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The fraction of children brought for care in the public sector and in the private sector was assumed to have a beta distribution, with the mean value being the estimated value in the survey and the standard deviation calculated from the range of the estimated 95% CIs.
The fraction of children not brought for care was assumed to have a rectangular distribution, with the lower limit being 0 and the upper limit calculated as 1 minus the proportion that were brought for care in the public and private sectors. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
The three distributions (fraction seeking treatment in public sector, fraction seeking treatment in private sector only and fraction not seeking treatment) were constrained to add up to 1.Values for the fractions seeking care were linearly interpolated between the years that had a survey, and were extrapolated for the years before the first or after the last survey. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
Missing values for the distributions were imputed in a similar way or, if there was no value for any year in the country or area, were imputed as a mixture of the distribution of the region for that year.
CIs were obtained from 10 000 draws of the convoluted distributions. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
The data were analysed using R statistical software (5).For India, the values were obtained at subnational level using the same methodology, but adjusting the private sector for an additional factor because of the active case detection, estimated as the ratio of the test positivity rate in active case detection over the test positivity rate for passive case detection. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
This factor was assumed to have a normal distribution, with mean value and standard deviation calculated from the values reported in 2010.No adjustment for private sector treatment seeking was made for the following countries and areas, because they report cases from the private and public sector together: Bangladesh, Bolivia (Plurinational State of), Botswana, Brazil, Colombia, Dominican Republic, French Guiana, Guatemala, Guyana, Haiti, Honduras, Myanmar (since 2013), Nicaragua, Panama, Peru, Rwanda, Senegal (70% of private sector reported together with public sector in 2018) and Venezuela (Bolivarian Republic of).Method 2Method 2 was used for high-transmission countries in Africa and for countries in the WHO Eastern Mediterranean Region in which the quality of surveillance data did not permit a robust estimate from the number of reported cases: Angola, Benin, Burkina Faso, Burundi, Cameroon, Central African Republic, Chad, Congo, Côte d’Ivoire, Democratic Republic of the Congo, Equatorial Guinea, Gabon, Ghana, Guinea, Guinea-Bissau, Kenya, Liberia, Malawi, Mali, Mozambique, Niger, Nigeria, Sierra Leone, Somalia, South Sudan, Sudan, Togo, Uganda, United Republic of Tanzania and Zambia. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
In this method, estimates of the number of malaria cases were derived from information on parasite prevalence obtained from household surveys.First, data on parasite prevalence from nearly 60 000 survey records were assembled within a spatio-temporal Bayesian geostatistical model, along with environmental and sociodemographic covariates, and data distribution on interventions such as insecticide-treated mosquito nets (ITNs), antimalarial drugs and indoor residual spraying (IRS) (6). | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
The geospatial model enabled predictions of Plasmodium falciparum prevalence in children aged 2–10 years, at a resolution of 5 × 5 km2, throughout all malaria endemic African countries for each year from 2000 to 2019.
Second, an ensemble model was developed to predict malaria incidence as a function of parasite prevalence (7). | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
The model was then applied to the estimated parasite prevalence in order to obtain estimates of the malaria case incidence at 5 × 5 km2 resolution for each year from 2000 to 2019.1 Data for each 5 × 5 km2 area were then aggregated within country and regional boundaries, to obtain both national and regional estimates of malaria cases (9).Other methodsFor most of the elimination countries and countries at the stage of prevention of reintroduction, the number of indigenous cases registered by NMPs are reported without further adjustments. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
The countries in this category were Algeria, Argentina, Armenia, Azerbaijan, Belize, Bhutan, Cabo Verde, China, Comoros, Costa Rica, Democratic People’s Republic of Korea, Djibouti, Ecuador, Egypt, El Salvador, Eswatini, Georgia, Iran (Islamic Republic of), Iraq, Kazakhstan, Kyrgyzstan, Malaysia, Mexico, Morocco, Oman, Paraguay, Republic of Korea, Sao Tome and Principe, Saudi Arabia, South Africa, Sri Lanka, Suriname, Syrian Arab Republic, Tajikistan, Thailand, Turkey, Turkmenistan, United Arab Emirates and Uzbekistan.i m p u t e d 2 0 0 0 – 2 0 1 0 For some years, information was not available or was not of sufficient quality to be used. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
For those countries, the number of cases was imputed from other years where the quality of the data was better, adjusting for population growth, as follows: for Afghanistan, values for 2000 and 2001 were imputed from 2002–2003; and for Bangladesh, values for 2001–2005 were imputed from 2006–2008.
For Ethiopia, the values for 2000–2019 were taken from a mixed distribution between values from Method 1 and Method 2 (50% from each method). | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
For Gambia, values f o r f r o m w e r e 2011–2013; for Haiti, values for 2000–2005, 2009 and 2010 were imputed from 2006–2008; for Indonesia, values for 2000–2003 and 2007–2009 were imputed from 2004–2006; for Mauritania, values for 2000–2010 were imputed from a mixture of Method 1 and Method 2, starting with 100% values from Method 2 for 2001 and 2002, and increasing to 90% values from Method 1 in 2010. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
For Myanmar, values for 2000–2005 were imputed from 2007–2009; for Namibia, values for 2000 were imputed from 2001–2003, and for 2012 from 2011 and 2013.
For Pakistan, values for 2000 were imputed from 2001–2003; for Papua New Guinea, values for 2012 were imputed from 2009–2011.
For Rwanda, values for 2000–2006 were imputed from a mixture of Method 1 and Method 2, starting with 100% values from Method 2 in 2000, with that percentage decreasing to 10% in 2006. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
For Senegal, values for 2000–2006 were imputed from a mixture of Method 1 and Method 2, with 90% of Method 2 in 2000, decreasing to 10% of Method 2 in 2006.
For Thailand, values for 2000 were imputed from 2001–2003; for Timor-Leste, values for 2000–2001 were imputed from 2002–2004; and for Zimbabwe, values for 2000–2006 were imputed from 2007–2009.
For Burkina Faso, Mali and Niger, values for 2000–2019 were imputed from the estimated series in the World malaria report 2019 (10). | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
For Côte d’Ivoire and Uganda, values were obtained from a combination of the values from the World malaria report 2019 (10) and the current series, extrapolated as the trend from the most 1 See the Malaria Atlas Project website for methods on the development of maps (8).124125WORLD MALARIA REPORT 2020 Annex 1 – Data sources and methodsrecent years for the 2019 estimation for Côte d’Ivoire and from the last incidence value for Uganda.The number of malaria cases caused by P. vivax in each country was estimated by multiplying the country’s reported proportion of P. vivax cases (computed as 1 − P. falciparum) by the total number of estimated cases for the country. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
For countries where the estimated proportion was not 0 or 1, the proportion of P. falciparum cases was assumed to have a beta distribution and was estimated from the proportion of P. falciparum cases reported by NMPs.To transform malaria cases into incidence, an estimate of population at risk was used.
The proportion of the population at high, low or no risk of malaria was provided by NMPs. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
This was applied to United Nations (UN) population estimates, to compute the number of people at risk of malaria.b) Global estimated malaria deathsNumbers of malaria deaths were estimated using methods from Category 1, 2 or 3, as outlined below.Category 1 methodThe Category 1 method was used for low-transmission countries and areas, both within and outside Africa: Afghanistan, Bangladesh, Bolivia (Plurinational State of), Botswana, Cambodia, Comoros, Djibouti, Eritrea, Eswatini, Ethiopia, French Guiana, Guatemala, Guyana, Haiti, Honduras, India, Indonesia, Lao People’s Democratic Republic, Madagascar, Myanmar, Namibia, Nepal, Pakistan, Papua New Guinea, Peru, Philippines, Solomon Islands, Somalia, Sudan, Timor-Leste, Vanuatu, Venezuela (Bolivarian Republic of), Viet Nam, Yemen and Zimbabwe. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
A case fatality rate of 0.256% was applied to the estimated number of P. falciparum cases, which represents the average of case fatality rates reported in the literature (11-13) and rates from unpublished data from Indonesia, 2004–2009.1 The proportion of deaths then follows a categorical distribution of 0.01%, 0.19%, 0.30%, 0.38% and 0.40%, each one with equal probability.A case fatality rate of 0.0375% was applied to the estimated number of P. vivax cases, representing the midpoint of the range of case fatality rates reported in a study by Douglas et al. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
(14), following a rectangular distribution between 0.012% and 0.063%. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
Following the nonlinear association explained for the Category 2 method below, the proportion of deaths in children aged under 5 years was estimated as:Proportion of deathsunder 5 = –0.2288 × Mortalityoverall0.823 × Mortalityoverall + 0.2239where Mortalityoverall is the number of estimated deaths over the estimated population at risk per 1000 (see Annex 3.F for national estimates of population at risk).2 + Category 2 methodThe Category 2 method was used for countries in Africa with a high proportion of deaths due to malaria: Angola, Benin, Burkina Faso, Burundi, Cameroon, Central African Republic, Chad, Congo, Côte d’Ivoire, Democratic Republic of the Congo, Equatorial Guinea, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Liberia, Malawi, Mali, Mauritania, Mozambique, Niger, Nigeria, Rwanda, Senegal, Sierra Leone, South Sudan, Togo, Uganda, United Republic of Tanzania and Zambia.In this method, child malaria deaths were estimated using a verbal autopsy multicause model that was developed by the WHO Maternal and Child Health Epidemiology Estimation Group (MCEE) to estimate causes of death in children aged 1–59 months (15). | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
Mortality estimates (and 95% CI) were derived for seven causes of post-neonatal death (pneumonia, diarrhoea, malaria, meningitis, injuries, pertussis and other disorders), four causes arising in the neonatal period (prematurity, birth asphyxia and trauma, sepsis, and other conditions of the neonate), and other causes (e.g.
malnutrition).
Deaths due to measles, unknown causes and HIV/AIDS were estimated separately. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
The resulting cause-specific estimates were adjusted, country by country, to fit the estimated mortality envelope of 1–59 months (excluding HIV/AIDS and measles deaths) for corresponding years.
Estimated prevalence of malaria parasites (see methods notes for Table 3.1) was used as a covariate within the model.
It was assumed that the number of deaths follows a rectangular distribution, with limits being the estimated 95% CI. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
The malaria mortality rate in children aged under 5 years estimated with this method was then used to infer malaria-specific mortality in those aged over 5 years, using the relationship between levels of malaria mortality in a series of age groups and the intensity of malaria transmission (16), and assuming a nonlinear association between under-5-years mortality and over-5-years mortality, as follows:Proportion of deathsover 5 = –0.293 × Mortalityunder 5× Mortalityunder 5 + 0.2896where Mortalityunder 5 is estimated from the number of deaths from the MCEE model over the population at risk per 1000.2 + 0.8918 Category 3 methodFor the Category 3 method, the number of indigenous malaria deaths registered by NMPs is reported without further adjustments. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
This category is used in the following countries: Algeria, Argentina, Armenia, Azerbaijan, Belize, Bhutan, Brazil, Cabo Verde, China, Colombia, Costa Rica, Democratic People’s Republic of Korea, Dominican Republic, Ecuador, Egypt, El Salvador, Georgia, Iran (Islamic Republic of), Iraq, Kazakhstan, Kyrgyzstan, 1 Dr Ric Price, Menzies School of Health Research, Australia, personal communication (November 2014).Malaysia, Mexico, Morocco, Nicaragua, Oman, Panama, Paraguay, Republic of Korea, Sao Tome and Principe, Saudi Arabia, South Africa, Sri Lanka, Suriname, Syrian Arab Republic, Tajikistan, Thailand, Turkey, Turkmenistan, United Arab Emirates and Uzbekistan.Fig. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
3.2.
Global trends in a) malaria case incidence rate (cases per 1000 population at risk), b) mortality rate (deaths per 100 000 population at risk), 2000–2019, c) distribution of malaria cases and d) deaths by country, 2019See methods notes for Table 3.1.Table 3.2.
Estimated malaria cases and deaths in the WHO African Region, 2000–2019See methods notes for Table 3.1.Fig.
3.3. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
3.3.
Trends in a) malaria case incidence rate (cases per 1000 population at risk), b) mortality rate (deaths per 100 000 population at risk), 2000–2019 and c) malaria cases by country in the WHO African Region, 2019See methods notes for Table 3.1.Table 3.3.
Estimated malaria cases and deaths in the WHO South-East Asia Region, 2000–2019See methods notes for Table 3.1.Fig.
3.4. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
3.4.
Trends in a) malaria case incidence rate (cases per 1000 population at risk), b) mortality rate (deaths per 100 000 population at risk), 2000–2019 and c) malaria cases by country in the WHO South-East Asia Region, 2019See methods notes for Table 3.1.Table 3.4.
Estimated malaria cases and deaths in the WHO Eastern Mediterranean Region, 2000–2019See methods notes for Table 3.1.Fig.
3.5. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
3.5.
Trends in a) malaria case incidence rate (cases per 1000 population at risk), b) mortality rate (deaths per 100 000 population at risk), 2000–2019 and c) malaria cases by country in the WHO Eastern Mediterranean Region, 2019See methods notes for Table 3.1.Table 3.5.
Estimated malaria cases and deaths in the WHO Western Pacific Region, 2000–2019See methods notes for Table 3.1.Fig.
3.6. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
3.6.
Trends in a) malaria case incidence rate (cases per 1000 population at risk), b) mortality rate (deaths per 100 000 population at risk), 2000–2019 and c) malaria cases by country in the WHO Western Pacific Region, 2019See methods notes for Table 3.1.Table 3.6.
Estimated malaria cases and deaths in the WHO Region of the Americas, 2000–2019See methods notes for Table 3.1.Fig.
3.7. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
3.7.
Trends in a) malaria case incidence rate (cases per 1000 population at risk), b) mortality rate (deaths per 100 000 population at risk), 2000–2019 and c) malaria cases by country in the WHO Region of the Americas, 2019See methods notes for Table 3.1.Fig.
3.8.
Cumulative number of cases and deaths averted globally and by WHO region, 2000–2019See methods for information on estimation of cases and deaths. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
Estimated cases and deaths averted were computed by comparing current estimates for each year since 2000 with estimates computed by holding the 2000 case incidence and mortality rates constant throughout the period 2000–2019.Fig.
3.9.
Percentage of a) cases and b) deaths averted by WHO region, 2000–2019See methods for information on estimation of cases and deaths.
See Fig.
3.8 for methods to estimate cases and deaths averted. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
The percentage of cases and deaths averted was estimated using overall global cases and deaths averted as denominator, and regional cases and deaths averted as numerator.Fig.
3.10. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
3.10.
Estimated prevalence of exposure to malaria infection during pregnancy, overall and by subregion in 2019, in moderate to high transmission countries in the WHO African RegionEstimates of malaria-exposed pregnancies and preventable malaria-attributable low birthweight (LBW) deliveries in the absence of pregnancy-specific malaria prevention (i.e. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
long-lasting insecticidal net [LLIN] delivery based on intermittent preventive treatment in pregnancy [IPTp] or antenatal care [ANC]) were obtained using a model of the relationship between these outcomes with slide microscopy prevalence in the general population and age- and gravidity-specific fertility patterns. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
This model 126127WORLD MALARIA REPORT 2020 Annex 1 – Data sources and methodswas developed by fitting an established model of the relationship between malaria transmission and malaria infection by age (17) to patterns of infection in placental histology (18) and attributable LBW risk by gravidity in the absence of IPTp or other effective chemoprevention (19). | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
The model was run across a 0.2 degree (5 km2) longitude/latitude grid for 100 realizations of the Malaria Atlas Project (MAP) joint posterior estimated slide prevalence in children aged 2–10 years in 2018 (9).
Country-specific, age-specific or gravidity-specific fertility rates, stratified by urban rural status, were obtained from demographic health surveys (DHS) and malaria indicator surveys (MIS), where such surveys had been carried out since 2014 and were available from the DHS program website (20). | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
Countries where surveys were not available were allocated fertility patterns from a survey from another country, matched on the basis of total fertility rate (21) and geography.
Fertility patterns of individual women within simulations at each grid-point were simulated according to the proportion of women estimated to be living in urban or rural locations. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
Urban or rural attribution at a 1 km2 scale was conducted based on WorldPop 1 km2 population estimates from 2018 (22) and an urban/rural threshold of 386/km2 (23); the estimates were then aggregated to the 0.2 degree (5 km2) resolution of the MAP surfaces. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
This provided a risk of malaria infection and malaria-attributable LBW in the absence of prevention, along with a modelled per capita pregnancy rate for each grid-point, which was aggregated to country level (using WorldPop population estimates) to provide a per pregnancy risk of malaria infection and per livebirth estimate of malaria-attributable LBW in the absence of prevention.
These were then multiplied by country-level estimates of pregnancies and estimates of LBW in 2019 (Fig.
3.11).Fig.
3.11. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
3.11).Fig.
3.11.
Estimated number of low birthweights due to exposure to malaria infection during pregnancy overall and by subregion in 2019, in moderate to high transmission countries in sub-Saharan AfricaMethods for estimating malaria infection in pregnancy and malaria-attributable LBWs are described in Walker et al.
(19).
Numbers of pregnancies were estimated from the latest UN population-estimated number of births and adjusted for the rate of abortion, miscarriage and stillbirths (24, 25). | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
The underlying P. falciparum parasite prevalence estimates were from the updated MAP series, using methods described in Bhatt et al.
(2015) (9).Fig.
3.12. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
Estimated number of low birthweights averted if current levels of IPTp coverage are maintained and the additional number averted if coverage of first dose of IPTp was optimized to match levels of coverage of first ANC visit in 2019, in moderate to high transmission countries in the WHO African RegionEfficacy of IPTp was modelled as a per-sulfadoxine-pyrimethamine (SP) dose reduction in the attributable risk of LBW and fitted to data from trials of IPTp-SP efficacy before the implementation of the intervention as policy; thus, they reflect impact on drug-sensitive parasites, with our central estimate being based on an assumed malaria-attributable LBW fraction of 40% within these trials. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
The modelling produced estimates of 48.5%, 73.5% and 86.3% efficacy in preventing malaria-attributable LBW for women receiving one, two or three doses of SP through IPTp, respectively.
See the methods for Fig.
3.11.Fig.
4.1.
Number of countries that were malaria endemic in 2000, with fewer than 10, 100, 1000 and 10 000 indigenous malaria cases between 2000 and 2019The figure is based on the countries where malaria was endemic in 2000 and had cases of malaria in 2000. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
The number of estimated cases was tabulated.Table 4.1.
Countries eliminating malaria since 2000Countries are shown by the year in which they attained zero indigenous cases for 3 consecutive years, according to reports submitted by NMPs.Table 4.2.
Number of indigenous malaria cases in E-2020 countries, 2010–2019Data were derived from NMP reports.Fig.
4.2. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
4.2.
Total malaria and P. falciparum cases in the GMS, 2000–2019Data were derived from NMP reports to the Greater Mekong subregion (GMS) Malaria Elimination Database (MEDB).Fig.
4.3.
Regional map of malaria incidence in the GMS by area, 2012–2019Data were derived from NMP reports to the GMS MEDB.Fig.
5.1.
HBHI: a targeted malaria response to get countries back on track to achieve the GTS 2025 milestonesThis figure on high burden high impact (HBHI) was taken from a recent WHO publication (26).Table 5.1. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
HBHI Response Element 2: work areas and status updatewas conducted and thus the trends presented in the main text should be interpreted carefully.The work areas shown in the table were developed by WHO and the RBM Partnership in consultation with countries and stakeholders as part of the HBHI response (26).Fig.
5.2. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
5.2.
Example of subnational tailoring of malaria intervention mixes and their projected impacts implemented as part of the HBHI response (in Nigeria)This is an example from Nigeria of analysis resulting from the HBHI Response Element 2 support involving subnational tailoring of malaria interventions using granular data on epidemiology and other factors developed by GMP. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
A mathematical model developed by the Institute for Disease Modeling1 was used to assess the impact of various scenarios, with different mixes of interventions.Fig.
5.3.
Estimated malaria a) cases, b) cases per 1000 population at risk, c) deaths and d) deaths per 100 000 population at risk, 2018 and 2019, in HBHI countriesSee methods notes for Table 3.1.Table 5.2. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
Comparisons of estimated malaria cases (millions) using the parasite rate-to-incidence model (Annex 1) and the reported data from the routine public health sector in high-burden countries of the WHO African Region, 2019See methods notes for Table 3.1.
The analysis compares, for 10 HBHI countries in Africa, the estimated number of malaria cases in 2019 if results from Method 2 (officially used to estimated cases in these countries) were compared with those in Method 1.Fig.
6.1. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
6.1.
Funding for malaria control and elimination, 2010–2019 (% of total funding), by source of funds (constant 2019 US$)Total funding for malaria control and elimination over the period 2000–2019 was estimated using data obtained from several sources, where available.
The methodology below describes the collection and analysis for all available domestic and international funding for Figs.
6.1–6.5.
For Figs. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
6.1–6.5.
For Figs.
6.1–6.5, data are represented for the years 2010–2019, because the Organisation for Economic Co-operation and Development (OECD) use of the multilateral system and the country-specific unit cost estimates were not available before 2010.
Figs. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
Figs.
6.3–6.5 reflect data available for 2000–2019, where, when there are no data available for a specific funder, no imputation 1 https://idmod.org/documentationContributions from governments of endemic countries were estimated as the sum of government contributions reported by NMPs for the world malaria report of the relevant year plus the estimated costs of patient care delivery services at public health facilities. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
If NMP contributions were missing for 2019, data reported from previous years were used after conversion to constant 2019 US$.
The number of reported malaria cases attending public health facilities was sourced from NMP reports, adjusted for diagnosis and reporting completeness.
Between 1% and 3% of uncomplicated reported malaria cases were assumed to have moved to the severe stage of disease, and 50–80% of these severe cases were assumed to have been hospitalized. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
Costs of outpatient visits and inpatient bed-stays were estimated from the perspective of the public health care provider, using unit cost estimates from WHO-CHOosing Interventions that are Cost-Effective (WHO-CHOICE) (27).
For each country, the 2010 unit cost estimates from WHO-CHOICE, expressed in the national currency, were estimated for the period 2011–2019 using the gross domestic product (GDP) annual price deflator published by the World Bank (28) on 7 July 2020, and converted in base year 2010. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
Country-specific unit cost estimates were then converted from national currency to constant 2019 US$ for each year during 2010–2019.
For each country, the number of adjusted reported malaria cases attending public health facilities was then multiplied by the estimated unit costs.
In the absence of information on the level of care at which malaria patients attend public facilities, uncertainty around unit cost estimates was handled through probabilistic uncertainty analysis. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
The mean total cost of patient care service delivery was calculated from 1000 estimations.
Contributions from governments of endemic countries as reported by NMPs were available for 2000-2019.International bilateral funding data were obtained from several sources.
Data on planned funding from the government of the United States of America (USA) were sourced from the US government Foreign Assistance website (29), with the technical assistance of the Kaiser Family Foundation. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
Country-level funding data were available for the US Agency for International Development (USAID) for the period 2006–2019. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
Country-specific planned funding data from other agencies, such as the US Centers for Disease Control and Prevention (CDC) and the US Department of Defense, were not available; therefore, data on total annual planned funding from each of these two agencies were used for the period 2001–2019, as well as total annual planned funding from USAID for 2001–2005 until the introduction of country-specific funding from 2006 through 2019. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
For the government of the United Kingdom of Great Britain and Northern Ireland 128129WORLD MALARIA REPORT 2020 Annex 1 – Data sources and methods(United Kingdom), funding data towards malaria control for 2017, 2018 and 2019 were sourced from the Statistics on International Development: final UK aid spend 2019 (30) (UK aid spend) with the technical assistance of the United Kingdom Department for International Development. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
The UK aid spend data do not capture all spending from the United Kingdom that may affect malaria outcomes.
The United Kingdom supports malaria control and elimination through a broad range of interventions; for example, via support to overall health systems in malaria endemic countries, and through research and development (R&D), which are not included in these data. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
For the period 2010–2016, United Kingdom spending data were sourced from the OECD creditor reporting system (CRS) database on aid activity (31).
For all other donors, disbursement data were also obtained from the OECD CRS database on aid activity for the period 2002–2018.
For each year and each funder, the country- and regional-level project-type interventions and other technical assistance were extracted.
All data were converted to constant 2019 US$. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
For years with no data available for a particular funder, no imputation was conducted so trends presented in the main text figures should be interpreted carefully.Malaria-related annual funding from donors through multilateral agencies was estimated from data on (i) donors’ contributions published by the Global Fund to Fight AIDS, Tuberculosis and Malaria (Global Fund) (32) from 2010 to 2019, and annual disbursements by the Global Fund to malaria endemic countries between 2003 and 2019, as reported by the Global Fund; and (ii) donors’ disbursements to malaria endemic countries published in the OECD CRS and in the OECD Development Assistance Committee (DAC) members’ total use of the multilateral system from 2011 through 2018 (31). | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
All funding flows were converted to constant 2019 US$.For (i), the amount of funding contributed by each donor was estimated as the proportion of funding paid by each donor out of the total amount received by the Global Fund in a given year, multiplied by the total amount disbursed by the Global Fund in that same year.For (ii), contributions from donors to multilateral channels were estimated by calculating the proportion of the core contributions received by a multilateral agency each year by each donor, then multiplying that amount by the multilateral agency’s estimated investment in malaria control in that same year.Contributions from malaria endemic countries to multilateral agencies were allocated to governments of endemic countries under the “funding source” category. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
Contributions from non-DAC countries and other sources to multilateral agencies were not available and were therefore not included.Annual estimated investments were summed to estimate the total amount each funder contributed to malaria control and the period elimination over 2010–2019, and the relative percentage of the total spending contributed by each funder was calculated for the period 2010–2019.Fig.
6.1 excludes household spending on malaria prevention and treatment in malaria endemic countries.Fig. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
6.2.
Funding for malaria control and elimination, 2010–2019, by source of funds (constant 2019 US$)See methods notes for Fig.
6.1 for sources of information on total funding for malaria control and elimination from governments of malaria endemic countries and on international funding flows.
Fig.
6.2 excludes household spending on malaria prevention and treatment in malaria endemic countries.Fig.
6.3. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
6.3.
Funding for malaria control and elimination, 2000–2019, by World Bank 2019 income group and source of funding (constant 2019 US$)See methods notes for Fig.
6.1 for sources of information on total funding for malaria control and elimination from governments of malaria endemic countries and on international funding flows.
Data on income group classification for 2019 were sourced from the World Bank (33). | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
For years with no data available for a particular funder, no imputation was conducted so trends presented in the main text figures should be interpreted carefully.
Fig.
6.3 excludes household spending on malaria prevention and treatment in malaria endemic countries.Fig.
6.4.
Funding for malaria control and elimination, 2000–2019, by channel (constant 2019 US$)See methods notes for Fig. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
6.1 for sources of information on total funding for malaria control and elimination from governments of malaria endemic countries and on international funding flows.
For years with no data available for a particular funder, no imputation was conducted so trends presented in the main text figures should be interpreted carefully.
Fig.
6.4 excludes household spending on malaria prevention and treatment in malaria endemic countries.Fig.
6.5. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
6.5.
Funding for malaria control and elimination, 2000–2019, by WHO region (constant 2019 US$)See methods notes for Fig.
6.1 for sources of information on total funding for malaria control and elimination from governments of malaria endemic countries and on international funding flows.
The “Unspecified” category includes all funding data for which there was no geographical information on the recipient. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
For years with no data available for a particular funder, no imputation was conducted so trends presented in the main text figures should be interpreted carefully.
Fig.
6.5 excludes household spending on malaria prevention and treatment in malaria endemic countries.Fig.
6.6.
Funding for malaria-related R&D, 2007–2018, by product type (constant 2019 US$)Data on funding for malaria-related R&D for 2007–2018 were sourced directly from Policy Cures Research through the G-FINDER data portal (34).Fig.
6.7. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
6.7.
Malaria R&D funding from 2007 to 2018, by sector (constant 2019 US$)See methods notes for Fig.
6.6.Fig.
7.1.
Number of ITNs delivered by manufacturers and distributed by NMPs, 2010–2019Data on the number of ITNs delivered by manufacturers to countries were provided to WHO by Milliner Global Associates.
Data from NMP reports were used for the number of ITNs distributed within countries.Fig.
7.2. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
7.2.
Indicators of population-level coverage of ITNs, sub-Saharan Africa, 2000–2019: a) percentage of households with at least one ITN, b) percentage of households with one ITN for every two people, c) percentage of population with access to an ITN, d) percentage of population using an ITN, e) percentage of children aged under 5 years using an ITN and f) percentage of pregnant women sleeping under an ITNEstimates of ITN coverage were derived from a model developed by MAP (8), using a two-stage process. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
First, a mechanism was designed for estimating net crop (i.e.
the total number of ITNs in households in a country at a given time), taking into account inputs to the system (e.g.
deliveries of ITNs to a country) and outputs (e.g.
loss of ITNs from households).
Second, empirical modelling was used to translate estimated net crops (i.e.
total number of ITNs in a country) into resulting levels of coverage (e.g. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
access within households, use in all ages and use among children aged under 5 years).The model incorporates data from three sources: ■ the number of ITNs delivered by manufacturers to countries, as provided to WHO by Milliner Global Associates; ■ the number of ITNs distributed within countries, as reported to WHO by NMPs; and ■ data from nationally representative household surveys from 39 countries in sub-Saharan Africa, from 2001 to 2018.Countries for analysisThe main analysis covered 40 of the 47 malaria endemic countries or areas of sub-Saharan Africa. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
The islands of Mayotte (for which no ITN delivery or distribution data were available) and Cabo Verde (which does not distribute ITNs) were excluded, as were the low-transmission countries of Eswatini, Namibia, Sao Tome and Principe, and South Africa, for which ITNs comprise a small proportion of vector control.
Analyses were limited to populations categorized by NMPs as being at risk.Estimating national net crops through timeAs described by Flaxman et al. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
(35), national ITN systems were represented using a discrete-time stock-and-flow model.
Nets delivered to a country by manufacturers were modelled as first entering a “country stock” compartment (i.e.
stored in-country but not yet distributed to households).
Nets were then available from this stock for distribution to households by the NMP or through other distribution channels. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
To accommodate uncertainty in net distribution, the number of nets distributed in a given year was specified as a range, with all available country stock (i.e.
the maximum number of nets that could be delivered) as the upper end of the range and the NMP-reported value (i.e.
the assumed minimum distribution) as the lower end. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
The total household net crop comprised new nets reaching households plus older nets remaining from earlier times, with the duration of net retention by households governed by a loss function.
However, rather than the loss function being fitted to a small external dataset – as per Flaxman et al.
(35) – the loss function was fitted directly to the distribution and net crop data within the stock-and-flow model itself. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
Loss functions were fitted on a country-by-country basis, were allowed to vary through time, and were defined separately for conventional ITNs (cITNs) and LLINs.
The fitted loss functions were compared with existing assumptions about rates of net loss from households. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
The stock-and-flow model was fitted using Bayesian inference and Markov chain Monte Carlo methods, which provided time-series estimates of national household net crop for cITNs and LLINs in each country, and an evaluation of under-distribution, all with posterior credible intervals.Estimating indicators of national ITN access and use from the net cropRates of ITN access within households depend not only on the total number of ITNs in a country (i.e. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
the net crop), but also on how those nets are distributed among households.
One factor that is known to strongly influence the 130131WORLD MALARIA REPORT 2020 Annex 1 – Data sources and methodsrelationship between net crop and net distribution patterns among households is the size of households, which varies among countries, particularly across sub-Saharan Africa.
Many recent national surveys report the number of ITNs observed in each household surveyed. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
Hence, it is possible not only to estimate net crop, but also to generate a histogram that summarizes the household net ownership pattern (i.e.
the proportion of households with 0, 1 or 2 nets, etc).
In this way, the size of the net crop was linked to distribution patterns among households while accounting for household size, making it possible to generate ownership distributions for each stratum of household size. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
The bivariate histogram of net crop to distribution of nets among households by household size made it possible to calculate the proportion of households with at least one ITN.
Also, because the numbers of both ITNs and people in each household were available, it was possible to directly calculate two additional indicators: the proportion of households with at least one ITN for every two people, and the proportion of the population with access to an ITN within their household. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
For the final ITN indicator – the proportion of the population who slept under an ITN the previous night – the relationship between ITN use and access was defined using 62 surveys in which both these indicators were available (ITNuse all ages = 0.8133 × ITN accessall ages + 0.0026, R2 = 0.773).
This relationship was applied to the MAP’s country–year estimates of household access, to obtain ITN use among all ages. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
The same method was used to obtain the country–year estimates of ITN use in children aged under 5 years (ITN usechildren under 5 = 0.9327 x ITN accesschildren under 5 + 0.0282, R2 = 0.754).Fig.
7.3.
Concentration index of ITN use by children aged under 5 years, sub-Saharan Africa at administrative level 1The distribution of ITN usage related to the distribution of wealth index was analysed from household surveys using the concindex command in Stata (36). | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
The concentration index (37) has a value of 0 if there is no difference in the distribution of the usage related to the distribution of wealth, a positive value if the usage is concentrated among the high-wealth population and a negative value if the usage is concentrated among the low-wealth population.Fig.
7.4.
Percentage of the population at risk protected by IRS, by WHO region, 2010–2019The number of people protected by IRS was reported to WHO by NMPs. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
The total population of each country was taken from the 2017 revision of the World population prospects (21), and the proportion at risk of malaria was derived from NMP reports.Fig.
7.5.
Subnational areas where SMC was delivered in implementing countries in sub-Saharan Africa, 2019Data were provided by Chemoprevention (SMC) Working Group.the Seasonal Malaria Table 7.1. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
Average number of children treated with at least one dose of SMC by year in countries implementing SMC, 2012-2019Data were provided by the London School of Hygiene & Tropical Medicine (LSHTM) and MMV.Table 7.2.
Average number of children targeted and treated, and total treatment doses targeted and delivered, in countries implementing SMC, 2019Data were provided by LSHTM and MMV.Fig.
7.6. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
7.6.
Percentage of pregnant women attending an ANC clinic at least once and receiving IPTp, by dose, sub-Saharan Africa, 2010–2019The total number of pregnant women eligible for IPTp was calculated by adding total live births calculated from UN population data and spontaneous pregnancy loss (specifically, miscarriages and stillbirths) after the first trimester (24).
Spontaneous pregnancy loss has previously been calculated by Dellicour et al.
(25). | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
(25).
Country-specific estimates of IPTp coverage were calculated as the ratio of pregnant women receiving IPTp at ANC clinics to the estimated number of pregnant women eligible for IPTp in a given year.
ANC attendance rates were derived in the same way, using the number of initial ANC visits reported through routine information systems.
Local linear interpolation or information for national representative surveys was used to compute missing values. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
Annual aggregate estimates exclude countries for which a report or interpolation was not available for the specific year. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
Dose coverage could be calculated for 34 of the 38 countries with an IPTp policy.Diagnostic testing and treatmentThe analysis is based on the latest nationally representative household surveys (DHS and MIS) conducted between 2015 and 2019, and surveys (latest from 2000–2005) considered baseline surveys from sub-Saharan African countries where data on malaria case management were available. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
Data are only available for children aged under 5 years because DHS and MIS focus on the most vulnerable population groups.
Interviewers ask caregivers whether the child has had fever in the 2 weeks preceding the interview and, if so, where care was sought; whether the child received a finger or heel stick as part of the care; what treatment was received for the fever and when; and, in particular, whether the child received an artemisinin-based combination therapy (ACT) or other antimalarial medicine. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
In addition to self-reported data, DHS and MIS also include biomarker testing for malaria, using rapid diagnostic tests (RDTs) that detect P. falciparum histidine-rich protein 2 (HRP2).
Percentages and 95% CIs were calculated for each country each year, taking into account the survey design. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
Median values and interquartile ranges (IQRs) were calculated using country percentages for the latest and baseline surveys.The following indicators are presented in Table 7.3:IndicatorNumeratorDenominatorMedian prevalence of fever in the past 2 weeksChildren aged under 5 years with a history of fever in the past 2 weeksChildren aged under 5 yearsMedian prevalence of fever in the past 2 weeks for whom treatment was soughtMedian prevalence of treatment seeking by source of treatment for fever (public health facility, private health facility or community health worker)Median prevalence of receiving finger or heel prickChildren aged under 5 years with a history of fever in the past 2 weeks for whom treatment was soughtChildren aged under 5 years with a history of fever in the past 2 weeks for whom treatment was sought in the public sector or private sector or community health workerChildren aged under 5 years with a history of fever in the past 2 weeks for whom treatment was sought and who received a finger or heel prickChildren aged under 5 years with fever in the past 2 weeksChildren aged under 5 years with fever in the past 2 weeks for whom treatment was sought Children aged under 5 years with fever in the past 2 weeks for whom treatment was sought Median prevalence of treatment with ACTsChildren aged under 5 years with a history of fever in the past 2 weeks for whom treatment was sought and who were treated with ACTsChildren aged under 5 years with fever in the past 2 weeks for whom treatment was sought in public, private or community health servicesMedian prevalence of treatment with ACTs among those who received a finger or heel prickReceived ACT treatmentChildren aged under 5 years with fever in the past 2 weeks for whom treatment was sought and who received a finger or heel prickThe use of household survey data has several limitations. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
One issue is that, because of difficulty recalling past events, respondents may not provide reliable information, especially on episodes of fever and the identity of prescribed medicines, resulting in a misclassification of drugs. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
Also, because respondents can choose more than one source of care for one episode of fever, and because the diagnostic test and treatment question is asked broadly and hence is not linked to any specific source of care, it has been assumed that the diagnostic test and treatment were received in all the selected sources of care.
However, only a low percentage (<5%) of febrile children were brought for care in more than one source of care. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
Data may also be biased by the seasonality of survey data collection, because DHS are carried out at various times during the year and MIS are usually timed to correspond with the high malaria transmission season.
Another limitation, when undertaking trend analysis, is that DHS and MIS are done intermittently, or not at all in some countries, resulting in a relatively small number of countries in sub-Saharan Africa or for any particular 4-year period. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
Countries are also not the same across each 4-year period.
In addition, depending on the sample size of the survey, the denominator for some indicators can be small – countries where the number of children in the denominator was less than 30 were excluded from the calculation.Fig.
7.7. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
7.7.
Number of RDTs sold by manufacturers and distributed by NMPs for use in testing suspected malaria cases, 2010–2019The numbers of RDTs distributed by WHO region are the annual totals reported as having been distributed by NMPs.
Numbers of RDT sales between 2010 and 2019 reflect sales by companies eligible for procurement. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
From 2010 to 2017, WHO received reports from up to 44 (cumulative number; figure differs from year to year) manufacturers that participated in the RDT Product Testing Programme by WHO, the Foundation for Innovative New Diagnostics (FIND), the CDC, and the Special Programme for Research and Training in Tropical Diseases.
Since WHO Prequalification became a selection criterion for procurement, 2018 and 2019 sales data mainly focus on sales by the 11 eligible companies. | https://docs-lawep.s3.us-east-2.amazonaws.com/1710417610444.pdf | https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/9789240015791-double-page-view.pdf?sfvrsn=2c24349d10 | Zambia |
Subsets and Splits