.56 (< 0.05) 0.08 0.25 (0.21–0.29)
Recent hair color or treatment
No — 0.20 (0.16–0.24)
Yes −0.11 (0.17) 0.08 0.18 (0.15–0.21)
Pregnancy
Not pregnant — 0.19 (0.16–0.22)
Pregnant 0.12 (0.31) 0.12 0.21 (0.17–0.26)
Age groups (years)
16–19 — 0.15 (0.12–0.19)
20–29 0.15 (0.19) 0.11 0.17 (0.14–0.21)
30–39 0.22 (0.05) 0.11 0.19 (0.15–0.23)
40–49 0.39 (< 0.05) 0.13 0.22 (0.18–0.28)
—, Reference level.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.6866ehp0112-00117215289162ResearchArticlesThe Effect of Arsenic Mitigation Interventions on Disease Burden in Bangladesh Lokuge Kamalini M. 1Smith Wayne 2Caldwell Bruce 1Dear Keith 1Milton Abul H. 11Australian National University, National Centre for Epidemiology and Population Health, Acton, Australian Capital Territory, Australia2University of Newcastle, Centre for Clinical Epidemiology and Biostatistics, Newcastle, New South Wales, AustraliaAddress correspondence to K. Lokuge, National Centre for Epidemiology and Population Health, Australian National University, Building 62, Mills Road, Canberra, Australian Capital Territory, 0200 Australia. Telephone: 61-26125-2378. Fax: 61-2-61250740. E-mail:
[email protected] authors declare they have no competing financial interests.
8 2004 17 6 2004 112 11 1172 1177 18 11 2003 17 6 2004 Publication 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 interventions have been advocated to mitigate the impact of arsenic contamination of drinking water in Bangladesh. However, there are few data on the true magnitude of arsenic-related disease in Bangladesh nationally. There has also been little consideration given to possible adverse effects of such interventions, in particular, diarrheal disease. The purpose of this study was to estimate and compare the likely impacts of arsenic mitigation interventions on both arsenic-related disease and water-borne infectious disease. We found that arsenic-related disease currently results in 9,136 deaths per year and 174,174 disability-adjusted life years (DALYs; undiscounted) lost per year in those exposed to arsenic concentrations > 50 μg/L. This constitutes 0.3% of the total disease burden in Bangladesh in terms of undiscounted DALYs. We found intervention to be of overall benefit in reducing disease burden in most scenarios examined, but the concomitant increase in water-related infectious disease significantly reduced the potential benefits gained from intervention. A minimum reduction in arsenic-related DALYs of 77% was necessary before intervention achieved any reduction in net disease burden. This is assuming that interventions were provided to those exposed to > 50 μg/L and would concomitantly result in a 20% increase in water-related infectious disease in those without access to adequate sanitation. Intervention appears to be justified for those populations exposed to high levels of arsenic, but it must be based on exposure levels and on the effectiveness of interventions not only in reducing arsenic but in minimizing risk of water-related infections.
arsenic/adverse effectsBangladeshburden of diseasediarrhearisk assessmentwater pollutantswater supply
==== Body
There has been growing concern regarding the widespread exposure of the Bangladeshi population to arsenic in tube-well water (Smith et al. 2000). Many interventions and alternative water sources have been advocated on the grounds that they are effective in reducing arsenic ingestion. However, limited consideration has been given to possible adverse effects of such interventions, in particular, water-related infectious diseases such as diarrhea. Although this issue has been raised (Caldwell 2003; MacDonald 2001), there has been no evaluation published allowing a meaningful assessment of the competing risks involved in mitigation. Such an assessment is required before the development of effective policy recommendations for arsenic mitigation in Bangladesh.
In this article, we present an evaluation of the possible change in overall burden of disease resulting from implementation of arsenic mitigation interventions in Bangladesh and compare likely impacts on both arsenic-related disease and water-borne infectious disease.
Materials and Methods
Arsenic-related disease due to chronic exposure through drinking water has a relatively low incidence and a latency of up to decades for most end points significant to a burden of disease assessment (National Research Council 2001). However, case fatality rates for arsenic-exposure–related sequelae such as internal cancer are high, particularly in a country such as Bangladesh where access to health care is limited. In contrast, diarrheal and other water-related infectious diseases, although having a comparatively low case fatality rate, have a much higher incidence. Additionally, 90% of the disease burden due to diarrhea occurs in children younger than 5 years of age (Pruss et al. 2002), unlike arsenic, which affects primarily older adults. To provide a basis for comparing end points with such diversity in the population profile, we calculated mortality rates and disability-adjusted life years (DALYs) lost for end points related to these two risk factors.
DALYs are the measure used by the Global Burden of Disease (GBD) study to assess and compare burden of disease due to varied risk factors and end points. The DALY is a summary health measure that accounts for mortality at different ages and for both the severity and duration of morbidity (Murray et al. 2002). By using DALYs, a comparison can also be made between the impacts of arsenic, of water-related infection, and of arsenic mitigation interventions overall, with other causes of disease in Bangladesh evaluated by the GBD study. DALYs, which measure loss of healthy life, are useful for assessing the impact of interventions and comparing predicted health states (Murray and Acharya 1997); therefore, we chose DALYs over other measures of quality-adjusted life. Because one purpose of the present study was to provide input into policies for arsenic mitigation, we include estimates discounted at 3% (the discounting rate used in the standard GBD figures) alongside those with zero discounting.
The disease burden attributable to each risk factor was estimated using cause-specific rates of mortality and DALYs for the Southeast Asia region (SEAR), D subregion, for the year 2001 published in the 2002 World Health Report [World Health Organization (WHO) 2002]. These estimates were regarded as the most appropriate for Bangladesh because the SEAR-D subregion includes those countries within the SEAR that have high child and adult mortality rates (India, Bangladesh, and Pakistan).
To calculate disease burden for those end points not disaggregated in the GBD study, we obtained background mortality rates applicable to the Bangladeshi population from the literature. These data were then entered into the formulas for DALYs given in the GBD study (Murray and Lopez 1996). The rates of disease and assumptions used for each specific exposure and sequelae are defined below.
We obtained demographic data from multiple sources. Total population by thana-level administrative unit was obtained from the Bangladesh Bureau of Statistics and derived from the 1991 national census (Bangladesh Bureau of Statistics 2002). The age structure of the Bangladeshi population was obtained from the 1999–2000 Bangladesh Demographic and Health Survey (BDHS) (National Institute of Population Research and Training 2001).
Relative risk estimates from published literature were entered into the formulas for attributable fraction given by Rockhill et al. (1998). The calculated exposure and disease-specific attributable fractions were then applied to relevant background estimates to obtain the total disease burden due to the factors under study.
Disease burden from arsenic exposure.
We calculated estimates of the arsenic-exposed population by different age strata by assuming that a) the exposed group had an overall similar age structure to the population surveyed in the 1999–2000 BDHS (National Institute of Population Research and Training 2001); b) total population numbers within each thana subunit were similar to those of the 1991 national census; and c) exposure was through use of water from shallow tube wells. Population estimates were adjusted for levels of shallow tube-well use, currently estimated at 87% (Caldwell 2003).
Data on arsenic levels in tube wells used in this study were obtained from a British Geological Survey (BGS) survey of tube wells in Bangladesh (Kinniburgh and Smedley 2001). This survey is the only one currently published that provides nationally representative data. Table 1 shows the average and median arsenic concentrations for various ranges calculated using these data.
We calculated the population exposed to arsenic at different levels by using the distribution of arsenic exposure estimated from the BGS data (Kinniburgh and Smedley 2001). and applying it to 1991 national census data (Bangladesh Bureau of Statistics 2002). This was done for each thana, the smallest administrative subunit for which data were available. The calculated proportions of the population in each exposure strata are shown in Table 2.
Arsenic-related end points.
The quality of studies detailing associations between health outcomes and arsenic exposure varies. The literature has been reviewed by expert committees from the U.S. Environmental Protection Agency (EPA) Science Advisory Board (U.S. EPA 2001a), in the 2001 United Nations Synthesis Report on Arsenic in Drinking Water (Abernathy 2001), and by the American Council on Science and Health (Brown and Ross 2002). Table 3 stratifies the level of evidence for a possible association between arsenic and these disease end points according to these key reviews. From those end points that any of these organizations considers to have strong or reasonably strong evidence for an association, we selected all that directly contribute to disease burden. We included lung, bladder, kidney, and skin cancers; ischemic heart disease; and diabetes mellitus.
End points not included.
Skin alterations are the most common manifestation of chronic arsenic exposure. However, as in the GBD study, the nonmalignant manifestations were assumed to cause minimal disability and no independent increase in mortality; therefore, we did not include nonmalignant skin alterations as end points in our study.
Peripheral vascular disease (PVD) has been noted in arsenic-exposed populations worldwide, but there is continuing debate over the association between arsenic and the more severe forms of PVD, in particular, the role of other chemicals in the causation of blackfoot disease (Yang et al. 2002). For this reason, and because it has not been noted in Bangladesh, PVDs such as blackfoot disease were not included.
Although hypertension has been found to be associated with arsenic exposure (Rahman et al. 1999), it is not a contributor to disease burden of itself, but a risk factor for end points that have been included. Hypertension was therefore not included as a separate end point.
Calculating arsenic-related attributable fraction of disease.
Although there are some studies on arsenic-related disease in Bangladesh, none provide reliable population-level estimates of risk. Despite limitations, and uncertainty regarding the level of exposure, the data we used were from studies carried out in Taiwanese populations (Tsai et al. 1999). We used these data because they are still recognized as the most reliable source of dose–response information on exposure to arsenic in drinking water currently available (National Research Council 2001; U.S. EPA 2001b).
Cancers associated with arsenic.
Relative risks for lung, liver, bladder, and kidney cancer were published in a review by Smith et al. (1992). A major issue related to the use of these estimates was the wide range within each category of exposure. In particular, the lowest exposure group covered the range from 0 to 300 μg/L [weighted average concentration, 170 μg/L (U.S. EPA 1988)]. However, the average concentration in tube wells in Bangladesh for this range is much lower: 170 μg/L is closer to the average concentration in tube wells in Bangladesh in a range of 100–300 μg/L and not 0–300 μg/L (Table 1). Most studies also suggest that the threshold for internal malignancies related to arsenic exposure is > 100 μg/L (Chiou et al. 2001; Guo 2000). The risks for the Taiwanese population exposed at an average concentration of 170 μg/L are therefore probably most applicable to the population in Bangladesh exposed within the 100–300 μg/L range. To account for this, we calculated a range of attributable fractions, each using the same relative risk for the lowest exposure category, but with the proportion of the population exposed to that risk level varied to the proportion in Bangladesh exposed at 0–30, 10–300, 50–300, or 100–300 μg/L.
We applied the relative risks due to arsenic exposure obtained from these Taiwanese data to estimates of DALYs and deaths for each end point in the GBD SEAR-D subregion (WHO 2002). However, the GBD study does not provide disaggregated data for either kidney or nonmelanotic skin cancer. In the case of kidney cancer, we calculated mortality using age-specific background cancer mortality rates in Bangladesh published by the International Agency for Research on Cancer (IARC; Ferlay et al. 2001). For Bangladesh, where cancer registry data are not available, mortality was estimated from Indian registry incidence data using country/regional survival (Parkin 1986). However, the background rate we used for kidney cancer is actually an aggregate of kidney cancer along with cancers of other urinary organs and therefore will overestimate the risk of kidney cancer alone.
There are no reliable estimates for either incidence or case fatality rates for nonmelanotic skin cancers applicable to Bangladesh. Arsenic is not associated with melanoma, which dominates the estimates from the combined skin cancer categories routinely reported, including by IARC; therefore these rates could not be used. By combining calculated lifetime excess risk of skin cancer due to arsenic exposure in Bangladesh (Khan et al. 2003) with a case fatality rate of 14.3% for arsenic-related skin cancers over a 5-year period (Yeh 1973), it was possible to derive an estimate of the number of skin cancer deaths per year due to arsenic exposure in Bangladesh.
For both skin cancer and kidney disease, lack of data meant that only total mortality and years of life lost (YLL), a subcomponent of DALYs, could be calculated.
Noncancer effects associated with arsenic.
Several studies have noted an association between arsenic exposure and cardiovascular disease in populations from Taiwan, Chile, and the United States, and an increased prevalence of hypertension has been noted in populations exposed to arsenic, including in Bangladesh (Rahman et al. 1999). Three possible sources of risk estimates were a cohort study by Chen et al. (1996), an ecologic level study by Tsai et al. (1999), and a study in Bangladesh on prevalence of hypertension (Rahman et al. 1999). Chen et al. (1996) were unable to provide precise estimates of risk at levels of exposure < 500 μg/L. We did not use the Rahman et al. (1999) study because exposure was not directly determined but inferred from the presence of arsenic-related skin lesions. The relative risk of death from ischemic heart disease in arsenic-exposed compared with nonexposed populations was therefore obtained from Tsai et al. (1999).
Diabetes has also been associated with arsenic exposure in some studies, including one conducted in arsenic-exposed populations in Bangladesh (Rahman et al. 1998), but again, this study suffered from the same limitations of exposure measurement. The ecologic study by Tsai et al. (1999) on the Taiwanese population found standardized mortality ratios (SMRs) for death from diabetes of 1.35 for women and 1.55 for men in a population exposed to elevated arsenic levels in drinking water compared with a local reference population; we used these figures to calculate attributable risk.
Water-related infectious diseases.
There are several infectious diseases that are water related, including infective causes of acute diarrhea, helminth infections, schistosomiasis, and water-washed diseases such as trachoma (Esrey et al. 1991). Within the SEAR-D subregion as a whole, > 99% of the disease burden from these infections is attributable to diarrheal disease. Pruss et al. (2002) estimated that the global disease burden due to diarrhea and other water-related infectious diseases attributable to water, sanitation, and hygiene is 4.0% of all deaths and 5.7% of the total burden of disease in DALYs lost. Proportionately, diarrheal disease is an even greater contributor to the burden of disease in developing countries such as Bangladesh (Hussain et al. 1999). Considering that the overall burden of diarrheal disease is so high, it is important to evaluate the possible impacts that currently recommended changes in water supply aimed at arsenic mitigation may have on water-related infectious disease, in particular, diarrheal disease.
Attributable risk due to change in water supply.
In the context of Bangladesh, the most appropriate technology in terms of microbiologically clean water was and is tube wells. To assess the possible additional burden of disease resulting from changes to current arsenic-contaminated water supplies, it is necessary to estimate the magnitude of this effect.
A recent study into the global burden of water-related illness disease categorized exposure to diarrheal disease from water supply into several levels (Pruss et al. 2002). The Global Water Supply and Sanitation Assessment classification of water supplies and sanitation infrastructure was the source of the definitions used by Pruss et al. (2002) to categorize the exposure level of subgroups of the world population in terms of access to water and sanitation services [WHO and the U.N. Children’s Fund (WHO/UNICEF) 2002]. Levels of relevance to this assessment and the risk reduction when moving between these levels for the present study are presented in Table 4.
A recent WHO/UNICEF joint report (WHO/UNICEF 2001) used serial surveys of coverage to estimate current levels of access to water supply and sanitation in Bangladesh. Access to improved sanitation in rural areas is 41%; therefore, 59% of the population has inadequate access to these services (WHO/UNICEF 2001).
The current status of most of the Bangladeshi population would primarily be within the Vb level of risk (improved water supply but no improved sanitation; Table 4). In evaluating possible arsenic mitigation options, the feasible alternatives include various forms of surface water, treatment of tube-well water before consumption, and the use of available uncontaminated tube wells. All involve a possible change in either the quality or quantity of water available to the household for use.
A transition to surface water sources such as unimproved dug wells, ponds, or streams would mean a change in exposure status from level Vb to level VI (no improved water supply and no sanitation) and therefore an estimated increase in diarrheal disease of 20%. The data presented below are based on the assumption of the equivalent of this change in risk for arsenic mitigation interventions for those in the subpopulation without access to improved sanitation.
Mortality due to diarrheal disease.
Although several studies in Bangladesh have examined diarrheal incidence in childhood, relatively few have assessed mortality. Because reliable estimates require large sample sizes, most studies evaluating diarrheal mortality do so in the context of hospital-based case studies and cannot provide estimates of overall mortality in the community.
One extensive source of data on Bangladesh comes from studies conducted in the long-term follow-up area of the International Center for Diarrhoeal Disease Research, Bangladesh (ICDDR-B; Fauveau 1994). However, this population has been studied intensively over many decades and may not therefore be representative of the general population. In the present study, we used the GBD SEAR-D water-related infectious disease morbidity rates (WHO 2002), which are conservative relative to ICDDR-B estimates.
Results
Table 1 gives the average and median arsenic concentrations for various ranges of exposure, calculated using BGS tube-well survey data from Bangladesh (Kinniburgh and Smedley 2001).
Table 2 details the proportion of population estimated to be within each exposure range in those regions of Bangladesh surveyed by the BGS (Kinniburgh and Smedley 2001).
Table 5 provides results of the estimated total burden of disease due to exposure to arsenic at concentrations > 50 μg/L.
Projections of intervention impact on water-related infectious disease are shown in Table 6, given that 59% of the population in Bangladesh does not have access to improved sanitation and assuming that interventions are used by all those exposed to > 10 μg/L (scenario A) or all those exposed to > 50 μg/L (scenario B).
Projections of the net change in disease burden as a result of intervention are shown in Table 7. These projections assume a 100% reduction in arsenic-related disease, along with a 20% increase in water-related infectious disease in the subgroup without access to sanitation. Also included is an estimate of the net effect with different thresholds for the effects of arsenic on lung, bladder, and kidney cancer. Table 8 presents the predicted increase in infectious disease burden as a percentage of current total arsenic-related disease burden and is therefore the minimum reduction in current arsenic-related disease burden necessary to achieve any net decrease in overall disease burden through intervention.
Table 9 presents the arsenic-related burden of disease in those exposed to concentrations > 50 μg/L as a proportion of total burden of disease in Bangladesh and as a proportion of the disease burden due to other selected causes.
Discussion
Arsenic contamination of drinking water is a major health issue for those living in affected areas of Bangladesh. However, the present study demonstrates that much of the benefit obtained from intervention may be negated by a concomitant increase in water-related infectious disease. Currently, in the evaluation of arsenic mitigation interventions, the emphasis is on assessing their impact on arsenic levels. Clearly this is important because inefficient interventions are likely to have little overall benefit and may even have adverse net impacts. However, all suggested mitigation interventions must be considered not only from the perspective of reducing arsenic-related morbidity and mortality but also from the overall health perspective.
There are a number of methodologic issues that are important in considering the results of the present study: a) estimates of exposure; b) estimates of disease burden from arsenic exposure, in particular the end points chosen for inclusion; c) extrapolation of risk estimates of arsenic exposure from different populations, with differing exposure levels; d) estimates of the effectiveness of arsenic mitigation interventions; e) estimates of disease burden from diarrhea; and f) estimates of diarrheal risk from arsenic mitigation interventions, including assumptions about changing from improved to unimproved water supplies.
Estimates of exposure.
Exposure data were obtained from the BGS survey of tube wells (Kinniburgh and Smedley 2001), the only nationally representative data on tube-well contamination currently available. However, this survey sampled fewer than 4,000 tube wells; therefore, at the thana level, exposure was inferred from relatively few data points. These estimates can be refined only if more comprehensive tube-well surveys, using nationally representative sampling frames, are conducted.
Estimates of disease burden from arsenic exposure.
We took an inclusive approach in estimating the disease burden from arsenic exposure. The GBD rates for cardiovascular disease and diabetes may be an overestimate for Bangladesh because members of this primarily poor and rural population are likely to have lower cardiovascular disease and diabetic mortality rates than those in the urban Indian populations, whose levels are more likely to match the SEAR-D estimates (WHO 2002). The disease burden due to these end points is therefore likely to be biased toward a beneficial effect of arsenic mitigation. The burden of disease estimates for arsenic are dominated by the contribution of cardiovascular disease, but the association between arsenic exposure and cardiovascular disease remains ambiguous, at least in strength of association. Because the strength of this association will determine whether interventions are likely to cause good or harm, it is crucial that valid estimates of this association are available, particularly at lower arsenic exposure levels.
End points resulting from chronic arsenic exposure chosen for inclusion were those that major reviews considered to have strong or reasonably strong evidence of a causal link. Numerous other end points have been found to be associated with arsenic exposure. However, the evidence is much less definitive, in terms of both whether an association exists and its strength. Additionally, data do not currently exist to allow a meaningful estimate of the burden of disease resulting from such end points. However, excluding them from this study may slightly underestimate arsenic-related burden of disease.
Extrapolation of risk estimates of arsenic exposure from different populations with differing exposure levels.
For all arsenic-related end points, we derived risk estimates from a different population to the study population, assuming the same exposure–risk relationship. The source Taiwanese population is described as being largely rural, engaged in farming, fishing, and salt production, of below average socioeconomic standard, and with a low-protein diet based primarily on rice and sweet potatoes (Wu et al. 1989). In terms of these factors, the current Bangladeshi population is fairly similar to the Taiwanese population of 40 years ago from which the data are derived (Bangladesh Bureau of Statistics 2002). The primary caloric source is rice, and malnutrition levels are high (Ahmed 1992). For these reasons, we made no adjustment for fluid intake and body mass, as has been done when extrapolating Taiwanese data to the U.S. population (Morales et al. 2000). However, the risks for the Taiwanese population exposed at an average concentration of 170 μg/L (range, 0–300 μg/L) are probably most applicable to the population in Bangladesh exposed within the 100–300 μg/L range and not the 0–300 μg/L range (discussed in “Materials and Methods”).
Estimates of the effectiveness of arsenic mitigation interventions.
Arsenic mitigation interventions, if given to those exposed to > 50 μg/L, would need to achieve at least a 77% reduction in arsenic-related disease burden to result in a net reduction in DALYs. Arsenic mitigation interventions cannot achieve a 100% reduction in disease burden for several reasons, and even reductions of 70–80% are doubtful. It is unlikely that any of the interventions widely accessible in Bangladesh would be 100% effective, due to both compliance and efficacy, and the degree to which arsenic contamination of irrigation water and resultant intake through food contributes to disease burden is unclear.
Therefore, assuming a 100% reduction in arsenic-related disease after intervention, as was done for all estimates in this study, is likely to bias results toward a beneficial outcome from intervention.
Estimates of disease burden from diarrhea.
The GBD study rates (WHO 2002) used in the estimations are lower than those from recent studies in Bangladesh on the disease burden from diarrhea; Streatfield et al. (2001) estimated the disease burden attributable to diarrheal disease in Bangladesh as 11% of all deaths and 12.1% of DALYs. The SEAR-D rates used (WHO 2002) were 6.2% of deaths and 7.2% of undiscounted DALYs, which are both almost half that of Streatfield et al. (2001). The background rates of diarrheal disease used are therefore conservative in the context of Bangladesh. This is again likely to bias results toward an overall beneficial effect of arsenic mitigation.
Estimates of diarrheal risk from arsenic mitigation interventions.
The association between incidence of diarrheal disease and water supply was categorized into several levels by Pruss et al. (2002). Based on these data, there is a 20.8% increase in risk when moving between level Vb (improved water supply but no basic sanitation) to level VI (no improved water supply and no basic sanitation). Studies conducted in developing countries including Bangladesh found that water and sanitation interventions have a proportionately greater impact on child mortality as opposed to morbidity [a 26% reduction in morbidity, compared with a 55% reduction in overall mortality, and a 65% reduction in diarrhea related mortality (Esrey et al. 1991)]. Because the attributable risks used applied to changes in morbidity, it is likely that we underestimated impacts on mortality.
It is clearly appropriate to assume an increased risk when the intervention involves moving from tube wells to dug wells that are not sanitary protected (constructed to be relatively protected from microbial contamination), or moving to other forms of surface water that are unimproved. However, a change from contaminated water to uncontaminated tube wells would, at face value, appear to involve no change in exposure status. Assuming any individual household would prefer to use the most convenient well, usually the closest and often within the household compound, any change in the tube well would presumably involve a change to an uncontaminated but less convenient tube well, in terms of either distance or the number of individuals using the well for water. Aside from compliance issues, this also increases the risk of water-borne disease. Studies have found that in terms of protection against infectious disease, the quantity of water used is as important or even more important than the quality of water used (Esrey et al. 1991), and that the quantity of water used is directly related to the distance to the water source and the number of users (Hoque et al. 1989). Thus, even a change in the tube well used may increase the risk of diarrheal disease.
There is also evidence to suggest that arsenic filtration systems may increase the risk of water-related infections. The main risk of filter systems is through increased handling and storage of water within the household, and past studies have shown that household storage and handling is a significant source of contamination, perhaps the major source (Molbak et al. 1989). This assumption is supported by a field study evaluating arsenic removal systems in Bangladesh, which found that such systems had higher levels of microbial contamination in the filtrated water than in the tube wells from which the water was taken (Sutherland et al. 2002) and may therefore increase the risk of water-related infectious disease.
Latency.
Any impact that changes in water supply have on incidence of arsenic-related disease will be delayed, probably for several years. Estimates of the latency period for arsenic-related chronic disease vary greatly, but most are in the range of several decades. For bladder, lung, and liver cancer, estimates range to > 40 years (Chen et al. 1986). However, the impact of arsenic mitigation interventions on diarrheal disease will be immediate. Because maximal arsenic-related reductions would be delayed for a number of years, there would be an overall increase in mortality in the period immediately after initiation of any intervention. Because the results given here apply only once equilibrium has been reached, they do not take into account this period and therefore, again, are biased toward a beneficial effect of mitigation.
Conclusions
There are many areas where limited data affected the validity of the estimates obtained in this study, including lack of data on the long-term effects of arsenic exposure at the lower ranges; lack of reliable population-level estimates of risk related to arsenic exposure in Bangladesh, particularly for those more common end points such as cardiovascular disease that are likely to constitute the bulk of disease burden; and imprecise data on exposure nationally.
At present, there are inadequate data to reliably meet these needs, and formulating policy options before the availability of such data carries potentially significant risks. The present study is an attempt to make a quantitative assessment of the impacts of intervention. As data become available in those areas where it is currently lacking, further refinements will allow more precise estimates of benefit and risk.
As Table 9 demonstrates, arsenic-related disease resulting from exposure to arsenic concentrations > 50 μg/L constitutes 0.3% of the total disease burden in Bangladesh in terms of undiscounted DALYs, and although it is a significant cause of disease burden in exposed groups, nationally it is of less importance than many other risk factors.
Interventions must be used effectively in a country such as Bangladesh, where resources are limited and multiple competing interests exist. In the case of arsenic mitigation, this means ensuring that interventions are targeted to those areas where exposure has been confirmed, and that those interventions provided achieve significant reductions in arsenic exposure without concomitantly causing substantial increases in other risks such as water-related infectious disease.
As these estimates demonstrate, the effects of arsenic mitigation are double-edged, and intervention appears to be clearly justifiable at present only within the higher levels of exposure. There is an urgent need for studies evaluating alternative water sources in terms of not only their effectiveness in reducing arsenic intake but also their associated effect on water-related infections.
Table 1 Arsenic concentrations in tube wells in Bangladesh.
Concentration range (μg/L) Average concentration within range (μg/L) Median concentration within range (μg/L)
0–300 33 < 10
10–300 82 56
50–300 132 108
100–300 180 170
> 300–600 421 406
> 10–500 107 63
> 500 628 572
> 50–500 167 130
> 600 755 668
Calculated using data from (Kinniburgh and Smedley (2001).
Table 2 Distribution of arsenic exposure across the population of Bangladesh.
Arsenic concentration range (μg/L) Percentage of population exposed to drinking water contaminated at this level
≤10 58.8
> 10–50 16.4
> 50–100 8.6
> 100–300 10.9
> 300–600 4.5
> 600 0.83
Calculated using BGS tube-well survey data (Kinniburgh and Smedley 2001) and population data from the 1991 Bangladesh national census (Bangladesh Bureau of Statistics 2002).
Table 3 Strength of evidence for a causal link between arsenic and various end points.
Reference Level of evidence Exposure-related disease end point
U.S. EPA 2001a Strong Lung, bladder cancer
Reasonably strong Ischemic heart disease, diabetes mellitus, hypertension, skin cancer
Suggestive Prostate cancer, nephritis and nephrosis, hypertensive heart disease, nonmalignant respiratory disease
Abernathy 2001 Strong Skin, lungs, bladder, kidney cancer, skin hyperkeratosis and pigmentation changes
Reasonably strong Hypertension, cardiovascular disease
Suggestive Diabetes, reproductive diseases
Weakest Cerebrovascular disease, long-term neurologic effects, cancer at sites other than skin, lung, bladder, and kidney
Brown and Ross 2002 May cause Skin, lung, bladder cancer, cutaneous effects
Possible Kidney, liver, prostate, and other cancers
Some evidence Cardiovascular/cerebrovascular diabetes, reproductive diseases
Table 4 Change in risk of diarrheal disease due to improvements in water supply and sanitation services.
Level Description of level Risk difference
VI No improved water supply and no basic sanitation in a country that is not extensively covered by those services, and where water supply is not routinely controlled Index
Vb Improved water supply and no basic sanitation in a country that is not extensively covered by those services, and where water supply is not routinely controlled 20.8%
Va Basic sanitation but no improved water supply in a country that is not extensively covered by those services, and where water supply is not routinely controlled 37.5%
IV Improved water supply and basic sanitation in a country that is not extensively covered by those services, and where water supply is not routinely controlled 37.5%
Data from (Pruss et al. (2002)
Table 5 Burden of disease incurred in Bangladesh each year due to arsenic levels > 50 μg/L.
DALYs
Disease Deaths Undiscounted Discounted at 3%
Diabetes mellitus 351 10,524 7,628
Ischemic heart disease 5,128 91,616 67,380
Tracheal, bronchial, lung cancers 2,100 39,759 28,921
Bladder cancer 1,346 25,432 17,121
Kidney cancera 85 3,463 1,840
Skin cancera 126 3,379 2,120
Total disease burden 9,136 174,174 125,010
a Includes only YLL and not years lived with disability.
Table 6 Estimated increase in water-related infectious disease burden caused by arsenic mitigation.
DALYs
Scenario Deaths Undiscounted Discounted at 3%
A: Assuming interventions were used by all those exposed to arsenic > 10 μg/L 3,370 218,198 97,659
B: Assuming interventions were used by all those exposed to arsenic > 50 μg/L 2,080 134,671 60,275
Table 7 Net impact of arsenic mitigation on burden of disease in Bangladesh.
DALYs
Population supplied with intervention Threshold for arsenic-related lung, bladder, and kidney cancer Deaths Undiscounted Discounted at 3%
All those exposed to arsenic levels > 10 μg/L No threshold 6,623 −27,251 39,173
> 50 μg/L 5,765 −44,024 27,351
> 100 μg/L 5,072 −58,785 17,324
All those exposed to arsenic levels > 50 μg/L No threshold 7,055 39,503 64,735
> 100 μg/L 6,362 24,741 54,707
A negative number signifies a net overall increase in DALYs lost.
Table 8 Predicted increase in infectious disease burden resulting from arsenic mitigation, given as a percentage of the disease burden currently incurred through arsenic exposure.a
DALYs (%)
Population supplied with intervention Deaths (%) Undiscounted Discounted at 3%
Exposed to arsenic levels > 10 μg/L 34 114b 71
Exposed to arsenic levels > 50 μg/L 23 77 48
a Assuming no threshold for arsenic-related disease.
b Percentage is > 100 because the total arsenic-related burden of disease that can be removed through mitigation is less than that predicted due to water-related infectious disease after mitigation.
Table 9 Current disease burden due to arsenic levels > 50 μg/L as a proportion of burden of disease due to other selected causes in Bangladesh.
DALYs (%)
Disease Deaths (%) Undiscounted Discounted at 3%
All causes 0.9 0.3 0.4
Childhood-cluster diseases 34.2 8.1 14.7
Nutritional deficiencies 71.0 12.0 15.8
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7024ehp0112-00117815289163ResearchArticlesLead, Diabetes, Hypertension, and Renal Function: The Normative Aging Study Tsaih Shirng-Wern 12Korrick Susan 12Schwartz Joel 23Amarasiriwardena Chitra 12Aro Antonio 12Sparrow David 4Hu Howard 121Occupational Health Program, Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA2Channing Laboratory, Department of Medicine, Brigham and Women’s Hospital, and Harvard Medical School, Boston, Massachusetts, USA3Environmental Epidemiology Program, Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA4The Normative Aging Study, Department of Veterans Affairs Medical Center, Boston, Massachusetts, USAAddress correspondence to S.-W. Tsaih, Jackson Laboratory, Box 303, 600 Main St., Bar Harbor, ME 04609 USA. Telephone: (207) 288-6000 ext. 1281. Fax: (207) 288-6077. E-mail:
[email protected] gratefully acknowledge the research management of S. Datta and G. Fleischaker and the research assistance of S.Y. Park, S. Oliveira, and N. Lupoli.
This research was supported by five National Institutes of Health (NIH) grants (R01-ES05257, R01-ES08074, P42-ES05947, General Clinical Research Center RR02635, and Center Grant ES00002). The Normative Aging Study is supported by the Cooperative Studies Program/Epidemiology Research and Information Center, Department of Veterans Affairs, and is a research component of the Massachusetts Veterans Epidemiology Research and Information Center. The KXRF instrument used in this work was developed by ABIOMED, Inc. (Danvers, MA) with support from NIH (ES03918).
The contents of this report are solely the responsibility of the authors and do not necessarily represent the official views of the National Institute of Environmental Health Sciences, NIH, or the U.S. Environmental Protection Agency.
The authors declare they have no competing financial interests.
8 2004 3 6 2004 112 11 1178 1182 11 2 2004 3 6 2004 Publication 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 prospective study, we examined changes in renal function during 6 years of follow-up in relation to baseline lead levels, diabetes, and hypertension among 448 middle-age and elderly men, a subsample of the Normative Aging Study. Lead levels were generally low at baseline, with mean blood lead, patella lead, and tibia lead values of 6.5 μg/dL, 32.4 μg/g, and 21.5 μg/g, respectively. Six percent and 26% of subjects had diabetes and hypertension at baseline, respectively. In multivariate-adjusted regression analyses, longitudinal increases in serum creatinine (SCr) were associated with higher baseline lead levels but these associations were not statistically significant. However, we observed significant interactions of blood lead and tibia lead with diabetes in predicting annual change in SCr. For example, increasing the tibia lead level from the midpoints of the lowest to the highest quartiles (9–34 μg/g) was associated with an increase in the rate of rise in SCr that was 17.6-fold greater in diabetics than in nondiabetics (1.08 mg/dL/10 years vs. 0.062 mg/dL/10 years; p < 0.01). We also observed significant interactions of blood lead and tibia lead with diabetes in relation to baseline SCr levels (tibia lead only) and follow-up SCr levels. A significant interaction of tibia lead with hypertensive status in predicting annual change in SCr was also observed. We conclude that longitudinal decline of renal function among middle-age and elderly individuals appears to depend on both long-term lead stores and circulating lead, with an effect that is most pronounced among diabetics and hypertensives, subjects who likely represent particularly susceptible groups.
blood leadbone leaddiabeteshypertensionkidney functionserum creatinine
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An association between lead poisoning and renal disease in humans has been recognized for more than a century (Wedeen et al. 1975). Numerous epidemiologic studies, mortality studies, and experimental studies in animals have reported lead nephrotoxicity at high levels of exposure; however, studies on the action of lead on renal function at lower levels of chronic exposure have produced a mixed pattern of findings. Most of the studies found no significant association between low-level lead exposure and renal dysfunction. To date, only a few cross-sectional studies (Payton et al. 1994; Staessen et al. 1990, 1992) and one longitudinal study (Kim et al. 1996) have reported a significant association between elevated blood lead levels and reduced renal function measured by serum creatinine (SCr) or creatinine clearance in members of the general population. In addition, a recent randomized trial among individuals with elevated environmental lead exposure demonstrating improved creatinine clearance in those receiving chelation therapy provides evidence of lead’s effect on the kidney (and its potential reversibility) at community levels of exposure (Lin et al. 2003).
Blood lead, which mostly reflects relatively recent exposure, is an inadequate measure of total body burden of lead, which may explain why most of the previous observational studies failed to find a significant association between low-level lead exposure and renal function impairment. Compared with concurrent blood lead, bone lead, which comprises > 95% of adult body lead burden and has a biologic half-life ranging from years to decades, is a better biologic marker for studying chronic toxicity of accumulated exposure and lead burden (Gonzalez-Cossio et al. 1997; Hu et al. 1996; Korrick et al. 1999). In addition, bone lead also serves as an endogenous source of lead exposure for individuals with increased bone turnover (Silbergeld 1991; Silbergeld et al. 1988). Therefore, bone lead may be a risk factor for impaired renal function either by serving as either a dosimeter of cumulative exposure of the kidney to lead or a measure of the major endogenous source of blood lead that, in turn, may affect the kidney.
Given that an increase in bone resorption is a characteristic of aging in both men and women, aging-associated release of bone lead into the circulation is a potentially important source of soft-tissue lead exposure and toxicity. Another factor associated with aging that may increase the nephrotoxicity of lead is diabetes. The more prevalent form, type 2 diabetes, affects approximately 10% or more of the general population (with substantially higher rates at ≥ 55 years of age) (Ford 2001) and is well known as an independent predictor of accelerated decline in kidney function. A third factor associated with aging that may also increase the nephrotoxicity of lead is hypertension.
In the present study, we used data from a cohort of middle-age and elderly men who had no previous known heavy lead exposure to examine the effects of low-level bone and blood lead levels on renal function. We also examined the potential modifying effect of diabetes and hypertension on these relationships.
Materials and Methods
Study population.
Study participants were from the Normative Aging Study (NAS), a longitudinal study of aging established by the Veterans Administration in 1961 (Bell et al. 1972). The study cohort initially consisted of 2,280 men from the Greater Boston area who were 21–80 years of age on enrollment. All participants were free of known chronic medical conditions at enrollment; men with any history of cancer, asthma, sinusitis, bronchitis, diabetes, gout, or peptic ulcer were excluded, as were those with a systolic blood pressure of > 140 mmHg or a diastolic blood pressure of > 90 mmHg. Since their enrollment in 1961–1968, participants have been reevaluated at 3- to 5-year intervals by a detailed core examination including collection of medical history information, routine physical examinations, laboratory tests, and questionnaires. The mean of blood pressure measurements in the left and right arms was used as each participant’s systolic and diastolic blood pressure. For the present study, “hypertensive” was defined as systolic blood pressure ≥ 160, or diastolic blood pressure ≥ 95 mmHg, or a physician’s diagnosis of hypertension with use of antihypertensive medication. The diagnosis of diabetes was based on clinical data from the study core examination; specifically, participants were classified as diabetic if they a) used oral hypoglycemic drugs, b) used insulin, or c) reported a physician’s diagnosis of diabetes whether or not they used diabetic drugs for treatment.
A blood sample for lead analysis has been collected at each NAS visit since July 1988. Beginning in August 1991, NAS participants were recruited for a substudy of K X-ray fluorescence (KXRF) bone lead measurement. Subjects included in the present investigation were those who participated in the KXRF bone lead substudy with concurrent blood lead, SCr, body mass index (BMI), alcohol intake, and blood pressure data and a follow-up measurement of SCr at least 4 years later.
All research performed in the present study was approved by the Human Research Committees of Brigham and Women’s Hospital and the Department of Veterans Affairs Outpatient Clinic.
Measurements.
Bone lead was measured in each subject’s midtibia shaft and patella with a KXRF instrument (ABIOMED, Inc., Danvers, MA). The tibia and patella have been targeted for bone lead research because they consist mainly of cortical and trabecular bone, respectively. A technical description and the validity specifications of this instrument have been published elsewhere (Burger et al. 1990; Hu et al. 1990). The KXRF instrument provides an unbiased estimate of bone lead levels (normalized for bone mineral content as micrograms of lead per gram of bone mineral) and an estimate of the uncertainty associated with each measurement.
Whole-blood samples were obtained and analyzed for lead by graphite furnace atomic absorption with Zeeman background correction (ESA Laboratories, Chelmsford, MA). Values below the minimum detection limit of 1 μg/dL were coded as 0. The instrument was calibrated with National Institute of Standards and Technology Standard Reference Material (NIST SRM 955a, lead in blood) after every 20 samples. Ten percent of samples were run in duplicate; at least 10% of the samples were controls, and 10% were blanks. In tests on reference samples from the Centers for Disease Control and Prevention (Atlanta, GA), precision [coefficient of variation (CV)] ranged from 8% for concentrations 10–30 μg/dL to 1% for higher concentrations. Compared with an NIST target of 5.7 μg/dL, 24 measurements by this method gave a mean ± SD of 5.3 ± 1.23 μg/dL.
SCr concentration was determined by a computerized automatic analyzer [Technicon SAM models (Technicon Corp., Tarrytown, NY) from 1979 to 1993; Boehringer Mannheim/Hitachi 747 analyzer (Boehringer-Mannheim Corp, Indianapolis, IN) from 1993 and on] at each examination. The analyzer measures creatinine based on the Jaffe procedure (Jaffe 1886) and demonstrated excellent reproducibility. This method of analysis has intraassay CVs of 1.3% at 1.2 mg/dL and interassay CVs of 3.3% at 1.1 mg/dL.
Statistical methods.
We used chi-square analysis and Student’s t-test to compare participants included in the analysis with eligible nonparticipants. Because many of the variables had skewed distributions, we used the nonparametric Wilcoxon signed-rank test for continuous variables to compare their distributions between baseline and follow-up visit. The main outcome of interest, annual change in SCr (milligrams per deciliter per year) was defined as (follow-up SCr – baseline SCr)/years of follow-up.
We used multiple linear regression analyses to determine the associations between baseline lead biomarkers (blood lead, patella lead, and tibia lead) and annual change in SCr. Because lead levels in blood and bone were skewed toward the upper end, we used lead biomarkers in the natural log scale to improve stability over the whole range of lead levels. The following variables at baseline were considered for possible inclusion in the models: age, BMI, baseline SCr, diabetic status, hypertensive status, smoking history [smoking status (ever/never) and cumulative smoking in pack years], alcohol consumption, and use of analgesic medication and diuretic medication. Alcohol consumption was analyzed both as a continuous variable (grams per day) and as a categorical variable: nondrinkers, light to moderate drinkers (< 20 g/day), and heavy drinkers (≥ 20 g/day).
To examine the modifying effect of diabetes on the nephrotoxicity of lead, we constructed models of the hypothesized interaction of lead with diabetes as follows: Annual change in SCr = intercept + β1(ID1) + β2(ID0 × mean-centered lead) + β3 (ID1 × mean-centered lead) + (other covariates), where ID0 = 1 if nondiabetes (reference group), 0 otherwise; ID1 = 1 if diabetes, 0 otherwise. We used this model to get the slopes for the two groups and their statistical significance. We constructed a second multiple regression model containing all main effects and a two-way interaction between diabetic status and natural-log–transformed baseline lead bio-markers. The model is expressed as annual change in SCr = intercept + β1(ID1) + β2(mean-centered lead) + β3 (ID1 × mean-centered lead) + (other covariates), where ID1 = 1 if diabetes, 0 otherwise. The second model was to do the statistical test of the interaction. If β3 differs significantly from zero, then diabetes is a significant effect modifier. The inclusion of specific covariates in the final multiple linear regression models was based on statistical and biologic considerations. To minimize the possibility of reverse causation, we repeated the analyses of annual change in SCr after excluding subjects with a high SCr at baseline, as defined by a value > 1.5 mg/dL. In addition, we examined the cross-sectional associations of baseline lead biomarkers with SCr measured at baseline and follow-up visits. The same set of confounders was considered for possible inclusion in the cross-sectional analyses of SCr.
We used the same approach to examine the modifying effect of hypertension on the nephrotoxicity of lead. Analyses were conducted using the Statistical Analysis System (Unix SAS version 8.2; SAS Institute, Cary, NC).
Results
An initial group of 707 NAS subjects who participated in the KXRF substudy between 1991 and 1995 and who had complete data on lead biomarkers, SCr, BMI, alcohol intake, medication use history, and diagnoses and blood pressure measurements were identified as eligible study subjects at baseline. Among them, 448 subjects had a follow-up measurement of SCr at, on average, 6 years later (range, 4–8). Selected characteristics of the 448 subjects at baseline and at follow-up are shown in Table 1. No significant differences were found with respect to the distributions of age, BMI, alcohol consumption, smoking status, diabetic status, hypertensive status, baseline SCr, and blood and bone lead levels among eligible nonparticipants and participants at baseline. At baseline, 8% were current smokers, 6% were classified as diabetic, 26% were classified as hypertensive, 7% reported using diuretic medication, and 78% reported using analgesic medication. Only 5% of the study subjects had reduced renal function (SCr > 1.5 mg/dL) at baseline. Subjects with diabetes at baseline had slightly higher bone lead levels and lower blood lead levels than those who were free of diabetes. Furthermore, subjects with diabetes had greater increase in SCr over time compared with those free of diabetes at baseline (p = 0.03 from Wilcoxon rank-sum test).
The nonparametric Wilcoxon signed-rank test showed that the mean follow-up SCr (1.06 mg/dL) was significantly lower than the mean baseline SCr (1.25 mg/dL), and blood lead levels decreased significantly over time in this population (p < 0.05).
Associations of baseline and follow-up SCr with blood and bone lead levels.
Both bone lead measures, but not blood lead, were consistently and positively associated with baseline SCr in cross-sectional analyses, but these associations were not statistically significant (Table 2). However, a significant interaction between diabetes and baseline tibia lead level regressed on baseline SCr was observed after adjusting for potential confounders (Table 3). Specifically, the positive cross-sectional association of tibia lead level with SCr was substantially stronger and statistically significant among diabetics compared with nondiabetics. Similar effect modification by diabetes was found with respect to the association of baseline patella lead with baseline SCr, but the interaction was not significant (Table 3). Exclusion of diuretic medication users or participants with SCr > 1.5 mg/dL did not materially change the observed associations between lead levels and baseline SCr. No significant interaction between hypertensives and baseline lead levels regressed on baseline SCr was observed (Table 3).
Similarly, both baseline tibia lead measurement and follow-up blood lead levels were consistently and positively associated with follow-up SCr, but only the association of follow-up blood lead with follow-up SCr was statistically significant (Table 2). In analogy to the cross-sectional analysis, a significant interaction between diabetes and tibia lead on follow-up SCr was observed (Table 3). Results remained unchanged after diuretic medication users at follow-up were excluded. Exclusion of participants with SCr > 1.5 mg/dL did not materially change the observed associations between baseline bone lead levels and follow-up SCr. However, the association of follow-up blood lead with follow-up SCr and the interaction of blood lead with diabetes in determining follow-up SCr became nonsignificant after we excluded participants with baseline SCr > 1.5 mg/dL. A significant interaction between hypertensive status and follow-up blood lead level regressed on follow-up SCr was observed (Table 3). Specifically, the positive cross-sectional association of blood lead level with SCr was substantially stronger and statistically significant among hypertensives compared with normotensives.
Association of annual change in SCr with blood and bone lead levels.
All three lead measures were positively associated with longitudinal increases in SCr, but none of these associations was statistically significant (Table 2). However, we observed significant interactions of both blood and tibia lead with diabetes in predicting annual change in SCr after adjusting for baseline covariates (Table 3). For example, increasing the tibia lead level from the midpoints of the lowest to the highest quartiles (9–34 μg/g) was associated with an increase in the rate of rise of SCr that was 17.6-fold greater in diabetics than nondiabetics (1.084 mg/dL over 10 years vs. 0.062 mg/dL over 10 years). Similarly, increasing baseline blood lead levels from the midpoints of the lowest to the highest quartiles (3–11.25 μg/dL) was associated with an increase in the rate of rise of SCr that was 12.8-fold greater in diabetics than nondiabetics (1.01 μg /dL over 10 years vs. 0.08 μg /dL over 10 years) (Figure 1). Exclusion of participants with baseline SCr > 1.5 mg/dL did not materially change the observed longitudinal associations between lead levels and change in SCr. The direction of the observed longitudinal associations between lead levels and SCr remained the same after we excluded diuretic medication users at baseline, but the interaction between blood lead and tibia lead and diabetes became nonsignificant. Similar findings were observed after exclusion of hypertensive subjects at baseline.
We also observed significant interactions of tibia lead with hypertension in predicting annual change in SCr after adjusting for baseline covariates (Table 3). Increasing the tibia lead level from the midpoints of the lowest to the highest quartiles (9–34 μg/g) was associated with an increase in the rate of rise of SCr that was > 50-fold greater in hypertensives than in normotensives (0.31 mg/dL over 10 years vs. 0.005 mg/dL over 10 years) (Figure 1).
There was no interaction of alcohol consumption or smoking with lead biomarkers in determining annual change in SCr. Assessment for a potential interaction between race and lead exposure in determining annual change in SCr was limited by small numbers (n = 12, 2.7% black participants). Excluding this group from the analysis did not change the observed associations
Discussion
In this study, significant associations of bone lead (particularly tibia bone) with prospective follow-up measures and annual change in SCr were observed among subjects with diabetes. We also observed significant positive associations of blood lead with prospective annual change in SCr among diabetics and cross-sectional increases in SCr (at the follow-up exam) among nondiabetics. Associations of higher blood lead with poorer renal function have been described elsewhere among non-occupationally exposed populations. A positive correlation between SCr concentration and blood lead levels was found in a survey of men in the British civil service (Staessen et al. 1990). In general population studies in Belgium and in the United States (the NAS cohort in Boston), creatinine clearance was inversely associated with blood lead levels (Payton et al. 1994; Staessen et al. 1992). Furthermore, in a recent longitudinal analysis of the NAS cohort, there was a positive association between low lead levels and SCr (Kim et al. 1996).
The analysis of lead, hypertension, and SCr indicates that both the association between follow-up blood lead with follow-up measures of SCr and the association between tibia lead and prospective annual change in SCr were significantly modified by hypertensive status, with hypertensive subjects having stronger and more significant associations. A recent analysis from the Third National Health and Nutrition Examination Survey (NHANES III) also showed a significant association of higher blood lead levels with chronic kidney disease and elevated SCr among hypertensives. Relationships among lead exposure, impaired renal function, and hypertension are complex: Lead exposure has been associated with an increased risk of hypertension, and essential hypertension, in turn, is a well-established risk factor for kidney disease. Whether lead affects blood pressure indirectly through alterations in kidney function or via more direct effects on the vasculature or neurologic blood pressure control is unknown. The interaction of hypertension, lead, and kidney function merits further investigation in a prospective cohort.
Studies of lead body burden estimated by EDTA mobilization tests have revealed a correlation of high body lead burden with declines in renal function (Batuman et al. 1983; Lin and Huang 1994; Lin et al. 2001). There have been a few studies of the association between body burden in the form of bone lead and renal function, but the results have been inconclusive. Furthermore, most prior studies have assessed occupationally exposed populations. For example, no adverse effects of bone lead on renal function were found in Swedish smelter works (Gerhardsson et al. 1992), whereas a positive association of tibia lead with glomerular hyperfiltration was reported in Belgian lead workers, suggesting the potential for a paradoxical protective effect of bone lead on renal function (Roels et al. 1994). The present study is among the first to assess the relation of bone lead levels from a general population sample with measures of renal function. Our findings support the hypothesis that long-term low-level lead accumulation (estimated by tibia bone lead) is associated with an increased risk of declining renal function particularly among diabetics or hypertensives, populations already at risk for impaired renal function.
However, there are several limitations to our findings. In the absence of diabetes or hypertension, we did not see statistically significant associations of bone lead levels with either cross-sectional or longitudinal measures of renal function. Our study population was not occupationally exposed and therefore had relatively low lead levels, whereas the clearest associations of lead with decrements in renal function have been demonstrated among heavily exposed populations. Although SCr is a widely used measure of renal function in clinical medicine, it provides only a rough estimate of glomerular function. Increases in SCr are relatively insensitive to declining glomerular filtration and are evident (i.e., > 1.5 mg/dL) only when kidney function has been reduced by about 50%. Therefore, low exposures and the relative insensitivity of our outcome measure may have limited our ability to detect more modest lead effects. Furthermore, we observed an unexpected overall decline in SCr over time in this population. SCr is a function of muscle mass and diet, as well as the glomerular filtration rate. A possible explanation for the lower follow-up SCr we observed includes decreased creatinine generation attributable to reduced muscle mass as a result of aging or reduced meat intake. However, SCr level was not associated with total energy-adjusted protein intake either at baseline or at the follow-up in the present study. Therefore, protein intake did not appear to confound the relation of SCr with lead exposure. Baseline and follow-up SCr were measured using the same technique and established standards and calibration methods, making measurement drift an unlikely explanation for lower follow-up values.
In addition, our diagnostic criteria for diabetes may misclassify individuals. However, this type of misclassification is likely to be non-differential with respect to the null hypothesis of no association, because nondiabetic individuals who had high or low lead exposure (and high or low SCr) would be equally likely to be misclassified as diabetic. The same is true regarding diabetic individuals being misclassified as nondiabetics. Such a nondifferential misclassification will tend to drive the overall effect toward a null finding (attenuated parameter estimates) but will not drive a true null finding toward an effect.
Our findings do not necessarily exclude the alternative hypothesis that elevated bone (or blood) lead levels were a result of impaired renal function. However, studies have shown that body lead burden was not elevated among patients with renal insufficiency or chronic renal failure if they did not have a history of childhood plumbism or high lead exposure (Batuman et al. 1983; Lin and Huang 1994; Sanchez-Fructuoso et al. 1996). For the most part, participants’ SCr levels were well within the normal range throughout the follow-up period of the study, and excluding individuals with elevated SCr at baseline did not materially alter our findings. These observations in combination with the prospective study design support the conclusion that the direction of the association is lead dose resulting in renal dysfunction. Hypertensive status has been shown to be associated with increases in SCr (Staessen et al. 1990, 1992) and with bone lead levels in the NAS (Hu et al. 1996). However, inclusion or exclusion of hypertension in the models did not make substantial differences in the observed associations except as noted in the interaction analyses.
Although tibia lead was clearly associated with longitudinal decrements in renal function among the study’s diabetics, patella lead was not. Differential sensitivity of tibia versus patella lead in predicting health outcomes has been observed previously (Payton et al. 1998) and may be a consequence of presumed different lead toxicokinetics in cortical (tibia) and trabecular (patella) bone. Previous research demonstrated overall declines in patella lead but stable tibia lead levels in this population (Kim et al. 1997) during 3 years of follow-up, which implies that patella lead may not reflect past cumulative exposures as accurately as tibia lead levels. In addition, the null finding for patella lead may be due to higher uncertainties, that is, greater measurement error, in patella lead measurements than in tibia lead measurements (Hu et al. 1991).
Several factors related to blood and bone lead levels, including age, cigarette smoking, and alcohol consumption are potential confounders of the lead–SCr relationship. However, SCr level was not associated with age, cigarette smoking, or alcohol use in the present study. Therefore, these factors did not appear to confound the relation of SCr with lead exposure.
In summary, our findings suggest that both blood lead and cumulative lead burden, reflected by tibia (cortical) bone lead levels, are predictors of prospective increases in SCr among middle-age and elderly men with diabetes or hypertension. To our knowledge, no previous studies have reported an analysis of the potential for diabetes to modify the relationship between lead exposure and renal function. Such an interaction may be related to the joint effect of the glomerular pathology associated with diabetes and the tubular atrophy and interstitial nephritis/fibrosis associated with lead. Given how common a history of environmental or occupational lead exposure is among adults and the high prevalence (and growing incidence) of type 2 diabetes in the general population, an interaction as suggested in this study would be of significant public health importance if confirmed. Additional research in this area—both epidemiologic and experimental involving, for example, the diabetic rat—would be helpful.
Figure 1 The modifying effect of diabetes and hypertension on the 10-year change in SCr associated with increasing tibia and blood lead levels from the midpoints of their lowest to their highest quartiles (25 μg/g and 8 μg/dL increases, respectively). Error bars indicate 95% confidence intervals.
Table 1 Characteristics of the 707 eligible subjects and the 448 NAS subjects at baseline (1991–1995) and at follow-up visit [mean ± SD or no. (%)].
Participants in this study
Follow-up
Characteristic Baseline eligible subjects Mean ± SDa Baseline Mean ± SDa No. Mean ± SDa
Tibia lead (μg/g) 21.9 ± 13.3 21.5 ± 13.5 247 23.8 ± 16.8*
Patella lead (μg/g) 32.0 ± 19.6 32.4 ± 20.5 258 31.1 ± 23.5*
Blood lead (μg/dL) 6.2 ± 4.1 6.5 ± 4.2 427 4.5 ± 2.5*
Age (years) 66.9 ± 7.2 66.0 ± 6.6 448 72.0 ± 6.5*
SCr (mg/dL) 1.2 ± 0.2 1.25 ± 0.2 448 1.1 ± 0.4*
Body mass index (kg/m2) 27.8 ± 3.8 27.8 ± 3.7 399 28.2 ± 3.9*
Alcohol consumption (g/day) 13.1 ± 17.9 13.4 ± 17.9 386 13.5 ± 20.2
Serum albumin (g/dL) 4.7 ± 0.3 4.7 ± 0.3 448 4.4 ± 0.3*
Pack-years of smokingb 22.3 ± 25.6 19.5 ± 23.6 437 19.7 ± 24.2*
Energy-adjusted protein intake (g/day) 82.2 ± 15.7 82.4 ± 16.4 386 80.5 ± 15.4*
Follow-up time (year) ND ND 448 6.0 ± 0.5
Changes in SCr (mg/dL-year) ND ND 448 −0.03 ± 0.1
Smoking status 443
Never 210 (29.7)c 145 (32.4)c 145 (32.7)c
Current 61 (8.6) 36 (8.0) 26 (5.9)
Former 436 (61.7) 267 (59.6) 272 (61.4)
Hypertensivesd 198 (28.0) 115 (25.7) 448 126 (28.1)
Clinical diagnosed diabetes mellitus 54 (7.6) 26 (5.8) 448 52 (11.6)
Use of diuretics (yes) 62 (8.8) 33 (7.4) 448 63 (14.1)
Use of aspirin or pain medication (yes) 540 (76.4) 349 (77.9) 448 355 (79.2)
SCr > 1.5 mg/dL (yes) 41 (5.8) 24 (5.4) 448 23 (5.1)
Alcohol consumption 386
None 184 (26.1) 108 (24.1) 94 (24.4)
0–20 (g/day) 371 (52.5) 243 (54.2) 215 (55.7)
≥ 20 (g/day) 152 (21.5) 97 (21.7) 77 (20.0)
ND, no data.
a Values are mean ± SD except where indicated.
b Nine eligible subjects and six baseline subjects were missing pack-year smoking data.
c No. (%).
d Hypertensive was defined as systolic blood pressure ≥ 160, or diastolic blood pressure ≥ 95 mmHg, or a physician’s diagnosis of hypertension with use of antihypertensive medication.
* Values at baseline and follow-up were significantly different (p < 0.05 by Wilcoxon signed-rank test).
Table 2 Multiple regression analysis of SCr on blood or bone lead in the NAS [β (SE)].
Variable Model of baseline SCra Model of follow-up SCrb Model of changes in SCrc
Loge(baseline blood lead) −0.023 (0.019) 0.009 (0.005)
Loge(follow-up blood lead) 0.149 (0.055)*
Loge(baseline patella lead) 0.011 (0.017) −0.006 (0.043) 0.001 (0.004)
Loge(baseline tibia lead) 0.017 (0.020) 0.065 (0.049) 0.007 (0.005)
a Adjusted for age, age squared, BMI, alcohol intake (< 20, ≥ 20 g/day vs. nondrinkers), ever smoking, pain medication, hypertension, and diabetes.
b Adjusted for follow-up variables of age, BMI, alcohol intake (< 20, ≥ 20 g/day vs. non-drinkers), ever smoking, pain medication, hypertension, and diabetes.
c Adjusted for baseline variables of SCr, SCr squared, age, BMI, alcohol intake (< 20, ≥ 20 g/day vs. nondrinkers), ever smoking, pain medication, hypertension, and diabetes.
* p < 0.05 for the -coefficient.
Table 3 Multiple regression analysis of SCr on blood or bone lead in the NAS stratified by baseline diabetic status [β (SE)].
Variable, diabetic or hypertensive status Model of baseline SCra Model of follow-up SCrb Model of changes in SCrc
Loge (baseline blood lead)
Diabetic (n = 26) −0.054 (0.089) 0.076 (0.023)*,**
Nondiabetic (n = 422) −0.022 (0.019) 0.006 (0.005)
Hypertensive (n = 115) −0.009 (0.039) 0.008 (0.010)
Normotensive (n = 333) −0.027 (0.021) 0.009 (0.006)
Loge (follow-up blood lead)
Diabetic (n = 24) 0.223 (0.183)
Nondiabetic (n = 403) 0.142 (0.058)*
Hypertensive (n = 108) 0.352 (0.097)*,**
Normotensive (n = 319) 0.058 (0.065)
Loge (baseline patella lead)
Diabetic (n = 26) 0.056 (0.065) 0.007 (0.107) 0.004 (0.017)
Nondiabetic (n = 422) 0.008 (0.017) −0.008 (0.047) 0.0004 (0.005)
Hypertensive (n = 115) 0.052 (0.034) −0.019 (0.075) 0.009 (0.009)
Normotensive (n = 333) −0.0003 (0.019) −0.0005 (0.051) −0.002 (0.005)
Loge (baseline tibia lead)
Diabetic (n = 26) 0.229 (0.102)*,** 0.699 (0.192)*,** 0.082 (0.027)*,**
Nondiabetic (n = 422) 0.011 (0.020) 0.029 (0.049) 0.005 (0.005)
Hypertensive (n = 115) 0.027 (0.037) 0.180 (0.097) 0.023 (0.010)*,**
Normotensive (n = 333) 0.013 (0.024) 0.030 (0.055) 0.0004 (0.006)
a Adjusted for age, age squared, BMI, alcohol intake (< 20, ≥ 20 g/day vs. nondrinkers), ever smoking, pain medication, hypertension, and diabetes.
b Adjusted for follow-up variables of age, BMI, alcohol intake (< 20, ≥ 20 g/day vs.nondrinkers), ever smoking, pain medication, hypertension, and diabetes.
c Adjusted for baseline variables of SCr, SCr squared, age, BMI, alcohol intake (< 20, ≥ 20 g/day vs. nondrinkers), ever smoking, pain medication, hypertension, and diabetes.
* p < 0.05 for the β-coefficient.
** p < 0.05 for the interaction between lead variable and diabetic or hypertensive status.
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Payton M Riggs KM Spiro AI Weiss ST Hu H 1998 Relations of bone and blood lead to cognitive function: the VA Normative Aging Study Neurotoxicol Teratol 20 1 19 27 9511166
Roels H Lauwerys R Konings J Buchet JP Bernard A Green S 1994 Renal function and hyperfiltration capacity in lead smelter workers with high bone lead Occup Environ Med 51 8 505 512 7951773
Sanchez-Fructuoso AI Torralbo A Arroyo M Luque M Ruilope LM Santos JL 1996 Occult lead intoxication as a cause of hypertension and renal failure Nephrol Dial Transplant 11 9 1775 1780 8918621
Silbergeld EK 1991 Lead in bone: implications for toxicology during pregnancy and lactation Environ Health Perspect 91 63 70 2040252
Silbergeld EK Schwartz J Mahaffey K 1988 Lead and osteoporosis: mobilization of lead from bone in postmenopausal women Environ Res 47 1 79 94 3168967
Staessen JA Yeoman WB Fletcher AE Markowe HL Marmot MG Rose G 1990 Blood lead concentration, renal function, and blood pressure in London civil servants Br J Ind Med 47 7 442 447 1974456
Staessen JA Lauwerys RR Buchet JP Bulpitt CJ Rondia D Vanrenterghem Y 1992 Impairment of renal function with increasing blood lead concentrations in the general population. The Cadmibel Study Group N Engl J Med 327 3 151 156 1608406
Wedeen RP Maesaka JK Weiner B Lipat GA Lyons MM Vitale LF 1975 Occupational lead nephropathy Am J Med 59 5 630 641 1200035
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Environ Health Perspect. 2004 Aug 3; 112(11):1178-1182
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.6946ehp0112-00118315289164ResearchArticlesPesticide Spraying for West Nile Virus Control and Emergency Department Asthma Visits in New York City, 2000 Karpati Adam M. 12Perrin Mary C. 34Matte Tom 5Leighton Jessica 3Schwartz Joel 67Barr R. Graham 6891Division of Disease Control, New York City Department of Health and Mental Hygiene, New York, New York, USA2Epidemiology Program Office, Centers for Disease Control and Prevention, Atlanta, Georgia, USA3Division of Environmental Health, New York City Department of Health and Mental Hygiene, New York, New York, USA4Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, USA5National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, Georgia, USA6Channing Laboratory, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA7Division of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA8Division of General Medicine, Department of Medicine, College of Physicians and Surgeons, and9Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, USAAddress correspondence to A.M. Karpati, Division of Epidemiology, New York City Department of Health and Mental Hygiene, 125 Worth St., Room 315, CN-06, New York, NY 10013 USA. Telephone: (646) 253-5700. Fax: (212) 788-4473. E-mail:
[email protected] authors declare they have no competing financial interests.
8 2004 8 7 2004 112 11 1183 1187 26 12 2003 6 7 2004 Publication 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. Pyrethroid pesticides were applied via ground spraying to residential neighborhoods in New York City during July–September 2000 to control mosquito vectors of West Nile virus (WNV). Case reports link pyrethroid exposure to asthma exacerbations, but population-level effects on asthma from large-scale mosquito control programs have not been assessed. We conducted this analysis to determine whether widespread urban pyrethroid pesticide use was associated with increased rates of emergency department (ED) visits for asthma. We recorded the dates and locations of pyrethroid spraying during the 2000 WNV season in New York City and tabulated all ED visits for asthma to public hospitals from October 1999 through November 2000 by date and ZIP code of patients’ residences. The association between pesticide application and asthma-related emergency visits was evaluated across date and ZIP code, adjusting for season, day of week, and daily temperature, precipitation, particulate, and ozone levels. There were 62,827 ED visits for asthma during the 14-month study period, across 162 ZIP codes. The number of asthma visits was similar in the 3-day periods before and after spraying (510 vs. 501, p = 0.78). In multivariate analyses, daily rates of asthma visits were not associated with pesticide spraying (rate ratio = 0.92; 95% confidence interval, 0.80–1.07). Secondary analyses among children and for chronic obstructive pulmonary disease yielded similar null results. This analysis shows that spraying pyrethroids for WNV control in New York City was not followed by population-level increases in public hospital ED visit rates for asthma.
asthmaobstructive airway diseaseozoneparticulatespesticidespollutantspyrethroidsWest Nile virus
==== Body
Outbreaks of encephalitis caused by West Nile virus (WNV) have occurred in the late summer and early autumn months yearly in New York City since 1999. Birds are the reservoirs for WNV, and transmission to humans occurs via mosquito vectors (Roehrig et al. 2002). One component of the New York City Department of Health and Mental Hygiene’s (DOHMH) response to the emergence of WNV was to initiate a citywide adult mosquito control program, which included the application of aerosolized pesticides via truck spraying to residential and commercial neighborhoods and to other areas such as parks and cemeteries. Beginning in 2000, the dates and ZIP codes of pesticide spraying were guided by the results of WNV testing of trapped mosquitoes and dead birds and by surveillance for human cases of WNV infection.
The active ingredients in the brand of pesticide used in 2000 were sumithrin (10%), a pyrethroid, and piperonyl butoxide (10%), a benzodioxole, which acts as a microsomal enzyme inhibitor. Exposure to pyrethroid pesticides or their synergists can cause respiratory irritation, hypersensitivity pneumonitis, exacerbation of asthma, and death (Carlson and Villaveces 1977; He et al. 1988; Kolmodin-Hedman et al. 1982; Lessenger 1992; Moretto 1991; Newton and Breslin 1983; Wax and Hoffman 1994); however, we could find no data on population-level respiratory effects of large-scale mosquito control programs using pyrethroids. Exacerbations of existing respiratory illness such as asthma after pyrethroid pesticide spraying is a concern, particularly given the high rates of asthma in some New York City communities. Public concern for respiratory effects of pesticides applied for WNV control has been high (Gonzalez 2001; Zhao 2001).
In this analysis we focused on the 2000 WNV season, the first year in which the New York City mosquito control program exclusively used a pyrethroid pesticide. Pyrethroids continue to be the only adulticide (a pesticide effective in killing adult mosquitoes, as opposed to larvae) used by the DOHMH and are used extensively in other areas of the United States. We conducted a time-series analysis across ZIP codes in New York City to determine whether truck-based ground spraying of pyrethroid pesticides precipitated an increase in asthma exacerbations requiring emergency department (ED) treatment during the 2000 WNV season.
Materials and Methods
We analyzed the dates and locations of pyrethroid spraying and ED visits to public hospitals for asthma exacerbations for all residential ZIP codes in four of the five New York City boroughs for the 14-month study period from 1 October 1999 through 30 November 2000. Because the DOHMH organized and instituted the pesticide application by ZIP code, we compiled daily counts of ED visits for each ZIP code. We used a time-series approach at the ZIP-code level to avoid confounding by intrinsic differences between sprayed and nonsprayed ZIP codes (e.g., in underlying asthma rates or patterns of public hospital use) and to maximize sensitivity to temporal determinants of asthma-related visit rates. Because the analysis compares visit counts in each ZIP code on each day with counts on other days, each ZIP code’s population serves as its own control. The unit of analysis was therefore the ZIP-day. All ZIP codes in New York City were included except those in Staten Island, which lacks a public hospital. Pesticide application was performed between July and September 2000; however, 14 months of data were included in the analysis to increase the power of models to account for potential confounders.
Pesticide exposure assessment.
In 2000, the DOHMH applied pesticides to localized residential areas, defined by ZIP code, after surveillance revealed local evidence of actively circulating WNV (i.e., presence of WNV-positive mosquitoes or dead birds, or a human case of WNV infection). Consequently, different ZIP codes in the city were sprayed on different days throughout the season (late summer through early fall). Rarely was a given ZIP code sprayed on consecutive days. Through radio, television, and print media, the public was notified 48 hr in advance of possible pesticide use. Before any spray action, instructions were given to residents to remain indoors and close all windows during spray times. Pesticides were applied to residential areas at night from trucks that drove through the streets between approximately 2200 hr and 0500 hr. In most cases, all streets in the ZIP code were sprayed. In some instances, only some of the streets were sprayed, depending on the size of the area and its proximity to the surveillance event (e.g., the location of the dead bird) that prompted the spraying. Truck-based spraying from streets was the only method employed in the study area; no more direct application (e.g., to back yards) was performed. Locations and dates of pesticide application were compiled from records of the DOHMH.
We defined a ZIP code as exposed to spraying on the date on which spraying began, which was usually at approximately 2200 hr. The principal exposure measure was a dichotomous variable defining the date for a given ZIP code as “exposed” if any portion of the ZIP code was sprayed and “unexposed” only if none of the ZIP code was sprayed on that day. We also constructed two other exposure variables for sensitivity analyses: a dichotomous variable in which “exposure” was attributed only if ≥ 90% of the area of the ZIP code was sprayed, and a continuous variable defining exposure by the percentage of the area of the ZIP code that was sprayed.
Asthma exacerbations.
We were interested in all asthma exacerbations requiring ED treatment in New York City; however, for this analysis, data were available only for public hospitals. These data were obtained from the New York City Health and Hospitals Corporation (HHC). The study outcome was therefore asthma-related visits to the 11 New York City public hospital EDs (including urgent care clinics), which are located in four of the five boroughs (Manhattan, the Bronx, Brooklyn, and Queens). These 11 EDs accounted for approximately 28% of the citywide ED volume in 1998 (Greater New York Hospital Association 2000). ED visits for asthma were defined by International Classification of Diseases, 9th Revision (ICD-9; 1997) coded discharge diagnoses (ICD-9 codes 493.0–493.9). Cases were attributed to the date of visit and the ZIP code of residence. We used the guarantor’s ZIP code to define the residence of each patient.
Secondary analyses included one restricted to asthma visits among children < 15 years of age and an analysis expanding the outcome of interest to include visits for exacerbations of chronic obstructive lung disease (COPD) and acute and chronic bronchitis (ICD-9 codes 466, 490–492, 496).
Additional variables.
We obtained air-quality data for the 14-month study period from the New York State Department of Environmental Conservation, Bureau of Air Quality Surveillance (unpublished data). Meteorologic data were obtained from the National Weather Service database (National Weather Service 2003). Daily minimum, maximum, and mean levels were calculated from hourly data for particulates [< 10-μm in diameter (PM10)] and ozone; temperature was obtained as daily minimum, maximum, and mean, and precipitation as a 24-hr total, which we dichotomized into a binary variable (zero or trace vs. more than trace). PM10 data were obtained from two real-time monitoring stations (one in Manhattan, one in the Bronx), ozone from three stations (in Manhattan, the Bronx, and Queens), and meteorologic data from one station (in Queens). For the air-quality measures, ZIP codes were assigned values measured at the site closest to the center of the ZIP code. For particulates, 19 days of data were missing from one station and 55 days from the other; data were missing from both stations on 2 days. For ozone, one station had 1 day of missing data, another station had 5 days, and the third had 14 days; on one day two stations’ data were missing, and on no days were all three stations missing data. We imputed missing data for PM10 and ozone as follows: If data from one station were missing on a given day, the other station’s value or mean of the two other stations for that day was used. If data were missing from all stations on a given day, values were imputed by averaging the measurements taken 5 days before and after the missing day. Additional time-varying variables were day of the week, date (for seasonal trend), and whether the day was a holiday.
Although non-time-varying differences in rates across ZIP codes would not be confounders of this time-series analysis, we extracted ZIP-code-level measures from the 2000 U.S. Census (New York City Department of City Planning 2003) for descriptive purposes. These were population size, median household income, median age, and percentage of population reporting non-Hispanic white race/ethnicity. We also calculated the distance between the ZIP code center and the nearest public hospital.
Statistical analysis.
We first conducted a bivariate analysis in which we counted the number of ED visits for asthma across all sprayed ZIP codes in the 3 days before and after spraying. Days on which spraying had occurred within the prior 7 days were excluded (n = 30, 11% of spray events). We calculated the proportion of visits that occurred in the 3 days after spraying; under the null hypothesis of no association between spraying and visit rates, this proportion would be 0.5. This proportion was tested as a one-sample test using the normal-theory method. This analysis was also performed using 1- and 2-day time windows.
We then used a time-series analysis of the number of cases per day within individual ZIP codes to assess the temporal relationship of spraying and asthma. To model seasonal, day-to-day, climatic, and pollution trends, we used 14 months of data from all included ZIP codes, whether or not they were sprayed. We estimated the effect of pesticide spraying on the daily number of ED asthma visits in each ZIP code by fitting a generalized additive model with a Poisson distribution. The natural log of ZIP-code population size was included as a variable with coefficient of 1 (offset). To account for differences between ZIP codes in mean visit rates and in the proportion of asthma exacerbations that result in ED visits, we estimated random effects for ZIP codes and included non-time-varying ZIP-code characteristics as covariates. Variables were added to the model based on likelihood ratio testing, and the optimal form of the variables (linear vs. nonlinear effects, daily maximum, minimum, or mean) was chosen through minimization of the Bayesian Information Criteria (Schwartz 1978). Nonlinear relationships, such as seasonal trends in asthma visits and temperature, were modeled using natural splines. Day-of-the-week and holiday effects were estimated using indicator variables. Lagged effects of the main exposure variable were assessed for lags of 0–6 days (and of potential confounders, from 1 to 2 days). Statistical significance was defined as a two-tailed p-value < 0.05. Models were implemented using both SAS version 8.2 (SAS Institute, Cary, NC) and S-Plus 6 (Insightful Corporation, Seattle, WA).
Results
The analysis included 162 ZIP codes and 427 days between 1 October 1999 and 30 November 2000, yielding 69,174 ZIP-days.
Pesticide application.
The number of ZIP codes sprayed per day is shown in Figure 1. Partial or complete spraying occurred in 1–31 ZIP codes per day on 27 days between 24 July and 24 September 2000, for a total of 278 ZIP-days of exposure. In Figure 2, of the 162 ZIP codes shown on the map, 143 (88%) were sprayed at least once (median = 2 days; range, 1–5 days). Fifty-seven percent of spraying events covered the entire area of the ZIP code; 80% of spraying events covered > 50% of the ZIP code area.
ED visits for asthma.
The range of ED visits for asthma per ZIP-day for all ages was 0–20 (median = 0 visits) and for children < 15 years of age, 0–11 (median = 0 visits) (Table 1). Over the 14-month analysis period, the rate of asthma ED visits across all ages was 28 per 10,000 population (interquartile range = 9–85), and for children 0–14 years of age, 68 per 10,000 population (interquartile range = 18–185). Figure 3 shows the daily number of asthma visits in all study hospitals. The sawtooth pattern indicates day-of-the-week variability. Notable seasonal trends include a midwinter peak, summer trough, and late summer/early autumn rise. Similar patterns were evident for those who were < 15 years of age. The pesticide application schedule coincided with both the summer trough and the subsequent autumn increase in asthma visits.
Characteristics of covariates.
Table 1 also summarizes the time-varying covariates, which include air quality and weather factors. Although there was considerable day-to-day variability in air quality, daily values across monitoring sites were highly correlated across the city (mean Pearson’s correlation coefficient = 0.90; range, 0.83–0.94); data are shown for a single monitoring station. Highly elevated particulate and ozone levels were rare; compared with national standards, mean daily PM10 levels exceeded 50 μg/m3 on 13 days, and maximum hourly ozone levels, according to the U.S. EPA database (U.S. EPA 2003), exceeded 0.12 ppm on 1 day. Table 1 also lists non-time-varying characteristics of ZIP codes from the U.S. Census (New York City Department of City Planning 2003) and distance from ZIP-code center to the nearest public hospital.
Pesticide application and ED visits for asthma.
There were 1,011 ED visits for asthma during the 3-day period that preceded spraying and the 3-day period that followed spraying across all sprayed ZIP codes. Of these, 510 (50.4%) occurred in the period that preceded spraying and 501 (49.6%) occurred in the period that followed spraying (p = 0.78). Using 1- and 2-day windows, the proportions of all visits that followed spraying were 0.47 (p = 0.32) and 0.49 (p = 0.70), respectively.
In the multivariate analysis, exposure to pesticide spraying was not associated with elevated ED visits for asthma on the day after spraying (Table 2). The multivariate rate ratio (RR) for exposure to pesticide spraying, defined as any part of the ZIP code being sprayed, was 0.92 [95% confidence interval (CI), 0.80–1.07]. ED visits for asthma also did not increase in the days after spraying. Multivariate models that incorporated lags between spraying and ED visits for asthma of 2–6 days showed no increase in ED visits for asthma (e.g., multivariate RR for ED asthma visits lagged 5 days after exposure was 0.94; 95% CI, 0.82–1.08). ED visits for asthma were also not elevated after a second or more application of pesticide in the 85 ZIP codes that received repeated spraying (e.g., multivariate RR for 2 spray events vs. no events, 0.93; 95% CI, 0.73–1.19).
Separate analyses examining possible associations in vulnerable populations demonstrated no effect of spraying. The analysis restricted to children < 15 years of age showed a multivariate RR of 0.78 (95% CI, 0.61–1.01). The analysis that included ED visits for exacerbations of COPD similarly showed no association (RR = 0.91; 95% CI, 0.80–1.04). Findings were similar in sensitivity analyses using different definitions of exposure to spraying and various smoothing spans for the seasonal term. In no case did the spray variable reach statistical significance, nor was there a trend toward increasing asthma rates.
Particulate matter, other covariates, and asthma.
In contrast to findings for pesticide spraying, daily PM10 and ozone were significantly associated with daily ED visits for asthma (Table 2). Each increase in PM10 of 20 μg/m3 was associated with a 7% rise in ED visits for asthma, and each 0.02-ppm increase in ozone was associated with a 4% rise in ED visits for asthma. Minimum daily temperature, precipitation, and whether the day was a holiday were also associated with ED visits for asthma. The increase in the ED visit rate for asthma comparing days at 50°F versus 70°F daily minimum temperature was approximately 30%.
Non-time-varying characteristics of ZIP codes were included in models for descriptive purposes rather than for control of confounding between ZIP codes (because comparisons were made within, rather than between, ZIP codes). These ZIP code characteristics were significantly associated with rates of ED visits for asthma as described in Table 2.
Discussion
In this study we examined the question of whether ground-based application of pyrethroid pesticides to urban residential areas was associated with population-level increases in asthma exacerbations requiring emergency care. We found no significant association between neighborhood spraying and subsequent rates of ED visits for asthma. Similarly null findings were obtained for pediatric asthma and for COPD exacerbations and in various sensitivity analyses using different lag times and exposure definitions.
Many jurisdictions in the United States use aerosolized pyrethroid pesticides to control insect populations, for either nuisance reduction or prevention of insect-borne diseases (Crockett et al. 2002; Groves et al. 1997; Rose 2000). These compounds (as well as the solvents in which they are suspended) can potentially stimulate asthma through allergic or irritant pathways. Symptoms that have been described after short-term exposures to pyrethroid insecticides include stuffy, runny nose, sneezing, and scratchy throat, as well as wheezing, shortness of breath, and chest tightness. Evidence suggests that pyrethroid pesticides may aggravate preexisting respiratory conditions in certain individuals. For example, one study found that exposure to a pesticide containing pyrethroids and piperonyl butoxide (a synergist) produced bronchospasm in seven persons with asthma several minutes after exposure (Newton and Breslin 1983). Most reports of respiratory effects of pyrethroids, however, involve few exposed subjects who are exposed to relatively high doses of pesticide, often in occupational settings. This report is the first of which we are aware that addresses the question of whether similar effects would be observed at a population level, where individual exposure would potentially be widespread and include more sensitive individuals but levels of exposure would likely be low.
Because the spraying program was implemented at the ZIP code level and occurred only on specific days in each ZIP code, we examined daily, ZIP code–level measures of asthma. The results, therefore, apply to the population level; spraying may have triggered asthma exacerbations in certain particularly susceptible or heavily exposed individuals. Also, although we used a residence-based exposure definition, exposure to pesticides might have occurred in other settings (e.g., occupational) for certain individuals. Our results, however, suggest that the number of individuals whose asthma was affected severely enough that they required ED treatment was not large. Also, public announcements were made before spraying to alert local residents. It is possible that residents with asthma or other respiratory illnesses took particular precautions (staying indoors, taking medication prophylaxis) to avoid exposure to or potential effects of the sprayed pesticides. The null results of the analysis should therefore be viewed as referring to the pesticide application program as a whole, rather than specifically addressing causal relations between the agents and asthma exacerbations.
The count of ED visits for asthma before and after spraying suggested no increase in asthma rates. The additional multivariate-modeling technique was designed to address potential confounding of the relationship between pesticide exposure and visit rates by a number of time-varying factors, such as time of year, day of week, weather, and air quality. The models also incorporated determinants of baseline heterogeneity in asthma visit rates between neighborhoods, such as socio-demographic characteristics. The results confirmed previously identified effects of air quality (e.g., particulate levels and ozone) and weather (e.g., temperature) on day-to-day variability in asthma rates (Brunekreef et al. 1995; Schwartz et al. 1993) and revealed expected ecologic determinants of asthma visit rates to New York City public hospitals, such as neighborhood socioeconomic status, racial composition, and proximity to such facilities. These positive findings suggest that the null association of pesticide exposure with visit rates was not caused by model misspecification or insensitivity to population-wide effects.
Only data from public hospitals were available for this analysis, rather than data from all New York City EDs. However, the absence of complete population-level data should not have had a biasing effect on the temporal variability of asthma visit rates from particular ZIP codes because the propensity of residents of a particular ZIP code to visit certain hospitals would likely not have changed significantly over the study period. Public hospital ED users might, in fact, be a more sensitive population for detecting triggers of asthma in the population, because they might preferentially use ED services rather than physicians’ clinics and tend to have less well-controlled asthma (Ortega et al. 2001). Another potential limitation is that ZIP code of residence was defined as that of the guarantor of the patient, rather than explicitly as the patient’s home address. Although it is possible that these addresses may differ for some patients, it is unlikely that this phenomenon occurred with sufficient frequency to substantially bias the results. Also, although Staten Island was the most heavily sprayed borough in 2000 and this analysis does not include data from that area, the null results found even in multiply sprayed ZIP codes suggest that the population-level experience might have been similar there.
As the circulation of WNV and the emergence of WNV-associated illness increases in the United States, public health agencies are increasingly called on to make risk–benefit calculations regarding vector control programs. Our results suggest that modest to large increases in ED visits for asthma did not occur in New York City during and after pyrethroid spraying for WNV control in 2000.
Figure 1 Pesticide application schedule, New York City, 24 July through 24 September 2000.
Figure 2 ZIP codes sprayed for WNV control in New York City, 24 July 2000 through 24 September 2000.
Figure 3 Daily number of asthma-related ED visits to public hospitals in New York City, October 1999 through November 2000.
Table 1 ED asthma visit rates and meteorologic and air quality measures, October 1999 through November 2000, and ZIP code characteristics, 2000, New York City.a
Characteristic Median Interquartile range
Daily ED visits for asthma (within ZIP codes)
All ages 0 0–1 (range 0–20)
Children < 15 years of age 0 0–0 (range 0–11)
Time-varying measures
Ozone (daily maximum, ppm) 0.02 0.01–0.03
PM10 (2-day mean, μg/m3) 19.3 14.6–27.2
Temperature (daily minimum, °F) 49 38–60
Precipitation (daily total, inches) 0 0–0.04
Non-time-varying measures (ZIP-code characteristics)
Population 42,309 26,000–65,576
Median household income (dollars) 31,800 21,900–40,800
Median age (years) 34 32–38
Percent non-Hispanic white 38 8–64
Distance to nearest public hospital (miles) 1.9 1.2–3.0
a Staten Island has no public hospitals; therefore, ZIP codes in that borough were excluded from the analysis.
Table 2 Adjusted RRs (95% CIs) for truck-based pyrethroid spraying for WNV and other time-varying predictors of asthma-related ED visits to public hospitals in New York City,a 1999–2000.
Characteristic RRb (95% CI)
Time-varying covariates
Truck-based pyrethroid sprayingc 0.92 (0.80–1.07)
PM10 (per 20-μg/m3 increase in 2-day mean) 1.07 (1.05–1.09)
Ozone (per 0.02-ppm increase in daily maximum, 2-day lag) 1.04 (1.02–1.05)
Holiday 0.93 (0.88–0.98)
Precipitation 0.97 (0.95–0.99)
Non-time-varying covariates
Median income (per $10,000 increase) 0.69 (0.68–0.70)
Median age [years (quantiles)]
24–31 Reference
31–33 0.74 (0.73–0.76)
33–35 0.71 (0.68–0.74)
35–38 0.88 (0.85–0.93)
> 38 0.94 (0.90–0.98)
Non-Hispanic white ethnicity [percent of population (quantiles)]
0–4 Reference
4–18 0.56 (0.55–0.57)
18–51 0.47 (0.46–0.48)
51–68 0.34 (0.33–0.36)
68–98 0.25 (0.23–0.26)
Distance to nearest public hospital [miles (quantiles)]
0.1–1.0 Reference
1.0–1.6 0.49 (0.48–0.50)
1.6–2.2 0.43 (0.42–0.44)
2.2–3.2 0.42 (0.41–0.43)
3.2–8.4 0.19 (0.18–0.20)
a Staten Island has no public hospitals; therefore, ZIP codes in that borough were excluded from the analysis.
b RRs were also adjusted for temperature, day of week, and a smoothed seasonal trend.
c Application of pesticide to any part of a given ZIP code, lagged by 1 day from the date on which application began.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.6972ehp0112-00118815289165Environmental MedicineArticlesIncreased Risk of Hepatocellular Carcinoma and Liver Cirrhosis in Vinyl Chloride Workers: Synergistic Effect of Occupational Exposure with Alcohol Intake Mastrangelo Giuseppe 1Fedeli Ugo 1Fadda Emanuela 1Valentini Flavio 2Agnesi Roberto 2Magarotto Giancarlo 3Marchì Teresio 3Buda Andrea 4Pinzani Massimo 5Martines Diego 41Department of Environmental Medicine and Public Health, University of Padua, Padova, Italy2Occupational Health Service, Local Health Unit 13, Dolo, Italy3Occupational Health Service, Local Health Unit 12, Venice, Italy4Department of Surgical and Gastroenterological Sciences, University of Padua, Italy5Department of Internal Medicine, University of Florence, ItalyAddress correspondence to G. Mastrangelo, Department of Environmental Medicine and Public Health, University of Padua, Via Giustiniani 2, 35128 Padova, Italy. Telephone: 0039-049-821-2543. Fax: 0039-049-821-2542. E-mail:
[email protected] thank M. Rugge for reviewing the histology of cases.
The study was supported in part by the Regione del Veneto, Italy, and the Italian Ministry of Health.
The authors declare they have no competing financial interests. Two of the authors (D. Martines and G. Mastrangelo) were the consultants of the Italian government and public prosecutors in a lawsuit opposing several hundred claimants (workers, local municipalities, and the Italian national government) against the management of an Italian plant producing vinyl chloride monomer and polyvinylchloride.
8 2004 27 5 2004 112 11 1188 1192 20 1 2004 27 5 2004 Publication 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. Hepatocellular carcinoma (HCC) and liver cirrhosis (LC) are not well-established vinyl chloride monomer (VCM)–induced diseases. Our aim was to appraise the role of VCM, alcohol intake, and viral hepatitis infection, and their interactions, in the etiology of HCC and LC. Thirteen cases of HCC and 40 cases of LC were separately compared with 139 referents without chronic liver diseases or cancer in a case–referent study nested in a cohort of 1,658 VCM workers. The odds ratios (ORs) and the 95% confidence intervals (CIs) were estimated by common methods and by fitting models of logistic regression. We used Rothman’s synergy index (S) to evaluate interactions. By holding the confounding factors constant at logistic regression analysis, each extra increase of 1,000 ppm × years of VCM cumulative exposure was found to increase the risk of HCC by 71% (OR = 1.71; 95% CI, 1.28–2.44) and the risk of LC by 37% (OR = 1.37; 95% CI, 1.13–1.69). The joint effect of VCM exposure above 2,500 ppm × years and alcohol intake above 60 g/day resulted in ORs of 409 (95% CI, 19.6–8,553) for HCC and 752 (95% CI, 55.3–10,248) for LC; both S indexes suggested a synergistic effect. The joint effect of VCM exposure above 2,500 ppm × years and viral hepatitis infection was 210 (95% CI, 7.13–6,203) for HCC and 80.5 (95% CI, 3.67–1,763) for LC; both S indexes suggested an additive effect. In conclusion, according to our findings, VCM exposure appears to be an independent risk factor for HCC and LC interacting synergistically with alcohol consumption and additively with viral hepatitis infection.
alcoholcase–referent studiescirrhosishepatocellular carcinomaoccupational diseasesvinyl chloride
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Although a large body of evidence from experimental and epidemiologic studies has demonstrated the relationship between exposure to vinyl chloride monomer (VCM) and angiosarcoma [International Agency for Research on Cancer (IARC) 1987; Lee et al. 1996], there is little evidence of a causal association between VCM and hepatocellular carcinoma (HCC) and liver cirrhosis (LC).
In their study on the U.S. cohort of VCM-exposed workers, Mundt et al. (2000) found an increased risk of liver cancer, mainly liver angiosarcomas. In the study, however, they distinguished HCC from angiosarcoma on the basis of information on the cause of death reported in death certificates. In the European cohort of VCM workers, Ward et al. (2001) searched for the best evidence of liver cancer by reviewing all available documentation and found a marked exposure–response relationship for all liver cancers (71 cases), angiosarcoma (37 cases), and HCC (10 cases). This evidence is also inconclusive because the number of HCC cases was small, there was a disproportionate excess of liver cancers with “other and unknown histology,” and the risk estimates were not adjusted for the influence of well-known risk factors for HCC: alcohol consumption and viral infection. Recently Wong et al. (2003) suggested an interaction between occupational VCM exposure and hepatitis B virus (HBV) infection in the development of liver cancer.
Data on an association between VCM and LC are even scarcer and are inconclusive. Du and Wang (1998) reported a significantly increased number of hospital admissions among Taiwanese VCM workers due to primary liver cancer and cirrhosis of the liver. In the European cohort of vinyl chloride workers, Ward et al. (2001) reported that overall mortality from cirrhosis was decreased, although there was a trend toward an increase in cirrhosis mortality proportionate to an increase in cumulative exposure. In this case, risk estimates were not adjusted for the confounding influence of alcohol consumption and HBV infection.
Pirastu et al. (2003) reported on a cohort of 1,658 workers employed in a VCM manufacturing plant, in which the standardized mortality ratio (SMR) for primary liver cancer of 2.78 was significantly increased. Because cohort studies are unavoidably affected by selection (healthy worker effect), information (misclassification of exposure and diagnosis of diseases based on death certificate), and confounding biases [alcohol intake, HBV/hepatitis C virus (HCV) carrier status], we carried out a case–referent study nested in the same cohort. In northeast Italy (Porto Marghera, Venice), where the plant is located, alcohol consumption is heavy and viral hepatitis common. These particular exposure conditions appeared suitable for the appraisal of the individual role of VCM exposure, alcohol intake, viral hepatitis infections, and their interactions in the etiology of HCC and LC.
Materials and Methods
The present case–referent study was carried out on the occasion of a lawsuit by hundreds of workers, local municipalities, and the Italian national government against the VCM plant management. At the beginning of the lawsuit, the company indemnified any health problem that claimant workers themselves attributed to their past exposure in the plant. Among the “claimants” were 13 cases of HCC [8 confirmed by histology and 5 based on the criteria recently issued by the Italian Association for the Study of the Liver and the British Society of Gastroenterology (Ryder 2003)—focal hepatic lesions at sonography and α1-fetoprotein > 400 μg/L (Ryder 2003)], and 40 cases of LC (24 with histologic confirmation and 16 with clinical evidence of portal hypertension, ascites, and/or esophageal varices). Out of the 13 HCC cases, 11 also had LC and are included in the series of LC cases.
We found information on diagnosis in hospital records, which we actively searched for deceased subjects (vital status and cause of death were ascertained for all the cohort members through 1999); incident cancer cases (ascertained through the regional cancer registry for all the cohort members from 1987 to 1999); and all other claimant workers.
Six hundred and forty-three former VCM workers belonging to the above cohort were examined from 1999 through 2002 by occupational physicians at the Occupational Health Services (OHS) of two local health authorities in the course of a medical surveillance program launched by the Regione Veneto and the Italian Ministry of Health. Among these subjects, we identified 139 subjects without clinical (including liver sonography) or biochemical (normal serum levels of aspartate aminotransferase, alanine aminotransferase, and γ-glutamyl transpeptidase) evidence of chronic liver disease or cancer in any site. HCC cases and LC cases were separately compared to the above 139 referents in the present cohort-based case–referent study.
For cases, information on the job performed and the corresponding entry/exit dates was obtained from company files; for referents, these data were obtained through the occupational history collected by OHS occupational physicians during the medical surveillance program. Using a job–exposure matrix developed by Pirastu et al. (1991), we estimated cumulative VCM exposure by summing across the calendar years of exposure the product of the average level of VCM exposure in a job (parts per million) and years worked in that job. The variable was split into four classes using the quartiles (160, 500, and 2,500 ppm × years); it was also dichotomized (cut point, 2,500 ppm × years) when examining interactions of VCM exposure with alcohol consumption or viral hepatitis infection.
We ascertained alcohol consumption in cases and referents through hospital clinical records and/or health surveillance records. The measure was computed in grams of ethanol per day. The variable was split into three classes using 30 and 60 g/day as cut points; it was also dichotomized [cut point, 60 g/day, a threshold considered necessary for alcohol-mediated injury (Donato et al. 2002)] in examining interactions between VCM exposure and alcohol consumption.
HBV and HCV chronic infection was determined in cases and in referents by serologic markers [HBV surface antigen (HbsAg) and anti-HCV antibodies]. At analysis, the variable was coded 1 in the presence of markers for HBV and/or HCV, and 0 otherwise.
We defined “age” as the age reached by each subject in 1999 (cases and referents) or at death (cases only).
Interval variables were analyzed using Student’s t-test and frequency variables analyzed using Fisher’s exact test. At univariate analysis, the odds ratio (OR) and the exact 95% confidence interval (CI) were estimated using the StatXact statistical package (Mehta and Patel 1999). When a variable was broken down into classes, the lowest class was the reference subgroup at a conventional risk of 1.0. We also calculated the chi-square test for linear trend across ordered categories as described by Breslow and Day (1980).
In order to evaluate interactions of VCM exposure with alcohol intake or HBV/HCV infections, we used the OR for joint exposure (ORAB), the OR for exposure to a single factor (ORA), and the OR for exposure to the other single factor (ORB) to calculate the S synergy index as S = (ORAB − 1) ÷ [(ORA + ORB) − 2] (Rothman 1986). The APAB proportion of disease attributable to the interaction was calculated as APAB = (S − 1) ÷ S. A multiple logistic model was used to evaluate departure from additivity, in which terms for confounding factors were also included (Rothman 1986).
Alcohol consumption (× 10), cumulative VCM exposure (× 1,000), and HBV/HCV carrier status were used as independent variables in two models of stratified logistic regression analysis (three strata of birth year), where the dependent variable was 1 for cases (either HCC or LC) and 0 for referents (always the same 139 referents). Conditional maximum likelihood estimate ORs with exact 95% CIs and two-tailed probability of error were obtained using the LogXact statistical package (Mehta and Patel 2002).
Results
Table 1 shows the general characteristics of 13 HCC cases, 40 LC cases, and 139 referents. With respect to referents, cases were born earlier (but they were younger because of early death), were more exposed, and drank more alcohol. The prevalence of drinkers was 92.3% (12 of 13), 97.5% (39 of 40), and 73.4% (102 of 139) in HCC, LC, and referents, respectively. The prevalence of HBV/HCV carriers was 23.1% (3 of 13), 17.5% (7 of 40), and 2.2% (3 of 139), in HCC, LC, and referents, respectively.
Table 2 shows the results at univariate analysis. The first two quartiles of cumulative exposure collapsed because of missing HCC cases. Increasing levels of cumulative VCM and alcohol consumption significantly increased the risks of HCC and LC. With VCM exposure, the trend was steeper for HCC than for LC, whereas the contrary occurred with alcohol consumption. The surprisingly high risk of HCC and LC in subjects consuming > 60 g/day of alcohol suggested an interaction with occupational exposure. Viral hepatitis infection significantly increased the risk of HCC and LC.
Table 3 shows a joint classification [by cumulative VCM exposure lower or higher than 2,500 ppm × years and a) alcohol consumption lower or higher than 60 g/day or b) viral hepatitis infection absent or present] of HCC cases and referents. The conventional risk of subjects unexposed to both of two risk factors (reference category) being 1.0, the OR estimating the effect of joint exposure to VCM and alcohol was one order of magnitude greater than the ORs estimating the effect of each factor in the absence of the other. Accordingly, the synergy index, which was close to 7, indicated a departure from an additive relation. The proportion of HCC attributable to the interaction of VCM exposure and alcohol consumption was as high as 85%. The joint effect from VCM exposure and viral hepatitis infection seemed less than multiplicative, and S indicated only a moderate departure from an additive relation; the interaction of two factors was responsible for 38% of the HCC cases.
Table 4 shows a joint classification [by cumulative VCM exposure lower or higher than 2,500 ppm × years and a) alcohol consumption lower or higher than 60 g/day or b) viral hepatitis infection absent or present] of LC cases and referents. The conventional risk of subjects unexposed to both risk factors being 1.0, the OR among subjects jointly exposed to VCM and alcohol was close to the product of ORs in those exposed to each factor in the absence of the other. The synergy index of 5 indicated a departure from an additive relation, and the proportion of disease among those with both exposures was 80%. The joint effect from VCM exposure and viral hepatitis infection (close to the sum of separate effects), and the S index (close to unity) indicated an additive relation.
Table 5 shows that by stratifying by tertiles of the birth year of the cases, holding constant the influence of HBV/HCV infection and alcohol intake, each extra increase of 1,000 ppm × years involved a 71% excess of HCC risk or a 37% excess of LC risk.
Discussion
At the beginning of the trial, the company granted compensation for any disease to all employees, without ascertaining its occupational origin. Detailed information on this was given by the labor union and the local media during the trial (Mastrangelo et al. 2003). From 1975 (when a cross-sectional study was carried out) onward (during their employment at the VCM plant), these workers underwent yearly medical surveillance, which included liver function tests. Using such records, all subjects with liver function alteration or liver disease were identified in the course of trial. Finally, mortality and incidence registers were scrutinized. It is therefore reasonable to assume that all cases of HCC and LC occurring in the cohort were collected.
If exposure in referents had been higher than, similar to, or lower than that in the whole cohort, the HCC/LC risk would have been underestimated, valid, or overestimated, respectively. It is therefore important to consider whether our method for selecting referents may have introduced a bias. The mean ± SD of cumulative VCM was 1367.5 ± 2209.1 ppm × years in our 139 referents and 1751.5 ± 2564.8 ppm × years in the remaining 504 VCM cohort workers in the medical surveillance program. This difference is not statistically significant (t = 1.61; p = 0.11). It is therefore reasonable to deduct that ours is a cohort-based case–referent study, in which the selection of referents did not lead to an underestimation or overestimation of the risk of HCC and LC. Nor was our study affected by selection (healthy worker effect), information (diagnosis of diseases based on death certificate), or confounding biases (alcohol intake, HBV/HCV carrier status).
The main finding of the present study is that VCM exposure is an independent risk factor for the development of HCC and LC. The association between VCM exposure and HCC was suggested in early studies showing the coexistence of nodules of angiosarcoma and hepatocarcinoma in histologic liver specimens. Jones and Smith (1982) found angiosarcoma, hepatocarcinoma, and hepatoadenoma nodules in a worker who had been exposed to high doses of VCM for several years. Furthermore, Evans et al. (1983) reported the association of cirrhosis, angiosarcoma, and hepatocarcinoma in a subject exposed to VCM. This finding was confirmed by experimental studies in rats, in which VCM exposure induced both angiosarcomas and hepatocarcinomas, the two tumor types found in the same animal (Froment et al. 1994). A recent study reported on 18 HCC patients with long-term exposure to VCM; all 18 patients lacked any further identifiable risk factors for developing HCC (Weihrauch et al. 2000). Confirmatory evidence has been reported in a recent meta-analysis combining the European and North American cohorts of VCM workers (Boffetta et al. 2003): liver cancers other than angiosarcoma resulted in a meta-SMR of 1.35 (95% CI, 1.04–1.77).
A multiplicative effect between VCM exposure and alcohol in hepatocarcinogenesis was found in an experimental study (Radike et al. 1981). However, the present study is, to our knowledge, the first to report a synergistic effect between VCM exposure in humans and alcohol consumption in the development of HCC and its associated preneoplastic condition, LC. An attributable proportion of nearly 80% indicates that VCM exposure and alcohol intake have little effect separately but, in association, produce most of the disease. This may explain why the relationship between HCC (or LC) and VCM has been overlooked in epidemiologic settings (where HCC cases would be in excess only if alcohol intake were high in VCM-exposed workers) and clinical settings (where nonoccupational causes of disease are often present).
The biologic interaction between VCM and alcohol during hepatocarcinogenesis may be due to several mechanisms. Alcohol is prevalently metabolized in the liver by the microsomal ethanol-oxidizing system (MEOS) and alcohol dehydrogenase, leading to the generation of acetaldehyde and reactive oxygen species (ROSs). Chronic alcohol consumption is associated with an increased activity of MEOS, which involves the specific P450 cytochrome CYP2E1 (Lieber and DeCarli 1970). An important feature of CYP2E1 is its capacity to convert different xenobiotics into highly toxic metabolites. VCM is primarily metabolized in the liver by CYP2E1 (Stickel et al. 2002) to chloroethylene oxide and chloracetaldehyde, metabolites that can react with DNA bases and promote mutations in bacterial and mammalian cells (Marion and Boivin-Angele 1999; Marion et al. 1996; Zhou et al. 2003). Thus, ethanol induction of CYP2E1 could contribute to hepatocarcinogenesis by enhancing the conversion of VCM into toxic intermediates. The induction of CYP2E1 is also responsible for an increased catabolism of retinoic acid (Leo and Lieber 1985). The reduction of the hepatic concentration of retinoids has been shown to be associated with an up-regulation of the AP-1 (c-jun and c-fos) transcriptional complex, leading to enhanced cellular proliferation (Wang et al. 1998). By sustaining parenchymal hyperproliferation, alcohol (or viral infection) may act as a promoter in VCM carcinogenesis. Acetaldehyde is highly toxic and mutagenic and evidence has accumulated that acetaldehyde is responsible for alcohol associated carcinogenesis (Stickel et al. 2002). ROSs promote lipid peroxidation and react with DNA, resulting in alterations of DNA structure. Besides these (carcinogenetic) effects, ethanol and acetaldehyde could also enhance VCM genotoxicity through the inhibition of DNA–adduct removal (Singletary et al. 2004).
In a cohort nested case–referent study, 18 cases of liver cancer and 68 referents matched for age and specific plant of employment were selected from among 3,293 workers from six polyvinyl chloride polymerization factories in Taiwan (Wong et al. 2003). Eighty-nine percent of cancer cases had a history of HBV infection, and none of the subjects was a habitual alcohol drinker. With respect to subjects unexposed to both risk factors, the OR was 396.0 (95% CI, 22.6 to infinity) among subjects jointly exposed (high VCM exposure and viral hepatitis infection). The latter OR was greater than the product of ORs in those exposed to each factor in the absence of the other, suggesting a synergistic effect. By contrast, we found only an additive effect of VCM cumulative exposure with viral hepatitis on the risk of HCC while controlling for alcohol consumption. In our cases the prevalence of drinkers was > 90%, and the prevalence of HBV/HCV carriers was about 20%; whether the conflicting results might be explained by the different distribution of risk factors in the two working populations is unclear.
Although the mechanism whereby VCM exposure and viral hepatitis infection act additively is unknown, one hypothesis is that both factors induce liver fibrosis and regeneration, which act as a tumor promoter in hepatocarcinogenesis (Blendis et al. 2000; Pinzani 1999).
It is widely accepted that exposure to increased concentrations of VCM causes liver fibrosis (Popper and Thomas 1975). Hepatic fibrogenesis, a dynamic tissue repair process, is characterized by the increased synthesis of extracellular matrix components and changes in the perisinusoidal space (Pinzani 1995). If the noxious agent persists, liver fibrosis progresses to cirrhosis. The rate of progression of fibrosis varies greatly from patient to patient, and epidemiologic studies have identified several cofactors related to the host. Alcohol consumption is an important factor, with a detrimental effect on liver fibrosis. The activation of hepatic stellate cells is the common pathway to liver fibrogenesis, and in vitro studies have shown that acetaldehyde, a highly reactive toxic product of alcohol metabolism, can directly induce collagen gene transcription and promote liver fibrosis, even in the absence of necro-inflammatory changes (Moshage et al. 1990). Likewise, because of its structural similarities, the VCM metabolite chloracetaldehyde could directly sustain the progression of liver fibrosis (Larson and Bull 1991), thus explaining our finding of an increased risk of LC after exposure to high doses of VCM only. As suggested by several studies performed in human hepatic stellate cells [reviewed by Parola and Robino (2001)], reactive aldehydes are able to directly induce pro-collagen type I and III gene and protein expression with a mechanism involving nuclear translocation and activation of c-Jun amino-terminal kinase (Parola et al. 1998).
Chronic alcohol consumption decreases glutathione levels (Shaw et al. 1983), a reductive tripeptide, which inactivates both the VCM hepatotoxic metabolites chloroethylene oxide and chloracetaldehyde. Thus, VCM exposure and alcohol intake may have a hyper-additive effect in the progression to LC either because of their intrinsic hepatotoxicity and profibrogenetic activity or because they compete and/or deplete the reductive detoxification system.
HBV and HCV cause chronic liver disease, which can progress into cirrhosis. However, not all hepatitis patients have this complication, and genetic factors, alcohol (Wiley et al. 1998), and obesity (Naveau et al. 1997) may play a role. VCM exposure could contribute to the development of LC by the same mechanisms described for the alcohol–hepatitis interaction.
In conclusion, according to our findings, VCM exposure appears to be an independent risk factor for HCC and LC interacting synergistically with alcohol consumption and additively with viral hepatitis infection. This could be relevant for new prevention strategies in high-risk individuals.
Table 1 VCM cumulative exposure, alcohol consumption, demographic variables, and prevalence of HBV/HCV infection in HCC cases, LC cases, and referents (Ref).
p-Valuea
HCC cases LC cases Ref HCC vs. Ref LC vs. Ref
No. of cases 13 40 139
VCM exposure (ppm × years) 4223.8 ± 2888.4 2845.3 ± 3041.7 1367.5 ± 2209.1 < 0.001 0.001
Alcohol (g/day) 90.8 ± 62.2 108.5 ± 53.2 29.1 ± 31.6 < 0.001 < 0.001
Year of hire 1960.5 ± 3.7 1961.8 ± 6.2 1964.9 ± 6.6 0.022 0.010
Year of birth 1933.2 ± 4.0 1930.9 ± 7.7 1935.5 ± 6.5 0.196 0.002
Age at death/end of follow-up 58.8 ± 4.5 59.6 ± 7.9 63.5 ± 6.5 0.013 0.010
HBsAg/HCV positive (%) 23.1 17.5 2.2 0.009 0.001
Values shown are mean ± SD except where indicated.
a p-Values for a two-tailed test (Student’s t-test for interval variables and Fisher’s exact test for frequency variable).
Table 2 HCC and LC risks in relation to cumulative VCM exposure, alcohol consumption, and viral hepatitis infection at univariate analysis.
Cases (n) Ref (n) OR 95% CI χ2 for trend
HCC
VCM cumulative exposure
< 500 ppm × years 1 78 Reference
500–2,500 ppm × years 3 37 6.32 0.48–336.0
> 2,500 ppm × years 9 24 29.3# 3.61–1,298 16.1#
Alcohol consumption
< 30 g/day 1 82 Reference
30–60 g/day 4 46 7.13 0.67–355.0
> 60 g/day 8 11 59.6# 6.51–2,676 24.3#
HBsAg/HCV
Negative 10 136 Reference
Positive 3 3 13.6* 1.55–111.0
LC
VCM cumulative exposure
< 160 ppm × years 7 38 Reference
160–500 ppm × years 7 40 0.95 0.26–3.51
500–2,500 ppm × years 9 37 1.36 0.47–3.72
> 2,500 ppm × years 17 24 3.95** 1.56–9.98 8.06**
Alcohol consumption
< 30 g/day 1 82 Reference
30–60 g/day 7 46 12.5* 1.50–569
> 60 g/day 32 11 238# 31.2–9,820 78.1#
HBsAg/HCV
Negative 33 136 Reference
Positive 7 3 9.62** 2.03–59.6
Ref, referents.
* p < 0.05,
** p < 0.01, and
# p < 0.001 by two-tailed t-test.
Table 3 Distribution of HCC cases and referents (Ref) according to a joint classification (VCM exposure and alcohol consumption or viral hepatitis infection).
VCM < 2,500 ppm × years VCM > 2,500 ppm × years
Alcohol < 60 g/day
Cases/Ref 1/105 4/23
ORa (95% CI) Reference 18.8* (1.62–218.0)
Alcohol > 60 g/day
Cases/Ref 3/10 5/1
ORa (95% CI) 42.9** (3.41–540.0) 409# (19.6–8553.0)
Alcohol summary S = 6.83; AP = 85%
HbsAg/HCV negative
Cases/Ref 3/113 7/23
ORb (95% CI) Reference 25.0** (2.77–226.0)
HbsAg/HCV positive
Cases/Ref 1/2 2/1
ORb (95% CI) 106.9** (4.43–2578.0) 210.3** (7.13–6203.0)
HbsAg/HCV summary S = 1.61; AP = 38%
Abbreviations: AP, proportion of disease attributable to interaction; S, Rothman’s synergy index for interaction.
a OR adjusted for age and viral hepatitis infection.
b OR adjusted for age and alcohol use.
* p < 0.05,
** p < 0.01, and
# p < 0.001 by two-tailed t-test.
Table 4 Distribution of LC cases and referents (Ref) according to a joint classification (VCM exposure and alcohol consumption or viral hepatitis infection).
VCM < 2,500 ppm × years VCM > 2,500 ppm × years
Alcohol < 60 g/day
Cases/Ref 3/105 5/23
ORa (95% CI) Reference 6.64* (1.03–42.8)
Alcohol > 60 g/day
Cases/Ref 20/10 12/1
ORa (95% CI) 144.1# (24.1–860.0) 752.7# (55.3–10248.0)
Alcohol summary S = 5.05; AP = 80%
HbsAg/HCV negative
Cases/Ref 20/113 13/23
ORb (95% CI) Reference 8.22* (1.57–43.0)
HbsAg/HCV positive
Cases/Ref 3/2 4/1
ORb (95% CI) 67.2** (5.14–877.0) 80.5** (3.67–1763.0)
HbsAg/HCV summary S = 1.08; AP = 7%
Abbreviations: AP, proportion of disease attributable to interaction; S, Rothman’s synergy index for interaction.
a OR adjusted for age and viral hepatitis infection.
b OR adjusted for age and alcohol use.
* p < 0.05,
** p < 0.01, and
# p < 0.001 by two-tailed t-test.
Table 5 HCC and LC risks in relation to cumulative VCM exposure, alcohol consumption, and viral hepatitis infection.
OR (95% CI) p-Value
HCC
VCM exposure (ppm × years × 1,000) 1.71 (1.29–2.44) 0.0008
Alcohol consumption (g/day × 10) 1.36 (1.18–1.62) < 0.0001
HBsAg/HCV positive 46.6 (1.79–4960.0) 0.0141
LC
VCM exposure (ppm × years × 1,000) 1.37 (1.13–1.69) 0.0009
Alcohol consumption (g/day × 10) 1.70 (1.44–2.01) < 0.0001
HBsAg/HCV positive 33.9 (3.66–410.0) 0.0007
Estimates were obtained by means of conditional regression analysis for stratified data (strata, tertiles of year of birth): OR, exact 95% CI, and exact error probability (p-value) for a two-tailed test.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.6916ehp0112-00119315289166Environmental MedicineArticlesThe Relationship between Levels of PCBs and Pesticides in Human Hair and Blood: Preliminary Results Altshul Larisa 1Covaci Adrian 2Hauser Russ 341Department of Environmental Health, Exposure, Epidemiology and Risk Program, Harvard School of Public Health, Boston, Massachusetts, USA2Toxicological Center, University of Antwerp, Wilrijk, Belgium3Department of Environmental Health, Occupational Health Program, Harvard School of Public Health, Boston, Massachusetts, USA4Vincent Memorial Obstetrics and Gynecology Service, Andrology Laboratory and In Vitro Fertilization Unit, Massachusetts General Hospital, Boston, Massachusetts, USAAddress correspondence to L. Altshul, Exposure, Epidemiology and Risk Program, Harvard School of Public Health, Building 1, Room B28, 665 Huntington Ave., Boston, MA 02115 USA. Telephone: (617) 432-0653. Fax: (617) 432-3349. E-mail:
[email protected] thank the staff of Organic Chemistry Laboratory at Harvard School of Public Health, S. Forsberg and R. Stolyar for chemical analysis, and the volunteers for participating in the study.
This work was supported by National Institute of Environmental Health Sciences grants ES09718, ES00002, and 5P42 ES05947.
The authors declare they have no competing financial interests.
8 2004 27 5 2004 112 11 1193 1199 12 12 2003 27 5 2004 Publication 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. Human hair as a biologic measure of exposure to persistent organic pollutants (POPs) has some advantages over the more commonly used blood and adipose tissue samples. However, one of the primary limitations is the difficulty in distinguishing between exogenous and endogenous contamination. In addition, there are currently no standardized methods for hair sample collection, washing, and chemical analysis. There is also very limited information describing the correlation between levels of organic contaminants in hair and other body compartments. To explore levels of POPs in blood and hair, samples from 10 volunteers were collected and analyzed for select organochlorine pesticides and 57 individual polychlorinated biphenyl (PCB) congeners. We demonstrated that the method for analyzing organic contaminants in human hair was reliable and reproducible. Washing hair with shampoo decreased levels of PCBs, pesticides, and lipids by 25–33% on average and up to 62% for low-chlorinated congeners. The percentage of lipids and the levels of organochlorines in hair were higher than in serum. We found strong correlation (r = 0.8) between p,p′-DDE (dichlorodiphenyldichloroethylene) levels in hair and blood and moderate correlations for the more persistent PCB congeners, but no correlations or weak correlations for other organochlorines. The present study provides preliminary evidence on the utility of hair analysis for POPs; however, further larger studies are recommended before hair analysis can be successfully applied in epidemiologic studies on POPs.
exogenous and endogenous contaminantsorganochlorinespesticidespolychlorinated biphenyls (PCBs)
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There is an extensive scientific literature describing the levels of organochlorines in human tissues, mainly in blood (Bush et al. 1984; Luotamo et al. 1985), including cord blood (Covaci et al. 2002b; Korrick et al. 2000), breast milk (Johansen et al. 1994; Mes et al. 1987), and adipose tissues (Covaci et al. 2002a; Focardi et al. 1986). Human hair is not commonly used as a biologic measure of exposure to persistent organic pollutants (POPs), although it has some advantages over the more commonly used blood and adipose tissue samples. Blood is not always available in sufficient amounts for a reliable analysis, whereas tissues need to be obtained by more invasive procedures or at surgery or autopsy. Because the collection of hair is simple, inexpensive, and noninvasive, subject compliance is high and hair can easily be collected from both adults and children. Hair sampling would not require the same precautions and conditions for handling, storage, and shipment as does blood, milk, or tissues. Hair sample collection would be especially useful in large epidemiologic studies where hair samples can be remotely collected by subjects and mailed to the investigator. In addition, studies with small children would be more feasible because the collection of blood limits participation rates. Finally, levels of environmental chemicals in hair are relatively stable (Bencze 1990a, 1990b) and may occasionally be high.
However, there are several limitations that need to be overcome before hair can be widely used as a matrix for biologic monitoring. One of the primary limitations is the difficulty in distinguishing between exogenous and endogenous contamination. In addition, there are currently no standardized methods for hair sample collection, washing, and chemical analysis. Another limitation is the lack of information regarding the rate of elimination, metabolization, and distribution of organic pollutants between hair and other body matrices. Finally, when exposure levels are low, as in general population studies, the sensitivity of the analytical methods for hair analysis might be a potential limitation.
There is a significant literature describing methods for analyzing hair for metals, specifically methyl mercury and arsenic (Jacobs 1996; Tsalev 1995), abused and therapeutic drugs (Beumer et al. 2001; Nakahara 1999), and anabolic steroids (Kicman and Gower 2003). These analyses have been performed for many years, and accordingly, analytical methods have been optimized and validated. Moreover, for some metals, reference values for levels found in the general population are available (Seifert et al. 2000). However, unlike metals, methods for hair analysis to assess exposure to polychlorinated biphenyls (PCBs), organochlorine pesticides (OCPs), and other POPs are not fully developed and validated.
Previous studies describing measurement methods for POPs in hair are limited. Schramm and colleagues (Schramm 1997; Schramm et al. 1992) were among the first scientists to recognize the potential utility of hair analysis for the assessment of human exposure to polychlorinated dibenzodioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs). Subsequently, simplified analytical methods for the extraction of PCBs, dichlorodiphenyltrichloroethane (DDT) and hexachlorocyclohexane (HCH) isomers from human hair were developed by Dauberschmidt and Wennig (1998) and by Covaci and Schepens (2001). Covaci and Schepens (2001) explored the relationship between select PCBs and OCPs in hair and breast milk from one individual and found that lipid-adjusted levels were comparable for most of compounds. Recently, Nakao et al. (2002) measured levels of PCDDs/PCDFs and coplanar PCBs in hair and blood collected from six healthy donors. They found moderate correlations between levels of 1,2,3,4,7,8-hexaCDD and 2,3,4,7,8-pentaCDF in hair and blood. The hair and blood levels of the other isomers of PCDD/PCDFs were not correlated. To the best of our knowledge, there are no other data describing the correlation between levels of organic contaminants in human hair and other body compartments, such as blood or tissues.
In June 2001, the Agency for Toxic Substances and Disease Registry convened a meeting of experts to review and discuss the current state of the science on hair analysis and the feasibility of using hair analysis in assessing environmental exposure (Harkins and Susten 2003). One of the conclusions from this meeting was that for most substances, with the exception of methyl mercury, for which the levels in hair are a biomarker of exposure, hair analysis is currently not a reliable indicator of environmental exposure or internal body burden. More research is needed before hair analysis can be considered a valid tool for human environmental exposure and health studies. They also concluded that there is limited available information on the utility of hair analysis for environmentally relevant organic pollutants but that the knowledge in this field should be expanded.
The objectives of the present study were to validate analytical methods for measuring PCBs and OCPs in human hair, to evaluate the effect of hair washing on the levels of hair contaminants, to assess endogenous versus exogenous exposure, and to determine the relationship between the levels of these compounds in hair and blood. To determine the utility of hair analysis for biomonitoring, we needed to compare this new method with well-established methods and to assess the comparability of the results from serum and hair analysis.
Materials and Methods
All subjects signed an informed consent approved by the Harvard School of Public Health Human Subjects Committee. The subjects were a convenience sample of volunteers.
Blood and hair samples.
Blood samples were collected in red top Vacutainer tubes, and the serum fraction was removed after being separated with centrifugation. The serum was stored in solventrinsed glass vials with Teflon-lined caps at −20°C until extraction. There was a significant interval (ranging from 10 to 25 months) between collecting serum and hair samples. Because there is no standard protocol for collecting hair samples, scalp hair was collected by study participants during their routine haircut. Because men and some of the women had short hair, the hair was cut from all areas of the scalp. For women with long hair, only the distal portion of hair was cut. We did not collect information on participants’ hair color or hair treatments, such as dyeing, permanent waving, or personal care products.
Hair washing.
Hair samples were covered with 35 mL hot water, sonicated for 30 min, and then dried with paper towel. For washing hair with shampoo, we placed hair in a 40-mL screw-cap vial, filled it with 35 mL deionized water, added one drop of mild Johnson & Johnson Baby Shampoo (Johnson & Johnson; Skillman, NJ) and vigorously shook the vial for 3 min. [The shampoo was separately tested for organochlorines, and all concentrations were < 0.02 ng/g, except that of hexachlorobenzene (HCB), which was 0.067 ± 0.004 ng/g.] The washing solution was decanted (and saved for analysis), the hair was rinsed five times with 30 mL deionized water each rinse, and the rinses were added to washing solution. For the hair washed with shampoo twice, the washing procedure was repeated by adding shampoo the second time before rinsing the hair.
Laboratory analysis.
All samples (blood serum, hair, and washing liquid) were analyzed for 57 individual PCB congeners and chlorinated pesticides. Details of hair extraction (Covaci and Schepens 2001), serum analyses (Korrick et al. 2000), and extraction of the washing liquid [U.S. Environmental Protection Agency (EPA) 1984] have been reported and are briefly described below. Before extraction, each sample was spiked with the surrogate compounds PCB-30 and PCB-112 [International Union of Pure and Applied Chemistry (IUPAC) nomenclature; Ballschmiter et al. 1992] to monitor the efficiency of the extraction procedure. Blood serum samples were denatured with methanol and extracted three times with a 1:1 mixture of hexane and ethyl ether. The hair samples were extracted by submerging hair in 3N hydrochloric acid, incubating it overnight at 40°C, and extracting three times with a mixture of n-hexane and dichloromethane (4:1, vol/vol). Water samples were extracted three times with dichloromethane. All solvent extracts were dried with anhydrous sodium sulfate, concentrated using a Kuderna-Danish evaporator followed by concentration under the stream of purified nitrogen. The percentage of lipids in hair and serum were determined gravimetrically by weighing an aliquot of the extract (20%). The remaining extract was concentrated to approximately 1–2 mL in a Kuderna-Danish apparatus followed by nitrogen evaporation.
For all three matrices, the extract was cleaned up using a chromatographic column packed with anhydrous sodium sulfate, 3% deactivated silica gel, and 2% deactivated aluminum oxide and eluted with 20 mL hexane. The sample extracts were concentrated to 100 μL, analyzed by dual capillary high resolution gas chromatography with electron capture detection and quantified based on the response factor of each analyte relative to the internal standard (PCB-166), added before gas chromatography injection. The average values obtained from both columns were reported for each target analyte unless the difference between two results exceeded 20%, in which case the lower value was reported. PCB concentrations were reported as individual congeners and as the sum of all congeners assayed (∑PCB). All final concentrations were reported after subtracting the amount of the analyte measured in the procedural blank.
Statistical analysis.
For data analysis, we used SAS, Version 8.2 (SAS Institute Inc., Cary, NC). Descriptive analyses of subject characteristics were performed. We used Spearman correlation coefficients to determine correlations between hair and blood levels of organochlorines.
Results
Ten Caucasian adults (five men and five women) participated. The men’s ages ranged from 25 to 43 years, with a mean ± SD of 34 ± 7.6 years. The women’s ages ranged from 39 to 53 years, with a mean ± SD of 43 ± 7.3 years. None of the subjects reported occupational exposure to PCBs or pesticides.
The average recoveries ± SD for two surrogates, PCB-30 and PCB-112, added to hair samples were 73 ± 5% and 82 ± 7% and for washing liquid they were 86 ± 18% and 86 ± 20%, respectively. The mean percentage of recovery for PCB congeners added to eight hair matrix spike samples was 91 ± 39%. The large SDs were a result of concentrations of some target analytes in hair being an order of magnitude higher than the amount of the spike added. Analytical precision, expressed as mean ± SD coefficient of variation for six triplicate and three duplicate hair samples, was 9 ± 8% for ∑PCBs, 9 ± 7% for p,p′-DDE (dichlorodiphenyldichloroethylene), 20 ± 9% for percentage lipid, and 7 ± 5% and 3 ± 3% for recoveries of two surrogates. The mean ± SD for ∑PCBs in procedural blanks for hair was 0.79 ± 0.08 ng. The average recoveries for two surrogates added to serum samples were 105 ± 6% and 93 ± 1%, respectively. The method detection limits (MDLs) for target analytes in serum ranged from 0.002 to 0.036 ng/g serum, with most MDLs < 0.01 ng/g serum (Korrick et al. 2000). The MDLs for hair samples ranged from 0.01 to 0.32 ng/g, with most MDLs < 0.1 ng/g. They were determined as 3 times the SD from the mean values for procedural blanks and using 0.5 g as the weight of the hair sample.
The percentage of lipids and the levels of organic contaminants in hair were generally higher than those in serum. The levels in hair were above the detection limits for all compounds, except PCB-25 and dieldrin, which were not detected in hair; in serum, PCB-8, PCB-18, PCB-33, and PCB-37 concentrations were below the MDLs in all subjects, and PCB-26, PCB-44, and PCB-84 concentrations were below the MDLs in most of the subjects. Table 1 lists the median levels together with the 25th and 75th percentiles for organochlorines in hair separately for females, males, and all subjects. In a comparison of hair concentrations between hair washed with only hot water and hair washed once or twice with shampoo, washing hair with shampoo decreased the levels of PCBs, pesticides, and lipids by 25–33% on average (Table 2). For the less-chlorinated congeners, such as PCB-8 and PCB-18, this decrease was even larger, up to 48% and 62%, respectively. Most of the decrease in levels of organochlorines and the percentage of fat occurred after the first shampoo washing, with 82% of the total loss for ∑PCBs, 88% for p,p′-DDE, and 93% for percentage of fat. The percent contribution of each PCB congener to ∑PCB (or congener profile) in hair and washing liquid and the percent contribution of individual OCPs to their sum are presented in Figure 1.
Two major contributors to the levels of organochlorines in serum are p,p′-DDE (47%), followed by the ∑PCB congeners (42%). Other contaminants contribute significantly less: 2% for p,p′-DDT, HCB, and trans-nonachlor and < 2% for other contaminants. There is a different percent distribution for the major contaminants in hair, with 70% of ∑PCB congeners, followed by only 14% of p,p′-DDE, and then 7% of p,p′-DDT. When the levels of OCPs in hair are compared between males and females, mean levels in females are significantly higher than in males (Figure 2A). The levels of pesticides, especially p,p′-DDE, in serum are also higher in females, although it is not as evident as for hair (Figure 2B).
PCB congener profiles for the average serum and hair concentrations are shown in Figure 3A, and the contribution of pesticides is presented in Figure 3B. The percentage of highly chlorinated and the more persistent PCBs were higher in serum than hair (Figure 3A). With the exception of PCB-74 (a persistent congener), the percentages of the less-chlorinated PCBs were higher in hair, for which a primary source may be from external exposure.
The mean ± SD ratios of p,p′-DDE: p,p′-DDT concentrations (nanograms per gram lipid) for all subjects were 28 ± 14 in serum versus 3 ± 2 in hair, which shows that DDE as a metabolization product is found in significantly higher proportions in serum than in hair.
The ratios and correlations of hair to serum concentrations for select PCB congeners and pesticides are shown in Table 3. A strong positive correlation was found between levels of p,p′-DDE in hair and blood, whereas moderate correlations were found for PCB-28, PCB-74, PCB-99, PCB-170, PCB-180, and PCB-194. A moderate negative correlation was found between levels of o,p′-DDE in hair and blood. The other PCB congeners and OCPs showed no correlation or weak correlation between the two matrices.
Discussion
In the present study, we demonstrated that the analytical method for analyzing organic contaminants in human hair is both reliable and reproducible (coefficients of variation were < 10%). We found strong correlations (r = 0.8) between hair and blood levels of p,p′-DDE, the most stable metabolite of p,p′-DDT, which was abundant in both matrices. The correlation was stronger than the hair-to-blood correlation for p,p′-DDT, which was expected because p,p′-DDT is easily metabolized. A moderate hair-to-blood correlation was found for PCB-28, PCB-74, PCB-99, PCB-170, PCB-180, and PCB-194, which are the more persistent congeners. The other PCB congeners and OCPs showed no correlations or weak correlations between the two matrices. The negative correlation found between hair and blood levels of o,p′-DDE was unexpected. Although this may represent a chance finding, it is worthy of follow-up in future studies. We also explored correlations between hair and blood for the sum of easily metabolized congeners (e.g., PCB-31, PCB-52, PCB-101, PCB-110, PCB-132, and PCB-149), which are present in hair in higher proportions than in serum, but did not find strong correlations.
Several analytical methodologies for hair analysis have been previously described, each having advantages and disadvantages. A special emphasis has been placed on the determination of PCDDs/PCDFs and coplanar PCBs (Luksemburg et al. 2002; Nakao et al. 2002; Schramm et al. 1992), whereas major PCB congeners and pesticides (e.g., DDT) have been studied to a lesser extent (Covaci and Schepens 2001; Dauberschmidt and Wennig 1998). Several conclusions become evident after the evaluation of existing methodologies: a) the efficiency of extraction of organic pollutants from the hair matrix is enhanced after a chemical treatment (acid or base digestion) of the hair; b) liquid–liquid extraction of the hair digest is faster than the Soxhlet extraction and more efficient than solid-phase extraction procedures; c) because relatively low amounts of hair (< 1 g) are usually used, hair analysis can be miniaturized for lower solvent consumption, and the resulting cleaned hair extracts have less interfering compounds than do extracts obtained from other body matrices; and d) the use of digestion procedures, as well as the choice of adsorbents for extract cleanup, is strongly dependent on the analytes of interest. It has been shown that alkaline digestion destroys several OCPs (e.g., HCHs) and converts p,p′-DDT to p,p′-DDE and p,p′-DDD (dichlorodiphenyldichloroethane), whereas the use of acidified silica gel (33–44% concentrated sulfuric acid on silica) does not allow for the determination of acid-labile pesticides such as dieldrin and heptachloroepoxide (Covaci and Schepens 2001), present in measurable concentrations in other body matrices (i.e., serum and adipose tissue).
Compared with levels of PCB congeners with assigned World Health Organization toxic equivalency factors measured by Tirler et al. (2001) in hair samples from one person collected at three different time points (2 and 3 months apart), the concentrations in our subjects were similar for PCB-156, PCB-167, and PCB-189 but 2.5 times lower for HCB. Levels of PCB-105 and PCB-118 were 4 and 3 times higher, respectively, in our study. However, further comparison is not appropriate because Tirler et al. (2001) did not measure the most prevalent congeners, such as PCB-138, PCB-153, PCB-170, and PCB-180.
Nakao et al. (2002) measured levels of PCDD/PCDFs and coplanar PCBs in hair and blood collected from six healthy donors. The correlation factors between these two matrices for 1,2,3,4,7,8-hexaCDD and 2,3,4,7,8-pentaCDF were 0.63 and 0.93, respectively, whereas the other PCDD/PCDF isomers showed weak or no correlations. Both of these congeners were relatively abundant in the samples, especially 2,3,4,7,8-pentaCDF. With the exception of one sample, where 2,3,4,7,8-pentaCDF was not detected in blood, it was the most abundant PCDF congener (except for octaCDF) in blood. Also, the levels of this congener in blood were significantly higher than in hair, which indicates the persistence of this congener. There was also no correlation for the two tetrachlorobiphenyls, PCB-77 and PCB-81, which were not detected in blood in most of the samples. The levels of penta- and hexachlorobiphenyls, PCB-126 and PCB-169, in hair and blood were correlated, with correlations of 0.66 and 0.67, respectively. The levels of these congeners in blood were significantly higher than in hair, with the blood-to-hair ratios ranging from 5 to 24 for PCB-126 and from 40 to 190 for PCB-169. This limited information suggested that there was a correlation between hair and blood levels for more persistent compounds.
Environmental organic pollutants are deposited on and in human hair via two major routes, endogenous (dietary exposure followed by excretion of pollutants into the hair shaft) and exogenous (atmospheric deposition) (Schramm 1997, 1999). Therefore, hair reflects internal exposure to organic contaminants, as well as contamination from the environment and hair care products. Permanent hair treatments may also alter organochlorine levels in hair. Thus, the difficulty in separating externally deposited compounds from endogenously deposited compounds makes the interpretation of hair analysis difficult. Washing hair with soap and hot water should remove most externally bound contaminants and, theoretically, may allow for the determination of internally bound analytes. However, an additional factor is the endogenous excretion of organic pollutants through the sebaceous glands onto the hair shaft, which complicates the picture of exogenous versus endogenous exposure.
In the present study, we washed hair with hot water and shampoo in a covered vessel using a sonication bath. This adequately removed dirt and dust from the hair exterior. We did not wash hair with organic solvents because this can also remove endogenously bound contaminants from hair. The decrease in hair levels of both lipids and most organochlorine pollutants after washing with shampoo was similar, and it was mainly observed after the first wash (~ 25–35%), whereas additional (5%), although small, loss was observed after the second wash. The relatively low variations between the loss of these compounds during washing may be due to the imperfect structure of hair with various scratches and holes in the matrix, acting as adsorbing sites (Valkovic 1988), which results in the similar loss of pollutants during the washing, independent of their persistence in the human body. The decrease in the levels of pollutants after washing hair with shampoo is in agreement with results from Nakao et al. (2002), who showed that by washing hair with a common surfactant, levels of PCDDs and PCDFs in hair samples decreased by 50% and 64%, respectively, and that a second washing had no further effect on the elimination of PCDD/PCDFs from hair samples. Interestingly, the percent contribution of PCB congeners and OCPs to their sum in hair and washing liquid were similar (Figure 1). This fact, together with the decrease in the total levels of pollutants and lipids after the first shampoo wash, suggests that the washing procedure is probably able to remove lipidic material deriving from sebaceous excretion. This material has the same PCB profile (profile of washing liquid) as the PCB profile found in the inner side of the hair shaft (profile in hair after wash). This further suggests that the exogenous contamination is insignificant for most PCBs, including the persistent PCBs, but not for the very volatile congeners (e.g., PCB-8 and PCB-18). Therefore, the washing procedure with shampoo may be excluded, but a simple washing step with water is still needed, especially when animal hair (which might contain fine soil particles and other solid materials) is analyzed (Covaci A, unpublished data).
In the present study, the percentage of highly chlorinated and more persistent PCBs was higher in serum than hair and with the exception of PCB-74 (a persistent congener), the percentage of the less chlorinated PCBs was higher in hair. A primary source of the less chlorinated PCBs in hair may be from exogenous exposure from gaseous or particulate sources. Low-chlorinated PCBs have a higher vapor pressure and therefore are found in higher concentrations in air than are higher chlorinated PCBs (Vorhees et al. 1997). Furthermore, lower-chlorinated PCBs have a significantly shorter half-life time in the human body (due to a faster metabolization rate) and therefore are expected to contribute less to endogenous organochlorine exposure. The strong decrease (up to 62%) in the hair concentrations of the more volatile PCB congeners (e.g., PCB-8 and PCB-18) after shampoo wash supports the exogenous exposure hypothesis. However, more readily metabolized congeners, such as PCB-52, PCB-101, PCB-110, and PCB-149, are present at high concentrations in hair even after two washes with shampoo. This may be the result of different elimination and distribution mechanisms between hair and internal organs or body tissues.
The readily metabolizable compounds are found in lower concentrations in body organs and tissues, where they will accumulate only after passing through the liver and thus after being metabolized. The hair root is vascularized during its growth, and thus contaminants present in the blood stream may enter the hair shaft via the root. If the subject has had a recent exposure to a cocktail of contaminants (including the easily metabolizable ones) or if they have been continuously exposed at low or background concentrations, these compounds will be present in the blood stream for a limited time until they are metabolized. However, the compounds will be sequestered in the hair shaft and will be present in relatively higher concentrations than in serum. A similar mechanism may be valid for heptachlor, an easy metabolizable compound, which is present only in hair and not in serum (Figure 3B). On the other hand, oxychlordane, a metabolization product, is found in much higher amounts in serum than in hair (Figure 3B). The same hypothesis may apply to nonpersistent PCB congeners and p,p′-DDT, which have a much higher abundance in hair compared with serum. Significantly higher ratios of p,p′-DDE:p,p′-DDT concentrations in serum than in hair show that DDE as a metabolization product is found in higher proportions in serum than in hair. However, this is just a hypothesis, because there are no studies on the distribution of POPs between hair, blood, and other tissues.
Neuber and Merkel (1999) used hair samples from preschool children to assess indoor air pollution from lindane and DDT from wood preservatives, woodworking, or imported furniture in the homes from rural areas in Germany. They studied children because children’s hair was assumed not to be bleached or colored with hair agents. They did not wash hair samples before analysis and detected lindane in most of the samples and DDT in almost 30% of all samples (although the levels in most of the samples were below the quantification levels by gas chromatography–mass spectrometry). Their conclusion was that hair analysis is a suitable method for detecting and quantifying indoor air pollution by lindane and DDT, especially for screening purposes, because of its easy and noninvasive sampling. Tirler et al. (2001) suggested that hair can serve as a passive sampler, similar to spruce needles, and provide information on environmental exposures.
In summary, in the present study, we validated analytical methods for measuring PCBs and OCPs in human hair and evaluated the effect of hair washing on the levels of these contaminants. There were correlations between the levels in hair and blood for select organochlorine pollutants, including p,p′-DDE and more persistent PCB congeners, such as PCB-99, PCB-170, PCB-180, and PCB-194. However, because most organochlorines had a weak correlation or no correlation between two matrices, it is too early to recommend hair as a reliable biomarker of exposure to organochlorines, which can replace serum or tissues as a biomonitoring tool. The present study had a number of limitations, which included a small sample size, the lack of consistency in hair collection location on the scalp, and the variable time period between collecting hair and blood samples from the same individual.
Because there are several distinct advantages of hair analysis compared with blood or tissue analysis for organochlorine pollutants, further larger studies are recommended. Potential advantages of hair analysis include its utility in studies where it is not feasible to collect blood or tissue. Examples include large epidemiologic studies in which subjects can remotely collect their own hair and mail it to the investigator. In addition, hair analysis may be practical in studies on small children where it is not possible to collect blood samples. Finally, hair samples may also prove useful as a screening media to identify individuals or groups of individuals with high levels (e.g., special populations), ultimately allowing for more targeted and efficient studies using more traditional matrices, such as blood, breast milk, or adipose tissue.
Figure 1 Contaminants in hair and washing liquid. (A) PCBs. (B) Pesticides.
Figure 2 Mean levels (ng/g fat) of organochlorines in females versus males in (A) hair and (B) serum.
Figure 3 Contaminants in human serum and hair. (A) PCBs. (B) Pesticides.
Table 1 Concentrations (ng/g fat) of select organochlorines and percent lipids in human hair, by percentile.
Females (n = 5)
Males (n = 5)
All subjects (n = 10)
PCBs (IUPAC nos.) and pesticides 25th Median 75th 25th Median 75th 25th Median 75th
PCB congener
6 5.4 12 33 13 18 60 11 15 39
8 15 18 53 27 41 43 18 34 53
16 7.9 18 51 6.4 17 27 8.0 18 32
18 14 45 103 8.6 21 31 11 24 51
26 18 27 52 17 17 17 17 22 39
28 26 37 136 22 35 50 22 36 53
31 24 51 144 26 30 35 24 32 60
33 18 34 119 29 30 42 27 32 65
37 24 24 24 5.1 41 122 14 33 81
41 14 14 14 6.0 41 51 10 27 46
44 26 85 136 35 40 68 29 54 85
49 13 42 93 15 20 22 13 21 42
52 40 130 165 45 59 96 40 77 130
60 13 21 87 16 18 21 13 20 23
66 46 127 137 39 48 63 39 56 137
70 41 146 188 31 59 72 38 65 146
74 17 48 70 5.2 6.0 17 6.0 17 48
77/110 114 253 316 76 106 158 76 136 253
84 34 94 157 44 58 76 34 67 112
87 39 104 108 26 35 56 26 47 104
95 50 116 144 39 79 92 46 85 144
97 24 81 104 21 24 46 21 35 81
99 18 71 108 14 14 27 14 23 71
101 102 207 214 63 115 157 63 136 214
105 36 49 75 17 22 23 17 25 49
118 62 172 218 44 57 83 44 72 172
135 35 44 50 30 36 36 30 36 50
136 17 31 34 11 37 41 11 33 39
138 184 245 268 71 163 213 71 198 245
141 38 84 86 13 91 99 15 85 99
146 29 36 37 10 25 29 10 29 37
149 145 216 229 65 224 234 65 220 234
151 59 70 78 53 92 154 59 78 87
153 208 238 254 74 189 236 74 222 254
156 8.9 19 20 4.7 6.7 8.0 5.1 8.5 19
167 4.0 7.3 9.2 2.2 3.4 3.5 2.2 3.8 7.3
170 15 32 47 16 29 29 15 29 40
171 11 16 18 7.0 16 18 7.0 16 18
174 32 62 71 25 70 79 25 66 79
177 16 31 38 11 31 34 11 31 37
180 37 76 106 37 70 71 37 71 106
183 21 37 41 13 41 46 13 39 46
187 54 90 93 32 87 104 32 88 104
189 0.4 0.6 1.3 0.9 0.9 1.0 0.5 0.9 1.2
196/203 10 14 27 6.0 14 15 6.0 14 16
199 10 13 24 7.0 12 13 7.0 13 18
∑PCBs 2,010 3,620 4,500 1,180 2,140 3,130 1,180 2,640 3,620
Pesticide
HCB 17 20 27 30 32 41 20 28 32
Aldrin 12 12 12 4.0 15 27 4.0 12 27
Heptachlor 10 23 50 12 21 22 10 21 50
o,p′-DDE 22 47 88 31 35 62 22 41 76
o,p′-DDT 24 156 488 29 34 47 29 34 94
p,p′-DDD 4.6 25 50 1.7 3.4 6.7 1.7 5.6 25
p,p′-DDE 241 517 820 128 199 217 128 229 731
p,p′-DDT 51 466 802 53 67 158 51 113 466
trans-Nonachlor 27 125 139 24 35 36 24 36 125
Percent lipids 2.1 2.2 3.1 0.77 1.6 1.8 1.6 1.8 2.2
Table 2 Organochlorine concentrations in hair and their percentage of loss after washing hair with shampoo.
PCBs (IUPAC nos.) and pesticides Hot water wash [mean (ng/g)]a Shampoo once [mean (ng/g)]a Loss after one shampoo (%) Shampoo twice [mean (ng/g)]a Additional loss after 2nd shampoo (%)
PCB congener
8 0.65 0.33 49 0.34 0
18 0.39 0.21 47 0.15 16
16 0.59 0.45 24 0.40 8
26 1.3 1.2 11 1.1 1
31 1.6 1.2 21 1.1 7
28 1.2 0.92 25 0.81 9
33 0.92 0.82 11 0.75 8
52 3.6 3.1 13 2.9 7
49 1.5 1.1 25 0.93 11
44 2.2 1.9 13 1.9 3
95/66 7.2 5.6 22 5.3 4
74 1.6 1.1 30 1.0 6
70 3.8 3.3 14 3.2 2
84 4.1 4.0 2 3.7 8
60 0.77 0.58 25 0.47 14
99 3.7 2.6 28 2.4 8
101 6.5 4.8 26 4.5 4
97 2.7 2.0 26 1.8 6
87 3.1 2.4 22 2.3 4
77/110 6.0 5.1 15 5.6 7b
151 2.2 1.4 34 1.3 7
135 1.2 1.1 11 0.97 9
149 4.7 3.4 28 3.2 4
118 6.9 5.1 26 4.8 5
146 1.3 0.83 36 0.82 1
153 9.2 6.6 28 6.3 4
105/141 4.0 2.9 28 2.7 5
138 8.5 6.4 25 5.9 6
187 2.7 1.9 28 2.0 5b
183 1.2 0.83 30 0.82 1
128 0.62 0.44 29 0.37 12
174 2.3 1.7 27 1.6 5
167 0.34 0.24 29 0.22 5
177 1.2 0.89 27 0.83 5
157/201 0.44 0.34 22 0.30 8
171 0.57 0.43 24 0.39 7
156 0.86 0.60 30 0.51 11
180 3.6 2.6 28 2.4 5
170 1.8 1.3 27 1.2 5
199 0.82 0.57 31 0.52 6
196/203 0.89 0.65 27 0.60 5
189 0.06 0.04 21 0.04 8
195 0.28 0.22 22 0.20 5
194 0.40 0.29 29 0.26 5
∑PCBs 110 85 23 80 5
Pesticide
p,p′-DDE 80 63 22 60 3
o,p′-DDE 3.0 2.4 21 2.2 6
p,p′-DDT 35 27 21 25 7
o,p′-DDT 22 17 24 15 6
p,p′-DDD 1.5 1.2 21 1.2 0
HCB 0.67 0.66 1 0.60 10
Heptachlor 0.61 0.51 15 0.51 1
trans-Nonachlor 4.3 3.1 27 2.7 9
Percent lipids 3.1 2.3 26 2.2 2
a Mean values for three replicate experiments.
b Increase.
Table 3 Ratios and correlations of hair to serum concentrations (ng/g fat) for select PCB congeners and pesticides.
Hair:serum ratios (mean ± SD)
PCBs (IUPAC nos.) and pesticides Females (n = 4)a Males (n = 5) All subjects (n = 9)a Spearman correlations
PCB congener
28 6.5 ± 5.5 12 ± 5.6 9.0 ± 5.9 0.5
52 53 ± 78 49 ± 35 51 ± 54 −0.03
74 2.3 ± 2.9 1.3 ± 1.3 1.7 ± 2.1 0.5
99 3.6 ± 5.0 2.0 ± 1.3 2.7 ± 3.4 0.5
101 52 ± 82 48 ± 33 50 ± 55 0.2
149 47 ± 48 68 ± 52 59 ± 48 0.1
118 3.1 ± 3.5 3.5 ± 2.0 3.3 ± 2.6 0.4
153 1.8 ± 1.4 2.9 ± 1.5 2.4 ± 1.5 0.2
105 6.8 ± 7.4 12 ± 17 10 ± 13 0.2
138 2.5 ± 2.3 3.3 ± 1.8 2.9 ± 1.9 0.2
180 0.9 ± 0.5 1.7 ± 0.75 1.3 ± 0.75 0.6
170 1.0 ± 0.6 2.0 ± 1.1 1.6 ± 1.0 0.5
194 0.5 ± 0.5 0.9 ± 0.5 0.7 ± 0.5 0.6
∑PCBs 5.5 ± 6.5 6.3 ± 3.4 6.0 ± 4.7 0.2
Pesticide
o,p′-DDE 6.7 ± 6.1 5.8 ± 4.2 6.2 ± 4.2 −0.6
p,p′-DDE 0.8 ± 0.7 0.9 ± 0.47 0.8 ± 0.47 0.8 *
p,p′-DDT 17 ± 17 7.9 ± 12.6 11.8 ± 12.6 0.4
o,p′-DDT 48 ± 53 9.0 ± 38.4 26.2 ± 38.4 0.4
a Percent lipids for one serum sample was not available.
* p < 0.05.
==== Refs
References
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Covaci A de Boer J Ryan JJ Voorspoels S Schepens P 2002a Distribution of organobrominated and organochlorinated pollutants in Belgian human adipose tissue Environ Res 88 210 218 12051799
Covaci A Jorens P Jacquemyn Y Schepens P 2002b Distribution of PCBs and organochlorine pesticides in umbilical cord and maternal serum Sci Total Environ 298 45 53 12449328
Covaci A Schepens P 2001 Chromatographic aspects of selected persistent organochlorine pollutants analysis in human hair Chromatographia 53 366 371
Dauberschmidt C Wennig R 1998 Organochlorine pollutants in human hair J Anal Toxicol 22 610 611 9847014
Focardi S Fossi C Leonzio C Romei R 1986 PCB Congeners, hexachlorobenzene and organochlorine insecticides in human fat in Italy Bull Environ Contam Toxicol 36 644 650 3085752
Harkins DK Susten AS 2003 Hair analysis: exploring the state of the sciences Environ Health Perspect 111 576 578 12676618
Jacobs RM 1996. Techniques employed for the assessment of metals in biological systems. In: Toxicology of Metals (Chang LW, ed). New York:Lewis Publishers, 81–107.
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Korrick SA Altshul LM Tolbert PE Burse VW Needham LL Monson RR 2000 Measurement of PCBs, DDE, and hexachlorobenzene in cord blood from infants born in towns adjacent to a PCB-contaminated waste site J Exp Anal Environ Epidemiol 10 743 754
Luksemburg WJ Mitzel RS Peterson RG Hedin JM Maier MM Schuld M 2002 Polychlorinated dibenzodioxins and dibenzofurans (PCDD/PCDFs) levels in environmental and human hair samples around an electronic waste processing site in Guiyu, Guangdong Province, China Organohalogen Compounds 55 347 349
Luotamo M Jarvisalo J Aitio A 1985 Analysis of polychlorinated biphenyls (PCBs) in human serum Environ Health Perspect 60 327 332 3928361
Mes J Turton D Davies D Sun WF Lau PY Weber D 1987 The routine analysis of some specific isomers of polychlorinated biphenyl congeners in human milk Int J Environ Anal Chem 28 197 205 3104221
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Nakao T Aozasa O Ohta S Miyata H 2002 Assessment of human exposure to PCDDs, PCDFs and Co-PCBs using hair as a human pollution indicator sample I: development of analytical method for human hair and evaluation for exposure assessment Chemosphere 48 885 896 12222782
Neuber K Merkel FFE 1999 Indoor air pollution by lindane and DDT indicated by head hair samples of children Toxicol Lett 107 189 192 10414795
Schramm KW 1997 Hair: a matrix for non-invasive biomonitoring of organic chemicals in mammals Bull Environ Contam Toxicol 59 396 402 9256392
Schramm KW 1999. Biomonitoring Ausgewahlter Organischer Chemikalien mit Haaren [in German]. Munchen, Germany:Herbert Utz Verlag GmbH.
Schramm KW Kuettner T Weber S Lutzke K 1992 Dioxin hair analysis as monitoring pool Chemosphere 24 3 351 358
Seifert B Becker K Helm D Krause C Schulz C Seiwert M 2000 The German Environmental Survey 1990/1992 (GerES II): reference concentrations of selected environmental pollutants in blood, urine, hair, house dust, drinking water and indoor air J Exp Anal Environ Epidemiol 10 6 552 565
Tirler W Voto G Donega M 2001 PCDD/F, PCB and hexachlorobenzene level in hair Organohalogen Compounds 52 290 292
Tsalev DL 1995. Atomic Absorption Spectrometry in Occupational and Environmental Health Practice. Boca Raton, FL:CRC Press.
U.S. EPA 1984 Methods for organic chemical analysis of municipal and industrial wastewater. 40CFR part 136, appendix A Fed Reg 49 209 89 104
Valkovic V 1988. Human Hair, Vols. 1 and 2. Boca Raton, FL:CRC Press.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.6555ehp0112-00120015289167Children's HealthArticlesThe Association between Environmental Lead Exposure and Bone Density in Children Campbell James R. 1Rosier Randy N. 2Novotny Leonore 2Puzas J. Edward 21Department of Pediatrics and2Department of Orthopedics, University of Rochester Medical Center, Rochester, New York, USAAddress correspondence to J.R. Campbell, Department of Pediatrics, MOB Suite 300, Rochester General Hospital, 1425 Portland Ave., Rochester, NY 14621 USA. Telephone: (585) 922-3919. Fax: (585) 922-3929. E-mail:
[email protected] thank C. Muzytchuk for conducting the bone density measures, and R. O’Keefe, M. Zuscik, and S. Schaffer for their thoughtful commentary on the manuscript.
This work was supported by University of Rochester Environmental Health Sciences Center grants NIEHS P30 ES01247 and NIEHS PO1 ES011854.
The authors declare they have no competing financial interests.
8 2004 7 4 2004 112 11 1200 1203 30 6 2003 7 4 2004 Publication 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. Osteoporosis is a decrease in bone mineral density (BMD) that predisposes individuals to fractures. Although an elderly affliction, a predisposition may develop during adolescence if a sufficient peak BMD is not achieved. Rat studies have found that lead exposure is associated with decreased BMD. However, human studies are limited. We hypothesized that the BMD of children with high lead exposure would be lower than the BMD of children with low lead exposure. We collected data on 35 subjects; 16 had low cumulative lead exposure (mean, 6.5 μg/dL), and 19 had high exposure (mean, 23.6 μg/dL). All were African American; there was no difference between the groups by sex, age, body mass index, socioeconomic status, physical activity, or calcium intake. Significant differences in BMD between low and high cumulative lead exposure were noted in the head (1.589 vs. 1.721 g/cm2), third lumbar vertebra (0.761 vs. 0.819 g/cm2), and fourth lumbar vertebra (0.712 vs. 0.789 g/cm2). Contrary to our hypothesis, subjects with high lead exposure had a significantly higher BMD than did subjects with low lead exposure. This may reflect a true phenomenon because lead exposure has been reported to accelerate bony maturation by inhibiting the effects of parathyroid hormone–related peptide. Accelerated maturation of bone may ultimately result in a lower peak BMD being achieved in young adulthood, thus predisposing to osteoporosis in later life. Future studies need to investigate this proposed model.
blood lead levelsbone mineral densitydual-energy X-ray absorptiometryparathyroid hormone–related peptide
==== Body
Research on the adverse effects of lead exposure on humans has focused on neurocognitive outcomes among children [Canfield et al. 2003; National Research Council (NRC) 1993]. However, a growing body of literature reports that the effects of childhood lead exposure continue into adolescence and adulthood. These include delinquent behavior (Dietrich et al. 2001; Needleman et al. 2002), dental caries (Moss et al. 1999), hypertension (Hu et al. 1996; Nash et al. 2003), cardiac arrhythmias (Cheng et al. 1998), and renal dysfunction (Kim et al. 1996). Research also demonstrates another potential late effect of childhood lead exposure: osteoporosis. Studies on rats have found that increased lead exposure is associated with decreased bone density (Escribano et al. 1997; Gruber et al. 1997; Puzas et al. 1999) and decreased bone strength (Ronis et al. 2001). Additional studies have found that lead exposure inhibits the function of osteoblasts (Klein and Wiren 1993; Puzas et al. 1999; Ronis et al. 2001), the cells that make bone.
However, human studies on this association are limited. In a study of children, Laraque et al. (1990) found no association between bone density and lead exposure. However, because the comparison group was made up of children with moderate-level lead exposure (i.e., blood lead level 12–29 μg/dL), such a study cannot exclude the possibility that lead exposure has an effect at lower levels. In addition, because the children were examined at a young age (range, 18–47 months), sufficient time may not have elapsed for the adverse effects on the bone to become manifest. A study by Alfvén et al. (2002) also found no association between lead exposure and bone mineral density (BMD) in a cross-sectional study of adults. However, the authors used concurrent blood lead level to define lead exposure and acknowledged that such a measure may be inadequate to measure body lead burden. In addition, subjects did not have high blood lead levels [the mean blood lead level was 3.1 μg/dL (Alfvén T, personal communication)]; thus, the lack of an association may be due to low lead exposure among the subjects.
Our objective was to determine whether an association between lead exposure and bone density exists in children. We hypothesized that the bone density of children with high lead exposure would be lower than the bone density of children with low lead exposure.
Materials and Methods
Subject identification and enrollment.
To identify potential subjects who had an adequate number of blood lead levels to define past lead exposure, we obtained a comprehensive database of blood lead levels from the local county health department (Department of Health, Monroe County, New York State). To minimize the effect of age on BMD, we limited the database to children 8–10 years of age. In the database, we excluded capillary blood lead levels ≥ 10 μg/dL, because of the possibility that these represent contaminated specimens [Centers for Disease Control and Prevention (CDC) 1991], and children who did not have at least one blood lead level at each of four age groups (13–24 months, 25–36 months, 37–48 months, and 49–60 months). For the remaining children, we calculated each child’s cumulative lead exposure (defined below) and created a list with the following information: child’s name, date of birth, race, and cumulative lead exposure. From the list, we identified children who attended the principal investigator’s (J.R.C.) pediatric practice or whom the principal investigator had medically managed for lead toxicity. To eliminate the effect of race, we contacted only subjects who were African American. The principal investigator (J.R.C.) subsequently called the parents of the potential subjects to ask if they would be interested in having the child enrolled. If a parent agreed, a short questionnaire was administered to determine whether exclusionary criteria existed. We excluded children who had medical conditions that affected bone density (metabolic bone disease, renal disease, sickle cell disease), used certain medications (corticosteroids, anticonvulsants, diuretics), had evidence of sexual maturation (i.e., Tanner stage ≥ 2), or had a parent who was not African American. If no exclusionary criteria existed, the study coordinator (L.N.) called the parent to schedule an appointment for the bone density procedure. At the appointment, the study coordinator obtained informed consent, completed a questionnaire to collect covariate date, and measured the child’s height and weight. Subsequently, one technician conducted the bone density measurement. The study coordinator and technician were blinded to the subject’s cumulative lead exposure status.
The Human Subjects Committee of the Monroe County Health Department, the Human Subjects Review Board of the University of Rochester Medical Center, and the Clinical Investigation Committee of Rochester General Hospital approved this study.
Measure of lead exposure.
The measure of lead exposure used in this study is termed the cumulative lead exposure. To compute it, we identified all blood lead levels collected during four age strata (i.e., 12–23 months, 24–35 months, 36–47 months, and 48–60 months) from the local health department database. Subsequently, we calculated the arithmetic mean of all blood lead levels for each of the four age strata. Finally, the cumulative lead exposure was calculated by computing the arithmetic mean of the four age strata means. Subjects were dichotomized as having high versus low cumulative lead exposure at a cutoff of 15 μg/dL.
There is a strong correlation (i.e., 0.92–0.95) between any single blood lead level in children between 2 and 4 years of age and cumulative lead exposure measure based on 24 serial blood lead levels in children between 3 months and 10 years of age (Dietrich K, personal communication); therefore, we conclude that our measure of cumulative lead exposure is a valid measure of the overall lifetime lead exposure for a school-age child.
Measure of bone density.
We used a fan-beam dual-energy X-ray absorptiometry (DEXA) scanner (Prodigy; GE/Lunar Corporation, Madison, WI) to measure BMD (Mazess et al. 1990). BMD was determined at various body regions (e.g., total body, arms, legs, trunk), the lumbar vertebrae, and hip regions (total hip, femoral neck, trochanter, femoral shaft).
Measure of covariates.
Variables associated with changes in bone density include age (Boot et al. 1997; Maynard et al. 1998), race (Nelson et al. 1997; Pollitzer and Anderson 1989), weight (Boot et al. 1997), physical activity (Cooper et al. 1988), and calcium intake (Johnston et al. 1992). Bone density does not vary by sex among children < 13 years of age (Maynard et al. 1998).
To minimize the effect of age, we enrolled only subjects within a narrow age range: 8–10 years. To eliminate the effect of race, we enrolled only subjects who were African American (and whose parents were both African American). We measured subject weight and height at the time of the BMD measurement. A parental questionnaire collected data on physical activity (i.e., the number of hours a day the child is physically active and inactive), calcium intake (i.e., current milk and milk-product intake and frequency), and socioeconomic status [head of household Hollingshead occupational level and socioeconomic score (Hollingshead 1958)].
Analyses.
We initially compared the covariates between subjects by cumulative lead exposure status (i.e., low vs. high). Because age and weight are strongly associated with BMD, we decided, a priori, to introduce both into adjusted analyses. Other comparisons with p-values ≤0.20 were also to be introduced into adjusted analyses. The primary analysis was, for each bony site, a comparison of the mean BMD by cumulative lead exposure status. Using SPSS software (version 4.0; SPSS Inc., Chicago, IL), we conducted adjusted analyses by use of analysis of covariance between the cumulative lead exposure groups.
A sample size calculation demonstrated that 44 subjects would be required to achieve a power of 80% in discerning a 1.0-SD difference in BMD between the groups. We conducted analyses during subject recruitment, thus allowing us to discontinue enrollment after significant findings were discerned at a sample size of 35 subjects.
Results
We collected data on 36 subjects. All were African American. One subject was excluded because of obesity [body mass index (BMI) = 33]. Among the remaining subjects, 63% were male. The mean age was 109.5 months. The mean weight was 33.6 kg, and the mean height 138.6 cm; these measures are approximately at the 75th and 60th percentiles, respectively.
Among the 35 subjects analyzed, 16 had a low cumulative lead exposure and 19 had a high cumulative lead exposure; the respective mean cumulative lead exposures were 6.5 μg/dL (range, 2.7–10.3 μg/dL) and 23.6 μg/dL (range, 15.5–43.5 μg/dL) (Table 1). The groups were otherwise comparable; there were no differences (i.e., p > 0.20) between the groups on sex distribution, age, BMI, socioeconomic status, physical activity, or calcium intake (Table 2).
Table 3 shows the adjusted mean BMD by bony site and cumulative lead exposure status. Contrary to our hypothesis, we found that subjects with high cumulative lead exposure had a higher BMD than did subjects with low cumulative lead exposure. Among 17 bony sites, four were significantly different (i.e., p ≤0.05).
Discussion
Contrary to our hypothesis, we found that subjects with high lead exposure had a significantly higher bone density than did subjects with low lead exposure. We initially considered whether this result derived from an artificial increase in the measure of bone density by DEXA due to the presence of lead in bone. A false elevation of DEXA-based bone density is reported to occur in bone containing strontium, a heavy metal with a lower atomic weight than lead (Nielsen et al. 1999). We found, in an in vitro study using an older Lunar DPX-L pencil-beam instrument, that bone density increased by 8–11% with increasing and clinically relevant bone lead levels (i.e., 10–100 μg/g) (Puzas et al. 2002). However, when this in vitro study was replicated using a newer Lunar Prodigy fan-beam instrument, the same used on the subjects of our study, the effect was minimal and within the precision of the DEXA measure (Muzytchuk et al. 2004).
The alternative interpretation of our findings is that high lead exposure is associated with truly higher bone density in childhood. Our results indicate that the magnitude of this association is clinically relevant. For example, the mean BMD of the lumbar vertebrae (L1–L4) was 0.770 g/cm2 versus 0.720 g/cm2 among subjects with high and low cumulative lead exposure, respectively (p = 0.03). Thus, in this study, children with high cumulative lead exposure had nearly 7% higher BMD at the lumbar vertebrae than did children with low cumulative lead exposure. This amounts to about 2 years of bone growth.
We now wish to speculate on the mechanism of this finding. An in vitro study found that lead inhibits parathyroid hormone–related peptide (PTHrP) and transforming growth factor-β1, proteins that decrease the rate of maturation of chondrocytes in endochondral bone formation (Zuscik et al. 2002). Further, this inhibition of PTHrP is associated with accelerated skeletal maturity. Mice homozygous for PTHrP gene deletion have advanced skeletal maturation at birth (Karaplis et al. 1994; Lee et al. 1996). Similarly, children with Blomstrand syndrome, a congenital chondrodysplasia due to nonfunctioning PTHrP receptors (Jobert et al. 1998; Karaplis et al. 1998), also have advanced skeletal maturation and higher than normal bone density at birth (Blomstrand et al. 1985; den Hollander et al. 1997; Loshkajian et al. 1997; Young et al. 1993). The inhibition of PTHrP causes premature maturation of the chondrocytes (Zuscik et al. 2002), which may result in a higher bone density.
Our findings differ from past research findings that lead exposure is associated with lower, not higher, bone density in mature animals (Escribano et al. 1997; Gruber et al. 1997; Puzas et al. 1999; Ronis et al. 2001). Nevertheless, the literature suggests that the higher bone density associated with PTHrP inhibition is transient. Although mice homozygous for PTHrP gene deletion have higher bone density at birth (Karaplis et al. 1994), mice heterozygous for PTHrP gene deletion have osteopenia as adults (Amizuka et al. 1996; He et al. 2000). The proposed mechanism is as follows: Besides its effects on endochondral bone formation, PTHrP also acts on bone remodeling in adult organisms. It promotes the differentiation of bone marrow stem cells toward osteoblasts and away from adipocytes and impedes the apoptosis of osteoblasts (Karaplis 2001). In a mature organism without endochondral bone formation, PTHrP inhibition on bone remodeling would predominate—that is, differentiation of stem cells toward adipocytes and an increased rate of osteoblast apoptosis—thus predisposing to osteoporosis (Karaplis et al. 2001). We therefore speculate that a lead-exposed individual may undergo a higher rate of bone loss when older than would individuals without lead exposure.
An alternative model for the development of osteoporosis is that a lead-exposed individual may achieve a lower peak bone mass as a young adult. Studies of children have found a negative association between blood lead level and height (Ballew et al. 1999; Schwartz et al. 1986; Selevan et al. 2003). Similarly, mice homozygous for PTHrP gene deletion, in addition to having advanced skeletal maturation, have shorter long bones and shorter vertebrae than do normal mice (Karaplis et al. 1994). These findings along with the findings described in the preceding paragraph suggest that lead targets its effects on the growth plate by inhibiting PTHrP and thus causing shorter stature in exposed children. We speculate that this inhibition of stature when young results in a lower peak bone mass being achieved, thus predisposing to osteoporosis in later life (Figure 1). Future studies are needed to investigate whether these proposed models are valid.
Figure 1 This study found that bone density for lead-exposed children is higher than that for children not exposed to lead. We propose that this increase may be transient (inset). A lower peak bone mass may occur in early adulthood (B rather than A), thus predisposing to osteoporosis in later life (D rather than C).
Table 1 Blood lead level measures by cumulative lead exposure status [low vs. high (μg/dL)].
BLL measure Low High
Mean BLL
12–23 months 7.3 23.8
24–35 months 7.4 22.4
36–47 months 5.4 24.5
48–60 months 4.9 21.1
Mean cumulative lead exposurea 6.5 23.6
Range 2.7–10.3 15.5–43.5
BLL, blood lead level.
a Defined in ”Materials and Methods.”
Table 2 Comparison of covariates by cumulative lead exposure status (low vs. high).
Covariates Low High p-Valuea
Demographics
Sex (% male) 56 68 0.46
Age (months) 109.9 109.2 0.73
HOH Hollingshead occupation levelb 5.6 6.3 0.30c
HOH Hollingshead socioeconomic scoreb 89.4 89.7 0.92
Body size
Weight (kg) 34.1 33.2 0.72
Height (cm) 137.4 139.6 0.40
BMI (kg/m2) 17.9 16.9 0.28
Physical activity
Active play (hr/day) 4.8 4.6 0.80
Inactive play (hr/day) 3.2 3.0 0.72
Calcium intake (portions/day) 3.6 3.6 1.00
HOH, head of household.
a By t-test, except where specified.
b HOH Hollingshead occupation level and socioeconomic level (Hollingshead 1958).
c By Mann-Whitney U-statistic.
Table 3 Adjusted BMD (g/cm2) by bony site and cumulative lead exposure (low vs. high).
BMD site Low High p-Valuea
Body regions
Head 1.589 1.721 < 0.01**
Arms 0.684 0.704 0.16
Legs 0.917 0.928 0.61
Trunk 0.693 0.720 0.06*
Ribs 0.594 0.615 0.09*
Pelvis 0.806 0.839 0.09*
Spine 0.720 0.749 0.14
Total body 0.911 0.940 0.06*
Lumbar vertebrae
L1b 0.682 0.707 0.28
L2 0.722 0.756 0.22
L3 0.761 0.819 0.01**
L4 0.712 0.789 0.01**
L1–L4 0.720 0.770 0.03**
Hip regions
Femoral neck 0.827 0.893 0.07*
Trochanter 0.682 0.732 0.11
Femoral shaft 0.939 1.006 0.11
Total hip 0.842 0.906 0.08*
a By analysis of covariance.
b First lumbar vertebra.
* Marginally significant (i.e., 0.05 < p < 0.10).
** Significant at p ≤ 0.05 level.
==== Refs
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Puzas JE Campbell J O’Keefe R Schwartz E Rosier R 2002 Lead in the skeleton interferes with bone mineral measurements [Abstract] J Bone Miner Metab 17 S1 S314
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Ronis MJ Aronson J Gao GG Hogue W Skinner RA Badger TM 2001 Skeletal effects of developmental lead exposure in rats Toxicol Sci 62 321 329 11452145
Schwartz J Angle C Pitcher H 1986 Relationship between childhood blood lead levels and stature Pediatrics 77 281 288 3951909
Selevan SG Rice DC Hogan KA Euling SY Pfahles-Hutchens A Bethel J 2003 Blood lead concentration and delayed puberty in girls N Engl J Med 348 1527 1536 12700372
Young ID Zuccollo JM Broderick NJ 1993 A lethal skeletal dysplasia with generalized sclerosis and advanced skeletal maturation: Blomstrand chondrodysplasia? J Med Genet 30 155 157 8445622
Zuscik MJ Pateder DB Puzas JE Schwarz EM Rosier RN O’Keefe RJ 2002 Lead alters parathyroid hormone-related peptide and transforming growth factor-β1 effects and AP-1 and NF-κB signaling in chondrocytes J Orthoped Res 20 811 818
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.6864ehp0112-00120415289168Children's HealthArticlesPerfluorooctane Sulfonate (PFOS) and Related Perfluorinated Compounds in Human Maternal and Cord Blood Samples: Assessment of PFOS Exposure in a Susceptible Population during Pregnancy Inoue Koichi 1Okada Fumio 1Ito Rie 1Kato Shizue 2Sasaki Seiko 2Nakajima Sonomi 2Uno Akiko 2Saijo Yasuaki 2Sata Fumihiro 2Yoshimura Yoshihiro 1Kishi Reiko 2Nakazawa Hiroyuki 11Department of Analytical Chemistry, Faculty of Pharmaceutical Sciences, Hoshi University, Tokyo, Japan2Department of Public Health, Hokkaido University Graduate School of Medicine, Hokkaido, JapanAddress correspondence to H. Nakazawa, Department of Analytical Chemistry, Faculty of Pharmaceutical Sciences, Hoshi University, 2-4-41 Ebara, Shinagawa-ku, Tokyo 142-8501, Japan. Telephone: 81-3-5498-5763. Fax: 81-3-5498-5062. E-mail:
[email protected] study was supported by the Grant-in-Aid for Health Sciences Research grants from the Ministry of Health, Labour and Welfare of Japan, and by a Grant-in-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science and Technology.
The authors declare they have no competing financial interests.
8 2004 13 4 2004 112 11 1204 1207 17 11 2003 13 4 2004 Publication 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. Fluorinated organic compounds (FOCs), such as perfluorooctane sulfonate (PFOS), perfluorooctanoate (PFOA), and perfluorooctane sulfonylamide (PFOSA), are widely used in the manufacture of plastic, electronics, textile, and construction material in the apparel, leather, and upholstery industries. FOCs have been detected in human blood samples. Studies have indicated that FOCs may be detrimental to rodent development possibly by affecting thyroid hormone levels. In the present study, we determined the concentrations of FOCs in maternal and cord blood samples. Pregnant women 17–37 years of age were enrolled as subjects. FOCs in 15 pairs of maternal and cord blood samples were analyzed by liquid chromatography–electrospray mass spectrometry coupled with online extraction. The limits of quantification of PFOS, PFOA, and PFOSA in human plasma or serum were 0.5, 0.5, and 1.0 ng/mL, respectively. The method enables the precise determination of FOCs and can be applied to the detection of FOCs in human blood samples for monitoring human exposure. PFOS concentrations in maternal samples ranged from 4.9 to 17.6 ng/mL, whereas those in fetal samples ranged from 1.6 to 5.3 ng/mL. In contrast, PFOSA was not detected in fetal or maternal samples, whereas PFOA was detected only in maternal samples (range, < 0.5 to 2.3 ng/mL, 4 of 15). Our results revealed a high correlation between PFOS concentrations in maternal and cord blood (r2 = 0.876). However, we did not find any significant correlations between PFOS concentration in maternal and cord blood samples and age bracket, birth weight, or levels of thyroid-stimulating hormone or free thyroxine. Our study revealed that human fetuses in Japan may be exposed to relatively high levels of FOCs. Further investigation is required to determine the postnatal effects of fetal exposure to FOCs.
cord bloodfluorinated organic compoundshumanPFOAPFOSPFOSApregnancy
==== Body
Fluorinated organic compounds (FOCs), such as perfluorooctane sulfonate (PFOS), perfluorooctanoate (PFOA), and perfluorooctane sulfonylamide (PFOSA), are stable chemicals with a wide range of industrial and consumer applications (Renner 2001). Recent reports have indicated the presence of these environmental contaminants in wildlife and river water (Giesy and Kannan 2001; Kannan et al. 2001; Saito et al. 2003; Taniyasu et al. 2003). FOCs have been manufactured for > 50 years and are used as refrigerants, surfactants, and polymers and as components of paper coatings, fire retardants, adhesives, cosmetics, and insecticides (Key et al. 1997).
In the United States, PFOS is a stable FOC with many industrial applications. The amount of PFOS used in consumer application totals 5.6 million lb [U.S. Environmental Protection Agency (EPA) 2000]. These findings prompted the major manufacturer of PFOS in the United States, 3M Company, to halt production in the end of 2002 (Renner 2001). However, PFOS is still available on the Japanese market. A recent evaluation of PFOS toxicity in green algae revealed that the no observable effect concentration (NOEC) ranges from 5.3 to 8.2 mg/L (Boudreau et al. 2003). In addition, PFOS is well absorbed but poorly metabolized and excreted, and it has a long half-life (200 days) in monkeys (Seacat et al. 2002). On the other hand, the potential toxicity of PFOS in general is not well characterized, and even less known are the mechanisms underlying its toxic effects. Hepatic toxicity and altered thyroid hormone levels have been found in monkeys and rodents (Luebker et al. 2002; Seacat et al. 2002, 2003). PFOS interferes with mitochondrial bioenergetics, and cell–cell communication has been implicated as a potential mechanism of toxicity (Hu et al. 2002, 2003). Recently, the neuroendocrine effects of PFOS were reported in the rat (Austin et al. 2003). In addition, maternal, developmental, and postnatal toxicities of PFOS were reported in the rat and mouse (Lau et al. 2003; Thibodeaux et al. 2003), showing that exposure to PFOS of pregnant rats and mice leads to significant physiologic alterations and that additional investigations are required to elucidate the pathophysiologic mechanisms.
PFOA is widely used in the production of chemicals and in aircraft and electronic products manufacturing. PFOA does not appear to be biomagnified in animals. However, it is a persistent pollutant, and low concentrations are detected in human blood, according to a risk assessment study (Renner 2003). In the same manner as PFOA, risk assessment of PFOSA is required for evaluating exposure levels and effects.
Because of the ubiquity of FOCs and their potential role in increasing the risk of neuroendocrine and reproductive dysfunction, human exposure assessment studies are urgently needed. A quantitative analysis of four FOCs in 65 human serum samples collected from several biologic supply companies in the United States detected PFOS (6.7–81.5 ng/mL), PFOA [limit of quantitation (LOQ), 35.2 ng/mL], and PFOSA (LOQ, 2.2 ng/mL) (Hansen et al. 2001). A study of human exposure to FOCs found that mean serum PFOS concentration was 17.7 ng/mL in 24 donors (Olsen et al. 2003b). In Japan, there is only one report of exposure in humans (Taniyasu et al. 2003), indicating that PFOS concentrations range from 2.4 to 14 ng/mL. Recently, a rapid and sensitive method for measuring 15 FOCs in human breast milk and serum has been developed (Calafat et al. 2003). The study of human exposure to FOCs requires information about the concentration of these toxicants in a non-occupationally exposed population. However, to our knowledge, there is no study of FOCs in human maternal and cord blood samples for fetal risk assessment. Human fetal and maternal exposure assessment studies are urgently needed because maternal and developmental toxicities of PFOS have been indicated (Lau et al. 2003; Thibodeaux et al. 2003).
The aim of the present study was to determine human cord and maternal serum concentrations of PFOS, PFOA, and PFOSA for fetal risk assessment. Although based on only a small sample set, our findings indicate the levels of FOCs to which Japanese women are exposed. We developed an easy, reliable, and high-throughput analytical method that uses liquid chromatography–electrospray mass spectrometry (LC-MS) coupled with online extraction to measure specific FOCs in human plasma and serum samples. This method enables the precise determination of standards in human blood samples and can be applied to the detection of PFOS, PFOA, and PFOSA in human blood samples for monitoring human exposure.
Materials and Methods
Clinical materials.
We recruited subjects between February and July 2003 at Sapporo Toho Hospitals in Hokkaido, Japan. This study was conducted with all the subjects’ written informed consent and was approved by the institutional ethical board for epidemiologic studies at the Hokkaido University Graduate School of Medicine. The data of physical and biologic examinations, laboratory tests, and questionnaires were recorded by the Department of Public Heath, Hokkaido University Graduate School of Medicine. Blood was sampled from pregnant women (n = 15; Table 1) between gestation weeks 38 and 41 (mean ± SD, 39.7 ± 1.05). Cord blood samples were collected immediately after birth by using standard procedure, which included careful cleansing of the cord and strict puncture of the umbilical vein to avoid maternal contamination. Body mass index (BMI) was calculated from the information given in Table 1 (height, prepregnancy weight, and weight at delivery).
FOC analysis.
PFOS [molecular weight (MW), 538.23; 98% purity], PFOA (MW, 414.07; > 90%), and PFOSA (MW, 199.14; 97%) were purchased from Wako Pure Chemical Inc. (Osaka, Japan), Fluka Chemie AG (Buchs, Switzerland), and ABCR GmbH & Co. (Im Schlehert, Germany). The internal standard (perfluorodecanoic acid) was purchased from Lancaster Company, Inc. (Morecambe, UK). Other reagents and solvents were of HPLC grade and were purchased from Wako Pure Chemical Inc. (Osaka, Japan). The distilled water purification system was Milli-Q gradient A 10 with an EDS polisher (Millipore, Bedford, MA, USA).
LC-MS with electrospray ionization was performed using an Agilent 1100 MSD-SL system (Agilent Technologies, Palo Alto, CA, USA). The working conditions were as follows: The drying nitrogen gas temperature was set at 350°C and was introduced into the capillary region at a flow rate of 12 L/min; the capillary was held at a potential of 3,500 V relative to the counter electrode in the negative-ion mode for all compounds. The fragmenter voltages were 220 V for PFOS, 130 V for PFOA, and 170 V for PFOSA during the chromatographic run. The direct injection volume was 30 μL. The column used was Inertsil C8–3 (2.1 × 100 mm, 5 μm; GL Sciences Inc., Tokyo, Japan) with a Mightysil RP-18 GP precolumn (2.0 × 5 mm, 5 μm; Kanto Chemical Inc., Osaka, Japan).
The column-switching LC-MS coupled with an on-line extraction system consisted of this LC-MS combined with an LC pump (Shimadzu LC-10 ADvp pump; Shimadzu, Kyoto, Japan) and Oasis HLB extraction column (20 × 2.1 mm, 25 μm; Waters Co., Milford, MA, USA). After a blood sample was injected by an autosampler, it was loaded onto the extraction column by flowing water/methanol (90/10, vol/vol) at a rate of 1.0 mL/min using a Shimadzu pump for 5 min. After on-line extraction for 5 min, the position of the switching valve was changed. This configuration connected the back-flashing extraction column to the analytical column and the MS detector in the flow path of the Agilent pump. After 20 min, the switching valve was returned to its original position. The run time for the assay of the sample mixture was 30 min. Gradient mobile phase of 1.0 mM ammonium acetate in water/acetonitrile (vol/vol) was used at a flow rate of 0.2 mL/min (5–15 min using a linear increase from 65 to 85% acetonitrile solution and holding at 85%).
In the quantitative procedure, standard solutions of PFOS, PFOA, and PFOSA were prepared in aqueous solution to cover the calibration range. Quantitative analysis was performed in the single ion monitoring mode to maximize sensitivity. PFOS, PFOA, and PFOSA concentrations in each sample were calculated relative to the internal standard added to the sample before direct analysis. Calibration curves of PFOS, PFOA, and PFOSA were performed daily for all samples with internal standard. We added 0.3 mL of sample to 0.3 mL of internal standard solution. The mixed sample was centrifuged at 3,000 × g for 10 min. The top clear layer was removed to the glass tube. This sample solution was filtered. This solution was analyzed by column-switching LC-MS. We analyzed quality-control materials (spiked samples in 25 ng/mL of PFOS) with each batch of samples on separated days. In the result, this material did not deviate from the 99% confidence interval (CI) (in this case, 99% CI, 24.17–25.53).
The method was developed previously. The compounds were separated by reverse-phase LC with a C8 column and detected by MS in the selected ion monitoring mode. When working in the selected ion monitoring mode, the m/z ions for PFOS, PFOA, and PFOSA were [M–K]− 499, [M–COOH]− 369, and [M–H]− 498. In addition, the m/z ion of the internal standard was designated as [M–COOH]− 469 in the negative ion mode.
The analysis of trace levels of PFOS, PFOA, and PFOSA in biologic samples is complicated by contamination, particularly by leaching from Teflon plastic. Thus, care must be taken to control contamination during experiments and, where possible, to eliminate the contamination. Investigations of PFOS, PFOA, and PFOSA contamination of the Milli-Q water system, the plastic tube, and the LC system produced negative results (below the limit of detection). We investigated whether the recoveries of PFOS, PFOA, and PFOSA (10 and 100 ng/mL) in the samples could be determined by this method. The average recoveries of PFOS, PFOA, and PFOSA ranged from 82.2 to 98.7%, with relative standard deviation < 5.2%. We used this method to assess FOC levels in human blood samples to obtain a reference range.
Thyroid hormone estimation.
Thyroid-stimulating hormone (TSH) and free thyroxine (T4) levels of newborns were measured in Sapporo City Institute of Public Health. Blood specimens on filter paper were collected from infants between 4 and 7 days of age. TSH and free T4 were determined in single 0.3-cm disks punched from the same filter-paper blood. TSH and free T4 were measured using enzyme-linked immunosorbent assay (TSH: Enzaplate N-TSH, Bayer Co., Tokyo, Japan; free T4: Enzaplate N-FT4, Bayer). Detection limits were for TSH, 0.5 μIU/mL, and for free T4, 0.20 ng/dL, respectively.
Results
We were able to realize a low LOQ and rapid analysis using the developed column-switching LC-MS coupled with on-line extraction method. The calculated LOQs when the signal-to-noise ratio was 10 were 0.5 for PFOS, 0.5 for PFOA, and 1.0 ng/mL for PFOSA in human serum samples.
We analyzed 15 pairs of maternal and cord serum samples for PFOS, PFOA, and PFOSA by this method. In the results (Table 2), the percentage detection of PFOS, PFOA, and PFOSA in the maternal and cord samples was 100% (30 of 30), 10% (3 of 30), and 0% (0 of 30), respectively. The concentrations of PFOS in maternal serum samples ranged from 4.9 to 17.6 ng/mL, whereas those in cord samples ranged from 1.6 to 5.3 ng/mL. The PFOS concentrations in maternal and cord blood samples were highly correlated (r2 = 0.876) (Figure 1). The average maternal age was 28.4 years, with a range of 17–37 years. In addition, the average BMIs based on prepregnancy weight and on weight at delivery were 20.3 and 24.4, respectively. PFOS concentration was not correlated with these parameters (Figure 2). On the other hand, Figure 3 shows no apparent correlation between cord PFOS concentration and sex and weight at birth. In addition, Figure 4 shows no apparent correlation between cord PFOS concentration and thyroid hormone factors such as TSH and free T4.
Discussion
To the best of our knowledge, this is the first report of PFOS and its related compounds in pregnant Japanese women and fetal cord blood. The issue of the bioavailability of PFOS in human, particularly in pregnant women and their cord blood, is controversial. A study of female human exposure levels of PFOS concentration found that levels in the United States are approximately twice those in Japan (Olsen et al. 2003b; Taniyasu et al. 2003). In contrast, PFOS concentrations in cord blood samples have not been reported so far. Therefore, we cannot compare our cord blood PFOS exposure levels with other data. However, a study of exposure to PFOS during pregnancy in the rat and mouse indicated that the amount of accumulated PFOS is proportional to the treatment dosage and the levels detected in fetal liver; in terms of concentration, the fetal liver level appears to contain approximately half as much PFOS as its maternal counterpart (Thibodeaux et al. 2003). Based on our study, the mean ratio of PFOS concentration in maternal blood to that in cord blood is 0.32 (range, 0.23–0.41). The studies of PFOS exposure during pregnancy in the rat and mouse (Lau et al. 2003; Thibodeaux et al. 2003) support our findings that PFOS accumulation can be measured in humans and that there is a high correlation between PFOS concentrations in maternal and cord blood.
On the other hand, PFOA was detected in a small number of maternal samples but not in cord samples (< 0.1 ng/mL). PFOA may pose a developmental risk to children at concentrations already found in the blood of women and children, according to a U.S. EPA preliminary risk assessment released in April 2003 (Renner 2003). Kannan et al. (2002a) measured FOCs, including PFOA, in bird livers collected from Japan and South Korea and found that the highest concentration of PFOA in the samples was 21 ng/g wet weight. PFOA has been found in environmental samples (Giesy and Kannan 2001; Kannan et al. 2002b). However, we do not know how people are exposed to PFOA according to a nonoccupational exposure assessment study. The occupational exposure levels of PFOA in humans are 1,780 ng/mL with a range of 40–10,060 ng/mL (Olsen et al. 2003a), and 899 ng/mL with a range of 722–1,120 ng/mL (Olsen et al. 2003c). By contrast, nonoccupational exposure to PFOA was found to be at trace levels (range of < 0.5–4.1 ng/mL, 15 of 21) in our other study (Okada et al. 2003). Therefore, we surmise that fetal exposure to PFOA is at trace levels. Recently, the dissociation constants for PFOA binding to human serum albumin (HSA) and the number of PFOA binding sites on HSA were determined (Han et al. 2003). At the same time, Han et al. (2003) predicted that PFOA bound to maternal blood protein may not be able to cross the placental barrier. The reasons for the trace levels of PFOA in fetal blood samples are nonoccupational exposure and the binding of PFOA to maternal blood protein.
Like polychlorinated biphenyls, organochlorine pesticides, and polybrominated diphenyl ethers, PFOS may be able to cross the placental barrier to enter fetal circulation (Covaci et al. 2002; DeKoning and Karmaus 2000; Mazdai et al. 2003; Sala et al. 2001; Waliszewski et al. 2000). Our data suggest that the slope is approximately 0.33 (Figure 1), indicating that PFOS does not pass into the fetal circulation completely; that is, there does seem to be a barrier effect. In contrast to PFOS, however, PFOA and PFOSA cannot cross the placental barrier to enter fetal circulation. PFOS is known to exhibit developmental toxicity and postnatal effects, as has been demonstrated in experimental animal studies (Lau et al. 2003; Thibodeaux et al. 2003). In those studies, exposure to PFOS during pregnancy led to significant physiologic alterations that indicate maternal toxicity. In addition, these results indicate that in utero exposure to PFOS severely compromises postnatal survival of neonatal rats and mice and causes delays in growth and development accompanied by hypothyroxinemia in the surviving rat pups. However, little research has been conducted on the effects of PFOS on the human fetus, including epidemiologic, analytic, and toxicologic studies. It is necessary to investigate PFOS effects on thyroid hormone levels in a large number of fetuses. PFOS affects the estrous cycle and functions as an endocrine disruptor (Austin et al. 2003). Thyroid hormones play an important role in brain development, and deficiencies in T4 are known to cause mental delay in humans. In the present study, there was no apparent association between fetal PFOS concentration and thyroid hormones; however, the sample size may have been too small to detect such a relationship in a human population. Large-scale follow-up studies are necessary to assess the adverse effects of exposure to PFOS and related compounds on fetal development. Further exposure assessment studies of PFOS in the susceptible population during pregnancy are needed to determine whether maternal exposure to PFOS can lead to adverse effects on the endocrine system in offspring. The description of a large-scale fetal population and the effects of PFOS on the endocrine system will be detailed in a forthcoming paper.
Figure 1 PFOS concentrations in maternal and cord blood samples (r2 = 0.8759; y = 0.3332x–0.0877).
Figure 2 Maternal age (A) and BMI (B) plotted against PFOS concentration in maternal blood samples (n = 15).
Figure 3 Infants’ sex (A) and birth weight (B) plotted against PFOS concentration in cord blood samples (n = 15). Error bars indicate mean ± SD.
Figure 4 Infants’ thyroid hormones levels (TSH and free T4) plotted against PFOS concentration in cord blood samples (n = 15).
Table 1 Characteristics of mothers and infants (n = 15).
Characteristics Median (range)
Maternal age (years) 28.4 (17–37)
Gestation (weeks) 39.7 (38–41)
Maternal height (cm) 157.2 (148–168)
Maternal prepregnancy weight (kg) 50.3 (40–61)
Maternal weight at delivery (kg) 60.4 (49.1–72)
Infants
Male [n (%)] 8 (53.3)
Female [n (%)] 7 (46.7)
Birth weight (g) 3125.7 (2,579–4,162)
Table 2 Concentrations (ng/mL) of FOC congeners (PFOS, PFOA, and PFOSA) in maternal and cord blood samples (n = 15).
Maternal
Fetal
Sample no. PFOS PFOA PFOSA PFOS PFOA PFOSA
1 10.4 0.7 ND 3.9 ND ND
2 17.6 2.3 ND 5.3 ND ND
3 9.5 ND ND 3.9 ND ND
4 7.9 ND ND 2.4 ND ND
5 12.8 ND ND 4.7 ND ND
6 5.4 ND ND 1.6 ND ND
7 7.9 ND ND 2.3 ND ND
8 9.5 0.5 ND 3.0 ND ND
9 7.9 ND ND 2.4 ND ND
10 5.8 ND ND 2.1 ND ND
11 9.9 ND ND 2.8 ND ND
12 8.1 ND ND 2.5 ND ND
13 4.9 ND ND 1.7 ND ND
14 8.3 ND ND 2.8 ND ND
15 6.9 ND ND 1.6 ND ND
ND, not detected: PFOS < 0.5 ng/mL; PFOA < 0.5 ng/mL; PFOSA < 1.0 ng/mL.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.6424ehp0112-00120815289169Children's HealthArticlesFetal Exposure to PCBs and Their Hydroxylated Metabolites in a Dutch Cohort Soechitram Shalini Devi 1Athanasiadou Maria 2Hovander Lotta 2Bergman Åke 2Sauer Pieter Jan Jacob 11University Hospital Groningen, Department of Paediatrics/Beatrix Children’s Hospital, Groningen, the Netherlands2Department of Environmental Chemistry, Stockholm University, Stockholm, SwedenAddress correspondence to S.D. Soechitram, University Hospital Groningen, Department of Paediatrics, PO Box 30.001, 9700 RB Groningen, the Netherlands. Telephone: 00-31-50-3612470. Fax: 00-31-3611704. E-mail:
[email protected] thank all mothers and children who participated in this study. We also thank I. Athanassiadis for skillful analytical assistance.
The European Commission, Environment and Climate Program (grant ENV-CT96-0170) financially supported this research.
The authors declare they have no competing financial interests.
8 2004 13 4 2004 112 11 1208 1212 29 4 2003 13 4 2004 Publication 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. Polychlorinated biphenyls (PCBs) are still the most abundant pollutants in wildlife and humans. Hydroxylated PCB metabolites (OH-PCBs) are known to be formed in humans and wildlife. Studies in animals show that these metabolites cause endocrine-related toxicity. The health effects in humans have not yet been evaluated, especially the effect on the fetus and newborn. The aim of this study is to measure the levels of PCBs and OH-PCBs in maternal and cord blood samples in a population with background levels of PCBs. We analyzed 51 maternal and corresponding cord blood samples in the northern part of the Netherlands. The PCB concentrations in maternal plasma ranged from 2 to 293 ng/g lipid, and OH-PCB concentrations from nondetectable (ND) to 0.62 ng/g fresh weight. In cord plasma, PCB concentrations were 1–277 ng/g lipid, and OH-PCB concentrations, ND to 0.47 ng/g fresh weight. The cord versus maternal blood calculated ratio was 1.28 ± 0.56 for PCBs and 2.11 ± 1.33 for OH-PCBs, expressed per gram of lipid. When expressed per gram fresh weight, the ratios are 0.32 ± 0.15 and 0.53 ± 0.23 for PCBs and OH-PCBs, respectively. A significant correlation between the respective maternal and cord levels for both PCBs and OH-PCBs was found. Our results indicate that OH-PCBs and PCBs are transferred across the placenta to the fetus in concentrations resulting in levels of approximately 50 and 30%, respectively, of those in maternal plasma. More research in humans is needed to evaluate potential negative effects of these endocrine disruptors on the fetus.
cord bloodhuman plasmahydroxylated PCBsPCBplacental transfer
==== Body
Polychlorinated biphenyls (PCBs) are, together with DDT (dichlorodiphenyltrichloroethane) and DDT-related chemicals, the most dominating classes of environmental pollutants worldwide, with concentrations varying in wildlife and in humans from different areas of the globe (Damstra et al. 2002). Although all PCB congeners, either present in commercial mixtures or as single chemicals, are lipophilic substances with low water solubility, only some of them, even within the same class of chemicals, have a strong tendency to accumulate in higher organisms. This is shown by a few strongly dominating PCB congeners retained in, for example, humans (Fängström et al. 2002; Koopman-Esseboom et al. 1994a), grey seals (Blomkvist et al. 1992), and polar bears (Letcher et al. 1996), all acting at top of the food chain. PCB concentrations are generally in the low micrograms PCB per gram lipid range among humans (Koopman-Esseboom et al. 1994b; Sjödin et al. 2000), but higher levels are occasionally found in individuals with a heavy consumption of fatty fish from contaminated waters (Sjödin et al. 2000) or with a pronounced diet based on subsistence food items (Ayotte et al 1997; Fängström et al. 2002). The levels of PCBs have been shown to slowly decrease (Norén and Meironyté 2000) as a result of the legislative measures taken to prohibit the production of PCBs in the early 1970s, leading to lower environmental releases.
Studies have shown negative effects of PCBs in animals and humans, especially in newborn infants (Patandin 1999; Winneke et al. 2002). Reported effects of background exposure in infants include reduced birth weight, less postnatal growth (Patandin et al. 1998; Rylander et al. 1996), neonatal hypotonia (Huisman et al. 1995a, 1995b), impaired development and impaired immune response (Weisglas-Kuperus 1998; Weisglas-Kuperus et al. 1995), and lower thyroid hormone levels (Brouwer et al. 1998, 1999; Koopman-Esseboom et al. 1994a, 1997; Osius et al. 1999). With few exceptions (e.g., Walkowiak et al. 2001), most negative effects of background levels of PCBs were primarily related to antenatal exposure, whereas post-natal effects from PCBs were related mainly to accidental exposure of infants to rather high levels of PCBs and other organohalogen substances (Kuratsune et al. 1996). It is unknown whether these effects are caused by the PCBs themselves or by their metabolites.
Hydroxylated PCBs (OH-PCBs), or polychlorobiphenylols, are major metabolites of PCBs (Letcher et al. 2000). These metabolites are formed by oxidative metabolism of PCBs, mediated by the cytochrome P450 enzymatic system, that generally involves an arene oxide intermediate (Jerina and Daly 1974). OH-PCBs, like most phenolic compounds, are readily conjugated and excreted, but several OH-PCB congeners and some other halogenated phenolic compounds have been found to be retained in human and wildlife blood (Bergman et al. 1994; Fängström et al. 2002; Hovander et al. 2002; Letcher et al. 2000; Sandau et al. 2000). The OH-PCB concentrations so far reported have been 10–20% of the PCB level in humans but were found to be higher in, for example, polar bear blood (Sandau et al. 2000). The three most abundant OH-PCB congeners retained in the blood are metabolites of CB105, CB118, CB138, CB153, and CB187 (Hovander et al. 2002), all known to be among the most persistent and bioaccumulative PCB congeners.
The toxicologic impact of OH-PCBs is still not known, but several studies indicate that these metabolites may have adverse effects in mammals (Meerts et al. 2002). In animals, OH-PCBs are transferred across the placenta (Bjerregaard and Hansen 2000; Korrick et al. 2000; Sala et al. 2001). It is not yet known at what rate OH-PCBs are transferred to the human fetus (Meironyté-Guvenius et al. 2003). The objective of the present study was to assess PCB and OH-PCB levels in mothers and children at birth and to determine the transplacental transfer of PCBs and OH-PCBs.
Materials and Methods
Cohort.
From September 1998 through December 2000, pregnant women from the northern part of the Netherlands were invited by their midwife or obstetrician to participate in a study on exposure to PCBs and OH-PCBs and their potential effects on the development of the newborn infant. The mothers had to be of Western European origin, and Dutch had to be their native language. To establish an optimal study population, pregnancy and delivery had to involve no serious illness or complications. Only infants born at term (37–42 weeks of gestation) without congenital anomalies or diseases were included. Admission of an infant at a hospital more than 1 day after birth was an exclusion criterion. The medical ethics committee of the University of Groningen approved the study. A blood sample was taken from the pregnant women in the second and/or third trimester of their pregnancy. Blood samples of the umbilical cord were taken directly after delivery. Blood was collected in a vacuum system EDTA tube (Ritmeester, Utrecht, the Netherlands) and centrifuged within 24 hr for 5 min at 4,000 rpm. The plasma was transferred to separate glass tubes with screw caps with Teflon inlayers and stored at −18°C to −20°C until analysis. A total of 51 paired maternal and cord blood plasma samples were analyzed in the present study. An additional 29 maternal blood samples and 11 cord blood samples were also analyzed, but these samples were not paired and are not included in this study. The 51 samples were analyzed at the analytical laboratory participating in the study.
Chemicals.
Hexane and dichloromethane were of pesticide grade (Fisons, Leicestershire, England). Methyl tert-butyl ether (MTBE), 2-propanol, and potassium hydroxide (Eka Nobel AB, Bohus, Sweden), as well as potassium chloride (Merck, Darmstadt, Germany) and sulfuric acid (98%; BDH Laboratory Supplies, Poole, England), were of analytic quality. Ethanol (99.5%) was purchased from Kemetyl (Haninge, Sweden). Diazomethane was synthesized as described by Furniss et al. (1989). Silica gel (< 0.063 mm; Macherey-Nagel, Düren, Germany) was activated by heating it overnight at 280°C and allowed to cool to room temperature before use. All glassware was heated at 300°C overnight before use.
Instruments.
Gas chromatography (GC) was performed on a Varian 3400 GC equipped with an electron capture detector, a Varian 8200 autosampler, and a split/splitless injector operated in the splitless mode. The fused silica capillary column used was a nonpolar column, CP-SIL 8CB (25 m × 0.15 mm × 0.12 μm), from Chrompack (EA Middelburg, the Netherlands). The column oven temperature was programmed as follows: for analysis of methylated derivatives of OH-PCBs, 80°C (2 min), then 50°C/min to 200°C, then 1°C/min to 230C°, then 30°C/min to 330°C (3 min); for analysis of PCBs, 80°C (1 min), then 20°C/min to 300°C (10 min). The injector and detector temperatures were 250°C and 360°C, respectively. Hydrogen was used as carrier gas, and nitrogen was used as makeup gas.
For the evaporation of solvents during cleanup of the samples, we used a centrifugal concentrator (Genevac SF50 Sales Development Ltd., Ipswich, England). For the phase separation during the extraction and lipid removal with concentrated sulfuric acid using test tubes, we used a Wifug centrifuge (Wifug Ltd., Bradford, England).
Analysis.
The following PCB congeners were used as analytical standards: 2,3,3′,4,4′-pentachlorobiphenyl (CB105), 2,2′,3,4,4′,5′-hexachlorobiphenyl (CB138), 2,2′,3,4′,5,5′-hexachlorobiphenyl (CB146), 2,2′,3,3′,4,4′,5-heptachlorobiphenyl (CB170), 2,2′,3,4,4′, 5,5′-heptachlorobiphenyl (CB180), and 2,2′,3,4′,5,5′,6-heptachlorobiphenyl (CB187), and they were purchased from Promochem AB (Ulricehamn, Sweden). 2,2′,4,4′,5,5′-Hexachlorobiphenyl (CB153), 2,3,3′,4,4′,5-hexachlorobiphenyl (CB156), and 2,2′,3,4,4′,5′,6-heptachlorobiphenyl (CB183) were synthesized in house (Bergman et al. 1990; Sundström et al. 1973); 2,3′, 4,4′,5-pentachlorobiphenyl (CB118) and 2,3,3′,4,4′, 5,5′-heptachlorobiphenyl (CB189, internal standard) were synthesized as described elsewhere (Sundström et al. 1973). A larger number of PCB congeners are given here than are given in “Results.” However, for this study, the ones mentioned here were all analyzed. The PCB numbering system as suggested by Ballschmiter et al. (1993) is applied in the present study.
The parent PCB compounds and their OH-PCB metabolites are given in Table 1. The following methoxylated (MeO-PCB) congeners were used as authentic reference standards to quantify the methyl ether derivatives of the hydroxylated PCBs: 4-methoxy-2,3,3′,4′, 5-pentachlorobiphenyl (4-MeO-CB107), 3-methoxy-2,2′,3′,4,4′,5-hexachlorobiphenyl (3′-MeO-CB138), 4-methoxy-2,2′,3,4′,5, 5′-hexachlorobiphenyl (4-MeO-CB146), 3-methoxy-2,2′,4,4′,5,5′-hexachlorobiphenyl (3-MeO-CB153), 4-MeO-2,2′,3,3′,4′,5,5′-heptachlorobiphenyl (4′-OH-CB172), and 4-methoxy-2,2′,3,4′,5,5′,6-heptachloro-biphenyl (4-MeO-CB187). 4-Methoxy-2,3,3′,4′,5,5′,6-heptachlorobiphenyl (4-MeO-CB193) and 4-hydroxy-2,3,3′,4′, 5,5′,6-heptachlorobiphenyl (4-OH-CB193) were synthesized in house (Bergman et al. 1995) and applied as internal standards. The MeO-PCB and OH-PCB congeners are numbered according to Letcher et al. (2000). For this study the following OH-PCBs were measured: OH-CB107, OH-CB153, OH-CB146, OH-CB138, OH-CB187, and OH-CB172 (all presented in “Results”).
Extraction and cleanup.
The extraction procedure applied in this study is identical to the method described by Hovander et al. (2000). The cleanup procedure that was required to obtain samples pure enough for analysis was a combination of sulfuric acid treatment and silica gel/sulfuric acid column chromatography separations. Both methods are described by Hovander et al. (2000). Before extraction, the samples were spiked with CB189 (2 ng/sample) and 4-OH-CB193 (1 ng/sample). Sample volumes smaller than approximately 4 g of plasma were adjusted to 5 g with an aqueous 1% potassium chloride solution before extraction. For each 10 samples, a solvent blank was run.
The procedure applied for cleanup may be summarized as follows: To approximately 5 g plasma spiked with internal standards, 1 mL 6 M hydrochloric acid was added and mixed well. Thereafter, 2-propanol (6 mL) was added and mixed well; each sample was extracted and reextracted with 6 and 3 mL of hexane:MTBE (1:1), respectively. The phases (a water phase and an organic phase) were separated by centrifugation. The organic phase also underwent a washing step with 4 mL aqueous 1% potassium chloride before reduction of the organic solvent. The lipid residue was determined gravimetrically.
The phenolic compounds were isolated after the extraction from the plasma by using potassium hydroxide (0.5 M in 50% ethanol) and were derivatized to their corresponding methyl ethers by addition of ethereal diazomethane (0.5 mL, 3 hr at 4–8°C) before cleanup and analysis (Hovander et al. 2000).
Results
In total, 214 pregnant women expressed interest in participating in this study; 104 of them actually participated in the study. Of these 104, a random sample of 51 mother–infant pairs were included in the study.
Clinical characteristics of the mothers and infants are given in Table 2. The mean ± SD maternal age was 31 ± 4 years; mean body mass index (BMI) was 20 ± 3. Infants were born after a gestational age of 40 ± 1 weeks with a birth weight of 3,714 ± 461 g. Fifty-five percent of the infants were male.
The results of all PCB and OH-PCB measurements are presented in Table 3 [mean (range)]. The sum PCB (sum of six congeners) in maternal plasma was 268 (113–619) ng/g lipid [mean (range)], compared with 345 (78–809) ng/g lipid in cord blood. The sum OH-PCB (sum of six congeners) in maternal blood was 54 (14–125) ng/g lipid weight compared with 114 (49–244) ng/g lipid in cord blood. Expressed per gram fresh weight—a more suitable way of expressing OH-PCB values because they are more hydrophilic than are PCBs—the levels were 0.340 (nondetectable to 0.622) ng/g fresh weight in maternal blood compared with 0.180 (nondetectable to 0.407) ng/g fresh weight in cord blood. The lipid content of maternal plasma was considerably higher than that of the fetus, 0.7 versus 0.2 g/100 mg plasma.
The ratios of cord versus maternal plasma concentrations for PCB and OH-PCB congeners, expressed in nanograms per gram lipid as well as nanograms per gram plasma, are shown in Figure 1. The sum PCBs have a ratio ± SD of 1.3 ± 0.56 when expressed per gram of lipid. The sum OH-PCBs have a ratio of 2.2 ± 1.33 when expressed per gram of lipid and a ratio of 0.5 ± 0.23 when expressed per gram of plasma.
Table 4 shows the correlation between the maternal and cord levels for both the PCBs and the OH-PCBs. Except for CB118 and CB156, there was a significant correlation between the maternal and cord plasma levels. This indicates a transfer of these compounds across the placental barrier. Figure 2 shows the correlation between PCB and OH-PCB levels between maternal and cord plasma.
The correlations between the parent compound and the resulting OH-PCB for both maternal plasma and cord plasma are given in Table 5. There was a significant correlation between the parent compound and resulting OH metabolite for all congeners.
Discussion
In this study, we found that OH-PCBs, metabolites of PCBs, are detectable in plasma of pregnant women who are exposed to background levels of PCBs in the Netherlands. Both the PCB pollutants and their OH-PCB metabolites are also detectable in cord plasma. Second, plasma levels of OH-PCBs in umbilical cord plasma are 50% of the levels in the mothers, indicating a considerable placental transfer. The placental transfer of OH-PCBs may be explained from their strong binding to transthyretin (TTR) and active transport across the placenta. PCBs, in contrast, are neutral lipophilic compounds strongly distributed to lipids and therefore less readily cross the placenta.
The low ratio in PCBs when expressed per gram of plasma may be explained by the fact that a newborn infant consists of 15% fat, in contrast to 25% fat in adults. In our study, the lipid content of cord plasma is about 0.2% lipids compared with 0.7% lipids in the mother. The PCB body burden of the infant expressed per gram of body weight therefore is lower than the body burden of the mother, although the PCB levels are equal or slightly higher when expressed per gram of lipid.
Because of the different transfer mechanism of OH-PCBs, the infant will have a body burden, expressed per body weight of OH-PCBs, of 50–70% of their mothers, an intriguing observation making OH-PCBs possibly more important from a risk assessment viewpoint than initially thought.
Our finding that there is a correlation between the maternal and cord levels of both PCBs and OH-PCBs further supports the transplacental transfer of both compounds.
PCBs can reach the fetus only by transplacental transfer. OH-PCBs in the fetus can be the result of transplacental transfer as well as hydroxylation by the fetus itself. From our observational study, no firm conclusions can be drawn regarding the source of OH-PCBs in the fetus. Levels of OH-PCBs were, on average, approximately 50% of maternal levels. At the same time, the correlation between parent PCB and resulting OH-PCB was stronger in the fetus than in the pregnant mother. The fetus excretes OH-PCBs to the mother, whereas the mother excretes OH-PCBs in feces and/or urine. That the correlation between the parent compound and the OH congener is rather weak in the mother can be explained by differences in kinetics between the PCBs and OH-PCBs. PCBs have a half-life longer than that of OH-PCBs.
Levels of PCBs found in this study are almost equal to levels we have found in the Netherlands in a comparable group of healthy pregnant women (Koopman-Esseboom et al. 1994b, 1994c). Plasma levels found in this study versus levels found 10 years ago are, for CB118, 0.19 versus 0.16 ng/g plasma; for CB138, 0.50 versus 0.60 ng/g; for CB153, 0.70 versus 0.91 ng/g; and for CB180, 0.30 versus 0.54 ng/g. Although the samples are analyzed in different laboratories by different methods, these results might indicate that PCB levels in the Netherlands do seem to have hardly declined in these 10 years. We cannot compare the OH-PCB levels because OH-PCB levels have not been measured before in the Dutch population.
One limitation to this study is the time difference between the blood taken from the pregnant mother and cord blood. We do not believe, however, that this time difference influenced our results. PCB levels in the mother are the result of lifelong exposure and do not change during pregnancy (Koopman-Esseboom et al. 1994a). Most likely, the conversion of PCB to OH-PCB does not change during pregnancy. Although we did not measure the level of OH-PCB at more times during pregnancy, we expected the levels of both PCBs and OH-PCBs to be constant during pregnancy and therefore accepted the time difference between maternal samples and cord samples, caused by practical considerations.
The mother–infant pairs included in this study were a random selection from a larger cohort of mother–infant pairs. The total group was included on a voluntary basis in our study. Although we expected to have a random sample of mothers in our region, we cannot exclude some bias in the mothers who volunteered to enter the study. Mothers might have had some concerns regarding these environmental compounds. These could be mothers who select their food carefully, but also mothers without such opportunities for food selection who are therefore concerned. Altogether we believe, however, that our population is a valid representation of the population in our region.
Sjödin et al. (2000) measured PCB and OH-PCB levels in Swedish and Latvian fishermen consuming either low or high amounts of fish from the Baltic Sea, known to be highly polluted by contaminants. They found CB153 levels in Swedish fishermen ranging from 226 ng/g lipid in low fish consumers to 534 ng/g lipid in high fish consumers. The CB153 levels in the pregnant women in our study were 101 ng/g lipid (range, 43–293). Sjödin et al. (2000) also measured OH-PCB levels in their subjects. The 4-OH-CB187 levels ranged from 31 ng/g lipid in low fish consumers to 176 ng/g lipid in high fish consumers from Latvia and from 43 to 75 ng/g lipid in their Swedish counterparts. In the pregnant women in our study, we found much lower 4-OH-CB187 levels of 20 ng/g lipid (range, 6.6–49).
Recently, Sandau et al. (2002) also analyzed OH-PCBs in umbilical cord plasma of neonates from coastal populations in Québec. They measured OH-PCB levels in three different areas. In one area (southern Québec), individuals were exposed to background levels of PCBs, and in the other two (Nunavik and Lower North Shore) they were selected because of their high fish consumption. The OH-PCB concentration range for OH-CB187 was 10–250 pg/g plasma; OH-CB146, 4–507 pg/g plasma; OH-CB153, 3–74 pg/g plasma; OH-CB107, 3–168 pg/g plasma; OH-CB138, 3–92 pg/g plasma; and OH-CB172, 1–75 pg/g plasma (Sandau et al. 2002). The concentrations measured in Québec are higher than those found in our study. This can be explained by the level of contamination and the mainly fish diet of Québec. Also remarkable is a different pattern in OH-PCBs between the study in Québec and our study. This might indicate a different source of PCBs.
OH-PCBs are considered endocrine disruptors in animals, with effects on thyroid hormones, estrogens, and testosterone. Animal studies have shown a significant reduction of thyroid hormones in the brain after exposure to OH-PCBs. The reduction in thyroid hormones is related to the binding of OH-PCBs to TTR, which can be explained by the strong structural resemblance between OH-PCB and thyroxine (Brouwer et al. 1998; Ghosh et al. 2000). Some OH-PCBs have > 60% higher affinity for TTR than does thyroxine itself (Ghosh et al. 2000; Meerts et al. 2000, 2002). In humans, thyroid hormones are mainly bound to thyroxine-binding globulin (TBG) (Brouwer et al. 1998). Whether the binding of OH-PCBs to TTR has negative effects in humans is unknown.
Meerts (2001) observed a significant prolongation of estrous cycles in the offspring of pregnant rats exposed to OH-CB107 but found no effect on their reproductive performance. Also, Meerts (2001) observed an impaired habituation in male rat offspring but not in female offspring.
Whether OH-PCBs do have an effect in the human on either the thyroid or sex hormones is unknown. TBG and not TTR is the main transport protein of thyroxine in humans. TTR, however, might be important for the transfer of thyroid hormones into the brain. Further studies in humans are needed to elucidate if OH-PCBs have a negative effect on the human fetus or older individuals.
Conclusion
Our study indicates that OH-PCBs can be found in the plasma of healthy pregnant women in the Netherlands. The level of OH-PCBs in cord plasma is approximately 50% of levels found in the mother. So both PCBs and OH-PCBs cross the placenta. In both cord and maternal plasma, OH-PCBs are correlated with the respective parent PCBs. The only way to reduce the exposure of the fetus to these potentially toxic compounds is to reduce the body burden of PCBs in pregnant women.
Figure 1 Ratio cord versus maternal plasma expressed (A) per lipid weight and (B) per fresh weight (mean ± SD).
Figure 2 Correlation between maternal and cord plasma levels for (A) PCB-153 (expressed per lipid weight) and (B) OH-PCB-153 (expressed per fresh weight).
Table 1 PCB congeners and their respective OH-PCB metabolites.
Parent PCB Metabolite
CB105 4-OH-CB107
CB118 4-OH-CB107
CB138 4-OH-CB146
CB138 3′-OH-CB138
CB153 4-OH-CB146
CB153 3-OH-CB153
CB170 4′-OH-CB172
CB180 4′-OH-CB172
CB187 4-OH-CB187
Table 2 Characteristics of the study group (n = 51).
Characteristic Value
Maternal age (years; mean ± SD) 31 ± 4
Weight gain during pregnancy (kg; mean ± SD) 13.3 ± 6
Mother’s BMI (mean ± SD) 20 ± 3
Parity, first-born/second- or third-born (%) 33/67
Smoking during pregnancy, yes/no (%) 21/79
Alcohol during pregnancy, yes/no (%) 23/77
Sex of child, male/female (%) 55/45
Gestational age (weeks; mean ± SD) 40 ± 1
Apgar score 1 min [median (range)] 9 (4–10)
Birth weight (g; mean ± SD) 3,714 ± 461
Table 3 PCB and OH-PCB concentrations in corresponding maternal and cord plasma samples [mean (range); n = 51].
PCB Maternal PCB
Cord PCB
Maternal OH-PCB
Cord OH-PCB
congener lw (ng/g) fw (ng/g) lw (ng/g) fw (ng/g) OH-PCB congener lw (ng/g) fw (ng/g) lw (ng/g) fw (ng/g)
CB118 28 (8–69) 0.188 (0.054–0.452) 56 (9–277) 0.093 (0.013–0.470) 4-OH-CB107 10 (0.8–38) 0.060 (ND–0.183) 14 (4–354) 0.022 (ND–0.048)
CB146 10 (2–29) 0.069 (0.015–0.312) 30 (1–106) 0.050 (0.002–0.154) 3-OH-CB153 5 (1.4–13) 0.035 (ND–0.101) 14 (3–38) 0.021 (ND–0.063)
CB153 101 (43–293) 0.700 (0.248–3.514) 115 (25–252) 0.193 (0.037–0.412) 4-OH-CB146 10 (3–27) 0.063 (ND–0.129) 23 (8–58) 0.036 (ND–0.097)
CB138 73 (32–171) 0.496 (0.183–2.040) 77 (20–213) 0.130 (0.029–0.399) 3′-OH-CB138 7 (1.3–26) 0.045 (ND–0.166) 18 (6–50) 0.028 (ND–0.079)
CB156 12 (4–22) 0.084 (0.028–0.215) 29 (7–96) 0.047 (0.013–0.135) 4-OH-CB187 20 (7–49) 0.022 (ND–0.048) 38 (17–69) 0.061 (ND–0.115)
CB180 44 (15–93) 0.300 (0.090–0.961) 37 (11–88) 0.063 (0.019–0.144) 4′-OH-CB172 2 (0.4–6) 0.015 (ND–0.034) 7 (2–18) 0.011 (ND–0.030)
∑PCB 268 (113–619) 1.837 (0.645–7.432) 345 (78–809) 0.585 (0.134–1.370) ∑OH-PCB 54 (14–125) 0.340 (ND–0.622) 114 (49–244) 0.180 (ND–0.407)
Lipid (%) 0.7 (0.4–1.2) 0.2 (0.1–0.3) Lipid (%) 0.7 (0.4–1.2) 0.2 (0.1–0.3)
Abbreviations: fw, fresh weight; lw, lipid weight; ND, nondetectable.
Table 4 Correlation between maternal and cord plasma levels of PCBs.
PCB congener (g lipid) Correlation coefficient OH-PCB congener (g fresh weight) Correlation coefficient
CB118 0.03 4-OH-CB107 0.15
CB146 0.34 3-OH-CB153 0.54
CB153 0.57 4-OH-CB146 0.54
CB138 0.36 3′-OH-CB138 0.57
CB156 0.15 4-OH-CB187 0.55
CB180 0.79 4′-OH-CB172 0.58
Sum 0.43 Sum 0.52
Table 5 Correlation between parent compound and hydroxy metabolite in maternal and cord plasma.
Parent PCB congener (g lipid) OH-PCB metabolite congener (g fresh weight) Correlation coefficient in maternal sample Correlation coefficient in cord sample
CB118 4-OH-CB107 0.21 0.46
CB138 4-OH-CB146 0.38 0.58
CB138 3′-OH-CB138 0.26 0.33
CB153 4-OH-CB146 0.39 0.49
CB153 3-OH-CB153 0.27 0.33
CB180 4′-OH-CB172 0.48 0.45
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7003ehp0112-00121315289170Children's HealthArticlesDrinking Water Contaminants, Gene Polymorphisms, and Fetal Growth Infante-Rivard Claire Department of Epidemiology, Biostatistics and Occupational Health, Faculty of Medicine, McGill University, Montréal, Québec, CanadaAddress correspondence to C. Infante-Rivard, Department of Epidemiology, Biostatistics, and Occupational Health, Faculty of Medicine, McGill University, 1130 Pine Ave. West, Montréal, Québec, Canada H3A 1A3. Telephone: (514) 398-4231. Fax: (514) 398-7435. E-mail:
[email protected] project was supported by grants from the Canadian Institutes of Health Research. The author holds a Canada Research Chair (James McGill Professorship).
The author declares she has no competing financial interests.
8 2004 26 5 2004 112 11 1213 1216 5 2 2004 26 5 2004 Publication 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. There are still many uncertainties regarding the risk of adverse pregnancy outcomes associated with exposure to drinking water disinfection by-products. In Montréal, Québec, Canada, we carried out a hospital-based case–control study including 493 cases of intrauterine growth restriction defined as birth weight below the 10th percentile for gestational age and sex, according to Canadian standards. Controls were babies (n = 472) delivered at the same hospital whose birth weight was at or above the 10th percentile, matched for gestational age, race, and sex. Exposure to total and specific trihalomethanes was measured using regulatory data collected by municipalities and the provincial Ministry of Environment. Residential history, water drinking, and shower habits during pregnancy, as well as known risk factors for intrauterine growth restriction, were measured with a face-to-face interview with all mothers. Mothers and newborns were characterized for two genetic polymorphisms, one in the CYP2E1 gene (G1259C), and another in the 5,10-methylenetetrahydrofolate reductase (MTHFR) gene (C677T). Exposure to specific and total trihalomethanes from drinking water, determined for 458 cases and 426 controls, did not result in an increased risk of intrauterine growth restriction. However, significant effect modification was observed between newborns with and without the CYP2E1 variant; among newborns with the variant, the adjusted odds ratio for intrauterine growth restriction associated with exposure to average total trihalomethanes above the 90th percentile (corresponding to 29.4 μg/L) was 13.20 (95% confidence interval, 1.19–146.72). These findings suggest that exposure to trihalomethanes at the highest levels can affect fetal growth but only in genetically susceptible newborns.
CYP2E1 gene disinfection by-productsdrinking watergene polymorphismgene–environment interactionintrauterine growth restrictionlow birth weightMTHFR genetrihalomethanes
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Chlorination by-products in drinking water come from the reaction of chlorine with organic material in the water. This reaction occurs naturally or originates from municipal, agricultural, and industrial wastes. Trihalomethanes (THMs) such as chloroform, bromoform, bromodichloromethane (BDCM), and chlorodibromomethane are the most prevalent class of disinfection by-products (DBPs) found in treated water. From the toxicologic literature, chloroform appears to affect fetal development (Geveker Graves et al. 2001), by mechanisms that have not yet been elucidated. In the last decade, a number of epidemiologic studies have been carried out to determine the effect of DBPs on adverse pregnancy outcomes (Bove et al. 2002). Two recent reviews propose that the weight of the evidence, although moderate and not fully conclusive, is in favor of an association between DBPs and fetal growth restriction (Bove et al. 2002; Geveker Graves et al. 2001). Most of the previous studies were based on information from birth records, and despite the fact that the number of records included was usually large, the information that was available on other risk factors for fetal growth, or on other personal variables influencing exposure to DBPs, was often limited.
The primary enzyme involved in the metabolism of low doses of chloroform is CYP2E1 (Meek et al. 2002). My group has previously shown that a polymorphism in the CYP2E1 gene can modify the effect of water contaminants (Infante-Rivard et al. 2002a). Another enzyme, 5,10-methylenetetrahydro-folate reductase (MTHFR), together with folic acid, is involved in the remethylation of homocysteine to methionine, as well as in the methylation of DNA, proteins, and phospholipids (Botto and Yang 2000). Alston (1991) reported that vitamin B12-dependent methionine biosynthesis could be inhibited by chloroform. Common polymorphisms in the MTHFR gene have been identified (Botto and Yang 2000). To my knowledge, no study has considered the role of genetic polymorphisms on the relationship between DBPs and fetal growth.
My group carried out a study on genetic and metabolic risk factors for intrauterine growth restriction (IUGR) (Infante-Rivard et al. 2002b, 2003a, 2003b). In the course of the study, we also collected personal and environmental information to analyze the association between chemical water contaminants and fetal growth.
Materials and Methods
Study subjects.
Details on study subjects have been reported elsewhere (Infante-Rivard et al. 2002b). Briefly, cases were newborns whose birth weight was below the 10th percentile for gestational age and sex, based on Canadian standards (Arbuckle et al. 1993). All cases seen at the largest university-based mother–child center in Montréal between May 1998 and June 2000 who were born singleton, alive after the 24th week of gestation, and without severe congenital anomalies were eligible for the study. During that period, 505 newborns met the eligibility criteria, and 493 were included in the study (97.6%). Controls were born at the same hospital and met the same eligibility criteria, except that their birth weight was at or above the 10th percentile. They were matched to cases for gestational week, sex, and race (white, black, Hispanic/Amerindian, and Asian) and usually born within 1 week of the matched case subject. Of those identified, 480 controls were invited to participate, and 472 accepted (98.3%). The project was approved by the hospital ethics committee. An informed consent was signed by the mother to collect cord and maternal blood.
Interview.
A face-to-face interview with all mothers of cases and controls was carried out in French or English at the hospital, generally within 2 days of delivery. It included questions about demographic factors, complications of pregnancy, maternal chronic diseases, obstetric history, and smoking. The medical record was used for variables such as height and weight and to confirm pregnancy diseases. To determine exposure to water contaminants for each pregnancy trimester until delivery, we collected the following information: maternal residential history, source of drinking water (community, private well, bottled), use and type of domestic water filter, average number of glasses of water per day at home or elsewhere (including those with reconstituted frozen fruit juices), usual way of consuming tap water (directly from tap, after refrigeration), average number of showers per week, and usual duration of showers.
Exposure ascertainment.
For the study period, exposure to THMs from drinking water according to place of residence was obtained from regulatory data collected by municipalities and the Ministry of Environment. There were 189 distribution systems involved; although for most systems there were multiple measurements on the same date, I was only provided with average measures. Of the 965 women in the study, 10 lived in other Canadian provinces, 37 lived in other countries, and there was no address for 2 others, leaving 916 study women reporting addresses in the province. Overall, THM information was available for 884 (91.6%) of the study women (458 cases and 426 controls).
Exposure from drinking water.
Estimates of exposure levels to total and specific THMs from drinking water were tabulated first as average level at the tap (from treatment plant data) over the pregnancy period [(sum of concentration i × duration in days at level i based on residence) ÷ (total number of pregnancy days)]; this measure was then categorized at the 90th percentile of the distribution for cases and controls. Another index was the cumulative level over the pregnancy period (sum of concentration i × duration in days at level i); it was also categorized at the 90th percentile of the distribution. When the source of drinking water was exclusively well water or bottled water, the exposure levels for drinking water were set at zero. Finally, the estimated average level of THMs at the tap from the municipal distribution system was multiplied by the number of glasses of tap water per day averaged over pregnancy. Another version of this index included applying an arbitrary weight of 0.9 to the average number of tap water glasses if a filter was used or if the water was refrigerated before consumption.
Exposure from showering.
Exposure to THMs from showering was set to zero for residences using exclusively well water, others were assigned their network level. An index of exposure to THMs from showering was defined as frequency of showers per week (times duration) multiplied by the average levels of exposure to THMs at the tap from the distributing network.
Genotyping.
Polymerase chain reaction (PCR) allele-specific oligonucleotide hybridization assays have been used to genotype the polymorphism G1259C (a G-to-C substitution at position 1259 in the promoter) that defines the allele CYP2E1*5 (Infante-Rivard et al. 2002a) as well as the MTHFR C677T polymorphism (Infante-Rivard et al. 2002b).
Statistical analysis.
Out of 493 cases and 472 controls, 451 were matched for gestational week, sex, and race. Because the matching involved only categorical factors, odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using unconditional logistic regression analysis, allowing all study subjects to be included. I included race, sex, and gestational age as confounding variables in all analyses, as well as the following risk factors known to be associated with IUGR: weight gain during pregnancy, prepregnancy body mass index (BMI), parity, history of preeclampsia, prior history of IUGR, primiparity, and smoking during pregnancy. I also tested for gene–environment interactions, that is, whether the effect of water contaminants (total THMs and chloroform in tap water) was modified by newborn and maternal genetic variants (one or two variant alleles vs. none), using a heterogeneity chi-square test (Hills and De Stavola 2002).
Results
Table 1 provides some background maternal characteristics for cases and controls. As expected, case mothers had gained less weight during pregnancy and had a lower BMI before pregnancy. In addition, they were more likely to be older, to have smoked during the third trimester of pregnancy, to be primiparous, to have preeclampsia, and to report a previous pregnancy with IUGR.
Table 2 shows the distribution of exposure variables such as different THMs as well as showering and drinking-water habits. The only notable difference between cases and controls was the use of domestic water filters, which was higher among controls.
Table 3 shows the results for exposure to specific and total THMs in drinking water using the 90th percentile cutoff for average level of exposure. No increased risk was observed for any of the specific THMs or for total THMs. All reported ORs were fully adjusted. Using a cutoff at the 95th percentile (instead of the 90th) for average level of exposure, I estimated an OR of 1.17 (95% CI, 0.60–2.29) for chloroform and of 1.26 (95% CI, 0.65–2.45) for total THMs. I also estimated ORs for cumulative exposure to specific and total THMs in drinking water and found that results were very similar to those for average exposure (data not shown). Using the other indices for drinking water and the index for showering, I observed no increased risks (data not shown).
Table 4 shows the adjusted ORs for exposure to average levels of chloroform and total THMs from drinking water (contrasting the group above the 90th percentile with the group at or below the 90th), according to whether the newborn or the mother carried one or two variant alleles, as opposed to none. The risk of IUGR associated with exposure to total THMs was different between newborn carriers and noncarriers of the CYP2E1 variant. An increased risk was also observed among the newborn carriers for exposure to chloroform, as well as among mother carriers for both exposures (chloroform and total THMs), but the risks were not statistically different across the gene strata. I also observed statistical heterogeneity in the risk of IUGR between newborns carriers and noncarriers of the CYP2E1 variant for exposure to average levels of chloroform and total THMs measured as numerical variables (data not shown). No effect modification was observed when contrasting carriers and noncarriers of the T allele in the MTHFR C677T gene. No significant effect modification was observed between newborn or maternal carriers and noncarriers of either polymorphism when exposure was defined with the other indices for drinking water or for showering.
Discussion
My results for the association between exposure to water contaminants were largely negative, whether average or cumulative levels from drinking water at the tap were used or when I also accounted for drinking water and showering habits. There were some indications that, with increased genetic susceptibility, especially in the newborn, measured by the presence of a variant in the CYP2E1 gene, exposure to total THMs was associated with substantial risk. I know of no similar results.
The results from previous studies are mixed with respect to IUGR (often referred to as small for gestational age): Four studies reported associations (Bove et al. 1995; Gallagher et al. 1998; Kramer et al. 1992; Wright et al. 2003), and five did not (Dodds et al. 1999; Jaakkola et al. 2001; Kallen and Robert 2000; Savitz et al. 1995; Yang et al. 2000). In the present study, selection bias was unlikely. Exposure assessment at the personal level was more detailed than in most previous studies because I accounted for the use of bottled water as drinking water, the drinking habits, and the showering habits (although not the bathing habits). A substantial proportion of women were drinking only bottled water, which influenced the levels of exposure to THMs reported in the study. Despite these positive features, misclassification of exposure to water contaminants was most certainly present; in particular, better measures could be achieved if assigned levels were based on specific locations within the distribution systems when multiple locations within the system were sampled. Another advantage of this study, compared with many of the previous studies using birth records, was the extensive control for confounding; gestational age, child’s sex, race, maternal smoking, primiparity, weight gain during pregnancy, BMI, previous IUGR, and pregnancy hypertension are all known risk factors for IUGR for which I was able to adjust.
Despite the absence of association between exposure to THMs and IUGR, the adverse effects of exposure of THMs were uncovered when taking into account genetic susceptibility. The study included subjects from many racial backgrounds; confounding by ethnicity, known as population stratification, can bias the results of case–control studies with genetic risk factors. However, I adjusted for race in all analyses. Other confounders for IUGR were measured and controlled for in the analysis.
The mechanism by which exposure to THMs affects fetal growth is not known; among humans, almost all studies have been epidemiologic, and therefore other types of studies addressing mechanisms are not available. A recent toxicologic study hypothesized that BDCM could disrupt the synthesis and/or secretion of placental syncytiotrophoblast-derived chorionic gonadotropin (Chen et al. 2003). The authors tested whether BDCM targets trophoblasts by examining the effect of BDCM on chorionic gonadotropin secretion by primary cultures of human trophoblasts. The results showed that BDCM reduced the secretion of immunoreactive and bioactive chorionic gonadotropin, and thus the component appears to target human placental trophoblasts. Trophoblasts are the sole source of chorionic gonadotropin during normal human pregnancy; thus, a decrease in the amount of this bioactive hormone could have adverse effects on pregnancy outcome. However, much more work is still needed to elucidate the possible effects on human fetal growth.
A few years ago, Chen et al. (1996) were the first to suggest that carriers of the common MTHFR C677T polymorphism could be at higher risk for the effects of chloroform in drinking water. MTHFR is involved in the metabolism of methionine and homocysteine through a mechanism that is vitamin B12 dependent; as suggested by Alston (1991), the latter could be inhibited by chloroform. Among carriers of the T allele, in particular the homozygotes, the transformation of homocysteine to methionine is less efficient and possibly the exposure to chloroform could inhibit this transformation even more. In this study, I found no indication that MTHFR C677T will modify the effect of exposure to chloroform. It is likely that this is not a promising hypothesis after all, at least for fetal growth.
The tested variant in CYP2E1 is in the regulatory region and is associated with an increased transcriptional activity (Hayashi et al. 1991). The carriers would be expected to have an increased metabolism of THMs resulting in the production of activated metabolites. Our results are coherent with this hypothesis. This indication for gene–environment interaction should lead to more similar investigations because it is very unlikely that only one polymorphic gene is involved in the metabolism of THMs.
In conclusion, in the present study I was unable to show effects of exposure to THMs from DBPs on the risk of IUGR. However, among newborn carriers of a CYP2E1 gene variant, important effects were observed. These results will need confirmation. They also suggest that accounting for genetic susceptibility is a sensible way to study the effects of environmental exposures when there is information on the candidate genes involved in the metabolism of these agents.
Table 1 Distribution of maternal characteristics between cases (n = 493) and controls (n = 472).
Characteristic Cases Controls
Race [no. (%)]
White 330 (66.9) 333 (70.5)
Black 117 (23.7) 110 (23.3)
Asian 24 (4.8) 13 (2.7)
Hispanic/Amerindian 22 (4.4) 16 (3.4)
Age ≥ 36 years [no. (%)] 86 (17.4) 70 (14.8)
Schooling ≤ 12 years [no. (%)] 108 (21.9) 96 (20.3)
Prepregnancy BMI (mean ± SD) 22.8 ± 4.3 23.2 ± 5.2
Weight gain during pregnancy, kg (mean ± SD) 12.7 ± 5.5 14.4 ± 5.6
Primiparous [no. (%)] 321 (65.2) 234 (49.6)
Preeclampsia [no. (%)] 69 (14.0) 12 (2.5)
Previous IUGR among parous [no. (%)] 66 (38.4) 23 (9.7)
Any cigarette smoking in 3rd trimester [no. (%)] 112 (22.7) 72 (15.7)
Table 2 Distribution of exposure variables between cases and controls.
Variable Cases Controls
THM concentration (μg/L) at the tap (mean ± SD)
Chloroform 11.84 ± 18.19 11.58 ± 16.31
Bromoform 0.42 ± 0.62 0.36 ± 0.65
BDCM 4.34 ± 2.94 4.24 ± 3.42
Chlorodibromomethane 2.21 ± 1.95 2.08 ± 2.30
Total THM 18.74 ± 19.76 18.26 ± 18.89
Other exposure characteristics
Drinking bottled water (%) 21.9 26.4
Private well (%) 0.66 0.95
Use of domestic water filter at tap (%) 14.7 9.9
Glasses of water/daya (mean ± SD) 6.8 ± 4.7 6.7 ± 4.2
No. of weekly showers (mean ± SD) 6.8 ± 3.9 6.9 ± 3.8
Duration of showers, min (mean ± SD) 12.6 ± 8.3 13.1 ± 7.3
a Includes water mixed with frozen juices.
Table 3 Adjusteda ORs (95% CIs) for IUGR in relation to exposure to specific and total THMs in drinking water measured as average levels at the tap.
Exposure index Value at cutoff (μg/L) OR (95% CI)
Average level ( > 90th percentile vs. ≤ 90th percentile)
Chloroform 23.7 1.06 (0.63–1.79)
Bromoform 1.22 2.44 (0.19–31.10)
BDCM 6.3 0.84 (0.50–1.43)
Chlorodibromomethane 3.9 0.62 (0.27–1.44)
Total THM 29.4 0.97 (0.57–1.62)
a Adjusted for gestational age, sex, race, mother’s weight gain during pregnancy, prepregnancy BMI, smoking during the third trimester, primiparity, preeclampsia in the current pregnancy, and previous IUGR.
Table 4 Adjusteda ORs (95% CIs) for exposure to THMs (chloroform and total THMs) in drinking water measured as average level at the tap, according to newborn and maternal polymorphisms in the CYP2E1 and MTHFR genes.
OR (95% CI)b
Gene Cases (n) Controls (n) Chloroform Total THMs
Newborns
CYP2E1*5 (G1259C)
Wild type 385 375 0.99 (0.57–1.74) 0.82 (0.47–1.45)
1 or 2 variant alleles 45 37 5.62 (0.82–38.39) 13.20 (1.19–146.72)*
MTHFR C677T
Wild type 239 212 1.78 (0.82–3.87) 1.63 (0.72–3.71)
1 or 2 variant alleles 195 204 0.83 (0.38–1.54) 0.76 (0.38–1.54)
Mothers
CYP2E1*5 G1259C
Wild type 395 380 0.88 (0.50–1.54) 0.83 (0.48–1.44)
1 or 2 variant alleles 57 39 4.40 (0.73–26.42) 6.54 (0.59–71.45)
MTHFR C677T
Wild type 244 214 1.00 (0.46–2.18) 0.98 (0.46–2.10)
1 or 2 variant alleles 212 206 1.12 (0.56–2.32) 0.94 (0.47–1.89)
a Adjusted for gestational age, sex, race, mother’s weight gain during pregnancy, prepregnancy BMI, smoking during the third trimester, primiparity, preeclampsia in the current pregnancy, and previous IUGR.
b For exposure defined as average level (> 90th percentile vs. ≤ 90th percentile).
* Chi-square (1 degree of freedom) for effect modification = 4.87; p = 0.027.
==== Refs
References
Alston TA 1991 Inhibition of vitamin B12 -dependent methionine biosynthesis by chloroform and carbon tetrachloride Biochem Pharmacol 42 R25 R28 1764111
Arbuckle TE Wilkins R Sherman GJ 1993 Birth weight percentiles by gestational age in Canada Obstet Gynecol 81 39 48 8416459
Botto LD Yang Q 2000 5,10-Methylenetetrahydrofolate reductase gene variants and congenital anomalies: a HUGE review Am J Epidemiol 151 862 877 10791559
Bove FJ Fulcomer MC Klotz JB Esmart J Dufficy EM Savrin JE 1995 Public drinking water contamination and birth outcome Am J Epidemiol 141 850 852 7717362
Bove F Shim Y Zeitz P 2002 Drinking water contaminants and adverse pregnancy outcomes: a review Environ Health Perspect 110 suppl 1 61 74 11834464
Chen ATL Reidy JA Sever LE 1996 Public drinking water contamination and birth outcomes [Letter] Am J Epidemiol 143 1179 1180 8633613
Chen J Douglas GC Thirkill TL Lohstroh PN Bielmeier SR Narotsky MG 2003 Effect of bromodichloromethane on chorionic gonadotrophin secretion by human placental trophoblast cultures Toxicol Sci 76 75 82 12970577
Dodds L King W Woolcott C Pole J 1999 Trihalomethanes in public water supplies and adverse birth outcomes Epidemiology 10 233 237 10230830
Gallagher MD Nuckols JR Stallones L Savitz DA 1998 Exposure to trihalomethanes and adverse pregnancy outcomes Epidemiology 9 484 489 9730025
Geveker Graves C Matanoski GM Tardiff RG 2001 Weight of evidence for an association between adverse reproductive and developmental effects and exposure to disinfection-byproducts: a critical review Reg Toxicol Pharmacol 34 103 124
Hayashi SI Watanabe J Kawajiri K 1991 Genetic polymorphisms in the 5’ flanking region change transcriptional regulation of the human cytochrome P450E1 gene J Biochem 110 559 565 1778977
Hills M De Stavola BL 2002. A Short Introduction to STATA for Biostatistics. London:Timberlake Consultants Ltd.
Infante-Rivard C Amre D Sinnett D 2002a GSTT1 and CYP2E1 polymorphisms and trihalomethanes in drinking water: effect on childhood leukemia Environ Health Perspect 110 591 593 12055050
Infante-Rivard C Lévy E Rivard GE Guiguet M Feoli-Fonseca JC 2003a Small babies receive the cardiovascular protective apolipoprotein ɛ2 allele less frequently than expected J Med Gen 40 626 629
Infante-Rivard C Rivard GE Gauthier R Théôret Y 2003b Unexpected relation between plasma homocysteine and intrauterine growth restriction? Clin Chem 49 1476 1482 12928228
Infante-Rivard C Rivard GE Yotov W Génin E Guiguet M Weinberg C 2002b Absence of association of thrombophilia polymorphisms with intrauterine growth retardation N Engl J Med 347 19 24 12097536
Jaakkola JJK Magnus P Skrondal A Hwang BF Becher G Dybing E 2001 Foetal growth and duration of gestation relative to water chlorination Occup Environ Med 58 437 442 11404447
Källén BAJ Robert E 2000 Drinking water chlorination and delivery outcome—a registry-based study in Sweden Reprod Toxicol 14 303 309 10908833
Kramer MD Lynch CF Isacson P Hanson JW 1992 The association of waterborne chloroform with intrauterine growth retardation Epidemiology 3 407 413 1391132
Meek ME Beauchamp R Long G Moir D Turner L Walker M 2002 Chloroform: exposure estimation, hazard characterization, and exposure-response analysis J Toxicol Environ Health 5 Part B 283 334
Savitz DA Andrews KW Pastore LM 1995 Drinking water and pregnancy outcome in central North Carolina: source, amount, and trihalomethane levels Environ Health Perspect 103 592 596 7556013
Wright JM Schwartz J Dockery DW 2003 Effect of trihalomethane exposure on fetal development Occup Environ Med 60 173 180 12598663
Yang CH Cheng BH Tsai SS Wu TN Lin MC Lin KC 2000 Association between chlorination of drinking water and adverse pregnancy outcome in Taiwan Environ Health Perspect 108 765 768 10964797
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0059815289171PerspectivesEditorialEditorial: Olden Times: Looking Back on a Career at the NIEHS Brown David NIEHS, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, E-mail:
[email protected] Tart Kimberly G. NIEHS, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, E-mail:
[email protected] Thomas J. NIEHS, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, E-mail:
[email protected] Brown is special assistant to the NIEHS director. Kimberly G. Thigpen Tart is news editor of EHP. Thomas J. Goehl is editor-in-chief of EHP.
8 2004 112 11 A598 A599 Publication 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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After 13 years of distinguished service as director of the NIEHS and the National Toxicology Program (NTP), Kenneth Olden is stepping down to return to the laboratory to continue his work on cancer biology. His tenure at the NIEHS has been marked by numerous scientific advances, as well as by the creation of inspired programs to translate these advances into practicable means for real improvement in the health and lives of human beings around the world. Intrinsic to this process has been his belief in the necessity of making often complicated and technical science understandable to all so that it might become a tool by which people can make informed decisions. His contribution to the development of EHP from a series of monographs to one of the leading international science journals is but one example of the concrete application of this philosophy.
Olden was appointed director of the NIEHS and NTP in June of 1991, becoming the first African American to head an NIH institute. He made it his challenge to introduce an orderly, reasoned approach to the management of human health research related to the environment. He worked to change a culture of conflict and opposition among environmental health stakeholders to one of trust, respect, and cooperation. Olden broadened the definition of “environment” to include not just chemical and physical agents, but also food and nutrients, biological agents, prescription drugs, lifestyle choices, social and economic factors, and the built environment.
Along with this broadened definition, he established a new paradigm for environmental health research. He strengthened peer review, increased public input, and orchestrated a major reorganization of the institute’s intramural research program. During his tenure, the NIEHS has become the focus of national and international attention for outstanding research accomplishments including the discovery of a breast cancer gene (BRCA1) and the isolation of a metastasis gene in prostate cancer. At the same time, Olden posted a new list of priorities that went beyond an emphasis on identifying environmental agents that cause cancer—the new list included identifying disease end points such as reproductive and developmental defects, developing molecular prevention and intervention programs, and establishing clinical programs in environmental health.
Many of Olden’s research initiatives grew out of an observation that became his mantra: that human diseases are the product of a triangle of influences comprising environment, genetics, and aging. Thus, under his leadership the NIEHS initiated the Environmental Genome Project (EGP) in 1997. This project is a major national effort to identify those genes that confer susceptibility to various diseases as a consequence of exposure to specific environmental agents. The first phase of this project identifed more than 20,000 single-nucleotide polymorphisms (SNPs) in 217 environmentally relevant candidate genes; the goal is to resequence and analyze for SNPs in a total of 554 genes. A companion program, the Comparative Mouse Genomics Centers Consortium, was initiated by the EGP to develop transgenic and knockout mouse models based on human DNA sequence variants in environmentally responsive genes. These mouse models are tools to improve our understanding of the biological significance of human DNA polymorphism.
Extending the work of the EGP, Olden established the National Center for Toxicogenomics (NCT) in 2000. Through this center, NIEHS researchers and their partners at various government and academic institutions in the Toxicogenomics Research Consortium are surveying the human genome for alterations in gene expression patterns of several thousands of genes using DNA microarray technology. The advantages of the toxicogenomics approach over traditional toxicity tests are greater speed and efficiency, and a reduction in animal use. The NCT is already spurring the development of gene-based toxicity studies. Initiatives to develop other “omics” technologies such as proteomics and metabolomics were begun under Olden’s leadership and are coming to fruition in both intramural and extramural research programs, and through the NTP. Olden’s vision to form multidisciplinary collaborations among numerous research entities will no doubt revolutionize the way research in toxicology and environmental health is conducted in the future.
Realizing that the research needs to be done but that it also must be effectively communicated to the scientific community and other stakeholders, Olden took his vision for the advancement of toxicogenomics a step further and directed the development of an EHP toxicogenomics section. Published in quarterly issues, this section is designed to facilitate scientific discourse in these rapidly emerging fields so that researchers may take advantage of each others’ efforts and pool their knowledge for a more complete understanding of genomics and systems biology.
Seeing the larger picture and understanding that the best results come from the combination of many and varied talents have been a hallmark of Olden’s tenure at the NIEHS. Olden created centers within the NIEHS and the NTP consisting of leaders in toxicology research and risk assessment from government, industry, academia, and the private sector. Examples include the Interagency Center for the Evaluation of Alternative Toxicological Methods, the Center for the Evaluation of Risks to Human Reproduction, and the Center for Phototoxicology. These centers provide objective interpretation of scientific data to be used in conducting more credible scientific health assessments, and in promoting the development of new methodologies to better evaluate environmental health issues. Because the centers focus only on scientific issues and not on regulatory policies, they are looked upon as a neutral forum.
In the grants programs, the Superfund Basic Research Program and the Worker Education and Training Program have experienced substantial growth over the last 10 years, and their budgets are now appropriated directly from Congress. Olden also expanded on the extensive network of NIEHS-funded Environmental Health Sciences Centers and established a number of specialty centers including the Centers for Children’s Environmental Health and Disease Prevention Research (cofunded with the U.S. Environmental Protection Agency), the Collaborative Centers for Parkinson’s Disease Environmental Research, and the Breast Cancer and the Environment Research Centers. All of the centers are based on a model of multidisciplinary research collaboration and coordination that has recently been recognized by the NIH in its Roadmap for Medical Research as the wave of the future in biomedical studies.
Think like a wise man but communicate in the language of the people.
William Butler Yeats (1865–1939)
Since taking the helm of the NIEHS, Olden has been at the forefront in another area as well. From early on he showed an awareness and understanding of a fact that had often been ignored by others in research administration—that local communities have the collective ability to identify environmental health problems but often lack the time, means, and research expertise to effectively resolve these problems. He immediately put into place a series of measures, expanded over the course of his tenure, that would link community groups with resources at the NIEHS and other research institutions.
Olden made it mandatory for each NIEHS-funded Environmental Health Sciences Center to have a Community Outreach and Education Program that was responsive to local environmental health problems, particularly those of poor and minority populations. The communities surrounding these centers have benefited from access to local researchers and resources to help them address the important environmental health issues in their homes, neighborhoods, and work-places. Olden then put forward the idea that community groups could apply for, and receive, funding from the NIEHS/NIH for research programs and outreach projects of their own. Such a grants program was a novel undertaking for a component of the NIH, which, as the nation’s largest biomedical research enterprise, traditionally funds only universities and research institutions.
Tradition notwithstanding, Olden initiated the Environmental Justice Grants Program and the Community-Based Prevention/Intervention Research Program. These programs have been the basis for science capacity building in local communities, such that several grantees have been able to compete successfully for much larger federal research programs in collaboration with neighboring academic institutions. Olden was one of the first NIH directors to initiate programs specifically designed to address health disparities, a measure that has now been adopted by the entire NIH. These programs are examples of many that Olden initiated with the goal of translating basic and clinical environmental health science into public health practice.
Central to Olden’s philosophy of translational research has been his belief that the research process must be a two-way street—that communication does not simply flow from the “ivory towers,” but that the people charged with setting national research agendas must be responsive to the needs and messages of the publics they serve. With this in mind, he invited all environmental health stakeholders to come to the table and voice their concerns. He made openness, transparency, and accessibility integral to the processes of the NTP. In 1998 he instituted a series of national town meetings in cities across the United States, where residents were invited to an open forum that he himself attended for discussion of the environmental health issues of importance to them. These discussions have become part of the basis for a national environmental health agenda. Olden has also taken bold steps, through the creation of the NIEHS Public Interest Liaison Group, to involve the public in brainstorming sessions with scientists, environmental professionals, and organizations concerned with diseases such as Parkinson disease, breast cancer, and respiratory disease, or that represent at-risk populations such as children, women, and minorities, to help the institute determine future research priorities.
One of the most visible aspects of Olden’s concerted effort to provide a forum for discussion of environmental health issues and communication of the most current scientific research is EHP itself. Almost immediately Olden recognized that the journal should work to become the most effective vehicle possible for accomplishing these goals. He began by directing the development of EHP into a monthly publication with an environmental news section written to be comprehensible to a lay audience. Over the years, EHP has expanded internally, adding sections on children’s health, environmental medicine, and toxicogenomics; the journal has also expanded internationally, publishing a Chinese-language edition and a dedicated section in the Spanish-language journal Ciencia y Trabajo. In a bold step forward for science communication, Olden deemed in 2003 that EHP would become entirely open access, and thus the information in it freely available to anyone in the world.
Through his vision, Olden has dramatically improved the visibility and reputation of the NIEHS while making its research programs more responsive to the needs of the American people. The institute’s emphasis on prevention and intervention is consistent with both public health and economic policies placing a high premium on disease prevention and the elimination of uncertainties in human risk assessment that lead to regulatory gridlock. Building on the core foundation of the NIEHS, Olden has created a host of programs to respond to new challenges and new opportunities. With his departure, Olden can rest safe in the knowledge that his leadership has ensured an NIEHS, and an EHP, that are poised to meet their futures as well.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0060015289172PerspectivesEditorialEditorial: Impacts of Our Built Environment on Public Health Dearry Allen NIEHS, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, E-mail:
[email protected] Dearry is associate director for Research Coordination, Planning, and Translation, NIEHS.
8 2004 112 11 A600 A601 Publication 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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We spend more than 90% of our lives indoors (National Research Council 1981), yet we know much more about ambient environmental factors and health than we do about the “built environment” and health. Conceptually, the built environment includes all of the physical structures engineered and built by people—the places where we live, work, and play. These edifices include our homes, work-places, schools, parks, and transit arrangements. How we design and build where we live has changed dramatically over the past century. In the early 1900s, urban areas tended to be compact and communities were walkable, with a central business district and a mix of housing and services. Then, connections between urban design and health and disease were more clearly recognized, and planners and public health practitioners often worked together to deal with problems related to poor sanitation and housing conditions. Increasing movement away from such urban locales over the last 50 years led to lower-density developments, segregation of land uses, and extensive roadway construction. Today, this trend, sometimes referred to as “urban sprawl,” is characterized by huge increases in urbanized land area and vehicle miles traveled [U.S. Environmental Protection Agency (EPA) 2001a]. These changes have both direct and indirect impacts on our environment and on public health.
Changes in land use and development patterns have contributed to habitat loss and declining water resources and quality (Soule 1991; U.S. EPA 1992). Increases in impervious surfaces and attendant surface water runoff contribute to deterioration in availability and use of safe, clean water supplies for both recreation and consumption. For example, suburban development is associated with a rising load of polycyclic aromatic hydrocarbons in nearby surface water (Van Metre et al. 2000).
Increases in vehicle travel affect our environment and our health in multiple fashions. As neighborhood density decreases, vehicle miles traveled (VMT) increase (Holtzclaw et al. 2002). With more driving comes more vehicle crashes as well as pedestrian injuries and fatalities. Moreover, further VMT contribute to overall releases of air pollutants (Kennedy and Bates 1989), which are associated with numerous adverse health outcomes (Samet et al. 2000). Additionally, carbon dioxide and other vehicle emissions contribute to accumulation of greenhouse gases in the atmosphere (U.S. EPA 2001b), which may ultimately impact public health by affecting the transmission and spread of infectious diseases (Epstein 2000).
Our built environment also affects individual mental health as well as population-wide well-being. Housing type and quality, neighborhood quality, noise, crowding, indoor air quality, and light have all been linked to personal mental health (Evans 2003). Indirectly, the built environment may influence development and maintenance of socially supportive networks within a community. Higher levels of this type of “social capital” are associated with lower levels of morbidity and mortality (Kawachi et al. 1999). Although the connection between the built environment and social capital remains to be well established, both walkability and mixed use of neighborhoods have been reported to be related to an enhanced sense of community and social capital (Glynn 1981; Nasar and Julian 1995).
Perhaps the most recently publicized link between the built environment and public health relates to the occurrence of overweight and obesity in the United States. The built environment influences weight management by affecting both food intake and energy expenditure. Communities characterized by less-dense development are associated with more vehicle travel and less walking and biking than are more densely developed communities (Frank and Pivo 1995). Physical activity has been shown to have a salubrious effect on health and quality of life (Lee and Paffenbarger 2000). However, only recently have investigators expanded such work to address more specifically the impact of community design not only on physical activity but also on obesity and associated comorbidities. One study reported that, after controlling for individual differences, those living in sprawling counties are more likely to walk less in their leisure time, weigh more, and have a greater prevalence of hypertension than those living in more compact places (Ewing et al. 2003). Similarly, a more walkable environment has been found to be associated with higher physical activity and lower obesity levels (Salens et al. 2003). In addition, the likelihood of obesity apparently declines with increases in mixed land use, but rises with increases in time spent in a car per day (Frank et al. 2004). To date, such work addresses important relationships but does not establish causation. In fact, Frank et al. (2004) pointed out that mixed land use, while being the most important variable of the built environment related to obesity, may not exert its effect via physical activity. Hence, significant methodologic and etiologic research remains to be conducted to clarify such issues.
The built environment may also play a role in controlling weight by shaping food access and availability. Recent research suggests that supermarkets are more likely to be located in wealthier and predominantly white areas, and that fruit and vegetable intake is positively associated with the presence of a supermarket, even after controlling for personal socioeconomic factors (Morland et al. 2002a, 2002b). Although the relationship between different types of eating places and dietary consumption has not been well examined, the availability, type, and distribution of restaurants and the diffusion of food advertising represent other means by which the environment may affect weight homeostasis.
Additional research will be necessary to enable us to understand the complicated pathways and intersections linking community design, transportation, and a variety of health outcomes. Such information will permit us to develop communities that promote health for both people and ecosystems rather than dealing with the health-damaging repercussions of a poorly designed built environment (Srinivasan et al. 2003). In pursuit of this goal, it will be important to reestablish the unity of health practitioners and public planners—not only to carry out needed research at the interface of these disciplines but also to ensure that the results of such research are properly translated and applied in order to lead to tangible improvements in our living arrangements and in public health.
==== Refs
References
Epstein P 2000 Is global warming harmful to health? Sci Am 283 50 57 10914399
Evans GW 2003 The built environment and mental health J Urban Health 80 536 555 14709704
Ewing R Schmid T Killingsworth R Zlot A Raudenbush S 2003 Relationship between urban sprawl and physical activity, obesity, and morbidity Am J Health Promotion 18 47 57
Frank L Pivo G 1995 Impacts of mixed use and density on utilization of three modes of travel: single-occupant vehicle, transit, and walking Transportation Res Rec 1466 44 52
Frank L Andresen M Schmid T 2004. Obesity relationships with community design, physical activity, and time spent in cars. Am J Prev Med. Available: http://www.ajpm-online.net/webfiles/images/journals/amepre/special.pdf [accessed 29 June 2004].
Glynn T 1981 Psychological sense of community: measurement and application Hum Relations 34 789 818
Holtzclaw J Clear R Dittmar H Goldstein D Haas P 2002 Location efficiency: neighborhood and socioeconomic characteristics determine auto ownership and use—studies in Chicago, Los Angeles, and San Francisco Transportation Plan Technol 25 1 27
Kawachi I Kennedy B Wilkinson R eds. 1999. Income Inequality and Health. New York:New Press.
Kennedy D Bates R eds. 1989. Air Pollution, the Automobile, and Public Health. Washington, DC:National Academy Press.
Lee I Paffenbarger R 2000 Associations of light, moderate, and vigorous intensity physical activity with longevity: the Harvard Alumni Health Study Am J Epidemiol 151 293 299 10670554
Morland K Wing S Diez Roux A Poole C 2002a Neighborhood characteristics associated with the location of food stores and food service places Am J Prev Med 22 23 29 11777675
Morland K Wing S Roux A 2002b The contextual effect of the local food environment on residents’ diets: the Atherosclerosis Risk in Communities Study Am J Public Health 92 1761 1768 12406805
Nasar J Julian D 1995 The psychological sense of community in the neighborhood J Am Plann Assoc 61 178 184
National Research Council 1981. Indoor Air Pollutants. Washington, DC:National Academy Press.
Salens B Sallis J Black J Chen D 2003 Neighborhood-based differences in physical activity: an environment scale evaluation Am J Public Health 93 1552 1558 12948979
Samet J Dominici F Curriero F Coursac I Zeger S 2000 Fine particulate air pollution and mortality in 20 US cities, 1987–1994 N Engl J Med 343 1742 1749 11114312
Soule M 1991 Land use planning wildlife maintenance. Guidelines for conserving wildlife in an urban landscape J Am Plann Assoc 57 313 323
Srinivasan S O’Fallon L Dearry A 2003 Creating healthy communities, healthy homes, healthy people: initiating a research agenda on the built environment and public health Am J Public Health 93 1446 1450 12948961
U.S. EPA 2001a. Our Built and Natural Environments. EPA 231-R-01-002. Washington, DC:U.S. Environmental Protection Agency.
U.S. EPA. 2001b. Inventory of US Greenhouse Gas Emissions and Sinks: 1990–1999. EPA 236-R-01-001. Washington, DC:U.S. Environmental Protection Agency.
U.S. EPA 1992. Environmental impacts of storm water discharges—a national profile. EPA 841-R-92-001. Washington, DC:U.S. Environmental Protection Agency.
Van Metre P Mahler B Furlong E 2000 Urban sprawl leaves its PAH signature Environ Sci Technol 34 4064 4070
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a00605PerspectivesCorrespondenceStudying Human Fertility: Response to Slama et al. and Joffe et al. Stanford Joseph B. Health Research Center, Department of Family and Preventive Medicine, University of Utah, Salt Lake City, UtahDunson David B. Tingen Candace National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, E-mail:
[email protected] authors declare they have no competing financial interests.
8 2004 112 11 A605 A606 Publication 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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Slama et al. provide valuable comments on sampling frames to study fecundity, and we agree that the sampling frame is a major methodologic problem in fecundity studies of all designs. The current duration strategy of enrolling couples currently attempting pregnancy is a promising approach, particularly when couples are followed after enrollment to obtain detailed prospective information. Data from the menstrual cycles before enrollment can then be combined with detailed data from cycles during the study period using recently proposed statistical methods (Dunson 2003).
However, it is important to note that this innovative combination of retrospective and prospective designs still does not address the vexing problem of couples who do not have a clearly defined pregnancy attempt. Demographic surveys and qualitative research reveal that many—perhaps most—pregnancies are not exactly planned in the sense of an exactly defined onset of intention to become pregnant (Trussell et al. 1999). Even the onset of sexual intercourse without contraception may not always be easy to define reliably, with periods of use interspersed with periods of nonuse. Ultimately, a complete evaluation of this issue will need to include couples using contraception, at least at study enrollment. Some studies have done this, at least for barrier contraceptives (Eskenazi et al. 1995).
Joffe et al. comment on alternative retrospective designs that can be considered to address the problem of a nonrepresentative sample. We agree that prospective studies are limited by the fact that individuals willing to participate may not be representative of the general population (as in prospective epidemiologic studies of other heath outcomes). However, many of Joffe et al.’s comments on the prospective design are unduly negative. For example, the stated methodologic problem of it being “impossible to distinguish the approximately 3% of couples who are sterile from those who merely take a long time to conceive” is not specific to the prospective design, but a general issue in distinguishing sterility from infertility in the absence of known causes of sterility (Dunson et al. 2004).
The “best” design (if it exists) really depends on the scientific questions of interest. Retrospective and population-based studies have an important role in assessing population fecundability in demographic studies, in studying effective fecundability, and in surveillance for possibly significant environmental exposures. However, our focus is on studies investigating the potentially complex and time-varying effects of environmental exposures on biologic fecundability. Intercourse timing relative to ovulation has a critical role, not only in determining the overall probability of conception in a menstrual cycle, and hence time to pregnancy, but also in predicting later outcomes, such as early pregnancy loss (Wilcox et al. 1998). Confounding resulting from differences in exposed and unexposed individuals in their sexual behavior, including timing and frequency of intercourse, is a major concern. There can be problems even if the individuals have the same intercourse frequency because there is substantial variability in the timing of ovulation (Wilcox et al. 2000). In addition, prospective data on mucus and hormones potentially provide important information about biologic mechanisms.
For all of these reasons, we continue to recommend that whenever possible, detailed prospective data of the type that we have outlined should be collected in epidemiologic studies of fecundity, as well as in studies that seek to relate periconception exposures to later reproductive and developmental outcomes. Daily sampling of urine (via samples sent to the laboratory, or onsite with commercially available computerized devices) is one way to achieve this, but not the only one. We detailed other currently available and feasible approaches in our article (Tingen et al. 2004).
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References
Dunson DB 2003 Math Popul Studies 10 127 143
Dunson DB Baird DD Colombo B 2004 Increased infertility with age in men and women Obstet Gynecol 103 57 62 14704245
Eskenazi B Gold EB Samuels SJ Wight S Lasley BL Hammond SK 1995 Prospective assessment of fecundability of female semiconductor workers Am J Ind Med 28 6 817 831 8588566
Tingen C Stanford JB Dunson DB 2004 Methodologic and statistical approaches to studying human fertility and environmental exposure Environ Health Perspect 112 87 93 14698936
Trussell J Vaughan B Stanford J 1999 Are all contraceptive failures unintended pregnancies? Evidence from the 1995 National Survey of Family Growth Fam Plann Perspect 31 5 246 247 260.10723650
Wilcox AJ Weinberg CR Baird DD 1998 Post-ovulatory ageing of the human oocyte and embryo failure Hum Reprod 13 394 397 9557845
Wilcox AJ Dunson D Baird DD 2000 The timing of the “fertile window” in the menstrual cycle: day specific estimates from a prospective study Br Med J 321 7271 1259 1262 11082086
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0060615289176PerspectivesCorrespondenceThe WTC Disaster and Asbestos Regulations Lange John H. Envirosafe Training and Consultants, Inc., Pittsburgh, Pennsylvania, E-mail:
[email protected] author declares he has no competing financial interests.
8 2004 112 11 A606 A607 Publication 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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Landrigan et al. (2004) reported on exposure to asbestos as a result of events involving the World Trade Center (WTC). Their results are somewhat lower than that reported by others (Lange 2004) for this unfortunate event. I reported a single asbestos bulk sample at 40% asbestos (Lange 2004), although Landrigan et al. suggested that most are in the range of 1–3%. Because there was one “high” bulk sample observed, it is likely that numerous other locations had similar “elevated” asbestos levels. Airborne exposures were also elevated for a considerable time period after the event (Lange 2004). Although measurements were reported as task-length averages (TLA), it is likely that some personal samples (Lange 2004) exceeded the U.S. Occupational Safety and Health Administration permissible exposure limit (PEL) of 0.1 fibers/cm3 well after the first few days of the event. For example, during 1 January 2002–11 February 2002 in the west area, the arithmetic mean exposure and an upper reported value (0.500 fibers/cm3-TLA) were above the PEL (Lange 2004).
Landrigan et al. (2004) also reported clearance samples as fibers per millimeter squared, which likely should be in structures per millimeter squared. It should be noted that this clearance standard has not been shown to be health based, and structures per millimeter squared cannot be equilibrated or converted to fibers per millimeter squared.
Even with evidence of higher exposure levels, on the basis of reported data (Lange 2004), it is unlikely that exposure to asbestos itself will result in any actual health effects. This is because the asbestos was mostly chrysotile (Landrigan et al. 2004) and the duration of exposure for most workers was short (Lange 2003). However, as previously reported (Lange 2001, 2002, 2004), regulatory agencies ignored their own regulations at the WTC, whereas asbestos concentrations (bulk and air) for other locations would probably trigger a regulatory response and most likely a citation with a requirement of some action plan. Thus, it appears that there are two standards to be taken from the WTC, one for agencies themselves and another for all others.
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References
Landrigran PJ Lioy PJ Thurston G Berkowitz G Chen LC Chillrud SN 2004 Health and environmental consequences of the World Trade Center Disaster Environ Health Perspect 112 731 739 15121517
Lange JH 2001 Has the World Trade Center tragedy established a new standard for asbestos? [Editorial] Indoor Built Environ 10 346 349
Lange JH 2002 How do you interpret regulations: through science or agency rules? Toxicol Ind Health 18 107 108 12868799
Lange JH 2003 Cough and bronchial responses in fire-fighters at the World Trade Center Site [Letter] N Engl J Med 348 76 77 12510689
Lange JH 2004 Emergence of a new policy for asbestos: a result of the World Trade Center tragedy Indoor Built Environ 13 21 34
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a00608PerspectivesCorrespondenceTrichloroethylene: Johnson et al.’s Response Johnson Paula D. University Animal Care, The University of Arizona, Tucson, Arizona, E-mail:
[email protected] Brenda V. University of Auckland, Faculty of Medical and Health Sciences, Auckland, New ZealandGoldberg Stanley J. Congenital Cardiology, University Physicians, Tucson, ArizonaMays Mary Z. Department of Family and Community Medicine, University of Arizona Health Sciences Center, Tucson, ArizonaThe authors declare they have no competing financial interests.
Editor’s note: EHP considers expert witness testimony to be a competing financial interest.
8 2004 112 11 A608 A609 Publication 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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We share Hardin et al.’s belief that any apparent conflict of interest should be reported. We note that Brent provided testimony for the defense in TCE litigation, notably for the same case in which Goldberg (based on his extensive epidemiologic and laboratory research on the effects of TCE) acted as an expert witness for the plaintiff. We did not report Goldberg’s experience acting as an expert witness because the point of expert witness is to provide unbiased, factual explanations of extant data. We believe this does not constitute a conflict of interest; we have included a caveat about extrapolating data to humans in our publications. To our knowledge none of our data have been used inappropriately.
The work published in 1993 (Dawson et al.) and in 2003 (Johnson et al.) was actually performed during a much shorter period of time. Many extraneous factors contributed to the late publication of the 2003 paper. Data from our previous work was included in the more recent paper because we needed “boundary values” between or below which we were looking for a threshold or a critical level. This was a long-term study, and it would have been an inappropriate use of animals to repeat the earlier animal studies for those groups. We should have stated more clearly that we were using the groups already studied to prevent repetition and to conserve animal resources, as recommended by the Animal Welfare Act (1990); however, we did refer to our previous paper. Our 2003 publication contained new data as well as previously published data. We welcome this opportunity to clarify our method.
Our alleged reclassification of defects in our Table 2 (Johnson et al. 2003) merely reflects careful reevaluation by the cardiologist and minor updates in terminology that mirror current clinical usage to clarify the nature of a defect (e.g., great vessel defect vs. the more specific aortic hypoplasia; L-transposition vs. abnormal looping, etc.). There are other minor numerical differences in the tables (Table 2, Johnson et al. 2003, and Tables 1 and 3, Dawson et al. 1993), not remarked upon by Hardin et al., which derive from the more extensive statistical analysis in the later paper. In an apparent typographic error, we failed to report a pulmonary valve defect for the 1.5 ppm TCE in the 2003 paper. This should have been included in Table 2; however, it would not have changed the number of hearts with defects.
Again, because this was a long-term continuous project, we did use all of the controls together in a cumulative manner. We used the larger sample size with data collected over a long period because it increases the generalizeability of our data, demonstrating clearly the background rate and the variability around rate estimates. Control values were consistent throughout our studies. The larger sample size did increase statistical power somewhat in our most recent paper (Johnson et al. 2003), again without inappropriate use of further valuable animal resources. It should be noted that the increase in statistical power is small compared to the increase generated by the effect sizes and the increase in the number of dose groups—data that can only be generated in a long-term project.
Our statistical analysis was simple and conventional. Hardin et al. are incorrect in stating that the differences at the 1.5-ppm dose were statistically significant in our recent paper (Johnson et al. 2003). The p-values were reported in Figures 1 and 2 of our paper as 0.14 and 0.08, respectively, values not conventionally seen as statistically significant. Different levels of statistical significance used in each of the studies for each of the groups were carefully listed in the tables and figures and explained in the text.
There are many references in the scientific literature about effects of halogenated hydrocarbons on development. We included only a few of these in our articles. We are a multidisciplinary team and have studied both TCE and its major metabolites, often basing some of our work on the findings of others in the field without duplicating the work of others. We have consulted with other prominent researchers in the field from time to time in establishing our experimental design or in interpreting our results. We have found only heart defects associated with these compounds, despite looking for other effects. This work has been consistent with the original epidemiological studies on which our laboratory work was based. We have been funded by government and other nonbiased agencies requiring competitive grant application and accountability. We have presented our results as peer-reviewed published articles in excellent journals. Our work has all been carried out at The University of Arizona. A major strength of our studies was microdissection of each heart by investigators fully versed in the pathology of congenital cardiac malformations as well as noncardiac anatomy.
We fully agree with Hardin et al. that studies in this area “have potential for important health and public policy implications, so it is particularly important for the scientific and regulatory communities to have confidence in the conduct and reporting of those studies.” We believe that our studies have been rationally planned, are statistically and scientifically sound, and are of value for this purpose. We welcome this opportunity for postpublication discussion of results.
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References
Animal Welfare Act. 1990 7USC2131–2159.
Dawson BV Johnson PD Goldberg SJ Ulreich JB 1993 Cardiac teratogenesis of halogenated hydrocarbon-contaminated drinking water J Am Coll Cardiol 21 1466 1472 8473657
Johnson PD Goldberg SJ Mays MZ Dawson BV 2003 Threshold of trichloroethylene contamination in drinking waters affecting fetal heart development in the rat Environ Health Perspect 111 289 292 12611656
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0061215289180EnvironewsForumChildren’s Health: Mother’s Thyroid, Baby’s Health Mead M. Nathaniel 8 2004 112 11 A612 A612 Publication 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 the 1970s, epidemiologic studies have linked maternal thyroid insufficiency during gestation with fetal brain malformation, fetal death, and miscarriage. The fetus is wholly dependent on the maternal thyroid during the first 10–20 weeks of gestation. U.S. women generally get enough iodine, the elemental nutrient essential for synthesis of the thyroid hormone thyroxine (T4). But regular daily intake may not be sufficient during pregnancy due to metabolic changes in the mother-to-be, and recent studies suggest that detection and treatment may be needed long before birth. These and other topics were discussed by scientists at a January 2004 symposium cosponsored by the Centers for Disease Control and Prevention, the National Center on Birth Defects and Developmental Disabilities, and the American Thyroid Association (ATA).
Early maternal thyroidal insufficiency, or EMTI, is a failure of the maternal thyroid to provide an adequate supply of T4 in early pregnancy. According to Steven Lamm, a pediatrician and director of the Washington, D.C.–based Consultants in Epidemiology and Occupational Health, EMTI may affect 0.5–5.0% of all pregnant women. When depletion occurs early in pregnancy, fetal brain formation can be markedly altered. Even subtle degrees of thyroid dysfunction in pregnant women might be associated with impaired psychomotor development in their infants, toddlers, and preschool children.
While there’s no doubt that EMTI is related to poor fetal outcomes, the follow-up data on child development are only available until 5–6 years of age, so it’s still unknown whether these developmental delays persist over the long term, said Victor Pop, a professor in the Department of Clinical Health Psychology at Tilburg University, Netherlands, whose landmark study on EMTI was published in the February 1999 issue of Clinical Endocrinology. In a later study published in September 2003 in Clinical Endocrinology, Pop found that women with the lowest tenth percentile of T4 concentrations at 12 weeks’ gestation bore children who experienced impaired mental and motor functioning at age 1–2 years. In EMTI women who showed an increase in T4 concentrations at 24 and 32 weeks’ gestation, child development was not adversely affected. Most of the concerns related to fetal risk have focused on the first half of gestation. However, the third trimester is a critical time for cerebellar development and myelination.
The limited amount and quality of the evidence to date is one reason it has been difficult to reach consensus on the etiology as well as screening and treatment requirements for EMTI. Researchers aren’t sure whether using T4 to treat women with EMTI benefits all children of these mothers, or whether there are unforeseen effects. Therefore, placebo-controlled studies are urgently needed, said Pop.
John Lazarus, a senior lecturer in medicine at the University of Wales, United Kingdom, described his upcoming randomized clinical study of 22,000 women at 13–16 weeks’ gestation. An experimental group will have T4 and the complementary thyroid-stimulating hormone (TSH) measured and thryoxine treatment applied if necessary, while the control mothers will remain untested until after their babies are delivered. Children from both groups will undergo developmental testing at ages 2 and 5 years. This study will rigorously evaluate the impact of both subclinical maternal hypothyroidism and hypothyroxinemia (inadequate TSH and free T4, respectively) on the IQ scores of the offspring, as well as the effect of prenatal treatment.
Additional discussions focused on the possible need for screening and treatment. “While it is not yet known whether early identification and treatment of thyroid deficiency will avoid fetal death and neuropsychological deficits in the offspring, it is clear that women themselves will benefit,” said James Haddow, medical director of the Foundation for Blood Research in Scarborough, Maine. “Many women go undiagnosed for longer periods of time, so that they lack the energy they need to function well in everyday life during their child’s early years, when the demands placed on them are greatest.”
Haddow contended that TSH measurement should be added to the list of tests routinely performed at the first prenatal visit (the ATA currently advocates testing for pregnant women with a history of miscarriage, fetal loss, infertility, autoimmune disease, goiter on exam, and family history of thyroid disease). Lamm and other participants also suggested that normal levels for both TSH and T4 should be determined for the different stages of pregnancy. Another suggestion was to supplement prenatal vitamins with 150 micrograms of iodine (many currently contain little or none).
But scientists still need to agree on other matters, such as TSH and/or T4 cut-off points for defining high risk. A TSH level of 2.5 milliunits per liter was proposed as a good initial cut-off. “This is a conservative cut-off,” said conference co-planner Joseph Hollowell, a professor of pediatrics at the University of Kansas Medical Center, “and it will prompt further investigation to see if there’s a real problem.”
Intimately connected. New studies are showing the significance of a healthy thyroid in mothers-to-be on the future health of their babies.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a00615EnvironewsForumEHPnet: European Pollutant Emission Register Dooley Erin E. 8 2004 112 11 A615 A615 Publication 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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Pollutant release and transfer inventories are a relatively new database-driven means of providing information on the who, what, and how much of industrial emissions. Though governments have for some time collected such data for their own use, it has only been in the last decade or so that a move has been under way to make this information publicly available. Agenda 21, the plan of action adopted at the 1992 United Nations Conference on Environment and Development, advocated the development of national registries in each of the participating countries as a means of educating the general public and others about pollution sources. Today, inventories of emissions from more than 9,000 large and medium facilities in 16 European countries are available for free online through the European Pollutant Emission Register (EPER), located at http://www.eper.cec.eu.int/eper/.
A joint project of the European Commission and the European Environment Agency, EPER allows users to compare data between such variables as industry type and locale so that interested parties can act to reduce disparities. Environment commissioner Margot Wallström commented at the 23 February 2004 launch of the register that people need to know about pollution in their environment because it directly affects their health and their quality of life. She added that by using the register, citizens can put pressure on government and industry—an essential aspect of the public’s involvement in protecting the environment.
The data included within EPER have been provided by facilities that exceed specified emission thresholds. The data cover 50 air and water pollutants that can harm human and environmental health, including arsenic, lead, mercury, nitrogen, phosphorus, and small particulate matter. Industrial sectors include pig and poultry farming, minerals, metals, pharmaceuticals, cement and glass, asbestos, and waste disposal. The current version of EPER includes data from the year 2001; a set of year 2004 data will be added in 2006.
Choosing the Facility Level search allows users to search for facilities by area—all of the European Union countries or any of 17 individual nations. Users can also choose from pull-down lists of pollutants and industrial activities, and search by facility name and/or address. Users can also choose to run an Industrial Activity or Pollutant search.
The Map Search tool of the website allows the user to create a customized color-coded map that can show such elements as the density of all EPER industries across a region, the density of certain types of industries in a certain area, and the industries in a single metropolitan area. The map can also be configured to show only facilities emitting a single substance across specified areas.
EPER has also provided a searchable glossary of terms related to industry and pollutants. Links to the national emissions registers that were used in helping to compile the EPER database, as well as to a number of European and international environmental organizations, are available as well, under the Links heading.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0061615289181EnvironewsNIEHS NewsFighting Obesity Through the Built Environment Wakefield Julie 8 2004 112 11 A616 A618 Publication 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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Although it’s easy to point the finger at everyone from Ronald McDonald to Bill Gates, no one entity or factor is specifically to blame for the nation’s raging obesity epidemic, according to speakers at the first-ever national conference on obesity and the built environment, held in late May in Washington, D.C. But every community can do something to combat it, participants agreed.
The built environment includes all aspects of the environment that are modified by humans, including homes, schools, workplaces, parks, industrial areas, and highways. Participants at Obesity and the Built Environment: Improving Public Health Through Community Design first probed how various aspects of the built environment currently contribute to obesity by affecting eating and physical activity habits and facilitating an increasingly sedentary lifestyle. Then participants discussed how the built environment can be changed to combat obesity, and how environmental health research and interventions can impact this growing public health problem.
The conference brought together researchers, planners, health care providers, developers, policy makers, and community and business leaders to develop agendas for future research and policy implementation, and to facilitate partnerships among these disciplines—goals that primary organizer Allen Dearry, NIEHS associate director of research coordination, planning, and translation, said the 600 attendees successfully realized by the meeting’s end. Highlighting evidence-based strategies for intervention, the conference also pointed to the need for interagency cooperation at all levels of government and for efforts to inform elected officials on the subject.
The conference was sponsored by the NIEHS. Its planning committee included officials from other federal agencies such as sister NIH institutes, the Department of Health and Human Services (DHHS), the Department of Housing and Urban Development (HUD), and the Centers for Disease Control and Prevention (CDC), as well as from academia.
A Spreading Epidemic
In the United States and elsewhere, the obesity epidemic is spreading virtually unchecked. Today, about two-thirds of Americans are overweight (defined as having a body mass index of 25 or more), and one-third are obese (with a body mass index of 30 or more), said NIEHS deputy director Samuel Wilson, citing figures from the CDC’s National Health and Nutrition Examination Survey. If the trend continues, 75% of the U.S. population will be overweight within the next five years, and 40% will be obese. Meanwhile, the cost of treating obesity-related illnesses and conditions such as cardiovascular disease, cancer, and type 2 diabetes mellitus will exceed $76 billion annually in direct costs, by some estimates. When indirect costs such as lost wages are factored in, the number already exceeds $117 billion a year, according to the CDC.
In 2001, U.S. surgeon general David Satcher issued the Call to Action to Prevent and Decrease Overweight and Obesity, which has largely gone unheard. In fact, the average American adult has continued to gain 1–2 pounds a year since then, according to James O. Hill, director of the Center for Human Nutrition at the University of Colorado Health Sciences Center, and colleagues, writing in the 7 February 2003 issue of Science. And the obesity rate continues to climb among young people, currently affecting about 15% of children aged 6–18. The surgeon general asserted that today’s youth may be the first generation not to outlive their parents. As it is, an estimated 300,000 deaths per year may be attributable to obesity in the United States alone. Meanwhile, the World Health Organization added “overweight/obesity” to its list of the top 10 preventable health risks worldwide.
“The highest rates of obesity are found among populations with the highest poverty rates and the least education,” especially among women, said Adam Drewnowski, director for the Center for Public Health Nutrition at the University of Washington. Yet all income and education groups are steadily becoming more obese. Not only socioeconomic phenomena but also features of the built environment limit access to healthy diets, he explained to participants.
NIH director Elias Zerhouni sees the growing epidemic as an evolutionary challenge to our species. “Our intelligence and our ability to understand through science and technology and the development of industry [has enabled] us to change our environment at speeds that our natural genetic evolutionary forces are unable to adapt to,” he explained. For example, 80% of our genes are tuned to respond to food scarcity, but only 20% are designed to maintain weight in a normal range, said Zerhouni.
Schools, Workplaces, and Communities at Large
Although obesity, like most other chronic health problems, is caused by complex interactions between genetics and environmental factors, the rapid increase in obesity over the past 30 years strongly suggests that environmental influences are responsible for this trend; the conference primarily focused on the environmental component. “Our built environment promotes a sedentary lifestyle today,” said Wilson, “and in addition to obesity there are many other attendant health challenges. . . . If we better understand the linkages between obesity and the built environment, we can create communities and workplaces that promote health and also promote well-being, an important feature in overall health.”
The conference covered obesity and the built environment in the context of three cross-cutting themes: schools and children; communities and families; and worksites, employers, and employees. In their presentations, speakers addressed a number of key questions: How do we develop, implement, and evaluate more “walkable” communities, where it is not necessary to drive everywhere due to sprawl and other poor design decisions? How do we create and assess incentives to encourage necessary changes at both the community and individual level? And how do we promote more physical activity and determine its effectiveness in maintaining a healthy weight? Across these themes, participants identified key environmental factors, from the intensive marketing of unhealthy foods, to the cultural belief that junk food tastes best, to the lack of full-service supermarkets and other nutritious food outlets in many neighborhoods, to poorly designed communities that discourage walking, biking, and other physical activity.
Part of the environmental component of obesity is an overall package of unhealthy lifestyle behaviors that contribute to the problem. These behaviors include anything that encourages a more sedentary lifestyle (such as playing video games excessively) and eating excess calories (such as a high-sugar, high-fat diet). Often the development of these behaviors starts in childhood, speakers stressed, and childhood obesity is strongly associated with adult obesity.
Many economic and political forces contribute to the problem, from budget cuts that slash school physical education and sports programs to the proliferation of vending machines on school campuses. Candy and snack food manufacturers, soft drink bottlers, and fast-food restaurants heavily market in schools, and many schools depend on revenues from these and other sources, such as annual fund-raisers selling doughnuts and candy bars. “The smartest people in the country are paid l o t s o f m o n e y t o manipulate children into behaviors that may be harmful to their health,” said Alex Molnar, director of the Education Policy Studies Laboratory at Arizona State University. And the changes have become systemic, he said, pervading the National School Lunch Program, school fundraisers, and more: “Corporate America has turned principals, teachers, and other school officials into cheerleaders that reinforce this behavior.”
Many issues regarding access to nutritious food spill over into the community at large as well. The evidence is clear that wholesome foods such as lean meats and fresh produce often cost more and that lower-cost diets are often high in starches, added sugars, and added fats, which are known to contribute to weight gain, according to Drewnowski and other conference speakers.
Emerging evidence further shows a direct association between community design and residents’ levels of physical activity. The likelihood of obesity declines with increases in mixed land use, but rises with increases in time spent in a car per day, according to recent results presented by Lawrence Frank, an associate professor of community and regional planning at the University of British Columbia. Every 30 additional minutes spent in a car was linked with a 3% increase in the risk of obesity in a recent study of nearly 11,000 Atlanta residents. “Taking into account multiple outcomes [such as residential density, land use mix, and commuting time] will likely help to explain the variation within individual outcome measures such as body mass index,” Frank explained. The study appears in the August 2004 issue of the American Journal of Preventive Medicine.
Obesity has become a growing concern for employers, as well, in terms of controlling health care costs and maintaining worker productivity. The numbers are daunting. Nationally, obesity costs U.S. companies more than $13 billion a year, including $8 billion for health insurance, $2.4 billion for sick leave, $1.8 billion for life insurance, and another $1 billion for disability insurance, according to the 2001 surgeon general’s Call to Action.
In addition to the increased use of health services by obese employees, “employees and employers alike incur additional costs from the impact of obesity on absenteeism, which results in lost employee income and lower corporate profits,” said David Chenoweth, president of Health Management Associates, a New Bern, North Carolina, health care research and consulting firm. In fact, obese workers are almost twice as likely to be frequently absent as people of a healthy weight, according to results by Brigham Young University health promotion professor Larry Tucker, published in the January/February 1998 American Journal of Health Promotion. The 2001 Call to Action noted that obesity-related illnesses cost employers 39.3 million lost workdays, 239 million days of reduced productivity, and 62.7 million doctor visits annually.
A Growing Body of Evidence
Overall, research into the links between obesity and the environment is in its infancy. To begin with, researchers to a large extent still can’t clearly define what a healthy diet is—witness the debate over low- and high-carbohydrate diets. And the questions only get more complex from there. Relationships between community design, patterns of social interaction, and the formation of a sense of community cooperation are all factors, as are aspects of safety and security, air and water quality, mental health, and more.
“Right now, we don’t really know what a healthy environment looks like,” said Hill. As a first step to achieving this yet-undefined environment that facilitates healthy lifestyles and healthy weights, Hill invited participants to envision what it would look like. Researchers admit much work remains to figure out exactly how obesity and the built environment are connected. Moreover, there are differences between what works for adults and what works for children as far as encouraging exercise goes. Still, “while we can’t safely say that certain changes in community design will lead to increases in physical activity, we can safely say that certain changes in community design will increase opportunities for physical activity,” said Susan Handy, an associate professor of environmental science and policy at the University of California, Davis.
Successful strategies require governments and local communities to work together to initiate programs in schools, workplaces, and communities, and to involve food producers, industries, and consumer associations. Examples of successful partnerships with industry that target physical activity and obesity include Gatorade’s “Get Kids in Action” (which has research and education components, as well as outreach to elementary and middle school children), Nike’s “NikeGO” (which funds physical activity programs and facilities for children), and General Motors’ “Just a Bit Gets You Fit” (which emphasizes the concept of exercising in manageable chunks of time). All three work to change lifestyles and behaviors through interventions at schools or worksites.
Studies such as a review article in the November/December 2001 issue of the American Journal of Health Promotion reveal that these and other interventions can be effective. “Social” marketing, which uses conventional marketing and advertising approaches to promote a change in behavior in a certain population (for example, those at risk for becoming overweight or obese), can help reverse trends in weight gain. Food labeling has also been shown to decrease caloric intake and fat consumption. Moreover, reducing prices of healthier foods increases their sale.
Stronger links still need to be forged between seemingly disparate disciplines, speakers stressed—issues that seem unrelated to obesity may, in fact, be connected. In addition, developers and planners should begin measuring and accounting for the health impact of proposed land use plans and future development projects. “Smart growth plans and policies need to be more explicit about addressing health,” said Marya Morris, a senior research associate at the nonprofit American Planning Association. For example, walkability should be factored in to school siting, just as creating bike trails and adequate walkways should be an inherent part of road and highway construction. “The lack of action on these issues is due in part to the lack of understanding by planners and others about the health consequences of how we shape the built environment,” Morris said.
Although much work remains, a growing body of evidence suggests that well-designed health promotion and disease prevention programs can improve workers’ health, morale, work relations, and productivity, as well as lessen disease risk, save businesses money, and boost financial performance of organizations, reported Ron Goetzel, vice president of consulting and applied research for The Medstat Group, a market intelligence firm. DHHS secretary Tommy Thompson suggested that all employers set aside time for their employees to exercise. Thompson believes better work-place practices should start in the federal government with its health agencies. And above all, public health workers and policy makers should practice what they preach by taking the stairs more, wearing a pedometer to count daily steps, losing excess weight, and improving their diets—even taking breaks through the day to do push-ups, and more. “I want to transform the American health system,” he declared, “and small steps can make a difference.”
A consensus emerged from the conference that complex environmental health problems require an integrated multilevel response strategy. “We cannot succeed without a comprehensive, multipronged view about the problem of obesity, including the relationship not only with our genes and biology but with our environment, which is changing at a speed that overwhelms our ability to adjust to it,” Zerhouni said.
Conference participants agree that prevention is critical for children, families, communities, and workplaces because obesity has been difficult to treat thus far. NIEHS director Kenneth Olden called for greater investments in the prevention of obesity and the translation of science-based information into effective policy and action for the public. And importantly, added Olden, “Somebody has to step out and bring all these agencies together to make sure we address this important public health issue.”
A large work in progress. With their junk food vending machines, restrictive schedules, and stationary tasking, workplace environments are contributing to the obesity epidemic.
A place for play. Playgrounds and bike lanes have sometimes been viewed as frivolous or out of place in the urban environment, but new research shows that making it convenient to have an active lifestyle may go a long way toward preventing serious—and expensive—health problems.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0061815309757EnvironewsNIEHS NewsBeyond the Bench: Keeping Migrant Families Safe Thigpen Kimberly G. 8 2004 112 11 A618 A619 Publication 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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For the thousands of migrant farmworkers who come to the United States in search of jobs, this country may seem like the land of opportunity. For their children, however, it also offers the opportunity for serious injury or even death from exposure to household toxicants that parents may not be aware of or have the language ability to recognize as a hazard. A program of the Community Outreach and Education Program at the University of California, Davis, Center for Environmental Health Sciences is seeking to provide migrant families in Northern California with the skills to protect their children from accidental poisonings.
The Safety Literacy for Migrant Farm Worker Families: Childhood Poison Prevention project uses a variety of means to reach and educate migrant families. The centerpiece of the program is training conducted in Spanish at migrant housing centers, in which parents and staff are taught how to read and interpret safety warnings and emergency first aid instructions. These classes cover basic safety information on over-the-counter medicines and vitamins, household and personal care products (such as bleach, cleansers, and bug sprays), plants and other environmental toxicants, and pesticides used in the home or at work. Participants are also taught how to read labels, what to do in case of poisoning, and how to use 9-1-1 and poison control centers. Participants receive a variety of printed safety resources in Spanish such as booklets, posters, and stickers, and each trainee is given four safety latches to secure chemicals stored in their homes.
To date, classes have been provided at five Head Start/Early Head Start child development centers in Yolo County, and at a series of parenting sessions organized by Yolo Connections, an umbrella agency whose mission is to promote volunteerism, mentoring, and community partnership. The poison prevention training has also been presented at the annual California Office of Migrant Services conference to 83 staff members representing all 26 migrant housing centers in California.
Center staff have also developed safety materials for posting and distribution in target camps that illustrate the proper use and storage of toxic household substances, as well as emergency first aid techniques. The materials are distributed to Head Start, parenting classes, and migrant camps through collaboration with the California Poison Control Center, the California Office of Migrant Services, and the nonprofit California Human Development Corporation.
The Safety Literacy for Migrant Farm Worker Families program goes beyond education to preventive action through the distribution and installation of child-proof locks in migrant housing. By the 2004 harvest season, more than 1,000 safety latches will have been purchased by the project and installed in migrant camps throughout Yolo County. More information on the project can be found at the center’s website at http://www.envtox.ucdavis.edu/cehs/.
Word to the wise. A graphic reminds parents that “children will eat almost anything”!
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a00619EnvironewsNIEHS NewsHeadliners: Liver Cancer: Hepatitis B Virus Mutation Predicts Liver Cancer Phelps Jerry 8 2004 112 11 A619 A619 Publication 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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Kuang SY, Jackson PE, Wang JB, Lu PX, Muñoz A, Qian GS, Kensler TW, Groopman JD. 2004. Specific mutations of hepatitis B virus in plasma predict liver cancer development. Proc Natl Acad Sci USA 101:3575–3580.
Liver cancer is the fifth most prevalent form of cancer worldwide, causing more than 500,000 deaths annually, according to the World Health Organization. Exposure to the hepatitis B virus (HBV) is a major risk factor in the development of liver cancer. Now a team including NIEHS grantees Alvaro Muñoz, John D. Groopman, and Thomas W. Kensler, all of the Johns Hopkins Bloomberg School of Public Health, has identified a biomarker that may predict future cases of liver cancer in HBV carriers.
Previous work by members of this team has shown that HBV exposure causes a 7-fold increase in risk. Exposure to aflatoxin, a mold product commonly found in peanuts and grains, increases the risk of liver cancer by 3.5 times. Combined exposure to these two agents results in a remarkable 60-fold increase in the risk of developing liver cancer. This is an especially troubling public health problem in China, where HBV and aflatoxin exposures are both very high.
In the current study, the researchers examined the prevalence a particular HBV mutation in the plasma and tumors of liver cancer patients living in Qidong, China. Initial studies determined that about three-fourths of the tumors from an initial group of 70 patients contained the mutation. In a second group of 15 liver cancer patients chosen from a cohort of high-risk individuals, the investigators determined that about half had detectable levels of the HBV mutation in their blood several years before the cancer appeared.
These findings suggest that detection of the mutated HBV in the blood is an early warning sign of subsequent liver cancer development, and suggest its use as an intermediate end point in prevention and intervention trials. –Jerry Phelps
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0062015289182EnvironewsFocusSprawl: The New Manifest Destiny? Schmidt Charles W. 8 2004 112 11 A620 A627 Publication 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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Seen from 400 miles above the Earth, the greater Washington, D.C.– Baltimore area is an aggressive consumer of farmland and open spaces. Computer-enhanced satellite images of the area show paved surfaces as crimson tentacles, pushing steadily out from the urban core. Recent studies by the National Aeronautics and Space Administration now suggest the land area occupied by Washington, D.C., and surrounding communities will expand 80% over its current size by 2030.
Urban sprawl so extensive that you can watch it from space is hardly limited to the nation’s capital. Indeed, sprawl—defined as low-density development that outpaces population growth—is endemic throughout much of the United States. Donald Chen, executive director of Smart Growth America, a nonprofit research coalition in Washington, D.C., says that the overall declines in urban density, loss of open spaces, and increased auto use that accompany sprawl are continuing “virtually unabated.” Those who leave cities for the suburbs may expect a healthier, cleaner environment, but sprawl developments actually present a range of health risks including poor air quality from rising vehicle use, watershed pollution, and a built environment that limits opportunities to walk from homes to businesses and schools, thereby exacerbating obesity and related medical problems, such as heart disease.
Sprawl first surfaced as a federal policy issue in the late 1990s, driven mainly by then–vice president Al Gore, who made it a centerpiece of his environmental platform. Researchers were increasingly aware that sprawl was a growing problem fraught with economic, ecologic, and, possibly, health consequences. However, these consequences were not well understood, says Reid Ewing, an associate and research professor at the University of Maryland National Center for Smart Growth Research and Education. “Sprawl was mainly a political issue back then,” he recalls. “There were various hypotheses about the magnitude of sprawl and its impacts, but sprawl had been neither measured in a sophisticated way nor related objectively to a range of outcomes such as loss of farmland and increased air pollution.”
Since the turn of the millennium, Ewing says, numerous studies have sought to quantify sprawl, define its causes, and investigate its health and environmental concerns. At the same time, alternatives to sprawl have been studied and applied in many areas, with varying levels of success.
Defining Sprawl and Its Effects
During the 1990s, there was no consistent definition for sprawl. Experts compared it to obscenity: hard to define, but obvious when you see it. But several years of focused study have since cleared up confusion over what sprawl actually is. In its groundbreaking 2002 report titled Measuring Sprawl and Its Impact, Smart Growth America defined sprawl as the outcome of four related factors: low residential density; a poor mix of homes, jobs, and services; limited activity centers and downtown areas; and limited options for walking or biking. This report—the first to create a multidimensional picture of sprawl and its effects—ranked 83 metropolitan areas according to a “sprawl index” derived from 22 separate measures based on the four factors described above. According to this ranking, Riverside–San Bernardino, California, about 60 miles east of Los Angeles, is the most sprawling metropolitan area in the country, while New York City is the least.
Along with a greater understanding of sprawl’s defining features has come improved knowledge of its related health hazards. For instance, the Smart Growth America report showed that sprawl correlated directly with rising vehicle use. The finding was based on a comparison of each city’s overall sprawl index and a parameter known as vehicle-miles traveled (VMT) per person (which Ewing has found is also a risk factor for crashes and traffic fatalities; for more on the growing problem of traffic crashes, see “Vehicular Manslaughter: The Global Epidemic of Traffic Deaths, p. A628 this issue). VMT can be derived from data gathered by the U.S. Department of Transportation. The correlation between sprawl and VMT is small, the report states, but sufficient to produce significant increases in vehicle emissions across metropolitan regions.
Among the most problematic vehicle emissions are nitrogen oxides (NOx), a group of highly reactive combustion gases. Automotive controls have lessened emissions of other pollutants, but NOx—because of its chemical properties—is still emitted at high levels. This is unfortunate because NOx combines with airborne particles and sunlight to form ground-level ozone, a toxic chemical with dangerous respiratory effects, especially among children, those with asthma, and the elderly.
“There doesn’t seem to be any doubt that sprawling metro areas have worse ozone pollution than more compact areas,” says Ewing. Data gathered by Smart Growth America show that high ozone levels are tightly linked to sprawl development. In fact, high-density areas were found to have ozone levels that averaged 51 parts per billion less than low-density areas; the U.S. Environmental Protection Agency (EPA) standard for ambient ozone is 80 parts per billion, averaged over an eight-hour period.
These results may appear at odds with common sense; after all, shouldn’t automotive pollution be worse in urban areas than in outlying communities? “You would think you’d have less congestion and cleaner air in the suburbs,” Ewing concedes. “But people drive so much more in sprawling areas that they offset the benefits of dispersal. We found ozone levels were higher and congestion was about the same, largely due to these offsetting effects.”
With its focus limited to ozone, Measuring Sprawl and Its Impact is silent on other automotive pollutants that may also elevate health risks. However, the Sierra Club recently conducted a broad investigation of highway health risks from polluted air, emphasizing in particular the role of carcinogenic hydrocarbon emissions from cars and trucks. The organization’s 2004 report, titled Highway Health Hazards, compiled the results of 24 academic studies published in peer-reviewed journals such as JAMA, The Lancet, and EHP, among others. These studies linked traffic-related air pollution to health problems such as asthma, cancer, premature birth, low birth weight, and a generally higher risk of death among residents who lived near busy roadways, particularly those roads carrying more than 150,000 vehicles per day.
Brett Hulsey, a transportation expert at the Sierra Club, says the findings reinforce the view that vehicle emissions and health effects are related. “Some of the worst air pollution is in the car itself,” Hulsey explains. “People who drive for hours every day are stuck in a plume of cancer-causing chemicals [spewing from the cars around them]. So, what we’re saying is that more sprawl equals more driving, and that more driving equals greater health risk. Therefore, sprawl and health risks are related.”
A Focus on Obesity
In a recent development, sprawl researchers have also begun to address the built environment’s influence on physical activity and obesity. The obesity epidemic in the United States and other countries throughout the world is now viewed as a growing public health crisis. Both child and adult obesity rates in the United States have doubled since 1980, according to the Centers for Disease Control and Prevention. The expanding waistline is a major factor in the rise of type 2 diabetes mellitus, which also has achieved epidemic proportions, affecting some 17 million Americans, according to the National Institute of Diabetes and Digestive and Kidney Diseases. Add cardiovascular disease, low self-esteem, and depression to the list of related health problems, and obesity will soon surpass smoking as the nation’s leading health threat, experts say.
Hypothesized links between the built environment and obesity are now being explored jointly by experts in planning, nutrition, and public health [see “Fighting Obesity Through the Built Environment,” p. A616 this issue]. This multidisciplinary union has produced important new evidence suggesting that sprawl and obesity are likely related. A study published in the August 2004 issue of the American Journal of Preventive Medicine related body mass to measures of sprawl within a one-kilometer distance of each participant’s residence. The study, led by Lawrence Frank, an associate professor of community and regional planning at the University of British Columbia, focused on 10,898 residents of Atlanta, Georgia, a city that ranks fourth on Smart Growth America’s top-10 list of the most sprawling U.S. metropolitan areas.
Frank’s results showed that sprawl development was associated with both increased time spent in cars and increases in body weight. Specifically, for every extra 30 minutes of commuting time per day, participants had a 3% greater likelihood of obesity than peers who drove less. The study also found that people who lived within walking distance (defined as a half-mile) of shops were 7% less likely to be obese than counterparts who lived farther away. “These findings are intuitively obvious,” Frank says. “But now we actually have the data to back them up.”
Frank is currently doing another study in which subjects wear accelerometers, which measure motion. This yields data on activity patterns, which he and colleagues will correlate with obesity and residential land use features. Data analysis is preliminary, but correlations between activity and residential land use features observed thus far are “very strong,” he says.
Frank’s research, with its kilometer-scale resolution, builds on an earlier study by Ewing and colleagues, published in the September/October 2003 issue of the American Journal of Health Promotion. This study showed that urban design at the county level in Atlanta also correlated with physical activity and obesity. When it was released, the study triggered widespread media coverage; it provided the most compelling evidence to date that sprawl promotes obesity by fostering a sedentary lifestyle. Specifically, the study showed that those who lived in sprawling counties were likely to walk less, weigh more, and have greater prevalence of hypertension than those living in more compact counties.
Ewing and Frank caution that the current evidence doesn’t conclusively establish a cause–effect relationship between sprawl and obesity. Other variables are also at play, chief among them the types of food available locally and the calories consumed compared to those expended. Furthermore, current evidence derives from cross-sectional studies that merely provide snapshots of weight and behavior at single time points. Longitudinal studies that track participants as they move in and out of sprawling areas are needed to bolster cause–effect hypotheses, Ewing says. “Right now, it’s not clear that sprawl makes people less active,” he explains. “It may be that people who are already less active choose sprawl development as a place to live.”
In choosing low-density development, sprawl inhabitants may also seek a greater connection with nature. But sprawl tends to highly disturb the natural environment. Michael Klemens, a senior conservationist with the Bronx Zoo–based Wildlife Conservation Society and coauthor of the book Nature in Fragments: The Legacy of Urban Sprawl (in press), has studied sprawl’s effects on biodiversity in the New York City metropolitan area for more then 25 years. His research, based on field observations and more than 100 years of existing baseline data, shows that 75% of plant and animal species impacted by sprawl in New York are in decline. A residual 25% of species experience population increases, he says, but these tend to be so-called weed species that are able to thrive in fragmented habitats.
Declines in biodiversity have far-reaching ecological impacts. “The gene pool is much smaller, so the system itself is at greater risk,” Klemens explains. “An ecosystem that contains just twenty-five percent of the original flora and fauna is less resilient to change.” Furthermore, he adds, some weed species are competent vectors for disease transmission. White-footed mice, for instance, which thrive in sprawl developments, carry Lyme disease and West Nile virus. Thus, sprawl also contributes to the spread of infectious illnesses, with serious public health effects.
Real-World Solutions
The chief development alternative to emerge in response to sprawl is “smart growth.” With its focus on urban revitalization and expanded transit options, smart growth seeks to make existing communities places that people want to live. The term was popularized by Parris N. Glendening, governor of Maryland from 1994 to 2002, who in 1997 launched the Smart Growth and Neighborhood Conservation Program to limit sprawl in his state. Today, dozens of environmental groups, civic organizations, and government agencies promote smart growth principles as part of their sprawl reduction programs. These principles include, among other concepts, the promotion of mixed land uses and the creation of attractive neighborhoods with a strong sense of “place,” or local identity and character, where residents can walk freely to the places they need to go.
The Smart Growth Network is a partnership between the EPA and a number of nonprofit, public, and governmental organizations working together to raise public awareness and promote smart growth principles. In its popular first volume of the manual Getting to Smart Growth, released in 2001 (a second volume was released in 2003), the Smart Growth Network suggested that towns should return to the designs of the early twentieth century. In those earlier times, land uses were more integrated, enabling people to walk to the corner store, to work, or to school. Today, such uses are more often placed so far apart they can only be reached by car. Numerous communities have sought to reverse this trend.
Portland, Oregon, is an oft-touted model of sprawl containment. The city established an “urban growth boundary” in 1980 that protects nearby farmland surrounding the city and tightly limits development in outlying areas. Portland’s approach has not been without controversy. For several years, the urban growth boundary was accompanied by skyrocketing housing costs and discontent among those who resented restrictions on development. But the high costs of housing—which are in fact attributable to a host of factors, including a high rate of migration to Portland from other states, particularly California—have since declined to the point that they are roughly equivalent to those of other West Coast cities, says Mary Volm, spokesperson for the City of Portland Office of Transportation.
Because of the urban growth boundary, Volm says, Portland has successfully assimilated a sharply rising population without encroaching on its valuable land resources. “We make solid investments to create lively districts and neighborhoods that people are attracted to,” she explains. Portland’s urban designs provide affordable and accessible public transit located close to schools, businesses, and residential communities. In addition, walking and bike paths connect the entire community, which is infused with a multitude of parks and green spaces.
Urban growth boundaries are but one tool among many to limit sprawl. Others include establishing more mixed-use areas (so residents can shorten or eliminate some trips) and creating more density in places that already have or could have transit services. Atlanta began its Livable Centers Initiative (LCI) in 1999 after a 13-county region surrounding the city fell out of compliance with the Clean Air Act. This major program committed $350 million toward alternative transportation projects in surrounding communities that plan for mixed land uses, affordable housing, and increased transportation efficiency. A total of 51 communities have been funded for planning under the program thus far.
Past experience in Atlanta permits an optimistic outlook. In preparation for the 1996 Summer Olympics, the city bolstered public transportation and other traffic control measures in part by substantially increasing service on the rail transit system and making major areas off-limits to vehicular traffic. Once these changes were in place, acute childhood asthma attacks fell by 44%, ozone concentrations fell by 28%, and morning peak traffic fell by 22.5%. These results are described in the 21 February 2001 issue of JAMA in a study by Michael Friedman, an epidemiologist at the Centers for Disease Control and Prevention, and colleagues. Thomas Weyandt, director of comprehensive planning with the Atlanta Regional Commission, says the experience also showed that “if you provide more transit, people will use it.”
And Still There Is Sprawl
Despite growing knowledge of its impacts and an array of development alternatives, sprawl continues to spread, leaving polluted resources and more sedentary populations in its wake. Why? Numerous factors drive the trend. First are the government subsidies that pay for sprawl. Rural roads are built and maintained with twice the federal funding that is devoted to urban road maintenance, according to the Surface Transportation Policy Project, a Washington, D.C.–based nationwide coalition that studies transportation issues. Gasoline, too, is heavily subsidized by the federal government—if the costs of air pollution and protection of national petroleum interests were incorporated into fuel pricing, then gas at the pump would be twice as expensive as it is now, according to the Surface Transportation Policy Project.
A sustained surge in the housing market has also played a significant role. Middle- to upper-middle-class citizens continue to flock to the suburbs in search of safe, affordable housing. Moreover, smart growth projects often conflict with local zoning codes that impede urban revitalization. These laws reflect decades-old efforts to segregate housing from industrial polluters that are rarely found in residential areas today, since heavy industry is no longer the primary engine of the economy. Variances for new urban development can take months or years to process; meanwhile, adequate parking, emergency response, and other related development issues required for urban renewal collapse into a morass of red tape. “A lot of developers just don’t want to fight that battle,” says Jessica Cogan Millman, deputy director of the Smart Growth Leadership Institute, a nonprofit project within Smart Growth America.
Perhaps the greatest barrier to smart growth is the diversity and number of stakeholders required to move the process forward, adds Geoffrey Anderson, director of the EPA Development, Community, and Environment Division. “The whole system is burdened with inertia,” he explains. “You have to interest private-sector developers, you need to secure financing, you need the government to issue permits, and you have to convince residents that well-designed density is in their best interests. At a fundamental level, smart growth requires all these stakeholders to work together. But that doesn’t usually happen. Instead, the system puts out the easiest and most familiar product: development that segregates housing and business and invests little into existing communities—in short, development that is land-consumptive and auto-dependent.”
Weyandt agrees that successful coordination under the LCI has depended on the engagement of local leadership and the extent of community involvement. He, too, points to the challenges raised by logistical issues, particularly zoning ordinances that stand in the way of the process. “Zoning has the perverse effect of discouraging what we want most,” he says. “We’ve looked at these ordinances to see how they stack up against smart growth principles, and it’s not a good record. Sometimes these communities have to amend ordinances before they can get funded under the LCI.” Public buy-in on the process can also pose challenges, Weyandt says.
But with a sensitive, well-prepared approach, planners can convince residents that urban revitalization is good for the city and ultimately good for their health. “Once you start talking about housing density at eighty [dwellings] per acre, some people are going to see that in a negative way,” Weyandt concedes. “But if you see a development that’s not only mixed-use and high-density but also pleasant and attractive, then maybe you can imagine yourself living there.”
In fact, Weyandt says, in-town housing is booming in Atlanta. “Our experience shows that the market responds positively to smart growth options,” he says. “We see this as a long-term process. We facilitate decisions at the local level and reward those who do well. And as for those that aren’t interested, perhaps in a few years they’ll change their minds.”
Mass exodus. A long line of taillights heading into the dusk as commuters leave the city for the suburbs is an increasingly common sight in metropolitan regions throughout the United States.
One sprawl solution. Suburban communities that encourage and support having an active lifestyle are one part of the answer to the health problems associated with sprawl.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0062815289183EnvironewsSpheres of InfluenceVehicular Manslaughter: The Global Epidemic of Traffic Deaths Dahl Richard 8 2004 112 11 A628 A631 Publication 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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Even though traffic-related death rates in the United States and other high-income countries have been declining steadily for several decades, death tolls on the roadways of the world’s poorer countries have been skyrocketing. The number of motorized vehicles has been escalating in developing countries, where roads (often poorly built to begin with) are shared by pedestrians, animal-driven carts, rickshaws, and bicycles, and traffic safety laws are weak or inadequately enforced. The result—an alarming increase in death and injury on the roadways of the world’s poorer nations—is now attracting the attention of international organizations as a major public health problem.
In recognition of this global problem, the World Health Organization (WHO) designated “road safety” as the theme of World Health Day 2004, held April 7. The same day, the WHO and the World Bank released the World Report on Road Traffic Injury Prevention, an exhaustive examination of the global problem and potential solutions, culminating in a call for action at both the national and international levels. A week later, the United Nations (UN) General Assembly devoted a plenary session to the global crisis in road safety for the first time in its history, discussing, among other things, how to implement the report. Then, in May 2004, at the WHO 57th World Health Assembly meeting in Geneva, delegates accepted resolution EB113.R3, “Road Safety and Health,” calling for the WHO to act as a coordinator on road safety issues within the UN system, working in close collaboration with UN regional commissions.
As the WHO/World Bank report makes clear, the increase in traffic deaths and injuries in the world’s poorer nations has been evident for years: global traffic deaths have risen from approximately 990,000 per year in 1990 to nearly 1.2 million per year in 2002, with 85–90% of these deaths occurring in low- and middle-income countries. Citing a study that examined changes in traffic fatality rates in various countries between 1975 and 1998, the report noted that during that time span Canada’s road fatality rates declined by 63.4%, Sweden’s by 58.3%, and those in the United States by 27.2%; the reasons for these declines have been multifold. At the same time, traffic fatalities increased by 237.1% in Colombia, by 243.0% in China, and by 383.8% in Botswana.
Furthermore, the report predicts that if nothing is done to stop the trend, it will rapidly escalate. By 2020, predict the WHO and the World Bank, if appropriate actions are not taken, overall global traffic deaths will increase by 67%; an 83% increase in poorer countries will offset a projected 30% decrease in high-income countries. In 1990, road traffic injuries were the ninth leading contributor to the global burden of disease, according to the report. Without appropriate action, by 2020 road injuries are predicted to be the third leading contributor.
Many physicians and public health professionals have been sounding the alarm for years and voicing frustration over the lack of international response. Samuel N. Forjuoh, a Ghanaian-born physician and professor of family and community health at Texas A&M University College of Medicine, believes that part of the problem has been that road crashes are simply not seen in the same way as diseases. “For a long time, injuries were considered haphazard events, acts of God,” he says. “There was a belief that there was nothing you could do about them.”
Another reason is that car crashes occur one at a time, says Mark L. Rosenberg, executive director of the U.S.-based nonprofit Task Force for Child Survival and Development and former director of the Centers for Disease Control and Prevention’s (CDC) National Center for Injury Prevention and Control. “You have three thousand people a day dying in car crashes,” he explains. “If you had three thousand people a day dying in one building that collapses or in a few big airplane crashes, it would get much bigger headlines.”
Still another reason is a sense of fatalism on the part of some observers. Rosenberg explains, “People say, ‘Oh, motor vehicle deaths are just what happens when you start development in a country. You build roads, you bring in cars, and this is inevitable.’ But nothing could be further from the truth. It’s not inevitable; [these deaths] are completely predictable and preventable.”
A Perfect Plague
Rosenberg borrows from the popular book and movie The Perfect Storm, which told how a rare combination of meteorological forces resulted in a monstrously destructive force, to describe the escalation in traffic deaths and injuries in poorer countries. “More and more motorized vehicles are being added to roadways that are inadequate,” he says. “You have these mixes of vulnerable pedestrians and motorized traffic on main roads with no center barriers. The roads are poorly marked, and they go right through villages. You have situations where people don’t know how to drive, they don’t obey laws, and even if you have laws, they’re not enforced. So you have this lethal mix, and into this we are adding more and more cars, accelerating the rate of injury and death as these cars are added. You have the ‘perfect plague.’” Rosenberg says this plague is also perfectly predictable because “we know how quickly the motor vehicle manufacturers are planning to increase their production in these ripest of markets.”
According to the World Report, a large portion of the motor vehicle increase in poorer countries has been in the number of motorcycles, minibuses, and trucks. And their increased presence on the roadways of those countries has been overwhelming in many instances. Since 1990, the number of motor vehicles in China, for instance, has quadrupled to more than 55 million. In Thailand, the number of motor vehicles nearly quadrupled between 1987 and 1997.
The new and growing mix of two-wheeled motor vehicles, larger multi-passenger vehicles, traditional wheeled vehicles, and pedestrians all often using the same roadways has created patterns of traffic death and injury that are much different from those in high-income countries. The WHO/World Bank report points out that while most of the people who die as the result of traffic crashes in the developed world are passengers in vehicles, traffic crashes in poorer countries are more likely to involve pedestrians and motorcyclists. The report states that between 1977 and 1994 in the city of Nairobi, for instance, 64% of traffic fatalities were pedestrians.
Another characteristic of poorer countries is that crashes are far more likely to result in deaths than crashes in high-income countries. Rosenberg explains, “The victims [in developing nations] are much, much more vulnerable: they are pedestrians or bike riders with nothing to protect them.” Further, says Michael R. Reich, a professor of international health policy at the Harvard School of Public Health, the typical U.S. car crash involves the driver running into a tree or into another vehicle. In contrast, crashes in poor countries typically involve pedestrians or people in poorly maintained multi-passenger vehicles, such as mini-buses crammed with 20–30 people and no seat-belts. According to “Road Traffic Injuries in Developing Countries: Strategies for Prevention and Control,” a resource paper by Reich and Harvard colleague Vinand Nantulya, 10,000 U.S. crashes result in 66 deaths; but in Kenya, the death rate per 10,000 crashes has been as high as 1,786, and in Vietnam it’s reached 3,181.
A variety of other factors lend to the troubling numbers in poor countries. Forjuoh believes that low literacy rates play a role because many drivers can’t understand road signs. David Sleet, associate director for science at the CDC National Center for Injury Prevention and Control and a coeditor of the WHO/World Bank report, contends that many pedestrians in developing countries have never driven a car and don’t understand the basic “rules of the road” governing motorists. They also are inexperienced in judging oncoming vehicle speeds and stopping distances.
Yet another factor exacerbating the traffic fatality trend in poor countries is the shortage—and often the absence altogether—of emergency medical response. Charles N. Mock, a surgeon and epidemiologist at the University of Washington’s Harborview Injury Prevention and Research Center in Seattle, has spent time in Ghana and has worked with groups in Mexico, Vietnam, and India to improve systems of emergency medical care in those nations. He points out that while organizations such as Doctors Without Borders and the International Committee of the Red Cross can provide physicians on episodic bases, such as in response to earthquakes and wars, there really isn’t an available pool of doctors that poor countries can call upon to provide sustained improvement of emergency medical response.
Mock admits that the situation may sound dismal, at least at the moment. “But to sit back and say nothing can be done because of the economic problems isn’t fruitful,” he says. “A tremendous amount can be accomplished. Mortality rates can be decreased, medically preventable death can be prevented, disabilities can be prevented, and part of the discrepancies in outcome between rich and poor countries can be eliminated by improving organization and planning for trauma care services without necessarily spending very much more.”
Sensible Responses to the Epidemic
Mock has been intimately involved in a project, cosponsored by the WHO and the International Society of Surgery, to formulate a low-cost model that nearly every country can implement to greatly improve emergency trauma care. The model identifies specific essential elements that are necessary for trauma care—human resources, equipment, supplies—for purposes of aiding countries in performing needs assessments. Mock has spent this year at WHO headquarters in Geneva working on the plan’s implementation.
Other organizations are embarking on other programs. In 1999, the World Bank created an organization called the Global Road Safety Partnership (GRSP), an international collaboration between business, civil society, and government organizations to improve road safety conditions around the world. GRSP is currently involved in a variety of projects in 10 countries. For example, in Bangalore, India, GRSP has created partnerships to launch an anti–drunk driving campaign and to improve roadways in high-traffic areas to enhance safety. In addition, the CDC has been working with the ministries of health and other groups in Mexico, Colombia, and El Salvador to devise creative new ways to reduce injuries to pedestrians, bicyclists, and motor vehicle occupants. And the World Bank is providing $25 million for the Vietnam Road Safety Project with a goal of achieving continuous, long-term reduction of traffic crashes in that country.
Sleet also points to other local strategies in place that reduce the likelihood of crashes. Some cities are pursuing better land use management for optimized traffic flow, and promoting alternative transportation modes such as mass transit. Other strategies target drunk driving and speeding behaviors, and promote the use of cycle helmets, seat belts, and other protective devices. Still others seek to separate non-motorized traffic (such as pedestrians and cyclists) from motorized traffic.
While these efforts are noteworthy, reducing the epidemic of traffic deaths and injuries around the globe will be impossible unless countries adopt the political will to do so on their own, states the World Report. In the United States, for instance, the will to do something about the nation’s ever-growing highway death tolls began in the 1960s. Ralph Nader’s 1965 book Unsafe at Any Speed detailed the minimal attention being paid to safety in the auto industry, and the next year President Lyndon Johnson signed two bills to create stricter safety standards in cars and roadways. The combination of safer cars and roadways coupled with successful efforts to promote safer behaviors (such as use of seat belts and “designated drivers”) has been credited with bringing about ever-diminishing rates of traffic death and injury on U.S. roadways.
While the United States and other developed nations possess the resources to invest in safer cars, better roadways, and improved emergency medical response, an obvious question is, how can poorer countries achieve improvements as well? According to Sleet and others who are knowledgeable about traffic safety, the answer is by adopting and adapting effective strategies, and by developing local evidence-based solutions.
In Colombia, for instance, a concerted effort to reduce traffic deaths in Bogotá and other cities has proven to be extremely successful. In 1995, Colombia first required that all vehicle owners must be insured and then instituted a 3% levy on all vehicle insurance policies, earmarking that money for a “road accident prevention fund.” Colombia had recorded its all-time record high for traffic fatalities—7,874—in 1995, according to a report in the March–June 2003 issue of Injury Control and Safety Promotion by a team led by Deysi Yasmin Rodríguez, an engineer with the Research Program on Traffic and Transport at the National University of Colombia. By 2002, the nation’s traffic deaths had dropped to 6,063.
In Bogotá, the nation’s capital and biggest city with 7 million inhabitants, a series of mayors have instituted several programs to reduce traffic deaths and injury, such as closing bars at 1 a.m. instead of 3 a.m. and urging people to drink in moderation if they’re driving, reducing the number of cars during rush hour by encouraging workers to find alternative means of transportation (such as carpooling), and reclaiming sidewalks for pedestrians by prohibiting the old practice of allowing drivers to park their cars there. The outcome has been striking: the number of traffic fatalities in Bogotá declined from 1,387 in 1995 to 697 in 2002.
According to Rodríguez, the national Ministry of Transport is now finishing plans to broaden the Bogotá approach to the entire nation. “Among the main strategies we have is the generation of a new culture about road safety in the country,” says Rodríguez, who is a consultant on the project.
Still, even though Colombia has made great progress, Rodríguez sees distinct obstacles ahead. There are “few economic and human resources capable of supporting the programs in a continuous way,” she says.
The WHO/World Bank report includes a series of recommendations to reduce global traffic deaths and injuries. It strongly advocates the identification of a lead agency in every nation to be in charge of traffic safety. It then recommends the routine collection of traffic crash and injury data to document the magnitude of the problem and the key risk factors. It goes on to recommend the formulation of a national plan of action in every country, together with implementation of the plan using science-based interventions.
But as Rosenberg emphasizes, there are serious impediments to the creation of good traffic safety plans in poor countries. “We say, ‘Form a coordinating agency in your country,’ but we’ve got to get them the resources to do it,” he says. “Each step takes money, and right now they don’t have the resources.”
To Rosenberg, the problem is akin to where AIDS/HIV stood 20 years ago. “We tended to the problem just within our own borders and completely ignored what was happening in sub-Saharan Africa until it was too late. We need to learn from that epidemic and apply it to this one, because with this one we have a chance to prevent it,” he says. “When AIDS came twenty years ago there were no good means to prevent it or treat it, and not even a good test to diagnose it. But with traffic safety there are many very clear and effective things we can do. If we let this one get out of control, we will have no excuse. We will have failed in our responsibility to become good ancestors.”
Collision course. Traffic crashes such as this one in Bangkok, Thailand, resulted in 1.2 million deaths worldwide in 2002, with as much as 90% of those occurring in low- and middle-income countries.
Street fight. Scenes like this one of congested vehicle and pedestrian traffic in Shanghai are an everyday occurrence worldwide as humans and machines compete for use of the roads.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0063215289184EnvironewsInnovationsFormula for a New Foam Frazer Lance 8 2004 112 11 A632 A635 Publication 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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By 2010, chlorofluorocarbons (CFCs) will be banned globally because of their adverse impact on the planet’s protective ozone layer. One industrial activity that has been significantly impacted by this ban is the manufacture of plastic foams—lightweight alternatives to solid plastic that are valued for their flexibility and ability to insulate, as well as their cushioning ability and (in marine applications) enhanced flotation.
Plastic foams are created by combining two chemicals that would otherwise form a solid plastic, or by melting an existing solid. A third substance, often a CFC, is then added as a “blowing agent.” This agent vaporizes at the reaction temperature, releasing gas bubbles into the molten plastic. “It’s much like soda,” explains Gordon Nelson, a professor of chemistry and dean of the Florida Institute of Technology College of Science and Liberal Arts. “Soda has carbon dioxide [CO2] trapped in it, but if you remove the pressure, you get fizz. With plastic, you get foam.” The resulting plastic foam can be exceptionally lightweight, given its size and application.
Today, the goal of the plastic foam industry is to make a material that remains lighter than solid plastic but has many of the same qualities of durability and flexible rigidity as the solid version, and to do so without having to rely on ozone-depleting gases. A team led by L. James Lee, a professor of chemical engineering at The Ohio State University, is pursuing one approach that relies on clay nanoparticles for strength and the green chemist’s old friend—supercritical CO2— to put the “foam” in plastic foam. Supercritical CO2, formed by putting CO2 gas under increasing temperature and pressure, has been used as an environmentally sound replacement for other toxic chemicals, including the solvents used in the manufacture of semiconductors [see “SCORR One for the Environment,” EHP 109:A382–A385 (2001)].
A number of researchers have been pursuing various types of nanoparticles as strengthening agents for plastic foams, while others have been looking at different substitutes for the banned CFCs; Lee, along with fellow associate chemical engineering professors Kurt W. Koelling and David L. Tomasko, decided to tackle both aspects at once. His ultimate goal—a lightweight, environmentally friendly plastic foam that may one day replace solid plastics in some applications.
Better Bubbles
According to Lee, supercritical CO2 was a logical alternative to ozone-depleting CFCs. “Supercritical CO2 is a pretty benign substance,” he says. “And it’s neither difficult to make and use, nor is it expensive. Additionally, we’re not generating new carbon dioxide, merely using what’s available atmospherically, which lessens the impact still further.”
Lee says another advantage to supercritical CO2 is that under conditions necessary to reach supercritical status, the gas becomes a liquid, which mixes more easily with the molten plastic than a solid or gas. Lee says the optimal conditions arrived at in his experiments—temperatures just above 31°C and pressures of about 1,100 pounds per square inch—are easily achievable with current technologies. These results were reported in the 16 October 2003 issue of Advanced Materials.
And while most structural-grade plastic foams contain bubbles close to several hundred micrometers across, Lee’s process generates bubbles as small as 5 micrometers in diameter. According to Nelson, the bubbles must be small and uniform, or foams with less desirable qualities will result. “Smaller equals nicer consistency and better insulating properties,” he says. “Larger bubbles can alter the physical and thermal properties of the foam.”
There is one obstacle, however, according to Roland Loh, a global applications specialist for plastic foam manufacturer Owens Corning and principal investigator in the company’s search for environmentally benign nanocomposite foam products, and that is one particular property of CO2: “Carbon dioxide is a low-cost blowing agent, but its solubility is low, so if you want it diffused throughout the polymer, you need to keep it under pressure,” he says. “That will obviously raise the cost for industry.” Owens Corning is currently looking for a viable substitute for its blowing agent of choice, the CFC chlorodifluoroethane.
Lee admits that’s one of the aspects of the new technology still to be addressed. “The good thing about CFCs is that they dissolve better and diffuse out more slowly. Carbon dioxide is a small molecule, so it diffuses out more quickly, making it difficult to control density,” he explains. That’s one area where he and his colleagues believe the clay nanoparticles will help—by acting as a diffusion barrier, to slow the progress of the supercritical CO2 in diffusing out of the polymer.
Mixing In the Particles
The addition of clay nanoparticles—the second aspect of Lee’s process—performs several critical functions in addition to improving the mechanical properties of the polymer. The clay also acts as a flame retardant and helps limit outgassing of potentially dangerous gases in the event the polymer does burn. These particles are added, less than 10% by volume, to the molten plastic.
According to Lee, the nanoparticles are derived from montmorillonite, an environmentally friendly, naturally occurring clay found in huge deposits in Europe and the United States. Clay naturally exists in a platelet-like structure, a structure maintained by particles even on the nanoscale, and this platelet-like nature can impart a number of desirable qualities. But the clay structure must first be modified before it can be added to the polymer.
In the 16 October 2003 Advanced Materials article, Lee explains: “In the natural state, [montmorillonite] platelets are held together by van der Waals forces and electrostatic forces to form crystallites (tactoids). Organic cationic surfactants, e.g., alkyl ammonium salts, are commonly used to modify the negatively charged clay surface through ion exchange, in order to improve wetting and lower the interfacial tension between the polymer and the clay that in turn enhances dispersion. Favorable interaction between the polymer matrix and clay surface and the resulting energy reduction are critical for the formation of exfoliated nanocomposites.”
Lee says one appealing aspect of clay as used in his work is that it forms a submicroscopic nucleation agent for the supercritical CO2, much the way impurities in a liquid or irregularities on the surface of a glass can trigger bubble formation. Reducing the pressure in the process that keeps the CO2 at a supercritical stage means the compound reverts to a gas, forming bubbles throughout the plastic. More nanoparticles within the molten plastic mean more bubbles will be formed throughout the plastic, and the large surface area of the nanoparticles provides much greater contact between the particles, the polymer matrix, and gases.
Clay, says Lee, is also an ideal strengthening agent because of its low cost. “Clay runs around two dollars per pound,” Lee says. “Other materials, like carbon nanofibers, would be even better, but a single-wall carbon nanotube can run five hundred dollars per gram.” Lee’s experiments have resulted in boards as strong as typical plastic foam, yet only two-thirds as thick. He’s experimenting with other strengthening materials, including aluminum and carbon.
Having thus far produced polystyrene and polymethyl methacrylate nanocomposite foams, Lee says his process can be used with any type of plastic, although certain polymers are more compatible. The process is better with polymers with high CO2 solubility, Lee explains. For example, poly-methyl methacrylate, used for optical fibers and as a shatterproof alternative to glass, has been a very good choice. Other good choices are polystyrene (used for applications including electric lawn and garden equipment and kitchen and bath accessories) and polyvinyl chloride (used for irrigation and other types of pipe). Polyolefins such as polyethylene (used in plastic bags, pipes, and packaging) and polypropylene (used for flexible food packaging) are more difficult. Additionally, the process will work as well, if not better, with non–petroleum-based plastics. “Clay is hydrophilic,” Lee explains, “so it doesn’t like to mix with oil. A non–petroleum-based plastic would be much easier for us to mix in the clay.”
Feet of Clay?
Nelson says test results to date may not fully support the flame retardant aspects of clay nanoparticles. The presence of these particles will definitely reduce peak rates of heat release, he says, but in other tests, they haven’t shown they provide much benefit.
“Heat release is only one aspect of these industry tests,” Nelson says. “Does the material undergo sustained ignition? If so, then it’s not usable. . . . And the industry tests look at a broad range of things, like ignition, heat release, ease of extinguishment, and smoke and toxic gas formation.”
Nelson also notes that tests have shown variable flammability depending on whether the clay-augmented sample is oriented vertically or horizontally. In horizontal tests, clay comes to the surface and creates a barrier, which stops degradation and heat transfer. But in vertical tests, the clay barriers break, exposing new melting plastic—but not before effectively hindering the flow of the plastic just long enough to make it burn faster. “I think, from what I’ve seen,” he says, “that nanoclays may be useful in combination with traditional flame retardants, thus lowering the volume of flame retardant you need to use.”
Another concern, says Loh, is the concept of scalability. “The whole process of going from the lab to a commercial setting is always a challenge,” he says. “In the lab, you might be producing fifty grams of this stuff, while our pilot line might produce several hundreds pounds to a batch. The question is, how do you keep the same quality that you have in the lab? Labs use different equipment, and have different quality control aspects. The next stage for us in this project will be a pilot project, which we plan to have operational by year-end.”
Scale-up is always a challenge, agrees Lee. “We are successful in one scale-up with one company, and while I don’t have other data points at this time, it should, in theory, work in other materials and processes,” he adds. “The critical issue is to have good mixing and pressure/temperature control.”
Nelson thinks the issue comes down to application. He thinks it’s more likely that solid plastics will be replaced by better solids, and foams with better foams. He says, “Along with application comes the question of cost, which will always be the issue for industry. . . . What are the changes, what do they cost, and are they within reason? Also, you have to look at the qualities of the foam. Does it have the same initial properties? How does it age? These are the kinds of factors that industry will have to evaluate before adopting anything new.”
Despite the questions still outstanding, momentum for that adoption appears to be building. Lee’s team recently received a $2 million award from the Ohio state government’s Wright Capital Project Fund to introduce novel nanocomposites to industrial manufacturers. Besides Owens Corning, his work has also attracted the interest of Procter & Gamble. Only time will tell whether clay nanoparticles and supercritical CO2 are the answer for the plastic foam industry—but by 2010, at least, we should have an answer.
A future in plastics? (left to right) Kurt Koelling, L. James Lee, and David Tomasko with samples of the plastic foam materials they developed.
Trans-foamation. Micrographs allow comparison of cell size and density in polystyrene foams: (top) without clay, (middle) with intercalated clay, and (bottom) with exfoliated clay. Adding clay produces smaller, more uniform bubbles, resulting in a stronger plastic that insulates better.
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Suggested Reading
Siegel RW Hu E Roco MC eds. 1999. Nanostructure Science and Technology: R&D Status and Trends in Nanoparticles, Nanostructured Materials and Nanodevices. Dordrecht, Netherlands: Kluwer Academic Publishers.
Tomasko DL Li H Liu D Han X Wingert MJ Lee LJ 2003 A review of CO2 applications in the processing of polymers Ind Eng Chem Res 42 25 6431 6456
Wallenberger FT Weston N eds. 2004. Natural Fibers, Plastics and Composites. Weimar, TX: Culinary and Hospitality Industry Publications Services.
Zeng C Han X Lee LJ Koelling KW Tomasko DL 2003 Polymer-clay nanocomposite foams prepared using carbon dioxide Adv Mater 15 20 1743 1747
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a00637EnvironewsScience SelectionsSpraying on a Summer Night: A Safer Way to Stop West Nile Virus McGovern Victoria 8 2004 112 11 A637 A637 Publication 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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A population-level study has shown that night-time pesticide spraying in the late summer and early fall, aimed at controlling adult mosquitoes that carry West Nile virus, can be done in a way that does not drive up the number of people seeking emergency care for asthma-related problems [EHP 112:1183–1187]. A team led by Adam M. Karpati, a physician in the New York City Department of Health and Mental Hygiene, reports that in studies of the city’s 2000 mosquito spraying season, no correlation could be found between broad application of sumithrin (a pyrethroid pesticide) and asthma cases presenting at the city’s 11 public hospital emergency departments.
Earlier studies had shown that high exposure to pyrethroid pesticides—often in an occupational setting—can trigger reactions in asthma sufferers ranging from mild symptoms such as sneezing and scratchy throat to more acute ones such as wheezing, chest tightness, and even death. But no data have been available showing on a population scale how the lower-level exposures that come from public health spraying of pesticides affect the large number of asthmatics that may live in a big city.
The researchers tabulated data for asthma-related emergency room visits around the dates when a sumithrin-based pesticide was sprayed in each of 162 residential zip code areas in the city during July–September 2000. The timing of spraying within each zip code depended on whether surveillance indicated it was warranted—for example, if a dead bird were found to be infected with the virus, or if a human case were identified. A zip code area was rarely sprayed on consecutive days. The study also incorporated air quality data including daily measures of ozone, air particulates, and temperature, which can all cause fluctuations in the number of people seeking treatment for asthma-related symptoms. For a control, the team used asthma-related emergency room visits on days prior to spraying. They also looked at the number of asthma-related emergency room visits before and after the spraying season.
The researchers found that the number of asthma-related visits in the three days before application of the pesticide and the three days after were nearly identical. Looking more specifically within the emergency department data for asthma flare-ups in children and for aggravation of chronic obstructive pulmonary disease similarly yielded no correlation between spraying and symptoms.
The study does not necessarily show that public health pyrethroid spraying is not a danger to asthmatics. Rather, it could suggest that the city’s method of application and/or the citizens’ behavior during spraying helped minimize exposure. During 2000, the first year when New York City exclusively used a pyrethroid pesticide, the city limited its spraying to areas where the virus was detected in birds, mosquitoes, or humans, with spray trucks usually beginning their rounds near 10 p.m. and continuing through the night to 5 a.m. Radio, television, and print media were used to alert residents 48 hours prior to any spraying and to instruct people to remain indoors and close their windows during the hours when spraying would occur.
Big Apple air okay. Mosquito pesticide spraying to prevent West Nile virus was not associated with an increase in asthma attacks.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a00639AnnouncementsNIEHS Extramural UpdateNIEHS Dual-Degree Predoctoral Fellowships for Training Clinician–Scientists Shreffler Carol PhDPROGRAM ADMINISTRATOR [email protected] Training and fellowship grants8 2004 112 11 A639 A639 Publication 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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Ruth L. Kirschstein National Research Service Awards issued under the Dual-Degree Predoctoral Fellowships for Training Clinician–Scientists program provide training for clinical scientists. First announced by the NIEHS in 1999, the program was reissued in 2001 under Program Announcement PA-01-132 (http://grants.nih.gov/grants/guide/pa-files/PA-01-132.html). The program offers fellowship and stipend support to trainees enrolled in a combined MD/PhD or MD/MPH program at an accredited medical school. Trainees must be conducting basic or clinical research in environmental medicine or on a specific environmentally related disease or disorder.
For this fellowship, environmental medicine is the area of medicine concerned with the development and application of knowledge directed at the etiology, diagnosis, pathophysiologic progression, treatment, and prevention of adverse effects from exposure to toxic agents. Adverse effects may include an identifiable disease, disorder, or decrement in mental or physical function. Environmental medicine is a cross-cutting issue in the medical arena in that it is problem-oriented and does not focus on a particular discipline, specialty, disease, or organ system. As the development of a disease or disorder can be viewed as resulting from the interplay of genetic and environmental factors over the life span of the individual, potent environmental influences may manifest at many stages of life: as a gamete, as an embryo, during epigenesis, in utero, during childhood, during adulthood, or with aging.
Linking exposures to chemical, physical, or biologically derived toxicants in the environment to clinical outcomes in humans continues to be a research focus for the NIEHS. The problems addressed are complex and of great public interest. However, the standard courses of study at most medical schools do not provide the experience to approach these issues. The NIEHS has therefore not supported a large number of physicians in its research portfolio. Individual fellowships for combined degree seekers is viewed as a way to develop highly trained clinical scientists who will be available to conduct the next generation of environmental health research, which is expected to emphasize the translation of animal- and cellular-level laboratory research to the clinical setting.
The NIEHS continues to enthusiastically support the dual-degree predoctoral fellowship program. So far 15 awards have been made to medical students at 11 medical schools. Applications are particularly sought for clinical and translational research in environmental medicine.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a00640AnnouncementsFellowships, Grants, & AwardsFellowships, Grants, & Awards 8 2004 112 11 A640 A641 Publication 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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Cancer Intervention and Surveillance Modeling Network (CISNET)
The Division of Cancer Control and Population Sciences (DCCPS) of the National Cancer Institute (NCI) invites applications from domestic and foreign applicants to support collaborative research using simulation and other modeling techniques to describe the impact of interventions in population-based settings that will shed light on U.S. population-based trends. It is well known that great progress in the war against cancer is possible by the complete use and adequate delivery of existing modalities of cancer control. The primary goals of this research are to determine the impact of cancer control interventions on observed trends in incidence and/or mortality, and to determine if recommended interventions are having their expected population impact by examining discrepancies between controlled cancer intervention study results and the population experience.
Once a general understanding of the various factors influencing current trends has been achieved, a number of secondary goals may be addressed. Applicants may propose secondary goals of modeling the potential impact of new interventions on future national trends, and/or evaluating optimal cancer control strategies.
The NCI has a long-standing function of providing answers to critical policy questions, which can only be answered through an indirect synthesis of available information and assumptions. A commitment to modeling of this type will allow the NCI to apply the most sophisticated tools available for evidence-based planning to several areas: 1) Be responsive to challenges due to the increasing pace of technology, and to provide short-term answers while randomized controlled trials (RCTs) are still in progress. In the future we will be increasingly faced with new interventions, biomarkers, and diagnostic and genetic tests that will become widely disseminated prior to rigorous testing in controlled settings, and therefore the evaluation of population impact will become even more important. 2) Address emerging questions while they are still being debated in the policy forum. For example, new smokeless tobacco products are coming on the market, and modeling of their potential impact can benefit the Federal Trade Commission and other policy makers. 3) Translate RCT evidence of quantities to the population setting. 4) Provide estimates of quantities that will never be derived from RCTs. For example, half of Americans alive today who ever smoked are ex-smokers. It is important to understand the patterns of quitting, the process of carcinogenesis for ex-smokers, and the implications for future lung cancer trends.
DCCPS, which fulfills a federal-level function to respond to evolving surveillance questions of national policy relevance, helps focus research questions and acts as a conduit to national data resources necessary for parameter estimation, model calibration, validation, and population trends. An emergent property of this collaborative agreement is progress toward a comprehensive understanding of the determinants of site-specific cancer trends at the population level and a better understanding of the science of modeling.
Modeling is the use of mathematical and statistical techniques within a logical framework to integrate and synthesize known biological, epidemiological, clinical, behavioral, genetic, and economic information. Prior to the Cancer Intervention and Surveillance Modeling Network (CISNET), many of the simulation and other modeling techniques had been utilized to describe the impact of cancer interventions (i.e., primary prevention, screening, treatment) for hypothetical cohorts or in trial and other clinical settings. The goal of this request for applications (RFA) is to promote the application and extension of these models to population-based settings in order to ascertain determinants of cancer trends. This information is critical to the NCI because of the necessity of understanding whether recommended interventions are having their expected population impact, and of predicting the potential impact of new interventions on national trends. These studies will often involve extrapolation of results of controlled cancer intervention studies to estimates of U.S. population and community effectiveness. This type of modeling addresses issues of population-based policies and programs, and is distinct from individual-level models of risk and models of clinical decision making used at the individual patient–physician level. An additional goal of this concept is to advance methodology for modeling and to develop more uniform criteria for model validation in the population setting.
It is not the purpose of this RFA to focus on the analysis of hypothetical or trial-based cohorts and/or cost-effectiveness analyses, but rather to support analyses based on realistic scenarios of population impact. Projects will focus on models describing the population impact of the observed dissemination of cancer control interventions as well as other factors on observed national incidence and/or mortality trends.
CISNET was originally funded as a cooperative agreement (U01) for two phased-in rounds of funding. In September 2000, RFA CA-99-013 funded seven grants in breast cancer, one in prostate cancer, and one in colorectal cancer. A second round, funded under RFA CA-02-010 in August 2002, funded five grants in lung cancer as well as two additional grants for colorectal cancer and one in prostate cancer.
CISNET investigators are currently engaged in a wide range of policy-relevant modeling studies including the following:
Development of base case questions. A major strength of having a consortium of modelers is the ability to employ a comparative modeling approach. While each modeler has areas of individual focus, whenever possible, common “base” questions have been developed that allow for comparisons across models. The sometimes widely different results from models are often difficult to resolve, and base cases provide a chance to reach consensus on important questions, and to better understand differences between models. In these base case questions, a set of common population inputs is used across all models (e.g., dissemination patterns of screening and treatment, mortality from noncancer causes), and a common set of intermediate and final outputs is developed to help understand differences and similarities across models.
Breast base case spin-off questions. The breast base case serves as a jumping-off point for each grantee as they vary the basic formulation to focus on areas of individual interest. Spin-off issues that are actively being pursued include a) modeling the impact of using alternative, more biologically based natural disease history formulations, especially continuous time tumor growth models (which include microscopic fatal metastases that are initially undetectable); b) analyses for different racial, ethnic, and insurance-status groups; c) a unique Bayesian approach to update its prior estimates of treatment efficacy to obtain posterior estimates of community effectiveness of adjuvant therapy and mammography that best reproduce national mortality trends; d) geographically based analyses; e) the role of risk factors in breast cancer trends; and f) the potential impact of optimal screening intervals.
Prostate cancer. CISNET researchers have published an analysis of trends in the use of the prostate-specific antigen (PSA) test for modeling prostate cancer incidence trends to obtain estimates of over-diagnoses associated with PSA screening. In addition, these researchers are investigating the use of modeling to better understand the results of ecologic analyses of the effectiveness of PSA screening.
Special issue of Statistical Methods in Medical Research. CISNET was invited to sponsor a special issue of the journal Statistical Methods in Medical Research titled “Uses of Stochastic Models for the Early Detection of Cancer,” with articles submitted in spring 2003. Articles in the issue include 1) “Distribution of Clinical Covariates at Detection of Cancer: Stochastic Modeling and Statistical Inference,” 2) “Planning Public Health Programs and Scheduling: Breast Cancer,” 3) “Planning of Randomized Trials,” 4) “The Use of Modeling to Understand the Impact of Screening on U.S. Mortality: Examples from Mammography and PSA Screening,” 5) “Parameter Estimation for Stochastic Models via Simulation,” and 6) “Diversity of Model Approaches.”
Linkages with other cancer surveillance and control activities. CISNET has sought linkages to be integrated with and responsive to situations where modeling may play an important role. For example, the Agency for Health Research and Quality and the Center for Medicare and Medicaid Studies approached the NCI for assistance in studying a reimbursement decision related to the immunochemical fecal occult blood test (iFOBT) (http://cisnet.cancer.gov/reports/medicare.html). CISNET modelers have also been asked to aid in a midcourse (2005) evaluation to help determine whether reaching Healthy People 2010 upstream goals for cancer treatment, screening, and prevention will enable us to fall short of, meet, or exceed the downstream 2010 cancer mortality goals, and to retarget our efforts if necessary.
This reissuance of CISNET will be limited to modeling applications focusing on breast, prostate, lung, and colorectal cancer. Although the reissuance of CISNET will not be limited to grantees previously or currently funded, CISNET will no longer fund models that either are starting from scratch or have not been previously applied to the analysis of population trends. This means that models should have been applied to multiple real birth cohorts representing the actual population experience. Models that have been applied only to hypothetical cohorts, as is sometimes done to model trial data or estimate cost-effectiveness, will not be considered. The emphasis in this reissuance is in the application of already developed models to study the population impact of existing or emerging cancer control interventions. In addition, applications are being solicited for cancer site–specific coordinating centers for breast, prostate, colorectal, and lung cancer.
Areas of application will include more refined analyses of current trends, and a renewed emphasis on future trends and optimal cancer control planning. While the original issuance focused primarily on discovery (basic mathematical and statistical relationships necessary for the development of multi-cohort population models) and development (data sources and realistic scenarios to evaluate past intervention impact in the population setting and project future impact), the reissuance will continue development efforts and will greatly enhance the delivery element (synthesizing relevant scenarios for informing policy decisions and cancer control planning implementation).
While some new mathematical and statistical derivations may be necessary, they should not be the centerpiece of these applications. Instead, the focus of the application should be on identifying important cancer surveillance and control questions, obtaining the data sources and making model modifications as necessary to run the model, and producing results that are meaningful and packaged in a way that policy makers and cancer control planners can understand. Inclusion of interdisciplinary expertise will be essential in this phase of CISNET. Applicants should demonstrate modeling capability and propose a specific research plan. However, applicants should be flexible enough to accommodate further refinement and integration with other efforts.
The purpose of these efforts is to model the impact of the observed dissemination of cancer control interventions in the population, rather than using observed population trends to postulate new etiologic factors. However, these models can include components that model the impact of population changes in both modifiable and nonmodifiable risk factors. Models that include the synergistic impact of multiple interventions simultaneously are desirable. Models can be of the entire U.S. population, a region of the country, some specific identified population where unique data exist on the implementation of an intervention, or in a subpopulation of specific interest (e.g., the rural poor). However, whenever possible, inference should relate to the United States as a whole. Models can be developed for non-U.S. populations, but should be justified based on their applicability to understanding U.S. cancer trends.
Examples of areas of interest and types of questions are given below. Note that these are examples only, and applicants should not feel constrained to choose areas of application from this list only.
What new quantifiable statements can be made concerning estimates and uncertainty in the adenoma-colorectal cancer sequence? What is the range of natural history models associated with in situ breast cancer, and what are the implications of these natural history models for the overdiagnosis of disease?
What is the contribution of treatment to observed declines in prostate cancer mortality, especially the transition from the use of androgen deprivation therapy after biochemical failure (i.e., rising PSA levels) to use in the adjuvant setting? How can future improvements in the quality of care and the general health status of older individuals result in increased use and responsiveness to treatment?
What is the impact on incidence and mortality of both the increased dissemination of currently established screening modalities (e.g., iFOBT, sigmoidoscopy) and the potential dissemination of new or more novel modalities (e.g., screening colonoscopy, advanced imaging modalities, iFOBT, fecal mutagen tests, other innovative biomarkers)? As screening trial results for PSA, flexible sigmoidoscopy, chest X ray, and spiral computed tomography start to become available over the next decade, how do these results alter our understanding of population trends in incidence and mortality?
Given that obesity is a major problem that is getting worse, what are the implications for projections of breast and colorectal cancer mortality? What is the expected dissemination of the use of tamoxifen for women with different risk profiles, and what is the projected mortality reduction associated with these levels of dissemination?
How would resource requirements be affected by the use of risk stratification models or biomarkers that would allow selective screening and/or selective surveillance monitoring of higher-risk individuals? What is the national burden of iatrogenic morbidity from prostate cancer treatment among screen-detected men, and how do we weigh this against the potential mortality gains?
Can we use population trends to better understand differences in the natural history of prostate cancer between white and black men, and how can we use this information to better target interventions? How do racial disparities in obesity impact future trends? What is the impact of racial, economic, and insurance-status disparities in the use of adjuvant therapy and mammography on breast cancer mortality?
What is the impact of changing Medicare reimbursement policies on screening, treatment, and cancer mortality?
CISNET models should be able to help translate (in a timely manner) the impact of specific emergent results from epidemiologic, genetic, treatment, prevention, and screening studies to the population setting. Recent examples include how the mutation of a gene involved in non–small cell lung cancer increases the likelihood that the drug gefitinib will show a beneficial response; how a prevention trial showed that although finasteride reduced the risk of developing prostate cancer, those who developed the cancer had higher-grade tumors; and how an international clinical trial found that post-menopausal survivors of early-stage breast cancer who took the drug letrozole after completing an initial five years of tamoxifen therapy had a significantly reduced risk of cancer.
CISNET models can help translate the relationship between upstream (e.g., screening, modifiable risk factors) and downstream (e.g., mortality) goals. It can also help target the upstream factors that have the most potential for influencing mortality. In addition, CISNET models can help target what types of emerging technologies have the largest potential to help us reach the NCI’s 2015 goal of eliminating suffering from cancer. Is enough known about these technologies to have confidence in these projections? Can modeling point to the most important studies that could be conducted to gain more confidence with respect to their operating characteristics?
In the first issuance of CISNET no funds were specifically allocated for coordination activities. In this reissuance we have set aside funds for coordinating centers for all four cancer sites: breast, prostate, colorectal, and lung cancer. Coordinating centers should be site-specific because each center needs to be totally conversant with the data sources, modeling issues, and policy questions specific to that cancer site. Coordination activities, under the general direction and consensus of the NCI and principal investigators, will include 1) formulating, prioritizing, and coordinating work on base case and other questions (including outside requests); 2) negotiating common requests for outside data sources; 3) consensus building and coordinating critical evaluation of disparate results; 4) preparing inputs, and collecting and processing common outputs for model comparisons; and 5) coordinating synthesis papers and group responses, bringing together disparate information to inform policy makers. Through the coordinating center, each CISNET cancer site group will constitute an established expert knowledge base that can provide technical advice on evolving policy-relevant surveillance questions. Because all of the expertise necessary to accomplish these goals is not likely to exist in one place, the coordinating center would have discretionary funds to tap outside expertise for particular tasks, pay for access to data sources, and provide funds to modeling groups to mount intensive efforts to provide technical advice while issues are still relevant in the policy area. Even though one group would be tasked with being the coordinating center, CISNET would be run through consensus, as it has in the past.
To keep applications focused, each will be limited to a single cancer site. The CISNET project requirements call for the development of site-specific working groups that will 1) facilitate comparative analyses, 2) allow modeling groups access to a broader array of data resources and interdisciplinary expertise, and 3) provide a forum for discussions of validation and other methodologic issues. CISNET will allow for diversity and originality of modeling approaches that can be compared using uniform criteria. New investigators will be expected to join in the ongoing collaborative activities already under way.
The NCI intends to commit approximately $1.8 million in total costs (direct and facilities and administrative [F&A] costs) in fiscal year 2005 to fund 6–9 new modeling grants in response to this RFA. In addition, the NCI intends to commit approximately $950,000 (direct and F&A costs) in fiscal year 2005 to fund 4 coordinating centers (one each in breast, prostate, colorectal, and lung cancer). An applicant may request a project period of up to five years. Although an applicant can submit applications for more than one cancer site (either for modeling grants or coordinating centers), each individual application must be limited to one cancer site. Coordinating center grants must be submitted separately from modeling grants, even if one applicant submits both.
Applications must be prepared using the PHS 398 research grant application instructions and forms (rev. 5/2001). Applications must have a Dun and Bradstreet Data Universal Numbering System (DUNS) number as the Universal Identifier when applying for federal grants or cooperative agreements. The DUNS number can be obtained by calling 1-866-705-5711 or through the website at http://www.dunandbradstreet.com/. The DUNS number should be entered on line 11 of the face page of the PHS 398 form. The PHS 398 document is available at http://grants.nih.gov/grants/funding/phs398/phs398.html in an interactive format. For further assistance, contact GrantsInfo by calling 301-435-0714 or e-mailing [email protected].
Letters of intent must be received by 14 September 2004. Applications must be received by 14 October 2004. The anticipated award date is July 2005.
Contact: Eric Feuer, DCCPS, NCI, 6116 Executive Blvd, Rm 5041, MSC 8317, Bethesda, MD 20892-8317 USA, 301-496-5029, fax: 301-480-2046, e-mail: [email protected]. Reference: RFA No. RFA-CA-05-018
Defender of the Earth Book Award
Red Hen Press announces the first annual Defender of the Earth Book Award to promote awareness and appreciation of the need to respect the beauty and fragility of the environment. This award is for a previously unpublished original manuscript of nonfiction writing on the environment, and is open to all authors. The award is to be presented by the David Family Foundation.
The winner will receive $5,000 and publication of the winning manuscript by Red Hen Press. The minimum page count for submissions is 64 pages. The author’s name should appear only on the cover sheet. Send a self-addressed stamped envelope for notification. Entries must be postmarked by 30 August 2004.
Contact: Red Hen Press, Attention: Defender of the Earth Book Award, PO Box 3537, Granada Hills, CA 91394 USA, 818-831-0649, e-mail: [email protected], Internet: http://www.redhen.org/2004literaryawards.htm
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0604a15289173PerspectivesCorrespondenceStudying Human Fertility and Environmental Exposures Slama Rémy Ducot Béatrice INSERM and INED (French National Institute for Health and Medical Research and the French National Institute for Demographic Studies), Unit 569, Le Kremlin-Bicêtre, France, E-mail:
[email protected] Niels Department of Biostatistics, University of Copenhagen, Copenhagen, DenmarkBouyer Jean INSERM and INED (French National Institute for Health and Medical Research and the French National Institute for Demographic Studies), Unit 569, Le Kremlin-Bicêtre, FranceThe authors declare they have no competing financial interests.
8 2004 112 11 A604 A604 Publication 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 review of approaches to studying the influence of environmental exposures on human fecundity, Tingen et al. (2004) compared several ways of assessing fecundity.
Fecundity—the probability of pregnancy in couples having regular intercourse without contraception—can be assessed by applying appropriate statistical approaches to time-to-pregnancy (TTP) data. Tingen et al. (2004) provided a thorough presentation of the detailed prospective approach to assess TTP. We agree that advantages of this approach, in which daily urine samples are collected, include allowing the estimation of the daily probability of pregnancy within a menstrual cycle and studying the early survival of the embryo; however, we have reservations about the authors’ conclusion that the detailed prospective approach should be seen as the gold standard for studying the effects of environmental exposures on fecundity.
We believe that prospective TTP studies, whether detailed or not, have one main limitation, which lies in the difficulty of defining precisely the target population: These studies are often based on the inclusion of couples soon planning to attempt conception or to stop using contraceptive methods. In our opinion, this population is ill-defined and lacks a sampling frame, which makes the estimation of participation rates difficult. Indeed, many published detailed prospective TTP studies had unreported or low participation rates (Buck et al. 2004), opening the door for selection biases. We also doubt that these “super pregnancy planners,” who program their pregnancy attempts months ahead, are representative of the general population. For example, detailed prospective TTP studies have sometimes included couples with higher-than-average educational level (Wilcox et al. 1988) or those who use natural family planning methods not widely used (Dunson et al. 2002). These characteristics may be associated with the probability of pregnancy and with the environmental exposures of interest, thus resulting in possible biases.
These limitations of the prospective approach do not justify a preference for retrospective studies. As pointed out by Tingen et al. (2004), the exclusion of infertile couples in most retrospective studies is indeed of particular concern; it reduces statistical power and leads to underestimation of the effect of the environmental exposure of interest (Slama et al. 2004).
The current duration approach, another approach not mentioned by Tingen et al. (2004), makes it possible to include infertile couples without resorting to detailed prospective studies. The current duration approach relies on the inclusion of couples currently trying to conceive or who are having intercourse without contraception (Keiding et al. 2002; Olsen and Andersen 1999). The recruited couples are asked how long they have been having unprotected sexual intercourse. Follow-up of these couples is not required (Keiding et al. 2002), but it is possible to obtain information on the occurrence of a pregnancy. In this case, the approach is based on principles from the case–cohort design (Olsen and Andersen 1999).
In the current duration approach, data on the frequency of sexual intercourse, the duration of the menstrual cycle during the attempt at pregnancy, and environmental exposures can be collected with virtually no recall bias. The collection of urine or other biologic samples is possible, at least from the date of inclusion; that is, some time after cessation of contraceptive use. The advantage of the current duration approach is that the inclusion criterion (currently having sexual intercourse without contraception) is more clear-cut than that of the prospective approach. This approach thus has a clearly defined sampling frame. We are currently testing this approach on a representative population of French women 18–45 years of age.
The four approaches to assessing TTP are based on different inclusion schemes. The retrospective approach is based on the inclusion of couples who already had a pregnancy; prospective approaches (detailed and not) are most often based on the inclusion of couples who will soon discontinue contraceptive use; and the current duration approach is based on the inclusion of couples currently trying to conceive. We believe that none of these methods can currently be considered a gold standard. In particular, unlike Tingen et al. (2004), we do not think that the potential bias from the exclusion of pregnancies occurring during contraceptive use (Baird et al. 1994) is specific to the retrospective approach, because prospective (and current duration) studies seldom include couples using contraceptive methods.
Instead, we believe that the existence of new, alternative approaches should provoke comparative studies, leaving room for debate before conclusions are drawn about which approach is preferable for a given purpose.
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References
Baird DD Weinberg CR Schwingl P Wilcox AJ 1994 Selection bias associated with contraceptive practice in time-to-pregnancy studies Ann NY Acad Sci 709 156 164 8154699
Buck GM Lynch CD Stanford JB Sweeney AM Schieve LA Rockett JC 2004 Prospective pregnancy study designs for assessing reproductive and developmental toxicants Environ Health Perspect 112 79 86 14698935
Dunson DB Colombo B Baird DD 2002 Changes with age in the level and duration of fertility in the menstrual cycle Hum Reprod 17 1399 1403 11980771
Keiding N Kvist K Hartvig H Tvede M Juul S 2002 Estimating time to pregnancy from current durations in a cross-sectional sample Biostatistics 3 565 578 12933598
Olsen J Andersen PK 1999 We should monitor human fecundity, but how? A suggestion for a new method that may also be used to identify determinants of low fecundity Epidemiology 10 419 421 10401877
Slama R Jensen TK Scheike T Ducot B Spira A Keiding N 2004 How would a decline in sperm concentration over time influence the probability of pregnancy? Epidemiology 15 458 465 15232407
Tingen C Stanford JB Dunson DB 2004 Methodologic and statistical approaches to studying human fertility and environmental exposure Environ Health Perspect 112 87 93 14698936
Wilcox AJ Weinberg CR O’Connor JF Baird DD Schlatterer JP Canfield RE 1988 Incidence of early loss of pregnancy N Engl J Med 319 189 194 3393170
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0604b15289174PerspectivesCorrespondenceStudying Human Fertility Joffe Michael Key Jane Best Nicky Department of Epidemiology & Public Health, Imperial College Faculty of Medicine, London, United Kingdom, E-mail:
[email protected] Tina Kold Institute of Public Health, Department of Environmental Medicine, University of Southern Denmark, Odense, DenmarkThe authors declare they have no competing financial interests.
8 2004 112 11 A604 A605 Publication 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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We very much welcome the National Children’s Study, which promises to raise the study of factors affecting reproduction and development to a new level. An impressive and exciting range of new methodologies is being developed (Chapin and Buck 2004; National Children’s Study 2004).
However, we think it important to correct some of the inaccurate statements concerning the use of retrospective time to pregnancy (TTP) made by Tingen et al. (2004). We do not see prospective methods and the retrospective approach as alternatives; they are complementary, each having their strengths and weaknesses. Unfortunately, Tingen et al. presented a negative and distorted view of retrospective TTP studies, describing things that are “often” or “typically” done but that do not represent current best practice; then they used their description to denigrate all such studies. Although it is true that retrospective studies are subject to multiple potential “bias in recruitment, recall, and behavior or exposure trends” (Tingen et al. 2004), careful sampling and questionnaire design and use of appropriate methods of analysis can address most of these issues.
Retrospective studies are not necessarily pregnancy based. They can be conducted in random population-based samples and frequently are cross-sectional or birth cohort studies (Joffe 2000; Joffe and Li 1994; Karmaus et al. 1999; Sallmén et al. 1995; Schaumburg and Boldsen 1992; Schaumburg and Olsen 1989; Thonneau et al. 1999), thereby overcoming the problem that only women who eventually conceived are included. Even in pregnancy-based studies, if there are concerns about differential prenatal care (an issue in the United States but not in western Europe, for example), recruitment could be based on births rather than pregnancies, obviously with loss of nonbirth outcomes. If sampling is population based, it is feasible to ascertain periods of unprotected intercourse not leading to conception (generally stipulating a minimum duration such as 6 months); these attempts can be added to the pregnancy-related TTP values to generate the “time of unprotected intercourse” (Karmaus et al. 1999).
Tingen et al. (2004) presented simple issues of questionnaire design negatively, but these problems can be easily solved. For example, if data are collected in relation to the starting time instead of the conception time (Weinberg et al. 1994), behavior change does not lead to bias but only to nondifferential loss of information.
A central issue is planning bias, the question being how to exclude accidental (unplanned) pregnancies without bias occurring if the exposure variable is associated with the degree of “plannedness.” Retrospective studies can readily investigate this by following the standard guidance to collect full information for all pregnancies, including all covariates, and carry out parallel analyses with “unplanned pregnancy rate” as outcome variable (Weinberg et al. 1994). Prospective studies are unable to do this because only planners are recruited.
Tingen et al. (2004) stated that in TTP studies, “women are asked to recount their contraceptive and sexual history.” This is incorrect; in TTP studies, women are not asked for this detailed information because it would be invasive and inaccurate. Instead, women are simply asked how long it took to conceive, a question that is acceptable and that most can answer. The replies give an accurate representation of the true TTP distribution (Baird et al. 1991; Joffe et al. 1993, 1995; Zielhuis et al. 1992), even with recall of up to 20 years (Joffe et al. 1995). Although digit preference (and other non-differential misclassification) can occur, the implication is that more respondents are required than would be the case with perfect information. Nevertheless, stable estimates of the TTP distribution can be obtained with approximately 200 values in each exposure group, or fewer in the case of ordered categories such as successive 5-year periods (Joffe 2000).
We agree that a major limitation of retrospective studies is that it is impossible to obtain detailed, timed information on exposures and key biologic events such as ovulation, and difficult to ascertain certain covariates such as frequency or timing of intercourse. This is the key strength of the prospective design. On the other hand, retrospective studies are representative because, as already noted, sampling from the general population is available and planning bias can be handled. The questions are easily administered and answered, and the response rate is high. Even response bias can be avoided by nesting the TTP questions within a more general population survey, thus decoupling survey nonresponse from differential fertility or other motivation that would convert low response rates to response bias (Joffe 2000). Selection bias remains a potential problem for some retrospective designs but can be handled by appropriate statistical analysis allowing for truncation effects (Scheike and Jensen 1997).
Not only are prospective studies time-consuming and costly, and therefore likely to be rarely used, but they have important methodologic drawbacks. For example, it is impossible to distinguish the approximately 3% of couples who are sterile from those who merely take a long time to conceive (> 10% typically take > 12 months), unless follow-up is extremely long.
More seriously, prospective studies are dominated by the lack of a sampling frame (except in occupational studies) and by a potent combination of planning bias and response bias. They can include only couples who deliberately plan and are willing to volunteer for onerous monitoring. This is acceptable for internal comparisons (e.g., studying day-specific conception rates, each subject being her own control) but raises serious problems with external validity. Tingen et al. (2004) referred to this only in their Table 1—“Participants might be less representative of target population”—but not in the text; in contrast, Buck et al. (2004) admitted that women who plan their pregnancies may be systematically different from those who do not, that this may adversely affect external validity to a degree which cannot be empirically evaluated, and that the findings may not be generalizable to all women.
==== Refs
References
Baird DD Weinberg CR Rowland AS 1991 Reporting errors in time-to-pregnancy data collected with a short questionnaire Am J Epidemiol 133 1282 1290 2063836
Buck GM Lynch CD Stanford JB Sweeney AM Schieve LA Rockett JC 2004 Prospective pregnancy study designs for assessing reproductive and developmental toxicants Environ Health Perspect 112 79 86 14698935
Chapin RE Buck GM 2004 Our once-in-a-lifetime opportunity Environ Health Perspect 112 67 68 14698933
Joffe M 2000 Time trends in biological fertility in Britain Lancet 355 1961 1965 10859042
Joffe M Li Z 1994 Male and female factors in fertility Am J Epidemiol 140 921 929 7977279
Joffe M Villard L Li Z Plowman R Vessey M 1993 Long-term recall of time-to-pregnancy Fertil Steril 60 99 104 8513966
Joffe M Villard L Li Z Plowman R Vessey M 1995 A time to pregnancy questionnaire designed for long term recall: validity in Oxford, England J Epidemiol Community Health 49 314 319 7629471
Karmaus W Juul S European Infertility and Subfecundity Group 1999 Infertility and subfecundity in population-based samples from Denmark, Germany, Italy, Poland and Spain Eur J Public Health 9 229 235
National Children’s Study 2004. National Children’s Study Homepage. Available: http://nationalchildrensstudy.gov/ [accessed 20 March 2004].
Sallmén M Lindbohm M-L Kyyrönen P Nykyri E Anttila A Taskinen H 1995 Reduced fertility among women exposed to organic solvents Am J Ind Med 27 699 713 7611306
Schaumburg I Boldsen JL 1992 Waiting time to pregnancy and pregnancy outcome among Danish workers in the textile, clothing, and footwear industries Scand J Soc Med 20 110 114 1496329
Schaumburg I Olsen J 1989 Time to pregnancy among Danish pharmacy assistants Scand J Work Environ Health 15 222 226 2781252
Scheike T Jensen T 1997 A discrete survival model with random effects: an application to time to pregnancy Biometrics 53 318 329 9147597
Thonneau P Abell A Larsen SB Bonde JP Joffe M Clavert A 1999 Effects of pesticide exposure on time to pregnancy: results of a multicenter study in France and Denmark. ASCLEPIOS Study Group Am J Epidemiol 150 157 163 10412960
Tingen C Stanford JB Dunson DB 2004 Methodologic and statistical approaches to studying human fertility and environmental exposure Environ Health Perspect 112 87 93 14698936
Weinberg CR Baird DD Wilcox AJ 1994 Sources of bias in studies of time to pregnancy Stat Med 13 671 681 8023042
Zielhuis GA Hulscher MEJL Florack EIM 1992 Validity and reliability of a questionnaire on fecundability Int J Epidemiol 21 1151 1156 1483821
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0607a15289177PerspectivesCorrespondenceThe WTC Disaster: Landrigan’s Response Landrigan Philip J. Mount Sinai School of Medicine, New York, New York, E-mail:
[email protected] author declares he has no competing financial interests.
8 2004 112 11 A607 A607 Publication 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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My colleagues and I thank Lange for his letter confirming our finding that asbestos was present in settled dust as well as in airborne samples obtained at Ground Zero, the site of the World Trade Center, and for his having agreed with us that this asbestos almost certainly represented an exposure hazard for workers. The asbestos that was detected in the dust at Ground Zero originated from asbestos that had been sprayed onto the steel skeleton of the Twin Towers as fireproofing when the structure was being built. It was well known that asbestos was applied in the North Tower up to about the 40th story and at other locations throughout the structure before the practice of spraying on asbestos was banned in New York City in the early 1970s (Nicholson et al. 1971; Reitze et al. 1972). Concentrations of asbestos in the dust at Ground Zero were highly variable, and the level in any particular sample reflects the location of sampling and the composition of the dust that happened to be in that area. We agree with Lange’s view that workers likely had intermittent exposures to asbestos that would have arisen unpredictably when, for example, they picked up a steel beam or turned over rubble and liberated asbestos fibers into the air. The asbestos hazard to workers was magnified by the fact that the U.S. Occupational Safety and Health Administration (OSHA) failed to require constant use of respirators at Ground Zero.
We disagree strongly with Lange’s statement that “it is unlikely that exposure to asbestos itself will result in any actual health effects.” Lange appears to base his assertion, first, on the fact that most of the asbestos at the World Trade Center was chrysotile asbestos, and second, that duration of exposure for most workers was brief. Unfortunately neither of those factors conveys protection. We remain concerned that there now exists a risk for mesothelioma caused by occupational exposure to asbestos for the brave men and women who worked and volunteered at Ground Zero.
All types of asbestos fibers, chrysotile included, have been shown in laboratory as well as clinical studies to be capable of causing malignant mesothelioma (Nicholson and Landrigan 1996). All types of asbestos fibers, chrysotile included, have been declared proven human carcinogens by OSHA, the U.S. Environmental Protection Agency, and the International Agency for Research on Cancer. Pathologic studies have found short chrysotile fibers, the predominant type of fiber in World Trade Center dust, to be the predominant fiber in mesothelioma tissue (Dodson et al. 1991; LeBouffant et al. 1973; Suzuki and Yuen 2002). Moreover, mesothelioma has been reported in persons with relatively low-dose, nonoccupational exposure to asbestos of brief duration (Anderson 1982; Camus et al. 1998; Magnani et al. 2001). The greatest future risk of mesothelioma would appear to exist among first responders who were covered by the cloud of dust on 11 September 2001 as well as in other workers employed directly at Ground Zero and workers employed in cleaning asbestos-laden dust from contaminated buildings. Although we agree with Lange that the number of mesothelioma cases will probably not be great, we think it quite misleading to state that no risk exists.
==== Refs
References
Anderson HA 1982. Family contact exposure. In: Proceedings of the World Symposium on Asbestos, 25–27 May 1982, Montreal, Quebec, Canada. Montreal, Quebec, Canada:Canadian Asbestos Information Centre, 349–362.
Camus M Siemiatycki J Meek B 1998 Nonoccupational exposure to chrysotile asbestos and the risk of lung cancer N Engl J Med 338 1565 1571 9603793
Dodson RF Williams MG Corn CJ Brollo A Bianchi C 1991 A comparison of asbestos burden in lung parenchma, lymph nodes and plaques Ann NY Acad Sci 643 53 60 1809166
LeBouffant L Martin JC Durif W Daniel H 1973 Structure and composition of pleural plaque. In: Biological Effects of Asbestos. (Bogovski P, Gilson JC, Timbrell V, Wagner JC, eds) IARC Sci Publ 8 249 257
Magnani C Dalmasso P Biggeri A Ivaldi C Mirabelli D Terracini B 2001 Increased risk of malignant mesothelioma of the pleura after residential or domestic exposure to asbestos: a case–control study in Casale Monferrato, Italy Environ Health Perspect 109 915 919 11673120
Nicholson WJ Rohl AN Ferrand EF 1971. Asbestos air pollution in New York City. In: Proceedings of the Second International Clean Air Congress (Englund HM, Beer WT, eds). New York:Academic Press, 36–139.
Nicholson WJ Landrigan PJ 1996 Asbestos: a status report Curr Issues Public Health 2 118 123
Reitze WB Nicholson WJ Holaday DA Selikoff IJ 1972 Application of sprayed inorganic fiber containing asbestos: occupational health hazards Am Ind Hyg Assoc J 33 178 191 5074675
Suzuki Y Yuen SR 2002 Asbestos fibers contributing to the induction of human malignant mesothelioma Annals NY Acad Sci 982 160 176
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0607b15289177PerspectivesCorrespondenceTrichloroethylene and Cardiac Malformations Hardin Bryan D. Kelman Bruce J. Brent Robert L. GlobalTox, Inc., Hilton Head, South Carolina, E-mail:
[email protected], Inc., Redmond, WashingtonAlfred I. DuPont Institute, Wilmington, DelawareB.D.H. has had no consulting relationships involving TCE. B.J.K. has provided testimony as a defense expert in TCE litigation pertaining to congenital malformations. R.L.B. has provided testimony as a defense expert in TCE litigation pertaining to congenital malformations of the heart.
8 2004 112 11 A607 A608 Publication 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 report of cardiac malformations in rats exposed to trichloroethylene (TCE) in drinking water, Johnson et al. (2003) used two (1.5 and 1,100 ppm) of the four treatment concentrations that they reported in a previous study (Dawson et al. 1993). To evaluate consistency of results in this single laboratory across the 10-year interval, we compared cardiac defects reported in 2003 by Johnson et al. with those reported in 1993 by Dawson et al. Data from the two papers are shown in Table 1.
Dawson et al. (1993) did not report the number of litters per group, so that correlation was not possible. Regardless, it would be an astonishing coincidence for two studies to produce exactly the same number of fetuses in each group. Still more astonishing is the identical number of “abnormal hearts.” Nothing reported by Johnson et al. (2003) gives notice that previously published data are being reported again, but that seems to be the inescapable conclusion. If this is a republication of 1993 data, then there has also been reclassification of “defects” with the passage of time.
Another feature of the article by Johnson et al. (2003) that attracted our attention was the uncharacteristically large control group (55 litters). One can surmise that in the earlier study (Dawson et al. 1993), each group would have consisted of approximately 10 females, which is consistent with the size of exposed groups (9–13) reported by Johnson et al. Their control group, however, was unprecedentedly large, both in the context of conventional study design and relative to the other groups in this study. Johnson et al. (2003) provided no rationale for designing their study with a concurrent control five times larger than the treatment groups, which leads us to ask whether the control group reported here is, in fact, a composite of controls from multiple, perhaps five, different studies. The immediate impact of this large control group is that the very cardiac “abnormalities” at the 1.5 ppm dose that did not differ significantly from controls in 1993 become statistically significant in 2003.
Conventional developmental and reproductive toxicology assays in mice, rats, and rabbits consistently fail to find adverse effects of TCE on fertility or embryonic development aside from embryo- or fetotoxicity associated with maternal toxicity [Cosby and Dukelow 1992; Dorfmueller et al. 1979; Hardin et al. 1981; Healy et al. 1982; Manson et al. 1984; National Toxicology Program (NTP) 1985, 1986; Schwetz et al. 1975]. Johnson and Dawson, with their collaborators, are alone in reporting that TCE is a “specific” cardiac teratogen (Dawson et al. 1990, 1993; Goldberg et al. 1992; Johnson et al. 1998; Johnson et al. 2003; Loeber et al. 1988). We have always considered those findings suspect, and our comparison of data from the studies of Dawson et al. (1993) and Johnson et al. (2003) serves only to intensify our reservations. Studies from this group have potential for important public health and public policy implications, so it is particularly important for the scientific and regulatory communities to have confidence in the conduct and reporting of those studies.
We are also concerned that S.J. Goldberg, one of the authors of the publications alleging that TCE is a selective cardiac teratogen, has been a plaintiff expert in TCE lawsuits and failed to reveal that fact in his publications.
Table 1 Cardiac malformations in rats exposed throughout pregnancy to drinking water containing 1.5 or 1,100 ppm TCE.
TCE dose
TCE dose
Cardiac abnormalitiesa (Dawson et al. 1993) 1.5 ppm 1,100 ppm Heart malformationsb (Johnson et al. 2003) 1.5 ppm 1,100 ppm
L-Transposition (left chest) 1 0 Abnormal looping 2 0
Great vessel defect 1 0 Aortic hypoplasia 1 0
Pulmonary valve defect 1 0 Pulmonary artery hypoplasia 1 0
Atrial septal defect 4 7 Atrial septal defect 4 7
Ventricular septal defects Ventricular septal defects
Subaortic 2 1 Perimembranous (subaortic) 3 3
Muscular 1 4 Muscular 1 1
Endocardial cushion defect 0 1 Atrioventricular septal defect 0 1
Aortic valve defect 0 2 Aortic valve defect 0 2
No. with abnormal hearts 9 11 9 11
No. fetuses examined 181 105 181 105
aData from Dawson et al. (1993) Tables 1 and 3, Groups III and IV.
bData from Johnson et al. (2003) Table 2; percentage was converted to number.
==== Refs
References
Cosby NC Dukelow WR 1992 Toxicology of maternally ingested trichloroethylene (TCE) on embryonal and fetal development in mice and of TCE metabolites on in vitro fertilization Fundam Appl Toxicol 19 2 268 274 1516784
Dawson BV Johnson PD Goldberg SJ Ulreich JB 1990 Cardiac teratogenesis of trichloroethylene and dichloro-ethylene in a mammalian model J Am Coll Cardiol 16 5 1304 1309 2229779
Dawson BV Johnson PD Goldberg SJ Ulreich JB 1993 Cardiac teratogenesis of halogenated hydrocarbon-contaminated drinking water J Am Coll Cardiol 21 1466 1472 8473657
Dorfmueller MA Henne SP York RG Bornschein RL Manson JM 1979 Evaluation of teratogenicity and behavioral toxicity with inhalation exposure of maternal rats to trichloroethylene Toxicology 14 2 153 166 538767
Goldberg SJ Dawson BV Johnson PD Hoyme HE Ulreich JB 1992 Cardiac teratogenicity of dichloroethylene in a chick model Pediatr Res 32 1 23 26 1635841
Hardin BD Bond GP Sikov MR Andrew FD Beliles RP Niemeier RW 1981 Testing of selected workplace chemicals for teratogenic potential Scand J Work Environ Health 7 suppl 4 66 75 7330632
Healy TE Poole TR Hopper A 1982 Rat fetal development and maternal exposure to trichloroethylene 100 p.p.m Br J Anaesth 54 3 337 341 7066129
Johnson PD Dawson BV Goldberg SJ 1998 A review: trichloroethylene metabolites: potential cardiac teratogens Environ Health Perspect 106 suppl 4 995 999 9703484
Johnson PD Goldberg SJ Mays MZ Dawson BV 2003 Threshold of trichloroethylene contamination in maternal drinking waters affecting fetal heart development in the rat Environ Health Perspect 111 289 292 12611656
Loeber CP Hendrix MJ Diez De Pinos S Goldberg SJ 1988 Trichloroethylene: a cardiac teratogen in developing chick embryos Pediatr Res 24 6 740 744 3205631
Manson JM Murphy M Richdale N Smith MK 1984 Effects of oral exposure to trichloroethylene on female reproductive function Toxicology 32 3 229 242 6474485
NTP 1985. Trichloroethylene (CAS # 79-01-6): Reproduction and Fertility Assessment in CD-1 Mice When Administered in the Feed. NTP Report RACB84113. Research Triangle Park, NC:National Toxicology Program.
NTP 1986. Trichloroethylene (CAS # 79-01-6): Reproduction and Fertility Assessment in F344 Rats When Administered in Feed. NTP Report RACB84112. Research Triangle Park, NC:National Toxicology Program.
Schwetz BA Leong KJ Gehring PJ 1975 The effect of maternally inhaled trichloroethylene, perchloroethylene, methyl chloroform, and methylene chloride on embryonal and fetal development in mice and rats Toxicol Appl Pharmacol 32 1 84 96 1135881
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0613a15309754EnvironewsForumAir Pollution: Asia’s Two-Stroke Engine Dilemma Potera Carol 8 2004 112 11 A613 A613 Publication 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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Asian cities face a serious air pollution problem from two- and three-wheeled vehicles that run on two-stroke engines. Global experts shared their knowledge about these vehicles at an international conference held 30 March–1 April 2004 at the Centre for Science and Environment (CSE) in Delhi, India. Anumita Roychowdhury, associate director of the CSE, said the inexpensive two-wheelers form a staggering 75–80% of the traffic in most Asian cities. She called them “an Asian dilemma.”
Because two-stroke engines burn an oil–gasoline mixture, they emit more smoke, carbon monoxide, hydrocarbons, and particulate matter than the gas-only four-stroke engines found in newer motorcycles. Making matters worse, many Asian two-wheelers are converted into three-wheeled “baby taxis” by adding a sidecar. However, “the vehicle is not designed for the extra weight, and the engine burns even dirtier,” said Michael Walsh, an independent consultant who advises nations worldwide about motor vehicle pollution and control issues.
The World Health Organization ranks urban outdoor air pollution as the thirteenth greatest contributor to disease burden and death worldwide. Air pollution raises the risk of respiratory illnesses; about two-thirds of the residents of Delhi and Calcutta suffer from respiratory symptoms such as common cold and dry and wet cough, which Twisha Lahiri, head of neuroendocrinology at India’s Chittaranjan National Cancer Institute, largely blames on two-stroke engine emissions.
In work presented at the conference, Lahiri and colleagues examined 2,000 non-smoking adults from Calcutta and Delhi and 300 from the rural Sunderban region, where air pollution is extremely low. Spirometry measurements found impaired lung function in 46% of Delhi adults and 56% of Calcutta adults, but only 21% of those from the Sunderban islands. Lahiri has also observed early indicators of lung cancer, such as metaplastic epithelial cells, in people exposed to traffic pollution. These findings “warrant immediate measures to abate the alarmingly high vehicular pollution in Indian cities,” she warned.
Measurements of how much pollution two-wheelers emit are rare, but one study of traffic intersections in Bangkok, Thailand, found that two-wheelers contributed up to 47% of particulates. When the city instituted a stringent inspection program and emissions standards in 2000, two-wheelers made up 96% of the city’s traffic; by March 2004 they made up only 40%, reported Supat Wangwongwatana, deputy director general of Thailand’s Pollution Control Department.
Similarly, when two-stroke baby taxis were phased out of Dhaka, Bangladesh, in 2002, particulate concentrations dropped up to 40%, and carbon monoxide and hydrocarbons fell significantly, reported S.M.A. Bari, director of engineering at the Bangladesh Road Transport Authority. However, no country has established particulate standards for two-wheelers, said Roychowdhury, and there are no standardized methods for measuring particulate emissions from these vehicles.
Economic incentives were what drove the transition from two-stroke to four-stroke tricycles in the Philippines’ San Fernando City. In 2001, three-quarters of the city’s 1,600 registered tricycles ran on two-stroke engines. The city council mandated a total phase-out of these vehicles by 2004 and offers interest-free loans for down-payments on four-stroke models. According to San Fernando City mayor Mary Jane Ortega, 400 four-stroke tricycles had replaced older two-stroke models as of March 2004.
The information presented at the conference supports public policies promoted by the CSE. “Small incremental steps will not help us beat the rapidly growing pollution,” said Roychowdhury. Instead, the CSE recommends stringent emissions standards for two-wheelers, an effective vehicle inspection program, fiscal incentive programs to replace existing two-stroke engines with four-stroke ones, and the development of efficient public transportation systems.
Two strokes and you’re out. Two-stroke engines, ubiquitous throughout Asia, are major contributors to air pollution and resulting respiratory illness in people.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0613b15309754EnvironewsForumThe Beat Dooley Erin E. 8 2004 112 11 A613 A615 Publication 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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Further Support for Sustainable Cities
In February 2004, representatives at the Asia and Pacific Leadership Forum adopted the Hong Kong Declaration on Sustainable Development for Cities. The document stems from Agenda 21, recently reaffirmed at the World Summit on Sustainable Development, and sets a goal of significantly improving the lives of at least 10% of the world’s estimated 1 billion slum dwellers. The declaration encourages cities to develop comprehensive strategies for not only economic development, but environmental protection as well, and notes the role that education and public health play in sustainable development. The declaration also notes the challenge that urban transportation poses to sustainable development, particularly in cities in the Asia/Pacific region.
BBC ’Toons Tout Healthier Snacks
No longer will popular BBC cartoon characters like the Teletubbies and the Tweenies grace the labels of unhealthy snack foods. In April 2004, network officials announced the characters’ removal from labels of products with high sugar, salt, and fat contents, in response to growing concerns over children’s diets and obesity. The network will continue to license its characters for healthier foods including yogurt, pasta, and bread, as well as special-occasion treats like birthday cakes. The network is also planning to license the characters for a line of staple foods including fruit, vegetables, meat, milk, and dairy products. This new move follows a July 2003 decision by the network to end a sponsorship deal with McDonald’s using BBC characters.
Fast Food Premieres
Director Morgan Spurlock has documented the impact the fast-food industry has on Americans’ waistlines in his film Super Size Me. With obesity affecting growing numbers of adults and children alike, Spurlock wanted to find out what was causing this epidemic. He interviewed people in 20 cities, from children eating at McDonald’s to the U.S. Surgeon General, and lived on nothing but fast food for an entire month while he made the movie, gaining 25 pounds and damaging his liver in the process. The website for the movie (http://www.supersizeme.com/) notes that each day 1 in 4 Americans visits a fast-food restaurant, and that most nutritionists recommend not eating fast food more than once a month.
London Hits Volume Control
Noting that noise can affect a person’s speech, learning, and concentration, the mayor of London, England, has set forth a citywide plan for a quieter capital. The plan requires reductions by all sources of ambient noise, at all times of day—the three main priorities for the strategy are improving and maintaining road surfaces, securing a night-time aircraft ban over the city, and reducing noise through better planning and design of new housing. Ongoing incentives for alternative vehicles will also help decrease noise, as these vehicles are often quieter than their conventional counterparts. The London initiative comes ahead of a requirement that the entire country enact an ambient noise strategy by 2007.
U.S. Signs Tobacco Treaty
In May 2004, the United States became the 108th country to sign the WHO’s international treaty on tobacco control, which outlines a plan of action for issues ranging from tobacco advertising to cigarette smuggling. The action was praised by many groups, but it is not apparent whether the United States will actually ratify the treaty; 40 governments must ratify the treaty for it to take effect, but only 9 have done so. With 5 million people around the world dying from tobacco-related causes each year, supporters hope the treaty will come into force. Among other actions, signatories must ban cigarette advertising, increase taxes on tobacco products, and require cigarette manufacturers to size health warnings to take up at least 30% of the package label.
Where Does the Old Oil Go?
The United States generates approximately 1 billion gallons of used automotive, hydraulic, and cutting oils each year, 75% of which is resold untreated as a cheap industrial fuel. This practice leads to significant emissions of toxic metals including lead and cadmium, according to a 15 January 2004 report in Environmental Science & Technology. The report compared three ways of dealing with used oil: re-refining it, distilling it, and burning it untreated. The authors found the toxicity potential of using the untreated oil was 150 times greater for terrestrial ecosystems and 5 times greater for humans. Development of better oil filters for cars and less frequent oil changes can greatly reduce the volume of used oil.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0614a15309755EnvironewsForumNoise Pollution: EU Ramps Up Road Improvements Holzman David C. 8 2004 112 11 A614 A614 Publication 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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Noise costs the European Union (EU) 10–40 billion annually, by various estimates, with roughly half of this due to road noise. Contributing factors include medical costs, reduced worker productivity, and de facto condemnation of noise-exposed land. Due mostly to the demands of greater population density, European noise mitigation efforts are far ahead of those of the United States, and U.S. officials are paying attention: this spring, officials and researchers toured the best European projects.
Tires hitting pavement can cause as much as 90% of road traffic noise, depending on the traffic conditions, vehicle type, and driving style, says Ulf Sandberg, a senior research scientist at the Swedish National Road and Transport Research Institute. The treads squeeze air as they strike the road and snap as they pull away, which sets the tread and sidewalls vibrating. The upward-curving treads and the road surface form a “horn,” amplifying the cacophony, and the highway surface reflects the noise, says Roger L. Wayson, an associate professor of civil and environmental engineering at the University of Central Florida.
One strategy to fight road/tire noise is single-layer porous highways sitting on an asphalt concrete foundation, which cover hundreds of miles in Europe. The pores, created by using stones of similar size in the asphalt mix, are thought to dampen the hiss of the pumped air and to impair the acoustic reflectivity of the road surface. The resulting 3-decibel reduction over a conventional European highway is readily perceptible.
Besides dampening noise, porous surfaces drain rain, potentially reducing accidents. But they also drain winter road salt (sand can’t be used because it blocks the pores). Europeans use wetted salt, which sticks longer to the road, but U.S. observers worry that more salt use could trade one environmental problem for another. Still, single-layer porous surfaces are successful in the European countries visited, says Christopher Corbisier, a civil engineer and noise specialist with the Federal Highway Administration who took the recent tour.
More experimental are roadways with two layers of porous asphalt atop the foundation, which shave another several decibels from the din, says Gijsjan van Blokland, general manager of the Dutch company M&P Consulting Engineers. At short test sites in Italy and the Netherlands noise-absorbing Helmholtz resonators are embedded in the concrete foundation, cutting several decibels more. These highways tend to wear more quickly than the single-layered ones.
Although noise costs have not been estimated for the United States, road noise is still a concern here. In the United States, noise mitigation must be considered if residential exposures reach 66 decibels, although it is not required if deemed not reasonably feasible. Tall concrete noise barriers are typically used, but cost more than $1 million per mile. Quiet roads offer a potentially cheaper, more aesthetically pleasing alternative.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0614b15309755EnvironewsForumMarine Science: Surf’s Yuck Adler Tina 8 2004 112 11 A614 A614 Publication 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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To get the real skinny on the health effects of coastal water pollution, talk to a surfer. While catching the waves, surfers are also catching colds, stomach bugs, and more. Surfers long ago made the connection between sick days and urban storm drains dumping untreated runoff from streets, yards, and waterways into beach water. But researchers have now calculated the likelihood of surfers succumbing to waterborne bacteria and viruses.
Environmental scientist Ryan H. Dwight of the University of California, Irvine, and colleagues interviewed 1,873 surfers in two California surfing hot spots: rural Santa Cruz County and urban northern Orange County. The researchers interviewed the surfers in April 1998, following a very wet El Niño winter with greater runoff than usual, and again in April 1999, following a very dry La Niña winter with less runoff than usual.
The first year, Orange County surfers reported almost twice as many symptoms over the previous three months compared with Santa Cruz surfers. Their symptoms included fever, nausea, stomach pain, sore throats, and eye, ear, and skin infections, the team reported in the April 2004 American Journal of Public Health. But even Santa Cruz surfers weren’t entirely safe that spring. Every additional 2.5 hours that surfers in either county spent in the water increased by 10% their likelihood of developing symptoms, the team writes. In the spring following the drier La Niña winter, Orange County surfers reported only slightly more symptoms than Santa Cruz participants.
All of the participants, whose mean age was 30, surfed at least once a week. For their water quality data, the researchers used mean monthly total coliform counts collected by the two counties’ health agencies. Orange County scored much worse on water quality tests in the first year than did Santa Cruz, which is a small, less-developed watershed.
Since the study was done, California has expanded its water quality testing requirements. In 1999, in accordance with updated state standards, California began measuring for enterococci, bacteria that inhabit the intestine. Dwight and others say that although overall water quality may not have improved, the change did result in many more beach closings, particularly in Orange County.
The work by Dwight and colleagues helps confirm in a tangible way what swimmers and surfers know from experience, says Cheryl McGovern, a program manager with the U.S. Environmental Protection Agency in San Francisco. People need studies to quantify the health risks associated with various recreational waters, especially if they will be paying for pollution cleanup, she says. She would like to see a follow-up study that uses more sophisticated water quality data, including measurements of enterococci.
Dwight notes that surfers are not the only people exposed to the waters in these or other coastal counties—several millions of tourists and local residents swim in these waters every year. “If the surfers are getting sick—and they are young and healthy—then the public is at risk as well,” he says.
Dangerous waves. A study of surfers connects a raft of health symptoms with waters flooded with toxic runoff following storms.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0636aEnvironewsScience SelectionsMeasuring Lead Effects: Blood and Bone Together Are Better Josephson Julian 8 2004 112 11 A636 A636 Publication 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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Many studies have reported impaired renal function and kidney disease at high levels of lead exposure, as estimated mainly through concentrations of serum creatinine (SCr) and rates of creatinine clearance from the body. However, lower-level lead exposure has not been correlated with renal effects as conclusively, perhaps because blood lead reflects relatively recent exposure, and therefore is not an adequate measure of total body burden. This month, Shirng-Wern Tsaih of the Harvard School of Public Health and her colleagues report that blood lead levels alone may not be enough to determine whether kidney effects are occurring at low exposure; lead levels in bone also need to be determined [EHP 112:1178–1182]. The Tsaih study is among the first to assess the relationship between low-level bone and blood lead levels and measures of kidney function in a general population sample.
In contrast with blood lead, bone lead makes up more than 95% of the adult body burden. The lead in more compacted cortical bones, such as the tibia, is less available for mobilization, because this type of bone is less prone to turnover than spongier trabecular bones, such as the patella. Yet, as people age, bone loss often does take place, so lead that has long been held in bone is released to soft tissue and can find its way to the kidneys. Thus, bone lead may be a better marker for studying the chronic toxicity of accumulated exposure and lead burden.
The Tsaih study examined data from a cohort of middle-aged and elderly Boston men with no known heavy exposure to lead. Participants were from the Normative Aging Study, a federal study of aging begun in 1961. A blood sample for lead analysis had been collected every 3–5 years since July 1988; bone lead measurements began in August 1991, when a subset of participants were recruited for a substudy in which bone lead was measured by K X-ray fluorescence.
Tsaih and colleagues examined data for 448 men who had a baseline bone lead measurement between 1991 and 1995, and follow-up measurements of SCr 4–8 years later. They examined blood and bone lead concentrations and their correlation with kidney function, taking into account the known nephrotoxic effects of diabetes mellitus and hypertension, which had been diagnosed in 5.8% and 25.7% of the men, respectively, at the time of baseline measurement. Bone lead was measured in the tibia and patella.
Tibia lead was observed to be associated with increases in SCr levels in follow-up participants with diabetes. The findings suggest that long-term low-level lead accumulation, estimated by tibia lead, is associated with an increased risk of reduced renal function. This is especially true for diabetics and hypertensives, who already are at risk for kidney impairment because of their disease. In addition, blood lead and tibia lead appeared to be associated with elevated SCr levels and chronic kidney disease among hypertensives. There was no statistical evidence of patella lead being associated with change in renal function, suggesting that chronic absorption of lead is a risk factor for impaired renal function.
The study, however, has some limitations. Although SCr is widely used in medicine to measure overall renal function, it provides only a rough estimate of the kidney’s filtration capacity. For instance, increases in SCr definitively show impairment only when kidney function has been reduced by about 50%. Thus, the researchers had great difficulty in detecting more modest effects of lead. In addition, the alternative hypothesis that elevated blood or bone lead levels actually result from impaired kidney function cannot be ruled out.
It has not yet been determined whether lead affects blood pressure indirectly through changes in kidney function, or via more direct effects on the circulatory system or neurological blood pressure control. The researchers also know of no studies to date that analyze the potential for diabetes to modify the relationship between lead exposure and renal function. Given that many adults have a history of environmental or occupational lead exposure and the incidence of both type 2 diabetes and hypertension, studies of such interactions, if confirmed, could be of significant public health value.
A step in the right direction. Correlating bone lead measurements (obtained through K X-ray fluorescence) with blood lead data yields insights into adverse renal effects.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0636bEnvironewsScience SelectionsRisky Trade-offs: Bangladeshi Quest for Safe Water Mead M. Nathaniel 8 2004 112 11 A636 A637 Publication 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 an attempt to eliminate epidemic levels of diarrhea and other infectious diseases associated with the use of surface waters, millions of shallow tube wells were drilled into the Ganges Delta alluvium in Bangladesh beginning in the early 1970s. This process reduced the rates of water-related infectious diseases but created a new public health dilemma: a surge in diseases such as skin ailments, diabetes mellitus, and various cancers, all resulting from habitual consumption of groundwater naturally high in arsenic.
A number of interventions have been proposed to help remedy the widespread arsenic exposure, but these interventions may only be bringing the catastrophic water situation in Bangladesh full circle. A new study by epidemiologists led by Kamalini Lokuge of the Australian National University suggests that, while these interventions will eventually result in less disease overall, they may initially cause a steady and considerable increase in diarrheal disease [EHP 112:1172–1177]. The study indicates that any large-scale transition away from household tube wells as a source of drinking water, without proper evaluation of the risks, may be premature.
In attempting to quantify the disease burden resulting both from arsenic exposure and from the potential side effects of widely available arsenic mitigation interventions, Lokuge and her colleagues used previously published information to estimate mortality rates and disability-adjusted life years (DALYs). Simply put, a DALY is a measure of the burden of disease; it reflects how much a person’s expectancy of healthy life is reduced by premature death as well as by disability caused by disease.
The Australian team used World Health Organization data to estimate the DALYs lost per year to arsenic-related effects including diabetes, ischemic heart disease, and a number of cancers. They calculated that arsenic exposure causes the loss of 174,174 DALYs per year in Bangladeshis exposed to arsenic concentrations above 50 micrograms per liter (μg/L), the nation’s cut-off point for safe drinking water.
Then they calculated the DALYs that would be lost to infectious disease, provided Bangladeshis adopted certain arsenic mitigation options currently advocated by the federal Bangladesh Arsenic Mitigation and Water Supply Project and immediately accessible to the majority of the Bangladeshi population year-round. These include surface water supplies, uncontaminated community tube wells, and low-cost filtration systems. These alternative options carry the potential for increased water-related infections, compared with household tube wells.
Assuming that mitigation efforts were undertaken only in those areas where the arsenic concentration of drinking water is highest (100–300 μg/L), the team found that the long-range benefits of arsenic mitigation in terms of DALYs gained and deaths avoided would outweigh any initial decline in public health due to water-related infectious diseases. However, there would initially be a period of some years (the number of which is still unknown) before any benefit would accrue, and some additional years until the total benefit outweighed the cost of the water-related infectious disease increase. The investigators also conclude, moreover, that if the Bangladeshi people gradually stop using the alternative water sources and processes (for example, because of the inconvenience of maintenance or complacency as disease drops off), the initial DALY-based cost of water-related infectious diseases would remain while the long-range benefits would disappear.
The study demonstrates that implementation of any arsenic-mitigating intervention must take into account not only the strategy’s effectiveness in reducing arsenic exposure but also its safety in terms of water-related infectious diseases, the likelihood of population-wide compliance, and different exposure levels within the population. The investigators contend that such information is vital to developing appropriate policies toward resolving the drinking water crisis in Bangladesh.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0650aAnnouncementsBook ReviewAn Air that Kills: How the Asbestos Poisoning of Libby, Montana, Uncovered a National Scandal Kipen Howard M. Howard M. Kipen is professor of Environmental and Occupational Medicine and director of the Division of Occupational Medicine at the UMDNJ–Robert Wood Johnson Medical School and its Environmental & Occupational Health Sciences Institute. He studies occupational respiratory disease and has consulted with the ATSDR on the Libby situation.8 2004 112 11 A650 A650 Publication 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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By Andrew Schneider and David McCumber
New York:G.P. Putnam’s Sons, 2004. 440 pp. ISBN: 0-399-15095-1, $25.95 cloth.
Asbestos has tragically affected communities throughout the world, largely through its effects on workers. This powerful history is presented with dramatic flair in the new investigative book by Andrew Schneider and David McCumber. An Air That Kills provides a compelling update on what should now be a closed chapter in occupational health in the United States. More remarkably, it also asserts the potential impact of the long-feared environmental health disaster arising from negligent use of asbestos. This book explores new territory in nonoccupational asbestos exposure.
The book records the history of some of the heroes of occupational medicine in the United States and also names some of the “black hats.” It also addresses risk and risk reduction related to nonoccupational exposure to asbestos. Here it lands on thin ice, and may even fall through it by omitting results of the Agency for Toxic Substances and Disease Registry (ATSDR) survey that could validate its thesis, as well as by making some extreme statements. Schneider and McCumber note that the federal government “did the biggest public health survey in its history. A third of the town got . . . the ‘death sentence.’” The data from the survey, not presented in this book although published in EHP (111:1753–1759), do not substantiate this statement. The nonrandom sample of 6,668 adults in Libby showed 17.8% with pleural abnormalities and < 1% with interstitial abnormalities. These findings are hard to reconcile with certain asbestos-related mortality of one-third of the town.
A wealth of investigative reporting exposes how environmental asbestos concerns were neglected by the Bush–Whitman Environmental Protection Agency (EPA) in the desire to get Wall Street going after September 11. The authors also describe vermiculite shipments to over 750 locations, 293 major users, and 45–73 expansion facilities throughout North America. Attic insulation, concrete, wallboard, roofing, crayons, and potting soil, as well as tremolite-contaminated talc, taconite ore, and Sierra foothill soil, contain contaminated vermiculite or other “naturally occurring asbestos.” No one has epidemiologically established the risk of mesothelioma from these environmental exposures. This book amply documents “an enormous hole in the safety net that people assume is out there in modern life” for toxic substance regulation and enforcement.
The allegation that Libby’s doctors rarely diagnosed asbestosis because doctors were high in the town’s caste system is telling but not documented. The story that W.R. Grace & Co. and others conspired to intimidate a doctor who was willing to diagnose asbestosis is convincing, as are other claims of Grace’s unethical behavior. The lack of chapter titles decreases the utility and approachability of this book. There are too many silly errors of fact, and it is frustrating to read a provocative statement characterizing the World Trade Center cleanup without a source citation. Such a source might likely be an interview rather than a report that could be reviewed by readers. This is not an academic book, but a popular and, to some extent, a muckraking book based on a lot of journalistic research.
While U.S. EPA scientists and investigators were struggling to document the Libby problem in the late 1990s, they uncovered reports from 1980 and 1982 that had already characterized the problem. But neither miners nor the public had been warned. The book alleges that 15 years before it took any concrete action, the U.S. EPA knew that asbestos was killing residents of Libby, stonewalled, and then in an about-face requested an investigation, eventually giving an award to the local administrators who had publicized both the situation and the U.S. EPA’s period of inaction. There are even implications that close ties between W.R. Grace CEO J. Peter Grace and the Reagan White House might have led to downplaying the earlier knowledge. The U.S. EPA had knowledge that could have begun a reduction in exposure, for miners and the town, 15–20 years sooner. W.R. Grace made denials until they declared bankruptcy.
Beyond the hyperbole and the melodrama of the individual tragedies, there is much new history and political insight to recommend this book. This is important material in the unfortunately still unfolding scientific and historical chapters on asbestos and disease.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0650bAnnouncementsNew BooksNew Books 8 2004 112 11 A650 A650 Publication 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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Academic Health Centers: Leading Change in the 21st Century
Linda T. Kohn, ed.
Washington, DC:National Academies Press, 2004. 216 pp. ISBN: 0-309-08893-3, $39
Bioethics in Complexity: Foundations and Evolutions
Sergio De Risio, Franco F. Orsucci, eds.
River Edge, NJ:World Scientific, 2004. 100 pp. ISBN:1-86094-399-3, $34
Biological Weapons Defense: Infectious Disease and Counterbioterrorism
Luther E. Lindler, Frank J. Lebeda, George Korch
Totowa, NJ:Humana Press, 2004. 625 pp. ISBN: 1-58829-184-7, $145
Bioterrorism and Food Safety
Barbara A. Rasco, Gleyn E. Bledsoe
Boca Raton, FL:CRC Press LLC, 2004. 296 pp. ISBN: 0-8493-2787-3, $99.95
Cell Growth: Control of Cell Size
Michael N. Hall, Martin Raff, George Thomas, eds.
Woodbury, NY:Cold Spring Harbor Press, 2004. 652 pp. ISBN: 0-87969-672-9, $135
Chemical Consequences: Environmental Mutagens, Scientist Activism, and the Rise of Genetic Toxicology
Scott Frickel
Piscataway, NJ:Rutgers University Press, 2004. 224 pp. ISBN: 0-8135-3412-7, $22.95
Earthly Politics: Local and Global in Environmental Governance
Sheila Jasanoff , Marybeth Long Martello, eds.
Cambridge, MA:MIT Press, 2004. 376 pp. ISBN: 0-262-10103-3, $67
Environmental Contaminants
Daniel Vallero
Burlington, MA:Elsevier, 2004. 832 pp. ISBN: 0-12-710057-1, $79.95
Folding and Self-Assembly of Biological Macromolecules
E. Westhof, N. Hardy
River Edge, NJ:World Scientific, 2004. 416 pp. ISBN: 981-238-500-2, $142
Free Radicals and Inhalation Pathology
Erich Schiller
New York:Springer-Verlag, 2004. 773 pp. ISBN: 3-540-00201-4, $225
Green Giants? Environmental Policies of the United States and the European Union
Norman J. Vig, Michael G. Faure, eds.
Cambridge, MA:MIT Press, 2004. 392 pp. ISBN: 0-262-22068-7, $67
Molecular Basis of Breast Cancer: Prevention and Treatment
Jose Russo, Irma H. Russo
New York:Springer-Verlag, 2004. 448 pp. ISBN: 3-540-00391-6, $249
Nature and Human Communities
T. Sasaki, ed.
New York:Springer-Verlag, 2004. 216 pp. ISBN: 4-431-20720-1, $99
Reviews of Environmental Contamination and Toxicology
George Ware, ed.
New York:Springer-Verlag, 2004. 202 pp. ISBN: 0-387-20519-5, $129
The Cancer Handbook
Malcolm R. Alison, ed.
Hoboken, NJ:John Wiley & Sons, 2004. 1,772 pp. ISBN: 0-470-02506-9, $495
The Economics of Groundwater Remediation and Protection
Paul E Hardisty, Ece Ozdemiroglu, Jonathan Smith
Boca Raton, FL:CRC Press LLC, 2004. 400 pp. ISBN: 1566706432, $149.95
The Practical Bioinformatician
Limsoon Wong, ed.
River Edge, NJ:World Scientific, 2004. 540 pp. ISBN: 981-238-846-X, $100
Urban Transport and the Environment
Edited by World Conference on Transport Research Society and the Institute for Transport Policy Studies
Burlington, MA:Elsevier, 2004. 450 pp. ISBN: 0-08-044512-8, $88
Water and Sustainable Development: Opportunities for the Chemical Sciences–A Workshop Report to the Chemical Sciences Roundtable
Parry Norling, Frankie Wood-Black, Tina M. Masciangioli, eds.
Washington, DC:National Academies Press, 2004. 106 pp. ISBN: 0-309-09200-0, $22.95
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7219ehp0112-00126515345337ResearchCommentariesDioxin Revisited: Developments Since the 1997 IARC Classification of Dioxin as a Human Carcinogen Steenland Kyle 1Bertazzi Pier 2Baccarelli Andrea 2Kogevinas Manolis 31Rollins School of Public Health, Emory University, Atlanta, Georgia, USA2Department of Occupational and Environmental Health, EPOCA Research Center for Occupational, Clinical, and Environmental Epidemiology, University of Milan, Milan, Italy3Respiratory and Environmental Health Research Unit, Municipal Institute of Medical Research, Barcelona, SpainAddress correspondence to K. Steenland, 1518 Clifton Rd., Rollins School of Public Health, Emory University, Atlanta, GA 30306 USA. Telephone: (404) 712-8277. Fax: (404) 727-8744. E-mail:
[email protected] authors declare they have no competing financial interests.
9 2004 10 6 2004 112 13 1265 1268 30 4 2004 10 6 2004 Publication 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 1997 the International Agency for Research on Cancer (IARC) classified 2,3,7,8-tetra-chlorodibenzo-p-dioxin (TCDD; the most potent dioxin congener) as a group 1 carcinogen based on limited evidence in humans, sufficient evidence in experimental animals, and extensive mechanistic information indicating that TCDD acts through a mechanism involving the aryl hydrocarbon receptor (AhR), which is present in both humans and animals. The judgment of limited evidence in humans was based primarily on an elevation of all cancers combined in four industrial cohorts. The group 1 classification has been somewhat controversial and has been challenged in the literature in recent years. In this article we review the epidemiologic and mechanistic evidence that has emerged since 1997. New epidemiologic evidence consists primarily of positive exposure–response analyses in several of the industrial cohorts, as well as evidence of excesses of several specific cancers in the Seveso accident cohort. There are also new data regarding how the AhR functions in mediating the carcinogenic response to TCDD. The new evidence generally supports the 1997 IARC classification.
carcinogendioxinTCDD
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The 1997 IARC Evaluation
In 1997 the International Agency for Research on Cancer (IARC) classified TCDD (2,3,7,8-tetrachlorodibenzo-p-dioxin, the most potent dioxin congener) as a group 1 carcinogen (IARC 1997) based on limited evidence in humans, sufficient evidence in animals, and extensive mechanistic information indicating that TCDD acts through a mechanism involving the aryl hydrocarbon receptor (AhR), which is present in both humans and animals. The 1997 IARC evaluation updated an older, obsolete evaluation that had classified TCDD as a group 2B (possible) human carcinogen. IARC’s criteria for “limited” evidence for the epidemiologic studies requires that a causal interpretation is “credible,” but that chance, bias, or confounding cannot be ruled out as the source of the observed association. TCDD was unprecedented in that it was judged to cause an increase in cancers at all sites rather than at a few specific sites. This judgment was supported by both the epidemiologic and animal data. In animals, there were no “hallmark” sites that were elevated; instead, different tumor sites were elevated in different species in different studies. Furthermore, tissue concentrations were similar both in heavily exposed human populations, in which increased overall cancer risk was observed, and in rats exposed to carcinogenic dosage regimens in bioassays. At the 1997 IARC meeting held 4–11 February in Lyon, France, there was consensus that the epidemiologic evidence was at least “limited,” with some consideration that it was “sufficient.” The main discussion and division of opinions concerned the use of mechanistic data to interpret cancer risk in humans.
In 1997, the epidemiologic evidence consisted of studies of a) several industrial cohorts of chemical workers producing chlorophenol and phenoxy herbicides; b) cohorts of civilian or military pesticide applicators; c) the Seveso accident cohort; and d) numerous community-based studies. The IARC working group on dioxins summarized all of the available data but based the epidemiologic evaluation on studies of four highly exposed subcohorts within the industrial cohorts, and on the Seveso cohort. The main criteria for relying on these studies were principally that the cohorts included subjects with levels clearly higher than background and that exposure was well characterized. The four industrial cohorts are listed in Table 1, which also defines the subcohorts and their all-cancer mortality in reference to external populations. Exposure information is also given in Table 1 in terms of parts per trillion in serum. To put this in context, the general population has serum levels of approximately ≤ 5 ppt, and levels have been gradually decreasing in recent decades (Aylward and Hays 2002; IARC 1997; Schecter et al. 2003). The four industrial subcohorts were consistent in showing significant although moderate elevations of cancer mortality. When the data were combined, the standardized mortality ratio for all four subcohorts was 1.40 [95% confidence interval (CI), 1.1–1.7]. An exposure–response analysis was available in 1997 for two of the four cohorts (Flesch-Janys et al. 1995; Ott and Zober 1996); both of these analyses showed a significant positive exposure response for all cancers. Confounding by smoking or by other chemicals was judged to be unlikely to explain the observed consistent all-cancer excess.
Evidence Published after 1997
New exposure–response analyses.
Since the IARC monograph on dioxins (IARC 1997), there have been several new exposure–response analyses using the industrial cohorts (Table 1). These analyses have used similar techniques to develop estimates of serum TCDD levels for all workers in the cohort.
Using a newly developed job-exposure matrix (JEM) (Piacitelli et al. 2000), Steenland et al. (1999) analyzed exposure–response analysis in the NIOSH (National Institute for Occupational Safety and Health) cohort using cumulative exposure scores. The JEM was based on knowledge of the amount of TCDD contamination in the chemicals produced in each of eight plants in the study, knowledge of plant processes over time, and knowledge of what the job of each worker was across time. Each job in each plant was assigned an exposure score by the JEM. The exposure score represented a relative ranking of exposure for each worker. The rate ratios for all cancers (mortality) by septile of cumulative exposure score (15-year lag) were 1.00, 1.00, 1.29, 1.38, 1.43, 1.88, and 1.76 (p-value for trend < 0.001). Steenland et al. (1999) used data on exposure scores and serum level, which were available for 170 workers, to determine the relationship between exposure score and serum level. This enabled assignment of estimated serum level, based on the exposure score, for all workers (n = 3,538) in the study (Steenland et al. 2001). Analyses by septile of estimated cumulative serum level resulted in rate ratios for all cancers of 1.00, 1.26, 1.02, 1.43, 1.46, 1.82, and 1.62 (p-value for trend = 0.003).
Additional analyses of the Dutch cohort (Hooiveld et al. 1998) used a similar approach. Serum TCDD levels from 144 workers were used to build a model to predict serum levels based on duration of exposure, exposure during an accident, and exposure before 1970. Predicted serum TCDD values from the model were assigned to the whole cohort of 1,031 workers. Workers were then classified as having received low, medium, or high exposure based on predicted serum TCDD values. Workers who received medium and high exposures had significant 5-fold increases in cancer mortality compared with workers at the same plant with low dioxin exposure.
Becher et al. (1998) and Flesch-Janys et al. (1998) used a similar approach to further analyze a German cohort in detailed exposure–response analyses. TCDD levels from a sample of 275 workers were used to construct a model based on job, age, and body mass index. This model predicted TCDD values over time for all 1,189 members of the cohort. These authors then used these data to estimate time-dependent cumulative exposure to TCDD in the serum for each cohort member. Prior analyses had been restricted to a fixed estimate of serum TCDD at the end of exposure. Rate ratios for all-cancer mortality by categorized ppt-years of TCDD were 1.00, 1.12, 1.42, 1.77, 1.63, and 2.19 (p-value for trend = 0.03) (Becher et al. 1998).
Crump et al. (2003) conducted a meta-analysis of three of these cohorts (Flesch-Janys 1998; Ott and Zober 1996; Steenland et al. 1999) and found a positive and significant exposure–response trend for all cancers. Crump et al. (2003) also showed that the slope of the dose response was not dependent on the pattern of the risk in heavily exposed workers and that, by contrast, the slope was slightly steeper at lower doses.
Harking back to Austin Bradford Hill and his criteria for assessing causation (Hill 1965), positive exposure–response analyses are important in supporting the assessment of causality. Furthermore, the dose–response analyses are internal comparisons among workers and are unlikely to be affected by confounding from occupational, lifestyle, or other factors related to socioeconomic status. These positive exposure–response analyses for TCDD since the IARC classification (IARC 1997) strengthen the decision by IARC to label TCDD a human carcinogen.
New results from Seveso.
Besides the new exposure–response findings, there has been new information from the Seveso cohort, which was exposed during an accident in Italy in 1976 (Bertazzi and di Domenico 2003). This cohort was exposed at one time to quite high levels of TCDD. People in zone A (the most highly exposed zone) had a median serum TCDD level of 72 ppt in 1992–1993 (back-extrapolated level to 1976, 379 ppt). In Seveso, exposure to nearly “pure” TCDD was well documented and affected all ages and both sexes. The exposed and reference populations both lived in a fairly homogeneous area and shared environmental, occupational, social, and cultural features. The limitations of the cohort are that the number of highly exposed subjects is relatively small and that follow-up has been of relatively short duration. However, recent data have shown significant cancer excesses that were not previously evident in this cohort.
Among those with relatively high exposure at Seveso (zones A and B), all-cancer mortality in the 20-year postaccident period and all-cancer incidence in the 15-year postaccident period failed to exhibit significant departures from the expected (Bertazzi et al. 2001; Pesatori et al. 2003). Among men, however, after 20 years of follow-up, both all-cancer (166 deaths) and lung cancer mortality (57 deaths) tended to be higher than expected [all cancer: relative risk (RR) = 1.1; 95% CI, 1.0–1.3; lung cancer: RR = 1.3; 95% CI, 1.0–1.7]. Furthermore, some specific cancer sites were significantly elevated. For lymphopoietic neoplasms, significant increases in mortality (20 years; RR = 1.7; 95% CI, 1.2–2.5) and morbidity (15-year latency; RR = 1.8; 95% CI, 1.2–2.6) were observed, consistent in both sexes. Furthermore, there was an increase in rectal cancer mortality in men (RR = 2.4; 95% CI, 1.2–4.6); a corresponding increase was seen for incidence. Among women, liver cancer incidence was elevated in the 15-year postaccident period (RR = 2.4; 95% CI, 1.1–5.1). Finally, in a separate analysis of 981 women in zone A who had stored serum, breast cancer incidence was significantly related to serum TCDD levels (a 2-fold increase for a 10-fold increase in serum TCDD), based on a limited number of cases (n = 15) (Warner et al. 2002).
Other new studies.
Another cohort with well-documented exposure, based on serum TCDD levels, is the Ranch Hand cohort of Air Force personnel who sprayed Agent Orange in Vietnam. This cohort was not exposed to TCDD at the high levels of the industrial cohorts but nonetheless was exposed to levels considerably beyond background. For example, the mean serum TCDD level in the mid-1980s was 46 ppt (geometric mean, 15), compared with a mean of 233 ppt among the NIOSH cohort in the late 1980s (Fingerhut et al. 1991). Until recently, the Ranch Hand cohort had not shown any cancer excesses, and the number of cancers was small. Although there is still no overall cancer excess [standardized incidence ratio (SIR) = 1.07), in the most recent update (through 1999) of this cohort, Akhtar et al. (2004) found a significant excess of melanoma [(SIR = 2.57; 95% CI, 1.52–4.09) when comparing Ranch Hand personnel with the general population (16 cases)]. This excess did not appear among other Air Force personnel who were also in Southeast Asia in the 1960s but did not spray Agent Orange. Furthermore, there appeared to be an exposure–response trend, using several different measures of exposure. Akhtar et al. (2004) also found excesses of prostate cancer incidence, but these occurred in both exposed and nonexposed Air Force personnel and may have been due to increased cancer surveillance in both groups; both are subject to intense medical follow-up.
Other dioxin studies published since 1997 include a study of Army Chemical Corps veterans who did or did not serve in Vietnam (Dalager et al. 1997), and an update of a subcohort contained within the NIOSH cohort (Bodner et al. 2003). The studies are largely uninformative because the numbers are quite small or because exposure is uncertain (Dalager et al. 1997).
Dioxin Risk Assessments
A separate issue is whether the findings that high levels of TCDD exposure lead to cancer has relevance for those exposed at low doses, that is, the general public. The classification of TCDD as a human carcinogen in 1997 strengthened the pressure to lower human exposure and was followed by subsequent World Health Organization (WHO) risk assessments that lowered considerably the accepted tolerable daily intake from previously set limits (WHO 1998
WHO 2001). There have also been several cancer risk assessment efforts to date (Becher et al. 1998; Crump et al. 2003; Starr 2001; Steenland et al. 2001; U.S. EPA 2000) using data on the high-exposure industrial cohorts to estimate risk at low doses. It should be noted that some of the high-exposure cohorts did have a fair number of low-exposed subjects, so the usual problem of extrapolating findings from high dose to low dose is not as pronounced as for some other agents for which risk assessment has been based on occupational cohorts. Nearly all these assessments concur in showing an appreciable excess risk of cancer due to relatively small increases above background levels. In the general population, such increases would be due to increased TCDD in the diet.
New Evidence on the AhR
Apart from new epidemiologic data since 1997, there are also new experimental studies (some of them used in the recent WHO risk assessments) and advances in the understanding of mechanisms of action of dioxins, particularly concerning the AhR. The AhR is a nuclear receptor and transcription factor. In the presence of TCDD, it forms an active heterodimer with the aromatic hydrocarbon nuclear translocator (ARNT/HIF-1β) and induces (or suppresses) the transcription of numerous genes, including P4501A1 (CYP1A1) (Whitlock 1999). In the last few years, additional components of the AhR complex have been identified, including the AhR repressor, AhR-interacting protein (also known as XAP2), Rb protein, receptor-interacting protein 140, SRC-1, p23, and the RelA NF-κB subunit (Carlson and Perdew 2002; Kumar and Perdew 1999; Mimura et al. 1999; Petrulis and Perdew 2002). Molecular mechanisms occurring downstream of AhR and possibly associated with cancer development, such as changes in cytosolic signaling proteins, calcium mobilization, tumor suppressor proteins, growth factors, oncogenes, and cell cycle proteins, have been characterized (Carlson and Perdew 2002; Enan et al. 1998; Matsumura 2003).
Recently, molecular epidemiology investigations have been conducted on random samples of the Seveso population highly exposed to TCDD (zones A and B) and from the reference noncontaminated area (non-ABR) to evaluate how TCDD exposure affects the AhR pathway in human subjects in vivo (Baccarelli et al. 2004; Landi et al. 2003). Because of the extremely long biologic half-life of TCDD, plasma TCDD levels were still substantially elevated in the exposed subjects, particularly in females and older subjects (Landi et al. 1997). Experimental studies indicate that, after a transient increase, cellular levels of AhR decrease following TCDD binding (Pollenz 2002). Nearly 20 years after the Seveso accident, the levels of AhR transcripts (measured in uncultured peripheral blood lymphocytes) were decreased in the exposed subjects and negatively correlated with current plasma TCDD levels (Landi et al. 2003). These results show that TCDD exposure causes a persistent alteration of the AhR pathway in human subjects and are consistent with down-regulation of this receptor, comparable with that observed in several other receptor-mediated systems (Pollenz 2002). The impact on the health of exposed individuals of the persistent decrease of AhR transcripts, which in turn may affect any AhR-regulated biologic function, is to be clarified. Down-regulation tends to decrease the amount of receptor available for ligand binding and to attenuate the resulting biologic responses. Thus, the AhR, like most receptor systems, may have high initial sensitivity to the ligand, whereas in the presence of high amounts of TCDD, down-regulation would buffer against excessive ligand-induced responses. High initial levels of exposure, rather than low persisting exposures, may be associated with the highest effects. In the industrial cohorts, cumulative exposure predicts cancer excess. However, it is likely that cumulative and peak exposures are highly correlated among industrial workers. The new evidence from animal studies and on the AhR should be used to refine quantitative risk assessment of TCDD and could modify estimates on tolerable intake in humans. This evidence, put together, supports the approach taken by IARC to consider the animal and mechanistic data in the evaluation of carcinogenicity of these compounds in humans.
Conclusion
The IARC classification of TCDD as a group 1 carcinogen (IARC 1997) has stirred some controversy. For example, Cole et al. (2003) argue that the original IARC classification of epidemiologic evidence for TCDD as “limited” (IARC 1997) was incorrect, claiming that “inadequate” would have been more appropriate (i.e., a causal interpretation was not “credible”). However, these authors ignored the original IARC focus on high-exposure subcohorts, ignored the positive exposure–response analyses, and raised the issue of possible confounding by smoking and other chemical carcinogens without any serious consideration of whether such possible confounding is likely, or whether it could account for the observed elevation of all-cancer mortality in those with higher TCDD exposure.
In our view, the epidemiologic and toxicologic evidence since the IARC (1997) classification of TCDD as a human carcinogen has strengthened the case for IARC’s decision. Furthermore, the dose–response assessments for TCDD and cancer indicate that TCDD exposure levels close to those in the general population may be carcinogenic and argue for caution in setting the upper ranges of long-term permissible exposure to dioxins.
Table 1 Four industrial cohorts that served as a basis for IARC (1997) TCDD determination.
Study originally available to IARC in 1997a Cancer SMR (95% CI) and definition of subcohort No. of cancer deaths Estimated TCDD at end of exposureb Exposure–response data for TCDD
Fingerhut et al. 1991 1.5 (1.2–1.8), > 1 year exposure, 20 years of latency (59% of cohort) 114 Mean 418 ppt (n = 119) Positive significant trend (p < 0.001, p = 0.003) in Steenland et al. (1999, 2001),c based on JEM and serum levels
Becher et al. 1996 1.3 (1.0–1.5), workers in two plants with documented chloracne and high serum TCDD levels 105 Plant 1: mean, 141 ppt (n = 190). Plant 2: mean, 402 ppt (n = 20) Positive significant trend (p < 0.01) in Flesch-Janys et al. (1995), in Flesch-Janys et al. (1998; p = 0.01),c and in Becher et al. (1998; p = 0.03),c based on JEM and serum levels
Hooiveld et al. 1996 1.5 (1.3–1.9), workers in the most highly exposed plant (n = 549) 51 Geometric mean, 286 ppt (n = 48) Medium- and high-exposure groups elevated (RRs = 4.7 and 4.1) versus low (Hooiveld et al. 1998),c based on work history and serum levels
Ott and Zober 1996 1.9 (1.1–3.0), chloracne and ≥20 years’ latency (n = 113) 18 Geometric mean, 400 ppt (n = 138) Positive significant trend (p = 0.05) in original 1996 publication, based on body burden
Abbreviations: CI, confidence interval; NIOSH, National Institute for Occupational Safety and Health; SMR, standardized mortality ratio.
a IARC (1997; Table 38).
b IARC (1997; Table 22).
c Post-1997 findings.
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WHO 2001. PCDDs, PCDFs, and Coplanar PCBs, Safety Evaluation of Certain Food Additives and Contaminants. WHO Food Additive Series. Geneva:World Health Organization. Available: http://www.inchem.org/documents/jecfa/jecmono/v48je20.htm#1.0 [accessed 2 June 2004].
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.6950ehp0112-00126915345338ResearchCommentariesThe Science and Practice of Carcinogen Identification and Evaluation Cogliano Vincent James Baan Robert A. Straif Kurt Grosse Yann Secretan Marie Béatrice Ghissassi Fatiha El Kleihues Paul International Agency for Research on Cancer, Lyon, FranceAddress correspondence to V.J. Cogliano, Chief, Carcinogen Identification and Evaluation, International Agency for Research on Cancer, 150 cours Albert Thomas, 69372 Lyon cedex 08, France. Telephone: 33-4-72-73-84-76. Fax: 33-4-72-73-83-19. E-mail:
[email protected] acknowledge the important contributions of administrative staff of the IARC Monographs program: S. Egraz, M. Lézère, J. Mitchell, and E. Perez.
The IARC Monographs are supported, in part, by grants from the U.S. National Cancer Institute, the European Commission, the U.S. National Institute of Environmental Health Sciences, and the U.S. Environmental Protection Agency.
The authors declare they have no competing financial interests.
9 2004 3 6 2004 112 13 1269 1274 31 12 2003 3 6 2004 Publication 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 national and international health agencies have established programs with the aim of identifying agents and exposures that cause cancer in humans. Carcinogen identification is an activity grounded in the scientific evaluation of the results of human epidemiologic studies, long-term bioassays in experimental animals, and other data relevant to an evaluation of carcinogenicity and its mechanisms. In this commentary, after a brief discussion of the science basis common to the evaluation of carcinogens across different programs, we discuss in more detail the principles and procedures currently used by the IARC Monographs program.
carcinogencarcinogen identificationconflict of interestshazard identificationIARC Monographs
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The global burden of cancer continues to increase. There were an estimated 10.1 million new cases, 6.2 million deaths, and 22.4 million persons living with cancer in the year 2000 (Kleihues and Stewart 2003). This represents an increase of 19% in incidence and 18% in mortality since 1990. Given current trends in smoking prevalence and other factors, the annual number of new cases is estimated to reach 15 million by 2020. It is possible to prevent at least one-third of these new cases through better use of existing knowledge.
Understanding how cancer develops creates opportunities for cancer prevention or early detection. An important part of this effort is to identify the agents and exposures that cause cancer. Several national and international health agencies have established carcinogen identification programs that provide a scientific basis for governmental and private efforts to control cancer by reducing exposure to known and suspected human carcinogens.
The IARC Monographs on the Evaluation of Carcinogenic Risks to Humans are published by the International Agency for Research on Cancer (IARC) of the World Health Organization (WHO). Each IARC Monograph represents the consensus of an international working group of expert scientists. The IARC Monographs include a critical review of the pertinent peer-reviewed scientific literature as the basis for an evaluation of the weight of the evidence that an agent may be carcinogenic to humans. Published continuously since 1972, the scope of the IARC Monographs has expanded beyond chemicals to include complex mixtures, occupational exposures, lifestyle factors, physical and biologic agents, and other potentially carcinogenic exposures. After publication of IARC Monograph volume 87, expected in 2004 or 2005, nearly 900 agents, mixtures, and exposures will have been evaluated. Among these, 91 have been characterized as carcinogenic to humans, 67 as probably carcinogenic to humans, and 240 as possibly carcinogenic to humans.
The U.S. National Toxicology Program (NTP; Research Triangle Park, NC, USA) publishes the Report on Carcinogens, which identifies and discusses substances that may pose a carcinogenic hazard to human health and to which a significant number of persons residing in the United States are exposed. Mandated in 1978 by an act of the U.S. Congress, the Report on Carcinogens lists agents as either “known to be a human carcinogen” or “reasonably anticipated to be a human carcinogen.” One nongovernmental and two federal scientific committees review the nominations for listing in or delisting from the Report on Carcinogens. The director of the National Toxicology Program reviews the three groups’ recommendations and all public comments before the Secretary of Health and Human Services reviews and approves the Report on Carcinogens (NTP 2002).
The U.S. Environmental Protection Agency (EPA) assesses the health hazards of chemical contaminants present in the environment. These assessments cover cancer and adverse effects other than cancer. The hazard assessments are coupled with dose–response assessments that the U.S. EPA uses in its regulatory and information programs. The principles that the U.S. EPA uses in its cancer assessments are discussed in an evolving series of guidelines (U.S. EPA 1986
U.S. EPA 1999
U.S. EPA 2003). Chemical assessments are developed through a process that includes a toxicologic review of the pertinent scientific literature written by U.S. EPA scientists or contractors, internal and external peer reviews, and an internal consensus review (U.S. EPA 2004).
The California Environmental Protection Agency (Cal/EPA) maintains a list of “chemicals known to the state to cause cancer” under Proposition 65 (Cal/EPA 2004), a 1986 ballot initiative enacted to protect citizens from chemicals known to cause cancer, birth defects, or other reproductive harm and to inform citizens about exposures to such chemicals. A chemical is listed if an independent committee of scientists and health professionals finds that the chemical has been clearly shown to cause cancer, if an authoritative body (currently the U.S. EPA, the U.S. Food and Drug Administration, the National Institute for Occupational Safety and Health, the NTP, and IARC) has identified it as causing cancer, or if a California or U.S. government agency requires that it be labeled or identified as causing cancer (Cal/EPA 2004).
These programs have developed the following descriptors:
IARC (IARC 2004)
Carcinogenic to humans (group 1)
Probably carcinogenic to humans (group 2A)
Possibly carcinogenic to humans (group 2B)
Not classifiable as to its carcinogenicity to humans (group 3)
Probably not carcinogenic to humans (group 4)
U.S. EPA (U.S. EPA 2003)
Carcinogenic to humans
Likely to be carcinogenic to humans
Suggestive evidence of carcinogenic potential
Inadequate information to assess carcinogenic potential
Not likely to be carcinogenic to humans
U.S. NTP (NTP 2002)
Known to be a human carcinogen
Reasonably anticipated to be a human carcinogen
Cal/EPA (Cal/EPA 2004)
Known to the state to cause cancer.
The Science of Carcinogen Identification and Evaluation
The risk assessment paradigm.
Decisions about reducing exposure to suspected carcinogens are often controversial, partly because the available data often do not allow us to identify human carcinogens with certainty and because the costs and benefits of risk reduction affect different segments of the population. In an effort to identify the scientific components of these decisions, the U.S. National Research Council (NRC) has distinguished “risk assessment,” which is the use of scientific data to describe the health effects of exposure to hazardous agents, from “risk management,” which is the process of weighing policy alternatives and selecting the most appropriate action (NRC 1983). Risk management integrates the results of a risk assessment with other technical data and with economic, social, and political concerns (Figure 1).
The NRC further divided risk assessment into a series of distinct steps (NRC 1983, 1994). Hazard identification determines whether exposure to an agent is linked to adverse health effects. Dose–response assessment characterizes the relation between the dose of an agent and the incidence of an adverse health effect. Exposure assessment determines the extent of human exposure to an agent. Risk characterization describes the nature and magnitude of human risk, including attendant uncertainty.
Under this paradigm, a cancer “hazard” is an agent that is capable of causing cancer under some circumstances, whereas a cancer “risk” is an estimate of the nature and incidence of cancer expected from a given exposure. Risk depends on both the existence of a hazard and exposure to that hazard. A cancer hazard exists even when current exposures suggest little or no cancer risk, because accidental or unanticipated exposures that are difficult to foresee may pose a risk of cancer. Thus, carcinogen identification is an exercise in hazard identification, distinct from the tasks of estimating human dose–response functions, estimating current or future human exposures, or characterizing the risk from current or future human exposures.
Pertinent data for carcinogen identification.
The term “carcinogen” generally refers to an agent, mixture, or exposure that increases the age-specific incidence of cancer. Carcinogen identification is an activity grounded in the evaluation of the results of scientific research. Pertinent data for carcinogen identification include human epidemiologic studies, long-term bioassays in experimental animals, and other relevant data on toxicokinetics and cancer mechanisms. Each source of data has a role in the overall assessment. Epidemiologic studies can provide unequivocal evidence of a carcinogenic hazard but often are not sufficiently sensitive to identify a carcinogenic hazard except when the risk is high or involves an unusual form of cancer. In addition, cancer’s latent period implies that many years of preventable human exposure would occur before informative epidemiologic studies are available. For these reasons, animal studies generally provide the best means of assessing potential risks to humans. To answer questions about the similarity of response between animals and humans, studies of toxicokinetics and mechanisms have been employed. Toxicokinetic studies are done to allow cross-species comparisons of absorption, distribution, metabolism, and elimination but often are done in detail in only one species. Mechanistic studies aim to eventually elucidate the chemical species and cellular processes involved in cancer initiation and development.
Evaluating evidence of cancer in humans.
Epidemiologic studies provide unique information about the response of humans exposed to potential carcinogens. Among these, cohort and case–control studies are especially useful for determining whether exposure to an agent is causally associated with human cancer. Criteria for assessing the adequacy of epidemiologic studies include selection and characterization of exposed and reference groups, identification and characterization of confounding factors and bias, duration of follow-up in view of cancer’s latent period, ascertainment of causes of disease and death, and power to detect specific effects.
In using human studies to identify carcinogens, epidemiologists often ask whether a causal interpretation is credible and whether chance, bias, or confounding factors can be excluded. On the question of causality, epidemiologists have found useful guidance in a set of factors known as the Hill criteria (Hill 1965; U.S. EPA 2003):
Consistency of the observed association. A pattern of elevated risks observed across several independent studies would support or strengthen an inference of causality. Reproducibility of findings constitutes one of the strongest arguments for causality.
Strength of the observed association. The finding of large, precise risks increases confidence that an association is not likely due to chance, bias, or confounding factors. A modest risk, however, does not preclude a causal association and may reflect a lower level of exposure, an agent of lower potency, or a common disease (e.g., when there is a relatively high incidence rate of a disease in the general population, it is more difficult to reach a doubling of that incidence rate).
Specificity of the observed association. As originally described, this refers to a single cause associated with a single effect (Hill 1965). Current understanding is that many agents cause cancer at multiple sites, and many cancers have multiple causes. Thus, although the presence of specificity supports causality, its absence does not exclude it.
Temporal relationship of the observed association. A causal interpretation is strengthened when exposure is known to precede development of the disease. Because cancer usually has a latent period ≥ 20 years, it is important to ascertain whether the study included sufficient follow-up time after exposure.
Biologic gradient (exposure–response relationship). A clear exposure–response relationship (i.e., increasing effects associated with increasing exposure) strongly suggests cause and effect, especially when such relationships are observed for both level and duration of exposure. Because an epidemiologic study may fail to detect an exposure–response relationship for several reasons (e.g., a small range of observed exposure levels or exposure misclassification), the absence of an exposure–response relationship does not exclude a causal relationship.
Biologic plausibility. An inference of causality is strengthened by consistency with experimental data that show plausible biologic mechanisms. A lack of mechanistic data, however, is not a reason to reject causality.
Coherence. Other lines of evidence—for example, experimental animal studies, toxicokinetic studies, short-term tests, and mechanistic studies—may strengthen an inference of causality. The absence of other lines of evidence, however, is not a reason to reject causality.
Experimental evidence (from human populations). Experimental evidence is seldom available from human populations and exists only when conditions of human exposure are altered to create a “natural experiment” at different levels of exposure, or for medical treatments tested in randomized controlled trials with a sufficient follow-up period. Strong evidence for causality can be provided when a change in exposure brings about a change in disease frequency, for example, the decrease in lung cancer risk that follows cessation of smoking.
Analogy. Evidence for causality can be strengthened by information on an agent’s structural analogues.
Evaluating evidence of cancer in experimental animals.
The most common method for identifying potentially carcinogenic agents is a long-term bioassay in experimental animals. Exposures can be tightly controlled and monitored in animal bioassays, although animal responses may not correspond strictly to human responses. Experimental carcinogenesis research is based on the scientific assumption that agents causing cancer in animals will have similar effects in humans (NTP 2002). Accordingly, in the absence of adequate data on humans, it is biologically plausible and prudent to regard agents and mixtures for which there is sufficient evidence of carcinogenicity in experimental animals as if they presented a carcinogenic risk to humans (IARC 2004).
Criteria for evaluating evidence in experimental animals include the breadth of the tumor response—for example, the induction of tumors in multiple species or in independent studies. When evaluating studies in experimental animals, it is important to incorporate scientific experience and current understanding of long-term carcinogenesis studies in laboratory animals and to consider the following points:
Adequacy of the experimental design and conduct (e.g., route, schedule, and duration of exposure; species, strain, sex, and age of animal; duration of follow-up; tissues examined)
Statistical significance of the observed tumor response, survival-adjusted analyses, and concerns about false positives or false negatives
Supporting information from proliferative lesions (hyperplasia) at the site of neoplasia or in other experiments (same lesion in another sex or species)
Progression (or lack thereof) from benign to malignant neoplasia as well as from preneoplastic to neoplastic lesions; where progression is a possibility, to assume that benign neoplasms have the potential to progress, and to combine benign and malignant tumors thought to represent stages of progression in the same organ or tissue
Occurrence of common versus uncommon neoplasia
Concurrent control tumor incidence, as well as the historical control rate and variability for a specific neoplasm (especially in the case of uncommon neoplasia)
Multiplicity in site-specific neoplasia
Latency in tumor induction
Metastases
Presence or absence of dose–response relationships
Structure–activity correlations
Genetic toxicity at the site of neoplasia.
Evaluating mechanistic data.
Consideration of mechanistic data has the potential to improve the analysis of studies in both humans and experimental animals. Elucidation of the mechanisms of carcinogenesis can give insight into the biology of cancer and help identify stages where intervention may be possible.
In evaluating human studies, mechanistic data contribute to the discussion of biologic plausibility when evaluating whether an observed association is causal. If the series of precursor events leading to tumors in humans is understood, the observation of tumor precursors in exposed humans can provide strong support for a carcinogenic hazard.
In evaluating experimental animal studies, mechanistic studies can provide data to address the question of correspondence of response between animals and humans. This implies sufficient data to identify the mechanisms contributing to the induction of the observed animal tumors and to determine whether analogous mechanisms may be operative in humans. If this determination is based on tumor site concordance between animals and humans, it is important to discuss and document the support for this assumption, because it is not valid in general.
There has been concern recently that some assessments have been based on untested or incomplete mechanistic hypotheses. To illustrate the difficulty in drawing conclusions from mechanistic data, plausible-sounding mechanistic conclusions had been made simultaneously that 1,3-butadiene both is (Melnick and Kohn 1995) and is not (Bond et al. 1995) carcinogenic to humans. Early guidance for evaluating mechanistic data focused on the question of whether the available studies support a hypothesized sequence of precursor events [International Programme for Chemical Safety (IPCS) 1999; U.S. EPA 1999]. Patterned after the Hill criteria, these approaches looked at associations between precursor events and tumors in experimental animals.
A problem can arise, however, if insufficient consideration is given to the possibility that more than one mechanism might be operating. This can lead to premature and false conclusions, because associations observed between one mechanism’s precursor events and tumors cannot by themselves rule out the operation of other mechanisms. (This is similar to the problem of confounding factors in epidemiology: There may be strong associations between exposure and disease, but if confounding factors are not examined thoroughly, the associations may be spurious.) It is necessary to consider that an uneven level of experimental support for different mechanistic hypotheses can reflect disproportionate resources spent on investigating one hypothesis and does not exclude the contribution of other mechanisms. More recent guidance puts greater emphasis on investigating whether multiple mechanisms may be operating and asks that three questions be addressed for each potential mechanism: Is it sufficiently supported in test animals, is it relevant to humans, and which populations or life stages can be particularly susceptible? (U.S. EPA 2003).
IARC’s Practice for Carcinogen Identification and Evaluation
The IARC Monographs are scientific evaluations developed by international working groups of expert scientists. These evaluations provide the scientific support for public health measures implemented by many national and international health agencies around the world. The process for developing the IARC Monographs is reviewed and updated from time to time. To promote better understanding of the process, here we discuss the principles and procedures currently in use.
Selection of agents and exposures for evaluation.
Agents are selected for evaluation based on evidence of human exposure and some evidence or suspicion of carcinogenicity. Agents and exposures can be reevaluated if significant new data become available. Periodically, IARC convenes advisory groups to advise on priorities for future evaluation or reevaluation (IARC 2003). These advisory groups consist of scientists from national and international health agencies and research institutions, striving to include scientists from many countries. Seeking such advice is meant to ensure that the IARC Monographs reflect the current state of scientific knowledge and remain relevant to the needs of national health agencies and the research and public health communities.
Structure of the IARC Monographs.
The IARC Monographs are published as a series of volumes. Each volume contains one or more monographs, which can cover either a single agent or a group of related agents. There is a standard structure, which has evolved to include sections on the following:
1. Exposure data
2. Studies of cancer in humans
3. Studies of cancer in experimental animals
4. Other data relevant to an evaluation of carcinogenicity and its mechanisms
5. Summary of data reported and evaluation
5.1. Exposure data
5.2. Human carcinogenicity data
5.3. Animal carcinogenicity data
5.4. Other relevant data
5.5. Evaluation
6. References.
Sections 1–4 provide a critical review of the pertinent scientific literature. Section 5 includes summaries of the scientific data and the evaluations developed by the working group.
The preamble to the IARC Monographs (IARC 2004) opens each volume and discusses the principles and procedures used in developing the IARC Monographs, including the scientific criteria that guide the working group’s evaluations. The preamble promotes consistency across different working groups and provides insight into the review process and evaluation criteria.
The critical review of the pertinent scientific literature.
The critical review of the pertinent peer-reviewed scientific literature (sections 1–4) considers all published articles, plus articles accepted for publication. Reports and documents from national and international government agencies are considered if they are publicly available. Consensus reports in the published literature are also considered, subject to the same critical review as other articles, including consideration of the composition and balance of the panel that produced the consensus. Research that is not publicly available, including articles in preparation, is not considered.
The critical review provides a brief, separate, factual synopsis of each study, summarizing the study’s design and results. After each study synopsis is a separate assessment by the working group of the study’s strengths and limitations. These comments, which generally appear in square brackets, provide insight into the working group’s reasoning by revealing the factors that might affect their interpretation or evaluation of that study.
The evaluations.
The working group develops its evaluations through a series of distinct steps (Figure 2). This stepwise evaluation process provides insight into the working group’s reasoning by revealing the weight given to each line of evidence. For each agent being evaluated, the process begins with separate evaluations of the evidence of cancer in humans and cancer in experimental animals, each choosing one of four descriptors: “sufficient evidence,” “limited evidence,” “inadequate evidence,” or “evidence suggesting lack of carcinogenicity” (for definitions of these terms, see IARC 2004).
These two partial evaluations are combined into a preliminary default evaluation that the agent is “carcinogenic to humans” (group 1), “probably carcinogenic to humans” (group 2A), “possibly carcinogenic to humans” (group 2B), “not classifiable as to its carcinogenicity to humans” (group 3), or “probably not carcinogenic to humans” (group 4). Then the mechanistic and other relevant data are considered to determine whether the default evaluation should be modified. This determination considers the strength of the mechanistic evidence and whether the mechanism operates in humans. The final overall evaluation is a matter of scientific judgment, reflecting the weight of the evidence derived from studies in humans, studies in experimental animals, and mechanistic and other relevant data. In considering all relevant scientific data, the working group may assign the agent to a higher or lower group than the default would indicate.
The goal is a consensus evaluation by the working group. The evaluation will include a synopsis that discusses the rationale for the conclusions. If the working group is not able to reach consensus, the overall evaluation is determined by majority vote. In this case, the synopsis will present the differing scientific positions, the data that support or are inconsistent with each position, and the rationale for the majority position. The evaluation can identify research needed to test different hypotheses, especially those that have not received adequate research attention.
IARC Monograph meetings.
Each volume, which may contain one or more monographs, is developed by a working group at an IARC Monograph meeting. Each year, IARC generally convenes three separate working groups on different topics. Meetings are announced on the Internet (IARC 2004).
Before each meeting, IARC staff searches and collects the pertinent scientific literature and makes it available to the working group. Working group members critically review the literature and write first drafts of sections 1–4 on exposure, cancer in humans, cancer in experimental animals, and other relevant data, respectively. IARC collects and formats these first drafts for review at the meeting.
The objectives of the meeting are review and consensus. The first days of the meeting are devoted to subgroup work. Four subgroups, each responsible for one section, peer review the individual members’ drafts, develop a joint revised draft (Figure 3), and then write the summaries that become section 5. For each agent, the subgroup on cancer in humans proposes a partial evaluation of the human evidence, and the subgroup on cancer in experimental animals proposes a partial evaluation of the animal evidence (Figure 2). The subgroup on other relevant data characterizes the mechanistic evidence using terms such as “weak,” “moderate,” or “strong” and discusses whether the mechanisms are likely to be operative in humans.
In the final days of the meeting, the subgroups come together in plenary session. The entire working group peer reviews and reaches consensus on the critical reviews in sections 1–4 and discusses and reaches consensus on the summaries and partial evaluations proposed by the subgroups. Then the working group as a whole develops and reaches consensus on an overall evaluation of each agent.
Declaration of interests by participants at IARC Monograph meetings.
IARC, part of the WHO, follows WHO procedures with respect to declaration of interests by participants in its meetings (WHO 2004). Each potential participant is asked to declare, in confidence,
any interests that could constitute a real, potential or apparent conflict of interest, with respect to his/her involvement in the meeting or work, between a) commercial entities and the participant personally, and b) commercial entities and the administrative unit with which the participant has an employment relationship.
The WHO defines conflict of interest to mean “the expert or his/her partner, or the administrative unit with which the expert has an employment relationship, has a financial or other interest that could unduly influence the expert’s position with respect to the subject-matter being considered.” An apparent conflict of interest exists when “an interest would not necessarily influence the expert but could result in the expert’s objectivity being questioned by others” (WHO 2004).
The WHO provides several examples of financial or other interests, including competing interests, that should be declared. The examples include consulting work or research support that can pose as much of a conflict as employment or stock ownership. In addition, a conflict can arise from an expectation of future support, to the expert individually or to the expert’s organization. On the other hand, an interest that is no longer current becomes immaterial after a period of time. In the case of research support given to an expert’s organization, determining whether the conflict warrants some limitation on participation includes consideration of several factors, such as the level of funding from interested parties, whether the organization’s research or positions depend on such funding, and whether such funding supports the expert’s own research or position.
Before an invitation is extended, each potential participant submits a declaration of interests (WHO 2004). IARC assesses these interests to determine whether there is a conflict that warrants some limitation on participation. Each participant updates the declaration of interests at the opening of the meeting. Interests pertinent to the subject matter of the meeting are disclosed to the meeting participants and in the published IARC Monograph.
Participants in IARC Monograph meetings.
Two principles govern the selection of working group members: to invite the best-qualified experts, and to avoid real or apparent conflicts of interest. Consideration is given also to demographic diversity. Members are chosen on the basis of knowledge and experience, which can come from research into the specific agents to be evaluated or from general experience in conducting or evaluating epidemiologic or experimental studies. Members chair the meeting and the subgroups and are the only participants who vote on the overall evaluations, if a vote is needed. Working group members are invited to serve in their individual capacities as scientists and not as representatives of their government or any organization with which they are affiliated.
A difficulty arises when an expert with relevant knowledge and experience has a real or apparent conflict of interest. This issue has become more visible in recent years because commercial interests sponsor many epidemiologic and experimental studies, and some investigators develop a history of receiving research support from interested parties. The selection of experts with real or apparent conflicts of interest can erode confidence in the integrity and impartiality of the results. This creates a tension between two competing ideals: evaluations developed by the best-qualified experts versus evaluations whose integrity and impartiality are above question.
The new category of invited specialist allows the IARC Monographs to achieve both ideals. An invited specialist is an expert with critical knowledge and experience who is recused from certain activities because of a real or apparent conflict of interests. These activities include serving as meeting chair or subgroup chair, drafting text that discusses cancer data or contributes to the evaluations (sections 2–4 and 5.2–5.5), and participating in evaluations reached by either consensus or vote. Invited specialists are available during subgroup and plenary discussions to contribute the benefit of their knowledge and experience. Invited specialists also agree to serve in their individual capacities as scientists and not as representatives of any organization or interest. Their conflicting interests are fully disclosed to the meeting participants and in the IARC Monograph. In this way, the meeting can include the best-qualified experts, and the evaluations are developed and written by experts with no real or apparent conflicts of interest.
In the interest of transparency, a limited number of scientifically qualified observers are welcome to attend IARC Monograph meetings. Consideration is given to admitting observers from different constituencies with differing interests. The main role of observers is to serve as sources of first-hand information from the meeting to the organizations that sponsor them. Observers can play a valuable role in ensuring that all published information and scientific perspectives are considered. At the meeting, the meeting chair and subgroup chairs may grant observers the opportunity to speak. Observers do not serve as meeting chair or subgroup chair, draft any part of an IARC Monograph, or participate in the evaluations. Observers may be presumed to serve the interests of the organizations that nominate and sponsor them, and these interests are fully disclosed to the meeting participants and in the IARC Monograph. Observers are asked to agree to ethics guidelines that include a requirement not to lobby working group members, both before and during the meeting. A challenge for IARC is to increase the diversity of observers in view of the unequal resources available to potential observers from different sectors.
There are two other categories of participants. Representatives of national and international health agencies (e.g., the U.S. National Cancer Institute) often attend and provide independent assurance and guarantee of the integrity of the IARC Monographs. Scientists employed by IARC comprise the IARC Secretariat. The secretariat hosts the meeting and drafts text or tables when requested by the meeting chair or subgroup chair. To facilitate consistency across different IARC Monographs, members of the secretariat serve as rapporteurs and answer questions about the preamble. After the meeting, the secretariat reviews all data cited in the text and tables to ensure scientific accuracy and clarity and publishes the finished volume. Representatives and secretariat participate in discussions but do not vote on the evaluations; thus, the evaluations are determined by working group members only.
Table 1 summarizes the roles of working group members, invited specialists, observers, representatives, and the secretariat. The published volume identifies all participants by name and affiliation and identifies the meeting chair and subgroup chairs.
Inclusion of all scientific views.
When planning a meeting, it is important to identify the pivotal issues in advance and convene a working group that includes all scientific views. There are two reasons for this. First, a balanced representation of all scientific views promotes confidence that all hypotheses and data have been considered fully and evenly. Second, identifying the pivotal issues can uncover issue-related conflicts that would not otherwise be apparent but may warrant some limitation on participation. For example, the pivotal issue of whether a particular mechanism is operative in humans not only affects the evaluation of the agent being considered but also can set a precedent for other agents that operate through similar mechanisms. Identifying pivotal issues and related agents can be difficult, but doing so will promote confidence in the working group’s objectivity.
Freedom from interference.
The working group should be free from all attempts at interference, before and during the meeting. This includes lobbying by interested parties, receipt of written materials from interested parties, and meals, drinks, social events, or other favors offered by interested parties. Attempts at interference outside the meeting are particularly insidious, because they occur outside the view of other participants. Such interference destroys transparency and invites suspicion. Working group members have assumed the responsibility to safeguard the integrity of their work by resisting any attempt at interference. To aid them in this responsibility, working group members are reminded not to discuss the subject matter of the meeting with those outside the meeting and are asked to report all attempts at interference.
The Future of Carcinogen Identification and Evaluation
The future of carcinogen identification will be one of continuing evolution to reflect changes in the underlying science. Future evaluations will continue to consider mechanistic data to aid in interpreting experimental animal results. The task will be not only to get the right answer based on publicly available scientific evidence, but also to build a broad-based scientific consensus around the answer. When sufficient data are available to identify a mechanism of carcinogenesis, these data will also be the key to identifying susceptible populations and life stages, including the prenatal and early postnatal periods. Another implication of using mechanistic data will be carcinogen identifications that are based on scientific inference in the absence of tumor studies in humans or experimental animals.
In addition to changes in the science, the milieu in which carcinogens are identified is changing rapidly. A key challenge is to maintain independence against the increasing demand for access and influence by advocates on all sides. Another is keeping current, as more agents need evaluation because of new scientific data or understanding.
In its practice of carcinogen identification, IARC is committed to the highest standards of scientific and ethical conduct. For > 30 years the IARC Monographs have achieved a reputation unmatched for thoroughness, accuracy, and integrity. The principles and procedures discussed here should ensure that this reputation remains solid well into the future.
Figure 1 The risk assessment paradigm.
Figure 2 The evaluation process used in the IARC Monographs.
Figure 3 The development of drafts at IARC Monograph meetings.
Table 1 Roles of participants at IARC Monograph meetings.
Working group members Invited specialists Observers Representatives IARC secretariat
Before the meeting
Draft section 1 (exposure data) × × ×
Draft sections 2–4 × ×
During subgroup sessions
Serve as subgroup chair ×
Peer review members’ drafts (sections 1–4) × × × ×
Draft summary of section 1 (section 5.1) × × ×
Draft summaries of sections 2–4 (sections 5.2–5.4) × ×
Propose evaluations of human, animal, or mechanistic data ×
During plenary session
Serve as meeting chair ×
Peer review subgroup drafts and summaries × × × ×
Discuss subgroup evaluations and develop the overall evaluation (section 5.5) × × ×
Vote on overall evaluation, if needed ×
==== Refs
References
Bond JA Recio L Andjelkovich D 1995 Epidemiological and mechanistic data suggest that 1,3-butadiene will not be carcinogenic to humans at exposures likely to be encountered in the environment or workplace Carcinogenesis 16 2 165 171 7859344
Cal/EPA 2004. Proposition 65. Sacramento, CA:California Environmental Protection Agency. Available: http://oehha.ca.gov/prop65.html [accessed 10 May 2004].
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IARC 2003. Report of An Ad-Hoc IARC Monographs Advisory Group on Priorities for Future Evaluations. Internal report no. 03/001. Lyon, France:International Agency for Research on Cancer. Available: http://monographs.iarc.fr [accessed 10 May 2004].
IARC 2004. Preamble to the IARC Monographs. Lyon, France:International Agency for Research on Cancer. Available: http://monographs.iarc.fr [accessed 10 May 2004].
IPCS 1999. IPCS Workshop on Developing a Conceptual Framework for Cancer Risk Assessment. IPCS/99.6. Geneva:International Programme for Chemical Safety.
Kleihues P Stewart BW eds. 2003. World Cancer Report. Lyon, France:International Agency for Research on Cancer.
Melnick RL Kohn MC 1995 Mechanistic data indicate that 1,3-butadiene is a human carcinogen Carcinogenesis 16 2 157 163 7859343
NRC (National Research Council) 1983. Risk Assessment in the Federal Government: Managing the Process. Washington, DC:National Academies Press.
NRC (National Research Council) 1994. Science and Judgment in Risk Assessment. Washington, DC:National Academies Press.
NTP 2002. Report on Carcinogens. 10th ed. Research Triangle Park, NC:National Toxicology Program. Available: http://ntp-server.niehs.nih.gov/NewHomeRoc/AboutRoc.html [accessed 10 May 2004].
U.S. EPA 1986. Guidelines for Carcinogen Risk Assessment. Fed Reg 51(185):33992–34003. Available: http://www.epa.gov/ncea/raf [accessed 10 May 2004].
U.S. EPA 1999. Guidelines for Carcinogen Risk Assessment (Review Draft). NCEA-F-0664. Washington, DC:U.S. Environmental Protection Agency. Available: http://www.epa.gov/ncea/raf/cancer.htm [accessed 10 May 2004].
U.S. EPA 2003. Draft Final Guidelines for Carcinogen Risk Assessment. EPA/630/P-03/001A. Washington, DC:U.S. Environmental Protection Agency. Available: http://www.epa.gov/ncea/raf/cancer2003.htm [accessed 10 May 2004].
U.S. EPA 2004. U.S. EPA’s Process for IRIS Assessment Development and Review. Washington, DC:U.S. Environmental Protection Agency. Available: http://www.epa.gov/iris/process.htm [accessed 10 May 2004].
WHO 2004. Declaration of Interests for WHO Experts. Geneva:World Health Organization. Available: http://www.who.int/pcs/ra_site/docs/Declaration_of_interest.pdf [accessed 10 May 2004].
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7070ehp0112-00127515345339ResearchArticlesPesticide Product Use and Risk of Non-Hodgkin Lymphoma in Women Kato Ikuko 1*Watanabe-Meserve Hiroko 1Koenig Karen L. 1Baptiste Mark S. 2Lillquist Patricia P. 2Frizzera Glauco 3**Burke Jerome S. 4Moseson Miriam 1Shore Roy E. 11Department of Environmental Medicine, New York University of School of Medicine, New York, New York, USA2Bureau of Chronic Disease Epidemiology and Surveillance, New York State Department of Health, Albany, New York, USA3Department of Pathology, New York University Medical Center, New York, New York, USA4Department of Pathology, Alta Bates Summit Medical Center, Berkeley, California, USAAddress correspondence to I. Kato, Karmanos Cancer Institute, 110 East Warren Ave., Detroit, MI 48201 USA. Telephone: (313) 833-0715. Fax (313) 831-7806. E-mail:
[email protected]*Currently at Karmanos Cancer Institute/Department of Pathology, Wayne State University, Detroit, MI, USA
**Currently at Department of Pathology, Weill Medical College of Cornell University, New York, NY, USA.
We thank E. Weiskopf, J. Rocklin, D. Heimowitz, F. Grab, and E. Aziel for their technical assistance.
This study was supported by National Cancer Institute (NCI) research Grant CA 63550 to R.E.S and, in part, by National Institute of Environmental Health Sciences Center Grant ES00260 and NCI Center grant 5P30CA16087.
The authors declare they have no competing financial interests.
9 2004 3 6 2004 112 13 1275 1281 5 3 2004 3 6 2004 Publication 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. A population-based, incidence case–control study was conducted among women in upstate New York to determine whether pesticide exposure is associated with an increase in risk of non-Hodgkin lymphoma (NHL) among women. The study involved 376 cases of NHL identified through the State Cancer Registry and 463 controls selected from the Medicare beneficiary files and state driver’s license records. Information about history of farm work, history of other jobs associated with pesticide exposure, use of common household pesticide products, and potential confounding variables was obtained by telephone interview. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using an unconditional logistic regression model. The risk of NHL was doubled (OR = 2.12; 95% CI, 1.21–3.71) among women who worked for at least 10 years at a farm where pesticides were reportedly used. When both farming and other types of jobs associated with pesticide exposure were combined, there was a progressive increase in risk of NHL with increasing duration of such work (p = 0.005). Overall cumulative frequency of use of household pesticide products was positively associated with risk of NHL (p = 0.004), which was most pronounced when they were applied by subjects themselves. When exposure was analyzed by type of products used, a significant association was observed for mothballs. The associations with both occupational and household pesticides were particularly elevated if exposure started in 1950–1969 and for high-grade NHL. Although the results of this case–control study suggest that exposure to pesticide products may be associated with an increased risk of NHL among women, methodologic limitations related to selection and recall bias suggest caution in inferring causation.
case–control studymothballsNHLpesticides
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The U.S. Environmental Protection Agency (EPA) estimates that approximately 1.2 billion pounds of pesticides were used in the United States in 1999 (Donaldson et al. 2002), which was equivalent to 4.4 pounds per capita in the U.S. population. Of these pesticides, 76% were used in agriculture, 11% in other industries/governments, and 13% in homes and gardens; also, they were used by 77% of U.S. households and 1.2 million certified professional applicators (Donaldson et al. 2002). Despite a recent decline in overall usage after a marked increase in the 1950s and 1960s, and despite the fact that registrations of some pesticides found to have unacceptable toxicity have been canceled, there has been a concern about their long-term effects on human health, because some pesticides persist in human tissues, soil, foods, and the home environment (Muller 2000).
One of the major health concerns is carcinogenicity. More than 30 pesticides or groups of pesticides have been identified as possible carcinogens to humans by several national and international institutions [International Agency for Research on Cancer (IARC) 1987, 1991; U.S. EPA 2004]. Pesticides may increase the risk of cancer through various mechanisms. Some are known to be genotoxic (mutagenic) or tumor promotive, whereas others possess hormonal, immunotoxic, or hematotoxic properties (Acquavella et al. 2003; Dich et al. 1997). Furthermore, it has been reported that exposure to certain pesticides synergistically increases the mutagenicity of diet-derived heterocyclic amines (Wagner et al. 2003). Higher frequencies of chromosome aberrations, sister chromatid exchanges, and micronuclei have been observed in peripheral lymphocytes of pesticide applicators and certain groups of farmers (Bolognesi 2003; Maroni and Fait 1993). Because of these chromosome abnormalities, cancers in the hematolymphoid tissues [e.g., non-Hodgkin lymphoma (NHL), Hodgkin lymphoma, multiple myeloma, and leukemia] have been a central issue in the evaluation for potential health consequences of pesticide exposure. Particularly, NHL has received research attention because the recent rapid increase in its incidence parallels an exponential growth in pesticide use with a few decades of lag (Ries et al. 2003).
There have been extensive reviews (Acquavella et al. 1998; Dich et al. 1997; Maroni and Fait 1993; Morrison et al. 1992; Zahm and Ward 1998) on cancer risk associated with farming and pesticide exposure as well as a number of more recent articles on specific types of cancer and specific classes of pesticides (Blair et al. 1998; Buckley et al. 2000; Cantor et al. 2003; Hardell et al. 2002; Kogevinas et al. 1995; McDuffie et al. 2001; Meinert et al. 2000; Nanni et al. 1996; Schroeder et al. 2001; Waddell et al. 2001; Woods et al. 1987; Zahm et al. 1990; Zheng et al. 2001). However, the vast majority of those studies have focused only on occupational exposures, except for some childhood cancer studies in which parental exposures in and around the home were assessed (Buckley et al. 2000; Meinert et al. 2000; Zahm and Ward 1998). Because of the widespread use of these chemicals in and around the home and because of the longer time spent at home than at work, especially among women, information about pesticide use around the home is critical to obtain a better picture of the overall effects of pesticides in the general population. In this population-based case–control study in upstate New York, we attempted to address whether pesticide product use at home as well as at work is associated with increased risk of NHL among women.
Materials and Methods
Study population.
This study was designed as a population-based case–control study of incident NHL in the upstate counties of New York State (NYS; i.e., excluding New York City and surrounding counties) to examine the associations with several environmental exposures. The study population base consisted of women 20–79 years of age who lived in the defined area of NYS at any time during the case-ascertainment period. Males were excluded because a primary focus of the study was on hair dyes, which will be reported separately. Women with a prior history of any type of hematologic cancer were also excluded from the study population.
Cases were newly diagnosed with NHL during the 3-year period between 1 October 1995 and 30 September 1998 and were identified through a rapid case-ascertainment system coordinated with the NYS Cancer Registry. The completeness of case ascertainment was verified by linkages with the whole state cancer registry database and with state death certificates. From 722 initially identified eligible cases, we excluded 3.4% because their physician’s consent could not be obtained and an additional 4.2% because we could not find a valid contact address of the patients. Population-based controls were frequency matched to the projected age distribution of the cases and were selected from an age-stratified random sample from the NYS Department of Motor Vehicles (DMV) driver’s license files for those < 65 years of age, or from the Health Care Financing Administration (HCFA) beneficiary files for those ≥ 65 years of age. However, the frequency matching was only partially successful because of age-related differences in response rates. To increase comparability between cases and controls, we excluded cases < 65 years of age without a valid NYS driver’s license. No monetary incentives were offered for participation. Among those with valid address information who met all other eligibility criteria, the final participation rate in the study was 56% (n = 376, with a median age at diagnosis of 65 years) among the cases, 30% (n = 248) among the DMV controls, and 67% (n = 215) among the HCFA controls. The participation rate of cases and DMV controls was low in part because of a requirement by the NYS Department of Health institutional review board that they first be sent a study solicitation letter by the NYS Cancer Registry; only if they returned a signed consent form could we contact them for an interview. Verbal consent to participate in the study was approved by the New York University (NYU) School of Medicine institutional review board for the HCFA controls.
Demographic characteristics of the participants have been published elsewhere (Kato et al. 2002). Briefly, both the case and control participants were primarily white (98%), born in NYS (77%), and married (59%). Mean age at the index date (defined below) was 60.5 years for the cases and 54.6 years for the controls. More controls had a college education (61%) than did cases (45%). The proportion of smokers was similar in the two groups (22% in cases and 19% in controls). Family history of hematologic cancer was more common in cases (11%) than in controls (6%).
Data collection.
Cases and controls were interviewed over the telephone by an interviewer at NYU who was not aware of the case–control status of the participants. The structured questionnaire was developed specifically for this study. Next of kin were interviewed for the cases (20.5%) and controls (3.2%) who were found to be deceased or medically incapable of participating in an interview. The most common surrogates were children (47%), followed by husbands (27%). In advance of the interview, each participant was mailed a package containing a letter outlining the study and a booklet displaying lists of product/chemical names to be discussed in the interview. The median time between NHL diagnosis and the telephone interview was 1.2 years, ranging from 2 months to 3.3 years. Information was collected on the lifetime history of living or working on a farm, exposures to pesticides from other types of jobs, and the lifetime history of pesticide product use in and around the home. For the subjects who worked on a farm, we asked whether pesticides were used on the farm and whether the pesticides were applied by the subject herself. When the subject applied or handled pesticides herself, details about pesticides (name and duration) were elicited. We asked about other occupational exposures in three separate categories: insecticides, herbicides, and wood preservatives. For each category, the number of hours exposed per day, week, month, or year and total duration of employment were elicited. We asked about pesticide product use in and around the home in 12 separate categories principally based on the purposes of use: to control ants, cockroaches/silverfish, bees, flies/mosquitoes, moths (mothballs), or termites; to treat indoor plants, trees/shrubs, plants in the garden/outdoor pots, or lawns; to control head lice; and use of an indoor/outdoor fogger. For each group of pesticide products, information on application methods (indoor/outdoor and by self/others), year or age first used, year or age last used, and average frequency of use in a year/season was elicited. Based on the average frequency and total duration of use, we calculated the cumulative number of uses for each product or group of products as well as for each mode of application.
Classification of NHL.
Copies of medical records of the cases were obtained and reviewed to confirm their diagnosis and eligibility. In addition, to allow for a uniform classification of NHL, pathology slides were obtained and reviewed by an expert hematopathologist at NYU (G.F.). It was possible to complete the review for 268 cases (71%). Approximately 26% of these slides were sent to a second expert hematopathologist consultant (J.S.B.) to resolve discrepancies between the original diagnoses and the review diagnoses at NYU. In our review, NHL was classified according to both the REAL (Revised European-American Classification of Lymphoid Neoplasms) system (Harris et al. 1994) and the Working Formulation (Weisenburger 1992). Classification by immunophenotype was based on the final REAL categories from our pathologic review whenever available, otherwise on the immunophenotype obtained at the original institution. If neither was available (9.8%), follicular lymphomas by histology were considered B-cell in type, and the others were left unclassified. As a result, 322 were considered B-cell, 25 T-cell, and 29 unclassified. Lymphomas were also grouped by grade based on the Working Formulation: 54 low grade, 189 intermediate grade, 25 high grade, and 8 unclassified.
Statistical analysis.
In order to eliminate reported exposures that occurred after diagnosis of NHL and to allow a minimum latency (lag) period of 1 year from exposure to diagnosis for each case, we set an index date, after which any exposures should be excluded from the analysis. The index date was defined as the date 1 year before diagnosis. To ensure comparable exposure assessment periods between cases and controls, within 5-year age strata we randomly assigned lag periods (i.e., index dates) to controls corresponding to the frequency distribution of lags among the cases of comparable age. Any exposures and events reported after their index dates were discounted for both cases and controls. The average lag time between the index date and the date of interview was 2.5 years for both cases and controls.
The odds ratios (ORs) and 95% confidence intervals (95% CIs) for NHL according to various indices for pesticide exposure were calculated using the unconditional logistic regression model (Breslow and Day 1980), adjusted for selected covariates: four continuous variables (age at index date, year of interview, and frequencies of use of pain-relieving drugs and of cortisone injections) and five indicator variables (college education, surrogate interview, family history of hematologic cancer, and personal history of eczema/hives and of antihistamine use). These covariates were selected according to the following three criteria: a) known risk factors for NHL (age and family history of hematologic cancer); b) factors that influence data quality (education, surrogate status, and year of interview); and c) potential risk factors associated with pesticide product use/farm work (frequencies of use of pain-relieving drugs and of cortisone injections and personal history of eczema/hives and of antihistamine use) (Holly et al. 1999; Kato et al. 2002; McWhorter 1988). Whenever possible, the ORs were calculated for ordered categories (in quartiles, tertiles, or halves) of cumulative number of uses or total duration of exposure, compared with nonusers or no-exposure groups. Tests for linear trend in the logit of risk with increasing frequency or duration of exposure were performed using natural-log–transformed continuous values. Selected analyses were repeated for subtypes of lymphoma. All statistical analyses were conducted using SAS software (SAS Institute, Cary, NC).
Results
First, we examined the associations with potential exposure to pesticides at work (Table 1). There was a marginal trend in risk of NHL with the number of years worked on a farm (p = 0.053). This trend became more significant (p = 0.03) when only farm work involving pesticide use was considered. The OR associated with such farm work of ≥ 10 years was 2.12 (95% CI, 1.21–3.71). Applying or handling pesticides by the women themselves was not associated with appreciably increased risk. Furthermore, < 50% of the women who applied/handled pesticides could recall the product names; thus, evaluation by chemical class of pesticides was not feasible. When types of crops handled by the study subjects were considered, the OR appeared to be higher for vegetables, grain, and other crops than for fruits and flowers, although none of them was statistically significant. Exposure to pesticides was also reported under various types jobs other than farming (n = 61). About half of these jobs (n = 32) involved a passive low level of exposure to periodic building/lawn treatment with pesticides. Common jobs in this category were restaurant jobs, office work, and miscellaneous other jobs. The second category of jobs (n = 9) represented a possible intermediate level of exposure, for example, retail jobs handling pesticides, crop-processing factory work, or working in an office adjacent to a farm or florist. The final category of jobs represented occupations that may have entailed direct exposure to pesticides through application (n = 20). This consisted of structure maintenance or environmental control jobs, horticultural work, veterinary jobs, and wood-handling factory jobs. The number of hours of actual exposure was reported to be much shorter for the low-exposure job category (median, 12 hr/year), compared with those in the intermediate- and high-exposure job categories (medians, 192 hr/year and 55 hr/year, respectively). With increasing cumulative number of hours exposed to pesticides at these jobs other than farming, there was a marginal increasing trend in risk of NHL (p = 0.08). When farming and other jobs associated with pesticide exposure were combined, the total duration at any of these jobs was significantly positively associated with the risk of NHL (p = 0.005). This increase in risk of NHL was more pronounced when exposure started in 1950–1969 than when it first occurred before or after this period.
The ORs and 95% CIs associated with pesticide use in and around the home are presented in Table 2. We grouped products based on the target pest. As a result, insecticides were categorized into those for crawling insects (ants, cockroaches/silverfish, and termites), for flying insects [bees, flies/mosquitoes, and moths (except mothballs) and indoor/outdoor fogger], mothballs, and antilice products. Products to treat indoor plants, trees/shrubs, or plants in garden/outdoor pots were combined into one group, that is, fungicides/plant pesticides. Products to treat lawns were considered herbicides/lawn pesticides. Products to control moths were assumed to be mothballs if they were used exclusively indoors; otherwise, they were counted in the categories for the flying insects. Correlations among these groups of home pesticide products ranged from –0.07 to 0.27. For all products combined, there was a linear increase in risk of NHL with increasing cumulative number of uses (p = 0.004). The positive trend was observed for most of the products groups, except for the herbicide and fungicide groups. Logistic regression for individual product groups with simultaneous adjustment for use of all other products revealed a significant positive association of NHL with mothballs (p = 0.03) and a marginally significant association with insecticides for flying insects/foggers (p = 0.07). When no-exposure groups were excluded from the trend analyses, the regression coefficient for mothballs approached zero, whereas those for the others changed minimally. When time of first use was analyzed for all household pesticide products combined, the association with NHL was significant only for pesticide use started during 1950–1969 (OR = 2.42; 95% CI, 1.16–5.02), whereas weaker associations were found for pesticide use started before 1950 or after 1969 (OR = 1.42 and 1.25, respectively; data not shown).
For pesticides for flying and crawling insects and for all pesticide products combined, we calculated the ORs for NHL according to application methods that were separated into three groups based on presumed exposure intensity, namely, pesticides applied by the respondent, applied indoors by others, or applied outdoors by others (Table 3). For individual groups of pesticide products, we also adjusted for other pesticide use via the same application method in these analyses. The positive linear trend with cumulative number of uses was most evident when pesticides were applied by women themselves for all products combined (p = 0.01), but the risk associated with insecticides for flying insects was only significant when they were applied outdoors by others. The association with mothballs was virtually the same when exposure occurred through self use or use by others, although a limited number of subjects were exposed through use by others (data not shown).
We also examined combined and separate effects of occupational and home pesticide exposure. To study combination effects, we divided exposures into two levels using the medians: 10 years for duration of jobs associated with pesticide exposure and 70 times for cumulative number of uses of any household pesticide products. The OR was 2.33 (95% CI, 0.93–5.85) for the subjects with higher exposures for both (n = 54), 1.46 (95% CI, 0.72–2.98) for those with higher exposure only at home or only at job (n = 381), and 1.00 (95% CI, 0.49–2.04) for those who had lower exposure for both or combinations of no exposure and lower exposure at home and job (n = 354), compared with the subjects with neither exposure (n = 48), and this trend was statistically significant (p = 0.005). When the subjects were limited to those without any occupational exposure to pesticides (n = 648), the association with cumulative number of uses of any type of home pesticide products remained highly statistically significant (p = 0.005). The number of women who were not exposed to any home pesticide products was too small (n = 54) to analyze the effects of occupational exposure separately. However, simultaneous adjustment for home pesticide use did not affect the association with occupational pesticide exposure (p = 0.01).
Table 4 presents the results of analysis by subtype of NHL according to levels of total pesticide exposure from work and around the home. There were no clear differences in trends in the ORs between B-cell and T-cell subtypes, but the increasing risk of NHL with the number of years worked in pesticide-related jobs and with the cumulative number of any pesticide product uses around the home was most pronounced for high-grade lymphoma (p < 0.001 and p = 0.002, respectively).
Discussion
The results of this case–control study suggest that exposure to pesticide products may lead to an increased risk of NHL among women. This finding was supported by the dose–response relationship observed with length of exposure, cumulative number of uses, and potential intensity of exposure.
Compared with studies using biologic or environmental samples at single time points, a questionnaire-based study has an advantage in the assessment of long-term exposure by reconstructing the whole personal history. However, it also has limitations. First, there may be bias in recall: cases with serious disease may be likely to report hypothesized exposures more completely than controls in good health. This especially may occur when there is enhanced public health concern about an exposure (Infante-Rivard and Jacques 2000; Weinstock et al. 1991), as may be the case for pesticides.
Obtaining information on specific chemicals over a long period of time is challenging, given the large number of products on the market, but is crucial when exposure effects may be cumulative. For nonoccupational exposure, Teitelbaum (2002) has suggested that asking about treatments for specific pest problems may be an effective way to help subject recall, a practice we implemented in designing our questionnaires. Notably, reasonable correlations have been observed between self-reported household chemical use and measurements of pesticides and their metabolites in urine of household members (Kieszak et al. 2002) and in indoor air (Van Winkel and Scheff 2001). Therefore, this type of questionnaire design seems useful in the assessment of household pesticides, at least for recent exposure. One shortcoming of our assessment of nonoccupational pesticide exposure is that we did not include dietary exposure, which may contribute a substantial fraction of pesticide exposures (Whitmore et al. 1994; Yess et al. 1991). However, mis-classification of exposure due to the omission of dietary sources is most likely to be nondifferential because many foods are known to contain pesticide residues (Yess et al. 1991).
It has been suggested that self-reported occupational pesticide exposure tends to overestimate exposure (Daniels et al. 2001; Meinert et al. 2000) because people often do not know for sure about actual chemical contents used at their work places. Farmers may be an exception (Blair and Zahm 1990), but indeed fewer than half of the women who applied pesticide themselves in this study could recall at least one of the product names they used. This proportion appears to be lower than in farmer studies (Dosemeci et al. 2002; Zahm et al. 1993) but may be because most of the farm work was in the distant past (median interval between last farm work and interview was 37 years, and median duration of farm work was only 8 years). Poor recall may also account for our failure to detect an excess risk among women who applied or handled pesticides. However, reentry to areas that were recently treated with pesticides for harvesting may result in greater cumulative exposure to pesticide residues than application itself (Garcia 2003); Coronado et al. (2004) recently reported that detectable levels of pesticide metabolite were not higher among workers who were engaged in mixing, loading, or applying pesticide formulations than among those who did not perform these tasks, contrary to expectation. Some investigators have found that including information from surrogates biases the results (Blair and Zahm 1990; Waddell et al. 2001), but when we limited our analysis to the subjects themselves, the strength of the associations remained almost the same as those observed in the entire sample.
Finally, the relatively low overall participation rate in this study raises issues of selection bias and of generalizability of the results. The probable reasons for the lower response rates among the DMV controls and the cases have been discussed elsewhere (Kato et al. 2002). Cases in this study were similar in age distribution to all the cases diagnosed in NYS during the same time period, but white and married women were overrepresented in both the case and control groups. Although we do not have external data to estimate the magnitude of selection bias, the results of hypothetical sensitivity analyses based on a selection bias factor defined by Rothman and Greenland (1998) suggest that the ORs obtained in this study are more likely to have been underestimated than overestimated. This relies on an assumption that exposed controls were more likely to respond to this survey than were nonexposed controls because both the study invitation letter and the study packet (product list) indicated that pesticides were one of our major research interests, whereas this selection should play a minor role among the cases who were already motivated because of their diagnosed disease. In addition, the DMV controls, who were < 65 years of age and had a lower overall participation rate than cases, may have been more motivated to participate in research related to environmental issues and therefore may have had better recall of pesticide exposure. This would tend to counterbalance the hypothesized biased recall among cases discussed above, unless such motivated people tend to live in better housing conditions that require less use of pesticides.
It is possible that pesticide use is a marker for other possible causative factors for NHL. For instance, occupational exposure to pesticides is often accompanied by exposure to other possible hazardous substances, such as solvents, fuels, and dusts (Maroni and Fait 1993; Morrison et al. 1992), that have been associated with increased NHL risk (Mao et al. 2000; Rego 1998). Similarly, people who use pesticides in and around the home may tend to use other household chemicals more often than those who do not. Another possibility is that pesticide use is an indicator of exposure to insects that may act as vectors to transmit viruses and bacteria. Certain types of viruses and bacteria have been identified as etiologic factors for NHL (Pagano 2002; Persing and Prendergast 1999).
Some earlier studies have pointed to associations between specific types of pesticide or pesticide groups and NHL risk (Dich et al. 1997). Three groups of pesticides have received special research attention: phenoxy herbicides and organochlorine and organophosphate insecticides. However, the results have been inconclusive because initial positive findings that were usually based on small numbers of subjects have often not been confirmed in larger studies or in multivariate analyses taking other pesticides into consideration (Cantor et al. 2003; Hardell et al. 2002; Morrison et al. 1992). In this study, we were not able to analyze any specific classes of chemicals because the women had limited recall of the particular chemicals used. Yet, the finding that pesticide use starting in 1950–1969 was associated with the most pronounced risk of NHL suggests a potential role of organochlorine insecticides that became widely available during this period. Alternatively, it may be a chance finding or simply indicate that a 25–45 year latency period is typical of pesticide-induced NHL.
A finding that is relatively unique in this study is the increased risk of NHL associated with mothball use, although a dose response was not clearly demonstrated among users. In the United States, major chemical constituents of mothballs are naphthalene or para-dichlorobenzene (p-DCB). These chemicals are also constituents of other common household products, such as air fresheners and solid toilet bowl deodorizers, which were not included in our questionnaire. Vapors from mothballs can be absorbed not only by inhalation but also by direct skin contact. Both of these chemicals are known to have hematotoxicity, including reports of hemolytic anemia (Hallowell 1959; Santucci and Shah 2000) and aplastic anemia (Harden and Baetjer 1978). In addition, in vitro and in vivo studies have demonstrated cytotoxicity and genotoxicity of these chemicals and their metabolites (Bagchi et al. 1998; Brusick 1986; Carbonell et al. 1991; Tingle et al. 1993), and carcinogenicity has been shown in animal models (Preuss et al. 2003; Umemura et al. 1992). Importantly, both naphthalene and p-DCB are among the most ubiquitously detected hazardous household chemicals in indoor air (Van Winkel and Scheff 2001), and concentrations in indoor air samples and urine samples of residents are correlated with reported mothball use (Kieszak et al. 2002; Van Winkel and Scheff 2001). This suggests that the association between mothball use and NHL merits further investigation.
We found that the association with pesticide exposure was most pronounced for high-grade lymphoma. The results for subtypes of NHL, however, should be interpreted cautiously because of small numbers of cases by subtype and because of the multiple comparisons involved. Data have been limited and inconsistent in earlier studies concerning types of lymphoma associated with pesticide exposure. There have been reports of relatively stronger associations of various types of agricultural insecticides with low-grade lymphoma (Nanni et al. 1996), carbamate insecticides with small lymphocytic lymphoma (Zheng et al. 2001), organophosphate pesticides and phenoxy herbicides with intermediate grade lymphoma (Waddell et al. 2001; Zahm et al. 1990), and phenoxy herbicides with B-cell lymphoma (Zahm et al. 1990). Schroeder et al. (2001) reported that a type of B-cell lymphoma that carries a specific chromosomal translocation was associated with occupational exposure to several types of pesticides. Finally, a case–control study of NHL among children revealed that the associations with parental occupational and household exposure to pesticides were more clear for higher grade lymphomas, whereas there were no differences between B- and T-cell types (Buckley et al. 2000). Although mechanistic bases for possible carcinogenic actions by pesticides are largely unknown, Schroeder et al. (2001) speculate that they are different from those for NHL linked to immunosuppression, based on their observation of a specific genetic change associated with pesticide exposure.
In conclusion, the results of our case–control study suggest an association of pesticide exposures with NHL. However, methodologic limitations related to selection and recall bias suggest caution in inferring causation. In order to draw more definitive conclusions and to make public recommendations, more research is needed, integrating various types of studies, such as surveillance for personal pesticide product use, development and application of new biomarkers for pesticide exposure, and assessment of genetic polymorphisms related to pesticide metabolism.
Table 1 ORs and 95% CIs for NHL associated with occupational pesticide exposures.
Type of exposure No. of cases/controls ORa 95% CI
Worked on a farm (years)
0 258/352 1.00 —
0.1–4 26/35 1.03 0.56–1.90
4.1–8 25/28 1.33 0.71–2.48
8.1–15 32/19 2.16 1.09–4.26
≥ 15.1 27/28 1.40 0.74–2.63
pb = 0.053
Worked on a farm using pesticides (years)c
< 10 30/35 1.09 0.61–1.95
≥ 10 43/32 2.12 1.21–3.71
p = 0.020
Applied pesticides on a farmc
Yes 25/24 1.18 0.59–2.38
Crops handledc
Fruit 30/35 1.18 0.65–2.13
Vegetables 62/55 1.50 0.96–2.35
Grain 40/33 1.53 0.87–2.69
Other 18/17 1.74 0.79–3.82
Other occupations with pesticide exposure (cumulative hours)
0 346/432 1.00 —
< 180 13/18 1.11 0.50–2.49
≥ 180 17/13 2.21 0.94–5.17
p = 0.077
Any occupations with pesticide exposure (years)
0 277/371 1.00 —
0.1–4.9 16/26 1.01 0.48–2.11
5.0–9.9 22/25 1.13 0.58–2.20
10–17.9 29/20 2.72 1.37–5.40
≥ 18.0 28/20 1.80 0.93–3.48
p = 0.005
Year of starting job with pesticide exposure
None 277/371 1.00 —
≤ 1949 39/35 1.24 0.71–2.16
1950–1969 32/21 2.86 1.50–5.45
1970–index date 23/35 1.19 0.63–2.26
a Adjusted for age at index date, family history of hematologic cancer, college education, surrogate status and year of interview, frequencies of use of pain-relieving drugs and of cortisone injections, history of eczema/hives, and history of antihistamine use.
b p-Values for trend based on natural-log–transformed continuous values.
c Compared with subjects who never worked on a farm.
Table 2 ORs and 95% CIs for NHL associated with home pesticide use.
Type of home pesticides Cumulative no. of uses No. of cases/controls ORa 95% CI
Insecticides for flying bugs or foggers 0 117/161 1.00 —
1–3 54/95 0.90 0.56–1.45
4–16 53/78 1.07 0.66–1.75
17–86 75/66 1.69 1.04–2.75
≥ 87 77/63 1.31 0.80–2.15
pb = 0.070
Insecticides for crawling bugs 0 124/171 1.00 —
1–3 63/81 1.16 0.73–1.83
4–15 51/77 0.76 0.46–1.24
16–46 71/65 1.40 0.86–2.28
≥ 47 67/69 1.18 0.73–1.92
p = 0.227
Anti-lice products 0 229/307 1.00 —
1 56/71 1.20 0.76–1.89
2–3 45/37 1.48 0.87–2.52
≥ 4 36/37 1.23 0.69–2.18
p = 0.224
Mothballs 0 217/354 1.00 —
1–10 39/24 2.19 1.21–3.97
11–25 34/32 1.36 0.77–2.42
26–44 38/27 1.82 1.01–3.29
≥ 45 39/25 1.33 0.70–2.52
p = 0.025
Herbicides/lawn pesticides 0 231/287 1.00 —
1–4 33/44 0.88 0.50–1.53
5–17 30/47 0.74 0.42–1.32
18–39 27/41 0.98 0.56–1.71
≥ 40 40/37 0.89 0.51–1.54
p = 0.658
Fungicides/plant pesticides 0 201/263 1.00 —
1–7 35/58 1.01 0.60–1.71
8–27 36/58 0.80 0.48–1.34
28–79 52/42 1.42 0.85–2.39
≥ 80 51/42 1.07 0.63–1.84
p = 0.596
Any type 0 23/33 1.00 —
1–20 60/135 0.81 0.40–1.68
21–69 91/105 1.62 0.80–3.31
70–184 94/102 1.38 0.67–2.82
≥ 185 108/88 1.62 0.79–3.32
p = 0.004
a Adjusted for age at index date, family history of hematologic cancer, college education, surrogate status and year of interview, frequencies of use of pain-relieving drugs and of cortisone injections, history of eczema/hives, and history of antihistamine use; use of each type of pesticide was adjusted for use of other types of pesticides combined.
b p-Values for trend based on natural-log–transformed continuous values.
Table 3 ORs and 95% CIs for NHL associated with selected home pesticides by application type.
Applied by self
Indoor application by others
Outdoor application by others
Pesticide type Quartilea No. of cases/controls ORb 95% CI No. of cases/controls OR 95% CI No. of cases/controls OR 95% CI
Insecticides for flying 1 42/48 1.60 0.92–2.77 28/40 1.10 0.59–2.02 27/51 0.68 0.37–1.28
bugs or foggers 2 39/52 1.09 0.62–1.91 13/26 0.95 0.42–2.12 37/54 1.07 0.62–1.84
3 48/43 1.73 0.97–3.03 29/29 1.76 0.90–3.42 43/42 1.56 0.88–2.78
4 46/44 0.97 0.55–1.71 28/27 1.28 0.65–2.52 53/32 2.37 1.32–4.24
pc = 0.653 p = 0.149 p = 0.005
Insecticides for 1 35/51 0.92 0.53–1.61 33/42 1.06 0.59–1.90 25/27 1.16 0.60–2.26
crawling bugs 2 36/50 0.82 0.46–1.44 32/44 1.16 0.65–2.08 25/27 1.12 0.57–2.20
3 48/40 1.65 0.94–2.88 38/39 1.14 0.62–2.07 25/27 0.87 0.42–1.78
4 42/44 1.27 0.72–2.24 38/36 1.52 0.84–2.75 31/21 1.69 0.85–3.38
p = 0.098 p = 0.327 p = 0.205
Any type 1 55/114 0.88 0.42–1.83 44/59 1.26 0.58–2.76 54/86 1.13 0.54–2.38
2 76/93 1.36 0.66–2.80 45/71 1.35 0.63–2.90 66/74 1.58 0.75–3.34
3 84/86 1.51 0.73–3.12 54/56 1.53 0.70–3.32 69/72 1.44 0.69–3.04
4 92/77 1.64 0.79–3.40 59/51 1.68 0.77–3.67 77/63 1.58 0.75–3.32
p = 0.012 p = 0.141 p = 0.177
a Quartile cutoff points for self, indoor by others, and outdoor by others are, respectively, 1–3, 4–15, 16–75, ≥ 76; 1, 2–3, 4–21, ≥ 22; and 1–2, 3–9, 10–48, ≥ 49 for insecticides for flying bugs/foggers; 1–3, 4–13, 14–41, ≥ 42; 1, 2–8, 9–24, ≥ 25; and 1, 2–8, 9–20, ≥ 21 for insecticides for crawling bugs; and 1–8, 9–36, 37–100, ≥ 101; 1–2, 3–9, 10–35, ≥ 36; and 1–6, 7–27, 28–80, ≥ 81 for any type.
b Adjusted for age at index date, family history of hematologic cancer, college education, surrogate status and year of interview, frequencies of use of pain-relieving drugs and of cortisone injections, history of eczema/hives, and history of antihistamine use, in comparison with a common reference group of subjects with no exposure to a given pesticide group through any application methods. Use of each type of pesticide was adjusted for use of other types of pesticides combined.
c p-Values for trend based on natural-log–transformed continuous values including level 0 (reference group with no exposure through any application methods).
Table 4 ORsa and 95% CIs for NHL associated with occupational and home pesticide exposure by type of NHL.
Level of pesticide exposure
0
1
2
3
Exposure type, NHL cell type, and grade No. of cases No. of cases OR 95% CI No. of cases OR 95% CI No. of cases OR 95% CI p-Value for trendb
At jobc
B-cell 238 32 1.06 0.61–1.82 24 2.48 1.21–5.08 24 1.77 0.90–3.48 0.014
T-cell 17 3 2.94 0.61–14.03 3 18.20 3.47–95.44 2 1.79 0.25–12.77 0.005
Low 107 16 1.03 0.52–2.04 17 3.99 1.80–8.80 12 1.80 0.79–4.11 0.007
Intermediate 151 16 1.04 0.53–2.05 10 1.64 0.67–4.04 10 1.34 0.56–3.20 0.276
High 14 4 3.07 0.79–11.98 2 7.27 1.31–40.40 5 6.11 1.46–25.57 < 0.001
No. of controls 371 51 20 20
At homed
B-cell 73 80 1.82 1.16–2.86 79 1.52 0.96–2.40 90 1.76 1.11–2.81 0.014
T-cell 5 5 4.22 0.88–20.25 3 1.56 0.27–8.93 12 3.58 0.83–15.42 0.077
Low 35 34 1.53 0.86–2.73 41 1.44 0.82–2.54 44 1.49 0.83–2.66 0.143
Intermediate 45 49 2.03 1.16–3.55 44 1.64 0.92–2.93 51 1.98 1.11–3.52 0.026
High 2 6 9.90 1.49–65.77 5 6.25 0.92–42.72 12 15.02 2.47–91.29 0.002
No. of controls 168 105 102 88
a Adjusted for age at index date, family history of hematologic cancer, college education, surrogate status and year of interview, frequencies of use of pain-relieving drugs and of cortisone injections, history of eczema/hives, and history of antihistamine use.
b p-Values for trend based on natural-log–transformed continuous values.
c Total number of years at job with pesticide exposure, defined as follows: 0, none; 1, < 10 years; 2, 10–17.9 years; 3, ≥ 18 years.
d Cumulative number of uses of any home pesticides, defined as follows: 0, 0–20; 1, 21–69; 2, 70–184; 3, ≥ 185.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.6980ehp0112-00128215345340ResearchArticlesA Bayesian Hierarchical Approach for Relating PM2.5 Exposure to Cardiovascular Mortality in North Carolina Holloman Christopher H. 1Bortnick Steven M. 1Morara Michele 1Strauss Warren J. 1Calder Catherine A. 21Statistics and Data Analysis Systems, Battelle Memorial Institute, Columbus, Ohio, USA2Department of Statistics, The Ohio State University, Columbus, Ohio, USAAddress correspondence to C. Holloman, Battelle Memorial Institute, 505 King Ave., Columbus, Ohio 43201-2693 USA. Telephone: (614) 424-4946. Fax: (614) 424-4611. E-mail:
[email protected] thank H. Özkaynak and R. Williams of the U.S. Environmental Protection Agency for many helpful suggestions regarding exposure modeling and health end points.
This work was funded by a Battelle internal research and development grant for fiscal year 2003.
The authors declare they have no competing financial interests.
9 2004 3 6 2004 112 13 1282 1288 23 1 2004 3 6 2004 Publication 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. Considerable attention has been given to the relationship between levels of fine particulate matter (particulate matter ≤ 2.5 μm in aerodynamic diameter; PM2.5) in the atmosphere and health effects in human populations. Since the U.S. Environmental Protection Agency began widespread monitoring of PM2.5 levels in 1999, the epidemiologic community has performed numerous observational studies modeling mortality and morbidity responses to PM2.5 levels using Poisson generalized additive models (GAMs). Although these models are useful for relating ambient PM2.5 levels to mortality, they cannot directly measure the strength of the effect of exposure to PM2.5 on mortality. In order to assess this effect, we propose a three-stage Bayesian hierarchical model as an alternative to the classical Poisson GAM. Fitting our model to data collected in seven North Carolina counties from 1999 through 2001, we found that an increase in PM2.5 exposure is linked to increased risk of cardiovascular mortality in the same day and next 2 days. Specifically, a 10-μg/m3 increase in average PM2.5 exposure is associated with a 2.5% increase in the relative risk of current-day cardiovascular mortality, a 4.0% increase in the relative risk of cardiovascular mortality the next day, and an 11.4% increase in the relative risk of cardiovascular mortality 2 days later. Because of the small sample size of our study, only the third effect was found to have > 95% posterior probability of being > 0. In addition, we compared the results obtained from our model to those obtained by applying frequentist (or classical, repeated sampling-based) and Bayesian versions of the classical Poisson GAM to our study population.
exposure simulatorfine particulate matterSHEDS-PMspatial modelingStochastic Human Exposure and Dose Simulation
==== Body
Researchers have found that acute episodes of increased particulate matter (PM) are associated with nonaccidental mortality (Goldberg et al. 2001), total mortality (Katsouyanni et al. 2001; Laden et al. 2000; Mar et al. 2000; Wichmann et al. 2000), cardiovascular deaths (Hoek et al. 2001; Ostro et al. 2000), respiratory deaths (Braga et al. 2001; Hoek et al. 2001), elderly deaths (Katsouyanni et al. 2001), asthma in children and the nonelderly (Lin et al. 2002; Norris et al. 1999; Sheppard et al. 1999), and morbidity (Schwartz 1999; Zanobetti et al. 2000). In all of these studies, the approach taken by the researchers to establish a connection between ambient PM levels and health end points consists of relating measured PM levels on a given day to mortality or morbidity rates on the same or closely following days while adjusting for possible confounding factors such as weather, day of the week, and long-term trends in mortality rates. By far, the most common model used to establish this relationship is the Poisson generalized additive model (GAM). Poisson GAMs are well suited for addressing the question of whether levels of ambient PM in the outdoor environment are associated with health end points, but they may not be the best approach for quantifying the relationship between PM exposure and health end points because direct exposure data cannot be collected for large populations over long periods of time. As a result, Poisson GAMs cannot give direct estimates of increases in the relative risk of morbidity and mortality as a result of exposure to PM.
In attempting to explore the relationship between PM exposure and morbidity or mortality, care should be taken not to assume that the relationship between ambient levels and mortality implies a similar connection between exposure and mortality. It is well documented that ambient levels poorly approximate true exposure (Dockery and Spengler 1981; Lioy et al. 1990; Spengler et al. 1985; Tamura and Ando, unpublished data), and ignoring the discrepancy between exposure and ambient levels in investigations of health effects can lead to biases and underestimation or overestimation of the uncertainty about effects even in simple models (Armstrong et al. 1992). One recent study from the Health Effects Institute (HEI; Cambridge, MA) shows that PM studies are no different: ignoring exposure information can result in biases and misrepresentation of uncertainty when linking PM to health effects (Samet et al. 2000).
In an effort to include exposure information in a model linking levels of PM ≤ 10 μm in aerodynamic diameter (PM10) and mortality, an HEI study (Samet et al. 2000) proposed a multistage Bayesian Poisson regression model, a generalization of the GAM, that includes exposure information. The focus of the HEI study was on Baltimore, Maryland, where daily mortality, PM10, and weather variables were collected from 1987 through 1994. Within Baltimore, Samet et al. used the Poisson GAM form to relate PM10 exposure (instead of ambient levels) to mortality. At the next stage of the hierarchy, the latent exposure is related to ambient PM levels using a linear regression form. To provide information about the coefficients of the regression relating the latent exposure to ambient levels, Samet et al. hypothesized that the same linear form is appropriate for each of five exposure studies and linked the coefficients in each study and the Baltimore population together through another level in the hierarchy.
Although the approach of Samet et al. (2000) takes an important step forward by including exposure information in an epidemiologic model, the method of relating ambient levels to exposure levels could be improved. The assumption that the linear relationship between PM10 levels and true exposure is similar between the Baltimore population and the populations in the five exposure studies may be unwarranted. In contrast to this HEI approach, an alternative approach for relating ambient pollutant levels to true personal exposure that has gained acceptance more recently is the use of computer exposure simulators. Zidek et al. (2003) presented a general statistical framework for the construction of these simulators. Exposure simulators use activity data and microenvironment pollutant-level data to estimate pollutant exposure levels for individuals. One of the most sophisticated exposure simulators to date for PM is the Stochastic Human Exposure and Dose Simulation (SHEDS-PM) (Burke et al. 2001). For a single individual, SHEDS-PM stochastically simulates a PM level for each of the environments in which the individual spends time. Once SHEDS-PM has defined the microenvironmental levels, the total PM exposure for the individual is estimated by weighting the PM levels in the various environments by the amount of time the individual spends in each of those environments. By examining the estimated PM exposure levels of several individuals created in this manner, the distribution of exposure levels for a population can be characterized.
Building upon the Bayesian model used in the HEI study (Samet et al. 2000), we propose a Bayesian hierarchical model for modeling the relationships among levels of ambient fine PM (particulate matter ≤ 2.5 μm in aerodynamic diameter; PM2.5), average exposure to PM2.5, and cardiovascular mortality that incorporates an exposure simulator similar to SHEDS-PM. Unlike most studies, our model allows us to directly quantify the effect of exposure to PM2.5 on cardiovascular mortality. Bayesian hierarchical modeling is a framework that allows multiple data sources and statistical modeling techniques to be incorporated into a single coherent statistical model (Gelman et al. 1995). In contrast to the Poisson GAM, our model describes the hierarchical nature of the process that connects monitor readings of PM2.5 to cardiovascular mortality by using a three-level hierarchy. The hierarchy is summarized in Table 1. At the first level, we describe the relationship between PM2.5 monitors and a continuous surface of ambient PM2.5 concentrations by allowing for monitor error and considering the spatial properties of PM2.5. At the next level, we link average ambient PM2.5 concentrations at the county level to average population exposure at the county level using an exposure simulator similar to SHEDS-PM. Finally, the third level links average exposure levels to daily cardiovascular mortality counts using the Poisson GAM form. By incorporating all of these levels into a single Bayesian hierarchical model, we are able to estimate the effect of PM2.5 exposure on cardiovascular mortality and to combine several disparate sources of data in a meaningful way. Although not clearly marked in Table 1, note that the modeled process from level 1 feeds into the modeling technique for level 2, and the modeled process from level 2 feeds into the modeling technique for level 3. By fitting our model using 3 years of data in seven counties in North Carolina (Alamance, Chatham, Durham, Guilford, Johnston, Randolph, and Wake), we found that increased PM2.5 exposure is related to increased risk of cardiovascular mortality on the same day and the next 2 days. The size of the observed effect is greater than that observed between ambient PM2.5 levels and cardiovascular mortality, although similar patterns in the effects appear.
Materials and Methods
Mortality data for North Carolina for the years 1999–2001 were obtained from the website of the Odum Institute at the University of North Carolina (Odum Institute 2003). These data were subdivided to include only deaths from cardiovascular causes [International Classification of Diseases, 10th Revision (ICD-10) codes I00 to I99; World Health Organization (WHO) 1992]. PM2.5 data for all available monitors in North Carolina during 1999–2001 were obtained from the U.S. Environmental Protection Agency (EPA) Aerometric Information Retrieval System/Air Quality Subsystem (AIRS/AQS) database (U.S. EPA 2003b). Each monitor in North Carolina takes readings on a daily, 1-in-3-day, or 1-in-6-day schedule. Daily meteorologic data across North Carolina were obtained from the National Oceanographic and Atmospheric Association’s (NOAA) National Climatic Data Center (Asheville NC) via online subscription (NOAA 2003). For each county, the values of the three variables of interest (daily maximum temperature, average wind speed, and relative humidity) were assumed to be equal to the values of those variables reported by the weather station closest to the centroid of the county. We imputed missing meteorologic data (~ 2% missing overall) by calculating the average value for all other counties with complete data on the same day and substituting that average value for the missing value. Data on human activity patterns were obtained from the Consolidated Human Activities Database (CHAD; U.S. EPA 2003a). This database contains the results of 12 studies in which individual 24-hr details of activities and the environments in which those activities took place were recorded. We restricted our use of the database to records contained in the National Human Activity Pattern Survey (NHAPS) portion of the CHAD and to records of individuals > 20 years of age. Demographic data on the county level were obtained from the U.S. Census Bureau (2003). The population counts for the 2000 census were assumed to be representative of the population counts across the time period studied (1999–2001). We used two level-3 summary files in our analysis, P1 and PCT35, which include total population counts by county and the number of individuals > 16 years of age in each county by sex, age, and employment status, respectively.
The model that we propose for relating PM2.5 readings at monitors to daily cardiovascular mortality counts is a three-level hierarchical Bayesian model. The three levels in our model are as follows: a) linking monitor readings to ambient levels over the study region, b) linking ambient levels to exposure levels, and c) linking exposure levels to mortality (Table 1).
Level 1.
Central to our model relating PM levels to mortality is that, for any given day, a continuous surface of ambient PM2.5 levels exists over the study region; this is what would be measured if we obtained an infinite number of monitor readings (spatially dense) without error each day. The first level of our model specifies the spatial distribution of PM2.5 and relates that distribution to readings taken at monitors on a single day.
We conducted a spatial analysis of PM2.5 and determined that PM2.5 exhibits strong spatial correlation over the region of interest [details reported by Calder et al. (2003)]. In order to incorporate this information into a statistical model, we assigned a joint multivariate normal distribution to any set of observations of the PM2.5 surface. Although we acknowledge that PM2.5 readings tend to be right-skewed rather than normally distributed, this simplification is not expected to have a strong impact on the overall model fit and simplifies model fitting considerably. On any day t and for any set of sites s(1), … , s(nψ), the distribution of the PM2.5 surface ψt at those points is ψt | θ ~ MNnψ (Mtθ, ∑), where ψt = [ψt(s1) … , ψt[s(nψ)]T, MN is the multivariate normal distribution, Mt is a design matrix of covariates, θ is a parameter vector, and ∑ is an nψ × nψ spatial covariance matrix constructed using information from our exploratory spatial analysis of outdoor PM2.5 levels. For each site, s(1), … , s(nψ), Mt includes a row with elements representing an overall mean, maximum temperature, average wind speed, and two sinusoidal terms that capture seasonal cycles. We considered the corresponding five regression coefficients, θ = (θ0, … , θ4), to be unknown, and we minimized prior influence by placing vague N(0, 100) priors on these parameters.
The sites s(1), … , s(nψ) for which the spatial distribution of PM2.5 is estimated need not be locations with monitors. The matrices Mt and ∑ are defined for any location in our modeled domain. In fact, in our implementation we modeled the spatial process at several locations that do not have monitors to better characterize the average ambient level over the entire spatial area of each county.
In relating monitor readings to the ambient surface we have defined, we assumed that the PM2.5 monitors measure the ambient PM2.5 surface with some error (measurement error and other random sources of error) at their locations: Xt(s) | ψt(s), σx2 ~ N[ψt(s), σx2], where Xt(s) is the monitor reading at monitoring site s at time t, ψt(s) is the value of the ambient surface at the location of monitoring site s at time t, and σx2 is the variance of the measurement error. This construction automatically incorporates the additional uncertainty about the ambient PM2.5 surface on days when fewer monitors take readings. Days when more monitors take readings (every third or sixth day) will carry more information about the ambient surface than will days when only a subset of daily monitors takes readings, so our uncertainty about the ambient surface will be smaller on these days.
In order to construct a prior distribution for σx2, the variance of the measurement error at the PM2.5 monitors, precision and accuracy data were downloaded from the AIRS/AQS database (U.S. EPA 2003b). Using these data, we developed an inverse-gamma (649, 1433.405) prior distribution (mean = 2.2, variance = 7.5 × 10−3) for σx2. This prior was developed using a simple conjugate inverse-gamma/normal model [e.g., Gelman et al. (1995)] with an inverse-gamma (1, 1) prior on σx2 before observing data.
By creating a continuous surface of ambient PM2.5 levels, we gained several advantages over the more common “monitor averaging” approach. First, information on the ambient PM2.5 level on any given day is shared across counties, allowing more accurate characterization of ambient levels in all locations. Second, the interpolation of a continuous ambient surface allows inference about the ambient level in counties that do not contain any PM2.5 monitors, thereby giving better representation to rural counties. Third, the Bayesian specification of the prior distribution on the ambient level allows natural incorporation of seasonal cycles and meteorologic effects on PM2.5 levels. Finally, we can characterize the average ambient level in any county on any day by averaging the spatial surface over the county.
Level 2.
Level 2 of our model links average ambient PM2.5 levels in a county to the average exposure level within that county. In this level of the model, we used a deterministic population-level exposure simulator to assist in relating ambient levels to true exposure. Our simulator uses human activity data, information about PM2.5 levels in indoor environments, and the average ambient concentration on a given day to approximate the exposure level of several individuals in a county on that day. Then, the exposure levels for these individuals are averaged to estimate an average exposure level for all individuals in the county on that day. The population-level exposure simulator used in our model is an adaptation of the SHEDS-PM simulator proposed by Burke et al. (2001). Like SHEDS-PM, our simulator calculates exposure for an individual person using an activity diary and ambient PM2.5 levels as inputs. This process is repeated for several individuals, and the resulting average exposure is estimated as the mean of the individual exposure levels.
Assuming that the outdoor PM2.5 level is known and the activity pattern of an individual is known, our simulator calculates individual exposure as follows:
where ζict is the exposure level for individual i in county c on day t, mico is the number of minutes the individual spends outdoors, mice is the number of minutes the individual spends in indoor microenvironment e (residential, office, school, store, vehicle, restaurant, and bar), mic,smoke is the number of minutes the individual spends with smokers present, mic,cook is the number of minutes the individual spends cooking, Loct is the ambient PM2.5 level in county c on day t, Lect is the PM2.5 level in indoor microenvironment e in county c on day t, Lsmoke is the addition to the PM2.5 level in the current microenvironment when smokers are present, Lcook is the addition to the PM2.5 level in the current microenvironment when the individual is cooking, and 1,440 is the number of minutes in a day. When the simulator is implemented in our statistical model, Loct is set equal to the average ambient level in the county at time t, ψ̄ct. Additional PM2.5 measures from smoking and cooking are fixed at 10 μg/m3 [based on values reported by Burke et al. (2001)] and 5 μg/m3 [based on findings of Wallace et al. (2003)]. We kept these values constant to simplify computation; a more accurate approach would be to account for the brief shock these activities give to indoor PM2.5 levels stochastically. Note that this equation makes no distinction between the toxicity of indoor and outdoor particles in our model. The values of Lect for indoor microenvironments are calculated as linear functions of the outdoor level: Lect = ae + beLoct for e in the set {residential, office, school, store, vehicle, restaurant, bar}. Values of ae and be are shown in Table 2. These values were calculated using simplifications of values reported by Burke et al. (2001) for SHEDS-PM.
In each of the counties in which we hope to model the relationship between exposure and cardiovascular mortality, we applied the exposure simulator to several individuals to estimate an average exposure value. In order to apply the simulator, we used activity data that are representative of the true activity patterns in each county in which we modeled the mortality/exposure link. We simulated the activity data by randomly sampling 100 individuals from the county of interest using census demographic information (U.S. Census Bureau 2003) and matching each individual with an activity record from the CHAD (U.S. EPA 2003a). These activity records are drawn from diaries kept across the entire country. Despite possible geographic mismatches, this method of obtaining activity information is usually sufficient for obtaining representative activity information (Özkaynak H, personal communication). To simplify model implementation, a single activity pattern was associated with each individual, and no adjustments were made for different times of the year (i.e., winter vs. summer activity patterns).
To account for possible discrepancy between the simulator predicted value of exposure and true exposure levels, we specified that the average exposure level in a given county is normally distributed around the –value predicted by the simulator: Zct | ψ̄ct, σz2 ~ N[ξ(ψ̄ct), σz2], where Zct is the average exposure level in county c at time t, ψ̄ct is the average ambient level in county c at time t, ξ(ψ̄ct) is the average exposure level predicted by the simulator in county c at time t as a function of the average ambient level, and σz2 is the variance of the error in the simulator. We place a uniform (0, 25) prior on σz2. Although there is not enough information in the data to estimate σz2 accurately, allowing it to be random incorporates our uncertainty in the simulator into the model resulting in more accurate uncertainty estimates at the third level.
Level 3.
In the third level of the model, we linked exposure directly to mortality using the Poisson GAM form commonly used in studies of the link between PM2.5 and mortality. Mortality was assumed to be Poisson distributed with a mean that depends on average PM2.5 exposure in the current and 3 previous days as well as the values of several confounders:
where Yct is the mortality in county c on day t, Ec is the expected daily mortality rate in county c (necessary for adjusting the mean level so that the β and η parameters have the same interpretation in all counties), λct may be interpreted as a relative risk of death in county c on day t, μ is an overall baseline relative risk of death in the study region over the time period studied, β0, … , β3 are parameters describing the influence of county-level average exposure on mortality rate, fp(Cpct) are transformations of confounding variables, and η1, … , ηP are parameters describing the influence of confounding variables on mortality. For our data set, confounding variables included a factor variable for the day of the week, a cubic spline transformation of time to account for long-term trends in cardiovascular mortality, a cubic spline transformation of maximum temperature, a cubic spline transformation of relative humidity, and cubic spline transformations of 1- to 3-day lagged values of maximum temperature and relative humidity. The cubic spline transformation of time included 21 evenly spaced knots, and the cubic spline transformations of maximum temperature and relative humidity each included five evenly spaced knots. The model was not assessed for sensitivity to the placement of these knot locations. We reparameterized the confounding variable term into a design matrix (&Ctilde;) and coefficient vector (γ), and we placed vague N(0, 100) priors on the coefficients. We also placed vague N(0, 100) priors on all of the β-parameters describing the strength of the relationship between PM2.5 exposure and cardiovascular mortality at different lags as well as on the overall mean relative risk parameter, μ.
Summary.
Although we have introduced a three-level model, we emphasize that the three levels of the model are all fitted simultaneously as a single coherent statistical model. There are three main advantages to creating a hierarchical Bayesian model for solving such a complex problem. The most important advantage is that uncertainty in parameters is propagated throughout the model. For example, our uncertainty about the true ambient surface (due to errors in the monitors and the necessity of spatial interpolation) carries through to result in a corresponding level of uncertainty about the effect of exposure on cardiovascular mortality. The second important advantage of hierarchical Bayesian modeling is that it is simple to specify large, complex models using simpler statements about conditionally independent parameters. It would be impossible to specify the joint distribution of the thousands of parameters involved in our model if we tried to model the spatial properties of PM2.5, the relationship between exposure and ambient levels, and the relationship between exposure and cardiovascular mortality simultaneously. In contrast, the hierarchical approach allows us to specify each level of the model conditionally independent of other levels and to combine the information at the end to obtain a joint distribution of all parameters. The third advantage is that elements of the hierarchy can be substituted without changing the overall form of the model. For instance, we could substitute a different exposure simulator in the second level of the model.
Results
Model fitting was performed using a Markov chain Monte Carlo algorithm (Gelfand and Smith 1990; Geman and Geman 1984; Hastings 1970). The algorithm was implemented with custom C++ software developed using Microsoft Visual Studio (Microsoft Corporation, Redmond, WA). Random number generation was performed using functions from the Numerical Algorithms Group library (NAG, Ltd, Oxford, UK). The algorithm was run for 200,000 iterations, 50,000 of which were discarded as “burn-in” iterations. To reduce the storage space for the samples, the remaining 150,000 samples were thinned by a factor of 50, resulting in a total of 3,000 draws from the joint posterior distribution.
The marginal posterior distributions of several important parameters are summarized in Table 3. For each of the parameters, we include an estimate of the posterior mean (calculated by averaging samples from the posterior distribution) and posterior median (calculated as the median of the sample), a Monte Carlo error for the mean, and a posterior 95% credible interval. The Monte Carlo error for the mean describes how far off our estimate of the true posterior mean is as a result of using a Monte Carlo method for exploring the posterior; it does not describe the uncertainty in the actual parameter. The 95% credible interval does describe the uncertainty in the parameter; it is an equaltail interval such that the posterior probability that the parameter falls within the interval is 95%. Credible intervals are the Bayesian analogue of the confidence interval but are much easier to interpret because they give direct information about the probability of a parameter falling within certain bounds.
The posterior analysis indicates a positive effect of PM2.5 exposure on the relative risk of cardiovascular mortality. The posterior marginal expectations of the parameters indicate that a 10-μg/m3 increase in average PM2.5 exposure is associated with a 2.5% increase (95% credible interval, –3.9 to 9.6) in the relative risk of current day cardiovascular mortality, a 4.0% increase (–3.3 to 12.2) in the relative risk of cardiovascular mortality the next day, an 11.4% increase (2.8 to 19.8) in the relative risk of cardiovascular mortality 2 days later, and a 1.1% decrease (–7.5 to 5.2) in the relative risk of cardiovascular mortality 3 days later. These rates were calculated by multiplying the β-value corresponding to the effect by 10 and exponentiating. Only the effect on the second day after exposure has a > 95% posterior probability of exceeding zero. Note that the estimates presented are marginal expectations and therefore cannot be added together (e.g., to get an overall risk of cardiovascular mortality from exposure to PM2.5) in a meaningful way. The negative estimate on the third day might be considered an unexpected effect, but it does lend some support to the theory of harvesting (Schwartz 2000). This theory hypothesizes that individuals close to dying of cardiovascular-related causes may die soon after a spike in PM2.5 exposure, leaving only healthier individuals and consequently decreasing the overall risk of cardiovascular mortality in the total population.
We are unaware of any other study that has attempted to directly estimate the effect of PM2.5 exposure on mortality, but some related estimates for PM10 are available from the HEI study (Samet et al. 2000). In that study, a 10-μg/m3 increase in PM10 exposure is associated with a 1.4% increase in same-day relative risk of mortality. Although the uncertainty about the HEI estimate is much smaller (probably as the result of a longer time period of study), the point estimate is similar to the one obtained in our analysis.
Although our main goal in this analysis was to demonstrate the effect of PM2.5 exposure on cardiovascular mortality, we can also address the effect of changes in the ambient level on the relative risk of cardiovascular mortality. To determine the relationship between ambient levels and relative risk induced by our model, we examined the joint posterior distribution of average ambient levels, ψ̄ct, and log relative risk, λct, on the same and closely following days. Figure 1 shows smoothed images of the joint distributions combining information across counties. Lines have been added to the figures to illustrate the overall direction of the effect; the line is chosen to minimize the sum of squared distances between samples from the distribution (not shown) and the line. The slope of the line is a summary of the effect of an increase in average ambient level on the log relative risk of cardiovascular mortality, although it is not a parameter in the model. By exponentiating the slope of the line, we obtain an estimate of the proportional increase in relative risk associated with a unit change in ambient level. The lines imply that a 10-μg/m3 increase in ambient level is associated with a 0.09% increase in the relative risk of cardiovascular mortality on the same day, a 0.2% increase the next day, a 1.0% increase 2 days later, and a 1.4% decrease 3 days later. As with the estimates of effect of exposure on cardiovascular mortality, these estimates are marginal effects and should be interpreted individually; they should not be combined to find an overall effect. These estimates tend to be lower than some comparable estimates reported in the epidemiologic literature. The effect of 2-day mean ambient levels on total mortality has been estimated at 3.3% for chronic obstructive pulmonary disease, 2.1% for ischemic heart disease [both estimates from Schwartz et al. (1996)], and 1.5% for total mortality from natural causes (Klemm et al. 2000), all higher than our largest estimate. This result is not surprising because the inclusion of an exposure link in our model should weaken the direct relationship between ambient levels and mortality. The trend of a weaker association between ambient levels and mortality than between exposure and mortality is similar to the trend reported in the HEI study (Samet et al. 2000).
Although the assessment of the relationship between PM2.5 and cardiovascular mortality is the main focus of this analysis, estimates of other parameters provide insights into some components of the model. For instance, the estimate of θ0, the baseline average ambient PM2.5 level over all days examined (temperature at 0°F, wind speed at 0 miles/hr), indicates that baseline ambient PM2.5 levels averaged approximately 9.7 μg/m3 over the study region from January 1999 through December 2001. The Bayesian model provides an uncertainty estimate for this parameter as well; the baseline ambient PM2.5 level averaged between 6.1 μg/m3 and 13.2 μg/m3 with 95% posterior probability. Some other effects to note are a positive relationship between maximum daily temperature and ambient PM2.5 levels (an increase of 1°F in maximum temperature is associated with an increase of 0.09 μg/m3 in daily average ambient PM2.5 level) and a negative relationship between daily average wind speed and ambient PM2.5 level (an increase of 1 mile/hr in average daily wind speed is associated with a decrease of 0.08 μg/m3 in daily average ambient PM2.5 level). Finally, it is of interest to examine the relationship between average ambient levels and average exposure levels in the counties of interest. The estimates of these values are presented in Table 4 along with some demographic information that was used to choose individuals for the simulator. No correlation between the demographic data and posterior mean exposure levels was observed for the seven counties in our study.
Another interesting parameter estimated in our model is the relative risk of cardiovascular mortality in each county at each time step, λct. Examining the relative risk of cardiovascular mortality over the time period studied reveals some interesting patterns. All counties showed similar patterns, so we only present the results for Alamance County (Figure 2). The relative risk of cardiovascular mortality in each county follows a sinusoidal pattern that peaks when the seasonal cycle for PM2.5 is at its lowest point (as implied by the estimates of θ3 and θ4). The relative risk includes the influence of all of the confounding variables (maximum temperature, relative humidity, long-term cardiovascular mortality trend, and day of the week) in addition to the effect of PM2.5 exposure on cardiovascular mortality. Therefore, we conclude that overall cardiovascular mortality is significantly affected by numerous factors other than PM2.5; however, our analysis shows that PM2.5 exposure plays an important role in determining the relative risk of cardiovascular mortality.
Model validation and comparison.
In order to assess whether our model gives reasonable results, we fitted different forms of the model and compared the results obtained in each case. We first considered the effect of eliminating both the spatial interpolation of ambient levels (level 1) and removing the exposure link (level 2 of our model). We call this alternate model 1. We can only fit this model in three of the seven original counties (Durham, Guilford, and Wake) because only these three counties contain at least one daily PM2.5 monitor. In each county, we first obtained a PM2.5 reading on each day by averaging the PM2.5 readings from all monitors that took readings on that day in the county. Prior distributions for all parameters that remain in the model (μ, β-parameters, and γ-parameters) are the same as in our full Bayesian model. We compared the results of this model with results obtained by fitting Poisson GAMs in each of the three counties individually.
The second alternate model that we fitted replaces level 2 of our Bayesian model with a simplified exposure link. Rather than including an exposure simulator, we constructed alternate model 2 by hypothesizing that exposure is equal to the ambient level plus some error [i.e., Zct | ψ̄ct, σz2 ~ N(ψ̄ct, σz2)]. The remainder of the model is specified exactly as in our original Bayesian model. Summaries of the parameters of most interest, the β-parameters, appear in Table 5, which reports marginal posterior means and 95% credible intervals for the Bayesian models (alternate models 1 and 2) and maximum likelihood estimates with 95% confidence intervals for the classical Poisson GAMs. Note that the parameters for alternate model 2 are interpreted as the effect of a one-unit increase in PM2.5 exposure on the log relative risk of cardiovascular mortality, whereas the parameters in the other models relate ambient PM2.5 levels to the log relative risk of cardiovascular mortality.
The results from alternate model 1, the Bayesian model with no spatial interpolation or exposure link, are comparable with the results obtained by fitting the classical Poisson GAM in each of the three counties. This similarity gives evidence that the Bayesian approach produces results similar to those ordinarily obtained using the classical Poisson GAM approach. However, using a Bayesian model allows the incorporation of additional data sources and levels into the hierarchy, so the Bayesian model is more readily expanded.
As expected, the results from alternate model 2 are different from the results obtained from the classical models and alternate model 1; alternate model 2 summarizes the effect of PM2.5 exposure, not ambient level, on mortality. The results from alternate model 2 are more comparable with those obtained from our full Bayesian model. This similarity indicates that our model is robust to our choice of exposure simulator. However, we do not conclude that the exposure simulator is unnecessary because increased accuracy of simulated exposures will lead to more accurate estimates of the effect of exposure on mortality.
Conclusions
By constructing a hierarchical Bayesian model that divides the process linking PM2.5 monitor readings and mortality into three intuitive levels, we have shown that elevated PM2.5 exposure is related to increased risk of cardiovascular mortality in the closely following days. We found that increases in the level of PM2.5 exposure are most closely related to increased relative risk of cardiovascular mortality 2 days later. In addition, we have demonstrated that the effect of increased levels of exposure on cardiovascular mortality is not equivalent to the effect of increased levels of ambient PM2.5 on cardiovascular mortality. Our results are similar to those reported in several studies lending additional support to our findings. In addition, we estimate that the association between ambient levels and relative risk of cardiovascular mortality on closely following days is lower than what has been previously reported in the literature.
Despite the sophistication of our model, the second level of the model leaves room for improvement. A deficiency of the second level is the absence of real exposure data. Another limitation of the second level is the simplicity of our exposure simulator; our exposure simulator ignores changes in people’s activity patterns over different days of the week and different seasons, uses fixed values to relate indoor and outdoor PM2.5 values, and may introduce biases in estimation by assuming that the outdoor level is the same for each individual, calculating individual exposures, and then averaging across individuals (Freedman 1999).
Future work on this type of model might focus on addressing the weaknesses in the second level of our model. For example, if real exposure data can be acquired, a data-driven version could be substituted without substantially changing the structure of the model. Similarly, a more complex exposure simulator that takes seasons and the day of the week into account could be substituted to improve the reliability of the results. Nonetheless, the results obtained by incorporating a simple exposure simulator into the model provide valuable insight into the relationship between PM2.5 exposure and cardiovascular mortality.
Figure 1 Joint distribution of ambient PM2.5 level and log relative risk on the same day (A), the next day (B), 2 days later (C), and 3 days later (D), with lines summarizing the direction of association (described in ”Results”). Darker areas represent regions of higher probability. The exponential of the slope of the line in each panel represents the proportion change in relative risk per unit change in ambient level.
Figure 2 Posterior means for relative risk of mortality in Alamance County over the period studied. Vertical bars indicate 1 January for each year in the analysis.
Table 1 Summary of levels of hierarchical model.
Level Data Modeling techniques Modeled process
1 Meteorology ambient monitor Spatial statistical model Spatial surface of ambient PM2.5 levels
2 Demographics activity patterns Exposure simulator Population exposure levels
3 Mortality confounders Poisson GAM Cardiovascular mortality
Table 2 Coefficients for relating ambient PM2.5 level to the level in indoor microenvironments.
Indoor microenvironment (e) ae be
Residential 0.0049 0.578
Office 3.6 0.18
School 6.8 0.6
Store 9.0 0.74
Vehicle 33 0.26
Restaurant 9.8 1.0
Bar 9.8 1.0
Table 3 Marginal posterior summaries of several model parameters.
Parameter Description Mean (median) MC error for mean 95% Credible interval
μ Overall log RR –0.5963 (–0.6064) 0.0651 –1.2493 to 0.07618
β0 Same-day mortality 0.0025 (0.0026) 0.0002 –0.0040 to 0.0092
β1 Lagged mortality (1) 0.0039 (0.0038) 0.0003 –0.0034 to 0.0115
β2 Lagged mortality (2) 0.0108 (0.0108) 0.0003 0.0028 to 0.0181
β3 Lagged mortality (3) –0.0011 (–0.0010) 0.0002 –0.0078 to 0.0051
σz2 Simulator variance 20.2853 (20.9932) 0.1489 12.3870 to 24.8422
σx2 Monitor error 1.6495 (1.6476) 0.0009 1.5594 to 1.7457
θ0 Mean PM2.5 (μg/m3) 9.6856 (9.6916) 0.0275 6.1121 to 13.1849
θ1 Maximum temperature (°F) 0.0879 (0.0872) 0.0006 0.0224 to 0.1527
θ2 Wind speed (miles/hr) –0.0799 (–0.0798) 0.0009 –0.1607 to 0.0024
θ3 Sine term –0.8764 (–0.8699) 0.0061 –1.4987 to –0.2455
θ4 Cosine term –1.3451 (–1.3528) 0.0091 –2.3660 to –0.3142
Abbreviations: MC, Monte Carlo; RR, relative risk.
Table 4 Posterior mean ambient PM2.5 levels and exposure levels, and demographic characteristics.
County Ambient PM2.5 level (μg/m3) Exposure level (μg/m3) Percent male Percent unemployed
Alamance 15.62906 13.83480 47 35
Chatham 15.64579 16.75560 48 36
Durham 15.65255 23.44071 47 34
Guilford 15.66802 28.88822 47 33
Johnston 15.61301 23.74197 48 34
Randolph 15.62650 24.23487 49 33
Wake 15.59123 12.85243 49 27
Table 5 Estimates of the β-parameters (credible intervals) in alternative models.
Model β0 β1 β2 β3
Bayesian models
Alternate model 1 –0.0025 (–0.0067 to 0.0018) –0.0055 (–0.0106 to –0.0005) 0.0049 (–0.0001 to 0.0098) –0.0016 (–0.0059 to 0.0025)
Alternate model 2 0.0013 (–0.0032 to 0.0057) 0.0004 (–0.0045 to 0.0054) 0.0061 (0.0013 to 0.0108) 0.0016 (–0.0028 to 0.0057)
Classical Poisson GAMs
Durham County –0.0036 (–0.0149 to 0.0077) 0.0024 (–0.0102 to 0.0149) 0.0124 (1.5 ×10−6 to 0.0248) –0.0100 (–0.0210 to 0.0009)
Guilford County 0.0009 (–0.0084 to 0.0102) –0.0073 (–0.0178 to 0.0033) 0.0018 (–8.5 × 10−3 to 0.0122) –0.0020 (–0.0110 to 0.0069)
Wake County –0.0032 (–0.0117 to 0.0054) –0.0058 (–0.0152 to 0.0037) 0.0061 (–3.1 × 10−3 to 0.0153) 0.0050 (–0.0032 to 0.0132)
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7004ehp0112-00128915345341ResearchArticlesSerum Dioxin Concentrations and Age at Menarche Warner Marcella 1Samuels Steven 12Mocarelli Paolo 3Gerthoux Pier Mario 3Needham Larry 4Patterson Donald G. Jr.4Eskenazi Brenda 11School of Public Health, University of California at Berkeley, Berkeley, California, USA2Division of Occupational/Environmental Medicine and Epidemiology, University of California at Davis, Davis, California, USA3Department of Laboratory Medicine, University of Milano-Bicocca, School of Medicine, Hospital of Desio, Desio-Milano, Italy4Division of Environmental Health Laboratory Science, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, Georgia, USAAddress correspondence to M. Warner, School of Public Health, University of California, 2150 Shattuck Ave., Suite 600, Berkeley, CA 94720-7380 USA. Telephone: (510) 642-9545. Fax: (510) 642-9083. E-mail:
[email protected] thank S. Casalini and P. Brambilla for coordinating data collection at the Hospital of Desio and W. Turner (CDC) for serum TCDD measurements.
This work was supported by the following grants: R82471 from the U.S. Environmental Protection Agency, R01 ES07171 and F06 TW0207501 from the National Institutes of Health, EA-M1977 from the Endometriosis Association, 2P30-ESO01896-17 from the National Institute of Environmental Health Sciences, and 2896 from Regione Lombardia and Fondazione Lombardia Ambiente, Milan, Italy.
The authors declare they have no competing financial interests.
9 2004 10 6 2004 112 13 1289 1292 5 2 2004 10 6 2004 Publication 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. 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD), a widespread environmental contaminant, is associated with delays in pubertal development in animal studies. On 10 July 1976, as a result of a chemical explosion, residents of Seveso, Italy, experienced the highest levels of TCDD exposure experienced by a human population. Twenty years later, we initiated the Seveso Women’s Health Study (SWHS), a retrospective cohort study of female residents of the most contaminated areas, to determine whether the women were at higher risk for reproductive disease. We examined the association of TCDD serum levels, based on measurements in serum collected soon after the explosion, with reported age at menarche among the 282 SWHS women who were premenarcheal at the time of the explosion. We found no change in risk of onset of menarche with a 10-fold increase in TCDD (e.g., 10–100 ppt; hazard ratio = 0.95; 95% confidence interval, 0.83–1.09; p-value for trend = 0.46). When TCDD levels were categorized, there was also no evidence of a dose–response trend (p = 0.65). In summary, we found that individual serum TCDD measurements are not significantly related to age at menarche among women in the SWHS cohort. The women in this study experienced substantial TCDD exposure during the postnatal but prepubertal developmental period. Given that animal evidence suggests in utero exposure has the most significant effect on onset of puberty, continued follow-up of the offspring of the SWHS cohort is important.
dioxinendocrine disruptorsenvironmental exposuresepidemiologymenarchepuberty2, 3, 7, 8-tetrachlorodibenzo-p-dioxin
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Polychlorinated dibenzodioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), and polychlorinated biphenyls (PCBs) constitute a group of polyhalogenated aromatic hydrocarbons that are persistent, widespread environmental contaminants, frequently detected at parts-per-trillion levels (lipid basis) in animals and humans throughout the industrialized world (Zook and Rappe 1994). 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD or dioxin) is the most toxic congener within this group of compounds and has been shown to cause a wide variety of effects in animals, including altered reproductive development [Birnbaum 1994, 1995; International Agency for Research on Cancer (IARC) 1997]. Increasing evidence suggests that exposure to TCDD during earlier stages of development is particularly hazardous to reproductive development (Chaffin et al. 1996). In utero and lactational TCDD exposure in rodents has been associated with delays in pubertal development [e.g., delayed vaginal opening, altered vaginal estrous cyclicity (Gray and Ostby 1995; Wolf et al. 1999)] and effects on ovarian function (Gray and Ostby 1995; Heimler et al. 1998), even at doses below those that induce overt maternal toxicity. A similar spectrum of reproductive alterations has been associated in rodents exposed in utero to other dioxin-like compounds, including PCDDs, PCDFs, and PCBs (Faqi et al. 1998; Hamm et al. 2003; Muto et al. 2003; Sager and Girard 1994).
To date, no epidemiologic studies have examined the association of TCDD exposure and age at menarche. Three studies, however, have examined the relation of dioxin-like compounds to pubertal development, with inconsistent conclusions. A study of daughters of Michigan women who had consumed polybrominated biphenyls (PBBs) in food in 1973 found earlier menarche in daughters whose mothers had higher serum PBB levels (Blanck et al. 2000). There were no differences in age at menarche in Taiwanese women who were exposed postnatally (but premenarche) to PCBs and PCDFs via consumption of contaminated rice oil (Yucheng) compared with unexposed women (Guo and Kao 2003). In Flemish adolescents, although breast development was inversely related, there was no relation of age at menarche to current serum levels of dioxin-like compounds as measured by chemical-activated luciferase gene expression bioassay toxic equivalents (CALUX-TEQ) or individual PCB congeners 118, 153, and 180 (Den Hond et al. 2002).
On 10 July 1976, as a result of a chemical explosion, residents of Seveso, Italy, experienced the highest levels of TCDD exposure in a human population. Shortly after the explosion, a cohort of residents was established and exposure status was classified by zone of residence (A, B, R, non-ABR) as determined by surface soil TCDD measurements (di Domenico et al. 1980). Twenty years after the explosion, we initiated the Seveso Women’s Health Study (SWHS) to measure TCDD in previously stored blood samples and to assess associations of serum levels of TCDD with reproductive disease.
In the SWHS, we have observed that serum TCDD levels were associated with an increase in menstrual cycle length among those who were premenarcheal at exposure, but not in those who were postmenarcheal at exposure (Eskenazi et al. 2002). Consistent with animal studies (Chaffin et al. 1996), this suggests that females may be particularly susceptible to the effects of TCDD during early stages of development, for example, in utero or prepubertal exposure. In this article we report the results of the association of individual serum TCDD and age of menarche among women who were premenarcheal in 1976, at the time of the explosion.
Materials and Methods
Study population.
Women eligible for the SWHS were 1 month to 40 years of age in 1976, had resided in one of the most highly contaminated zones (A or B), and had adequate stored sera collected soon after the explosion. Enrollment began in March 1996 and ended in July 1998. Of 1,271 eligible women, 17 could not be located or contacted, 33 had died or were too ill to participate, and 240 declined to participate, leaving 981 women. The age distribution of those who declined to participate was not significantly different from those who did participate. For this analysis, we included all women who were premenarcheal on 10 July 1976, the date of the explosion (n = 282).
Procedure.
The institutional review boards of the participating institutions approved the study. Details of the study have been presented elsewhere (Eskenazi et al. 2000). Briefly, participation included signed informed consent, blood draw, personal interview, and for most women, a gynecologic examination and ultrasound. The interview was conducted by a trained nurse-interviewer who was blinded to serum TCDD levels and zone of residence. Age at menarche was determined from the question, “At what age did you get your first menstrual period?”
Laboratory analyses.
TCDD was measured in archived sera by high-resolution gas chromatography/high-resolution mass spectrometry methods (Patterson et al. 1987). Values are reported on a lipid-weight basis in parts per trillion (Akins et al. 1989).
Details of serum sample selection have been presented elsewhere (Eskenazi et al. 2000). For the 282 women in this analysis, we measured TCDD in sera collected between 1976 and 1977 for 257 women, between 1978 and 1981 for 23 women, and in 1996 for two women whose earlier samples had insufficient volume. For women with detectable post-1977 TCDD measurements (n = 20), the TCDD exposure level was back-extrapolated to 1976 using the Filser model (Kreuzer et al. 1997). For nondetectable values (n = 22), a serum TCDD level equal to one-half the detection limit was assigned (Hornung and Reed 1990). For the median serum sample weight of 0.65 g, the median detection limit was 18.8 ppt, lipid adjusted.
Statistical analyses.
We considered serum TCDD both as a continuous variable (log10 TCDD) and a categorical variable. TCDD was first categorized into quartile groups (≤ 55.9, 56–140.2, 140.3–300, > 300 ppt). Because the lower limit of the serum TCDD level was relatively high, the lowest group was subdivided into women with levels ≤ 20.0 and 20.1–55.9 ppt. We selected 20 ppt (body burden ~ 4 ng/kg) as the cutoff point because this was the average TCDD level of 1976 serum pools collected from Italian women living in an unexposed area (Eskenazi et al. 2004). For additional analyses, we categorized preexplosion experience as “unexposed.”
Statistical analyses were performed using Cox survival models in Stata 7 (Stata Corporation, College Station, TX, USA). We did not censor data because for each woman age of menarche was observed. Each woman was entered into the denominator (“risk set”) for her year-group at the date of the accident or on her seventh birthday, whichever was later.
The Cox model assesses effects on age-specific probabilities of beginning menstruation by the relative hazard, or hazard ratio (HR), the ratio of probabilities computed for each categorized level of exposure versus the reference group or for the effect of a 10-fold increase in TCDD (log10 TCDD). For the categorical analysis, we used the highest dose group (> 300 ppt) as the reference group because the lowest dose group (≤ 20 ppt) had the smallest sample size. We report model-free standard errors, which are valid even when conventional assumptions for regressions are violated (Huber 1967). We examined the effect of potential confounders and effect modifiers, including height, weight, body mass index (BMI), and report of participation in athletic training at the time of interview (we did not obtain this information for early time periods), and smoking and alcohol consumption habits between 10 and 14 years of age.
The Cox model with constant HR may not be plausible when there is an inevitable event and the age-specific rates increase to 100%. We therefore also considered parametric regression survival-time models in which the natural log of the age at menarche is expressed as a linear function of the covariates.
The youngest age at menarche reported by the women who were premenarcheal at the time of the explosion was 8 years. We addressed the possibility of bias associated with the inclusion of women who, relative to their birth cohort, might already be at risk for late age at menarche at the time of the explosion (e.g., a woman who was 14 years of age but still premenarcheal in 1976). We therefore repeated the analysis on the subset of 158 women who were < 8 years of age at the time of the explosion and who were presumably not yet at risk for menarche.
To further assess the possibility of bias, we added to the analysis data 153 women who were in the same birth cohort as the 282 women in the analysis sample (birth years 1959–1976) but who had begun menstruating before the explosion date, 10 July 1976. These 153 women would have been at risk for menarche after the explosion had they not reached menarche before the explosion; their premenarche ages are all “unexposed.” For the enlarged sample of 435 (153 + 282) women, we repeated the analysis with this additional “unexposed” exposure category (unexposed, ≤ 20, 20.1–55.9, 56.0–140.2, 140.3–300, and > 300 ppt) and each “unexposed” woman entered the denominator on her seventh birthday.
Results
Demographic characteristics of the 282 women who were premenarcheal at exposure are presented in Table 1. On 10 July 1976, the age of the 282 women was 6.9 ± 3.7 years (mean ± SD; range = 0–17 years), and 158 (56%) were < 8 years of age. The mean age at menarche reported for the 282 women was 12.8 ± 1.6 year. The mean age at follow-up (1996–1998) was 27.3 ± 3.8 years and the mean BMI was 21.4 ± 3.1 mg/kg2. Women who had higher current BMIs or who consumed alcohol or smoked regularly between 10 and 14 years of age reported earlier ages of menarche.
Serum TCDD levels are presented in Table 2 by reported age at menarche for all premenarcheal women (n = 282) and for women who were < 8 years of age (n = 158) at exposure. The median serum TCDD level was 140.3 ppt (range, 3.6–56,000 ppt) for all premenarcheal women and 205.0 ppt (range, 3.6–56,000 ppt) for those who were < 8 years of age at exposure. Serum TCDD levels did not vary by reported age of menarche for either group [p > 0.5, analysis of variance (ANOVA)].
Results of Cox models are presented in Table 3. When we examined the effect of potential confounders and effect modifiers, we found no variables to confound (i.e., change the TCDD parameter estimate by > 10%) or to modify the association between TCDD and age of menarche. Thus, we report unadjusted results. When log10 TCDD was entered as the exposure variable, the HR associated with a 10-fold increase in TCDD was 0.95 [95% confidence interval (CI), 0.83–1.09]. That is, there was no change in risk of onset of menarche with a 10-fold increase in TCDD levels (e.g., 10–100 ppt). When the analysis was restricted to women < 8 years of age at exposure, the HR associated with a 10-fold increase in TCDD was 1.08 (95% CI, 0.89–1.30).
When TCDD was categorized (Table 3), there was also no evidence of a dose–response trend (p = 0.65). None of the four lower exposure groups (≤ 20.0, 20.1–55.9, 56–140.2, 140.3–300 ppt) had significantly different age-specific menarche rates than the highest category (> 300 ppt), and all CIs for the HR contained 1.0. The conclusions were similar when the analysis was restricted to women who were < 8 years of age at the time of the explosion. The conclusion of no association of age at menarche and TCDD persisted when we applied alternative models (log-normal, log-logistic) in which the mean of the log of age at menarche was the response (results not shown).
Finally, the analysis of all 435 women in the 1959–1976 birth cohort, with preexplosion ages categorized as “unexposed” also showed no association of TCDD level and age-specific hazard of menarche (data not shown).
Discussion
The results of this study of women residing in Seveso, Italy, in 1976 at the time of an explosion that released high levels of TCDD provide little evidence of an association of exposure and age of menarche. That is, we found no evidence of an association between TCDD levels measured in serum collected near the time of exposure among the 282 women who were premenarcheal at the time of the explosion, the subset of 158 women who were < 8 years of age at the time of the explosion, or the 435 women who belonged to the 1959–1976 birth cohort.
A limitation of our study is the retrospective recall of age of menarche. However, moderate to high correlations between actual and recalled menarche have been reported for females up to 19 years of age after the event (Must et al. 2002). In our study, the time between onset of menarche and study interview ranged from 5 to 19 years. The women in the SWHS reported age at menarche in whole years, presumably age at last birthday, and age was not rounded to the nearest “biological age.” Such nondifferential measurement error would reduce precision and would tend to bias our findings toward no effect.
A second limitation of the present study is that members of the lowest TCDD exposure group (≤ 20 ppt) experienced relatively high serum levels in comparison with contemporary levels reported for this area (~ 2 ppt) (Warner M, unpublished data). If there is a threshold for TCDD effects on age of onset of menarche but it is < 20 ppt, we would not be able to detect it in this population. However, we also found no association in analyses that counted preexplosion experience as “unexposed.”
Another limitation of this study is that, although the explosion resulted in exposure specifically to TCDD, analyses of pooled serum from residents of an unexposed zone suggest there was substantial background exposure to other PCDDs, PCDFs, and PCBs during this time period [80 ppt TCDD toxic equivalents (TEQ), on average] (Eskenazi et al. 2004). Therefore, individuals with TCDD levels < 20 ppt might still have had substantial total TEQ exposure. Because we considered only TCDD in this study, our results may have underestimated an effect due to total TEQ exposure.
An advantage of this study over previous studies is that we were able to measure TCDD levels in individual serum samples collected near the time of exposure. Previous studies have used cross-sectional exposure measures (Den Hond et al. 2002) or had to rely upon alternative exposure assessment methods including ecologic measures (Guo and Kao 2003) or modeling (Blanck et al. 2000).
Our finding of no association of TCDD with age at menarche is consistent with results reported in studies with postnatal exposure to other dioxin-like compounds, including PCBs and PCDFs (Den Hond et al. 2002; Guo and Kao 2003). However, our results differ from those of Blanck et al. (2000), which, in contrast to animal studies, showed an earlier rather than later age of menarche with in utero and perinatal PBB exposure. There are several reasons why our results may differ. The PBB studied by Blanck et al. (2000), 2,2′,4,4′,5,5′-hexabromobiphenyl, which was the main congener (60–80%) in the Fire Master mixture, is not a dioxin-like congener and does not bind to the aryl hydrocarbon receptor, unlike coplanar PBBs (Darnerud 2003). Second, the PBB-exposed cohort was exposed in utero and via lactation, unlike the SWHS cohort, in whom no exposure occurred in utero and only three women reported having been breast-fed postexplosion. It is possible that the fetus is more sensitive to the effects of exposure to dioxin-like compounds in utero. In fact, although in utero and lactational TCDD exposure in animal studies has been associated with significant effects on onset of puberty (Gray and Ostby 1995; Wolf et al. 1999) and ovarian function (Gray and Ostby 1995; Heimler et al. 1998), the evidence for these adverse effects after only postnatal exposure is limited, based on studies using the immature intact and immature hypophysectomized rat models (Gao et al. 1999; Li et al. 1995, 1997; Son et al. 1999). Thus, postnatal (but prepubertal) TCDD exposure experienced by the SWHS cohort, although substantial in dose, likely missed the critical window for exposure effects.
In summary, we have shown that individual serum TCDD measurements are not significantly related to age at menarche among women in the SWHS cohort. The women in this study experienced substantial TCDD exposure during the postnatal but prepubertal developmental period. Given that animal evidence suggests in utero exposure has the most significant effect on onset of puberty, continued follow-up of the offspring of the SWHS cohort is important.
Table 1 Distribution and age of menarche by select characteristics of women who were premenarcheal at exposure, SWHS, Seveso, Italy, 1996–1998.
Age at menarche (years)
Characteristic All premenarcheal women [No. (%)] Women < 8 years of age in 1976 [No. (%)] All premenarcheal women (mean ± SD) Women < 8 years of age in 1976 (mean ± SD)
Total 282 (100) 158 (56) 12.8 ± 1.6 12.5 ± 1.5
Age at exposure (years)
0–4 84 (30) 84 (53) 12.6 ± 1.5 12.6 ± 1.5
5–7 74 (26) 74 (47) 12.4 ± 1.6 12.4 ± 1.6
8–10 69 (24) 0 (0) 12.4 ± 1.3 —
11–17 55 (20) 0 (0) 13.8 ± 1.5 —
Year of birth
1959–1966 93 (33) 0 (0) 13.3 ± 1.5 —
1967–1970 90 (32) 59 (37) 12.4 ± 1.6 12.6 ± 1.7
1971–1976 99 (35) 99 (63) 12.5 ± 1.4 12.5 ± 1.4
Zone of residence
A 58 (21) 37 (23) 13.0 ± 1.8 12.6 ± 1.2
B 224 (79) 121 (77) 12.7 ± 1.5 12.5 ± 1.6
Current BMI (kg/m2)
< 19.8 93 (33) 63 (40) 13.2 ± 1.6 13.1 ± 1.6
19.8–26 166 (60) 87 (55) 12.6 ± 1.5 12.2 ± 1.4
26–29 12 (4) 5 (3) 12.5 ± 2.5 11.4 ± 1.9
> 29 8 (3) 2 (1) 12.4 ± 1.1 12.0 ± 1.4
Physical activity
No 117 (42) 55 (35) 12.7 ± 1.5 12.1 ± 1.5
Yes 165 (58) 103 (65) 12.8 ± 1.6 12.8 ± 1.5
Alcohol use at 10–14 years
No 271 (96) 153 (97) 12.8 ± 1.5 13.0 ± 1.5
Yes 11 (4) 5 (3) 11.8 ± 1.7 12.0 ± 2.5
Cigarette smoking at 10–14 years
No 273 (97) 153 (97) 12.9 ± 1.6 12.7 ± 1.5
Yes 9 (3) 5 (3) 12.6 ± 1.4 12.2 ± 1.5
—, No observations.
Table 2 Frequency (%) of age of menarche and serum TCDD levels (ppt) of women who were premenarcheal at exposure, SWHS, Seveso, Italy, 1996–1998.
All premenarcheal women in 1976
Women < 8 years in 1976
Age at menarche (years) No. (%) Serum TCDD Median (IQR)* No. (%) Serum TCDD Median (IQR)**
< 11 16 (6) 183.5 (107–292) 13 (8) 167.0 (96–244)
11 36 (13) 109.7 (42–323) 22 (14) 231.5 (84–553)
12 83 (29) 122.0 (45–288) 48 (30) 179.0 (59–477)
13 56 (20) 207.5 (74–324) 32 (20) 221.5 (78–403)
14 62 (22) 135.0 (51–214) 27 (17) 176.0 (128–407)
> 14 29 (10) 136.0 (51–340) 16 (10) 163.0 (50–570)
Total 282 (100) 140.3 (56–300) 158 (100) 205.0 (76–417)
IQR interquartile range.
* ANOVA, p = 0.64.
** ANOVA, p = 0.97.
Table 3 Age at menarche (mean ± SD) of women who were premenarcheal at exposure and Cox proportional hazards estimates, SWHS, Seveso, Italy, 1996–1998.
All premenarcheal women in 1976
Women < 8 years in 1976
Exposure No. (%) Age at menarche (years) HR (95% CI) No. (%) Age at menarche (years) HR (95% CI)
Log10TCDD 282 12.8 ± 1.6 0.95 (0.83–1.09) 158 12.5 ± 1.5 1.08 (0.89–1.30)
TCDD (ppt)
≤ 20 24 (8) 13.1 ± 1.7 1.11 (0.75–1.64) 8 (5) 13.0 ± 2.0 0.76 (0.40–1.44)
20.1–55.9 47 (17) 12.7 ± 1.4 1.10 (0.83–1.45) 21 (13) 12.7 ± 1.6 0.94 (0.61–1.43)
56.0–140.2 70 (25) 12.8 ± 1.4 1.14 (0.90–1.42) 27 (17) 12.2 ± 1.4 1.20 (0.87–1.64)
140.3–300.0 72 (26) 12.6 ± 1.7 1.07 (0.85–1.35) 50 (32) 12.4 ± 1.8 1.01 (0.75–1.35)
> 300.0 69 (24) 12.8 ± 1.6 1.00 50 (33) 12.7 ± 1.3 1.00
CI, confidence interval.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7108ehp0112-00129315345342ResearchArticlesExhaled Breath Condensate as a Suitable Matrix to Assess Lung Dose and Effects in Workers Exposed to Cobalt and Tungsten Goldoni Matteo 12Catalani Simona 3De Palma Giuseppe 1Manini Paola 12Acampa Olga 2Corradi Massimo 12Bergonzi Roberto 3Apostoli Pietro 3Mutti Antonio 21National Institute of Occupational Safety and Prevention, Research Centre at the University of Parma, Parma, Italy2Department of Clinical Medicine, Nephrology and Health Sciences, University of Parma, Parma, Italy3Department of Experimental and Applied Medicine, University of Brescia, Brescia, ItalyAddress correspondence to A. Mutti, Laboratory of Industrial Toxicology, Department of Clinical Medicine, Nephrology, and Health Sciences, University of Parma, Via Gramsci 14, 43100 Parma, Italy. Telephone: 0039-0521-033075. Fax: 0039-0521-033076. E-mail:
[email protected] thank I. Cortesi and D. Folli for their cooperation during the field survey and R. Andreoli and A. Caglieri for skillful technical assistance.
This work was supported by the National Heart, Blood and Lung Institute (NHLBI), Bethesda, MD, USA (grant 1R01 HL72323-01), and by the Italian Ministry of Education, University and Research (PRIN 200306145).
Contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of the NHLBI or the National Institutes of Health.
The authors declare they have no competing financial interests.
9 2004 10 6 2004 112 13 1293 1298 22 3 2004 10 6 2004 Publication 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 aim of the present study was to investigate whether exhaled breath condensate (EBC), a fluid formed by cooling exhaled air, can be used as a suitable matrix to assess target tissue dose and effects of inhaled cobalt and tungsten, using EBC malondialdehyde (MDA) as a biomarker of pulmonary oxidative stress. Thirty-three workers exposed to Co and W in workshops producing either diamond tools or hard-metal mechanical parts participated in this study. Two EBC and urinary samples were collected: one before and one at the end of the work shift. Controls were selected among nonexposed workers. Co, W, and MDA in EBC were analyzed with analytical methods based on mass spectrometric reference techniques. In the EBC from controls, Co was detectable at ultratrace levels, whereas W was undetectable. In exposed workers, EBC Co ranged from a few to several hundred nanomoles per liter. Corresponding W levels ranged from undetectable to several tens of nanomoles per liter. A parallel trend was observed for much higher urinary levels. Both Co and W in biological media were higher at the end of the work shift in comparison with preexposure values. In EBC, MDA levels were increased depending on Co concentration and were enhanced by coexposure to W. Such a correlation between EBC MDA and both Co and W levels was not observed with urinary concentration of either element. These results suggest the potential usefulness of EBC to complete and integrate biomonitoring and health surveillance procedures among workers exposed to mixtures of transition elements and hard metals.
cobaltexhaled breath condensatehard metalslungmalondialdehydeoxidative stresstungsten
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Cobalt is a transition element occurring in four valences (0, +2, +3, and +4), the divalent oxidation state being the most common. Tungsten, also known as wolfram (W), is a hard metal that can occur in the natural state only in the form of chemical compounds with other elements. Co is a major constituent (5–10%) of hard metal alloys, mainly based (> 80%) on tungsten carbide (W-C) and small percentages of other carbides (Lauwerys and Lison 1994). Other industrial uses of Co include diamond polishing with Co-containing disks and production of drying agents, pigments, and catalysts. W and its alloys are used extensively for filaments for electric lamps, electron and television tubes, and metal evaporation work. W has no recognized physiologic roles, whereas Co is a cofactor of vitamin B12 and therefore is an essential element (Taylor and Marks 1978).
Occupational exposure to Co can lead to various lung diseases, such as interstitial pneumonitis, fibrosis, and asthma [Agency for Toxic Substances and Disease Registry (ATSDR) 1999; Cirla 1994; Lauwerys and Lison 1994; Lison 1996; Nordberg 1994]. Although the mechanisms of Co-induced lung toxicity are not completely known, there is evidence from both in vivo and in vitro experiments supporting the view that Co induces the production of reactive oxygen species (ROS) with subsequent oxidative stress [reviewed by Lison et al. (2001); Nemery et al. (1994); Salnikow et al. (2000)]. In addition, ROS generation by Co administration is significantly increased by coexposure to W-C particles, through a physical–chemical mechanism of interaction (Lison et al. 1995). Although W-C alone seems to cause marginal genotoxic effects (De Boeck et al. 2003; Van Goethem et al. 1997), when administered alone it would be unable to induce lung parenchymal lesions (Lasfargues et al. 1992) and oxidative stress (Lison et al. 1995). Although Co was classified as group 2B, possibly carcinogenic to humans [International Agency for Research on Cancer (IARC) 1991], its association with W-C has recently been included in group 2A, probably carcinogenic to humans (IARC 2003). Therefore, strict control of dust level and regular health monitoring are recommended for workers employed in the hard metal industry, where such a coexposure may occur.
Urinary Co excretion (Co-U) has been proposed as a biomarker of exposure because of its correlation with airborne Co concentration (Alexandersson 1988; Apostoli et al. 1994; Christensen and Poulsen 1994; Linnainmaa and Kiilunen 1997; Scansetti et al. 1985). Little information is available about W metabolism and kinetics, mainly because of the lack of suitable methods for its determination in biological matrices other than the relatively recent and expensive technique based on inductively coupled plasma–mass spectrometry (ICP-MS). W measurement in urine has been proposed as a suitable exposure biomarker in the hard metal industry, where exposure to W-C is variable and has been reported to reach 417 μg/m3 (Kraus et al. 2001).
A key issue for risk assessment in occupational health is the characterization of dose at the target organ level. In the case of inhalable pneumotoxic metals, such as Co and W, a noninvasive method for sampling from the lung would be extremely useful. Standard methods for the evaluation of lung pathobiology (bronchoscopy, induced sputum) have a high degree of invasiveness, which limits their applicability to occupational monitoring. Exhaled breath condensate (EBC), obtained by cooling exhaled air under condition of spontaneous breathing, is a new technique to assess pulmonary status, and published data suggest that its composition has a good correlation with bronchoscopic specimens (Kharitonov and Barnes 2001; Mutlu et al. 2001). EBC is a noninvasive and simple procedure, and portable devices have been developed; therefore, it has the potential for application in occupational settings (Antczak and Gorski 2002; Lemiere 2002). Preliminary studies on smokers and patients with chronic obstructive pulmonary disease have revealed that several toxic metals and trace elements are detectable in EBC, raising the possibility of using this new matrix to quantify the lung tissue dose of pneumotoxic substances (Mutti et al. 2003). Furthermore, EBC analysis can be used to quantify relevant biomarkers reflecting lipid peroxidation of membrane cells (Corradi et al. 2003; Kharitonov and Barnes 2001; Mutlu et al. 2001). To the best of our knowledge, EBC analysis has never been used in occupational settings.
The aim of the present study was to investigate whether EBC can be employed for a better risk assessment among workers exposed to pneumotoxic metals, using workers from the hard metal industry as a first paradigmatic application.
Materials and Methods
Subjects.
Thirty-three workers from three factories producing either diamond tools (group A, n = 12 subjects; group B, n = 11 subjects) or hard-metal inserts (group C, n = 10 subjects) were recruited to participate to this study. Groups A and B were exposed either to Co and W-C powders, which were mixed to produce hard-metal alloys by synterization, or to metal dust originating from dry-grinding activities. Subjects from group C were exposed to metal dust produced during the dry grinding of hard-metal pieces. Sixteen adult healthy subjects, not occupationally exposed to metals, were recruited as the control group. Controls were defined as individuals with normal spirometry and without a significant history of lung diseases.
Demographic and clinical data of participants are summarized in Table 1. Workers belonging to groups A, B, and C presented normal spirometric indexes, with the exception of a single worker (heavy smoker) from group B showing mild airway obstruction. Two subjects from group A and one from group C had a history of mild intermittent asthma, and both were under therapy with inhaled salbutamol as needed. One subject from group B had radiologic evidence of pulmonary fibrosis ascribed to the occupational exposure to hard metals. All other workers were asymptomatic and did not refer a significant current or past respiratory diseases. Symptoms of acute respiratory illness within the 4 weeks preceding the study were ruled out in all subjects. All workers participating in the study denied vitamin B12 supplementation or beer drinking (which are potential confounders as sources of Co) over the week preceding the study.
Study design.
Workers belonging to groups A and C were evaluated both before (~ 15 hr after the end of the last exposure) and at the end of the same 8-hr work shift. Samples from subjects in group B were obtained only at the end of the work shift. In addition to EBC and urine collection, a short questionnaire about current and past medical history was completed and a spirometry was performed. We carried out the same procedures in controls in our laboratory during a normal working day. In order to verify whether possible air contamination in the offices could influence exhaled metals, three controls collected EBC inside the offices of the plants. Because the levels of Co and W in EBC of these subjects were undetectable, we concluded that the contamination of office environmental air, if any, provided a negligible contribution to the concentration of these elements in EBC.
All subjects enrolled in the study provided written informed consent to the procedures, which were approved by the local institutional human ethical committee. The sampling of biological material was carried out according to the Declaration of Helsinki (World Medical Association 2002).
Spirometric measurements.
Spirometry was performed with a pneumotachograph (Koko Spirometer; Sensormedics, Milan, Italy). We obtained mean values for forced expiratory volume in the first second (FEV1) and forced vital capacity (FVC) from the three best acceptable test values of lung function, according to the recommendation of the American Thoracic Society (1995).
Environmental measurements.
Environmental monitoring was carried out by stationary samplers. The inhalable fraction of particulate matter was collected using a selector, following the procedures suggested by the American Conference of Governmental Industrial Hygienists (ACGIH 2003). Airborne particulate was collected on cellulose ester membranes (0.8 μm porosity, 25 mm diameter) at a constant flow of 3 L/min for a period ranging from 4 to 7 hr during the same day of biological monitoring. Membranes conditioned before and after dust sampling were weighted in a thermohygrometrically conditioned cabinet using a precision microbalance reading 0.0001 mg. Membranes were then dissolved in concentrated hyperpure nitric acid, and the solution was diluted with ultrapure water. The analytical blank was obtained from virgin membranes, nitric acid, and water. Co and W were analyzed by an ICP-MS instrument using the same method applied to analyze biological samples (Apostoli et al. 1998). Measured dusts were expressed as micrograms per cubic meter.
Airborne concentrations of Co and W are reported in Table 2. The highest concentrations of Co and W were observed in factory 3 (group C). In factories 1 and 2 (groups A and B, respectively), comparable Co levels were observed, whereas factory 2 showed slightly higher W levels.
We did not use personnel biomonitoring of Co and W, mainly because, in the three plants considered, the work places were very narrow and the workers had almost no mobility within the working area. Therefore, the collectors were placed in the proximity of the workers’ breathing area. In addition, because of the expected low airborne levels, the use of stationary samplers allowed the use of higher flow rates, thereby increasing sampling efficiency and reducing analytical errors, which may have a greater impact at relatively low airborne concentrations.
EBC collection.
EBC was collected with a simple homemade apparatus formed by five components: a) a mouthpiece set up to work also as a saliva trap; b) a nonrebreathing polypropylene valve; c) a 10-cm Tygon tube (Nalgene 890 FEP tubing; Nalge Nunc International, Rochester, New York, USA); d) a 50-mL polypropylene vial; and e) a Dewar flask refrigerated with gel refrigerant (Ice-Brix; BDH Laboratory Supplies, Poole, Dorset, UK); the apparatus was placed at –20°C the night before the measurements. The five disposable components can be easily assembled, giving rise to simple portable apparatus essentially composed of two parts: a) a disposable part, which is maintained at room temperature and is composed of the mouthpiece with the rebreathing valve and the tube (which connects the valve to the vial); and b) the condensing part, which is composed of a disposable vial immersed in the refrigerant gel inside the Dewar flask. Inside the Dewar flask kept at room temperature, the gel refrigerant remains completely frozen up to 6 hr at a constant temperature (–20°C). Therefore, it can be used for several EBC collections. Exhaled air condenses along the internal surface of the vial, whose temperature is close to –20°C. Upon condensation, EBC droplets collect at the bottom of the tube, which, unlike the tube walls, is not in contact with the refrigerant gel; this keeps the temperature slightly above 0°C. Therefore, EBC collected with this device remains liquid. This is an important feature because some proteins and peptides may change their quaternary structure and lose immunoreactivity upon repeated thawing and freezing.
In EBC samples collected with this device from 12 subjects, salivary contamination was excluded through the colorimetric detection of α-amylase (Infinity amylase reagent; Sigma, St. Louis, MO, USA). Any possible release of Co and W from plastics or contamination during EBC collection was excluded in repeated experiments made by extensive washing of each component of the collection circuit (data not shown).
Subjects were asked to breathe tidally through the mouthpiece for 10 min, sitting comfortably in the workplace office (workers) or in our laboratory (controls). Subjects were instructed to form a complete seal around the mouthpiece with their mouth and to maintain a dry mouth during collection by periodically swallowing excess saliva. In addition, they were asked to rinse their mouths thoroughly before the maneuver and each 5 min during the test. To prevent any contamination from skin, subjects were asked to wash their hands before EBC and urine collection and to wear disposable latex gloves during the collecting procedures. EBC samples (almost 1 mL) were transported in dry ice to the laboratory and stored at –80°C in polypropylene tubes until analytical determinations.
Analyses of Co and W in urine and EBC.
Co-U and urinary excretions of W (W-U) were measured using flow injection (FI) ICP-MS and expressed as a function of creatinine, as previously described (Apostoli et al. 1998).
The protocol used for water analysis was applied to EBC samples because EBC does not show any matrix effect. Briefly, 2.5 mL of ultrapure bidistilled water (MilliQ; Millipore, Milan, Italy) was added to 0.5 mL EBC, which was then vigorously shaken before analysis by FI ICP-MS. We determined the accuracy of methods by means of analyzing standard reference material 1640 [National Institute of Standards and Technology (NIST) Gaithersburg, MD, USA]. The precision expressed as coefficient of variation varied from 4 to 8% among series and from 6 to 12% between the series. The detection limit, determined on the basis of 3 SDs of the background signal, was 0.003 μg/L for both Co and W.
The method for Co analysis in EBC was compared with the most used technique relying on atomic absorption spectroscopy with Zeeman background correction (ETAAS-Z). The agreement between the two methods was assessed both by correlation analysis and by applying the Bland-Altman method, a suitable approach to verify the agreement between two independent methods (Bland and Altman 1986), and is presented in Figure 1.
Determination of malondialdehyde (MDA) in EBC.
We determined MDA in EBC (MDA-EBC) by liquid chromatography tandem mass spectrometry (LC-MS/MS) using an Applied Biosystems-Sciex API 365 triple quadrupole mass spectrometer (Sciex, Concord, Ontario, Canada) equipped with a heated nebulized interface for atmospheric pressure chemical ionization. Aldehydes were separated by reversed-phase liquid chromatography after derivatization with dinitrophenylhydrazine, as previously described (Andreoli et al. 2003). The limit of detection was 1.0 nmol/L of MDA, and the limit of quantitation was 3 nmol/L.
Statistical analyses.
Statistical analysis was performed using SPSS 11.5 software (SPSS, Chicago, IL, USA). When separate groups were considered, Co and W in biological media were not normally distributed. Also, the corresponding log-transformed values did not result in Gaussian distributions (one-sample Kolmogorov-Smirnov Z and Shapiro-Wilk test). Therefore, results were reported as median and range, and Wilcoxon and Mann-Whitney tests were used to assess differences between groups. Owing to the lack of interference of tobacco smoking on metal levels, further statistical analyses were performed irrespective of smoking habits.
In the overall sample, all measured parameters showed a log-normal distribution (Kolmogorov-Smirnov Z and Shapiro-Wilk test). Therefore, all regression analyses were performed on log-transformed values. Pearson’s R was used to assess the correlation between variables, with p-values < 0.05 (two-tailed) considered statistically significant. To compare the difference between the slopes of different regression lines, we used an appropriate form of Student’s t-test (Glantz 2002). Finally, to assess the effect of Co and W on the MDA levels, we performed an analysis of covariance (ANCOVA) on log-transformed values. The significance level for all the used tests was p = 0.05.
Results
Analytical determinations on urine and EBC samples collected at the end of the work shift are summarized in Table 3. In controls, Co-EBC was measurable by ICP-MS but not by ETAAS-Z, whereas both techniques revealed measurable and much higher levels in all exposed workers. We observed a similar behavior for Co-U, whose values among exposed workers were higher by several orders of magnitude compared with both controls and the corresponding Co-EBC concentrations.
In samples collected before the work shift among workers from group A, neither Co-EBC [median, 2.0 nmol/L (interquartile range, 0.5–3.4 nmol/L)] nor Co-U [2.1 μmol/mol creatinine (1.7–3.8 μmol/mol)] was significantly different compared with controls. In group C, basal levels of both Co-EBC [median, 57.7 nmol/L creatinine (interquartile range, 10.2–219.0 μmol/mol creatinine)] and Co-U [7.8 μmol/mol creatinine (2.7–11.8 μmol/mol creatinine)] were much higher than control values (p < 0.01).
In workers from groups A and C, paired analysis showed significant increases in Co-EBC levels at the end of exposure compared with values recorded before the working shift (p < 0.01 and p < 0.05, respectively). In workers from group C, but not from group A, a significant difference between preexposure and postexposure levels was observed for Co-U (p < 0.01).
At both sampling times, W was not detectable in EBC or in most urine samples from group A workers and controls. In samples collected from group C workers before the working shift, W-EBC [median, 8.7 nmol/L (interquartile range, 3.3–16.9)] and W-U [2.3 μmol/mol creatinine (1.1–3.5 μmol/mol creatinine)] were higher than were control values but about three times lower compared with the corresponding postexposure levels reported in Table 3 (p < 0.01).
MDA-EBC was measurable in all samples. In groups B and C, postexposure MDA-EBC levels were higher than control values (p < 0.01 and p < 0.05, respectively), whereas the difference did not reach statistical significance for less-exposed workers in group A (p = 0.1). A significant increase in MDA-EBC values over preexposure levels [median, 9.2 nmol/L (interquartile range, 5.2–15.0 nmol/L )] was apparent in group C (p < 0.01), whereas no differences in MDA-EBC levels were observed in group A compared with preexposure concentrations [7.9 nmol/L (6.5–10.3 nmol/L)].
Co-EBC levels did not correlate with airborne Co (R = 0.27, p = 0.15). A positive correlation between airborne Co and Co-U levels was observed in groups A and C, either considered separately (R = 0.77, p < 0.01, and R = 0.78, p < 0.01, respectively) or taken together (R = 0.79, p < 0.01). The inclusion of group B lowered the correlation coefficient (R = 0.46, p < 0.01). In group B, the correlation between airborne Co and Co-U levels observed in groups A and C was no longer apparent. In workers from group C only, a positive correlation (R = 0.70, p < 0.05) was observed between airborne W and W-U levels.
In group B, we observed an apparent inconsistency between the relatively low airborne Co levels and the high concentrations of Co-U. A possible explanation is represented by poor hygienic conditions (a couple of workers smoked during work hours), which may lead to hand contamination and Co absorption from routes other than inhalation (dermal and oral).
In preexposure and postexposure pooled samples (Figure 2A), Co-EBC was significantly related to W-EBC both for group B (R = 0.70, p < 0.05) and group C (R = 0.72, p < 0.01), although with a different slope (1.11 ± 0.41 for group B vs. 0.50 ± 0.11 for group C, mean ± SEM). In groups B and C, a similar correlation was also found (Figure 2B) between Co-U and W-U (R = 0.80, p < 0.01, and R = 0.91, p < 0.01, respectively). Although we observed no difference between the slopes of the two regression lines (0.97 ± 0.27 for group B vs. 0.86 ± 0.10 for group C), a shift between the intercepts of the two regression lines was evident.
In a pooled analysis of preexposure and postexposure samples from all workers (Figure 3A), Co-EBC correlated with Co-U (R = 0.62, p < 0.01). A similar relationship on pooled data (Figure 3B) was observed between W-U and W-EBC (R = 0.48, p < 0.01).
In group C, MDA-EBC levels at both times were significantly correlated with Co-EBC (R = 0.91, p < 0.01) but not with Co-U. In workers from groups A and B, such a correlation was apparent in postexposure samples (R = 0.68, p < 0.05, and R = 0.67, p < 0.05, respectively). In group C, we also found a correlation between W-EBC and MDA-EBC (R = 0.77, p < 0.01).
To test whether the concomitant W exposure interferes with Co pneumotoxicity, we identified two different groups of workers on the basis of the median of W-EBC. Regression lines between Co-EBC and MDA-EBC among subjects with W-EBC, respectively, lower and higher than 16.3 nmol/L (Figure 4) showed different slopes (mean ± SEM, 0.21 ± 0.04 vs. 0.37 ± 0.11; p < 0.01, Student’s t-test for the regression lines). In an ANCOVA model using MDA-EBC as the dependent variable, both W-EBC (p = 0.002) and Co-EBC (p < 0.001) had a significant influence on the variability of MDA-EBC. A highly significant Co by W interaction was also observed (p < 0.001). Interestingly, smoking habits did not enter in the model either alone or in combination with covariates.
Discussion
The present study demonstrates that Co and W can be measured in the EBC of occupationally exposed workers and thus suggests the potential use of this matrix as a novel approach to monitor target tissue dose and effects occurring in the respiratory tract upon exposure to pneumotoxic substances. Indeed, inhaled toxic chemicals can act locally on the lung, which represents the route of entry of most environmental pollutants.
Biological monitoring of exposure to trace elements and toxic metals is mainly based on their measurement in blood and urine. However, these biomarkers integrate the overall intake from different absorption routes and can be used to assess the risk of systemic effects, rather than local effects on the respiratory tract. These limitations are confirmed in the present study by the lack of correlation between either Co-U or W-U and MDA concentration in EBC. On the contrary, both Co and W in EBC were correlated with MDA-EBC levels, thus suggesting that exhaled elements may reflect the lung dose responsible for local toxic effects. In addition, the relationship between Co levels in EBC and urine seems to indicate that Co-EBC really may represent the fraction of body dose (represented by Co-U), which has been inhaled and has not yet moved from lung tissue in the systemic circulation at the sampling time. However, the relatively weak (for W in particular) correlation between Co and W levels in EBC and the respective urinary levels suggests that other absorption routes (i.e., dermal and oral absorption) may provide a sizable contribution to absorbed dose and subsequent urinary excretion. Furthermore, the different bioavailability and solubility of Co and W (Kraus et al. 2001) could account for the weak correlation between the levels into two compartments.
Because of the novelty of the EBC measurements, the analytical validity of determinations was strictly controlled. We employed analytical methods based on mass spectrometric reference techniques, namely, LC-MS/MS for MDA and FI ICP-MS for metals, in order to obtain accurate and reliable results. Compared with other biological matrices, such as urine and blood, EBC represents a “clean” matrix, consisting mostly of water. The reliability of the elemental measurements in EBC was also assessed by performing the analysis of Co in EBC samples with two techniques relying on different principles, namely, FI ICP-MS and ETAAS-Z. The results showed a high repeatability, and therefore Co in EBC can be easily measured relying on a readily available technique such as ETAAS-Z.
The assessment of metal concentrations in different biological fluids can also provide useful information about kinetics and a better comprehension of physical–chemical interactions between metals, for example, between Co and W. In the present study, Co-U was related to both air concentrations and Co-EBC, despite the lack of correlation between Co-EBC and airborne Co. This could be consistent with a fast kinetic of inhaled Co, which can be readily absorbed via the lungs in the organism (ATSDR 1999). Therefore, we speculate that Co-EBC reflects not only exposure but also the amount of the element retained in the lung and eliminated with exhaled air after its interaction with—and possibly damage to—resident cells. In fact, there is a relationship between MDA-EBC (a marker of lipoperoxidation of membranes) and Co-EBC, which might represent a marker of effective dose or dose at the target. The correlation between MDA-EBC and Co-EBC is consistent with the mechanism of action of Co, which is known to cause oxidative stress (Lewis et al. 1991; Lison et al. 1995, 2001; Nemery et al. 1994). Considering that Co-U decreases rapidly (within 24 hr) after Co exposure has ceased (Alexandersson 1988; Apostoli et al. 1994), it is likely that the kinetics of absorption of Co at the level of the primary target organ (respiratory tract) are even faster. This is also consistent with the drastic drop in Co-EBC values at preexposure level about 16 hr after the last exposure.
A better understanding of the physical–chemical interactions between Co and W in vivo is another issue to which EBC can contribute. Exposure to W was quite different despite a similar productive cycle, probably depending on a different composition of hard metal alloys (covered by industrial secrets) and, perhaps, on different behaviors of workers from the point of view of personal hygiene. In a couple of outliers, urinary levels were higher by orders of magnitude than expected on the basis of the corresponding air and EBC concentrations. The significant Co-EBC by W-EBC interaction in the ANCOVA model with MDA-EBC as a dependent variable strongly suggests that W exposure has a synergistic effect in vivo on Co pneumotoxicity. This is in agreement with published data obtained from in vitro experiments (Lison et al. 2001). In fact, although W-C alone is known to be inert, there is some evidence that the physical–chemical association of Co and W-C generates electrons (provided by Co and transferred on the surface of W-C), which can reduce oxygen, thus giving rise to ROS (Lison et al. 1995).
Although Figure 2 shows that the levels of Co and W in EBC and urine are factory dependent, the concentration of MDA in EBC is strongly associated with both Co and W levels in EBC, thus confirming the most recent understanding of hard metal lung disease as a consequence of the combined effects of these elements.
In conclusion, the present study shows that EBC analysis is a promising matrix to assess the target tissue dose of pneumotoxic substances from polluted workplaces and to assess early effect markers, such as MDA.
Figure 1 Correlation (A) and Bland-Altman graph (B) of the comparison between ETAAS-Z and FI ICP-MS techniques for the measurements in 12 EBC samples. In (A), the solid line represents the best fit of experimental values, and the dashed line shows the theoretical identity line. In (B), the solid line shows the mean deviation between the two methods; dashed lines indicate the mean ± 2 SD. A = –3.08381 ± 2.28647; B = 1.14266 ± 0.01921; R = 0.99817; p < 0.0001.
Figure 2 Correlation (A) between Co-EBC and W-EBC levels and (B) between Co-U and W-U levels. In (A), group B log(W-EBC) = 2.44 + log(Co-EBC1.12)and Group C log(W-EBC) = 0.15 + log(Co-EBC0.51). In (B), group B log(W-U) = 1.47 + log(Co-EBC0.97) and Group C log(W-U = 0.35 + log(Co-EBC0.86).
Figure 3 Correlation between (A) Co-U and Co-EBC levels and (B) between W-U and W-EBC levels. In (A), log(Co-EBC) = 0.99 + log(Co-U0.79); in (B), log(W-EBC) = 0.65 + log(W-U0.59).
Figure 4 Correlation between Co-EBC and MDA-EBC levels. Group 1, workers with W-EBC < 16.3 nmol/L [log(MDA-EBC) = 0.66 + log(Co-EBC0.21)]; group 2, workers with W-EBC > 16.3 nmol/L [log(MDA-EBC) = 0.44 + log(Co-EBC0.37)].
Table 1 Demographic and clinical characteristics of study groups.
Characteristic Controls Group A Group B Group C
No. of subjects 16 10 11 12
Age (years) 34.8 ± 2.1 39.1 ± 3.9 33.2 ± 2.0 37.9 ± 3.2
Sex (male/female) 11/5 10/0 10/1 8/4
No. of current/ex-/never-smokers 6/1/9 4/5/1 8/0/3 6/0/6
Pack-years of current/ex-smokers (mean ± SEM) 10.2 ± 1.5/20 13.1 ± 5.9/10.2 ± 2.9 12.6 ± 4.5/0 7.8 ± 1.5/0
FVC, percent of predicted (mean ± SEM) 104.3 ± 4.3 114.7 ± 11.0 98.5 ± 2.8 117 ± 3.4
FEV1, percent of predicted (mean ± SEM) 102.5 ± 0.1 116.5 ± 8.4 96.3 ± 3.4 110 ± 3.5
FVC/FEV1, percent (mean ± SEM) 83.1 ± 0.01 84.5 ± 1.0 81.9 ± 2.09 98 ± 1.5
Table 2 Airborne concentrations (mg/m3) of Co and W [median (range)] measured in the three working environments.
Group Co W
A 8.25 (0.1–16.4) < 0.01
B 8.45 (0.9–16.0) 0.10 (0.01–0.2)
C 26 (14.6–37.4) 3 (1.1–4.9)
Table 3 End-of-shift values [median (interquartile range)] of biomarkers in the three factories.
Variables Controls (n = 16) Group A (n = 10) Group B (n = 11) Group C (n = 12)
Co-EBC (nmol/L) 0.7 (0.5–1.0) 40.7 (11.9–54.3)** 126 (44.1–628)** 163 (37.3–741)**
Co-U (μmol/mol creatinine) 0.09 (0.06–0.4) 2.9 (1.7–5.3)** 50.0 (16.2–366)** 18.9 (7.2–49.2)**
W-EBC (nmol/L) < 0.5 < 0.5 1.1 (0.5–4.9) 25.6 (15.2–76.1)
W-U (μmol/mol creatinine) < 0.06 (< 0.06 – 1.5) < 0.06 (< 0.06 – 1.0) 1.2 (0.6–4.9) 8.2 (3.2–16.1)
MDA-EBC (nmol/L) 7.6 (7.0–8.5) 11.5 (6.3–14.6) 14.2 (12.4–16.6)** 26.5 (6.5–44.0)*
Co, 1 nmol/L = 58.9 ng/L, 1 μmol/mol creatinine = 0.52 μg/g creatinine; W, 1 nmol/L = 185 ng/L, 1 μmol/mol creatinine = 1.64 μg/g creatinine.
* p < 0.05 versus controls (Mann-Whitney test).
** p < 0.01.
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World Medical Association 2002. World Medical Association Declaration of Helsinki. Ethical Principles for Medical Research Involving Human Subjects. Ferney-Voltaire, France:The World Medical Association. Available: http://www.wma.net/e/policy/pdf/17c.pdf [accessed 12 July 2004].
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7001ehp0112-00129915345343ResearchArticlesParticulate Matter Exposure Impairs Systemic Microvascular Endothelium-Dependent Dilation Nurkiewicz Timothy R. 12Porter Dale W. 13Barger Mark 3Castranova Vincent 13Boegehold Matthew A. 121Department of Physiology and Pharmacology and2Center for Interdisciplinary Research in Cardiovascular Sciences, West Virginia University School of Medicine, Morgantown, West Virginia, USA3Pathology and Physiology Research Branch, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, West Virginia, USAAddress correspondence to T.R. Nurkiewicz, Department of Physiology and Pharmacology, Box 9229, Robert C. Byrd Health Sciences Center, West Virginia University, Morgantown, WV 26506-9229 USA. Telephone: (304) 293-7328. Fax: (304) 293-3850. E-mail:
[email protected] thank K. Wix for technical assistance.
Support was provided by National Institutes of Health Research Service Award HL-67562 (T.R.N.) and National Institutes of Health RO1 HL-44012 (M.A.B.).
The authors declare they have no competing financial interests.
9 2004 23 6 2004 112 13 1299 1306 4 2 2004 23 6 2004 Publication 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. Acute exposure to airborne pollutants, such as solid particulate matter (PM), increases the risk of cardiovascular dysfunction, but the mechanisms by which PM evokes systemic effects remain to be identified. The purpose of this study was to determine if pulmonary exposure to a PM surrogate, such as residual oil fly ash (ROFA), affects endothelium-dependent dilation in the systemic microcirculation. Rats were intratracheally instilled with ROFA at 0.1, 0.25, 1 or 2 mg/rat 24 hr before experimental measurements. Rats intratracheally instilled with saline or titanium dioxide (0.25 mg/rat) served as vehicle or particle control groups, respectively. In vivo microscopy of the spinotrapezius muscle was used to study systemic arteriolar dilator responses to the Ca2+ ionophore A23187, administered by ejection via pressurized micropipette into the arteriolar lumen. We used analysis of bronchoalveolar lavage (BAL) samples to monitor identified pulmonary inflammation and damage. To determine if ROFA exposure affected arteriolar nitric oxide sensitivity, sodium nitroprusside was iontophoretically applied to arterioles of rats exposed to ROFA. In saline-treated rats, A23187 dilated arterioles up to 72 ± 7% of maximum. In ROFA- and TiO2-exposed rats, A23187-induced dilation was significantly attenuated. BAL fluid analysis revealed measurable pulmonary inflammation and damage after exposure to 1 and 2 mg ROFA (but not TiO2 or < 1 mg ROFA), as evidenced by significantly higher polymorphonuclear leukocyte cell counts, enhanced BAL albumin levels, and increased lactate dehydrogenase activity in BAL fluid. The sensitivity of arteriolar smooth muscle to NO was similar in saline-treated and ROFA-exposed rats, suggesting that pulmonary exposure to ROFA affected endothelial rather than smooth muscle function. A significant increase in venular leukocyte adhesion and rolling was observed in ROFA-exposed rats, suggesting local inflammation at the systemic microvascular level. These results indicate that pulmonary PM exposure impairs systemic endothelium-dependent arteriolar dilation. Moreover, because rats exposed to < 1 mg ROFA or TiO2 did not exhibit BAL signs of pulmonary damage or inflammation, it appears that PM exposure can impair systemic microvascular function independently of detectable pulmonary inflammation.
arterioleendotheliumnitric oxideparticulate matterresidual oil fly ashROFAspinotrapezius musclesystemic microcirculationtitanium dioxide
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Evidence from industrialized countries indicates that acute exposure to airborne pollutants, such as solid particulate matter (PM), is associated with an increased risk of morbidity and mortality (Dockery 2001; Fairley 1990; Samet et al. 2000a, 2000b). The collective implication of this evidence is that PM affects tissues and organs outside the respiratory tract, as evidenced by the occurrence of cardiovascular dysfunction on high pollution days (Goldberg et al. 2000, 2001; Samet et al. 2000a). Although the epidemiologic evidence is convincing, the biologic mechanisms by which PM evokes systemic effects remain to be defined.
Only recently have scientists started to recognize that the systemic effects of PM may be as important as the pulmonary effects. Despite its obvious importance in regulating the convective delivery of cells and molecules to all tissues, and in the etiology of most cardiovascular diseases, no research has investigated how systemic microvascular function is affected by pulmonary PM exposure. However, the following results suggest that systemic microvascular effects of pulmonary PM exposure are possible. Constriction of the brachial artery has been observed in humans exposed to PM and ozone (Brook et al. 2002). PM elicits vasorelaxation in aortic rings that had been precontracted with phenylephrine but has no effect on small isolated arteries (Bagate et al. 2004; Knaapen et al. 2001). Although intriguing, conclusions from these latter findings are limited, because isolation of any tissue and subsequent exposure to PM in vitro assumes that PM must access the systemic circulation and interact directly with the vasculature.
Endothelial products can have potent effects on microvascular smooth muscle tone. Ulrich et al. (2002) found that pulmonary exposure to PM caused a 60% decrease in endothelin-1 and angiotensin-converting enzyme mRNA levels in lung tissue. They interpreted this result as an indication of possible endothelial damage in pulmonary blood vessels. However, the effect of pulmonary exposure to PM on endothelial cell function in the systemic microcirculation has not been evaluated.
Although several investigations have documented systemic responses to PM exposure, the underlying mechanisms of action remain unclear. However, inflammation appears to be a common denominator in the studies that report a systemic effect of PM exposure (Gardner et al. 2000; Moyer et al. 2002; Salvi et al. 1999; Tan et al. 2000; van Eeden et al. 2001). Cultured human alveolar macrophages (AMs) produce tumor necrosis factor (TNF-α) and proinflammatory cytokines such as granulocyte-macrophage colony-stimulating factor, interleukin-6 (IL-6), and IL-1 after phagocytosing PM. Additionally, circulating IL-1 is elevated in humans exposed to PM (van Eeden et al. 2001). Pulmonary production of such mediators may induce systemic responses. Indeed, TNF-α, IL-6, and IL-1 can stimulate microvascular dilation, alter leukocyte dynamics and increase microvascular permeability (Baudry et al. 1996; Brian and Faraci 1998; Kunkel et al. 1998; Matsuki and Duling 2000; Vicaut et al. 1991). Acute exposure to PM accelerates the transit of polymorphonuclear leukocytes (PMNLs) from bone marrow, whereas chronic exposure increases the size of the bone marrow PMNL pool (van Eeden and Hogg 2002). Blood samples from healthy humans exposed to PM reveal a systemic inflammatory response in the form of elevated immature PMNL (Tan et al. 2000), neutrophils, and platelets (Salvi et al. 1999). In addition to elevating circulating PMNLs, PM exposure has also been shown to accelerate the formation of athero-sclerotic lesions and increase plaque cell turnover (Suwa et al. 2002). Collectively, these systemic inflammatory events associated with PM exposure have been proposed to be linked to cardiovascular dysfunction in compromised individuals. However, specific changes and/or effects of PM exposure at the systemic microvascular level, where the majority of peripheral resistance resides (Renkin 1984; Zweifach et al. 1981; Zweifach and Lipowsky 1984) and vascular dysfunction can be lethal, have not been evaluated.
Microvascular resistance is responsible for the largest dissipation of pressure across the systemic circulation (Renkin 1984; Zweifach et al. 1981; Zweifach and Lipowsky 1984). The resistance of a given microvascular network is a function of network branching patterns, vessel anatomy, and vessel density (Zweifach et al. 1981). Because arteriolar resistance is a highly dynamic state that is chiefly determined by internal vascular diameter, it is acutely dependent on a delicate balance between local, humoral, and neural stimuli, as well as the microvascular wall anatomy. Disruption, impairment, or imbalance of these stimuli, often accompanied by changes in wall structure, have been well characterized in cardiovascular diseases such as hypertension and ischemia, with myocardial infarction and stroke being the most lethal (Baumbach et al. 2002; Hein et al. 2003; Prewitt et al. 2002; Schwartzkopff et al. 1992, 1998; Suzuki et al. 1994).
Based on the prevalence of cardiovascular dysfunction on high pollution days, we hypothesized that impairment of normal microvascular endothelial cell function constitutes an important component of the systemic effects associated with pulmonary PM exposure. The purpose of this study was to determine the effects of pulmonary PM exposure on systemic endothelium-dependent arteriolar dilation. Intratracheal (IT) instillation and bronchoalveolar lavage (BAL) were used in conjunction with intravital microscopy to study the effects of residual oil fly ash (ROFA) exposure on microvascular function in the rat spinotrapezius muscle. The spinotrapezius muscle preparation has been used for > 30 years as an experimental model to evaluate physiologic and pathophysiologic phenomena within the microcirculation (Bailey et al. 2000; Gray 1973). This preparation has been an essential tool in the fundamental understanding of capillary network development (Skalak and Schmid-Schonbein 1986); neurogenic, humoral, and myogenic control of microvascular resistance (Lash and Shoukas 1991; Nakamura and Prewitt 1991; Nurkiewicz and Boegehold 1998); and the physiologic roles of oxygen, nitric oxide, and calcium (Ca2+) in microcirculation (Linderman and Boegehold 1999; Pries et al. 1995; Toth et al. 1998). Additionally, the spinotrapezius muscle preparation has been extensively used to characterize pathophysiologic microvascular consequences of chronic diseases such as diabetes (Kindig et al. 1998), hypertension (Boegehold 1991; Nurkiewicz and Boegehold 1998; Zweifach et al. 1981), and heart failure (Kindig et al. 1999). Arterioles of the first branching order were studied because these vessels, together with their upstream feed arteries, are responsible for approximately 60% of total spinotrapezius muscle vascular resistance and therefore are of major importance for local blood flow regulation (Boegehold 1991).
Experimental Objectives
Objective 1.
Our first objective was to determine if pulmonary exposure to PM has an effect on systemic microvascular function. Rats were exposed by IT instillation to various doses of ROFA or saline. At 24 hr postexposure, we evaluated systemic microvascular function by intravital microscopy of arterioles in the spinotrapezius muscle. Microvascular function was determined by measurement of resting arteriolar diameter and tone and by responsiveness to dilation dependent on the calcium ionophore A23187.
Objective 2.
Our second objective was to determine if the effects of pulmonary exposure to PM on systemic microvascular function were due to modification of endothelial or smooth muscle function. Rats were exposed to PM by IT instillation, and arteriolar smooth muscle sensitivity to NO was determined by iontophoretically applying the NO donor sodium nitroprusside (SNP) to the exterior arteriolar wall and measuring dilation.
Objective 3.
Our third objective was to determine if the effects of pulmonary exposure to PM on systemic microvascular function were dependent on pulmonary and/or microvascular inflammation. Rats were exposed to PM by IT instillation. Pulmonary inflammation and damage were measured 24 hr postexposure as BAL cell counts and activity, acellular BAL albumin levels, and BAL fluid lactate dehydrogenase (LDH) activity. Microvascular inflammation was monitored microscopically 24 hr postexposure by measuring systemic leukocyte adhesion and rolling in first-order venules of the spinotrapezius muscle.
Materials and Methods
ROFA preparation.
ROFA was collected from a precipitator at Boston Edison Company (Mystic Power Plant #4, Everett, MA). ROFA particle size and elemental composition from this source have been previously characterized (Antonini et al. 2002; Roberts et al. 2004). ROFA particles were of respirable size with a count mean diameter of 2.2 μm (as determined by scanning electron microscopy (JSM 5600 SEM; JEOL Ltd., Peabody, MA). The major metal contaminants were iron, aluminum, vanadium, nickel, calcium, and zinc. The main soluble metals were aluminum, nickel, and calcium. Vanadium comprised < 1% of the soluble metals in ROFA samples from this source. ROFA (suspended in sterile saline) was sonicated for 1 min with a Sonifer 450 cell disruptor (Branson Ultrasonics, Danbury, CT) before IT instillation.
IT instillation.
Male Sprague-Dawley rats (7–8 weeks of age) were lightly anesthetized by an intraperitoneal (ip) injection of sodium methohexitol (Brevital) and instilled with ROFA (0.1, 0.25, 1, or 2 mg/rat, IT, in a 300 μL sterile saline suspension) according to the method of Brain et al. (1976). Rats in the vehicle control group were IT dosed with 300 μL sterile saline. Rats in the particle control group were dosed with titanium dioxide (0.25 mg/rat, IT, in a 300 μL sterile saline suspension). The higher ROFA doses chosen for these experiments (1 mg and 2 mg) have been previously shown to produce significant pulmonary inflammation (Antonini et al. 2002) and fall within the range of concentrations consistently used in other animal studies evaluating pulmonary responses to ROFA (Antonini et al. 2002; Dreher et al. 1997; Gavett et al. 1997; Kodavanti et al. 1998). After IT instillation, all rats were allowed to recover for 24 hr before subsequent BAL or intravital microscopy experiments.
Collection of BAL samples and blood for measurement of systemic and pulmonary inflammation.
Rats were euthanized with sodium pentobarbital (≥100 mg/kg, ip). A tracheal cannula was inserted, and BAL was performed through the cannula using ice-cold Ca2+/Mg2+-free phosphate-buffered saline (PBS). The first lavage was 6 mL and was kept separate from the rest of the lavage sample. Subsequent lavages used 8 mL PBS until a total of 80 mL lavage fluid was collected. The samples were centrifuged (650 × g, 5 min at 4°C), and the acellular first lavage supernatant (BAL fluid) was aspirated and saved for LDH activity and albumin protein assays. Cells were combined, resuspended in HEPES-buffered medium (10 mM N-[2-hydroxyethyl]piperazine-N′-[2-ethanesulfonic acid], 145 mM NaCl, 5 mM KCl, 1 mM CaCl2, and 5.5 mM d-glucose, pH 7.4) and centrifuged a second time (650 × g, 5 min, 4°C). The cells were then resuspended in HEPES-buffered medium, and cell counts were determined with an electronic cell counter equipped with a cell-sizing attachment (Porter et al. 2002). Whole blood was drawn from a carotid cannula (inserted for intravital microscopy experiments, described below) using a Vacutainer blood collection tube with sodium ethylenediamine tetraacetate as an anticoagulant. Blood cell differentials were determined with a Cell-Dyne 3500R hematology cell counter (Abbott Diagnostics, Abbott Park, IL) (Porter et al. 2001).
BAL fluid LDH activity and albumin protein assays.
All measurements used for the current study were made on the day that samples were taken, and any remaining samples were frozen for long-term storage (−80°C). BAL fluid LDH activities were determined as a marker of cytotoxicity and were measured by monitoring the LDH catalyzed oxidation of pyruvate coupled with the reduction of nicotinamide adenine dinucleotide at 340 nm using a commercial assay kit (LDH Reagent; Roche Diagnostic Systems, Montclair, NJ) and a Cobas Fara II Analyzer (Roche Diagnostic Systems), as previously described (Porter et al. 2002). BAL fluid albumin concentrations were determined as an indicator of the integrity of the blood–pulmonary epithelial cell barrier. BAL fluid albumin was measured colorimetrically at 628 nm based on albumin binding to bromcresol green using a commercial assay kit (Albumin BCG diagnostic kit; Sigma Chemical Co., St. Louis, MO) and a Cobas Fara II Analyzer, as previously described (Porter et al. 2002).
AM chemiluminescence.
AM chemiluminescence was determined as an indicator of reactive oxygen species production by AM. The use of unopsonized zymosan in the chemiluminescence assay allows only AM chemiluminescence to be measured, because unopsonized zymosan stimulates AM chemiluminescence but not PMNL chemiluminescence. The assay was conducted in a total volume of 0.25 mL HEPES-buffered media. Resting AM chemiluminescence was determined by incubating 1.0 × 106 AM/mL at 37°C for 20 min, followed by the addition of 5-amino-2,3-dihydro-1,4-phthalazinedione (luminol) to a final concentration of 0.08 μg/mL followed by the measurement of chemiluminescence. To determine zymosan-stimulated chemiluminescence, unopsonized zymosan (Sigma) was added to a final concentration of 2 mg/mL immediately before the measurement of chemiluminescence. All chemiluminescence measurements were made with an automated luminometer (Autolumat LB 953; Berthold, Gaithersburg, MD) at 390–620 nm for 15 min. The integral of counts per minute versus time was calculated. Zymosan-stimulated chemiluminescence was calculated as the counts per minute in the zymosan-stimulated sample minus the counts per minute in the resting sample. NO-dependent chemiluminescence was determined by subtracting the zymosan-stimulated chemiluminescence from cells pre-incubated with 1 mM 1400W, an inhibitor of NO synthase, from the zymosan-stimulated chemiluminescence from AM without 1400W.
Intravital microscopy.
Male Sprague-Dawley rats (7–8 weeks of age) were anesthetized with sodium thiopental (100 mg/kg ip) and placed on a heating pad to maintain a 37°C rectal temperature. The trachea was intubated to ensure a patent airway, and the right carotid artery was cannulated to measure arterial pressure. The right spinotrapezius muscle was then exteriorized for microscopic observation, leaving its innervation and all feed vessels intact. After exteriorization, the muscle was gently secured over an optical pedestal at its in situ length. The muscle was next enclosed in a tissue bath for transillumination and observation. In Sprague-Dawley rats of this age, the spinotrapezius muscle weighs 285 ± 56 mg (mean ± SE) and has a surface area of 192 ± 28 mm2 (Linderman and Boegehold 1996). Throughout the surgery and subsequent experimental period, the muscle was continuously superfused with an electrolyte solution (119 mM NaCl, 25 mM NaHCO3, 6 mM KCl, and 3.6 mM CaCl2), warmed to 35°C, and equilibrated with 95% N, 2–5% CO2 (pH 7.35–7.40). Superfusate flow rate was maintained at 4–6 mL/min to minimize equilibration with atmospheric oxygen (Boegehold and Bohlen 1988).
The animal preparation was then transferred to the stage of an intravital microscope coupled to a CCD (charge-coupled device) video camera. Observations were made with a 20× water immersion objective (final video image magnification, 1,460×). One to three microvessels were studied per rat. Video images were displayed on a high-resolution color video monitor and videotaped for off-line analysis. During videotape replay, arteriolar inner diameters were measured with a video caliper (Cardiovascular Research Institute, Texas A&M University, College Station, TX), and venular leukocyte adhesion was monitored.
Experimental protocol 1.
In the first series of experiments, arteriolar endothelium-dependent dilation was evaluated in the four ROFA groups and the saline and TiO2 groups by assessing the capacity for Ca2+-dependent endothelial NO formation in response to intraluminal infusion of the calcium ionophore A23187 (Sigma). Glass micropipettes were beveled to a 23–25° angle and a 2–4 μm inner tip diameter. One mg of A23187 was dissolved in 50 μL dimethyl sulfoxide and then serially diluted in PBS to produce a 10−7 M solution that was loaded into the micropipettes. A23187 increases endothelial NO synthase activity, which produces NO and therefore relaxes exposed vessels (Schneider et al. 2003). The micropipette was inserted into the lumen of the selected arteriole approximately 100 μm upstream from the diameter measurement site, and A23187 was then infused directly into the flow stream. A Picospritzer II pressure system (General Valve Corporation, Fairfield, NJ) was used to continuously infuse A23187 for 2-min periods at net ejection pressures of 5, 10, 20, and 40 psi. In preliminary tests, we verified that the amount of agonist ejected from the pipette tip is directly proportional to the ejection pressure. The order in which the different ejection pressures were applied was randomized in each experiment, and a 2-min recovery period followed each ejection. Given the resistance of the pipette tips, the fluid volumes ejected at these pressures are relatively small and increase total arteriolar volume flow by no more than 10% (Nurkiewicz TR, Boegehold MA, unpublished observations). At the end of each experiment, adenosine (ADO) was added via a syringe pump to the superfusate at a final concentration of 10−4 M to fully dilate the microvascular network and determine the passive diameter of each arteriole studied. We have previously shown that the magnitude of arteriolar dilation induced by 10−4 M ADO is not further augmented by subsequent exposure to SNP plus nifedipine in a Ca2+-free superfusate, indicating that 10−4 M ADO completely abolishes arteriolar tone in the exteriorized spinotrapezius muscle without altering systemic arterial pressure (Nurkiewicz and Boegehold 2000).
In a separate series of experiments, the contribution of NO to endothelium-dependent arteriolar dilation in the spinotrapezius muscle was evaluated by inhibiting local NO synthesis with NG-monomethyl-l-arginine (L-NMMA). Rats in this group of experiments were of the same age and weight range as those used in other protocols but did not receive IT saline. A23187 was infused into arterioles first under the normal superfusate at the four ejection pressures and again in the presence of L-NMMA in the superfusate. A syringe pump was used to continuously infuse L-NMMA at 0.4 mL/min into the superfusate delivery line. The stock L-NMMA concentration was adjusted to produce a final superfusate concentration of 10−4 M. We have previously shown that, in this vascular bed after 15 min of preincubation, L-NMMA at this concentration maximally inhibits the dilation of spinotrapezius muscle arterioles to acetylcholine (Boegehold 1995) and that L-NMMA is a specific inhibitor of NO synthesis (Nurkiewicz and Boegehold 1999).
Experimental protocol 2.
To evaluate arteriolar responsiveness to NO, the NO donor SNP was iontophoretically applied to individual arterioles in rats exposed to either saline or 0.25 mg ROFA. Glass micropipettes were prepared as above and filled with a 0.05 M solution of SNP in distilled water. The pipette tip was placed in light contact with the arteriolar wall, and a current programmer (model 260; World Precision Instruments, New Haven, CT) was used to deliver continuous 2-min ejection currents of 5, 10 and 20 nA. A recovery period of at least 2 min followed each application. The order of the 5- and 10-nA ejection currents was randomized, but the 20-nA ejection current was always performed last because of a considerably slower recovery from this stimulus. At the end of each experiment, ADO was added to the superfusate at a final concentration of 10−4 M to determine the passive diameter of each arteriole studied.
Experimental protocol 3.
Adhering or rolling leukocytes in first-order venules of rats exposed to either saline or 2 mg ROFA were quantified to characterize potential microvascular inflammation. Leukocytes that were either stationary or moving but in constant contact with the venular wall for at least 200 μm were counted for 1 min in each venule studied.
Data and statistical analysis.
Arteriolar diameter (D, in micrometers) was sampled at 10-sec intervals during all control and infusion periods. Resting vascular tone was calculated for each vessel as follows: tone = [(Dpass − Dc)/Dpass] × 100, where Dpass is passive diameter under ADO and Dc is the diameter measured during the control period. A tone of 100% represents complete vessel closure, whereas 0% represents the passive state. For comparisons of arteriolar responses to A23187 infusion among different treatments and experimental groups, responses were normalized as follows: percent change from control = [(Dss/Dc) − 1] × 100, where Dss is the steady-state diameter during A23187 exposure. All data are reported as mean ± SE. Statistical analysis was performed by commercially available software (Sigmastat; SPSS, Chicago, IL). One-way repeated-measures analysis of variance (ANOVA) was used to determine the effect of a treatment within a group or differences among groups. Two-way repeated measures ANOVA was used to determine the effects of group, treatment, and group–treatment interactions on measured variables. For all ANOVA procedures, the Student-Newman-Keuls method for post hoc analysis was used to isolate pair wise differences among specific groups. Significance was assessed at the 95% confidence level (p < 0.05) for all tests.
Results
The general characteristics of rats used for intravital microscopy experiments are reported in Table 1. At the time of study, mean age, body weight, and arterial pressure were not significantly different in the experimental groups. Rats used for BAL data were of the same age and body weight as those reported in Table 1 (data not shown). Resting variables of all arterioles studied are reported in Table 2. Resting arteriolar diameters were not significantly different among the experimental groups. Passive diameters (in the presence of ADO) in the 1-mg ROFA group were significantly greater than those in the 0.25-mg TiO2 and 0.25-mg ROFA groups; however, none of the particle groups was different from saline. Resting arteriolar tone was not different among the experimental groups.
The effects of pulmonary exposure to ROFA on BAL fluid and pulmonary cells are reported in Table 3. AMs were not significantly different in the experimental groups. However, ROFA exposure did cause dose-dependent changes in other indicators of pulmonary inflammation and damage. PMNL counts were significantly elevated in the 1- and 2-mg ROFA groups versus those in the saline and 0.25-mg TiO2 groups. Additionally, PMNL counts in the 1- and 2-mg ROFA groups were significantly greater than those in the 0.25- and 0.1-mg ROFA groups, which did not differ from either the saline or particle control (TiO2) groups. BAL fluid albumin was significantly elevated in the 1- and 2-mg ROFA groups versus that in the saline and 0.25-mg TiO2 groups. BAL fluid albumin in the 1- and 2-mg ROFA groups was also significantly greater than that in the 0.25- and 0.1-mg ROFA groups, which did not differ from either the saline or particle control (TiO2) groups. BAL fluid LDH in the 1- and 2-mg ROFA groups was significantly higher than that in the saline and 0.25-mg ROFA groups. BAL fluid LDH in the 1-mg ROFA group also was significantly greater than that in the 0.25-mg TiO2 group. In three rats (one exposed to 0.1 mg ROFA and two exposed to 0.25 mg TiO2), insertion of the tracheal cannula during BAL caused bleeding that distorted the albumin and LDH data in these rats. Therefore, BAL fluid data from those animals were omitted from Table 3. Total zymosan-stimulated AM chemiluminescence was significantly greater in the 2-mg ROFA group than that in the saline, TiO2, 1-mg, or 0.1-mg ROFA groups. NO-dependent AM chemiluminescence was significantly higher in the 2-mg ROFA group than that in all other groups.
In spinotrapezius muscle arterioles, A23187 infusion produced dose-dependent dilation that was near maximal at the highest ejection pressure in the IT-saline control group (Figure 1). This dilation was not an artifact of ejection pressure because infusion of the vehicle did not elicit a significant vasoactive response at any of the ejection pressures used in this study.
IT treatment of rats with 0.25 mg or 1 mg ROFA completely inhibited A23187-induced dilation of spinotrapezius muscle arterioles (Figure 2). Arterioles in rats exposed to 2 mg ROFA also failed to respond to A23187 infusion (data omitted from Figure 2 for clarity). In rats exposed to 0.1 mg ROFA, A23187 infusion produced an intermediate dilation at 20 and 40 psi that was significantly greater than that observed in rats exposed to 0.25 mg, 1 mg, or 2 mg ROFA but was less than that in IT-saline control rats. Therefore, pulmonary exposure to ROFA inhibited systemic microvascular function in a dose-dependent manner.
To determine if the inhibition of systemic vasodilation after pulmonary exposure to ROFA was substance specific, arteriolar responses to A23187 after IT instillation of a noninflammatory particle (TiO2) were evaluated. A23187 infusion had no effect on arteriolar diameter in rats exposed to 0.25 mg TiO2 (Figure 3). The arteriolar responses to A23187 infusion between rats exposed to 0.25 mg ROFA and 0.25 mg TiO2 were not different. Therefore, inhibition of systemic microvascular function in response to pulmonary PM exposure does not appear to depend on the pulmonary toxicity of the particle.
In Figure 4, the effect of A23187 infusion on arteriolar diameter is expressed in terms of percent change from control. A comparison is made between arteriolar responses under the normal superfusate, during L-NMMA superfusion, and in rats exposed to 0.25 mg ROFA. The normal superfusate and L-NMMA data shown here were previously collected from spinotrapezius muscle first-order arterioles in Sprague-Dawley rats of the same age and weight range as those used in the present study but did not receive IT saline (Nurkiewicz and Boegehold 2004). During L-NMMA superfusion, arteriolar dilation in response to A23187 was significantly decreased at each ejection pressure, indicating that > 50% of the arteriolar dilation due to A23187 infusion is NO dependent. The residual response to A23187 that persists in the presence of L-NMMA is NO independent. In rats exposed to 0.25 mg ROFA, arteriolar responses to A23187 at 10, 20, and 40 psi were significantly less than those responses observed during superfusion with L-NMMA. Thus, IT exposure to ROFA inhibits both NO-dependent and NO-independent components of A23187-induced dilation of systemic arterioles.
Figure 5 displays arteriolar responsiveness to iontophoretically applied SNP in rats exposed to either saline or 0.25 mg ROFA. The arteriolar responses to SNP at each current dose represent significant dilations from control diameter and were dose dependent. The arteriolar responses to SNP in the IT-saline control group and ROFA group were not different at any current dose. Thus, pulmonary exposure to ROFA did not alter the responsiveness of arteriolar smooth muscle to the dilator effects of NO.
Microvascular leukocyte activity in the spinotrapezius muscle is characterized in Figure 6. Such characterization involved monitoring the number of adhering and rolling leukocytes in venules that were closely paired to the arterioles of interest. Figure 6A shows a representative venule in a saline-treated rat. Figure 6B shows a representative venule in a rat exposed to 2 mg ROFA, where an increased leukocyte presence is clearly visible. Figure 6C indicates that leukocyte adherence and rolling was 4-fold greater in venules of ROFA-exposed rats than in saline-treated rats. This result is consistent with a local inflammatory response at the systemic microvascular level. Figure 6D indicates that systemic leukocyte concentrations were not different between ROFA-exposed and saline-treated rats.
Discussion
To our knowledge, this is the first study to demonstrate a significant effect of acute pulmonary PM exposure at the systemic microvascular level. There are four prominent observations within this study.
First, pulmonary PM exposure significantly impairs A23187-induced arteriolar endothelium-dependent dilation in the rat spinotrapezius muscle (Figures 2 and 3). In saline-treated rats, this microvascular response to intraluminal infusion of A23187 was dose dependent and near the maximal level of dilation. Resting arteriolar diameter was unchanged after ROFA or TiO2 exposure (Table 2). However, A23187-induced arteriolar endothelium-dependent dilation was either significantly impaired or abolished after ROFA or TiO2 exposure. A profound result of this study is that, despite our finding that ROFA or TiO2 exposure at 0.25 mg/rat did not cause significant pulmonary inflammation or damage (as judged by BAL PMNL influx, BAL albumin, and LDH release; Table 3), complete impairment of systemic A23187-induced endothelium-dependent arteriolar dilation was observed.
Second, pulmonary PM exposure appears to abolish both NO-dependent and NO-independent systemic arteriolar dilation (Figure 4). In the spinotrapezius muscle of the normal rat, NO synthase inhibition reduced the effect of intraluminal A23187 infusion by approximately 50% (open bars vs. black bars). The remaining responsiveness to A23187 may be due to a variety of NO-independent factors such as bradykinin, cyclooxygenase products, or ADO. Because the arteriolar response to intraluminal A23187 infusion after ROFA exposure is significantly less than that during L-NMMA superfusion in normal rats (blue bars vs. black bars), it appears that ROFA has the capacity to impair both NO-dependent and NO-independent arteriolar dilation.
Third, pulmonary PM exposure does not affect systemic arteriolar smooth muscle responsiveness to NO (Figure 5). Regardless of the iontophoretic ejection current, SNP applied to the arteriolar wall produced equivalent arteriolar dilation in both ROFA-exposed and saline-treated control rats. This finding is essential because it is impossible to characterize the adverse endothelial effects of PM exposure if the microvascular smooth muscle sensitivity to NO is not clearly defined.
Fourth, PM exposure increases the number of adhering and rolling leukocytes in spinotrapezius muscle venules (Figure 6). This significant increase in observable venular leukocytes (Figure 6C) could be due to a) an increased systemic leukocyte concentration and/or b) an increase in the adhesion activity between leukocytes and the venular endothelium. We did not observe a difference in the systemic leukocyte concentrations between ROFA-exposed and saline-treated control rats (Figure 6D). However, it is conceivable that the increase in venular leukocyte adhesion after ROFA exposure is sufficient to lower free circulating leukocytes, thereby preventing an accurate measurement of systemic leukocytes. This may account for the similar systemic leukocyte concentrations between saline-treated and ROFA-exposed rats.
Few studies have investigated the effect of acute PM exposure on vascular function. Inhalation of concentrated ambient particles (CAPs) and ozone in humans causes constriction of the brachial artery but has no effect on endothelium-dependent or -independent dilation (Brook et al. 2002). Our finding that endothelium-independent dilation is unaltered (Figure 5) after PM exposure is in agreement with Brook et al. (2002), but we did not observe an effect of PM exposure on resting arteriolar diameter (Table 2). However, because vascular tone reflects an integrated response to a variety of signals that vary in importance among vascular beds (Cornelissen et al. 2002; Hill et al. 2001; Zweifach 1991), our finding that arteriolar tone is unaltered after PM exposure is not necessarily at odds with the observations of Brook et al. (2002). Using flow-dependent dilation as an indirect index of endothelial function, Brook et al. (2002) found no effect of CAPs on endothelium-dependent dilation. In contrast, we delivered an agonist directly to the endothelium of a single arteriolar segment and found that endothelium-dependent dilation was potently impaired after PM exposure (Figures 2–4). A possible explanation for this discrepancy is that flow-dependent dilation activates endothelial NO synthase substantially through a Ca2+-independent pathway (Muller et al. 1999), but A23187 does so via a Ca2+-dependent pathway (Schneider et al. 2003). Additionally, the technique used to induce flow-dependent dilation (total flow stoppage by inflation of a cuff around the proximal forearm and subsequent release) would likely disturb the local environment, and the influence of local metabolites or myogenic activity under these conditions cannot be ruled out. The type of PM and exposure methods may also contribute to the discrepancies between our findings and those of Brook et al. (2002).
In vitro treatment of rat aortic rings and small arteries with PM stimulates vascular smooth muscle relaxation (Bagate et al. 2004; Knaapen et al. 2001). In these studies, the vessel segments were precontracted with phenylephrine and then exposed to a PM solution. PM exposure in this preparation induced a dose-dependent relaxation that was partially endothelium dependent. The tissue isolation required for in vitro experiments precludes any neural, local, physical, and/or hormonal influence on vascular physiology. In vitro observations on tissues exposed to PM may be of limited relevance because they mandate a direct interaction between PM and the systemic vasculature. In support of this possibility, PM has been reported to enter the systemic circulation. PM deposition in the arteriolar wall has been documented in stray Mexico City canines (Calderon-Garciduenas et al. 2001). Inhaled ultrafine 13C particles have been shown to translocate to extrapulmonary organs (Oberdorster et al. 2002). In contrast, studies with ultrafine 192Ir indicate that only a small fraction of inhaled ultrafine PM exits the lung (Kreyling et al. 2002).
Circulating leukocytes and platelets are known to increase after PM exposure (Salvi et al. 1999; Tan et al. 2000; Terashima et al. 1997). Up-regulation of bronchial adhesion molecules, such as ICAM-1 and VCAM-1, has also been documented after PM exposure (Salvi et al. 1999). However, the state of systemic microvascular adhesion molecules after PM exposure is unknown. Our observations that venular leukocyte adhesion and rolling are increased despite no change in the systemic leukocyte concentration after PM exposure (Figure 6) suggest that systemic adhesion molecules are up-regulated and/or activated. NO plays a vital role in protecting the endothelium from potential injury, as well as preventing inflammatory responses and atherosclerosis development (Cannon 1998). A loss of endothelial NO production could contribute to the altered venular leukocyte dynamics after PM exposure shown in Figure 6.
Tissue blood flow depends primarily on perfusion pressure and the caliber of resistance vessels. Changes in caliber are dictated by chemical and physical stimuli that originate within the immediate environment of resistance vessels and by changes in neural activity and circulating humoral agents (Shepherd 1983). Diminished tissue perfusion will disturb the local environment, most notably in the form of tissue hypoxia. Compensatory mechanisms, such as arteriolar dilation, attempt to restore normal tissue perfusion and therefore normoxia. Low arteriolar wall PO2 (partial pressure of O2) may serve as a stimulus for the release of endothelium-derived NO (Sauls and Boegehold 2000), which would dilate arterioles in an attempt to restore wall PO2. Typical responses such as these contribute to the maintenance of cardiovascular homeostasis under normal conditions. However, in diseased states such as hypercholesterolemia, hypertension, and coronary artery disease, endothelium-dependent dilation is often compromised (Drexler and Hornig 1999). If these populations respond to PM exposure as reported here, the impairment of endothelium-dependent dilation would compromise an already reduced vasodilator reserve, if not obliterate it, and may precipitate a cardiovascular incident.
In the present investigation, pulmonary exposure was accomplished by a single IT instillation of saline, ROFA, or TiO2. Some have questioned the relevance of such an exposure to the human condition of a continuous exposure by inhalation. A review sponsored by the Inhalation Specialty Section of the Society of Toxicology has addressed this issue (Henderson et al. 1995). Results indicate that pulmonary responses to particle exposure via inhalation versus IT instillation correlate well when lung burdens are equivalent. Additionally, our laboratory has shown that adverse response, that is, enhanced susceptibility to bacterial infection, is well distributed throughout the different regions of the lung after IT instillation of ROFA (Roberts et al. 2004). In light of such results, IT instillation has been judged as a useful exposure method for hazard identification and elucidation of mechanisms of toxic responses (Henderson et al. 1995).
Toxic pulmonary responses to ROFA have been linked to the high concentrations of soluble metals associated with these particles (Antonini et al. 2002; Dreher et al. 1997). In addition, ROFA particles are acidic, having a pH of approximately 4.1 suspended in saline. For these reasons, some have questioned whether ROFA particles are a good enough surrogate for ambient PM. Data from the present study indicate that pulmonary exposure to ROFA or TiO2 resulted in similar alterations of systemic microvascular function. These results argue against soluble metals being a prime factor in this response. Additionally, IT instillation of acidic saline solutions did not reproduce the pulmonary effects of ROFA (data not shown). Therefore, the effects of pulmonary exposure to ROFA on systemic microvascular function may be generalized to ambient PM.
In the present study, a significant impairment of systemic arteriolar dilation was observed 24 hr after IT instillation of 0.1 mg ROFA (Figure 2). How would this lung burden compare with that of humans exposed to ambient air? The current U.S. Environmental Protection Agency limit for ambient PM is 150 μg/m3 (U.S. EPA 1987). Levels in problem cities on high pollution days can easily exceed this limit by 3-fold. Assuming that a) the resting minute ventilation of 7.5 L/min means that 10.8 m3 of air is inhaled over a 24-hr period; b) 25% of inhaled PM with a mean diameter of approximately 2 μm would be deposited in the respiratory zone; and c) the alveolar surface area of humans is 250 times that of rats, pulmonary exposures in this study would exceed human exposures to ambient PM by 20-fold. Considering that special populations (i.e., the elderly and those with preexisting cardiovascular disease) exhibit increased susceptibility to PM, the results reported here using young healthy rats may be relevant.
Conclusions
This study has identified significant effects of pulmonary PM exposure on the systemic microcirculation, which is the primary site of total peripheral resistance, tissue blood flow regulation, and plasma–tissue exchange. PM exposure studies in large conduit arteries are important, but the substantial anatomical and functional differences that exist between conduit arteries and arterioles preclude extrapolation of those findings to the microvascular level. Microvascular dysfunction is associated with essentially all cardiovascular diseases and is among the first targets of inflammatory and immune responses. Therefore, this study is a crucial and fundamental first step in identifying the mechanisms by which pulmonary PM exposure may increase morbidity and mortality. Results of the present study argue strongly for future investigations to a) identify the “no effect” lung burdens for ROFA and TiO2 for impairment of endothelium-dependent arteriolar dilation; b) determine what characteristics of ROFA are responsible for the impaired endothelium-dependent arteriolar dilation observed herein; c) determine the time course of microvascular effects after PM exposure; d) determine if other types of PM also elicit the same effect on the microvascular endothelium; and e) determine to what degree these observations are altered by age or pathologic states, such as hypertension, hypercholesterolemia, and diabetes, where microvascular structure and function are already compromised.
Figure 1 Intraluminal A23187 infusion produces graded, near-maximal arteriolar dilation in rats exposed to IT saline; the vehicle is not inherently vasoactive. Values are mean ± SE. For A23187 infusion, n (number of arterioles studied) = 10; for vehicle infusion, n = 7.
*p < 0.05 vs. vehicle at the same ejection pressure. **p < 0.05 vs. 5 psi response. #p < 0.05 vs. 10 psi response. ##p < 0.05 vs. 20 psi response.
Figure 2 ROFA exposure impairs or abolishes spinotrapezius muscle arteriolar responsiveness to intraluminal A23187. Values are mean ± SE. For saline control, n (number of arterioles studied) = 10; for 0.1 mg ROFA, n = 9; for 0.25 mg ROFA, n = 8; for 1 mg ROFA, n = 8.
*p < 0.05 vs. 0.25 mg ROFA. **p < 0.05 vs. 0.1, 0.25, and 1 mg ROFA. #p < 0.05 vs. 0.25 mg and 1 mg ROFA.
Figure 3 TiO2 exposure abolishes spinotrapezius muscle arteriolar responsiveness to intraluminal A23187. Values are mean ± SE. For saline control, n (number of arterioles studied) = 10; for 0.25 mg TiO2, n = 8; for 0.25 mg ROFA, n = 8.
*p < 0.05 vs. 0.25 mg TiO2 and 0.25 mg ROFA.
Figure 4 ROFA exposure impairs both NO-dependent and NO-independent arteriolar responsiveness to intraluminal A23187 in the spinotrapezius muscle. NO synthase was competitively inhibited by simultaneous superfusion with L-NMMA (10−4 M final superfusate concentration). Values are mean ± SE. For normal superfusate and with L-NMMA, n (number of arterioles studied) = 8; for 0.25 mg ROFA, n = 8.
*p < 0.05 vs. normal superfusate. **p < 0.05 vs. 5 psi response. #p < 0.05 vs. +L-NMMA. ##p < 0.05 vs. 10 psi response. †p < 0.05 vs. 20 psi response.
Figure 5 ROFA exposure does not alter spinotrapezius muscle arteriolar responsiveness to SNP. Values are mean ± SE. For saline control, n (number of arterioles studied) = 8; for 0.25 mg ROFA, n = 9.
*p < 0.05 vs. 0 nA in both groups. **p < 0.05 vs. 10 nA in both groups.
Figure 6 ROFA exposure increases venular leukocyte rolling and adhesion in the spinotrapezius muscle. Abbreviations: n, number of venules studied; N, number of rats studied. (A) Representative venule in an IT-saline control rat. (B) Representative venule in a rat exposed to 2 mg ROFA. (C) Rolling and adhering venular leukocytes observed per minute in a 200-μm segment [for IT-saline control, n = 15; for 2 mg ROFA, n = 15]. (D) Systemic leukocyte concentration in saline control rats (N = 3) and rats exposed to 2 mg ROFA (N = 3). Values in (C) and (D) are mean ± SE.
*p < 0.05 vs. IT-saline control.
Table 1 Profiles of experimental animals used for intravital studies (mean ± SE).
Experimental group No. of rats Age (days) Weight (g) Mean arterial pressure (mm Hg)
Saline 10 57 ± 4 233 ± 5 88 ± 6
0.25 mg TiO2 3 56 ± 3 219 ± 7 91 ± 15
0.1 mg ROFA 5 51 ± 1 223 ± 7 96 ± 9
0.25 mg ROFA 7 51 ± 3 209 ± 6 80 ± 6
1 mg ROFA 3 52 ± 3 238 ± 17 80 ± 6
2 mg ROFA 6 55 ± 4 218 ± 13 84 ± 5
Table 2 Resting variables for all arterioles studied (mean ± SE).
Experimental group
Saline 0.25 mg TiO2 0.1 mg ROFA 0.25 mg ROFA 1 mg ROFA 2 mg ROFA
No. of arterioles 18 8 9 17 8 6
Resting diameter (μm) 43 ± 2 41 ± 2 41 ± 2 41 ± 1 44 ± 2 48 ± 4
Passive diameter (μm) 107 ± 4 100 ± 3 111 ± 6 100 ± 3 117 ± 5* 108 ± 5
Resting tone (percent of maximum) 59 ± 2 59 ± 2 62 ± 3 59 ± 1 62 ± 2 56 ± 3
* p < 0.05 vs. 0.25 mg TiO2 and 0.25 mg ROFA.
Table 3 BAL data in saline-, TiO2-, and ROFA-exposed rats (mean ± SE).
Cellular content
BAL fluid
AM (CL)
Experimental group AM PMNL Albumin (mg/mL) LDH (U/L) Total CL NO-dependent CL
Saline 6.20 ± 0.63 1.09 ± 0.12 0.12 ± 0.01 46 ± 5 8.87 ± 1.99 1.44 ± 0.35
0.25 mg TiO2 4.89 ± 0.65 1.11 ± 0.15 0.19 ± 0.02 65 ± 8 3.64 ± 0.73 0.42 ± 0.26
0.1 mg ROFA 5.22 ± 0.93 1.24 ± 0.28 0.20 ± 0.04 75 ± 2* 14.47 ± 2.60 4.40 ± 0.95
0.25 mg ROFA 6.25 ± 0.76 1.91 ± 0.20 0.17 ± 0.01 46 ± 4## 17.42 ± 1.11 5.38 ± 0.20
1 mg ROFA 6.12 ± 0.94 2.87 ± 0.38*,**, #,## 0.46 ± 0.08*,**, #,## 97 ± 9*,**, # 10.47 ± 1.86 3.45 ± 1.01
2 mg ROFA 6.47 ± 0.76 4.02 ± 0.40*,**, #,##, † 0.36 ± 0.04*,**, #,## 84 ± 9*, # 26.26 ± 7.14*,**, ##, † 9.81 ± 3.77*,**, #,##, †
CL, chemiluminescence. The number of rats in experimental groups were as follows: 17 for saline, 6 for TiO2, and 5 for all ROFA doses. We evaluated 106 cells/rat for AM and for PMNL. Total CL and NO-dependent CL were determined as follows: counts per minute × 105/0.25 × 106 AM/15 min.
* p <0.05 vs. saline.
** p < 0.05 vs. TiO2.
# p < 0.05 vs. 0.25 mg ROFA.
## p < 0.05 vs. 0.1 mg ROFA.
† p < 0.05 vs. 1 mg ROFA.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7059ehp0112-00130715345344ResearchArticlesEffects of Low Sulfur Fuel and a Catalyzed Particle Trap on the Composition and Toxicity of Diesel Emissions McDonald Jacob D. 1Harrod Kevin S. 1Seagrave JeanClare 1Seilkop Steven K. 12Mauderly Joe L. 11Lovelace Respiratory Research Institute, Albuquerque, New Mexico, USA2SKS Consulting Services, Siler City, North Carolina, USAAddress correspondence to J.D. McDonald, Lovelace Respiratory Research Institute, 2425 Ridgecrest Dr. SE, Albuquerque, NM 87108 USA. Telephone: (505) 348-9455. Fax: (505) 348-4980. E-mail:
[email protected] study was supported by the Department of Energy Office of FreedomCAR and Vehicle Technologies. Partial support was received from the National Institute of Environmental Health Sciences through grant P30 ES-012072 to the New Mexico Center for Environmental Health Sciences.
This article does not represent the views and opinions of any federal sponsor.
The authors declare they have no competing financial interests.
9 2004 7 7 2004 112 13 1307 1312 1 3 2004 7 7 2004 Publication 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 compared a “baseline” condition of uncontrolled diesel engine exhaust (DEE) emissions generated with current (circa 2003) certification fuel to an emissions-reduction (ER) case with low sulfur fuel and a catalyzed particle trap. Lung toxicity assessments (resistance to respiratory viral infection, lung inflammation, and oxidative stress) were performed on mice (C57Bl/6) exposed by inhalation (6 hr/day for 7 days). The engine was operated identically (same engine load) in both cases, and the inhalation exposures were conducted at the same exhaust dilution rate. For baseline DEE, this dilution resulted in a particle mass (PM) concentration of approximately 200 μg/m3 PM, whereas the ER reduced the PM and almost every other measured constituent [except nitrogen oxides (NOx)] to near background levels in the exposure atmospheres. These measurements included PM, PM size distribution, PM composition (carbon, ions, elements), NOx, carbon monoxide, speciated/total volatile hydrocarbons, and several classes of semi-volatile organic compounds. After exposure concluded, one group of mice was immediately sacrificed and assessed for inflammation and oxidative stress in lung homogenate. Another group of mice were intratracheally instilled with respiratory syncytial virus (RSV), and RSV lung clearance and inflammation was assessed 4 days later. Baseline DEE produced statistically significant biological effects for all measured parameters. The use of low sulfur fuel and a catalyzed trap either completely or nearly eliminated the effects.
diesel exhaustemissions reductionhealth effectsmetalsorganic carbonparticulate matter health effects
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In response to regulatory pressure aimed at decreasing the health hazards of engine emissions, diesel engine exhaust (DEE) is changing rapidly as a result of engine and fuel modifications and emissions-reduction (ER) technologies. The most drastic changes to DEE are yet to come, as fuel and ER technologies are implemented to meet the particulate matter (PM) and nitrogen oxide (NOx) regulatory benchmarks in 2007 and 2010 [U.S. Environmental Protection Agency (EPA) 2000]. A wide range of engine (e.g., fuel injection, combustion chamber), fuel (e.g., decreased sulfur, aromatic content), and after-treatment (e.g., particle traps, oxidation catalysts, catalyzed traps) technologies are being developed to meet these more-stringent emissions standards. Although these changes will certainly decrease regulated emissions, it is not clear how health hazards might change from historically understood DEE (U.S. EPA 2002).
With changes in DEE composition come new challenges for measurement of emissions (Durbin et al. 2003) and determination of health hazards. There is a need to understand more about the composition of exhaust produced from these emerging technologies and the resulting health benefits as the emissions change. The composition of emissions affects toxicity, as has been demonstrated by differences in the in vitro (bacterial mutagenicity) and in vivo (lung responses to instilled material) responses among seven samples of engine emissions collected from “normal-emitting” and “high-emitting” gasoline and diesel vehicles (Seagrave et al. 2002). In another study, the bacterial mutagenicity of PM collected from exhaust generated using “old” and “new” technology fuels showed decreased mutagenicity with the new fuel composition (Bagley et al. 1996). Certainly new engineering controls will change the composition of tailpipe emissions, and it is important to ensure that the new technologies provide health benefits but not produce unintended health hazards. There is a need to evaluate both the change [relative to baseline (uncontrolled) DEE emissions] in composition and health hazard of emissions as new technologies emerge.
Most of the work addressing health hazards of new ER technologies has been limited to in vitro assays (primarily bacterial mutagenicity) of exhaust sample extracts. Typically, samples collected to do this work only account for a small fraction of exhaust (e.g., PM), and that fraction may not accurately represent the physical, and perhaps not even the chemical, composition of the exhaust as it exists in the environment. Moreover, in vitro and in vivo assays have been shown to provide quite different rankings of toxicity among engine exhaust samples of different composition (Seagrave et al. 2003). In vivo responses are considered more relevant to human health hazards, and exposure by inhalation is considered the “gold standard” for hazard assessment (Driscoll et al. 2000).
In the present study, we compared a baseline case of DEE to that of a single ER case (low sulfur fuel/catalyzed ceramic trap), both generated from a “model” small-scale engine system previously shown to produce exhaust having an environmentally relevant composition (McDonald et al. 2004a). The study combined detailed characterization of the exposure atmosphere with measurements of pulmonary proinflammatory responses, heme oxygenase (HO-1) up-regulation, and resistance to infection with respiratory syncytial virus (RSV). Although we did not address the full range of health concerns related to DEE, we did include indices of the responses to acute exposure we found to be most sensitive. HO-1 is a stress-response enzyme that has been implicated as an indicator of oxidant-induced lung injury (Choi and Alam 1996; Morse and Choi 2002) and has been shown to be induced in vitro by DEE PM extracts (Li et al. 2002) and ambient PM extracts (Li et al. 2002, 2003). RSV is the most common cause of respiratory infection in young children (Collins et al. 2001); it can infect immune-compromised older individuals (Mlinaric-Galinovic et al. 1996); and we previously observed diminished clearance (and increased inflammation) of RSV at low exposure levels of DEE (30 μg/m3) generated either with a multicylinder engine (Harrod et al. 2003b) or with the single cylinder engine used in the present study (Harrod et al. 2003a).
ER markedly reduced nearly all of the measured components (both PM and gases) of the exhaust and diminished all toxicity observed with baseline DEE. These findings suggest that the use of low sulfur fuel and a catalyzed trap should markedly reduce certain health hazards and provide encouragement that ER technology will provide substantial public health benefits.
Materials and Methods
This study included two separate inhalation exposures (termed DEE and DEE + ER, conditions summarized in Table 1) conducted at the same dilution ratio (620:1). This dilution ratio was determined by the dilution required to obtain 200 μg/m3 PM for the baseline DEE, which is not the minimum concentration for which we have observed effects (for RSV infection), but it is a concentration for which strongly significant effects have been reported to occur (Harrod et al. 2003a, 2003b). We conducted measurements of the biological responses and composition of the exposure atmospheres identically for the two exposures as described below.
Exhaust generation.
The exhaust generation/exposure system has been described previously (McDonald et al. 2004a). Briefly, DEE was produced by a 5500-watt single cylinder diesel engine generator (Model YDG 5500E; (Yanmar, Osaka, Japan) that contains a 406-cc displacement air-cooled engine. Engine oil (15/40-weight, Rotella T, Shell, Houston, TX) was changed immediately prior to each 1-week exposure. The baseline DEE was generated used number 2 diesel certification fuel (Phillips Chemical Company, Borger, TX) and a high engine load condition. This fuel represented current (circa 2003) national average on-road diesel fuel. Eleven 500-watt halogen lights provided a constant load targeted at the rated capacity of the generator (5,500 watts, corresponding to 9 horsepower/3,600 rotations/min) for both exposures. For DEE + ER, the engine was operated with low sulfur fuel and an in-line catalyzed ceramic trap in the exhaust line. The low sulfur fuel/trap combination is necessary (for both large and small scale) because sulfur is detrimental to the performance and lifetime of the trap. We used a commercially available trap specifically designed for abatement of exhaust from diesel generators (fca060w4cn30; Clean Air Systems Inc., Santa Fe, NM). The trap and catalyst technologies were similar to those manufactured by the same company for larger on-road and off-road applications. Because these traps are used with low sulfur fuel, we used a prototype ultra-low sulfur diesel fuel (ECD1; provided by BP, Naperville, MD) in the ER case. The characteristics of both fuels are shown in Table 2. To ensure efficient operation, the trap was maintained at a minimum of 300°C for the duration of the exposure by thermostatically controlled heat tape. This temperature was recommended by the manufacturer, and it has been used for other DEE trap studies with multicylinder engines (e.g., SAE 1998).
Exposure system and exposure atmosphere.
The animal exposure chamber was a 1-m3 whole-body inhalation chamber (Hazleton H-1000; Lab Products, Maywood, NJ) operated at a flow rate (250 L/min) that produced approximately 15 air exchanges/hr. Temperature, relative humidity, and flow (orifice plate mated to electronic pressure transducer) were monitored and recorded at all times. Temperature was maintained between 22 and 26°C. Exposures were conducted 6 hr/day (~ 0730–1330 hours) for 7 consecutive days. DEE concentration for baseline conditions was controlled by manually adjusting the air dilution to the predetermined PM concentration. These adjustments were based on PM concentration measurements made both in “real time” and integrated over 30-min periods as described below. For DEE + ER, the catalyzed trap decreased the PM concentration sufficiently that it was not practical to use PM for system control. The trap also decreased the gases to levels below those useful for control of the system; one exception was NOx, which could not be used because the NOx analyzer failed during the exposure study. To control the system for DEE + ER, the trap was by-passed before the exposure started and the exhaust dilution was adjusted to match the DEE test based on the concentration of carbon monoxide measured directly in the engine exhaust and exposure chamber. Once the dilution valves were set, the engine was turned off, the exposure chamber was flushed for 10 min, and the animals were placed in the chamber. The exhaust was then routed through the trap for the entire exposure period. After the exposure, the animals were removed and the dilution was measured again by the same approach to ensure that it did not change during the exposure. The DEE and DEE + ER exposures were accompanied by separate concurrent control groups exposed to filtered air (HEPA filter and charcoal scrubber). The filtered air controls were treated exactly the same as the DEE-exposed animals (including animal movement immediately before and after DEE + ER exposures).
Exposure atmosphere characterization.
The composition of the exposure atmospheres were characterized in detail (> 250 analytes) using methods reported previously (McDonald et al. 2004a). The measurements, measurement techniques, and laboratories that conducted the analyses are summarized in Table 3. As mentioned above, during the exposure study, the NOx analyzer malfunctioned; therefore, NOx values are reported from measurements collected at a later date with identical fuel/engine/trap operational conditions and dilutions used during the exposures.
PM concentration, the metric we used to target the DEE dilution rate, was measured gravimetrically by sampling from the exposure chamber for 30-min intervals on 47-mm Pallflex filters (Pall-Gelman, East Hills, NY) in aluminum in-line filter holders (In-Tox Products, Inc., Albuquerque, NM). We measured pre- and postsample filter weights using a Mettler MT5 microbalance (Mettler, Columbus, OH). A static discharger was used before weighing filters to avoid any interference from electrical charge on the filters.
Animals and husbandry.
Young (8–10 weeks of age) C57Bl/6 mice (Charles River Laboratories, Inc., Wilmington, MA) were housed under pathogen-free conditions according to Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC)-approved guidelines and protocols. Routine serologic screens for mouse pathogens showed no preexisting infections in the study groups. Mice were housed in the whole-body exposure chambers and were provided water ad libitum at all times. Food (Harlan TEKLAD Rodent Diet; Harlan Teklad, Madison, WI) was provided ad libitum during nonexposure hours. At the end of the 7-day exposure period, a portion of each treatment group was infected with RSV, and the remainder was immediately sacrificed for analysis of lung inflammation and HO-1.
Resistance to infection.
We assessed viral clearance and lung histopathology as described by Harrod et al. (2003b). Briefly, control and DEE-exposed C57Bl/6 mice (eight per group) were instilled intratracheally with 106 plaque-forming units of cultured RSV immediately after the last day of exposure. Mice were housed individually in pathogen-free conditions in a designated animal room for 4 days. At 4 days postinfection, we analyzed one lobe of the lung for the presence of RSV virus by densitometric analysis of virus-specific mRNA transcripts that were isolated by gel electrophoresis after amplification(reverse transcriptase-polymerase chain reaction). RSV mRNA transcripts were ratioed to amplified β-actin (internal control) mRNA levels to account for intersample variability in mRNA isolation and amplification. RSV was thus compared in each treatment group as the average of the RSV/β-actin responses for each animal.
Lung cross-sections for histopathology of the RSV infected mice were obtained approximately 500 μm caudal to the junction of the mainstream bronchus, stained with hematoxylin and eosin, and analyzed by a pathologist under light microscopy. The pathologist scored (0–4 scale) the levels of inflammation in the airways and vessels without knowledge of the origin of the sample.
Inflammation.
We measured inflammatory signaling proteins (cytokines) in homogenates of the right caudal and middle lung lobes (six per group). Immediately after sacrifice, lungs were frozen in 1 mL Dulbecco’s phosphate-buffered saline (PBS) with a cocktail of proteinase inhibitors. Before analysis, lungs were removed from the freezer and brought to room temperature. Lungs were homogenized for 1 min at full speed in a Tissuemizer (Tekmar, Mason, OH) and centrifuged for 5 min at 14,000 × g. The supernatant was transferred to clean microfuge tubes and kept on ice. Cytokines [tumor necrosis factor-α (TNF-α), interferon -γ (IFN-γ), interleukin-6 (IL-6)] were determined (two measurements for each cytokine for each sample) by ELISA using commercially available mouse analysis kits (Biosource International, Camarillo, CA). To normalize the cytokine measurements to total protein, supernatants were diluted to 2 mg/mL in PBS and total protein was assayed by the Coomassie-dye binding assay (Pierce, Rockford, IL) with bovine serum albumin as the standard.
HO-1.
We measured HO-1 induction in lung homogenate by Western blotting using 30 μg of a sample of lung homogenate supernatant (prepared as described above for inflammatory indicators) in 1× Laemmli sample buffer containing 25 mM dithiothreitol. Samples were heated for 5 min at 95°C and resolved on a 15% polyacrylamide gel. Proteins were electroblotted to polyvinylidene difluoride membranes. The blots were blocked with 5% nonfat dry milk in Tris-buffered saline with 0.1% Tween-20, and incubated with 1 μg/mL polyclonal anti-HO-1 (Calbiochem, San Diego, CA) followed by 1 μg/mL horse-radish peroxidase-labeled goat anti-rabbit IgG. HO-1 was detected by chemiluminescent (ECL, Amersham, Piscataway, NJ) exposure of BioMax Film (Kodak, Rochester, NY) and quantified by densitometry as described by Li et al. (2003).
Statistical analysis.
We used analysis of variance (ANOVA) to evaluate DEE and DEE + ER responses relative to values from concurrent control groups. Levene’s test (Levene 1960) was first performed to evaluate the appropriateness of the standard ANOVA assumption of equality of variances among experimental group responses. These tests showed that for all end points except lung histopathology, there was significant evidence of inequality of variances (p < 0.05). To address this problem, we used a weighted least-squares analysis (Neter et al. 1996) using the reciprocals of the variances in experimental groups as weights. F-test contrasts (Searle 1971) were used to compare DEE and DEE + ER responses with baseline values in concurrent control groups. Because the baseline values for the two control groups differed substantially for some end points, reported means and SEs were scaled by the mean values from concurrent control groups. Statistical significance was assessed at p = 0.05 and p = 0.01; however, several treatment groups showed p-values much lower than this. Calculations were performed using SAS software (SAS Institute, Cary, NC).
Results
Exposure characterization.
Table 4 summarizes the composition, reporting the concentrations of major components (by mass) and several composite (summed) subclasses of material along with select organic compounds, primarily those designated as hazardous air pollutants by the U.S. Environmental Protection Agency. (Data for individual compounds are available from the corresponding author upon request.) The percent change in the concentration of each component or component class after ER implementation is also shown in Table 4. Data are reported here as average concentrations of multiple (2–3) samples that were collected during the exposure period.
ER significantly reduced the concentration of nearly every component. Most constituents (except NOx, select elements, and the carbonyls) were in the range of the background concentrations observed in the control exposure chamber. NOx (~ 98% nitrogen monoxide and 2% nitrogen dioxide, both with and without ER) reductions were not expected, as the manufacturer of the catalyzed trap (Clean Air Systems Inc.) reported modest to no reduction in NOx concentrations in their product description. ER reduced the PM and particle number concentration so that it was too low to accurately measure particle size or particle number (< 103 particles/cm3). The particle number and mass distributions for the baseline DEE are illustrated in Figure 1, which shows a distribution having a mass median aerodynamic diameter of 110 nm and a particle number median diameter of 80 nm. The majority of the other components in the ER atmosphere were also reduced. Most of these reductions were > 60% relative to baseline DEE; there was no detectable black (elemental) carbon and reduced particle organic carbon content. Similar to the control atmosphere, the PM component of the ER atmosphere was nearly 100% organic carbon. Small amounts of the elements, especially calcium and zinc, which are lube oil and fuel additives (Docekal et al. 1992), were observed in DEE and decreased substantially with ER.
The ER atmosphere had low quantities of both gases and PM, many of which were in the same range as the concentrations observed in the control atmosphere. However, several individual organic compounds were present in DEE and DEE + ER at concentrations significantly above background. Acetylene, a compound that has been used in ambient source apportionment studies as an indicator of mobile source emissions, was enriched in DEE but reduced in DEE + ER to background levels, as was also true for most of the aromatic [including polycyclic aromatic hydrocarbons (PAHs)] and alkene (including 1,3-butadiene) compounds. However, removal of the carbonyl compounds, especially formaldehyde and acetaldehyde, was much less efficient (~17–45%).
Lung toxicity.
The results of the cytokine/HO-1 up-regulation in noninfected animals and the lung viral burden/histopathology scores after exposure/RSV infection are shown in Figures 2–4. The DEE exposure resulted in statistically significant differences from control exposed animals for all measured lung responses, but the DEE + ER exposure resulted in no significant differences from control for any biological measurement. Figure 2 summarizes the lung viral burden and lung histopathology of virus-infected mice. As expected from previous studies (Harrod et al. 2003a, 2003b), DEE exposure significantly (p = 0.002) decreased the clearance of virus from the lung and significantly increased(p = 0.003) the histopathology scores. Figure 3 shows the increase in cytokines. All cytokines were significantly elevated above control values in the DEE group, but not in the ER group. HO-1, the oxidative stress response indicator, also significantly increased after DEE exposure but not after DEE + ER exposure (Figure 4).
Discussion
The present study showed that implementation of a low sulfur fuel/catalyzed trap combination decreased the concentration of most components of emissions and diminished the biological effects of DEE on viral clearance, inflammation, and oxidative stress. These findings suggest that this type of ER technology may have substantial health benefits. Of course, ER technologies may vary considerably, and it is not known how broadly these results might apply to other technologies.
The ER case significantly decreased nearly every measured exposure constituent except NOx to background levels. Except for a few volatile organic compounds and elements, the ER and control exposure atmosphere had similar low concentrations of both gases and PM. These similarities suggest that a portion of the constituents observed in the DEE exposure atmosphere downstream of the trap was contributed by background in the dilution air or by the rodents themselves. Although the dilution air was pretreated by filtration through HEPA and charcoal filters, these filters do not efficiently remove CO or methane. The contribution of rodent respiration and excretion to the composition whole-body exposure atmospheres has been discussed previously (McDonald et al. 2004b). Among the compounds that are contributed by respiration and background are the C2-C12 alkanes, for which there were similar concentrations among all of the exposure atmospheres (including DEE). Similar to the control chamber, the small PM component of the DEE + ER exposure atmosphere was nearly 100% organic carbon, which was likely contributed by the rodents (dander, exhaled organics, etc.).
Despite the contribution of rodents and dilution air to the exposure atmospheres, several individual organic compounds were present in DEE and DEE + ER at concentrations significantly above background, indicating a variable efficiency of removal. Acetylene, a compound that has been used in ambient source apportionment studies as an indicator of mobile source emissions, was enriched in DEE but reduced in the DEE + ER to background levels. This also occurred for most of the aromatic (including PAH) and alkene (including 1,3-butadiene) compounds. However, removal of the carbonyl compounds, especially formaldehyde and acetaldehyde, was much less efficient (~ 17–45%). These findings agree with previous reports comparing a baseline DEE to DEE with low sulfur fuel and a trap (Durbin et al. 2003), where the ER was most efficient at removing acetylene, moderately efficient at removing alkenes/aromatics, and poor at removing volatile carbonyls.
Although it provided an important first look at the effects of ER, this study had several limitations. First, the exhaust was not produced by an engine that would be used on-road. We previously demonstrated the usefulness of this model system by showing both similar composition (McDonald et al. 2004a) and similar biological responses (Harrod et al. 2003a) at selected operating conditions compared to DEE produced from a multicylinder diesel engine operated on a heavy-duty engine cycle. This model system was therefore considered adequate to show “proof of concept” or to develop testing protocols. However, the applicability of the present results to emissions generated from larger on-road and off-road engine systems needs to be confirmed. In addition, it may be important to assess the performance of a wider range of ER technologies operating under a variety of engine operation conditions. The high constant workload and new particle trap (emissions may change after trap “ages”) used in this study allowed the optimal performance of the ER. Under this condition, the emissions were substantially decreased. Rudell et al. (1996) reported that humans exposed to DEE from an idling vehicle both with and without a ceramic particle trap (no catalyst) had inflammation (as assessed by increase in neutrophils and infiltration of alveolar macrophages into their airways) in both cases. In that study the ceramic trap removed only half of the particle count.
Although the results of the present study clearly demonstrated a near total mitigation of the effects of DEE exposure on retardation of viral clearance and pathology, inflammation, and oxidative stress, the results must be extrapolated to humans with caution. There is no direct evidence for the effect of DEE on human resistance to RSV infection, although RSV is certainly a pervasive human pathogen (Collins et al. 2001); proximity to heavy traffic has been associated with increased categories of respiratory illnesses that encompass viral infection (e.g., Romieu et al. 2002). The correspondence between responses of mice and humans can be questioned, but the use of mice as models for the pathophysiology of human RSV infection is widely accepted (Graham et al. 1988). The use of only one exposure concentration for DEE was another limitation of this study; however, we previously demonstrated that the effect of DEE inhalation on RSV clearance was concentration related (Harrod et al. 2003b). We believe that the single concentration, which is in the range of occupational exposures to DEE (e.g., McDonald et al. 2002) was adequate to explore the effects of the ER strategy.
The induction of respiratory inflammation by exposure to whole, diluted DEE and its partial mitigation by a particle trap (also at single concentrations) has been demonstrated in humans (Rudell et al. 1996). Although evidence for the role of oxidative stress in responses to DEE is derived largely from animal and in vitro studies, the induction of HO-1 stress response protein has been well documented in humans as an indirect indicator of oxidative stress (Morse and Choi 2002). In this study we did not attempt to fully characterize the nature and magnitude of DEE-induced oxidative damage.
The approach used in this study had the advantage of being a) by inhalation, b) short-term, and c) relevant to known public health hazards. It provided data using contemporary chemical and physical characterization techniques coupled to three biological response categories that are relevant to human health end points observed by laboratory (e.g., Rudell et al. 1996; Sydborn 2001) and epidemiology studies (e.g., Nicolai et al. 2003; Romieu et al. 2002; Samet et al. 2000; Van Vliet et al. 1997). The present study illustrates one approach to the challenge posed to the scientific and regulatory community to develop appropriate testing protocols aimed at placing changing DEE health hazards in context. We did not assess several classes of health effects that may be of importance (e.g., tumor formation, cardiovascular toxicity, exacerbation of asthma/inflammation), including effects that are commonly studied after long-term exposure periods. The study included only a few of the biological responses that have been reported in response to DEE, but these are among the most sensitive (e.g., RSV end points respond to DEE diluted as low as 30 μg/m3). The concordance in response among the biological end points lends confidence in the overall conclusions of decreased health hazard.
Conclusions
ER (low sulfur fuel/catalyzed trap) technology decreased or diminished the emissions and the toxicity of DEE. With ER in place there was no detectable black (elemental) carbon, particle organic carbon in the range of background air, and decreased (relative to uncontrolled emissions) concentrations of the elements. Nearly all-gaseous components (except NOx, which was only slightly reduced, and select carbonyls) were in the range of background air. Baseline DEE exposures (no emission controls) produced significant biological responses in all measured end points. These responses, including lung inflammation (response to lung injury), resistance to a viral infection, and induction of a lung oxidative stress indicator, were not observed with ER in place. These results indicate that the use of low sulfur fuel and a catalyzed trap markedly reduce the DEE health hazard associated with resistance to infection, inflammation, and oxidative stress.
Figure 1 Particle mass and number size distribution in the DEE exposure atmosphere. Abbreviations: ae, aerodynamic diameter; d, derivative; m, mass; p, particle diameter; n, number. Control and DEE + ER particle mass and number concentrations are not shown because they were too low to measure accurately.
Figure 2 Viral retention and lung histopathology in RSV-infected mice exposed to either clean air (DEE or DEE + ER control), DEE, or DEE + ER. Error bars indicate SE. DEE and DEE + ER exposures were conducted at equivalent dilutions.
*p = 0.002 and **p = 0.003 compared to control.
Figure 3 Inflammatory signaling proteins measured in lung homogenates of mice exposed to clean air (DEE control, DEE + ER control), DEE, or DEE + ER. Error bars indicate SE. DEE and DEE + ER exposures were conducted at equivalent dilutions.
*p = 0.003, **p = 0.036, and #p = 0.001, compared to control.
Figure 4 HO-1 measured in lung homogenates of mice exposed to clean air (DEE Control, DEE + ER Control), DEE, or DEE + ER. Error bars indicate SE. DEE and DEE + ER exposures were conducted at equivalent dilutions.
*p = 0.003.
Table 1 Summary of exposure atmosphere generation test conditions.
Engine operation Fuel After-treatment Dilution target
DEE High load No. 2 Cert None 200 μg/m3 PM
DEE + ER High load ECD1 Catalyzed trap Same dilution as DEE
No. 2 Cert, number 2 diesel certification fuel.
Table 2 Properties of the number 2 diesel certification fuel (No. 2 Cert) and the ECD1 low sulfur fuel.
No. 2 Cert ECD1
API gravity 35.8 35.3
Specific gravity 60/60 0.85 0.85
Viscosity 2.4 2.8
Sulfur (ppm) 371 14
Aromatics (volume %) 29 32
Cetane index 47.6 46.1
Cetane number 47.3 47.7
API gravity is an arbitrary scale representing the gravity of liquid petroleum; cetane number is a measure of ignition quality of diesel fuel; and cetane index is an approximation of cetane number based on the API gravity and mid-boiling point of a fuel.
Table 3 Summary of measurements, measurement conditions, and analytical techniques used to characterize exposure atmosphere composition.
Analysis Collection device Collection media Sample flow rate (L/min) Analytical instrument
Particle mass Aluminum in-line filter holder TIGF filter 10 MB
NOx Chemiluminescence analyzer NA 0.4 NA
CO Photoacoustic analyzer NA 1 NA
Organic/elemental carbona Aluminum in-line filter holder Quartz filter 20 TOR
Ions (sulfate/nitrate/ammonium)a Aluminum in-line filter holder Quartz filter 20 IC
Metals and other elementsb Teflon in-line filter holder Teflon filter 20 ICPMS
Speciated organic compounds
Volatile hydrocarbons (C1–C12) a Volatile organic sampler Electropolished canister 0.1 GCFID
Volatile carbonyls Volatile organic sampler DNPH cartridge 0.3 LC/UV
Semivolatile/aromatic/alkane Filter/PUF sampler TIGF filter/PUF/XAD/PUF XAD-4/PUF 60 GCMS
Size distribution
0.05–10 μm particle mass distribution MOUDI impactorc Aluminum 30 MB
0.02–0.7 μm particle number distribution SMPS NA 0.3 NA
Abbreviations: DNPH, dinitrophenylhydrazine; GCFID, gas chromatography flame ionization detection; GCMS, gas chromatography/mass spectrometry; IC, ion chromatography; ICPMS, inductively coupled plasma mass spectrometry; LC/UV, liquid chromatography/ultraviolet detection; MB, microbalance; NA, not applicable; PUF, polyurethane foam; SMPS, scanning mobility particle sizer; TIGF, Teflon impregnated glass fiber; TOR, thermal/optical reflectance; XAD, XAD resin.
a Analyses conducted at the Desert Research Institute, Reno, NV.
b Analyses conducted at the Carlsbad Environmental Monitoring and Research Center, Carlsbad, NM.
c Source: MSP Corp, St. Paul, MN.
Table 4 Comparative composition of DEE, DEE + ER, and control (clean air) exposure chambers.
Analyte or chemical class Control DEE DEE + ER DEE vs. DEE + ER (percent decrease)
NOxa (ppm) < 0.04 2.1 1.9 10
Nonmethane volatile organic (μg/m3) 54.4 162.3 63.2 61
CO (ppm) 0.3 2.0 0.2 90
Particle mass (μg/m3) 5.1 235.7 7.0 99
Particle composition
Black (elemental) carbon (μg/m3) 0.0 200.3 0.0 100
Organic carbon (μg/m3) 4.5 39.9 4.2 90
Nitrate (μg/m3) 0.5 0.2 0.0 100
Sulfate (μg/m3) 0.2 0.0 −0.1 NA
Ammonium (μg/m3) 0.0 −0.1 −0.1 NA
Sum of elements (μg/m3) 0.0 2.1 0.7 67
Speciated organic classes
Sum carbonyl (μg/m3) 5.3 37.7 21.9 42
Acetylene (alkyne) (μg/m3) 0.5 16.7 0.4 98
Sum of C2–C12 alkanes (μg/m3) 26.6 27.7 21.1 24
Sum of C2–C12 alkenes (μg/m3) 3.0 31.7 1.6 95
Sum of volatile aromatics (μg/m3) 8.5 25.2 13.2 48
Sum of C15–C30 alkanes (μg/m3) 6.8 26.7 9.6 64
Sum of naphthalenes (μg/m3) 1.0 4.7 1.0 80
Sum of phenanthrenes (μg/m3) 0.5 6.2 0.4 93
Sum of other SVOC PAHs (μg/m3) 0.4 1.7 0.6 65
Sum of particle PAHs (ng/m3) 0.0 23.0 0.0 100
Sum of Oxy-PAHs (μg/m3) 0.05 1.29 0.08 94
Select speciated organics
Formaldehye (μg/m3) 1.8 14.1 11.6 17
Acetaldehyde (μg/m3) 1.5 17.0 9.4 45
Benzaldehyde (μg/m3) 0.5 1.9 0.3 84
Ethene (μg/m3) 0.5 25.9 0.5 98
1,3-Butadiene (μg/m3) 0.0 2.2 0.0 100
Benzene (μg/m3) 0.4 4.5 0.2 95
Pyrene (μg/m3) 0.03 0.34 0.02 93
Benzo[a]pyrene (ng/m3) 0.00 0.08 0.00 100
Dibenzothiopene (μg/m3) 0.06 0.10 0.05 43
9-Fluorenone (μg/m3) 0.05 1.07 0.05 95
Xanthone (μg/m3) 0.00 0.12 0.00 100
Select elements
Zinc (μg/m3) −0.01 0.71 0.07 90
Calcium (μg/m3) −0.03 0.41 0.22 47
Iron (μg/m3) −0.02 0.24 0.07 71
Potassium (μg/m3) −0.01 0.16 0.04 73
Silicon (μg/m3) −0.09 0.26 0.07 73
Magnesium (μg/m3) 0.00 0.08 0.03 58
Copper (μg/m3) 0.01 0.06 0.05 11
Lead (μg/m3) 0.01 0.07 0.02 74
Abbreviations: PAHs, polycyclic aromatic hydrocarbons; SVOC, semivolatile organic compound.
a Concentrations not obtained during exposures due to analyzer failure; data was obtained from an identical fuel and engine operation exposure study.
==== Refs
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U.S. EPA 2000. Regulatory Announcement: Final Emission Standards for 2004 and Later Model Year Highway Heavy-Duty Vehicles and Engines. EPA420-F-00-026. Washington, DC:U.S. Environmental Protection Agency. Available: http://www.epa.gov/oms/regs/hd-hwy/2000frm/f00026.pdf [accessed 16 July 2004].
U.S. EPA 2002. Health Assessment Document for Diesel Engine Exhaust. EPA/600/8-90/057F. Washington, DC:U.S. Environmental Protection Agency, National Center for Environmental Assessment, Office of Research and Development. Available: http://www.epa.gov/ncea [accessed 21 January 2004].
Van Vliet P Knape M de Hartog J Janssen N Harssema H Brunekreef B 1997 Motor vehicle exhaust and chronic respiratory symptoms in children living near freeways Environ Res 74 122 132 9339225
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.6920ehp0112-00131315345345ResearchArticlesDevelopmental Dental Aberrations After the Dioxin Accident in Seveso Alaluusua Satu 12Calderara Pier 1Gerthoux Pier Mario 3Lukinmaa Pirjo-Liisa 4Kovero Outi 5Needham Larry 6Patterson Donald G. Jr.6Tuomisto Jouko 7Mocarelli Paolo 31Department of Pedodontics and Orthodontics, Institute of Dentistry, University of Helsinki, Helsinki, Finland2Department of Oral and Maxillofacial Diseases, Helsinki University Central Hospital, Helsinki, Finland3Department of Laboratory Medicine, Desio Hospital, University of Milano, Bicocca, Italy4Department of Oral Pathology, Institute of Dentistry, University of Helsinki, and Department of Pathology, Helsinki University Central Hospital, Helsinki, Finland5Department of Radiology, Institute of Dentistry, University of Helsinki, Helsinki, Finland6Division of Laboratory Sciences, Centers for Disease Control and Prevention, Atlanta, Georgia, USA7Department of Environmental Health, National Public Health Institute, and Department of Public Health and General Practice, University of Kuopio, Kuopio, FinlandAddress correspondence to S. Alaluusua, Department of Pedodontics and Orthodontics, Institute of Dentistry, P.O. Box 41, FIN-00014 University of Helsinki, Helsinki, Finland. Telephone: 358-9-19127314. Fax: 358-9-19127266. E-mail: satu. [email protected] work was supported by the European Commission (QLK4-1999-01446), Region Lombardia, Italy (2896), and the Research Program for Environmental Health, Academy of Finland (contract 203395). Planmeca Oy, Helsinki, Finland, provided us with a dental unit.
The authors declare they have no competing financial interests.
9 2004 1 7 2004 112 13 1313 1318 17 12 2003 1 7 2004 Publication 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. Children’s developing teeth may be sensitive to environmental dioxins, and in animal studies developing teeth are one of the most sensitive targets of toxicity of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Twenty-five years after the dioxin accident in Seveso, Italy, 48 subjects from the contaminated areas (zones A and B) and in patches lightly contaminated (zone R) were recruited for the examination of dental and oral aberrations. Subjects were randomly invited from those exposed in their childhood and for whom frozen serum samples were available. The subjects were frequency matched with 65 subjects from the surrounding non-ABR zone for age, sex, and education. Concentrations of TCDD in previously analyzed plasma samples (zone ABR subjects only) ranged from 23 to 26,000 ng/kg in serum lipid. Ninety-three percent (25 of 27) of the subjects who had developmental enamel defects had been < 5 years of age at the time of the accident. The prevalence of defects in this age group was 42% (15 of 36) in zone ABR subjects and 26% (10 of 39) in zone non-ABR subjects, correlating with serum TCDD levels (p = 0.016). Hypodontia was seen in 12.5% (6 of 48) and 4.6% (3 of 65) of the zone ABR and non-ABR subjects, respectively, also correlating with serum TCDD level (p = 0.05). In conclusion, developmental dental aberrations were associated with childhood exposure to TCDD. In contrast, dental caries and periodontal disease, both infectious in nature, and oral pigmentation and salivary flow rate were not related to the exposure. The results support our hypothesis that dioxins can interfere with human organogenesis.
developmental enamel defectdioxinhypodontiahypomineralizationhypoplasiaSevesoTCDDteeth
==== Body
2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) is referred to as the most toxic man-made chemical known [International Agency for Research on Cancer (IARC) 1997]. Data on environmental accidents and occupationally exposed subjects have increased our knowledge on human health effects of TCDD (Sweeney and Mocarelli 2000).
The best-known dioxin accident took place in Seveso, Italy, in 1976. In a chemical factory, a trichlorophenol production reactor exploded and ≥30 kg TCDD was spread over an area of approximately 18 km2 (Di Domenico et al. 1980). Several thousand people, children among them, were exposed to substantial quantities of TCDD. The contaminated area was divided into three major zones (A, B, and R) on the basis of decreasing order of surface soil concentrations of TCDD (Bisanti et al. 1980), and soon after the accident a health assessment of the population was initiated. As part of this effort, blood samples were collected for clinical chemistry tests, and small amounts of serum were stored for later analyses. These samples rendered it possible to quantify individual TCDD exposures (Mocarelli et al. 1988; Needham et al. 1997/1998).
Children exposed to TCDD had higher body burdens than adults. This was seen in zones A and B but also in the non-ABR zone surrounding the three major zones (Eskenazi et al. 2004). Approximately 20% of the children < 10 years of age who had been exposed and had been living in the most severely contaminated zone A developed chloracne (Mocarelli et al. 1986). Exposure of males before and during puberty was linked to a lower male:female ratio in their offspring, and it was suggested that TCDD permanently affected the function of human epididymis (Mocarelli et al. 2000).
In two episodes of epidemic poisoning in Japan and Taiwan (so-called Yusho and Yucheng accidents, respectively), severe developmental effects were observed in infants and children born to mothers who had been exposed to polychlorinated dibenzofurans/biphenyls (PCDFs/PCBs) (Rogan et al. 1988; Yamashita and Hayashi 1985; Yao et al. 2002). These included intrauterine growth retardation, low birth weight, hyperpigmentation, natal teeth, increased incidences of skin and respiratory infections, neurodevelopmental delay, and alterations in sexual development (Chen and Hsu 1994; Chen et al. 1992; Ikeda 1996; Rogan et al 1988).
Although there are differences among species in susceptibility, embryonic development of most vertebrate species is sensitive to TCDD (Peterson et al. 1993). Developmental effects in laboratory rodents include cleft palate, disturbed development of the mandible, various ureteric and kidney malformations in mice (Abbott and Birnbaum 1989; Abbott et al. 1987; Allen and Leam 2001; Peters et al. 1999), and alterations in the reproductive tract development and function in mice and rats (Hurst et al. 2000; Theobald and Peterson 1997). Impaired mammary gland development and differentiation were found in female mice after gestational and lactational exposure (Lewis et al. 2001). In rhesus macaques TCDD causes developmental jaw cysts (McNulty 1985), and in avian and fish embryos it causes cardiovascular toxicity and disturbs craniofacial development (Cantrell et al. 1996; Hornung et al. 1999; Teraoka et al. 2002; Walker and Catron 2000).
Teeth develop as a result of a series of inductive, sequential, and reciprocal interactions between the ectoderm and the subjacent mesenchyme (Thesleff et al. 1995). Tooth development is genetically regulated but sensitive to environmental disturbances. Aberrations in the function of tooth-forming cells lead to permanent morphologic consequences. Because development of the first primary tooth begins in the fourth week in utero and the development of the roots of the wisdom teeth is completed around 20 years of age, teeth serve as a record that covers a long time period of life.
We have shown that in a normal child population, polychlorinated dibenzo-p-dioxins (PCDDs) and PCDFs in mother’s milk may cause mineralization defects in the child’s permanent first molar teeth undergoing mineralization during the first 2 years of life (Alaluusua et al. 1996, 1999). A variety of dental and oral changes were also reported in children exposed to PCB/PCDF in the Yusho and Yucheng accidents. At birth natal teeth and oral pigmentation were prevalent, and later, missing permanent teeth, delayed eruption of permanent teeth, and disturbed root development were observed (Akamine et al. 1985; Fukuyama et al. 1979; Rogan et al. 1988). In adulthood, periodontal disease was common (Hashiguchi et al. 2003). However, in these studies, individual serum or human milk levels of the contaminants in the exposed subjects were not available.
Previous studies in vivo and in vitro show that rat and mouse teeth are sensitive to TCDD throughout their development (Alaluusua et al. 1993; Kattainen et al. 2001; Kiukkonen et al. 2002; Lukinmaa et al. 2001; Miettinen et al. 2002; Partanen et al. 1998). The dental toxicity of TCDD appears to have two phases (Partanen et al. 1998). First, the sequence of morphogenetic events can be affected. This leads to failure of tooth germs to develop as a result of accelerated and increased apoptosis in the dental lamina connecting the oral epithelium and the tooth germ (Lukinmaa et al. 2001; Partanen et al. 2004). Later, tooth size can be reduced (Kattainen et al. 2001; Miettinen et al. 2002; Partanen et al. 1998, 2004). In more advanced teeth, root development can be arrested (Lukinmaa et al. 2001). Second, the interference by TCDD with the formative stage of tooth development—that is, the function of secretory ameloblasts and odontoblasts—results in delayed or defective mineralization of the molar teeth (Gao et al. 2004; Partanen et al. 1998) and failure of enamel and dentin formation to be completed in the continuously erupting rat incisors (Alaluusua et al. 1993; Kiukkonen et al. 2002; Lukinmaa et al. 2001).
In rodents, effective doses of TCDD are very low; for example, a single dose of 30 ng/kg to the rat dam during gestation reduced molar tooth size in the pup (Kattainen et al. 2001). That dose produces maternal concentrations in adipose tissue that are not markedly different from those at the high end of human adipose tissue concentrations without occupational or accidental exposure and is one to three orders of magnitude lower than the serum lipid concentrations measured in subjects after accidental exposure (Hurst et al. 2000; Needham et al. 1999).
Because of all these facts, 25 years after the Seveso accident we invited subjects exposed to TCDD in 1976 in their childhood to receive a dental examination. The goal was to determine the dental and oral changes after heavy exposure to TCDD. We hypothesized that TCDD causes developmental dental defects that can still be seen in adulthood. Furthermore, we expected to see increased prevalences of periodontal disease and oral pigmentation among the exposed subjects, as found in the Yusho subjects (Hashiguchi et al. 2003). We have found that TCDD causes morphologic changes in cultured mouse embryonic salivary glands (Kiukkonen A, et al., unpublished data). Therefore, we also wanted to find out whether salivary flow rate is normal in the exposed subjects.
Materials and Methods
Sixty-five subjects from the contaminated A and B zones and from the R zone, which was lightly contaminated in patches, and 130 subjects from the surrounding, so-called non-ABR zone were invited for the study. At the time of the accident, the subjects were < 9.5 years of age. Demographic information was officially obtained through the different municipalities’ censuses and consisted of date of birth and town of residence in July 1976. The subjects from ABR zones were randomly selected among those for whom we had frozen serum samples since 1976. TCDD in serum had been previously measured by high-resolution gas chromatography/isotope-dilution high-resolution mass spectrometry (Needham et al. 1999; Patterson et al. 1987). Serum samples from non-ABR zone subjects were not available. The eligible subjects were contacted by letter and by phone by the same person. The compliance for the zone ABR subjects and non-ABR subjects was 74 and 58%, respectively. Written informed consent was obtained and approval given by the local institutional review board.
A structured questionnaire, which included a collection of a detailed personal dental and medical history, education, and smoking habits, was administered to all subjects through personal interview. The subjects were frequency matched for age, sex, and education. Education, which is known to modify dental health, was categorized into five levels. Elementary school and secondary school not finished were scored as “lower education”; vocational school, secondary school finished, and high school finished, with or without university training, were scored as “higher education” (Table 1). Because data obtained by the questionnaire showed that the non-ABR group had a higher education level, we randomly dropped out 10 subjects who had finished high school or had university training. Thus, the final number of subjects from the ABR zones was 48 and from the non-ABR zone was 65.
One dentist (P.C.) undertook the examination in a dental unit kindly provided by Planmeca Oy (Helsinki, Finland). During the examination, the dentist had no knowledge whether the subject had been an ABR or non-ABR resident or of the serum TCDD concentration. The subjects were not aware of the hypothesis under study. All teeth (excluding wisdom teeth) were recorded for agenesis of tooth and developmental dental defects using the Developmental Defects of Enamel Index (Fédération Dentaire Internationale 1992). The lesions were grouped into three types: demarcated opacities and diffuse opacities (both of which are qualitative defects of enamel) and hypoplasia (quantitative defect of enamel). Defects involving a local alteration in the translucency of the enamel with clear boundary to the adjacent normal enamel were recorded as demarcated opacities. Diffuse type of opacities included defects involving an alteration in the translucency of the enamel with no clear boundary to the adjacent normal enamel. Hypoplasia included defects with reduced thickness of enamel, the morphology of the defects ranging from shallow or deep pits, small or large, wide or narrow grooves to partial or complete absence of enamel over small or considerable areas of dentine. Lesions < 2 mm in diameter and hereditary defects in tooth structure or tetracycline staining were not included in the analysis. To evaluate the intra-examiner reproducibility of the investigator, 25 subjects were reevaluated a few weeks later and results were assessed using Cohen’s κ-coefficient (Pine et al. 1997). Reproducibility on the presence of defects in a subject was 100%. The κ-statistic on tooth basis was 0.73.
Caries was assessed according to recommendations of the World Health Organization (WHO 1997). Periodontal status was determined by examining six surfaces of all teeth (midbuccally, midlingually, and approximally both buccally and lingually) for the presence or absence of gingival bleeding on probing, subgingival calculus, and pocket depth as assessed by a ball point probe graded in 2 mm (probe force ~ 20 g; LM Instruments Oy, Parainen, Finland). Gingiva, palate, buccal mucosa, and tongue were examined for pigmentation. For the measurement of salivary flow, paraffin-stimulated saliva was collected for 5 min.
The clinical examination was supplemented by radiographic examination using panoramic tomography. Missing or retention of teeth, alveolar bone loss, root deformities, and the presence of jaw cysts were recorded. One dentist (O.K.) undertook the radiographic examinations, and during the examination she had no knowledge whether the subject belonged to the control or the study group or of the serum TCDD concentration.
Pearson’s correlations were computed on the entire study population and for both sexes separately to examine associations between different variables. For analysis of associations between serum TCDD concentrations and other variables, residents from ABR zones were divided into three groups, in an increasing order of serum TCDD concentrations. Each group included 16 subjects. For studying the role of age on the prevalence of developmental enamel defects, the study population was divided into two groups: subjects < 5 years of age and subjects > 5 years of age at the time of the accident. This grouping was based on the fact that enamel development in the permanent dentition (excluding wisdom teeth) is most sensitive to environmental disturbances up to the first 5–7 years of life. Cumulative odds ratio for developmental enamel defects was calculated on ranked serum TCDD concentrations for the < 5 year age group. Logistic regression, where the presence of developmental defects of enamel was a response variable and education (two levels) and serum TCDD concentration (three levels) were explanatory variables, was calculated. Corresponding odds ratios with their 95% confidence intervals were determined. Differences between the means were evaluated by independent-sample t-test or by Mann-Whitney U-test. Comparisons between the categorized variables were done by chi-square test and Mantel-Haenszel chi-square test. In all statistical tests, probabilities ≤0.05 were considered statistically significant.
Results
At the time of the dental and oral examination, the subjects from ABR zones were 25.4–34.0 years of age (mean, 29.1), and those from the non-ABR zone were 24.6–34.1 years of age (mean, 29.2). None of them had experienced severe disease such as cancer in their childhood.
Characteristics of the study population are shown in Table 1. Like sex, age, and education level, smoking habits were not significantly different in zone ABR and non-ABR residents. TCDD concentrations of the zone ABR subjects ranged from 23 to 26,000 ng/kg (median, 476 ng/kg) in serum lipid. At the lowest tertile, TCDD values ranged from 23 to 226 ng/kg in serum lipid, at the mid-tertile from 238 to 592, and at the highest from 700 to 26,000, respectively.
Developmental defects of enamel.
Developmental defects of enamel were found in 27 subjects (14 males, 13 females). All but two of them had been < 5 years of age at the time of the accident (p < 0.0009; Table 2). The prevalence of defects in the subjects from the ABR zones was 42% (15 of 36) and that from non-ABR zone was 26% (10 of 39; Table 2). Subjects with higher serum TCDD levels had more frequent developmental defects of enamel than did those with lower TCDD levels (Mantel-Haenszel χ2 = 5.76, p = 0.016; χ2 = 6.26, p = 0.044; Tables 2 and 3), and at ranked serum concentration levels, the ratio of subjects with developmental defects of enamel to those without such defects increased (Figure 1). In the older age group, the prevalence of defects was only 5.3% (2 of 38).
Education level was negatively associated with the presence of developmental enamel defects in subjects who had been < 5 years of age at the time of the accident (χ2 = 7.14, p < 0.0075; Tables 2 and 3). The association was clearer among the zone ABR subjects (χ2 = 5.14, p = 0.023) than in the zone non-ABR subjects (χ2 = 2.21, p = 0.14). Logistic model revealed no significant interaction between low educational level and the level of serum TCDD concentration.
The difference in the prevalence of developmental defects of enamel between zone ABR and non-ABR subjects who had been < 5 years of age at the time of the accident was due to the high number of teeth with enamel hypoplasia in the zone ABR subjects. Seven of the zone ABR subjects (19.4%) had at least one hypoplastic tooth compared with two subjects from the non-ABR zone (5.1%). Demarcated opacities occurred in 8 of 36 zone ABR subjects (22.2%) and in 10 of 39 zone non-ABR subjects (25.6%). Two subjects had both demarcated opacities and hypoplasia. Diffuse opacities related to fluorosis were not seen.
Hypodontia.
A total of 12.5% of the zone ABR subjects (three males, three females) had missing permanent teeth (excluding wisdom teeth), compared with 4.6% of the zone non-ABR residents (two males, one female). The teeth missing were lateral incisors and second premolars. Zone ABR subjects with higher serum TCDD levels more often lacked permanent teeth than did those with lower TCDD levels or the zone non-ABR residents (Mantel-Haenszel χ2 = 3.83, p = 0.05).
Other dental and oral aberrations.
Other pathologic changes in the oral cavity that could be related to the exposure to TCDD were few. Dilaceration of tooth roots was not seen. Two zone ABR and two zone non-ABR subjects had gingival pigmentation. No significant associations between any periodontal parameters and serum TCDD levels were seen, and the number of deepened periodontal pockets, percentage of subgingival calculus sites, and percentage of bleeding sites after probing were similar in subjects from zones ABR and non-ABR (Table 4). Prevalence of caries and salivary flow rate were also on similar levels in both groups (Table 4). Significant associations between caries or salivary flow rate and serum TCDD concentrations were not found.
Discussion
Twenty-five years after the Seveso accident we found that serum TCDD levels in childhood were associated with the presence of developmental enamel defects in the permanent dentition. This study, supporting our earlier results of the sensitivity of developing teeth to dioxins, was possible to perform because of two important conditions. First, serum samples from children after the accident had been collected and stored for further analysis. Second, tracing of developmental defects of enamel after such a long time was possible because teeth, once they have developed, do not undergo remodeling; that is, the defects are permanent in nature.
Ninety-three percent of the subjects with enamel defects had been < 5 years of age at the time of the accident. Enamel development of the permanent dentition (excluding wisdom teeth) is most sensitive to environmental disturbances up to the first 5–7 years of life, when mineralization of the crowns is radiographically completed (Haavikko 1970). Because of the vulnerability of the forming enamel, the distribution pattern supports the role of TCDD as causative of the defects.
Among both zone ABR and non-ABR subjects, developmental enamel defects were more prevalent in those subjects who were < 5 years of age at the time of the accident than in those who were older. A recent study by Eskenazi et al. (2004) showed that in two pooled samples from 40 children (0–12 years of age) who lived in the non-ABR zone in 1976, serum contained 33.4 and 47.6 ng/kg TCDD in lipid. These concentrations overlap those measured from children who lived in the ABR zones. Effects of TCDD and other dioxin-like compounds (also found in the pooled samples) on prevalence figures of developmental defects of enamel (26% in zone non-ABR subjects vs. 42% in zone ABR subjects) on these “background” levels cannot be excluded.
Likewise, in an epidemiologic study on 8-to 15-year-old children pre- and postnatally exposed to polychlorinated aromatic hydrocarbons, mainly PCBs, Jan and Vrbič (2000) found that significantly more developmental defects of enamel were found in a contaminated region than in the control area. This suggests that exposure to PCBs can also be associated with developmental enamel defects. However, whether dioxins and PCBs share the mechanism(s) of interference with tooth development is not known.
The subjects from the ABR and non-ABR zones were frequency matched with age, sex, and education. Developmental defects of enamel are persistent but can be masked by caries and can be removed in connection of treatment of caries (e.g., fillings, crowns). Education and age are related to dental health (Brodeur et al. 2000; Reisine and Psoter 2001). To exclude the possible bias caused by difference in education level between the groups, we randomly dropped 10 subjects initially included in the non-ABR group. The DMFT (number of decayed, missing, and filled teeth due to caries) index, which tells the number of teeth affected by caries, was 11.7 in the subjects from ABR zones and 10.6 in the subjects from the non-ABR zone, indicating that “masking” of developmental defects was at a similar level in the dentitions of both groups.
We found that education was negatively correlated with the presence of developmental defects of enamel in subjects who at the time of the accident were < 5 years of age, but not in the older age group. In the younger age group 8 of 12 subjects with basic education only had developmental defects of enamel, whereas in the older age group the proportion was 1 of 8. The difference in the distribution is difficult to explain. We therefore studied whether an interaction between the education level and the serum TCDD level could explain the higher prevalence of developmental dental defects in subjects with basic education only, but such an interaction was not found. Education thus remained as an independent explanatory factor.
Here we evaluated the association between the exposure to TCDD and the presence of developmental dental defects. Earlier, almost 100 different factors had been listed as being responsible for developmental defects of enamel (Small and Murray 1978). Therefore, it is more than likely that many defects seen in the study population were not related to exposure to TCDD. Hypoplastic enamel defects were found here in higher numbers than in many other populations (Fédération Dentaire Internationale 1992; Wiktorsson et al. 1994). However, the same etiologic factor may cause enamel opacity and hypoplasia, the end result depending on timing, duration, and severity of the influence of the disturbing agent and the susceptibility of the individual. Therefore, here, as well as in other conditions, it is rarely possible to connect a clinical appearance of a defect with a particular causative agent.
Mineralization of the first permanent teeth starts around birth and of the last usually between years 2 and 3 of life, although the normal range is wide (Schour and Massler 1940). Before mineralization, severe damage such as mechanical trauma to the tooth germ as well as multiagent chemotherapy and radiation therapy may arrest the development, leading to absence of a tooth. However, in most cases the basis for a missing tooth is genetic. Prevalence figures for nonsyndromic hypodontia (one to six teeth missing) in the permanent dentition in a normal population differ to some extent among countries, but most studies show prevalences of 3–8% (Arte 2002). Childhood exposure to PCBs and PCDFs may have increased the prevalence of hypodontia in Yusho subjects. Accordingly, 3 of 37 children (9%) had missing teeth (Fukuyama et al. 1979). In our study the prevalence was 3 of 16 (19%) in subjects with the highest tertile of serum TCDD concentrations, compared with 3 of 65 (4.6%) in the subjects from the non-ABR zone. The result of an increased prevalence of hypodontia in our study is in line with the studies in Yusho and our observations in laboratory rodents, but should be interpreted with caution because of the small study population and small number of subjects with hypodontia. To confirm that TCDD is related to hypodontia, a larger study population of children < 3 years of age (the most critical age for agenesis of a tooth) at the time of the accident should be performed.
Gingival pigmentation was observed in two (Caucasian) subjects from the ABR zones and in two from the non-ABR zone. In all subjects the pigmentation was mild and may be related to factors other than TCDD exposure. This is in contrast to the findings in Yusho patients. Oral pigmentation was observed in 75 of 121 patients approximately 30 years after the accident, with gingival pigmentation predominating (Hashiguchi et al. 2003). This striking difference is difficult to explain but may be partly related to differences in the properties of the causative compounds.
Prominent and confirmed risk factors or predictors of periodontal diseases in adults include smoking and low education (Pihlstrom 2001). In our study the subjects were frequency matched with education, and smoking was as common in subjects from the ABR zones (35%) as in subjects from the non-ABR zone (33%). Because we found no significant associations between serum TCDD levels and the periodontal disease parameters and no differences in the prevalence of periodontal disease among subjects from the ABR zones and the non-ABR zone, we suggest that exposure to TCDD in childhood is not associated with the development of periodontal disease.
In Yusho patients periodontal disease was a frequent finding (Hashiguchi et al. 2003). About 30 years after the accident, 95 of 110 examined patients (86%) had at least one tooth with a periodontal pocket deeper than 3 mm. Unfortunately, no information on the controls was available. The authors suggested that PCBs and related compounds might play an important role in the development of periodontal disease (Hashiguchi et al. 2003). Such a tendency was not seen in our study population exposed mainly to TCDD.
In a case report, Shimizu et al. (1992) described dento-orofacial characteristics of a 24-year-old female who had been exposed to PCBs and PCDFs at 6 years of age. Her main clinical findings were oral hyperpigmentation, periodontitis, and delayed eruption of teeth. Radiologic findings were a cystic radiolucency in the mandible and hypoplastic and dilacerated roots in developing teeth. Some teeth were impacted, malposed, and ankylosed. Distortion of the roots has also been reported in Yusho patients by Fukuyama et al. (1979). Unexpectedly, in the present study radiographic examination did not reveal developmental cysts, root dilacerations, or impacted teeth (except for an upper canine in one subject). Nor did the dental histories reveal remarkable aberrations.
Our recent findings show that TCDD impairs branching morphogenesis of mouse embryonic salivary glands in vitro (Kiukkonen A et al., unpublished data). Given our present results, it seems that the function of salivary glands measured as salivary flow rate was not affected in subjects exposed to high amounts of TCDD in Seveso.
Taken together, these results from Seveso show that developmental dental aberrations, which are permanent in nature, were related to childhood exposure to TCDD. In contrast, dental caries or periodontal diseases, which are infectious in nature, were not associated with the exposure. The results support our hypothesis that dioxins can interfere with human organogenesis.
Figure 1 Ratio of subjects with developmental dental defects to those without at each exposure level. Subjects < 5 years of age at the time of the Seveso accident and from whom serum samples were collected soon after the accident were included (n = 36). Range of the TCDD concentrations was 31–26,000 ng/kg in serum lipid.
Table 1 Characteristics of the study population.
Variable Zone ABR Zone non-ABR p-Value
Total 48 65
Sex (female) 23 (48) 37 (57) 0.34
Age
Mean (range), years 29.1 (25.4–34.0) 29.2 (24.6–34.1) 0.93
< 5 years at the time of the accident 36 (75) 39 (60) 0.095
Education
Elementary school 2 (4) 5 (8)
Secondary school not finished 6 (12) 7 (11)
Vocational school 11 (23) 6 (9)
Secondary school finished 23 (48) 34 (52)
High school finished, university training 6 (12) 13 (20) 0.28
Smoker 15 (31) 21 (32) 0.90
Values shown are number (%) except where noted.
Table 2 Presence of developmental defects of enamel by age and by zone of residence at the time of the accident, serum TCDD level, and education in children < 5 years of age at the time of the accident.
Variable No. of subjects No. of subjects with developmental defects of enamel (%) p-Value
> 5 years of age at time of accident 38 2 (5.3)
< 5 years of age at time of accident 75 25 (33) 0.0009
Non-ABR zone 39 10 (26)
ABR zone 36 15 (42) 0.14
31–226 ng/kg TCDD 10 1 (10)
238–592 ng/kg TCDD 11 5 (45)
700–26,000 ng/kg TCDD 15 9 (60) 0.016
Educational level
ABR zone
Lowera 6 5 (83)
Higherb 30 10 (33) 0.023
Non-ABR zone
Lower 6 3 (50)
Higher 33 6 (18) 0.14
a Lower education level refers to subjects with elementary school or secondary school not finished (basic education).
b Higher education level refers to subjects with vocational school, secondary school finished, high school finished, or university training.
Table 3 Independent explanatory factors associated in the logistic regression model with the presence of developmental enamel defects in subjects < 5 years of age at the time of the Seveso accident.
Variable p-Value OR (95% CI)
TCDD exposure level 0.007
Resident from non-ABR zone or serum TCDD 31–226 ng/kg in serum lipid 1.0
Increase of the serum TCDD level (238–592 and 700–26,000 ng/kg in serum lipid) 2.4 (1.3–4.5)
Education 0.014
Higher than basic education 1.0
Basic education only 5.8 (1.4–23.7)
Abbreviations: CI, confidence interval; OR, odds ratio. Residents from zone non-ABR and those with the lowest tertile of the ranked serum TCDD concentrations from zones ABR are grouped.
Table 4 Dental caries, periodontal disease, and salivary flow in subjects from ABR zones (n = 48) and from the non-ABR zone (n = 65) [mean ± SD (range)].
Variable Zone ABRa Zone non-ABR
DMFT 11.7 ± 4.05 (3–21) 10.6 ± 5.26 (0–22)
Attachment loss at probing > 4 mm (no. of sites) 7.7 ± 6.8 (0–28) 7.2 ± 8.6 (0–33)
Attachment loss at probing > 5 mm (no. of sites) 1.3 ± 2.2 (0–10) 1.7 ± 4.5 (0–31)
Bleeding on probing (percent of sites) 33.9 ± 16.0 (6–63) 31.0 ± 15.6 (3–71)
Subgingival calculus (percent of sites) 29.1 ± 14.4 (1–61) 27.0 ± 16.2 (1–77)
Stimulated salivary flow rate (mL/min) 1.3 ± 0.64 (0.4–2.4) 1.3 ± 0.75 (0–3.6)
a The difference between the mean values of all variables of zone ABR subjects and zone non-ABR subjects was insignificant (range of p-values, 0.11–0.67).
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.6995ehp0112-00131915345346Environmental MedicineArticleNeurologic Abnormalities in Workers of a 1-Bromopropane Factory Ichihara Gaku 1Li Weihua 2Shibata Eiji 3Ding Xuncheng 2Wang Hailan 1Liang Yideng 4Peng Simeng 5Itohara Seiichiro 1Kamijima Michihiro 1Fan Qiyuan 2Zhang Yunhui 2Zhong Enhong 2Wu Xiaoyun 2Valentine William M. 6Takeuchi Yasuhiro 71Field of Social Life Science, Nagoya University Graduate School of Medicine, Nagoya, Japan2Shanghai Institute of Planned Parenthood Research, Shanghai, China3Department of Health and Psychosocial Medicine, Aichi Medical University, Aichi, Japan4Division of Neurobiology, Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA5Yixing Anti-Epidemic and Health Station, Yixing, China6Department of Pathology, Vanderbilt University Medical Center, Nashville, Tennessee, USA7Emeritus Professor, Nagoya University, Nagoya, JapanAddress correspondence to G. Ichihara, Field of Social Life Science, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya 466-8550, Japan. Telephone: 81-52-744-2123. Fax: 81-52-744-2126. E-mail:
[email protected] thank Z.-Q. Chen, School of Medicine, Fudan University, and K. Yokoyama, Department of Public Health, School of Medicine, Mie University, for their help in conducting neurobehavioral tests. We also thank Y. Koike, Department of Medical Technology, Nagoya University School of Health Sciences, and T. Kondo, Nihon Kohden Co. Ltd., for their support in electrophysiologic examination.
This study was supported by grants 13470088 and 14406015 from the Japan Society for the Promotion of Science.
The authors declare they have no competing financial interests.
9 2004 30 6 2004 112 13 1319 1325 2 2 2004 30 6 2004 Publication 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 reported recently that 1-bromopropane (1-BP; n-propylbromide, CAS Registry no. 106-94-5), an alternative to ozone-depleting solvents, is neurotoxic and exhibits reproductive toxicity in rats. The four most recent case reports suggested possible neurotoxicity of 1-BP in workers. The aim of the present study was to establish the neurologic effects of 1-BP in workers and examine the relationship with exposure levels. We surveyed 27 female workers in a 1-BP production factory and compared 23 of them with 23 age-matched workers in a beer factory as controls. The workers were interviewed and examined by neurologic, electrophysiologic, hematologic, biochemical, neurobehavioral, and postural sway tests. 1-BP exposure levels were estimated with passive samplers. Tests with a tuning fork showed diminished vibration sensation of the foot in 15 workers exposed to 1-BP but in none of the controls. 1-BP factory workers showed significantly longer distal latency in the tibial nerve than did the controls but no significant changes in motor nerve conduction velocity. Workers also displayed lower values in sensory nerve conduction velocity in the sural nerve, backward recalled digits, Benton visual memory test scores, pursuit aiming test scores, and five items of the Profile of Mood States (POMS) test (tension, depression, anxiety, fatigue, and confusion) compared with controls matched for age and education. Workers hired after May 1999, who were exposed to 1-BP only (workers hired before 1999 could have also been exposed to 2-BP), showed similar changes in vibration sense, distal latency, Benton test scores, and depression and fatigue in the POMS test. Time-weighted average exposure levels in the workers were 0.34–49.19 ppm. Exposure to 1-BP could adversely affect peripheral nerves or/and the central nervous system.
1-bromopropanedistal latencynerve conduction velocityneurobehavioral testingneurotoxicityozone-depleting solventspostural sway testingreproductive toxicityvibration sense
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Ozone-depleting solvents, such as specific chlorofluorocarbons and 1,1,1-trichloroethane, have been banned since 1996 in developed countries. Because they were used in large amounts in various industries, alternative compounds were introduced to the workplace. One such alternative compound is 1-bromopropane (1-BP; n-propylbromide, CAS Registry no. 106-94-5), which is used in the United States and Japan as a cleaning agent for metals, precision instruments, electronics, optical instruments, and ceramics (Ichihara, in press). It is also used in spray form as an adhesive in the United States (Ichihara et al. 2002). Environ Tech (2001) estimated the total amount of 1-BP commercially available for sale in the United States in the year 2000 was 1,967.9 metric tons (4,338,583 lb), which is comparable to 9.0, 31.0, and 10.6% of the amount of methylene chloride, perchloroethylene, and trichloroethylene used in adhesive/foam fabrication and metal cleaning in the same year in the United States. In Japan, the amount of 1-BP sold in 2003 was 1,125 metric tons, which is about double the 645 metric tons sold in 1998 (Association of Bromopropane Producers of Japan, unpublished data). In addition, in the workplace where cases of neurotoxicity had been reported, 1-BP was introduced as an alternative for methylene chloride (Ichihara et al. 2002). The benefits of using 1-BP instead of the chlorinated carbons are not clear. However, under pressure to regulate the use of chlorocarbons, 1-BP has been used as a surrogate, which is encouraged by the lack of measures to define the exposure limits. In this regard, previous animal studies revealed neurotoxicity and reproductive toxicity of 1-BP (Ichihara et al. 2000a, 2000b; Wang et al. 2002, 2003; Yamada et al. 2003; Yu et al. 1998, 2001). Exposure to 1-BP resulted in a dose-dependent limb muscle weakness and reduction of nerve conduction in rats (Ichihara et al. 2000a). It also resulted in myelin degeneration of peripheral nerves and swelling of preterminal axons in the medulla oblongata (Ichihara et al. 2000a). It was also revealed that 1-BP exhibits reproductive toxicity in both male and female rats (Ichihara et al. 2000b; Yamada et al. 2003). Thus, animal studies preceded human studies and warned about the potential neurotoxicity and reproductive toxicity of 1-BP in humans. The most recently reported cases also confirmed the neurotoxicity of 1-BP in humans (Ichihara et al. 2002; Sclar 1999). However, these case reports have limitations in terms of quantitative analysis. In 1999 we investigated a 1-BP factory, but this investigation was also limited because it was originally oriented to study the effects of 2-bromopropane (2-BP), which targets mainly reproductive and hematopoietic systems (Ichihara et al. 2004).
The aim of the present study was to assess the neurologic function and other health-related changes in workers exposed to 1-BP and compare the results with those of control workers in a beer factory.
Materials and Methods
Factories and workers.
The subjects were female workers of a 1-BP production factory located in Yixing, Jiangsu Province, China. The survey was conducted 16–18 January 2001. The same factory mainly produced 2-BP in 1996 (Ichihara et al. 1999), but shifted the main production to 1-BP between 1996 and 1999 (Ichihara et al. 2004), and the product was only 1-BP at the time of the present survey. 1-BP was synthesized by incubating n-propranolol and hydrogen bromide under concentrated sulfuric acid. The product was purified by distillation and temporarily stored in ceramic containers. The crude product was then transferred to 20-L plastic vessels through hose pipe from the cock of the container and subsequently neutralized with hydrogen carbonate. The product was finally transferred to 1,000-L drums for storage and transport. The workers were at risk of exposure to 1-BP when a) placing the chemicals into the reaction pots; b) sitting close to the reaction pots to observe and record the temperature; c) taking out the crude product; d) adding the hydrogen carbonate and stirring; and e) pouring the product into the drums. In the final step, the workers added the product with hand scoops to adjust the product volume in the drum.
The surveyed factory has two similar-sized manufacturing plants, each measuring 9.7 × 24.4 × 7 m (width × depth × height). In each plant, a ventilating fan was ineffectively installed 6 m from the floor; no local ventilation fan was installed in the vicinity of the areas where workers might be exposed to 1-BP. The 27 surveyed workers who were engaged in the production of 1-BP in the factory were all female. As controls, we selected age-matched (± 2 years) females at random from 202 female workers in a beer factory in the same city. The control workers lived in the same area.
In the analysis of paired t-tests between 1-BP workers and controls, four 1-BP workers were excluded because no corresponding match of control workers from the beer factory could be recruited. However, the analysis by exposure level or period of exposure included those 1-BP female workers for whom no corresponding age-matched controls could be recruited. All workers who were hired after 1991 and for whom corresponding age-matched controls could be recruited were identified as 1991 workers. Among them, the workers who were hired after 1999 and were exposed only to 1-BP were defined as 1999 workers.
Medical examination.
Signed informed consent was obtained from each worker for all examinations and interviews, according to the Declaration of Helsinki (World Medical Association 2002). All female workers in the 1-BP factory and the 23 age-matched beer-factory workers were clinically examined by a trained Chinese neurologist who was conducting medical research at the Department of Neurology, Nagoya University, Japan, and had a good command of both Chinese and Japanese languages.
The vibration sensation was evaluated using a vibrating tuning fork (128 Hz); the fork was placed on the dorsum of the metatarsophalangeal joint of the big toe or the dorsum of the metacarpophalangeal joint of the thumb, and the worker was asked to report when the vibration ceased. Immediately after reporting, the tuning fork was moved to the same site (big toe or thumb) of the examiner and the duration of the lasting vibration after the worker’s report was recorded. It was difficult to assess the actual time when the delay time was < 2 sec, because it took some time (but < 2 sec) to move the tuning fork from the worker’s body to the examiner’s body. In addition, one worker reported total loss of vibration sense in the right toe. Therefore, the value could not be treated as a continuous value in the statistical analysis. The examiner was a trained female (38-year-old) neurologist who worked with every worker throughout the investigation.
Electrophysiologic studies.
We conducted electrophysiologic studies in an air-conditioned room maintained at 24°C. The workers were acclimated to the room temperature for 30 min before the electrophysiologic studies. We examined distal latency (DL), motor nerve conduction velocity (MCV), F-wave conduction velocity (FWCV), and sensory nerve conduction velocity (SNCV). Electric stimulation and recordings were performed with a Neuropack evoked potential/electromyogram measurement system (model MEB5508; Nihon Kohden, Co., Tokyo, Japan). For measurement of DL and MCV, the stimulation site was just behind the medial malleolus (distal) and the center of poples (proximal), and the recording site was fixed 11 cm distal to the distal stimulation site on the abductor hallucis muscle.
Blood tests.
The following blood tests were performed in each worker: red blood cell (RBC) count, hemoglobin, hematocrit, white blood cell (WBC) count, and platelet count, using a hematocell counter (Coulter JT, Coulter Electronics, Hialeah, FL, USA), as well as fructosamine (colorimetric method), blood urea nitrogen [urease ultraviolet (UV) method], creatinine (enzyme method), total protein (Biuret method), total cholesterol (enzyme method), creatine kinase (UV N-acetylcysteine method), aspartate aminotransferase (UV method), alanine aminotransferase (UV method), γ-glutamyl transferase (l-γ-glutamyl-3-carboxy-4-nitroanilide substrate method), lactate dehydrogenase (Wroblewski-LaDue method), alkaline phosphatase (p-nitrophenol substrate method), serum creatinine (alkaline picric acid method), vitamin B1 (HPLC method), iron [2-nitroso-5-(N-propyl-N-sulfopropylamino)phenol method], ferritin, thyroid-stimulating hormone [radioimmunoassay (RIA)], luteinizing hormone (LH; RIA), follicle-stimulating hormone (FSH; RIA), and estradiol (RIA).
Neurobehavioral tests and postural sway test.
Neurobehavioral testing [simple reaction time, digit span, Santa Ana, digit symbol, Benton, pursuit aiming test, Profile of Mood States (POMS)] was conducted based on the Chinese edition of the World Health Organization Neurobehavioral Core Test Battery (Chen 1988; Liang 1987) by trained Chinese researchers. Because neurobehavioral tests can be influenced by education level, we also conducted analyses with controls matched for age and education level. Postural balance was measured with a Gravicorder GS-30 stabilometer (Anima Co., Tokyo, Japan). The same instrument was used in all subjects throughout the investigation. Postural sway testing was performed as described previously (Yamamoto et al. 2001; Yokoyama et al. 1997). Briefly, the subject was asked to stand with big toes touching each other on the platform of the Gravicorder. The center of gravity was recorded every 50 msec with both eyes open for 1 min and closed for 1 min. The calculated values based on the center of gravity were a) the total length of excursion; b) envelope area; c) length of excursion per envelope area; d) rectangular area, representing the product of the range of the x-component (lateral) and that of the y-component (anteroposterior); e) root mean square area; f) the mean of x-axis or y-axis component of each recorded point; g) the center of range of the x-axis or y-axis component of points; h) power spectrum of the x-axis or y-axis at 0.02–0.2 Hz, 0.2–2.0 Hz, and 2.0–10.0 Hz, obtained by frequency analysis, with both eyes open and closed; and i) the Romberg quotient, representing the ratio of values measured with eyes closed to the value with eyes open for items a through h.
Assessment of exposure to 1-BP.
Individual exposure levels during work shifts were evaluated with passive samplers (Sibata Scientific Technology Ltd., Tokyo, Japan) using the method described previously by Ichihara et al. (2004). A passive sampler was attached to each worker during one 8-hr shift and was collected immediately after the shift and kept in separate sealed bags at 4°C until analysis. The absorbed solvent in the sampler was analyzed 2 weeks after the investigation. In our previous study (Ichihara et al. 2004), we confirmed the stability of absorbed 1-BP in charcoal at 4°C for 2 weeks. For analysis, activated charcoal particles were taken from the samplers and then immersed in 2 mL carbon disulfide (Wako Pure Chemicals, Osaka, Japan) in a glass tube with a screw cap. The tube was shaken vigorously for 5 min and left to stand for 1 hr; the supernatant was then injected into a gas chromatograph equipped with an electron ionization detector (GCD system G1800A, Hewlett Packard, Palo Alto, CA, USA). The concentration of 1-BP was quantified by the selected ion mode. The detection limit was 0.007 ppm by this method. The time-weighted average (TWA) was calculated based on the formula
In our calculations, we used the value of 0.134 as the sampling rate of 1-BP. The value was determined by the diffusing cell method.
Statistical analysis.
We used the paired t-test to compare continuous parameters of the exposure group and controls matched for age or age and education level. In this analysis, all indices of electrophysiologic studies, neurobehavioral tests, POMS test, stabilometer testing, and blood tests were compared with the age-matched controls, and the indices of neurobehavioral tests and POMS were also compared with controls matched for age and education level. We used the Wilcoxon test and Fisher’s exact test to compare the delay time and abnormality of menstrual cycles, respectively, of the exposure group and the age-matched controls. In the analysis by exposure levels, the 27 exposed workers were classified into two groups: ≤2.64 ppm (n = 17) and ≥ 8.84 ppm (n = 7); data missing (n = 3). For analysis by length of exposure, the 27 exposed workers were again classified into two groups: ≤ 9.31 months (n = 10) and ≥ 16.33 months (n = 16); data missing (n = 1). We selected these cutoff values because they divided the two peak distributions when the histograms with column width of 2.5 ppm and 5 months, respectively, were drawn, whereas no values were found between 2.64 and 8.84 ppm and 9.31 and 16.33 months. In comparisons between groups stratified with fructosamine [≤ 246 μmol/L (n = 14) and 248–284 μmol/L (n = 13)] or vitamin levels [20–30 ng/mL (n = 13) and ≥ 31 ng/mL (n = 13); data missing (n = 1)], the groups were divided according to the median because there was no split in the distribution that formed two peak distributions. The t-test was applied when comparing continuous variables (electrophysiologic tests, neurobehavioral tests, POMS test, stabilometer tests, and blood tests) by exposure levels or length of exposure as well as the levels of fructosamine or vitamin B1. For the analysis of delay time and frequency of menstrual cycles, we used Wilcoxon test and Fisher’s exact test, respectively, for comparison according to exposure levels, length of exposure, and the level of fructosamine or vitamin B1. We defined significance as the probability of p < 0.05.
Results
There were no differences in age and height between 1-BP workers and the age-matched controls (Table 1). The control group had a higher education level than the exposure group. Job duration of the exposure group was shorter than for the controls, probably because the area where the workers lived had been developed quite recently, so they had engaged in agriculture before employment in the factory. Four workers in the beer factory (controls) had been exposed to various chemicals (formalin, n = 2; ammonia, n = 1; alkaline reagent, n = 1) in occupational settings before their present jobs. None of the workers investigated was a smoker, and only one exposed worker and one control worker were alcohol drinkers. None of the workers investigated had a history of diabetes mellitus, which could cause polyneuropathy. Individual exposure levels ranged from 0.34 to 49.2 ppm (median, 1.61 ppm; geometric mean, 2.92; Figure 1).
Bromopropane workers, all of whom were hired after 1991 (1991 workers), had significantly longer DL and lower SNCV than did the age-matched controls (Table 2). Because the main product in the factory had shifted from 2-BP to 1-BP between 1996 and May 1999 (Ichihara et al. 2004), we also analyzed data for 1999 workers to examine the effects of exposure to 1-BP only. Examination of these workers showed the only significant change to be an increase in the DL compared with age-matched controls. However, the extent of the change in any electrophysiologic parameter in the 1999 workers tended, in general, to be similar to that of the 1991 workers. Reduced vibration sensation as tested on the right toe, left toe, right finger, and left finger was detected in 15, 13, 4, and 4 female workers, respectively (Tables 3 and 4). One worker showed complete loss of vibration sense on the right toe by tuning fork stimulation. The exposure level for this worker was 1.10 ppm, and she had a relatively high DL (8.8 msec) and low MCV (43.1 m/sec), FWCV (53.7 m/sec), and SNCV (38.8 m/sec). In contrast, none of the age-matched beer workers showed any abnormalities in vibration sensation in the toe and finger. The Wilcoxon test showed significant differences in the delay time bilaterally both in the feet and in the fingers between 1991 workers and controls. Analysis of 1999 workers also showed significant prolongation of the delay time on the toes bilaterally but not in the fingers. The percentage of 1999 workers who showed reduced vibration sensation (delay time ≥ 2 sec) on both sides of the foot and in the fingers was similar to that of 1991 workers.
Neurobehavioral tests showed lower values for the forward and backward digit span, Benton visual memory test, pursuit aiming test, POMS test (scores for tension, depression, anxiety, fatigue, and confusion) in the 1-BP workers than in the controls (Tables 5 and 6). Because the education level of 1-BP workers was different from that of the age-matched controls and because the education level could affect the results of neurobehavioral tests, these tests were analyzed after matching both education level and age (Tables 5 and 6). 1-BP workers had lower levels of backward digit span; correct scores in the Benton visual memory test; completed response in the pursuit aiming test; and tension, depression, anxiety, fatigue, and confusion in the POMS test than did controls matched for age and education level. Further analysis was conducted for these neurobehavioral tests on 1999 workers (Tables 5 and 6). Significant differences with the controls were found only in the Benton visual memory test and in POMS depression and fatigue.
The postural sway tests showed significantly lower power spectrum of the x-axis at 2.0–1.0 Hz with eyes open and y-axis at 0.02–0.2 Hz with eyes closed and significantly higher power spectrum of the y-axis at 0.2–2.0 Hz with eyes closed (Table 7) in the 1999 workers than in the age-matched controls, but other parameters were not significantly different between the two groups (Table 7; Romberg quotients for all items, which also did not show any statistical difference, are not shown). The comparison of the 1999 workers and age-matched controls did not show any significant differences in postural sway tests (Table 7; Romberg quotients not shown).
Laboratory tests did not show any significant differences between the 1991 workers and age-matched controls (data not shown) except for significantly lower levels of vitamin B1 (31.0 ± 5.6 vs. 34.3 ± 5.4 ng/mL) and low WBC count (5.7 ± 1.7 × 103/μL vs. 6.7 ± 1.8 × 103/μL) in the 1991 workers than in age-matched controls. For 1999 workers, only the WBC count was significantly lower than in the age-matched controls. In only one worker (42 years of age) in the control group, the fructosamine level (286 μmol/L) was above the upper limit of reference value (205–285 μmol/L). This worker had rather high DL (8.24 msec) and low levels of MCV (42.5 m/sec), FWCV (49.8 m/sec), and SNCV (39.5 m/sec) but did not show abnormal vibration sensation. This worker was not included in the education-matched testing because she had no education-matched individual in the exposure group. Comparison between the two groups stratified by fructosamine levels within all exposed workers (n = 27) showed significant differences only in higher levels of total protein (8.22 ± 0.53 g/dL), total cholesterol (197.7 ± 32.1 mg/dL), choline esterase (ChE; 366.1 ± 86.7 IU/L), LH (14.3 ± 14.3 IU/L), WBC (6.62 ± 5.15 × 103/μL), RBC (4.19 ± 0.38 × 106/μL), POMS confusion (5.31 ± 4.35), and lower estradiol level (35.4 ± 25.1 pg/mL) in the high-fructosamine group compared with the low-fructosamine group (total protein, 7.64 ± 0.24 g/dL; LH, 4.2 ± 3.4 IU/L; total cholesterol, 166.5 ± 28.7 mg/dL; ChE, 288.4 ± 37.8 IU/L; WBC, 5.16 ± 0.97 × 103/μL; RBC, 3.84 ± 0.38 × 106/μL; POMS confusion, 2.36 ± 1.91; estradiol, 63.2 ± 38.3 pg/mL).
Fisher’s exact test did not show any difference between the 1991 and 1999 worker groups and their corresponding age-matched control groups with regard to the frequency of menstrual abnormalities after starting working in the 1-BP factory. Two workers in the exposure group had a short menstrual cycle. Similarly, one worker in the control group had a short menstrual cycle, and another reported a prolonged period of menstrual bleeding.
On the other hand, a comparison based on the exposure levels (≤ 2.64 or ≥ 8.84 ppm) showed that workers with high exposure levels showed significantly high values of MCV (56.4 ± 12.9 m/sec), FWCV (54.7 ± 2.8 m/sec), hematocrit (0.393 ± 0.032), and POMS tension (5.14 ± 1.77) and lower values of FSH (9.0 ± 6.3 mIU/mL) and POMS vigor (18.6 ± 2.5), compared with the low-exposure group (MCV, 47.3 ± 8.3 m/sec; FWCV, 52.0 ± 1.9 m/sec; hematocrit, 0.356 ± 0.034; POMS tension, 2.73 ± 1.49; FSH, 27.7 ± 35.3 mIU/mL; POMS vigor, 24.3 ± 4.0) but did not show any significant association with other examined indices. In the comparison by the length of exposure (≤ 9.31 or ≥ 16.33 months), the longer-exposure group had high levels of LH (13.5 ± 13.7 mIU/mL) and FSH (34.9 ± 34.9 mIU/mL) and lower levels of total protein (7.77 ± 0.30 g/dL) and vitamin B1 (29.2 ± 5.1 ng/mL) than did the shorter-exposure group (LH, 3.3 ± 1.8 mIU/mL; FSH, 5.5 ± 2.1 mIU/mL; total protein, 8.18 ± 0.66 g/dL; vitamin B1, 33.2 ± 5.0 ng/mL) but did not show any significant association with other examined indices.
Because the mean concentration of vitamin B1 was significantly lower in the exposure group than in the controls, the values were compared between the two groups stratified by vitamin B1 level within all exposed workers (n = 27). The comparison did not reveal any difference in the frequency of low vibration sensation or results of electrophysiologic tests, apart from lower levels of alkaline phosphatase (ALP; 129.3 ± 30.7 IU/L) and ChE (293.8 ± 52.8 IU/L) in the low vitamin group than high vitamin group (ALP, 169.5 ± 43.1 IU/L; ChE, 361.8 ± 84.1 IU/L).
Discussion
In the tested factory, isopropanol, hydrogen bromide, and sulfuric acid were also used as materials in the process of producing 1-BP. These chemicals are not considered to have neurotoxic effects, so it is unlikely that the low vibration sensation or change in DL is due to these chemicals. In the last survey of the same factory (Ichihara et al. 2004), we found that the main product of this factory was shifted from 2-BP to 1-BP. 1991 Workers include the workers who were hired before May 1999 and might have been exposed to not only 1-BP but also 2-BP before 1999 (Ichihara et al. 1999). In contrast, 1999 workers were exposed to 1-BP only. Therefore, the observed changes in the DL, vibration sense in both feet bilaterally, Benton visual memory test score, and depression and fatigue in the POMS test that were noted in 1999 workers are considered to be due to exposure to 1-BP. However, the effects of 2-BP cannot be excluded in 1991 workers. The SNCV showed significant changes in the analysis of 1991 workers but not in 1999 workers with age-matched controls. This is most likely due to the lack of power as a result of the reduction in the number of subjects, given the fact that the extent of change in sensory nerve conduction, as well as other electrophysiologic parameters, and the percentage of workers who showed reduced vibration sense among 1999 workers was similar to that of 1991 workers. This explanation might also be valid for other parameters that showed significant change in 1991 workers but not in 1999 workers.
Our animal studies (Ichihara et al. 2000a; Yu et al. 1998) preceded human case reports in revealing the neurotoxicity of 1-BP, which is far more potent than that of 2-BP (Yu et al. 1999, 2001). However, the results of animal studies had certain limitations in predicting symptoms or signs in human cases; for example, animal studies cannot detect any subjective symptoms that might reflect abnormalities of sensation or the central nervous system. It is sometimes difficult especially for morphologic studies to substantiate the adverse effects on the central nervous system because the structure of the central nervous system is far more robust than that of peripheral nerves or other organs. It is also difficult to evaluate imbalance during walking in rodents because four-footed animals are completely different from bipedal humans regarding the clinical signs of imbalance. Thus, information from human cases should help us understand the toxicologic targets of 1-BP. The first case was reported by Sclar (1999), and three other cases were recently reported by our group (Ichihara et al. 2002). All four cases showed diminished vibration sensation in the toe. Moreover, the present study showed that more than half of the workers exposed to 1-BP suffered from reduced vibration sensation. Considered together, these results suggest that vibration sensation in the toe might be susceptible to exposure with 1-BP. The previously reported cases also complained of urinary incontinence; numbness in the perineum, low back, and front of the thighs or buttocks; or headache (Ichihara et al. 2002); however, our factory workers did not report any such symptoms. This difference might depend on the levels or period of exposure to 1-BP because it is possible that our workers adapted to low levels but longer periods of exposure, leading to unawareness of symptoms.
In comparisons with age-matched controls, both the 1991 workers and 1999 workers showed prolonged DL but no change in MCV. This prolongation of DL without decrease in MCV parallels the results of animal studies, which showed earlier changes in DL than MCV in the tail nerve (Ichihara et al. 2000a). Such a pattern of changes might indicate predominant deterioration of the distal portion of the peripheral nerve or delay in chemical transmission between nerve terminals and muscle.
Comparison of data of 1991 workers with age-matched controls showed that the exposure group had lower levels of forward and backward digit span, Benton scores, pursuit aiming test scores, and POMS tension, depression, anxiety, fatigue, and confusion than did the controls. Because education level could influence the results of neurobehavioral tests, the results of the tests were reanalyzed after matching age and education levels. This reanalysis also revealed changes in the above items excluding forward digit span. When the analysis was limited to the 1999 workers, significant differences were found only in Benton visual memory test scores, POMS depression, and POMS fatigue, which could reflect the lack of power due to the small sample number. Digit span, pursuit aiming test, and the POMS test are considered the most sensitive indicators of exposure to organic solvents or neurotoxic agents such as lead (Zhou et al. 2002). Poorer performance in the POMS test was also observed in a Venezuelan study of workers exposed to organic solvents (Escalona et al. 1995). The present results of neurobehavioral tests could also suggest that 1-BP adversely affects the central nervous system in humans. Postural sway tests showed higher power of the y-axis (anterior–posterior sway) at 0.2–2.0 Hz and lower at 0.02–0.2 Hz with eyes closed, although such significant differences were not observed in 1999 workers. These results might be important because the cases found in the United States also showed unstable balance in walking. Clinically, patients with cerebellar disease and anterior lobe atrophy show anteroposterior sway, often with a spontaneous high-frequency body tremor of around 3 Hz (Diener et al. 1984). This anteroposterior sway might resemble the present result of the increase in the power of the y-axis at 0.2–2 Hz. However, the results of the postural sway tests noted in our study await further confirmation because the presence of cerebellar disorder in the formerly reported cases or present workers is not conclusive, and it is possible to attribute the unstable balance to a disorder of the peripheral nerves or spinal cord.
Diabetes mellitus could be a common confounding factor related to neurologic disorders by solvent intoxication. HbA1C and fructosamine are used as long-term (Bunn et al. 1976) and intermediate-term (1–3 weeks) (Baker et al. 1983) indicators of glucose levels in clinical settings. In the present study, we measured serum fructosamine levels. For the measurement of HbA1C, the blood samples had to be kept at 4°C but not frozen. However, the long transportation from the factory site to the laboratory could have potentially caused hemolysis of the collected blood and thus may have resulted in marked variability and errors in estimations. For this reason, HbA1C was not measured in the present study. The comparison between the exposed group and the controls did not show any difference in the level of fructosamine, and the comparison between the high-fructosamine group and low-fructosamine group within the exposed group also did not show any difference in indices related to the nervous system.
The levels of vitamin B1 were lower in the entire exposure group than in the controls and in the longer-exposure group compared with the shorter-exposure group. Lack of vitamin B1 is known to cause polyneuropathy, but the relatively low level of vitamin B1 in the 1-BP factory workers could not fully explain the neurologic abnormalities. First, the level of vitamin B1 in the exposed workers ranged from 20 to 43 ng/mL, which was within the normal range (20–50 ng/mL). Second, the low-level vitamin B1 group showed no neurologic deficit such as vibration sensation or electrophysiologic indices, apart from a low score of POMS confusion, which would be weak evidence in substantiating the adverse effects on the nervous system.
Letz and Gerr (1994a, 1994b) investigated the confounding factors that could affect nerve conduction velocity and amplitude as well as vibrotactile and thermal thresholds, based on data from 4,464 subjects. Their studies revealed that the major covariates were height, examiner, skin temperature, and body mass index for sural sensory nerve and height, examiner, age, and body mass index for peroneal motor nerve conduction velocities. For vibrotactile threshold in toe, the major covariates were height, examiner, age, and body mass index. Our study design could control for the effect of examiner-, sex-, and age-matching pairs but not skin temperature-, body height-, or body mass index-matching pairs. Although body height was comparable on average between the exposure group and the controls and workers were acclimated to the room temperature before the electrophysiologic studies, the lack of pair matching for height, skin temperature, and body mass index should be carefully noted as a limitation of this study. Previous animal experiments demonstrated that exposure to 1-BP disrupted the estrous cycle and inhibited follicular development (Yamada et al. 2003). Two patients who worked in a cushion company in the United States also reported temporary irregularities of menstrual cycle (Ichihara et al. 2002). Although the exposure level for the two patients was not evaluated directly, such levels would be higher than 60–261 ppm, which were determined with the third case from the same factory after the former two cases were identified and ventilation was improved in the workplace. On the other hand, our study did not demonstrate significant differences in the prevalence of menstrual cycle abnormalities between the two groups. This might be due to the difference in exposure levels between U.S. cases (≥ 60–261 ppm) and our Chinese 1-BP factory workers (0.34–49.19 ppm).
Comparisons based on the exposure period showed higher levels of FSH and LH in the longer-exposure group than in shorter-exposure group. One explanation for this difference is that our group included four elderly women, who were excluded from the paired t-test analysis because of the lack of matched controls and who had high levels of FSH (42–100 mIU/mL) and LH (16–42 mIU/mL). Analysis based on exposure level did not show any relationship between exposure levels and these parameters, which were different between the exposure group and age-matched controls (paired t-test). The present analysis by exposure period and level has certain limitations. First, the number of subjects was too small and did not control for age. Second, the experimental design allowed only a single measurement of the exposure level, although the task of workers was not fixed and thus the exposure levels could vary. The exposure levels in 1999 in the same factory ranged from 0.9 to 170.5 ppm (geometric mean = 52.5 ppm) (Ichihara et al. 2004), which was far higher than in the present study. It is possible that the workers were exposed to 1-BP at higher levels than those measured in our study. Further assessment of long-term exposure levels is required to determine the relationship between 1-BP and exposure levels.
In summary, the present study suggested that exposure to 1-BP produces adverse effects on peripheral sensory and motor nerves and/or the central nervous system in humans. Estimation of long-term exposure levels is required to confirm the precise association between the health effects of 1-BP and exposure levels.
Correction
In the manuscript published online, the numbers of workers listed in Table 1, especially in the footnotes, were incorrect; also, the statistical significance of values for the right and left fingers for 1999 workers and age-matched controls was incorrect. These errors have been corrected here.
Figure 1 Exposure levels of each worker in a 1-BP factory (TWA for 8-hr shift). Values were obtained with passive samplers from workers who had age-matched controls (n = 23). Maximum = 49.19; minimum = 0.34; median = 1.61; geometric mean = 2.92 ppm.
Table 1 Characteristics of workers.
Characteristic 1-BP exposed (n = 23) Control (n = 23)
Age (years) 36.2 ± 5.7a 36.2 ± 5.2
Height (cm) 160.3 ± 6.6a 158.8 ± 5.9
Education
Elementary school 4 4
Junior high school 19 12
High school 0 6
University 0 1
Job duration (months) 27 ± 31 168 ± 67
Past job exposure to chemicals 0 4b
Previous medical condition 2c 8d
Data for age, height, and job duration are mean ± SD.
Other values are numbers of workers.
a Not significantly different from the controls (paired t-test).
b Includes formalin (2), ammonia (1), alkaline (1).
c Includes cholecystitis (1), contraceptive use (1).
d Includes anemia (2), gastritis (2), hysteromyoma (2), oophoritic cyst (1), cholecystitis (1), taking antihypertensive medications (1).
Table 2 Electrophysiologic indices of workers exposed to 1-BP and of the controls.
1991 workers Age-matched controls for 1991 1999 workers Age-matched controls for 1999
No. of pairs 23 12
DL of nervus tibialis (msec) 8.05 ± 2.17* 5.96 ± 1.38 8.36 ± 2.38* 6.06 ± 1.43
MCV of nervus tibialis (m/sec) 49.8 ± 10.3 49.9 ± 8.2 51.3 ± 12.0 51.7 ± 10.7
FWCV of nervus tibialis (m/sec) 52.8 ± 3.5 55.1 ± 3.2 51.8 ± 2.8 55.0 ± 2.9
SNCV of nervus suralis (m/sec) 39.2 ± 3.5* 46.2 ± 6.6 39.2 ± 2.6 47.5 ± 8.5
Data are mean ± SD.
* p < 0.05 compared with age-matched controls (paired t-test).
Table 3 Number of workers with reduced vibration sensation in the foot.
1991 workers and age-matched controls (n = 23 pairs)
1999 workers and age-matched controls (n = 12 pairs)
Right foot* Left foot* Right foot* Left foot*
Delay timea (sec) 1-BP workers Controls 1-BP workers Controls 1-BP workers Controls 1-BP workers Controls
< 2 8 23 10 23 5 12 5 12
2 0 0 1 0 0 0 1 0
3 3 0 1 0 1 0 1 0
4 2 0 4 0 1 0 1 0
5 2 0 1 0 1 0 0 0
6 4 0 4 0 3 0 2 0
8 3 0 1 0 1 0 1 0
10 0 0 1 0 0 0 1 0
∞b 1 0 0 0 0 0 0 0
a Delay time for vibration sensation by tuning fork stimulation (see “Materials and Methods” for details); time 0 is the time when the worker reported becoming unaware of the vibration.
b One worker felt no vibration sense in the right foot.
* p < 0.05, Wilcoxon test.
Table 4 Number of workers with reduced vibration sensation in the finger.
1991 workers and age-matched controls (n = 23 pairs)
1999 workers and age-matched controls (n = 12 pairs)
Right finger* Left finger* Right finger
Left finger
Delay timea (sec) 1-BP workers Controls 1-BP workers Controls 1-BP workers Controls 1-BP workers Controls
< 2 19 23 19 23 10 12 10 12
2 3 0 2 0 2 0 2 0
3 1 0 2 0 1 0 0 0
a Delay time for vibration sensation by tuning fork stimulation (see “Materials and Methods” for details); time 0 is the time when the worker reported becoming unaware of the vibration.
* p < 0.05, Wilcoxon test.
Table 5 Results of neurobehavioral tests in the 1-BP group and controls matched for age or for age and education (mean ± SD).
Test 1991 workers (age-matched controls) 1991 workers (age/education-matched controls) (age/education-matched controls) 1999 workers
No. (pairs) 22 12 6
Simple reaction time (sec) 0.38 ± 0.12 (0.36 ± 0.12) 0.38 ± 0.12 (0.36 ± 0.12) 0.40 ± 0.14 (0.39 ± 0.12)
Digit span (digits recalled) forward 10.6 ± 2.3* (11.7 ± 1.4) 10.8 ± 2.5 (11.8 ± 1.3) 10.2 ± 3.1 (12.0 ± 1.1)
Digit span (digits recalled) backward 4.5 ± 2.2* (5.8 ± 1.8) 5.0 ± 2.6* (5.6 ± 1.4) 4.2 ± 2.3 (6.2 ± 1.6)
Santa Ana preferred hand 35.2 ± 3.6 (36.6 ± 4.8) 35.3 ± 4.0 (36.1 ± 4.3) 36.0 ± 2.4 (35.8 ± 5.2)
Santa Ana nonpreferred hand 33.5 ± 4.6 (32.8 ± 5.1) 33.8 ± 5.2 (33.7 ± 5.6) 32.8 ± 4.4 (35.5 ± 5.9)
Digit symbol (no. completed) 47.0 ± 17.5 (54.0 ± 10.2) 48.6 ± 19.8 (55.5 ± 5.6) 45.3 ± 21.9 (56.7 ± 6.7)
Benton (no. correct) 7.2 ± 1.7* (8.3 ± 1.4) 7.8 ± 1.5* (8.2 ± 1.3) 7.3 ± 1.8* (8.3 ± 1.0)
Pursuit aiming test (no. completed) 103.1 ± 16.9* (119.9 ± 19.1) 101.6 ± 17.9* (119.3 ± 20.4) 98.0 ± 11.4 (125.7 ± 17.0)
* p < 0.05, paired t-test.
Table 6 Results of POMS tests in the 1-BP group and controls matched for age or for age and education (mean ± SD).
Test 1991 workers (age-matched controls) 1991 workers (age/education-matched controls) 1999 workers (age/education-matched controls)
No. (pairs) 20 12 6
Profile of mood state
Tension 4.4 ± 3.9* (7.7 ± 7.1) 4.1 ± 5.2* (10.2 ± 8.5) 6.8 ± 7.0 (9.6 ± 7.2)
Depression 4.8 ± 7.5* (10.5 ± 13.0) 5.6 ± 10.1* (13.3 ± 17.1) 10.0 ± 14.4* (12.8 ± 16.0)
Anxiety 4.1 ± 5.0* (10.2 ± 10.2) 4.7 ± 6.3* (12.6 ± 13.2) 7.0 ± 9.0 (13.4 ± 12.7)
Vigor 22.2 ± 4.3 (20.7 ± 6.7) 23.7 ± 3.9 (20.9 ± 6.9) 23.4 ± 4.5 (21.4 ± 9.2)
Fatigue 3.1 ± 2.6* (6.4 ± 4.0) 3.0 ± 3.4* (6.7 ± 5.2) 4.4 ± 4.8* (7.2 ± 3.8)
Confusion 3.7 ± 3.7* (7.1 ± 4.3) 3.3 ± 4.4* (7.7 ± 5.6) 5.0 ± 6.2 (5.6 ± 5.0)
* p < 0.05, paired t-test.
Table 7 Stabilometer test results of 1-BP exposure group and controls.
1991 Workers Age-matched controls 1999 Workers Age-matched controls
No. (pairs) 23 12
LNG (cm)
Eyes open 71.7 ± 15.5 69.9 ± 20.8 71.5 ± 19.3 74.0 ± 19.4
Eyes closed 100.4 ± 25.1 91.1 ± 27.3 106.3 ± 29.7 95.0 ± 26.0
E AREA (cm2)
Eyes open 3.38 ± 1.26 3.69 ± 2.86 3.60 ± 1.52 3.88 ± 2.64
Eyes closed 4.94 ± 2.27 4.56 ± 3.62 5.65 ± 2.67 4.80 ± 4.15
LNG E AREA (per cm)
Eyes open 22.9 ± 6.3 24.9 ± 10.9 21.8 ± 6.6 26.3 ± 13.7
Eyes closed 23.1 ± 7.9 26.3 ± 10.4 22.0 ± 8.8 28.8 ± 12.2
REC AREA (cm2)
Eyes open 7.53 ± 2.76 8.26 ± 6.39 7.87 ± 3.42 8.51 ± 6.08
Eyes closed 10.5 ± 5.6 10.3 ± 8.6 12.8 ± 6.1 10.5 ± 9.6
RMS (cm2)
Eyes open 1.62 ± 0.70 1.98 ± 1.67 1.82 ± 0.91 2.09 ± 1.46
Eyes closed 2.05 ± 1.01 2.05 ± 1.65 2.30 ± 1.25 2.22 ± 2.01
Mx (cm)
Eyes open 0.019 ± 0.581 −0.123 ± 1.162 −0.166 ± 0.485 −0.008 ± 0.724
Eyes closed 0.010 ± 0.573 0.053 ± 1.228 −0.044 ± 0.557 0.217 ± 0.679
My (cm)
Eyes open −2.43 ± 1.15 −2.09 ± 1.37 −2.70 ± 1.05 −2.20 ± 1.41
Eyes closed −2.29 ± 1.06 −2.06 ± 1.28 −2.50 ± 0.95 −2.41 ± 1.07
XO (cm)
Eyes open −0.004 ± 0.621 −0.142 ± 1.170 −0.186 ± 0.627 −0.009 ± 0.756
Eyes closed 0.119 ± 0.657 −0.003 ± 1.347 0.106 ± 0.771 0.231 ± 0.788
YO (cm)
Eyes open −2.50 ± 1.15 −2.07 ± 1.39 −2.79 ± 1.14 −2.22 ± 1.41
Eyes closed −2.29 ± 1.01 −2.30 ± 1.42 −2.48 ± 0.86 −2.48 ± 1.09
Power spectrum of x-axis (lateral)
Eyes open (%)
0.02–0.2 Hz 61.1 ± 12.4 54.7 ± 17.0 62.5 ± 12.1 53.7 ± 14.5
0.2–2.0 Hz 38.5 ± 12.3 42.1 ± 13.6 37.1 ± 11.9 45.8 ± 14.4
2.0–10 Hz 0.36 ± 0.21* 0.46 ± 0.21 0.38 ± 0.24 0.46 ± 0.21
Eyes closed (%)
0.02–0.2 Hz 45.7 ± 17.3 47.9 ± 12.2 48.5 ± 17.8 46.1 ± 12.7
0.2–2.0 Hz 52.5 ± 19.7 49.2 ± 16.3 48.4 ± 21.8 49.2 ± 19.7
2.0–10 Hz 0.53 ± 0.37 0.59 ± 0.33 0.60 ± 0.44 0.58 ± 0.34
Power spectrum of y-axis (anteroposterior)
Eyes open (%)
0.02–0.2 Hz 66.6 ± 14.0 70.7 ± 11.4 73.5 ± 11.1 70.9 ± 10.1
0.2–2.0 Hz 32.4 ± 12.8 28.9 ± 11.4 26.1 ± 11.1 28.6 ± 10.1
2.0–10 Hz 0.97 ± 2.62 0.42 ± 0.28 0.35 ± 0.16 0.45 ± 0.35
Eyes closed (%)
0.02–0.2 Hz 51.6 ± 14.0* 61.2 ± 13.7 54.0 ± 11.6 55.0 ± 11.2
0.2–2.0 Hz 47.3 ± 13.0* 38.3 ± 13.7 45.4 ± 11.6 44.4 ± 11.2
2.0–10 Hz 1.03 ± 2.38 0.50 ± 0.28 0.56 ± 0.26 0.57 ± 0.25
Abbreviations: E, envelope; LNG, length of excursion; Mx, mean of x-axis (lateral) component of each recorded points; My, mean of y-axis (anteroposterior) component of each recorded points; REC AREA, rectangular area; RMS, root mean square area; XO, center of range of x-axis component of points; YO, center of range of y-axis component of points. Data are mean ± SD.
* p < 0.05, paired t-test. No significant difference was found between the exposed group and the controls in the Romberg quotient for all items (the ratio of values measured with eyes closed to the values with eyes open; data not shown).
==== Refs
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Ichihara G Li W Ding X Peng S Yu X Shibata E 2004 A survey on exposure level, health status, and biomarkers in workers exposed to 1-bromopropane Am J Ind Med 45 63 75 14691970
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Ichihara G Yu X Kitoh J Asaeda N Kumazawa T Iwai H 2000b Reproductive toxicity of 1-bromopropane, a newly introduced alternative to ozone layer depleting solvents, in male rats Toxicol Sci 54 416 423 10774824
Letz R Gerr F 1994a Covariates of human peripheral nerve function: I. Nerve conduction velocity and amplitude Neurotoxicol Teratol 16 95 104 8183195
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Yu X Ichihara G Kitoh J Xie Z Shibata E Kamijima M 1999 Effect of inhalation exposure to 2-bromopropane on the nervous system in rats Toxicology 135 87 93 10463765
Yu X Ichihara G Kitoh J Xie Z Shibata E Kamijima M 1998 Preliminary report on the neurotoxicity of 1-bromopropane an alternative solvent for chlorofluorocarbons J Occup Health 40 234 235
Yu X Ichihara G Kitoh J Xie Z Shibata E Kamijima M 2001 Neurotoxicity of 2-bromopropane and 1-bromopropane, alternative solvents for chlorofluorocarbons Environ Res 85 48 52 11161652
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.6891ehp0112-00132615345347Environmental MedicineCase ReportSubcutaneous Injection of Mercury: “Warding Off Evil” Prasad Venkat L. Tri County Community Health Center, Dunn, North Carolina, USAAddress correspondence to V.L. Prasad, Tri County Community Health Center, 3331 Easy St., Dunn, NC 28334 USA. Telephone: (910) 567-6194. Fax: (910) 567- 4570. E-mail:
[email protected] author declares he has no competing financial interests.
9 2004 22 7 2004 112 13 1326 1328 4 12 2003 22 7 2004 Publication 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. Deliberate injection of mercury, especially subcutaneous injection, is rare but is seen in psychiatric patients, individuals who attempt suicide, those who are accidentally injected, and boxers who wish to build muscle bulk. Metallic mercury plays a major role in ethnic folk medicine. Neurologic and renal complications can result from high systemic levels of mercury, and subcutaneous injection usually results in sterile abscesses. Urgent surgical evacuation and close monitoring for neurologic and renal functions as well as chelation (if toxicity is indicated) are key aspects of treatment. Education of the adverse effects and dangers of mercury is important, especially in pregnant women and children. As increased immigration changes demographic patterns, proper disposal of mercury and preventing its sale and use should become urgent societal priorities. Psychiatric consultation should be obtained whenever appropriate.
case reportlocal abscessesmercury injectionsubcutaneous
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Case Presentation
Injection of elemental mercury is uncommon, and only 72 cases have been reported in the literature over the past 75 years. Of these 72 cases 46 were deliberate; most involved direct intravenous administration, usually with suicidal intent (Kayias 2003), or they were a complication of drug abuse. Bradberry et al. (1996) reported an attempted homicide by this means. Self-injection has also been reported in psychiatric patients (Soo et al. 2003), and accidental injections have been reported (Ellabban et al. 2003). Subcutaneous injection of mercury by accident (including injuries from broken thermometers), self-injection, and suicide attempts has been reported (Chodorowski et al. 1997; Ellabban et al. 2003; Smith et al. 1997; Soo et al. 2003).
A search in MEDLINE and PubMed (National Library of Medicine, Bethesda, MD) did not reveal any study or report on injection of mercury in the subcutaneous space of the hands for the sole purpose of preventing infections and “evil” during foreign travel. This practice is apparently common in several Central and South American countries. In this case report, I present such an injection received by a couple in Honduras before they traveled to the United States.
G.B., a 41-year-old Hispanic woman, and her partner, V.V., a 35-year-old Hispanic male, came to the clinic together. Both had wet towels wrapped around both their forearms and hands. They reported having pain for 5 days as well as swelling in the hands and low-grade subjective fever. The pain was localized to the dorsum of the hand and forearm, with no radiation, and was moderate in intensity and continuous, with no specific aggravating or relieving factors. The swelling and redness was localized to the same areas on the dorsum of the hand. They reported no history of bites or stings, and they had no swollen glands or joint pain. A review of systems was otherwise negative.
Both patients gave a history of having received multiple injections of mercury at a roadside nonmedical facility in Honduras about 1 week before their clinic visit. They did not know about the sterility of the procedure or if needles/syringes used were disposable. On further questioning, they indicated that the injection of mercury is a common practice among people who wish to travel abroad. The reason for their injections was to ward off “evil” and also to protect against exposure to any unknown diseases while traveling in a foreign country. The patients estimated that the injections for both hands in both patients was < US$1.00.
Both G.B. and V.V. denied any significant allergies or past medical history. They were both nonsmokers and denied alcohol or drug abuse.
A physical exam revealed G.B. to be an obese Hispanic woman in obvious distress due to pain in both hands and forearms. The general exam was unremarkable, and a local exam revealed a diffuse soft tissue swelling on the dorsum of both hands, with fluctuation, redness, and pointing (most prominent part of swelling in an abscess that marks the area of imminent rupture) in the first web space of both hands. Redness and swelling was also noted all along both forearms, with significant tenderness. No lymphadenopathy was noted. Lungs and heart were normal, and there was no renal angle tenderness and no hepatosplenomegaly. The neurologic exam was normal.
V.V. was a tall, medium-built Hispanic male in distress from pain. The general exam was unremarkable, and the local exam revealed findings similar to those for his partner, with fluctuation, redness, and tenderness in the dorsum of the hand and first web space and in the forearms. Otherwise, the exam was unremarkable.
Laboratory values for G.B. were as follows: glucose, 101 mg/dL; blood urea nitrogen (BUN), 14 mg/dL; creatinine, 0.8 mg/dL; sodium, 138 mmol/L; potassium, 4.1 mmol/L; chloride, 105 mmol/L; carbon dioxide, 22 mmol/L; calcium, 9.5 mmol/L; liver function tests, normal; white blood cell (WBC) count, 8,700/μL; hemoglobin, 12.6 g/dL; hematocrit, 37.6%; urine mercury, 11.3 μg/L; and serum mercury, < 5.0 μg/L.
Laboratory values for V.V. were as follows: glucose, 108 mg/dL; BUN, 26 mg/dL, creatinine, 1.1 mg/dL; sodium, 138 mmol/L; potassium, 4.2 mmol/L; chloride, 97 mmol/L; carbon dioxide, 26 mmol/L; calcium, 10.2 mg/dL; liver function tests, normal except for alanine aminotransferase, 64 U/L (normal, 4–60 U/L); WBC count, 8,700/μL; hemoglobin, 16.0 g/dL; hematocrit, 48.3%; and blood mercury, 100 μg/L (normal < 10 μg/L). Urine mercury analysis was not performed because V.V.’s urine samples were lost by the laboratory.
A diagnosis of abscess was made, and both patients underwent incision drainage of both hands. Thick pus was evacuated along with beads of metallic mercury (Figures 1–3). Complete evacuation of all visible mercury, about 0.5 mL, was performed and wounds were thoroughly washed with copious amounts of saline. The fluid removed was sterile pus (result of milder inflammation caused by irritants, foreign bodies, etc., but not due to infection). The soaked gauze and dirty sheets were disposed in regular waste.
Postoperatively, the wounds granulated and healed well by secondary intention (left open to heal by epithelization). Since that time, the patients have been lost to follow-up.
Discussion
Mercury is sold as “azogue” in religious stores, or botanicas, for use in Esperitismo (spiritual belief in Puerto Rico), Santeria (Cuban practices), and voodoo. The mercury is often carried personally in a pouch or spread around the house or bed, mixed in the bath, or burned in devotional candles. Mexican-Americans take it orally to relieve empacho (indigestion), especially in infants and children. Mercury is difficult to remove, and it can remain in carpets, walls, and homes for long periods.
The form of mercury consumed in fish is mainly methyl mercury, and mercury from occupational and dental exposure is elemental mercury. Both forms are absorbed and can have serious consequences (Magos 1997).
Concerns about mercury contamination have been growing in predominantly Hispanic and Caribbean neighborhoods. In New York City, neurotoxic levels of mercury vapor from magicoreligious and ethnomedical uses of mercury have been reported (Wendroff AP, personal communication). Wastewater samples from a residential neighborhood in Washington Heights had highly elevated mercury levels on two occasions. Secondhand exposure from previous tenants sprinkling mercury on floors also remains a problem because the contamination can remain for over a decade. Mercury exposures resulting from magicoreligious use are often greater than those occurring by eating fish or from dental amalgams (Wendroff AP, personal communication).
In a survey at the Montefiore Medical Center in New York in 1996, Zayas and Ozuah (1996) studied the sales of mercury in the Bronx area of New York City. Of the 41 botanicas they located, 38 sold elemental mercury; in 1995, 35 of the 38 botanicas sold about 25,000–155,000 capsules or vials (mean weight, 9 g) for spiritual practices. Of the users, 29.3% said that it was “sprinkled in the home” (Zayas and Ozuah 1996).
In an effort to raise the awareness among pediatricians about the possibility of toxic exposure to mercury in children, Goldman (2001) reported on the use of mercury in Santeria among immigrants from Haiti and other Caribbean nations, in which elemental mercury was sprinkled around the house. Riley et al. (2001) reported a 5% prevalence of elevated mercury levels in urine of 100 children in Bronx, New York, in August 2001. Of these children, 55% were Latino and 43% were African American (Riley et al. 2001).
In a study in Massachusetts, 898 people were surveyed in the Lawrence area, which has significant Latino and Caribbean populations (JSI Center for Environmental Health Studies 2003). The survey showed that 91 people swallowed mercury in a drink, 143 applied it to their skin, 152 burned it in candles, and 108 sprinkled it around their homes. The study authors estimated that a minimum of 6.8 lb of mercury had been released into the community through magicoreligious use. Forty percent of the Latinos in the Lawrence area knew about azogue or used it themselves. The authors were especially concerned about the large number of apartments that may have been severely contaminated.
Attempts by power companies to replace pressure-control devices for domestic gas supply has led to mercury spills, affecting 200,000 homes in one incident (Clarkson et al. 2003). High levels of mercury exposure can result from sprinkling mercury on the floor of a home or car, burning it in a candle, and mixing it with perfume. Because mercury vapor is heavy and tends to form layers close to the ground, infants and children, whose breathing zones are closest to the floor, are at highest risk. Ingested mercury passes through the gut unabsorbed. For centuries it has been used to treat constipation (Clarkson et al. 2003).
In Latin American and Central American countries, mercury is dispensed in small centers for psychic readings and in fortune telling stores, usually not a medical establishment. The entire process is very ritualistic. Clients are often requested to bathe and then have eggs smeared over their bodies. Of the various indigenous herbs and heavy metals used for treatment, mercury is popular; it is often consumed in a mixture of port wine, eggs, nutmeg, and milk. In many South American countries, mercury is often administered by intravenous injection to help athletes and boxers build muscle mass, a practice based on superstition (Smith et al. 1997).
The oral route of metallic mercury use does not cause poisoning symptoms, but its use in infants and children could cause subclinical developmental problems. Concentrations in blood and urine after ingestion of mercury remain low because very little is absorbed. However, mercury injected subcutaneously causes sterile, inflammatory, and necrotic reactions resulting in abscesses and granulomas. Environmental and occupational exposure to mercury can be determined by measuring toenail mercury levels (Garland et al. 1993; MacIntosh et al. 1997; Yoshizawa et al. 2002).
Intra-arterial injection can cause digital ischemia and/or gangrene secondary to embolization. One case of cardiac granuloma secondary to intra-arterial injection has been reported (Kedziora and Duflou 1995). When mercury is injected intravenously, it goes mainly to the lungs and can cross over to systemic circulation (Givica-Perez et al. 2001).
Cases of foreign body granuloma on the thumbs or hands have been reported after rubbing mercurial ointments (Bradberry et al. 1996). In cases of subcutaneous metallic mercury injection, patients usually present weeks to months later with an inflammatory mass at the site of injection. The diagnosis may be apparent on X-ray examination or it may be obvious at the time of surgery (Bradberry et al. 1996).
Patients may be seen remote from the mercury exposure with swelling at the injection site. Pathologic findings of granuloma, fibrosis, and histiocytes suggest a local foreign-body–type reaction to metallic mercury. Abnormal serum levels suggest that there is some lymphatic and vascular migration following subcutaneous injection (Soo et al. 2003).
Mercury can be detected by imaging X rays or ultrasound. In the case of a 32-year-old nurse who had cut the palm of her right hand with a broken thermometer 30 days earlier, sonography showed multiple small echogenic dots surrounded by a hypoechoic halo, suggesting the presence of small crystal fragments or droplets of mercury (Romero et al. 2004). No reverberation, acoustic shadowing, or flow on color or power Doppler imaging was noted. Mercury is hyperechoic on sonograms despite being liquid at room temperature. It is a safe, inexpensive, portable, and readily available imaging modality (Romero et al. 2004). Two deaths have been reported following subcutaneous injection (Chodorowski et al. 1997); cause of death was renal failure in one patient and empyema in the lung of the second patient.
There is no ban on the sale of mercury, although the Federal Hazardous Substances Act (1994) mandates that it be sold only with an attached warning label. Current U.S. public advice on disposal of mercury is confusing and inconsistent; 45% of requests for advice from local and state waste management centers resulted in advice to use regular household collections to dispose thermometers (DiCarlo et al. 2002).
Under a voluntary agreement between the U.S. Environmental Protection Agency (EPA), the American Hospital Association, Hospitals for a Healthy Environment (H2E) was formed. A pledge was made to eliminate mercury, identify pollution prevention opportunities, and reduce waste. As of March 2002, the H2E had as partners 260 hospitals, 36 clinics, 8 nursing homes, and 25 other facilities across the United States (Wendroff A, personal communication). Information on the safe disposal of mercury is available on the U.S. EPA website (U.S. EPA 2004).
With changes in demographic and population ethnic mixes, controlling the sale of mercury and ensuring its proper disposal become more urgent. Serious environmental contamination and long-term consequences could otherwise cause severe consequences in the future.
Figure 1 Incision made in hand of V.V. shows mercury pellets inside the incision and the inflammation of the injection site.
Figure 2 Incision site of V.V.’s hand before irrigation.
Figure 3 Significant amount of mercury pellets spilled during irrigation of the incision in V.V.’s hand.
==== Refs
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Environ Health Perspect. 2004 Sep 22; 112(13):1326-1328
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.6964ehp0112-00132915345348Children's HealthArticlesWater Arsenic Exposure and Children’s Intellectual Function in Araihazar, Bangladesh Wasserman Gail A. 12Liu Xinhua 23Parvez Faruque 3Ahsan Habibul 3Factor-Litvak Pam 3van Geen Alexander 4Slavkovich Vesna 3Lolacono Nancy J. 3Cheng Zhongqi 4Hussain Iftikhar 5Momotaj Hassina 6Graziano Joseph H. 31Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York, New York, USA2New York State Psychiatric Institute, New York, New York, USA3Mailman School of Public Health and4Lamont-Doherty Earth Observatory, Columbia University, New York, New York, USA5National Institute of Preventive and Social Medicine, Dhaka, Bangladesh6Columbia University Bangladesh Arsenic Project, 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] thank J. Kline for her epidemiologic contributions. We also acknowledge our Bangladeshi field staff and the people of Araihazar.
This work was supported by National Institute of Environmental Health Sciences grants P42 ES 10349, P30 ES 09089, the Mailman School of Public Health, and the Earth Institute at Columbia University.
The authors declare they have no competing financial interests.
9 2004 28 4 2004 112 13 1329 1333 14 1 2004 28 4 2004 Publication 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 arsenic has long been known to have neurologic consequences in adults, but to date there are no well-controlled studies in children. We report results of a cross-sectional investigation of intellectual function in 201 children 10 years of age whose parents participate in our ongoing prospective cohort study examining health effects of As exposure in 12,000 residents of Araihazar, Bangladesh. Water As and manganese concentrations of tube wells at each child’s home were obtained by surveying all wells in the study region. 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 on tests drawn from the Wechsler Intelligence Scale for Children, version III, was assessed 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 and hemoglobin concentrations. Exposure to As from drinking water was associated with reduced intellectual function after adjustment for sociodemographic covariates and water Mn. Water As was associated with reduced intellectual function, in a dose–response manner, such that children with water As levels > 50 μg/L achieved significantly lower Performance and Full-Scale scores than did children with water As levels < 5.5 μg/L. The association was generally stronger for well-water As than for urinary As.
arsenicchildrenIQ
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In Bangladesh, approximately 30–40 million people (British Geological Survey 2001) have been chronically exposed to high concentrations of naturally occurring arsenic in drinking water, supplied by approximately 10 million tube wells (British Geological Survey 2001; van Geen et al. 2002). Aside from carcinogenic and vascular effects, the literature contains reports (e.g., Rodriguez et al. 2003) of neurologic consequences of acute and chronic exposure in adults, although the dosimetry is poorly described. Clinical and industrial reports of heavy exposure in adults (Bolla-Wilson and Bleecker 1987; Morton and Caron 2003) document adverse impacts on a range of cognitive functions, including learning, memory, and concentration, as well as peripheral and central neuropathies (Morton and Dunnette 1994; Pershagen et al. 1981; Schoolmeester and White 1980).
In addition to elevated As concentrations in Bangladesh groundwater, the British Geological Survey (2001) has reported that many of the existing wells in Bangladesh also have manganese concentrations that exceed the World Health Organization (WHO) standard of 500 μg/L. Occupational Mn exposure has been associated with neurologic sequelae in adults, specifically Parkinsonism (Gorell et al. 1999).
Given the absence of a significant research base concerning the consequences of As or Mn in children, we sought to examine the possible associations between As exposure and intellectual function, taking into account possible effects of Mn. In 2000, we began a prospective study of the health effects of As in 12,000 adult residents of Araihazar, Bangladesh. The study site, a 25-km2 region located approximately 30 km east of Dhaka, was chosen because of its wide range of As concentrations in drinking water. Our survey of 6,000 contiguous wells in the region (van Geen et al. 2003b) revealed that 75% exceed the WHO As standard of 10 μg/L and 53% exceed the Bangladesh standard of 50 μg/L; water As concentrations ranged from < 1 to 900 μg/L. In an analysis of a subset of 629 well-water samples, 82% exceeded the WHO Mn standard (Cheng et al. 2004).
In the same region, we are examining the consequences of As exposure on children’s health. We report here the results of a cross-sectional investigation of intellectual function in 201 children 10 years of age.
Materials and Methods
Overview.
The present project is part of a larger ongoing multidisciplinary study by health, earth, and social scientists working collaboratively in Araihazar, Bangladesh. People in Araihazar, as in most of rural Bangladesh, live in houses with cement or mud floors and tin or straw walls and roofs. Members of extended families live in clusters of individual houses (called 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 Bangladesh standards.
Given the absence of a research base concerning the effects of As on children’s intellectual functioning, we extrapolated our assessment plan from experience gained in 12 years of studying lead exposure in Kosovo, Yugoslavia (e.g., Factor-Litvak et al. 1999). That research, consistent with the findings of others, documented adverse impact of Pb exposure on intellectual development, with effects stronger on visuomotor than on verbal functioning.
Subjects.
Of the 11,749 adults enrolled in our cohort study, we selected at random a pool of 400 of their children (using 400 different wells) between 9.5 and 10.5 years of age. During summer of 2002, field staff visited families at home to verify child age and school attendance, to discuss the proposed study, and to make an appointment for a clinic visit. Of the initial pool of 400, 176 children were assessed during the summer of 2002. Informed parental consent and assent of the children were obtained. The study was approved by the Bangladesh Medical Research Council and the Columbia University institutional review board. Of the remaining 224 potential study children, 105 were never visited because seasonal flooding made access hazardous; of those visited, 14 families refused participation and 8 were excluded because the child was not attending school or was > 10.5 years of age (n = 33), the family was not at home, or other, unspecified reasons (n = 64). To bolster our sample size, in 25 instances when a home visit identified an excluded child, interviewers selected a child of participating parents from the same village until 201 children, using 196 wells, had been assessed.
Procedure.
Children and their mothers came to our field clinic, where children participated in the assessments described below and received a medical examination by a study physician. Weight, height, and head circumference were measured. Children provided urine specimens for the measurement of urinary As and creatinine and were asked to provide a blood sample for the measurement of blood lead (BPb) and hemoglobin (Hgb) concentrations. Of the 201 children assessed, 107 agreed to provide blood samples. Information on family demographics (e.g., parental education, occupation, housing type) was available 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 whether their home included a television; about parental age, education, and occupation; and about child birth order. For an additional surrogate for social class, the type of roofing on the well owner’s home was recorded as thatched, tin, or cement (thatched lowest, cement highest). Children were given a toy as thanks for their participation; families participating in the larger cohort study receive primary medical care at our own field clinic.
Measures.
Water analyses.
Water As concentrations of tube wells at each child’s home were obtained during a survey of all wells in the study region (van Geen et al. 2003b) and shipped to Columbia University’s Lamont Doherty Earth Observatory for analysis. Water samples were analyzed by graphite furnace atomic absorption (GFAA), which had a detection limit of 5 μg/L. Those water samples found to have < 5 μg/L were subsequently reanalyzed by inductively coupled plasma–mass spectrometry (ICP-MS), which has a detection limit of 0.1 μg/L (Cheng et al. 2004). Of the 196 well-water samples, 194 were also analyzed for Mn by standard flame atomic absorption spectrophotometry.
Biochemical measurements.
Urinary As concentrations were assayed by GFAA at the Mailman School of Public Health, using a Perkin-Elmer Analyst 600 system as previously described (Nixon et al. 1991). Our laboratory participates in a quality control program coordinated by P. Weber at the Québec Toxicology Center (Québec City, 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. Levels of As in urine were also adjusted for urinary creatinine levels, which were analyzed by a colorimetric Sigma Diagnostics Kit (Sigma, St. Louis, MO, USA). In addition, urinary As metabolites were speciated using a method adapted after Heitkemper et al. (2001). This method employs high-performance liquid chromatography separation of arsenobetaine (AsB), arsenocholine (AsC), arsenate, arsenite, monomethylarsonic acid (MMA), and dimethylarsinic acid (DMA), followed by detection by ICP-MS. The percentages of inorganic As (InAs; i.e., arsenate + arsenite), MMA, and DMA were calculated after subtracting AsC and AsB from total urinary As.
Venous blood samples were obtained for measurements of BPb (Fernandez and Hilligoss 1982) and Hgb. Whole-blood samples were appropriately stored and transported to a laboratory at Columbia University that participates in the BPb quality control program of the Centers for Disease Control and Prevention (CDC; Atlanta, GA, USA). Intraclass correlation coefficients between our laboratory’s values and samples calibrated at CDC ranged between 0.97 and 0.99. Children providing blood samples had mothers with significantly more years of education and received significantly higher Verbal raw scores than did mothers of children not providing blood samples (Wilcoxon tests, degrees of freedom = 1, p-values < 0.05); there were no other differences between those providing and not providing blood samples.
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 upon administration) verbal subtests, which together provide a Verbal intellectual quotient (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 Western settings where this test was developed, which necessitated adaptations for use in this culture. For example, a typical 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; commonly this consists of one or two bulbs used for lighting. Many common Western household items, such as telephones and bathtubs, are rare.
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. Two items with no recognizable analog were eliminated from the Picture Completion subscale (telephone, bathtub), and close substitutions were made for four others from the Similarities subscale (“mango and banana” for “apple and banana”; “flute and drum” for “piano and guitar”; “dog and cow” for “cat and mouse”; and “tire and ball” for “wheel and ball”). The WISC-III subtests include items of graduated difficulty, with more points awarded for harder items or faster completion. We summed these weighted items across Verbal, Performance, and Full-Scale domains to create Verbal, Performance, and Full-Scale raw scores; we 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. Below we use “IQ” to represent this estimated measure.
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, items deemed to be culturally inappropriate were altered or omitted. Materials were piloted to ensure maternal and child comprehension; two interviewers were then trained by a competent tester (G.A.W.) and then continued with supervised practice sessions for 2 weeks. All written test responses were rechecked when data were sent to the Columbia University Department of Psychiatry for entry.
Statistical analyses.
Outcomes.
Because of concerns about 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 analytic models to the prediction of estimated Verbal “IQ,” Performance “IQ,” and Full-Scale “IQ.”
Covariate adjustment.
In Bangladesh, grammar school extends to fifth grade. Therefore, mother’s and father’s education were categorized as none, 1–5 years, and 6–13 years. Parental occupation was recoded as laborer/farmer, factory/other paid job, business, or missing/other. Because just 6% (n = 11) of mothers reported working outside the home, only paternal occupation was included in the regression models. From a pool of potential demographic covariates, we retained those that were empirically or theoretically importantly related to child intelligence, as well as those that made an initial contribution (at significance p ≤ 0.20 or better), in initial regression analyses, either to any of the outcomes of interest or to the measures of As exposure.
Analytic model.
The analyses first sought to predict the outcomes of interest from the set of sociodemographic factors using linear regression models; once this “core” model was derived, we examined the incremental association of exposures (Mn, As) singly and together, measured continuously. We repeated our analyses, categorizing children into groups, based on quartiles of water As to illustrate dose–response relationships. We next repeated these analyses for the subset of children providing blood samples for the measurement of BPb and Hgb, measured continuously. In all analyses, BPb and water As were log-transformed and water Mn was square root–transformed to make distributions approximately symmetric.
For the most part, analyses are based on n = 201 children. Analyses considering well-water Mn employ n = 194. Analyses involving urinary As and its metabolites are based on n = 200; for analyses considering BPb and Hgb, n = 107.
Results
Sample characteristics.
Table 1 presents descriptive information for all demographic, water, and biochemical variables. Average child age was 10 years; approximately half the children in the sample were male; one-third had regular access to a television. On average, mothers and fathers reported 3.7 and 2.9 years of education, respectively. Average child height was 125.6 cm, and average weight was 21.9 kg, values that correspond to roughly the fourth percentile by U.S. norms (CDC 2003).
Exposure characteristics.
Water As concentrations ranged from 0.094 to 790 μg/L, with a mean (117.8 μg/dL) and distribution comparable to those in the larger set of 6,000 contiguous wells in Araihazar (van Geen et al. 2003b). The mean water Mn concentration of 1,386 μg/L was well in excess of the U.S. and WHO recommended maximum contaminant level (MCL) of 500 μg/L, with a range up to 5,438; water Mn values are not available for the entire set of 6,000 wells. Indeed, 82% of children were consuming water in excess of the MCL for Mn. The association between water As and water Mn was significant (Spearman r = 0.39; p < 0.0001) but not strong enough to preclude examination of their independent effects on child intelligence. The correlation between water As and urinary As (Spearman r = 0.45, p < 0.0001) was comparable to that previously reported for adults in this region (Ahsan et al. 2000). In the subsample of children for whom BPb measures were obtained (n = 107), Spearman correlations (necessitated by skewed distributions) with well-water As (−0.16 ) and with urinary As (−0.06) were not significant.
Relationship between covariates and intellectual function.
Linear regression analyses predicting test raw scores from the sociodemographic features retained in the final “core” model revealed generally better scores in children of more educated mothers and of mothers with higher Raven scores, those living in more adequate dwellings, those with access to television, and those who were taller and had larger head circumference (data not shown).
Relationship between well-water Mn and intellectual function.
Without adjustment for either core variables or water As, water Mn was significantly associated with Full-Scale and Performance raw scores (B-values = −0.33 and −0.29, p < 0.002 and p < 0.001, respectively) but not with Verbal raw score (B = −0.04, p = 0.15). Addition of Mn to analytic models made little change in associations between core model variables and intellectual function raw scores. Controlling for sociodemographic features, Mn levels were significantly negatively associated with Performance and Full-Scale raw scores (B-values = −0.20 and −0.22, respectively, p < 0.03) but not with Verbal raw score (B = −0.02, p > 0.5). However, when water As was added to these models, Mn made no significant (p > 0.25) contribution to intellectual function. With both water As and water Mn in the model, there was no significant interaction in their prediction of Full-Scale, Verbal, or Performance raw scores.
Relationship between well-water As and intellectual function.
Table 2 presents associations between water As and intellectual function, before and after adjustment for sociodemographic features. In each case, associations between water As and intellectual function raw scores were stronger before adjustment for sociodemographic features. In unadjusted analyses, water As explained 7.29, 2.61, and 7.04% of the variance in Performance, Verbal, and Full-Scale raw scores, respectively. With covariate adjustment, water As remained significantly negatively associated with both performance and Full-Scale raw scores, explaining an incremental 4.33, 0.89, and 3.88% of the variance in Performance, Verbal, and Full-Scale raw scores, respectively. Results were similar when “IQ” outcomes were substituted for raw scores (data not shown).
Dose–response relationships between water As and intellectual function.
Figure 1 illustrates the adjusted Full-Scale, Performance, and Verbal raw scores by As quartile. As water As increased, there were dose-dependent changes in adjusted and unadjusted (data not shown) scores. With adjustment, compared with the lowest quartile of As exposure, the third and fourth quartiles had significantly lower scores on both Full-Scale (B = −7.8 and −11.3, p < 0.05 and p < 0.01, respectively) and Performance raw scores (B = −7.3 and −9.7, p < 0.05 and p < 0.01, respectively). The highest exposure quartile was marginally lower on Verbal raw score than the lowest exposure quartile (B = −1.6, p < 0.10). The relationship between water As (measured continuously) and Full-Scale raw score is illustrated in Figure 2. Water As concentrations of 10 and 50 μg/L were associated with decrements in Full-Scale raw scores of 3.8 and 6.4 points, respectively.
Relationship between urinary As, As metabolites, and intellectual function.
We examined relationships between total urinary As concentration, as micrograms per gram creatinine, and child intellectual function. After adjustment for core variables, the associations between urinary As and measures of intellectual function were not statistically significant for Full-Scale (B = −2.9, p = 0.09), Performance (B = −2.2, p = 0.14), or Verbal scores (B = −0.7, p = 0.11) but were in the anticipated direction.
Urine samples were analyzed by high-performance liquid chromatography/ICP-MS for the relative amounts of InAs, MMA, and DMA. The percentages of InAs, MMA, and DMA were calculated after subtracting the contribution of AsC and AsB to total As concentration. The mean ± SD AsC and AsB concentrations were 3.9 ± 3.5 μg/g creatinine and 5.0 ± 7.5 μg/g creatinine, respectively. The frequency distributions of InAs, MMA, and DMA are illustrated in Figure 3. There was a wide variability in the extent to which children eliminated As in the dimethylated form. On average, the percentages of urinary As eliminated as InAs, MMA, and DMA were 12.2, 8.9, and 74.1%, respectively. We posited that children who were poor methylators might be particularly adversely affected by As. However, when both DMA and urinary As were included in the core model, DMA failed to make a significant contribution to intellectual function and did not alter the estimates for total urinary As.
Relationships between BPb, Hgb, and intellectual function.
Analyses predicting intellectual raw scores from other hematologic measures, adjusted for the same demographic features, were conducted for the subset of 107 children providing blood samples. No significant associations were detected for log BPb or for Hgb on Verbal, Performance, or Full-Scale raw scores or “IQ,” with or without the inclusion of water As (data not shown).
Discussion
This is the first systematic study of effects of As on children’s intellectual function. Exposure to As from drinking water was associated with reduced scores on measures of intellectual function, before and after adjusting for sociodemographic features known to contribute to intellectual function. With covariate adjustment, water As remained significantly negatively associated with both Performance and Full-Scale raw scores. Exposure to As was associated with reduced intellectual function, in a dose–response manner, such that children with exposures > 50 μg/L received significantly lower Performance and Full-Scale scores than did children with exposures < 5.5 μg/L. The association was stronger for well-water As than for urinary As. Children in the highest quartile of water As scored approximately 10 points lower in Performance raw scores than did those in the lowest quartile.
We have made diligent efforts to reduce the consumption of As-contaminated water in the Araihazar population since our original well survey was conducted in the first half of 2000. For example, each well was labeled to indicate As concentrations > and < 50 μg/L, with either a skull and cross-bones or a picture of a child drinking water. In addition, a village education program that encouraged well switching (van Geen et al. 2002) successfully reached roughly half of all residents. Beyond these, new low-As private and community wells have been installed in parts of the region during this time frame (van Geen et al. 2003a). It is therefore likely that some recent reduction in these children’s As exposure occurred between January 2001 (when well labeling began) and the summer of 2002 (when children were assessed). Indeed, in our simultaneous prospective cohort study in adults, repeated measurements of urine As concentrations over the same interval have declined. Because urinary As reflects recent exposure, reduced exposure may explain the weaker associations between intellectual function and levels of As in urine, compared with levels in water.
Two published studies of As exposure also found adverse associations with children’s intellectual function. In a small (n = 80) sample of children from a Pb smelter area in Mexico, Calderon et al. (2001) found negative associations between children’s urinary As and Verbal intelligence, controlling for a small set of demographic factors. Although investigating anthropogenic exposure to As and Pb, that study did not consider other potential toxicants to which nearby residents were exposed. In a second ecologic study, Tsai et al. (2003) compared adolescents in Taiwan from regions with and without elevated As in well water, with no measure of individual exposure. With minimal control for sociodemographic factors, adolescents in the exposed group showed inconsistently poorer scores on Performance-type tests; some outcomes were adversely affected in adolescents with low exposure (but not in those with high exposure) relative to those without exposure.
As metabolism.
Humans excrete MMA and DMA after ingestion of arsenate or arsenite, but the extent of metabolism is remarkably variable and may influence both pre- and postnatal toxicity (Hopenhayn-Rich et al. 1996). This variability in methylation is likely due to both genetic (Chung et al. 2002; Vahter et al. 1995) and dietary factors. Maintenance of an adequate supply of the ultimate methyl donor (i.e., S-adenosylmethionine) requires an adequate supply of dietary folate and B vitamins. Two previous studies of As metabolism in very small groups of children have suggested that children are poor methylators compared with adults (Chowdhury et al. 2003; Concha et al. 1998). For example, children in two exposed villages in Argentina eliminated 49 and 42%, respectively, as InAs in urine, significantly more than did women in the same villages (25 and 29%, respectively) (Concha et al. 1998). In our study, children were not poor metabolizers. Only 12.2% of urinary As was in the InAs form; mean levels of MMA and DMA were 8.8 and 74.1%, respectively. These metabolite levels compare favorably with those reported for adults (Concha et al. 1998; Hopenhayn-Rich et al. 1996) in various parts of the world and with those of a subset of 300 adults in our cohort study (data not shown).
Mn and Pb exposure.
The relationship between water Mn and children’s intellectual function suggested a possible adverse effect, above and beyond the contribution of social factors. However, that relationship did not persist once water As was added to the regression model. This study was not designed to examine the effects of Mn exposure on intellectual function, and in fact there was a moderate and significant positive association between water Mn and water As. A rigorous examination of the possible relationship between Mn exposure and intellectual function in children calls for a study design in which As exposure is extremely low.
We did not observe the anticipated relationship between BPb and child intellectual function. Our ability to detect this relationship was severely hampered by low statistical power, because approximately half of the study children refused to provide a blood sample.
Limitations.
We cannot comfortably make a statement about IQ points lost in relationship to As exposure, because of limitations in the application of the U.S. standardization norms to the generation of IQ scores in the present study population. As we have 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 (derived from our related work in Kosovo) 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.
Employing raw scores avoids many pitfalls that would result from using nonstandardized procedures; however, the removal of culturally bound items and subscales diverges from common practice. On the other hand, other simpler 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. This gives us confidence in the validity of the observed associations with As. To provide estimates of the impact of As exposure on IQ that would be more directly useful to policy makers, future research should either standardize an IQ test for Bangladesh (a considerable undertaking) or replicate the present effort in a well-defined sample of Western children. Given that the prevalence of malnutrition is quite high in Bangladesh, and that children in our study were of small stature relative to U.S. norms (although not anemic), the dose–response relationship in U.S. children may be different.
The present investigation examines a single age group at a single point in time. We do not know whether the present level of deficit can be detected earlier, whether continued exposure is associated with increased intellectual loss, or, conversely, whether a reduction in exposure would be associated with improved functioning. Better understanding of the exposure–outcome relationship could be obtained by following a group of children from an earlier age and tracking both exposure and outcome regularly.
We believe that our finding of a strong association between As exposure and intelligence is both important and tragic and adds urgency to the need for effective remediation in Bangladesh and other regions of South Asia where consumption of As-contaminated water is prevalent. The global community has been slow in responding to the public health significance of As exposure in Bangladesh, despite the enormous scope of the problem. We hope that the present findings add a new sense of urgency to efforts aimed at alleviating and eliminating As exposure in Bangladesh.
Figure 1 Adjusted scores by quartiles of water As 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 television.
Figure 2 Continuous relationship between water As and Full-Scale raw score, adjusted as in Figure 1. The dotted perpendicular lines illustrate the loss in Full-Scale raw score associated with water As concentrations of 10 and 50 μg/L.
Figure 3 Frequency distributions of InAs, MMA, and DMA in urine, expressed as a percentage of total urinary As.
Table 1 Sample characteristics [no. (%) or mean ± SD].
Variable No. (%)
Male 98 (48.8)
Television access 70 (34.8)
House type
Thatched roof or poorer 20 (10.0)
Corrugated tin 149 (74.1)
Concrete construction 32 (15.2)
Father’s occupation
Other/missing 23 (11.4)
Laborer/farmer 47 (23.4)
Factory/other paid job 67 (33.3)
Business 64 (31.8)
Child age 10.0 ± 0.4a
Full-Scale “IQ” 53.0 ± 6.3
Verbal “IQ” 55.4 ± 5.2
Performance “IQ” 58.4 ± 8.0
Full-Scale raw score 70.5 ± 20.8
Verbal raw score 16.5 ± 5.1
Performance raw score 54.0 ± 17.4
Height (cm) 125.6 ± 6.5
Weight (kg) 21.9 ± 3.3
Body mass index (kg/m2) 13.8 ± 1.1
Head circumference (cm) 49.5 ± 1.4
Mother’s education (years) 2.9 ± 3.4
Father’s education (years) 3.7 ± 3.7
Mother’s age (years) 32.6 ± 6.7
Mother’s Raven’s score 14.4 ± 3.5
Water Mn (μg/L) 1,386 ± 927
Water As (μg/L) 117.8 ± 145.2
Urinary As (μg/L) 116.6 ± 148.8
Urinary creatinine (mg/dL) 43.3 ± 34.1
Urinary As (μg/g creatinine) 296.6 ± 277.2
Hgb (g/dL) 12.6 ± 1.1
BPb (μg/dL) 10.1 ± 3.3
Except where noted, sample size is 201. Values are no. (%) except where indicated.
a Mean ± SD.
Table 2 Prediction of Verbal, Performance, and Full-Scale raw scores from water As before and after covariate adjustment.
Variable Performance Verbal Full-Scale
Before adjustment
Water As −1.84# −0.32* −2.16#
After adjustment
Maternal education
None −6.25## −2.57* −8.82*
1–5 years −2.75 −0.98 −3.74
5–13 years (Reference group)
Maternal intelligence 0.46 −0.02 0.44
House type
Thatched roof or poorer −7.03 −1.93 −8.96##
Corrugated tin −1.50 −0.34 −1.83
Concrete (Reference group)
Television access 3.53 1.60* 5.13##
Height 0.69# 0.13* 0.81#
Head circumference 2.31** 0.78** 3.08**
Water As −1.45** −0.19 −1.64**
Total R2 (%) 29.02 22.83 31.63
* p < 0.05,
** p < 0.01,
# p < 0.001,
## p < 0.10.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.6954ehp0112-00133415345349Children's HealthArticlesPostweaning Exposure to Aflatoxin Results in Impaired Child Growth: A Longitudinal Study in Benin, West Africa Gong Yunyun 1Hounsa Assomption 2Egal Sharif 2Turner Paul C. 1Sutcliffe Anne E. 1Hall Andrew J. 3Cardwell Kitty 2Wild Christopher P. 11Molecular Epidemiology Unit, Centre for Epidemiology and Biostatistics, Leeds Institute of Genetics Health and Therapeutics, Faculty of Medicine and Health, University of Leeds, Leeds, United Kingdom2International Institute of Tropical Agriculture, Cotonou, Benin, West Africa3London School of Hygiene and Tropical Medicine, London, United KingdomAddress correspondence to C.P. Wild, Molecular Epidemiology Unit, Centre for Epidemiology and Biostatistics, Leeds Institute of Genetics Health and Therapeutics, Faculty of Medicine and Health, University of Leeds, Leeds, UK LS2 9JT. Telephone: 0113-343-6601. Fax: 0113-343-6603. E-mail:
[email protected] thank the following people who participated in the fieldwork: A. Agognon, Z. Assani, G. Ayeni, M. Elegbede, A. Gandjeto, M. Koube, and J. Djossou.
This study was supported by Gesellschaft für Technische Zusammenarbeit project 98.7860.4-001.00 and National Institute of Environmental Health Sciences grant ES06052.
The authors declare they have no competing financial interests.
9 2004 27 4 2004 112 13 1334 1338 6 1 2004 27 4 2004 Publication 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. Aflatoxins are dietary contaminants that are hepatocarcinogenic and immunotoxic and cause growth retardation in animals, but there is little evidence concerning the latter two parameters in exposed human populations. Aflatoxin exposure of West African children is known to be high, so we conducted a longitudinal study over an 8-month period in Benin to assess the effects of exposure on growth. Two hundred children 16–37 months of age were recruited from four villages, two with high and two with low aflatoxin exposure (50 children per village). Serum aflatoxin–albumin (AF-alb) adducts, anthropometric parameters, information on food consumption, and various demographic data were measured at recruitment (February) and at two subsequent time points (June and October). Plasma levels of vitamin A and zinc were also measured. AF-alb adducts increased markedly between February and October in three of the four villages, with the largest increases in the villages with higher exposures. Children who were fully weaned at recruitment had higher AF-alb than did those still partially breast-fed (p < 0.0001); the major weaning food was a maize-based porridge. There was no association between AF-alb and micronutrient levels, suggesting that aflatoxin exposure was not accompanied by a general nutritional deficiency. There was, however, a strong negative correlation (p < 0.0001) between AF-alb and height increase over the 8-month follow-up after adjustment for age, sex, height at recruitment, socioeconomic status, village, and weaning status; the highest quartile of AF-alb was associated with a mean 1.7 cm reduction in growth over 8 months compared with the lowest quartile. This study emphasizes the association between aflatoxin and stunting, although the underlying mechanisms remain unclear. Aflatoxin exposure during the weaning period may be critical in terms of adverse health effects in West African children, and intervention measures to reduce exposure merit investigation.
aflatoxinbiomarkerschild growthdietary exposurelongitudinal studyweaning
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Aflatoxins are fungal metabolites that contaminate dietary staple foods such as groundnuts and maize in agroecologies where hot, humid climates combine with poor food storage conditions to facilitate fungal growth and toxin production [International Agency for Research on Cancer (IARC) 2002]. Aflatoxins are proven hepatocarcinogens in many animal species. In populations in parts of Africa and Southeast Asia, exposure is associated with an increased risk of hepatocellular carcinoma, particularly in individuals with chronic hepatitis B virus infection (Hall and Wild 1994; IARC 2002; Wild and Turner 2002). In addition to their carcinogenic properties, aflatoxins can cause growth retardation and impairment of immune function in animals (Raisuddin et al. 1993). However, to date there has been little investigation of these latter parameters in exposed human populations. In one study of Gambian children, Turner et al. (2003) found evidence of a reduced level of salivary immunoglobulin A (IgA) in exposed individuals but no effect on antibody titers to pneumococcal and rabies vaccines.
Aflatoxin exposure cannot be measured accurately at the individual level through a combination of questionnaire-based approach and food analysis, primarily because the heterogeneity of toxin distribution within a particular food product makes representative sampling impractical. Exposure biomarkers have been developed to circumvent this problem, including serum aflatoxin–albumin (AF-alb) adducts that reflect recent past exposure (previous 2–3 months) (Wild and Turner 2002). In a cross-sectional study in Benin and Togo, young children showed a consistently high prevalence and level of AF-alb, with detection of the marker in 99% of children [geometric mean (GM), 32.8 pg/mg; 95% confidence interval (CI), 25.3–42.5]. Exposure was significantly related to weaning status in children 1–3 years of age, with mean AF-alb levels approximately 2-fold higher in fully weaned children compared with those receiving a mixture of breast milk and solid foods. Furthermore, the level of AF-alb was strongly associated with growth faltering, particularly stunting (Gong et al. 2002, 2003). Although breast milk may contain aflatoxins (Zarba et al. 1992), these are generally less toxic metabolites (AFM1) than are the parent toxins found in the diet (AFB1, AFG1), and they occur at lower levels. Thus, breast-feeding provides a period of relatively low aflatoxin exposure in a population whose primary weaning foods, particularly maize, are at high risk of contamination. Toxin exposure during the postweaning period may be a critical factor in young children in determining the adverse health effects of aflatoxins in terms of growth, immune status, and eventually liver cancer risk.
Our earlier study of aflatoxin in relation to weaning and growth was of cross-sectional design (Gong et al. 2002, 2003). The study reported here is of longitudinal design over 8 months examining these associations with repeat measures of aflatoxin exposure and anthropometry.
Materials and Methods
Subject recruitment and survey time.
Fifty children (16–37 months of age) from each of the four villages (Bagbe, Sedje, Djidja, and Dovi-Cogbe) in Benin were recruited into the study in February 2001 and were followed up in June and October 2001. Bagbe and Sedje are located in the coastal savannah (CS), the southernmost zone in the country, and were expected to have lower aflatoxin exposure. Djidja and Dovi-Cogbe are in southern Guinea savannah (SGS), the zone immediately to the north of CS, and were expected to have higher aflatoxin exposure. Rainfall and humidity decrease from south to north (Hell et al. 2000a). The two agroecologic zones each have two maize-growing seasons per year. SGS was the zone with the highest aflatoxin exposure in the country in our previous study (Gong et al. 2002, 2003). Ethical approval was obtained from the Ministry for Health in Benin. The head of household and the mother of the chosen child were informed about the nature of the study and, if they agreed to participate, signed a statement of informed consent.
A questionnaire, administered by trained interviewers to the mothers of children recruited to the study, obtained information on the child, namely, age, sex, food consumption (including frequency of maize and groundnut consumption during 3 days before blood sampling), weaning status, weaning foods, and general health status. Information was also obtained at each of the later two survey points. Additional data were gathered concerning the economic status of the household and the mother. These were used to generate an index of relative socioeconomic status (SES) based upon actual material belongings and potential for income generation. Questionnaires were administered at each of the three survey periods. Only the mother’s SES was used in the analysis because it was considered more relevant to the child’s diet (Gong et al. 2002, 2003). The mother’s mean SES calculated from the February and October surveys was used for the analysis. Data collected at the February survey with regard to personal information (parent’s religion and ethnicity and mother’s education and body mass index) were used in the analysis.
Aflatoxin exposure assessment.
A 5-mL blood sample was obtained from each child at each survey date. The serum was separated and the samples stored at –20°C in Benin before shipment on dry ice to the University of Leeds for analysis. The levels of AF-alb adduct were determined after albumin extraction, digestion, and enzyme-linked immunosorbent assay (ELISA) as previously described (Chapot and Wild 1991). The detection limit was 3 pg of aflatoxin-lysine equivalents per milligram albumin. Controls included three positive and one negative control analyzed alongside batches of samples. Samples were measured in ELISA in quadruplicate on at least two occasions on separate days.
Blood micronutrients.
Plasma vitamin A was measured by reverse-phase high-performance liquid chromatography by the method of Thurnham et al. (1988) with the minor modification that hexane was used for extraction instead of heptane. Zinc was measured by atomic absorption spectroscopy.
Anthropometry.
Child and mother’s body weight and height were measured at all three survey dates, using accurately calibrated instruments [electronic scales: Soehnle (BCB Ltd., Cardiff, UK), 20 kg maximum weight, accurate to 10 g; height measurement: Schorr (Olney, Maryland, USA)]. Field workers, trained to maximize repeatability, made all height and weight measurements. Height for age Z-score (HAZ), weight for age Z-score (WAZ), and weight for height Z-score (WHZ) were calculated at the end of the study (October) as described previously (Gong et al. 2002, 2003), according to the World Health Organization reference population (WHO 1986).
Statistical analysis.
The AF-alb adduct data were not normally distributed and were natural log transformed for statistical analysis. The mean AF-alb level from all three surveys for a given individual was calculated and used as a measure of aflatoxin exposure in some of the analyses. Growth velocity was calculated either as the height difference between two survey points or the difference over the whole 8-month period. The difference between means was tested by t-test or analysis of variance (ANOVA). Significant variables of age, village, and mother’s SES were entered into multivariable models to analyze effects of weaning status on AF-alb level and AF-alb on growth velocity. All the analyses were performed using Stata version 8.0 software (StataCorp., College Station, TX, USA). GMs for AF-alb with 95% CIs are reported in the tables and text unless otherwise stated.
Results
Demographic data for the 200 children at recruitment (February) are presented by village in Table 1. There were no significant differences in age and sex distribution by village. The majority religion was Christian in three of the four villages, with Voodoo being the most common in Djidja. Dovi-Cogbe had the lowest mean measure of mother’s SES, whereas Djidja had the highest. In terms of the major dietary sources of aflatoxin during the period of the study, most of the children (> 80%) in all four villages had consumed maize (including in weaning foods) daily in the 3 days before recruitment in February, and this pattern was maintained in the latter two surveys. In contrast to the almost uniform consumption of maize, the frequencies of groundnut consumption in February in the four villages did differ significantly (p < 0.0001). Groundnut consumption was more common at this time in Djidja and Dovi-Cogbe than in the other villages (Table 1). The same general pattern was also found at the two later survey points, except for somewhat increased groundnut consumption in Bagbe in June (data not shown).
AF-alb levels.
AF-alb was detected in almost every individual at all time points, with a prevalence of 98, 99.5, and 100% in February, June, and October respectively. At the individual level, AF-alb showed a highly significant positive correlation between the three survey points (r = 0.6253 for February vs. June, 0.5624 for February vs. October, and 0.5398 for June vs. October; p < 0.0001 in all cases), suggesting that individuals track over time in terms of their exposure level, although this was predominantly a feature of the higher exposure villages (data not shown). There was no association between AF-alb adduct levels and sex of the child or mother’s SES, body mass index, or level of education. Although there were some differences in adduct level in relation to religious group, these differences were not significant in multivariable analysis (data not shown). Mother’s SES was included in the multivariable analysis because of the relatively strong rationale behind this parameter affecting the child’s diet and hence aflatoxin exposure and, more generally, as a way of controlling for unmeasured dietary confounding. In addition, plasma vitamin A was measured in February and June and zinc in June to assess whether dietary deficiency in these nutrients has a confounding effect on the association between aflatoxin exposure and growth. However, neither nutrient was correlated with AF-alb level (data not shown).
More frequent groundnut consumption was correlated with higher AF-alb in a univariate analysis (Spearman correlation, p < 0.0001). However, groundnut consumption did not make a significant contribution to AF-alb level after adjusting for age, weaning status, village, and SES (p = 0.256). There was no significant correlation between maize consumption and AF-alb, which is probably explained by the relatively uniform consumption frequency.
The AF-alb levels among the four villages showed a similar pattern to that predicted from earlier work, in that Dovi-Cogbe and Djidja had the highest exposures. Longitudinally, AF-alb levels did not differ between February and June (GMs across all four villages, 37.4 vs. 38.7 pg/mg, respectively) but were markedly higher in October (GM, 86.8 pg/mg, p < 0.0001) compared with both February and June. This pattern was in general observed for each of the villages individually, although the increase was particularly marked in the two higher-exposure villages, whereas in Sedje there was little variation in AF-alb over the 8-month period (Figure 1).
Village was one of the strongest determinants of AF-alb in the present study together with the time of sampling. To analyze the contribution of both the village and the timing of sampling to the AF-alb, a repeated ANOVA model was used. This analysis showed that village made the major contribution to AF-alb (F = 89.7, p < 0.0001) followed by the survey time point (F = 46.8, p < 0.0001).
Weaning food and weaning practice.
In addition to the data on foods consumed by the children during this 8-month study, we also obtained information concerning the introduction of weaning foods for each child. Because when the children entered the study they were 16–37 months of age, these data were retrospective for most of them. None of the children were receiving exclusively breast milk at the time of the longitudinal study.
The mean age at which children were fully weaned was 22 months, with the youngest recorded age for complete weaning being 9 months. By 3 years of age, all but eight children were completely weaned. In terms of weaning foods, 95% of the children were given a maize-based porridge, although in Bagbe this maize-based porridge was less frequently consumed (only 64%) than in the other three villages, with millet and sorghum used as an alternative. The porridge was introduced quite early in life, with 25% of the children starting from 3 months of age and almost all children (96%) eating this food to some degree by 7 months.
In addition to the porridge or other specified weaning foods, different types of family foods are introduced to the child’s diet between 5 and 12 months of age. Specifically, at 5 months only 6% of children were reported to be receiving family foods additional to the specific weaning foods, whereas by 12 months of age 90% of the children were consuming such foods. The data in Table 1 for maize and peanut consumption refer to all children, both partially and fully weaned. Patterns of weaning across villages were generally similar, although Bagbe had a lower frequency of weaned children at recruitment (Table 1; 44%) even though the age range did not differ from the other villages.
Weaning and AF-alb.
We examined whether increases in AF-alb with age can be explained by the change in weaning status. As expected in a cohort of this age group, the percentage of fully weaned children increased over the three survey dates from 64% (February) to 79% (June) to 96% (October). When examining the relationship between age and AF-alb, there was a strong positive correlation at recruitment (February), but this became progressively less significant over time and was no longer significant at the end of the 8-month follow-up (p = 0.001, 0.033, and > 0.05 for the February, June, and October time points, respectively).
To separate the effect of age on AF-alb from that of weaning status, we dichotomized the children in to fully weaned or partially breast-fed groups and examined the age effect in these two separate groups. In this analysis, we found no correlation between age and AF-alb within either group alone (data not shown). Given the small numbers of children still partially breast-fed at the later two survey points, we were able to conduct this analysis only with the February data. When considering the fully weaned group of children compared with those partially breast-fed from all villages in February, we found 2.7-fold higher GM AF-alb level in the former group (53.5 vs. 19.5 pg/mg). The mean AF-alb adduct levels at recruitment in fully weaned and partially breast-fed groups after adjustment for age and SES are shown in Figure 2, revealing a higher mean AF-alb in fully weaned children in each of the villages, even when absolute levels of exposure are significantly different.
To take advantage of the information on change in weaning status over time in individual children, we further categorized the children into four groups. Two groups were partially breast-fed at recruitment (February) but were fully weaned by June or October, respectively, one small group of children (n = 7) were partially breast-fed at recruitment and remained so throughout the study, and one group were fully weaned throughout the whole study period. In this analysis (adjusted for age at recruitment, village, and SES), we expressed the results for each child as a ratio of the AF-alb level in October compared with February. The increase in AF-alb was significantly different between the four groups over the 8-month period (F = 4.50, p = 0.0046; Table 2); however, among the four groups, the greatest increase occurred in children who were fully weaned between February and June and, to a lesser extent, in those fully weaned between June and October. This is further evidence that the change from partial breast-feeding to fully weaned is associated with an increase in aflatoxin exposure.
Growth and AF-alb.
When AF-alb levels, either in February or the mean level from the three survey points, were analyzed by quartiles, there was a significant inverse correlation with HAZ and WHZ score but not WAZ score. After adjustment for age, sex, height, weaning status (all data from the February point), SES, and village, a significant correlation remained between HAZ and both measures of AF-alb (p = 0.009 for February AF-alb, p < 0.0001 for mean AF-alb over three survey points), but there was no significant correlation between WHZ and AF-alb.
Height and weight were measured at each of the survey dates. The increase in height and weight between the first and last time point (8 months apart) was calculated and compared with mean AF-alb (represented as quartiles) for each individual over the three survey points or the level at recruitment (February). There was a significant inverse association between mean AF-alb at the three survey points and increase in height but not weight (data not shown) from February to October (Table 3). This association remains highly significant after adjustment for age, height, weaning status (all at recruitment), SES, and village (p < 0.0001). The retardation in height increase was 1.7 cm over the 8-month period between the highest and lowest quartiles of aflatoxin exposure (Table 3). In addition, when AF-alb at entry into the study was used as the measure of exposure, the results were quite similar to those found when exposure was integrated over the whole period (Table 3; p = 0.003).
Discussion
The present study confirmed that children in Benin have exceptionally high aflatoxin exposure, with some individual levels of AF-alb (> 1,100 pg aflatoxin-lysine equivalents per milligram albumin) being higher than we have observed in any other population. This biomarker has permitted studies of the health effects of aflatoxin exposure that were previously precluded because of the inability to accurately estimate individual exposure by dietary assessment. Together with previous studies of children in other parts of West Africa using this biomarker, a picture emerges of consistently high levels of exposure in this part of the world (Allen et al. 1992; Turner et al. 2000, 2003). The present data from Benin are consistent with the previous cross-sectional study in Benin and Togo with regard to both high levels and the fact that villages in the SGS (i.e., Djidja and Dovi-Cogbe) were found to have the highest exposure (Gong et al. 2002, 2003). In fact, the AF-alb levels are characterized by marked geographic variations with Dovi-Cogbe, the village with highest levels, having a mean AF-alb 10 times that in Bagbe. Overall, village was the strongest determinant of AF-alb level. Temperature and humidity are two factors that favor growth of Aspergillus species and production of the associated aflatoxins as secondary metabolites, and these will vary geographically. Harvest and storage practices also differ from village to village, and these will influence the susceptibility of crops to fungal infestation and toxin production (Hell et al. 2000a).
Seasonal changes in aflatoxin exposure have been reported in previous work, presumably as a result of toxin accumulation during storage under hot, humid conditions, often complicated by insect infestation (Hell et al. 2000a; Turner et al. 2000; Wild and Hall 2000). In the present study, only minor changes in AF-alb were observed between February and June, but there was a substantial increase between June and October in all but one village (Sedje). These dynamics are difficult to relate directly to any specific factor because of variations in annual climatic conditions, the fact that there are two maize harvests per annum, and that maize can be stored for > 1 year. Consequently, the variations in toxin level are more complex than they are for groundnuts, a crop that tends to be eaten within the year following harvest. The AF-alb level will also be influenced by the frequency, quality, and quantity of maize and groundnut consumption throughout the year due to availability of these and alternative food sources.
The observations from this longitudinal study confirmed our earlier report (Gong et al. 2002, 2003) that weaning onto family foods represents a period of increasing aflatoxin exposure. Although age was significantly correlated with AF-alb at recruitment, this association became weaker over the study period. In further analysis, it was apparent that weaning status was the underlying cause of this observation, for the following reasons. First, the correlation at the February survey between age and AF-alb level disappeared when the correlation was considered separately in children categorized as fully weaned or partially breast-fed. Second, when grouping the children according to their change in weaning status over time, we found that it was the change from partial breast-feeding to complete weaning that was correlated with the largest increase in AF-alb. Nevertheless, it is noteworthy that even in those children who continued to receive some breast milk throughout the follow-up, there was a modest increase in AF-alb, possibly reflecting the increasing proportion of total food consumption coming from the weaning and family foods as the child becomes older.
The most likely source of aflatoxin exposure during the weaning period in this population is maize. The main source of aflatoxins will vary by region, and in other parts of West Africa groundnuts are the major contributor to exposure (Turner et al. 2002); in the present study the precision of our dietary analysis does not permit us to completely exclude groundnuts as a contributing factor to aflatoxin exposure, but groundnuts are eaten less frequently and in smaller amounts than is maize. Maize is one of the main dietary staples frequently contaminated with aflatoxins worldwide, including in West Africa (Hell et al. 2000a, 2000b; Jelinek et al. 1989; Setamou et al. 1997). Levels of aflatoxins in maize in Benin have previously been reported to range up to 532 ppb (Hell et al. 2003). Maize-based porridge was found to be the principle weaning food in all four villages, and AF-alb levels increased when this food replaced breast milk, probably because of the lower toxin levels in milk compared with foods. The fact that maize-based porridge as a weaning food is consumed less frequently in Bagbe, the village with the lowest AF-alb level, is consistent with this interpretation that the maize porridge is a major source of aflatoxin. In Bagbe alternative weaning foods were sorghum and millet, and the prevalence of fully weaned children was somewhat lower than in the other villages. Estimated carryover of aflatoxin from dietary intake to milk in animals is around 1%, and similar estimates were made from studies measuring intakes versus excretion in individual women in The Gambia (Zarba et al. 1992). An alternative hypothesis for the increase in AF-alb after weaning is that breast milk could have an effect on the intestinal absorption of aflatoxin or on its metabolism to reactive metabolites once ingested. However, this hypothesis is not supported by any experimental data so far to our knowledge.
Fetal and early childhood environment is considered critical for growth and disease risk in later life (Terry and Susser 2001). Aflatoxin has been shown to cause both immune suppression and growth impairment in animals (Hall and Wild 1994; Raisuddin et al. 1993). Exposure has been linked to kwashiorkor, a severe protein-energy–deficient disease in African children (Hendrickse et al. 1982); however, this association awaits confirmation in appropriately designed epidemiologic studies (Hall and Wild 1994). In a previous cross-sectional study in Benin and Togo, we found an inverse association between HAZ score and AF-alb adduct level in 480 children 1–5 years of age (Gong et al. 2002). Comparatively, growth velocity is more persuasive than a cross-sectional measure in clarifying the growth impairment associated with aflatoxin. In this longitudinal study, the reduction in height increase was significantly correlated both with higher AF-alb level at recruitment and with high mean AF-alb level over the three time points studied. This association was present after adjusting for age, weaning status, height at recruitment, SES, and village. Categorizing the children into quartiles for mean AF-alb over the three time points in the study, there is a mean 1.7-cm reduction in height gain in the highest versus lowest quartile of exposure over just an 8-month period. This corresponded to a difference in GM of 160.2 pg/mg (174.2–14.0 pg/mg) in AF-alb over the 8-month period between the lowest and highest quartiles of exposure. It should be noted that all the levels of aflatoxin exposure in this study are high and chronic in nature compared with developed countries. If the effects on growth were compared with children infrequently exposed to negligible toxin levels, the observations may appear even more striking.
The strong association between aflatoxin exposure and impaired growth may have significant effects on other aspects of child health, such as immunity and susceptibility to infectious diseases. Nevertheless, the underlying biology to explain the effect of aflatoxin on growth is not understood and is important to investigate. Recently, we reported a reduction in salivary IgA in Gambian children exposed to aflatoxin (Turner et al. 2003). If aflatoxins can alter mucosal barriers and affect resistance to intestinal infections, for example, then this would provide one mechanism for the observations we have made on growth impairment. It is also recognized that mycotoxins occur commonly as mixtures; most notably for the present study, aflatoxins would be expected to co-occur with fumonisins in maize (IARC 2002), and the role of possible interactions between these co-contaminants in the underlying mechanisms of growth impairment is of interest. It might be argued that AF-alb is a surrogate marker for food of poor nutritional quality and that reduced dietary intakes of nutrients are the underlying cause of the association between AF-alb and impaired growth. Evidence that this is not the case comes from the fact that blood micronutrient levels (vitamin A and zinc) were not correlated with AF-alb levels in this study. However, we did not have a measure of consumption of other dietary components or of total energy intake. In fact, to fully distinguish the effects of the toxin from other confounding factors in the diet would require a randomized intervention study where the impact of lowering aflatoxin exposure on child immunity, growth, and disease susceptibility can be assessed. This would also permit a better understanding of the relative contribution of aflatoxin to growth impairment in relation to other important determinants in these communities. Given the potential adverse health effects on West African children of this ubiquitous dietary toxin, it is important to evaluate intervention strategies appropriate to these populations (Wild and Hall 2000).
Figure 1 AF-alb adduct GM level across the four villages at the three survey time points. The “Total” bars show the adduct level of all the villages at three survey points, with the overall adduct level at the October survey being significantly higher than the other two survey points (p < 0.01). Djidja and Dovi-Cogbe had significantly higher AF-alb than did Bagbe and Sedje at both the October and June survey points (p < 0.01 for both). However, at the February survey, the AF-alb adduct levels are different across all the villages (p < 0.01).
Figure 2 Adjusted mean AF-alb level in weaned and partially weaned children. Data used in this figure are from the February survey point, and the mean AF-alb levels (95% CI) are adjusted for age and SES. The differences between partially breast-fed and fully weaned children are significant in ANOVA, p = 0.0001.
Table 1 Descriptive data at the time of recruitment (February) for each of the four villages.
Village
Characteristic Bagbe Sedje Djidja Dovi-Cogbe
AF-alb (pg/mg)a 11.8 (9.2–15.2)* 31.1 (25.4–38.0)** 45.9 (35.7–59.0)# 119.3 (96.2–148.1)#,*
Age (months)a 25 (23.6–26.4) 26 (24.5–28.2) 27 (25.7–29.0) 27 (25.5–28.7)
Sex (male:female)b 22:28 29:21 26:24 25:25
Religion (C:I:V)b 41:1:4* 37:1:9* 13:0:30** 40:0:7*
Weaned (%) 44* 66** 72** 74**
Maize consumptionb,c 1:1:3:45 0:0:4:46 0:0:3:47 0:1:5:44
Groundnut consumptionb,c 32:10:7:0* 24:12:6:7** 5:10:12:23# 10:8:13:19#,*
Mother’s SESd 10.8 (8.0–13.5)* 11.0 (8.7–13.4)* 14.7 (12.0–17.3)** 8.9 (7.0–10.8)*
Abbreviations: C, Christian; I, Islam; V, traditional/Voodoo.
a Mean (95% CI).
b Ratio.
c Maize and groundnut consumption refers to the number of children (both partially and fully weaned) having consumed the commodity on 0, 1, 2, or 3 days of the 3 days before the survey date.
d Median (25–75%).
* Data with different symbols are significantly different.
Table 2 Mean ratio (95% CI) of AF-alb in October compared with February with respect to change in weaning status over the study period.
GM AF-alb (pg/mg)
Ratioa of AF-alb in October compared to February (95% CI)
Weaning groupb No.c February October Unadjusted Adjustedd
Weaned at entry 123 54.4 99.4 1.8 (1.5–2.2) 1.6 (1.2–2.1)
Not weaned 7 8.9 24.0 2.7 (1.2–6.1) 2.1 (0.9–4.9)
Weaned at June 27 26.4 127.4 4.8 (3.2–7.2)** 4.2 (2.7–6.6)
Weaned at October 24 13.0 44.3 3.4 (2.0–5.8)* 2.9 (1.7–5.0)
a The ratio of the AF-alb level in February and that in October was calculated for each child, and then the GM of these ratios was calculated for each group.
b Children who were fully weaned at the start of the study are categorized as “weaned at entry.” Those who remained partially breast-fed throughout the study period, “not weaned”; those who were partially breast-fed in February but were fully weaned by June or October.
c Nineteen of the 200 children had incomplete information on weaning status or missing data on AF-alb.
d ANOVA test, p = 0.0046 after adjusted for age at recruitment, village, and SES.
* p = 0.068 compared with weaned at entry group;
** p < 0.0001 compared with weaned at entry group.
Table 3 Height increase in comparison with AF-alb level [mean (95% CI)].
Mean AF-alb over 8 months height increase (cm)
AF-alb at February height increase (cm)
Aflatoxin exposure groupa Unadjusted Adjustedb Unadjusted Adjustedb
Lower quartile 4.9 (4.5–5.3)*,c 5.9 (5.2–6.6) 4.9 (4.5–5.2)* 5.3 (4.6–6.1)
Mid-lower quartile 4.4 (4.1–4.7)** 5.3 (4.8–5.9) 4.4 (4.1–4.7) 5.0 (4.5–5.5)
Mid-upper quartile 4.1 (3.8–4.5)** 4.8 (4.4–5.2) 4.0 (3.7–4.4)** 4.7 (4.3–5.1)
Upper quartile 4.1 (3.8–4.5)** 4.2 (3.9–4.6) 4.2 (3.9–4.5)** 4.3 (4.0–4.7)
a The quartiles for mean AF-alb over 8 months are < 23.3, 23.3–53.0, 53.0–101.5, and > 101.5 pg/mg. The quartiles for AF-alb in February are < 17.1, 17.1–39.6, 39.6–82.3, and > 82.3 pg/mg.
b Data are adjusted for age, height, and weaning status in February (p = 0.003) and for mother’s SES and village over the 8 months (p < 0.0001).
c *Data with different symbols are significantly different.
==== Refs
References
Allen SJ Wild CP Wheeler JG Riley EM Montesano R Bennett S 1992 Aflatoxin exposure, malaria, and hepatitis B infection in rural Gambian children Trans R Soc Trop Med Hyg 86 426 430 1440826
Chapot B Wild CP 1991. ELISA for quantification of aflatoxin-albumin adducts and their application to human exposure assessment. In: Techniques in Diagnostic Pathology , Vol 2 (Warhol M, van Velzen D, Bullock GR, eds). San Diego, CA:Academic Press, 135–155.
Gong YY Cardwell K Hounsa A Egal S Turner PC Hall AJ 2002 Dietary aflatoxin exposure and impaired growth in young children from Benin and Togo: cross sectional study Br Med J 325 20 21 12098724
Gong YY Egal S Hounsa A Turner PC Hall AJ Cardwell KF 2003 Determinants of aflatoxin exposure in young children from Benin and Togo, West Africa: the critical role of weaning Int J Epidemiol 32 556 562 12913029
Hall AJ Wild CP 1994. Epidemiology of aflatoxin related disease. In: The Toxicology of Aflatoxins: Human Health, Veterinary and Agricultural Significance (Eaton DA, Groopman JD, eds). San Diego CA:Academic Press, 233–258.
Hell K Cardwell KF Poehling HM 2003 Relationship between management practices, fungal infection and aflatoxin for stored maize in Benin J Phytopathol 151 690 698
Hell K Cardwell KF Setamou M Poehling H-M 2000a The influence of storage practices on aflatoxin contamination in maize in four agroecological zones of Benin, West Africa J Stored Prod Res 36 365 382 10880814
Hell K Cardwell KF Setamou M Schulthess F 2000b Influence of insect infestation on aflatoxin contamination of stored maize in four agroecological regions in Benin Afr Entomol 8 169 177
Hendrickse RG Coulter JB Lamplugh SM Macfarlane SB Williams TE Omer MI 1982 Aflatoxins and kwashiorkor: a study in Sudanese children Br Med J [Clin Res] 285 843 846
IARC 2002 Some traditional herbal medicines, some mycotoxins, naphthalene and styrene IARC Monogr Eval Carcinog Risks Hum 82 1 556 12687954
Jelinek CF Pohland AE Wood GE 1989 Worldwide occurrence of mycotoxins in foods and feeds—an update J Assoc Off Anal Chem 72 223 230 2651391
Raisuddin S Singh KP Zaidi SI Paul BN Ray PK 1993 Immunosuppressive effects of aflatoxin in growing rats Mycopathologia 124 189 194 8022466
Setamou M Cardwell KF Schulthes F Hell K 1997 Aspergillus flavus infection and aflatoxin contamination of preharvest maize in Benin Plant Disease 81 1323 1327
Terry MB Susser E 2001 Commentary: the impact of fetal and infant exposures along the life course Int J Epidemiol 30 95 96 11171864
Thurnham DI Smith E Flora PS 1988 Concurrent liquid-chromatographic assay of retinol, alpha-tocopherol, beta-carotene, alpha-carotene, lycopene, and beta-cryptoxanthin in plasma, with tocopherol acetate as internal standard Clin Chem 34 377 381 3342512
Turner PC Mendy M Whittle H Fortuin M Hall AJ Wild CP 2000 Hepatitis B infection and aflatoxin biomarker levels in Gambian children Trop Med Int Health 5 837 841 11169271
Turner PC Moore SE Hall AJ Prentice AM Wild CP 2003 Modification of immune function through exposure to dietary aflatoxin in Gambian children Environ Health Perspect 111 217 220 12573908
Turner PC Sylla A Diallo MS Castegnaro JJ Hall AJ Wild CP 2002 The role of aflatoxins and hepatitis viruses in the etiopathogenesis of hepatocellular carcinoma: a basis for primary prevention in Guinea-Conakry, West Africa J Gastroenterol Hepatol 17 S441 S448 12534775
WHO 1986 WHO working group: Use and interpretation of anthropometric indicators of nutritional status Bull WHO 64 929 941 3493862
Wild CP Hall AJ 2000 Primary prevention of hepatocellular carcinoma in developing countries Mutat Res 462 381 393 10767647
Wild CP Turner PC 2002 The toxicology of aflatoxins as a basis for public health decisions Mutagenesis 17 471 481 12435844
Zarba A Wild CP Hall AJ Montesano R Hudson GJ Groopman JD 1992 Aflatoxin M1 in human breast milk from The Gambia, West Africa, quantified by combined monoclonal antibody immunoaffinity chromatography and HPLC Carcinogenesis 13 891 894 1587004
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.6901ehp0112-00133915345350Children's HealthArticlesFollow-Up Study of Adolescents Exposed to Di(2-Ethylhexyl) Phthalate (DEHP) as Neonates on Extracorporeal Membrane Oxygenation (ECMO) Support Rais-Bahrami Khodayar 1Nunez Susan 2Revenis Mary E. 1Luban Naomi L.C. 3Short Billie L. 11Departments of Neonatology,2Endocrinology, and3Transfusion Medicine, Children’s National Medical Center and The George Washington University School of Medicine, Washington, DC, USAAddress correspondence to K. Rais-Bahrami, Department of Neonatology, Children’s National Medical Center, 111 Michigan Ave., NW, Washington, DC 20010 USA. Telephone: (202) 884-4764. Fax: (202) 884-3459. E-mail:
[email protected] study was supported by grant M01-RR13297 from the General Clinical Research Center, Program of the National Center for Research Resources, National Institutes of Health, Department of Health and Human Services.
The authors declare they have no competing financial interests.
9 2004 7 4 2004 112 13 1339 1340 8 12 2003 7 4 2004 Publication 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. Di(2-ethylhexyl) phthalate (DEHP) is used to make polyvinyl chloride (PVC) plastic tubing soft and flexible. Animal data show that adverse effects of DEHP exposure may include reduced fertility, reduced sperm production in males, and ovarian dysfunction in females. Known treatments that involve high DEHP exposures are blood exchange transfusions, extracorporeal membrane oxygenation (ECMO), and cardiovascular surgery. Although potential exposure to DEHP in ECMO patients is significant, the exposure has not been associated with short-term toxicity. To evaluate long-term toxicity, we undertook a study of neonatal ECMO survivors to assess their onset of puberty and sexual maturity. We evaluated 13 male and 6 female subjects at 14–16 years of age who had undergone ECMO as neonates. All subjects had a complete physical examination including measurements for height, weight, head circumference, and pubertal assessment by Tanner staging. The testicular volume and the phallic length were measured in male participants. Laboratory tests included thyroid, liver, and renal function as well as measurements of luteinizing hormone, follicle-stimulating hormone, testosterone for males, and estradiol for females. Except for one patient with Marfan syndrome, the rest had normal growth percentile for age and sex. All had normal values for thyroid, liver, and renal functions. Sexual hormones were appropriate for the stage of pubertal maturity. Our results indicate that adolescents exposed to significant quantities of DEHP as neonates showed no significant adverse effects on their physical growth and pubertal maturity. Thyroid, liver, renal, and male and female gonadal functions tested were within normal range for age and sex distribution.
DEHPECMOtoxicity
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Human exposure to di(2-ethylhexyl) phthalate (DEHP) occurs throughout life. Of particular concern is the exposure of fetuses, preterm infants, and babies because the developing human reproductive system may be affected when the metabolic pathways of detoxification are immature. DEHP has been shown to damage the male and female reproductive systems in newborn animals. Animal studies have shown DEHP to be particularly harmful to developing fetuses: Adverse effects in the reproductive system include changes in the testes, specifically the Sertoli cell, leading to reduced fertility and changes in sperm production in males (Foster et al. 2001; Park et al. 2002; Poon et al. 1997) and ovarian dysfunction and decreased hormone production in females (Davis et al. 1994; Lovekamp-Swan and Davis 2003). Respiratory distress and changes in kidney and liver function have also been linked to DEHP exposure (Crocker et al. 1988; Kevy and Jacobson 1982; Latini 2000; Rock et al. 1987; Roth et al. 1988; Ward et al. 1998).
DEHP derives from a family of chemicals called phthalates. These chemicals are used to make polyvinyl chloride (PVC) plastic tubing soft and flexible. Because DEHP does not bind to the plastic, it can leach out of the PVC products. DEHP is widely used in PVC disposable medical devices. As in other products, DEHP can leach out of flexible PVC medical devices into the solution or medication it contains and subsequently into the patient (Rubin and Schiffer 1976).
Species differences in toxicity and metabolism of DEHP have created considerable debate about the relevance of studies in rodents to human health. However, exposures in neonatal intensive care units (NICUs) are potentially at or above levels known to cause adverse health effects in relevant animal studies (e.g., Tickner et al. 2001). For infants requiring intensive care, DEHP exposure can occur at three orders of magnitude greater than average adult exposures and at or above levels shown to cause adverse reproductive effects in animals (e.g., Tickner et al. 2001).
DEHP concentrations in blood and blood products are of particular concern for neonates who receive regular blood transfusions. The most commonly used blood products—packed red blood cells and plasma—are typically stored in DEHP plasticized bags and administered to patients through DEHP plasticized intravenous tubes. Less common treatments that involve potentially high DEHP exposures are blood exchange transfusions and extra-corporeal membrane oxygenation (ECMO). Although potential exposure to DEHP in ECMO patients is significant, it has not been associated with short-term toxicity. To evaluate long-term toxicity, we undertook a study of adolescents who had previously undergone ECMO treatment in the neonatal period to assess their onset of puberty and sexual maturation in comparison to an age- and sex-matched reference population.
Methods
This prospective study was approved by the institutional review board at Children’s National Medical Center. After obtaining informed consent and assent, we evaluated 19 (13 male and 6 female) adolescents 14–16 years of age who had undergone ECMO as neonates. All subjects had a complete physical examination including measurements for height, weight, head circumference, and pubertal staging according to the method of Tanner (Morris and Udry 1980; Tanner 1975). In addition, the testicular volume and the phallic length were measured in all male participants. Laboratory tests included measurements of thyroid function [thyroid-stimulating hormone, free thyroxine (T4) by dialysis, and T4], liver function (aspartate aminotransferase, alanine aminotransferase, γ-glutamyl transpeptidase, and total and direct bilirubin), renal function (blood urea nitrogen and creatinine), as well as measurements of luteinizing hormone (LH), follicle-stimulating hormone (FSH), testosterone for males, and estradiol for females.
Results
Except for one female participant with a diagnosis of Marfan syndrome, the rest had normal growth percentiles for age and sex. All the participants had normal laboratory values for thyroid, liver, and renal functions. The levels of LH, FSH, testosterone in males, and estradiol in females were normal and appropriate for the degree of pubertal development. Results of the sex hormones related to pubertal maturation are shown in Tables 1 and 2 as mean values (normal ranges).
Discussion
Our study did not show long-term adverse outcome related to physical growth and pubertal development in adolescents previously exposed to DEHP in the neonatal period. This is in contrast to the animal data in multiple species, which show a variety of reproductive and developmental toxicities when this plasticizer is administered both orally and parenterally.
Individuals who have among the highest exposures to DEHP are those undergoing medical treatments or procedures such as dialysis, exchange transfusion, ECMO, and cardiovascular surgery. Shneider et al. (1989) have shown that babies undergoing ECMO, in which the blood is circulating through PVC tubing, are exposed to 42–140 mg DEHP/kg body weight over a treatment period of 3–10 days (Shneider et al. 1989). Karle et al. (1997) reported a lower level of exposure that ranged from nondetectable to 34.9 mg/kg/treatment period. The nondetectable level resulted from the use of a heparin coating on the DEHP-plasticized PVC circuit. In addition to the heparin coated tubing, Karle et al. (1997) attributed the difference between their study and that of Shneider et al. (1989) to the smaller surface area of the newer ECMO configurations and the varying percentage of DEHP composition in each type of tubing.
Although intravenous exposure to DEHP through the ECMO circuit or other intravenous routes exceeds recommended oral exposure limits, it is difficult to directly compare the two because one is an assumed lifetime daily oral exposure and the other an acute temporary exposure during ECMO therapy. Also, the routes of exposure differ: oral versus intravenous (Doull et al. 1999). Because the human exposure can be similar to the doses that are toxic in rodents, there is an ongoing concern that exposure to DEHP in neonatal intensive care units may adversely affect the developing reproductive organs in these infants (Huber et al. 1996). The most sensitive system appears to be the immature male reproductive tract, especially the Sertoli cell (Parks et al. 2000; Poon et al. 1997).
When DEHP enters the human body, the compound is metabolized into various substances that are more rapidly excreted. The most important of these metabolites, monoethylhexyl phthalate (MEHP) is thought to be responsible for much of DEHP’s toxicity. The enzymes that break down DEHP into MEHP are found mainly in the intestines but also occur in the liver, kidney, lungs, pancreas, and plasma. Because conversion of DEHP to MEHP occurs primarily in the intestinal tract, exposure to DEHP by ingestion may be more hazardous than by intravenous exposure, which largely bypasses the intestinal tract (Huber et al. 1996; Lewandowski et al. 1980; Thomas et al. 1979).
Our study of adolescents exposed to significant quantities of DEHP as neonates showed no significant adverse effects of DEHP on their physical growth and pubertal maturity. Thyroid, liver, renal, and male and female gonadal functions tested were within normal range for age and sex distribution. We hypothesize that the acute and short-term exposure to DEHP in an intravenous form and lack of significant conversion of DEHP to MEHP may be protective against its long-term side effects.
Table 1 Results of sexual hormones in female subjects matched for Tanner stage [mean value (normal reference range)].
Females (n) Tanner stage LH (IU/L) FSH (IU/L) Estradiol (pg/mL)
4 4 6.05 (0.72–15.01) 4.58 (1.26–7.37) 48.75 (25–345)
2 5 3.7 (0.30–29.38) 2.65 (1.02–9.24) 118.5 (25–410)
Table 2 Results of sexual hormones, testicular volume, and phallic length in male subjects matched for Tanner stage [mean value (normal reference range)].
Males (n) Tanner stage LH (IU/L) FSH (IU/L) Testosterone (ng/dL) Testicular volume (mL) Phallic length (cm)
4 2–3 1.83 (0.26–3.74) 2.40 (0.72–10.37) 119 (15–280) 11 (5–10) 8.0 (6.3–8.6)
9 4–5 3.02 (0.55–7.00) 3.61 (1.70–7.00) 387 (105–800) 22 (20–29) 11.2 (8.6–9.9)
==== Refs
References
Crocker J Safe S Acott P 1988 Effects of chronic phthalate exposure on the kidney J Toxicol Environ Health 23 433 444 3361614
Davis BJ Maronpot RR Heindel JJ 1994 Di-(2-ethylhexyl) phthalate suppresses estradiol and ovulation in cycling rats Toxicol Appl Pharmacol 128 216 223 7940536
Doull J Cattley R Elcombe C Lake BG Swenberg J Wilkinson C 1999 A cancer risk assessment of di(2-ethylhexyl)-phthalate: application of the new U.S. EPA risk assessment guidelines Regul Toxicol Pharmacol 29 3 327 357 10388618
Foster PM Mylchreest E Gaido KW Sar M 2001 Effects of phthalate esters on the developing reproductive tract of male rats Hum Reprod Update 7 231 235 11392369
Huber WW Grasl-Kraupp B Schulte-Hermann R 1996 Hepatocarcinogenic potential of di(2-ethylhexyl)phthalate in rodents and its implications on human risk Crit Rev Toxicol 26 365 381 8817083
Karle VA Short BL Martin GR Bulas DI Geston PR Luban NCL 1997 Extrcorporeal membrane oxygenation exposes infants to the plasticizer, DEHP Crit Care Med 25 696 703 9142038
Kevy S Jacobson M 1982 Hepatic effects of a phthalate ester plasticizer leached from poly(vinyl-chloride) blood bags following transfusion Environ Health Perspect 45 57 64 7140697
Latini G 2000 Potential hazards of exposure to di-(2-ethylhexyl)-phthalate in babies. A review Biol Neonate 78 269 276 11093005
Lewandowski M Fernandes J Chen TS 1980 Assessment of the teratogenic potential of plasma-soluble extracts of diethyl-hexyl phthalate plasticized polyvinyl chloride plastics in rats Toxicol Appl Pharmacol 54 141 147 7394782
Lovekamp-Swan T Davis BJ 2003 Mechanisms of phthalate ester toxicity in the female reproductive system Environ Health Perspect 111 139 146 12573895
Morris MN Udry JR 1980 Validation of a self-administered instrument to assess stage of adolescent development J Youth Adolesc 9 271 280 24318082
Park JD Habeebu SS Klaassen CD 2002 Testicular toxicity of di-(2-ethylhexyl)phthalate in young Sprague-Dawley rats Toxicology 171 105 115 11836017
Parks LG Ostby JS Lambright CR Abbott BD Klinefelter GR Barlow NJ 2000 The plasticizer diethylhexyl phthalate induces malformations by decreasing fetal testosterone synthesis during sexual differentiation in the male rat Toxicol Sci 58 339 349 11099646
Poon R Lecavaliert P Mueller R Valli VE Procter BG Chu I 1997 Subchronic oral toxicity of di-n -octyl phthalate and di(2-ethylhexyl) phthalate in the rat Food Chem Toxicol 35 225 239 9146736
Rock G Labow RS Franklin C Burnett R Tocchi M 1987 Hypotension and cardiac arrest in rats after infusion of mono (2-ethylhexyl) phthalate (MEHP) a contaminant of stored blood N Engl J Med 316 1218 1219 3574376
Roth B Herkenrath P Lehmann HJ Ohles HD Homig HJ Benz-Bohm G 1988 Di-(2-ethylhexyl)-phthalate as plasticizer in PVC respiratory tubing systems: indications of hazardous effects on pulmonary function in mechanically ventilated, preterm infants Eur J Pediatr 147 41 46 3422189
Rubin RJ Schiffer CA 1976 Fate in humans of the plasticizer, di-2-ethylhexyl phthalate, arising from transfusion of platelets stored in vinyl plastic bags Transfusion 16 330 335 985623
Shneider B Schena J Truog R Jacobson M Kevy S 1989 Exposure to di (2-ethylhexyl) phthalate in infants receiving extracorporeal membrane oxygenation N Engl J Med 320 1563 2725593
Tanner JM 1975. Growth and endocrinology of the adolescent. In: Endocrine and Disease of Childhood (Gardner LJ II, ed). Philadelphia:W.B. Saunders, 14–64.
Thomas JA Schein LG Gupta PK McCafferty RE Felice PR Donovan MP 1979 Failure of monoethylhexyl phthalate to cause teratogenic effects in offspring of rabbits Toxicol Appl Pharmacol 51 523 528 538762
Tickner JA Schettler T Guidotti T McCally M Rossi M 2001 Health risks posed by use of di-2-ethylhexyl phthalate (DEHP) in PVC medical devices: a critical review Am J Ind Med 39 100 111 11148020
Ward JM Peters JM Perella CM Gonzalez FJ 1998 Receptor and nonreceptor mediated organ-specific toxicity of DEHP in peroxisome proliferator-activated receptor alpha-null mice Toxicol Pathol 26 240 246 9547862
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0072215345351PerspectivesEditorialGuest Editorial: Breast Milk: An Optimal Food Pronczuk Jenny Moy Gerald Vallenas Constanza World Health Organization Geneva, Switzerland E-mail:
[email protected] Pronczuk is a medical officer working at the WHO in the area of chemical safety, and children's health and the environment.
Gerald Moy is a scientist in the WHO Department of Food and Safety.
Constanza Vallenas works in the WHO as a medical officer on infant and young child feeding.
The authors alone are responsible for the views expressed in this editorial.
9 2004 112 13 A722 A723 Publication 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
Human breast milk offers the optimal nutrition for all infants and provides immunological, developmental, psychological, economic, and practical advantages when compared to artificial feeding. For proper growth, development, and health, infants should be exclusively breast-fed with no other food or drink—not even water—for their first 6 months of life [World Health Organization (WHO) 2001]; they should then receive nutritionally adequate and safe complementary foods while breast-feeding continues up to 24 months of age or beyond.
Given the considerable benefits of breast-feeding for mothers and children everywhere, special efforts are being undertaken by the WHO and partners to promote it in all countries. The Global Strategy for Infant and Young Child Feeding (WHO 2003) recommends critical interventions such as the implementation and monitoring of the International Code of Marketing of Breast-milk Substitutes and the subsequent relevant World Health Assembly resolutions; the adoption and monitoring of maternity entitlements consistent with the International Labour Organization (ILO) Maternity Protection Convention (ILO 2000); and the expanded implementation of the WHO/UNICEF Baby-Friendly Hospital Initiative (WHO/UNICEF 1992). Education of women as well as men about the benefits of breast-feeding is being promoted to establish broader social acceptance of and support for breast-feeding.
New knowledge is emerging on the importance of breast-feeding and the origin of some adult diseases. Breast-feeding may be related to the prevention of diabetes, heart disease, and other diseases that appear in adulthood.
When there is a risk of infectious and toxic agents being present in human milk, however, specific recommendations may apply. To address these and other concerns, the WHO promotes collaborative research studies and develops guidance on the prevention of exposure and the reduction of risk. This represents a challenging task because guidelines must address global public health issues while taking into account the needs of countries and peoples with different health care, sociocultural, and economic conditions.
Among the infectious agents, tuberculosis, hepatitis B virus (HBV), and human immunodeficiency virus (HIV) are considered the main global threats to the health of mothers and infants. In the case of maternal tuberculosis, infants should remain with their mothers and be immunized with BCG (bacillus Calmette-Guérin) as soon as possible after birth to protect them from meningeal and pulmonary tuberculosis. Mothers are treated with the standard short course antibiotic regimes compatible with breast-feeding (WHO 1998a). HBV is another major public health problem. Breast-feeding seems to be an additional mechanism by which infants acquire HBV infection; however, the risk associated with breast-feeding is negligible compared with that of exposure to maternal blood and body fluids at birth. In industrialized countries pregnant mothers are screened for hepatitis B surface antigen, and infants are treated with specific hyperimmune globulin and HBV vaccine, but in developing countries only the routine immunization of infants with HBV vaccine is possible and breast-feeding is still recommended (WHO 1996a). Mother-to-child transmission of HIV is the most significant source of HIV infection in children, and 5–20% of infants born to HIV-infected mothers may acquire it through breast-feeding. Given the need to reduce the risk of transmission to infants while minimizing the risk of other causes of morbidity and mortality, current guidelines state that when replacement feeding is acceptable, feasible, affordable, sustainable, and safe, HIV-infected mothers should avoid breast-feeding completely (WHO/UNICEF/UNFPA/UNAIDS 2003). When these conditions are not present, HIV-infected women who choose to breast-feed are recommended to do so exclusively for the first few months. Then, over a period of a few days to a few weeks, they may gradually stop breast-feeding (exclusive breast-feeding with early cessation), provided the conditions for replacement feeding or other breast-milk options are in place.
There is a myriad of potential chemical contaminants that can be detected in breast milk as analytical methods become ever more sensitive. Most research studies deal with dioxins, polychlorinated biphenyls (PCBs), and organochlorine pesticides. These chemicals belong to the group of persistent organic pollutants (POPs) and are being studied in view of their potential endocrine-disrupting effects. Studies undertaken by the WHO over the past 15 years on dioxins and PCBs demonstrated that in most countries levels of these chemicals in breast milk continue to decrease (WHO 1988, 1989, 1996b). The latest study (Van Leeuwen and Malisch 2002) concluded that in view of this trend, breast-feeding should be encouraged and promoted because of its multiple benefits for the overall health and development of infants. A safety evalutation by the WHO (2002) noted that for PCBs, the exposure of infants through breast milk may be less important than exposure in utero and that most of the subtle effects observed are associated more with transplacental exposure than with exposure through breast-feeding.
The risk assessment of selected organochlorine contaminants in breast milk undertaken by the WHO in 1998 showed that DDT concentrations were higher in developing countries and that hexa-chlorobenzene levels were higher in industrialized countries (WHO 1998b). However, it was stressed that the primary preventive measures to control and reduce the introduction of organochlorine compounds in the environment were the most effective means to eliminate and minimize contaminants in breast milk. Under the Stockholm Convention (United Nations Environment Programme 2001), which was ratified in May 2004, the production and emission of the first group of 12 POPs are to be reduced or eliminated.
Tobacco smoking deserves special consideration because it increases the exposure of mothers and infants to a large number of toxicants, including pesticide residues and known carcinogens, and is linked to reduced duration of breast-feeding and higher levels of abdominal distress in the child. Women who smoke are encouraged to breast-feed and to eliminate cigarette use during pregnancy and lactation.
In view of existing and new information available on infectious and chemical breast milk contaminants, appropriate mechanisms for assessing, preventing, and communicating potential health risks should be considered. Risk communication is of paramount importance—“do not hide, do not scare”—and should enable the informed choice of the mother. In most cases, mothers can and should be reassured that breast milk is by far the best food to give to their babies.
==== Refs
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ILO (International Labour Organization) 2000. C183 Maternity Protection Convention, 2000. Available: http://www.ilo.org/ilolex/cgi-lex/convde.pl?C183 [accessed 10 August 2004].
United Nations Environment Programme 2001. Stockholm Convention on Persistent Organic Pollutants (POPS). Available: http://www.pops.int [accessed 11 August 2004].
Van Leeuwen FXR Malisch R 2002 Results of the third round of WHO-coordinated exposure study on the levels of PCBs, PCDDs and PCDFs in human milk Organohalogen Compounds 56 311 316
WHO 1988. Assessment of Health Risks in Infants Associated with Exposure to PCBs, PCDDs and PCDFs in Breast Milk: Report on a WHO Working Group, Abano Terme/Padua, 16–19 February 1987 (Grandjean P, ed). Environmental Health Series 29. Copenhagen:World Health Organization Regional Office for Europe.
WHO 1989. Levels of PCBs, PCDDs and PCDFs in Breast Milk: Results of WHO-Coordinated Interlaboratory Quality Control Studies and Analytical Field Studies (Yrjänheikki E, ed). Environmental Health Series 34. Copenhagen:World Health Organization Regional Office for Europe.
WHO 1996a. Hepatitis B and Breastfeeding. Child and Adolescent Health and Development Update No 22. Available: http://www.who.int/child-adolescent-health/publications/NUTRITION/Up_22.htm [accessed 10 August 2004].
WHO 1996b. Levels of PCBs, PCDDs and PCDFs in Human Milk: Second Round of WHO-Coordinated Exposure Study. Environmental Health in Europe No 3. Bilthoven, Netherlands:World Health Organization European Centre for Environment and Health.
WHO 1998a. Breastfeeding and Maternal Tuberculosis. Child and Adolescent Health and Development Update No 23. Available: http://www.who.int/child-adolescent-health/publications/NUTRITION/Up_23.htm [accessed 10 August 2004].
WHO 1998b. GEMS/Food International Dietary Survey: Infant Exposure to Certain Organochlorine Contaminants from Breast Milk—A Risk Assessment (Schutz D, ed). WHO/FSF/FOS/98.4. Geneva:Food Safety Unit, Programme of Food Safety and Food Aid, World Health Organization.
WHO 2001. The Optimal Duration of Exclusive Breastfeeding. Report of an Expert Consultation. WHO/NHD/01.09 and WHO/FCH/CAH/01.24. Geneva:World Health Organization.
WHO 2002. Safety Evaluation of Certain Food Additives and Contaminants. WHO Food Additives Series 48. Geneva:World Health Organization, 311–316.
WHO 2003. Global Strategy for Infant and Young Child Feeding. Geneva:World Health Organization.
WHO/UNICEF 1992. The Global Criteria for the WHO/UNICEF Baby-Friendly Hospital Initiative. In: Baby-Friendly Hospital Initiative. Part II. Hospital Level Implementation. Geneva:World Health Organization.
WHO/UNICEF/UNFPA/UNAIDS 2003. HIV and Infant Feeding: Guidelines for Decision-makers. Geneva:World Health Organization. Available: http://www.who.int/child-adolescent-health/publications/NUTRITION/ISBN_92_4_159122_6.htm [accessed 10 August 2004].
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a00725PerspectivesCorrespondenceActivities and Organophosphate Exposures: Response Coronado Gloria D. Thompson Beti Strong Larkin Griffith William C. Islas Ilda Cancer Prevention Research Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, E-mail:
[email protected] authors declare they have no competing financial interests.
9 2004 112 13 A725 A726 Publication 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, Krieger and Zhang note that our article (Coronado et al. 2004) is “founded on an erroneous premise” and that it “presents virtually no data to estimate levels of worker or child exposure.” Our analyses show that the children of workers who reported thinning plants had significantly higher proportions of detectable urinary metabolites of organophosphate (OP) pesticides than the children of workers who reported that they did not perform this task. We concur that no urinary metabolite dose estimates were presented. However, we cited Curl et al. (2002), whose analyses using the same data set show significant correlations between pesticide exposure levels (geometric means and percentiles) between adult farm-workers and children who live in the same household, and high correlations between pesticide residues in house and vehicle dust. When our research team embarked upon investigating the relationship between job task and pesticide exposure, we agreed that calculating the percent detection of metabolites in the urine samples of workers who did and did not perform a given task would permit exploration of this issue. Thus, we noted that the analyses were exploratory in nature (see “Methods”; Coronado et al. 2004). This meant that significant findings would offer directions for further inquiry and investigation.
Krieger and Zhang also state that there is “substantial … data available related to work tasks of handlers,” and they specifically cite the Pesticide Handlers Exposure Database and the U.S. Environmental Protection Agency (EPA) Transfer Coefficients. The Pesticide Handlers Exposure Database aims to determine how much residue (as a percentage of pesticide applied) ends up as external exposure to workers (Van Hemmen 1992). These data are used in worker risk analysis, taking an activity, translating it to external exposure, then translating it to a body burden or toxicologic risk (e.g., using a dermal absorption rates). The database generates hypothesized toxicologic risk estimates and relies on no biomonitoring data, such as testing for pesticide residues in urine or blood. Transfer coefficients estimate the amount of treated foliage with which a worker comes in contact while performing a given task (Knaak et al. 1996). A formula based on fixed assumptions about the clothing that a worker wears and the rate of dermal exposure is used to calculate the body burden of pesticide exposure when a worker performs a given task on a given crop.
We agree that these databases represent important sources of data on both exposure and body burden. However, because formulas used in the calculations of exposure rely on fixed values for protection incurred by personal protective equipment, work hours, absorption rates, and spray patterns, they generate hypothesized risk estimates. Various studies have reported that < 100% of workers routinely use personal protective equipment while applying pesticides, and many may enter recently treated fields (before the expiration of the reentry interval), resulting in potentially higher exposures than estimated in these databases.
Our data are unique in that they provide real-world data on differences in proportion of detection of dimethyl urinary metabolites of adult workers. Moreover, by documenting differences in proportions of detection of urinary metabolites among children of farm-workers, our analyses begin to answer the question of whether or not pesticide residues are being brought into homes where children may be exposed. Because it is widely believed that pesticide residues accumulate in home environments, that they degrade more slowly than pesticides in fields, and that children have unique susceptibilities to and frequencies of exposure (given their propensity for hand-to-mouth behaviors and their frequent contact with floors), such a question merits investigation. Moreover, the relationship between workers’ job task and pesticide residues in collected house dust and vehicle dust samples provide compelling evidence that the take-home pathway is an important source of exposure.
Krieger and Zhang argue that urinary OP metabolite levels of children are more likely linked to dietary exposure than to environmental sources. The findings of Curl et al. (2002)—showing a significant correlation between adult and child urinary metabolite levels and showing lower median total dimethyl urinary metabolite concentrations among children adhering to an organic diet compared to children consuming conventional diets—support the claim that dietary sources contribute to children’s pesticide exposure (Curl et al. 2003). However, there is a growing body of evidence that supports the claim that environmental sources contribute to children’s pesticide exposure. Data from Curl et al. (2002) might also support the take-home pathway because it argues that children are affected by the residues brought home by their parents. McCauley et al. (2001) showed that home pesticide residues in dust are associated with home practices such as changing out of work clothing within 2 hr of returning home. Further research by McCauley et al. (2001)—showing that greater numbers of agricultural workers who live in a house and in close proximity to treated fields is associated with elevated residues of pesticides in house dust—offers additional support for the nondietary pathway of exposure. Our analyses (Coronado et al. 2004) show that the proportion of detectable pesticides residues in home and vehicle dust are greater for workers who thin plants than for workers who do not perform this task (home dust p-value = 0.003; vehicle dust p-value = 0.001). Although in our analyses we did not assess the associations between dust levels and urinary metabolite concentrations, our results do suggest that contamination of the home environment varies by occupational characteristics (Coronado et al. 2004).
In the next 5 years we will explore the take-home pathways as well as other pathways of pesticide exposure among children, including the dietary pathway. It is our hope that continued research will help clarify the important pathways involved in children’s exposure to pesticides. We believe that the most meaningful and relevant scientific discoveries result from hypotheses that are controversial. We look forward to further research and discovery on this topic.
==== Refs
References
Coronado GD Thompson B Strong L Griffith WC Islas I 2004 Agricultural task and exposure to organophosphate pesticides among farmworkers Environ Health Perspect 112 142 147 14754567
Curl CL Fenske RA Kissel JC Shirai JH Moate TF Griffith W 2002 Evaluation of take-home organophosphorus pesticide exposure among agricultural workers and their children Environ Health Perspect 110 A787 792 12460819
Curl CL Fenske RA Elgethun K 2003 Organophosphate pesticide exposure of urban and suburban preschool children with organic and conventional diets Environ Health Perspect 111 377 382 12611667
Knaak JB Al Bayati MA Rabbe OG Blancato JN 1996. Use of a multiple pathway and multiroute PBPK model for predicting organophosphorus pesticide toxicity. In: Biomarkers for Agrochemicals and Toxic Substances: Applications and Risk Assessment (Blancato JN, Brown RN, Dary CC, Saleh MA, eds). ACS Symposium Series 643. Washington, DC:American Chemical Society, 206–228.
McCauley LA Lasarev MR Higgins G Rothlein J Muniz J Ebbert C 2001 Work characteristics and pesticide exposures among migrant agricultural families: a community-based research approach Environ Health Perspect 109 533 538 11401767
McCauley LA Micheals S Rothlein J Muniz J Lasarev M Ebbert C 2003 Pesticide exposure and self reported home hygiene: practices in agricultural families Am Assoc Occup Health Nurs J 51 113 119
Van Hemmen JJ 1992 Agricultural pesticide exposure data bases for risk assessment Rev Environ Contam Toxicol 126 1 85 1598424
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0072715345358PerspectivesCorrespondencePast and Future Considerations for Heavy-Duty Diesel Engine Emissions Schaeffer Allen R. Diesel Technology Forum, Frederick, Maryland, E-mail:
[email protected] author declares he has a competing financial interest because he is the executive director of the Diesel Technology Forum, a not-for-profit educational organization representing the interests of the diesel industry.
9 2004 112 13 A727 A728 Publication 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 her Commentary in the June issue of EHP, “Diesel Exhaust: A Moving Target,” Janet Arey (2004) makes a strong point for standardizing diesel exhaust reference material for future research due to the changes in diesel technology and resulting emissions.
Arey (2004) appropriately recognized that diesel exhaust particulates are a small category of emissions in what is truly a complex mixture of ambient particles. According to the U.S. Environmental Protection Agency’s (EPA’s) most recent emissions inventory (U.S. EPA 2003), emissions from all diesel sources (on-road light and heavy-duty, off-road, marine and rail) in 2001 accounted for 4.37% of the nation’s fine particle inventory.
Recognizing the shift toward dramatically lower emissions and potential changes in the composition of those emissions, diesel engine manufacturers have initiated a unique broad-ranging stakeholder project known as the Advanced Collaborative Emissions Study, or ACES (French 2003). The objective of this collaborative government, academic, and industry research program is to develop the necessary data to assess and characterize in a timely manner (i.e., in the 2007–2008 timeframe) the emissions and any potential health effects from real-world exposures to exhaust from advanced prototype 2007–2010 heavy-duty engines, after-treatment systems, and reformulated fuels. This effort includes 794 emissions characterizations, chronic animal exposure studies, and short-term studies on allergic responses, and it is expected to publish findings in 2009–2010 (Warren 2004).
In further noting Arey’s appeals on the “need to understand the atmospheric chemistry of diesel and other vehicle exhaust” (Arey 2004), alternative fuels should also be evaluated. Now, more than ever, this is of particular significance as the use of alternative-fueled vehicles has increased, but the understanding and research of these emissions in the atmosphere have not kept pace.
A number of recently published studies have assessed the emissions from alternative-fueled heavy-duty vehicles. In a study of school buses running on diesel and compressed natural gas (CNG), low-emitting clean diesel technology had the lowest level of both U.S. EPA regulated emissions and toxic air compounds as defined by the California Air Resources Board (Ullman et al. 2003).
Similarly, the California Air Resources Board conducted a small-scale research project comparing transit buses using CNG to buses using advanced clean diesel technology (cleaner diesel fuel and particulate traps) (Ayala et al. 2002). This limited study found that the clean diesel bus had fewer emissions of toxic compounds than the CNG bus, and that both types of buses (CNG and those using filters and cleaner fuel) were superior to conventional diesel fuel and engines (Holmean and Ayala 2002). Even though the CNG bus was not equipped with an oxidation catalyst, the higher emissions of air toxics (1,3-butadiene, formaldehyde, etc.) could be expected by similar technology configurations currently in use around the country. For some areas that have aggressively promoted the use of so-called clean alternative fuels such as CNG without a complete understanding of their emissions profiles, these findings call for an additional consideration by Arey and other atmospheric chemists.
As the subtitle on the cover of the June issue of EHP appropriately suggests, diesel technology is a moving target—moving rapidly toward very low emissions across the board in all engine types and categories, with clearly defined pathways and time frames. For highway vehicles, 2004 model heavy-duty diesel engines have only one-eighth the level of emissions of nitrogen oxides and particulate matter compared to those built a dozen years ago, with 90% in additional reductions of particulate matter on track beginning in 2007 (U.S. EPA 2001). The U.S. EPA recently issued final rules for the fourth round of new lower emissions standards for off-road machines and equipment in the farming, construction, and industrial sectors, along with proposed rules for cleaner fuel requirements for marine vessels and locomotives (U.S. EPA 2004). Taking effect beginning in 2008 and at full implementation in 2014, these standards will converge at virtually the same low levels as highway engines.
Finally, an additional consideration, which was not identified by Arey (2004) but is significant, is the future impacts of applying new reformulated lower-sulfur diesel fuels and emissions filters on existing engines and equipment of various ages and types, an effort increasingly under way at the state and federal levels. Given this level of rapid change, establishing standardized reference materials will be particularly challenging.
==== Refs
References
Arey J 2004 A tale of two diesels Environ Health Perspect 112 812 813 15175165
Ayala A Kato N Okamoto R Gebel M Riegel P Holmën B 2002. ARB’s Study of Emissions from Diesel and CNG Heavy-duty Transit Buses. Available: http://www.arb.ca.gov/research/cng-diesel/deer2002-arb.pdf [accessed 11 June 2004].
French T 2003. Advanced Collaborative Emissions Study “ACES.” Available: http://www.healtheffects.org/ACES/French.pdf [accessed 26 July 2004].
Holmëan B Ayala A 2002 Ultrafine PM emissions from natural gas, oxidation-catalyst diesel, and particle-trap diesel heavy-duty transit buses Environ Sci Technol 36 5041 5050 12523418
Ullman T Smith L Anthony J Slowdowske W Trestall B Bunn W 2003. Comparison of Exhaust Emissions, Including Toxic Air Contaminants, from School Buses in Compressed Natural Gas, Low Emitting Diesel, and Conventional Diesel Engine Configurations. SAE 003-01-1381. Warrendale, PA:Society of Automotive Engineers.
U.S. EPA 2001 Control of Air Pollution from New Motor Vehicles: Heavy-Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements; Final Rule Fed Reg 66 5001 5050
U.S. EPA 2003. National Emissions Inventory (NEI) Air Pollutant Emission Trends. Current Emission Trend Summaries 1970–2001. Washington, DC:U.S. Environmental Protection Agency. Available: http://www.epa.gov/ttn/chief/trends/ [accessed 11 June 2004].
U.S. EPA 2004 Control of Emissions of Air Pollution from Nonroad Diesel Engines and Fuel; Final Rule Fed Reg 69 38958 39273
Warren J 2004. Update on the ACES Diesel Assessment Program. Available: http://www.healtheffects.org/Slides/AnnConf2004/Warren.pdf [accessed 11 June 2004].
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Environ Health Perspect. 2004 Sep; 112(13):A727-A728
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0073415345362EnvironewsForumHazardous Waste: Electronics, Lead, and Landfills Brown Valerie J. 9 2004 112 13 A734 A734 Publication 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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Ironically, some of our most advanced technologies, when discarded, may represent a rapidly expanding and sometimes unregulated exposure to a toxicant that plagued even the ancient Romans: lead. Almost all electronic devices contain lead, and such devices are proliferating—and becoming obsolete—at breathtaking speed. A University of Florida environmental engineer is researching the potential environmental fate of the lead found in electronics sent to landfills. In a report sponsored by the U.S. Environmental Protection Agency (EPA) and issued 15 July 2004, Timothy G. Townsend described his study of 12 different types of electronic items and his finding that the items leached lead at concentrations exceeding the EPA threshold for categorizing a waste as hazardous.
Townsend’s goal is to help landfill regulators and managers decide how to allocate scarce resources. He explains, “Maybe they have to choose what type of waste to recycle—tires or electronics?” By discovering whether electronics leach toxic chemicals, he says, “we might help a community decide.” He focused on testing for lead because it happens to extract well under the test procedure he used—which is modeled on landfill conditions—and thus may be likely to leach from a landfill.
Townsend’s report, RCRA Toxicity Characterization of Computer CPUs and Other Discarded Electronic Devices, expanded on his earlier research on cathode ray tubes (CRTs) used in computer monitors and televisions. CRTs contain an average of about four pounds of lead. There are smaller quantities in the solder used in other electronic devices.
Townsend performed an EPA test known as the toxicity characteristic leaching procedure (TCLP) on a variety of electronic items including computer CPUs (central processing units), televisions, videocassette recorders, printers, cellular phones, remote controls, computer mice and keyboards, and smoke alarms. The TCLP test determines the mobility of analytes present in waste. Following the protocol, the devices were ground up, mixed with an acetic acid–based simulated leachate fluid, and rotated in a drum container for 18 hours, after which the leachate was tested for metal concentrations. In the TCLP, lead concentrations above 5 milligrams per liter are considered hazardous. All the devices Townsend tested leached lead over this threshold under some conditions.
But is the lead that is actually in landfills a health threat? “It has never been shown that lead is actually leaching out of landfills,” says Fern Abrams, director of environmental policy at IPC–Association Connecting Electronics Industries, an industry group based in Northbrook, Illinois. And although lead is known to be present in landfills, some of it may come from other constituents. “Electronics in general are one percent of the waste that goes into a landfill,” says Jan Whitworth, a policy analyst with the Oregon Department of Environmental Quality. So if lead were to be found in leachate, it would be very hard to say for sure whether it had come from electronics.
Even so, the European Union has banned lead solder in certain electronic devices beginning in 2006, due to landfill concerns. California already bans disposal of CRTs and televisions in household waste landfills. Oladele Ogunseitan, an associate professor of social ecology at the University of California, Irvine, who is evaluating the phaseout of lead solder, thinks it makes sense to allow manufacturers to use hazardous materials when alternatives are not available, but to require recycling. Today, many computer manufacturers will recycle discarded computers, but often will charge a fee.
Others believe hazardous substances must be removed from products altogether. Mamta Khanna, pollution prevention program manager at the nonprofit activist Center for Environmental Health in Oakland, California, would like electronics manufacturers to take cradle-to-grave responsibility for their products. “Once they have to bear the burden of disposal, they will use less hazardous materials,” says Khanna. “Why wait for years of study to determine when these toxic materials will start leaching and poisoning us, when electronics makers can start using safer materials today?” Khanna also points out that electronics waste is associated with other potentially toxic chemicals, including mercury, chromium, and brominated flame retardants.
To simulate landfill conditions more accurately than can be done in a lab with the TCLP, Townsend is now conducting an experiment in which he has buried garbage and electronics waste. Simulated rainfall is added periodically, with leachate forming as the water percolates through the waste. Results will be available in about two years. Next year the EPA expects to issue a rule limiting how CRTs can be disposed of nationwide, according to agency environmental protection specialist Marilyn Goode.
Obsolete and overflowing. Certain electronic items have become practically disposable, and are tossed into landfills as soon as the newest version arrives. Once there, however, are they leaching lead at hazardous rates?
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a00737EnvironewsForumEHPnet: Environmental Technology Opportunities Portal Dooley Erin E. 9 2004 112 13 A737 A737 Publication 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 2003, Congress mandated that the U.S. Environmental Protection Agency (EPA) set up a centralized office for facilitating public–private partnerships established to commercialize cost-effective environment-related technologies. As part of this effort, the EPA has created the Environmental Technology Opportunities Portal (ETOP), located at http://www.epa.gov/etop/, where technology developers can access the numerous programs—financial and otherwise—that the EPA offers them. The site is designed to help developers understand EPA programs on offer so that they can better take advantage of the money and other resources available through these programs.
The site has three primary sections. The largest is the For Technology Developers section. From this section, visitors can go to subsections to learn more about getting financial support, finding ways to demonstrate and verify their technologies, marketing their products, disseminating information, building partnerships, and advocating for their innovations.
The Financial Support subsection of this page includes information not just about EPA sources, but also about monies available from other federal agencies and the private sector. The Demonstration/Verification subsection has links to various programs designed specifically for field-testing and otherwise demonstrating new technologies in certain areas, such as the Superfund Innovative Technology Evaluation Program.
The Marketing subsection provides links to the VENDINFO database of pollution prevention equipment, products, and services, as well as to marketing/labeling programs such as Energy Star. Finally, the Information, Partnership, and Advocacy Programs subsection includes links to resources such as the EPA’s Technology Innovation Program, an information and advocacy group that promotes the use of new technologies in remediation of a variety of polluted sites. This program works with other federal agencies, states, engineering firms, responsible parties, investors, and developers to provide technology and market information and to facilitate the implementation of these innovations.
Back at the homepage, the Technology Users section of the ETOP site connects those searching for environmental technologies to appropriate solutions, sorted by type: air, water, solid and hazardous waste, and pollution prevention. Included is information on EPA research and development activities. This section also provides the Thesaurus of Environmental Technology Terms, a compendium of relevant terminology, technologies, programs, and offices.
The ETOP site offers a number of features on its homepage to help ensure that visitors can easily get the information they need. The Where You Live link leads to an interactive map that allows visitors to pull up information by EPA region or state (the map currently contains information just for Region 1). Visitors can also subscribe to two mailing lists: the ETOP mailing list provides information about funding opportunities as they are announced and updates to the ETOP site, while the EnvirotechNews mailing list features a calendar of upcoming events, information on federal funding opportunities, and items on enforcement actions. Finally, ETOP News pulls together news items of interest to the environmental technology developer community, such as the recent awarding of $900,000 to four companies to develop environmentally relevant nanotechnologies.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0073815345363EnvironewsNIEHS NewsBeyond the Bench: Fish Tales to Ensure Health Thigpen Kimberly G. Petering David 9 2004 112 13 A738 A738 Publication 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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Fishermen are known for telling tales of their catches that tend toward exaggeration. A new kind of fish tale, however, doesn’t stretch the truth when making a point to the Hmong community in Milwaukee, Wisconsin, about the hazards of eating fish contaminated with methylmercury and polychlorinated biphenyls. A video produced by the Community Outreach and Education Program at the NIEHS Marine and Freshwater Biomedical Sciences Center at the University of Wisconsin–Milwaukee communicates in a simple, understandable, and culturally sensitive way the risks of eating contaminated fish and teaches methods of catching and preparing fish that can reduce these risks.
The Hmong, refugees from Southeast Asia, are avid anglers and traditionally fish to support large families with an average of 7–8 children. But these fishers often have little understanding of the pollution and contamination of the waterways of Wisconsin and the fish that populate them. The goal of the center program, developed in partnership with the Hmong/American Friendship Association and the Sixteenth Street Community Health Center, is to communicate to the inner-city Hmong population the hazards associated with eating contaminated fish in a way that results in active consideration of the issues within the context of the group’s fishing practices.
The centerpiece of the outreach program is a bilingual Hmong–English video titled Nyob Paug Hauv Qab Thu (Below the Surface). The video presents scientifically sound information on safe fish consumption. It also acknowledges the Hmong cultural tradition of fishing while showing which fish are safest to catch and ways to make fish safer to eat prior to cooking, including how to remove fins, fat, and other parts of the fish where toxicants accumulate. The video is packaged with a laminated card that provides shorthand tips on safer catching and preparation, and a kitchen magnet with similar information.
To date, approximately 750 video/card/magnet packets have been distributed by community workers through local stores, doctors’ offices, and Hmong festivals where the video has been showcased. Follow-up to assess the impact of the videos is currently under way.
John Dellinger, a center researcher who studies the effects of fish consumption in Native American populations and who is featured in the film, has also shown the video or supplemental materials to audiences of Intertribal Council and InterTribal Fisheries Assessment Program officials in the Upper Peninsula of Michigan, as well as to Tahitian Ministry of Health officials. Officials of the Michigan Ojibwa and the government of Tahiti have asked that the film be adapted for their communities. Dellinger plans to work on productions for both of these groups in 2005.
In an extension of the community outreach program, the center has developed a life sciences classroom module for middle-school students that explores the behavioral effects of mercury and lead contamination, both of which affect inner-city Hmong populations. The module provides a hands-on, inquiry-based experiment about a complex organism’s behavioral integration with its environment, and what happens when that environment becomes contaminated.
In the module, students observe fathead minnows in the classroom to learn and characterize their normal reproductive behavior. Students then watch a video produced by the center that shows the behavior of mercury- and lead-exposed fish. Based on their understanding of normal behavior, students analyze the differences that exposure to the toxic metals makes in the fish. The differences are dramatic because although the behavior is affected, the fish show no outward physiological signs of toxicity. Teachers can then draw an analogy to human exposure to mercury through fish consumption, and to lead through paint chip ingestion, and the potential resulting effects on human behavior.
Teachers were trained during a summer workshop to teach and evaluate the experiment. In the next two years, 11 teachers from Milwaukee public schools and from area suburban schools with the largest Hmong student populations will introduce this module, using the video as a cross-cultural tool to support it.
New tool for the tacklebox. Laminated cards with fish safety information are distributed to Hmong anglers.
Translating into health. A bilingual video educates Hmong fishers on health risks and safety measures.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a00739EnvironewsNIEHS NewsHeadliners: Breast Cancer: Regular Aspirin Use May Decrease Breast Cancer Risk Phelps Jerry 9 2004 112 13 A739 A739 Publication 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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Terry MB, Gammon MD, Zhang FF, Tawfik H, Teitelbaum SL, Britton JA, Subbaramaiah K, Dannenberg AJ, Neugut AI. 2004. Association of frequency and duration of aspirin use and hormone receptor status with breast cancer risk. JAMA 291:2433–2440.
Aspirin has been used as a nonprescription pain reliever for more than 100 years, with more than 80 million tablets currently consumed in the United States every day. However, it was not until the 1970s that the mechanism of action was discovered; aspirin was found to inhibit the production of proinflammatory prostaglandins. In the past 20 years, regular aspirin use has been shown to protect against heart disease, stroke, and colorectal cancer. Now NIEHS grantee Marilie Gammon of the University of North Carolina School of Public Health and colleagues report that regular aspirin use may also protect against breast cancer.
Research suggests that inhibition of prostaglandin synthesis may prevent cancer. Cyclooxygenase (COX) is involved in the synthesis of prostaglandins. Aspirin and other nonsteroidal anti-inflammatory drugs (NSAIDs) are known to block the active site of COX and, therefore, inhibit prostaglandin production. Because the final reaction in the synthesis of estrogen depends upon a cytochrome P450 enzyme that is stimulated by prostaglandin E2, inhibition of prostaglandin production will also decrease the production of estrogen. Given the importance of estrogen in the development of breast cancer, Gammon and colleagues undertook an epidemiologic study to determine whether there was any association between regular NSAID use and reduced risk of breast cancer.
The team conducted a population-based study of 1,442 women with breast cancer and 1,420 controls. The women were interviewed and asked to report their intake of aspirin, ibuprofen, and acetaminophen. Dose was not considered; instead, the team looked at duration and frequency of use. Regular use was defined as women who took aspirin at least 4 times per week for at least 3 months and initiated use at least 1 year prior to the reference age (age at diagnosis of breast cancer or corresponding age for controls). All exposure information was truncated to 12 months prior to the reference age.
Regular aspirin use was inversely associated with hormone-responsive breast tumors, with the strongest results for women who took 7 or more tablets per week. The results of ibuprofen use were generally weaker. There was no association with use of acetaminophen, which does not inhibit prostaglandin synthesis.
This study adds to the growing body of data that supports the regular use of aspirin as an effective chemopreventive agent for hormone-responsive breast cancer tumors. This effect most likely occurs through the inhibition of prostaglandin and subsequent inhibition of estrogen biosynthesis. However, the reduced risk must be confirmed before clinicians can make definite recommendations to women at risk for breast cancer. –Jerry Phelps
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0074015345364EnvironewsFocusNanotechnology: Looking As We Leap Hood Ernie 9 2004 112 13 A740 A749 Publication 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 1989, when IBM researchers whimsically demonstrated a scientific breakthrough by constructing a 35-atom depiction of the company’s logo, the ability to manipulate individual atoms has spawned a tidal wave of research and development at the nano (from the Greek word for “dwarf”) scale. Nanomaterials are defined as having at least one dimension of 100 nanometers or less—about the size of your average virus. Nanotechnology—the creation, manipulation, and application of materials at the nanoscale—involves the ability to engineer, control, and exploit the unique chemical, physical, and electrical properties that emerge from the infinitesimally tiny man-made particles.
Nanoparticles behave like neither solids, liquids, nor gases, and exist in the topsy-turvy world of quantum physics, which governs those denizens small enough to have escaped the laws of Newtonian physics. This allows them to perform their almost magical feats of conductivity, reactivity, and optical sensitivity, among others.
“That’s why nanomaterials are useful and interesting and so hot right now,” says Kristen Kulinowski, executive director for education and policy at the Rice University Center for Biological and Environmental Nanotechnology (CBEN). “Being in this quantum regime enables new properties to emerge that are not possible or not exhibited by those same chemicals when they’re much smaller or much larger. These include different colors, electronic properties, magnetic properties, mechanical properties—depending on the particle, any or all of these can be altered at the nanoscale. That’s the power of nanotech.”
Many observers not normally given to hyperbole are calling nanotechnology “the next Industrial Revolution.” The National Nanotechnology Initiative (NNI), the interagency consortium overseeing the federal government’s widespread and well-funded nanotechnology activities, has predicted the field will be worth $1 trillion to the U.S. economy alone by 2015—or sooner. Clearly, nanotechnology is poised to become a major factor in the world’s economy and part of our everyday lives in the near future. The science of the very small is going to be very big, very soon.
The Springboard
The first swells presaging the approaching nanotechnology tidal wave have already reached the shore. Engineered nanoparticles are already being produced, sold, and used commercially in products such as sporting goods, tires, and stain-resistant clothing. Engineered nanomaterials designed to provide nontoxic, noncorrosive, and nonflammable neutralization of chemical spills or chemical warfare agents are currently on the market. Even sunscreens have gone nano—some now contain nanoscale titanium dioxide or zinc oxide particles that, unlike their larger, opaque white incarnations, are transparent, while still blocking ultraviolet rays effectively. Fullerenes, which are used in commercial products from semiconductors to coatings for bowling balls, are being produced by the ton at a Mitsubishi plant in Japan.
Within a few years, experts say, these initial market forays will seem as quaint as eight-track tapes. According to Mihail Roco, senior advisor on nanotechnology to the National Science Foundation (NSF) and coordinator of the NNI, nanotechnology will have four generations, or phases of development. We’re already in the first, consisting of “passive” nanostructures—simple particles designed to perform one task. Roco predicts the second phase will start in 2005, with the appearance of commercial prototypes of “active” nanostructures such as special actuators, drug delivery devices, and new types of transistors and sensors.
As evidence of progress toward this second phase, a team of Northwestern University chemists led by Chad Mirkin recently announced that they have discovered ways to precisely construct nanoscale building blocks that assemble into flat or curved structures. The ability to create unusual nanostructures such as bundles, sheets, and tubes holds promise for new and powerful drug delivery systems, electronic circuits, catalysts, and light-harvesting materials.
By 2010, Roco says, the third generation will arrive, featuring nanosystems with thousands of interacting components. And a few years after that, the first “molecular” nanodevices will appear, devices that will be composed of systems within systems operating much like a cell.
As manufacturing methods are perfected and scaled up, nanotechnology is expected to soon pervade, and often revolutionize, virtually every sector of industrial activity, from electronics to warfare, from medicine to agriculture, from the energy we use to drive our cars and light our homes to the water we drink and the food we eat. Nanotechnology is today’s version of the space race, and countries around the globe are enthusiastically pouring billions of dollars into support of research, development, and commercialization.
In terms of the environment and human health, nanotechnology presents the same conundrum as past major technological advances: there may be enormous benefits in terms of benign applications, but there are inherent risks as well. What will happen when nanomaterials and nanoparticles get into our soil, water, and air, as they most assuredly will, whether deliberately or accidentally? What will happen when they inevitably get into our bodies, whether through environmental exposures or targeted applications? The answers to those vital questions remain largely unanswered, although some early findings are less than reassuring, as evidenced by a recent study implicating fullerenes in oxidative stress in the brains of large-mouth bass [see “Fullerenes and Fish Brains: Nanomaterials Cause Oxidative Stress,” EHP 112:A568 (2004)].
Questions of another sort also need to be answered. Is anyone looking at these health and safety issues? And can enough solid, reliable risk assessment knowledge be gained in time to ensure that the public will—or even that it should—be comfortable with the proliferation of the technology? Will the paradigm shift smoothly into the nano world, or will issues of safety and trust surround nanotechnology with controversy that may hinder its potential, as has happened in the past with such achievements as genetically modified organisms (GMOs)?
Kulinowski expresses the nearly universal sentiments of the field’s advocates: “We think nanotechnology has enormous potential to benefit society in a whole variety of sectors and applications, from the next cancer treatment, to environmental applications, to energy—you name it. So we don’t want to see that potential limited or eliminated by real or perceived risk factors associated with engineered nanomaterials.” To ensure that nanotechnology flourishes responsibly and with strong public support, Kulinowski says, advocates believe it’s very important to gather risk data so that questions can be answered and problems addressed early on in the trajectory of the technology development.
Sean Murdock, executive director of the NanoBusiness Alliance, a nanotechnology trade association, thinks it is possible to avoid past mistakes of rolling out a new technology too far ahead of health and safety information. “The risks are there, they’re real, but they’re manageable,” he says. “And on balance, with the right processes in place, we’re going to be able to deal with all of those risks, we’re going to mitigate those risks, and we’re going to realize the upside of the potential.”
Nanomedicine: A Tiny Dose for Health
One of the most promising applications of nanotechnology, known as nanomedicine, involves the development of nanoscale tools and machines designed to monitor health, deliver drugs, cure diseases, and repair damaged tissues, all within the molecular factories of living cells and organelles. The NIH Roadmap for Medical Research—the agency’s master plan to accelerate the pace of discovery and speed the application of new knowledge to biomedical prevention strategies, diagnostics, and treatments—contains a significant nanomedicine initiative that will begin with the establishment of 3–4 Nanomedicine Development Centers. These multidisciplinary facilities will serve as the intellectual and technological centerpiece of the endeavor. Funding for the centers of $6 million per year will begin in September 2005.
Today, the initiative’s long-term goals sound like scenarios straight out of Isaac Asimov’s Fantastic Voyage: nanobots that can search out and destroy cancer cells before they can form tumors . . . nanomachines that can remove and replace broken parts of cells . . . molecule-sized implanted pumps that can deliver precisely targeted doses of drugs when and where they’re needed . . . even “smart” nanosensors that can detect pathology or perturbation in any or every cell in the body, and instantly communicate that information to doctors. Science fiction may soon become science fact—these and many other nanomedicine innovations are currently in development, and the NIH predicts that its nanomedicine initiative will start yielding medical benefits in as soon as 10 years. Roco also foresees that fully half of all drug discovery and delivery technology will be based on nanotechnology by 2015.
Experts predict that nanosensors will also provide significantly improved tools to determine both internal and external exposures in real time, assess risk, link exposure to disease etiology, characterize gene–environment interactions, and ultimately improve public health. The NIEHS, through its extramural grants and Superfund Basic Research Program, is funding the research and development behind many of these expected innovations.
For example, with a Small Business Innovation Research grant, the institute is supporting Platypus Technologies of Madison, Wisconsin, in its work on smart nanosensors designed to act as personal dosimeters for real-time and cumulative exposure to toxic compounds. Combining scaled-down photo optics and nanomaterials to form a uniquely sensitive platform for exposure detection, the initial prototype device is intended to detect even very low exposures to organophosphate pesticides. The sensor, expected to be available commercially within two years, is small, lightweight, passive, inexpensive, and easily operated—one immediate application will be monitoring the chemical environments of children.
Platypus CEO Barbara Israel elaborates: “Our product is ‘tunable’ for different anticipated concentration ranges and monitoring time periods. Therefore, it can be applied to monitor workers for occupational exposure to toxic compounds during manufacturing, as well as to the monitoring of field exposure of agricultural workers.” The company is also developing sensors that will immediately respond to the ambient presence of very low concentrations of other toxic agents, and expects that units will be networked by the thousands in security systems at facilities such as airports and train stations, as well as having industrial applications.
“This technology is going to revolutionize how we do business”—the business of environmental health science, that is—according to William Suk, director of the Center for Risk and Integrated Sciences within the NIEHS Division of Extramural Research and Training. Suk oversees many of the institute’s extramural grants involving nanotechnology. “One of the real potentials of this technology is to truly be able to understand gene–environment interactions, to be able to take the ‘omics’ revolution and scale it down in such a way that you have a comprehensive global approach to understanding how things fit together,” he says. “We’re really looking at the use of these technologies in systems biology, to understand how systems communicate—how cells communicate amongst themselves, and within themselves, and with other cell systems within our body. It’s all connected.”
A wide variety of extraordinarily sophisticated nanobiosensors fitting Suk’s vision are well along in development among institute grantees. For example, neurotoxicologist Martin Philbert at the University of Michigan is perfecting a sensor that measures and identifies chemical perturbations within the mitochondria of neurons, and may eventually allow intervention or prevention of such cellular disturbances. Roger Tsien, a professor of pharmacology, biochemistry, and chemistry at the University of California, San Diego, is developing toxicity sensors that can indicate exposures and the perturbations they cause at the genomic level in real time. Kenneth Turtle-taub, a scientist at Lawrence Livermore National Laboratory, uses an accelerator mass spectrometer to look at nanostructures for biomarkers of exposure to carcinogenic chemicals, characterizing perturbations at the atomic level. According to Suk, these and other nanodevices will be making major contributions to the field of environmental health within the next five years. When nanotechnology achieves its full impact, he says, toxicogenomics will evolve beyond its infancy and begin to fulfill its promise of significant improvements in public health.
Small Improvements in A Big World
Although research and development of environmental applications is still a relatively narrow area of nanotechnology work, it is growing rapidly, and nanomaterials promise just as dazzling an array of benefits here as they do in other fields. Nanotechnology will be applied to both ends of the environmental spectrum, to clean up existing pollution and to decrease or prevent its generation. It is also expected to contribute to significant leaps forward in the near future in environmental monitoring and environmental health science.
The Science To Achieve Results (STAR) program of the U.S. Environmental Protection Agency (EPA), administered by the agency’s National Center for Environmental Research, was an early investor in and promoter of environmental applications of nanotechnology. Beginning in 2001, the agency devoted a small discretionary portion of its grant-making budget to nanotechnology. “We decided to do applications with respect to the environment first,” says Barbara Karn, who oversees the nanotechnology aspect of the program. “We wanted to make a case for the new technology being useful for the legacy issues of EPA.”
Contaminated soil and groundwater are among the most prominent of those legacy issues, and there has been considerable progress in nanotechnology-based remediation methods. Environmental engineer Weixian Zhang of Lehigh University, a STAR grantee who also receives funding from the NSF, has been working since 1996 to develop a remediation method using nanoscale metallic particles, particularly iron nanoparticles, which he has found to be powerful reductants. “If any contaminant can be degraded or transformed by reduction,” he says, “you can use the iron nanoparticles.” He has been field-testing the method since 2000, both in pilot studies and at several industrial sites contaminated with such toxicants as polychlorinated biphenyls, DDT, and dioxin, and the results have been encouraging.
Zhang’s nanoremediation offers several potential advantages over existing methods. The implementation is very simple—the nanoparticles are suspended in a slurry and basically pumped directly into the heart of a contaminated site. By comparison, current methods often involve digging up the soil and treating it.“You can inject [the nanoparticles] in some difficult situations, for example, under a runway, under a building, or other sites where typical engineering methods may not be feasible,” says Zhang.
Nanomaterials have a large proportion of surface atoms, and the surface of any material is where reactions happen. Because of nanoparticles’ huge surface area and thus very high surface activity, workers can potentially use much less material. The amount of surface area also allows a fast reaction with less time for intermediates to form—a boon in biodegradation, where the intermediate products are sometimes more toxic than the parent compound. Finally, Zhang’s method is also much faster. “Because of the higher activity, it takes much less time to achieve remediation goals than conventional technology, which, using biological processes, can take years,” he says. With the iron nanoparticles, in most cases the team saw contaminants neutralized into benign compounds in a few days.
Zhang is currently focusing on scaling up production of the iron nanoparticles to make them more cost-competitive, and plans to establish a business based upon his techniques. His is just one of dozens of nanoremediation methods being developed, but is probably the closest to large-scale deployment—he expects that within a year or two, there will be tens to hundreds of projects using the metallic nanoparticle technology. And this type of “passive” application is only the beginning.
“In the future,” says Zhang, “we’ll have more sophisticated devices that can function not only as a treatment device, but also as a sensor with detection functions and communication capability that you can put into the ground and get feedback on different environmental parameters.” That type of device will give remediators the ability to determine when a treatment has been adequately completed, currently a problematic determination to make. Similar nanosensors that will allow real-time in situ detection and analysis of pollutants are being developed for environmental monitoring purposes.
The environmental benefits portended by nanotechnology go farther still. Improvements in membrane technology afforded by nanomaterials, for example, will allow greatly enhanced water filtration, desalination, and treatment of wastewater through finer and “smarter” selective filtration. The technology that is expected to be proliferated is also anticipated to be very simple and very inexpensive. These developments are expected to eventually go a long way toward ameliorating the shortages of clean, plentiful, low-cost drinking water that plague many areas of the world.
Murdock says nanotechnology is also likely to help prevent a great deal of pollution in the future by affording the opportunity to “reinvent the energy infrastructure that powers the economy, which ultimately has been driving many of the issues that environmentalists . . . have been worrying about over the past few decades.” Nanoscale materials and devices could result in game-changing breakthroughs in energy production through advances in hydrogen and solar energy, and could even beget vast improvements in the efficiency and cleanliness of carbon-based energy. There is serious talk, for example, that nanotechnology could make it possible to sustainably expand the use of coal in energy production, using a nanocatalyst that turns coal directly into cleaner-burning diesel fuel and gasoline.
On the other hand, nano-based lighting is already a reality—traffic lights across the country now use tiny light-emitting diode displays that remain in service longer and use less energy than bulbs. The NNI has projected that widespread proliferation of the technology for home and office lighting could cut U.S. energy consumption by as much as 10%, dropping carbon emissions by up to 200 million tons annually.
With their extremely high reactivity, nanomaterials may also enable “green” chemistry and “exact” manufacturing, in which chemicals and other products are manufactured from the bottom up, atom by atom. This development would allow the creation of less-toxic products while reducing or eliminating both hazardous waste and the need for large quantities of toxic raw materials—so-called source reduction. The green chemistry concept applies to the production of nanoparticles themselves. University of Oregon chemist James Hutchinson recently patented a more benign (and faster and cheaper) method for producing gold nanoparticles, which are particularly important in the semiconductor industry.
Karn is excited by this and similar developments: “We really have such an opportunity here with this new technology, to make it without waste, to make the particles in an environmentally friendly way, so that we don’t have to worry about the emissions [and] we don’t have to worry about the cleanup afterwards.”
A Yellow Light
The same properties that confer such incredible utility to engineered nanoparticles are those that raise concerns about the nature of their interactions with biological systems: their size, their shapes, their high reactivity, how they are coated, and other unique characteristics could prove to be harmful in some physiologic circumstances. Several recent studies have appeared in the literature showing that some nanomaterials are not inherently benign. Some can travel readily through the body, deposit in organ systems, and penetrate individual cells, and could trigger inflammatory responses similar to those seen with ambient nanoparticles—better known in environmental science as ultrafine particles—which are known to often be far more toxic than their larger counterparts. The primary difference between ambient and engineered nanoparticles is that the former have widely varying shapes, sizes, and compositions, whereas the latter are single, uniform compounds.
University of Rochester environmental toxicologist Günter Oberdörster has shown in rodent studies, published in June 2004 in Inhalation Toxicology, that inhaled nanoparticles accumulate in the nasal passages, lungs, and brains of rats. And in the January 2004 issue of Toxicological Sciences, National Aeronautics and Space Administration scientist Chiu-Wing Lam recently reported that a suspension of carbon nanotubes (one of the most widely used and researched engineered nanoparticles) placed directly into mouse lungs caused granulomas, unusual lesions that can interfere with oxygen absorption. David Warheit, a DuPont researcher, conducted a similar experiment in rats, reported in the same issue of Toxicological Sciences, and discovered immune cells gathering around clumps of nanotubes in the animals’ lungs. At the highest dose, 15% of the rats essentially suffocated due to the clumping of the nanotubes having blocked bronchial passages. Although Lam’s and Warheit’s studies did not reflect potential real-world exposures, their results were nonetheless troubling, showing at least that nanotubes are biologically active and possibly toxic.
A study published in the July 2004 issue of EHP documenting oxidative stress (a sign of inflammation) in the brains of largemouth bass exposed to aqueous fullerenes has received perhaps the most attention and raised the most warnings of any nanomaterial health implication experiment to date. Eva Oberdörster (Günter’s daughter), an environmental toxicologist at Southern Methodist University, describes herself as “shocked” at the amount of mainstream national press coverage the study has received. She is quick to stress that although some reports have described “brain damage” or even “severe brain damage” in the fish, she has actually characterized her findings as “significant damage in the brain, which is very different from brain damage.” After 48 hours’ exposure to fairly high doses of fullerenes, the fish probably had the same effect as a very bad headache, she says, but they did survive the exposure. As to the inflammation, Oberdörster says it could have been an appropriate response to a foreign stressor or a symptom of real physiologic damage. She plans to study this issue further in gene microarray experiments designed to more thoroughly characterize the inflammatory response involved, and to see whether the fish might actually metabolize and excrete the particles.
Oberdörster has described the findings as “a yellow light, not a red one,” and explains further that there are some indications from the inhalation and fish studies that there is a potential for nanoparticles to react with tissues and create inflammation. “So the next step then is to look at it in a broader spectrum before we bring all these products out into the market, to make sure that they are safe so that consumers are protected,” she says.
Kulinowski feels that the early studies raise more questions than answers, and she cautions against overinterpreting individual studies. She is optimistic, however, that with technological advances, the potential negative impacts of engineered nanoparticles can be minimized or eliminated altogether. “The good news I see is that with the control we have over engineered nanoparticles, we may be able to engineer them to confer the benefits, but not the risks, not the hazards.” Again, it’s all about the surface, she says: “If we can control surface properties of nanoparticles, we may be able to tune out the toxicity. . . . It’s like sliding a dimmer switch on a lamp—you can just tune it right down to pretty much beyond our capacity to measure it.”
Big Issues for Nanotechnology
Nanotechnology accomplishments could soon affect every person on the planet. But the opinion is virtually unanimous, among advocates and skeptics alike, that the full realization of nanotechnology’s potential benefits is threatened by ongoing concerns about the potentially negative effects nanomaterials could have on human health and the environment. Applications are being vigorously pursued. The question is, will knowledge of the implications keep pace?
In August 2002, the Action Group on Erosion, Technology, and Concentration (ETC Group), a Canadian environmental activist group that played a key role in the battle against acceptance of GMOs in the 1990s, called for a worldwide moratorium on research and commercialization of engineered nanomaterials until there are protocols in place to protect workers, including lab workers. They cited a dearth of research data about the potential negative implications of nanomaterials, and the lack of specific regulatory oversight or established best practices in the handling of nanoparticles in either the laboratory or the manufacturing setting.
Perhaps coincidentally, in the two years since the ETC Group’s moratorium call there has been a palpable upsurge in research and bureaucratic activity regarding those missing pieces of the puzzle. All of the stakeholders, casting a weather eye at the GMO experience, apparently agree that the bountiful benefits of nanotechnology cannot be harvested without full and transparent characterization of the risks they could pose to human health and the environment.
“We’re much better off over the long haul if we make sure that we address concerns and issues proactively,” says Murdock. “That doesn’t mean we should be hypersensitive and shy away from exploring new areas, because fundamentally we only make progress through exploration. But it needs to be balanced and tempered with a continuous examination of the implications.”
Roco, who has been instrumental in the NNI’s ongoing attention to both safety implications and the potential societal impacts of nanotechnology worldwide, agrees that the time for responsible risk assessment is now: “This is no longer something you do after the fact, after you do the other research, but has to be done from the beginning, to be an integral part of the research. You have to look at the whole cycle of activity, not only at the first phase when you create something.”
The CBEN has been investigating the environmental fate of nanoparticles since its inception in 2001 as one of six Nanoscale Science and Engineering Centers established by the NSF, and Kulinowski has noted the recent explosion in interest and funding in nanotech environmental health and safety research. “We have seen tremendous movement on this issue over the last year and a half, from the point where we almost felt like we were calling out into the darkness, to where people are now moving forward independently,” she says. “Most encouraging has been the federal government’s response. We’ve also seen a tremendous response from industry . . . that gives us hope that as we move toward commercialization of nanotechnology products, these questions will be addressed early on in the development, before or when products come to market.”
Critics and even some participants maintain that funding of implications research is still inadequate in proportion to the nearly $1 billion the government is currently investing in nanotechnology development. But efforts to better understand the implications of nanotechnology are clearly gaining momentum. Important new research initiatives are getting under way, and coordination and collaboration are increasing among the federal regulatory agencies and research organizations involved.
The NNI is the central locus, with a number of agencies participating. Representatives from several of those agencies, including the NIEHS, the EPA, and the National Institute for Occupational Safety and Health (NIOSH), have formed a working group on the environmental and health impacts of nanotechnology. The group meets monthly to share knowledge, coordinate activities, identify research gaps and goals, and address urgent issues such as regulation and nomenclature.
Two further major research initiatives designed to establish fundamental knowledge about the toxicologic properties of engineered nanomaterials are in their initial stages. Both will contribute significantly to the knowledge base and allow more rational risk assessment in the future.
The first of these major initiatives arose from the CBEN’s 2003 nomination of nanomaterials for study by the National Toxicology Program (NTP). The NTP, which is headquartered at the NIEHS, has embarked upon a research program involving safety studies of representative manufactured nanomaterials. “The aim of our program,” says Nigel Walker, lead scientist of the investigation, “is actually to help guide the nanomaterial industry in identifying the key parameters that lead to biocompatibility of nanomaterials, versus toxicity of nanomaterials, so that we can avoid having problems such as the genetically modified food situation, where the industry and the technology got ahead of the biocompatibility issues.”
The NTP program will focus initially on studies of single-walled carbon nanotubes, titanium dioxide, quantum dots (fluorescent semiconductor nanocrystals used in imaging equipment), and fullerenes. Because the most likely route of exposure to those nanomaterials as they are used today is through the skin, several studies will concentrate on dermal toxicity. Other exposure routes will be examined as well, however, all looking at general, acute, subchronic, and chronic levels of exposure.
One of the broad goals of the NTP initiative is to create models of nanomaterial chemical, physical, and pharmacokinetic properties that can be used to help evaluate new engineered nanostructures as they come along. According to John Bucher, deputy director of the NIEHS Environmental Toxicology Program, the purpose of this initiative is not to prevent or understand the toxicity of every material that can be manufactured under the “nano” rubric. Instead, he says, “What we’re trying to do is understand some of the fundamental properties of nanomaterials—how they move, what kind of toxicities they have, what kinds of organ systems are generally targeted, what the effects of surface coatings are. . . . We’re not trying to make the world safe from nanotechnology, nor do we believe that the world is necessarily at great risk from nanomaterials at this moment, or potentially even in the future. But the total absence of any information makes this an area that we just have to pursue.”
The NTP, in association with the University of Florida, is also planning a workshop for November 2004 designed to bring together scientists from the toxicology community, environmental engineers, and representatives of the pharmaceutical and chemical industries. The workshop will focus on questions about how best to assess exposure to nanomaterials and evaluate their toxicity and safety.
Walker thinks these efforts are timed perfectly. “If we’d tried to do this two or three years ago, we may actually have been targeting things that weren’t important,” he says. “You don’t want to be too early on the curve, but then you don’t want to be too late. This is about the right time . . . and we are being very open about how things are moving along, because the NTP is completely open, and all the data is ultimately the public’s.”
The second major initiative—research on occupational health risks associated with manufacturing and using nanomaterials—is being spearheaded by NIOSH. The institute recently organized a Nanotechnology Research Center to coordinate, track, and measure outcomes, and disseminate the output of nanotechnology-related activities throughout the institute.
NIOSH has also undertaken a five-year multidisciplinary initiative known as the NIOSH Nanotechnology and Health and Safety Research Program. As with the NTP’s efforts, the idea is to characterize risks early in the industry’s development, and the workplace is the most likely location of exposures at present. “There is some concern that these materials are of unknown effect, and there is interest in getting generalized industrial hygiene, generalized control measures, and best work practices involved early on,” says researcher Vincent Castranova, who is principal investigator for the program’s coordinating project as well as a separate study exploring particle surface area as a dose metric. “Normally, interest in these elements has come after proof of disease outcome. This is one case where the concerns are sufficient to cause the industry and the governmental agencies to try to get good work practices and prevention measures up front, before we know full health outcomes.”
Another NIOSH scientist, Andrew Maynard, is investigating methods of characterizing and monitoring airborne nanoparticles. “Part of my project,” says Maynard, “is developing and using the characterization techniques, so that we can understand very precisely the chemical and physical nature of the particles, and also the concentration of the particles being used in these experiments. Also, we will look at how we can effectively monitor exposures in the workplace, so that we can have simple, robust, inexpensive techniques that people can use in the workplace.”
Although dermal exposure to nanomaterials is occurring as a result of their use in sunscreens and some cosmetics, inhalation is suspected to be the most likely route of exposure in the workplace, so other projects in the program will focus on pulmonary toxicity questions, particularly with respect to carbon nanotubes. Those questions will be tricky, again due to the unique attributes of nanomaterials—they are technically ultrafine particles, but can they be judged the same way?
“This is one of the big areas of debate at the moment,” says Maynard. “To what extent do you treat engineered nanoparticles as just another ultrafine particle? It’s fair to say that most of our concerns over nanomaterials are being driven by our experiences with ultrafine particles, which are substantially more inflammatory and toxic than fine particles.”
“Another issue that is unresolved is that these nanoparticles tend to aggregate, and the aggregates often tend not to be under a hundred nanometers in diameter,” adds Castranova. “So do they then behave as fine particles rather than ultrafine? That depends on whether they disaggregate either in handling or once they’re in the lung, which is unknown. Their ability to enter the lungs, to cross the air–blood barrier, or to cause inflammation would be affected by [disaggregating].” NIOSH is helping to organize the First International Symposium on Occupational Health Implications of Nanomaterials, which will convene in the United Kingdom in October to discuss these issues.
The Ripple Effect
The NTP and NIOSH initiatives are the major new programs in the works, but a great deal of activity is continuing or beginning in other circles as well. The NNI is expanding its support of implications research, and recently held a landmark international meeting that brought together the leaders of nanotechnology programs in 25 countries and the European Union. The International Dialog on Responsible Research and Development of Nanotechnology took place 17–18 June 2004 in Arlington, Virginia, and was designed to help develop a global vision of how the technology can be fostered with the appropriate attention to and respect for concerns about the societal issues and environmental, health, and safety implications.
Roco, who called the meeting “a historic event,” proposed the establishment of an ongoing international organization dedicated to responsible nanotechnology development. Participants agreed to form a “preparatory group” charged with exploring possible actions, mechanisms, timing, institutional frameworks, and principles involved in constructing a permanent institution designed to ensure international dialogue, cooperation, and coordination in nanotechnology research and development.
ETC Group executive director Pat Mooney reacted favorably to the gathering as well: “That’s the first time we’ve had an international meeting like this, and I think it’s a very encouraging sign.”
Encouraging signs of commitment and progress were also evident at two other landmark events held earlier this year. In March, the NIEHS held a workshop called Technologies for Improved Risk Stratification and Disease Prevention that brought together a panel of experts to formulate specific recommendations on how the institute should incorporate nanotechnology into its research agenda in the coming years. Participants embraced the idea that the NIEHS should lead the way in developing a single small-scale platform to detect individual chemical exposures, eliminate toxicants from the system, and intervene to reverse any harmful effects that might have been initiated by the exposure. Then, in May, a one-day public discussion was held by the Institute of Medicine’s Roundtable on Environmental Health Sciences, Research, and Medicine, in which experts and members of the public explored the issues raised by nanotechnology from a public health perspective. The discussion illuminated potential public health benefits while acknowledging recent toxicological concerns. Events such as these serve to inform the scientific community and the public alike, encouraging the responsible development of the technology.
Recognizing the enormous opportunities at hand, the chemical industry is also placing a high priority on nanotechnology implications research. A consortium called the Chemical Industry Vision2020 Technology Partnership, in cooperation with the NNI and the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy, released a comprehensive white paper in 2003 titled “Chemical Industry R&D Roadmap for Nanomaterials by Design: From Fundamentals to Function.” This document calls for an unprecedented level of cooperation and collaboration among U.S. chemical companies to foster the long-term success of the nanochemical industry, and stresses that environment, safety, and health knowledge will be an essential component. “The anticipated growth in nanoparticle utilization warrants parallel efforts in hazard identification, exposure evaluation, and risk assessment,” the paper states. “Chemical companies are prepared to serve a major role in this process as leaders in characterizing materials, identifying their potential risks, and providing guidelines for their safe and effective utilization.”
The EPA’s STAR program is planning to award new grants soon in nanotechnology implications research, and the CBEN is continuing its work on what it calls “the wet–dry interface”—the interactions between engineered nanomaterials and systems that are active in aqueous or water-based environments, including ecosystems and living beings. “We have several research projects we would characterize as implications research,” says Kulinowski, “looking at what happens when nanomaterials get into the soil or into a water supply.” By understanding how nanoparticles (which are typically not soluble in water, hence the “dry” side) interact with aqueous environments (the “wet” side), the researchers hope to create technologies that will improve human health and the environment, such as biocompatible nanoparticles or nanostructured catalysts that will break down organic pollutants. The wet–dry interface also plays a major role in determining the environmental fate and transport of nanomaterials.
Regulatory agencies such as the EPA, the FDA, and the Occupational Safety and Health Administration are all participating in the NNI, following the progress of the research carefully, and building their own knowledge bases with an eye toward the eventual development and implementation of nano-specific regulatory frameworks within their purviews. At present the consensus seems to be that existing regulations are sufficiently robust to appropriately address concerns related to nanomaterials, but as risks and hazards are characterized in more detail, that stance could change.
Even the ETC Group, although it has not rescinded its call for a moratorium, seems encouraged by recent progress. “We do feel like we have had a reasonable response—as reasonable as to be expected—from the governments,” says Mooney, “and that there is work under way to try to correct the problems that we have identified.” Mooney says that as individual nations put nano-specific laboratory protocols into place, his group will no longer call for a moratorium in those countries.
It appears that all of this research activity is reaching critical mass at just the right time. The nanotechnology bullet train has left the station with the power to take us to some magical places we’ve barely even dreamed of. Although public distrust of the technology could potentially derail the train, many passengers are hoping that increased understanding of both its potential benefits and dangers will keep it on track and allow the journey toward discovery to continue.
Nanotech-Knowledge-y
Centers and Initiatives
National Nanotechnology Initiative (NNI)
Consortium of 19 agencies oversees the federal government’s widespread and well-funded nanotechnology activities.
http://www.nano.gov/
Center for Biological and Environmental Nanotechnology (CBEN)
The CBEN, housed at Rice University in Houston, was begun in 2001 as one of six Nanoscale Science and Engineering Centers established by the NSF.
http://www.ruf.rice.edu/~cben/
National Institute for Occupational Safety and Health (NIOSH)
NIOSH is particularly interested in nanomaterials with regards to occupational safety and health. This page includes information on the NIOSH Nanotechnology Health and Safety Research Program and the NIOSH Nano technology Research Center.
http://www.cdc.gov/niosh/topics/nanotech/
NIH Roadmap for Medical Research: Nanomedicine
The NIH Roadmap for Medical Research provides a framework for NIH research priorities in upcoming years. The roadmap contains a significant nanomedicine initiative that includes the establishment of multidisciplinary Nano medicine Development Centers.
http://nihroadmap.nih.gov/nanomedicine/index.asp
Chemical Industry Vision2020 Technology Partnership
This industry-led consortium aims to accelerate innovation and technology development in the chemical industry. The consortium, in cooperation with the NNI and the US Department of Energy Office of Energy Efficiency and Renewable Energy, released a comprehensive white paper in 2003 called the Chemical Industry R&D Roadmap for Nanomaterials By Design: From Fundamentals to Function.
http://www.chemicalvision2020.org/pdfs/nano_roadmap.pdf
National Toxicology Program (NTP) Nanotechnology Safety Assessment
The CBEN nominated nanoscale materials for study by the NTP. Based on the nomination, the NTP is developing materials and protocols to test a broad spectrum of nanoscale materials for toxicity in animal models over the next several years.
http://www.niehs.nih.gov/oc/factsheets/nano.htm
Other Resources
NanoBusiness Alliance
This nanotechnology trade association is developing a range of initiatives to support and strengthen the nanotechnology business community.
http://www.nanobusiness.org/
Action Group on Erosion, Technology, and Concentration (ETC Group)
This Canadian group of environmental activists has called for a worldwide moratorium on research and commercialization of engineered nanomaterials. http://www.etcgroup.org/
Recent and Upcoming Events
Technology and Environmental Health: Implication of Nanotechnology Public Discussion
Participants at this May 2004 meeting of the Institute of Medicine Roundtable on Environmental Health Sciences, Research, and Medicine discussed human and environmental health implications of nanotechnology as well as legislative and societal issues.
http://www.iom.edu/subpage.asp?id=19612
The International Dialog on Responsible Research and Development of Nanotechnology
The NNI brought together the leaders of nanotechnology programs from countries around the world at this June 2004 workshop.
http://www.nsf.gov/home/crssprgm/nano/dialog.htm
First International Symposium on Occupational Health Implications of Nanomaterials
This workshop, convened by NIOSH, the U.K. Health and Safety Laboratory, and the U.K. Health and Safety Executive, will convene in the United Kingdom in October 2004 to discuss workplace issues related to nanomaterials.
http://www.hsl.gov.uk/news/nanosymp.htm
The NTP, in association with the University of Florida, is also planning a workshop for November 2004 to focus on questions about how best to assess exposure to nanomaterials and evaluate their toxicity and safety.
For comparison’s sake. A micrograph shows a nanowire curled into a loop in front of a human hair. Nanowires can be as slender as 50 nanometers, about one-thousandth the width of a hair.
Small learning curve. Self-assembly of gold polymer nanorods results in a curved structure. The ability to control the size and curvature of nanostructures could aid in applications in drug delivery and electronics.
Probing insights. Nanoprobes studded with molecules that bind ions such as zinc, calcium, and potassium are injected into cells to reveal the patterns of ion exchange that make cells function. Computer models are used to interpret the fluorescent signatures probes emit when they capture a target ion.
Shapes and sizes. A visualization of a nanohydraulic piston consists of common nanotechnology components including a carbon nanotube (blue), helium atoms (green), and a "buckyball" molecule (gray).
Tiny beach umbrellas. A titanium dioxide microsphere (approximately 1–50 microns in diameter) with closed-packed spherical inclusions functions in sunscreens as a small “photonic crystallite,” scattering light very effectively.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0075015345365EnvironewsSpheres of InfluenceBotanical Supplements: Weeding Out the Health Risks Taylor David A. 9 2004 112 13 A750 A753 Publication 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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For the past decade, the U.S. medical establishment has been adjusting to the rising popularity of herbal remedies and other dietary supplements. The 1994 Dietary Supplement Health and Education Act (DSHEA) created a new regulatory approach for products that included herbal products, vitamins, and minerals. Intended to streamline the entry of lower-risk products to the marketplace, DSHEA has since become viewed by some as having unleashed a deluge of relatively unregulated pharmacologically active products onto unwary consumers. At the same time, reports have mounted describing products of variable quality and manufacturers who lack accountability for their claims. Today, regulatory agencies are ramping up their efforts to ensure the safety of botanical supplements.
The Last Straw
Under DSHEA, herbal products, vitamins, and minerals were to be regulated as foods, and were not subject to the rigors of drug or food additive approval. Supplements that had been on the market prior to 1994 were presumed to be safe. They could be marketed without further testing or approval from the Food and Drug Administration (FDA), even if they were used in new combinations. [For more on DSHEA, see “Herbal Medicine at a Crossroads,” EHP 104:924–928 (1996).]
According to the FDA, the number of supplement products on the market grew more than sevenfold since DSHEA’s 1994 passage, to about 29,000 in 2003. Many manufacturers entered the industry with little investment and easy access to a growing market, and were not careful in processing or labeling their products. Even under the best conditions, quality control is tough for herbal supplements because they start from plants containing many chemical compounds, and constituent concentrations vary from batch to batch. And unlike regulated pharmaceuticals, the active ingredients for botanical medicines and dietary supplements are not well-characterized or in some cases even known.
Consumer concerns about quality cropped up in the late 1990s, as many companies rushed to bring herbal products to mainstream markets. Consumers grew confused by the flood of new brands. Widespread newspaper reports of deaths and other serious adverse reactions, scandals over product labels that misrepresented ingredient content, the discovery of contaminants such as heavy metals, and mixed results in efficacy trials further dampened public enthusiasm. Supplement sales have plateaued since. “Consumers seem to be more skeptical than they were in 1998,” says Floyd Leaders, CEO of Botanical Enterprises, a company that develops natural products.
Nevertheless, botanical supplements were still popular in February 2003, when a pitcher for the Baltimore Orioles major league baseball team died as a result of taking a supplement containing ephedra. Until then, ephedra (also known as ma huang) was one of the most popular products for losing weight and enhancing athletic performance, although for years reports of adverse reactions had signaled potential health risks.
Weeks after the baseball player’s death, the FDA proposed a set of mandatory “good manufacturing practices” (GMPs) for dietary supplements to ensure more reliable quality. These resembled practices enacted for over-the-counter drugs, including guidelines for regulating temperature, sanitation, and equipment maintenance. By law, the supplement GMPs followed requirements for food GMPs rather than those for drugs. The proposed GMPs were enacted early this year, with phased-in requirement depending on the size of the manufacturer (large manufacturers must comply within one year, medium-sized manufacturers within two years, and small manufacturers within three years).
Articles in the 26 March and 17 September 2003 issues of JAMA raised concerns about ephedra’s safety, drug interactions caused by other supplements, and the rise in unscrupulous advertisements for supplements on the Internet, keeping up the call for regulatory action. At the end of 2003, the FDA finally announced a ban of dietary supplements containing ephedra, which took effect in April 2004. Manufacturers were warned that other risky supplements could be next.
Shortly thereafter, to improve oversight specifically of botanicals used in prescription drugs, the FDA issued a new guidance document, a move that industry watchers found encouraging. The guidance gives incentives for companies to take products through the clinical trials that can lead to stronger claims: if a botanical product is legally marketed in the United States with no known toxic effects, the manufacturer can delay certain preclinical trials on toxicity and move more quickly into the clinical trials phase to determine efficacy. Without gaining premarket approval to sell the product as a drug, a supplement maker can make claims about the product’s effect on the body (so-called structure–function claims, such as “calcium builds strong bones”), but they cannot claim to treat disease (for instance, “calcium reduces the risk of osteoporosis”).
Leaders welcomes the new FDA guidance, which he says puts the agency “two or three years ahead of the industry” in terms of understanding that public perception of an industry’s reliability is critical to product acceptance. What many in the industry don’t yet fully comprehend, says Leaders, is that the new guidance streamlines the process by which manufacturers can gain exclusive claims of benefit. It also gives the FDA a better basis for determining a product’s safety.
The FDA’s pursuit of ephedra brought a shift in the government’s approach to making its case for regulatory action, according to Ilene Ringel Heller, senior staff attorney for the Center for Science in the Public Interest (CSPI), a consumer advocacy group based in Washington, D.C. “What took FDA so long in the case of ephedra is that FDA has the burden of proving that a supplement poses a significant or unreasonable risk before it can take action [to restrict its use],” she says—an extremely difficult case to make in the absence of adverse event reports. “Eventually, FDA decided . . . to do a risk–benefit analysis and decide whether it poses an unreasonable risk. And that’s how ephedra got banned. But it’s not certain that this will be upheld in court.” Furthermore, points out George Lucier, an advisor to the National Toxicology Program (NTP), good risk–benefit analysis is simply not possible without good data on both efficacy and toxicity.
A New Crop of Research
The framework for federal research on botanical products has evolved to help produce those sorely needed data. The NIH Office of Dietary Supplements (ODS), created by DSHEA, has grown from a $5 million budget in 1999 to one more than five times that size in 2004. Yet according to ODS director Paul Coates, the office’s new five-year strategic plan does not mark a dramatic shift in direction. The new plan has major strategic goals very similar to the ones in the first plan. The difference, says Coates, is one of emphasis: the ODS will continue to support research to improve analytical methods and enhance understanding of the mechanisms by which popular herbal supplements act, and plans to assess the role of dietary supplements in reducing the risk of chronic disease.
In its work, the ODS collaborates with most of the NIH institutes and centers, including the National Center for Complementary and Alternative Medicine (NCCAM), the NIEHS, and the John E. Fogarty International Center for Advanced Studies in the Health Sciences. Referring to NCCAM, Coates says, “We have areas of natural complementarity, given that a great many dietary supplements have been used in traditional healing environments.”
NCCAM’s history parallels that of the ODS: established by Congress in 1998, the center quickly developed a program of research trials that included studies of herbal products used in traditional medical systems such as Indian Ayurvedic medicine and Chinese herbal medicine. NCCAM also established an Office of Clinical and Regulatory Affairs.
Jonathan Berman, director of that office, says the center studies dietary supplements according to a drug model to see if they are safe and effective, and to determine dosage. Throughout its existence, while exploring various alternative therapies, NCCAM has approached dietary supplements with the aim of steering as many as possible toward the FDA’s drug approval process and its benefits of premarket approval and more accountable manufacturing. Berman says NCCAM leaders believe the new FDA guidelines for botanicals used in prescription drugs will lead to safer products all around.
Research on botanical dietary supplements is also being conducted by other branches of the NIH. In recent years, the ODS, NCCAM, and the NIEHS joined forces to create six new research centers devoted to such studies. These university-based centers meet annually to share progress and compare notes, according to Diane Birt, director of the Center for Research on Dietary Botanical Supplements at Iowa State University. Other centers are located at the University of California, Los Angeles (Center for Dietary Supplements Research: Botanicals), the University of Illinois at Chicago (Center for Botanical Dietary Supplement Research in Women’s Health), the University of Missouri–Columbia (Center for Phytonutrient and Phytochemical Studies), Purdue University and the University of Alabama at Birmingham (Botanicals Research Center for Age Related Diseases), and the University of Arizona (Center for Phytomedicine Research).
Researchers at the NIEHS and the NTP also are conducting studies on the safety of compounds found in dietary supplements. Since 1998, the NTP has performed literature reviews for 41 candidate substances, with studies actually conducted on about 20. Among the substances studied were the alkaloids in comfrey (Symphytum officinale), an herb used by the ancient Greeks to stop bleeding and heal wounds. Comfrey, however, contains pyrrolizidine alkaloids, which are known to cause liver cancer (in 1993 the FDA cited these effects in its report Unsubstantiated Claims and Documented Health Hazards in the Dietary Supplement Marketplace). The NTP quantified the alkaloid content of comfrey samples and relayed the results to the FDA, and in July 2001 the FDA advised manufacturers to take comfrey products off the market. “Although the FDA cannot require toxicological data for herbal products, they can take regulatory action if data indicative of risk become available. So studies like the NTP studies are important for addressing public health concerns,” says Lucier.
Besides preclinical studies, the ODS supports efforts to standardize methods for assessing supplements, as Congress mandated in 2002. This mandate answered a need voiced by the industry itself, according to Michael McGuffin, president of the American Herbal Products Association (AHPA), a trade association based in Silver Spring, Maryland. The industry welcomes standardized methods, McGuffin says, so that the same analytical methods are used by producers, the media, and the FDA alike. This can avert conflicting safety and efficacy reports that leave consumers baffled.
Some experts maintain that even the big NCCAM-supported clinical trials are not as definitive as they should be. One such major study, focusing on St. John’s wort (Hypericum perforatum) and published 10 April 2002 in JAMA, found the herb to be “no better than placebo for the treatment of major mental depression,” says Berman. Observers inside and outside the industry were dismayed perhaps less by the findings than by how they were reported. “There appeared to be bias in the reporting,” Leaders says—what the medical journal didn’t note was the authors’ finding that the currently accepted treatment for depression likewise performed no better than placebo, thus casting doubt on the entire experiment.
Adds Mark Blumenthal, who directs the nonprofit American Botanical Council of Austin, Texas, “Just as there have been problems with quality control in some aspects of the herbal industry, there are also some serious problems of quality control in the way that the media and the medical journals themselves have reported on the herbals.”
Coates acknowledges the need for clearer public information. “The people who commented on our strategic planning process more than once said that we have to pay more attention to the development of appropriate communications and information tools,” he says, “and we’re planning to do that.”
Growth Under DSHEA
In recent years, the FDA has reorganized itself to better reckon with the challenges posed by DSHEA. In February 2003, the Office of Nutritional Products, Labeling, and Dietary Supplements created a separate Division of Dietary Supplement Programs. Susan Walker, who heads that division, says it continues to explore relationships with the NIH and its research institutes, including the NTP, and to coordinate with other parts of the FDA, notably the National Center for Toxicological Research.
The FDA and other agencies have become more vigilant in enforcing laws pertaining to supplements. In late 2003 and early this year, the FDA sent 119 warning letters to distributors of supplements and refused entry of over 1,100 imported products, according to a 19 April 2004 agency press release. Enforcement by other agencies has been stepped up too. The Federal Trade Commission, the FDA’s partner in enforcement, cracked down on unscrupulous advertisers of dietary supplements in 2003 as part of an ongoing effort known as Operation Cure-All. The result was 83 warning letters demanding that companies cease making illegal claims on their websites and in their literature.
Manufacturers, too, are finding ways to live with the new policies. In the past, companies complained that there was no incentive for drug research on herbs because they couldn’t patent a natural product. But there are ways around that obstacle, for example by patenting the process for extraction. “The process defines the product,” says Leaders. He gives the example of brewing coffee: Starting from the same coffee beans, you can either brew very strong Turkish coffee or a drink as weak, he says, “as my mother used to make.”
Leaders says one of the two main weaknesses of the 1994 law was that it did not require manufacturers to report adverse reactions to the FDA. The latter point was echoed recently by a review panel of the Institute of Medicine in its April 2004 report Dietary Supplements: A Framework for Evaluating Safety, and by consumer groups. “FDA needs authority to require mandatory adverse event reporting,” says Heller. “It’s a major shortcoming that FDA doesn’t have it.”
The FDA does have a voluntary system in place for reporting adverse events—the Center for Food Safety and Applied Nutrition Adverse Event Reporting System is a computerized database of records submitted by consumers, health care providers, and industry. Yet despite some revamping of the system, Heller says the fact remains that reporting to this system is, by virtue of being voluntary, largely ineffective. Indeed, the April 2001 Department of Health and Human Services report Adverse Event Reporting for Dietary Supplements: An Inadequate Safety Valve cites an unnamed FDA-commissioned report as finding that the FDA receives reports of less than 1% of all adverse events associated with dietary supplements.
Other nations, too, are looking at issues related to botanical supplement safety. Both the European Union and Canada have added a new “traditional medicine” category for products that have a history of use in the literature without adverse reactions. Like the United States, Europe is reviewing how to assess the risks and benefits of botanical dietary supplements. An expert group of the European branch of the International Life Sciences Institute (ILSI Europe), a Brussels-based nonprofit foundation that is funded primarily by industry members, published a paper in the December 2003 issue of Food and Chemical Toxicology offering new guidance on how to assess the safety of botanicals. The authors stated that “ultimate safety in use depends on the establishment of an adequate safety margin.” This margin is the ratio between the dose demonstrated to be safe from research and the dose actually consumed, explains Nico van Belzen, executive director of ILSI Europe.
Defining what size margin is adequate, however, is a thorny proposition and requires a case-by-case determination, admits van Belzen. ILSI Europe suggests a decision tree approach to the evaluation process, and has created a model for risk–benefit analysis of micronutrients that can provide a tool to help officials weigh the risk of deficiency (making the dosage too low for users to experience a product’s benefit) against the risk of toxicity (exceeding the tolerable upper limit). That model will be published in an upcoming issue of Food and Chemical Toxicology.
In the shadow of the ephedra mêlée, the system for balancing the potential risks and benefits of botanical dietary supplements clearly is still taking shape. With better understanding of herbal compounds and improved regulation of the claims made on their behalf, however, manufacturers and the public alike could reap a rich harvest.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0075415345366EnvironewsInnovationsNew Spin on an Old Fiber Frazer Lance 9 2004 112 13 A754 A757 Publication 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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Every year, cotton growers in the United States produce 20 million bales—some 9.6 billion pounds—of cotton fiber, or about one-fifth of total global production. The great majority of this fiber is destined for use in cloth, yet more than a quarter may never reach the fabric market: at each step throughout the production process, from harvesting the puffy white cotton bolls to weaving the cloth for the shirt you’re wearing as you read this, some portion of the fiber is lost to scrap or waste. Now a Cornell University researcher has developed a new process for electro-spinning waste cotton into nanofibers using a less harmful solvent, a change that could both profit the cotton industry and afford environmentally friendly applications.
According to Margaret Frey, an assistant professor of textile science at Cornell, some 4–8% of cotton fiber is lost at the textile mill in so-called opening and cleaning, which involves mechanically separating compressed clumps of fibers for removal of trapped debris. Another 1% is lost in drawing and roving—pulling lengths of fiber into longer and longer segments, which are then twisted together for strength. An average of 14–20% more is lost during combing and yarn production. Typically, waste cotton is used in relatively low-value products such as cotton balls, yarn, and cotton batting.
Cotton is 90% cellulose—a very pure source of this fiber. Perhaps, Frey theorizes, more productive use could be made of this waste cotton. “My idea,” she says, “was to . . . give the industry a way to produce some high-end products.”
Frey’s process involves dissolving the cotton with ethylene diamine, a relatively benign solvent, and using an electrospinning process to produce fibers 100 times smaller than anything obtainable by conventional spinning technologies. In electrospinning, a polymer solution is pulled by an arcing electrical charge through the air and onto an electrical ground. Electrospun materials can then be incorporated into a traditionally woven product to add strength or durability.
Frey says the great thing about nanofibers is that they have a very high surface-to-volume ratio, so much less material will accomplish more. For example, she says, adding no more than 0.1 gram of nanofiber material per square meter to conventional filter material—for example, in a biohazard suit or air filter—will dramatically improve the efficiency of the filter.
“The military can also use it in protective systems for soldiers at risk from chemical or biological weapons,” Frey says. “The tremendous filtration capabilities can protect personnel without making them feel like they’re wrapped in plastic.” Frey also suggests that these fibers could be made into mats that could absorb fertilizers, pesticides, and similar substances, later releasing them in a timed, targeted fashion.
Fiber Options
Frey says cellulose has a large number of hydrogen bond sites. “The trick, then,” she says, “is to break these hydrogen bonds without depolymerizing the cellulose and turning it back into its basic constituent—glucose. Once we have a good solvent for cellulose, we’ll be able to process it into any shape we want: small fibers, films, and so on.” At the same time, another challenge is to find a solvent that is benign, yet volatile enough to work with electrospinning. “In electro-spinning, you apply a voltage to a solution,” Frey explains. “As that charge arcs across to a ground, the solvent needs to evaporate across the path of the arc so that what you collect is fiber.” Frey thinks ethylene diamine may be just the solvent to meet both these needs.
The solvents typically used to convert cellulose into a soluble compound—for example, to process wood pulp into rayon—include carbon disulfide and sodium hydroxide, both of which carry substantial health baggage. Carbon disulfide, in the impure form typically used in industrial processes, has an odor that has been likened to rotting radishes. Breathing low concentrations of carbon disulfide over extended periods can cause headache, tiredness, and nerve damage, while breathing high concentrations can be fatal. The vapor also combusts and explodes easily. Sodium hydroxide is a room-temperature solid that generates tremendous heat when dissolved in water or neutralized with acid, and can react with a variety of metals to create flammable hydrogen gas. It can cause irritation of the skin and eyes at low exposure, while higher concentrations can cause severe burns to the eyes, skin, and gastrointestinal tract. Frey says rayon production in the United States resulted in a number of Superfund sites.
In the early 1990s, a second cellulose fiber, Lyocell, became available, created using amine oxides as the solvent. Amine oxides dissolve cellulose without the issues of the chemical reactions in rayon manufacturing, says Frey, but it’s not as flexible a process as the rayon process—the types of fibers that can be made have properties in a fairly limited range.
“Amine oxides . . . have a number of very reactive nitrogen–oxygen bonds,” explains Richard Kotek, an assistant professor in North Carolina State University’s Department of Textile Engineering, Chemistry, and Science. He adds, “Those oxides can not only have a lot of side reactions, they can also decompose very rapidly if the temperature is too high.”
Frey believes her system, using ethylene diamine, offers the best of both worlds. “It’s a direct solvent system, with no unwanted side reactions,” she says, “where we can capture and reuse the solvent and use the process to produce fibers for a full range of end users.”
Cottoning to a New Technology
Frey developed the process in collaboration with assistant professor Yong Joo and graduate student Choowon Kim, both from Cornell’s School of Chemical and Biomolecular Engineering. The team discovered that ethylene diamine swells cellulose and separates individual polymer chains from each other without dissolving the molecules.
To actually dissolve the cellulose, they added thiocyanate salts. In experiments presented at the fall 2003 American Chemical Society national meeting, dried cellulose was dispersed in ethylene diamine using a vortex mixer. Sodium thiocyanate or potassium thiocyanate was added, and the mix was blended again.
The samples were then put through a freeze-and-thaw system to complete the dissolution. Samples were frozen at –20°C for four hours, then thawed in a 40°C water bath for 20 minutes. Three cycles of freezing and thawing were sufficient to complete dissolution in all samples where dissolution occurred (in samples where solutions didn’t form, no number of cycle repetitions was sufficient to trigger solution formation; the reason why this is so is one subject of ongoing research). “We’re not sure why [the freeze-and-thaw system] works,” says Frey, “but we have several thermodynamic studies ongoing to find out.” The solutions were then successfully electrospun.
Discovery of ethylene diamine as the solvent of choice for Frey’s process was based partly on previous research and partly on serendipity. In recent years, an ammonia-based solvent with a high vapor pressure had been produced and shown to be effective with cellulose. However, the vapor pressure was “too high for what we wanted,” says Frey. “So we went up to the next large molecule, which is hydrazine [a colorless liquid used in rocket fuels and chemical manufacturing, which can cause nervous system, kidney, and liver damage]. Hydrazine’s issues are pretty well documented, so we went up to the next larger molecule, which was ethylene diamine.” She says one advantage to ethylene diamine is that it remains a liquid at room temperature, unlike ammonia-based solvents, which volatilize at or near room temperature.
Hurdles to High Cotton
Frey admits there have been some perceptual hurdles to overcome with regards to providing another use for waste cotton. “There is this concern that if you’re dealing with a waste product, you somehow have an ‘impure’ stream, and that has caused a distraction [within the industry],” she says. “But I think it’s a distraction of perception, rather than reality.”
Don Bailey, vice president of textile research and implementation for the industry research organization Cotton Incorporated, is cautiously interested in Frey’s process, with some reservations. Bailey has worked with textiles science since 1971. “In that time,” he says, “every solvent I’ve ever run across that was supposed to be the next best solvent for textile processing has been banned or heavily restricted shortly thereafter. That’s why I don’t generally get too excited when I hear people talk about solvents.”
Bailey also points to the question of basic economics. “The original rayons used cotton waste fibers, but the industry found they just weren’t able to compete economically with wood pulp,” he says. “And even though there’s a cotton oversupply in the world today, it would take some serious economic studies to see if [Frey’s] process was competitive.”
Finally, he says, is the question of whether and how the process might alter the cotton fibers. As an example, he points to bamboo fibers. “There’s a lot of bamboo in the world, so people think it will make a great feedstock for fiber production and might offer unique properties,” he says. “But one problem is that while the bamboo itself is mildew-resistant, the processed fiber loses those properties. Problematic changes like this require more study.”
The environmental friendliness of the process is also still somewhat debatable. “Solvents do, for the most part, have attendant health and environmental issues,” Frey says. “We still have concerns with [ethylene diamine], as we do with most solvents. The Environmental Protection Agency has not identified it as a problem, and there are no specific regulations covering its usage, but it still needs to be recaptured and recycled, like any other solvent.”
It should also be noted that the thiocyanates added to make the process work have their own risks, including respiratory irritation, weakness, low blood pressure, and death at doses of 15–30 grams. And if heated to decomposition, they can release toxic fumes of ammonia, nitrogen oxides, and cyanide.
Ultimately, says Bailey, if the new process proves itself to be environmentally benign, “that would be great, but I’ll need to see a long-term proven track record first. . . . If it can indeed make nanofibers through electrospinning, it would be of interest to industry.”
“I think [Frey’s] approach is an excellent idea,” says Kotek. “Nanofibers are very popular, and although I don’t see a multitude of applications for nanofibers in industry at this time, it’s certainly something that could find a number of applications in the near future.”
Frey agrees. “So far, there are no limitations to the types of cellulose that can be dissolved using the process we’ve developed,” she says. “We have tried a wide range of samples, including cotton, wood pulps, bacterial cellulose, and anything else I could get my hands on. I think the process has tremendous potential.”
Spinning gold out of waste? Nearly a quarter of the 9.6 billion pounds of cotton fiber produced each year in the United States is lost to waste during harvesting, transport, and processing. A new technology uses electrospinning of waste cotton fibers, made possible by addition of a relatively benign solvent, to create high-value nanofibers (above).
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Suggested Reading
American Fiber Manufacturers Association FiberSource: Cellulose [online tutorial]. Arlington, VA: American Fiber Manufacturers Association. Available: http://www.fibersource.com/f-tutor/cellulose.htm [accessed 30 July 2004].
Cuculo JA Aminuddin N Frey MW 2001. Solvent spun cellulose fibers. In: Salem DR, ed. Structure Formation in Polymeric Fibers. Munich, Germany: Hanser Gardner Publications; 296–328.
Graham K Gogins M 2004. Incorporation of electrospun microfibers into functional structures. Intl Nonwovens J 13(2):21–27. Available: http://www.inda.org/subscrip/inj04_2/p21–27-graham.pdf [accessed 30 July 2004].
Hattori K Abe E Yoshida T Cuculo JA 2004 New solvents for cellulose. II. Ethylenediamine/thiocyanate salt system Polym J 36 2 123 130
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a00759EnvironewsScience SelectionsAflatoxin Exposure after Weaning: Solid Food Contaminant Impairs Growth Barrett Julia R. 9 2004 112 13 A759 A759 Publication 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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Given the heat, humidity, and poor storage conditions of many tropical developing nations, mold readily grows in harvested crops such as maize and groundnuts. Such foods are dietary staples in many of these countries, and their consumption can lead to widespread exposure to aflatoxin, a mold toxin produced by Aspergillus species that is known to cause liver cancer. Aflatoxin is also associated with impaired growth and immune function in animals, but minimal data exist regarding comparable effects in humans. To examine a potential link more closely, a team of researchers in the United Kingdom and Benin built upon an earlier cross-sectional study that demonstrated impaired growth among West African children with high aflatoxin exposure [EHP 112:1334–1338]. The researchers now present evidence from a longitudinal study that aflatoxin does impair growth in humans.
Previous studies indicated that aflatoxin exposure is high in West African populations, and dietary exposure begins with the introduction of solid foods at weaning. Maize, in the form of porridge, is often the first solid food given to young children here. To study the effects on growth of probable aflatoxin exposure at a young age, the team recruited 50 children from each of four villages in the West African nation of Benin. The children were 16–37 months old when the study began in February 2001. The children’s mothers were interviewed in February, June, and October to gather information about each child’s diet, health, and other factors. Blood samples collected from the children at each survey point were analyzed for levels of aflatoxin–albumin, a biomarker of recent aflatoxin exposure. Vitamin A and zinc levels also were obtained as markers of nutrition. The children and their mothers were weighed and measured at each survey point.
At the first survey point, the researchers found that levels of aflatoxin–albumin were significantly higher in weaned children than in those still partially breastfeeding. Throughout the study, more children became fully weaned, and the levels of the biomarker increased in these children. More than 98% of the children were positive for aflatoxin–albumin at all three time points. Most exposure was likely due to maize consumption, although eating other foods such as groundnuts may have contributed.
Children with the highest levels of the aflatoxin biomarker grew an average 1.7 centimeters less than those with the lowest levels. Poor nutrition did not appear to be a factor in the reduced growth, as blood concentrations of vitamin A and zinc were not correlated with aflatoxin–albumin levels.
The mechanism by which aflatoxin could affect growth is currently being investigated. Defining aflatoxin’s effects is complicated by confounding dietary variables (including co-contamination of food with additional mycotoxins) and the presence of infection. For example, previous research by this group revealed an association between aflatoxin exposure and reduced levels of protective antibodies in the saliva of Gambian children. The team therefore theorizes that aflatoxin could affect growth by altering mucosal barriers and lowering resistance to intestinal infection.
The group is now conducting research aimed at better understanding such relationships. They suggest that controlling for many confounding factors will require a randomized intervention study in which aflatoxin exposure would be reduced to assess the toxin’s impact on children’s immunity, growth, and disease susceptibility.
A somber start. Maize porridge—a potential source of growth-limiting aflatoxin exposure—is often the first solid food given to West African children such as this boy in Burkina Faso.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a00761AnnouncementsNIEHS Extramural UpdateWorker Education and Training Branch Editor’s note: This is the third in a series of articles describing the four extramural program branches at the NIEHS.
What does training really mean? Firefighters from Rock Hill, South Carolina, used the expertise gained in several NIEHS grant–funded courses to rescue a victim who had fallen into a 20-foot-deep vertical sewer. The initial rescuer was equipped with proper protective gear and lowered into the space to find the victim drowning in the raw sewage. The rescuer established a patent airway and cervical–spinal stabilization and requested assistance from a second rescuer. Both rescuers then worked to package the patient for safe removal from the space. The result: good training had saved another life.*
9 2004 112 13 A761 A761 Publication 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 Worker Education and Training Branch (WETB) plans and administers grants, contracts, cooperative agreements, and interagency agreements to help organizations develop institutional competency to train hazardous waste workers and emergency responders. The WETB staff has worked to develop specific initiatives to support the programmatic functions of the branch.
The WETB is responsible for the Worker Education and Training Program (WETP), a training grants program established under the Superfund Amendments and Reauthorization Act of 1986. The mission of the WETP is to support the development of a network of nonprofit organizations that are committed to protecting workers and their communities by creating and delivering high-quality, peer-reviewed safety and health curricula to train hazardous waste workers and emergency responders.
Over the past 16 years, the WETP supported its core program, the Hazardous Waste Worker Training Program, by providing over a million workers in all regions of the country with health and safety training. Since 1986, the scope of the program expanded to include the following grant activities:
the Department of Energy (DOE) Nuclear Training Program—awardees trained nearly 175,000 environmental response and cleanup workers at the DOE Nuclear Weapons Complex;
the Minority Worker Training Program—awardees successfully trained more than 2,600 young minority adults in worker health and safety for construction and environmental cleanup;
the Brownfields Minority Worker Training Program—awardees trained nearly 2,000 workers in 15 brownfields communities, in the process positively changing the lives of the trainees and their families in many different ways; and
the Small Business Innovation Research E-Learning Program—awardees created e-learning technology that supports high-quality health and safety training for hazardous waste workers and emergency responders.
In addition to these activities, since 11 September 2001 the WETP has also trained workers in cleaning up environmental problems stemming from the World Trade Center attacks, as well as potential bioterrorism and use of weapons of mass destruction.
WETB Staff
Joseph Hughes, Jr.—PROGRAM DIRECTOR | [email protected]
Hazardous Waste Worker Training, DOE Nuclear Training
Sharon Beard—INDUSTRIAL HYGIENIST | [email protected]
Hazardous Waste Worker Training, DOE Nuclear Training, Minority Worker Training, Brownfields Minority Worker Training
Ted Outwater—PUBLIC HEALTH EDUCATOR | [email protected]
Small Business Innovation Research E-Learning
Patricia Thompson—PROGRAM ANALYST | [email protected]
*Alan Veasey, director of workplace safety training, University of Alabama at Birmingham, in NIEHS Progress Report.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a00762AnnouncementsFellowships, Grants, & AwardsFellowships, Grants, & Awards 9 2004 112 13 A762 A763 Publication 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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Understanding and Promoting Health Literacy
The participating institutes, centers, and offices of the NIH and the Agency for Healthcare Research and Quality (AHRQ) invite investigators to submit research grant applications on health literacy. The goal of this program announcement (PA) is to increase scientific understanding of the nature of health literacy and its relationship to healthy behaviors, illness prevention and treatment, chronic disease management, health disparities, risk assessment of environmental factors, and health outcomes including mental and oral health. There is a need for increased scientific knowledge of interventions that can strengthen health literacy and improve the positive health impacts of communications between health care/public health professionals (including dentists, health care delivery organizations, and public health entities) and consumer or patient audiences that vary in health literacy. Such knowledge will help enable health care and public health systems to serve individuals and populations more effectively, and employ strategies that reduce health disparities in the population. Once a general understanding of the various factors influencing current trends has been achieved, a number of secondary goals may be addressed. Applicants may propose secondary goals of modeling the potential impact of new interventions on future national trends and/or determining the impact of targeted cancer control interventions on population outcome (i.e., evaluating optimal cancer control strategies).
Healthy People 2010 defines “health literacy” as the degree to which individuals have the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions. Many factors affect individuals’ ability to comprehend, and in turn use or act on, health information and communication. Proficiency in reading, writing, listening, interpreting, oral communication, and visual analysis is necessary as the modern health system typically relies on a variety of interpersonal, textual, and electronic media to present health information. Individuals and families both must be able to 1) communicate with health professionals; 2) understand the health information in mass communication; 3) understand how to use health-related print, audiovisual, graphic, and electronic materials; 4) understand basic health concepts (e.g., that many health problems can be prevented or minimized) and vocabulary (e.g., about the body, diseases, medical treatments, etc.); and 5) connect this health-related knowledge to health decision making and action taking.
Access to and understanding of health information and services is a reciprocal process among health professionals, communication professionals, and patients. For instance, these professionals must use science-based strategies and tactics, develop resources and materials, and understand communication interactions between providers and patients.
Research on health literacy should assist the NIH in its mission of communicating scientifically based health information to the public and to the health care providers and related professionals who serve the public. The application of scientific knowledge from health literacy research may also strengthen the health information knowledge and communication skills of the public, and further one of the national goals of Healthy People 2010: to improve health literacy by the decade’s end.
Health literacy is a complex phenomenon that involves individuals, families, communities, and systems. For instance, consumers, patients, caregivers, and other laypersons may vary with respect to 1) access (e.g., to audience-appropriate information, media, or professionals); 2) skills (e.g., to gather and comprehend health information; to speak and share personal information about health history and symptoms; to act on information by initiating appropriate follow-up visits and conveying understanding back to the information source; to make decisions about basic healthy behaviors, such as healthy eating and exercise; to engage in self-care and chronic disease management); 3) knowledge (e.g., of health and medical vocabulary, concepts such as “risk,” the organization and functioning of health care systems); 4) disabilities (e.g., sensory, communication, cognitive, or physical challenges or limitations); 5) the features of their health care providers and the public health systems in which these providers practice (e.g., the communication skills of health professionals, platforms employed for patient education, built environments and signage); or 6) other important characteristics (including development or life stage; cultural, linguistic, or educational differences that affect health beliefs, knowledge, or communication).
Too often, people with the greatest health burdens have limited access to relevant health information. In part, this is due to the complex and cumbersome ways in which health information often is presented; it is also due to individuals’ limited abilities to fully interpret and understand complex health terminology and instructions, and to make personal decisions related to risk-avoidance or -reduction strategies. For instance, to follow health care instructions, patients need to be able to comprehend written and oral prescription instructions, directions for self-care, and plans for follow-up tests and appointments.
In addition, health care providers may not communicate effectively with individuals with limited levels of literacy. For instance, achieving informed consent for treatment is difficult when health care personnel cannot explain biological processes or treatment procedures in simplified language, and patients cannot interpret health information. These situations hamper the effectiveness of health professionals’ efforts to prevent, diagnose, and treat medical conditions, and limit many health care consumers’ abilities to make important health care decisions.
Low health literacy is a widespread problem, affecting more than 90 million adults in the United States. Low health literacy results in patients’ inadequate engagement in and benefit from health care advances, as well as medical errors. Low health literacy is likely to be a major contributor of adverse health outcomes. Research has linked low or limited health literacy with such adverse outcomes as poorer self-management of chronic diseases, less healthful behaviors, higher rates of hospitalizations, and overall poorer health.
This PA invites applications to develop research on health literacy in general areas that include, but are not limited to, the following: 1) modeling and measuring the nature and scope of health literacy; 2) variation in health literacy over the life course or among native and nonnative speakers of English; 3) mediators and moderators of low health literacy; 4) the impact of low health literacy on health outcomes, diseases, behaviors, and treatments, including the contribution of health literacy to informed decision making, adherence to preventative or therapeutic regimens, utilization of health care services, risk avoidance strategies, and other consumer health care–related actions; 5) the identification of effective preventive and other interventions to improve health literacy among populations and to enable the health care and public health systems to communicate effectively across different health literacy levels; and 6) the development of effective methods and new technologies in health literacy research.
Applications should be relevant both to the objectives of the PA and to at least one of the participating institutes’ general research interests. Prior to preparing an application, researchers are strongly encouraged both to review the general research interests of the participating institutes and to contact program staff of the relevant institutes to discuss the proposed research.
A wide variety of research approaches are encouraged under this PA: basic research that investigates or describes the nature of health literacy and the magnitude of health literacy problems, and applied research addressing issues pertinent to health literacy practices (e.g., systems-level interventions) and research in practice (e.g., active potential end users participate as supportive research partners). Applications also may develop theoretical models, refine research constructs, improve methods and measurements, and establish causal relationships (e.g., between low health literacy and lack of effective health promotion). Researchers also may address the effectiveness of interventions, or adapt and test existing programs (including those that are not research-based) to reduce low health literacy and its adverse consequences (e.g., interventions implemented by health care systems and systems outside of health care such as systems of public education).
The research must involve either 1) health literacy, or one of its many components, as a key outcome; 2) health literacy as a key explanatory variable for some other outcome; 3) methodological or technological improvement to strengthen research on health literacy; or 4) health literacy–focused preventions and interventions. Studies to develop or evaluate the readability or utility of specific materials that are intended for single uses or single audiences are not responsive to this PA unless these investigations are integral to testing a significant research hypothesis related to health literacy. Some potential areas of focus are as follows.
Nature and scope.
1) Assess the prevalence and causes of low health literacy; 2) identify the nature of the mix of abilities and skills required to be functionally “health literate” (e.g., including media and health care system navigation skills, etc.) and the roles of basic literacy (i.e., reading, writing, speaking, listening, visual interpretation skills) and mathematics abilities (e.g., graphical interpretation and other quantitative skills) in health literacy; 3) explore the magnitude and variation, by socioeconomic and/or other group characteristics, in accessing, seeking, evaluating, interpreting, and using health information from a variety of sources; 4) examine the problems and factors involved in the presentation and interpretation of quantitative information (e.g., graphic interpretation, “risk” or probability statistics, the influence of information context and information formats, etc.) from either the provider or user perspective, or investigate how specific health referents, such as basic genetics and/or environmental risk concepts, are best understood and conveyed; 5) create a conceptual model of “health literacy” or the skill sets that influence the comprehension of relevant health information (e.g., visual information comprehension skills that permit understanding of such visual messages as color coding, representation of risk, or disease processes); or 6) evaluate the different strategies and channels available, including the role of information technologies, that enable consumers to seek, access, and interpret relevant health information effectively, and how these may differ by cultural and health literacy backgrounds (e.g., research on the information-seeking or service-utilization characteristics among health consumers with different levels of health literacy).
Life span and cultural differences.
Applications addressing health literacy as an age-differentiated phenomenon might explore the developmental precursors of low health literacy and the age-related changes in reading and other cognitive skills throughout the life course that may contribute to these difficulties. 1) Identify the reading and oral language comprehension skills crucial for the satisfactory acquisition and understanding of basic health information by children, adolescents, and adults of various ages; 2) determine how intuitive or everyday notions of germs, contagion, environmental exposures, disease, drugs, bodily processes, and other health-related concepts influence health literacy and consequent illness prevention behaviors across the life course, and identify age-appropriate intervention techniques that can be used to mitigate these problems; 3) examine the role of social and cultural factors in the development of health literacy (e.g., how children acquire health-related knowledge as they age, especially those children in households where the parents speak limited English and the children serve as interpreters); 4) explore how the quantity and quality of structured interactions with adult caregivers affects the health literacy of the child from birth to age three; or 5) examine the effect of current age-related differences in media use (e.g., children versus the elderly) on health literacy.
Mediators and moderators of health literacy: protective and risk factors.
1) Describe how patients’ information-seeking abilities and health information interpretation mediates or moderates the effects of provider practices on health literacy; 2) examine bidirectional communication processes between providers and patients/clients in the health care/health promotion system that affect health literacy, including systemic and cultural barriers that help create and sustain health literacy problems, as well as adaptation strategies used by providers and consumers to minimize health literacy problems (e.g., how patients’ use of print and electronic health information mediates or moderates their communication with providers); 3) examine how physicians’ or dentists’ nonverbal communication influences patients’ comprehension and implementation of health-related information; 4) examine the influence of social, contextual, and environmental factors (e.g., urban versus rural, housing type, workplace features, social support and social network members, etc.) on health literacy outcomes; 5) examine the media (including TV, radio, movies, newspapers, the Internet, and interactive systems) as a socializing agent of health literacy (e.g., determine how newspaper articles, TV drug advertising, soap operas, and medical dramas affect health literacy, and how different media can be used to communicate more effectively with consumers varying in health literacy levels); or 6) examine the factors that influence the desire for or processing of health literacy information (e.g., how self-efficacy in decision making and/or financial planning, time perspectives as presented in socio-emotional selectivity theory, ease of cognitive access via intuitive and reasoning processes, and coping and anxiety reduction behaviors influence the use of or desire to access health care knowledge).
Impacts and consequences of low health literacy.
1) Examine the relationship between health literacy and health disparities; 2) analyze the role of health literacy in the prevention and treatment of chronic diseases; 3) identify the relationship between health literacy variation and the ability to engage in informed decision making for a variety of health issues, such as chronic disease management and participation in clinical trials; 4) evaluate the magnitude of the problems caused by low levels of health literacy or by professionals’ lack of effective communication skills for adapting to the communication needs of consumers with differing levels of literacy; or 5) assess the role of health literacy as a mediator or moderator of health care access across adulthood.
Preventative interventions: education and training.
1) Explore the role of kindergarten through twelfth-grade (K–12) education systems in increasing levels of health literacy and improving health communication skills (e.g., assess the treatment of health literacy in K–12 health education, biology, or general science classes; assess the effectiveness of such course-work, curricula, and pedagogy on improving health literacy among school-age children; evaluate the effectiveness of arts-based interventions on children’s development of health literacy); 2) assess the role of K–12 education (e.g., in basic literacy skills such as reading, writing, comprehension, speaking, and listening skills, or in mathematics) on health literacy; or 3) determine the specific content and components of undergraduate, graduate, and in-service training experiences needed to adequately prepare provider groups to communicate with low-literacy patient populations (e.g., assess the effect of cultural competence on provider communication skills; assess innovative training approaches that allow providers to help patients deal with shame over low health literacy and facilitate negotiating the modern health care system); examine policies that support the development, implementation, and effectiveness of such training experiences; and evaluate the roles of information technology in training to improve health literacy.
Other health literacy interventions.
1) Evaluate the effectiveness of health literacy interventions directed at the general public, different audience segments, patients, providers, or the health care or public health systems (e.g., how health care systems can be designed to better support the information needs of consumers with different levels of health literacy; the effectiveness of interventions within the health care system that are designed to increase the access of intended audiences to relevant health information and appropriate materials); 2) examine the development and dissemination of effective information sources and materials for audiences with different levels of health literacy (e.g., how prevention campaigns should be designed to effectively communicate with audiences with differing levels of health literacy); 3) design and evaluate health literacy diagnostic and/or communication tools to help health care professionals identify and communicate more effectively with consumers with different levels of health literacy (e.g., technology tools for automatically converting health information to a variety of appropriate levels); 4) identify innovative strategies, practices, and policies currently in use that can be disseminated immediately to promote health literacy across the various participants in the health care systems; 5) conduct cost-effectiveness analyses of various health literacy interventions; or 6) further multilevel health literacy intervention approaches (e.g., by developing paradigms and/or statistical models to test the interaction of such variables as knowledge, prior education, cognitive status, social support, community influence, technology, and health care access on health care decisions).
Methodology and research technology development.
1) Assess the efficacy of current methods of health literacy assessment and develop, as needed, audience-appropriate methodologies to understand the prevalence of low health literacy in different populations, the interaction of low health literacy with other demographic and social factors, and the contribution of low health literacy to health care costs and health outcomes; 2) identify effective approaches of combining qualitative and quantitative methods to further knowledge of health literacy; 3) identify a core set of constructs, variables, and quantitative measures for conducting health literacy research; 4) develop and pilot-test new tools and technologies to identify health literacy barriers (e.g., an assessment to distinguish, among persons with low literacy skills, those who have learning disabilities or communication disorders such as auditory processing, aphasia, or hearing loss); or 5) in the context of understanding and promoting health literacy, develop technologies related to data reduction, data mining, and knowledge extraction; develop tools for meta-databases and integrative services to enhance the utility of existing databases; or develop new methods or technologies for timely, appropriate communication of pertinent health information and knowledge (e.g., as seen through the creation of telemedicine, or to enhance patient, doctor, or administrator decision making regarding health literacy, etc.).
Projects may employ any one or combination of study designs, research approaches, and data collection techniques. Secondary analyses of existing data sets as well as meta-analytic studies are also suitable for this PA. Multilevel, multidisciplinary, and interdisciplinary research is also encouraged, especially studies that incorporate individual, family, community, and societal mediators of health literacy in childhood and adulthood, or state-of-the-art health communication theory and knowledge. Researchers are encouraged to address ongoing investigations of prevention, healthy living, chronic disease management, patient-based health care, cultural competence, and health disparities to inform the research on health literacy. Research questions can focus on consumers, patients, clients, or other population groups; the strategies and tactics used by providers of medical and health information/communication to enable them to effectively reach literacy-challenged populations; or the influences of health literacy upon interactions between consumers, patients, clients, providers, and organizations or systems.
The Institute of Medicine’s 2004 report Health Literacy: A Prescription to End Confusion reviews the current body of knowledge about health literacy, and identifies actions for the promotion of health literacy in society. Applicants are encouraged to consult this report as a general reference.
This PA will use the NIH R01 award mechanism. As an applicant, you will be solely responsible for planning, directing, and executing the proposed project. This PA uses just-in-time concepts. It also uses the modular budgeting format (see http://grants.nih.gov/grants/funding/modular/modular.htm). Specifically, if you are submitting an application with direct costs in each year of $250,000 or less, use the modular budget format. This program does not require cost sharing as defined in the current NIH Grants Policy Statement at http://grants.nih.gov/grants/policy/nihgps_2003/NIHGPS_Part2.htm.
Applications must be prepared using the PHS 398 research grant application instructions and forms (rev. 5/2001). Applications must have a Dun and Bradstreet Data Universal Numbering System (DUNS) number as the Universal Identifier when applying for federal grants or cooperative agreements. The DUNS number can be obtained by calling 1-866-705-5711 or through the website at http://www.dunandbradstreet.com/. The DUNS number should be entered on line 11 of the face page of the PHS 398 form. The PHS 398 document is available at http://grants.nih.gov/grants/funding/phs398/phs398.html in an interactive format. For further assistance, contact GrantsInfo by calling 301-435-0714 or e-mailing [email protected].
Applications submitted in response to this PA will be accepted at the following receipt dates: 13 October 2004, 13 October 2005, and 13 October 2006.
Contact: For the complete listing of contacts, please consult the full PA, available online at http://grants1.nih.gov/grants/guide/pa-files/PAR-04-116.html. Reference: PA No. PAR-04-116
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0724a15345353PerspectivesCorrespondencePrenatal Lead Exposure and Schizophrenia: A Plausible Neurobiologic Connection Guilarte Tomás R. Department of Environmental Health Sciences, The Johns Hopkins University, Bloomberg School of Public Health, Baltimore, Maryland E-mail:
[email protected] work on Pb2+ and the NMDAR is supported by grant ES06189 from the National Institute of Environmental Health Sciences.
The author declares he has no competing financial interests.
Editor’s note: In accordance with journal policy, Opler et al. were asked whether they wanted to respond to this letter, but they chose not to do so.
9 2004 112 13 A724 A724 Publication 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 April issue of EHP, Opler et al. (2004) raise the intriguing possibility that prenatal exposure to the ubiquitous developmental neurotoxicant lead (Pb2+) may be associated with schizophrenia, an adult psychiatric disease. Although the study has certain limitations that the authors discussed, it brings to light the possibility that prenatal Pb2+ exposure may be a risk factor for the expression of schizophrenia later in life. If an association between developmental Pb2+ exposure and schizophrenia exists, then identifying plausible neurobiologic substrate(s) would be useful in future studies. A common and potentially critical link between developmental Pb2+ exposure and schizophrenia is the disruption of glutamatergic synaptic activity—specifically, hypoactivity of the N-methyl-d-aspartate subtype (NMDAR) of glutamatergic receptors.
The “glutamatergic hypothesis” of schizophrenia originated from observations that administration of NMDAR noncompetitive antagonists exacerbates psychotic symptoms in schizophrenics and mimics schizophrenia in nonpsychotic subjects (Coyle et al. 2003; Konradi and Heckers 2003). Further, the administration of such antagonists in animals models certain aspects of the disease. There is experimental evidence that Pb2+ is a potent and selective inhibitor of the NMDAR, and the NMDAR plays an important role in neuronal development, synaptic plasticity, and learning and memory (Nihei and Guilarte 2001). Similar to rats exposed to Pb2+ during development, several lines of evidence have implicated NMDAR hypofunction in the pathophysiology of schizophrenia (Coyle et al. 2003; Konradi and Heckers 2003).
Developmental exposure to Pb2+, in the same concentration range as implied in the work by Opler et al. (2004), alters gene and protein expression of NMDAR subunits in the rat brain (Nihei and Guilarte 2001). A consistent change in NMDAR subunits measured in young adult Pb2+-exposed rats is a decrease in NR1 subunit gene expression (Nihei and Guilarte 2001). These findings resemble some of the changes in NMDAR subunit expression described in the brain of schizophrenic patients (Konradi and Heckers 2003; Tsai and Coyle 2002). Further, there is compelling evidence for a common molecular target, the glycine modulatory site of the NMDAR. A proposed mechanism by which Pb2+ inhibits NMDAR function is by binding to a divalent cation site associated with the glycine site and allosterically inhibiting glycine binding (Hashemzadeh-Gargari and Guilarte 1999). The significance of the antagonistic action of Pb2+ at the glycine site of the NMDAR is that studies have identified abnormalities associated with schizophrenia that interfere with the activation of the glycine modulatory site of the NMDAR (Coyle and Tsai 2004a). Further, the use of NMDAR glycine site agonists such as glycine, d-serine, or d-cycloserine in clinical trials has demonstrated some efficacy in ameliorating the negative symptoms and cognitive disabilities in schizophrenics (Coyle and Tsai 2004a, 2004b).
Although an environmental component to the etiology of schizophrenia has been proposed (Tsuang 2000), developmental Pb2+ exposure has not been considered a potential risk factor for schizophrenia until the article by Opler et al. (2004) was published. It is possible that in susceptible individuals, the presence of Pb2+ during the development of the central nervous system may be directly related or may contribute to the expression of schizophrenia later in life.
==== Refs
References
Coyle JT Tsai G 2004a The NMDA receptor glycine modulatory site: a therapeutic target for improving cognition and reducing negative symptoms in schizophrenia Psychopharmacology 174 32 38 15205876
Coyle JT Tsai G 2004b NMDA receptor function, neuroplasticity, and the pathophysiology of schizophenia Int Rev Neurobiol 59 491 515 15006500
Coyle JT Tsai G Goff D 2003 Converging evidence of NMDA receptor hypofunction in the pathophysiology of schizophrenia Ann NY Acad Sci 1003 318 327 14684455
Hashemzadeh-Gargari H Guilarte TR 1999 Divalent cations modulate N -methyl-d -aspartare receptor function at the glycine site J Pharm Exp Ther 290 1356 1362
Konradi C Heckers S 2003 Molecular aspects of glutamate dysregulation: implications for schizophrenia and its treatment Pharmacol Ther 97 153 179 12559388
Nihei MK Guilarte TR 2001 Molecular changes in glutamatergic synapses induced by Pb2+ : association with deficits of LTP and spatial learning Neurotoxicology 22 635 643 11770885
Opler MGA Brown AS Graziano J Desai M Zheng W Schaefer C 2004 Prenatal lead exposure, δ-amino-levulinic acid, and schizophrenia Environ Health Perspect 112 548 552 15064159
Tsai G Coyle JT 2002 Glutamatergic mechanisms in schizophrenia Annu Rev Pharmacol Toxicol 42 165 179 11807169
Tsuang M 2000 Schizophrenia: genes and environment Biol Psychiatry 47 210 220 10682218
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0724b15345352PerspectivesCorrespondenceActivities and Organophosphate Exposures: Need for the Numbers Krieger Robert I. Zhang Xiaofei Personal Chemical Exposure Program, Department of Entomology, University of California, Riverside, Riverside, California, E-mail:
[email protected] authors declare they have no competing financial interests.
9 2004 112 13 A724 A725 Publication 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
The article “Agricultural Task and Exposure to Organophosphate Pesticides among Farmworkers” (Coronado et al. 2004) seems to be founded on an erroneous premise and presents virtually no data to estimate levels of worker or child exposure. Useful data generated in conjunction with this research probably exists, but they were not published.
In the abstract, Coronado et al. (2004) state that
Little is known about pesticide exposure among farmworkers, and even less is known about the exposure associated with performing specific tasks.
The investigators open weakly by ignoring the substantial exposure (amount per person) data available related to work tasks of handlers [Pesticide Handlers Exposure Database; U.S. Environmental Protection Agency (EPA) 1995] and harvesters (U.S. EPA Transfer Coefficients) in the open literature and regulatory files of registrants and the U.S. EPA (U.S. EPA 1998).
We commend Coronado et al. (2004) for their use of a very large random sample of 213 farmworkers from 24 communities. The sensitive metabolite analyses of urine were reported as “percent detectable dimethyl metabolites” without reference to the total amounts measured in the various urine specimens. This is unacceptable for exposure assessment if their intent was, as they stated, to “examine the association between specific agricultural tasks and levels of exposure among adult workers and children living in the same household.” Failure to report urine metabolite levels deprives readers of the opportunity to transform percentages to dose, a measure of exposure. Dose (micrograms per person) defines the relationship of agricultural task to organophosphate (OP) exposure. Coronado et al. (2004) must have calculated the metabolite levels, but their failure to present those data seriously devalues the contribution and the cooperation of their subjects.
Coronado et al. (2004) reported the percentage of detectable dimethyl urinary metabolites in children (n = 211; 2–6 years of age). These data do not permit estimation of dose, and they prohibit full evaluation of the relationship of exposure from parents’ work tasks or other sources to the dimethyl metabolites from residential exposures, particularly diet (Krieger et al. 2003). It seems that the urine OP metabolite levels of children are more likely linked to dietary exposure (Zhang and Krieger 2004) than to environmental sources (Lowenhurz et al. 1997) proposed by Coronado et al. (2004). Meaningful discussion is again prohibited by the lack of metabolite urine levels presented.
The data presented by Coronado et al. (2004) are not adequate. We believe that the metabolite levels in urine should be published in EHP or otherwise made available to investigators.
==== Refs
References
Coronado GD Thompson B Strong L Griffith WC Islas I 2004 Agricultural task and exposure to organophosphate pesticides among farmworkers Environ Health Perspect 112 142 147 14754567
Krieger RI Dinoff TM Williams RL Zhang X 2003 Preformed biomarkers in produce inflate human organophosphate exposure assessements [Letter] Environ Health Perspect 111 A688 14527854
Lowenhurz C Fenske RA Simcox NJ Bellamy G Kalman D 1997 Biological monitoring of organophosphorus pesticide exposure among children of agricultural workers Environ Health Perspect 105 1344 1353 9405329
U.S. EPA 1995. Pesticide Handlers Exposure Database. Washington, DC:U.S. Environmental Protection Agency, Office of Pesticide Programs, Occupational and Residential Exposure Branch.
U.S. EPA 1998. EPA Series 875—Occupational and Residential Exposure Test Guidelines. Group B—Postapplication Exposure Monitoring Test Guidelines. Washington, DC:U.S. Environmental Protection Agency, Office of Pesticide Programs.
Zhang X Krieger RI 2004 Dialkyl phosphates (DAPs) in produce confound biomonitoring in organophosphate risk assessment [Abstract] Toxicologist 78 528
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0726a15345355PerspectivesCorrespondenceElectromagnetic Fields and Free Radicals Stevens Richard G. University of Connecticut Health Center, Farmington, Connecticut, E-mail:
[email protected] author declares he has no competing financial interests.
9 2004 112 13 A726 A726 Publication 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
The article “Magnetic-Field–Induced DNA Strand Breaks in Brain Cells of the Rat” by Lai and Singh (2004) is interesting. The possibility that exposure to anthropogenic nonionizing radiation and/or electromagnetic fields (EMFs) might increase oxidative potential and free radical burden in cells may be a unifying theme for possible adverse biological consequences. Two articles published in EHP in the past explored two ideas in this regard. In the first article, we (Stevens and Kalkwarf 1990) pointed out a) that ferritin has a stable magnetic moment of 3.8 Bohr magnetons, and b) that on the basis of reports from Bawin and Adey (1976) and others that EMFs could alter calcium homeostasis, increases in free radicals could be expected. In the second article, I postulated specifically that “EMF-induced loss of iron from its intracellular storage protein, ferritin, might increase oxidative stress” (Stevens 1993).
This is an intriguing area of inquiry at the scientific level that may also have health implications.
==== Refs
References
Bawin SM Adey R 1976 Sensitivity of calcium binding in cerebral tissue to weak environmental electric fields oscillating at low frequency. Proc Natl Acad Sci USA 73 1999 2003
Lai H Singh NP 2004 Magnetic-field-induced DNA strand breaks in brain cells of the rat Environ Health Perspect 112 687 694 15121512
Stevens RG Kalkwarf DR 1990 Iron, radiation, and cancer Environ Health Perspect 87 291 300 2269234
Stevens RG 1993 Biologically based epidemiological studies of electric power and cancer Environ Health Perspect 101 suppl 4 93 101 8206047
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0726bPerspectivesCorrespondenceElectromagnetic Fields: Lai’s Response Lai Henry Department of Bioengineering, University of Washington, Seattle, Washington, E-mail:
[email protected] author declares he has no competing financial interests.
9 2004 112 13 A726 A726 Publication 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
I agree with Stevens that free radicals and changes in oxidative state in cells could play an important mediating role in some biological effects of nonionizing electromagnetic fields (EMFs), such as DNA damage. Certainly, cellular iron metabolism affects these processes. Genetic damage in cells can lead to malignancy and cancer. However, excessive cumulative genetic damages could also result in cell death. One possibility is that EMFs may be useful in treating cancer.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0726c15345357PerspectivesCorrespondenceComplexity of Factors Involved in Human Population Growth Hobbs Larry Inland Whale, Bainbridge Island, Washington, E-mail:
[email protected] Charles Systemic Management Studies Program, National Marine Mammal Laboratory, Seattle, Washington, E-mail:
[email protected] authors declare they have no competing financial interests.
9 2004 112 13 A726 A727 Publication 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
We would like to thank Bob Weinhold (2004) for his informative article documenting the issues facing humans in regard to infectious disease and the growing concern within the medical community that traditional thinking, approaches, and methods may well be inadequate to face the challenges ahead. We would also like to thank Steven Salmony (2004) for his thoughtful letter regarding Weinhold’s (2004) article, in which he presents another extremely important issue: that of human population growth and its interconnection with food resources. Both of these articles report the results of good science, and both describe well some of the critical issues facing humans at this time. We would like to present another viewpoint that we believe is both more helpful and more accurate for describing the problems we face and for setting research and decision-making goals that will involve the health of all systems.
Our approach (Fowler and Hobbs 2002, 2003) stems from systemic thinking as a paradigm that is emergent from modern systems theory, cybernetics, and information theory from their beginnings in the late 1940s. Basically, this way of thinking posits that all things are intricately interconnected in very complex ways, so that any action (or inaction, for that matter) will always result in a variety of consequences. Some of these we can predict and some we cannot; some will be evaluated as positive and some will be evaluated as negative in human value systems. Examples of these systemic reactions can be given for any field of inquiry (e.g., environmental, social, political, religious, personal) and at any level of organization (e.g., individual, species, ecosystem, biosphere); what we find is that there is never a single cause or a single outcome. It is always more complex than that. As humans, we have been able to ignore this complexity until very recently because simple cause-and-effect models were accurate enough to help us deal with the problems we faced.
However, as we have become more sophisticated with our technologies, we are experiencing unprecedented success at altering our world. The resulting changes are so profound that simple models no longer adequately describe the problems or define goals and guidelines to solve these problems. Certainly, as Hopfenberg (2003) so clearly pointed out, humans are biological organisms, and food availability is one of the factors that contribute to the wealth of factors that determine population size. It is, however, also true that the number of other factors that influence human population size is beyond human capacity to list, comprehend, and synthesize. We cannot measure them all nor can we accurately weigh the relative importance of each factor’s influence on the actual number of humans (e.g., disease, parasites, social upheaval, religious viewpoints, economics).
Each such factor is, in turn, influenced by other factors. For example, weather patterns influence the amount of food available. Ocean currents influence weather patterns; the orbiting of the earth and moon influence ocean currents; the orbits of other planets and the gravitational forces of the sun and other celestial bodies influence the orbits of the earth and moon; and so on. In each case, there are multitudes of other factors involved. The amount of food available is dependent on, or influenced by, microbes, other consumers, and predators and prey at all levels. A huge variety of physical forces is also at play in influencing primary and other levels of production, including volcanoes, hurricanes, floods, forest fires, and various human influences such as the use of pesticides and fertilizers and increased carbon dioxide production.
Human population numbers are also dependent on an enormous number of factors beyond food, including disease and all the other factors that were listed by Weinhold (2004). Had we been unable to curtail the effects of smallpox, for example, the human population would probably be smaller than it is today, as is the case for so many wildlife species whose populations are regulated, in part, by the effects of disease. However, when considering human population numbers, human value systems, economics, politics, and religion, all factors over which we have some limited measure of control, must also be taken into account.
We believe that any approach to dealing with human problems must take into account all of this complexity or it will lead to more problems. A systemic approach, such as we propose in our work (Fowler and Hobbs 2002, 2003), takes into account all of this complexity and also gives empirical guidelines for how to deal with the problems. It not only allows us to deal with how much food can be sustainably extracted from the various resource systems to feed ourselves but addresses the deeper and, we believe, more important question: how many of us should there be to feed?
==== Refs
References
Fowler CW Hobbs L 2002 Limits to natural variation: implications for systemic management Anim Biodiversity Conserv 25 2 7 46
Fowler CW Hobbs L 2003 Is humanity sustainable? Proc Roy Soc Lond B Biol Sci 270 2579 2583
Hopfenberg R 2003 Human carrying capacity is determined by food availability Popul Environ 25 2 109 117
Salmony SE 2004 Food and population growth. [Letter] Environ Health Perspect 112 A339 A340 15121530
Weinhold B 2004 Infectious disease: the human cost of our environmental errors Environ Health Perspect 112 A32 A39 14698949
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0728a15345360PerspectivesCorrespondenceHeavy-Duty Engine Emissions: Response Arey Janet Department of Environmental Sciences, Air Pollution Research Center, University of California, Riverside, Riverside, California, E-mail:
[email protected] author declares she has no competing financial interests.
9 2004 112 13 A728 A728 Publication 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 his letter, Schaeffer concludes that because of the ongoing changes in diesel technology, “establishing standardized reference materials [of diesel exhaust particles (DEPs)] will be particularly challenging.” As amply illustrated by the work of DeMarini et al. (2004) and Singh et al. (2004), which prompted my commentary (Arey 2004), the effort is worth making because multidisciplinary studies on representative DEP samples are needed if meaningful assessments of the health hazards associated with DEPs are to be made. DeMarini et al. (2004) and Singh et al. (2004) highlighted the chemical, physical, and biological differences between two widely used DEP samples, one mainly studied for pulmonary toxicity and the other for genotoxicity; before their studies, the chemical composition and biologic activity of the samples had not been compared.
In his letter, Schaeffer describes the Advanced Collaborative Emissions Study (ACES), an important diesel assessment project currently in the planning stage by the Health Effects Institute (Boston, MA). Perusing Warren’s presentation on the project (Warren 2004) cited by Schaeffer, I found that the utility of standard reference materials that allow for collaborations and exhaustive characterization of DEPs is reinforced by several issues Warren highlighted; for example, which of the “794 measurements under consideration” should be made; what should the results be compared to; and what health effect testing should be conducted? Until we fully understand the mechanisms of action of diesel and ambient particles that are involved in their adverse health effects, we need more multidisciplinary, collaborative efforts to study samples that can be shared among researchers.
==== Refs
References
Arey J 2004 A tale of two diesels Environ Health Perspect 112 812 813 15175165
DeMarini DM Brooks LR Warren SH Kobayashi T Gilmour MI Singh P 2004 Bioassay-directed fractionation and Salmonella mutagenicity of automobile and forklift diesel exhaust particles Environ Health Perspect 112 814 819 15175166
Singh P DeMarini DM Dick CAJ Tabor DG Ryan JV Linak WP 2004 Sample characterization of automobile and forklift diesel exhaust particles and comparative pulmonary toxicity in mice Environ Health Perspect 112 820 825 15175167
Warren J 2004. Update on the ACES Diesel Assessment Program. Available: http://www.healtheffects.org/Slides/AnnConf2004/Warren.pdf [accessed 1 July 2004].
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0728b15345360PerspectivesCorrespondenceMonitoring for Asbestos: U.S. EPA Methods Callahan Kathleen C. U.S. Environmental Protection Agency, Region 2, New York, New York, E-mail:
[email protected] author declares she has no competing financial interests.
Editor’s note: In accordance with journal policy, Landrigan et al. were asked whether they wanted to respond to this letter, but they chose not to do so.
9 2004 112 13 A728 A728 Publication 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
I would like to correct a misimpression about the methods used by the U.S. Environmental Protection Agency (EPA) in monitoring for asbestos in the air following the collapse of the World Trade Center in “Health and Environmental Consequences of the World Trade Center Disaster” (Landrigan et al. 2004). The authors state that
More than 10,000 ambient air samples from lower Manhattan were tested for asbestos by the U.S. EPA using phase-contrast light microscopy (PCM) to identify fibers > 5 mm in length; more than 8,000 of these samples were also examined by transmission electronic microscopy (TEM) to identify fibers of ≥0.5 mm in length.
This suggests that the U.S. EPA placed more emphasis on the analysis of asbestos in air samples using phase-contrast light microscopy (PCM) than those examined by transmission electron microscopy (TEM). This is not the case.
Recognizing the potential asbestos hazard, the U.S. EPA initiated its asbestos environmental sampling on the afternoon of September 11, employing TEM analysis as the primary method of recording the presence of asbestos fibers. The agency relied more heavily on the TEM data because PCM analysis cannot distinguish asbestos from other mineral fibers and would therefore not provide as accurate a measure of airborne asbestos concentrations as TEM.
As directed in the procedures outlined in the Asbestos Hazard Emergency Response Act (AHERA) (U.S. EPA 1987), TEM counts were recorded for both short (0.5–5 mm) and long (> 5 mm) asbestos fibers. The U.S. EPA’s World Trade Center website (U.S. EPA 2004) summarizes the results of 9,604 asbestos samples from 22 monitoring stations in lower Manhattan that were analyzed by TEM, not the 8,000 samples cited in the article (Landrigan et al. 2004).
Most of the asbestos samples were also analyzed by PCM. The PCM analysis was performed to provide ancillary information about total fiber counts and data for the Occupational Safety and Health Administration.
Because there has been much public confusion about the use of the two analytic methods in the World Trade Center response, I felt it was especially important to correct and clarify that the U.S. EPA used the most accepted and appropriate method to protect the health of residents and response workers in the aftermath of the disaster.
==== Refs
References
Landrigan PJ Lioy PJ Thurston G Berkowitz G Chen LC Chillrud SN 2004 Health and environmental consequences of the World Trade Center disaster Environ Health Perspect 111 16 731 739 15121517
U.S. EPA 1987. Asbestos Hazard Emergency Response Act. 40 CFR Part 763, Subpart E – Asbestos Containing Materials in Schools. Washington, DC:U.S. Environmental Protection Agency.
U.S. EPA 2004. World Trade Center Website. Available: http://www.epa.gov/wtc [accessed 15 July 2004].
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0729a15345361PerspectivesCorrespondenceEvaluating the Toxicity of Chemical Mixtures LeBlanc Gerald A. Olmstead Allen W. Department of Environmental and Molecular Toxicology, North Carolina State University, Raleigh, North Carolina, E-mail:
[email protected] authors declare they have no competing financial interests.
Editor’s note: In accordance with journal policy, Tinwell and Ashby were asked whether they wanted to respond to this letter, but they chose not to do so.
9 2004 112 13 A729 A730 Publication 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
Tinwell and Ashby (2004) provided a detailed evaluation of the joint action of a mixture of estrogenic chemicals using the immature rat uterotrophic assay. The researchers demonstrated that a mixture of estrogenic chemicals in which each individual chemical was present in the mixture at levels approximating the no observed effect level (NOEL) elicited a measurable response. This work advances our understanding of the toxicity of endocrine-active substances, and Tinwell and Ashby are to be commended for providing detailed results of their experiments suitable for evaluation by others.
The analysis of the data, however, stopped short of providing insights into the joint action of mixtures of endocrine disruptors. Tinwell and Ashby (2004) proposed three avenues for the analysis of the joint action of chemicals. The first, a simple addition-of-effects approach, is overly simplistic and unrealistic, as demonstrated by the authors. The second, graphic isobole analysis, was rejected by the authors for any mixture in excess of three chemicals. We concur that isobole analysis poses limitations for more complex mixtures of chemicals. The third, concentration addition, was deemed impractical by Tinwell and Ashby due to the requirement of detailed characterization of the concentration–response relationship of each chemical within the mixture. We agree that analysis of mixtures toxicity using concentration addition requires an understanding of the toxicity of the individual constituents within a mixture. However, we disagree that such a data requirement should discourage efforts to model and predict toxicity of chemical mixtures using this approach. Results reported by Tinwell and Ashby (2004), along with published data cited by the authors, provided sufficient information on the toxicity of the individual chemicals for us to accurately model the joint action of the mixture based upon concentration addition.
The authors’ recommendation that toxicity of chemical mixtures be directly assessed on a case-by-case basis (Tinwell and Ashby 2004) would provide a Band-Aid but not a cure to the dilemma of characterizing the hazards of chemical mixtures. Chemical mixtures are ever varying with respect to constituents and to concentrations of those constituents. Granted, the individual toxicity of many, if not most, chemicals has not been adequately evaluated to provide the concentration–response information required for the joint evaluation of toxicity. Rather than avoid such endeavors, the scientific community should mobilize to generate such data; the data should be made available in the public domain; and, alternative approaches (i.e., in vitro analyses of ligand–receptor interactions) should be explored as means to rapidly generate surrogate data for use in mixtures toxicity assessments. Thanks to the efforts of investigators such as Tinwell and Ashby, who are generous with the data they have generated, a growing database exists for estrogenic chemicals. Hopefully, key agencies (i.e., the National Institute of Environmental Health Sciences, the U.S. Environmental Protection Agency) will take the initiative to generate public-domain databases on chemicals harboring other mechanisms of toxicity. With such data resources, we may someday have the ability to routinely model the toxicity of chemical mixtures.
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References
Tinwell H Ashby J 2004 Sensitivity of the immature rat uterotrophic assay to mixtures of estrogens Environ Health Perspect 112 575 582 15064164
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0729b15345361PerspectivesCorrectionCorrections 9 2004 112 13 A729 A729 Publication 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 “Cause-Specific Mortality and the Extended Effects of Particulate Pollution and Temperature Exposure” by Goodman et al. [
Environ Health Perspect 112:179–184 (2004)], Figures 2–4 were incorrect; the corrected figures appear below. EHP regrets the errors.
The April 2004 news article “Reaching across the Border with the SBRP” [
Environ Health Perspect 112:A278–A279 (2004)] listed an incorrect URL for the University of Arizona website where visitors may download a Spanish-language environmental toxicology textbook. The correct URL is http://superfund.pharmacy.arizona.edu/outreach.html.
In the May 2004 toxicogenomics news article “Diet and DNA” [Environ Health Perspect 112:A404 (2004)], the European Nutrigenomics Organisation (NuGO) was described as “a network of 22 scientists” when in fact it is a network of 22 organizations.
EHP regrets the errors.
Figure 2 Polynomial distributed lag analysis of total nontrauma mortality versus minimum temperature adjusted for same-day minimum temperature for ages (A) 0–64, (B) 65–74, and (C) ≥75 years. Percent increase in total mortality for each 1°C decrease in minimum daily temperature for lags 1–41 days fitted with a sixth-degree polynomial.
Figure 3 Polynomial distributed lag analysis of (A) cardiovascular, (B) respiratory, and (C) other mortality versus minimum temperature adjusted for same-day minimum temperature. Percent increase in cause-specific mortality for each 1°C decrease in daily minimum temperature for lags 1–41 days fitted with a sixth-degree polynomial.
Figure 4 Polynomial distributed lag analysis of total nontrauma mortality versus BS adjusted for minimum temperature for ages (A) 0–64, (B) 65–74, and (C) ≥75 years. Percent increase in total mortality for each 10-μg/m3 increase in mean BS for lags 0–40 days fitted with a sixth-degree polynomial.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0735a15384232EnvironewsForumLead: Washington’s Water Woes Adler Tina 9 2004 112 13 A735 A735 Publication 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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For at least two years the concentration of lead in Washington, D.C., drinking water has dramatically exceeded the action level at which the Safe Drinking Water Act requires water systems to address the problem. By this summer, additional steps had been taken to address water quality through treatment, but these steps will take months to become fully effective. Indeed, the controversy surrounding the problem resembles the plot of a political potboiler, and blood tests and water filters are still hot topics among Washingtonians.
Of approximately 130,000 residences served by the District of Columbia Water and Sewer Authority (DCWASA), an estimated 18% have lead service pipes. Lead is in some older solder and plumbing fixtures as well. Paint and dust remain the main sources of lead exposure in the United States, but on average 10–20% of U.S. environmental lead exposure comes from drinking water, according to the EPA. (Experts largely agree, however, that the Safe Drinking Water Act amendments have greatly reduced exposure from the lead service pipes that still serve many households in older communities throughout the country.) Lead exposure impairs intellectual and physical development in fetuses and young children. In adults, it appears to increase the risk for hypertension and kidney disease.
Under the Lead and Copper Rule of the U.S. Environmental Protection Agency (EPA), water systems are required to develop a plan to lower lead levels if 10% of residences tested exceed 15 parts per billion (ppb). According to Alexandra Teitz, minority counsel for the House Committee on Government Reform, 73% of one set of water samples from Washing-ton homes exceeded the action level, with numerous samples exceeding 100 ppb and some exceeding 300 ppb. Moreover, before 2002, DCWASA was required to test only 50 residences each year.
Washington’s recent water quality troubles may have begun as early as November 2000. That’s when health officials, with the EPA’s approval, stopped using chlorine disinfection because of its by-products. The city switched to a chlorine–ammonia compound called chloramine to disinfect the water, while using pH adjustments to control corrosion. Unbeknownst to scientists and water utilities at the time, says Johnnie Hemphill, interim director for public affairs at DCWASA, pH adjustments are not as effective without chlorine. The absence of chlorine was not implicated until 2004—water system officials used chlorine in April and May of that year, and lead levels temporarily dropped, says Hemphill.
Consumers were first informed of the elevated lead levels in October 2002 via water bill inserts and a mailed brochure—means that some critics say tended to downplay the situation. As Hemphill explains it, the EPA then demanded that DCWASA explain whether it had failed to adequately monitor for lead or to adequately alert the public and the EPA about the elevated levels.
At the same time, members of Congress charged the EPA with failing to adequately protect the country’s drinking water. “The District and its residents were unknowingly forced to serve as a ‘canary in the coal mine’ for lead in drinking water,” asserted Representative Henry Waxman (D–California) in a statement presented at a congressional hearing in May 2004. “We have now been clearly warned about the flaws in our national program on lead in drinking water.”
In June, officials in Washington began adding phosphoric acid, a food additive, to a small portion of the system to protect the pipes. In July, DCWASA accelerated its timetable for replacing its lead service lines, promising to complete the job by 2010 (under EPA regulations, water systems need replace only a small percentage of public service lines per year and may approve lines using lead testing in lieu of actual pipe replacement). The city is offering loans to those residents who want to replace the part of the line on their property, which is the homeowner’s responsibility.
Blood tests, which the city has offered for free to residents, are indicating that the number of Washingtonians with high blood lead levels has not increased, Hemphill says. But this good news is overshadowed by studies showing that even at blood levels below the current cutoff of 10 micrograms per deciliter (μg/dL), lead can lower children’s IQ and cause behavior problems, says Lynn Goldman, an environmental health scientist at The Johns Hopkins University. A task force from the Centers for Disease Control and Prevention is considering recommending that the cutoff be lowered to 5 μg/dL, although Goldman notes that many experts think there is no threshold for the toxic effects of lead.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0735b15384232EnvironewsForumThe Beat Dooley Erin E. 9 2004 112 13 A735 A737 Publication 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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Scratching Out Data on Animal Antibiotic Effects
Farmers use antibiotics to help keep livestock healthy and make them grow faster. Because of concerns that this practice encourages microbial resistance to these drugs, the GAO studied research needs and federal agency efforts on the problem. The GAO’s April 2004 report found that agencies lack the data on linkages between antibiotic use in animals and emerging resistant bacteria that are needed to support research on human health risks. It recommended that the FDA expedite risk assessment of drugs used in animals that are also critical for human health, and that a plan be developed and implemented to fill data gaps in this area.
Counting Hydrocarbs to Curb U.S. Oil Hunger
In analyzing U.S. fossil fuel consumption, a Cornell University team has determined that energy conservation, along with the development and implementation of energy-efficient technologies, could save consumers $438 billion per year by 2014; conserve chemicals, paper, lumber, and metals; and reduce energy consumption by 33%—just over the amount provided by annual U.S. oil imports. In the June 2004 issue of Environment, Development, and Sustainability, the team reported that government subsidies of traditional energy industries, which cost American families about $410 each year in taxes, keep fuel prices artificially low, thus encouraging greater consumption and importation.
Obesity Report Cards
In June 2004, as part of a state antiobesity program, the nonpartisan Arkansas Center for Health Improvement began mailing annual health reports to the parents of all 450,000 Arkansan public school students. Schools submit each child’s weight and body mass index to the center, which then notifies parents of their child’s weight category and provides healthy lifestyle tips. The center found that 40% of the state’s children are either overweight or at risk for becoming so. Arkansas has also banned vending machines from elementary schools and set up school nutrition, exercise, and child health advisory committees.
Protein Discovery Sparks Hope for Malaria Vaccine
An international team of researchers reports finding a protein, PfEMP1, on the surface of red blood cells in young children infected with severe malaria, a major cause of morbidity and mortality among children in sub-Saharan Africa. This variant surface antigen could be the target for a vaccine to help children build up antibodies against the disease.
PfEMP1 is not found in other forms of malaria or in older people. Like other variant surface antigens, it enables infected cells to remain in the blood stream and reproduce, rather than being removed by the spleen. The report was published 3 May 2004 in The Journal of Experimental Medicine.
Renewed Commitment to Renewables
At June’s Renewables 2004 conference, a follow-up to the 2002 World Summit on Sustainable Development, representatives from 154 governments pledged anew to promote alternative energy sources, and the World Bank announced it will double loans for renewables projects by 2010. A total of 192 commitments were announced. Currently renewables make up only 5% of the world’s energy supplies.
Meeting attendants also adopted a political declaration, including a vision for equitable access to energy and increased energy efficiency. UNEP estimates that some 1.6 billion people do not have access to electricity. UNEP director Klaus Töpfer cited “energy poverty” as contributing to poverty overall and the associated environmental degradation.
Floods: Double the Devastation
Today, 25,000 people worldwide are killed each year by flooding, and many more face homelessness, disease, and crop failure following such catastrophes. Blaming such factors as deforestation, climate change, and population growth, United Nations University researchers announced in June 2004 that the number of people affected by devastating floods will double to 2 billion by 2050. Weather-related disasters cost the global economy $50–60 billion annually, and developing countries face the highest relative death toll from these disasters.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0736aEnvironewsForumAsthma: Smoking Clouds Treatment Benefits Susman Ed 9 2004 112 13 A736 A736 Publication 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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Although there are numerous ways children with asthma and allergies can reduce attacks and live a more normal life, researchers at the 2004 annual meeting of the American Academy of Allergy, Asthma, and Immunology (AAAAI) in San Francisco said cigarette smoking in the home virtually negates those interventions. “The data are clearly there,” said Robert Holzhauer, a clinical assistant professor of pediatrics at the University of Rochester School of Medicine and Dentistry. “We have unequivocal data to show that sidestream smoke is dangerous to people with asthma.”
In one study presented at the March meeting, Holzhauer and colleagues identified Rochester schoolchildren aged 3–7 who had mild persistent to severe persistent asthma. Children were assigned randomly to school-based care groups. One group received daily inhaled corticosteroids—a proven, effective treatment to prevent asthma attacks—at school, while the other did not.
Children in the treated group had fewer attacks and school absences; their parents reported fewer worries about their children’s health, work absences, and unexpected changes in plans. But if there was smoking in the home, those advantages were almost completely nullified. These findings have since been published in the May 2004 issue of Archives of Pediatrics and Adolescent Medicine.
In another presentation, Dennis Ownby, chief of allergy and immunology at the Medical College of Georgia, described his examination of the relationship between exposure to cats and dogs during the first year of life and the risk of allergy at age 6–7 years. He selected a birth cohort of 474 children, who were classified as having no exposure to cats or dogs during the first year of life, exposure to 1 cat or dog, or exposure to 2 or more cats or dogs.
Children of nonsmoking parents were significantly less likely to have allergies if they were exposed to 2 or more cats or dogs; about 14% of these children were allergic, compared to 37.5% of children with 1 pet and 36.8% of children with no pets. But this benefit was not seen in children of smoking parents. “This research shows that cigarette smoking is not innocuous to young children,” Ownby said. “We see evidence that it’s affecting their immune system.”
Holzhauer said parents must be convinced of how important it is to stop smoking in the home if they have children with asthma. However the doctors stopped short of advocating persuasion through legislation. “I think that if we were to report these parents to the authorities for child abuse, we would lose the children as patients,” Holzhauer said.
Rather, Kathleen Sheerin, a private-practice allergy specialist and chair of the AAAAI Public Education Committee, suggested that pediatricians and other health care professionals more strongly emphasize to parents the link between asthma, allergies, and smoking. She said, “We counsel parents to go to another room to smoke or to go outside if there is a child in the house.”
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0736bEnvironewsForumAgriculture: Tracking Antibiotics in Groundwater Barrett Julia R. 9 2004 112 13 A736 A736 Publication 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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Antibiotics are commonly used in food animal production to treat illness, promote growth, and ward off disease. These drugs and their metabolites appear in animal wastes and can eventually enter ground and surface waters following the common practice of applying manure to agricultural fields. Given that low levels of antibiotics can promote the development of microbial drug resistance, their presence in ground and surface waters constitutes an environmental health concern. Current methods for measuring trace amounts of antibiotics in water samples are costly and time-consuming, but researchers now show that a common food-test kit yields comparable information quickly and cheaply.
Researchers led by Kuldip Kumar at the University of Minnesota describe in the January–February 2004 Journal of Environmental Quality their use of the kits, which rely on the enzyme-linked immunosorbent assay (ELISA), a widely used technique based upon antibody recognition of target compounds. Food inspectors use the kits to test for drug residues in meat and milk. Using the kits, the researchers found trace amounts of tylosin, tetracycline, and chlortetracycline in surface and ground waters, field runoff, and swine manure. These results were confirmed with liquid chromatography–mass spectrometry (LC-MS). “Our bigger [question] is whether this small concentration of antibiotics in the environment is producing antibiotic-resistant bugs,” says Kumar.
The researchers, who are among the first to employ ELISA to test environmental samples for antibiotics, say the assay is as sensitive as LC-MS for detecting target compounds in parts per billion, but is quicker, easier, and less expensive ($5–15 per sample, compared to about $150 for LC-MS, including sample preparation and instrumentation). However, ELISA would best serve as a screening tool rather than a means of precise quantitation, because structural similarities between antibiotics, their metabolites or degradation products, and other compounds can yield false-positive results due to cross-reactivity. For example, the tetracycline test used by the researchers detected not just that drug but also several others in the same class.
Chemist Diana Aga of the University at Buffalo, who has also used ELISA to detect antibiotics in environmental samples, concurs that cross-reactivity is its key limitation. “This method shouldn’t be the basis of any policy making because ELISA is a semi-quantitative technique,” she says. “It’s a good technique because it is cheap and easy and fast, but it could also give you some false-positives or overestimate results.”
Despite this limitation, ELISA is a useful tool, says Ching-Hua Huang, an environmental engineer at the Georgia Institute of Technology. Researchers might use it to rapidly evaluate the presence of antibiotics in the environment, identify hot spots, and use the information for further studies. There is little dispute that antibiotics are in our source waters, says Huang—the question now is whether, and how, these compounds are linked to adverse effects in the environment.
Farm folly? Animal antibiotic use may contribute to microbial resistance, making it important to track these drugs in the environment.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0758aEnvironewsScience SelectionsThe Monster in the Closet: Mothballs’ Link to Non-Hodgkin Lymphoma Tenenbaum David J. 9 2004 112 13 A758 A758 Publication 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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Each year, according to the American Cancer Society, about 54,300 Americans are diagnosed with non-Hodgkin lymphoma (NHL), a cancer that originates in the lymph tissue, and about 19,400 people die from it. Several lines of evidence point to a possible association with pesticides. The incidence of NHL has roughly doubled since the 1970s, a few decades after a marked rise in U.S. household and agricultural pesticide use, and previous studies have found increases in chromosome aberrations and micronuclei in lymphocytes among pesticides applicators and some groups of farmers. This month, Ikuko Kato of Wayne State University and colleagues report an increased risk of NHL among New York State women with several types of pesticide exposure at home and on the job [EHP 112:1275–1281].
In the retrospective case–control study, 376 women recruited at NHL diagnosis in the late 1990s were compared to 463 age-matched controls. Cases were identified through the New York State Cancer Registry; controls were found through the Health Care Financing Administration or state Department of Motor Vehicle records. All participants answered a survey regarding past exposure to pesticides of all types. Whereas most previous studies of the association between cancer and pesticide exposure have focused on occupational exposure, Kato and colleagues also asked about home exposure to products such as mothballs, flea and ant killers, head lice treatments, and house plant products.
The highest risk of NHL was associated with pesticide exposure that began between 1950 and 1969. The authors speculate that this relationship could reflect a long latency period for NHL, or the historic use of compounds that are particularly toxic and now banned, such as the organochlorine pesticides.
Among women who used pesticides at home, the 25% with the highest use had a 62% greater chance of developing NHL than women who never used such products. Also, NHL risk was 2.12 times greater among women who had worked at least 10 years on a farm where pesticides were used, compared with women who never worked on a farm.
When analyzing use of specific products, the researchers found a significant correlation between use of mothballs and NHL, although not a clear dose–response relationship. The authors note that the active ingredients of mothballs may be inhaled or absorbed through the skin during contact with treated clothing. Naphthalene and paradichlorobenzene, common active ingredients in mothballs, are among the most common toxic chemicals detected in indoor air. Earlier studies correlated these compounds with blood diseases including aplastic anemia and hemolytic anemia. In vivo and in vitro studies have shown cytotoxicity, genotoxicity, and carcinogenicity for naphthalene, paradichlorobenzene, and their metabolites.
The findings were limited by the possibility of recall bias—cancer patients may be more motivated than controls to remember pesticide exposure. However, a counterbalancing bias may have existed: exposed controls, upon learning that pesticides were one of the major research interests, may have been more interested in participating than nonexposed controls. Additionally, fewer than 50% of the subjects could recall the names of pesticides that had been used at their workplaces. While establishing a correlation between exposure to pesticides and disease does not prove that the pesticides caused disease, it does add detail to the growing picture of pesticide-caused hematologic toxicity, and suggests a need for further study of mothballs in particular.
Saves clothes, not health. The naphthalene and paradichlorobenzene in mothballs may put those who use them at risk for non-Hodgkin lymphoma.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0758bEnvironewsScience SelectionsArsenic and Intellectual Function: Bangladeshi Children at Risk Tibbetts John 9 2004 112 13 A758 A759 Publication 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 Bangladesh, naturally occurring arsenic contaminates some 10 million tube wells that about 30–40 million people depend on for drinking water. Scientists have already established that adults with heavy exposure to arsenic can suffer adverse impacts on cognitive functions such as learning and memory. However, there have been no well-controlled studies of the neurological consequences of arsenic exposure in children. This month, a group of U.S. and Bangladeshi researchers led by Gail Wasserman of Columbia University provides evidence that even modest exposure to arsenic in drinking water is associated with reduced intellectual function in children in Araihazar, Bangladesh [EHP 112:1329–1333].
The investigators studied a group of 201 10-year-old children. The children’s parents were participating in an ongoing study of arsenic exposure among residents in a 25-square-kilometer region located about 30 kilometers east of Dhaka. The study site, Araihazar, was chosen because of its wide range of arsenic concentrations in drinking water.
The research team’s earlier survey of 6,000 contiguous tube wells in the region showed concentrations in individual wells ranging from less than 1 microgram per liter (μg/L) to 900 μg/L. Of the wells surveyed, 75% exceeded the World Health Organization (WHO) arsenic standard of 10 μg/L, and 53% exceeded the Bangladeshi standard of 50 μg/L.
In the current study, children and their mothers came to the research team’s field clinic for examination by a physician. The children provided urine specimens for the measurement of urinary arsenic and creatinine; about half also agreed to provide blood samples for measurement of blood lead and hemoglobin. Each child’s mother provided information about the family’s primary source of drinking water, and these sources were matched to the previously surveyed wells. In an effort to control for sociodemographic variables, the research team asked parents about parental age, education, and occupation, among other questions. The team also controlled for drinking water exposure to manganese, another known neurotoxicant (in their earlier survey, they had found that 82% of wells surveyed for manganese exceeded the WHO standard of 500 μg/L).
In addition to the medical evaluation, the children were assessed using an adaptation of the Wechsler Intelligence Scale for Children, version III (WISC-III). Because of the lack of standardized IQ measures in Bangladesh, Wasserman, a child psychologist, adapted the WISC-III for this cultural context. The WISC-III is a comprehensive series of tests that measures intellectual abilities such as comprehension and problem solving. Verbal subtests together provide a Verbal IQ, and a number of performance subtests (such as Picture Completion, Coding, Block Design, and Mazes) together provide a Performance IQ.
The researchers found that consumption of water contaminated by arsenic was associated with reduced intellectual function in a dose–response fashion. Children with exposures above 50 μg/L had significantly lower Performance and Full Scale scores than children with exposures under 5.5 μg/L. The children with the highest quartile of water arsenic also had marginally reduced Verbal scores. Lead and manganese exposures were not conclusively associated with impaired intellectual function, likely due to the low number of blood samples and confounding between arsenic and manganese, respectively.
The research team is working to curb exposure to arsenic in the study region. Since arriving in 2000, U.S. researchers, along with Bangladeshi colleagues, have overseen the installation of low-arsenic private and community wells and implemented a village education program that has successfully reduced some exposure. The authors note that the associations between arsenic and intellectual function were stronger for well-water concentrations than for urinary concentrations, which reflect recent exposure. The urinary concentrations at the time of testing may not reflect the full magnitude of the children’s earlier exposure, and the authors write that recently reduced exposure may explain the weaker associations between intellectual function and urine arsenic, compared to well-water arsenic.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0774aAnnouncementsBook ReviewUnnatural Disasters: Case Studies of Human-Induced Environmental Catastrophes Havenaar Johan M. Johan M. Havenaar, a psychiatrist, is director of the residency training program at Altrecht Institute for Mental Health Care in Utrecht, the Netherlands. He has published on the psychological consequences of human-made disasters, especially the Chernobyl disaster in Ukraine in 1986.9 2004 112 13 A774 A774 Publication 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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Angus M. Gunn
Westport, CT: Greenwood Press, 2004. 143 pp. ISBN: 0-313-31999-5, $55 cloth.
This book was written as a sequel to an earlier volume by the same author on the impact of natural disasters, The Impact of Geology on the United States: A Reference Guide to Benefits and Hazards (Westport, CT: Greenwood Press, 2001). In this second volume, Angus M. Gunn provides an overview of human-made environmental disasters. He shows that although technology has given humankind enormous control over the environment, it has also proven to be a threat to our survival. Gunn categorizes these human-made disasters into a number of subtypes—for example, mining disasters, dam failures, government actions, industrial explosions, oil spills, nuclear energy catastrophes, and terrorism. For each of these types of disaster, the book contains 26 case examples describing the events that led up to the disaster, the technical details of the event itself, the cleanup it necessitated, and its consequences. Some of the examples described in the book are famous—for example, the Minimata mercury poisoning in Japan, the Buffalo Creek dam collapse in West Virginia, and the near accident at the Three Mile Island nuclear plant in Pennsylvania. Others have been almost forgotten, such as the deliberately induced great famine in Ukraine in 1932, which resulted from the massive collectivization of farms ordered by Stalin.
The book is well written and successfully combines factual information with good journalism. Gunn professes to stick to the tried-and-true methods of the hard physical sciences. The consequence of this choice is that the book makes no reference to the societal and psychological impacts of disasters. Interestingly, it is exactly some of the case examples given in the book, such as the Three Mile Island incident, that have led to the recognition of the importance of these secondary effects. This omission is especially obvious in the case of terrorist events, which are precisely intended to cause fear and social discord as much as physical damage. Readers who are interested in the full picture of the impact of human-made disasters, including their underlying psychological and societal dynamics, should therefore turn to other volumes (e.g., Havenaar JM, Cwikel JG, Bromet EJ, eds. Toxic Turmoil: Psychological and Societal Consequences of Ecological Disasters. New York:Kluwer Academic, 2002). Another shortcoming of the book is that some of the medical information cited in the book appears to be incorrect, as presented in the case of the Chernobyl accident. Without due reference the author states that this accident caused a steady rise in miscarriages and birth defects in Belarus and will eventually have generated a death toll of over 5 million people. Claims such as these are entirely unfounded, as noted by Bard et al. [Chernobyl, 10 years after: health consequences. Am J Epidemiol 19:1–18 (1997)].
In summary, Unnatural Disasters is a well-written book containing a wealth of historical details about some classical environmental disasters, but it is not suitable as a reference for public health purposes.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0774bAnnouncementsNew BooksNew Books 9 2004 112 13 A774 A774 Publication 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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Biostatistics: A Bayesian Introduction
George G. Woodworth
Hoboken, NJ:John Wiley & Sons, 2004. 352 pp. ISBN: 0-471-46842-8, $89.95
Biostatistics and Epidemiology: A Primer for Health and Biomedical Professionals, 3rd ed.
Sylvia Wassertheil-Smoller
New York:Springer-Verlag, 2004. 243 pp. ISBN: 0-387-40292-6, $34.95
Burning Season: The Murder of Chico Mendes and the Fight for the Amazon Rain Forest
Andrew Revkin
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.6427ehp0112-00134115471723ResearchArticlesApplication of Benzo(a)pyrene and Coal Tar Tumor Dose–Response Data to a Modified Benchmark Dose Method of Guideline Development Fitzgerald D. James 1Robinson Neville I. 23Pester Beverly A. 11Environmental Health Service, Department of Health, Adelaide, South Australia, Australia2Division of Mathematical and Information Sciences, Commonwealth Scientific and Industrial Research Organisation, Urrbrae, Adelaide, South Australia, Australia3School of Chemistry, Physics and Earth Sciences, Flinders University, Bedford Park, South Australia, AustraliaAddress correspondence to J. Fitzgerald, Environmental Health Service, Department of Health, P.O. Box 6 Rundle Mall, Adelaide, South Australia 5000, Australia. Telephone: 61-8-82267134. Fax: 61-8-82267102. E-mail:
[email protected] Material is available online (http://ehp.niehs.nih.gov/members/2004/6427/supplemental.pdf).
We thank S. Culp for provision of detailed animal data from mouse studies; E. Weyand and L. Goldstein for discussions around 7H-benzo(c)fluorene; H. Rubin, A. Rubin, and J. Hengstler for discussions on interspecies BaP potency; and P. DiMarco and M. Moore for comments on the manuscript.
The opinions and scientific judgments expressed in this article do not necessarily represent the viewpoint of the authors’ organizations. Any guideline value proposed is not currently endorsed and may not necessarily represent what government legislative bodies eventually adopt.
The authors declare they have no competing financial interests.
10 2004 15 7 2004 112 14 1341 1346 29 4 2003 14 7 2004 Publication 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. Assessment of cancer risk from exposure to polycyclic aromatic hydrocarbons (PAHs) has been traditionally conducted by applying the conservative linearized multistage (LMS) model to animal tumor data for benzo(a)pyrene (BaP), considered the most potent carcinogen in PAH mixtures. Because it has been argued that LMS use of 95% lower confidence limits on dose is unnecessarily conservative, that assumptions of low-dose linearity to zero in the dose response imply clear mechanistic understanding, and that “acceptable” cancer risk rests on a policy decision, an alternative cancer risk assessment approach has been developed. Based in part on the emerging benchmark dose (BMD) method, the modified BMD method we used involves applying a suite of conventional mathematical models to tumor dose–response data. This permits derivation of the average dose corresponding to 5% extra tumor incidence (BMD0.05) to which a number of modifying factors are applied to achieve a guideline dose, that is, a daily dose considered safe for human lifetime exposure. Application of the modified BMD method to recent forestomach tumor data from BaP ingestion studies in mice suggests a guideline dose of 0.08 μg/kg/day. Based on this and an understanding of dietary BaP, and considering that BaP is a common contaminant in soil and therefore poses human health risk via soil ingestion, we propose a BaP soil guideline value of 5 ppm (milligrams per kilogram). Mouse tumor data from ingestion of coal tar mixtures containing PAHs and BaP show that lung and not forestomach tumors are most prevalent and that BaP content cannot explain the lung tumors. This calls into question the common use of toxicity equivalence factors based on BaP for assessing risk from complex PAH mixtures. Emerging data point to another PAH compound—7H-benzo(c)fluorene—as the possible lung tumorigen.
benzo(a)pyrenecancer risk assessmentdose-response modelingmodified benchmark dose methodPAH7H-benzo(c)fluorenesoil carcinogens
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Polycyclic aromatic hydrocarbons (PAHs) are found at a variety of contaminated sites throughout the world from industries such as coal gasification, coke production, aluminum production, iron and steel foundries, and creosote and asphalt production. Some PAHs, for example, the well-studied benzo(a)pyrene (BaP), are mutagenic and carcinogenic in experimental animals and probably in humans also [Boffetta et al. 1997; International Agency for Research on Cancer (IARC) 1987; Rubin 2001]. Therefore, health risk assessment of PAHs with a view to setting acceptable levels in contaminated soil is an important challenge for regulatory toxicologists.
Various methods are employed by agencies to estimate the risk posed by a certain level of soil contaminant, all of which have advantages and disadvantages. Threshold methods seek to determine a threshold below which no adverse effects are expected and that yield values such as the tolerable daily intake (TDI) or reference dose (RfD). These methods have the disadvantage that they hinge upon the no observed adverse effect level (NOAEL), which must be one of the chosen exposure levels in a toxicologic study. This exposure level is unlikely to be the actual threshold no effect level. For setting guideline levels based on cancer risk, the linearized multistage (LMS) model is used by the U.S. Environmental Protection Agency (EPA); this model assumes that the dose–response curve is linear in the low-dose region of the curve and that no threshold exists. This assumption has the disadvantages of not taking into account the complexities of the carcinogenic process and of not accommodating the possibility that the dose–response data may be best explained by a curve that is nonlinear in the low-dose region. In addition, the LMS approach requires a societal judgment on what constitutes an “acceptable” level of risk. Use of the LMS method results in the most conservative regulatory guidelines.
An alternative approach to the preparation of regulatory guidelines is the benchmark dose (BMD) method to model toxicologic end points. This method uses conventional mathematical models to obtain dose–response curves; that is, it does not assume linearity in the low-dose region. The BMD approach has been developed particularly by Crump (1984, 1995) and the U.S. EPA (1995). The current U.S. EPA default approach is to calculate the 95% lower confidence limit on a dose associated with a 10% extra tumor risk level (U.S. EPA 2003). However, the disadvantage of this method is that it applies a statistically derived 95% lower confidence limit on the dose–response curve that may not be valid for the small data sets often encountered in toxicology studies.
The National Health and Medical Research Council (NHMRC) of Australia embarked on a project to identify a cancer risk assessment process that avoided the extreme conservatism inherent in the assumption of low-dose linearity but that used any available dose–response data. The NHMRC Technical Working Party on Carcinogenic Risk Assessment for Soil Contaminants developed the modified BMD method (NHMRC 1999). This approach combines toxicologic dose–response data (usually from animal studies) and conventional mathematical models to generate dose–response curves for the chemical in question, even in the subexperimental region, and does not assume a linear relationship in this region. The approach avoids the conservatism of other BMD models by relying on best-fit modeling rather than 95% lower confidence limits on dose. For the various models applied, the technique determines an average dose at which 5% extra risk is incurred (BMD0.05); this level of risk was chosen because it is near the lower limit of responses that can be experimentally measured. Modifying factors reflecting the degree of uncertainty in extrapolating from animal exposure are then applied to yield a guideline dose for human exposure.
The purpose of this study was to use the modified BMD method to construct tumor dose–response curves for BaP using data from a recently published 2-year feeding study on female B6C3F1 mice (Culp et al. 1998). Previous rodent BaP feeding studies were also evaluated but either lacked sufficient data points or exposure times (Brune et al. 1981; Neal and Rigdon 1967) or suggested lesser sensitivity (Kroese et al. 2001). We used the BMD obtained from the Culp et al. (1998) data set to calculate a guideline value for BaP in soil.
The recent BaP bioassay (Culp et al. 1998) also examined the tumor response of female B6C3F1 mice to two coal tar mixtures. Although data generated would be reasonably assumed to be useful in assessing risk from exposure to complex mixtures, such data reveal some unresolved issues. These relate primarily to the difficulties of the simplistic BaP-equivalence approach of PAH additivity in the mixture, and the emerging notion that perhaps a PAH other than BaP ought to be the risk driver in these mixtures. This is further discussed in the present article.
In conducting this exercise, it has been necessary to adhere to the process set out in the nationally developed modified BMD method document (NHMRC 1999). However, as with any emerging field, refinements will be proposed over time that will decrease uncertainties in this risk assessment approach.
Materials and Methods
Dose calculation.
Culp et al. (1998) reported their dose data as dietary concentrations. To permit us to convert dietary concentrations to average daily doses on a body weight basis, S. Culp (National Center for Toxicological Research, Jefferson, AR, USA) provided information on the average amount of food consumed per animal per day and the average animal body weights. We calculated average doses in units of milligrams per kilogram per day for each of the 12 dose groups every 4 weeks until the end of the study, or until all animals were removed from the study in a particular dose group. These doses were then averaged to obtain an “average lifetime dose” for each group, as presented in Table 1. The doses for the coal tar mixtures are also given in BaP equivalents for comparison purposes. These BaP equivalents were calculated using previously published toxicity equivalence factors (TEFs) for PAH mixtures (Fitzgerald 1998).
Culp et al. (1998) reported results for several types of tumors induced by BaP and two coal tar mixtures. For BaP, forestomach tumors proved to be the most sensitive end point (Table 1), and these dose–response data are used here to determine a guideline value. In the case of the coal tar mixtures, lung tumors were shown to be the most sensitive end point (Table 1).
Mathematical modeling.
The NHMRC Technical Working Party document on the modified BMD method (NHMRC 1999) requires the construction of dose–response curves by fitting dose–response data with a suite of mathematical models. A suite is used to overcome bias when using a single model that is attempting to simulate an underlying but unknown model. The models are parametric and are the cumulative probability distribution functions (cdfs) for the well-known Weibull, log normal (probit), log logistic, gamma (multi-hit), and linear exponential (single hit) distributions as well as the truncated logistic and truncated normal distributions. The NHMRC recommends use of the Weibull, probit, and linear exponential models as a default selection with the option of an expanded or alternative selection of models. The expanded set of seven models has three parameters to be found from the data (except for the linear exponential, with two) and includes the zero dose background response. We chose three-parameter models for parsimony and because data sets for carcinogens often consist of just three or four data points.
Dose–extra-risk curves are determined by transformation of dose–response curves in the following way. If the cdfs are represented by P*(d) for a dose d, such that P*(d) ranges from 0 at d = 0 to 1 for a very high dose, then the fitted models all have the form P(d) = c + (1 – c)P*(d), where c is the background response. Extra risk is then defined by R = [P(d) – P(0)] ÷ [(1 – P(0)] = P*(d). At R = 5%, the BMD0.05 for a particular model is that value of d such that P*(d) = 0.05. This value is then determined for each model. Results from any particular curve are discarded only if it is clear that the model does not fit. The NHMRC procedure (NHMRC 1999) uses the BMD0.05 as determined by each acceptable model and then arithmetically averages them. The details of calculation of the BMD0.05 using the maximum likelihood estimate (MLE) for fitting cdf values to the data are provided in the Supplemental Material available online (http://ehp.niehs.nih.gov/members/2004/6427/supplemental.pdf).
Modifying factors.
To develop the guideline value from the BMD0.05 requires dividing the BMD0.05 by a modifying factor that takes into account interspecies extrapolation, intraspecies variability, the quality of the data set as a whole, the ability of the compound to induce malignant tumors, and the genotoxicity of the compound in question (NHMRC 1999).
Modifying factors for BaP.
Table 2 lists the modifying factors established for BaP, the numerical range of the factors, and the factors proposed for use in this guideline value development. The development of modifying factors for BaP was previously discussed in the use of a preliminary BMD method to derive a guideline value for BaP (Fitzgerald 1998). The modifying factor of 6,000 is slightly altered here in light of the additional data obtained from the recent studies by Culp et al. (1998).
BaP exhibits high lipophilicity and is metabolized in all tissues studied, and its metabolites are potent gene and chromosome mutagens, suggesting that the response of humans to BaP is likely to be more similar to that of mice than a maximum (default) inter-species extrapolation factor of 10 would imply (i.e., there is no evidence indicating that humans could be 10 times more sensitive than mice to BaP carcinogenicity). Several in vitro studies of BaP metabolism, mutagenicity, and DNA adduct formation using human and animal cells or tissue components suggest that BaP is not more toxicologically active in human cells than in mouse cells (Hengstler et al. 1999; Hsu et al. 1987; Oesch et al. 1977; Roggeband et al. 1993). However, an exception to this is seen with comparative studies of mammary cells exposed to BaP (Hengstler et al. 1999). Given this, and the limitations of extrapolating from in vitro data, we propose a modifying factor of 5 for interspecies extrapolation.
The intraspecies variability factor is set at 10 because of the lack of human data available. The adequacy of database factor, whereby the better the quality of the relevant tumor studies the smaller the factor, is given a value of 2 to reflect a high degree of confidence. The study of Culp et al. (1998) extended over the lifetime of the animals and included a suitable number of dose levels. The malignancy of BaP in a range of tissues is well established and—together with the Culp et al. (1998) bioassay study in which BaP induced tumors in the esophagus, tongue, larynx, and forestomach—engenders a proposed modifying factor of 9. The maximum factor of 5 for genotoxicity was assigned because this property of BaP is well established and BaP is a potent mutagen.
Thus, the overall modifying factor is 5 × 10 × 2 × 9 × 5 = 4,500.
Results
BaP.
The fitting of forestomach tumor dose–response curves to the the BaP data of Culp et al. (1998) (Table 1) is shown in Figure 1. Figure 1A depicts the plotted models relative to the Culp et al. BaP data, and Figure 1B shows the extra risk–dose curves derived from them. The calculated value of 0.362 mg/kg/day for BMD0.05, as shown in the Supplemental Material (http://ehp.niehs.nih.gov/members/2004/6427/supplemental.pdf), is an average from six of the models. The excluded model is the truncated normal model because it could not be fitted to the data. This lack of fit occurs when the curves are “supra-linear” or nearly so, as is the case here. This may also occur with some data sets for the truncated logistic model.
Development of a soil BaP guideline value from the BMD0.05.
Taking the BaP BMD0.05 of 0.362 mg/kg/day and applying the modifying factor of 4,500 yields the following guideline dose:
This yields the following maximum daily intakes (MDI) for adults (assuming 70 kg body weight) and children (assuming 13.2 kg for a 2-year-old child):
These MDIs represent the total daily BaP intake that should not be exceeded in order to safeguard human health. Some of this intake is assumed to come from food; consequently, the TDI from soil is calculated to be the MDI minus the intake from food, divided by 2 for a measure of safety and to allow for some exposures via air and water (Fitzgerald 1998).
In a previous BaP guideline value calculation (Fitzgerald 1998), a U.S. EPA upper estimate for BaP intake in food of 1.6 μg/day (U.S. EPA 1980) and a U.K. estimate of a child’s BaP intake being 40% of an adult’s intake (RPS 1995) were used. If we used these same values, the allowable daily intake for BaP from soil would be (5.6 – 1.6) ÷2 = 2.0 μg for adults and (1.06 – 0.64) ÷2 = 0.21 μg for children. Based on an assumed adult soil ingestion rate of 25 mg/day [Australia and New Zealand Environment Conservation Council (ANZECC) and NHMRC 1992], a BaP soil guideline value would be
For children, an assumed soil ingestion rate is 100 mg/day (ANZECC and NHMRC 1992); thus, a BaP soil guideline value would be
One of the key data sets in this approach is the estimate for daily dietary BaP intake. Better estimates for intake than those used above may be obtained from recent data from a U.S. study of 200 food items and 228 subjects (Kazerouni et al. 2001), which indicated that all adults in the study consumed < 0.16 μg BaP/day. Applying this to the above method, the allowable daily BaP intake from soil would be (5.6 – 0.16) ÷2 = 2.72 μg for adults and (1.06 – 0.06) ÷2 = 0.50 μg for children. Further, a BaP soil guideline based on adult soil ingestion would be
For children, the calculation would be
As previously suggested (Fitzgerald 1998), the least value of such calculations is proposed as the BaP soil guideline value, in this case, 5 ppm.
Coal tar mixtures.
Using the coal tar doses from the cancer bioassay study of Culp et al. (1998) (Table 1) to develop a guideline value is complicated by numerous factors. There is insufficient toxicologic information available on coal tar mixtures to confidently establish defensible modifying factors. In addition, there are no published MDI values for coal tar (or PAH) mixtures and no figures available on average coal tar (or PAH) intake from diet. Even if these figures were available, mixtures of coal tars and their bioavailability differ according to their source, the soil type, and degree of “aging” in the environment (Abdel-Rahman et al. 2002; Bordelon et al. 2000; Reeves et al. 2001). Consequently, any guideline value developed from cancer bio-assay data on a particular coal tar mixture may not apply to subsequently encountered coal tar mixtures.
Instead, a pragmatic approach commonly taken with PAH mixtures is to calculate the BaP equivalence dose, based on TEFs with BaP as the reference carcinogen (Boström et al. 2002; Fitzgerald 1998). For the present coal tar mixtures, this addition of BaP equivalents using previously proposed equivalence factors (Fitzgerald 1998) resulted in BaP equivalence doses approximately twice the actual BaP concentrations (Table 1). These calculations showed < 30% variance from BaP equivalence doses generated by two other TEF schemes (Larsen and Larsen 1998; Nisbet and LaGoy 1992). Not previously considered for TEFs were the naphthalene derivatives 1-methyl-naphthalene and 2-methylnaphthalene, which were prominent PAHs in the coal tar mixtures (Culp et al. 1998). Available toxicity data were limited, and TEFs of 0.001 were assigned to both isomers.
The most sensitive tumorigenic response to the coal tar mixtures was with lung tumors (Culp et al. 1998). Preliminary modeling of BaP equivalence doses and lung tumor data of coal tar exposures (not shown) revealed non-simple fits and considerable variability between the mixtures. Further detailed analysis is beyond the scope of the present study.
Discussion
The present study represents the first significant attempt to use the modified BMD method as developed in Australia for generating guideline values for environmental carcinogens. The present program focuses on BaP as the key surrogate for PAHs and builds on preliminary work in this area (Fitzgerald 1998). With this approach, we propose a BaP soil guideline value of 5 ppm. This would represent a significant departure from the current Australian soil guideline for BaP of 1 ppm that was based on consideration of proportionality of dietary BaP and related cancer risk derived from U.S. EPA LMS modeling (Fitzgerald 1991; National Environment Protection Council 1999).
In the absence of human data, the described method has employed experimental animal data. The BMD0.05 of 0.362 mg BaP/kg/day we used is considered a refinement of the 0.815 mg BaP/kg/day BMD0.05 determined in previous work, employing the MLE method with quantal Weibull and polynomial regression modeling of earlier bioassays (Fitzgerald 1998; Neal and Rigdon 1967). Although Neal and Rigdon’s data set was the best BaP tumor dose response available at the time and includes more groups in the low-dose region than does the data set of Culp et al. (1998), we consider it to be less suitable for BMD development principally because of the less-than-lifetime BaP exposures (3–6 months vs. 24 months).
Although the use of computer-based modeling in guideline development is quite sophisticated and reasonably defensible scientifically, the component of guideline value derivation that involves modifying factors is probably the most subjective part of the entire process. Nonetheless, such factors are used routinely in regulatory toxicology and are often termed uncertainty factors or safety factors. For some of these, for example, default interspecies extrapolation and intraspecies variability, there are empirical data to indicate a fair degree of confidence that they are not unreasonable although likely to be conservative (Dourson et al. 1996; Fitzgerald 1993; Lewis et al. 1990). Where information exists to allow a factor other than the default, for example, comparative toxicokinetic data or, as in the present case, a range of intuitive arguments around interspecies BaP extrapolation, a nondefault factor can be used.
For the “safety factor” portion of the overall modifying factors, namely, database adequacy, malignancy, and genotoxicity, judgment is somewhat subjective. Nonetheless, the suggested approach is based on internationally used assessment methods [NHMRC 1999; World Health Organization (WHO) 1994].
A further variable of the guideline value equation that has a major bearing on the outcome is the estimate of daily dietary BaP intake. Recent data indicating that BaP intake may be decreasing over time (Kazerouni et al. 2001; U.S. EPA 1980) could perhaps be explained by the stricter emission controls on industries that release PAHs; reduced PAHs in air pollution would mean reduced deposition on plants and reduced uptake by farm animals (Kazerouni et al. 2001).
The final variable affecting the guideline value generation is that of soil ingestion rate. For the present study, we used daily rates of 25 mg for adults and 100 mg for children because they are generally adopted by regulatory toxicologists in Australia (ANZECC and NHMRC 1992). However, we recognize that these values may be different in other countries (Gargas et al. 2000), although the intake used for children is similar to 95th percentile estimates determined recently for children residing near a U.S. Superfund site (Stanek and Calabrese 2000).
Potentially the most significant aspect of the data of Culp et al. (1998) is that concerning tumor responses to dietary coal tar mixtures in which the tumor profile was quite different from that with BaP, both qualitatively and quantitatively. Of particular note was the finding that, purely in terms of concentration, the BaP in the mixtures could explain the forestomach tumors induced by the mixtures but could not explain the lung tumors (Figure 2); BaP alone was a weak inducer or noninducer of lung tumors at the doses tested. Such preferential induction of lung tumors in mice by a PAH mixture compared with BaP has been previously reported (Weyand et al. 1995).
Speculatively, one may propose either that the action of BaP (or BaP equivalents) is synergized in the mixture milieu in a way that selectively induces lung tumors, or that some other component of the mixture is tumorigenic in the mouse lung. The latter notion, together with indication of a non-BaP compound interacting with lung DNA (Culp et al. 2000; Goldstein et al. 1998; Weyand and Wu 1995), has led to the finding that the causative agent may be 7H-benzo(c)fluorene (BcF) (Goldstein 2001; Koganti et al. 2000, 2001) and that in vivo bioavailability and metabolism of this PAH are probably much greater than for BaP (Koganti et al. 2001). Recent evidence further points to dihydrodiol and diol epoxide metabolites of BcF as being the proximate and ultimate carcinogenic moieties, respectively, that bind to mouse lung DNA (Wang et al. 2002). Recent studies have also examined separately the lung carcinogenicity of BcF (about 9 mg/kg/day) and equimolar BaP (about 10 mg/kg/day) given in the diet of lung-tumor–susceptible female A/J mice over 260 days (Weyand EH, personal communication; Weyand et al. 2002). The data showed that BcF increased the prevalence of mice with lung tumors (from 77% for BaP to 100%) but, most significantly, increased the multiplicity of lung tumors 33-fold (Weyand et al. 2002). This suggests that for future PAH risk assessments and setting of regulatory guidelines, more consideration of BcF levels will be needed, as well as some rethinking of the prominence afforded BaP and associated TEFs in current regulatory science (Goldstein 2001). Culp et al. (1998) did not report on BcF levels in the coal tar mixtures used in their studies.
A further possible consideration stems from the notion that lung tumorigenesis ought to be the cancer risk assessment driver for ingested PAH mixtures. That is, because lung tumorigenesis is the risk assessment driver for air/PAH inhalation, then evaluation of soil particle inhalation should be contemplated. However, mitigating against this are recent data suggesting that the deposition efficiency of airborne soil particles in the tracheobronchial and pulmonary regions of the lung is very low (Khalili and Thomas 2001).
The BaP forestomach tumor data from Culp et al. (1998) have been used to revise the U.S. EPA LMS cancer slope factor for BaP (Gaylor et al. 1998, 2000). Also, risk assessors have proposed BaP soil guideline values using TDI based on the LMS paradigm and “acceptable” lifetime cancer risk estimates (Boyd et al. 1999). It is beyond the scope of this article to examine such approaches. However, the present BMD method is a departure from LMS that does not operate on an assumption of low-dose linearity or attempt any policy decision on acceptable human population cancer risk. Instead, it makes fuller use of all the tumor dose–response data and is based on a more realistic central estimate.
Conclusion
We have proposed a guideline value for BaP in soil using a modified BMD method developed within the Australian regulatory toxicology community. As now required by the Australian government health authorities, this work will be extended to include other carcinogens that exist in a range of environmental media. Further work may also examine the general validity of the safety factors employed and whether scientific uncertainty around the 5% extra risk starting point may be reduced.
Figure 1 Suite of models fitted to BaP dose–response data (mouse forestomach tumors) reported by Culp et al. (1998). (A) MLE fitting of models except the truncated normal, which could not be fitted. (B) The extra-risk dose curves of (A) in the low-dose region around the 0.05 risk level and averaged dose at 0.362 mg/kg/day.
Figure 2 Comparison of dose responses for tumors reported by Culp et al. (1998), plotted for BaP alone and BaP content of coal tar mixtures. (A) Forestomach tumors. (B) Lung tumors.
Table 1 Doses for coal tar mixtures and BaP administered for 2 years in the diet of B6C3F1 mice,a and tumorigenic responses in forestomach and lung.
Mice with tumors/total mice
Dose group Concentration in diet (ppm) Average lifetime dose (mg/kg/day)b BaP equivalent dose (mg/kg/day)c Actual BaP dose (mg/kg/day) Forestomachd Lunge
Coal 0 0 0 0 0/47 2/47
Tar 100 12.4 0.051 0.023 2/47 3/48
Mix 1 300 35.8 0.15 0.066 6/45 4/48
1,000 121 0.49 0.222 3/47 4/48
3,000 367 1.46 0.675 14/46 27/47
6,000f 707 2.92 1.299 15/45 25/47
10,000f 1,234 5.01 2.268 6/41 21/47
Coal 0 0 0 0 0/47 2/47
Tar 300 36.4 0.21 0.100 3/47 4/48
Mix 2 1,000 124 0.72 0.342 2/47 10/48
3,000 339 1.97 0.936 13/44 23/47
BaP 0 0 1/48 5/49
5 0.65 3/47 0/48
25 3.5 36/46 4/45
100f 15.3 46/47 0/48
a Details from Culp et al. (1998) and S.J. Culp (personal communication); dose groups included zero dose controls, and animals in all groups were dosed for 2 years from 5 weeks of age.
b From animal weight and food intake data, averaged over the study period (Culp SJ, personal communication).
c From PAH levels in Culp et al. (1998) and from TEFs in Fitzgerald [1998; BaP, 1; dibenz(a,h)anthracene, 4; benz(a)anthracene, 0.1; benzo(b)fluoranthene, 0.1; benzo(k)fluoranthene, 0.1; indeno[1,2,3-c,d]pyrene, 0.1; anthracene, 0.001; benzo(g,h,i)perylene, 0.1; chrysene, 0.1; acenaphthene, 0.001; acenaphthylene, 0.001; fluoranthene, 0.01; fluorene, 0.001; naphthalene, 0.001; phenanthrene, 0.001; pyrene, 0.001].
d Forestomach papillomas and carcinomas.
e Alveolar and bronchial adenomas and carcinomas.
f At these doses, all tumor-bearing animals died before the end of the 2-year exposure period.
Table 2 Modifying factors for BaP.a
Factor Range of value BaP value
Interspecies extrapolation ≤1–10 5
Intraspecies variability 1–10 10
Database adequacy 1–2 (high) 2
3–7 (medium)
8–10 (low)
Malignancy 3–10 9
Genotoxicity 1–5 5
Overall factor 4,500
a See NHMRC (1999) and Fitzgerald (1998).
==== Refs
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Weyand EH Chen Y-C Wu Y Koganti A Dunsford HA Rodriguez LV 1995 Differences in the tumorigenic activity of a pure hydrocarbon and a complex mixture following ingestion: benzo(a )pyrene vs manufactured gas plant residue Chem Res Toxicol 8 949 954 8555410
Weyand EH Goldstein LS Reuhl KR Wang JQ Harvey RG 2002 Induction of lung tumors by 7H -benzo[c ]fluorene [Abstract] Toxicologist 61 909
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7167ehp0112-00134715471724ResearchArticlesQuinones and Aromatic Chemical Compounds in Particulate Matter Induce Mitochondrial Dysfunction: Implications for Ultrafine Particle Toxicity Xia Tian 12Korge Paavo 3Weiss James N. 3Li Ning 12Venkatesen M. Indira 4Sioutas Constantinos 25Nel Andre 121Division of Clinical Immunology and Allergy, Department of Medicine2The Southern California Particle Center and Supersite3Department of Physiology and Division of Cardiology, Department of Medicine, and4Institute of Geophysics and Planetary Physics, University of California, Los Angeles, California, USA5Department of Civil and Environmental Engineering, University of Southern California, Los Angeles, California, USAAddress correspondence to A. Nel, Department of Medicine, Division of Clinical Immunology and Allergy, UCLA School of Medicine, 52-175 CHS, 10833 Le Conte Ave., Los Angeles, CA 90095-1680 USA. Telephone: (310) 825-6620. Fax: (310) 206-8107. E-mail:
[email protected] work was supported by U.S. Public Health Service grants PO1 AI50495, RO1 ES10553, and RO1 ES10253 and by a U.S. Environmental Protection Agency (EPA) STAR award to the Southern California Particle Center and Supersite.
This work has not been subjected to the U.S. EPA for peer and policy review.
The authors declare they have no competing financial interests.
10 2004 7 7 2004 112 14 1347 1358 8 4 2004 7 7 2004 Publication 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. Particulate pollutants cause adverse health effects through the generation of oxidative stress. A key question is whether these effects are mediated by the particles or their chemical compounds. In this article we show that aliphatic, aromatic, and polar organic compounds, fractionated from diesel exhaust particles (DEPs), exert differential toxic effects in RAW 264.7 cells. Cellular analyses showed that the quinone-enriched polar fraction was more potent than the polycyclic aromatic hydrocarbon (PAH)–enriched aromatic fraction in O2•− generation, decrease of membrane potential (ΔΨm), loss of mitochondrial membrane mass, and induction of apoptosis. A major effect of the polar fraction was to promote cyclosporin A (CsA)–sensitive permeability transition pore (PTP) opening in isolated liver mitochondria. This opening effect is dependent on a direct effect on the PTP at low doses as well as on an effect on ΔΨm at high doses in calcium (Ca2+)-loaded mitochondria. The direct PTP effect was mimicked by redox-cycling DEP quinones. Although the aliphatic fraction failed to perturb mitochondrial function, the aromatic fraction increased the Ca2+ retention capacity at low doses and induced mitochondrial swelling and a decrease in ΔΨm at high doses. This swelling effect was mostly CsA insensitive and could be reproduced by a mixture of PAHs present in DEPs. These chemical effects on isolated mitochondria could be reproduced by intact DEPs as well as ambient ultrafine particles (UFPs). In contrast, commercial polystyrene nanoparticles failed to exert mitochondrial effects. These results suggest that DEP and UFP effects on the PTP and ΔΨm are mediated by adsorbed chemicals rather than the particles themselves.
apoptosisDEPsdiesel exhaust particlesPAHspermeability transition porepolycyclic aromatic hydrocarbonsquinonesultrafine particles
==== Body
There is increasing evidence that particulate pollutants induce inflammatory responses in the cardiorespiratory system (Nel et al. 1998; Nightingale et al. 2000; Saldiva et al. 2002). These proinflammatory effects have been linked to the ability of particulate matter (PM), such as diesel exhaust particles (DEPs), to generate reactive oxygen species (ROSs) and oxidative stress in macrophages, bronchial epithelial cells, and lung microsomes (Gurgueira et al. 2002; Hiura et al. 1999; Kumagai et al. 1997; Nel et al. 2001). The pro-oxidative effects of the intact particles can be mimicked by organic chemical components extracted from these particles (Hiura et al. 1999; Kumagai et al. 1997; Li et al. 2002). The PM-induced oxidative stress response is a hierarchical event, which is characterized by the induction of antioxidant and cytoprotective responses at lower tiers of oxidative stress and by pro-inflammatory and cytotoxic responses at higher levels of oxidative stress (Li et al. 2002; Xiao et al. 2003).
Mitochondrial damage is a key event in PM-induced cytotoxicity (Hiura et al. 1999, 2000). The initial response to PM is a decrease in mitochondrial membrane potential (ΔΨm) and increased O2•− production, followed by cytochrome c release and inner mitochondrial membrane damage (Hiura et al. 1999, 2000; Upadhyay et al. 2003). It is also of interest that the smallest and potentially most toxic ambient particles, ultrafine particles (UFPs), lodge inside damaged mitochondria (Li et al. 2003). UFPs have a physical diameter < 0.1 μm, which allows them to penetrate deep into the lung as well as into systemic circulation (Nemmar et al. 2002). Although it is still a matter of debate whether UFPs target the mitochondrion directly or enter the organelle secondary to oxidative damage (Li et al. 2003), PM-induced mitochondrial perturbation has important biologic effects, which include the initiation of apoptosis and decreased ATP production (Hiura et al. 2000). Although the particles themselves may play a role in mitochondrial damage, it has been demonstrated that the organic chemicals adsorbed on the particle surface mimic the effects of the intact particles (Hiura et al. 1999). These effects can also be reproduced by functionalized aromatic and polar chemical groups fractionated from DEPs by silica gel chromatography (Alsberg et al. 1985; Li et al. 2000). These compounds are toxicologically relevant because the aromatic fraction is enriched in polycyclic aromatic hydrocarbons (PAHs), whereas the polar fraction contains several oxy-PAH compounds, including quinones (Alsberg et al. 1985; Li et al. 2000). Quinones are able to redox cycle and to produce ROSs, whereas PAHs can be converted to quinones by cytochrome P450, epoxide hydrolase, and dihydrodiol dehydrogenase (Penning et al. 1999).
A key mitochondrial target for oxidizing chemicals is the permeability transition pore (PTP) (Jajte 1997; Susin et al. 1998; Zoratti and Szabo 1995). This calcium (Ca2+)-, voltage-, and pH-sensitive pore is permeant to molecules of < 1.5 kDa and opens in the mitochondrial inner membrane when matrix Ca2+ levels are increased, especially when accompanied by oxidative stress (Bernardi 1999; Kushnareva and Sokolove 2000; Zoratti and Szabo 1995). PTP opening causes massive in vitro mitochondrial swelling, outer membrane rupture, and release of proapoptotic factors such as cytochrome c (Susin et al. 1998). In addition, mitochondria become depolarized, causing inhibition of oxidative phosphorylation and stimulation of ATP hydrolysis. PTP opening is inhibited by cyclosporin A (CsA), which inhibits the peptidyl-prolyl cis-trans isomerase activity of cyclophilin D (Bernardi 1999). This has led to the proposal that PTP transition is mediated by a Ca2+-triggered conformational change of inner membrane proteins (Woodfield et al. 1998). However, although this model may explain the action of some PTP modulators, PTP open–close transitions are also regulated by physiologic factors, drugs, and chemicals (Jajte 1997; Kushnareva and Sokolove 2000). Walter et al. (2000) characterized endogenous ubiquinones that stimulate or inhibit pore function by means of a putative quinone binding site in the PTP.
The goal of our study was to clarify how redox-cycling DEP chemicals affect mitochondrial function, as well as to compare ambient UFPs with commercial nanoparticle effects on mitochondria. Aromatic, polar, and aliphatic chemical fractions, prepared by silica gel chromatography, were used to study CsA-sensitive mitochondrial swelling (PTP opening), ΔΨm, Ca2+ loading capacity, and mitochondrial respiration. We also compared isolated mitochondrial responses with perturbation of mitochondrial function in intact RAW 264.7 cells. Our data show that mitochondrial perturbation and induction of apoptosis by polar DEP chemicals involve CsA-sensitive PTP opening that can be mimicked by representative redox-cycling quinones present in DEPs. In contrast, the aromatic chemical fraction induced mostly CsA-insensitive mitochondrial swelling, which can be mimicked by a mixture of PAHs. Ambient UFPs induced a combination of aromatic and polar effects, whereas polystyrene nanoparticles were inactive.
Materials and Methods
Reagents.
Tetramethylrhodamine methyl ester (TMRM), propidium iodide (PI), sucrose, HEPES buffer salts, EGTA, ascorbic acid, succinate, malate, glutamate, carbonyl cyanide m-chlorophenylhydrazone (CCCP), alamethacin (Ala), and tetraphenylphosphonium chloride were from Sigma (St. Louis, MO). The annexin V–fluorescein isothio-cyanate (FITC) kit was obtained from Trevigen (Gaithersburg, MD). 3,3′-Dihexyl-oxabarbocyanine iodide (DiOC6), 10 N-nonylacridine orange (NAO), Calcium Green-5N, and hydroethidine (HE) were obtained from Molecular Probes (Eugene, OR). The PAH working standard (no. 8310) was purchased from Cerilliant Corporation (Round Rock, TX). All organic solvents used were of Fisher optima grade (Fisher Scientific, Hampton, NH), and the solid chemicals were of analytical reagent grade.
Preparation of crude DEP extracts.
DEPs were obtained from M. Sagai (National Institute of Environment Studies, Tsukuba, Ibaraki, Japan). These particles were collected from a 4JB1-type light-duty, 2.74-L, four-cylinder Isuzu diesel engine (Isuzu Automobile Co., Tokyo, Japan) under a load of 6 kilogram meter onto a cyclone impactor (Kumagai et al. 1997). The particles were scraped from the glass fiber filters and stored as a powder under nitrogen gas. The particles consist of aggregates in which individual particles are < 1 μm in diameter. The chemical composition of these particles, including PAH and quinone analysis, has been previously described (Li et al. 2000). DEP methanol extracts were prepared by suspending 100 mg particles in 25 mL methanol, followed by sonication and centrifuging the suspension at 2,000 rpm for 10 min at 4°C (Hiura et al. 1999). The supernatant was transferred to a preweighed polypropylene tube and dried under nitrogen gas. The tube was reweighed to determine the methanol-extractable phase. The dried extract was dissolved in DMSO, and aliquots stored at −80°C in the dark.
DEP fractionation by silica gel chromatography.
DEPs (1.2 g) were sonicated in 200 mL methylene chloride, and the extract was filtered with a 0.45-μm nylon filter in a Millipore filtration system (Li et al. 2000). The methylene chloride extract was concentrated by rotoevaporation, and asphaltenes (insoluble, aromatic chemicals with nitrogen, oxygen, and sulfur heteroatoms) were precipitated by adding 25 mL hexane and shaking. The contents were left overnight in the freezer and then centrifuged, and the supernatant was collected. The precipitate was washed twice with hexane, and the washings were combined with the first hexane extract, concentrated, and dried over anhydrous sodium sulfate. The extract thus prepared was subjected to gravity-fed silica gel column chromatography. Three columns (1.5 × 50 cm) were packed with 26 g activated silica gel between 1 cm anhydrous sodium sulfate and conditioned with hexane. The extract was split into three equal aliquots and applied to each column. Aliphatic, aromatic, and polar fractions were successively eluted at 1.5 mL/min with 70 mL hexane, 150 mL hexane:methylene chloride (3:2, vol/vol), and 90 mL methylene chloride:methanol (1:1, vol/vol), respectively. The elution of the aromatic fraction was monitored by ultraviolet light at 365 nm. The respective eluates were combined and concentrated by rotoevaporation and made up to 1 mL in a 4-mL graduated vial, the aliphatic fraction in hexane and the others in methylene chloride. The vials were tightly sealed with a silicone-lined cap and stored at −80°C until use. The weight of the fractions was determined in a microbalance after evaporating off the hexane or methylene chloride from a known sample volume. Alkanes in the aliphatic fraction were characterized by gas chromatography (Varian 3400 with an SPI injector; Varian Inc., Palo Alto, CA) equipped with a flame ionization detector and a DB-5 column (30 m, 0.25 mm inner diameter, 0.25 μm film). The fractions were dried with N2 gas and redissolved in DMSO for in vitro biologic studies.
PAH and quinone analyses.
PAH content in each fraction was determined by an HPLC-fluorescence method that detects a signature group of 16 PAHs (Li et al. 2003). Quinone content was analyzed as described by Cho et al. (2004). Briefly, quinones in the samples were derivatized and evaporated to approximately 50 μL under nitrogen; then, 100 mg zinc, anhydrous tetrahydrofuran, and 200 μL acetic anhydride were added to samples. After heating at 80°C for 15 min, samples were cooled to room temperature and an additional 100 mg zinc was added, followed by an additional 15 min of heating. The reaction was quenched with 0.5 mL water and 2 mL pentane. After centrifugation at 750 × g for 10 min, the pentane layer was evaporated to dry and the residue was reconstituted in 50–100 μL dry acetonitrile. 1,2-Naphthoquinone (NQ), 1,4-NQ, phenanthraquinone (PQ), and anthraquinone (AQ) were analyzed by the electron-impact gas chromatography/mass spectrometry technique using an HP MSD mass spectrometer (Hewlitt Packard, Palo Alto, CA) equipped with an automatic sampler (Cho et al. 2004).
Cell culture and stimulation.
RAW 264.7 cells were cultured in a 5% carbon dioxide in Dulbecco modified Eagle medium (DMEM) containing 10% fetal calf serum, 5,000 U/mL penicillin, 500 μg/mL streptomycin, and 2 mM l-glutamine. For exposure to DEP extracts and its fractions, aliquots of 3 × 106 cells were cultured in six-well plates in 3 mL medium at 37°C for the indicated time periods.
Cellular staining with fluorescent probes and flow cytometry.
Cells were stained with fluorescent dyes diluted in DMEM, except for annexin V and PI, which were prepared in a commercial binding buffer (Trevigen). The following dye combinations were added for 15–30 min at 37°C in the dark: a) 0.25 μg/mL annexin V plus 47.5 μg/mL PI in 500 μL binding buffer (assessment of apoptosis); b) 20 nM DiOC6 plus 2 μM HE (assessment of ΔΨm and mostly O2•− production, respectively); c) 100 nM NAO plus 2 μM HE (to assess cardiolipin mass and O2•− production, respectively). Flow cytometry was performed using a FACScan (Becton Dickinson, Mountain View, CA) equipped with a single 488-nm argon laser. DiOC6, NAO, and annexin V-FITC were analyzed using excitation and emission settings of 488 nm and 535 nm (Fl-1 channel); PI, 488 nm and 575 nm (Fl-2 channel); and HE, 518 nm and 605 nm (Fl-3 channel). Forward and side scatter were used to gate out cellular fragments.
Preparation of mouse liver mitochondria and experimental conditions.
We removed livers from Balb/c mice and isolated mito-chondria by a standard differential centrifugation procedure as previously described (Xia et al. 2002). Briefly, liver tissue was homogenized with four strokes of a Teflon pestle in buffer A (250 mM sucrose, 1 mM EGTA, and 5 mM HEPES, pH 7.4) on ice. After centrifugation at 1,000 × g for 10 min at 4°C, the supernatant was removed and recentrifuged at 10,000 × g for 10 min. The pellet was sequentially washed with buffer A and buffer B (buffer A without EGTA). The pellet was resuspended in buffer B and used within 5 hr after isolation. Mitochondrial protein content was determined by the Bradford method (Xia et al. 2002).
Most of the isolated mitochondrial experiments were conducted in a fiberoptic spectrofluorimeter (Ocean Optics, Dunedin, FL), which uses a closed, continuously stirred cuvette at room temperature (Korge et al. 2002). Mitochondria were added to the cuvette at 0.1 mg/mL in a standard buffer containing 250 mM sucrose and 5 mM HEPES, pH 7.4. Substrates, Ca2+, PI, inhibitors, and fluorescent indicators were added at the indicated concentrations as shown for each experiment.
Mitochondrial swelling assay.
Mitochondria (0.1 mg/mL) were incubated in swelling buffer containing 250 mM sucrose, 5 mM HEPES (pH 7.4), 2 μM rotenone, 1 mM PI, and 4.2 mM succinate at room temperature. Mitochondria were then exposed to different chemicals.
Changes in mitochondrial volume were estimated by measuring 90° light scatter with excitation and emission wavelengths set at 520 nm (Walter et al. 2000). Changes in matrix volume were reported as a percentage of maximum (100%) swelling induced by 10μg Ala at the end of each run.
Measurement of ΔΨm.
TMRM was included at 400 nM, and ΔΨm was estimated at a wavelength of 570 nm (Korge et al. 2002). Decrease in ΔΨm was expressed as percentage decrease in TMRM fluorescence compared with the effect of 1 μM CCCP (100%) in fully energized mitochondria. Light scattering was recorded simultaneously with TMRM fluorescence. In some experiments, ΔΨm was estimated using an ion-selective electrode to measure tetraphenylphosphonium ion (TPP+) distribution with a Flex-Ref electrode and Duo 18 recording system (World Precision Instruments, Sarasota, FL). TPP+ was added to a final concentration of 3 μM, and the mitochondria were energized by adding succinate at 4.2 mM.
Calcium Green-5N assay to assess mitochondrial Ca2+ retention capacity.
Changes in extramitochondrial Ca2+ concentration were followed by measuring Calcium Green-5N (1 μM, salt form) fluorescence at excitation and emission wavelengths of 475 and 530 nm, respectively. Individual Ca2+ additions were calibrated by adding known quantities of Ca2+ to the buffer in the presence of mitochondria and CCCP to block Ca2+ uptake. Addition of chemical materials did not exhibit autofluorescence in our spectrofluorimetry assays.
Assessment of mitochondrial respiration.
Mitochondrial respiration was carried out in the fiberoptic spectrofluorimeter in the presence of different substrates: succinate, 4.2 mM (complex II); malate/pyruvate/glutamate, 5 mM each (complex I); tetramethyl-p-phenylenediamine (TMPD) and ascorbate, 0.2 mM and 2.5 mM, respectively (complex IV) (Korge et al. 2002). The addition of 2 μM CCCP was used as an inducer of maximal respiration. The partial pressure of O2 in the buffer was continuously recorded by a fiber-optic oxygen sensor (Foxy Al-300; Ocean Optics, Dunedin, FL).
Collection of UFPs and assessment of their chemical composition.
UFPs were collected using the Versatile Aerosol Concentration Enrichment System (VACES) in Downey, California, as previously described by Li et al. (2003). Highly concentrated liquid particle suspensions were obtained by connecting the concentrated output flow from the VACES to a liquid impinger (BioSampler; SKC West Inc., Fullerton, CA). Particles were injected into the BioSampler in a swirling flow pattern so that they could be collected in a small volume of water by a combination of inertial and centrifugal forces.
For chemical analysis, we collected two reference filter samples in parallel with the VACES. The first sample was collected on a Teflon filter (47 mm, polytetrafluoroethylene, 2μm pore; Gelman Science, Ann Arbor, MI). Mass concentrations were determined by weighing the Teflon filter before and after each field test in a Mettler 5 Microbalance (Mettler-Toledo Inc., Highstown, NJ). Laboratory and field blanks were used for quality assurance. The Teflon filters were then analyzed by X-ray fluorescence for measurement of trace-element and metal concentrations. The second collection was done on two 47-mm quartz filters (Pallflex Corp., Putnam, CT). These filters were used for measurement of inorganic ions as well as for determining PAH, elemental carbon (EC), and organic carbon (OC) concentrations. A slice of approximately 0.2 cm2 from each filter was placed in a platinum boat containing manganese dioxide. The sample was acidified with an aliquot of HCl and heated to 115°C to form CO2 as an index of particle-associated carbon. The boat was then inserted into a dual-zone furnace, where MnO2 oxidized OC at 550°C and EC at 850°C. A flame ionization detector converted the CO2 combustion product to CH4 for detection. The remaining filter was divided in two equal parts: one half was analyzed by means of ion chromatography to determine the concentrations of particulate sulfate and nitrate; the other half was analyzed by a HPLC-fluorescence method for detection of a group of signature PAHs as previously described (Li et al. 2003).
Statistics.
The experiments were reproduced four times, except where otherwise stated. Results were analyzed by Student’s t-test, and changes were considered significant at p < 0.05.
Results
Differential toxicity and mitochondrial effects exerted by aliphatic, aromatic, and polar DEP fractions.
Previous data from our laboratory showed that crude organic DEP extracts mimic the effects of intact particles in ROS production and cytotoxicity (Hiura et al. 1999). Mitochondria play a key role in DEP-induced toxicity, as shown by an early decrease in ΔΨm, loss of inner membrane mass, caspase 9 activation, and onset of apoptosis (Hiura et al. 2000). To clarify which organic chemicals play a role in this cytotoxicity, the crude extract was fractionated by silica gel chromatography, as previously described (Li et al. 2000). Elution with increasingly polar solvents resulted in the recovery of aliphatic, aromatic, and polar fractions in the amounts shown in Table 1. Although the aromatic fraction was enriched for PAHs (Table 2), the polar fraction was devoid of this chemical group but contained an abundance of quinones (Table 3). No quinones were present in the aromatic fraction (Table 3).
RAW 264.7 cells were treated with these chemicals and assessed for evidence of apoptosis (Figure 1). Figure 1A and 1B show representative flow cytometry panels of an experiment that was performed in triplicate. The results demonstrate the induction of annexin V+/PI− (lower right) and annexin V+/PI+ (upper right) cells by the crude extract. These represent early and late apoptotic events, respectively, and can be combined with live (annexin V−/PI−, lower left) and dead (annexin V−/PI+, upper left) cells to provide a graphic display of cellular viability/toxicity (Figure 1C). This presentation format demonstrates that the polar fraction is considerably more toxic than the aromatic fraction, whereas the aliphatic fraction has no effect on cell viability (Figure 1C).
To explore mitochondrial perturbation, we assessed ΔΨm and ROS production by dual-color DiOC6/HE fluorescence (Hiura et al. 1999). DiOC6 reflects ΔΨm, whereas HE measures mostly O2•− production. This analysis shows that although the aliphatic fraction was inactive, the aromatic and polar fractions induced the appearance of DiOC6low/HEhigh subpopulations (Figure 2A). These effects were dose dependent (not shown), with the polar being more active than the aromatic fraction at comparable dose levels (Figure 2). To test whether O2•− production is related to inner membrane damage, we also performed dual-color NAO/HE fluorescence (Figure 2B). NAO binds to the inner membrane phospholipid, cardiolipin. Although NAO fluorescence is ΔΨm sensitive, a decrease in fluorescence reflects inner membrane damage. Both polar and aromatic compounds led to a decrease in inner membrane mass, whereas the aliphatic fraction was inactive (Figure 2). Cells with damaged mitochondria also showed increased HE fluorescence, which is in accordance with increased O2•− production by cells with reduced ΔΨm (Figure 2A). Overall, the polar fraction was more active than the aromatic fraction in its ability to induce these mitochondrial effects (Figure 2). Taken together, these results demonstrate that the aliphatic, aromatic, and polar fractions exert differential toxic effects on mitochondria and cellular viability.
Differential effects of the polar fraction on membrane depolarization and PTP opening.
To further explore the action of functionalized DEP chemical groups on mitochondrial function, we performed a series of studies in isolated liver mitochondria. First, ΔΨm was recorded with a TPP+ electrode after the addition of phosphate and succinate to the mitochondrial preparation (Kushnareva and Sokolove 2000). The addition of CCCP, a protonophore uncoupler, led to a quick dissipation of the ΔΨm (Figure 3A). Although the carrier (DMSO) and the aliphatic fraction were inactive (Figure 3A,B), the crude extract as well as the polar fraction induced a dose-dependent decline in ΔΨm (Figure 3C,D). The polar material was more potent and induced a faster rate of depolarization (Figure 3D).
If mitochondria are well polarized, addition of a large Ca2+ load leads to matrix Ca2+ uptake and PTP opening (Korge et al. 2002). PTP opening leads to mitochondrial swelling, which can be followed by using 90° light scatter in a spectrophotometer (Figure 4A, a). In mitochondria that had not been subjected to a Ca2+ load, addition of a small and nondepolarizing polar dose (1–2.5 μg/mL; Figure 3) caused spontaneous induction of mitochondrial swelling (Figure 4B, c and d). Compared with the lack of response to the DMSO carrier, these results were statistically significant (p < 0.01). In contrast, higher doses of the polar fraction (≥5 μg/mL) caused a statistically significant (p < 0.01) inhibition of Ca2+-induced mitochondrial swelling (Figure 4A). The same effect (p < 0.01) was seen with the crude DEP extract (not shown). This inhibition of swelling can be attributed to the ΔΨm-reducing effects of these higher concentrations. This is similar to the ΔΨm dissipation by CCCP, which prevents the rise in matrix Ca2+ required for PTP opening. If, on the other hand, matrix Ca2+ is already elevated, ΔΨm depolarization promotes PTP opening because the PTP open probability is voltage dependent and increases with depolarization. To test this theory, isolated mitochondria were preexposed to a small Ca2+ load (10 μM) that is insufficient to induce PTP opening, and then exposed to a higher polar concentration range. This led to a dose-dependent induction of mitochondrial swelling at all doses tested (Figure 4C). DMSO and the aliphatic fraction had no effect on mitochondrial swelling (not shown).
To confirm that mitochondrial swelling induced by the crude extract and polar fraction was due to PTP opening, we examined the effects of the PTP inhibitor CsA (Figure 5). Similar to its effect on Ca2+-induced swelling, CsA added before the addition of the polar fraction (Figure 5A, a) abrogated polar-induced mitochondrial swelling in a statistically significant fashion (p < 0.01) (Figure 5B). Ca2+-dependent mitochondrial swelling by the polar fraction was confirmed by prior addition of EGTA, which led to a significant reduction in the rate and magnitude of mitochondrial swelling in the presence of 1 μg/mL of the polar material (Figure 5C, b vs. c).
The polar fraction contains a number of chemicals, among which the quinones participate in the generation of oxidative stress and covalent protein modification (Penning et al. 1999). We tested a number of DEP quinones (Table 3) for their effects on mitochondrial swelling, including PQ, 1,2-naphthaquinone, and AQ. PQ induced statistically significant (p < 0.01) mitochondrial swelling with slower kinetics than did the Ca2+ load stimulus (Figure 5D). This effect was totally suppressed by CsA, indicating that quinones stimulate PTP activity in a Ca2+-dependent fashion (Figure 5D). Similar results were obtained with 1,2-naphthaquinone, whereas a nonredox-cycling quinone, AQ, was inactive (not shown). These results suggest that redox-cycling quinones play a role in the cytotoxic effects of DEPs on the mitochondrion.
All considered, the data presented indicate that polar chemicals induce mitochondrial swelling due to PTP opening. This involves direct action on the PTP at low doses, as well as rapid-onset ΔΨm depolarization at higher doses, provided that the matrix Ca2+ concentration is already elevated. In the absence of Ca2+ loading, higher polar doses inhibit mitochondrial swelling, most likely due to interference in Ca2+ accumulation as a result of ΔΨm depolarization.
Interference in the function of respiratory complexes by the polar fraction.
Mitochondrial uncoupling increases mitochondrial respiration, which can be assessed by measuring oxygen consumption with an oxygen-sensing electrode (Figure 6). Although the polar fraction increased mitochondrial respiration as a consequence of its depolarizing effect (not shown), the induction of maximal respiration by CCCP in the presence of succinate showed that subsequent addition of the polar fraction caused a slowing of respiration (Figure 6A). The crude DEP extract had the same effect, whereas the aromatic or aliphatic fractions did not affect maximal mitochondrial respiration (Figure 6A). These findings indicate that the polar fraction and crude DEPs interfere in the function of complex II in the inner membrane. Similar results were obtained when using malate/glutamate/pyruvate, which are substrates for complex I (not shown). However, there was no effect when ascorbate and TMPD were used, implying that complex IV was not affected by the polar chemicals (Figure 6B). We propose that exogenous quinones present in the polar fraction might compete with the ubiquinones, which play a critical role in electron transfer in the inner membrane complexes. Transfer of those electrons to molecular dioxygen could explain O2•− production.
Unique effects on ΔΨm, mitochondrial swelling, and Ca2+ retention capacity exerted by the aromatic fraction and PAHs.
Treatment with the aromatic fraction induced a dose-dependent ΔΨm decrease in isolated liver mitochondria at doses > 10 μg/mL (not shown). Unlike that observed with the polar fraction (Figure 3D), this depolarization was incomplete compared with CCCP (not shown). In addition, the aromatic fraction induced spontaneous mitochondrial swelling in a dose-dependent fashion (Figure 7A, b–f). In non-Ca2+-loaded mitochondria, this effect started at aromatic doses ≥10 μg/mL (Figure 7A), whereas lower doses (e.g., 5 μg/mL) actually inhibited Ca2+-induced swelling (Figure 7B). This is the opposite from the effect observed with the polar fraction, which interfered in mitochondrial swelling at high doses but induced spontaneous swelling at low doses (Figure 4B,C). Taken together, these data suggest that differences in the chemical composition of the aromatic and polar fractions lead to differential effects on mitochondrial function.
PAHs are the main components of the aromatic fraction and are capable of inducing apoptosis (Li et al. 2000). To test if PAHs exert an effect on isolated mitochondria, we used a commercial source composed of 16 DEP PAHs to conduct the swelling assay. This demonstrated that the PAH mix can induce slow-onset swelling in non-Ca2+-loaded mitochondria, which mimics the effects of the aromatic fraction (Figure 8). This swelling effect was incomplete and was partially but statistically significantly (p < 0.05) inhibited by CsA (Figure 8B). CsA exerted the same effect on the induction of swelling by the aromatic fraction (Figure 8A).
Use of mitochondrial calcium retention capacity to study differences between the polar and aromatic fractions on PTP opening.
Calcium Green-5N is a fluorescent dye that can be used to assess the Ca2+ retention capacity of isolated mitochondria. The addition of small amounts of Ca2+ leads to a rapid matrix uptake into isolated energized mitochondria (Figure 9A). With repeated Ca2+ pulses, matrix Ca2+ eventually triggers PTP opening, which leads to depolarization and release of Ca2+ from the matrix (Figure 9A). This leads to a precipitous and sustained increase in fluorescence intensity (Figure 9A). This response is statistically significantly (p < 0.01) inhibited by CsA, which increased the number of Ca2+ pulses from 4 to 14 (Figure 9B). The aliphatic fraction had no effect on the number of Ca2+ pulses (Figure 9C), whereas 1 μg/mL of the polar material reduced the number of Ca2+ pulses required to trigger PTP transition (Figure 9D). This finding is compatible with the ability of the polar fraction to induce spontaneous mitochondrial swelling in a Ca2+-dependent fashion (Figure 4C). Higher polar concentrations induced immediate release of ambient accumulated Ca2+, which reflects its depolarizing effect (Figure 9C). Similar results were obtained with the crude DEP extract: a reduction in the required number of Ca2+ pulses at low doses and precipitous Ca2+ release at high doses (not shown).
Because we have shown that DEP quinones mimic the effect of the polar fraction in spontaneous mitochondrial swelling, we also tested these quinones in the Calcium Green-5N assay. PQ reduced the required number of Ca2+ applications to achieve PTP from 3, to 2, to 0 at PQ concentrations of 0.25, 1, and 5 μg/mL, respectively (Figure 9F–H). CsA could significantly (p < 0.01) increase the number of Ca2+ pulses required for precipitous Ca2+ release in the presence of PQ, suggesting PTP involvement. Similar results were obtained with 1,2-NQ but not with AQ (not shown).
Examination of the aromatic fraction in the Calcium Green-5N assay showed that doses < 10 μg/mL increased the Ca2+ retention capacity (Figure 10A,B). This is in keeping with the ability of the aromatic fraction to inhibit Ca2+-induced PTP opening in this dose range (Figure 7B). At higher doses, the aromatic fraction induced a short Ca2+ burst, probably related to ΔΨm depolarization, which is followed by a progressive decline in the ability of the matrix to accumulate Ca2+ (Figure 10C). This depolarization was incomplete and not CsA sensitive (not shown). In order to determine whether this effect is related to the PAHs present in the aromatic fraction, the DEP PAH mixture was separately tested. PAHs mimicked the effect of the aromatic fraction in the low and high dose range (Figure 10D,E). Taken together, these results confirm that the polar and aromatic DEP compounds exert fundamentally different actions on mitochondria.
Effects of ambient UFPs on mitochondrial responses.
A key question is whether the effects of the DEP chemicals can be reproduced with intact DEP and “real-life” ambient particles (Li et al. 2003). Intact DEPs induce apoptosis (Hiura et al. 1999), and ambient UFPs induce structural damage and lodge inside mitochondria in RAW 264.7 cells and epithelial cells (Li et al. 2003). When UFPs, collected by a particle concentrator in the Los Angeles Basin (Kim et al. 2001), were tested in the mitochondrial swelling assay, we observed spontaneous PTP opening at doses of 4.8 and 7.7 μg/mL in non-Ca2+-loaded mitochondria (Figure 11, b and c). Swelling was partially reversed by CsA (Figure 11, d). At a dose of 1.9 μg/mL, UFPs did not induce spontaneous PTP opening but interfered with Ca2+-induced swelling (not shown). This is similar to the effect of sonicated DEP, which interfered in Ca2+-induced mitochondrial swelling in a dose-dependent fashion but failed to induce spontaneous swelling (Table 4). This could relate to differences in the particle size (the DEP powder used here contains particle aggregates) as well as differences in the bioavailability of surface chemical compounds on these particles. The chemical composition of UFPs is shown in Table 5. In contrast to the particulate pollutants, artificial polystyrene microspheres (size < 100 nm) did not exert an effect on mitochondrial swelling, and the mitochondria remained fully responsive to Ala (Figure 11, a).
In the Calcium Green-5N assay, ambient UFPs induced instantaneous Ca2+ release but reduced Ca2+ retention capacity in a dose-dependent manner (Figure 12A vs. Figure 12C–F). CsA prevented this effect (Figure 12G). Sonicated DEPs had a similar effect that was also CsA sensitive (Table 4). In contrast, polystyrene microspheres (80 nm) had no effect on Ca2+ retention capacity (Figure 12B). This suggests that the effect of the ambient UFP is dependent on their content of redox-cycling chemicals. Taken together with the data shown in Figure 11, the UFP effects appear to be a summation of the effects of polar and aromatic chemical compounds.
Discussion
In this study we looked at the effects of distinct DEP chemical fractions on mitochondrial function. A major effect of the polar fraction was to promote mitochondrial swelling, both directly at the level of PTP opening and indirectly by promoting ΔΨm depolarization. Mitochondrial swelling by the polar fraction and the redox-cycling quinones involved the induction of Ca2+-dependent PTP opening, as determined by the inhibitory effect of CsA and EGTA. Polar interference in inner membrane function likely targets membrane complexes I–III, as determined using different substrates in the mitochondrial respiratory chain. The polar fraction also contains chemical substances that induce mitochondrial swelling, even at low doses that have no effect on ΔΨm. This effect could be mimicked by DEP quinones, which are enriched in the polar fraction. Although the aliphatic fraction failed to affect mitochondrial function, the aromatic fraction induced a decrease in ΔΨm that is likely secondary to PTP perturbation. This effect is mostly Ca2+ independent and can be mimicked by PAHs. At low doses, the aromatic fraction increased the Ca2+ retention capacity, suggesting interference in PTP function. However, at higher doses, the aromatic fraction induced partial ΔΨm depolarization, which could promote swelling if matrix Ca2+ was already elevated. The polar and aromatic effects on isolated mitochondria could be mimicked, in part, by ambient UFPs and intact DEPs, which contain an abundance of both functionalized chemical species. In contrast, commercial polystyrene nanoparticles, which lack these chemicals, were inactive. The above effects on isolated mitochondria were accompanied by effects on apoptosis and ΔΨm in intact RAW 264.7 cells.
There is a paucity of data about the mechanisms by which ambient PM induces adverse health effects. There is also a considerable debate as to whether the particles themselves or their chemical components are responsible for injurious effects in the respiratory tract and cardiovascular system (Brown et al. 2000; Oberdörster 1996). Our view is that both the particles and the chemicals are important. First, the particles are effective carriers of chemical compounds, many of which are semi-volatile organic substances that will not otherwise gain access to the deeper regions of the lung. Second, the particle surface may act as an important catalyst for chemical reactions involved in ROS generation (Brown et al. 2000). Third, particles localize inside target cells, and it is possible that their subcellular localization may be determined by chemical composition. This could explain why ambient UFPs lodge inside mitochondria in epithelial cells and macrophages and why these particles are more potent than larger-sized particles in perturbing mitochondrial function (Figure 12). One possibility is that the negative charge of the mitochondrial matrix or the positive charge in the intermembrane space attracts chemical dipoles that are present in the polar material. Another possibility is that the large surface area of UFPs may promote the bioavailability of the absorbed chemicals. UFPs are known to have increased solubility, compared with larger sized particles of the same composition because of the increased surface-to-volume ratio for smaller particle sizes (Navrotsky 2001). This could explain why UFPs induce spontaneous swelling, whereas the major effect of DEPs is inhibition of Ca2+-induced swelling (Table 4). PAHs and quinones are representative chemical groups that may be released in different amounts from DEPs and UFPs. The type of PAH (e.g., 4-, 5-, or 6-ring PAHs) could also play a role in determining bioavailability.
How does mitochondrial perturbation lead to adverse PM health effects? An obvious mechanism is ROS production in mitochondria (Hiura et al. 1999). Although oxidative stress is increasingly recognized as a key component in tissue damage by DEPs, there is still a great deal of uncertainty about the origin of ROS. It is possible that one-electron transfers to molecular dioxygen in the mitochondrial inner membrane could contribute to O2•− generation. This effect is compatible with the effects of the polar fraction on inner membrane complexes I–III (Figure 6) and increased HE fluorescence in RAW 264.7 cells (Figure 2). We propose that quinones play a role in redirecting electron transfer to molecular O2 in the inner membrane. This effect could be enhanced by PTP transition, which disrupts the ΔΨm and increases O2•− generation (Zoratti and Szabo 1995). This does not imply that O2•− generation by mitochondria is the only PM-induced source of ROS production. In fact, it is well known that in phagocytic cells mitochondria are a minor source for ROS production compared with NADPH oxidase and lysosomes (Bassoe et al. 2003).
PM contains a number of polar chemical substances, including quinones, ketones, aldehydes, sulfur compounds, and dibutyl phthalate (Shuetzle et al. 1981). Although much needs to be learned about the biologic effects of these substances, there is a substantive biologic literature describing tissue injury by quinones (Penning et al. 1999). The endogenous ubiquinones play a key role in one-electron transfers in the mitochondrial inner membrane as well as PTP transition (Fontaine et al. 1998; Walter et al. 2000). Walter et al. (2000) described three classes of ubiquinones that affect the PTP: group I ubiquinones (Ub0, decyl-Ub, Ub10, 2,3,5-trimethyl-6-geranyl-1,4-benzoquinone, and 2,3-dimethyl-6-decyl-1,4-benzoquinone) act as PTP inhibitory quinones that enhance the Ca2+ load required for PTP opening; group II quinones [2,3-dimethoxy-5-methyl-6-(10-hydroxydecyl)-1,4-benzoquinone and 2,5-dihydroxy-6-undecyl-1,4-benzoquinone] act as PTP-activating quinones that dramatically decrease the Ca2+ load required for PTP opening; group III or PTP-inactive quinones [2,3,5-trimethyl-6-(3-hydroxyisoamyl)-1,4-benzoquinone and Ub5] are neutral in their effect but have the ability to counteract the effects of group I and II quinones (Walter et al. 2000). Although the mechanism of PTP perturbation is unclear, it has been proposed that competition between these groups is mediated through the occupancy of a common quinone binding site in the PTP (Walter et al. 2000). According to this hypothesis, ligation by stimulating (group II) quinones facilitates PTP opening at a relatively small Ca2+ load, whereas a larger Ca2+ load would be required to access the Ca2+ binding site when liganded with inactive (group III) quinones, and an even larger Ca2+ load when liganded with inhibitory (group I) quinones (Walter et al. 2000). If a mixture of quinones is present, they could compete in a concentration- and affinity-dependent manner for binding to the PTP site.
Although the applicability of this model to exogenous quinones is uncertain, it is interesting that redox-cycling NQs have been shown to induce Ca2+-dependent, CsA-sensitive PTP transition (Henry and Wallace 1995; Palmeira and Wallace 1997). On the other hand, non–redox-cycling quinones with sulfhydrylarylating potential (e.g., benzoquinone) induce direct, Ca2+-independent depolarization and mitochondrial swelling that is insensitive to CsA inhibition (Henry and Wallace 1995; Palmeira and Wallace 1997). These findings are compatible with our data that redox-cycling DEP quinones (e.g., PQ and 1,2-NQ) induce a Ca2+-dependent, CsA-sensitive PTP transition, whereas a non–redox-cycling DEP quinone (AQ) had no effect (Figure 5D). This suggests that the redox-cycling quinones present in DEPs are responsible for PTP transition. In the absence of Ca2+ loading, this effect disappears at higher polar concentrations that prevent Ca2+ accumulation (Figure 4C, Figure 9D,E). The mechanism by which exogenous quinones perturb PTP activity is unknown. One possibility is binding to the putative ubiquinone binding site mentioned above. Another is the oxidative modification of thiol-dependent PTP components by redox-cycling quinones (Henry and Wallace 1995; Palmeira and Wallace 1997). Whatever the exact explanation, our data indicate that DEP quinones affect mitochondrial function independent of other biologic effects these compounds may have.
It is interesting that the aromatic fraction differs from the polar fraction in its effect on mitochondrial function. The key difference appears to be the ability of the aromatic compounds to interfere in Ca2+-induced PTP opening at low doses (Figure 10B) while inducing mostly CsA-insensitive swelling at higher doses (Figure 7A). These effects are mimicked by the PAHs, suggesting that they play a key role in the toxic effect of the aromatic compounds (Figure 10D,E). Although we lack a definitive molecular explanation for the PAH effects, their action at lower doses resembles PTP inhibition by CsA (Figure 10D). Whether this represents occupation of an inhibitory binding site similar to group II ubiquinones or interference in cyclophylin D binding to the pore is unknown. Lemasters and colleagues have postulated that the PTP has two open conductance modes: one activated by Ca2+ and inhibited by CsA and the other independent of Ca2+ and CsA insensitive (He and Lemasters 2002; Lemasters et al. 2002). Induction of the Ca2+-independent open state has been suggested to be mediated by oxidative chemicals, such as phenylarsine oxide (PAO) and HgCl2, which lead to misfolding of integral membrane proteins at high doses (He and Lemasters 2002). It is possible that high doses of aromatic chemicals could act in similar fashion (Lemasters et al. 2002). According to the protein misfolding hypothesis, cyclophilin D protects against this effect by acting as a chaperone for the damaged proteins (Lemasters et al. 2002). That could lead to decreased cyclophilin D binding to the PTP, which may explain why the aromatic fraction interferes in Ca2+-induced PTP opening (Figure 7B). At a high aromatic dose, the number of misfolded protein clusters could overwhelm the ability of the chaperones to prevent nonspecific channel formation, leading to CsA-insensitive mitochondrial swelling (Figure 7A).
We have frequently referred to the role of Ca2+ in PM-induced mitochondrial effects, including the fact that certain quinones affect mitochondrial function and PTP opening in a Ca2+-dependent fashion (Henry and Wallace 1995). PAH diol epoxides have been shown to increase cytosolic Ca2+ in airway epithelial cells (Jyonouchi et al. 2001), which theoretically could affect mitochondrial function, as demonstrated by the ability of some PAH species to induce apoptosis (Solhaug et al. 2004). In addition to the contribution of chemicals, the particles themselves play a role in intracellular Ca2+ release, as demonstrated by treating alveolar macrophages with carbon black particles (Brown et al. 2004).
In addition to using a Ca2+-dependent pathway, redox-cycling DEP chemicals may perturb the PTP in a thiol-dependent manner. In this regard, Constantini et al. (1996) proposed that oxidation of vicinol thiol groups in the PTP by ROS or electrophilic chemicals may lead to induction of permeability transition. Bernardi and colleagues have provided data that suggest that two distinct thiol groups are implicated in modulating PTP activity (Chernyak and Bernardi 1996; Constantini et al. 1996). One thiol group is sensitive to glutathione (GSH) oxidation, whereas the other responds to the redox state of the matrix NAD(P). The adenine nucleotide transporter (ANT) protein, a proposed structural PTP component, has three cysteine residues that show differential reactivity toward various thiol and oxidizing reagents in a conformation-dependent fashion (Majima et al. 1993, 1994, 1995). These cysteines could represent the thiol groups that regulate cyclophilin D binding as well as the effects of membrane potential on the PTP. This could explain the synergy between intracellular Ca2+ flux and oxidative stress in PTP opening. Interestingly, ANT uses its vicinal thiols to bind to a PAO column (Halestrap et al. 1997). Treatment of isolated mitochondria with a crude DEP extract prevents ANT binding to PAO, suggesting that this protein is oxidatively modified at vicinal thiol groups (Xia et al., unpublished data). The thiol hypothesis also explains the prevention of mitochondrial damage by N-acetylcysteine, which, in addition to its effect as a radical scavenger, serves as a precursor for GSH synthesis as well as electrophilic binding to prooxidative DEP chemicals (Xiao et al. 2003). Under physiologic conditions, GSH may play an important role in protecting the vicinal thiols associated with the PTP, hence the association of a drop in GSH levels with DEP-induced apoptosis.
A final point of interest is the potent effect of ambient UFPs on mitochondrial function, compared with no effect from commercial UFPs (Figure 11). This finding is of great importance to the burgeoning field of nanotechnology, which has attracted attention because of the possible interference of nanoparticles in biologic processes (Brumfiel 2003). Although it is possible that very small particles may exert toxic effects and induce intracellular Ca2+ flux based on their small size and high surface area, independent of their chemical makeup (Brown et al. 2001, 2004), our data indicate that the injurious effect of ambient UFP is dependent on chemical composition. In addition to the presence of organic chemicals, transition metals may contribute to particle toxicity. By using a mitochondrial end point, we have shown that it is possible to develop a mechanistic approach to particle toxicity. Similar approaches could be used to study the effects of commercial nanoparticles, which, in addition to their chemical composition, may exert mitochondrial effects based on size, surface area, and surface charge.
Correction
The concentration of DEP extract and its fractions was incorrect in Figure 2 of the manuscript published online; it has been corrected here.
Figure 1 Flow cytometry showing that DEP fractions induce apoptosis in RAW 264.7 cells. (A) Control. (B) DEP. Cells were treated with 25 μg/mL of the crude DEP extract for 12 hr, stained with annexin V-FITC and PI, and analyzed by flow cytometry. (C) Flow data expressed as a stack diagram, in which the crude extract data are compared with the effects of aliphatic, aromatic, and polar fraction, each used at 25 μg/mL; the data are representative of three experiments in which the induction of apoptosis by the crude DEP material, as well as the aromatic and polar fractions, was statistically significant (p < 0.05).
Figure 2 Changes in ΔΨm, mitochondria mass, and ROS production induced by DEP chemicals in RAW 264.7 cells dual-color stained with either (A) HE (detects mostly O2•−) plus DiOC6 (ΔΨm) or (B) NAO (mitochondria mass) plus HE. RAW 264.7 cells were treated with 100 μg/mL DEP extract or its fractions for 5.5 hr before staining. Data are representative of two experiments.
Figure 3 Effects of organic DEP chemicals on ΔΨm in isolated mitochondria (Mito) incubated with 3 μM TPP+, 1 mM phosphate (PO43−), 4.2 mM succinate, and chemicals. (A) DMSO carrier. (B) Aliphatic fraction at 100 μg/mL. (C) Crude DEP extract. (D) Polar fraction. DEP extract and polar fraction were added as indicated by the arrows; CCCP was used to completely depolarize the mitochondria and to serve as a quantitative control. Data are representative of four experiments.
Figure 4 Effects of DEP and the polar fraction on mitochondrial swelling. (A) 50 μM Ca2+ added after DMSO and different doses of polar fraction (5, 10, 20, 30, 50 μg/mL); the control was DMSO alone. The data are representative of four experiments in which the inhibitory effect of polar concentrations ≥5 μg/mL on Ca2+-induced swelling was statistically significant (p < 0.01). (B) 50 μM Ca2+ introduced to induce swelling as a positive control; polar material (0.5, 1, 2.5, 5 μg/mL) was added in the absence of a Ca2+ stimulus, and the control was DMSO alone. See “Materials and Methods” for details. (C) When previously loaded with a small amount of 10 μM Ca2+, the subsequent addition of the polar material (1, 5, 10 μg/mL) induced near-maximal mitochondrial swelling at all doses tested.
Figure 5 Calcium-dependent PTP transition by the polar fraction and PQ in mitochondria incubated in swelling buffer. Mitochondria were then incubated with 1 μM CsA or DMSO before the addition of 50 μM Ca2+ (A), 1 μg/mL polar fraction (B), and 5 μM PQ (D). (C) EGTA was added before the introduction of 1 μg/mL polar fraction. See “Materials and Methods” for details. The data are representative of four experiments, in which the swelling effect of the polar fraction and PQ where both statistically significant at p < 0.01. The inhibition by CsA was also statistically significant at p < 0.01.
Figure 6 Effects of organic DEP chemicals on mitochondrial respiration. (A) Succinate as a complex II substrate. (B) Ascorbic acid/TMPD as complex IV substrates. See “Materials and Methods” for details. Maximal mitochondrial respiration was initiated by 2 μM CCCP before the addition of DEP or its fractions at 50 μg/mL. Data are representative of three experiments.
Figure 7 Effects of the aromatic fraction on mitochondrial swelling. (A) 50 μM Ca2+, DMSO alone, or different doses of aromatic (Aro) fraction (5, 10, 20, 30, 50 μg/mL); mitochondrial swelling was statistically significant (p < 0.01) at aromatic doses ≥20 μg/mL. (B) 50 μM Ca2+, 5 μg/mL aromatic fraction (Aro) followed by 50μM Ca2+, 5 μg/ml Aro alone, or control (DMSO alone). The data are representative of four experiments.
Figure 8 Effects of the aromatic fraction and PAHs on mitochondrial swelling. (A) 1 μM CsA followed by the addition of 20 μg/mL aromatic fraction (Aro) or 20 μg/mL Aro alone; the experiment was reproduced four times, with statistically significant (p < 0.05) inhibition of mitochondrial swelling by CsA. (B) CsA followed by 7.8 μg/mL PAHs or PAHs alone; the experiment was reproduced four times, with statistically significant stimulation by PAHs (p < 0.01) and inhibition (p < 0.01) of the swelling effect by CsA. See “Materials and Methods” for details.
Figure 9 Effect of the polar fraction and quinones on the Ca2+ retention capacity of isolated mitochondria incubated with 1 μM Calcium Green-5N. After the addition of mitochondria, the following chemicals were added: (A) DMSO (carrier), (B) CsA, (C) aliphatic (Ali), (D) 1 μg/mL polar fraction, (E) 10 μg/mL polar fraction, (F) 0.25 μM PQ, (G) 1 μM PQ, and (H) 5 μM PQ. Each arrow represents one 5 μM Ca2+ pulse. Data are representative of four experiments.
Figure 10 Effect of the aromatic fraction and PAHs on the Ca2+ retention capacity of isolated mitochondria incubated with 1 μM Calcium Green-5N. After the addition of mitochondria, the following chemicals were added: (A) DMSO, (B) aromatic (Aro) 10 μg/mL, (C) Aro 50 μg/mL, (D) PAH mix 3.9 μg/mL, and (E) PAH mix 7.8 μg/mL. Each arrow represents one 5 μM Ca2+ pulse. Data are representative of three experiments.
Figure 11 Effects of UFP on mitochondrial swelling conducted in the presence of 10 μg/mL UFP followed by Ca2+ (50 μM), 7.7 μg/mL UFP without Ca2+ loading, 4.8 μg/mL UFP without Ca2+ loading, or 1 μM CsA followed by 7.7 μg/mL UFP. Data are representative of three experiments.
Figure 12 Effect of UFPs on Ca2+ retention capacity of isolated mitochondria incubated with 1 μM Calcium Green-5N. After the addition of mitochondria, the following chemicals were added: (A) carrier buffer, (B) 10 μg/mL polystyrene microspheres, (C) 1 μg/mL UFP, (D) 1.9 μg/mL UFP, (E) 4.8 μg/mL UFP, (F) 7.7 μg/mL UFP, (G) CsA followed by the addition of 4.8 μg/mL UFP. Each arrow represents one 5 μM Ca2+ pulse. Data are representative of three experiments.
Table 1 Recovery of each fraction from 1.2 g DEPs.
Fraction Elution solvent Solvent Amount (mg) Recovery (%)a
Aliphatic Hexane Hexane 281.4 23.5
Aromatic Hexane:MC (3:2)b MC 125.6 10.5
Polar MC:methanol (1:1)b MC 119.8 10.0
Total 526.8 44.0
MC, methylene chloride.
a From 1.2 g DEPs, 347.6 mg asphaltene was recovered; this represents 29% recovery.
b Vol:vol.
Table 2 PAH content in each DEP fraction (ng/1.2 g DEPs).
PAH Crude extract Aliphatic Aromatic Polar
NAP 10,149 25.5 4,420 0
ACE 7,470 0 513 0
FLU 17,483 0 7,461 0
PHE 179,714 17.2 133,069 0
ANT 2,759 0 1,133 145
FLT 77,278 0 54,122 1,266
PYR 60,425 0 28,024 59.6
BAA 10,349 0 7,392 0
CRY 18,026 0 9,237 0
BBF 5,510 0 2,053 0
BKF 2,275 0.33 391 0
BAP 1,777 0.51 30.2 0
DBA 1,841 0.69 106 0
BGP 2,104 1.32 130 0
IND 2,045 0 119 0
Abbreviations: ACE, acenaphthalene; ANT, anthracene; BAA, benzo(a)anthracene; BAP, benzo(a)pyrene; BBF, benzo(b)fluoranthene; BGP, benzo(g,h,i)perylene; BKF, benzo(k)fluoranthene; CRY, chrysene; DBA, dibenz(a,h)anthracene; FLT, fluoranthene; FLU, fluorene; IND, indeno(1,2,3-c,d)pyrene; NAP, naphthalene; PHE, phenanthrene; PYR, pyrene.
Table 3 Quinone content in DEP fractions (ng/mg fraction).
Quinone Crude extract Aliphatic Aromatic Polar
1,2 NQ 22.34 ND ND 25.09
1,4 NQ 19.94 ND ND 75.88
9,10 PQ 18.73 ND ND 66.25
9,10 AQ 69.34 ND ND 405.02
ND, none detected.
Table 4 Comparison of DEP and UFP effects on isolated mitochondria.
Assay DEP particle Ambient UFPs
ΔΨm Dose-dependent delayed or rapid depolarization Rapid depolarization
CsA insensitive CsA insensitive
Mitochondrial Ca2+ retention capacity Decreased retention capacity Decreased retention capacity
CsA sensitive CsA sensitive
Mitochondrial swelling Dose-dependent inhibition of Ca2+-induced swelling Inhibition of Ca2+-induced swelling at low doses (1 μg/mL)
No spontaneous swelling effects at any dose Spontaneous swelling at doses > 1.9 μg/mL
Partially CsA sensitive
All assays were performed as described in “Materials and Methods”; DEPs were sonicated and tested in the dose range 1–50 μg/mL.
Table 5 Chemical composition of UFPs (percentage of PM mass).
Major elements (%) Inorganic ions (%) EC OC PAH
Na (0.84) Nitrate (4.9) PHE (1.75)
Al (8.80) Sulfate (17.6) FLT (2.72)
Si (14.19) PYR (2.94)
Cl (0.10) BAA (1.90)
K (0.67) CRY (2.53)
Ca (2.05) BBF (2.39)
Ti (0.47) BKF (1.04)
V (0.08) BAP (2.45)
Cr (0.07) BGP (10.38)
Mn (0.09) IND (3.04)
Fe (3.20)
Ni (0.05)
Cu (0.19)
Zn (0.10)
Br (0.01)
Sr (0.01)
Zr (0.01)
Ba (0.10)
Pb (0.02)
Total 31% 23% 2% 41% 31.1%
Abbreviations: BAA, benzo(a)anthracene; BAP, benzo(a)pyrene; BBF, benzo(b)fluoranthene; BGP, benzo(ghi)perylene; BKF, benzo(k)fluoranthene; CRY, chrysene; FLT, fluoranthene; IND, indeno(1,2,3-cd)pyrene; PHE, phenanthrene; PYR, pyrene. All species are expressed as a percentage of the total PM mass except PAHs, which are expressed in nanograms per milligram of PM mass. The data show an excellent balance between the total mass and the sum of the measured chemical species, which account for 97% of the total UFP mass. OC is the most predominant species, contributing 41% of the mass. Trace elements and metals, such as Al, Si, Ca, and Fe, are also significant. BGP is the most abundant PAH in the UFP mode.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7255ehp0112-00135915471725Environmental MedicineArticleAcute Infections and Environmental Exposure to Organochlorines in Inuit Infants from Nunavik Dallaire Frédéric 1Dewailly Éric 1Muckle Gina 1Vézina Carole 1Jacobson Sandra W. 2Jacobson Joseph L. 3Ayotte Pierre 11Department of Social and Preventive Medicine, Laval University, and Public Health Research Unit, CHUQ-Laval University Medical Center, Québec, Canada2Department of Psychiatry and Behavioral Neurosciences, and3Department of Psychology, Wayne State University School of Medicine, Detroit, Michigan, USAAddress correspondence to É. Dewailly, Public Health Research Unit, 945 Wolfe St., Sainte-Foy, Québec, G1V 5B3 Canada. Telephone: (418) 650-5115, ext. 5240. Fax: (418) 654-3132. E-mail:
[email protected] are grateful to the Nunavik population for their participation in this research. We thank the medical and health care professionals from the Inuulitsivik Health Center and the nursing stations in Puvirnituk, Inukjuak, and Kuujjuarapik for their assistance in recruiting this cohort. We acknowledge the support of the Nunavik Nutrition and Health Committee; the Municipal Councils of Puvirnituk, Inukjuaq and Kuujjuarapik; the Pauktuutit Inuit Women’s Association; and the Nunalituqait Ikaluqatigiitut Association. We thank G. Lebel for his involvement in the management of the exposure data and E. Lachance, C. Bouffard, K. Poitras, L. Chiodo, C. Couture, and B. Tuttle for their involvement in all phases of the data collection and instrument coding processes. We thank D. Pereg for her valuable inputs during the preparation of the manuscript.
This study was funded by the National Institute of Environmental Health Sciences (R01-ES07902), the Department of Indian and Northern Affairs of Canada (Northern Contaminants Program), Health Canada, and Hydro-Québec (Environmental Child Health Initiative). F.D. is supported by the Canadian Institutes of Health Research.
The authors declare they have no competing financial interests.
10 2004 18 8 2004 112 14 1359 1364 14 5 2004 18 8 2004 Publication 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 Inuit population of Nunavik (Canada) is exposed to immunotoxic organochlorines (OCs) mainly through the consumption of fish and marine mammal fat. We investigated the effect of perinatal exposure to polychlorinated biphenyls (PCBs) and dichlorodiphenyldichloroethylene (DDE) on the incidence of acute infections in Inuit infants. We reviewed the medical charts of a cohort of 199 Inuit infants during the first 12 months of life and evaluated the incidence rates of upper and lower respiratory tract infections (URTI and LRTIs, respectively), otitis media, and gastrointestinal (GI) infections. Maternal plasma during delivery and infant plasma at 7 months of age were sampled and assayed for PCBs and DDE. Compared to rates for infants in the first quartile of exposure to PCBs (least exposed), adjusted rate ratios for infants in higher quartiles ranged between 1.09 and 1.32 for URTIs, 0.99 and 1.39 for otitis, 1.52 and 1.89 for GI infections, and 1.16 and 1.68 for LRTIs during the first 6 months of follow-up. For all infections combined, the rate ratios ranged from 1.17 to 1.27. The effect size was similar for DDE exposure but was lower for the full 12-month follow-up. Globally, most rate ratios were > 1.0, but few were statistically significant (p < 0.05). No association was found when postnatal exposure was considered. These results show a possible association between prenatal exposure to OCs and acute infections early in life in this Inuit population.
cord bloodenvironmental healthgastrointestinal infectionshumaninfantinfectionsInuitorganochlorinesotitispesticidespolychlorinated biphenylsprenatal exposurerespiratory tract infections
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Substantial information concerning the contamination of northern marine food by organochlorines (OCs) is now available (Braune et al. 1999; Burkow and Kallenborn 2000; Muir et al. 1999). This family of compounds includes chlorinated pesticides [dichlorodiphenyltrichloroethane (DDT), dieldrin, mirex, and toxaphene] and industrial compounds [hexachlorobenzene (HCB) and polychlorinated biphenyls (PCBs)]. Several OCs are chemically stable. They are thus resistant to biodegradation and can accumulate in adipose tissue of living organisms. This leads to their biomagnification in the aquatic and terrestrial food chain, resulting in the highest levels in top predator species (Braune et al. 1999; Evans et al. 1991; Muir et al. 1999; Skaare et al. 2000). The manufacture of most OCs was halted in the 1970s when regulatory actions were adopted to limit their production and use. Today, OCs are still released into the environment due to improper storage and ongoing use in certain parts of the world.
For cultural and economical reasons, carnivorous fish and marine mammals constitute an important part of the diet of the Inuit population of Nunavik (northern Québec, Canada). Their exposure to bio-magnified substances, such as OCs, is thus proportionally high. Several studies have identified markedly higher concentrations of OCs in adult blood, umbilical cord blood, and breast milk of Nunavik inhabitants compared with those of the southern Québec population (Ayotte et al. 1997, 2003; Dewailly et al. 1989, 1993, 1998; Muckle et al. 1998, 2001; Rhainds et al. 1999).
Exposure to most OCs produces a wide variety of immunotoxic effects in animals and humans. OCs that have a chemical structure similar to 2,3,7,8-tetrachlorodibenzo-p-dioxin, such as dioxin congeners and coplanar PCBs, are especially immunotoxic. Alterations of T-cell subsets, of serum IgA and IgM concentrations, of delayed-type hypersensitivity, and of complement function, have been documented in primates and humans (Belles-Isles et al. 2002; Chang et al. 1982; Hoffman et al. 1986; Lu and Wu 1985; Neubert et al. 1992; Tryphonas et al. 1991a, 1991b). The development of the immune system in utero and during infancy is particularly sensitive to immunotoxic agents. High exposure during early life could lead to permanent defects in the immune system and thus decrease resistance to infectious agents (Badesha et al. 1995).
The high incidence of acute infectious diseases in infants and children from Nunavik has been known for many years (Bruneau et al. 2001; Dufour 1988; Proulx 1988; Thérien 1988). In this context, we hypothesized that the incidence of infections among Inuit infants was related in part to the high maternal body burden of immunotoxic food-chain contaminants during pregnancy. In 2000, we published a first study on susceptibility to infection in Inuit infants recruited between 1989 and 1990 (Dewailly et al. 2000). We found that the risk of acute otitis media and recurrent otitis media was positively associated with prenatal exposure to OCs. However, postnatal exposure was not considered, and some potential confounding factors could not be evaluated. To confirm the association observed, we investigated the association between exposure to OCs and the incidence rate of acute infections during the first year of life in a second cohort of 199 Inuit infants recruited between 1995 and 2001.
Material and Methods
Study population and recruitment.
The Nunavik region is located north of the 55th parallel in the province of Québec, Canada, and is composed of 14 isolated villages scattered along the coasts of the Ungava Bay, the Hudson Strait, and the Hudson Bay (Figure 1). The targeted participants for this study were Inuit infants born in Puvirnituq, Inukjuaq, and Kuujjuarapik, the three largest Inuit communities on the Hudson Bay coast in Nunavik. The recruitment procedures have been described elsewhere (Muckle et al. 2001). Briefly, between November 1995 and March 2001, we attempted to contact every pregnant woman after their first prenatal medical visit either by phone or by the community radio (for those without a telephone at home). Pregnant women were invited to meet with our research assistant, and women willing to participate were asked to sign an informed consent form. The study was part of a larger study focusing on environmental contaminants and neurobehavioral development. The study protocol was reviewed and approved by the Nunavik Health and Nutrition Committee and by the ethics committee of Laval University.
Data collection and biological sampling.
In order to gather biological samples and information on confounding variables, we conducted four interviews: one at midpregnancy (prenatal interview, median of 21 weeks gestation) and three with the infant and the mother at 1, 6, and 11 months postpartum. We collected information on maternal age, breast-feeding duration, socioeconomic status of the care-giver (Hollingshead index), smoking habits during pregnancy, environmental tobacco exposure during the first year of life, number of children living with the participant, village of residence, and day care attendance. Many other characteristics were also documented for the neurobehavioral arm of this cohort but were not included in this study.
We sampled maternal blood at delivery or, when it was impossible, as soon as possible after delivery (median, 2 days postpartum). We also obtained umbilical cord blood at delivery and infant blood at midfollow-up (median, 7.0 months of age). All blood samples were immediately centrifuged and frozen at −80°C. Frozen blood and plasma samples were sent to the Centre de Toxicologie (Institut National de Santé Publique du Québec, Québec City, Canada) every 3–6 months for contaminants and biochemical analyses. Finally, we extensively reviewed the medical charts of the mother and the infant for the pregnancy period and for the infant’s first year of life.
Determination of OCs.
We determined the concentrations of p,p′-dichlorodiphenyl-dichloroethylene (DDE) and 14 PCB congeners (International Union of Pure and Applied Chemistry numbers 28, 52, 99, 101, 105, 118, 128, 138, 153, 156, 170, 180, 183, and 187) in plasma samples by high-resolution gas chromatography. OCs were extracted from plasma with ammonium sulfate:ethanol: hexane (1:1:3). The extracts were cleaned on florisil columns, taken to a final volume of 100 μL, and analyzed on an HP-5890 series II gas chromatograph equipped with dual-capillary columns and dual Ni-63 electron-capture detectors (Hewlett-Packard, Palo Alto, CA, USA). We identified peaks by their relative retention times obtained on the two columns. Quality control procedures were described previously (Rhainds et al. 1999). Percent recovery ranged from 89 to 100%, and the detection limit was approximately 0.02 μg/L for all compounds. Coefficients of variation (n = 20, different days) ranged from 2.1 to 9.1%. The difference between the concentration of reference material and that found using the analytic method ranged from 10.9 to 3.8%. Because OCs are stored mainly in body fat, all results for contaminants are expressed on a lipid basis.
Determination of blood lipids.
We measured total cholesterol, free cholesterol, and tri-glycerides in plasma samples by standard enzymatic procedures. Concentrations of phospholipids were determined according to the enzymatic method of Takayama et al. (1977) using a commercial kit (Wako Pure Chemical Industries, Richmond, VA, USA). We estimated the concentrations of total plasma lipids using the formula developed by Phillips et al. (1989).
Estimation of exposure using plasma concentrations.
In this population, concentrations of maternal OCs are highly correlated with those of cord plasma (R = 0.94 for DDE and PCB-153). Because of logistic problems, we were not able to collect cord blood samples for more than half of the participants. Therefore, we used the concentration of OCs in maternal plasma as an estimate of prenatal exposure to OCs. For six subjects, a cord blood sample was available but not a maternal blood sample. For these six subjects, we estimated maternal concentrations from the cord plasma results using linear regression. Postnatal exposure was estimated using plasma concentration of OCs in infant blood at 7 months of age. The concentration of OCs in blood is well correlated with that found in adipose tissues, and it has been shown that either blood or adipose tissue concentrations are valid exposure measurements in epidemiologic studies (Dewailly et al. 1994).
We used PCB-153 concentration (log-transformed) as a surrogate measure for the total PCB burden. PCB-153 is the most abundant PCB congener. Its concentration is strongly correlated with all the moderate-to-heavily chlorinated congeners and with most chlorinated pesticides (except p,p′-DDT). It has been shown to be a good marker of exposure to most organochlorines in the Arctic (Muckle et al. 2001).
Medical chart review and incidence of infectious diseases.
Trained research nurses used a standardized questionnaire to review the medical charts of infants for the first 12 months of life. For every diagnosed health problem, we noted the date of diagnosis and the duration of hospitalization (if hospitalized). We also attributed a code corresponding to the International Classification of Primary Care, 2nd edition (ICPC-2; World Organization of National Colleges, Academies and Academic Associations of General Practitioners 1998). We then formed four groups of infections: upper respiratory tract infections (URTIs), otitis media, gastrointestinal (GI) infections, and lower respiratory tract infections (LRTIs). We also added a fifth group labeled “all infections,” which included all of the four preceding groups. Because previous studies on OCs and infections in children seem to point toward a greater association between OCs and otitis media compared with other infectious diseases, we excluded ear infections from the URTI category so that otitis and URTIs could be analyzed independently (Chao et al. 1997; Dewailly et al. 2000; Weisglas-Kuperus et al. 2000). The URTI category included streptococcal pharyngitis and tonsillitis, acute upper respiratory tract infection not otherwise specified (NOS), acute rhinitis, head cold, nasopharyngitis, pharyngitis, and coryza. The otitis category included acute suppurative otitis media, otitis media NOS, acute tympanitis, otitis media with effusion, serous otitis media, and glue ear. The LRTI category included acute bronchitis and bronchiolites, acute lower respiratory infection NOS, chest infection NOS, laryngotracheobronchitis, tracheobronchitis, bacterial and viral pneumonia, broncho-pneumonia, influenzal pneumonia, and pneumonitis. The GI infection category included GI infection and dysentery with specified organism, diarrhea or vomiting presumed to be infective, dysentery NOS, and gastric flu.
For every health problem identified, we trusted the diagnosis of the attending physician. When two physicians disagreed, we only recorded the last diagnosis made. In some Inuit communities, nurses are trained to identify and treat benign infections, especially otitis media and URTIs. When the child was not seen by a physician, we recorded the diagnosis of the nurse. We considered two episodes of the same infection type to be separate when there was at least 15 days between the two diagnoses and when it was not specified in the chart that the second episode was related to the first. When an episode of URTI led to a LRTI, we only included the latter in the analysis. We did not attempt to investigate infectious episodes for which treatment at the health center was not sought by the parents. Data on complications or abnormal events during pregnancy, infant sex, and birth weight were also gathered from the medical charts.
Statistical analyses.
We assigned a value of one-half the detection limit of the analytical method when a compound was not detected in a sample. OC concentrations had log-normal distributions and were log-transformed in all analyses. Therefore, results for contaminants are presented as geometric means. The correlation between contaminant concentrations was evaluated using Pearson’s method on log-transformed values. To evaluate associations between OC exposure and infection incidence rates, we used Poisson regression with quartiles of OC concentration as the main independent variable, and individual incidence rates as the dependent variable (both for bivariate and multivariate analyses). We categorized the exposure using quartiles boundaries, with the first quartile as the group of reference (Table 1). Regression results are, therefore, an estimate of the incidence rate ratios (RRs) for infants in the three highest quartiles of exposure, when infants in each of these quartiles are compared to infants in first quartile. To test the hypothesis of a dose–response association between incidence rates and OC concentrations (p-value for trend), we included the contaminant concentration (log-transformed) directly in the model and treated it as a continuous variable.
We based the selection of potential confounding variables on clinical knowledge and a literature review. Every identified potential confounding variable was tested in the model, but only those influencing the incidence rate ratios by > 5% were included in the final model. The variables initially excluded from the model were retested one by one in the final model to ensure that their exclusion did not influence the results. The variables included in the final model were maternal age at delivery (continuous), season of birth, year of birth (category), breast-feeding duration (categories), sex of the infant, socioeconomic status of the caregiver (continuous), smoking during pregnancy (yes/no), number of cigarettes smoked per day during pregnancy (continuous), number of children < 6 years of age living with the infant (continuous), and village of residence. The following variables were excluded from the final model because they did not significantly affect the association of interest: day care frequentation (ever/never), mean hours per week in day care (continuous), maternal omega-3 fatty-acid concentration in blood (continuous), proportion of omega-3 highly unsaturated fatty acids (continuous), number of smokers in the house where the infant resided (continuous), birth weight, gestational age, and reviewer of the medical chart. When postnatal exposure was investigated, we included in the model the infant’s age when the blood sample was drawn. We considered vaccination coverage a potential confounding factor. Information on vaccination was gathered through the review of the medical chart, but information was missing for many children. Preliminary analyses showed that vaccination coverage was not related to contaminant burden. We thus excluded it from the final models.
All modeling results are presented for both the crude model (only exposure categories) and the adjusted model (exposure categories and all the confounding variables mentioned above). Statistical analyses and database management were conducted using the SAS system 8.02 (SAS Institute, Cary, NC, USA). By convention, a p-value < 0.05 was considered significant.
Results
Recruitment and participation.
During the study period, 417 pregnancies were identified in the targeted communities. Of them, we excluded 47 pregnant women (11.3%) who had already been enrolled in the study during a previous pregnancy and 3 women (0.7%) due to miscarriage, and we were unable to contact 9 women (2.2%). Of the 358 eligible women asked to participate, 110 (30.7%) refused. This refusal rate is comparable with that of other prospective studies with several interviews in populations of low socioeconomic status. Of the 248 women willing to participate, we were unable to review the medical charts of 43 infants for the following reasons: 10 (4.0%) moved to another village, 14 (5.6%) were adopted in another village, 11 (4.4%) because of miscarriage or perinatal mortality, and 8 (3.2%) because the mother withdrew from the study. Finally, we excluded 6 (2.4%) participants because no biological samples were available for exposure analysis. A total of 199 participants were included in the final analyses.
Population characteristics.
Mothers included in the analysis were mostly from Puvirnituq (45.4%) and Inukjuaq (39.3%). The mean age at delivery was 25.2 years, and most of them smoked during pregnancy (91.4% reported smoking at least 1 cigarette/ day; mean, 10.6 cigarettes/day). Only 2.6% of the infants were not exposed to secondhand smoke during their first year of life. The mean parity was 2.1. There were more males than females (57.6%), and the mean birth weight and length were 3,454 g and 50.3 cm, respectively. Breast-feeding was very common, and only 12.2% were not breast-fed (most of them because they were adopted).
Incidence of infections.
Incidence proportions and rates for selected infections are shown in Table 2. Otitis media was the most frequent infection diagnosed, with a mean of 2.8 episodes per infant-year, followed by URTIs, with 2.4 episodes per infant-year. During the first year of life, almost all infants had at least one episode of otitis (96.0%), and 17.1% had five episodes or more. LRTIs required hospitalization in 31.4% of cases. More than half of the infants (56.8%) were hospitalized at least once during their first year of life.
Contaminant burden in plasma.
Table 1 shows the concentration of contaminants in maternal and infant plasma. The geometric mean concentration of the sum of the 14 PCB congeners (∑PCBs) in maternal plasma was 308 μg/kg (range, 60–1,951 μg/kg). The concentration of the ∑PCBs was highly correlated with that of PCB-153 in maternal plasma (r = 0.99). The correlation between cord plasma and maternal plasma was also very high, both for the ∑PCBs and for PCB-153 (r = 0.95 and 0.94, respectively). The geometric mean concentration for DDE in maternal plasma was 294 μg/kg (range, 54–2,269 μg/kg). The correlation between cord and maternal plasma samples for DDE was also very strong (r = 0.94). Mean concentrations of PCBs and DDE were lower in infant plasma compared to those in maternal plasma.
Prenatal exposure to PCB-153 and infections.
The association between prenatal exposure to PCB-153 and incidence of infections is shown in Table 3. In preliminary analyses we found that the associations between OCs and incidence rates were somewhat stronger during the first 6 months of life. Although this study was designed for a 12-month follow-up, we also present the results for the first 6 months of life. Regarding infections during the first 6 months of life and prenatal exposure to PCBs, we observed statistically significant associations only for LRTIs (3rd quartile; RR = 1.54 and 1.68 for the unadjusted and adjusted models, respectively). Although not statistically significant, almost all other RRs were above the unity. When the four types of infections were combined, the relative rates ranged from 1.19 to 1.20 in the unadjusted model and from 1.17 to 1.27 in the adjusted model. The trend was statistically significant in the adjusted model (p = 0.04).
Compared to the first 6 months of life, the effect size was lower when the first 12 months of life were considered, and only GI infections still pointed toward a positive association. The association was significant for the 3rd quartile in the adjusted model only (RR = 1.59). Globally, rate ratios were similar in the unadjusted and adjusted models.
Prenatal exposure to DDE and infections.
The association between incidence of infections and prenatal exposure to DDE (Table 4) was similar to that observed for exposure to PCB-153. For the first 6 months of life, we detected significant associations with otitis (RR = 1.63, 3rd quartile) and LRTIs (RR = 1.52, 2nd quartile) in the unadjusted model, and with URTIs (RR = 1.56, 2nd quartile) and otitis (RR = 1.83, 3rd quartile) in the adjusted model. The trend was significant for otitis in the unadjusted model (p = 0.04) and borderline significant in adjusted model (p = 0.07). When the four types of infections were combined, we observed significant associations for the 2nd quartile (RR = 1.49) in the unadjusted model, and for the 2nd (RR = 1.38) and 3rd (RR = 1.33) quartiles in the adjusted model. As observed for PCB exposure, almost all RRs were above the unity.
When considering the first 12 months of life, we observed significant associations for GI infections (RR = 1.49, 2nd quartile) in the unadjusted model, and for URTIs (RR = 1.34, 2nd quartile) and GI infections (RR = 1.59, 2nd quartile) in the adjusted model. For all infections combined, the association reached statistical significance only for the 2nd quartile in the unadjusted model (RR = 1.17).
Postnatal exposure to OCs and infections.
We used OC concentrations in infant plasma to evaluate the effect of postnatal exposure on incidence of infections (sampling done at a median age of 7.0 months). We observed no association between postnatal exposure and the incidence of infections (data not shown). The only significant association was for PCBs (12-month follow-up, 2nd quartile, RR = 1.19) in the unadjusted model, but the statistical significance was lost when adjustment for confounding was done.
Effects of exposure to OCs on hospitalization rate.
We found no significant association between prenatal or postnatal exposure and incidence rate of hospitalization for LRTIs (data not shown). However, statistical power was poor because of the limited number of admissions.
Discussion
Accidental and occupational exposure to PCBs has already been associated with increased susceptibility to infections in infants. Rogan et al. (1988) observed that mothers who were exposed to PCBs through the consumption of contaminated rice oil (Yu-Cheng) reported a higher rate of bronchitis in their children than did control mothers. After examination by two otolaryngologists, the same children were also shown to have a higher prevalence of middle ear diseases than matched controls (Chao et al. 1997). In Japan, Hara (1985) noted that infants born to women who had handled PCBs in a capacitor factory had a higher incidence of colds and GI complaints.
However, evidence of an effect of environmental OC exposure on susceptibility to infection in children is scarce and inconsistent. To our knowledge, the first study addressing this question was conducted in the Great Lakes area (Smith 1984); the author observed that fish consumption during pregnancy (a proxy of PCB exposure) was positively associated with colds, earaches, and flu symptoms in infants. Rogan et al. (1987) followed 900 families in North Carolina (USA) between 1978 and 1982. They reviewed children’s medical charts and did not find any evidence of harmful effects of PCBs or DDE during the first year of life. In the Netherlands, Weisglas-Kuperus et al. (1995) observed no association between PCBs and the number of episodes of rhinitis, bronchitis, tonsillitis, and otitis during the first 18 months of life. However, in the same group of children at 42 months of age, current PCB burden was associated with a higher prevalence of recurrent middle ear infections and chicken pox (Weisglas-Kuperus et al. 2000). Karmaus et al. (2001) also observed a higher risk of otitis media, but the association was only present with the combined exposure to DDE and PCBs. Finally, our laboratory previously reported that exposed Inuit infants had a higher risk of acute otitis media during the first year of life (third tertile of exposure compared to the first) (Dewailly et al. 2000). The association was significant with exposure to DDE and HCB but remained above the unity for PCBs, dieldrin, and mirex.
In this study, we showed that prenatal exposure to some environmental OC contaminants was possibly associated with a higher incidence rate of infections during the first 6 months of life. Although the associations were not always statistically significant because of limited statistical power, infants in the highest quartiles of PCB and DDE exposure had systematically more episodes of infections than their counterparts in the first quartile of exposure. This was mostly observed during the first 6 months of life, as the effect size was lower when infections during the first 12 months of life were considered. Postnatal exposure to OCs was not associated with infection incidence.
In the literature, middle ear infections are the most consistently reported infections associated with prenatal exposure to OCs. In our study, the strongest dose–response relationship was seen with ear infections. However, it is likely that insults of OCs on the developing immune system would result in the increase of incidence of many different types of acute infections and not only ear infections. Consistent with that assumption, our results showed a higher incidence rate for the four most frequent infections in infants in the higher exposure groups, and the rate ratios were similar to that observed for otitis. Furthermore, when these four types of infections were combined, the association was more stable and the magnitude of the dose–response relationship was increased, compared with that of the four types of infection taken separately.
We also observed that the effect of pre-natal exposure was mostly present during the first few months of life and that this effect seemed to vanish after 6 months of life. Furthermore, we found no effect of postnatal exposure to OCs with infections. It has already been suggested that the immune system is vulnerable to immunotoxic compounds during its development and that high maternal burden during pregnancy and lactation could lead to permanent defects on the infant’s immune system (Badesha et al. 1995; Barnett et al. 1987). Our results support the hypothesis of a stronger effect during early infancy, but we were unable to clearly identify any harmful effect persisting after the age of 6 months. After a few months of life, cumulative environmental influences on the immune system may begin to play a larger role, thus increasing the variability of responses to infections. Furthermore, contributions of the OC exposure via breast milk, entangled with the beneficial effect of breast-feeding on infections, might have masked the effect. This could explain in part the discrepancies in results of other studies on OCs and infections because the age of children during disease and exposure assessment varied considerably between studies. Further studies are needed to clarify the time period during which environmental exposure to OCs has a detrimental effect on children health.
In this population, plasma concentrations of many environmentally persistent OCs are strongly correlated (Muckle et al. 2001). Muckle et al. (2001) also showed that concentrations in cord plasma, maternal plasma, and breast milk samples are also strongly correlated. With such exposure, it is therefore not possible to attribute the effect observed to one specific OC compound, nor are we able to unravel the specific contribution of PCB-153 exposure from DDE exposure. Furthermore, our data did not allow us to determine whether the association between DDE and infections was due to an immune modulation property of DDE, to co-linearity with PCB-153, or both.
We used a review of the medical charts to evaluate disease frequency. There is only one health center in each of the three Inuit communities included in this study, and participants almost always go to that heath center when they seek medical attention; copies of consultations performed elsewhere are routinely requested to complete medical charts. We are therefore confident that we have reviewed a majority of the medical consultations sought by the participants. Nevertheless, we did not attempt to verify every diagnosis, nor did we try to inquire about infections for which medical attention was not sought by the parents. Furthermore, we did not find a suitable proxy for the propensity to go to the clinic when symptoms were present (health services are free of charge in Canada). Our results are therefore likely to be an underestimation of the true incidence. This underestimation is expected to be present for benign infection, but is unlikely to be significant for LRTIs. This underestimation may be associated with traditional lifestyle, and thus with OC exposure, but the direction of the bias is unknown. However, if such a bias was present, we could assume that it would have persisted beyond 6 months of age. RRs for the 12-month follow-up are close to unity; therefore, the bias seems to have little effect on our results.
Because of the relatively small number of subjects involved (n = 199), our results must be regarded with caution. Many factors can greatly influence the rate of acute infections. We have assessed several potential confounding factors, but unknown factors might still be present. Specifically, we cannot rule out the possibility that the infants in the lowest exposure group (first quartile) had better general health due to an unknown cause or simply due to chance. This would have resulted in RRs above the unity for the three highest quartiles of exposure without any dose–response association, which is similar to what we observed. This should be kept in mind in interpreting our results.
The high rate of infectious episodes in young Inuit children has been observed in northern Canada, the United States (Alaska), and Greenland (Banerji et al. 2001; Holman et al. 2001; Koch et al. 2002; Proulx 1988; Wainwright 1996). Many cultural, environmental, and economical factors contribute to this situation. Our study population is no exception, with a mean of almost nine infection-related medical consultations per infant during the first 12 months of life. In the context of such a high rate of infections, rate ratios of around 1.25, like the ones observed in this study, could have a tremendous impact on the public health of this population. This is the second study identifying a possible association between acute infections and prenatal exposure to OCs in Nunavik. However, the relatively small number of subjects raises the possibility of an association that could be due to chance. To further clarify the potential contribution of persisting contaminants in the high infection rate of these children, we are currently conducting another study in which a third cohort of Inuit children from the same population is being followed during the first 5 years of life. Other studies are also needed to identify which immune mechanisms are involved and to better understand the role of maternal passive immunity in these infants. In the meantime, awareness and precautions regarding the selection of marine food items before and during pregnancies are warranted.
Figure 1 Location of Inuit communities in Nunavik (province of Québec, Canada).
Table 1 Contaminant concentrations in plasma (μg/kg lipid-based).
Quartile boundaries
Contaminant Percent detected Geometric mean (95% CI) Range 1st 2nd 3rd 4th
Maternal plasma (n = 199)
∑ PCBs NA 308 (279–340) 59.6–1,951 < 190 190–296 296–500 > 500
PCB-153 100 102 (91.4–113) 14.6–709 < 57.6 57.6–98.4 98.4–170 > 170
DDE 100 294 (267–324) 54.3–2,269 < 183 183–281 281–472 > 472
Infant plasma (n = 172)
∑PCBs NA 259 (218–307) 26.9–3,801 < 99.0 99.0 –283 283–609 > 609
PCB-153 96.5 76.1 (62.4–92.9) ND–1,441 < 28.0 28.0–95.3 95.3–199 > 199
DDE 100 256 ( 214–307) 15.6–4,386 < 100 100–355 355–618 > 618
Abbreviations: CI, confidence interval; NA, not applicable; ND, not detected.
Table 2 Incidence proportion and mean infection incidence rate for all participants (n = 199).
Percentage of participants who had at least
Infection Mean incidence (episodes per person × year) Percentage of episodes requiring hospitalization 1 episode 3 episodes 5 episodes
URTIs 2.4 ± 1.7 1.3 90.0 42.7 12.6
Otitis media 2.8 ± 1.7 0 96.0 52.8 17.1
GI infections 1.0 ± 1.1 3.4 58.8 10.6 0.5
LRTIs 1.7 ± 1.7 31.4 73.4 26.6 5.5
Table 3 Incidence RR of each PCB-153 quartile of prenatal exposure compared to the first quartile.
Unadjusted (n = 199)
Adjusted (n = 177)a
Incidence RR (95% CI)b Incidence RR (95% CI)b
Infection type 2nd quartile (n = 50) 3rd quartile (n = 50) 4th quartile (n = 50) p-Value for trendc 2nd quartile (n = 40) 3rd quartile (n = 46) 4th quartile (n = 45) p-Value for trendc
6-Month follow-up
URTIs 1.08 (0.76–1.55) 0.98 (0.68–1.41) 1.19 (0.84–1.68) 0.69 1.08 (0.69–1.67) 1.08 (0.71–1.65) 1.32 (0.87–2.00) 0.22
Otitis media 1.33 (0.85–2.07) 1.15 (0.73–1.82) 1.30 (0.83–2.02) 0.17 1.11 (0.65–1.89) 0.99 (0.59–1.66) 1.39 (0.82–2.35) 0.17
GI infections 1.63 (0.80–3.34) 1.31 (0.62–2.76) 1.55 (0.75–3.20) 0.33 1.89 (0.78–4.56) 1.52 (0.65–3.54) 1.54 (0.66–3.60) 0.38
LRTIs 1.12 (0.71–1.76) 1.54 (1.01–2.35)* 1.01 (0.63–1.61) 0.61 1.16 (0.65–2.09) 1.68 (1.00–2.81)* 1.18 (0.68–2.04) 0.38
All infectionsd 1.19 (0.95–1.50) 1.18 (0.94–1.48) 1.19 (0.95–1.50) 0.14 1.17 (0.88–1.55) 1.19 (0.92–1.54) 1.27 (0.98–1.66) 0.04*
12-Month follow-up
URTIs 0.93 (0.72–1.20) 0.87 (0.67–1.13) 1.12 (0.88–1.43) 0.81 0.99 (0.71–1.36) 0.96 (0.71–1.29) 1.23 (0.92–1.65) 0.29
Otitis media 1.05 (0.83–1.32) 0.97 (0.76–1.22) 0.94 (0.75–1.20) 0.89 1.02 (0.77–1.35) 0.89 (0.68–1.17) 0.97 (0.73–1.28) 0.89
GI infections 1.27 (0.86–1.88) 1.22 (0.82–1.82) 1.05 (0.69–1.58) 0.81 1.53 (0.94–2.49) 1.59 (1.01–2.49)* 1.26 (0.78–2.04) 0.29
LRTIs 0.88 (0.65–1.19) 1.08 (0.81–1.45) 0.96 (0.71–1.29) 0.48 0.86 (0.57–1.28) 1.10 (0.78–1.55) 1.03 (0.72–1.48) 0.36
All infectionsd 1.00 (0.87–1.15) 0.99 (0.86–1.14) 1.01 (0.88–1.16) 0.67 1.02 (0.86–1.21) 1.01 (0.86–1.19) 1.08 (0.92–1.28) 0.24
CI, confidence interval.
a Model included mother’s age, season of birth, year of birth, breast-feeding duration, sex, socioeconomic status of the caregiver, tobacco use during pregnancy, village of residence, and number of children living with the participant.
b Incidence RR when the given quartile was compared to the first quartile of exposure (Poisson regression).
c p-Values for trends were calculated by Poisson regression in which the contaminant concentration (lipid-based) was entered as a continuous variable (log-transformed).
d Only infections with a mean incidence > 1.0 episode/year/infant were included; see details in “Materials and Methods”).
* p < 0.05.
Table 4 Incidence RR of each DDE quartile of prenatal exposure compared to the first quartile
Unadjusted (n = 199)
Adjusted (n = 177)a
Incidence RR (95% CI)b Incidence RR (95% CI)b
Infection type 2nd quartile (n = 50) 3rd quartile (n = 50) 4th quartile (n = 50) p-Value for trendc 2nd quartile (n = 40) 3rd quartile (n = 46) 4th quartile (n = 45) p-Value for trendc
6-Month follow-up
URTIs 1.50 (1.05–2.13) 1.06 (0.72–1.55) 1.19 (0.82–1.73) 0.91 1.56 (1.05–2.33)* 1.15 (0.75–1.75) 1.40 (0.90–2.16) 0.24
Otitis media 1.27 (0.79–2.05) 1.63 (1.04–2.57)* 1.50 (0.95–2.38) 0.04* 1.03 (0.59–1.77) 1.83 (1.09–3.07)* 1.55 (0.90–2.68) 0.07
GI infections 2.16 (1.02–4.55)* 1.76 (0.81–3.82) 1.67 (0.76–3.64) 0.34 1.91 (0.84–4.35) 1.66 (0.69–3.97) 1.35 (0.54–3.42) 0.58
LRTIs 1.52 (1.00–2.32)* 1.01 (0.64–1.59) 1.01 (0.64–1.59) 0.75 1.40 (0.86–2.29) 1.22 (0.72–2.05) 0.96 (0.55–1.66) 0.89
All infectionsd 1.49 (1.19–1.87)* 1.23 (0.97–1.55) 1.25 (0.99–1.57) 0.22 1.38 (1.07–1.78)* 1.33 (1.03–1.73)* 1.27 (0.96–1.67) 0.11
12-Month follow-up
URTIs 1.27 (0.98– 1.63) 1.03 (0.79– 1.34) 1.11 (0.85–1.44) 0.85 1.34 (1.00–1.78)* 1.09 (0.81–1.47) 1.30 (0.96–1.78) 0.27
Otitis media 1.00 (0.79–1.27) 1.12 (0.89–1.42) 1.08 (0.85–1.36) 0.36 0.89 (0.68–1.17) 1.08 (0.83–1.41) 1.02 (0.76–1.35) 0.72
GI infections 1.49 (1.00–2.23)* 1.30 (0.86–1.96) 1.20 (0.79–1.82) 0.98 1.59 (1.03–2.47)* 1.27 (0.81–2.00) 1.43 (0.87–2.34) 0.59
LRTIs 1.15 (0.85–1.55) 0.96 (0.70–1.30) 1.05 (0.78–1.42) 0.89 1.07 (0.75–1.51) 0.98 (0.69–1.40) 1.00 (0.69–1.45) 0.99
All infectionsd 1.17 (1.02–1.35)* 1.08 (0.93–1.24) 1.09 (0.95–1.26) 0.59 1.13 (0.97–1.33) 1.08 (0.92–1.26) 1.13 (0.95–1.34) 0.38
CI, confidence interval.
a Model included mother’s age, season of birth, year of birth, breast-feeding duration, sex, socioeconomic status of the caregiver, tobacco use during pregnancy, village of residence, and number of children living with the participant.
b Incidence RR when the given quartile was compared to the first quartile of exposure (Poisson regression).
c p-Values for trends were calculated by Poisson regression in which the contaminant concentration (lipid-based) was entered as a continuous variable (log-transformed).
d Only infections with a mean incidence > 1.0 episode/year/infant were included; see details in “Materials and Methods”).
* p < 0.05.
==== Refs
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Weisglas-Kuperus N Patandin S Berbers GA Sas TC Mulder PG Sauer PJ 2000 Immunologic effects of background exposure to polychlorinated biphenyls and dioxins in Dutch preschool children Environ Health Perspect 108 1203 1207 11133402
Weisglas-Kuperus N Sas TCJ Koopman-Esseboom C Van Der Zwan CW De Ridder MAJ Beishuizen A 1995 Immunologic effects of background prenatal and posnatal exposure to dioxins and polychlorinated biphenyls in Dutch infants Pediatr Res 38 3 404 410 7494667
World Organization of National Colleges, Academies and Academic Associations of General Practitioners 1998. International Classification of Primary Care (ICPC-2). 2nd ed. New-York:Oxford University Press.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.6857ehp0112-00136515471726Children's HealthReviewsDoes Particulate Air Pollution Contribute to Infant Death? A Systematic Review Glinianaia Svetlana V. Rankin Judith Bell Ruth Pless-Mulloli Tanja Howel Denise Public Health Research Group, School of Population and Health Sciences, Faculty of Medical Sciences, University of Newcastle, Newcastle upon Tyne, United KingdomAddress correspondence to S.V. Glinianaia, School of Population and Health Sciences, Faculty of Medical Sciences, University of Newcastle, Newcastle upon Tyne, NE2 4HH, United Kingdom. Telephone: 0191-222-5891. Fax: 0191-222-8211. E-mail:
[email protected] major part of the work on this review was supported by funds from the School of Population and Health Sciences (Epidemiology and Public Health), University of Newcastle. Additional literature searching and reviewing of articles published in 2002–2003 and revision to the manuscript in response to reviewers’ comments were supported by Wellcome Trust grant 072465/Z/03/Z.
The authors declare they have no competing financial interests.
10 2004 3 6 2004 112 14 1365 1370 12 11 2003 3 6 2004 Publication 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. There is now substantial evidence that both short- and long-term increases in ambient air pollution are associated with increased mortality and morbidity in adults and children. Children’s health is particularly vulnerable to environmental pollution, and infant mortality is still a major contributor to childhood mortality. In this systematic review we summarize and evaluate the current level of epidemiologic evidence of an association between particulate air pollution and infant mortality. We identified relevant publications using database searches with a comprehensive list of search terms and other established search methods. We included articles in the review according to specified inclusion criteria. Fifteen studies met our inclusion criteria. Evidence of an association between particulate air pollution and infant mortality in general was inconsistent, being reported from locations with largely comparable pollution levels. There was some evidence that the strength of association with particulate matter differed by subgroups of infant mortality. It was more consistent for post-neonatal mortality due to respiratory causes and sudden infant death syndrome. Differential findings for various mortality subgroups within studies suggest a stronger association of particulate air pollution with some causes of infant death. Research is needed to confirm and clarify these links, using the most appropriate methodologies for exposure assessment and control of confounders.
infant mortalityparticulate air pollutionpostneonatal respiratory mortalitysudden infant death syndromesystematic review
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The historic 1952 pollution episode in London, when a rapid increase in smog led to dramatic increases in daily mortality, including infant mortality (Her Majesty’s Public Health Service 1954), stimulated early studies into the effect of air pollution on population health. There is now widespread evidence that short-term increases in ambient air pollution increase mortality and morbidity in adults and children, even at exposure levels below the World Health Organization (WHO) Air Quality Guidelines for Europe, and U.S. Environmental Protection Agency (EPA) standards (Brunekreef et al. 1995; Committee on the Medical Effects of Air Pollution 1998; Dockery and Pope 1994; Holgate et al. 1999; Katsouyanni et al. 1997; Künzli et al. 2000; Lebowitz 1996; Pope et al. 1995; Samet et al. 2000; Schwartz 1994a; Sydbom et al. 2001; U.S. EPA 1987; WHO 1987). The findings are particularly consistent for particulate air pollution; most of the current evidence is available for inhalable particulate air pollution [particulate matter (PM) with a 50% cutoff aerodynamic diameter < 10 μm (PM10) and < 2.5 μm (PM2.5)] (Dominici et al. 2003; Katsouyanni et al. 1997). The effects were found to be stronger in susceptible population groups, such as the elderly, young children, and people with preexisting cardiovascular and respiratory conditions (Gouveia and Fletcher 2000; Pope 2000; Saldiva et al. 1995; Schwartz 1994b). Long-term exposure to particulate air pollution has also been associated with increases in total mortality and in cardio-pulmonary mortality and respiratory morbidity (European Environment Agency 2003; Pope 2000; Pope et al. 1995). The large overall impact of air pollution on human health and a nonthreshold linear relationship with some health outcomes (e.g., mortality and hospital admission) have prompted the WHO to put air pollution and its health effects on its agenda. It also led the U.S. EPA to draft its 2003 criteria document, which forms the basis for reevaluating the current U.S. ambient air quality standards for PM (U.S. EPA 2003; WHO 2002).
The health of infants and children is particularly vulnerable to environmental pollution and is the focus of the recently published European Environment and Health Strategy (European Comission 2003). Infant mortality remains the major contributor to childhood mortality worldwide, despite significant declines over the last two decades. Infant mortality rates vary considerably across regions and population groups, and the reasons for this variation are not fully understood. Environmental exposures, including ambient air pollution, may account partly for excesses in infant deaths. We undertook a systematic review to summarize the epidemiologic evidence for an association between levels of particulate air pollution and infant outcomes. This work was part of a broader systematic review of the association between ambient air pollution and fetal (Glinianaia et al. 2004) and infant health outcomes.
Materials and Methods
Identification of publications and review process.
Our methods were based on the guidelines published by the U.K. National Health Service Centre for Reviews and Dissemination (2001). We identified relevant publications using database searches with a comprehensive list of search terms and other established search methods (for details, see Glinianaia et al. 2004).
The inclusion criteria for articles were a) nonaccidental exposure to directly measured PM; b) an infant (during the first year of life) health outcome; c) publication between 1 January 1966 and 31 December 2003 in the English language; and d) availability through the British Library (London, UK) or the Internet. Articles describing outcomes related to occupational or accidental exposure were excluded, as were articles that were available as abstracts only. Only one relevant article on infant morbidity was identified by our comprehensive literature search (Gehring et al. 2002); all others explored infant mortality as an infant health outcome.
Those articles meeting the inclusion criteria were appraised by pairs of reviewers using a piloted data extraction form based on previous reviews (Bell et al. 1998; Rankin et al. 1998). We extracted information on study design, measurement methods for pollutants and outcomes, statistical techniques, confounding factors, and results.
Exposure measurements.
Most reviewed studies used total suspended particulates (TSP) (Bobak and Leon 1992, 999b; Chay and Greenstone 1999, 2003; Ha et al. 2003; Hunt and Cross 1975; Joyce et al. 1989; Lave and Seskin 1972; Penna and Duchiade 1991; Shinkura et al. 1999), PM10 (Lipfert et al. 2000; Woodruff et al. 1997), or PM2.5 (Gehring et al. 2002; Loomis et al. 1999) as measures of exposure to PM. One study used visibility as a measure of particulate air pollution (Knöbel et al. 1995). Where possible, we recalculated effect estimates (odds and risk ratios, mean changes) as the expected change in outcome for an increase in air pollution levels by 10 μg/m3 (TSP, PM10, PM2.5). This facilitated comparison across studies using the same particle size measurements.
Infant mortality.
The following definitions were used as reported in the studies: Infant mortality is the number of deaths within the first year of life per 1,000 live births; neonatal mortality (NM), the number of neonatal deaths within 0–27 days of life per 1,000 live births; postneonatal mortality (PNM), the number of deaths between 28 days and 1 year of life per 1,000 live births (Bobak and Leon 1992, 1999b; Lipfert et al. 2000) or per 1,000 neonatal survivors (Woodruff et al. 1997). Infant deaths are conventionally divided into neonatal and postneonatal deaths. The underlying causes of death differ in these time periods; in particular, preterm birth is the largest contributor to neonatal death (Maternal and Child Health Research Consortium 2001). Many reviewed studies also categorized mortality by cause of death (definitions are given in Table 1).
Study design.
A study was described as ecologic if both outcome and exposure data were measured at a geographic area–based level. A study was described as time series when an ecologic study was based in one geographically defined population with data collected at different points in time. In semi-individual studies (cohort, case–control, and cross-sectional), outcome data were collected at an individual level and air pollution data were measured at an area-based level.
Although considered, a meta-analysis was not undertaken due to the heterogeneity of methodologies used in the studies. The results are summarized narratively with 95% confidence intervals (CIs) for estimates where possible. Given the different ways in which the studies have reported results, we could consistently report only whether any association or difference was statistically significant at the 5% level.
Results
Study methods.
Fifteen studies met our inclusion criteria, and the findings of the 14 articles addressing mortality are summarized in Table 1. Key aspects of study quality (i.e., study design, sample size, control for confounders) are also reported. Table 1 gives estimates unadjusted for other pollutants because there was no consistency as to whether associations with PM were reported after adjustment for other pollutants.
The studies varied by design, geographic setting, PM source and composition, copollutant exposures, exposure period investigated, and outcome. Ten studies were ecologic or time series (Bobak and Leon 1992; Chay and Greenstsone 1999
Chay and Greenstsone 2003; Ha et al. 2003; Joyce et al. 1989; Knöbel et al. 1995; Lave and Seskin 1972; Loomis et al. 1999; Penna and Duchiade 1991; Sinkura et al. 1999), two were cross-sectional (Hunt and Cross 1975; Lipfert et al. 2000), two were cohort studies (Gehring et al. 2002; Woodruff et al. 1997), and one a matched case–control study (Bobak and Leon 1999b). All used area-based estimates of air pollution exposure, except for the German study, which used a Geographic Information Systems model to provide individual ambient pollution estimates (Gehring et al. 2002). Thirteen studies used direct measurements of PM from routine monitoring of the ambient air pollution level by monitoring stations in the study areas (Bobak and Leon 1992, 1999b; Chay and Greenstone 1999, 2003; Ha et al. 2003; Hunt and Cross 1975; Joyce et al. 1989; Lave and Seskin 1972; Lipfert et al. 2000; Loomis et al. 1999; Penna and Duchiade 1991; Sinkura et al. 1999; Woodruff et al. 1997), and one study used visibility as a measure of particulate air pollution, which was reported to be highly correlated with PM10 levels (Knöbel et al. 1995).
Five of the ecologic and time-series studies used the annual mean concentrations of particles (Bobak and Leon 1992; Chay and Greenstone 1999, 2003; Lave and Seskin 1972; Penna and Duchiade 1991), whereas the others used means over other periods (Joyce et al. 1989; Shinkura et al. 1999) or investigated different periods before death (Ha et al. 2003; Knöbel et al. 1995; Loomis et al. 1999). In the case–control study, exposure was assigned as the mean of all 24-hr particulate air pollution measurements for the period between birth and death of the index case (Bobak and Leon 1999b), whereas in the U.S. cohort study, the mean of the PM levels for the first 2 months of life was used (Woodruff et al. 1997). In the two cross-sectional studies, the exposure period was not specified in one (Hunt and Cross 1975), whereas the other used the annual mean of PM10 (Lipfert et al. 2000). In the German cohort study on respiratory morbidity, the estimated annual averages of PM2.5 were used (Gehring et al. 2002).
Adjustments for some maternal and socioeconomic factors were made by a number of studies (Table 1); a few also adjusted for maternal smoking (Gehring et al. 2002; Lipfert et al. 2000; Woodruff et al. 1997), other air pollutants (Bobak and Leon 1992, 1999b; Lipfert et al. 2000; Loomis et al. 1999), and/or season/ weather (Chay and Greenstone 1999; Ha et al. 2003; Knöbel et al. 1995; Lave and Seskin 1972). One older cross-sectional study did not adjust for any confounding factors (Hunt and Cross 1975). Considering the comparative precision of the exposure measurements and the adjustment for key confounding factors in mortality studies, the Czech case–control study (Bobak and Leon 1999b) and the U.S. cohort study (Woodruff et al. 1997) used the strongest designs, and their results are highlighted in the findings below.
Study findings.
Mortality outcomes: infant mortality.
The eight studies exploring PM and total infant mortality found little evidence of a consistent association (Table 1). Five studies of varying designs reported some positive associations (Chay and Greenstone 2003; Hunt and Cross 1975; Lave and Seskin 1972; Lipfert et al. 2000; Loomis et al. 1999), although the strength of evidence and critical exposure period differed. Three other studies (Bobak and Leon 1999b; Chay and Greenstone 1999; Penna and Duchiade 1991) reported non-significant associations. The case–control study (Bobak and Leon 1999b) found little evidence of any association with TSP levels [odds ratio (OR) = 1.03; 95% CI, 0.99–1.06].
The three studies investigating infant mortality due to respiratory causes reported a significant association with PM (Bobak and Leon 1999b; Lipfert et al. 2000; Penna and Duchiade 1991) but used different measures of effects. The case–control study (Bobak and Leon 1999b) reported a weak association with TSP levels (OR = 1.12; 95% CI, 1.01–1.28). These three studies also reported total infant mortality, and the strength of association was consistently lower than for respiratory mortality, although no formal comparisons were made.
The single study reporting infant mortality due to nonrespiratory causes found no significant association between PM levels and mortality due to this cause (Bobak and Leon 1999b) (OR = 1.01; 95% CI, 0.98–1.05), in contrast to their more positive findings for respiratory mortality.
Neonatal mortality.
Total NM did not show a consistent association with PM, with one U.S. study reporting a positive association (Lipfert et al. 2000), two studies with borderline findings (Bobak and Leon 1992; Chay and Greenstone 1999), and five studies from different geographic settings reporting no evidence of an association (Bobak and Leon 1999b; Chay and Greenstone 2003; Joyce et al. 1989; Lave and Seskin 1972; Shinkura et al. 1999).
The case–control study was the only one to explore NM due to both respiratory and non-respiratory causes. It reported little evidence of an association between TSP levels and either type of NM: respiratory, OR = 0.93 (95% CI, 0.67–1.32); nonrespiratory, OR = 1.00 (95% CI, 0.96–1.06) (Bobak and Leon 1999b). Another study examining respiratory NM reported a significant association with PM10 similar in strength to the association reported for total NM (Lipfert et al. 2000).
Postneonatal mortality.
Four of five studies investigating a relationship between PM and total PNM, including a cohort study (OR = 1.04; 95% CI, 1.02–1.07) (Woodruff et al. 1997), reported significant positive associations (Bobak and Leon 1992; Ha et al. 2003; Lipfert et al. 2000). The case–control study did not find a significant association (OR = 1.04; 95% CI, 0.99–1.10) (Bobak and Leon 1999b); the difference between this and the cohort study was in the precision of the estimates. Two (Bobak and Leon 1999b; Lipfert et al. 2000) of the five studies explored PNM in addition to total infant and total NM and reported similar strengths of association for all these mortality types.
In all studies examining both total and respiratory PNM (Bobak and Leon 1992, 1999b; Ha et al. 2003; Lipfert et al. 2000; Woodruff et al. 1997), the association between PM level and respiratory mortality was statistically significant and stronger than for total mortality, although no formal comparisons were made. This was true for infants of normal birth weight in the cohort study, but the results were inconclusive for the subgroup of infants with low birth weight (Woodruff et al. 1997). In the two studies where both postneonatal respiratory and nonrespiratory mortalities were investigated, there was little evidence of an association between PM levels and nonrespiratory mortality (Bobak and Leon 1999b; Woodruff et al. 1997).
Sudden infant death syndrome (SIDS) was found to be associated with ambient PM concentrations in two U.S. studies (Lipfert et al. 2000; Woodruff et al. 1997). A Taiwanese time-series study found a positive association between SIDS and reduced visibility during 1–9 days before death (Knöbel et al. 1995), but adjustment for confounders was limited. Although the U.S. cohort study found a significant association with PM10 (OR = 1.12; 95% CI, 1.07–1.17), the Czech case–control study did not find a significant association with TSP (OR = 0.91; 95% CI, 0.75–1.12) (Bobak and Leon 1999b).
Morbidity outcomes: respiratory morbidity.
The only study investigating respiratory morbidity in infants reported significant associations between exposure to PM2.5 and some (cough without infection and dry cough at night) but not other (wheeze, asthmoid or other types of bronchitis, respiratory infections, and sneezing, runny/stuffed nose) respiratory symptoms (Gehring et al. 2002).
Discussion
Main findings.
Our review suggests some evidence of an association between PM levels and different subgroups of infant mortality. There were differences in the magnitude and consistency of association by cause of death, with PNM due to respiratory causes and SIDS being more consistently associated with PM levels. However, it is problematic to compare cause-specific associations between studies because of variations in definitions and diagnostic criteria of causes of death. Differential findings for various mortality subgroups within some studies suggest a stronger association of particulate air pollution with some causes of infant death. The only study investigating respiratory morbidity in infants reported significant associations between exposure to PM2.5 and some but not other respiratory symptoms (Gehring et al. 2002).
Methodologic issues.
We were unable to take publication, language, and reporting biases into account when identifying relevant publications, which may have overestimated the strength of any associations.
Summarizing the findings was complicated by the considerable differences in methodologies used. Many articles reported the results relating to a number of combinations of PM, outcome, and exposure period, resulting in multiple comparisons, which in turn increased the likelihood of positive findings.
More than half of the reviewed studies were ecologic or time series in design. Controlling for confounding factors in such studies is more difficult than in individual-based studies because of the extra potential sources of bias due to the aggregation of subjects into groups (Morgenstern and Thomas 1993). Even in semi-individual studies, few adjusted for key confounding factors at an individual level, because some used area-based level data and others did not adjust for confounders. Other important individual risk factors, such as smoking and environmental exposures from other air pollutants (e.g., sulfur dioxide, nitrogen dioxide), were rarely controlled for. Adequate control for confounders is essential to accurately estimate the magnitude of any association between low-level particulate air pollution exposure and infant health, and inadequate control may partly account for some inconsistency between studies.
Air pollution exposure was generally estimated by small numbers of monitors, which may not estimate individual exposures accurately for all infants; this could result in misclassification of exposure. The potential for bias is also affected by monitoring decisions (e.g., annual or daily means). The absence of information about indoor air pollution may underestimate individual exposure. These factors are likely to be nondifferential and therefore reduce the precision of effect estimates.
Studies exploring the health effects of PM may report inconsistent results because the definitions and measurement techniques are variable. The toxicity of equal-sized PM depends on its chemical composition, which, in turn, depends on the mixture of sources generating them and their dispersion (Mage 2002). For example, the PM10:TSP ratio ranges from 50 to 60% for U.S. sampling sites (Dockery and Pope 1994), whereas in the Czech Republic PM10 has been estimated to constitute about 80% of TSP (Bobak and Leon 1999a). The reviewed studies also varied in relation to average levels and ranges of PM, and copollutant exposures. Despite differences in air pollution sources and levels, the findings of an association between PM levels and postneonatal respiratory mortality are fairly consistent across studies and regions.
Another possible explanation for inconsistency of findings is differences between settings in the distribution of timing and cause of death within infant mortality. For instance, the definitions of respiratory causes of death and SIDS varied across studies (Bobak and Leon 1992, 1999b; Knöbel et al. 1995; Lipfert et al. 2000; Woodruff et al. 1997) and were not always fully reported (Lipfert et al. 2000). Accurate diagnosis of deaths due to SIDS depends on a postmortem investigation, and this was not available for all cases coded as SIDS in one study (Knöbel et al. 1995). For this reason, within-study comparisons, when different subgroups and causes of death were examined in the same study, may be more valid than between-study comparisons.
The magnitudes of association reported in the reviewed studies are low and could be accounted for by the factors considered above. However, their magnitude is of the same order as that found between PM and adult mortality, which is accepted as a true relationship (Committee on the Medical Effects of Air Pollution 1998; WHO 2002).
Potential mechanisms.
Although the epidemiologic evidence linking increases in PM with excess mortality and morbidity in adults is now strong, the mechanisms for such a link are not yet well understood. To date, toxicologic studies have not identified unequivocally specific PM constituents or mechanisms to account for the epidemiologic observations. Infants and children are considered potentially susceptible populations in risk assessments, including risk from PM (U.S. EPA 1999), because of their immature immune system, potential impact on lung growth and development, and viral infections common in infants. However, few human and animal studies have compared immature and adult organisms with regard to their susceptibility to inhaled particles (Mauderly 2000). For adults, three potential mechanisms have been put forward for the PM effect: an inflammatory response that alters blood coagulation, an allergic immune response, and an alteration in cardiac autonomic function resulting in the reduction of heart rate variability (Donaldson et al. 2001; Granum and Lovik 2002; Liao et al. 2004; Pope 2000). All three potential mechanisms may be pertinent in infants, but the degree of their influence may vary at various stages of infant development. In particular, they may be more applicable to postneonatal death, because this is thought to be affected more by the infant’s external environment than is NM (Pharoah and Morris 1979). Neonatal deaths are more affected by conditions originating in the perinatal period, with immaturity-related conditions being the main cause of death. However, if there is a small adverse effect of particulate air pollution on fetal growth and duration of pregnancy, as discussed previously (Glinianaia et al. 2004), it may also indirectly contribute to neonatal deaths.
The mechanisms of SIDS, the most common cause of postneonatal death in developed countries, are not well understood, although a number of pathways have been proposed. One of the currently most compelling hypotheses for the occurrence of SIDS is an abnormality of brain development and maturation, with a tendency to central apnea and disturbed cardio-respiratory control mechanisms (Goldwater 2003; Harper 2000; Kahn et al. 2003; Kinney and Filiano 2001). Unsafe sleeping environment, exposure to environmental tobacco smoke (ETS), and lower socioeconomic status are critical risk factors for SIDS. It has been suggested that the association between post-natal exposure to tobacco smoke and SIDS is causal (Anderson and Cook 1997; McMartin et al. 2002). The potential mechanisms of action proposed for ETS (abnormal pulmonary development, reduced pulmonary function, abnormalities in cardiorespiratory control system, promotion of respiratory infections) (Chan-Yeung and Dimich-Ward 2003; Goldwater 2003; Hofhuis et al. 2003; Strachan and Cook 1997) might be similar to those for particulate air pollution, because ETS is known to contain a substantial proportion of PM.
Implications.
Current epidemiologic evidence suggests a link between ambient PM exposure and some subgroups of infant mortality, even at relatively low PM levels reported in the reviewed studies, which are comparable with current levels experienced in developed countries. More research is needed to further clarify whether there is a real effect of particulate air pollution on infant health and to quantify this effect. Future studies should explore overall and cause-specific infant mortality, using robust study designs with individual level information on key confounding variables. Exposure assessment should include details of level, size, and composition of PM and co-pollution exposure. The use of physiologic measurements (e.g., lung function in older children) and biomarkers of exposure or effect (e.g., methemoglobin as a biomarker of carbon monoxide poisoning, cotinine as a marker of exposure to ETS, placental DNA adduct levels as biomarkers of effect of polycyclic aromatic hydrocarbons) could promote understanding of causal effects of air pollution on infant and children’s health. If a causal association between exposure to PM and infant death exists, widespread exposure to particulate air pollution may be an important determinant of infant mortality at a population level.
Table 1 Studies investigating the association between PM and infant mortality.
Reference, country, data collection period Study design, sample size, exposure period(s) considered Estimate (95% CI) by exposure and type of mortality Adjustment for confounding factors
Studies exploring multiple outcomes
Bobak and Leon 1999b Matched case–control Per 10-μg/m3 increase in TSP: Socioeconomic factors, maternal age and parity, gestational age, BW, birth length
Czech Republic 1989–1991 2,006 cases and 7,952 controls with data on TSP Total infant: AOR = 1.03 (0.99–1.06)
Infant respiratory: AOR = 1.12 (1.01–1.28)a
Infant nonrespiratory: AOR = 1.01 (0.98–1.05)
Total neonatal: AOR = 1.00 (0.96–1.06) Also adjusted for other pollutants examined (SO2 and NOx), but the results are not given
Neonatal respiratory: AOR = 0.93 (0.67–1.32)a
Neonatal nonrespiratory: AOR = 1.00 (0.96–1.06)
Mean over period between birth and death Total postneonatal: AOR = 1.04 (0.99–1.10)
Postneonatal respiratory: AOR = 1.14 (1.02–1.32)a
Postneonatal nonrespiratory: AOR = 1.02 (0.96–1.08)
SIDS: AOR = 0.91 (0.75–1.12)
Lipfert et al. 2000 Cross-sectional Per 10-μg/m3 increase in PM10: Socioeconomic factors, mother’s smoking, month of birth
USA 1990 1,443,768 births and 13,041 infant deaths; 2,354 infant deaths due to respiratory causesb Total infant: AOR = 1.12 (1.09–1.15)
Infant respiratory: AOR = 1.18 (1.11–1.26)b Not adjusted for other pollutants examined (SO2, CO, SO42–)
AOR = 1.14 (0.96–1.35)a
Total neonatal: AOR = 1.13 (1.09–1.18)
341 infant deaths due to respiratory causesa Neonatal respiratory: AOR = 1.17 (1.09–1.26)b
8,362 neonatal deaths; 4,679 postneonatal deaths 1,918 SIDS deaths Total postneonatal: AOR = 1.10 (1.04–1.15)
Postneonatal respiratory: AOR = 1.21 (1.05–1.39)b
SIDS: AOR = 1.15 (1.07–1.24)
Annual mean
Bobak and Leon 1992 Ecologic > 84.7 (top quintile) vs. < 53.6 μg/m3 TSP (bottom quintile): Socioeconomic factors
Czech Republic 1986–1988 ~22,370 live births 2,699 infant deaths Total neonatal: AOR = 1.18 (1.00–1.39), test for trend p = 0.071 Also adjusted for other pollutants examined (SO2, NOx), but the results are not given
Annual mean Total postneonatal: AOR = 1.53 (1.20–1.97), test for trend p < 0.001
Postneonatal respiratory: AOR = 3.16 (1.52–6.55),a test for trend p = 0.001
Woodruff et al. 1997 Cohort Per 10-μg/m3 increase in PM10: Socioeconomic factors, mother’s smoking, month of birth
USA 1989–1991 All infants: 3,788,079 Total postneonatal: AOR = 1.04 (1.02–1.07)
Postneonatal respiratory mortality in normal-BW infants: AOR = 1.20 (1.06–1.36)a
First 2 months of life
Postneonatal respiratory in LBW infants:
AOR = 1.05 (0.91–1.22)a
Postneonatal nonrespiratory:
AOR = 1.00 (0.99–1.00)
SIDS: AOR = 1.12 (1.07–1.17)
Ha et al. 2003 Time series Per 10-μg/m3 increase in PM10: Seasonality, temperature, relative humidity, day of week
South Korea 1995–1999 1,045 postneonatal deaths Total postneonatal: ARR = 1.03 (1.02–1.04)
Daily mean on event day Postneonatal respiratory: ARR = 1.18 (1.14–1.21)
Chay and Greenstone 1999 Ecologic Total infant mortality: a 10-μg/m3 increase in TSP associated with 3.5 more deaths per 10,000 live births (SE = 1.9) Maternal, infant, and socioeconomic factors, and weather data (measured at county level)
USA 1980–1982 101,730 infant deaths of > 8.5 million births
70,649 neonatal deaths Total neonatal mortality: a 10-μg/m3 increase in TSP associated with 3.4 more deaths per 10,000 live births (SE = 1.7)
Changes in annual mean
TSP induced by recession
Chay and Greenstone 2003 Ecologic Total infant mortality: a 10-μg/m3 increase in TSP associated with 13 more deaths per 10,000 live births (SE = 5.6) Maternal, infant, and socioeconomic factors (measured at county level)
USA 1971–1972 > 4 million births in 489 U.S. counties
Total neonatal mortality: a 10-μg/m3 increase in TSP associated with 6.6 more deaths per 10,000 live births (SE = 4.4)
Change in annual mean TSP induced by Clean Air Act
Penna and Duchiade 1991 Ecologic Total infant mortality: no significant association with TSP level Socioeconomic factors
Brazil 1980 Sample size not given
Infant mortality due to pneumonia: a 10-μg/m3 increase in average PM level associated with 2.2 more deaths per 10,000 live births, p = 0.014
Annual geometric mean and the year’s daily maximum
No significant association with annual maximum TSP
Lave and Seskin 1972 Ecologic Total infant mortality: a 10-μg/m3 increase in minimum TSP level associated with 3.4 more deaths per 10,000 live births, p < 0.05 Socioeconomic factors, weather
USA 1960 Minimum biweekly level
Annual mean Total neonatal mortality: a 10-μg/m3 increase in TSP associated with 0.66 more deaths per 10,000 live births, p > 0.05
Exact sample size not given (based on 117 SMSAs)
Studies exploring one outcome only
Total infant mortality
Loomis et al. 1999 Time series Per 10-μg/m3 increase in PM2.5: lag 3–5 days; ARR = 1.069 (1.025–1.113) Mean temperature of the 3 days before death. Also adjusted for other pollutants examined (O3, NO2), but the results are not given
Mexico 1993–1995 2,798 infant deaths 0–6 days before death
Hunt and Cross 1975 Cross-sectional Higher risk of IM in one of four 3-month periods (with reported air pollution episodes), p < 0.001 No adjustment for any confounders
USA 1970 66 infant deaths in 3,739 live births
Proportion of IM from respiratory causes was higher in same 3-month period (p = 0.02) Primarily descriptive statistics
Exposure period not specified
Total neonatal mortality
Joyce et al. 1989 Ecologic Per 10-μg/m3 increase in TSP: AOR = 1.04 (p > 0.05) Low BW
USA 1976–1978 Sample size not given 4-year mean (1975–1978)
Shinkura et al. 1999 Time series Per 10-μg/m3 increase in TSP: RR = 1.01 (0.99–1.04) Season, calendar year, sex, but data not reported
Japan 1978–1988 98 neonatal deaths in ~29,790 live births
Mean of birth month
SIDS
Knöbel et al. 1995 Time series For visibility 1–3 km vs. 22–37 km: Weather, season, population size, level of urbanization, daily incidence of respiratory tract infections
Taiwan 1981–1991 3,816,000 live births ARR on day of death = 3.8 (2.8–5.1), ARR during 9 days before death = 5.1 (3.2–8.1)
3,005 deaths (estimated based on the crude rate)
Day of death, 1–9 days before death
Abbreviations: AOR, adjusted odds ratio; ARR, adjusted rate ratio; BW, birth weight; IM, infant mortality; LBW, low birth weight, < 2,500 g; RR, relative risk; SIDS, sudden infant death syndrome; SMSAs, standard metropolitan statistical areas. Values in parentheses after AOR or ARR are 95% CI. Where possible, study results were re-expressed as the estimated effect of increasing air pollution levels by 10 μg/m3 (TSP, PM10, PM2.5). PNM due to respiratory causes identified by the International Classification of Diseases, 9th Revision (ICD-9; World Health Organization 1977).
a ICD-9 codes 460–519 (Bobak and Leon 1999b; Lipfert et al. 2000; Woodruff et al. 1997).
b ICD-9 codes 460–519 and 769, 770 (Lipfert et al. 2000). SIDS deaths were defined as those with an ICD-9 cause code of 798.0 (Bobak and Leon 1999b; Woodruff et al. 1997); in one study the SIDS cases also included deaths from suffocation (Knöbel et al. 1995).
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7216ehp0112-00137115471727Children's HealthReviewsDrinking-Water Nitrate, Methemoglobinemia, and Global Burden of Disease: A Discussion Fewtrell Lorna Centre for Research into Environment and Health, Crewe, Cheshire, United KingdomAddress correspondence to L. Fewtrell, Centre for Research into Environment and Health, 5 Quakers Coppice, Crewe Gates Farm, Crewe, Cheshire, CW1 6FA UK. Telephone: 44-0-1270-250583. Fax: 44-0-1270-589761. E-mail:
[email protected] work was funded by the World Health Organization; however, the views are those of the author.
The author declares she has no competing financial interests.
10 2004 22 7 2004 112 14 1371 1374 29 4 2004 22 7 2004 Publication 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. On behalf of the World Health Organization (WHO), I have undertaken a series of literature-based investigations examining the global burden of disease related to a number of environmental risk factors associated with drinking water. In this article I outline the investigation of drinking-water nitrate concentration and methemoglobinemia. The exposure assessment was based on levels of nitrate in drinking water greater than the WHO guideline value of 50 mg/L. No exposure–response relationship, however, could be identified that related drinking-water nitrate level to methemoglobinemia. Indeed, although it has previously been accepted that consumption of drinking water high in nitrates causes methemoglobinemia in infants, it appears now that nitrate may be one of a number of co-factors that play a sometimes complex role in causing the disease. I conclude that, given the apparently low incidence of possible water-related methemoglobinemia, the complex nature of the role of nitrates, and that of individual behavior, it is currently inappropriate to attempt to link illness rates with drinking-water nitrate levels.
burden of diseasedrinking watermethemoglobinemianitrates
==== Body
The Global Burden of Disease project, coordinated by the World Health Organization (WHO), is an attempt to quantify and compare the level of illness at both world and regional levels. This can be done on a disease-by-disease basis (Murray and Lopez 1996) or in relation to various risk factors such as malnutrition; exposure to poor water, sanitation, and hygiene; or indoor air pollution (Murray and Lopez 1996; Prüss et al. 2002; WHO 2002). Information relating to environmental risk factors, such as the amount of illness attributable to lead in the environment (Fewtrell et al. 2004), can be very powerful in terms of informing policy decisions. Disease burden, in relation to environmental risk factors, is generally determined by establishing the exposure of the population (on a regional basis) to the chosen risk factor and combining these data with exposure–response relationships for the selected health outcomes to estimate the number of people affected with each outcome. This may then be converted into disability-adjusted life years, accounting for the severity and duration of each health outcome.
Nitrate pollution of drinking water (which has been linked with certain health outcomes) is known to be increasing (Croll and Hayes 1988; Tandia et al. 2000; WHO 1985; Young and Morgan-Jones 1980). WHO therefore considered it useful to determine whether it was possible to establish a disease burden estimate.
Health Outcomes
Nitrate is a naturally occurring ion, which makes up part of the nitrogen cycle. The nitrate ion (NO3−) is the stable form of combined nitrogen for oxygenated systems. Although it is chemically unreactive, it can be microbially reduced to the reactive nitrite ion. Nitrate has been implicated in methemoglobinemia and also a number of currently inconclusive health outcomes. These include proposed effects such as cancer (via the bacterial production of N-nitroso compounds), hypertension, increased infant mortality, central nervous system birth defects, diabetes, spontaneous abortions, respiratory tract infections, and changes to the immune system [Centers for Disease Control and Prevention (CDC) 1996; Dorsch et al. 1984; Gupta et al. 2000; Hill 1999; Kostraba et al. 1992; Kozliuk et al. 1989; Malberg et al. 1978; Morton 1971; Super et al. 1981]. Although the role of N-nitroso compounds and nitrite in the promotion of cancer would appear to be incontrovertible, the evidence relating to the role of nitrates is less clear (Pobel et al. 1995). Thus, methemoglobinemia was the only health outcome I considered further in this investigation.
Methemoglobin (MetHb) is formed when nitrite (for our purposes, formed from the endogenous bacterial conversion of nitrate from drinking water) oxidizes the ferrous iron in hemoglobin (Hb) to the ferric form (Fan et al. 1987). MetHb cannot bind oxygen, and the condition of methemoglobinemia is characterized by cyanosis, stupor, and cerebral anoxia (Fan et al. 1987). Under normal conditions, < 2% of the total Hb circulates as MetHb (Fan et al. 1987). Signs of methemoglobinemia appear at 10% MetHb or more, as shown in Table 1 [Craun et al. 1981; Kross et al. 1992; National Academy of Sciences (NAS) 1981]. Symptoms include an unusual bluish gray or brownish gray skin color, irritability, and excessive crying in children with moderate MetHb levels and drowsiness and lethargy at higher levels (Brunning-Fann and Kaneene 1993). Diagnosis is through the observation of chocolate-colored blood or a laboratory test showing the presence of elevated MetHb levels (Brunning-Fann and Kaneene 1993).
Infant methemoglobinemia was first linked to nitrates in drinking water by Hunter Comly in the United States in 1945. He reported on two cases and concluded that methemoglobinemia may occur in an infant after ingestion of water high in nitrates, especially if the infant was suffering from a gastrointestinal disturbance (Comly 1945). Fan et al. (1987) have noted since then that microbially poor water (i.e., high in microbes) and high drinking-water nitrate levels often go “hand in hand,” and gastrointestinal illness, as a result of exposure to poor water quality, may play a role in methemoglobinemia.
Nitrate-related drinking-water methemoglobinemia is principally a disease of young children, with bottle-fed or weaned infants < 4 months of age being the most susceptible. This age group is the most susceptible because of a combination of factors (Ayebo et al. 1997), including:
A higher gastric pH, which allows greater bacterial invasion of the stomach and hence an enhanced conversion of ingested nitrate to nitrite
A greater fluid intake relative to body weight
A higher proportion of fetal Hb (which may be more rapidly oxidized to MetHb than adult Hb)
Lower NADH-dependent MetHb reductase activity (the enzyme that converts MetHb to Hb).
However, although the gastric pH in infants may be higher than that seen in adults, L'hirondel and L’hirondel (2002) have suggested that the general stomach conditions are still not really suitable for the microbial conversion of nitrate to nitrite.
Exposure Assessment
Methemoglobinemia has several causes, as shown in Table 2, including exposure to nitrite or nitrate through the diet (although high dietary nitrate levels are generally accompanied by high nitrite levels). The principal area of interest in this study, however, was drinking water; therefore, the exposure assessment was based on the concentrations of nitrate in drinking water.
Guidelines and regulatory limits relating to the amount of nitrate in drinking water, of 10 mg/L nitrate–nitrogen (NO3–N) and 50 mg/L nitrate [nitrate concentrations are typically expressed either as mg/L NO3–N or nitrate (NO3); 50 mg/L NO3 is equivalent to 11.3 NO3–N], were established to prevent infantile methemoglobinemia [U.S. Environmental Protection Agency (EPA) 1977; WHO 1958, 1996], and were based principally on the results of a survey conducted by the American Public Health Association (APHA) and reported by Walton (1951). This survey reported on > 270 cases of methemoglobinemia in infants in the United States (for whom nitrate drinking-water levels were available for 214 cases), although APHA emphasized restricting the data to those cases thought to be definitely associated with nitrate-contaminated water. As noted by Walton (1951), no cases were observed with drinking-water concentrations < 10 mg/L NO3–N. High nitrate for the purposes of the exposure assessment has been taken, therefore, to mean anything exceeding the current recommendations.
Natural levels of nitrate in groundwater depend on soil type and geology. In the United States, naturally occurring levels of nitrate are in the range of 4–9 mg/L. Agricultural activities, however, can result in elevated levels (in the region of 100 mg/L; WHO 1996).
High-nitrate drinking water is most often associated with privately owned wells, especially with shallow wells with depths < 15 m in regions with permeable soils (Fan et al. 1987). It is exactly this situation of small community water supplies, in which poorly regulated and unsanitary waters are found, that could induce gastrointestinal symptoms in consumers (Fewtrell et al. 1998). Shearer et al. (1972) note that the factors responsible for elevated nitrate contents in well-water sources include geography, geology, groundwater hydrology, and the addition of nitrates naturally and from surface contamination by nitrogenous fertilizers or by organic waste of human or animal origin. Although water derived from privately owned wells may be the most common source of high-nitrate drinking water, municipal drinking water supplies may also be contaminated. Vitoria Minana et al. (1991) report on nitrate levels in the Valencia region of Spain, where concentrations exceeded the WHO guideline level (50 mg/L) in 95 towns, with 18 municipalities reporting levels > 150 mg/L.
It has been estimated that 15 million families in the United States receive their drinking water from private wells [U.S. General Accounting Office (GAO) 1997]. Assuming an exceedence rate of 13% (based on a survey of 5,500 wells in nine Midwestern states; CDC 1998), an estimated 2 million household supplies would exceed the federal standard of 10 mg/L NO3–N. Using current birth rates Knobeloch et al. (2000) estimate that 40,000 infants < 6 months of age are expected to be living in homes with high-nitrate drinking water.
Except for the United States, most literature on nitrate contamination covers small areas and does not allow estimates of the number of people exposed to be calculated.
Exposure–Response Relationship
Complex co-factor relationships do not currently allow the establishment of a quantitative exposure–response relationship for human exposure to nitrates in food or water and the subsequent development of methemoglobinemia. Two factors make estimates of the number of cases of methemoglobinemia hard to establish: Generally, methemoglobinemia is not a notifiable disease; and definitions of methemoglobinemia (in terms of the required level of MetHb) vary in the literature. Some authors, however, do report incidence rates.
In three counties in Transylvania (Romania), mean incidence rates varied between 117 and 363 of 100,000 live births (for the full 5 years between 1990 and 1994). These rates, reported by Ayebo et al. (1997), were considerably below the previously reported levels of 13,000/100,000 live births, or 13%, which may reflect a decrease in the availability of cheap inorganic fertilizer (hence a decrease in nitrate contamination levels). In 1985, WHO reported that > 1,300 cases of methemoglobinemia (with 21 fatalities) occurred in Hungary over a 5-year period. Indeed, up until the late 1980s methemoglobinemia was a serious problem in Hungary (Hill 1999). Although there are reports of high nitrate concentrations in drinking water (i.e., > 50 mg/L nitrate) from around the world (Hoering and Chapman 2004), these are rarely paralleled by reports of methemoglobinemia. Where illness has been reported, many of the cases predate the early 1990s, and Hanukoglu and Danon (1996) have proposed that the apparent decline in the incidence of methemoglobinemia is suggestive of an infectious etiology.
Discussion
In addition to the problem of limited data (relating to both the population exposed to nitrate in drinking water and the rate of illness), examination of the literature also revealed a number of factors that would either lead to uncertainty in the disease burden estimate (e.g., avoidance behavior) or cast doubt on the validity of the whole exercise.
Limited data.
Numerous reports from all over the world describe water supplies (often privately or community-owned wells, rather than municipal supplies) with nitrate concentrations greater than the WHO guideline value of 50 mg/L (Hoering and Chapman 2004). These rarely, however, also include figures on the population supplied by these water sources. Because agricultural and organic waste disposal activities (e.g., through inappropriate sanitation measures) can greatly influence water nitrate concentrations, it is not possible to use geologic data as a possible means to estimate affected population. Thus, it is currently difficult to estimate the population that might be exposed to elevated drinking-water nitrate. Even where the number of people known to have supplies with high nitrate levels can be assessed, this is unlikely to be an accurate estimate of those actually exposed to high-nitrate drinking water. In a number of countries, such as the United States and United Kingdom, health advisories are issued to pregnant women and mothers with formula-fed infants known to be living in high-nitrate areas (Fraser and Chilvers 1981; Schubert et al. 1999). Indeed, Schubert et al. (1999) found that avoidance behavior (i.e., the use of water from another source, such as bottled water or installation of a nitrate removal system) was common, especially in high-risk groups. On the other hand, owners of private wells often boil the water before using it in infant food, an action that, when done excessively, may concentrate nitrate (Ayebo et al. 1997).
A literature review (conducted by searching publication databases and bibliographic lists from collected references) revealed few reported cases of methemoglobinemia linked to water consumption in the last 12 years (Hoering and Chapman 2004). It is possible that because methemoglobinemia is generally not a notifiable disease, there may be under-reporting. It is also possible that there is under-diagnosis, although this is less likely with severe cases, where extensive cyanosis is seen.
Role of nitrate.
Since the 1940s, when the first cases of methemoglobinemia related to drinking water were reported, there has been the suggestion that gastrointestinal upset, and hence infection, may play a role in the development of the disease (Comly 1945). Comly (1945) suggested that it was advisable to use well water containing no more than 10 or, at the most, 20 mg/L NO3–N for infant feeding. This level seemed to be confirmed by the survey conducted by the APHA [cited by Walton (1951)], which suggested that, in instances where drinking-water nitrate had been determined, there were no cases of methemoglobinemia where water concentrations were < 10 mg/L NO3–N (~ 45 mg/L nitrate). However, this conclusion would have been influenced by the methodology, which placed an emphasis on cases thought to be linked to nitrate-contaminated water. However, the APHA survey noted that most cases of methemoglobinemia studied were related to NO3–N concentrations > 40 mg/L. Additionally, Walton (1951) noted a number of factors that may play a role in the development of infant methemoglobinemia, yet at some point a simple role for nitrate became accepted.
It is becoming increasingly clear, however, that the early authors were correct to be cautious, and now it appears that there is an association between gastrointestinal illness and symptoms of methemoglobinemia in the absence of exogenous nitrate exposure (Bricker et al. 1983; Dagan et al. 1988; Danish 1983; Gebara and Goetting 1994; Kay et al. 1990; Lebby et al. 1993; Smith et al. 1988; Yano et al. 1982). Yano et al. (1982) suggested that diarrhea produces an oxidant stress that increases MetHb production and that acidosis impairs the MetHb reductase systems. Nitric oxide, produced by several tissues in response to infection and inflammation, has also been proposed as a possible mechanism (Gupta et al. 1998; Levine et al. 1998), because nitrite is a product of nitric oxide metabolism. Avery (1999) suggested that exogenous nitrates (e.g., through the consumption of drinking water), rather than causing methemoglobinemia, increase its severity. L’hirondel and L’hirondel (2002) suggested that in cases where methemoglobinemia has been associated with infant formula made with drinking water containing elevated nitrate or carrot soup preparations, it is possible that bacterial growth within the bottle or stored soup and exogenous conversion of nitrate to nitrite is the source of the problem.
Conclusions
This study did not set out to review the role of nitrates in the causation of methemoglobinemia; however, examination of the literature suggests that a number of authors are starting to question the simple association between nitrate and infant methemoglobinemia, in favor of seeing nitrate as a co-factor in one of several causes of the disease. This factor, coupled with the paucity of data in terms of both population exposure and the level of suspected water-related cases of methemoglobinemia, suggests that attempts to estimate a global burden of disease are currently inappropriate.
Table 1 Signs and symptoms of methemoglobinemia.
MetHb concentration (%) Clinical findings
10–20 Central cyanosis of limbs/trunk
20–45 Central nervous system depression (headache, dizziness, fatigue, lethargy), dyspnea
45–55 Coma, arrhythmias, shock, convulsions
> 60 High risk of mortality
Adapted from Kross et al. (1992).
Table 2 Causes of methemoglobinemia.
Designation Examples
Hereditary NADH-cytochrome b5 reductase deficiency, cytochrome b5 deficiency, M Hb, unstable Hb
Drug/chemical induced Acetaminophen, amyl nitrite, benzocaine, dapsone, nitroglycerin, nitroprusside, phenazopyridine (pyridium), sulfanilamide, aniline dyes, chlorates, nitrofurans, sulfones
Diet induced Nitrites, nitratesa
Adapted from Mansouri and Lurie (1993). M HB is an abnormal type of Hb.
a When followed up, cases have generally been linked to high nitrite levels (e.g., Keating et al. 1973).
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7197ehp0112-00137515471728Children's HealthArticlesArsenic on the Hands of Children after Playing in Playgrounds Kwon Elena 1Zhang Hongquan 1Wang Zhongwen 1Jhangri Gian S. 1Lu Xiufen 1Fok Nelson 2Gabos Stephan 3Li Xing-Fang 1Le X. Chris 11Department of Public Health Sciences, University of Alberta, Edmonton, Alberta, Canada2Environmental Health, Capital Health, Edmonton, Alberta, Canada3Health Surveillance Branch, Alberta Health and Wellness, Edmonton, Alberta, CanadaAddress correspondence to X.C. Le, Department of Public Health Sciences, Faculty of Medicine, University of Alberta, 10-102 Clinical Sciences Building, Edmonton, Alberta, Canada T6G 2G3. Telephone: (780) 492-6416. Fax: (780) 492-7800. E-mail:
[email protected] thank P. Cardinal, D. Ehrman, C. Englot, D. Kirchner, R. Zolkiewski, W. Ma, and K. Carastathis for their contribution to this study; EnviroTest Laboratories (Edmonton, Alberta, Canada) for performing the analysis of arsenic in soil samples; and the participating children and their parents for their cooperation.
This study was supported by the Natural Sciences and Engineering Research Council, the Alberta Heritage Foundation for Medical Research, Alberta Health and Wellness, Capital Health, the City of Edmonton, and Environment Canada.
The authors declare they have no competing financial interests.
10 2004 17 6 2004 112 14 1375 1380 21 4 2004 17 6 2004 Publication 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. Increasing concerns over the use of wood treated with chromated copper arsenate (CCA) in playground structures arise from potential exposure to arsenic of children playing in these playgrounds. Limited data from previous studies analyzing arsenic levels in sand samples collected from CCA playgrounds are inconsistent and cannot be directly translated to the amount of children’s exposure to arsenic. The objective of this study was to determine the quantitative amounts of arsenic on the hands of children in contact with CCA-treated wood structures or sand in playgrounds. We compared arsenic levels on the hands of 66 children playing in eight CCA playgrounds with levels of arsenic found on the hands of 64 children playing in another eight playgrounds not constructed with CCA-treated wood. The children’s age and duration of playtime were recorded at each playground. After play, children’s hands were washed in a bag containing 150 mL of deionized water. Arsenic levels in the hand-washing water were quantified by inductively coupled plasma mass spectrometry. Our results show that the ages of the children sampled and the duration of play in the playgrounds were similar between the groups of CCA and non-CCA playgrounds. The mean amount of water-soluble arsenic on children’s hands from CCA playgrounds was 0.50 μg (range, 0.0078–3.5 μg). This was significantly higher (p < 0.001) than the mean amount of water-soluble arsenic on children’s hands from non-CCA playgrounds, which was 0.095 μg (range, 0.011–0.41 μg). There was no significant difference in the amount of sand on the children’s hands and the concentration of arsenic in the sand between the CCA and non-CCA groups. The higher values of arsenic on the hands of children playing in the CCA playgrounds are probably due to direct contact with CCA-treated wood. Washing hands after play would reduce the levels of potential exposure because most of the arsenic on children’s hands was washed off with water. The maximum amount of arsenic on children’s hands from the entire group of study participants was < 4 μg, which is lower than the average daily intake of arsenic from water and food.
arsenicCCAchildren’s exposurechromated copper arsenateplaygroundstreated wood
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Chromated copper arsenate (CCA) is a common wood preservative (American Wood Preservers Institute 2001; Bull 2000, 2001; CPSC 2002). Starting 1 January 2004, the U.S. Environmental Protection Agency (EPA) has banned CCA as a preservative for wood intended for residential use. Despite the ban in the United States, the CCA-treated wood is used in many existing structures in residential decks and public playgrounds. Approximately 70% of single-family homes in the United States have decks and porches containing CCA, and 14% of public playgrounds have CCA wood structures (U.S. EPA 2002a, 2003a, 2003b, 2004). In Edmonton, Alberta, Canada, at the time of this study, 222 of 316 city-owned public playgrounds were constructed either totally or partially with CCA-treated wood.
Although there are numerous studies on arsenic dislodging and leaching from CCA-treated wood [Cooper 1990, 1991; Cooper et al. 2001; Henningsson and Carlsson 1984; Hingston et al. 2001, 2002; Stilwell and Gorny 1997; Stilwell et al. 2003; Taylor et al. 2001; Townsend et al. 2003; U.S. Department of Agriculture (USDA) 1996], there is little quantitative information on arsenic exposure due to CCA-treated wood in playgrounds. Studies on CCA-treated wood have mostly examined soil and sand samples from playgrounds (Balasoiu et al. 2001; Stilwell and Gorny 1997; Townsend et al. 2003; Zagury et al. 2003). None of these studies have measured the levels of arsenic found on the hands of children playing on the CCA-treated structures. Thus, risk assessment has relied on estimates of exposure assessment with many assumptions [Consumer Product Safety Commission (CPSC) 1990; Hemond and Solo-Gabriele 2004; U.S. EPA 2001, 2002b]. In the absence of more reliable measured values of arsenic actually found on children’s hands, predictions were made using calculations of average child hand surface area, adherence factors for various types of soil, and activity patterns and behaviors of children at playgrounds (Hemond and Solo-Gabriele 2004; U.S. EPA 2001
U.S. EPA 2002b). To improve exposure assessment of arsenic from playgrounds, it is essential to obtain information on the levels of arsenic on children’s hands. However, there is no direct measurement of arsenic levels on the hands of children in contact with either CCA-treated wood or the soil/sand. The purpose of this project is to fill this gap.
Our objective in this study was to determine the quantitative amount of arsenic on the hands of children in contact with CCA-treated wood structures and sand in playgrounds. We chose eight playgrounds that were constructed with CCA-treated wood and another eight playgrounds that did not contain CCA-treated wood. After play, the children’s hands were washed and the arsenic concentration on the hands and in the sand was measured in the washing. Arsenic levels on the hands of 66 children playing in CCA-treated playgrounds are compared with levels of arsenic found on the hands of 64 children playing in playgrounds that are not constructed with CCA-treated wood. This information on the level of arsenic exposure is essential to a reliable assessment of the health risks to the public with the existence of CCA-treated wood structures in playgrounds.
Materials and Methods
Playground selection.
The city of Edmonton owned and operated 316 public playgrounds at the time of this study, of which 222 were constructed either totally or partially with CCA-treated wood. For this study, we selected 16 playgrounds constructed between 1985 and 2003. Eight playgrounds contained CCA-treated wood structures and the other eight did not. We selected the playgrounds to represent various characteristics of the playgrounds in the city. In particular, the age of playgrounds, the manufacturers, and the geographic locations of the playgrounds in the city were similar between the CCA and non-CCA groups.
Children’s hand-washing samples.
During 5–21 August 2003, the 16 playgrounds were visited for sampling in a randomized order, with CCA and non-CCA playgrounds interspersed throughout the sampling period. Weather conditions as well as the date and time of arrival were recorded for every playground visited. Except for damp conditions recorded for three CCA playgrounds (C, D, and N) and two non-CCA playgrounds (E and H) because of light rainfalls during the previous night, dry conditions were recorded for all other sampling days.
The time that children arrived at each playground was recorded. Parents of the children were asked for permission to allow their children’s participation in the study. The study objectives, procedures, and potential risks and benefits were explained. Information sheets were made available to the parents. Written consent was obtained from the parents of participating children.
On average, seven to nine children participated at each playground. This number varied because it was determined by an uncontrollable factor, the number of children actually playing at each playground on a given sampling day. The only exclusion criterion was the absence of a parent’s consent. Study protocols were approved by the University of Alberta Health Research Ethics Board.
After playing in the playgrounds, the participating children provided hand-washing samples. The hand-washing sampling consisted of collecting the washings of children’s hands, after playing in the playgrounds, with deionized water. Sterile medium-sized Ziploc bags (18 × 20 cm; Johnson and Son Ltd., Brantford, ON, Canada) were filled with 150 mL of deionized water at the beginning of every day of sampling. At each playground, once children had finished playing, their hands were rinsed for 1 min in the Ziploc bags containing deionized water. The age of each child and the length of time the child had played in the playground were recorded to correspond with the correct hand-washing sample.
The hand-washing samples were then brought back to the laboratory, where they were carefully poured into sterile polystyrene bottles. Each bag was rinsed with 80 mL of deionized water, and the rinse solution was added to the corresponding sample in the polystyrene bottle. The samples (total of 230 mL) were stored at 4°C until analysis. A control sample was also prepared in the same manner for every day of sampling, in which no hands were washed, but all other steps were followed.
Determination of arsenic in hand-washing samples.
We analyzed the washings collected for every child in all of the 16 playgrounds together on the same day for total arsenic concentrations. Because the hand washing contained residual sand from children’s hands, the concentrations of arsenic in the solution and the sand were determined separately after filtration. Hand-washing samples were filtered using Whatman glass filters with 1.2-μm pore size (Whatman International Ltd., Maidstone, UK). The sand collected on the filters was dried at 140°C and weighed. This provided direct measurements of the amount of sand on children’s hands.
We collected the filtrate for the analysis of soluble arsenic in the washing. To 10 mL of filtered samples we added 100 μL of concentrated HNO3 to give an overall concentration of 1% nitric acid. Concentrations of total arsenic (micrograms per liter) in each hand-washing sample were determined from triplicate analyses. Arsenic concentration multiplied by the volume of hand-washing solution (230 mL) provided the total amount (micrograms) of soluble arsenic on children’s hands.
We quantified the arsenic using an inductively coupled plasma-mass spectrometer (6100DRC Plus; PerkinElmer Sciex, Concord, ON, Canada). The standard liquid sample introduction system consisted of a Meinhard nebulizer coupled to a cyclonic spray chamber (Glass Expansion, West Melbourne, Australia). We used an ASX-500 autosampler (CETAC Technologies Inc., Omaha, NE, USA) to introduce the samples. The flow rate for sample introduction was set to 0.8 mL/min. The radio-frequency power was 1100 W. The argon gas flow rates were 15 L/min (plasma gas), 1.2 L/min (auxiliary gas), and 0.9 L/min (nebulizer gas), respectively. Rhodium (5 μg/L) was used as an internal standard. Calibration of the inductively coupled plasma mass spectrometer (ICPMS) using eight arsenic concentrations (0, 0.4, 0.8, 1.2, 1.6, 2.0, 5.0, and 10.0 μg/L) was carried out every 50 samples, and standard reference material (SRM) 1640 [National Institute of Standards and Technology (NIST), Gaithersberg, MD, USA], trace element in natural water (after 10-fold dilution) was analyzed every 10 samples as a quality control. The measured values of arsenic in the SRM were 24.1 ± 1.8 μg/L from 14 repeat analyses spaced over 2 days. This is in good agreement with the certified value (26.67 ± 0.41 μg/L) of the SRM.
Playground sand/soil samples.
Three composite sand/soil samples were collected from each playground on the same day when the children’s hand-washing samples were obtained. They were collected from the under-deck areas, the areas in which children frequently played, and the areas away from any playground structures. Sand and soil were taken from these areas to a depth of 0–6 inches, mixed, and placed in separate clean glass containers. The exact locations of sampling were marked on a detailed plan of each playground, as was the time of sampling. In addition, two playgrounds (G and R) containing CCA-treated wood structures were extensively sampled, with 24 samples collected at 0- to 6-inch depth from various locations of these playgrounds, particularly the areas frequently accessed by children.
Determination of arsenic in playground soil/sand samples.
The level of arsenic in the sand/soil samples was determined by EnviroTest Laboratories (Edmonton, AB, Canada) according to its Standard Operating Procedures. Briefly, we followed U.S. EPA SW-846 method 3050B (U.S. EPA 1996) for the acid digestion of the sand/soil samples. A representative 1–2 g sample was digested in nitric acid and hydrogen peroxide. The digest was then refluxed with nitric acid until all solid material was completely dissolved. The digest was diluted with deionized water, and the final solution contained 5% nitric acid.
We determined the arsenic concentration in the digest using an Elan 6000 ICPMS (PerkinElmer Sciex), following U.S. EPA method 6020 (U.S. EPA 1994). Included in the ICPMS analytical runs were the following quality control samples: calibration verification (every 10 samples), reagent blanks (every 10 samples), method blanks (one per batch), matrix spikes (10% of samples), sample duplicates (10% of samples), and NIST SRM 2709 (one per batch).
Statistical analysis.
Statistical analysis was performed using SPSS (version 11.5; SPSS Inc., Chicago, IL, USA). Data are expressed as mean ± SD. The total amount of soluble arsenic on children’s hands and the amount of arsenic in soil/sand values were compared between CCA and non-CCA playgrounds using two-independent samples t-test. The age of the children, the length of time the children played, and the concentration of arsenic in the sand/soil from the playgrounds were also compared by using t-test. The Pearson correlation coefficient was computed between all the continuous measurements. In multivariate analysis, arsenic on children’s hands was compared between CCA and non-CCA playgrounds after controlling for all the other variables using a general linear model. A p-value < 0.05 was considered statistically significant.
Risk evaluation.
Risk calculation followed the U.S. EPA’s risk assessment framework (U.S. EPA 2001
U.S. EPA 2002b). Ingestion was considered the main route of exposure. Exposure to arsenic by dermal absorption and inhalation was considered negligible (Bernstam et al. 2002; Wester et al. 2004). We used the measured values of arsenic on children’s hands for risk estimation. For comparison, we also used the amount of incidental ingestion of soil arsenic, estimated from the amount of sand ingested per day and the concentration of arsenic in the sand. The latter followed the usual assumption that the amount of sand ingested by children (2–6 years of age) is 50% of the total amount of sand (100 mg) on children’s hands.
Results
Demographics of the participating children.
One hundred thirty children participated in this study, of whom 66 (50.8%) were from CCA playgrounds and 64 (49.2%) were from the non-CCA playgrounds. Seventy participating children (53.8%) were boys, and 60 were girls (46.2%). The ages (mean ± SD) of the participating children were 4.7 ± 2.5 years for the CCA playgrounds and 4.8 ± 2.4 for the non-CCA playgrounds (Figure 1). There was no significant difference in the children’s ages between the two groups (p = 0.82).
We selected all children at each playground whose parents were able to provide written consent. Most children (> 80%) playing at each playground during the sampling period of 3–5 hr participated in the study. Thus, on average there were seven to nine participating children from each playground. A total number of 130 participating children is reasonable considering that 42% of children 2–6 years of age spend < 3 hr/day outdoors and that 80% of children under the age of 11 years spent ≤1 hr each day playing outdoors on sand, gravel, dirt, or grass (U.S. EPA 2002b).
Length of play time in the playgrounds.
Figure 2 compares the length of time children played in the playground before hand-washing samples were obtained from the children. The mean length of play time was 74.4 ± 45.7 min (median, 60 min; range, < 30–240 min) for the CCA playgrounds and 49.4 ± 27.6 min (median, 45 min; range, < 30–120 min) for the non-CCA playgrounds. Although the average length of play between the two groups was different, this is mainly driven by a few children (n = 8) who played > 120 min in the CCA playgrounds compared with the non-CCA playgrounds, where three children played for 120 min and no children played > 120 min.
Concentration of arsenic in the sand/soil from the playgrounds.
Table 1 shows the concentration of arsenic in sand/soil samples collected from the 16 playgrounds. The values of arsenic concentration in sand/soil (mean ± SD) were 3.3 ± 1.7 (median, 2.9; range, 0.8–7.4) for the CCA playgrounds and 1.9 ± 1.2 (median, 1.8; range, 0.5–5.3) for the non-CCA playgrounds. Although the concentrations of arsenic in the samples from the different playgrounds vary, there is no significant difference between the two groups (p = 0.07).
To examine possible heterogeneity of arsenic concentration in sand/soil samples from the playgrounds, we conducted extensive multiple sampling from two CCA playgrounds (G and R). We collected 24 samples from each of these playgrounds and analyzed them for arsenic concentration. The values (mean ± SD) of arsenic concentration were 3.5 ± 1.4 (median, 3.1; range, 1.3–6.0) for playground G and 3.5 ± 1.5 (median, 2.9; range, 1.7–7.4) for playground R. Both of these playgrounds contained CCA-treated wood structures.
Amount of soluble arsenic in the hand washing.
Table 2 shows the amount of soluble arsenic in hand washing of children playing in the 16 playgrounds. The hand washing was filtered to remove residual sand and the filtrate was directly analyzed for soluble arsenic present in the hand washing. Thus, these results represent the amount of soluble arsenic on children’s hands that were washed with 150 mL water. The overall values were 501 ± 512 ng (median, 398 ng; range, 8–3,536 ng) for the CCA playgrounds and 95 ± 70 ng (median, 72 ng; range, 11–407 ng) for the non-CCA playgrounds. The levels of arsenic on children’s hands were significantly (p < 0.001) higher for children playing in the CCA playgrounds compared with those in the non-CCA playgrounds.
Amount of arsenic in the sand residue collected in the hand washing.
Table 3 shows the amount of sand collected from children’s hand washing. The fine sand particles were collected in the hand washing and were filtered, dried, and weighed. The amount of sand collected from children’s hands in dry weight was 22.0 ± 19.1 mg (median, 16.4 mg; range, 0.8–95.8 mg) for the CCA playgrounds and 25.2 ± 23.3 mg (median, 16.6 mg; range, 3.7–116.2 mg) for the non-CCA playgrounds. There was no significant difference between the two groups (p = 0.23) regarding the amount of sand on the children’s hands. This is not surprising because of the similar age distributions (thus similar distribution of the size of hands) and the similar dry weather conditions during the sampling (thus similar adsorption of sand).
Total amount of arsenic in the hand washing.
Table 4 summarizes the total amount of arsenic in the hand washing. The values correspond to the sum of soluble arsenic and the arsenic in the sand residue collected in the hand washing for each child. The overall values were 561 ± 552 ng (median, 416 ng; range, 8–3,865 ng) for the CCA playgrounds and 143 ± 95 ng (median, 124 ng; range, 23–475 ng) for the non-CCA playgrounds. The levels of total arsenic on children’s hands were significantly (p < 0.001) higher for children playing in the CCA playgrounds than for those in the non-CCA playgrounds. This difference is primarily driven by the soluble arsenic (Table 2).
Additional statistical analysis.
There is no difference between boys and girls with regard to the amount of arsenic in their hand washing. Figure 3 shows that the amount of arsenic seems to increase with increasing age of children. However, there is no clear correlation (r = 0.24) between the children’s age and the amount of arsenic on their hands. Similarly, there is a very weak correlation (r = 0.33) between the length of play time and the amount of arsenic on their hands (Figure 3).
In multivariate analysis, the amount of arsenic on children’s hands remains significantly (p < 0.001) higher for those children who played in the CCA playgrounds than for those who played in the non-CCA playgrounds, even after controlling for the age of children and length of play time.
Discussion
The playgrounds were selected to represent the geographic locations of the entire city. The age and the manufacturers of the playgrounds were matched between the two groups. Sampling from the CCA and non-CCA playgrounds was carried out on alternate days. The weather conditions during sample collection were similar between the two groups. Therefore, with these variables controlled, we are able to examine any other differences between the playgrounds with or without the CCA-treated wood structures.
The ages of children and the length of their playing time in both groups of playgrounds were not controlled by design because we wanted to include as many participating children as possible. All children playing in the playgrounds during the time of our visit were approached, and those with their parents’ written consent participated in the study. The ages of children and the length of their playing time in both groups of playgrounds were not significantly different (Figures 1 and 2).
Results for arsenic in the sand/soil samples from the playgrounds show that there is no significant difference between the CCA and non-CCA playgrounds. The concentrations of arsenic in these samples from both types of playgrounds (3.3 ± 1.7 and 1.9 ± 1.2 mg/kg, respectively; Table 1) are below the Canadian guideline value of 12 mg/kg, established by the Canadian Council of Ministers of the Environment (CCME) National Contaminated Sites Remediation Program for all land use (residential/parkland) in Canada (CCME 1995). The guideline value was based on an estimated lifetime incremental risk of 10−6 and a soil ingestion rate of 20 mg/day. We found 22 ± 19 and 25 ± 23 mg sand from the hands of children playing in the CCA and non-CCA playgrounds, respectively (Table 3). Assuming that all the soil on children’s hands is ingested, the measured amount and the estimated values are similar.
The most important and significant difference between the playgrounds with or without the CCA-treated wood structures is the levels of arsenic found in the washings of children’s hands. The total amount of arsenic (Table 4), including both water-soluble arsenic in the washing water (Table 2) and arsenic in the sand collected from children’s hands (Table 3), was significantly higher for the CCA group (561 ± 552 ng) than for the non-CCA group (143 ± 95 ng). This difference was dominated by the soluble arsenic in the hand-washing water, which was approximately 5-fold higher in the CCA group (501 ± 512 ng) than in the non-CCA group (95 ± 70 ng; Table 2). Young children (2–6 years of age) have, on average, a hand-to-mouth frequency of 8–10/hr (Reed et al. 1999; Tulve et al. 2002). Young children putting hands and/or fingers in their mouths could lead to ingestion of arsenic that is on their hands (Tulve et al. 2002). Therefore, CCA-treated wood structures in playgrounds could potentially contribute to a higher exposure to arsenic by children playing in these playgrounds.
It is interesting to note that although there is a significantly higher concentration of arsenic on the hands of children playing in the CCA playgrounds, there is no significant difference in the concentration of arsenic in the sand/soil. A most likely reason responsible for this difference is that children in the CCA playgrounds may be in contact with CCA-treated wood structures directly. It has been found that arsenic from the CCA-treated wood can be transferred onto the hands when rubbing hands against the wood surface [California Department of Health Services (CDHS) 1987]. This is consistent with the results of analysis of swipe samples of CCA-treated wood carried out by others (Osmose Research Division 1983) and by us (data not shown). A subsequent investigation comparing hand washing from children in contact with CCA-treated wood and the same group of children playing with sand in CCA playgrounds suggests that direct contact of hands with CCA-treated wood is a major contributor to the increases in concentration of arsenic on children’s hands (Lu X and Le XC, unpublished data).
The maximum amount of arsenic on children’s hands was < 4 μg (Table 4). This is equivalent to 0.22 μg/kg body weight using a default body weight value of 17.8 kg for young children (2–6 years of age). With a safe conservative assumption that all the arsenic on children’s hands is ingested, the measured value is below the estimated average daily intake of inorganic arsenic from water and food by Canadian children, which is approximately 0.6 μg/kg body weight (CCME 1995). The average daily dietary ingestion of total arsenic was estimated to be 38 μg (15 μg for children 1–4 years of age) for Canada (Dabeka et al. 1993), 62 μg for the United States (Gartrell et al. 1988), 89 μg for the United Kingdom (Food Additives and Contaminants Committee 1984), 55 μg for New Zealand (Dick et al. 1978), and 160–280 μg for Japan (Tsuda et al. 1995). A range of arsenic species that have different toxicities may be present in food (Le et al. 2004). Estimated daily dietary intake of inorganic arsenic was 8.3–14 μg in the United States (Yost et al. 1998), 4.8–12.7 μg in Canada (Yost et al. 1998), and 15–211 μg in Taiwan (Schoof et al. 1998).
It is important to point out to the general public that arsenic is naturally present in the soil and sand regardless of whether the playgrounds contain CCA-treated wood structures. An important approach to reducing children’s exposure to arsenic is to wash hands after playing, particularly after contact with CCA-treated wood. We have measured arsenic in sequential hand washings and found that the most arsenic was present in the first hand washing (data not shown). This confirms that hand washing is effective in removing arsenic from hands.
Conclusions
Children playing in playgrounds constructed with CCA-treated wood have approximately five times more arsenic on their hands than do those playing in playgrounds that do not have CCA-treated wood structures. The higher values of arsenic on the hands of children playing in the CCA playgrounds are probably caused by direct contact with CCA-treated wood. Most of the arsenic on children’s hands is water soluble and is readily washed off with water. We recommend that children wash their hands after playing to reduce their potential exposure to arsenic.
The concentrations of arsenic in soil/sand samples from both CCA and non-CCA playgrounds were below the Canadian guideline levels. The maximum amount of arsenic on children’s hands from the entire group of study participants was < 4 μg. This amount is lower than the average daily intake of arsenic from water and food.
This study provides direct measurements of the amount of arsenic on children’s hands. The results—along with other information, such as the frequency and habit of hand-to-mouth activity, efficiency of transfer of arsenic from hands to mouth, and repeated contact of hands with CCA-treated wood surface after hand-to-mouth activity—are useful for assessing children’s exposure to arsenic.
Figure 1 Distribution of the ages of children who provided hand-washing samples.
Figure 2 Distribution of the length of time children played in the CCA playgrounds and non-CCA playgrounds.
Figure 3 Plots showing weak correlation of arsenic concentration from CCA and non-CCA playgrounds on children’s hands with (A) children’s age (r = 0.24) and (B) the length of playing time (r = 0.33). The arsenic concentration was logarithmically transformed. The results for soluble arsenic on the hands of 66 children who played in CCA playgrounds and 64 children who played in non-CCA playgrounds are included.
Table 1 Concentration of arsenic (mg/kg) in sand/soil samples collected from 16 playgrounds.
Playground Mean ± SD Median Range
CCA playgrounds
A 3.9 ± 2.6 3.8 1.4–6.5
C 1.2 ± 0.4 1.3 0.8–1.5
D 2.6 ± 2.0 1.6 1.3–4.9
F 3.4 ± 3.3 1.5 1.5–7.3
G 3.5 ± 1.4 3.1 1.3–6.0
I 2.2 ± 1.3 1.5 1.4–3.7
N 2.7 ± 2.6 1.6 0.8–5.6
R 3.5 ± 1.5 2.9 1.7–7.4
Overall 3.3 ± 1.7 2.9 0.8–7.4
Non-CCA playgrounds
B 1.8 ± 0.1 1.7 1.7–1.9
E 1.9 ± 0.1 1.9 1.8–2.0
H 3.3 ± 1.8 2.9 1.8–5.3
J 0.6 ± 0.1 0.6 0.5–0.7
K 2.2 ± 0.6 2.3 1.6–2.8
L 1.1 ± 0.8 0.7 0.5–2.0
M 1.2 ± 0.4 1.3 0.8–1.6
O 3.0 ± 1.3 2.2 2.2–4.5
Overall 1.9 ± 1.2 1.8 0.5–5.3
Table 2 Amount of water-soluble arsenic (ng) in hand washing from children playing in the 16 playgrounds.
Playground Mean ± SD Median Range
CCA playgrounds
C 272 ± 152 330 50–479
D 956 ± 247 871 570–1,263
F 167 ± 84 167 108–226
G 670 ± 300 691 185–1,126
I 359 ± 223 271 84–784
N 196 ± 157 147 8–500
R 987 ± 1,161 485 163–3,516
Overall 501 ± 512 398 8–3,536
Non-CCA playgrounds
E 82 ± 27 79 51–113
H 68 ± 36 60 26–138
J 60 ± 44 51 23–136
K 123 ± 51 113 46–225
L 38 ± 13 39 19–56
M 215 ± 95 193 129–407
O 61 ± 37 58 11–114
Overall 95 ± 70 72 11–407
Table 3 Amounts of sand and sand arsenic collected in hand washing from children playing in the 16 playgrounds.
Sand (mg)
Arsenic (ng)
Playground Mean ± SD Median Range Mean ± SD Median Range
CCA playgrounds
A 24.2 ± 24.9 13.9 5.0–77.7 70 ± 72 40 15–225
C 21.9 ± 23.0 14.0 5.2–76.5 26 ± 28 17 6–92
D 26.7 ± 10.3 26.3 15.0–42.8 69 ± 27 68 39–111
F 20.4 ± 21.8 20.4 5.0–35.8 70 ± 75 70 17–123
G 31.7 ± 20.4 29.5 3.3–67.7 110 ± 71 103 11–235
I 18.9 ± 11.1 14.5 6.6–38.2 42 ± 24 32 15–84
N 11.4 ± 10.1 7.7 0.8–38.3 30 ± 27 20 2–102
R 29.7 ± 31.0 16.4 10.8–95.8 102 ± 106 56 37–329
Overall 22.0 ± 19.1 16.4 0.8–95.8 60 ± 60 43 2–329
Non-CCA playgrounds
B 40.5 ± 40.3 28.7 7.2–116.2 72 ± 71 51 13–205
E 21.3 ± 13.2 21.2 6.5–37.3 41 ± 25 40 12–71
H 15.1 ± 9.2 13.0 5.7–38.2 50 ± 31 43 19–127
J 27.5 ± 32.9 13.3 9.1–86.1 17 ± 20 8 5–52
K 25.3 ± 10.8 24.8 10.2–45.7 56 ± 23 55 23–102
L 9.0 ± 2.9 9.5 3.7–11.7 10 ± 3 10 4–12
M 38.2 ± 23.9 45.3 10.8–73.1 47 ± 30 56 13–90
O 23.8 ± 22.6 15.3 3.8–70.2 71 ± 67 46 11–208
Overall 25.2 ± 23.3 16.6 3.7–116.2 49 ± 44 42 4–208
Table 4 Amount of total arsenic (ng) in hand washing from children playing in the 16 playgrounds.
Playground Mean ± SD Median Range
CCA playgrounds
A 587 ± 385 555 48–1,260
C 298 ± 172 356 57–571
D 1,025 ± 252 947 646–1,325
F 237 ± 158 237 125–349
G 780 ± 350 850 196–1,295
I 400 ± 229 297 168–855
N 225 ± 170 204 8–563
R 1,089 ± 1,256 561 219–3,865
Overall 561 ± 552 416 8–3,865
Non-CCA playgrounds
B 163 ± 134 102 57–420
E 122 ± 40 134 74–171
H 118 ± 55 111 48–265
J 77 ± 63 59 31–188
K 175 ± 55 163 69–281
L 48 ± 15 49 23–67
M 262 ± 112 250 142–475
O 132 ± 92 122 23–277
Overall 143 ± 95 124 23–475
==== Refs
References
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Wester RC Hui X Barbadillo S Maibach H Lowney YW Schoof RA 2004 In vivo percutaneous absorption of arsenic from water and CCA-treated wood residue Toxicol Sci 79 287 295 15056813
Yost LJ Schoof RA Aucoin R 1998 Intake of inorganic arsenic in the North American diet Hum Ecol Risk Assess 4 137 152
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.6616ehp0112-00138115471729Children's HealthArticlesEffect of Breast Milk Lead on Infant Blood Lead Levels at 1 Month of Age Ettinger Adrienne S. 12Téllez-Rojo Martha María 3Amarasiriwardena Chitra 2Bellinger David 4Peterson Karen 56Schwartz Joel 12Hu Howard 27Hernández-Avila Mauricio 31Environmental Epidemiology Program, Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA2Channing Laboratory, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA3Centro de Investigación de Salud Poblacional, Instituto Nacional de Salud Pública, Cuernavaca, Morelos, México4Department of Neurology, Children’s Hospital, Harvard Medical School, Boston, Massachusetts, USA5Departments of Nutrition,6Society, Human Development, and Health, and7Occupational Health Program, Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USAAddress correspondence to A.S. Ettinger, Harvard School of Public Health, Landmark Center, East 3-110-A, 401 Park Dr., Boston, MA 02215 USA. Telephone: (617) 384-8968. Fax: (617) 384-8994. E-mail:
[email protected]. Address reprint requests to H. Hu, Harvard School of Public Health, Landmark Center East 3-110-A, 401 Park Dr., Boston, MA 02215 USA.This study was supported by a National Institute of Environmental Health Sciences (NIEHS) grant P42-ES05947, Superfund Basic Research Program NIEHS R01-ES07821, NIEHS Center Grant 2 P30-ES 00002, and NIEHS T32-ES07069 NRSA Training Grant; and by Consejo Nacional de Ciencia y Tecnología (CONACyT) grant 4150M9405 and Consejo de Estudios para la Restauración y Valoración Ambiental (CONSERVA), Department of Federal District, México. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIEHS.
The authors declare they have no competing financial interests.
10 2004 11 5 2004 112 14 1381 1385 28 7 2003 10 5 2004 Publication 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. Nursing infants may be exposed to lead from breast milk, but relatively few data exist with which to evaluate and quantify this relationship. This route of exposure constitutes a potential infant hazard from mothers with current ongoing exposure to lead as well as from mothers who have been exposed previously due to the redistribution of cumulative maternal bone lead stores. We studied the relationship between maternal breast milk lead and infant blood lead levels among 255 mother–infant pairs exclusively or partially breast-feeding through 1 month of age in Mexico City. A rigorous, well-validated technique was used to collect, prepare, and analyze the samples of breast milk to minimize the potential for environmental contamination and maximize the percent recovery of lead. Umbilical cord and maternal blood lead were measured at delivery; 1 month after delivery (± 5 days) maternal blood, bone, and breast milk and infant blood lead levels were obtained. Levels of lead at 1 month postpartum were, for breast milk, 0.3–8.0 μg/L (mean ± SD, 1.5 ± 1.2); maternal blood lead, 2.9–29.9 μg/dL (mean ± SD, 9.4 ± 4.5); and infant blood lead, 1.0–23.1 μg/dL (mean ± SD, 5.5 ± 3.0). Infant blood lead at 1 month postpartum was significantly correlated with umbilical cord (Spearman correlation coefficient rS = 0.40, p < 0.0001) and maternal (rS = 0.42, p < 0.0001) blood lead at delivery and with maternal blood (rS = 0.67, p < 0.0001), patella (rS = 0.19, p = 0.004), and breast milk (rS = 0.32, p < 0.0001) lead at 1 month postpartum. Adjusting for cord blood lead, infant weight change, and reported breast-feeding status, a difference of approximately 2 μg/L (ppb; from the midpoint of the lowest quartile to the midpoint of the highest quartile) breast milk lead was associated with a 0.82 μg/dL increase in blood lead for breast-feeding infants at 1 month of age. Breast milk lead accounted for 12% of the variance of infant blood lead levels, whereas maternal blood lead accounted for 30%. Although these levels of lead in breast milk were low, they clearly have a strong influence on infant blood lead levels over and above the influence of maternal blood lead. Additional information on the lead content of dietary alternatives and interactions with other nutritional factors should be considered. However, because human milk is the best and most complete nutritional source for young infants, breast-feeding should be encouraged because the absolute values of the effects are small within this range of lead concentrations.
blood leadbreast milk leadbreast-feedingKXRF bone leadlactation
==== Body
Breast milk has been suggested as a significant potential source of lead exposure to nursing infants (Silbergeld 1991), but relatively few data exist with which to evaluate and quantify this relationship. This phenomenon constitutes a potential public health problem in countries where environmental lead exposure is continuing as well as in countries where environmental lead exposure has declined (Abadin et al. 1997). Previously, we reported that maternal blood and bone lead levels are both important determinants of lead in breast milk (Ettinger et al. 2004). Lead from current maternal exposure, as well as that accumulated in bone from past environmental exposures and subsequently released into blood, is excreted into breast milk and thus may be ingested by the nursing infant.
Studies of lead in human milk have found concentrations ranging over three levels of magnitude from < 1 to > 100 μg/L (ppb) (Gulson et al. 1998; Namihira et al. 1993). However, there are limited epidemiologic data available regarding the potential exposure that this represents for the breast-feeding infant.
There are some data from rodents on the lactational transfer and uptake of lead in the newborn. Kostial and Momcilovic (1974) showed that the peak transfer of radiolabeled lead in mice from mother to litter occurred during lactation. Keller and Doherty (1980) found that 25% of maternal bone lead burden in mice was transferred to infant mice, and most of this activity occurred during lactation. Mouse breast milk was found to concentrate lead at around 25 times the level circulating in plasma. Amount of lead transferred seems to vary considerably by species (Oskarsson et al. 1995); however, there may be more efficient absorption of lead by the neonate compared with the adult (Oskarsson et al. 1998; Palminger Hallén et al. 1996).
In humans, Rabinowitz et al. (1985) described a log-linear dose–response relationship between breast milk lead and infant blood lead at 6 months of age (β = 3.0 μg/dL, SE = 1.1 μg/dL, r2 = 10%, p = 0.009). By examining the lead isotopic ratios in a small number of infants born to recent immigrants to Australia (and infants of Australian controls), Gulson et al. (1998) found that for the first 60–90 days postpartum the contribution from breast milk to blood lead in the infants varied from 36 to 80%.
We evaluated the effect of breast milk lead on infant blood lead levels to quantify the dose–response relationship in a large, population-based sample of infants exclusively or partially breast-fed through 1 month of age. We used a rigorous, well-validated technique to collect, prepare, and analyze the samples of breast milk to minimize the potential for contamination and maximize the percent recovery of lead.
Materials and Methods
We conducted a cross-sectional study of 255 nursing infants at 1 month postpartum in Mexico City. Subjects included infants born to a subcohort of women recruited for later participation in a randomized placebo-controlled trial of calcium supplementation during lactation. Informed consent, questionnaire information, and samples for the present study were obtained before the initiation of calcium supplementation. All participating mothers received a detailed explanation of the study and counseling on reduction of lead exposure. The research protocol was approved by the human subjects committees of the National Institute of Public Health of Mexico, Harvard School of Public Health, and the participating hospitals.
Data collection methods have been described in detail elsewhere (Hernández-Avila et al. 2003). Between January 1994 and June 1995, 2,945 potential study participants were interviewed at three maternity hospitals in Mexico City. Of these, 1,398 were eligible for the trial. From the women identified as eligible, 629 (45%) agreed to participate in the study. These women completed a baseline evaluation including a questionnaire that assessed known risk factors for environmental lead exposure, dietary assessment of nutrient intake, and breast-feeding practices. At 1 month post-partum (± 5 days), field personnel visited study participants at home to obtain anthropometric measurements, blood, and breast milk samples. Maternal bone lead was estimated by K X-ray fluorescence (KXRF) at the research facility at the American British Cowdray (ABC) Hospital. Three hundred ten samples of breast milk from the 1 month postpartum visit were analyzed for lead content. This report is limited to the 255 subjects with both breast milk and infant blood lead levels available at 1 month postpartum.
Blood lead.
Blood lead measurements were performed using graphite furnace atomic absorption spectrophotometry (model 3000; PerkinElmer, Norwalk, CT, USA) at the ABC Hospital Trace Metal Laboratory according to a technique described by Miller et al. (1987). The laboratory participates in the Centers for Disease Control and Prevention blood lead proficiency testing program administered by the Wisconsin State Laboratory of Hygiene (Madison, WI, USA). The laboratory standardization program provided external quality control specimens varying from 2 to 88 μg/dL. Our laboratory maintained acceptable precision and accuracy over the study period (correlation = 0.98; mean difference = 0.71 μg/dL; SD = 0.68).
Bone lead.
We used a spot-source 109Cd KXRF instrument constructed at Harvard University and installed at the research facility in Mexico City to measure maternal bone lead. Thirty-minute in vivo measurements of each subject’s mid-tibial shaft (representing cortical bone) and patella (trabecular bone) were obtained after each region had been washed with a 50% solution of isopropyl alcohol. The physical principles, technical specifications, validation, and use of the KXRF technique have been described in detail elsewhere (Hu et al. 1991). The instrument provides an estimate of the uncertainty associated with each measurement. For quality control, we excluded bone lead measurements with uncertainty estimates that were > 10 and 15 μg lead/g mineral bone for tibia (n = 12) and patella (n = 38), respectively, from the entire cohort of 629 women. These measurements generally reflect excessive patient movement outside the measurement field or excessive thickness of overlaying tissue and do not produce acceptable results.
Breast milk lead.
Breast milk samples were collected at 1 month postpartum from lactating women using techniques to minimize potential for environmental contamination. Before manual expression of milk, the breast was washed with deionized water, which also was collected and analyzed for lead contamination. Ten milliliters of milk was collected in preleached polypropylene tubes. Samples were frozen, shipped to the Channing Laboratory, and stored at −30°C (Fisher IsoTempPlus, New York, NY, USA) until analysis.
Breast milk sample preparation was performed at University Research Institute for Analytical Chemistry (Amherst, MA, USA), and instrumental analysis was performed at the Trace Metals Laboratory of Harvard School of Public Health. Digestion was performed using HNO3 acid in high-temperature high-pressure asher (HPA; Anton Paar USA, Ashland, VA, USA). Lead content in the samples was analyzed by isotope dilution–inductively coupled plasma mass spectrometry (ID-ICPMS; Sciex Elan 5000; PerkinElmer,) by methods previously described in detail (Ettinger et al. 2004). The limit of detection for lead analysis in breast milk by HPA digestion and ID-ICPMS is 0.1 ng/mL (ppb) milk.
Statistical analysis.
Univariate and bivariate summary statistics and distributional plots were examined for all variables. Infant blood lead levels were highly positively skewed, so for the subsequent regression analyses, the log (base e)-transformed values of the dependent variable were used. Possible associations between infant blood lead and the independent variables were separately explored with bivariate linear regression models. Spearman correlation coefficients with p-values are reported. Characteristics of the participants were compared by reported breast-feeding practice (partial vs. exclusive) using Wilcoxon sign rank/chi-square tests of equality of two sample population means/proportions. Extreme values of infant blood lead (n = 3) and breast milk lead (n = 9) were identified using the generalized extreme studentized deviation many-outlier procedure (Rosner 1983) and excluded from the multivariate regression analyses. We used multiple linear regression models to describe the relationships between infant blood lead, breast milk lead, and the covariates of interest, which were determined a priori based on biologic considerations. Infant weight change (weight at 1 month minus birth weight) was used as a surrogate for the amount of breast milk consumed. The final model for infant blood lead included breast milk lead, umbilical cord lead at delivery, breast-feeding status (exclusive vs. partial), and infant weight change. Breast milk lead was divided into quartiles, and the midpoint of the quartile was used to predict the infant blood lead level for exposure at that level based on the final model for infant blood lead. To explore potential nonlinear associations between breast milk lead and infant blood lead levels, we examined the relations between the variables using generalized additive models. All statistical analyses were performed using Statistical Analysis System (SAS) software (release 8.01; SAS Institute, Inc., Cary, NC, USA) and S-PLUS (6.0 professional edition for Windows; Insightful Corp., Seattle, WA, USA).
Results
Summary statistics for the lead biomarkers of mothers and infants in the study (n = 255) are shown in Table 1. Levels of lead in breast milk ranged from 0.3 to 8.0 μg/L (ppb). Infant blood lead levels (mean ± SD) were 5.5 ± 3.0 μg/dL and ranged from 1.0 to 23.1 μg/dL. Figure 1 shows the unadjusted relationships of maternal blood lead and breast milk lead on infant blood lead levels at 1 month postpartum. Infant blood lead at 1 month postpartum was significantly correlated with umbilical cord (Spearman correlation coefficient rS = 0.40, p < 0.0001) and maternal (rS = 0.42, p < 0.0001) blood lead at delivery and with concurrent maternal blood (rS = 0.67, p < 0.0001), patella (rS = 0.19, p = 0.004), and breast milk (rS = 0.32, p < 0.0001) lead at 1 month postpartum (Table 2).
On average, mothers in the study were 24.3 years of age (range, 14–40 years of age) and had lived in Mexico City for 20 years (range, 0.5–40 years). Forty percent of women were primiparous. Of the 152 women with prior pregnancies, 22% (n = 55) had completed 12 or more months of total breast-feeding of their previous infants.
Differences in maternal and infant characteristics by reported breast-feeding practice (exclusive n = 88 vs. partial n = 165) at 1 month postpartum are shown in Table 3. Breast milk lead levels (mean μg/L ± SD) were similar (p = 0.84) among women who reported practicing exclusive breast-feeding (1.4 ± 1.1) compared with women who practiced partial lactation (1.5 ± 1.2). With respect to other subject characteristics, subjects differed somewhat by lead-glazed ceramics use. Subjects who were exclusively breast-feeding at 1 month postpartum were less likely to have used lead-glazed ceramics to store, prepare, or serve food in the past (p = 0.03), with 69% of women reporting past use of lead-glazed ceramics compared with 81% of partially breast-feeding mothers. In addition, those subjects who were partially breast-feeding reported slightly higher, although not statistically significant, current use of lead-glazed ceramics (p = 0.08). However, exclusively breast-feeding women (10%) were more likely to have reported current smoking or smoking during pregnancy than were partially breast-feeding women (3.6%; p = 0.03). Partially breast-feeding women were more likely to be married (74 vs. 61%, p = 0.04) and reported slightly higher dietary calcium intake (1,193 vs. 1,036 mg, p = 0.002) than were women who were exclusively breast-feeding at 1 month postpartum.
Figure 2 shows the nonparametric dose–response relationship of maternal blood lead and breast milk lead on infant blood lead levels at 1 month postpartum from the generalized additive model, adjusted for umbilical cord blood lead (micrograms per deciliter), infant weight change (grams), and breast-feeding practice (exclusive vs. partial).
In multivariate linear regression models, breast milk was a significant predictor (p = 0.02) of infant blood lead after controlling for umbilical cord lead, infant weight change, and breast-feeding practice. Breast milk accounted for 12% of the variance of infant blood lead levels (Table 4), whereas maternal blood lead accounted for 30% of the variance of infant blood lead levels in a similar model (data not shown). To predict the effect of breast milk lead on infant blood lead level, we calculated infant blood lead for each quartile of breast milk exposure based on the final model. Adjusting for cord blood lead, infant weight change, and reported breast-feeding practice, we found that a difference of approximately 2 μg/L (from the midpoint of the lowest quartile to the midpoint of the highest quartile) of breast milk lead was associated with a 0.82-μg/dL increase in blood lead for infants at 1 month of age (Figure 3). This effect was almost identical among the exclusive and partial breast-feeding groups, so the combined data are presented.
Discussion
From birth to 6 months, the infant’s exposure to lead is typically dominated by dietary sources. Although the levels of lead in breast milk reported here were low, they clearly had a strong influence on infant blood lead levels over and above the influence of maternal blood lead. In our study, breast milk lead accounted for 12% of the variance of infant blood lead levels at 1 month of age. In the only other large-scale study of breast milk and infant blood lead levels, milk lead accounted for 10% of the variance in 6-month blood lead (Rabinowitz et al. 1985).
It is important to estimate the contribution from the non–breast milk sources to total lead exposure from dietary intake. Rabinowitz et al. (1985) found breast milk to be the strongest correlate of 6-month blood leads, whereas formula lead correlated poorly with infant blood lead levels. Gulson et al. (1998) showed that the contribution of formula to infant blood lead varied from 24 to 68% in formula-fed infants. Therefore, it would have been important to document the sources and amount of lead in diet (other than from breast milk) consumed by infants in this population.
Our study was completed during the voluntary removal of lead soldered cans from the market in Mexico (De León 1996), so lead in canned infant formula may have been an additional source. We can only speculate that the contribution to lead exposure from foods and beverages used as alternatives to or in combination with breast milk may have been similar to or greater than that of breast milk. Although there may be more lead in infant formula, the relative bioavailability of such lead may be less than that of lead in breast milk. For example, it has been documented that iron is more readily absorbed from breast milk than from infant formula (Lonnerdal 1985).
Estimating the potential lead dose to infants from breast milk requires information about the quantity of breast milk consumed per day and the duration over which breast-feeding occurs (U.S. EPA 1997). Average intakes are about 750–800 g/day (range, 450–1,200 g/day) for the first 4–5 months of life [Institute of Medicine (IOM) 1991]. However, infant birth weight and nursing frequency have been shown to influence the rate of intake (IOM 1991). We attempted to control for consumption using infant weight change from birth to 1 month as a surrogate in our analyses.
It may also be important to estimate the contribution from the nondietary sources of lead to total body burden of young children. Although it is widely assumed that infant exposures to lead during the first 4–6 months of life are derived from diet, Manton et al. (2000) showed that lead dust contributed to exposure in U.S. infants in the first 4 months of life. However, lead dust is not a common source of exposure in Mexico. Also, neonatal bone turnover is a potential endogenous source of lead in infant blood (Gulson et al. 2001).
Our previous research (Hernández-Avila et al. 1996; Hu et al. 1996) and the research of others (Gulson et al. 1997; Rothenberg et al. 2000) have clearly shown that maternal bone stores of lead are mobilized to a marked degree during lactation. Breast-feeding practices and maternal bone lead are important predictors of maternal blood lead levels over the course of lactation (Téllez-Rojo et al. 2002). Previously, we reported that maternal blood and bone lead levels are both important determinants of lead in breast milk (Ettinger et al. 2004). Our data suggest that despite the potential for lead exposure, even among this population of women who have been relatively highly exposed, levels of lead in breast milk are low. However, we have demonstrated here that breast milk lead levels are highly influential on infant blood lead levels at 1 month of age. This is a cross-sectional analysis at 1 month postpartum and cannot evaluate changes in breast milk, infant blood, and bone lead levels over the course of lactation. It will be important to determine whether the degree of this influence changes over the course of lactation.
Due to the unique nutritional characteristics of human milk, breast-feeding is thought to be the optimal mode of nutrient delivery to term infants [American Academy of Pediatrics 1997; IOM 1991; World Health Organization (WHO) 1995]. Better understanding of the potential for neonatal exposure, including kinetics in the lactating mother and knowledge about alternative dietary sources of lead, is needed for risk assessment. Given the correlation of breast milk lead levels with maternal and infant blood lead levels, milk lead can be used as an indicator of both maternal and neonatal exposure (Hallén et al. 1995). Additional information on the lead content of dietary alternatives should be investigated in comparison with breast milk levels in a specific population and interactions with other nutritional factors should also be considered. This highlights the need to further investigate interventions that may reduce lead exposure from endogenous sources. Because bone lead has a half-life of years to decades, infants will continue to be at risk for exposure long after environmental sources of lead have been abated. In addition, efforts to reduce ongoing environmental exposure to lead should be continued, and ways to mitigate the effects of past exposures should be investigated. However, because human milk is the best and most complete nutritional source for young infants, breast-feeding should be encouraged because the absolute values of the effects are small within this range of lead concentrations.
Figure 1 Smooth scatter plots (Lowess; bandwidth = 0.75) of infant blood lead by (A) maternal blood lead and (B) breast milk lead at 1 month postpartum.
Figure 2 Generalized additive model-adjusted dose–response function for log-scaled infant blood lead and breast milk lead concentrations at 1 month postpartum adjusted for umbilical cord blood lead, infant weight change, and breast-feeding practice. The dashed lines represent 95% pointwise confidence intervals.
Figure 3 Geometric mean infant blood lead level (μg/dL) predicted at each level of breast milk lead. The midpoints of quartile 1 = 0.53 μg/L; quartile 2 = 0.83; quartile 3 = 1.28; quartile 4 = 2.34. A difference of approximately 2 μg/L (ppb; from the midpoint of the lowest quartile to the midpoint of the highest quartile) breast milk lead was associated with a 0.82-μg/dL increase in blood lead for breast-feeding infants at 1 month of age.
Table 1 Summary statistics for lead biomarkers among mothers and infants in the study.
Biomarker of lead exposure No. Mean ± SD Minimum Maximum
At delivery
Maternal blood lead (μg/dL) 251 8.7 ± 4.2 2.1 23.7
Umbilical cord lead (μg/dL) 222 6.7 ± 3.6 1.2 26.3
At 1 month postpartum
Breast milk lead (μg/L) 255 1.5 ± 1.2 0.3 8.0
Maternal blood lead (μg/dL) 255 9.4 ± 4.5 1.8 29.9
Maternal patella lead (μg/g)a 246 15.3 ± 15.0 < 1 67.2
Maternal tibia lead (μg/g)a 250 10.0 ± 10.4 < 1 76.6
Infant blood lead (μg/dL) 255 5.5 ± 3.0 1 23.1
a Includes measurements with negative values: patella (n = 37), tibia (n = 34).
Table 2 Correlation matrix for lead biomarkers.a
At delivery
At 1 month postpartum
Biomarker of lead exposure Umbilical cord (n = 222) Maternal blood (n = 220) Breast milk (n = 255) Maternal blood (n = 255) Maternal patella (n = 246) Maternal tibia (n = 250) Infant blood (n = 255)
At delivery
Umbilical cord 1.00 0.82 0.34 0.51 0.019 0.12 0.40
p < 0.0001 p < 0.0001 p < 0.0001 p = 0.006 p = 0.07 p < 0.0001
Maternal blood 1.00 0.36 0.56 0.22 0.18 0.42
p < 0.0001 p < 0.0001 p = 0.0006 p = 0.006 p < 0.0001
At 1 month postpartum
Breast milk 1.00 0.42 0.14 −0.005 0.32
p < 0.0001 p = 0.03 p = 0.94 p < 0.0001
Maternal blood 1.00 0.30 0.19 0.67
p < 0.0001 p < 0.0001 p < 0.0001
Maternal patella 1.00 0.27 0.19
p < 0.0001 p = 0.004
Maternal tibia 1.00 0.08
p = 0.2
Infant blood 1.00
a Spearman correlation coefficients; prob > |r| under H0; rho = 0.
Table 3 Maternal and infant characteristics by reported breast-feeding practice.
Reported breast-feeding practice
Exclusive lactation
Partial lactation
Characteristic No. Mean ± SD No. Mean ± SD p-Valuea
At delivery
Umbilical cord lead (μg/dL) 78 6.4 ± 2.9 143 6.9 ± 3.9 0.26
Maternal blood lead (μg/dL) 86 8.1 ± 3.8 164 9.0 ± 4.4 0.09
Infant birth weight (g) 88 3,140 ± 372 165 3,121 ± 380 0.71
Infant birth length (cm) 86 50.6 ± 2.1 162 50.3 ± 2.3 0.30
Infant head circumference (cm) 84 34.0 ± 1.4 157 33.9 ± 1.4 0.45
At 1 month of age (infant)
Blood lead (μg/dL) 88 5.4 ± 3.2 165 5.6 ± 3.0 0.54
Weight (g) 87 4,263 ± 516 165 4,178 ± 534 0.22
Length (cm) 88 53.5 ± 2.1 165 53.6 ± 2.0 0.79
At 1 month postpartum (maternal)
Breast milk lead (μg/L) 88 1.4 ± 1.1 165 1.5 ± 1.2 0.85
Blood lead (μg/dL) 88 9.4 ± 4.8 165 9.5 ± 4.3 0.82
Patella lead (μg/g) 85 15.4 ± 12.6 159 15.4 ± 16.1 0.98
Tibia lead (μg/g) 87 9.9 ± 9.5 164 10.0 ± 10.9 0.96
Age (years) 88 24.6 ± 5.4 165 24.2 ± 4.7 0.54
Years living in Mexico City 88 19.2 ± 9.4 165 20.7 ± 8.2 0.20
Education (years) 85 8.8 ± 3.1 165 9.2 ± 3.0 0.30
Married (%) 88 61 165 74 0.04
Estimated calcium intake (mg) 88 1,036 ± 358 164 1,193 ± 397 0.002
Previous lactation > 12 months (%) 88 28.4 165 18.2 0.06
No. of pregnancies 88 2.2 ± 1.3 165 2.0 ± 1.2 0.22
Primiparous (%) 88 35.2 165 43.6 0.19
Current use of lead-glazed ceramics (%) 88 34.1 165 45.5 0.08
Past use of lead-glazed ceramics (%) 88 69.3 165 81.2 0.03
Current smoking or during pregnancy (%) 88 10.2 165 3.6 0.03
a p-Value from Wilcoxon sign rank test/chi-square test of equality of two sample population means/proportions.
Table 4 Multivariate regressions for infant blood lead.a
β-coefficient SE p-Value Partial R2b
Intercept 1.06 0.15 < 0.0001 —
Breast milk leadc (μg/L) 0.10 0.04 0.02 0.12
Umbilical cord blood lead (μg/dL) 0.05 0.009 < 0.0001 0.11
Infant weight change (g) −0.00009 0.00007 0.2 0.007
Breast-feeding practiced 0.09 0.06 0.15 0.015
a Infant blood lead levels log (base e) transformed, n = 3 extreme outliers excluded.
b Adjusted model R2 = 0.2259.
c Breast milk lead n = 9, extreme outliers removed.
d Exclusive lactation = reference group.
==== Refs
References
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Ettinger AS Téllez-Rojo MM Amarasiriwardena C González-Cossío T Peterson K Aro A 2004 Levels of lead in breast milk and their relation to maternal blood and bone lead levels at one month postpartum Environ Health Perspect 112 926 931 15175184
Gulson BL Jameson CW Mahaffey KR Mizon KJ Korsch MJ Vimpani G 1997 Pregnancy increases mobilization of lead from maternal skeleton J Lab Clin Med 130 1 51 62 9242366
Gulson BL Jameson CW Mahaffey KR Mizon KJ Patison N Law AJ 1998 Relationships of lead in breast milk to lead in blood, urine, and diet of the infant and mother Environ Health Perspect 106 667 674 9755144
Gulson BL Mizon KJ Palmer JM Patison N Law AJ Korsch MJ 2001 Longitudinal study of daily intake and excretion of lead in newly born infants Environ Res 85 3 232 245 11237512
Hallén IP Jorhem L Lagerkvist BJ Oskarsson A 1995 Lead and cadmium levels in human milk and blood Sci Total Environ 166 149 155 7754354
Hernández-Avila M Gonzalez-Cossío T Hernández-Avila JE Romieu I Peterson KE Aro A 2003 Dietary calcium supplements to lower blood lead levels in lactating women: a randomized placebo-controlled trial Epidemiology 14 2 206 212 12606887
Hernández-Avila M González-Cossío T Palazuelos E Romieu I Aro A Fishbein E 1996 Dietary and environmental determinants of blood and bone lead levels in lactating postpartum women living in Mexico City Environ Health Perspect 104 1076 1082 8930549
Hu H Hashimoto D Besser M 1996 Levels of lead in blood and bone of women giving birth in a Boston hospital Arch Environ Health 51 1 52 58 8629865
Hu H Milder FL Burger DE 1991 The use of K X-ray fluorescence for measuring lead burden in epidemiological studies: high and low lead burdens and measurement uncertainty Environ Health Perspect 94 107 110 1954919
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Keller CA Doherty RA 1980 Bone lead mobilization in lactating mice Toxicol Appl Pharmacol 55 220 228 7191586
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Lonnerdal B 1985 Dietary factors affecting trace element bioavailability from human milk, cow’s milk and infant formulas Prog Food Nutr Sci 9 1–2 35 62 3911269
Manton WI Angle CR Stanek KL Reese YR Kuehnemann TJ 2000 Acquisition and retention of lead by young children Environ Res 82 1 60 80 10677147
Miller DT Paschal DC Gunter EW Stroud PE D’Angelo J 1987 Determination of lead in blood using electrothermal atomisation atomic absorption spectrometry with a L’vov platform and matrix modifier Analyst 112 12 1701 1704 3445938
Namihira D Saldivar L Pustilnik N Carreon GJ Salinas ME 1993 Lead in human blood and milk from nursing women living near a smelter in Mexico City J Toxicol Environ Health 38 3 225 232 8450554
Oskarsson A Palminger Hallén I Sundberg J 1995 Exposure to toxic elements via breast milk Analyst 120 765 770 7741226
Oskarsson A Palminger Hallén I Sundberg J Petersson Grawe K 1998 Risk assessment in relation to neonatal metal exposure Analyst 123 1 19 23 9581014
Palminger Hallén I Jonsson S Karlsson MO Oskarsson A 1996 Kinetic observations in neonatal mice exposed to lead via milk Toxicol Appl Pharmacol 140 1 13 18 8806865
Rabinowitz M Leviton A Needleman H 1985 Lead in milk and infant blood: a dose-response model Arch Environ Health 40 5 283 286 4062363
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Rothenberg SJ Khan F Manalo M Jiang J Cuellar R Reyes S 2000 Maternal bone lead contribution to blood lead during and after pregnancy Environ Res 82 1 81 90 10677148
Silbergeld EK 1991 Lead in bone: implications for toxicology during pregnancy and lactation Environ Health Perspect 91 63 70 2040252
Téllez-Rojo MM Hernández-Avila M González-Cossío T Romieu I Aro A Palazuelos E 2002 Impact of breast-feeding on the mobilization of lead from bone Am J Epidem 155 420 428
U.S. EPA 1997. Exposure Factors Handbook. Vol 2: PB98-124233—Food Ingestion Factors. Chapter 14: Breast Milk Intake. EPA/600/P-95/002Fa:1–8. Washington, DC:U.S. Environmental Protection Agency.
WHO 1995 The World Health Organization’s infant-feeding recommendation Wkly Epidemiol Rec 70 17 119 120
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7107ehp0112-00138615471730Children's HealthArticlesOutdoor, Indoor, and Personal Exposure to VOCs in Children Adgate John L. 1Church Timothy R. 1Ryan Andrew D. 1Ramachandran Gurumurthy 1Fredrickson Ann L. 1Stock Thomas H. 2Morandi Maria T. 2Sexton Ken 31Division of Environmental Health Sciences, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA;2University of Texas, Houston Health Science Center, School of Public Health, Houston, Texas, USA;3University of Texas, School of Public Health, Brownsville Regional Campus, Brownsville, Texas, USAAddress correspondence to J.L. Adgate, University of Minnesota School of Public Health, Division of Environmental Health Sciences, Room 1260 Mayo, 420 Delaware St. SE, Minneapolis, MN 55455 USA. Telephone: (612) 624-2601. Fax: (612) 626-4837. E-mail:
[email protected] are especially grateful to the participant families and to K. Meyer, D. Heistad, S. Mullett, S. Poston, D. Schultz, O. Brooks-James, B. Cefalu, S. Bishop, L. Zeno, and other staff at the Minneapolis Public Schools for their help in making this study possible. Collaboration with the Minnesota Department of Health Indoor Air Program was invaluable to the success of the study.
This research was funded by U.S. Environmental Protection Agency STAR grants (R825813 and R826789) and a grant from the Legislative Commission on Minnesota Resources.
The authors declare they have no competing financial interests.
10 2004 15 7 2004 112 14 1386 1392 22 3 2004 14 7 2004 Publication 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 measured volatile organic compound (VOC) exposures in multiple locations for a diverse population of children who attended two inner-city schools in Minneapolis, Minnesota. Fifteen common VOCs were measured at four locations: outdoors (O), indoors at school (S), indoors at home (H), and in personal samples (P). Concentrations of most VOCs followed the general pattern O ≈ S < P ≤ H across the measured microenvironments. The S and O environments had the smallest and H the largest influence on personal exposure to most compounds. A time-weighted model of P exposure using all measured microenvironments and time–activity data provided little additional explanatory power beyond that provided by using the H measurement alone. Although H and P concentrations of most VOCs measured in this study were similar to or lower than levels measured in recent personal monitoring studies of adults and children in the United States, p-dichlorobenzene was the notable exception to this pattern, with upper-bound exposures more than 100 times greater than those found in other studies of children. Median and upper-bound H and P exposures were well above health benchmarks for several compounds, so outdoor measurements likely underestimate long-term health risks from children’s exposure to these compounds.
air pollutionelementary school childrenethnicityhealth riskraceSHIELD study
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Although ambient levels of the major criteria pollutants have declined in the United States over the last 30 years, much less is known about exposure to many of the 189 hazardous air pollutants identified in the 1990 Clean Air Act Amendments (Clean Air Act Amendments 1990). Volatile organic compounds (VOCs) are an important class of outdoor air toxics because they are ubiquitous and associated with increased long-term health risks (Pratt et al. 2000; Woodruff et al. 1998). VOCs are also an indoor air quality issue because humans spend, on average, nearly 90% of their time indoors (Klepeis et al. 2001). VOCs in ambient air largely originate from mobile and industrial sources. The cumulative risk from exposure to multiple VOCs and other air pollutants is not known, and limited evidence suggests that the minority populations residing in inner-city neighborhoods have disproportionately higher exposures (Kinney et al. 2002; Metzger et al. 1995). There are relatively few data on VOC exposures in children or for minority populations in the United States (Adgate et al. 2004).
Past research has shown that VOCs are typically higher indoors than outdoors and that construction materials and building characteristics, such as the presence of an attached garage or air exchange rate, can influence levels or indoor:outdoor ratios (Levin 1989; Otson et al. 1994; Wallace 2001; Wallace et al. 1985). The contribution of indoor sources, such as consumer products and environmental tobacco smoke (ETS), is the largest source of variability in measured personal and indoor levels of many compounds (Sexton et al. 2004a; Wallace 2001). Compounds associated with consumer product use include p-dichlorobenzene (moth cakes, room air fresheners, toilet bowl deodorizers), chloroform (chlorinated water), and the fragrances α- and β-pinene and d-limonene (cleaning products, room fresheners) (Wallace 1991a, 1991b). Benzene and styrene have been shown to be elevated in homes with smokers, but these compounds also originate from traffic and are often higher in urban areas (Edwards et al. 2001).
Characterizing air pollution exposures in inner-city children is important for providing benchmarks for assessing environmental justice, estimating health risks, recommending interventions, and designing epidemiologic studies. We measured outdoor, indoor at school and home, and personal VOC concentrations for an ethnically and racially diverse sample of inner-city children in Minneapolis, Minnesota. These children were participants in the School Health Initiative: Environment, Learning, and Disease (SHIELD) study (Sexton et al. 2000, 2003), which selected participants at random with known probabilities from a defined sampling frame so inferences could be drawn about sociodemographic groups in two elementary schools. This analysis examines the distribution of exposures to common VOCs measured in the personal air and three primary microenvironments where these children spent time. We also examine how VOC exposures vary by sociodemographics, source/housing characteristics, and time–activity patterns and compare these results with health benchmarks and levels observed in recent VOC exposure studies in children and adults.
Materials and Methods
The SHIELD study was approved the University of Minnesota Research Subjects’ Protection Program Institutional Review Board: Human Subjects Committee and examined children’s exposures to a complex mixture of environmental agents, including VOCs and other chemical and biological agents. A detailed description of the SHIELD study design, eligibility criteria, sample selection, informed consent process, and response rates has been published (Sexton et al. 2000, 2003) and is briefly summarized here.
Children from two inner-city schools serving predominantly low-income households (> 90% qualified for free or reduced-price meals in the National School Lunch/Breakfast Program) in Minneapolis were recruited between November 1999 and January 2000. The three largest racial/ethnic groups that enrolled in SHIELD were African Americans, Hispanics, and Somalis, with a smaller number of Caucasians, Native Americans, Southeast Asians, and those declaring “other” or mixed-race ancestry. We used a stratified random sample to ensure an adequate number of subjects within the following defined subgroups of children: school (Lyndale, Whittier), grade (2nd, 3rd, 4th, 5th), language (English or non-English language spoken at home), and sex (female, male). This produced 32 distinct strata, with a target of five “index” children per stratum, for a target sample size of 80 children/school. A total of 153 index children were recruited for the study and eligible for VOC monitoring.
VOC measurements were obtained in winter (24 January–18 February) and spring (9 April–12 May) 2000. Fifteen VOCs were monitored concurrently in four locations using organic vapor monitors (OVMs; model 3520; 3M Corporation, St. Paul, MN): in the personal breathing zone (P), indoors in the child’s primary residence (H), indoors in five randomly selected classrooms in each school (S), and outdoors (O) at each school. To ensure a relatively high percentage of detectable concentrations, the duration of VOC sampling varied by location, based on logistical considerations and VOC concentrations measured during a pilot study: a) P and H measurements were collected continuously for 2 days (~ 48 hr), b) S measurements were collected each school day by capping the OVMs after school hours (average weekly sampling duration ~ 31 hr over 5 days), and c) O measurements were collected at each school continuously from Monday morning to Friday afternoon each week (~ 103 hr).
The P and H samplers were deployed simultaneously during a household visit on a Sunday, Monday, Tuesday, or Wednesday evening by SHIELD field teams. The P samplers were attached to the clothes in the breathing zone of the child, and the H sampler was placed in the room where the child spent the most time while awake. At night subjects were instructed to place the P monitor beside their bed. During the monitoring period each subject kept a time–activity diary (TAD), recording time the child spent in seven primary microenvironments (inside at home, school, and other; outside at home, school, and other; and in transit) as well as data on exposure to ETS and other potential exposure modifiers (e.g., use of cleaning products) and the number of hours that doors and windows were open. On the second day after deploying the P/H OVMs, study staff collected the samplers and checked the TAD for completeness. The SHIELD baseline questionnaire provided data on housing type, some source-related characteristics, and other sociodemographic information.
The OVMs are charcoal-based passive air samplers. The precision, accuracy, and suitability of these VOC badges for outdoor, indoor, and personal sampling have been demonstrated in previous studies (Chung et al. 1999a, 1999b; Stock et al. 1999). Target VOCs were extracted from OVMs using a 2:1 (vol/vol) mix of double-distilled acetone and carbon disulfide (Sigma-Aldrich, St. Louis, MO), which provided a very low background for target analytes. All extracts were analyzed by gas chromatograph/mass spectrometer with a Hewlett-Packard (HP) 5890 Series II Plus gas chromatograph with an HP 5972 mass spectrometer detector, HP 18593B autosampler, Vectra 486 computer with EnvironQuant ChemStation software, and NBS75K Spectra Library (all from Hewlett Packard, Palo Alto, CA), using an RTX-1/60 m/0.25 μm inner diameter/1 mm film thickness capillary column. Approximately 10% of H and S samples collected during the study were duplicates: Correlation coefficients (R2) for measurable values were > 0.90 for most VOCs, with lower values observed for β-pinene (0.85), benzene (0.84), and methylene chloride (0.59). The percent median relative absolute difference (the median of the ratios of within-pair absolute differences divided by the within-pair mean, multiplied by 100) for duplicate samples was 9% and ranged from 6% (β-pinene) to 14% (methylene chloride).
Statistical analyses were performed using SAS (version 8.0; SAS Institute Inc., Cary, NC) and S-plus (version 6.1; Insightful Corp., Seattle, WA) using log-transformed data because most VOC measurements were right skewed. Concentrations that were less than the analytical detection limit but produced an instrument reading > 0 were included in calculations. Concentrations that produced an instrument reading ≤ 0 (typically due to blank subtraction) were also included in calculations by assigning them a value of one-half the analytical detection limit. This substitution was infrequently done and had little effect on the P and H results: Among the 120 sample sets reported here (15 chemicals × 2 seasons × 4 sampling locations), more than half had no ≤ 0 instrument readings, and 85% had ≤ 0 instrument readings occurring < 10% of the time. The seven sample sets with a high proportion (> 50%) of ≤ 0 instrument readings were either O or S measurements of chloroform, d-limonene, β-pinene, or styrene.
To estimate the contribution of the indoor and outdoor microenvironments to P exposure, we examined the time-weighted model
where i is the index of chemical and the j the index of microenvironment M (H, S, or O), F is the fraction of time spent in microenvironment j, and ɛ is the error term (Figure 1). To examine the potential influence of school or sociodemographics (study design variables) and VOC emitters (sources) and modifiers (e.g., ventilation) of personal exposure across the population, we also conducted weighted linear regression using the model
where Design (season, school, English or non-English-speaking home, race/ethnicity, and grade), Source (source variables, e.g., presence of a smoker in household), and Ventilation [high (> 12 hr windows/doors open) vs. low ventilation] were included as covariates. Variables included in the regression were selected based on associations observed in previous studies and cut points for comparison groups based on continuous variables (e.g., high or low average hours of ventilation) constructed to obtain groups of approximately equal sizes. We also tested two-way interactions between season, source variables, and ventilation but report only significant associations in subsequent figures. Results for carbon tetrachloride are not included in Figures 2 and 3 because it is no longer produced, and measured levels represent global background (Sexton et al. 2004b).
Results
We obtained 181 matched P and H measurements (n = 93 winter, n = 88 spring) and a completed TAD questionnaire from 113 subjects (68 subjects in both seasons, 25 winter only, and 20 spring only), for a weighted response rate of 84.5% in winter and 73.6% in spring. The sociodemographics and household characteristics of the children who provided these samples are summarized in Table 1. The subjects who volunteered for VOC sampling were evenly distributed across grades and schools; were predominantly African American, Hispanic, or Somali; and were slightly more likely to be male. Even though the number of non-English-speaking participants was substantially larger than the number of English-speaking participants (n = 76 vs. n = 37), the former group represented only about half (52%) of the weighted sample due to adjustment for oversampling in the non-English-speaking strata. More than one-third of the children (35.6%) were born outside the United States, and nearly 22% lived in households where at least one person smoked, although this factor was reported to be more common in African-American households than for other races/ethnicities. Most of the families lived in rented apartments and did not have an attached garage or central air conditioning. Three-quarters of the families reported using room deodorizers. The children who provided VOC samples were between 7 and 13 years of age, with a mean age of 9.1 ± 1.4 years.
Weighted summary statistics that estimate the median and variability in VOC concentrations for the O, S, H, and P samples are presented in Table 2. Six VOCs (benzene, carbon tetrachloride, ethylbenzene, toluene, o-xylene, and m/p-xylene) were detected in all O samples. All O VOC concentrations were low compared with other measurement locations but displayed some seasonal variability, with five compounds (chloroform, p-dichlorobenzene, d-limonene, α-pinene, and trichloroethylene) more frequently detectable and at higher concentrations in spring compared with winter. Benzene, carbon tetrachloride, ethylbenzene, tetrachloroethylene, trichloroethylene, toluene, and the xylenes were present in more than half of the O, S, H, and P samples, but methylene chloride was above the detection limit in < 25% of samples from all measurement locations. Concentrations of β-pinene and styrene did vary substantially by measurement location and were more frequently detectable in H and P samples compared with S and O samples. Excluding trichloroethylene and carbon tetrachloride (because their concentrations were uniform across all microenvironments) and methylene chloride (because of low percent detectable), the relative concentration of VOCs at each location was either S < O < P ≤ H (benzene, d-limonene, and p-dichlorobenzene) or O ≈ S < P ≤ H (all other compounds). Across both seasons, the ratios of P:O and P:S medians were much greater than 1 for all compounds; the ratios of P:H medians ranged from 0.6 to 0.9 for all compounds except p-dichlorobenzene (1.4); and the ratios of S:O medians were > 1 for chloroform, p-dichlorobenzene, and d-limonene, and ≤ 1 for the other compounds. The within-measurement location variability (90th:50th percentile ratio) was relatively small for O samples (range, 1.0–3.5) and S samples (range, 1.0–4.2). For P samples, the 90th:50th percentile ratio ranged from 2.2 to 6.8 for all compounds except p-dichlorobenzene, which was 167 in winter and 67 in spring, indicating substantial between-child variability for this compound. This pattern is also clear in the H samples: The 90th:50th percentile ratio values ranged from 2.1 to 8.5 for all compounds except p-dichlorobenzene, which was > 475 in both seasons, indicating substantial between-residence variability for this compound.
Time–activity patterns for these children indicate that they spent an average of 25% of their time at school and > 90% of their time indoors at any location (Table 3). On average, SHIELD study children spent slightly more time in a vehicle than they did outdoors at any location, but time outdoors or in vehicles averaged < 8% of the day. Although an adult smoker was reported present in about one in five households, slightly more than one-quarter of the children reported some exposure to ETS, with the 90th percentile for minutes of ETS exposure being slightly less than 1 hr per day.
To examine the contribution of H, S, and O microenvironments to personal exposure, we plotted P versus time-weighted exposures estimated using concentrations and time–activity data for each microenvironment (Equation 1, Figure 1). The combination of H, S, and O microenvironments explain > 50% of the observed variability for all compounds except styrene (0.21) and β-pinene (0.41). As shown in Table 4, P exposure was dominated by the contribution from the residential environment for all compounds: The time-weighted model with H, S, and O measurements explained little more variability in personal exposure than did the model with the H measurement alone.
Regression results for P exposures in Figure 2 show 95% confidence intervals (CIs) for the variability in age-adjusted VOC concentrations for various population subgroups compared with a referent group (results not including the 100% line represent a statistically significant departure from geometric mean values). Variability in P exposure was not associated with sex, time spent in travel, residence ventilation, or use of room deodorizers. Adult tobacco use was associated with elevated styrene and benzene personal exposures, and use of cleaning supplies was associated with higher d-limonene, p-dichlorobenzene, and trichloroethylene P exposures. Variation among racial/ethnic groups was explored by comparing each major group sampled with the Other group, which consisted of Caucasians, Native Americans, and those of mixed race/ethnicity. Compared with this group, African Americans had higher chloroform exposures, Hispanics had higher p-dichlorobenzene and β-pinene exposures, Somalis had higher β-pinene exposures, and Southeast Asians had higher d-limonene and p-dichlorobenzene exposures. Compared with the referent group, Somalis had lower ethylbenzene, methylene chloride, and xylene exposures, and African Americans had lower methylene chloride and α-pinene exposures. Significant associations were observed for the interaction between ventilation and household cleaner use (lower d-limonene) and ventilation and room deodorizers (higher styrene).
Regression results for H exposures in Figure 3 display both divergence and some consistency with the trends observed for P exposures. In divergence with the P results, adult tobacco use was not associated with elevated indoor styrene or benzene levels in SHIELD residences. Although the use of cleaning supplies was still associated with higher p-dichlorobenzene levels, it was also associated with lower β-pinene and chloroform levels. Room deodorizer use was associated with higher indoor α-pinene but lower trichloroethylene levels. African-American households had higher chloroform, p-dichlorobenzene, and trichloroethylene levels relative to referent households. Exposures to air freshener and fragrance compounds still varied substantially among immigrant subgroups but showed consistency with P results: Higher concentrations of d-limonene (Southeast Asians) and p-dichlorobenzene (Hispanics and Southeast Asians) were observed for some households, and lower levels of the xylenes were observed in Somali residences. Significant associations were observed for the interaction between ventilation and household cleaner use (elevated chloroform but decreased benzene levels). Dry cleaning use was infrequently reported in this population, and therefore this exposure factor was not included in the regression models, although tetrachloroethylene levels were lower in households with greater ventilation.
Discussion
In this study we obtained concurrent outdoor, school and home indoor, and personal VOC samples from a diverse population of inner-city school children. The suite of VOCs reported here are compounds with long-term health risks and include a) those that are primarily from indoor sources (e.g., chloroform, p-dichlorobenzene, d-limonene, α- and β-pinene), b) those that have indoor and outdoor sources (e.g., benzene, ethylbenzene, styrene, toluene, tetrachloroethylene, m/p-and o-xylene), and c) those that originate mainly from outdoor air (e.g., carbon tetrachloride). For compounds detected in > 70% of samples, both median and 90th percentile concentrations followed the general pattern O ≈ S < P ≤ H or S < O < P ≤ H across the measured microenvironments. The only exceptions to this were carbon tetrachloride, which had similar concentrations in every measured microenvironment because measured levels represent global background, and trichloroethylene, for unknown reasons. It is clear from our analysis that the H microenvironment was the largest and that the O and S microenvironments were relatively small contributors to children’s personal exposure to these hazardous air pollutants.
The O VOC levels measured in Minneapolis for this study are relatively low compared with those in other large metropolitan areas in the United States (Sexton et al. 2004b), primarily because the Twin Cities metropolitan area is downwind of rural areas in the United States and Canada that have low VOC emissions, have relatively infrequent atmospheric inversions, and have no physical barriers that trap pollutants. The 2- to 5-day sampling durations used in this study allowed for enough material to be collected so that the percentage of samples above the detection limit was reasonably good for most compounds: Only methylene chloride, α- and β-pinene, and styrene were found in less than half of the O and S samples in both seasons, and all compounds except methylene chloride were found in > 70% of H and P samples.
Indoor VOC concentrations are a function of both outdoor sources (e.g., vehicle exhaust) and indoor sources (e.g., ETS, consumer products). Previous population-based studies in the United States suggest that levels of many VOCs are typically higher inside residences than in matched outdoor concentrations (Sexton et al. 2004b; Wallace 2001) because the source strength of indoor emissions is a stronger influence than the infiltration of outdoor air for many of these pollutants, especially those associated with fragrances and other consumer products (Kim et al. 2001). There was considerable variability in exposure to ETS between racial/ethnic groups: Time-diary and biomarker data indicate that African-American children in SHIELD had higher tobacco exposures than did other racial/ethnic groups (Sexton et al. 2004a). In all cases the indoor home environment had higher concentrations than did indoor air at these schools, possibly because of greater air exchange in the schools, but also because there are fewer strong sources in schools than exist in the residential environment. Children’s P concentrations were slightly lower than H for all compounds except p-dichlorobenzene. Research in adults indicates that P concentrations tend to be higher than matched residential concentrations, which are higher than matched outdoor levels, although in some communities with higher ambient concentrations, indoor and outdoor levels may be nearly the same (Kim et al. 2001; Kinney et al. 2002; Sexton et al. 2004b; Wallace et al. 1985).
To put our results in context, we compared them with three recent studies that measured personal and indoor VOC exposures in the central northern United States: a) nonsmoking Minneapolis–St. Paul adults who participated in the Hazardous Air Pollution Study (HAPS) (Sexton et al. 2004b, 2004c); b) midwestern U.S. (OH, IL, IN, MI, WI, and MN) adults (n = 258 > 21 years old) and children (n = 55 < 14 years old) who participated in the National Human Exposure Assessment Survey (NHEXAS) Region V study (Pellizzari et al. 1999); and c) 3- to 13-year-old participants (n = 73) who provided VOC measurements in the Minnesota Children’s Pesticide Exposure Study (MNCPES) (Adgate et al. 2004). All three studies used OVMs to measure VOCs, although a slightly different suite of compounds was measured in MNCPES and NHEXAS, whereas the same compounds were measured in the HAPS and SHIELD studies.
Median and upper-bound exposures in SHIELD were similar to or lower than those experienced by HAPS adults and NHEXAS Region V adults/children for all VOCs except p-dichlorobenzene. This difference likely occurs because children are in fewer “high-exposure” microenvironments outside the home, such as vehicles in traffic. With p-dichlorobenzene the only exception, median and upper-bound exposures in SHIELD were lower than those experienced by children in the MNCPES study. The MNCPES VOC samples were collected from a probability sample of urban, suburban, and rural Minnesota households with children who were similar in age to the SHIELD population. The main differences between the two studies were that the MNCPES samples were collected during the summer, and the MNCPES households were > 90% nonminority and had substantially higher household incomes (median > $50,000) than did the households that participated in SHIELD (median < $20,000) (Adgate et al. 2000; Sexton et al. 2003). In our judgment, the effect of season is unlikely to explain the observed differences, which are more likely related to differences in housing stock and indoor source use between these two study populations.
It is notable, however, that p-dichlorobenzene exposures were substantially higher in SHIELD P and H samples than in the other three studies, especially at the upper end of the exposure distribution. For example, SHIELD median P exposures to p-dichlorobenzene were 1.6–2.9 times higher than in the other three studies, with 90th percentile concentrations ranging from 17 to 30 times higher in SHIELD. This difference is even more striking for H concentrations: SHIELD median concentrations were 2- to 4-fold higher than in the HAPS, NHEXAS, and MNCPES, with 90th percentile concentrations ranging from 33 times higher to more than 250 times higher in SHIELD than in the other three studies. The distribution of exposure to p-dichlorobenzene in SHIELD children is bimodal, with 25 individuals with 48-hr average mean P exposures > 24 μg/m3 (or a time-weighted exposure of > 33 μg/m3). These high-exposure subjects were somewhat more likely to be male (16 of 25) and were predominantly Hispanic (13 of 25) and African American (6 of 25), and a large proportion (14 of 25) were in the 4th grade. This association with grade level seems due to chance, because there is no clear grouping of the data within classroom or across time. The primary sources of p-dichlorobenzene indoors are consumer products, such as toilet bowl blocks, room deodorizers, and moth cakes (Wallace 1991a, 1991b). Higher p-dichlorobenzene levels were not associated with questionnaire responses on frequency of room deodorizer use: Reported use rates in the 25 high-exposure households were similar to reported rates in the remaining study residences.
Overall, children’s time–activity patterns in SHIELD do not vary substantially from those observed in MNCPES and other studies of children that show that children spend 80–90% of their time indoors [U.S. Environmental Protection Agency (EPA) 2002]. Although it is possible to explain a large proportion of the variability in personal exposure for compounds found primarily in the home (e.g., p-dichlorobenzene, d-limonene), neither residential measurements nor our time-weighted model explains more than half the observed variability in exposure to compounds with both indoor and outdoor sources (e.g., benzene). Although some “high-exposure” microenvironments, such as inside vehicles, are important sources of variability in personal VOC exposure (Weisel et al. 1992), residential concentrations appear to be an important source of variability as well. It is likely that VOC levels are higher when people are at home and using sources, and that average levels obtained using OVMs do not adequately capture the peak levels that presumably exist during these periods of source use. Nonetheless, our data suggest that measuring VOCs in the home environment may be a reasonable proxy for assessing children’s exposure to many of these compounds. Longitudinal studies with repeat measurements over all seasons are needed to confirm the useful approach for estimating children’s VOC exposures in epidemiologic studies (Sexton et al. 2004c).
The main limitation of this work is that we have performed a relatively large number of comparisons, so the results of the regression analysis on the P and H measurements (Figures 2 and 3) should be interpreted with caution. Most of the significant statistical associations we observe appear plausible; for example, the significantly elevated exposures to chloroform, p-dichlorobenzene, d-limonene, and β-pinene are consistent with existing data on indoor sources, as is the observed relationship between ETS, benzene, and styrene (Wallace 1991a, 1991b). In our judgment, a few of the statistically significant associations we observed are implausible because they lack a clear link to known sources; for example, room deodorizer use was associated with significantly lower trichloroethylene levels in H samples (Figure 3), and cleaning product use was significantly associated with higher carbon tetrachloride levels in H samples (point estimate = 120%; 95% CI, 106–136%). Although we observed elevated exposures to some compounds for the African-American, Hispanic, Somali, and Southeast Asian subpopulations compared with the predominantly Caucasian reference group, the specific exposures are related to known strong indoor VOC sources, so any interventions should be primarily directed at reduction of source use. It is also notable that Somali children had significantly lower exposures to some VOCs associated with vehicle exhaust, such as ethylbenzene. Consistent with this evidence, Somali children also reported somewhat less time spent in transit. Increased ventilation was associated with reduced concentrations of some VOCs with indoors sources (e.g., d-limonene) as well as increased concentrations of compounds with outdoor sources (e.g., styrene). Analysis of interactions among source use, ventilation, and season indicated that these three factors together and season alone had no discernable effect on VOC levels.
To put measured values in the context of related health effects, we compared VOC levels in this study with acceptable risk limits for benzene, carbon tetrachloride, chloroform, p-dichlorobenzene, methylene chloride, and trichloroethylene. These six VOCs are designated human carcinogens by several regulatory authorities and thus have regulatory guidance for outdoor air levels (Sexton et al. 2004b). The established risk threshold in Minnesota is the airborne concentration (micrograms per cubic meter), which, if breathed over a 70-year lifetime, is estimated (using a 95th percentile upper-bound estimate) to increase an exposed individual’s lifetime cancer risk by 1 × 10−5 (Minnesota Pollution Control Agency 2004). All median and 90th percentile concentrations in P, H, S, and O samples were below the acceptable risk level for methylene chloride (20 μg/m3) and trichloroethylene (5.0 μg/m3). All measured concentrations of carbon tetrachloride, which were relatively constant across O, S, H, and P samples, were at or near the risk threshold value (0.7 μg/m3). Median and 90th percentile concentrations in outdoor air were below acceptable risk limits for chloroform (0.4 μg/m3) and p-dichlorobenzene (0.9 μg/m3). For p-dichlorobenzene and chloroform, median levels in winter and 90th percentile H and P samples in both seasons exceeded the applicable reference levels. For benzene, the median (O winter only) and 90th percentile concentrations in both seasons exceeded the acceptable risk value (1.3 μg/m3) in O, H, and P samples. Further research is needed to better understand the significance of these results for health risk assessments of children as well as potential interventions to reduce exposure.
Figure 1 Scatter plot of correlation between a 48-hr P VOC exposure and a time-weighted average of VOC exposure from major microenvironments where children spent time each day.
Figure 2 Age-adjusted regression results showing variability (95% CIs) in P VOC concentrations for study design (e.g., school, demographic categories), source (e.g., use of cleaning products), or exposure modification (e.g., ventilation) variables. Vent, ventilation. Interaction terms are displayed only if statistically significant: season, winter 2000 vs. spring 2000; school, Whittier vs. Lyndale; sex, female vs. male; African American, Somali immigrant, Hispanic, and Southeast Asian vs. other (including Caucasian); travel, > 1.5 hr on highway or road today; cleaners, > 0 hr spent using cleaning supplies today; cigarettes, > 0 cigarettes smoked in your presence today; Vent, > 12 hr doors and windows were left open for ventilation today; room deodorizers; cleaners × Vent; room deodorizers × Vent.
Figure 3 Age-adjusted regression results showing variability (95% CIs) in H VOC concentrations for study design (e.g., demographic categories), source (e.g., use of cleaning products), or exposure modification (e.g., ventilation) variables. Vent, ventilation. Interaction terms are displayed only if statistically significant: season, winter 2000 vs. spring 2000; African American, Somali immigrant, Hispanic, and Southeast Asian vs. other (including white); cleaners, > 0 hr spent using cleaning supplies today; cigarettes, > 0 cigarettes smoked in your presence today; Vent, > 12 hr doors and windows left open for ventilation today; room deodorizers; cleaners × Vent.
Table 1 Sociodemographic characteristics of the 113 SHIELD households providing time–activity data and a matched H and P sample in either season.
Sociodemographic variables Total no. (weighted %)a
School
Whittier 60 (46.3)
Lyndale 53 (53.7)
Language
English speaking 37 (48.0)
Non-English speaking 76 (52.0)
Race/ethnicity
African American 24 (32.9)
Somali 27 (17.5)
Hispanic 40 (26.9)
Southeast Asian 9 (7.8)
Otherb 13 (14.9)
Sex
Male 58 (55.9)
Female 55 (44.1)
Grade
2 27 (24.3)
3 26 (23.8)
4 32 (27.2)
5 28 (24.8)
Place of birth
United States 62 (64.4)
Other 51 (35.6)
Household characteristics
Smokers present 19 (21.7)
Attached garage 5 (5.5)
Use of room deodorizers 84 (73.6)
Central air conditioning 11 (8.5)
House type
Single-family detached 32 (28.5)
Single-family attached 15 (14.6)
Apartment 64 (55.3)
Other 2 (1.7)
Rent 94 (81.0)
Own 19 (19.1)
a Totals within categories may not add to 100% due to rounding.
b Caucasian, Native American, and those indicating mixed race.
Table 2 Summary of VOC concentration (μg/m3) distributions for 15 VOCs in matched P, S, H, and O samples from 113 subjects in winter and spring 2000.
Oa Sb Hc Pc
VOC Season %Det Median Q10 Q90 %Det Median Q10 Q90 %Det Median Q10 Q90 %Det Median Q10 Q90
Benzene Winter 100.0 1.3 0.4 2.2 77.1 0.6 0.1 1.6 100.0 2.2 0.8 6.2 100.0 2.1 0.7 6.5
Spring 100.0 1.1 0.7 1.6 90.5 0.6 0.2 1.0 99.0 2.1 0.6 7.2 100.0 1.5 0.7 4.2
Carbon tetrachloride Winter 100.0 0.5 0.5 0.7 100.0 0.6 0.5 0.7 99.0 0.6 0.5 0.6 100.0 0.5 0.4 0.6
Spring 100.0 0.5 0.4 0.7 93.7 0.5 0.2 0.9 100.0 0.5 0.4 0.8 100.0 0.5 0.4 0.6
Chloroform Winter 25.0 0.1 0.1 0.1 81.3 0.2 0.1 0.3 98.0 0.8 0.3 2.6 100.0 0.7 0.3 2.1
Spring 60.0 0.1 0.1 0.3 42.9 0.1 0.1 0.4 97.0 1.5 0.5 3.4 98.9 1.2 0.5 2.9
p-Dichlorobenzene Winter 12.5 0.1 0.0 0.2 87.5 0.5 0.1 1.1 82.8 0.7 0.1 344.6 92.5 1.0 0.2 167.2
Spring 70.0 0.2 0.1 0.4 85.7 0.5 0.1 1.1 89.9 0.9 0.2 429.0 93.2 1.3 0.2 87.2
Ethylbenzene Winter 100.0 0.6 0.2 0.8 97.9 0.6 0.2 1.0 100.0 1.0 0.6 2.8 100.0 1.0 0.6 2.4
Spring 100.0 0.5 0.3 0.7 93.7 0.3 0.2 0.5 100.0 1.0 0.5 3.8 100.0 0.9 0.5 2.0
d-Limonene Winter 12.5 0.1 0.0 0.3 100.0 4.6 1.8 12.1 100.0 28.6 6.4 122.3 100.0 24.7 7.5 159.5
Spring 80.0 0.4 0.1 0.6 100.0 1.9 0.9 7.9 100.0 21.2 7.2 124.8 100.0 22.2 8.3 110.0
Methylene chloride Winter 0.0 0.3 0.2 0.6 2.1 0.4 0.1 0.6 23.2 0.4 0.2 1.3 19.4 0.4 0.2 1.3
Spring 0.0 0.2 0.1 0.6 1.6 0.3 0.1 0.5 20.2 0.3 0.2 1.2 17.0 0.3 0.2 1.3
α-Pinene Winter 0.0 0.0 0.0 0.1 87.5 0.2 0.1 0.3 100.0 2.4 0.7 6.5 100.0 1.9 0.7 5.1
Spring 10.0 0.1 0.1 0.1 63.5 0.2 0.1 0.4 100.0 2.4 0.7 8.6 100.0 1.8 0.6 5.4
β-Pinene Winter 0.0 0.1 0.1 0.1 4.2 0.1 0.1 0.1 94.9 2.5 0.5 11.7 93.5 1.7 0.4 11.5
Spring 0.0 0.1 0.1 0.1 9.5 0.1 0.1 0.2 89.9 1.5 0.1 7.4 87.5 1.1 0.1 5.3
Styrene Winter 0.0 0.1 0.0 0.1 31.3 0.1 0.0 0.4 91.9 0.7 0.2 1.5 93.5 0.5 0.2 1.2
Spring 0.0 0.0 0.0 0.1 39.7 0.1 0.1 0.3 91.9 0.8 0.3 2.1 85.2 0.5 0.1 1.2
Tetrachloroethylene Winter 75.0 0.2 0.1 0.4 95.8 0.3 0.2 0.4 98.0 0.5 0.2 1.3 100.0 0.4 0.2 1.3
Spring 100.0 0.3 0.2 0.4 85.7 0.3 0.1 0.6 94.9 0.4 0.2 1.0 96.6 0.4 0.2 0.9
Toluene Winter 100.0 2.6 0.9 4.2 97.9 2.9 1.4 5.6 100.0 8.2 3.5 19.2 100.0 7.7 3.4 17.7
Spring 100.0 2.7 1.1 3.6 95.2 1.6 0.2 3.2 100.0 8.9 4.2 25.1 100.0 7.7 3.1 18.0
Trichloroethylene Winter 62.5 0.3 0.0 1.0 72.9 0.2 0.1 0.8 82.8 0.3 0.1 0.9 90.3 0.3 0.1 0.8
Spring 80.0 0.2 0.1 0.7 55.6 0.1 0.0 0.3 73.7 0.2 0.1 1.7 72.7 0.2 0.1 0.8
m/p-Xylene Winter 100.0 2.3 0.9 3.3 100.0 2.3 1.1 3.6 100.0 3.7 2.2 10.4 100.0 3.5 2.1 8.0
Spring 100.0 2.0 1.1 2.8 100.0 1.2 0.7 1.5 100.0 3.3 1.5 13.2 100.0 2.9 1.4 6.9
o-Xylene Winter 100.0 0.8 0.3 1.1 100.0 0.8 0.3 1.2 100.0 1.2 0.7 3.2 100.0 1.1 0.7 2.6
Spring 100.0 0.7 0.4 0.9 100.0 0.4 0.3 0.5 100.0 1.1 0.5 4.1 100.0 1.0 0.5 2.7
Abbreviations: %Det, equal to the proportion of the individual chemical concentrations above the analytical detection limit (results ≤ 0 were reset to one-half the analytical detection limit for all analyses); Q10, 10th percentile; Q90, 90th percentile.
a Five-day average; samples taken outdoors at two schools over 4 weeks in winter (n = 8) and 5 weeks in spring (n = 10).
b Five-day average; samples taken indoors in five rooms in two schools over 4 weeks in winter (n = 39) and 5 weeks in spring (n = 47), with missing results for four badges, one winter and three spring.
c H and P samples (2-day average) collected concurrently for a single child in each household (winter, n = 93; spring, n = 88). Quantiles were calculated using weights to adjust for nonselection and nonresponse.
Table 3 Weighted percentage of each day in each microenvironment or conducting exposure-related activities for 113 subjects during both seasons.
Percentile
Time spent in location/activity Mean ± SD Minimum 25th 50th 75th 90th Maximum
Inside at home 65 ± 6.6 45 62.5 66 68.9 70.6 87
Inside at school 25 ± 4.4 0.5 24 25.2 26.6 28.6 40.7
Inside at other 3.2 ± 5.4 0 0 0.5 4 8.2 24.1
Outside at home 1.2 ± 2 0 0 0 1.6 4.2 7.8
Outside at school 1.3 ± 1 0 0.9 1 1.4 2.1 7
Outside at other 0.7 ± 1.3 0 0 0 1.4 2.7 7.7
In transit (traveling in vehicle) 3.6 ± 1.9 0 2.1 3.8 4.7 5.9 10.6
Indoors with smoker(s) 1.3 ± 3.8 0 0 0 0.3 4.2 22.8
In vehicle with smoker(s) 0.1 ± 0.2 0 0 0 0 0 2
Doors or windows open in residence 10.8 ± 22.4 0 0 0 6.6 49.2 100
Table 4 Comparison of weighted correlation coefficient (r2)for P versus H measurements and a time-weighted microenvironmental model.
VOC Vs. H VOC concentration Vs. time-weighted model (H, S, and O locations)
Benzene 0.50 0.51
Chloroform 0.56 0.58
p-Dichlorobenzene 0.80 0.83
Ethylbenzene 0.56 0.56
d-Limonene 0.67 0.69
Methylene chloride 0.64 0.61
α-Pinene 0.59 0.60
β-Pinene 0.39 0.41
Styrene 0.20 0.21
Tetrachloroethylene 0.59 0.58
Toluene 0.49 0.50
Trichloroethylene 0.72 0.74
m/p-Xylene 0.58 0.58
o-Xylene 0.57 0.56
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7187ehp0112-00139315471731Children's HealthArticlesThe Association between Asthma and Allergic Symptoms in Children and Phthalates in House Dust: A Nested Case–Control Study Bornehag Carl-Gustaf 123Sundell Jan 2Weschler Charles J. 24Sigsgaard Torben 5Lundgren Björn 1Hasselgren Mikael 3Hägerhed-Engman Linda 11Swedish National Testing and Research Institute, Borås, Sweden2Technical University of Denmark, Lyngby, Denmark3Public Health Science, Karlstad University, Karlstad, Sweden4University of Medicine and Dentistry New Jersey–Robert Wood Johnson Medical School and Rutgers University, Piscataway, New Jersey, USA5Aarhus University, Aarhus, DenmarkAddress correspondence to C.G. Bornehag, Public Health Sciences, Karlstad University, 651 88 Karlstad, Sweden. Telephone: +46-54-700-25-40. Fax: +46-54-700-25-23. E-mail:
[email protected] study was supported by the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (Formas), Swedish Asthma and Allergy Association’s Research Foundation, the Swedish Foundation for Health Care Sciences and Allergy Research, and European Council of Plasticizers and Intermediates.
The authors declare they have no competing financial interests.
10 2004 15 7 2004 112 14 1393 1397 15 4 2004 15 7 2004 Publication 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. Global phthalate ester production has increased from very low levels at the end of World War II to approximately 3.5 million metric tons/year. The aim of the present study was to investigate potential associations between persistent allergic symptoms in children, which have increased markedly in developed countries over the past three decades, and the concentration of phthalates in dust collected from their homes. This investigation is a case–control study nested within a cohort of 10,852 children. From the cohort, we selected 198 cases with persistent allergic symptoms and 202 controls without allergic symptoms. A clinical and a technical team investigated each child and her or his environment. We found higher median concentrations of butyl benzyl phthalate (BBzP) in dust among cases than among controls (0.15 vs. 0.12 mg/g dust). Analyzing the case group by symptoms showed that BBzP was associated with rhinitis (p = 0.001) and eczema (p = 0.001), whereas di(2-ethylhexyl) phthalate (DEHP) was associated with asthma (p = 0.022). Furthermore, dose–response relationships for these associations are supported by trend analyses. This study shows that phthalates, within the range of what is normally found in indoor environments, are associated with allergic symptoms in children. We believe that the different associations of symptoms for the three major phthalates—BBzP, DEHP, and di-n-butyl phthalate—can be explained by a combination of chemical physical properties and toxicologic potential. Given the phthalate exposures of children worldwide, the results from this study of Swedish children have global implications.
allergyasthmaBBzPchildrenDEHPhomesphthalates
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Airborne phthalate esters are present at detectable levels across the surface of Earth. They were first identified in outdoor urban air (Cautreels and Van Cauwenberghe 1976a, 1976b) and subsequently have been recognized as global pollutants (Atlas and Giam 1981; Giam et al. 1978) and major constituents of indoor air (Weschler 1980, 1984). Their presence in outdoor and indoor environments reflects their large emission rates coupled with moderate atmospheric lifetimes. The total global consumption of phthalate esters is estimated to exceed 3.5 million metric tons/year, with di(2-ethylhexyl) phthalate (DEHP) constituting roughly 50% of the market share (Cadogan and Howick 1996). Consumption of di-n-butyl phthalate (DnBP) and n-butyl benzyl (BBzP) phthalate is smaller but still quite large (> 100,000 metric tons/year each) (Cadogan and Howick 1996). Although DEHP plasticizes numerous products, roughly 95% of the current production is used in polyvinyl chloride (PVC) (National Toxicology Program 2003), where it typically constitutes 30% of PVC by weight (Cadogan and Howick 1996; Kavlock et al. 2002b). DnBP is used in latex adhesives, in nail polish and other cosmetic products, as a plasticizer in cellulose plastics, as a solvent for certain dyes, and, to a lesser extent than DEHP, as a plasticizer in PVC (Kavlock et al. 2002c). BBzP is a plasticizer for vinyl tile, carpet tiles, and artificial leather and is also used in certain adhesives (Kavlock et al. 2002a).
Research groups have assessed the exposures of various populations to phthalate esters by using their metabolites in human urine as biomarkers [Barr et al. 2003; Blount et al. 2000; Centers for Disease Control and Prevention (CDC) 2003; Koch et al. 2003]. The biomarker results translate to daily exposures for DnBP, BBzP, and DEHP of 1.5, 0.88, and 0.71 μg/kg/day in the United States (Kohn et al. 2000); 0.95, 0.71, and 0.84 μg/kg/day in the United States (derived from data from Barr et al. 2003, their Table 1, using the procedure outlined by Kohn et al. 2000); and 5.22, 0.60, and 13.8 μg/kg/day in Germany (Koch et al. 2003). These findings confirm the relatively large daily exposure to phthalates in industrialized countries. Although the dominant route of exposure to DnBP, BBzP, and DEHP is thought to be via ingestion (Fromme et al. 2004; Kavlock et al. 2002a, 2002b, 2002c), few if any population-based data are available to support this statement. Indeed, a recent study has demonstrated associations between phthalate concentrations in inhaled air and urinary monoester metabolites (Adibi et al. 2003).
The incidence of asthma and allergy has increased throughout the developed world over the past 30 years (Beasley et al. 2003). The short interval over which it has occurred implies that the increase is caused by changes in environmental exposures rather than genetic changes (Etzel 2003; Strachan 2000). Changes in indoor environments warrant special attention because indoor air constitutes a dominant exposure route. Increased exposures to allergens and/or adjuvants (enhancing factors) may each be partially responsible for the increase. Multidisciplinary reviews of the scientific literature on associations between indoor exposures and asthma and allergies (Ahlbom et al. 1998; Andersson et al. 1997; Bornehag et al. 2001; Schneider et al. 2003; Wargocki et al. 2002) indicate that the underlying causal factors responsible for these increases remain unknown.
The use of plasticized products and, consequently, exposures to phthalate esters have increased dramatically since the end of World War II. Phthalate esters have been suggested to act as either allergens or adjuvants (Jaakkola et al. 1999; Oie et al. 1997). Several recent studies have examined the ability of different phthalate esters to function as adjuvants in BALB/c mice injected with a known antigen. DEHP displayed an adjuvant effect with immunoglobulin G1 at a concentration of 2,000 mg/mL after both one and two boosters (Larsen et al. 2001b). In contrast, DnBP only showed an adjuvant effect with immunoglobulin G1 after the second booster (Larsen et al. 2002), and BBzP showed no adjuvant effect (Larsen et al. 2003). Consistent with these results, the monoester of DEHP showed an adjuvant effect whereas the monoesters of DnBP and BBzP did not (Larsen et al. 2001a).
The present study is a nested case–control study on 198 symptomatic children and 202 healthy controls, including detailed clinical examinations by physicians in parallel with extensive inspections and measurements within the subjects’ homes. The cases and controls were selected from the first phase (Dampness In Buildings and Health, phase I), which was a cross-sectional questionnaire soliciting health and environmental information regarding all 14,077 children 1–6 years of age in the county of Värmland, Sweden; responses were obtained for 10,852 (Bornehag et al. 2003).
The aim of the present study was to investigate potential associations between persistent allergic symptoms in children and the concentrations of different phthalates in dust collected from their homes.
Materials and Methods
Inclusion criteria for cases and controls.
The selection criteria for the cases (Dampness In Buildings and Health, phase II) were as follows: a) in the initial questionnaire, reports of at least two incidents of eczema, or wheezing or rhinitis without a cold, during the preceding 12 months; and b) in the follow-up questionnaire 1.5 years later, at least two of three possible symptoms reported. Inclusion criteria for the controls were a) no symptoms in the first questionnaire and b) no symptoms in the follow-up questionnaire. For both groups they had to c) not have rebuilt their homes because of moisture problems and d) not have changed residence since the first questionnaire. All children with at least two symptoms in the first questionnaire were invited to participate in the case–control study (n = 1,056, corresponding to 9.7% of the total population). In the first questionnaire, 5,303 (48.9%) reported no airway, eye, nose, or skin symptoms. Of these, 1,100 children were randomly selected and invited to participate in the case–control study. This process ultimately yielded 198 cases and 202 controls.
Families were more inclined to participate if the child was reported to have more symptoms, if there was no smoking in the family, and if they belonged to a higher socioeconomic group.
Medical examination.
The medical examination of the 400 children (3–8 years of age) was performed during the same 2 weeks that the technical investigations of the homes, including dust collection, were carried out. Medical doctors examined the children and took a detailed history of each child. Blood samples were drawn from 387 children and screened for common allergens (Phadiatop, Pharmacia & Upjohn Diagnostics, Uppsala, Sweden), timothy, birch, mugworth, cat, horse, dog, house dust mites (Dermatophagoides pteronyssinus and Dermatophagoides farinae), and one mold (Cladosporium).
Physicians’ diagnoses of the children agreed well with the case–control status as reported in the questionnaire. All children with obvious asthma were found among cases, whereas 10 cases were found among controls (two children with rhinitis and eight children with eczema). Furthermore, 13 cases were found to be misclassified. In the analyses regarding case–control status, the study design has been used; that is, the 23 (10 plus 13) misclassified children have not been reclassified.
Building investigations.
There were 10 pairs of siblings among the 400 children; hence, they lived in 390 buildings. Between October 2001 and April 2002, six professional inspectors performed visual inspections and indoor air quality assessments, including dust sampling, in these 390 dwellings. During these investigations, a preestablished checklist was followed regarding building characteristics, mold and water damages, surface materials, and other building-related items.
Phthalates in dust.
Samples of dust from 390 homes were collected from molding and shelves in the children’s bedroom. The dust was collected on 90-mm membrane filters in holders made of styrene-acrylonitrile polymer mounted on a sampler made of polypropylene (VacuuMark disposable nozzle; Petersen Bach, Bjerringbro, Denmark) connected to a vacuum cleaner. The filter was weighed before and after sampling under controlled conditions. Conditioning the filters before weighing (23°C, 50% relative humidity) was critical to obtaining reproducible filter weights. From the 390 homes there were 9 missing samples, 13 samples with errors in the laboratory analysis, and 6 samples with a negative dust weight. Consequently, there were 362 valid samples. Only filters with a reliably measurable net increase in weight (≥ 25 mg) were included in the present analysis; 346 of the 362 dust samples met this criterion.
The dust samples were extracted in pre-cleaned 10-mL glass vials for 30 min using 2 mL dichloromethane. This procedure was repeated, and the two extracts were then combined and transferred to 3-mL autosampler vials. Aliquots from these vials were injected into either a gas chromatograph/mass selective detector (GC/MSD) for phthalate identification or a GC/flame ionization detector for quantitation. GC was performed using a 25-m capillary column (HP 1C; Agilent, Folsom, CA, USA; inner diameter, 0.2 mm; stationary phase, polydimethyl siloxane). The injector temperature was 280°C; column temperature started at 100°C for 3 min and then increased at 8°C/min to 300°C, which was maintained for 20 min. The detector temperature and transfer line to the MSD were maintained at 280°C. The analytical and field sampling techniques were tested in a preliminary study that found only limited influence from background contributions to the analyzed samples. In the present study, field blanks have indicated no significant background contributions. The dust concentrations (milligram per gram dust) of six phthalates were determined: diethyl phthalate (DEP), diisobutyl phthalate (DIBP), DnBP, BBzP, DEHP, and diisononyl phthalate (DINP).
Statistical method.
The concentrations of phthalates in the dust were log-normally distributed. Hence, analyses of potential associations between concentrations of phthalates in dust and health outcomes have been conducted using nonparametric tests (Mann-Whitney U-test). Log-transformed, normally distributed concentrations were tested with parametric tests (t-test). The concentrations are reported as medians, as arithmetic means, and as geometric means with 95% confidence intervals (CIs). The CIs were calculated with a back-transform of mean log ± 2 × SE. Dose–response relationships were tested by factoring the phthalate concentrations into quartiles and using both uni- and multivariate logistic regression analyses. Adjustments have been made for environmental tobacco smoke as well as sex and age of the child, because these have been associated with asthma and allergic symptoms. Adjustments for type of building were made, because living in a privately owned single-family house was a selection factor for both cases and controls (Bornehag et al., unpublished data). Indeed, cases and controls lived mainly in single-family houses (88.7%). Furthermore, the frequency of PVC as flooring material was lower in single-family houses than in multifamily houses (51.6 vs. 71.8%). Adjustments for the construction period of the building and self-reported water leakage in the home during the previous 3 years were made because these are associated with the concentrations of phthalates in dust. Finally, adjustments were made for exposure to other phthalates. Multiple logistic regressions were performed by a backward elimination technique where only significant variables were included in the final model. The analyses were considered statistically significant when p < 0.05.
The study was approved by the local ethics committee.
Results
Compared with other types of flooring materials, PVC flooring in the child’s bedroom was positively associated with case status [adjusted odds ratio (OR), 1.59; 95% CI, 1.05–2.41].
Phthalates in dust.
Results are presented in Tables 1–3 and Figure 1. In Tables 1 and 2, median phthalate dust concentrations are reported for data sets that include all valid samples with a reliably measurable net increase in weight (346 of 390 homes), and geometric mean concentrations are reported for data sets that exclude samples with phthalate dust concentrations less than the detection limit. (If, instead, nondetects were assigned concentrations of one-half the detection limit, then for phthalates with a large number of nondetects, their dust concentrations would no longer be log-normally distributed.) The geometric mean concentrations of BBzP and DEHP were higher in bedrooms with PVC flooring than in bedrooms without such flooring [BBzP: 0.208 (n = 164) vs. 0.147 (n = 107) mg/g dust; DEHP: 0.994 (n = 186) vs. 0.638 (n = 155) mg/g dust; both p < 0.001 by t-test]. DEP, DIBP, DnBP, and DINP were not associated with PVC flooring.
Association between phthalates in dust and health effects.
Cases had a higher concentrations of BBzP in the dust samples from the children’s bedrooms than did the controls in parametric as well as in nonparametric tests (Table 1). Cases with physician-diagnosed rhinitis or eczema had higher BBzP concentrations in the bedroom dust compared with controls (Table 2). Furthermore, cases with doctor-diagnosed asthma had higher DEHP concentrations in the dust compared with controls. In analyses restricted to single-family and row houses, the same associations were found (data not shown).
In an analysis restricted to homes with PVC flooring in the child’s bedroom (n = 189), the median BBzP concentration was significantly higher among cases compared with controls (0.21 vs. 0.16 mg/g dust, respectively; Mann-Whitney U-test, p = 0.042), and BBzP was associated with rhinitis and eczema (Table 2). Such differences between cases and controls were not observed for DEHP.
BBzP concentrations in the highest quartile were associated with an increased risk of being a “case child” (Table 3). The same association was found after adjusting for possible confounders. Table 3 also shows associations between phthalates in dust and doctor-diagnosed asthma, rhinitis, or eczema. A dose–response relationship was found between concentrations of BBzP in dust and doctor-diagnosed rhinitis and eczema in both crude and adjusted analyses. For DEHP, a dose–response relationship was found for asthma in both crude and adjusted analyses, as well as in analysis restricted to single-family houses (data not shown for the latter).
Specific immunoglobulin E in blood.
Figure 1 presents the concentration of phthalates in dust among cases and controls with and without specific immunoglobulin E in blood (i.e., atopics and nonatopics). Within the group of cases, the highest geometric mean concentrations of BBzP were found in dust from the bedrooms of atopics. However, when comparing cases with and without atopy, the difference was not statistically significant (p = 0.564).
Discussion
In the present study we found associations between dust concentrations of specific phthalate esters and asthma, rhinitis, and eczema. As shown in Tables 2 and 3, BBzP is significantly associated with doctor-diagnosed rhinitis and eczema, whereas DEHP is significantly associated with doctor-diagnosed asthma. Interestingly, no such associations are found for DnBP despite the fact that the median concentrations of BBzP and DnBP in the settled dust were comparable (0.150 vs. 0.135 mg/g; Table 1). Hence, these three phthalates display strikingly different associations between their dust concentrations and the health outcomes monitored in this study. From a physical chemistry viewpoint, DnBP, BBzP, and DEHP are significantly different from one another; they possess different vapor pressures, polarities, water solubilities, and octanol/air partition coefficients. For example, the vapor pressures of DnBP and BBzP are two orders of magnitude greater than that of DEHP. This means that greater fractions of DnBP and BBzP are in the gas phase as opposed to the condensed phase (i.e., associated with dust and airborne particles). We estimate that, for a particle concentration of 20 μg/m3, > 80% of airborne DnBP and > 80% of airborne BBzP are in the gas phase, whereas > 85% of airborne DEHP is associated with airborne particles (Weschler 2003). The deposition of a compound in the respiratory tract is strongly influenced by whether it is present in the gas phase or associated with airborne particles. Furthermore, as a consequence of their inherent chemical differences, DnBP, BBzP, and DEHP, as well as their monoester metabolites, produce different effects in a mouse model (Larsen et al. 2001a, 2001b, 2002, 2003). Furthermore, each of these phthalates has its distinct human metabolic pathway (Barr et al. 2003). We suspect that the different relative distributions between the gas and condensed phases, coupled with different toxicologic and pharmacokinetic behaviors, contribute to the fact that DnBP, BBzP, and DEHP are associated with different health outcomes (i.e., DnBP, no associations; BBzP, skin and mucosa symptoms; DEHP, lower airway symptoms).
In the present study there is a general association between PVC flooring and case status (OR, 1.59). Both BBzP and DEHP correlate with the amount of PVC flooring in the subjects’ homes. However, these two phthalates are not associated with health effects simply because they are associated with PVC flooring. This conclusion is supported by a number of observations: First, specific associations between BBzP and DEHP dust concentrations and doctor-diagnosed diseases (Table 3) are more pronounced than associations between PVC flooring and such diseases. Second, although BBzP and DEHP dust concentrations do correlate, the correlation is weak (R = 0.52), and they are associated with different health effects. Third, in a restricted analysis, including only homes with PVC flooring, higher concentrations of BBzP were found in dust from case homes than in that from control homes.
The reported concentrations of phthalates in the bedroom dust (Table 1) are consistent with those reported in other studies. In dust samples from 120 U.S. homes located on Cape Cod, Massachusetts (Rudel et al. 2003), the median concentrations were 0.34, 0.045, and 0.020 mg/g dust for DEHP, BBzP, and DnBP, respectively. In a study of 59 apartments in Berlin, Germany (Fromme et al. 2004), the median concentrations were 0.70, 0.030, and 0.047 mg/g dust for DEHP, BBzP, and DnBP. Clausen et al. (2003) measured mean DEHP concentrations of 3.2 mg/g dust in 15 Danish schools and 0.86 mg/g dust for 23 Danish homes. Oie et al. (1997) reported mean concentrations of 0.64 mg DEHP/g dust and 0.11 mg BBzP/g dust for 38 homes in Norway. Pohner et al. (1997) reported a 95th percentile DEHP concentration of 2.0 mg/g dust for 272 German homes, whereas another German study on 286 homes reported a 95th percentile DEHP concentration of 2.6 mg/g dust (Butte et al. 2001).
Regarding atopic status and its association with phthalate dust concentrations, the chosen study design is not optimal. Because there were only 16 atopic controls, the power of the analysis on atopic children is limited. On the other hand, our findings could be interpreted to mean that the mechanism is of a non-immunologic nature (e.g., exposure increases the risk for irritation).
To identify potential selection biases in the study group, we obtained information for all invited families from the first cross-sectional questionnaire. This revealed that the final study group contained significantly more single-family houses than the eligible population. Adjusting and restricting the analyses have addressed this problem. There was no selection bias regarding PVC flooring because included and nonincluded cases and controls reported about the same frequency of occurrence of PVC flooring in the child’s bedroom (Bornehag et al., unpublished data). Furthermore, 10 controls and 13 cases were misclassified when comparing self-reported symptoms and doctors diagnoses. However, when these children were excluded from the analyses, the reported associations remained. Finally, to be included as a “case,” a child was required to have at least two symptoms. Consequently, this study was not fine-tuned to examine associations between building factors and single symptoms (i.e., asthma, rhinitis, or eczema). However, even if the design is suboptimal, meaning it was more difficult to find associations between single symptoms and exposures, the association between selected building factors and single symptoms is meaningful and possibly underestimates true associations.
The reported analyses are based on samples with a weight > 25 mg. However, when including all samples (n = 362), the reported associations between exposure and symptoms remained or became stronger (data not shown).
Koo et al. (2002) present weak associations between exposure estimates for different phthalate esters, based on their urinary biomarkers, and the level of education, family income, and residency (urban or rural) in a reference U.S. population. Given that study, one might speculate that the associations reported in the present study are driven by demographic factors. However, in contrast to the United States, where 22.4% of the children live in households with incomes < 50% of the national median, in Sweden only 2.6% of the children live in such households (Unicef 2000). Additionally, the association in our study holds when the analysis is restricted to single-family houses; such homes have an even more homogeneous socioeconomic status. Hence, different demographic factors between cases and controls appear to be an unlikely explanation for the associations observed in the present study. Furthermore, given that the dust concentrations of DnBP, BBzP, and DEHP display quite different associations with different symptoms, the associations reflect a biologic response rather than just lifestyle or demographic factors associated with an increased use of plasticized materials.
This study demonstrates associations between BBzP and DEHP concentrations in dust and selected allergies and asthma. Although multiple factors likely are responsible for the increases in allergies and asthma that have been documented in developed countries over the past 30 years, it is striking that these increases have occurred during a period when plasticized products have become ubiquitous in the homes, schools, and workplaces of the developed world.
Figure 1 Geometric mean concentrations (95% CIs) of phthalates (A), BBzP, and (B), DEHP in surface dust from bedrooms of nonatopic and atopic children.
Table 1 Concentrations of phthalates in surface dust from children’s bedrooms.
Median (arithmetic mean) concentration of phthalates (mg/g dust)
Cases
Controls
Phthalate No. of homesa All samples (n = 346) Cases (n = 175)b Controls (n = 177)b U-testc (p-value) No. of homesd All samples GM conc No. GM conc [(95% CI) mg/g dust] No. GM conc [(95% CI) mg/g dust] t-Teste (p-value)
DEP 346 0.000 (0.031) 0.000 (0.046) 0.000 (0.018) 0.628 47 0.073 22 0.102 (0.049–0.211) 26 0.058 (0.035–0.097) 0.200
DIBP 346 0.045 (0.097) 0.042 (0.102) 0.048 (0.092) 0.424 290 0.056 141 0.058 (0.048–0.070) 154 0.055 (0.046–0.065) 0.635
DnBP 346 0.150 (0.226) 0.150 (0.228) 0.149 (0.220) 0.914 308 0.174 158 0.171 (0.152–0.193) 154 0.178 (0.157–0.201) 0.639
BBzP 346 0.135 (0.319) 0.152 (0.472) 0.118 (0.163) 0.014 272 0.181 139 0.209 (0.180–0.244) 137 0.157 (0.139–0.178) 0.004
DEHP 346 0.770 (1.310) 0.828 (1.384) 0.723 (1.229) 0.160 343 0.789 173 0.836 (0.724–0.964) 176 0.741 (0.643–0.855) 0.232
DINP 346 0.041 (0.639) 0.000 (0.671) 0.047 (0.589) 0.848 175 0.451 87 0.453 (0.352–0.583) 90 0.446 (0.351–0.566) 0.925
Abbreviations: conc, concentration; GM, geometric mean.
a Number of homes with a dust sample weight > 25 mg.
b The sum of cases and controls is 352 because, among the 346 bedrooms, there were six bedrooms shared by siblings.
c Mann-Whitney U-test.
d Number of homes with a dust sample weight > 25 mg and a phthalate concentration greater than the detection limit (0.040 mg/g dust for DnBP, BBzP, and DEHP).
e Test of the difference between cases and controls made on mean log-transformed concentration.
Table 2 Concentration of phthalates (BBzP and DEHP) in surface dust for case children with a doctor-diagnosed disease compared with controls.
Casesa Controls
Cases
Controls
Phthalate Disease No. Median conc (mg/g dust) No. Median conc (mg/g dust) U-testb (p-value) No. GM conc [(95% CI) mg/g dust] No. GM conc [(95% CI) mg/g dust] t-Testc (p-value)
All homes
BBzP Asthma 106 0.152 177 0.118 0.064 82 0.219 (0.177–0.270) 137 0.157 (0.139–0.178) 0.005
Rhinitis 79 0.181 177 0.118 0.007 65 0.237 (0.185–0.304) 137 0.157 (0.139–0.178) 0.001
Eczema 115 0.181 177 0.118 0.001 95 0.224 (0.186–0.269) 137 0.157 (0.139–0.178) 0.001
DEHP Asthma 106 0.899 177 0.723 0.008 106 0.966 (0.807–1.156) 176 0.741 (0.643–0.855) 0.022
Rhinitis 79 0.783 177 0.723 0.383 78 0.811 (0.638–1.030) 176 0.741 (0.643–0.855) 0.510
Eczema 115 0.844 177 0.723 0.111 115 0.855 (0.721–1.014) 176 0.741 (0.643–0.855) 0.207
Homes with PVC flooring in the child’s bedroom
BBzP Asthma 59 0.195 82 0.159 0.168 52 0.237 (0.177–0.316) 71 0.177 (0.148–0.212) 0.076
Rhinitis 45 0.216 82 0.159 0.008 43 0.265 (0.192–0.366) 71 0.177 (0.148–0.212) 0.018
Eczema 70 0.216 82 0.159 0.003 66 0.257 (0.204–0.324) 71 0.177 (0.148–0.212) 0.011
DEHP Asthma 59 1.006 82 0.855 0.149 59 1.148 (0.904–1.459) 82 0.938 (0.752–1.169) 0.228
Rhinitis 45 0.792 82 0.855 0.924 44 1.040 (0.771–1.403) 82 0.938 (0.752–1.169) 0.586
Eczema 70 0.904 82 0.855 0.379 70 1.045 (0.845–1.291) 82 0.938 (0.752–1.169) 0.491
Abbreviations: conc, concentration; GM, geometric mean.
a Cases with doctor diagnosed disease (asthma, rhinitis, or eczema).
b Mann-Whitney U-test.
c Test of the difference between cases and controls made on mean log-transformed concentration.
Table 3 Crude and adjusted ORs (95% CIs) between phthalates (BBzP and DEHP) in surface dust and case status or doctor-diagnosed disease.
Quartile
Groupa 1 (ref; n = 88) 2 (n = 88) 3 (n = 88) 4 (n = 88) p-Valueb
BBzP
Ranges (mg BBzP/g dust) 0.00–0.05 0.05–0.13 0.13–0.25 0.25–45.55
Crude analysis
Case status 1.0 0.69 (0.38–1.26) 1.00 (0.55–1.81) 2.01 (1.10–3.69) 0.012
Asthma 1.0 0.63 (0.31–1.27) 0.59 (0.45–1.76) 1.92 (0.98–3.79) 0.039
Rhinitis 1.0 0.85 (0.38–1.89) 1.12 (0.51–2.47) 2.69 (1.26–5.76) 0.006
Eczema 1.0 0.74 (0.36–1.52) 1.44 (0.73–2.81) 2.52 (1.26–5.00) 0.002
Adjustedc analysis
Case status 1.0 0.77 (0.40–1.46) 1.01 (0.53–1.90) 1.95 (1.02–3.74) —
Asthma 1.0 0.67 (0.33–1.38) 0.88 (0.43–1.80) 1.87 (0.92–3.81) —
Rhinitis 1.0 1.03 (0.44–2.39) 1.23 (0.53–2.88) 3.04 (1.34–6.89) —
Eczema 1.0 0.84 (0.40–1.76) 1.45 (0.71–2.97) 2.56 (1.24–5.32) —
DEHP
Ranges (mg DEHP/g dust) 0.00–0.46 0.46–0.77 0.77–1.30 1.30–40.46
Crude analysis
Case status 1.0 0.91 (0.50–1.65) 1.05 (0.58–1.89) 1.44 (0.80–2.61) 0.199
Asthma 1.0 1.11 (0.53–2.31) 1.51 (0.74–3.07) 2.36 (1.17–4.75) 0.009
Rhinitis 1.0 1.12 (0.53–2.36) 0.96 (0.44–2.11) 1.55 (0.73–3.28) 0.331
Eczema 1.0 1.00 (0.50–1.97) 1.35 (0.70–2.62) 1.50 (0.76–2.96) 0.161
Adjustedc analysis
Case status 1.0 NS NS NS —
Asthma 1.0 1.56 (0.70–3.46) 2.05 (0.94–4.47) 2.93 (1.36–6.34) —
Rhinitis 1.0 NS NS NS —
Eczema 1.0 NS NS NS —
— , no analyses have been done because linear-by-linear association cannot be done in a multivariate manner; NS, not significant in model, using backward elimination; ref, reference.
a Case status and subgroups with asthma, rhinitis, or eczema compared with controls.
b Linear-by-linear association.
c Adjustments made for sex, age, smoking at home, type of building, construction period, self-reported flooding during preceding 3 years, and the other phthalate variable (in quartiles), using backward elimination method; only significant variables were included in the final model.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7065ehp0112-00139815471732Children's HealthArticlesEstimated Risk for Altered Fetal Growth Resulting from Exposure to Fine Particles during Pregnancy: An Epidemiologic Prospective Cohort Study in Poland Jedrychowski Wieslaw 1Bendkowska Ivona 2Flak Elzbieta 1Penar Agnieszka 1Jacek Ryszard 1Kaim Irena 3Spengler John D. 4Camann David 5Perera Frederica P. 21Epidemiology and Preventive Medicine, Medical College, Jagiellonian University, Krakow, Poland2Center for Children’s Environmental Health, Mailman School of Public Health, Columbia University, New York, New York, USA3Obstetrics and Gynecology, Medical College, Jagiellonian University, Krakow, Poland4Department of Environmental Health, School of Public Health, Harvard University, Boston, Massachusetts, USA5Southwest Research Institute, San Antonio, Texas, USAAddress correspondence to W. Jedrychowski, Epidemiology and Preventive Medicine, Medical College, Jagiellonian University, 7A, Kopernika St., Krakow, Poland. Telephone: 48-12-423-1003. Fax: 48-12-422-8795. E-mail:
[email protected] study received funding from 5 RO1 ES0165 from the National Institute of Environmental Health Sciences and from the Gladys and Roland Harriman Foundation.
The authors declare they have no competing financial interests.
10 2004 21 6 2004 112 14 1398 1402 2 3 2004 21 6 2004 Publication 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 purpose of this study was to estimate exposure of pregnant women in Poland to fine particulate matter [≤2.5 μm in diameter (PM2.5)] and to assess its effect on the birth outcomes. The cohort consisted of 362 pregnant women who gave birth between 34 and 43 weeks of gestation. The enrollment included only nonsmoking women with singleton pregnancies, 18–35 years of age, who were free from chronic diseases such as diabetes and hypertension. PM2.5 was measured by personal air monitoring over 48 hr during the second trimester of pregnancy. All assessed birth effects were adjusted in multiple linear regression models for potential confounding factors such as the size of mother (maternal height, prepregnancy weight), parity, sex of child, gestational age, season of birth, and self-reported environmental tobacco smoke (ETS). The regression model explained 35% of the variability in birth weight (β = −200.8, p = 0.03), and both regression coefficients for PM2.5 and birth length (β = −1.44, p = 0.01) and head circumference (HC; β = −0.73, p = 0.02) were significant as well. In all regression models, the effect of ETS was insignificant. Predicted reduction in birth weight at an increase of exposure from 10 to 50 μg/m3 was 140.3 g. The corresponding predicted reduction of birth length would be 1.0 cm, and of HC, 0.5 cm. The study provides new and convincing epidemiologic evidence that high personal exposure to fine particles is associated with adverse effects on the developing fetus. These results indicate the need to reduce ambient fine particulate concentrations. However, further research should establish possible biologic mechanisms explaining the observed relationship.
air pollutantscohort studyfetal growthpregnancyprenatal exposure
==== Body
A large body of evidence demonstrates that, in addition to parental smoking (Bosley et al. 1981; Jedrychowski et al. 1977; Kallen 1997a, 1997b; Lowe 1959; Martinez et al. 1994; Miller et al. 1976; Savitz et al. 1991; Shaw et al. 1996; Simpson 1957; Underwood et al. 1967; Wasserman et al. 1996; Wyszynski et al. 1997) and environmental tobacco smoke (ETS) [Jedrychowski and Flak 1996; Mainous and Hueston 1994; Martin and Bracken 1986; National Research Council 1986; Ogawa et al. 1991; Perera et al. 2004; Rubin et al. 1986; U.S. Environmental Protection Agency (EPA) 1992; Windham et al. 1999), outdoor and indoor air pollutants may increase the risk of adverse birth outcomes, including low birth weight (LBW), premature births, and intrauterine growth retardation (IUGR) (Antipenko and Kogut 1993; Axelsson and Molin 1988; Bhopal et al. 1994, 1998; Bobak 2000; Bobak and Leon 1999; Dejmek et al. 1999, 2000; Kavlock et al. 1979; Landgren 1996; Lin et al. 2001; Longo 1997; Loomis et al. 1999; Norska-Borowka and Bursa 1993; Pereira et al. 1998; Perera et al. 1998, 2003; Ritz and Yu 1999; Ritz et al. 2000; Singh 1988; Smrcka and Leznarova 1998; Sram 1999; Tabacova and Balabaeva 1993; Wang et al. 1997; Woodruff et al. 1997; Xu et al. 1995). Despite the large number of studies dealing with air pollutants and birth outcomes, the evidence for a causal association remains still weak. Other studies comparing areas with wide ranges of exposure are needed to show the evidence for small effects.
It is assumed that areas in and around the home are important sources of chemical exposures for pregnant women, fetuses, and newborns. Toxic chemicals particularly may be present in proximity to industrial complexes and hazardous waste sites, as well as deriving from local combustion sources such as cars, trucks, and bus routes. The home can also be subject to contamination by particulate matter (PM) and compounds such as nitrogen dioxide, sulfur dioxide, carbon monoxide, and polycyclic aromatic hydrocarbons (PAHs).
Reproductive epidemiology provides evidence that fetuses and infants are likely to be significantly more sensitive to a variety of environmental toxicants than are adults. They are more sensitive because of differential exposure, physiologic immaturity, and a longer lifetime over which disease initiated in the early life can develop. Newborns and young children are especially vulnerable to the toxic effects of ETS, PAHs, PM, nitrosamines, pesticides, polychlorinated biphenyls, metals, and radiation (Perera et al. 2002).
The major difficulty in studying birth outcomes associated with air pollution lies in assessing exposure. Previous studies have attempted to quantify the concentration of outdoor air pollutants in the residence area such as total suspended particulates (TSP), particulate matter ≤10 μm in diameter (PM10), SO2, or CO and assign exposure values to the study subjects or use the area-based exposures to approximate individual exposures. Most prior studies, especially ecologic ones, did not consider important confounding factors such as maternal height and prepregnancy weight, smoking status, or occupational exposure.
The purpose of the present study was to estimate the exposure of pregnant women in Poland to potentially hazardous fine PM [≤2.5 μm in diameter (PM2.5)] and to assess its effects on the birth outcomes [weight, length, and head circumference (HC) at birth]. To avoid potential methodologic limitations of previous studies regarding the assessment of exposure, we included assessment of personal individual exposure to fine particulate pollutants from all potential sources indoors and outdoors. In the analysis of the association between air pollutants and birth outcomes, we also considered important confounders such as maternal anthropometric characteristics, parity, sex of child, gestational age, and birth season.
Materials and Methods
The design of this cohort prospective study and the detailed selection of the population have been described previously (Jedrychowski et al. 2003). Briefly, this study is part of an ongoing comparative longitudinal investigation of the health impact of prenatal exposure to outdoor/indoor air pollution on infants and children being conducted in New York City and Krakow. The ethics committee of the Jagiellonian University approved the study.
We analyzed data from 362 women who gave birth between 34 and 42 weeks of gestation from January 2001 through March 2003. Women attending ambulatory prenatal clinics in the first and second trimesters of pregnancy were eligible for the study. The enrollment included only nonsmoking women with singleton pregnancies who were 18–35 years of age and were free from chronic diseases such as diabetes and hypertension. Recruited women were interviewed and given a description of the study and requirements for participation in the project. Each subject was given a detailed questionnaire at entry to the study and in the third trimester to solicit information on demographic data, house characteristics, date of the last menstrual period (LMP), medical and reproductive history, occupational hazards, alcohol consumption, and smoking practices of others present in the home. After participating women had given birth, maternal and child hospital records were reviewed to obtain data on complications of delivery. Weight, length, and HC at birth and Apgar score at 1 and 5 min were recorded for all infants. Gestational age at birth was defined as the interval between the last day of the mother’s LMP and the date of birth.
Dosimetry of prenatal personal exposure to fine particles.
During the second trimester, a member of the air monitoring staff instructed the women in the use of the personal monitor, which is lightweight and quiet and is worn in a backpack. The women were asked to wear the monitor during daytime hours for 2 consecutive days and to place the monitor near the bed at night. During the morning of the second day, the air monitoring staff person and interviewer visited the women’s homes to change the battery pack and administer the full questionnaire. They also checked to see that the monitor had been running continuously and that no technical or operating failures had occurred. A staff member returned to the women’s homes on the morning of the third day to pick up the equipment.
A personal environmental monitoring sampler (PEMS) was used to measure particle mass. The PEMS is designed to achieve the particle target size of ≤2.5 μm at a flow rate of 4.0 L/min with an array of 10 impactor nozzles. Flow rates were calibrated (with filters in place) before the monitoring and were checked again with a change of the battery pack on the second day and at the conclusion of the monitoring. Pumps operated continuously at 2 L/min over the 48-hr period. To modify the sampler to achieve the 2.5-μm size cutoff at 2 L/min, five of the nozzles were blocked. Particles were collected on Teflon membrane filter (37 mm Teflo; SKC, Inc., Eighty Four, PA, USA). The combination of low pressure drop (permitting use of a low-power sampling pump), low hygroscopicity (minimizing bound water interference in mass measurements), and low trace element background (improving analytical sensitivity) of these filters makes them highly appropriate for personal particle sampling. Dust air samples were analyzed by J.D.S. and his staff.
Statistical methods.
The main birth outcomes were birth weight, length, and HC at birth, and association with exposure was examined by univariate and multivariate models. We constructed several models where exposure to PM2.5 was treated as continuous and dichotomous variables. First, crude effects were estimated, and subsequently they were adjusted to confounders. All maternal factors included in multivariate analyses were related to birth outcomes in bivariate analysis. In the final statistical analysis, we assessed the effect of PM2.5 and ETS exposure on the birth out-comes by multiple linear regression, controlling for potential confounders (parity, height of mother, prepregnancy weight, sex of infant, gestational age, and season of birth). We tested seasons of year for confounding because of their association with exposure and their potential association with fetal growth. Season of birth was introduced in the regression models as a dummy variable, with summer defined as the reference level. Because the distribution of the air pollutants in question was skewed, the PM2.5 values were log transformed before entry into the regression models. Because the outliers of exposure to PM2.5 did not change the regression estimates, they were not removed from the analysis. The ETS variable was categorized as follows: 1, no exposure; 2, exposed to ≤10 cigarettes smoked daily at home; 3, exposed to 11–20 cigarettes smoked daily at home; 4, exposed to > 20 cigarettes smoked daily at home. In all statistical analyses, the significance level was set at p < 0.05.
For testing the functional relationship between PM2.5 and birth outcomes, we used generalized additive models in S-Plus (Mathsoft Inc., Seattle, WA, USA; Mathsoft 1999), including gestational age, sex of child, parity, height and prepregnancy weight of mother, and the delivery season. The analysis showed that the relationship between PM2.5 and birth length did not differ from the linear relationship established by the linear multivariate regression model. The reduction of residual sum of squares from 1978.73 for linear fit to 1955.09 was not significant (p = 0.237). The corresponding estimates for birth weight and HC were similar.
Results
Analysis of personal air samples from the 362 pregnant women enrolled in our study showed that PM2.5 exposures averaged 43 μg/m3, with a range of 10.3–147.3 μg/m3. The mean weight, length, HC at birth, and gestational age for infants under study were 3439.8 g, 54.6 cm, 33.9 cm, and 39.5 weeks, respectively. The newborns of mothers with higher exposures to fine particles in the period of monitoring (above the median of 36.3 μg/m3) showed shorter length at birth by 0.9 cm. The corresponding reductions in HC and birth weight were 0.3 cm and 128.3 g, respectively (Table 1).
In the subsequent statistical analysis of the data, we used multiple linear multivariate regression models to examine the relationship between birth outcomes and the effect of fine particles and ETS. For each birth outcome, we constructed a separate model where dependent variables included gestational age, sex of child, season of birth, and variables on quality of air (ETS and level of PM2.5), as well as the anthropometry of the mother. Both variables of air quality correlated significantly (p = 0.01) with each other (r = 0.35), and the level of personal exposure to PM2.5 depended on the number of cigarettes smoked daily at home (Figure 1).
Tables 2–4 present the standardized β regression coefficient of PM2.5 on the birth outcomes after accounting for all dependent variables. The regression model explained 33.4% of the variability in birth weight (β = −200.8, p = 0.03), and both regression coefficients for PM2.5 and birth length (β = −1.44, p = 0.01) and HC (β = −0.73, 0.02) were significant as well. In all regression models the effect of ETS was insignificant. Predicted reduction in birth weight at an increase of exposure from 10 to 50 μg/m3 was 140.3 g. The corresponding predicted reduction of birth length would be 1.0 cm, and of HC, 0.5 cm.
Finally, we explored the hypothesis that PM2.5 had an adverse effect on gestational age. We did not find in the linear multivariate regression model that PM2.5 or ETS shortened the duration of pregnancy.
Discussion
Until now, there have been no studies of effects of personal exposure to fine particles on reproductive health and birth outcomes. Our study draws attention to the fact that not only lower birth weight but also reduction in length and HC at birth might be caused by prenatal exposure to pollutants during pregnancy.
Analysis of personal air samples from the pregnant women enrolled in the Krakow study showed that total personal PM2.5 exposures averaged 43.1 μg/m3 with a range of 10.3–147.3 μg/m. None of the women under study in Krakow reported heavy exposure to dusty environments in the working hours. The PM2.5 level in the Krakow study was very high and had a wide range of exposure, compared with data from the United States, where the range of annual mean for PM2.5 measured in various sites is 1.2–14.2 μg/m3 (U.S. EPA 2003). However, the PM2.5 exposure observed in Krakow would be comparable with that observed in the Czech Republic, where the daily mean is 35.6 μg/m3 (Dejmek et al. 1999).
The analysis of birth outcomes indicated a significant inverse correlation between concentrations of fine particles and fetal growth. The adjusted effect of exposure to PM2.5 was reflected in significantly lower mean weight and length at birth and lower mean HC of newborns. The newborns of mothers exposed to higher concentrations of fine particles (above the median of 36.3 μg/m3) showed shorter length at birth by 0.9 cm. The corresponding reductions in HC and birth weight were 0.3 cm and 128.3 g. We estimated from the regression equations that an increase of exposure from 10 to 50 μg/m3 of fine particles would reduce length at birth by 1.0 cm. The corresponding reductions of HC and birth weight would be 0.5 cm and 140.3 g, respectively.
Our study showed a significant positive interrelationship between self-reported ETS level and total personal exposure to PM2.5, which to a great extent depended on the number of reported cigarettes smoked daily at home. This interrelationship creates difficulties in separating the effect of ETS on the birth outcomes from that attributed to fine particles. However, none of the models with both ETS and PM2.5 showed a significant effect of ETS. Moreover, stepwise regression indicated that adding the ETS variable into the model did not explain better the amount of variability in birth outcomes due to air contamination. Therefore, we think that the effect of ETS confirmed in many previous studies may result from its interrelationship with PM2.5. In studies where the birth outcomes were not controlled by the PM2.5 level, the effect of ETS could be demonstrated.
The biologic mechanisms whereby PM2.5 might cause adverse pregnancy outcomes are unclear. PM2.5 might be a proxy measure of a whole complex of toxic agents present in the environment—including PAHs—that could adversely affect fetal growth. It is well known that fine particles are virtually always present in particle-generating processes, especially combustion processes that generate other toxic agents as well. Typically, the ambient fine particle fraction contains constituents of tobacco and wood smoke, organic compounds, sulfates, metals, and soot (Spengler et al. 2001). Therefore, it would be reasonable to assume that PM2.5 represents a wide spectrum of environmental hazards that may be implicated in intrauterine fetal growth. Air pollutants may affect DNA, as evidenced by observations that placental DNA adducts are more common in areas with higher levels of pollution (Topinka et al. 1997) and that altered fetal growth has been associated with PAH–DNA adducts (Perera et al. 1998).
Our data indicating that personal exposure to fine particles has a stronger relationship with birth outcomes than does ETS may result from the fact that the measurement of ETS exposure based on interviews with pregnant women could be biased. If respondents underestimated their ETS exposure, then its effect may appear much weaker in comparison with the objective measurements of fine particles. However, the level of fine particles is the function not only of ETS, which is the major source of indoor pollution, but also of PM generated from other sources such as fossil fuel combustion.
Another potential limitation of our study comes from the fact that personal monitoring of exposure to fine particles among pregnant women was performed over the short period of 48 hr in the second trimester of pregnancy. However, to evaluate the correlation between the level of PM2.5 measured over 48 hr in the second trimester of pregnancy with those in the second and the third trimesters, a series of repeated measurements in each trimester was carried out in the subsample of 51 pregnant women who were recruited in the first trimester. The concentration of PM2.5 (mean ± SD) in the second trimester was 44.4 ± 46.5 μg/m3, but it was not significantly different from the mean concentration in the first trimester (46.2 ± 34.0 μg/m3) or in the third trimester (35.9 ± 35.3 μg/m3). The latter results suggest that the mean levels of fine particles were rather stable over the whole pregnancy. This provides some confidence that the measurements of personal level of exposure to fine particles taken in the second trimester may also be representative for other pregnancy periods.
We could also demonstrate that total personal exposure to PM2.5 measured over 48 hr correlated well with the PM10 concentrations obtained from the monitors of the municipal air pollution network of Krakow, which were located in the residence areas of the subjects under study (Figure 2). We observed consistency between monthly means of PM10 measured by the local ambient monitors and the monthly means of total personal exposure to fine particles measured over 48 hr in the second trimester. This suggests that the extrapolation of ambient measurements to personal exposure may be reasonably approximated. However, the extent to which the ambient measurements reflect the individual exposure level may be different in various populations. First, it would depend largely on the quality of the ambient network of air pollution stations and its appropriate coverage of the given residency areas. Besides different lifestyles and mobility of women over the study period, substantial seasonal changes in air pollution due to weather and meteorologic conditions may be significant.
In our study, the most important confounders of the birth outcomes such as the presence of chronic diseases or active tobacco smoking by mothers in pregnancy have been removed through entry criteria. Other risk factors thought to affect the probability of delivery of newborns with lower growth, such as maternal height or prepregnancy weight, gestation age, sex of child, and season of birth, were also accounted for in the analysis.
Over the last decades there has been growing concern over the health effects associated with air pollution. The studies were concerned mainly with morbidity and mortality from respiratory diseases, occurrence of respiratory symptoms, pulmonary function, and physician office visits. To date, there have been a limited number of studies investigating the association between air pollution and adverse birth outcomes, and the conclusions were somewhat inconsistent. A study conducted in China suggested that exposure to TSP and SO2 was associated with an excess risk of preterm delivery (Xu et al. 1995; Yang et al. 2002) and LBW (Wang et al. 1997). Several studies observed an association of TSP and SO2 with LBW and found increased risk of IUGR in a highly polluted region in the Czech Republic (Bobak and Leon 1999; Dejmek et al. 1999, 2000; Sram 1999). Ritz and Yu (1999) found that high concentrations of CO and PM10 during the last trimester of pregnancy may increase the risk of LBW for term babies. Our prior study also showed that, after controlling for dietary and smoking sources of the pollutants, PAH–DNA adducts in cord blood were inversely associated with birth weight, length, and HC (Perera et al. 1998). In contrast, in a study performed in southern Sweden, Landgren (1996) could not confirm the hypothesis that air pollution affected the incidence of short gestation and LBW. Some researchers (Bhopal et al. 1994; Smrcka and Leznarova 1998) found no association between either residential proximity to a coking plant or major steel and petrochemical industries and birth outcomes in the United Kingdom. The study period, size of the population, and number of cases were large. However, Axelsson and Molin (1988) found that the miscarriage rate was slightly elevated in areas exposed to emissions from petrochemical industries. None of the studies used personal monitors of PM2.5 in the assessment of exposure.
The results of our study are of public concern because adverse birth outcomes have been associated in other studies with more health problems and reduced cognitive development in childhood (Perera et al. 2002). Follow-up of the cohort will permit determination of the longer-term sequels of prenatal exposures and adverse birth outcomes. The results of our study may have implications not only for the health and development of children but also for adult health. Epidemiologic studies in children indicate that prenatal hazards that restrict fetal growth may be associated with small but measurable delays in motor and social development through childhood and reduced cognitive development (Bacharach and Baumeister 1998; Hack et al. 1991; Hediger et al. 2002; Saigal 2000). There is also evidence of associations between birth size and future development of adult diseases, such as type 2 diabetes and coronary artery disease (Godfrey and Barker 2001; Phillips 2000). It is believed that these associations arise as a result of the phenomenon of “programming,” which involves persisting changes in structure and function caused by environmental factors during critical and vulnerable periods of early development. However, other explanations, including the operation of genetic factors and programming of certain endocrine axes, have also been suggested to explain this observation.
Our study provides convincing epidemiologic evidence based on a cohort that prenatal exposure resulting from high personal maternal exposure to fine particles is associated with adverse effects on the developing fetus. These results indicate the need to reduce ambient fine particulate concentrations. However, further research should help establish possible biologic mechanisms explaining the observed relationship.
Figure 1 Personal PM2.5 level by passive smoking category [cigarettes (cig)/day]. Data are mean ± SE.
Figure 2 Correlation between mean monthly personal PM2.5 measurements and mean monthly PM10 concentrations from areawide ambient monitoring (circles). Solid line, regression; dashed lines, 95% CI.
Table 1 Characteristics of the study sample by PM2.5 level of personal exposure during pregnancy (mean ± SD).
Variable Low levela (n = 180) High levelb (n = 182)
Mother’s age 28.1 ± 3.4 28.1 ± 3.9
Mother’s height (cm) 164.7 ± 5.3 165.3 ± 6.0
Mother’s weight (kg) 58.6 ± 10.2 58.3 ± 7.6
Gestational age (weeks) 39.6 ± 1.3 39.4 ± 1.4
Length at birth (cm) 55.1 ± 2.7* 54.2 ± 2.6
Birth weight (g) 3504.3 ± 471.1* 3376.0 ± 453.1
HC at birth (cm) 34.1 ± 1.5* 33.8 ± 1.4
a Low level, ≤36.3 μg/m3.
b High level, > 36.3 μg/m3.
* Significantly higher (analysis of variance, p < 0.05) compared with the group with higher exposure to PM2.5.
Table 2 Regression summary of dependent variable (birth weight) on log PM2.5 exposure and confounding variables (number of pregnancies, height and prepregnancy weight of mother, sex of newborn, and gestational age).a
Variable Coefficient 95% CI t-Value p-Value
Intercept −4876.72
Education (years of schooling) 10.886 −3.814 to 25.586 1.48 0.14
No. of pregnancies 61.451 13.121 to 109.781 2.54 0.01
Maternal height (cm) 10.958 3.278 to 18.638 2.85 0.00
Prepregnancy weight (kg) 9.743 4.867 to 14.619 4.00 0.00
Gestational age (weeks) 160.288 130.575 to 190.001 10.79 0.00
Sex of child −212.802 −293.914 to −131.691 −5.25 0.00
Season
Autumn −63.658 −182.217 to 54.900 −1.07 0.28
Winter 49.179 −67.036 to 165.395 0.85 0.40
Spring 23.544 −92.276 to 139.363 0.41 0.68
Log PM2.5 −200.821 −385.968 to −15.674 −2.17 0.03
ETS 32.008 −91.554 to 155.569 0.52 0.60
CI, confidence interval.
a R = 0.588; R2 = 0.334.
Table 3 Regression summary of dependent variable (length at birth) on log PM2.5 exposure (continuous) and confounding variables (number of pregnancies, height and prepregnancy weight of mother, sex of newborn, and gestational age).a
Variable Coefficient 95% CI t-Value p-Value
Intercept 13.8235
Education (years of schooling) 0.021 −0.070 to 0.111 0.45 0.65
No. of pregnancies 0.267 −0.032 to 0.566 1.79 0.07
Maternal height (cm) 0.067 0.020 to 0.115 2.83 0.00
Prepregnancy weight (kg) 0.040 0.010 to 0.070 2.66 0.01
Gestational age (weeks) 0.792 0.608 to 0.975 8.62 0.00
Sex of child −1.150 −1.652 to −0.649 −4.59 0.00
Season
Autumn −0.464 −1.197 to 0.269 −1.27 0.21
Winter 0.068 −0.650 to 0.787 0.19 0.85
Spring −0.063 −0.779 to 0.654 −0.17 0.86
Log PM2.5 −1.439 −2.583 to −0.294 −2.51 0.01
ETS −0.244 −1.008 to 0.520 −0.64 0.52
CI, confidence interval.
a R = 0.518; R2 = 0.262.
Table 4 Regression summary of dependent variable (HC at birth) on log PM2.5 exposure (continuous) and confounding variables (number of pregnancies, height and prepregnancy weight of mother, sex of newborn, and gestational age).a
Variable Coefficient 95% CI t-Value p-Value
Intercept 17.2662
Education (years of schooling) 0.043 −0.006 to 0.092 1.76 0.08
No. of pregnancies 0.186 0.025 to 0.347 2.31 0.02
Maternal height (cm) 0.028 0.002 to 0.053 2.17 0.03
Prepregnancy weight (kg) 0.026 0.009 to 0.042 3.14 0.00
Gestational age (weeks) 0.301 0.202 to 0.400 6.08 0.00
Sex of child −0.782 −1.052 to −0.511 −5.78 0.00
Season
Autumn −0.020 −0.416 to 0.375 −0.10 0.92
Winter 0.111 −0.277 to 0.498 0.57 0.57
Spring 0.134 −0.253 to 0.520 0.69 0.49
Log PM2.5 −0.729 −1.347 to −0.112 −2.36 0.02
ETS 0.151 −0.261 to 0.563 0.73 0.46
CI, confidence interval.
a R = 0.472; R2 = 0.214.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7051ehp0112-00140315471733Children's HealthArticlesPaternal Occupational Exposure to 2,3,7,8-Tetrachlorodibenzo-p-dioxin and Birth Outcomes of Offspring: Birth Weight, Preterm Delivery, and Birth Defects Lawson Christina C. 1Schnorr Teresa M. 1Whelan Elizabeth A. 1Deddens James A. 1Dankovic David A. 1Piacitelli Laurie A. 1Sweeney Marie H. 2Connally L. Barbara 11National Institute for Occupational Safety and Health, Cincinnati, Ohio, USA2Office of Global Health Affairs, Department of Health and Human Services, Hanoi, VietnamAddress correspondence to C.C. Lawson, National Institute for Occupational Safety and Health, 4676 Columbia Parkway (R-15), Cincinnati, OH 45226 USA. Telephone: (513) 841-4171. Fax: (513) 841-4486. E-mail:
[email protected] thank M. Fingerhut and J. Reefhuis for their scientific contributions and expertise. We also thank B. Jenkins and C. Gersic for their assistance in data collection and data management.
The authors declare they have no competing financial interests.
10 2004 23 6 2004 112 14 1403 1408 23 2 2004 23 6 2004 Publication 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. Agent Orange is a phenoxy herbicide that was contaminated with 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). We studied pregnancy outcomes among wives of male chemical workers who were highly exposed to chemicals contaminated with TCDD and among wives of nonexposed neighborhood referents. For exposed pregnancies, we estimated serum TCDD concentration at the time of conception using a pharmacokinetic model. The mean TCDD concentration for workers’ births was 254 pg/g lipid (range, 3–16,340 pg/g). The mean referent concentration of 6 pg/g was assigned to pregnancies fathered by workers before exposure. A total of 1,117 live singleton births of 217 referent wives and 176 worker wives were included. Only full-term births were included in the birth weight analysis (≥37 weeks of gestation). Mean birth weight among full-term babies was similar among referents’ babies (n = 604), preexposure workers’ babies (n = 221), and exposed workers’ babies (n = 292) (3,420, 3,347, and 3,442 g, respectively). Neither continuous nor categorical TCDD concentration had an effect on birth weight for term infants after adjustment for infant sex, mother’s education, parity, prenatal cigarette smoking, and gestation length. An analysis to estimate potential direct exposure of the wives during periods of workers’ exposure yielded a nonstatistically significant increase in infant birth weight of 130 g in the highest exposure group (TCDD concentration > 254 pg/g) compared with referents (p = 0.09). Mothers’ reports of preterm delivery showed a somewhat protective association with paternal TCDD (log) concentration (odds ratio = 0.8; 95% confidence interval, 0.6–1.1). We also include descriptive information on reported birth defects. Because the estimated TCDD concentrations in this population were much higher than in other studies, the results indicate that TCDD is unlikely to increase the risk of low birth weight or preterm delivery through a paternal mechanism.
birth defectsbirth weightcongenital anomaliesdioxinoccupationpaternal exposurepreterm birthTCDD
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Agent Orange is a phenoxy herbicide that was widely used as a defoliant in Vietnam. A mixture of the herbicides 2,4-D [(2,4-dichlorophenoxy)acetic acid] and 2,4,5-T [(2,4,5-trichlorophenoxy)acetic acid], Agent Orange was contaminated with 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Most of the general population is exposed to low levels of TCDD, primarily through dietary intake of animal fats. Occupational exposure to TCDD occurred in the United States during the manufacture of Agent Orange, as well as among U.S. Vietnam veterans who were exposed to Agent Orange during Operation Ranch Hand. Because most of these workers and veterans were men, there has been heightened interest in male reproductive health outcomes associated with exposure to TCDD.
Extensive data on laboratory animals suggest that developing tissues are highly sensitive to TCDD, so associations between TCDD exposure and adverse reproductive outcomes are biologically plausible [Eskenazi and Kimmel 1995; National Academy of Sciences (NAS) 2003]. A recent review of TCDD by the NAS concluded that TCDD is one of the most toxic chemicals known to affect animals, although there is an extreme range of effects among species, and that the most sensitive time of exposure to TCDD is during fetal development (NAS 2003). There have been few studies of paternal TCDD exposure and birth weight or preterm delivery in humans, however. In the fourth biennial update of the health effects of Vietnam veterans of exposure to herbicides, therefore, the NAS concluded there was insufficient or inadequate evidence to determine whether there is an association between paternal herbicide exposure and low infant birth weight or preterm delivery (NAS 2003). The NAS further concluded that there was limited suggestive evidence of an association between paternal exposure to herbicides and spina bifida (NAS 1997), but the evidence was inadequate regarding other birth defects (NAS 2003).
Low birth weight has been associated with infant mortality as well as outcomes later in life such as asthma, lower IQ, and hypertension (Wilcox 2001). Low-birth-weight babies either are born preterm (< 37 weeks of gestation) or are full-term but small (Wilcox 2001). Historically, etiologic research on pregnancy outcomes, such as birth weight or birth defects, has focused on maternal and fetal exposures. However, paternal exposures could be related to adverse reproductive outcomes through genetic damage to the male germ cell, transfer of chemicals via seminal fluid, or exposure from chemicals that the father brings home from the workplace or hobbies (referred to as take-home exposure).
We studied the pregnancy outcomes among wives of male chemical workers who were highly exposed to chemicals contaminated with TCDD and among nonexposed neighborhood referents who participated in a cross-sectional medical study. Previous reproductive health analyses of this cohort reported subtle alterations in reproductive gonadotrophin and testosterone levels in male workers (Egeland et al. 1994) but no association between paternal TCDD exposure and spontaneous abortion or sex ratio (Schnorr et al. 2001). In the present study we evaluated the association between paternal exposure to TCDD at the time of conception and birth weight and preterm delivery of offspring. We also describe birth defects as reported in maternal interviews.
Materials and Methods
Study population.
The reproductive health study was conducted as part of a cross-sectional medical study, described previously in detail (Sweeney et al. 1989, 1993). Briefly, the study was conducted in 1987–1988 by the National Institute for Occupational Safety and Health (NIOSH) and examined workers from plants in New Jersey and Missouri. Workers were exposed to TCDD during the production of sodium trichlorophenol or one of its derivatives, such as hexachlorophene [2,2′-methylene-bis-(3,4,6-trichlorophenol)] or 2,4,5-T, which was used to formulate Agent Orange.
For comparison, referents with no self-reported occupational exposure to TCDD were selected from the workers’ neighborhoods, matched on age (± 5 years), race, and sex. Selection of worker-matched referents was based on a procedure requiring neighborhood door-to-door solicitation by trained interviewers. Each eligible resident was assigned a random number from 1 to 6 (n = 938). Selection was based on the matching referent’s numerical position in the random sequence and continued until the sample size was met (Sweeney et al. 1989).
A questionnaire was used to collect information on health status and risk factors. Subjects were also asked to participate in a medical examination, which included drawing blood for determination of serum TCDD. The methods of serum collection, analytical methods, and quality control standards have been published previously (Fingerhut et al. 1989, 1991; Patterson et al. 1986, 1989; Turner et al. 1989). The study was voluntary, and informed consent was obtained from all study subjects.
Current and former wives/partners (hereafter referred to as wives) of male participants were contacted and administered a telephone interview, which collected detailed information on reproductive history, medical history, lifestyle factors, and occupational factors. Data for 14 women who worked at the plants and their referents were not included in the analysis because of the small number of births.
Exposure assessment.
Dates of employment in TCDD-related processes defined the exposure period (Piacitelli et al. 1992). Pregnancies conceived after the father’s first date of exposure were considered exposed, whereas referent pregnancies and pregnancies conceived before the fathers’ exposure at the plants (hereafter referred to as preexposure pregnancies) were considered unexposed.
For exposed pregnancies, we estimated the worker’s serum TCDD concentration at the time of conception using a pharmacokinetic model (Dankovic et al. 1995; Thomaseth and Salvan 1998). This model was based on the following factors: serum TCDD concentration at the time of examination, dates of employment in TCDD-related processes, body mass index (BMI) measured by NIOSH at the time of examination, and BMI measured by the employer during employment. Analyses for lipid-adjusted TCDD concentrations were calculated on a lipid weight basis because TCDD accumulates in the lipid stores of the body (Fingerhut et al. 1989). BMI change over time was modeled as a continuous function of age (Thomaseth and Salvan 1998). Data for workers who had both BMI values were used to create a linear regression model of BMI change over time using age at first employment, age at examination, and BMI at examination. This model was then applied to workers who had only one BMI value (n = 23).
The pharmacokinetic modeling technique has the advantage that it allowed for changes in individual body burden over time. The model assumed that there was no occupational exposure to TCDD after termination of employment, with a continuation of the background exposure to TCDD throughout life. In general, the modeled concentrations of TCDD in workers increased from first exposure to last exposure and then gradually declined to the concentration measured at the examination (Schnorr et al. 2001).
TCDD serum measurements were obtained for a random sample of 79 referents at examination. Because the referent serum concentrations were assumed to be the accumulation of a lifetime of background environmental exposures, we assigned the TCDD serum values from the examination to each referent pregnancy. For the remaining referents, the median referent value of 6 pg/g lipid was assigned (Piacitelli et al. 1992; Thomaseth and Salvan 1998). Pregnancies fathered by workers before exposure were also assumed to have exposure at the background level and so were assigned the median referent value of 6 pg/g.
Outcome definitions.
We analyzed three outcome variables for this study: birth weight, preterm delivery, and birth defects. Only live singleton births were included in the birth weight and preterm delivery analyses. Furthermore, the birth weight analysis was conducted only among full-term births. Although previous studies of birth weight and environmental or occupational exposure have included preterm births, the most current literature recommends analyzing birth weight only among full-term births for two reasons: An exposure that affects fetal growth does not necessarily affect preterm delivery, and among term births, the influence of gestational age is minor (Wilcox 2001). For the birth defects analysis, only singleton births and stillbirths were included. Pregnancies not fathered by the study males were excluded in all analyses.
We requested birth certificates for all births, as well as neonatal death certificates and medical records where applicable. Birth certificates were obtained for 82% of the births (86% for worker births and 77% for referent births). Birth weight as recorded on the birth certificates was used when available. If no certificate was available, then the mother’s report of her child’s birth weight was used. Among women who had both birth certificates and mother’s report, the correlation coefficient was 0.91. We analyzed birth weight in grams to be consistent with previous literature.
We defined full-term birth as a live birth of ≥37 completed weeks from last menstrual period (LMP) or no more than 3 weeks before due date. For gestational age, we used the mother’s response to the question “Was the baby born on time, early, or late,” and if early or late, “by how many weeks?” We compared the mother’s report of gestational age to what was reported on the birth certificate, when available, and found that 49% were in agreement within the exact week and an additional 25% agreed within 1 week, leaving 26% who disagreed by > 1 week. We found little difference among exposure groups when comparing the percentages of those who disagreed by > 1 week (referents, 25%; pre-exposure workers, 27%; postexposure worker pregnancies, 28%).
We used the criteria described by Erickson et al. (1984) to define infants with major birth defects—that is, those defects that would affect survival, require substantial medical care, or result in marked physical or psychological challenges.
Statistical analysis.
For the birth weight analysis, we modeled the primary independent variable, TCDD concentration, both as a continuous variable using the log and as a categorical variable (referents, < 20 pg/g, 20 to < 255 pg/g, ≥255 pg/g) using dummy variables. Unlogged TCDD and log-transformed TCDD gave similar results, but the model fit better with logged data, indicating a transformation was necessary. Therefore, the log-transformed TCDD data are presented to linearize the regression model. We selected the < 20-pg/g category as the lowest TCDD category because all of the referent serum samples were < 20 pg/g. The other categories were created based on the distribution of the TCDD estimates in our previous report (Schnorr et al. 2001). The pregnancies fathered by the workers fell into the four exposure categories as follows: 20% of pregnancies had serum values < 20 pg/g; 20% of pregnancies had serum values ≥1,120 pg/g; and the remaining 60% of the pregnancies were split equally into two categories, 20 to < 255 and 255 to < 1,120. For the analyses in this report, the upper two categories are combined into > 255 pg/g.
Repeated-measures analyses of variance were performed for the birth weight analysis using the SAS PROC MIXED procedure (SAS Institute, Inc., Cary, NC, USA) to account for the lack of independence among multiple pregnancies per mother. Restricted maximum likelihood was used to estimate the parameters, and a compound symmetric variance covariance structure was assumed.
To analyze preterm birth, we dichotomized gestational age into two categories: < 37 weeks or ≥37 weeks. The primary independent variable, TCDD, was modeled as a continuous (log) variable (too few subjects in the preterm category prevented analysis by TCDD categories). Repeated-measures analyses of variance were performed using the SAS PROC GEN-MOD procedure to account for the lack of independence among multiple pregnancies per mother.
For the birth weight and preterm analyses, we used univariate analyses to search for medical, lifestyle, and exposure factors that could potentially confound multivariate analyses. Potential confounders included mother’s medical conditions, prenatal medication use, alcohol consumption, cigarette smoking, maternal age at conception, parity, year of the birth, and prenatal weight gain; length of gestation; infant sex; accident, injury, or falls during pregnancy; maternal workplace factors such as occupational exposure to chemicals, prolonged standing, and heavy lifting; and mother’s and father’s education, race, and ethnicity. A variable was considered a potential confounder and retained for further modeling if it was significantly related to both log TCDD concentration and the outcome (p < 0.20) or changed the TCDD estimate by more than 15%. The variables that were retained for multivariate modeling for the birth weight analysis were sex of the infant, education of the mother, parity, cigarette smoking during pregnancy, and length of gestation. Inclusion in the model of the squared term for gestational length did not improve the model. The variables retained for the preterm birth analysis included having an accident, injury, or fall during pregnancy; mother’s age; and cigarette smoking and medication use during pregnancy.
For the birth defects analysis, we reviewed 1,153 live births and 13 stillbirths for a total of 1,166 births. We attempted to confirm all birth defects with medical records and/or vital or death records; however, because not all birth defects were confirmed, and because the maternal recall period could be several years, we only report those that were considered to be serious enough have a high likelihood of accurate recall. Small numbers of birth defects (n = 41) limited a meaningful analysis of birth defect categories by TCDD concentration, so only descriptive data of birth defects are reported.
Results
Characteristics of the study participants have been reported in previous publications (Schnorr et al. 2001; Sweeney et al. 1989, 1993). A total of 281 workers (70% of the 400 living locatable workers) participated in the medical examination. Nine hundred thirty-eight men from matching neighborhoods were eligible to participate. Random selections of the eligible referents were invited to participate until 260 were enrolled. More than half (62%) of the individuals who were first or second in the random sequences of 1–6 agreed to participate in the study (Sweeney et al. 1989). Among living current and former wives of these men, we interviewed 245 (77.5%) of the workers’ wives and 221 (73.9%) of the referents’ wives.
Of those who were interviewed, 176 of the worker wives and 217 of the referent wives had at least one singleton live birth and were included in the birth weight analyses. Most of the study population was Caucasian race (89.4% referent wives and 90.3% worker wives), and a small percentage were of Hispanic ethnicity (1.8% of referent wives and 3.1% of worker wives). A higher percentage of the referent wives had more than a high school education (38.2 vs. 34.5% of the worker wives).
Included in the birth weight analyses were a total of 1,117 live full-term births, 604 to referent wives and 513 to worker wives. Of the babies fathered by workers, 221 were conceived before the father was exposed to TCDD at the study company (preexposure births), and 292 were conceived during or after exposure and are considered exposed births. Table 1 shows demographic characteristics of referent births and preexposure and exposed births. Mothers of preexposure births were younger than mothers of referent or exposed births (p < 0.01). Births conceived during or after fathers’ exposure to TCDD had a higher average exposure to cigarette smoke compared with referents (p = 0.04), although the percentages of mothers who smoked were similar. Alcohol use was higher in exposed pregnancies compared with referent and preexposure births, although the differences were not statistically significant.
The median estimated TCDD concentration for exposed births was 254 pg/g (range, 3–16,340 pg/g; Table 1). The median TCDD serum concentration for referent fathers who participated in the medical exam was 6 pg/g (range, 2–19), which was the value assigned to all referent and preexposure births. As reported previously (Schnorr et al. 2001), there was a lower percentage of male births in the pre-exposure group (50.7%) than in the referent (54.5%) and exposed (56.2%) groups, although these differences were not statistically significant.
The same percentage of referent and worker wives worked during their pregnancies (28%), and few reported exposure to chemicals or radiation during the pregnancies (0.3–3.0%; data not shown). A portion of the wives worked in jobs that involved physical stressors such as heavy lifting, continuous standing, or use of vibrating tools (12% referent births, 10% pre-exposure births, and 8% exposed births).
Mean birth weights were similar in the three exposure groups (mean ± SD: referent births, 3,420 ± 490 g; preexposure births, 3,347 ± 485 g; exposed births, 3,442 ± 507 g). Figure 1 shows average birth weight by length of gestation for referent births and preexposure and exposed worker births. The graph is limited to gestational length of 37–43 weeks because of small numbers in the other weeks, as shown in Table 2. The three exposure groups presented in Figure 1 show similar mean birth weight patterns.
The results of a crude analysis show a small but statistically significant birth weight increase of 25 g with each increase in the log TCDD concentration (p = 0.01). Thus, for every 2- to 3-fold increase in TCDD, there was a 25-g increase in birth weight. There was no effect of continuous (data not shown) or categorical TCDD concentration (Table 3) on birth weight when adjusting for the following confounding variables: sex of the infant, education of the mother, parity, cigarette smoking during pregnancy, and length of gestation.
During the time of their husband’s employment, some of the workers’ wives may have been exposed directly to TCDD via contaminated clothing or vehicles (take-home exposure). To examine this, we restricted the analysis to the subset of births whose gestations overlapped the father’s dates of exposure at the plant. For the referents in this subanalysis, we included only births in which at least 1 day of the pregnancy occurred during the dates the two plants were in operation. The crude analysis showed a small increase in birth weight of 22 g (SE = 14) with each increase in the log of TCDD (p = 0.1). The adjusted analysis using TCDD as a continuous variable, however, showed a statistically significant increase of 31 g (SE = 14) in birth weight with each increase in the log of TCDD concentration(p = 0.03). In the categorical analysis (Table 4), the highest category of paternal TCDD (≥255 pg/g) showed an increase of 130 g (SE = 76) compared with the referents (p = 0.09). When we further restricted the analysis to pregnancies in which the entire pregnancy occurred during the father’s occupational exposure, or dates of plant operation for the referents, the results were similar to those shown in Table 4.
Because there were some differences among referent mothers, mothers of preexposed births, and mothers of exposed births in variables such as maternal age and parity (Table 1), we conducted an analysis of birth weight using the final adjusted model but limited only to exposed births (i.e., pregnancies conceived by workers during or after exposure; n = 287). This analysis showed a statistically significant increase in birth weight of 38 g with each increase in the log of TCDD concentration (p = 0.04).
There were 59 worker wives who had births in both exposure periods (preexposure and during/after exposure), with a total of 219 births (110 conceived preexposure and 109 conceived during or after exposure). An analysis of this subset showed that the mean birth weight for preexposure births was lower (mean ± SD, 3,388 ± 444 g) compared with exposed births (3,524 ± 563 g), similar to the main analysis. As would be expected, mean parity was also different between preexposure and exposed pregnancies (0.9 and 2.6, respectively). The difference in birth weight between the two exposure periods was not statistically significant after adjusting for parity. There was no difference in length of gestation or sex ratio between the two exposure periods in this subset.
To examine preterm births, we included all live births in the cohort (n = 1,153), consisting of 618 referents births, 238 preexposure births, and 297 exposed births. Of the 51 preterm births in the cohort (4.4%), 22 were among the 618 referent births (3.6%), 21 were among the 238 preexposure births (8.8%), and 8 were among the 297 exposed births (2.7%). A crude analysis of the preterm births showed a somewhat protective association with TCDD (log) concentration at LMP [odds ratio (OR) = 0.8; 95% confidence interval (CI), 0.6–1.0]. Results changed little when adjusted for accident during pregnancy, age of the mother, cigarette smoking during pregnancy, and medication use during pregnancy (OR = 0.8; 95% CI, 0.6–1.1; results not shown).
Table 5 shows selected birth defects that were reported on the maternal interviews, stratified by whether or not the father was exposed. The birth defect categories include central nervous system (CNS) defects, cardiovascular defects, genitourinary defects, clubfoot, hip and lower limb defects, orofacial clefts, and Down syndrome.
Six major CNS defects were reported: one case of spina bifida, two anencephalus cases, two hydrocephalus cases, and one case of multiple congenital anomalies. Two CNS cases were referents, two were in the background category (< 20 pg/g), one was in the low exposure category (20 to < 255 pg/g), and one case (spina bifida) was in the highest exposure category (≥1,120 pg/g). Four of these cases were confirmed. Two cases were not confirmed, specifically the spina bifida case and one of the hydrocephalus cases, because the records were not available. The mother of the spina bifida case reported that the child was diagnosed at age 5, with “neurologic impairment, spina bifida, and cerebral palsy.”
There were three cardiovascular defects, including tetralogy of Fallot, single ventricle, and Wolf-Parkinson-White syndrome. All three cases were in the referent or background (< 20 pg/g) categories.
Discussion
The results of our analyses do not support a causal relationship between low birth weight and high paternal TCDD exposure. In fact, subanalyses showed a positive association between paternal dioxin exposure and the birth weight of offspring. There are few previous studies of paternal exposure to TCDD and birth weight. Michalek et al. (1998) studied Vietnam veterans who were exposed to Agent Orange and TCDD during Operation Ranch Hand. The median estimated TCDD concentration at the time of conception among Ranch Hand veterans was 79 ppt, with a range from 0 to 1,425 ppt (Michalek et al. 1998). The risk of intrauterine growth retardation was not increased in any Ranch Hand exposure category. In a study of sawmill industry workers, no increase was found in risk for lower birth weight in wives of men occupationally exposed to dioxin-contaminated chlorophenols, as measured by expert raters’ estimations of hours of exposure (Dimich-Ward et al. 1996). Although the chlorophenols in that study were not contaminated with TCDD, the workers were exposed to other polychlorinated dioxins. A study of Australian veterans who served in Vietnam showed a higher prevalence of low birth weight (< 2,500 g) among veterans than among controls (5.8% and 3.7%, respectively), although the difference was not statistically significant (Field and Kerr 1988). Multivariate analyses were not presented in that study, and no data on exposure were collected. Studies of maternal TCDD exposure and birth weight have had contradictory results (Eskenazi et al. 2003; Kimbrough and Krouskas 2001).
Overall, the proportion of preterm births in the present population was low (4.4%), and it was lower among exposed births (2.7%) than among preexposure births (8.8%) or referent births (3.6%). Multivariate analysis showed a somewhat protective effect of paternal TCDD concentration with respect to preterm birth. In a previous study of the Ranch Hand veterans, there was a moderate increase in risk of preterm birth among children in the high and background (unexposed) categories compared with referents, although there was no increased risk in the low exposure category, suggesting that the risk was not likely due to TCDD exposure (Michalek et al. 1998).
Our data were too limited to present statistical analysis of reported birth defects and paternal TCDD exposure, although descriptive information on birth defects did not suggest any relationship to TCDD. The NAS concluded in 1996 that there was limited suggestive evidence of an association between paternal exposure to herbicides and spina bifida, although the evidence was inadequate regarding other birth defects (NAS 1997). The NAS conclusion was based on three high-quality studies of Vietnam veterans: the Centers for Disease Control and Prevention (CDC) Birth Defects Study (Erickson et al. 1984), the CDC Vietnam Experience Study (CDC 1989), and the Ranch Hand study (Wolfe et al. 1995). All three studies suggested an association between herbicide exposure and an increased risk of spina bifida in offspring. In our study, there was one reported case of spina bifida. The medical records were not available to confirm the case, and the mother reported that it was not diagnosed until age 5; however, it is interesting to note that the father’s exposure was in the highest category of TCDD.
Many previous occupational studies of paternal exposure to dioxin and birth defects suffer from small numbers and inexact exposure methods. Two studies did not find any association between birth defects and occupational exposure to TCDD or 2,4,5-T, but both acknowledged limited statistical power (Smith et al. 1982; Townsend et al. 1982). In a study of sawmill workers, paternal exposure to dioxin-contaminated chlorophenol was associated with increased risks for congenital anomalies of the eye, anencephaly, spina bifida, and genital defects (Dimich-Ward et al. 1996).
Our study’s limitations include a lengthy recall period and the reliance on maternal report of gestational age, birth defects, and birth weight when birth certificates or medical records were not available. However, previous literature shows that mothers’ recall of birth weight is generally found to be accurate (Sanderson et al. 1998; Tomeo et al. 1999; Troy et al. 1996). Correlation coefficients between mother’s report of birth weight and birth weight recorded on the birth certificate ranged from 0.84 to 0.89, even when recall was two to four decades after birth (Sanderson et al. 1998; Troy et al. 1996) with a mean difference in birth weight of −25 g (Tomeo et al. 1999). When we conducted a subanalysis that excluded those individuals (18%) for whom birth weight was not confirmed by certificate, the results were similar. We conducted another subanalysis of birth weight in which the definition of full term was estimated from birth certificates rather than mothers’ questionnaires (excluding 250 additional births) that also showed similar results. Another limitation of our study is that serum TCDD was measured several years after the pregnancies, and therefore, the concentrations at LMP are estimates. However, we used a pharmacokinetic model to estimate concentrations at LMP, allowing for changes in individual body burden over time.
Because of the random sequencing procedure used in this study, we could not calculate a true participation rate for male referents. To address participation bias, a telephone interview was attempted with all of the male workers who refused to be examined and a 10% random sample of the referents who refused to participate. Of the 115 refusant workers and 129 refusant referents who were contacted, 68 (57%) and 99 (77%), respectively, agreed to be interviewed. The differences in reporting of selected chronic diseases between participants and nonparticipants were not statistically significant (Calvert et al. 1991, 1992, 1999). No significant difference in age was found between participants and refusants (Calvert et al. 1999). In addition, because male subjects agreed to participate in a cross-sectional medical health study and then later were asked to enroll in the reproductive health study, we do not anticipate that male refusants would differ from participants with respect to reproductive outcomes.
The men in our occupational study group were exposed to TCDD at substantially higher concentrations than other cohorts, with estimated concentrations at the time of conception ranging up to 16,340 pg/g. The strengths of our study include biologic measurements of internal dose and a pharmacokinetic modeling technique. Another strength is that we were able to adjust for confounding variables that were collected with a telephone interview. In addition, most of our outcome data were verified by birth certificate and/or medical records.
In conclusion, these results do not support a relationship between paternal TCDD exposure and lowered birth weight or preterm delivery. Because the estimated TCDD concentrations in this population were much higher than in other studies, the results indicate that TCDD is unlikely to increase the risk of low birth weight or preterm delivery through a paternal mechanism.
Figure 1 Mean birth weight versus weeks of gestation by exposure category.
Table 1 Selected characteristics of live full-terma births.
Workers
Variable Referents (n = 604) Preexposure (n = 221) During exposure (n = 292)
Age of mother
Mean ± SD 26.1 ± 5.4 23.4 ± 4.6 27.4 ± 5.3
Range 13.2–43.7 15.3–38.0 16.1–43.1
Year of conception
Mean 1959 1956 1965
Range 1935–1987 1935–1971 1951–1987
Mother smoked cigarettes during pregnancy
No. (%) 142 (23.7) 59 (26.9) 90 (31.0)
Cigarettes, average number per week
Mean ± SD 23.2 ± 52.1 32.0 ± 63.9 37.5 ± 64.5
Range 0–280 0–280 0–280
Mother drank alcohol during pregnancy
No. (%) pregnancies 182 (31.6) 44 (19.9) 112 (38.4)
Drinks per month
Mean 2.2 1.2 3.6
Range 0–60 0–120 0–180
Paternal TCDD pg/g at conception
Median 6 6b 254
Range 2–19 3–16,340
Parity
Mean ± SD 1.5 ± 1.6 1.0 ± 1.2 1.8 ± 1.5
Range 0–9 0–6 0–8
Sex of infant: males
No. (%) 329 (54.5) 112 (50.7) 164 (56.2)
a Gestational length ≥37 weeks.
b All preexposure births were assigned the median referent value of 6 pg/g.
Table 2 Number of births per length of gestation by exposurea category.
Length of gestation (weeks)
Exposure category ≤36 37 38 39 40 41 42 43 44–45 Total
Referents 22 20 35 40 368 41 55 19 18 618
Preexposed 21 15 12 7 142 14 20 6 1 238
Exposed 8 11 16 17 171 31 27 9 7 297
a Preexposure infants were conceived before the father worked in a dioxin-exposed job. Exposed infants were conceived during or after the father worked in a dioxin-exposed job.
Table 3 Mean difference in birth weight among terma infants by paternal TCDD exposure category.
Mean difference in birth weight compared with referents (g)
Crude analysis
Adjusted analysisb
TCDD category No. Difference No. Difference
Referent pregnancies (mean ± SE) 596 3,401 ± 27 592 3,402 ± 26
< 20 pg/gc (mean difference ± SE) 264 −57 ± 46 262 −8 ± 44
20 to 254 pg/g (mean difference ± SE) 98 −59 ± 62 98 −42 ± 59
≥255 pg/g (mean difference ± SE) 144 120 ± 55* 142 83 ± 52
a Gestational age ≥37 weeks.
b Adjusted for sex of the infant, education of the mother, parity, cigarette smoking during pregnancy, and length of gestation.
c This category includes preexposed and exposed births.
* p-Value ≤0.05 compared with referents.
Table 4 Mean difference in birth weight among term infants by paternal TCDD exposure category during employment.a
Mean difference in birth weight compared with referents (g)
Crude analysis
Adjusted analysisb
TCDD category No. Difference No. Difference
Referent pregnancies (mean ± SE) 334 3,397 ± 33 330 3,393 ± 32
< 20 pg/g (mean difference ± SE) 27 −182 ± 93* 26 −146 ± 91
20 to 254 pg/g (mean difference ± SE) 20 73 ± 105 20 156 ± 101
≥255 pg/g (mean difference ± SE) 51 74 ± 80 50 130 ± 76
a Pregnancies where at least 1 day in the pregnancy occurred during the father’s employment. For referents, pregnancies occurring during the dates the plants were manufacturing chemicals contaminated with 2,3,7,8 TCDD.
b Adjusted for sex of the infant, education of the mother, parity, cigarette smoking during pregnancy, and length of gestation.
* p-Value ≤0.05 compared with referents.
Table 5 Number of selected birth defects reported among 1,166 live born and stillborn infants.
Type of defect Referents and preexposeda Exposeda Total reported Total confirmedb
CNS 2 4 6 4
Cardiovascular 1 2 3 3
Genitourinaryc,d 5 3 8 5
Clubfootd 9 3 12 3
Unspecified hip and lower limb 5 1 6 2
Cleft lip and/or palate 2 1 3 1
Down’s syndrome 3 0 3 2
Total selected reported birth defects 27 14 41 20
a Preexposed infants were conceived before the father worked in a dioxin-exposed job. Exposed infants were conceived during or after the father worked in a dioxin-exposed job.
b Confirmation from vital or death records or medical records.
c Genitourinary defects included anorchism (n = 2), hypospadias (n = 3), adrenogenital syndrome (n = 1), a kidney defect (n = 1), and ureteropelvis obstruction (n = 1).
d One infant had clubfoot and hypospadias.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7144ehp0112-00140915471734Mini-Monograph: Public Health TrackingArticlesNational Environmental Public Health Tracking Program: Bridging the Information Gap McGeehin Michael A. Qualters Judith R. Niskar Amanda Sue Division of Environmental Hazards and Health Effects, National Center for Environmental Health/Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention, Atlanta, Georgia, USAAddress correspondence to M.A. McGeehin, CDC, 1600 Clifton Rd., NE, MS F52, Atlanta, GA 30333. Telephone: (770) 488-3400. Fax: (770) 488-3460. E-mail:
[email protected] article is part of the mini-monograph “National Environmental Public Health Tracking,” which is sponsored by the Centers for Disease Control and Prevention (CDC).
We acknowledge CDC’s Environmental Health Tracking Branch staff and the Environmental Public Health Tracking cooperative agreement partners for their contributions to the conceptualization and development of the National Environmental Public Health Tracking Network.
Contributions are acknowledged to the development of this mini-monograph from the members of the National Environmental Public Health Tracking Program 2003 Publication Committee: A.S. Niskar (Guest Editor for mini-monograph), Centers for Disease Control and Prevention; T.A. Burke, Johns Hopkins Bloomberg School of Public Health; J.I. Joyner, City of Houston Department of Health and Human Services; J. Leighton, New York City Department of Health and Mental Hygiene; G. Lomax, California Environmental Public Health Tracking Program; T.E. McKone, School of Public Health, University of California, Berkeley; A.E. Smith, Maine Department of Human Services; L.E. White, DABT, Tulane School of Public Health and Tropical Medicine.
This article was supported by an environmental public health tracking cooperative agreement from CDC. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of CDC.
The authors declare they have no competing financial interests.
10 2004 3 8 2004 112 14 1409 1413 1 4 2004 3 8 2004 Publication 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 January 2001 the Pew Environmental Health Commission called for the creation of a coordinated public health system to prevent disease in the United States by tracking and combating environmental health threats. In response, the Centers for Disease Control and Prevention initiated the Environmental Public Health Tracking (EPHT) Program to integrate three distinct components of hazard monitoring and exposure and health effects surveillance into a cohesive tracking network. Uniform and acceptable data standards, easily understood case definitions, and improved communication between health and environmental agencies are just a few of the challenges that must be addressed for this network to be effective. The nascent EPHT program is attempting to respond to these challenges by drawing on a wide range of expertise from federal agencies, state health and environmental agencies, nongovernmental organizations, and the program’s academic Centers of Excellence. In this mini-monograph, we present innovative strategies and methods that are being applied to the broad scope of important and complex environmental public health problems by developing EPHT programs. The data resulting from this program can be used to identify areas and populations most likely to be affected by environmental contamination and to provide important information on the health and environmental status of communities. EPHT will develop valuable data on possible associations between the environment and the risk of noninfectious health effects. These data can be used to reduce the burden of adverse health effects on the American public.
environmental monitoringenvironmental public health surveillanceinformation system integrationtracking
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Why We Need an Environmental Public Health Tracking Network
At the turn of the 20th century, the American population faced significant health challenges. The recent shift in population from rural to urban that accompanied industrialization resulted in overcrowding in dilapidated housing served by inadequate water supplies and nonexistent waste disposal systems. These conditions led to continued outbreaks of infectious diseases that ravaged the population. In 1900 one-third of all deaths were caused by pneumonia, tuberculosis, or diarrhea, and 40% of these deaths were among children younger than 5 years (Bureau of the Census 1906). After the discovery of the “germ theory” of disease, much of the dramatic decrease in mortality from infectious disease in Western civilization was attributable to environmental public health measures such as disinfection of water, food safety regulations, and housing improvements, among others [Centers for Disease Control and Prevention (CDC) 1999].
The last half century witnessed a dramatic shift in the health burden of the U.S. population from infectious diseases to diseases such as cancer, birth defects, and asthma, many of which may be associated with environmental exposures. During the same period, advances in industrial science and technology led to the development and production of tens of thousands of chemical compounds. Unheard of 50 years ago, these chemicals are now ubiquitous in our air, water, food, workplaces, and homes. Mankind has benefited substantially from these products, but the health implications of long-term exposure to low levels of these compounds are not well understood. The American people feel strongly that the environment plays a role in their health. A poll taken in 1999 by the Pew Charitable Trusts found that 87% of Americans believed that environmental factors such as pollution cause increased rates of diseases and health problems (Pew Charitable Trusts 1999).
In September 2000, after 18 months of review, the Pew Environmental Health Commission released a report on the state of environmental public health in the United States (Environmental Health Tracking Project Team 2000). The commission found that the environmental public health system was fragmented, neglected, and ineffective. The report stated that the current system does not have the capability to respond adequately to environmental threats. The first of a number of recommendations made by the commission called on the federal government to establish a national environmental public health tracking (EPHT) network to link information on environmentally related diseases, human exposures, and environmental hazards. The information from this tracking network would be used to respond to, and eventually reduce, the burden of these diseases on the nation’s population. The commission estimated the cost of this tracking network to be $275 million annually.
Public health surveillance or tracking systems are critical in preventing and controlling disease in populations. Accurate and timely surveillance data permit public health authorities to determine disease impacts and trends, recognize clusters and outbreaks, identify populations and geographic areas most affected, and assess the effectiveness of public health interventions (Teutsch 2000). Most of the public health surveillance currently in place in the United States focuses on infectious diseases. We urgently need a more comprehensive national approach to the collection and analysis of noninfectious disease data and the integration of that information with environmental hazard monitoring and exposure data. The availability of these types of data in a standardized tracking network will enable researchers and health authorities to begin to understand the possible associations between the environment and adverse health effects.
An Approach for Environmental Public Health Tracking
Environmental public health tracking is the ongoing collection, integration, analysis, and dissemination of data from environmental hazard monitoring, human exposure tracking, and health effect surveillance (Figure 1). Currently, the Centers for Disease Control and Prevention (CDC) is leading an initiative to build a national EPHT network that will meld data from these three components into a network of standardized electronic data systems and will provide valid scientific information on environmental exposures and adverse health conditions and the possible spatial and temporal relations between them.
CDC and our partners are applying the conceptual model first proposed by Thacker et al. (1996) to design the EPHT network. This model outlines the causal pathway starting with a hazardous agent present in the environment, followed by a population exposed to this agent and receiving a dose, and ending with a clinically apparent adverse health effect. Hazard, exposure, and health effects tracking represent data collection points along this continuum. Collecting, analyzing, and disseminating data from any one of these types of data systems or a combination of them provide important information for public health practice and comprise environmental public health surveillance activities. Development of a national EPHT network depends on the availability, quality, timeliness, compatibility, and utility of existing hazard, exposure, and health effect data. Both Thacker et al. (1996) and the Pew Commission (Environmental Health Tracking Project Team 2000) describe data that could potentially comprise part of a national EPHT network, such as vital statistics and the Aeromatic Information Retrieval System. Improvements to existing data systems, development of new systems, and integration of the data from these systems will be required to fully implement this network.
Hazards include chemical agents, physical agents, biomechanical stressors, and biologic toxins that can be found in our air, water, soil, food, and other environmental media. Often, data regarding these hazards are collected for regulatory purposes, and the characteristics of data collected are mandated by federal or state statutes. Thus, the types of data collected, the frequency of data collection, the location of data collection, and the collection methods may be optimal for enforcement activities but are less than ideal for public health surveillance use. For example, the U.S. Environmental Protection Agency’s (EPA) Safe Drinking Water Information System/federal version (SDWIS/FED) collects data from states on drinking water utilities’ noncompliance with federal drinking water standards (U.S. EPA 2004b). These data allow the U.S. EPA to track contaminant levels and to determine whether new regulations are needed to protect human health. However, the utility of SDWIS/FED for environmental public health surveillance is limited because actual monitoring data are available at the federal level only when results exceed the maximum contaminant levels, and the consistency of data elements over space and time can vary (Niskar 2003). At a minimum, hazard data included in the national EPHT network will need to be obtained through routine standardized data collecting and reporting and must have ongoing quality control, appropriate geographic coverage for the population at risk, and be available in a timely manner.
Exposure tracking is the monitoring of individuals, communities, or population groups for the presence of an environmental agent or its metabolite. Exposure data can include estimates derived from hazard data through sophisticated modeling. For example, the U.S. EPA and the National Oceanic and Atmospheric Administration are leading a project to model source emissions data, air monitoring data, and meteorologic data to forecast population exposure to ozone (U.S. EPA 2004c). Another example is the work conducted by the National Cancer Institute to estimate county-level exposures and thyroid doses received by American citizens from iodine-131 in atmospheric nuclear weapons testing fallout (National Cancer Institute 1997). Exposure data can also include direct measurements of individual exposure obtained from use of personal monitors such as passive air samplers and personal radiation dosimeters. However, neither of these types of exposure data is currently available for tracking exposures in an ongoing, systematic manner.
Exposure tracking of biomonitoring data represents the only method that actually measures the presence of hazardous agents in the human body. This type of exposure data provides information on the levels of chemicals or their metabolites in human biologic specimens such as blood or urine. Depending on the chemical agent, these measurements can serve as indicators of recent, long-past, or cumulative exposure to a hazard. For example, chemicals such as benzene are metabolized and excreted rapidly from the body, whereas the lipophilic compounds tetrachlorodibenzo-p-dioxin and polychlorinated biphenyl are retained for years (Thornton et al. 2002). Although more research is needed to understand fully the complex relationship between external hazard concentrations, internal dose, and health effects, biomonitoring data are an important component for comprehensive environmental public health surveillance.
Currently, few biomonitoring data are being tracked. At the national level, human samples are collected through the National Health and Nutrition Examination Survey and analyzed and reported by CDC in the “National Report on Human Exposures to Environmental Chemicals.” Blood and urine levels of 116 environmental chemicals are currently available for a sample of the noninstitutionalized U.S. population (CDC 2003). At the state level, state laboratories have limited capacity for biomonitoring. Childhood blood lead levels are the only measures that are routinely collected across most states, and these levels are collected in screening programs of high-risk children.
The final component in the conceptual model of Thacker et al. (1996) is health effects tracking, which represents traditional public health surveillance efforts. Disease registries, vital statistics data, annual health surveys such as the National Health Interview Survey, and administrative data systems such as hospital discharge data are sources that have been used for tracking health conditions. These varied sources have created a patchwork of health effect measures, and reliance on these data demonstrates the need for standardization for most disease surveillance. EPHT limits itself to those health effects with scientific evidence of possible environmental etiology. Health end points recommended as starting points for a national EPHT network by the Pew Commission focus on the following chronic conditions: birth defects; developmental disabilities such as cerebral palsy, autism, and mental retardation; asthma and other chronic respiratory diseases such as bronchitis and emphysema; cancer; and neurologic diseases, including Parkinson disease, multiple sclerosis, and Alzheimer disease. Additionally, the commission recommended tracking sentinels of exposures and health outcomes requiring rapid public health responses such as heavy metal poisoning and pesticide poisoning (Environmental Health Tracking Project Team 2000).
A key distinction between EPHT and traditional surveillance is the emphasis on data integration across health, human exposure, and hazard information systems (Figure 1). Our program to build a national EPHT network is the first national effort to provide the United States with standardized data from multiple health, exposure, and hazard information systems, that includes linkage of these data as part of regular surveillance activities. The network builds on separate ongoing efforts within the public health and environmental sectors to improve health surveillance, hazard monitoring, and response capacity (CDC 2004; U.S. EPA 2004a). This system will be used to identify potential relations between exposure and health conditions that either indicate the need for additional research or require intervention to prevent disease, disability, and injury.
Network Vision and Strategy
The national EPHT network is still being formulated. However, expanding on the work of Hertz-Picciotto (1996), an ideal environmental public health surveillance system should include the following elements:
Data systems that use compatible data standards and vocabularies
High-quality, timely mortality and morbidity data with high resolution geographic coordinates
A wide range of exposure information based on biomonitoring, personal monitoring, and exposure modeling
Relevant, high-quality, and timely emissions data and monitoring data for air, water, soil, food, and other environmental media as well as geographic and temporal characteristics
Access to population data, including information on migration and sociodemographic factors
Tools to link data geographically
Tools for descriptive and small-area analyses
Tools for data dissemination
Support for public health action
CDC and our partners are endeavoring to achieve the ideals listed above. We envision a tracking network that will be multi-tiered with functional components at the local, state, and federal levels. The main building blocks of the network will be statewide EPHT networks (or city-wide in the case of large municipalities) and national data surveys. As a major component of CDC’s Public Health Information Network (PHIN), the national EPHT network will be standards-based and compliant with the federal health architecture being developed by the Department of Health and Human Services (CDC 2004; Office of Management and Budget 2004). Additionally, it will be compatible with the U.S. EPA National Environmental Information Exchange Network (U.S. EPA 2004a) to facilitate bridging the current gap between health and environmental data.
As conceptualized, the network will include a core set of linkable health, exposure, and hazards data systems as well as data that have already been linked at local, state, regional, and national levels. CDC and our partners are currently evaluating the network’s priorities. At the federal level, implementation of the tracking network will require that CDC be able to access agreed-upon state and national data. Individually identifiable information will not be available at the federal level for surveillance purposes, and, at all levels, privacy will be protected. At the state and local levels, the network structure will be flexible enough to allow states to track their own unique priority issues as well as core national diseases, exposures, and hazards. The network will allow direct electronic data reporting and linkage within and across health effects, exposure, and hazard data while protecting confidentiality of individual records. Also, the network will enable exchange and aggregation of data across states.
The demand for better information about our environment and health comes from the public, the media, researchers, and policymakers. Although a main goal of the network is to make information available to a wide variety of stakeholders, state and federal privacy laws will restrict the types of information available to specific users.
Building Bridges
Developing and maintaining partnerships are essential to building and sustaining the national EPHT network. Before the initiation of the tracking program, federal, state and local public health and environmental agencies, nongovernmental organizations, and academic institutions provided recommendations to CDC and the Agency for Toxic Substances and Disease Registry (ATSDR) that were incorporated into program development (CDC/ATSDR 2002). Collaborative activities continue to support the development of the national EPHT network as its infrastructure and methods are being developed and evaluated. Since 2002, CDC has funded 21 state health departments, three local health departments, and three schools of public health to conduct activities that will form the basis of a nationwide tracking network (Figure 2). The schools of public health are developing methods and conducting epidemiologic studies to advance the science of environmental public health that underlies the network and providing support to state and local partners. Eleven state partners and New York City are conducting projects to demonstrate a) an approach for linking existing health effect surveillance data with exposure or hazard data as part of ongoing surveillance activities, b) a sustainable effort to build capacity, and c) the usefulness of linked data in guiding public health policy and practice. Other state and local partners are conducting planning and capacity-building activities. In this mini-monograph, we present initial results from some of these projects.
Additionally, we are collaborating on improving communications and disseminating information about the national EPHT network with national professional organizations and advocacy groups, including the Association of State and Territorial Health Officials (ASTHO), the National Association of County and City Health Officials (NACCHO), the Environmental Council of States, the National Environmental Health Association, the Association of Public Health Laboratories, the Council of State and Territorial Epidemiologists, Physicians for Social Responsibility (PSR), and the Trust for America’s Health. For example, NACCHO is developing and circulating educational materials about EPHT to their constituency; ASTHO is serving as a conduit of information among CDC, state grantees, Centers of Excellence, and the unfunded states; and PSR is collaborating with NACCHO to increase the knowledge base and technical skills of physicians with regard to EPHT.
At the national level, both the U.S. EPA and the National Aeronautics and Space Administration (NASA) are active partners in development of the tracking network. As a cornerstone of this collaborative commitment, the U.S. EPA and CDC are taking advantage of the work being done on the U.S. EPA’s Exchange Network and CDC’s national EPHT network to increase health and environmental infrastructure and capacity at the local, state, and national level; to evaluate and improve data compatibility; and to collaborate on projects that develop and validate methods and tools to estimate exposure to environmental hazards for state and local partners. CDC and NASA also are working together to explore innovative public health applications of NASA technology such as existing remote sensing data collected via satellites for use in EPHT.
All three agencies (CDC, U.S. EPA, and NASA) are also collaborating in a new effort initiated by the CDC called Health and Environment Linked for Information Exchange, Atlanta (HELIX-Atlanta). We are coordinating this project with more than 70 representatives from local, state, federal, and academic partners. The public health significance of HELIX-Atlanta is to provide information regarding the five-county metropolitan Atlanta area through a network of integrated environmental monitoring and public health information systems so that all sectors can take action to prevent environmentally related health effects.
Challenges and Expectations for Environmental Public HealthTracking
A neglected public health infrastructure and the lack of a trained workforce are monumental challenges to establishing a national EPHT network. In 1988 the Institute of Medicine (IOM) referred to the American public health system as a “shattered vision” (IOM 1988). Fifteen years later, a follow-up IOM committee found that improvements in the public health infrastructure and workforce development were still needed to ensure provision of essential public health services and to address emerging public health issues (IOM 2003). A trained, motivated, and dedicated workforce will be a necessity not only for the establishment of a national EPHT network but also for ensuring the health of the American people through the coming decades.
Depending on resource availability, the national EPHT network may need to build some surveillance infrastructure from the ground up. Surveillance systems currently do not exist at the local, state, or national levels to track many of the exposures and health effects that may be associated with environmental hazards (CDC 1998, 2000). Kass et al. (2004) describe the challenges of pulling together pesticide data for environmental public health surveillance. When information systems do exist, quality of the data, data vocabularies, and case definitions vary, and electronic reporting is still not an option for all sources of data (Goldman et al. 1992; Steenland and Savitz 1997). Laflamme and VanDerslice (2004) discuss the lack of standard environmental health questions in the Behavioral Risk Factor Surveillance System. Mather et al. (2004) present a table describing uses and limitations of hazard, exposure, and health effects data. These limitations present significant challenges to developing a national integrated data network. Standardization of technology and data specifications is ongoing within CDC (CDC 2004) and the national EPHT network is part of this effort. CDC and its partners are currently identifying data needs of their constituents and evaluating mechanisms and costs for improving data and filling data gaps.
The national EPHT network has received attention from the media, elected officials, and the public. Along with that attention has come some misunderstanding of the capacity of the network to provide answers to etiologic questions. As a surveillance system, EPHT cannot answer questions about causes of diseases. It can, however, generate hypotheses about etiology and identify areas where additional research is needed. We expect the national EPHT network will provide information to estimate the magnitude of a health effect in the population at risk, to detect epidemics or clusters, to document the distribution and spread of a health effect, to evaluate interventions, and to facilitate planning (Teutsch 2000).
Being able to link health, exposure, and hazard data on an ongoing basis will enable environmental public health practitioners to evaluate the spatial and temporal relations between environmental factors and health. However, detecting even these ecologic relations through the network will require careful analysis and interpretation. The pitfalls of drawing etiologic conclusions based on these ecologic relations are well documented and include issues such as confounding, measurement error, variation in event classification, and migration patterns (English 1996; Greenland 2004). In this mini-monograph, Mather et al. (2004) provide further discussion on ecologic bias in describing the statistical framework for analyzing the exposure, hazard, and disease relationship.
One factor to consider in interpreting relationships seen from analysis and visualization of tracking data is the lag time for most health effects thought to be associated with exposure to environmental hazards. Disease may occur in a specific area during a prescribed time, but exposure associated with the disease may have occurred months, years, or even decades earlier. Other factors contribute to the difficulties in characterizing environmental exposures because of the uncertainties inherent in exposure modeling and the mobility of the American public, whether related to daily commutes or relocating residence. Finally, many of the health effects of interest for environmental public health have multi-causal pathways. Other factors will confound or modify our ability to interpret the role of specific environmental exposures on disease risk when analyzing surveillance data.
Lessons Learned in Building the EPHT Network
In this mini-monograph, we present the broad scope of activities of our partners to develop and evaluate methods for the science of EPHT. An initial step in building a national EPHT network has been to determine a core set of priority environmental public health problems and to identify existing data to describe these problems. State and local priority-setting activities, in combination with the recommendations from the Pew Environmental Health Commission (Environmental Health Tracking Project Team 2000), the Healthy People 2010 Objectives (U.S. Department of Health and Human Services 2000), and environmental public health indicator efforts (CSTE 2004; U.S. EPA 2003) will assist in identifying the core priority health, exposure and hazard data for the national EPHT network. Litt et al. (2004) describe the approach used by the Pew Environmental Health Commission as a model for priority setting, including the availability of existing data as a useful criterion for setting priorities.
State, local, and national data linkage demonstration projects are useful in identifying data gaps and compatibility issues in the environmental public health surveillance infrastructure. To fill data gaps, EPHT partners are collaborating to evaluate existing information systems that are not traditionally used for public health surveillance. Common challenges include data access, data completeness, data quality, and methods for integration that address temporal and spatial factors. This mini-monograph presents results of some unique and innovative methods for adapting and integrating non-traditional information systems for use in EPHT. For example, the work of Knorr et al. (2004) demonstrates the feasibility and utility of working with school nurses to get a more reliable estimate of asthma prevalence in school-age children at the local level than was previously available through traditional sources.
Data system compatibility issues are being addressed through ongoing work at the federal and state levels to standardize data vocabularies and messaging so that various data systems will “speak the same language.” CDC’s PHIN provides the architectural framework and specifications for the national EPHT network. The work of Hanrahan et al. (2004) on development of a PHIN-compliant module for Wisconsin’s childhood cancer surveillance project may serve as a model for other states and illustrates the challenges of designing an integrated data repository.
Summary
EPHT is the ongoing collection, integration, analysis, and dissemination of environmental hazard, exposure, and health data. CDC and our partners are developing this approach for the EPHT network based on concepts from infectious and chronic disease surveillance and environmental hazard monitoring. The unique feature of the national EPHT network is the emphasis on data integration and standardization from all sources to improve data utility to the end user. With adequate funds, the EPHT network will provide valid scientific information on environmental exposures and adverse health effects that will bridge the existing data gap and provide a foundation for actions to improve community health. A key component of the EPHT network is dissemination. This mini-monograph is an opportunity to disseminate the first lessons learned about the innovative process of developing a national EPHT network. The articles represent the diversity of partners, priorities, and activities that are underway to build the EPHT network and to provide more information to the American people on how the environment contributes to human health.
Figure 1 Environmental public health tracking. Reproduced from CDC (http://www.cdc.gov/nceh/tracking/diagram.pdf).
Figure 2 CDC’s EPHT Program grantees, 2004. Reproduced from CDC (http://www.cdc.gov/nceh/tracking/aag04.pdf).
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7147ehp0112-00141415471735Mini-Monograph: Public Health TrackingArticlesIdentifying Priority Health Conditions, Environmental Data, and Infrastructure Needs: A Synopsis of the Pew Environmental Health Tracking Project Litt Jill 1Tran Nga 2Malecki Kristen Chossek 3Neff Roni 3Resnick Beth 3Burke Thomas 31University of Colorado Health Sciences Center, Department of Preventive Medicine and Biometrics, Denver, Colorado, USA2Exponent Consulting, Washington, DC, USA3The Johns Hopkins Bloomberg School of Public Health, Department of Health Policy and Management, Baltimore, Maryland, USAAddress correspondence to T. Burke, The Johns Hopkins Bloomberg School of Public Health, Department of Health Policy and Management, 624 N. Broadway, Room 484, Baltimore, MD 21205 USA. Telephone: (410) 614-4587. Fax: (410) 614-4535. E-mail:
[email protected] article is part of the mini-monograph “National Environmental Public Health Tracking,” which is sponsored by the Centers for Disease Control and Prevention (CDC).
This article was supported by an environmental public health tracking cooperative agreement from CDC. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of CDC.
The authors declare they have no competing financial interests.
10 2004 3 8 2004 112 14 1414 1418 1 4 2004 3 8 2004 Publication 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 article we describe the methodologic approaches of the Pew Environmental Health Commission at the Johns Hopkins Bloomberg School of Public Health used to identify priority environmental health conditions and develop recommendations to establish a national environmental public health tracking network. We present the results of a survey of public health and environmental practitioners to uncover state and local health tracking needs and priorities. We describe the steps that combined the findings from the state and local health tracking survey and a review of the state of the science on environmental impacts on health to identify priority health end points. Through an examination of national health and health care databases, we then describe trends and public health effects of those diseases that may be linked to the environment. Based on this analysis, respiratory diseases and neurologic diseases are recommended as priorities for tracking. Specific end points recommended for tracking include asthma and chronic respiratory diseases, and chronic neurodegenerative diseases such as multiple sclerosis. Based on trends in reported prevalence, consideration should also be given to developmental disabilities, reproductive disorders, and endocrine/metabolic disorders. Strengthening of current efforts to track cancer and birth defects should also be included as components of a nationwide health tracking network. Finally, we present the recommendations for environmental public health tracking. These recommendations provided the groundwork for the development of the Centers for Disease Control and Prevention’s National Environmental Public Health Tracking Progam that now includes 21 states, three cities, and three academic centers throughout the nation.
biomonitoringdisease surveillanceenvironmental health indicatorsenvironmental public health trackingexposurehealth policyinformation systems
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The new millennium has brought unprecedented challenges and opportunities for the advancement of public health. Scientific breakthroughs provide new insights into the genetic basis of health and disease, while analytical advances allow the identification and measurement of previously unrecognized threats. The translation of this new knowledge to effective prevention will require new approaches to evaluating the interaction of health and environment. Despite the many advances in identification, measurement, and control of contaminants in the environment, fundamental questions about the role of environmental exposures in human disease remain unanswered. This information gap includes a number of chronic diseases and conditions that may be increasing in prevalence, including asthma, neurologic disorders, developmental disabilities, and even diabetes.
The Pew Environmental Health Commission at the Johns Hopkins Bloomberg School of Public Health was established in 1998 to develop a blueprint to rebuild the nation’s public health defenses against environmental threats, including the ability to track hazards, environmental exposures, and health outcomes. Greater capacity in tracking can help investigators a) identify populations at risk and respond to outbreaks, clusters and emerging threats; b) establish the relationship between environmental hazards and disease; c) guide intervention and prevention strategies, including lifestyle improvements; d) identify, reduce, and prevent harmful environmental risks; e) improve the public health basis for policymaking; f ) enable the public’s right to know about health and the environment; and g) track progress toward achieving a healthier nation and environment.
In 1999–2000 the Pew Environmental Health Commission’s Tracking Project (Pew Environmental Health Commission 2000) conducted an extensive evaluation of the national environmental public health infrastructure and found a fundamental information gap in our understanding of the relationship between environmental exposures and the health of the public. In this article, we present a series of methodologic approaches used to identify priority health conditions for environmental health tracking and develop the recommendations of the Pew Commission.
Methods
We describe a multistep approach to address the goals of the Pew Commission’s tracking project. First, we describe the development and implementation of a tracking survey administered to public health and environmental practitioners. Second, we describe steps taken to inform the selection of priority health end points for environmental health tracking, which included a synthesis of review articles on environmental health indicators and an examination of existing national environmental emissions, health, and health care databases to understand trends in environmentally related health end points. Finally, we discuss the process for translating the findings from our research to Pew Commission recommendations.
State and local survey.
State and local public health practitioners were surveyed by telephone to obtain information about their environmental public health tracking programs. Questions focused on organization, data collection and use, financial and technical resources, barriers, and priorities. State respondents (n = 49) were primarily environmental epidemiologists. Local respondents (n = 21 counties) were mostly health department directors who had expressed interest in environmental public health, suggesting a best-case scenario. Representatives included all 12 local representatives on the National Association of City and County Health Officials (NACCHO) Environmental Health and Prevention Advisory Committee in addition to 10 local health departments randomly selected from a NACCHO list of 133 local health departments that identified environmental health as an interest area. Surveys were conducted from March to September 2000. A follow-up survey is under way in 2004.
Identification of priority health conditions.
Tracking the health of a population is an essential component of effective public health practice. However, given the many gaps in our current understanding of the role of the environment in disease, the Pew Commission was faced with a fundamental question: “What health end points should be tracked?” To address this question, we developed an approach to evaluate available national data to identify health end points that may be appropriate for inclusion in a national environmental health tracking network.
Step 1—Literature review of environmental health indicators.
We evaluated the scientific literature to identify specific health effects that have been related to environmental exposures and that may serve as environmental health indicators. These indicators can be useful in providing measures of population health that can be related to environmental exposures, thereby providing a public health yardstick for measuring environmental progress. This review included published work by health and environmental agencies identifying diseases or health end points that have a possible link to environmental exposures.
Step 2—Connecting environmental releases and health conditions.
We used the Environmental Defense Scorecard analysis that provides listings of chemicals that impact human health (Environmental Defense 2000; http://www.scorecard.org). These listings were derived from toxicologic and epidemiologic studies and information from regulatory agencies. The U.S. Environmental Protection Agency (EPA) Toxics Release Inventory (TRI; http://www.epa.gov/tri/) served as the starting point for the chemical listings. The Scorecard ranking exercises combined the TRI data with information on potential health hazards of these substances. These reports account only for pollution from industrial facilities that reported to TRI in 1997 (the most recent year available for our analysis) and include only the 644 chemicals covered by TRI at that time.
Step 3—Examination of national health databases.
On the basis of Scorecard’s list of substances, related broad health categories, and the literature review, we analyzed available health outcome data at the national level for selected end points to identify those with high or increasing prevalence or those responsible for heavy utilization of health care. There is virtually no comprehensive national tracking of noninfectious diseases (except cancer). However, four National Center for Health Statistics (NCHS) national survey data sets including the National Health Interview Survey (NHIS), the National Ambulatory Medical Care Survey (NAMCS), the National Hospital Ambulatory Medical Care Survey (NHAMCS), and the National Hospital Discharge Survey (NHDS) were examined for this analysis and provided useful insights into some of the end points identified in steps 1 and 2 (NCHS 2000; 2004a; 2004c; 2004d).
Initially the analysis was intended to be limited to environmental health outcomes that are clinically observable and classifiable by the International Classification of Diseases, 9th Revision (ICD-9) codes and that have been linked to identifiable exposures to environmental agents (Health Care Financing Agency 1998). Because of the limitations of the available survey and emerging interests in a broader range of health end points, the analysis included a number of end points without known environmental causes. Inclusion of a disease in this analysis is not meant to imply environmental etiology. Given the present limitations of knowledge, even in most cases where an environmental exposure has been shown to contribute to the development of adverse effects, it is not possible to quantify the proportion of risk attributed to the environment.
The NHIS provides valuable national-level information on the prevalence of and trends for some key health outcomes. Published NHIS estimates for both chronic and acute conditions are available as far back as 1984. Categories of end points are grouped by ICD-9 codes in a process known as the NHIS recode (NCHS 2000). The 1997 survey was used to estimate rates of childhood disorders previously not covered by NHIS and adult conditions for comparison to earlier years. These data were obtained from the NCHS web site (NCHS 2004c), imported into SAS (version 8.0; SAS Institute, Inc., Cary, NC), and were used to develop estimates separately for children (< 18 years of age) and adults (18 years +) using the appropriate weights.
Depending on the disease category, these groupings may or may not be specific to environmental health end points. In addition, these categories may include a limited number of end points and may provide a misleading estimate of the prevalence of disease in the population. For example, the NHIS grouping for neurologic diseases includes migraine headaches but excludes diseases of growing interest such as Alzheimer and Parkinson diseases, thus resulting in an underestimation of prevalence of neurologic diseases in the population. This is a major limitation of the NHIS data set when evaluating disease trends that may be influenced by environmental exposures. Conversely, respiratory diseases are captured more accurately by the NHIS recode. The disease prevalence and incidence rates give a better assessment of respiratory conditions with environmental etiology such as asthma and chronic obstructive pulmonary disease.
In addition to disease trends, we reviewed health care information from the 1996 NHDS, NAMCS, and NHAMCS. Estimates from the NHDS were obtained for 1996 (NCHS 2004d). This source provided estimates of the number of discharges for individual ICD-9 codes, already weighted to the U.S. population. To estimate rates, U.S. census data were used to obtain the total U.S. population numbers for 1996, as well as population estimates by age, region, and sex (Population Estimates Program 1999a, 1999b).
Estimates from the 1996 NHAMCS and the 1996 NAMCS were obtained from public data sets available from the NCHS web site (NCHS 2004d). For analysis, the data sets were input into SAS, version 8.0. To estimate the number of visits for a particular ICD-9 code, that code was selected as the principal diagnosis and a weighted PROC FREQ command was used. To estimate the proportion of office visits because of a particular condition, the total number of office visits was used as the denominator. To estimate visit rates, the denominator was the 1996 U.S. population, obtained from U.S. census publications (Population Estimates Program 1999a, 1999b). Once weighted, these estimates represented U.S. population visits to physicians’ offices, hospital emergency departments, and hospital outpatient clinics for medical care.
Environmental summit.
As a follow-up to the previous research steps, the Pew Commission, in partnership with the Association of State and Territorial Health Officials, NACCHO, and the Public Health Foundation, hosted over 50 federal, state, and local environmental health leaders for 2 days to develop recommendations to establish and implement national environmental health tracking.
Results
State and local survey.
Data collection and use: hazards, exposures, and health outcomes.
Environmental public health tracking programs were diverse. In general, much of the hazard tracking was handled by environmental agencies, while public health agencies, especially at the state level, most frequently handled health outcome tracking. Exposure tracking was rare with the exception of lead (81% of state public health agencies) and personal air monitoring data (one-fourth of state health agencies.) States reported the following for health outcome tracking activities. Most states tracked cancer (94%), infectious outbreaks (80%), and birth defects (69%), but many did not track asthma (55%) and few tracked emerging environmental health concerns such as developmental disabilities (16%), learning disorders (12%), or autoimmune diseases (8%). Some of the larger local public health agencies collected significant quantities of tracking data, but many locals relied on other agencies to collect data, often seeing their role more in dissemination and follow-up investigations.
Coordination.
To improve their effectiveness and leverage limited capacities, state and local officials worked with other federal, state, and local agencies on environmental public health issues. Often, state public health departments facilitated relationships between federal and local agencies. Given the coordination challenges, 53% of states and 81% of local departments designated lead individuals for overseeing tracking activities.
Financial and technical resources.
Among states, major sources of funding for environmental public health tracking were state general funds (81% of states), federal funds from grants and cooperative agreements with the Centers for Disease Control and Prevention (CDC), the Agency for Toxic Substances and Disease Registry (ATSDR), the U.S. EPA and the National Institute for Occupational Safety and Health (55, 45, 34 and 23%, respectively), prevention block grants (32%), and fee-based regulatory programs (21%). At the local level, 90% of departments received local general funds, and 86% received funds from fees and permits to support their tracking activities. Forty-eight and 38% of departments, respectively, received state department of health and environment funding, and only 33% received federal funding. (Some federal funds may have been channeled through state programs.)
From a technologic perspective, 81% of the states indicated that health and environmental data were electronically available for internal use, and 69% made health data available to the public in limited electronic formats and geographic scales. Local public health departments varied widely in their access to current computer software and/or hardware, and in their ability to use technology to its full extent. The lack of standardization in state, county, and other data systems hindered the ability to access and interpret data.
Environmental public health tracking priorities.
States revealed a diverse set of priorities for environmental public health tracking. Drinking water, metals, food protection, asthma, cancer, and for some states, the need to establish basic tracking capacity and assure room for flexible approaches to tracking were top priorities. At the local level, top priorities, as reflected by spending, were food protection, waste management, and water and air quality. Further priority needs included indoor air, bioterrorism and emergency response, and land use issues.
Barriers.
At both the state and local levels, the greatest identified barrier to effective environmental public health tracking was scarce financial resources, followed by the related need for staff. Respondents wanted flexible funding strategies and increases in staff and expertise. Local agencies were particularly concerned about unfunded tracking recommendations or requirements. Other important barriers included a lack of political will, limited reporting requirements, lack of established databases, and their own organizational structure. Further, some wanted guidance on tracking system design, implementation, and priorities.
Identification of priority health conditions results.
Step 1—Literature review.
Through the literature review, we identified key health outcomes partially determined by environmental exposures. In 1993 the ATSDR authored a report (Lybarger and Spengler 1993) that identified seven broad groupings of health conditions where research is needed to elucidate the exposure–disease relationship. These conditions included respiratory diseases, neurologic disorders, congenital anomalies, reproductive disorders, kidney diseases, immune disorders, and cancer.
Rios et al. (1993) presented a review of research on biologic susceptibility of minority populations to environmental pollutants that may result from genetic makeup, occupation, pre-existing health conditions, exposure to mixtures of pollutants, substance abuse, unemployment, and other social inequalities in health care, education, and political power. The outcomes of concern included respiratory diseases (e.g., asthma and chronic obstructive pulmonary disease); chronic liver disease and cirrhosis; heart disease; sickle cell anemia; kidney disease; and endocrine disorders including diabetes mellitus.
In 1994 Silbergeld (1994) identified three health conditions whose etiologies remain largely unexplained but where environmental exposures are implicated. She suggested that epidemiologic research on asthma, low birth weight, and neurodegenerative diseases was central to improving our environmental health policies and their subsequent benefits.
Turning to the international literature, Kjellstrom and Corvalan (1995), as part of the World Health Organization project HEADLAMP (Health and Environment Analysis and Indicators for Decision-Making) identified seven indicators that would serve as monitoring tools for the United Nations initiative on sustainable development. These indicators included asthma; skin disorders; aplastic anemia; birth defects, including congenital anomalies and low birth weight; spontaneous abortions and cancer.
Regarding the connections between air pollution and mortality, particularly respiratory and cardiovascular mortality, Kelsall et al. (1997) provided one example of the growing body of epidemiologic work establishing this association. They presented scientific evidence that particular health conditions increase human susceptibility to environmental pollutants.
The U.S. Department of Health and Human Services (U.S. DHHS) Healthy People initiative regularly tracks the public’s health and has set objectives for quantifiable reductions in disease and disability over the past 20 years. Community health and environmental health indicators drawn from Healthy People objectives included asthma and chronic obstructive pulmonary disease, chronic liver disease and cirrhosis, heart disease, methemoglobinemia, congenital anomalies, low birth weight, developmental disabilities, kidney diseases, cancer and endocrine disorders including diabetes mellitus (U.S. DHHS 1998).
Step 2—Connecting environmental releases and health conditions.
Based on the reviews of the Scorecard data and related health categories, we found that substances with potential respiratory effects were the highest category of releases in 1997 and this continues to be the highest ranking category based on 2001 TRI data. Neurologic, skin, gastrointestinal, and liver toxicants were next highest categories in total pounds released. Based on 2001 data, these categories have shifted slightly, with skin disorders replacing neurologic conditions in the second highest category and gastrointestinal diseases moving up to the third category. Other categories included cardiovascular, developmental, and reproductive effects.
Step 3—National health outcome and health care databases.
Figure 1 shows 10-year trend data for the self-reported prevalence of a number of broad categories of health conditions, including respiratory conditions, skin diseases, neurologic disorders, reproductive and fertility conditions, endocrine and metabolic conditions. Specifically, endocrine and metabolic disorders show the greatest increase (21.7%), followed by neurologic (20%) and respiratory diseases (20%). As previously noted, because of the NHIS recodes, these broad categories of health conditions are combinations of conditions, reflecting end points with and without known environmental etiologies.
We also evaluated trend data over the same time period within specific health categories. For example, within respiratory conditions, asthma rates increased by 38.6% and chronic bronchitis increased by 15.3%. For endocrine and metabolic diseases, thyroid disorders increased by 36.3% and diabetes mellitus increased by 19.1% over this time period. For neurologic diseases, multiple sclerosis increased by 21.2% and migraine headaches increased by 26%. Within the reproductive health category, prostate diseases (noncancer including hyperplasia, inflammation) increased by 48% and disorders of female reproductive organs (e.g., ovarian cysts, disorders of the uterus and cervix) increased by 28.6%.
We also summarized the number of hospital discharges, emergency department visits, hospital outpatient care visits, and doctor office visits for six broad groupings of health outcomes, including lung and respiratory conditions, neurologic conditions, reproductive and fertility conditions, blood disorders, liver disease, and cardiovascular disorders. Specific disease end points within these broad classifications were included where information was available.
Cardiovascular diseases required the most health care resources, including over 48 million doctor visits and 4.6 million hospitalizations. Although pollution exposures have been indicated for some types of cardiovascular diseases, many other environmental risk factors, including lifestyle and obesity, have been implicated as contributing causes. Lung and respiratory diseases (33.6 million doctor visits and over 3 million emergency department visits) and neurologic conditions (8.7 million doctor visits) also required large amounts of health care services. Of all the lung and respiratory health conditions resulting in utilization of the health care system, asthma and chronic bronchitis accounted for the largest proportion of hospital, emergency department, outpatient and doctor visits in 1996. Among the endocrine conditions, diabetes resulted in the most health care use (over 15 million physician visits). Of the neurologic conditions requiring health care, a relatively small fraction was due to neurodegenerative diseases such as senility, cerebral degeneration, and Alzheimer and Parkinson diseases. However, these diseases have a devastating impact on the quality of life and require care that may not be measured by these surveys.
Environmental Summit—Stakeholder Priorities
Summit participants developed specific recommendations for improving the national environmental health infrastructure and capacity for tracking. They did not identify specific exposures or health end points for tracking. Recognizing current scientific limitations concerning the role of environment in disease and state and regional differences in environmental health priorities, they recommended a flexible tiered approach. Recommendations included
National tracking of high priority exposures and health outcomes
A sentinel network to identify emerging hazards
A coordinated network of pilot regional, state, and local tracking programs
A supportive research program to guide and evaluate tracking progress
These recommendations provided the basis for the Pew Commission recommendations that have shaped the development of the CDC Environmental Public Health Tracking Program.
Conclusions
The surveys of state and local public health department officials provided baseline data on the state of environmental public health tracking in 2000. The surveys found great variety in tracking organization, functions, and resources among state and local health departments. The overall infrastructure for environmental public health tracking lacked adequate support, personnel, coordination, and data resources. Collection of these baseline data will provide an opportunity to evaluate the impact of the new tracking initiatives and resources as well as increased focus on preparedness since 2000.
The priority health condition analysis identified a number of limitations that must be addressed. These findings are constrained by available epidemiologic and toxicologic data and are driven by high-volume chemical release reporting under the TRI. Multiple health effects can be associated with an individual toxicant, and complex interactions between toxicants can further affect human health. Nevertheless, this approach provided the Pew Commission with a starting point for identifying the categories of health end points to be considered for tracking. Given the large amount of toxic pollutants released, there is a need to improve the tracking of population exposures and look for any evidence of adverse health impacts.
The end points identified through the literature represent conditions for which environmental exposures have been implicated or are pre-existing health conditions that may be exacerbated by exposure to environmental pollutants. The end points also reflect agency priorities, including ATSDR’s seven broad categories of priority health conditions and the U.S. DHHS Healthy People objectives for community and environmental health indicators (U.S. DHHS 1998
U.S. DHHS 2000). Although this list of health end points is culled from numerous sources with diverse criteria, the categories and end points show a general convergence that can shape priorities for the developing tracking network.
An examination of available national survey data indicated that the reported prevalence of several categories of disease potentially related to the environment has been increasing. Between 1986 and 1995 the largest increases were reported in endocrine and metabolic disorders (21.7%), followed by neurologic (20%) and respiratory diseases (20%). Reproductive disorders also increased during this time (7.3%).
Available data on health care utilization for these outcomes indicate that cardiovascular disease requires the greatest use of health care, with respiratory (over 33 million doctor visits and 3 million emergency department visits) and neurologic diseases (over 8 million doctor visits) also requiring large amounts of health care services.
Based on this analysis of the weight of evidence and trends in health outcomes and impacts, respiratory diseases and neurologic diseases are recommended as priorities for tracking. Specific end points recommended for tracking include asthma and chronic respiratory diseases, and chronic neurodegenerative diseases such as multiple sclerosis. Based on increasing trends in reported prevalence and the potential for environmental exposures to increase population risks, consideration should also be given to developmental disabilities, reproductive disorders, and endocrine and metabolic disorders. Strengthening of current efforts to track cancer and birth defects should also be included as components of a nationwide environmental health tracking network.
The role of the environment in the etiology of these health outcomes remains unknown. Identification of specific outcomes as tracking priorities should not be interpreted as an implication of environmental causality. However, the increasing incidence and prevalence of a number of diseases with potential links to environmental exposures underscores the need for improved tracking to increase our understanding of risk factors, identify populations at high risk, inform priorities for research, and develop coordinated prevention efforts.
Discussion
Advances in hazard identification, exposure assessment, health outcome data collection, and information technology provide mechanisms for advancing tracking and improving our understanding of the environment and health. These advances, coupled with deep public concern, provide a window of opportunity to strengthen the national infrastructure for environmental health information, expand public access to this important information, and protect the privacy of individuals. New technologies in biomonitoring have the potential to transform the nation's capacity to track exposures to pollutants and understand their impacts on health. Advances in communication and information technology have expanded opportunities for public access and given us new tools to analyze, map, and disseminate health data. New technology also can improve safeguards to protect the confidentiality of identifiable personal health information.
Building on the findings and recommendations of the Pew Commission, CDC developed the Environmental Public Health Tracking Program (NCEH 2004). CDC was allocated funds in fiscal years 2002 and 2003 for $14.2 and $14.6 million, respectively, to fund 24 state and local health departments and three public health schools to a) build environmental public health capacity, b) increase collaboration between environment and health agencies, c) identify and evaluate environmental and health data systems, d) build partnerships with nongovernmental organizations and communities, and e) develop model systems that link environmental and health data and that other states or localities can use. Three public health schools are funded to support state and local health departments and investigate possible links between health effects and the environment (NCEH 2004).
In addition, CDC together with the Center for State and Territorial Epidemiologists (CSTE) have developed a set of environmental public health indicators that can be used to assess baseline status and trends and build core surveillance capacity in state and local agencies (CSTE 2004). The U.S. EPA has also contributed to the national capacity for tracking by producing the Draft EPA Report on the Environment (U.S. EPA 2003), which provides information on the status of and trends in environmental conditions and their effects on human health and the nation's natural resources. These indicator initiatives provide summary measures of environmental health relationships that are fundamental to future environmental tracking efforts (U.S. EPA 2003).
Beyond these indicator programs, the CDC’s biomonitoring program and “National Report on Human Exposure to Environmental Chemicals” are enhancing our abilities to measure environmental chemicals in the human body (NCEH 2003). The data are collected as part of the National Health and Nutrition Examination Survey (NCHS 2004b), and specimens are analyzed as part of the CDC biomonitoring program. Between 2001–2003 the number of chemicals being measured and reported expanded from 27 to 116. The samples provide estimates of population exposures to key contaminants of concern and begin to fill a critical gap in our ability to link exposure and health outcome data (NCEH 2003).
Beyond strengthening the science and supporting data, efforts are under way to better link environment and health, including new relationships and collaboration between environment and health agencies. This integrated thinking brings new understandings and approaches to improve environmental protection efforts, to better characterize and control sources through public health surveillance, and to understand the links between adverse exposures and health effects.
The “building blocks” of knowledge provided by a nationwide environmental public health tracking network will enable scientists to answer many of the troubling questions we are asking today about what is making us sick. The result will be new prevention strategies aimed at reducing and ultimately preventing many of the chronic diseases and disabling conditions that afflict millions of Americans.
Figure 1 Self-reported prevalence for selected categories of disease. Data from National Health Interview Survey, 1986–1995 (NCHS 2004c). Changes in reproductive and fertility outcomes reflect years 1988–1995.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7149ehp0112-00141915471736Mini-Monograph: Public Health TrackingArticlesDeveloping a Comprehensive Pesticide Health Effects Tracking System for an Urban Setting: New York City’s Approach Kass Daniel E. 1Thier Audrey L. 2Leighton Jessica 1Cone James E. 1Jeffery Nancy L. 11New York City Department of Health and Mental Hygiene, Bureau of Environmental Disease Prevention, New York, New York, USA2Consultant, Williamstown, Massachusetts, USAAddress correspondence to D.E. Kass, New York City Department of Health and Mental Hygiene, Environmental Public Health Tracking Program, 253 Broadway, CN-34C, New York, NY 10007 USA. Telephone: (212) 442-2638. Fax: (212) 676-2911. E-mail:
[email protected] article is part of the mini-monograph “National Environmental Public Health Tracking,” which is sponsored by the Centers for Disease Control and Prevention (CDC).
We gratefully acknowledge the guidance provided by members of the New York City Department of Health and Mental Hygiene Environmental Connections Advisory Panel.
This work was generously supported by cooperative agreements U50/CCU223290 and U50/CCU222455 from CDC.
This article was supported by an environmental public health tracking cooperative agreement from CDC. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of CDC.
The authors declare they have no competing financial interests.
10 2004 3 8 2004 112 14 1419 1423 1 4 2004 3 8 2004 Publication 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 recent years, there have been substantial investments and improvements in federal and state surveillance systems to track the health effects from pesticide exposure. These surveillance systems help to identify risk factors for occupational exposure to pesticides, patterns in poisonings, clusters of disease, and populations at risk of exposure from pesticide use. Data from pesticide use registries and recent epidemiologic evidence pointing to health risks from urban residential pesticide use make a strong case for understanding better the sale, application, and use of pesticides in cities. In this article, we describe plans for the development of a pesticide tracking system for New York City that will help to elucidate where and why pesticides are used, potential risks to varied populations, and the health consequences of their use. The results of an inventory of data sources are presented along with a description of their relevance to pesticide tracking. We also discuss practical, logistical, and methodologic difficulties of linking multiple secondary data sources with different levels of person, place, and time descriptors.
data linkageepidemiologyNew York Citypest controlpesticide usepoison controlsurveillance
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Pesticides are chemicals or other products used to kill, repel, or control pests and include insecticides, rodenticides, fungicides, and herbicides. Effectively assessing human health risks from pesticide use and exposure depends on the timely availability of data that describe how, where, when, by whom, why, what type, and in what quantities pesticides are used. A report by the Pew Environmental Health Commission (2000) has called for the development of state and local pilot environmental tracking systems to track, among other things, pesticide hazards and related health outcomes. This report helped to launch the national Environmental Public Health Tracking (EPHT) Program funded and organized by the U.S. Centers for Disease Control and Prevention (CDC). CDC defines environmental public health tracking as
the ongoing collection, integration, analysis, and interpretation of data about environmental hazards, exposure to environmental hazards, and human health effects potentially related to exposure to environmental hazards. (National Center for Environmental Health 2003)
Few examples exist where these three data domains—hazard, exposure, and health effects—are simultaneously tracked and linked, despite clear benefits of doing so (Council of State and Territorial Epidemiologists 1999).
In 2002 CDC awarded the New York City (NYC) Department of Health and Mental Hygiene (DOHMH) funding to develop its capacity to track environmental public health indicators. The following year, DOHMH was awarded an EPHT grant to develop a pilot pesticide tracking system for NYC. The goal of the EPHT program is to
demonstrate and evaluate methods for linking data from ongoing, existing health effects surveillance systems with data from existing surveillance/monitoring systems for human exposure and environmental hazards. (CDC 2003b)
Collectively, these programs are known at DOHMH as “Environmental Connections.” This article describes gaps in pesticide surveillance systems, a rationale for tracking pesticides in NYC, and NYC’s operational plan to create such a system.
Existing Pesticide-Related Surveillance
EPHT defines hazard as a factor that may adversely affect health. Many sources of pesticide hazard data exist. For example, national databases exist that describe the names and classes of pesticides, their federal and state registration status, and their toxicologic properties, although there is no single database that consolidates information on both the acute and chronic health effects of pesticides. The U.S. Food and Drug Administration (FDA) samples domestic and foreign food products for pesticide residues and funds states for local food surveillance (FDA 2001). However, no national-scale surveillance system exists that makes data available on pesticide production, import/export, sale, application, or use (Donaldson et al. 2002). Absent such data, the U.S. Environmental Protection Agency (EPA), Department of Agriculture, and Geological Survey estimate annual pesticide use by linking manufacturer, industry, grower, and crop survey data. The industry and trade association production and sales data used for these estimates are available for purchase (Kegley et al., unpublished data). Several states estimate pesticide use through similar combination of sales, use, and crop surveys, but the utility of this approach is limited to characterizing agricultural use and may be incomplete and inadequate to characterize geographic areas smaller than states or even regions (Thier 1997).
Five states mandate some form of comprehensive pesticide use reporting (PUR) and sales. California’s regulations require that agricultural and commercial applicators and government institutions file pesticide use reports with the state. Agricultural reports must contain information on the identity, quantity, location, method, date, and other volume and acreage data of restricted-use applications. Reports for nonagricultural applications are less detailed because they are aggregated by month and county. In addition, all pesticide sales must be reported at the first point of sale (California Department of Pesticide Regulation 2000). California’s use and sales systems permit public access to line-item data. Massachusetts, Oregon, New Hampshire, and New York require PUR that includes agricultural, nonagricultural, building, and institutional applications, with varying degrees of experience and public access (Kegley et al., unpublished data).
Oregon is the only state that currently requires tracking of household pesticide use through point-of-sale reporting, although the state’s fiscal crisis has prevented Oregon from collecting use and sales reports (PURS-Oregon 2004). New York’s system is the best equipped among state PURs to characterize urban pesticide use because address, type, and quantity must be provided for all structural and rodent applications. However, the New York legislature imposed the most restrictive of the states’ public access requirements, permitting release of raw data only for human health research and only if approved by a stakeholder health science board (New York State Environmental Conservation Law 1997).
Data from many of these state PUR systems have been used to produce research papers, reports, and white papers explaining the purpose, distribution, and quantities of largely agricultural pesticides. Investigators have used California’s PUR data for ecologic studies examining Parkinson disease (Ritz and Yu 2000) and cancer incidence (Mills 1998). Less specific pesticide use data from state and federal agricultural agencies have also been used to identify a pattern of birth defects associated with certain pesticide use (Garry et al. 1996; Schreinemachers 2003).
Exposure, for tracking purposes, is defined as the proximity and/or contact with a source of a disease agent in such a manner that effective transmission of the agent or harmful effects of the agent may occur (CDC 2003a). Pesticide exposure surveillance in the United States is largely limited to particular occupational cohorts—medical monitoring of applicators, for example—and to biomonitoring efforts to characterize exposures among representative samples of regional and national populations. In many states, occupational exposures resulting in depressed cholinesterase levels are reportable conditions and are useful for monitoring regulatory compliance, enforcing work rules, managing disease cases, and identifying risk factors (Calvert et al. 2004). But these data have limited generalizability to larger and more varied populations. The U.S. EPA National Human Exposure Assessment Survey, completed in the 1990s, evaluated pesticide exposures among representative populations in Arizona, the Midwest, and Maryland (Berry et al. 2000). This effort has not been replicated. As part of the Third National Health and Nutrition Examination Survey (NHANES III), CDC carried out biomonitoring for metabolites of several classes of pesticides. These data provide, for the first time, baseline exposure estimates for a representative U.S. population to a variety of pesticides (Barr et al. 2004). NHANES III has produced a wellspring of reports based on these results and illustrates how providing exposure data linked to personal descriptors can fill in critical knowledge gaps.
Health effects, for tracking purposes, are chronic or acute health conditions that affect the well-being of an individual or community and are measured in terms of illness and death (CDC 2003a). Although the health effects from pesticides may include acute and chronic conditions and reproductive effects, surveillance of their health impacts is effectively limited to nearly immediate toxic effects. The Toxic Exposure Surveillance System (TESS) is a national surveillance program that collects poison control data from all state and regional poison control centers. TESS records basic hazard, exposure, and individual information on pesticide-related inquiries, of which there were more than 96,000 in 2002 (Watson et al. 2003). Poison Control Center data are useful for identifying educational and outreach needs, identifying risk factors for poisonings, and investigating and identifying clusters and outbreaks. The Sentinel Event Notification System for Occupational Risk (SENSOR) at the National Institute for Occupational Safety and Health (NIOSH) supports pesticide-related illness and injury surveillance in 12 states and is used to identify outbreaks and emergency pesticide health effects (NIOSH 2004).
More than 40 states collect and report hospital discharge data, and pesticide-related hospitalizations are rare. Although more patients report to emergency departments for pesticide exposures than are admitted to hospitals, few states systematically collect and report these data.
The Case for Urban Pesticide Tracking
The data described above that are systematically collected about pesticide hazards, exposures, and health effects describe the risks experienced by agricultural communities better than those experienced by other groups. There are many reasons, however, why large cities may be interested in developing pesticide tracking systems. A 1999 analysis of New York’s PUR data found that even though NYC accounts for < 1% of the total land area of the state, > 7% by volume of all pesticides applied in the state, and 13% by weight, were applied in NYC. Also, all five counties of NYC were included in the top 10 counties statewide for use of pesticides (Thier 2000).
Several events have elevated the city’s level of awareness about pesticides. Spraying of adulticides for controlling mosquitoes that carry West Nile virus (CDC 2003c), the rise in asthma hospitalizations in the late 1980s through the mid 1990s, the growing awareness of the links between pest infestations and health symptoms, high profile experiments in least-toxic pest control in low-income housing, and public hearings on methods of controlling rats (Kass and Outwater 2002) have all contributed to public concern regarding pesticide health effects. NYC residents have been the subject of several recent studies that have associated negative reproductive health outcomes among low-income women with residential exposure levels to chlorpyrifos (Berkowitz et al. 2003, 2004; Perera et al. 2003; Whyatt et al. 2004). As a result of these events, pesticides have taken on greater importance for public health and housing agencies.
Populations residing in large urban areas face special health risks from a variety of environmental concerns. In NYC and other older, densely populated, largely immigrant cities, environmental hazards tend to concentrate spatially, ethnically, and socioeconomically. Awareness of these hazards may sometimes be great, prompting important and appropriate advocacy and action by communities to ameliorate conditions that contribute to acute and chronic illness. Other times, communities or governmental officials have so little information that speculation, hyperbole, or inaction may result. Under these circumstances, public health agencies play a largely reactive role to public concerns. Failing to unite disparate information on hazards leaves agencies with an incomplete story, and inappropriate policy decisions may result. By linking data sources on pesticide use, housing quality and finance, demographics and socioeconomic status, exposures, and health, much more can be learned about where and why pesticides are used. This deeper understanding may promote the improved targeting of resources, education, and toxic use reduction efforts, as well as inform scientifically sound policy and legislation.
Materials and Methods
With feedback from a stakeholder advisory panel created to guide the development of the public health tracking program, DOHMH identified seven principles that would guide decision making on data acquisition, data architecture, analytic priorities, and public engagement: The pesticide tracking system should a) build upon existing and ongoing data collection systems; b) link hazard, human exposure, and human health effects data in scientifically valid and defensible ways; c) automate, to the extent possible, the importing, cleaning, and linking of data sources; d) build on, rather than duplicate, data and technical systems already under development by data providers; e) enable the development and tracking over time of public health environmental indicators; f ) satisfy the needs of a wide community of data users, analysts, advocates, and residents; and g) inform the development of public health and environmental interventions whose goals are to reduce health risks and improve environmental quality. In this section, we describe preliminary progress toward the creation of NYC’s pesticide tracking system.
Data sources.
In 2003 the DOHMH, in cooperation with the NYC Department of Information and Telecommunications Technology, began a comprehensive data and metadata inventory of NYC and New York State environmental data. We reviewed data systems at health, housing, finance, planning, and environmental protection agencies for their applicability and relevance to a pesticide tracking system. A metadata database is being populated that includes descriptive information about the data, process information on its collection, contact information, identifiers, geospatial descriptors, system architecture, distribution methods, and anticipated modifications.
Our initial inventory revealed two significant data gaps in the hazard–exposure–outcome tracking triad. First, there is no existing source of data to describe, on a population basis, the exposures of NYC residents to pesticides. Fortuitously, DOHMH’s Division of Epidemiology was already 6 months into its planning for a NYC Health and Nutrition Examination Survey (HANES) when it became clear to the staff of Environmental Connections that by adding pesticide biomonitoring, similar to that carried out in NHANES III, one part of the gap could be closed. In collaboration with CDC’s National Center for Environmental Health Pesticide Laboratory, we plan to collect and analyze urine for organophosphate and pyrethroid metabolites as part of the 2004 NYC HANES. The second gap is a temporary one. Data on emergency department use will first become available in New York in 2005. Until then, we are collaborating with the DOHMH Bureau of Injury Surveillance to abstract charts in 23 emergency departments 1 week each quarter to determine the frequency, scope, and risk factors associated with pesticide poisonings, again opportunistically expanding an existing program for environmental tracking.
Table 1 summarizes results from the data inventory process and identifies the utility of each data source for a pesticide tracking system. In addition to data sources already described, the system will include data from NYC’s annual Community Health Survey, an annual telephone survey of 10,000 city residents, based on CDC’s Behavioral Risk Factor Surveillance System (Karpati et al. 2003). Questions on personal and commercial pesticide applications and cockroach infestations were included in the 2003 questionnaire.
Additional public and commercially available data sets will be linked, including pesticide registration and toxicity data (for grouping and lookup purposes) and Dunn and Bradstreet Business Locator (Providence, RI) (for identifying information on state-registered commercial pest control companies). Table 1 reveals several obstacles in building the tracking system. Hazard, health outcome, and related housing and population data are being acquired from three municipal and three state agencies and from surveys conducted by the U.S. Census Bureau. There is a steep learning curve for researchers to become familiar with the strengths and weaknesses of most large data sets; only some data sets have substantial documentation and data that have been used in published studies. For example, indices of housing disrepair exist and have been validated with housing and vacancy survey data.
For data originally gathered for purposes other than those contemplated here, the task is more difficult. For example, poison control data may have multiple reports of a single incident, redundancies not easily remedied. Building finance data, another example, is a historical data set that maintains all transactions related to parcels. Determining property value from the system’s tax and mortgage records requires algorithms that manage different assessment periods, overlapping loans, and asset transfers into account.
Some data sets may describe different stages of the same incident, such as poison control center, emergency department and hospitalization discharge data. The frequency of update differs among the data sets, posing logistical and methodologic challenges for creating analyzable data sets. Finally, negotiating multiple data use agreements, human subjects assurances, and stakeholder boards is time-consuming and imposes difficult-to-reconcile security requirements on data reporting and public availability.
Data links.
Although each source of data provides useful information for the development of environmental public health indicators, it is the ability to link them that differentiates this effort from simple reporting. Figure 1 describes the individual, building, and hazard identifiers shared among the key data sources for this system. Two types of links are highlighted, embedded, and derived. An embedded link occurs when data fields are shared by two data sets. For example, address data are contained within the PUR applications database and can be directly associated with housing complaint and inspections data in the NYC Department of Housing Preservation and Development data set. A derived link is one made possible through the use of geosupport tools, by the hierarchical nature of the data structure, or via probabilistic matching. For example, once an address is known, a building identification number can be imported into the record using a geosupport system created for NYC. Once a compound’s registration number is known, its pesticide class (e.g., organophosphate) can be determined. If an address is missing from poison control data, then time, age, gender, ZIP code, and other variables can be used to create probabilistic matches to emergency department or hospitalization records.
Figure 1 displays myriad connections among the data sources and can be thought of as a cognitive map of relationships from which hypotheses can be formulated and analyses carried out. The following are some of the questions that can be explored by using these data links:
Which building-related conditions are associated with the application of pesticides?
Do hospitalizations reflect the “tip of the iceberg” of health outcomes?
Is there an association between commercial pesticide applications and biomonitored exposure and type of residential building?
What is the correlation between reported use of pest-control services in the community health survey and pest control operator-reported applications?
What are the predictors of the personal use of hazardous pesticides?
Over time, is the use of pesticide associated with reductions in infestations?
Many methodologic issues confront this analysis. A system with so many sources of data and so many links may yield, by virtue of multiple comparisons, random associations. There are many unresolved issues involved in carrying out geospatial analysis, including the selection of geographic units of analysis, exposure modeling, and determining the potential for exposure, that may dramatically affect findings (Maantay 2002). The quality of some data to be assembled in this tracking system remains largely unknown until additional data sources are gathered and analyzed. Linking data originally gathered for fiscal or regulatory purposes to describe environmental hazards, exposures, and health outcomes raises concerns about the validity of variables, indicators, and indices derived from them.
The system described will also have limited ability to observe associations among hazards and exposures on the one hand, and chronic health outcomes on the other. Neither the hazard nor exposure data necessarily reflect long-term chronic exposures and risks. Poisonings, emergency department visits, and hospitalizations from pesticide-related problems reflect acute conditions resulting from acute exposures. Chronic conditions such as asthma, neurologic disorders, and many cancers may be observed in hospitalization and registry data but cannot be assumed to be related to short-term exposures reflected in the hazard data.
Discussion
We have completed the initial steps in the identification, acquisition, and assessment of data that can be used to characterize pesticide use, exposure, and health problems in NYC. The system we describe will be built largely on data sources that are pesticide related. Stakeholders are interested not only in the characterization of pesticide hazards, exposures, and poisonings but also in learning more about whether pesticide exposures are associated with Parkinson disease, neurologic disorders, development disabilities, and respiratory health. The potential of pesticide tracking to explore these concerns begins with building a base hazard and exposure system.
The final form, breadth, and analytic strength of this system will depend on many factors—data quality and completeness, the degree of sustained institutional and public support, sufficient funding, and staff resources among them. Despite logistical, resource, and methodologic limitations associated with the development of an urban pesticide tracking system, this system offers the potential for significant benefits for researchers, policy makers, residents, industry, and advocates. A hazard, exposure, and health outcome system has the potential to reveal relationships impossible to assess without linking data sets and to close significant gaps in our knowledge about how, where, when, why, and with what consequences pesticides are used in an urban environment.
Figure 1 Linkable identifiers among key data sources. HPD, NYC Department of Housing Preservation and Development.
Table 1 Key data sources for pesticide tracking system.
Data seta Managed by Type of tracking data Applicability Update frequency Required for acquisition
West Nile virus pesticide applications NYC DOHMH Hazard Applications data Continuous None
Food pesticide residue NYS Dept. of of Agriculture Hazard Indicators to be tracked Annual None
Pest control firm survey NYC DOHMH Hazard Indicators to be tracked Every 2 years IRB
Pesticide applications NYS DEC Hazard County/ZIP code applications Annual None
Address-level applications Annual Research application, IRB
Pesticide sales NYS DEC Hazard ZIP code–level sales Annual None
HPD building information NYC HPD Hazard, risk factors Address-level complaints and violation data Continuous Use agreement
Housing and vacancy survey U.S. Census Hazard, risk factors Neighborhood-level housing quality, occupancy Every 3 years None
NYC HANES NYC DOHMH Exposure, risk factors Exposure to organophosphates and pyrethroids Every 3 years IRB
Community health survey NYC DOHMH Hazard, outcome Neighborhood-level health and pesticide use data Annual None
Poison control data NYC DOHMH Hazard, outcome Suspected poisonings Continuous Use agreement, IRB
Emergency department chart abstraction NYC DOHMH Outcome Poisoning incidence Quarterly None
Hospital and emergency department discharge data NYS DOH Outcome Address-identified outcomes Annual IRB
Vital statistics birth records NYC DOHMH Population Intercensus populations Continuous Data use agreement
U.S. Census U.S. Census Population, risk factors Fine geography–level demographic, socioeconomic data Every 10 years None
Automated city register NYC Finance Risk factors Address-level financial data Continuous Data use agreement
Abbreviations: DEC, Department of Environmental Conservation; DOH, Department of Health; HPD, Department of Housing Preservation and Development; IRB, institutional review board; NYS, New York State.
a A list of web site addresses that describe or make available some of these data sets may be obtained by contacting the corresponding author of this article.
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Thier A 1997. A Review of Pesticide Use Reporting Policies. Report to the Pesticide Right to Know Campaign. Available: http://www.wsn.org.pesticides/farmwrkr.pdf [accessed 13 August 2004].
Thier A 2000. The Toxic Treadmill—Pesticide Use and Sales in New York State 1997–1998. Albany, NY:Environmental Advocates/New York Public Interest Research Group Fund Inc. Available: http://www.eany.org/reports/treadmill/index.html [accessed 6 September 2003].
Watson WA Litovitz TL Rodgers GC Klein-Schwartz W Youniss J Rose SR 2003 2002 Annual report of the American Association of Poison Control Centers toxic exposure surveillance system Am J Emerg Med 21 5 353 421 14523881
Whyatt RM Rauh V Barr DB Camann DE Andrews HF Garfinkel R 2004 Prenatal insecticide, exposures, birth weight and length among an urban minority cohort Environ Health Perspect 112 10 1125 1132 15238288
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7146ehp0112-00142415471737Mini-Monograph: Public Health TrackingArticlesTracking Pediatric Asthma:The Massachusetts Experience Using School Health Records Knorr Robert S. Condon Suzanne K. Dwyer Frances M. Hoffman Danielle F. Massachusetts Department of Public Health, Center for Environmental Health, Boston, Massachusetts, USAAddress correspondence to R.S. Knorr, Massachusetts Department of Public Health, 250 Washington St., 7th Floor, Boston, MA 02108 USA. Telephone: (617) 624-5757. Fax: (617) 624-5777. E-mail:
[email protected] article is part of the mini-monograph “National Environmental Public Health Tracking,” which is sponsored by the Centers for Disease Control and Prevention (CDC).
The Massachusetts Department of Public Health thanks school nurses, the Pediatric Asthma Surveillance Advisory Committee, and the staff of the Department’s Bureau of Family and Community Health who collaborated on this project.
This project is funded through cooperative agreement u50/ccu122451-02 from CDC, National Center for Environmental Health, Environmental Public Health Tracking Program.
This article was supported by an environmental public health tracking cooperative agreement from CDC. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of CDC.
The authors declare they have no competing financial interests.
10 2004 3 8 2004 112 14 1424 1427 1 4 2004 3 8 2004 Publication 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 Massachusetts Department of Public Health, in collaboration with the U.S. Centers for Disease Control and Prevention Environmental Public Health Tracking Program, initiated a 3-year statewide project for the routine surveillance of asthma in children using school health records as the primary data source. School district nurse leaders received electronic data reporting forms requesting the number of children with asthma by grade and gender for schools serving grades kindergarten (K) through 8. Verification efforts from an earlier community-level study comparing a select number of school health records with primary care provider records demonstrated a high level of agreement (i.e., > 95%). First-year surveillance targeted approximately one-half (n = 958 schools) of all Massachusetts’s K–8 schools. About 78% of targeted school districts participated, and 70% of the targeted schools submitted complete asthma data. School nurse–reported asthma prevalence was as high as 30.8% for schools, with a mean of 9.2%. School-based asthma surveillance has been demonstrated to be a reliable and cost-effective method of tracking disease through use of an existing and enhanced reporting structure.
environmental public health trackingepidemiologyindoor air qualitypediatric asthmaprevalenceschool healthsurveillance
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Asthma is one of the most common chronic diseases among children [American Lung Association (ALA) 2003] and has increased in prevalence over the past decades [Centers for Disease Control and Prevention (CDC) 2000]. According to CDC, the prevalence of current asthma among children 5–14 years of age increased from 4 to 7% between 1980 and 1996 (Mannino et al. 1998). More recent National Health Interview Survey (NHIS) findings suggest that prevalence rates may be leveling off, but more data are needed before the trend is clear (Akinbami et al. 2003). Findings from the 2001 Behavioral Risk Factor Surveillance System (BRFSS) show that the prevalence of current asthma for children in Massachusetts younger than 18 years of age is estimated to be 8.8%, whereas the prevalence of lifetime childhood asthma is 12.4% [New England Asthma Regional Council (ARC) 2004]. Asthma health care costs $3.2 billion annually for American children under the age of 18 (ALA 2003).
The reasons for the reported increase in asthma prevalence are unclear (Redd 2002). The increase may be a result of greater exposure to allergens and pollutants (Teague and Bayer 2001; Walker et al. 2003), improved identification of the disease (Barraclough et al. 2002), or the influence of other risk factors such as obesity (Gilliland et al. 2003) or infection (Camara et al. 2004). It is clear that asthma affects families through increased medical visits, school absenteeism, and lost work (Mannino et al. 2002). Statistics from national surveys also show disparities in asthma statistics among those affected by the disease (ARC 2004; Bloom et al. 2003; CDC 2004). For example, findings from the NHIS indicate that children 5–14 years of age have higher asthma prevalence than do other age groups and that, generally, African-American children experience more hospitalizations and mortality from asthma than children classified as white or other ethnicity. The NHIS also describes disparities by geographic region, with the northeastern United States experiencing more hospitalizations from asthma than other regions (Bloom et al. 2003). Data from the BRFSS show an inverse relationship between lifetime childhood asthma and household income in New England (ARC 2004). These observations suggest that environmental factors may be important.
The magnitude of prevalence and cost of asthma is a priority concern among public health organizations across the country. Promoting respiratory health and reducing morbidity and mortality from asthma are goals of the U.S. Department of Health and Human Services (U.S. DHHS) Healthy People 2010 (U.S. DHHS 2000). Environmental factors, such as indoor air quality (IAQ), and social factors, such as access to health care, are thought to explain some of the health disparities noted. However, our understanding of the strength of these relationships and our ability to identify opportunities to reduce morbidity and mortality are limited by the lack of systematically collected asthma data at the community level.
Available asthma prevalence information for Massachusetts has been generally limited to prevalence figures for the entire state or selected urban populations estimated through the BRFSS, a random telephone survey implemented by state health departments in conjunction with CDC. National figures have been available through the NHIS, which annually collects health and behavioral information through personal interviews. Historically, community-level data have been limited to communities with specialized surveillance programs or where research studies have been implemented.
In 2002 CDC established the national Environmental Public Health Tracking (EPHT) program. This program, building upon the recommendations of CDC work groups, the Pew Environmental Health Commission (Pew Foundation 2000), and other public health investigations (Lanphear and Gergen 2003), aims to develop a national network for the systematic collection, evaluation, and dissemination of health outcome and environmental hazard data. In response to the CDC program announcement, the Massachusetts Department of Public Health (MDPH) developed a proposal to track pediatric asthma through school health records based on previous work carried out in the Merrimack Valley of Massachusetts. Preliminary findings of this work suggested that school health records were a reliable data source for community-level asthma tracking, or surveillance, in children. This article describes the results of the first year of the Massachusetts pediatric asthma surveillance project and discusses project goals for years 2 and 3.
Materials and Methods
Surveillance design.
The objective of the MDPH pediatric asthma surveillance project is to determine the prevalence by school building of pediatric asthma among children enrolled in grades kindergarten (K) through 8. The surveillance system is designed to use the existing infrastructure of the school health system. Massachusetts school health records document demographic and emergency information, immunization history, past medical history, medication administration at school, and results of school physical exams. School nurses also keep medication administration plans for students receiving medications at school. Therefore, the information contained in the school health record is used as the data source for all health and demographic information. The school nurse or school health contact person for each school was asked to complete a pediatric asthma surveillance form reporting the number of children with asthma by gender and by grade. Only aggregate data were requested.
Target population.
In year 1 of the MDPH pediatric asthma surveillance project, all schools participating in the MDPH Essential School Health Service (ESHS) program were requested to provide information on the number of children with asthma in grades K–8 during the 2002–2003 academic year. The ESHS is a program designed to build school health capacity in Massachusetts public and private schools. ESHS districts are required to have a full-time master’s-prepared district nurse leader coordinating the health activities of that district’s schools. All Massachusetts communities were eligible to apply for ESHS grants. The target population included 958 public schools in 173 cities and towns (111 school districts) serving more than 395,000 children, or approximately 57% of Massachusetts’s K–8 students.
Surveillance definition of asthma.
School nurses were asked to provide information contained in school health records on the number of K–8 students attending the school “who have asthma of any type or severity” for the 2002–2003 school year. MDPH also requested the number of records documenting diagnosis of asthma made by a health care provider.
Data collection.
During January 2003, the MDPH mailed introductory letters regarding the asthma surveillance project to school superintendents, principals, and nurse leaders in eligible school districts. Project staff also made presentations at professional school nurse meetings to address questions or concerns. Additionally, an advisory committee was formed consisting of district nurse leaders from across the state. During the initial stages of the project, advisory committee members reviewed the surveillance form to ensure its ease of use. In March 2003, district nurse leaders in each target community were asked to distribute the two-page surveillance form asking for aggregate numbers of children with asthma by grade, gender, and school building (MDPH, unpublished protocol). Table 1 shows the information requested. When possible, surveillance forms were distributed to nurse leaders via E-mail to facilitate electronic data submission. If E-mail was not available, forms were sent via fax or the U.S. Postal Service. Follow-up telephone calls were placed to nurses who did not respond by April 2003. School enrollment data were collected from the Massachusetts Department of Education or from a school’s administrative staff. Schools that did not return complete surveillance data, or for which student enrollment data could not be obtained by June 2003, were considered nonresponders.
Data analysis.
Data analysis was performed with SAS (version 8.02; SAS Institute Inc., Cary, NC) and Microsoft Access (Microsoft Office 2000 SR-1 Professional; Microsoft Corp., Redwood, WA). The prevalence of asthma with 95% confidence intervals (95% CIs) was calculated for each participating school and school district and by grade level.
Results
Participation.
MDPH received complete information from a total of 760 schools. Of these schools, 668 were targeted ESHS schools, translating to a 70% participation rate. The remaining 92 schools were private schools (n = 52), charter schools (n = 9), and public schools not included in the ESHS (n = 31). At the district level, MDPH received data from at least 1 school in 87 of the 111 targeted ESHS districts (78%). Participation ranged from 6 to 100% within school districts.
Reported asthma prevalence.
The reported prevalence of asthma among the 311,610 students enrolled in the 760 participating schools was 9.2% (95% CI, 9.1–9.3%). Sixty percent of students reported to have asthma were male. Reported prevalence by individual schools ranged from 0 to 30.8%, with a median school asthma prevalence of 8.9%. Reported asthma prevalence by school district ranged from 2.7 to 16.2%, with a median district asthma prevalence of 8.8%. Figure 1 presents the frequency distribution of district-wide reported asthma prevalence figures. Reported asthma prevalence by grade ranged from 7.7 to 10.3 % (Table 2).
Other variables.
Analyses were conducted to determine the percentage of students with documentation of a health care provider diagnosis of asthma and/or asthma medication order. Results showed that half of all nurses reported that 90–100% of their students with asthma had documentation in the health record of a provider diagnosis of asthma and/or asthma medication orders. Approximately 25% of nurses indicated that 75–85% of student health records contained a diagnosis, and the remaining 25% of nurses reported that less than 75% of the student health records had this documentation.
Responses to questions eliciting other sources of information used by nurses to identify children with asthma showed that almost 90% listed parent or student communications as an alternative source of knowledge of a student's asthma status (41 and 48%, respectively). Direct observation of an asthma attack was rarely a source of information (< 0.5%).
Discussion
Comparison with other data sources.
The MDPH was successful in obtaining asthma surveillance data from 70% of targeted schools serving more than 311,000 students through its school-based pediatric asthma surveillance system. While the reported prevalence of pediatric asthma observed during the first year of the MDPH pediatric asthma surveillance project was 9.2%, it is important to note that prevalence ranged as high as 16.2% by district and nearly 31% by individual school. The statewide prevalence estimate is somewhat higher but nonetheless similar to the 8.8% prevalence of current childhood asthma in Massachusetts reported by the ARC based on BRFSS data collected in 2001 (ARC 2004). A Connecticut school-based surveillance effort by Environment and Human Health, Inc., similar to the one implemented in Massachusetts, reported a 9.7% asthma prevalence among students in grades K–5 (Storey et al. 2003). In comparison, K–5 prevalence estimate in Massachusetts was 8.8%.
Practical considerations.
A number of issues are important in assessing the utility of school health records as a pediatric asthma surveillance tool. These include the resource impacts on the individual school, the completeness of the data, the utility of the data to decisions makers, the ability to link health data with environmental databases, and compatibility with other state and national asthma surveillance programs. As a part of its CDC-funded EPHT program, the MDPH has begun addressing these issues.
Through close collaboration with school nurses and school nurse leaders, the MDPH has been able to develop a surveillance system that is responsive to concerns regarding impacts on schools. These concerns included requesting information once per year and at a time that is in less competition with other school nurse work demands, simplifying the data collection form, keeping school administrators informed, and sharing results in a timely fashion.
During the next 2 years, the MDPH will be evaluating the reliability and quality of the surveillance data collected. However, preliminary work carried out as part of the Merrimack Valley project suggested that data reliability and quality are excellent. In that project 184 schools serving grades K–8 located in 21 communities with 64,000 students participated. As in the current surveillance project, nurses were asked to provide data from school health records on the number of children with asthma. MDPH staff worked with school nurses and area physicians to confirm the diagnostic information contained in the school record and to validate the information collected to determine if asthma had been identified in children but not reported in the school record. The findings confirmed that the diagnostic information was accurate in 98% of the records evaluated and suggested that children with physician-diagnosed asthma were usually identified in the school health record as having asthma.
Although there was notable variation in reported asthma prevalence between schools and school districts, caution is needed when comparing the prevalence estimates between specific schools or districts during the surveillance project’s first year. Some school district prevalence estimates were based on reporting by a small percentage of the district’s schools and therefore may not be representative of that district’s actual asthma prevalence. Differences in school health systems between schools may further complicate the issue of comparability of asthma prevalence estimates. Such differences arise because there is not presently a requirement for systematic and standardized collection of asthma information in Massachusetts schools. Opportunities exist to improve the collection of asthma information through enhancements of the school-required medical history form and through encouraging the use of asthma action plans for all students with asthma. These improvements would facilitate more systematic and standardized data collection and aid in managing a student's asthma.
It is also important to note that a higher prevalence of asthma within one school or district does not necessarily indicate the presence of environmental problems within that district’s schools. Pediatric respiratory symptoms have been associated with a number of factors including exposures in the outdoor environment (Boezen et al. 1999; Delfino et al. 2002; Tolbert et al. 2000), exposures in the home environment (Rosenstreich et al. 1997; Smith et al. 2000; Sturm et al. 2004), genetic factors (El-Sharif et al. 2003; Lee et al. 2003), and lifestyle factors (Aligne et al. 2000; Heinrich et al. 2002). The MDPH pediatric asthma surveillance project is a surveillance system, and information about risk factors is not available. The collected information can be used to target intervention activities and to generate hypotheses about possible etiology. For example, IAQ is being assessed in approximately 100 schools as part of the MDPH's overall EPHT program. The assessments are conducted following a standardized protocol (MDPH, unpublished protocol) and include the measurement of total volatile organic compounds, particulate matter with an aerodynamic diameter < 2.5 μm, carbon monoxide, carbon dioxide, and evaluation of indicators of moisture and mold. IAQ assessment data for individual schools will be linked with asthma data to evaluate whether IAQ may be associated with asthma prevalence in students. School asthma data can also be linked with ambient air quality data by geocoding school addresses and connecting to existing ambient air quality data.
Local public health officers and other stakeholders often express interest in community-level prevalence estimates, but little information is available (Boss et al. 2001; Lanphear and Gergen 2003; White et al. 2002). This interest is based on the desire to identify and address the impacts of local environmental factors, as well as to delineate the need for health intervention programs. In a surveillance system that relies on aggregate data from school health records, prevalence estimates are generated by school and by school district. Therefore, the ability to generate community-specific prevalence is somewhat limited. Although it usually is possible to estimate town/city prevalence based on school data, some school districts are regional and draw students from multiple communities. Nevertheless, even school district–level prevalence estimates offer a more comprehensive view of pediatric asthma prevalence on the local level than do other surveillance data currently available. Sources such as hospitalization, emergency department, and Medicaid data look only at select segments of the population. These data sources can provide important insights into certain high-risk populations but exclude most individuals with asthma (Boss et al. 2001).
Another factor that may warrant consideration relates to the definition of asthma, which may not conform to the definitions used in the NHIS and BRFSS surveys and recommended by the Council of State and Territorial Epidemiologists (CSTE 1998). These definitions estimate asthma prevalence based upon responses to questions such as “[Has this child] ever been diagnosed with asthma?”, “Does this child still have asthma?” (CDC 2001), and “During the past 12 months has [child’s name] had an episode of asthma or an asthma attack?” (Bloom et al. 2003). It is unclear at this time which of the above definitions compares best with school nurse–reported asthma. The MDPH will be evaluating this issue over the next 2 years of the surveillance project.
Finally, the lack of electronic reporting to the MDPH may inhibit the utility of school-based surveillance. Many school nurses do not have direct access to a computer and/or the Internet, which presently limits electronic reporting of asthma data. In addition to the reporting methods employed in year 1 (fax, postal mail, and E-mail), other options are being explored that include web-based reporting and using electronic data collection forms on computer disks. To facilitate the transfer of information to CDC and other public health officials, the MDPH will use the National Electronic Disease Surveillance System (NEDSS). NEDSS is a standards-based electronic information system architecture that states can use to gather and disseminate information from a variety of sources.
Whether school-based asthma surveillance would be as successful in other states is an important question to resolve in order to meet the long-term goal of developing a national environmental public health tracking program. A Healthy People 2010 objective is to increase the proportion of U.S. schools with a nurse-to-student ratio of at least 1:750 (U.S. DHHS 2000). At present, however, not every school (including those in Massachusetts) has a nurse, or a nurse may be responsible for more than one school. Implementation of computerized school health records may help to overcome this limitation.
Additionally, the MDPH is working with the ARC to determine the feasibility of a coordinated asthma surveillance program for New England. Differences in laws governing school health, the definition of asthma, and the school health infrastructure in the region are among the issues being discussed.
This public health surveillance effort provides community-level asthma surveillance data for the first time in Massachusetts. It represents an important first step in the establishment of a statewide asthma surveillance system and in identifying the components and methodologic issues for a nationwide tracking system for pediatric asthma. During years 2 and 3 of the pediatric asthma surveillance project, the MDPH is expanding its target population to include all public, private, and charter schools serving any of grades K–8 in each of the state's 372 school districts. Preliminary analysis suggested that on the local level, asthma prevalence might not follow the socioeconomic patterns typically referenced as determinants of asthma patterns and trends. For that reason, it may be important to consider potential contributions of environmental factors in the indoor and ambient environments. As the project is extended statewide, MDPH will conduct statistical analyses to help characterize school populations in relation to reported asthma prevalence. Additionally, the MDPH plans to evaluate pediatric asthma prevalence in relation to school IAQ. The MDPH pediatric asthma surveillance project may prove a valuable tool for tracking asthma prevalence, planning intervention activities, and improving our understanding of pediatric asthma by providing both community-level and statewide asthma prevalence data for the first time in Massachusetts.
Figure 1 Distribution of district-wide reported asthma prevalence. MDPH pediatric asthma surveillance project, 2002–2003.
Table 1 Information collected by the MDPH pediatric asthma surveillance project, 2002–2003.
Variable name Description
School address Street address of the school
Male Number of male K–8 students with asthma
Female Number of female K–8 students with asthma
Grades K–8 Number of students in each grade with asthma (9 separate variables, 1 for each of grades K–8)
Percentage documented Percentage of students with health care provider documentation of asthma in health records
Sources Source(s) other than health care provider documentation that supplied nurses with knowledge of student asthma status
Table 2 Reported Asthma Prevalence by Grade. MDPH pediatric asthma surveillance project, 2002–2003.
Grade Prevalence % (n) 95% CI
K 8.1 (2,561) 7.8–8.4
1 7.7 (2,598) 7.4–8.0
2 8.3 (2,780) 8.0–8.6
3 9.0 (3,052) 8.7–9.3
4 9.5 (3,266) 9.2–9.8
5 10.0 (3,535) 9.7–10.3
6 10.3 (3,692) 10.0–10.6
7 10.0 (3,656) 9.6–10.2
8 9.8 (3,598) 9.5–10.2
Total 9.2 (28,738) 9.1–9.3
Total number of K–8 students enrolled in participating schools is 311,610.
==== Refs
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Teague WG Bayer CW 2001 Outdoor air pollution. Asthma and other concerns Pediatr Clin North Am 48 5 1167 1183 , ix.11579667
Tolbert PE Mulholland JA MacIntosh DL Xu F Daniels D Devine OJ 2000 Air quality and pediatric emergency room visits for asthma in Atlanta, Georgia, USA Am J Epidemiol 151 8 798 810 10965977
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7148ehp0112-00142815471738Mini-Monograph: Public Health TrackingArticlesUsing the Behavioral Risk Factor Surveillance System (BRFSS) for Exposure Tracking: Experiences from Washington State Laflamme Denise M. VanDerslice James A. Office of Environmental Health Assessments, Washington State Department of Health, Olympia, Washington, USAAddress correspondence to D. Laflamme, Office of Environmental Health Assessments, Washington State Department of Health, 7171 Cleanwater Lane, Building 2, P.O. Box 47846, Olympia, WA 98504-7846 USA. Telephone: (360) 236-3174. Fax: (360) 236-2251. E-mail:
[email protected] article is part of the mini-monograph “National Environmental Public Health Tracking,” which is sponsored by the U.S. Centers for Disease Control and Prevention (CDC).
We thank H. Ammann, Washington State Department of Ecology, and K. Wynkoop Simmons, Washington State Department of Health, for their comments.
This work is funded in part by CDC Environmental Public Health Tracking cooperative agreement U50/CCU022H38-01. BRFSS is supported in part by cooperative agreements U58/CCU002118-1 through -17 and U58/CCU022819-01-01.
This article was supported by an environmental public health tracking cooperative agreement from CDC. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of CDC.
The authors declare they have no competing financial interests.
10 2004 3 8 2004 112 14 1428 1433 1 4 2004 3 8 2004 Publication 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. One of the goals of the National Environmental Public Health Tracking Network is to link environmental data with chronic disease data as a means of improving our understanding of the environmental determinants of disease. Such efforts will rely on the ongoing collection of population exposure information, and there are few systems in place to track population exposures. In many cases, exposures can be estimated by combining environmental contaminant data with data about human behaviors. The Behavioral Risk Factor Surveillance System (BRFSS) provides a good opportunity to implement tracking of exposure-related behaviors. Washington State has used the BRFSS to collection information on environmentally related knowledge, attitudes, and behaviors. In this article we present case studies of modules covering drinking water, perceptions of environmental risk, and radon awareness and testing. Data on exposure-related behaviors have been useful for population exposure assessments and program evaluation. Questions about knowledge and attitudes and perceptions of environmental issues were not as useful because they lacked sufficient detail from which to modify existing education efforts. In some cases these data had not been used at all, indicating that the need for the data had not been well established. National development efforts should focus on compiling existing questions and developing questions on topics that are a priority at the state and national levels to be included as core questions and optional modules in future BRFSS surveys.
BRFSSenvironmental healthenvironmental public health trackingexposure assessment
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One of the primary goals of the National Environmental Public Health Tracking Network (EPHTN) is the development of the methods and data systems to link environmental data with chronic disease data in order to improve our understanding of the environmental determinants of disease [Centers for Disease Control and Prevention (CDC) 2004c]. Understanding exposure patterns in a population is a key element in linking environmental contamination to health outcomes. Although many studies have measured or estimated exposures in a defined population, there are few ongoing, systematic data collection efforts designed to track population exposures, particularly at the state or local level (CDC 2003b; Schober et al. 2003).
Many exposures are strongly influenced by behavior, including the types, frequencies, and amounts of foods and water consumed; the time spent and level of activity while breathing in different indoor environments; the time spent and level of hand-to-mouth activity in children; and the frequency of hand washing (Wallace et al. 1989; Yang et al. 1998). Individual efforts to test private wells or radon levels in the home and to take actions to reduce contaminant levels in one’s immediate environment are examples of individual behaviors that influence exposure by changing contaminant levels in their immediate environment.
In addition to the behaviors themselves, it is important to have a good understanding of the knowledge and attitudes of the individuals because these underlie the resulting behaviors. Given the importance of knowledge, attitudes, and behavior (KABs) as determinants of exposure, current data systems designed to capture information about KABs may be valuable tools for developing ongoing population exposure tracking efforts. One of the most widely developed systems is the Behavioral Risk Factor Surveillance System (BRFSS), an ongoing state-based telephone survey of randomly selected noninstitutionalized adults. The BRFSS is sponsored by CDC and is conducted in all 50 states, the District of Columbia, Puerto Rico, the Virgin Islands, and Guam (CDC 2004a). Survey participants are at least 18 years of age, live in the United States, and speak English (some states include the BRFSS in Spanish).
The survey instrument consists of a core set of questions developed by CDC that are used in all locations, optional modules of questions also developed by CDC that cover specific topic areas that may be used by a state, and questions developed and added by states. CDC’s optional modules of questions are available for state health departments to include based on state data needs and availability of funds to pay for them. In addition, states and local municipalities can develop their own modules of questions to include on the survey for their area.
The BRFSS primarily collects data on chronic diseases, injuries, infectious illnesses, and the behavioral factors underlying these conditions (Figgs et al. 2000). For many of these topics, BRFSS is the main source of state-level prevalence information, and BRFSS data are routinely used to set and track national health objectives such as Healthy People 2010 (Mokdad et al. 2003).
Many states have included environmental health–related questions on the BRFSS; however, most of these questions have been developed at the state level to address specific issues for that state. The heterogeneity of these issues is evidenced by the wide range of topics addressed, including asbestos, drinking water testing, food handling and safety, hanta-virus, lead, Lyme disease, rabies, and West Nile virus.
The Washington State Department of Health (WA DOH) has used the BRFSS to collect data on a variety of environmental health topics since 1990. Previously, there had been no analyses of how successful these questions were in addressing data needs within our agency or any assessment of how programs within the agency had used the results. The purpose of the present study was to compile and examine all environmental health data gathered in Washington using the BRFSS, assess the use and usefulness of these data, and examine the types of information gathered through the BRFSS that may have the greatest utility for ongoing exposure tracking efforts.
Materials and Methods
We collected and compiled Washington’s BRFSS instruments and data sets and reviewed them to identify questions pertaining to environmental health. We then generated weighted frequencies and/or summary statistics using STATA (version 7.0; Stata Corporation, College Station, TX). Data were weighted to account for different probabilities of selection of each household and the number of adults in each household and to account for differences in the surveyed population compared with the general population. Some questions asked about characteristics of the household instead of the respondent. For those questions, analyses were weighted to adjust for the probability of selection of the household only. For some results, the percentage of responses does not add up to 100% because of respondents who reported they did not know or who refused to answer some questions. In all cases the percentages reported are based on weighted proportions and thus are estimates of the proportion of the entire population having that characteristic.
To evaluate the use and usefulness of the environmental health data collected using the BRFSS, we met with program managers from each program in the Division of Environmental Health as well as program managers from other assessment units in WA DOH to ascertain if the data had been used, how the results influenced policy or programmatic decisions, and how useful the data were in helping the program managers in developing new approaches or in evaluating their current programs. The compiled BRFSS results were also presented to senior managers at the Division of Environmental Health in order to obtain their input on the usefulness of the existing data and to identify what data they needed to more effectively run their programs. This process was conducted as part of the development and production of a comprehensive document addressing health indicators in the state (WA DOH 2002). A main goal of this exercise was to help plan for and critically evaluate future BRFSS environmental health questions.
Results
From 1990 through 2004 WA DOH incorporated 13 environmental health–related modules into the BRFSS (Table 1). Environmental health–related modules were included in 9 of the 15 years. The statewide sample size ranged from 2,101 in 1990 to 4,826 in 2002.
The following sections summarize the BRFSS results and data use on three of these topic areas: drinking water, perceptions of environmental problems, and radon. These topic areas were chosen to illustrate different types of questions and the variability in how WA DOH programs used the results. The compiled results from all the environmental health questions are available elsewhere (WA DOH 2004a).
Drinking water.
Between 1996 and 2000, Washington State included three modules of questions regarding drinking water. These questions covered exposure-related behaviors (e.g., source of drinking water, testing of private wells, and treatment of tap water) and attitudes (e.g., reasons for using bottled water or water filters).
Most households reported receiving their drinking water from city water systems. This proportion increased from 68 to 77% between 1996 and 2000 (Table 2) and appeared to have been offset by a reduction in the proportion of households using private wells, which decreased from 17 to 10% over the same time period.
In 1996, 76% of households using private wells reported having their well tested at some point (Table 3). This percentage increased to 83% for 1998. About two-thirds reported testing their well within the last 3 years in both the 1996 and 1998 surveys (data not shown). The percentage of people who reported they did not know when their well had been tested decreased from 7 to 2% between 1996 and 1998. Of the households who reported testing their well, 6% recalled that the tests indicated some type of contamination.
In 2000 most households (83%) reported getting their drinking water from the tap, with the remainder reporting using bottled water or water from a water cooler as their usual source of drinking water (Table 4). Less than 1% reported using some “other source.” Forty percent of households using tap water reported using a water filter (Table 4). When asked why they used a water filter or bottled water, 37% responded that it was because of the water’s appearance, taste, or smell; 19% responded that it was because they were concerned that their water was unsafe; and 33% said it was for both of these reasons.
Uses of data.
Results concerning household water supply have provided the only reliable source of information on the number of people using private wells in the state. Previous estimates were derived by summing the reported number of service connections by all public water supplies for the total number of households in the state. The data from the BRFSS were used to revise substantially the previous estimates of the number of households on private wells (WA DOH 2002).
The responses to questions from households using private wells, combined with responses to behavior questions regarding the type of water used for drinking and the use of water filters, have been used to estimate population exposures to bacterial contamination and nitrate among private well owners. These behavior data along with the attitude data about why bottled water or water filters were used have been used in training water utility operators about consumer perceptions of water quality in public water supplies. Finally, behavior data on private well testing have been provided to local public health authorities to help guide their efforts in managing private well-water quality.
Perceptions of environmental problems.
In 1995 Washington State included questions on BRFSS to gauge public perceptions about the importance of various environmental issues. These questions were derived from those developed by the Northeast Tri-County Health District, which consists of Ferry, Stevens, and Pend Oreille counties in northeastern Washington State, and used in their 1994 county-level BRFSS (Gilmore Research Group 1995). For each environmental issue, the respondent was asked if it was “a problem in your community” and allowed to respond “yes,” “yes somewhat,” or “no.” The environmental issues were indoor and outdoor air quality, drinking water, workplace hazards, solid waste, pesticide use, wastewater, and hazardous waste sites.
Outdoor air quality was the issue most frequently identified as a problem in the community, with 22% responding that it was a problem or somewhat of a problem (Table 5). Indoor air quality was perceived to be a problem by only 6% of the respondents. For the other issues, about 10–15% of the respondents thought they were at least somewhat of a problem. Just more than half the respondents (54%) did not think that any of the environmental issues were a problem in their community.
Uses of data.
We could find no documentation of the rationale for including these questions on the statewide BRFSS. Environmental health program managers at WA DOH did not recall using these data and did not feel that the results of these questions provided useful information for program activities or public outreach.
Although these data have not been used at the state level, they have been used at the county level to help set priorities. The Northeast Tri-County Health District used these results as part of a comprehensive assessment of environmental hazards for the three-county area (Gilmore Research Group 1995). In 1996 similar perception questions were included on the BRFSS for Clark and Snohomish counties (Snohomish Health District 1997; Southwest Washington Health District 1997). The results of these perception questions were used in conjunction with local environmental data and stakeholder input to set health priorities and to guide public health planning efforts at the local level.
Radon.
Several behavior and knowledge questions about radon were included on the 1990, 1993, and 1997 BRFSS’s. These questions were part of an optional module developed by CDC. The intent of this module for Washington State was to gather information on the impact of efforts to educate the public about the risks of radon.
The proportion of the population that had heard of radon gas increased slightly from 72% in 1990 to 77% in 1993 (Table 6). In 1990, 81% of the respondents agreed with the statement that radon gas was harmful to health (data not shown). However, there was a steady decline in the proportion agreeing and a corresponding increase in the proportion answering “don’t know,” indicating an erosion in public awareness about radon (15% in 1990 to 27% in 1997). Less than one-third reported that they knew how to test for radon.
The percentage of households tested for radon gas was relatively low and did not change over time, ranging from 7 to 9% between 1990 and 1997 (Table 6). The percentage of households planning to test for radon gas was also low, staying between 6 and 8% during this time. The percentage of households planning to test for radon gas was even lower in households never tested for radon (4–6%).
Uses of data.
The questions in the radon module provide information about awareness, knowledge, and protective behaviors regarding radon. Program managers felt that information about behaviors as well as knowledge was more useful for program planning and evaluation than information from knowledge or attitude questions alone. Early results were used to guide and evaluate WA DOH’s radon awareness program; however, that program was discontinued in 1994. Data on household testing for radon have been used in a recent state compilation of environmental health problems (WA DOH 2002) and to help set priorities for Washington State’s Comprehensive Cancer Control Plan (WA DOH 2004b).
Discussion
CDC (2004c) defines environmental public health tracking as
the ongoing collection, integration, analysis, and interpretation of data about environmental hazards, exposure to environmental hazards, and human health effects potentially related to exposure to environmental hazards.
In this context, “environmental hazards” refers to chemical, radiologic, or biologic agents in the environmental that, because of their inherent characteristics, may pose a risk to people who are exposed. Operationally, data about environmental hazards include measures of the levels of these agents in environmental media, rates, or amounts of agents released into the environment and estimated environmental concentrations or emissions derived from modeling.
Although environmental monitoring and disease surveillance systems are well established throughout the country, there are few working examples of systems collecting ongoing, systematic data about environmental exposures. Such systems are the cornerstone of efforts to link environmental data to health data.
The BRFSS provides perhaps the best opportunity for a national systematic collection of data on behavioral determinants of exposure as well as the knowledge, perceptions, and attitudes underlying these behaviors. The marginal cost of adding questions to this established program is much less than the cost of designing and implementing a new survey. The sampling design and protocols are well established, the administrative mechanisms are in place, and there are a number of contractors who have experience using this survey in the field.
Perhaps the most attractive feature of BRFSS is its design, which allows individual states to tailor the content of the survey to meet state needs through the use of state-added questions and the optional use of CDC-developed modules. In addition, states or localities can increase the sample size overall or selectively target specific groups to meet analytical objectives. Consequently, prevalence estimates can be derived for each state, and for localities within states, with appropriate standard errors through the use of weights. This is in contrast to the other available national health surveys such as the National Health and Nutrition Examination Survey (NHANES) and the National Health Interview Survey, which are designed to provide national-level prevalence estimates.
One alternative is the development of surveys similar to the NHANES that could be conducted at the state or regional level to generate results for specific states or geographic regions. Such surveys could incorporate the use of biomarkers similar to the Second National Report on Human Exposure to Environmental Chemicals (CDC 2003b) to provide distributions of body burdens for state or regional populations. However, only New York City has developed and deployed such a survey, and sources of funding for a national or multistate effort to develop and conduct a state-level health and nutrition examination survey have yet to be identified.
The constant effort needed to collect exposure-related behavior information over time will require an ongoing institutional demand or mandate. Most environmental data are collected because it is required by federal or state legislation as part of regulatory activities. Disease surveillance systems were developed originally to meet a very real need to control outbreaks of communicable diseases, and this need was codified into rules and regulations covering notifiable conditions at the state level. Although an EPHTN would require ongoing, systematic data on a variety of environmental exposures, at present there are few examples of regulations or laws that require such information be collected. The first environmental health BRFSS core questions to be developed by CDC asked whether the respondent had experienced an illness due to environmental factors; these questions were part of the 2004 BRFSS and are currently included in the draft 2005 survey instrument. CDC has also developed optional modules on radon, environmental tobacco smoke, indoor air quality, and the home environment. The value of collecting such information needs to be clearly demonstrated to institutionalize these data collection efforts.
Standardizing questions across states and increasing the dissemination to a national audience will enhance the value of these data. Data from environmental health–related BRFSS questions have only rarely been published in the scientific literature (CDC 2003a; Kreutzer et al. 1999), and many of the existing results are found exclusively in state health department reports that are not abstracted by the major search services. A list of questions used in the core and optional modules is available on the CDC web site (CDC 2004b); however, there is no comprehensive database of state-added environmental health–related questions. Such a database would help states identify new BRFSS topics to consider and would provide a resource for program staff in states considering development of new BRFSS questions. Through the current EPHTN cooperative agreements, Washington State will be developing a repository of environmental health questions that have been used in the BRFSS, to share specific questions, results, and information about the validity of the available questions.
Within WA DOH there has not been a consistent approach for recommending and developing environmental health questions. Because of this, it has been difficult to determine how well some of the BRFSS questions met program needs within the agency or how some of the results were used. The information needs of the organization may not have been adequately developed and/or communicated to the staff designing the BRFSS module.
For many of the older questions (e.g., questions from the early 1990s), it was difficult to determine the program need that the questions were meant to address and how questions had been developed. In some cases it was difficult to ascertain how or if the WA DOH programs had used results of BRFSS questions they had requested because the program staff had changed and there was little or no documentation to show that the data had been analyzed or used. It appeared that the lack of use may have been because of changes in staff between the time the questions were proposed and when the results were available, or because of a lack of personnel with the skills to access and correctly analyze these data.
Even if the information needs were well conceived and communicated, the questions developed for the module may not have generated the type of data needed to address the information needs. For example, the environmental perception results did not accurately reflect actual known risks, indicating a need for better risk communication: Ambient air quality was identified as an environmental problem by a much larger proportion of respondents than indoor air quality (22.3 vs. 5.2%; Table 5). However, exposure studies have identified indoor air as the main source of exposure to many air pollutants and a source of some of the highest noncancer and cancer risks (U.S. Environmental Protection Agency 1990; Wallace et al. 1987). While knowing that such knowledge gaps exist is important, most questions addressing knowledge and attitude were generally too broad to provide sufficient detail from which to base modifications of existing educational materials.
Cognitive testing, pretesting, and studies of question validity are essential for ensuring that questions generate meaningful information (Aday 1996). Finally, the need for the information may not have been great enough to have managers take the time and resources to access, analyze, and incorporate the results into their programs, or the information needs may have changed during the 1.5–2 years that elapse between deciding to use the BRFSS and receiving the final data set from CDC.
The use of BRFSS does have clear limitations. WA DOH programs are charged $850 for each state-added question included in the BRFSS. While this cost has been a barrier for some programs, the overall length of the survey has become a more important constraint. Because of concerns about the declining response rate, WA DOH decided to limit the total length of the survey to 25 min. Given the length of the core survey, usually < 12 min are available for all optional modules and state-added questions. This constraint needs to be managed to avoid competition between state health department programs wishing to use the BRFSS.
In response to an increase in interest in using the BRFSS, our process for selecting state-added questions to be added to the survey has been modified over the last 5 years to require explicit descriptions of the information needs, how the data will be analyzed to meet these needs, and who will be responsible for conducting the data analysis. These factors, as well as evidence that previously collected data have actually been used by WA DOH programs, are used as criteria in the selection of questions to be included.
From our experience in Washington State, many environmental health professionals do not see the value in using tools such as the BRFSS to monitor KABs that lead to environmental exposures. We have observed this among WA DOH managers as well as among environmental health directors from local health jurisdictions around the state. This may be due in part to the nature of traditional environmental public health functions, which have centered on developing and enforcing standards in areas such as food safety, drinking water safety, radiation protection, and solid waste disposal.
Understanding environmentally related KABs will likely become more important as environmental health professionals begin to face issues such as large-scale polychlorinated biphenyl and arsenic contamination. In these circumstances, risk management cannot focus on regulating releases or mandating environmental remediation but rather must rely on efforts of health promotion programs to educate and motivate individuals to take appropriate steps to minimize their exposures. Tools such as the BRFSS will be critical for designing and evaluating such efforts.
Conclusions
The BRFSS offers an excellent opportunity to implement a system for tracking important exposure-related behaviors as part of the EPHTN. The relatively low marginal cost of adding nationally developed optional modules or state-added questions, the flexibility inherent in the sample design, and the well-developed infrastructure and procedures make the BRFSS an attractive option for exposure tracking. Although environmental health topics have not typically been included in the BRFSS at the national level, several states have developed and successfully used the BRFSS to collect data about exposure-related behaviors and the knowledge and attitudes that underlie these behaviors. As with any survey, there are limitations to the accuracy of data recall. Even so, such data on past behaviors have been useful for population exposure assessments and program evaluation. Questions about perceptions of environmental problems alone have not been seen as useful because they have lacked sufficient detail from which to modify existing education efforts. National development efforts should focus on compiling existing questions and experiences and identifying topics that are a priority at the state and national levels to be included as core questions and optional modules in future BRFSS surveys.
Table 1 Environmental health–related modules included on Washington State BRFSS and use of data at the state level, 1990–2004.
Topic Year Data used?
Drinking water source, well testing 1995, 1996, 1998, 2000 Yes
Environmental tobacco smokea 2000 Yes
Fish consumption, levels and awareness of fish advisories 2002, 2004 Yes
Hazardous waste sites, perception of problem 1995 No
Household heating source 1996 No
Household mold presence 2004 NA
Indoor air quality, perception of problem 1995, 1996 No
Outdoor air quality, perception of problem 1995, 1996 No
Illnesses perceived to be caused by indoor and outdoor air contaminationb 2004 NA
Pesticides, household use and perception of problem 1995, 2000 Yes
Radon awareness and testing behaviorsa 1990, 1993, 1997 Yes
Waste water and solid waste disposal, perception of problem 1995, 1996 No
Water recreation, frequency of use 1990 No
West Nile virus, awareness and protective behaviors 2004 NA
Workplace hazards, perception of problem 1995 No
NA, not applicable.
a CDC optional module.
b CDC core question for 2004.
Table 2 Source of household drinking water, Washington State, 1996–2000.
1996
1998
2000a
Question No. % (95% CI) No. % (95% CI) No. % (95% CI)
City water system 2,519 68.4 (66.6–70.2) 2,627 72.3 (70.5–74.1) 2,757 76.9 (75.5–78.3)
Small community system 215 6.0 (5.0–7.0) 248 7.1 (6.1–8.1) 235 6.6 (5.8–7.4)
Private well 545 16.5 (15.1–17.9) 472 13.7 (12.3–15.1) 368 10.0 (9.0–11.0)
Other 80 2.2 (1.6–2.8) 59 1.6 (1.2–2.0) 65 1.9 (1.5–2.3)
Don’t know 191 5.5 (4.5–6.5) 155 4.4 (3.4–5.4) 140 4.1 (3.3–4.9)
95% CI, 95% confidence interval.
a The wording of the drinking water source question for 2000 was modified from the question used in 1996 and 1998. Questions in 1996 and 1998: “What is the source of your home’s drinking water? Does it come from: a city or district supply, a community system, a private well, or some other source?” Questions in 2000: “Where does the water for your household come from? A private well serving just your household, a community well or other small water system which serves fewer than 15 homes, a city or municipal water supply, other?”
Table 3 Private domestic water well testing, Washington State, 1996 and 1998.
1996
1998
Question No. % (95% CI) No. % (95% CI)
Has your well water ever been tested?
Yes 420 75.5 (71.0–80.0) 395 82.7 (78.4–87.0)
No 75 13.1 (9.8–16.4) 38 8.8 (5.5–12.1)
Don’t know 50 11.4 (7.7–15.1) 39 8.4 (5.5–11.3)
Did the results from well testing indicate the presence of any contaminants?
Yes 25 5.9 (3.4–8.4) 27 6.1 (3.6–8.6)
No 371 89.1 (85.8–92.4) 357 91.5 (88.6–94.4)
Don’t know 23 4.8 (2.6–7.0) 11 2.3 (0.7–3.9)
Table 4 Drinking-water source, use of water filters among users of tap water, and reasons for water-filter use, Washington State, 2000.
Question No. % (95% CI)
Where do you usually get the water that you drink at home?
Tap 2,962 83.0 (80.5–85.5)
Bottled water or from water cooler 572 15.7 (13.3–18.1)
Other source 25 0.7 (0.1–1.3)
Do you use a water filter for your household drinking water? (tap water users only)
Yes 1,188 39.7 (37.9–41.5)
No 1,762 59.8 (58.0–61.6)
What is the main reason that you use a water filter or bottled water for your drinking water at home?
Don’t like the way the water looks, tastes, or smells 643 37.0 (34.6–39.4)
Concerned that the water is not safe to drink 332 18.8 (16.8–20.8)
Both of these two reasons 588 33.3 (30.9–35.7)
Some other reason 170 9.4 (8.0–10.8)
Table 5 Opinions about environmental problems in the community, Washington State, 1995.
Yes
Yes, somewhat
No
Questiona No. % (95% CI) No. % (95% CI) No. % (95% CI)
In your opinion, is (topic is inserted) a problem in your community?
Outdoor air quality? 458 13.3 (12.1–14.5) 305 9.0 (8.0–10.0) 2,543 76.6 (73.5–79.7)
Drinking water quality? 376 11.1 (8.7–13.5) 150 4.4 (2.8–6.0) 2,742 82.3 (79.2–85.0)
Hazards in your workplace? 207 9.5 (8.1–10.9) 85 4.1 (3.1–5.1) 1,842 85.5 (83.9–87.1)
Solid waste management? 258 7.3 (6.3–8.3) 82 2.7 (2.1–3.3) 2,942 88.2 (87.0–89.4)
Pesticide use and control? 253 7.1 (6.1–8.1) 98 2.8 (2.2–3.4) 2,790 84.3 (85.4–85.7)
Wastewater management? 232 7.0 (6.0–7.0) 77 2.3 (1.7–2.9) 2,895 86.8 (85.6–88.0)
Hazardous waste sites? 216 6.3 (5.5–7.1) 48 1.6 (1.0–2.2) 2,942 86.8 (85.6–88.0)
Air quality inside your home? 87 2.6 (2.0–3.2) 83 2.6 (2.0–3.2) 3,138 93.6 (92.6–94.6)
a Introductory statement: “These questions ask about the quality of the environment in your community. I’m going to read you a list of items and for each item I’d like you to tell me if, in your opinion, it is a problem in your community.
Table 6 Radon awareness and radon testing, Washington State, 1990, 1993, and 1997.
1990
1993
1997
Question No. % (95% CI) No. % (95% CI) No. % (95% CI)
Have you heard of radon, which is a radioactive gas that occurs in nature?
Yes 1,522 71.9 (69.7–74.1) 1,996 76.5 (74.7–78.3)
No 539 26.4 (24.3–28.4) 568 22.6 (20.8–24.4) Not asked
Don’t know 40 1.7 (1.1–2.3) 22 0.9 (0.5–1.3)
Refused 0 NA 0 NA
Do you know how to test your home for the presence of radon gas?
Yes 428 28.6 (26.2–31.0) 515 24.9 (22.9–26.9)
No 1,034 67.3 (64.7–69.9) 1,456 72.7 (70.6–74.8) Not asked
Don’t know 60 4.1 (3.0–5.2) 45 2.3 (1.6–3.0)
Refused 0 NA 2 0.1 (0–0.3)
Has your household air been tested for the presence of radon gas?
Yes 112 6.8 (5.4–8.2) 164 8.7 (7.3–10.1) 273 8.3 (7.1–9.5)
No 1,346 89.0 (87.2–90.8) 1,753 86.5 (84.9–88.1) 3,030 83.5 (82.0–85.0)
Don’t know 64 4.2 (2.9–5.5) 98 4.7 (3.7–5.7) 285 7.7 (6.7–8.7)
Refused 0 NA 3 0.2 (0.02–0.4) 16 0.4 (0.1–0.7)
Do you, or does anyone in your home plan to have your household air tested for radon within the next year?
Yes 101 6.8 (5.2–8.4) 138 7.8 (5.9–9.8) 198 6.1 (5.1–7.1)
No 1,280 84.3 (82.1–86.5) 1,708 83.4 (81.6–85.2) 3,090 84.9 (83.5–86.3)
Don’t know 141 8.9 (7.2–10.6) 170 8.9 (7.4–10.3) 300 8.5 (7.4–9.6)
Refused 0 NA 2 0.1 (0–0.3) 16 0.5 (0.2–0.8)
NA, not applicable.
==== Refs
References
Aday LA 1996. Designing and Conducting Health Surveys. 2nd ed. San Francisco:Jossey-Bass.
CDC (Centers for Disease Control and Prevention) 2003a Knowledge, attitudes, and behaviors about West Nile virus—Connecticut, 2002 MMWR Morbid Mortal Wkly Rep 52 37 886 888
CDC 2003b. Second National Report on Human Exposure to Environmental Chemicals. NCEH 02-0716. Atlanta, GA:Centers for Disease Control and Prevention, National Center for Environmental Health.
CDC 2004a. Behavior Risk Factor Surveillance System. Atlanta, GA:Centers for Disease Control and Prevention. Available: http://www.cdc.gov/brfss [accessed 13 January 2004].
CDC 2004b. Behavioral Risk Factor Surveillance System—State Information Web Page. State Publications Search. Atlanta, GA:Centers for Disease Control and Prevention. Available: http://www2.cdc.gov/nccdphp/brfss2/publications/index.asp [accessed 13 January 2004].
CDC 2004c. Environmental Public Health Tracking Program—at a Glance. Atlanta, GA:Centers for Disease Control and Prevention. Available: http://www.cdc.gov/nceh/tracking/trackingAAG.pdf [accessed 13 January 2004].
Figgs LW Bloom Y Dugbatey K Stanwyck CA Nelson DE Brownson RC 2000 Uses of behavioral risk factor surveillance system data, 1993–1997 Am J Public Health 90 774 776 10800428
Gilmore Research Group 1995. Behavioral Risk Factor Survey of Adults Living in the Northeast Tri-County Health District. Prepared for Northeast Tri-County Health District and the Area Health Education Center, WSU Spokane. Seattle, WA:Gilmore Research Group.
Kreutzer R Neutra RR Lashuay N 1999 Prevalence of people reporting sensitivities to chemicals in a population-based survey Am J Epidemiol 150 1 12 10400546
Mokdad AH Stroup DF Giles WH 2003 Public health surveillance for behavioral risk factors in a changing environment: recommendations from the Behavioral Risk Factor Surveillance Team MMWR Recomm Rep 52 RR-9 1 12 12817947
Schober SE Sinks TH Jones RL Bolger PM McDowell M Osterloh J 2003 Blood mercury levels in US children and women of childbearing age, 1999–2000 JAMA 289 13 1667 1674 12672735
Snohomish Health District 1997. Snohomish County Behavioral Risk Factor Surveillance Report 1996. Everett, WA:Snohomish Health District.
Southwest Washington Health District 1996. Clark County Behavioral Risk Factor Survey. Appendix D: Behavioral Risk Factor Survey—Environmental Health Questions. Vancouver, WA:Southwest Washington Health District.
U.S. Environmental Protection Agency 1990. Reducing Risk: Setting Priorities and Strategies for Environmental Protection. EPA-SAB-EC-90-021. Washington, DC:U.S. Environmental Protection Agency, Science Advisory Board.
WA DOH 2002. The Health of Washington State—A Statewide Assessment of Health Status, Health Risks, and Health Care Services. Olympia, WA:Washington State Department of Health. Available: http://www.doh.wa.gov/HWS/default.htm [accessed 29 March 2004].
WA DOH 2004a. Behavioral Risk Factor Surveillance System (BRFSS), Results of Environmental Health Questions, 1990–2004. Olympia, WA:Washington State Department of Health.
WA DOH 2004b. Washington State Comprehensive Cancer Control Plan, 2004–2008. Olympia, WA:Washington State Department of Health.
Wallace LA Pellizzari ED Hartwell TD Davis V Michael LC Whitmore RW 1989 The influence of personal activities on exposure to volatile organic compounds Environ Res 50 1 37 55 2792060
Wallace LA Pellizzari ED Hartwell TD Sparacino C Whitmore R Sheldon L 1987 The TEAM (Total Exposure Assessment Methodology) Study: personal exposures to toxic substances in air, drinking water, and breath of 400 residents of New Jersey, North Carolina, and North Dakota Environ Res 43 2 290 307 3608934
Yang S Leff MG McTague D Horvath KA Jackson-Thompson J Murayi T 1998 Multistate surveillance for food-handling, preparation, and consumption behaviors associated with foodborne diseases: 1995 and 1996 BRFSS food-safety questions MMWR CDC Surveill Summ 47 4 33 57 9750563
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7150ehp0112-00143415471739Mini-Monograph: Public Health TrackingArticlesWisconsin’s Environmental Public Health Tracking Network: Information Systems Design for Childhood Cancer Surveillance Hanrahan Lawrence P. 1Anderson Henry A. 1Busby Brian 2Bekkedal Marni 1Sieger Thomas 1Stephenson Laura 1Knobeloch Lynda 1Werner Mark 1Imm Pamela 1Olson Joseph 11Division of Public Health, Wisconsin Department of Health and Family Services, Madison, Wisconsin, USA2E-Commerce, University of Wisconsin Division of Information Technology, Madison, Wisconsin, USAAddress correspondence to L.P. Hanrahan, Bureau of Health Information and Policy, Division of Public Health, Wisconsin Department of Health and Family Services, Room 372, 1 West Wilson St., Madison, WI 53702 USA. Telephone: (608) 267-7173. Fax: (608) 267-4853. E-mail:
[email protected] article is part of the mini-monograph “National Environmental Public Health Tracking,” which is sponsored by the Centers for Disease Control and Prevention (CDC).
This work was funded by CDC through the Wisconsin Environmental Public Health Tracking Program (grant U50/CCU522439-01).
This article was supported by an environmental public health tracking cooperative agreement from CDC. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of CDC.
The authors declare they have no competing financial interests.
10 2004 3 8 2004 112 14 1434 1439 1 4 2004 3 8 2004 Publication 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 article we describe the development of an information system for environmental childhood cancer surveillance. The Wisconsin Cancer Registry annually receives more than 25,000 incident case reports. Approximately 269 cases per year involve children. Over time, there has been considerable community interest in understanding the role the environment plays as a cause of these cancer cases. Wisconsin’s Public Health Information Network (WI-PHIN) is a robust web portal integrating both Health Alert Network and National Electronic Disease Surveillance System components. WI-PHIN is the information technology platform for all public health surveillance programs. Functions include the secure, automated exchange of cancer case data between public health–based and hospital-based cancer registrars; web-based supplemental data entry for environmental exposure confirmation and hypothesis testing; automated data analysis, visualization, and exposure–outcome record linkage; directories of public health and clinical personnel for role-based access control of sensitive surveillance information; public health information dissemination and alerting; and information technology security and critical infrastructure protection. For hypothesis generation, cancer case data are sent electronically to WI-PHIN and populate the integrated data repository. Environmental data are linked and the exposure–disease relationships are explored using statistical tools for ecologic exposure risk assessment. For hypothesis testing, case–control interviews collect exposure histories, including parental employment and residential histories. This information technology approach can thus serve as the basis for building a comprehensive system to assess environmental cancer etiology.
childhood cancerenvironmentexposuresinformaticsinformation systemspublic healthsurveillancetracking
==== Body
Even though the environment is known to play an important role in human health, no comprehensive, integrated, state or national system exists to track the countless hazards, exposures, and ensuing health effects that could be due to environmental factors [Environmental Public Health Tracking Network (EPHTN) 2004]. For example, when environment is broadly defined to include occupational exposures, environmental pollution, and ionizing and ultraviolet radiation, 9% of cancer deaths have been attributed to known environmental causes (Harvard Report on Cancer Prevention 1996). And yet, what ultimately is known may be extremely limited precisely because a comprehensive, ongoing environmental health tracking system linking hazards and exposures to health effects does not exist.
In response to this challenge, the EPHTN program was established to develop a comprehensive environmental public health surveillance system. The program involves
the ongoing collection, integration, analysis, interpretation, and dissemination of data on environmental hazards; exposures to those hazards; and related health effects. The goal of tracking is to provide information that can be used to plan, apply, and evaluate actions to prevent and control environmentally related diseases. (EPHTN 2004)
Public health depends heavily upon information science. Increasingly, modern public health practice requires advanced, networked, computer-assisted technology to process a wide variety of information assets that monitor disease, analyze and detect risks, provide decision support, alert and communicate with those who need to know, continuously educate and train, support and manage public health response, and measure effectiveness. This recognition has brought about the specification and development of the Public Health Information Network (PHIN) by the Centers for Disease Control and Prevention (CDC) and its public health partners (e.g., state and local public health agencies and professional associations, for example, the Association of State and Territorial Health Officials, Council of State and Territorial Epidemiologists, Association of Public Health Laboratories, National Association of County and City Health Officials).
It has been estimated that environmental pollutants are responsible for a substantial attributable fraction of certain childhood diseases and their associated health care costs (Landrigan et al. 2002). The attendant environmental causes for childhood lead poisoning, asthma, cancer, and developmental disabilities alone may account for as much as $64.8 billion or 2.8% of total U.S. health care costs annually (Landrigan et al. 2002). The environmental attributable fraction for childhood cancer is estimated at 5% (range, 2–10%), and annual health care costs are estimated at $333 million (Landrigan et al. 2002).
Indeed, studies of childhood cancer have discovered a number of biologically plausible environmental associations (Zahm and Devesa 1995), including hazardous air pollutants and leukemia (Reynolds et al. 2003), leukemia and pesticide use (Reynolds et al. 2002), leukemia and electric and magnetic fields (Brain et al. 2003), leukemia and ionizing radiation (Axelson et al. 2002), nervous system cancers and parental pesticide exposures (Feychting et al. 2001), and road traffic (benzene exposures) and leukemia (Crosignani et al. 2004).
In Wisconsin an estimated 25,800 new cases of cancer are expected to have occurred in 2003 (American Cancer Society 2004). Approximately 269 will occur in children. Presently, the environmental contribution and etiology of these cases are unknown. Over the years, many of these childhood cancer cases have been the source of numerous requests for labor-intensive, systematic environmental cancer cluster investigations and assessments (Fiore et al. 1990). However, because of the many intrinsic limitations of the “self-selected” cluster analytic approach (Rothman 1990), our experience has resulted in little, if any, insight into the potential causes, environmental or otherwise.
The maturation of networked information systems holds the promise of automating much of public health practice (Yasnoff 2001). By automating practice with advanced information technology, comprehensive surveillance and tracking systems may be created that have the statistical power and considerable information depth needed to understand the operation of complex disease causal factors. To this end, an information technology platform is described that is in development to support environmental public health tracking in Wisconsin. One application of effort is illustrated using cancer registry data.
Materials and Methods
Information technology development.
A number of stakeholder committees were established to guide Wisconsin’s PHIN (WI-PHIN) development and establish functional system requirements. Members included public health staff from the Wisconsin Department of Health and Family Services, the University of Wisconsin [university information technology staff (UW-DoIT)], WiscNet (networking provider), University of Wisconsin Medical School, and Wisconsin State Laboratory of Hygiene); other state agencies; local public health services; hospitals, the health insurance industry; and the Wisconsin business community.
The “Wisconsin Idea,” used to develop the state’s PHIN, involves rapid, state-of-the-art technology transfer from the university to government, businesses, and all citizens of the state and nation (Stark 1995). The WI-PHIN program is a 21st century embodiment of the idea, providing cutting-edge information technology services through research and development. UW-DoIT provides the information systems research, development, technical support, and hosting for WI-PHIN.
Wisconsin has combined its financial support from several categorical funding sources to develop a secure, web-based WI-PHIN portal to respond to bioterrorism and all other public health threats. Funding resources have included bioterrorism public health preparedness funding, National Electronic Disease Surveillance System (NEDSS)–NEDSS Base System (NBS) deployment, Wisconsin Maternal and Child Health Program, audiometric newborn screening, environmental public health tracking, among others.
Nationally, the PHIN has nine architectural functional specifications to guide development. NEDSS (2001) also specifies standards for database structure and electronic surveillance systems. Using the PHIN specifications as a guide (CDC 2002), EPHTN program area module (PAM) architectural requirements were developed for the childhood cancer tracking system. Together, the following attributes were applied to the design of the WI-PHIN portal: The EPHTN PAM must include the secure, automated exchange of cancer case data between public health–based and hospital-based cancer registrars; web-based supplemental data entry for environmental exposure confirmation and hypothesis testing; automated data analysis, visualization, and exposure–outcome record linkage; directories of public health and clinical personnel for role-based access control (RBAC) to sensitive surveillance information; public health information dissemination and alerting; and information technology security.
Surveillance example: hypothesis generation.
Available data systems were inventoried and included systems describing hazards, exposures, health outcomes, and populations at risk. Sources of data included statewide health and environmental monitoring information and nationally available environmental and demographic data sets. Each system was qualitatively evaluated for its ability to be linked with other systems and for its coverage (years, geographic completeness, etc.). These systems would be contained in an integrated data repository (IDR) and linked through common attributes such as time and geographic location. System specifications required the support of two surveillance tracks—hypothesis generation and hypothesis testing—to provide a more complete view of environmental disease risk. Under the first track (hypothesis generation), cancer case data are sent electronically to WI-PHIN to populate the IDR, and basic surveillance/descriptive analyses are performed. Environmental data are then linked, and exposure–disease relationships are modeled using statistical and geographic information system (GIS) tools for ecologic exposure risk assessment. In the second track (hypothesis testing), case follow-back interviews are conducted using secure web-based data entry forms to obtain person-level exposure histories, including parental employment and residential histories, on cases and controls.
Childhood cancer data were obtained from the Wisconsin Cancer Registry for 1990 through 2000 (the most recent available year) (Wisconsin Department of Health and Family Services 2004). Cases were selected where individuals were younger than 20 years of age at diagnosis. Case frequencies were arrayed by cause, and age-adjusted rates were plotted by county for the most frequent cancer types.
Known exposure–disease relationships were ascertained by performing searches of the National Library of Medicine’s PubMed (2004) database along with Internet searches. A work group was established to review findings and determine the web-based interview structure that would obtain risk-confirming and hypothesis-testing person-level exposure data on cases and controls.
To begin work on hypothesis generation, initial ecologic risk assessments were performed by correlating county air pollution exposure estimates (Technology Transfer Network 2002) with county age-adjusted cancer rates. Age-adjusted rates were constructed using the direct method and the 2000 census standard million population (U.S. Census Bureau 2004). A nonparametric correlation was then calculated between the ranks of county air pollutants and the rank of age-adjusted cancer rate.
Results
Information technology.
Substantial progress has been made on the secure WI-PHIN portal since its start in 1999. Since its inception, > $7 million has been expended by combining public health information technology funding sources. Table 1 illustrates the information technology function requirements and Wisconsin’s progress toward achieving them. Figure 1 provides a conceptual diagram of portal information flows and services.
The WI-PHIN has the capability to perform automated data exchange, use electronic clinical data for event detection, and use the web for secure data entry for case follow-up. An online survey capability was created that can support web-based manual entry for case reporting. Storage capacity for laboratory results is established, as is case management capability. Specific case management rules continue to be refined with the integration of PAM business requirements. SAS (version 8.2; SAS, Inc., Cary, NC) has been integrated into the portal for automating statistical analyses and visualization, and GIS services (using Environmental Systems Research Institute, Inc., Redlands, CA, products) continue to be developed. A directory of personnel has been established containing more than 2,400 registered WI-PHIN users from more than 900 agencies (state and local public health agencies, hospitals, local emergency response agencies, clinics, etc.). The directory contains user contact information (e.g., E-mail, phone, fax, pager, cell phone) along with other attributes (public health role, occupation, agency affiliation, professional skills/competencies/certifications, volunteer for emergency response, etc.). Users can create personal groups from the directory and synchronize entries to their personal digital assistants (PDAs).
A considerable alerting and information dissemination capability has been developed. A commercial call-tree service (simultaneous phone, fax, pager, E-mail for public health emergencies and other alerts) has been integrated into the portal. Scenarios are being developed that contact appropriate responders to specific public health emergencies. In addition, the web portal has public and private topic areas and threaded discussion forums that are associated with public health programs such as environmental tracking. Users may bookmark topic areas and receive E-mail updates (digests) when new content is added to their subscribed content areas. All users can easily add content to the portal (text, web links, upload documents, streaming media) and add events to the calendar. Calendar entries can be synchronized to a PDA. Distance training and streaming media services are available within the portal. These features have been used to create on-line courses to train public health staff throughout the state on public health topics (e.g., bioterrorism) and on portal features and use techniques.
Advanced security controls are a part of the portal design. Users must register with the State of Wisconsin Web Access Management System and obtain a login ID and password to access the system. Users then reach the web site with an encrypted secure socket layer (SSL) connection. RBAC determines end-user access to surveillance programs such as the NBS (infectious disease reporting), SPHERE (Secure Public Health Electronic Record of the Wisconsin Maternal and Child Health Program), WE-TRAC (Wisconsin Early Hearing Detection and Intervention Referral and Coordination System), and the EPHTN childhood cancer pilot.
Hardware is also protected. Servers have redundant firewalls, virus scanning, continuous external port scanning and probing, and intrusion detection appliances. The system is continually backed up, and continuity of operations is assured through site mirroring planning and procedural implementation. Administrative security policies cover appropriate conduct and use documents, access auditing and logging, and on-line training.
Hypothesis generation example: benzene and leukemia.
Table 2 displays the environmental, population, and health outcome data systems that are under evaluation for inclusion and linkage in the EPHTN IDR. Wisconsin childhood cancer cases are displayed in Table 3. A total of 2,960 cases were selected. Leukemia, lymphatic, and brain cancers accounted for 51% of the cases. These were selected for rate analysis, plotting, literature review, and follow-back to assess environmental contributions. More than 1,000 articles/sources were obtained, and environmental exposure history development continues (Agency for Toxic Substances and Disease Registry 2004) for web-based data entry.
A preliminary hypothesis-generating assessment was made with some of the currently available data. These data consisted of the Wisconsin Cancer Registry (Wisconsin Department of Health and Family Services 2004), National Air Toxics Assessment data for 1996 (Technology Transfer Network 2002), and census county population estimates. Age-adjusted county cancer rates were correlated to each of the pollutants. Estimated inhalation concentrations for benzene are depicted by county in Figure 2. Figure 3 plots the age-adjusted leukemia incidence by county. Correlating the two revealed a significant rank correlation between exposure and disease (R = 0.31, p < 0.01). Indeed, benzene appears repeatedly in the literature as a potential cause of leukemia.
Discussion
The PHIN information technology functions provide a clear implementation plan to automate public health practice. Automation will be a tremendous benefit to the public health system, improving efficiency, coordination, assurance, response, and evaluation. However, substantial resources are necessary to accomplish this. An EPHTN program meeting the PHIN requirements is only possible by combining multiple funding sources that support public health information technology. Wisconsin has done this and has made substantial progress in its development of WI-PHIN and EPHTN, but much work remains. Continued federal funding and support from other sources will be necessary if the many ambitious PHIN goals are to be achieved.
Wisconsin’s EPHTN childhood cancer pilot PAM has established a PHIN-compatible framework to track the environmental causes of disease. Two mechanisms are specified—hypothesis generating and hypothesis testing. Hypothesis generation occurs through the linking of environmental exposure databases (by time and place) with health outcome and population data. Ecologic risks can be generated that suggest avenues for further investigation. When a subset of the available data was used, a population-level rank correlation was found between estimated human inhalation benzene exposures and childhood leukemia risk, corroborating previous findings. However, the “ecologic fallacy” is the chief limitation of this approach: There can be underlying heterogeneity of exposure levels and covariates within the group or area assigned its population-level fixed value (Rothman and Greenland 1998). Hypothesis testing provides a solution to this by creating a web-based follow-back form to capture individual, person-level exposure histories on cases and controls. Known risks can be assessed on the questionnaire, and hypothesized causes can be tested in the case–control framework. These results can then be used to construct environmental attributable fractions for case incidence.
But both of these approaches are limited in that neither obtains biologic or environmental markers of actual exposures or individual susceptibility. In addition, pre-existing exposure monitoring data may be further limited because much of the available information is collected for regulatory purposes. These environmental monitoring systems have not been designed to substantially support environmental health tracking systems. Reliable and valid laboratory measures of environmental exposures, cancer risk, and individual susceptibility (i.e., gene–environment interactions) are needed, and they would considerably increase our understanding of the environment’s contribution to childhood cancer (Grufferman 1998). Although this detailed environmental monitoring activity is outside of the project scope largely because of funding, the EPHTN PAM is positioned to integrate these kinds of measures because it can accept laboratory result messaging.
Through the Wisconsin Idea, the WI-PHIN program has developed innovative information technology solutions that can serve as an implementation model for others. Best practices and lessons learned are emerging as the WI-PHIN develops its pilot program for environmental childhood cancer tracking. This experience will be shared with other states seeking to better understand the relationship between childhood cancer and the environment using advanced information technology. This approach can then serve as the foundation building toward a comprehensive system to assess environmental cancer etiology while extending the method to tracking other environmental exposure and disease relationships.
Figure 1 WI-PHIN information flows and services. Abbreviations: HAN, Health Alert Network; PAMs, program area modules; WAMS, State of Wisconsin Web Access Management System.
Figure 2 National Air Toxics Assessment: 1996 estimated Wisconsin county median exposure concentration of benzene. From U.S. Environmental Protection Agency/Office of Air Quality Planning and Standards National-Scale Air Toxics Assessment (Technology Transfer Network 2002).
Figure 3 Wisconsin age-adjusted childhood leukemia incidence rates per 100,000 by county, 1990–2000.
Table 1 PHIN information technology functional standards and Wisconsin implementation status.
PHIN IT function specification PHIN standard implementationa WI-PHIN status
1. Automated data exchange Establish ebXML-compliant SOAP web service via an HTTPS connection after appropriate authentication;encrypted messages use industry standard ebXML format and include standardized HL7, version 2.3; HL7, version 3.0; X12; and LDIF message content. Web service capability established; test deployment with several laboratories.
2. Electronic clinical data: event detection Data received via ebXML messaging identified in function 1 above stored using NEDSS logical data model specification of the HL7 Reference Information Model and extensions. This allows standards-based interaction with commercial products for reporting, statistical analysis, geographic mapping, and automated outbreak detection algorithms, as well as the processing of queued data from and for electronic messages; the data repository should implement common database technology (e.g., Sybase, Oracle, or SQL Server) running on servers using Windows NT/2000/XP, LINUX, or UNIX and supporting ODBC, ANSI standard SQL, and JDBC access. Data repository established using Oracle 9i; messaging of laboratory data in pilot/production; hospital tumor registries contacted for case messaging; pilot volunteers identified.
3. Web: manual data entry Secure browser-based data entry for data input and results other reporting from and to primary care clinical care sites and sources; develop web browser–based data systems using open-platform web servers supporting generic web browsers (HTML 3.0+/Java) Function established; system operates on Sun Solaris using Weblogic application server; capability will be used to obtain supplemental risk and exposure history data.
4. Laboratory result information Data stored in HL7-compatible data formats; coding of request and results messages with the LOINC and SNOMED vocabularies; information messaging using function 1. Storage capability established; vocabulary capability in development.
5. Case management Using functions 1–4 above, cases should be “linked” and traceable from detection via electronic sources of clinical data or manual entry of case data, and through confirmation via laboratory result reporting. Capability established; PAM-specific business rules in development for linkage and tracking.
6. Analysis and visualization Commercial reporting systems integrated using ODBC and JDBC data access; security and access control applied for remote access using SSL and certificate- or token-based authentication with appropriate authentication and authorization. SAS product integrated; ESRI GIS capability in development; SSL and RBAC established.
7. Personnel directories Directories present an LDAP version 3.0 standard-based service allowing data access and sharing across multiple computer systems and appropriate organizational boundaries; directory information transfer and sharing supports standard message format (LDIF); data fields use X.500 standards for field type and length. Capability established; directory contains contact information and roles of > 2,400 registered PHIN users from > 900 organizations.
8. Information dissemination and alerting Receive, manage, and disseminate alerts, protocols, procedures, and other information for dissemination to public health workers, primary care physicians, public health laboratories, and other partners; ability to “push” information via messages and allow participants to “pull” information via the browsing of secure web sites; support of interactive communication sites for threaded discussion capabilities. Capability established; call-tree alerting system integrated (voice technologies); public and private topic areas, threaded discussion forums established; push digest subscriptions available from bookmarked topic areas, directing appropriate content to audience.
9. IT Security Meet/exceed HIPAA requirements; client and server X.509 digital certificates or comparable strong authentication methodology for access; establish RBAC protocols and effective administrative policies; employ desktop/server virus scanning, intrusion detection, network vulnerability analysis, security policy monitoring, regular penetration testing, and active threat intelligence; ensure continuity of operations through planning and procedure implementation. Capability established, including RBAC, administrative policies, auditing, and training; ongoing virus scanning, intrusion detection, threat intelligence, continuity of operations; independent validation and verification in development; client digital certificates in exploratory phase.
Abbreviations: ANSI, American National Standards Institute; ebXML, Electronic Business using eXtensible Markup Language; ESRI, Environmental Systems Research Institute; HIPAA, Health Insurance Portability and Accountability Act (1996); HL7, Health Level 7; HTTP, Hypertext Transfer Protocol; IT, information technology; JDBC, JAVA Database Connectivity; LDAP, Lightweight Directory Access Protocol; LDIF, Lightweight Data Interchange Format; LOINC, Logical Observation Identifiers; ODBC, Open Database Connectivity; SNOMED, Systemized Nomenclature of Medicine; SOAP, Simple Object Access Protocol; SQL, Structured Query Language.
a From CDC (2002).
Table 2 Wisconsin EPHTN data inventory.
Abbreviation Data set Scope Description
AEI Air Emissions Inventory State Emissions from mobile sources
BRRTS Bureau of Remediation and Redevelopment Tracking System State Database of environmental contamination sites including spills, leaking underground storage tanks, state-response sites, and federal Superfund sites
Census Census National Decennial population counts, age, gender, race, census tract, county, ZIP code
DWS Drinking Water System State Drinking-water quality in Wisconsin public wells
GEMS Groundwater Environmental Monitoring System State Environmental monitoring data for Wisconsin landfills, including landfill gas, groundwater, and other sample types
GLAT Great Lakes Air Toxic Emissions Inventory Regional Airborne toxic pollutant emissions affecting air and water quality in eight Great Lakes states
GRN Groundwater Retrieval Network State Groundwater quality in Wisconsin private, public, and monitoring wells
NATA National Air Toxics Assessment National Estimates of 33 air toxics (a subset of 32 air toxics on the Clean Air Act’s list of 188 air toxics, plus diesel particulate matter) (U.S. EPA 1993)
NEI National Emissions Inventory National Hazardous and criteria air pollutants
PEI Periodic Emissions Inventory State Annual emissions of criterion air pollutants and some noncriterion pollutants
RR GIS Registry Remediation and Redevelopment GIS Registry State Sites closed with residual water or soil contamination
SHWIMS Solid and Hazardous Waste Information Management System State Sitings for waste management facilities
SWAP Source Water Assessment Plan Database State Assessment of possible contamination sources within a specified distance from a drinking water well
TRI Toxics Release Inventory National Toxic chemical releases and other waste management activities for specific industry groups and federal facilities
WCR Wisconsin Cancer Registry State Cancer incidence by age, gender, race, county, ZIP code, histology, cytology, staging
WI Hosp Wisconsin Hospital Discharge State Hospitalizations by age, gender, race, county, ZIP code, cause
WMOR Wisconsin Mortality State Mortality by age, gender, race, county, cause
Table 3 Wisconsin Cancer Registry 1990–2000: childhood cancer cases frequency by cause (children < 20 years of age).
Cause Frequency (%)
Leukemia 672 (22.7)
Lymphatic cancers 428 (14.5)
Brain cancer 413 (14.0)
Cervical cancer 283 (9.6)
Bone cancer 174 (5.9)
Soft tissue cancer 151 (5.1)
Kidney and other urinary cancer 126 (4.3)
Thyroid cancer 91 (3.1)
Skin cancer/melanoma and other reportable 88 (3.0)
Other endocrine gland cancer 74 (2.5)
Testicular cancer 66 (2.2)
Eye cancer 59 (2.0)
Ovarian cancer 58 (2.0)
Other central nervous system cancer 47 (1.6)
All other cancers/unknown cancers 42 (1.4)
Oral cancer 33 (1.1)
Peritoneal cancer 27 (0.9)
Liver cancer 24 (0.8)
Nasal cancer 15 (0.5)
Colorectal cancer 14 (0.5)
Other respiratory/thoracic cancer 14 (0.5)
Bladder cancer 13 (0.4)
Bronchus and lung cancer 11 (0.4)
Other female genital cancer 11 (0.4)
Prostate cancer 7 (0.2)
Small intestine cancer 3 (0.1)
Breast cancer 3 (0.1)
Uterine cancer 3 (0.1)
Other leukemias 3 (0.1)
Stomach cancer 2 (0.1)
Pancreatic cancer 2 (0.1)
Laryngeal cancer 1 (0.0)
Pleural cancer 1 (0.0)
Other male genital cancer 1 (0.0)
Total 2,960 (100.0)
==== Refs
References
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7145ehp0112-00144015471740Mini-Monograph: Public Health TrackingArticlesStatistical Methods for Linking Health, Exposure, and Hazards Mather Frances Jean 1White LuAnn Ellis 2Langlois Elizabeth Cullen 2Shorter Charles Franklin 2Swalm Christopher Martin 3Shaffer Jeffrey George 1Hartley William Ralph 21Department of Biostatistics, Academic Information Systems,2Department of Environmental Health Sciences, Center for Applied Environmental Public Health, and3Academic Information Systems, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USAAddress correspondence to F.J. Mather, Department of Biostatistics, SL 18, Tulane University School of Public Health and Tropical Medicine, 1440 Canal St., New Orleans, LA 70112 USA. Telephone: (504) 988-7329. Fax: (504) 988-1706. E-mail:
[email protected] article is part of the mini-monograph “National Environmental Public Health Tracking,” which is sponsored by the Centers for Disease Control and Prevention (CDC).
We thank our reviewers for their thoughtful suggestions. Any errors are our responsibility.
This work was supported by the CDC Center of Excellence for Environmental Public Health Tracking grant U50/CCU622412.
This article was supported by an environmental public health tracking cooperative agreement from CDC. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of CDC.
The authors declare they have no competing financial interests.
10 2004 3 8 2004 112 14 1440 1445 1 4 2004 3 8 2004 Publication 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 Environmental Public Health Tracking Network (EPHTN) proposes to link environmental hazards and exposures to health outcomes. Statistical methods used in case–control and cohort studies to link health outcomes to individual exposure estimates are well developed. However, reliable exposure estimates for many contaminants are not available at the individual level. In these cases, exposure/hazard data are often aggregated over a geographic area, and ecologic models are used to relate health outcome and exposure/hazard. Ecologic models are not without limitations in interpretation. EPHTN data are characteristic of much information currently being collected—they are multivariate, with many predictors and response variables, often aggregated over geographic regions (small and large) and correlated in space and/or time. The methods to model trends in space and time, handle correlation structures in the data, estimate effects, test hypotheses, and predict future outcomes are relatively new and without extensive application in environmental public health. In this article we outline a tiered approach to data analysis for EPHTN and review the use of standard methods for relating exposure/hazards, disease mapping and clustering techniques, Bayesian approaches, Markov chain Monte Carlo methods for estimation of posterior parameters, and geostatistical methods. The advantages and limitations of these methods are discussed.
Bayesian modelingdata linkageexposureGIShazardshealth outcome datastatistical methods
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The environment plays an important role in health and human development. Acute effects from exposure to environmental contaminants, such as pesticide poisoning, are well recognized, but the environmental link to most chronic diseases remains unclear. Researchers have linked exposure to specific environmental hazards with a health effect, such as benzene and leukemia. Other associations are suspect, such as exposure to mixtures of drinking water disinfection by-products and bladder cancer. In other cases, linkages between environmental agents, individually or as mixtures, and health outcomes lack epidemiologic evidence and are postulated from laboratory animal studies. The Pew Environmental Health Commission (2000) calls the lack of information linking environmental hazards and chronic disease the “environmental health gap.” To address this gap, the Centers for Disease Control and Prevention (CDC) established the National Environmental Public Health Tracking Network (EPHTN; CDC 2003a), which is developing the infrastructure, resources, and methods for assembling and using available environmental hazard, exposure, and health outcome data (HOD). This initiative presents great methodologic challenges such as using existing data in new ways and for purposes other than for which they were collected; expanding the limited guidance for using available statistical methods to analyze and link data; and closing gaps in methodology for linking disparate data sets. Despite the challenges, great opportunities exist to forge partnerships to make data more available, develop standards to facilitate data exchange, and analyze data to describe the impact of environmental hazards on human health. However, without defining the appropriate rules for data linkage, indiscriminate linking may lead to erroneous conclusions. This highlights the need to understand each data set, articulate the uses and limits of each data set, and standardize methods for using the data.
Fundamental Premise for Linking Data
Fundamental questions must be asked before linking different types of data. For example: Is there a scientific basis for connecting the data sets? Are the data to be linked adequate and appropriate for addressing the issue? A useful framework for examining these questions has been presented by Thacker et al. (1996) in their description of a seven-step “hazard-exposure-outcome” axis that outlines the process of how an environmental hazard produces an adverse health outcome. In this process, Thacker et al. elucidate the steps whereby an environmental agent moves through the environment (hazard), enters a person (exposure), and produces an effect (health outcome). Associations made between hazard and health are not conclusive without considering actual exposure, although exposure is often estimated from hazard data.
Thacker et al. (1996) further describe the type of public health surveillance associated with each of these steps: a) hazard surveillance tracks the presence of an agent in the environment and environmental pathways (e.g., air, water) leading to the routes of exposure; b) exposure surveillance monitors exposure of the host to the agent (biomarkers of exposure), the distribution to a target tissue, and production of effects (biomarkers of effect); c) outcome surveillance follows clinically observable disease (Thacker et al. 1996). The linkage of two or more types of data provides a powerful tool but only if the steps in the process are taken into account.
Data Issues and Implications of Data Inadequacies
Before examining statistical methods for linking various types of data, it is necessary to examine data sources that are available for tracking and linking hazards, exposure, and health effects. Fundamental factors that provide confidence in the results of data linkage are data quality, appropriate use of the data, and consideration of data limitations. The quality of hazard, exposure, and HOD are diverse, and the uses and limitations of data outside of its original purpose are not yet well defined.
Hazard data.
Hazard data provide information about the presence and quantity of contaminants in environmental media. A hazard has the potential for harmful effects, but its presence alone may not be sufficient to produce an adverse effect in a population. Most environmental data collection by federal and state environmental agencies is mandated by legislation such as the Clean Air Act (1970), Clean Water Act (1977), and Safe Drinking Water Act (1974), and data are used for regulatory purposes. Examples of environmental data include the Toxic Release Inventory (TRI; http://www.epa.gov/tri/), criteria air pollutant data (ozone, sulfur dioxide, particulate matter), pesticide exposures, and the Safe Drinking Water Information System (SDWIS; http://www.epa.gov/enviro/html/sdwis/). Data analysis is often limited to comparison of each data point with an environmental standard or guideline, and regulatory action is triggered if the data point exceeds the standard. Standards are not available for all environmental contaminants, and those that do exist are extrapolated primarily from toxicologic studies. Standards are developed for regulatory purposes and include uncertainty and safety factors that are meant to be protective of public health; exceeding the standard does not predict health outcomes in the population.
Hazard data alone are not measures of individual exposure; in some cases, certain types of hazard data; for example, monitoring contaminants in drinking water, may contribute to the characterization of population exposure. Table 1 lists limitations in the use of hazard data for EPHTN.
Exposure data.
Exposure data are the essential link between environmental hazards and health outcomes. Optimally, exposure data include biomarkers such as a compound or its metabolite(s) in a biologic sample (e.g., blood, urine, hair, fat). Levels of agents in blood or urine are not necessarily proportional to environmental concentrations. Additionally, factors such as exposure level, route of exposure, frequency and duration of exposure, baseline health status, behavioral factors, and genetics influence the internal dose of the compound in an individual.
Exposure data represent the largest data gap for EPHTN. Ideally, exposure data would be available at the individual level, but very few of these types exist. Childhood lead levels are one of the few nationwide exposure data sets. The CDC initiative to monitor exposure of the general population to 116 environmental chemicals through biomonitoring as a part of the National Health and Nutrition Examination Survey (NHANES) is providing essential baseline exposure data (CDC 2003b). However, there are no other systematic biomonitoring programs that provide nationwide exposure data. Data from sporadic, single-event biomonitoring that occurs in response to specific incidents or investigations are not widely available. Another limitation is the rapid metabolism and clearance of many compounds of concern. Because of the limited availability of exposure data, these parameters are often estimated rather than measured. Exposure modeling and assessments, as used in regulatory risk assessment, are meant to be protective of public health and therefore use conservative assumptions that overestimate actual exposure (e.g., consumption of 2 L/day of water for 70 years; U.S. Environmental Protection Agency 1989). Multiple routes of exposure and chemical mixtures are difficult to estimate. Individual factors that modify exposure, such as age, race, gender, time near the hazard, behavioral factors, and genetics, are often not incorporated into the exposure assessment models. Inexact exposure estimates limit the ability to ascribe health outcomes to a contaminant.
The lack of exposure data further complicates issues because the level of exposure influences the appropriate health end point. At high exposure levels, health effects are observed within a relatively short time frame and are often documented in the scientific literature. Low-dose chronic exposures present greater challenges: health outcomes may not be known; effects of low-dose exposures are not observable for years or decades; effects may require repeated exposures over time; multiple agents may need to interact simultaneously or sequentially; and the effect may occur only in sensitive subpopulations or those with existing health conditions. Table 1 lists additional limitations in using exposure data for environmental public health tracking.
Health outcome data (HOD).
Adverse health events such as morbidity and mortality are the outcomes of interest in efforts to associate environmental hazards and exposure with effects in humans. Of particular interest are asthma, birth defects, cancer, and sequelae of lead and pesticide poisoning/exposure. Health events may be identified and evaluated at the individual level, as is the case in traditional epidemiologic studies, or may be aggregated for populations, such as those available from local, regional, and national surveys. The uses and limitations of population-based aggregate data can differ from those of individual-level data, and this affects the use of each type of data.
EPHTN efforts focus on the use of existing (secondary) data rather than on the generation of new data (primary). Sources of such data include local, regional, and national health surveys such as the NHANES and the Behavioral Risk Factor Surveillance System, and health events and reportable disease registries such as those for cancer—the National Cancer Institute’s (NCI) geographic information system (GIS) web site (NCI 2004b)—and birth defects. Other sources include state vital records, hospital discharge data, emergency room data, and health insurance statistics. Several potential limitations must be considered when using secondary HOD, including those related to evolving or changing diagnostic criteria, misclassification, generalizability, measurement error, and completeness. Secondary HOD are also limited because many diseases/conditions are not reportable, resulting in incomplete information for many outcomes of interest. Further, confidentiality may prohibit access to the data at the level of detail that might be needed for correlation with an exposure, for example, address of an individual. Table 1 lists additional limitations in using HOD for EPHTN.
Other relevant data (covariates).
The association of a disease with a risk factor in epidemiologic research requires more than observing corresponding fluctuations between the two factors. For example, examining the effect of air pollution as measured by criteria pollutants on the incidence of asthma must consider related factors. Other factors may include residence, proximity to known asthma-causing sources, socioeconomic status, age, race, and adherence to treatment regimens that may be related to incidence and hazard/exposure. These characteristics related to the factors of interest must also be considered and controlled. In epidemiologic research, strict control is not feasible, in the sense that these characteristics are not assigned by the researcher. Instead, important factors are observed, measured, and controlled in the analysis. Information regarding many of the standard covariates is routinely collected and made available through the U.S. census and surveys, including information regarding race, age, and sex distributions, as well as a variety of variables describing socioeconomic status, employment, and housing statistics, among others. These data are aggregated by specified units (e.g., state, county, census tract) and have the same limitations identified above for HOD. The decennial census data have limitations with respect to how current or complete the data are for a given time period. Migration is problematic, especially in estimating populations for small areas. Nevertheless, these data provide important information regarding population characteristics necessary for establishing denominator estimates and associating hazard/exposure with health outcomes.
Statistical Framework
Data limitations directly affect the outcome of statistical analyses and may at times limit the level of analyses that might be performed with a given data set. The lack of consistency in the quality of geocoding in the U.S. spatial infrastructure provides an example of such a limitation. The street reference files in urban areas may be of very good quality, whereas in rural areas they may have limited accuracy. Consider the impact of this varying quality on geocoding when comparing urban and rural areas. If unmatched cases are omitted, urban/rural differences may be attributed to geographic covariates rather than the inaccuracy of geocoding. Alternatively, if unmatched cases are allocated to the geocenter of the spatial area, distances from a putative exposure are still less accurate in the rural area than in the urban area. Current statistical methods do not account for this variability in quality of geocoding. These limitations highlight the need to closely examine available data sets and determine the appropriate use of the data as they exist and the assessment of data quality before more complex statistical analyses are applied. Additionally, data are collected from disparate sources (e.g., U.S. Environmental Protection Agency, CDC, U.S. Census Bureau, state and local health and environmental agencies) for a wide variety of purposes (regulatory vs. surveillance). These data sources have varying levels of quality related to completeness, consistency of definitions, accuracy, and timeliness. Further, the locations (i.e., state, county, ZIP code, census tract, point source) of data collection can be inconsistent or “misaligned.”
The complex relationship between exposure and health outcome requires careful consideration of the type of statistical model that should be used to represent this relationship. Ecologic bias is a potential problem when hazards ascertained from data aggregated over a larger geographic area are attributed to individuals within the area. It is fairly common to see analyses in which an aggregate measure of socioeconomic status for a ZIP code is assigned to each individual within the ZIP code and subsequently analyzed by methods ignoring the aggregation. The bias will decrease with smaller geographic areas and may be decreased by measuring confounding variables. But all the confounders may not be known, so whether bias has been eliminated is generally unknown (Wakefield and Elliott 1999). In general, data containing variables measured at individual and aggregate levels should be analyzed by means of hierarchical models that better describe the data and account for the limitations of these data in terms of the potential for ecologic bias.
Wakefield and Elliott (1999) and Banerjee et al. (2004) review a variety of statistical methods appropriate for the analysis of environmental and health data as well as the health/environment relationship. These methods attempt to realistically represent the hazard–exposure–disease process while also considering measurement issues. We organized these into three groups generally representing increasing complexity of study design.
The first group is composed of descriptive analyses and includes tracking trends (surveillance), temporally and spatially, of hazard, exposure, and HOD. These analyses provide information on recognizing and defining the scope of a problem, describing trends, generating baselines, and comparing changes in temporal and spatial indices of health and environmental data. GIS methods have been developed for exploratory data analysis and identification of space–time patterns.
The second group is ecologic analysis, which includes more advanced methods of spatial analysis and involves observational studies that provide information on the relationship between hazards and health outcomes. These hierarchical models can accommodate data from several levels such as cancer registry case point data and such regional data as poverty at the census, block group level. Ecologic analysis is appropriate for hypothesis generation and provides essential information needed before moving on to the more rigorous study designs in group three.
Group three consists of etiologic research using full-scale epidemiologic studies to test hypotheses describing the relationships between environmental exposures and a health outcome. These studies require high-quality, well-defined data and complex statistical methods that account for issues present in the data.
Group 1:Tracking and Trend Analysis
Time trends.
Examining time trends in HOD is helpful in identifying disease clusters. An example is the observation of birth defects (rare limb malformations) within a short time among women who took thalidomide for morning sickness. This cluster helped to identify thalidomide as a human teratogen (Taussig 1962). Trends identified through hazard surveillance are also important for characterizing background and changes in environmental contamination. Seasonal patterns are also identified through surveillance, for example, deaths from heat waves in the summer and pneumonia and influenza deaths in the winter season. Statistical approaches to the detection of clusters of disease include cell-count methods that compare observed with expected counts of events (Knox 1964; Openshaw et al. 1987, 1988), adjacency methods that examine whether areas of high rates of disease are likely to be adjacent to other high-rate areas (Moran 1948), and distance or nearest-neighbor methods that compare physical distances between cases to expected distance (Besag and Newell 1991; Cuzick and Edwards 1990; Mantel 1967). Alexander and Boyle (1996) provide extensions to the basic methods. Other statistical methods can be used to determine interarrival times between rare disease cases or to model seasonal patterns using time series methods, autoregression methods, and joinpoint regression (Kim et al. 2000). These trends are informative; however, confounding may prohibit correct interpretation. An example is an observation in prostate cancer incidence: A study was conducted to examine the prostate cancer incidence in a plant manufacturing triazine (MacLennan et al. 2002). Subsequent investigation of prostate cancer incidence indicated that the increase may have reflected increased prostate-specific antigen (PSA) screening rates rather than increased incidence of disease.
Spatial analysis and geographic distribution.
Environmental and health data have very few commonalities; in many cases, the only commonalities of the data are the general geographic area and time frame. GIS is a useful tool for examining each type of data and one of the few methods that can be used to compare disparate health and environmental data. These include the “brush and link” exploratory data tools such as GeoDa (Anselin et al. 2004) and identification of space–time patterns as available in SaTScan software (SaTScan 2004) and the TerraSeer Space Time Intelligence System (TerraSeer 2004). The common denominator is the geocode for each type of data. Issues arise in reconciling the point of sampling and the location (and often lack of location) of individuals with the health outcome. Nonetheless, simple mapping of each data set and overlay of the maps can assist in targeting geographic areas. GIS software often comes with an array of tools to produce maps with impressive visual displays of data, overlay of disparate environmental and HOD, and conduct trend analysis. Because of this, caution is necessary to ensure that the maps are not misleading. This can occur when health and hazard data are mapped independently and there is confounding in the data that may lead the investigator to an inappropriate conclusion. For example, the potential confounding due to the latency period and ecologic fallacy must be considered in interpretation. Additionally, the residential address of a cancer case at diagnosis may not be a good proxy indicator for the lifetime environmental exposure given the mobility of the U.S. population. Geocoding methods need to be developed that allow better cumulative estimation of exposure that individuals might experience during their lifetimes. These should be available for regular or irregular points in residence or occupational histories.
Disease mapping summarizes the spatial and temporal–spatial variation in risk. When compared with spatial and temporal–spatial variation in exposure/hazard, one may get some disease etiology clues. The National Cancer Institute’s Cancer Mortality Atlas (NCI 2004a) and State Cancer Profiles (NCI 2004c) show county-specific maps that have been instrumental in identifying cancer disease patterns with the location of major industries by means of ecologic studies—nasal cancer in areas with furniture manufacturing (Brinton et al. 1977), lung cancer in counties with petrochemical manufacturing (Blot and Fraumeni 1976), bladder cancer where chemical industries were located (Hoover et al. 1975), and oral cancer in regions where snuff use was common (Blot and Fraumeni 1977). Comparing county-level health rates from maps requires that rates be adjusted for potentially confounding variables such as age, race, sex, and socioeconomic status. Elliott et al. (2000) discuss problems in the comparison of indirectly adjusted rates [standard mortality ratios (SMRs)] and directly adjusted rates [comparative mortality figures (CMFs)]. CMFs may be unstable if the stratum-specific rates are based on small numbers; however, they are unbiased estimates of the relative risk to the standard population. SMRs may be biased if proportionality assumptions are not met. Neither of these rates (SMRs, CMFs) adjusts for the overdispersion typically found in this type of data. Nevertheless, Breslow and Day (1987) observe that the use of SMRs or CMFs often leads to the same results. A clearer exposition of which summary measure to use would be a welcome addition to the literature. Maps of rates can be very deceptive if they are composed of different-sized units; for example, large units are overwhelming, and small units are often lost in interpretation. The microplot map (NCI 2004c) shows 95% confidence intervals for the units, and their intervals may overlap even though a choropleth map indicates a difference. Additionally, maps at different scales may give different patterns, further obscuring the interpretation. Observed rates are quite variable, especially when the expected rates are small, so maps of county-specific rates are smoothed by a variety of means to enhance the visualization of regional patterns of distribution. Smoothing methods need to consider and adjust for differences in the population size (denominator) for different geographic units. Methods to compute and visualize the spatial and temporal–spatial variability in disease/mortality controlling for such covariates as age, race, sex, and deprivation are currently available, as are extensions of the method to smooth the data and model heterogeneity and clustering of the area-specific effects in both the traditional approach and Bayesian framework (Banerjee et al. 2004; Wakefield and Elliott 1999).
These temporal, spatial, cluster, and exploratory methods have standard software to analyze the data. These should become standard procedures in health departments. Problems in interpretation will arise because of geographic resolution of disease and environmental data, data quality, confounding, and ecologic bias. Replication of the analyses in widely differing settings should provide scientists with background information to make the judgment as to whether to abandon the examination of a particular health outcome–hazard link, replicate the study, and/or proceed to an ecologic study if significant effects are apparent or sufficient evidence to warrant further investigation exists (see “Descriptive analysis” in Figure 1).
Group 2: Ecologic Analysis
Ecologic epidemiologic studies.
Ecologic studies describe the coexistence of risk factors with disease among and within populations. Aggregate exposure data are correlated with aggregate health data for each unit of observation, usually defined by geographic or administrative boundaries (e.g., city, county, state). Rates of exposure and rates of disease within each unit are known, but the exposure status of diseased individuals is not known. Analysis of ecologic studies can be conducted visually by interpreting the slope of a line plot of the exposure rate by the disease rate for each unit or using the correlation coefficient, r, as a measure of association. In ecologic trend analyses, changes in rates of exposure are correlated with changes in rates of disease over time. The most salient limitation of these studies is ecologic bias, resulting from spuriously ascribing aggregate-level observations to individuals. These studies are useful for generating hypotheses, for assessing the impact of community-level interventions, or for initial evaluation of suspected associations, although more rigorous studies are necessary to support a causal relationship between exposure and disease.
Geographic correlation studies.
These studies model the interrelationships of hazard, exposure, and health over time and space. The objective is to relate environmental variables to disease and control for other factors such as life style (Banerjee et al. 2004; Wakefield and Elliott 1999). Poisson regression provides the framework for modeling the rates for rare diseases, whereas binomial regression or survival analysis is suitable when disease is more common. Counts or rates of events are described as a function of exposure, space, time, demographics, and other variables. The models are hierarchical when hazard data are used and are thus subject to ecologic bias. Spatial correlation in the data should be anticipated. For example, screening for PSA is likely to be correlated among counties when the screening initiative is directed toward a large area. Overdispersed data are an additional problem. The methods for estimating parameters relating hazard to health outcomes in these models are by means of likelihood methods and by Bayesian methods. Advantages of the likelihood methods include the readily available software and, in some cases, the incorporation of dispersion common in these data. Disadvantages include the difficulty of specifying complex covariance structures in the data, the unreliability of SMRs based on sparse data, and the problems of estimating variances of the relative risks. Bayesian methods applied to the data require the assumption of a prior distribution on these parameters from which a posterior distribution can be used to provide point (mean, median) and interval (95% confidence intervals) estimates for the parameters of interest. These estimates are obtained by means of Markov chain Monte Carlo methods using WinBUGS software (MRC Biostatistics Unit, Cambridge, UK; http://www.mrc-bsu.cam.ac.uk/bugs/welcome.shtml). Advantages of the Bayesian methods include the use of random effects to represent overdispersion, the availability of WinBUGS software for computation, and the provision of smoothed estimates of SMRs based on sparse data. Disadvantages of the Bayesian methods include the sensitivity of the estimates to the choice of the prior, and problems of convergence in complicated models (Elliott et al. 2000). Wakefield and Morris (1999) extend their analyses to accommodate spatial correlation and overdispersed data in the Bayesian analysis of the relationship between heart disease mortality and magnesium and calcium in water. Another example using Bayesian methods is a study measuring ozone levels at 10 fixed sites and emergency department visits—total visits and those for asthma by ZIP code—to assess the effect of ozone levels on pediatric emergency department asthma visits. In this example, the data for ozone and emergency department visits are “misaligned,” and before the data can be analyzed, a surface (kriging) for ozone from the site data must be developed so that an ozone level can be estimated for each ZIP code. Carlin demonstrates the use of hierarchical Bayesian models in solving this and other misalignment problems (Banerjee et al. 2004)
Multilevel models estimating hazard and health outcome effects and controlling for potential confounders and covariates provide hypothesis-generating information; however, they will likely require more refined hazard and disease data. Statistical methods are available but are not trivial to run and interpret, and the potential for ecologic bias remains. Replication of these studies in several areas should be guided by the descriptive analyses. Judgment of the scientific community is necessary to decide whether to halt studies at this level or, if significant effects and consensus of the scientific community indicate, then to design an in-depth study (etiologic research) (“Ecologic analyses” in Figure 1).
Group 3: Etiologic Research Studies
Epidemiologic studies associate exposure in individuals to health outcome by means of case–control studies in rare diseases and by cohort studies in groups such as in occupational settings. Hertz-Picciotto (1998) summarizes a number of studies illustrating the difficulties of associating environmental exposure to the risk of disease, for example, the risk of serious illness (seizures and/or death) from drinking cider in Devonshire (Baker 1767); the risk of cholera associated with ingestion of water contaminated by fecal matter from infected patients (Snow 1855); poor mental development among children exposed to lead (Needleman et al. 1979, 1990; Needleman and Gatsonis 1990); and the many studies on the risk of health effects associated with air pollution (Hertz-Picciotto 1998). Statistical analysis of environmental data when appropriate exposure measures are of high quality is handled quite readily by multiple logistic regression in case–control studies and survival analysis methods such as Cox regression.
These stronger studies requiring better hazard/exposure and disease definition should benefit from results of the earlier studies. Statistical methods are available for hypothesis testing of the risk associated with exposure. The limitations of these studies are well known, but the problem of identifying weak effects in small populations remains. If the results of these analyses show promise and, in the opinion of scientists and policymakers, an intervention is both possible and desirable, then intervention studies in the form of community trials or policy decisions effecting current or new standards should be evaluated for impact. Intervention analyses may be implemented at early stages of study, after ecologic studies (“Etiologic analyses” in Figure 1).
Discussion
The EPHTN is developing a framework to assess the impact of environmental agents on human health that will begin to fill in the “environmental health gap” described in the Pew Environmental Health Commission (2000) report. The environmental and health data that reside in federal and state health and environmental agencies hold a wealth of information if unlocked with appropriate linkages and analyses. The challenge is in the details for analyzing and linking disparate data in a scientifically sound manner while considering appropriate data uses and limitations. The magnitude of the endeavor dictates that we carefully articulate questions that might be reasonably answered with existing data in the near term and set an agenda to proceed to more complex and difficult questions.
The Thacker et al. (1996) model should be extended to account for exposures that effect only a susceptible population. Further, models specific for disease–exposure relationships explaining the fate and effect of an agent should be considered. Incorporation of policy changes and treatment interventions should also be considered. Guidelines on measurement and acquisition of data should be prepared. The Guide to Community Preventive Services (CDC 2004) program provides a model for how public health interventions and treatments could be represented in environmental situations.
The analytical framework presents groups of analyses to facilitate a progression from descriptive analyses to more complex linkages; this builds the foundation for etiologic research using epidemiologic studies.
The first group of analyses describes spatial and temporal distribution of hazards and outcomes independently and elucidates trends and relationships that can be further explored. These descriptive methods provide basic information to agencies and policymakers and suggestions for further studies. The second group of analyses focuses on ecologic studies that associate hazard with health outcomes using recently developed methods such as GIS spatial analysis, hierarchical models, and Bayesian methods. These methods address environmental and disease measurement issues, and experience with their use will generate hypotheses and a core set of analyses that may become the standard methods for linking health and environmental data. The third group relates exposure to outcome using traditional epidemiologic study designs to test hypotheses. These are research studies that should build on preceding descriptive and ecologic analyses.
The framework for analyses highlights the necessity for collaborations and partnerships. Data sharing is essential to EPHTN and requires overcoming the organizational and functional problems limiting collaboration between health and environmental agencies. Further, multidisciplinary teams with expertise in epidemiology, statistics, toxicology, environmental health, database management, GIS, and other areas will be required to ensure sound science and appropriate analysis of data. The EPHTN has established academic/agency partnership and designated Centers of Excellence to serve as a resource to state health agencies. As more complex linkages and analyses are conducted, greater statistical expertise will be needed in this initiative.
Conclusions
The linkage of two or more types of data provides a powerful tool, but only if the steps in Thacker et al. (1996) “hazard-exposure-outcome” model are considered. The lack of exposure data is an impediment to more complex linkages. In the descriptive analyses, the lack of exposure data may be acceptable, but studies of more complex linkages will require more and better data. Additional efforts to generate exposure data, perhaps in partnership with public health laboratories, need to be formulated.
Statistical methods are available to link hazards and covariates to health outcomes; however, the appropriate uses and limitations of each data set must be taken into account. The analysis of hazard data and their linkage to health outcomes are subject to ecologic bias; use of smaller geographical area may minimize that bias. Newer methods such as GIS spatial analysis, hierarchical models, and Bayesian methods are promising but require experience and repeated use with various types of linkages before they become standard techniques. If used properly, statistical methods are available to begin analyses and linkage of environmental hazard, exposure, and health data that in turn will provide information to the public, policymakers, and the scientific community.
Figure 1 Decision tree for statistical framework.
Table 1 Uses and limitations of hazard data, exposure data, and HOD.
Uses Limitations
Hazard data
Regulatory compliance Not representative of individual exposures
Standard setting Gaps in geographic coverage of monitors
Policymaking High percentage of nondetected values in data
Characterization of pollution sources Sampling and measurement errors are often unknown
Reflect current levels of pollutants
Insufficient data quantity for trend analysis
Objectives for monitoring vary across environmental media
Exposure data
Indicator of individual exposure to a hazard Data rarely available at the individual level
Required to link hazard with health outcome Misclassification of exposure
Difficult to account for multiple exposure pathways
Exposure models based on assumptions and uncertainties not included in statistical analysis
Lack of data amount, frequency, and duration of exposure
Variability within populations impedes generalizing exposure
Difficult to reconstruct past exposure
Health outcome data
Describes health status of populations Data completeness
Describes distribution and frequency of disease Misclassification of disease
Generalizability to population
Confidentiality issues (HIPAAa)
All three types of data
Completeness of records
Timeliness of reporting
Availability of access to data
Geographic resolution of the data (scale)
Frequency of data collection
Lack of data collection standards
a HIPAA, Health Insurance Portability and Accountability Act of 1996 (1996).
==== Refs
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Environ Health Perspect. 2004 Oct 3; 112(14):1440-1445
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Environ Health Perspect
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10.1289/ehp.7145
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