Mapping Plasmodium falciparum Mortality in Africa between 1990 and 2015

Abstract

Background: Malaria control has not been routinely informed by the assessment of subnational variation in malaria deaths. We combined data from the Malaria Atlas Project and the Global Burden of Disease Study to estimate malaria mortality across sub-Saharan Africa on a grid of 5 km² from 1990 through 2015.

Methods: We estimated malaria mortality using a spatiotemporal modeling framework of geolocated data (i.e., with known latitude and longitude) on the clinical incidence of malaria, coverage of antimalarial drug treatment, case fatality rate, and population distribution according to age.

Results: Across sub-Saharan Africa during the past 15 years, we estimated that there was an overall decrease of 57% (95% uncertainty interval, 46 to 65) in the rate of malaria deaths, from 12.5 (95% uncertainty interval, 8.3 to 17.0) per 10,000 population in 2000 to 5.4 (95% uncertainty interval, 3.4 to 7.9) in 2015. This led to an overall decrease of 37% (95% uncertainty interval, 36 to 39) in the number of malaria deaths annually, from 1,007,000 (95% uncertainty interval, 666,000 to 1,376,000) to 631,000 (95% uncertainty interval, 394,000 to 914,000). The share of malaria deaths among children younger than 5 years of age ranged from more than 80% at a rate of death of more than 25 per 10,000 to less than 40% at rates below 1 per 10,000. Areas with high malaria mortality (>10 per 10,000) and low coverage (<50%) of insecticide-treated bed nets and antimalarial drugs included much of Nigeria, Angola, and Cameroon and parts of the Central African Republic, Congo, Guinea, and Equatorial Guinea.

Conclusions: We estimated that there was an overall decrease of 57% in the rate of death from malaria across sub-Saharan Africa over the past 15 years and identified several countries in which high rates of death were associated with low coverage of antimalarial treatment and prevention programs. (Funded by the Bill and Melinda Gates Foundation and others.).

Figure 1. Main Components of the Modeling Framework for Estimating Malaria Mortality.

The use of insecticide-treated bed nets was defined as the proportion of people sleeping under such a net during the previous night. Relative drug use was defined as the relative proportion of patients with malaria who were treated with chloroquine, sulfadoxine–pyrimethamine, or artemisinin-based combination therapy (the values in the figure indicate the number of country-years of data per drug). Any antimalarial coverage was defined as the fraction of all patients who were treated with any antimalarial drug. Antimalarial drug efficacy was defined as the fraction of the time that treatment was effective; artemisinin-based combination therapy was assumed to be 100% efficacious (the values in the figure indicate the number of country-years of data per drug). Incidence of clinical malaria was defined as cases per person per year, according to age and year at the level of a 5-km² grid. Effective treatment coverage was defined as the fraction of malaria cases that were effectively treated. Untreated incidence was defined as the rate of untreated or ineffectively treated cases of malaria. CoDCorrect is the Global Burden of Disease Study (GBD) algorithm that standardizes estimates of cause-specific mortality (including from malaria) to match estimates of all-cause mortality. MAP denotes Malaria Atlas Project, NMCP National Malaria Control Program, and WHO World Health Organization.

Figure 2. Estimated Changes in Plasmodium falciparum Mortality in Sub-Saharan Africa.

Shown are estimated time series from 1990 through 2015 of the rate of death (Panel A) and number of deaths (Panel B) from malaria for each country in which malaria is endemic (gray lines, with the two countries with the largest burden, Nigeria and Democratic Republic of Congo, labeled for deaths) and aggregated into West, Central, East, and Southern Africa regions. Also shown is the change in malaria mortality for each country between 2000 and 2015 (Panel C), with each country identified by its International Organization for Standardization (ISO) country code. AGO denotes Angola, BDI Burundi, BEN Benin, BFA Burkina Faso, BWA Botswana, CAF Central African Republic, CIV Ivory Coast, CMR Cameroon, COD Democratic Republic of Congo, COG Congo, DJI Djibouti, DZA Algeria, EGY Egypt, ERI Eritrea, ESH Western Sahara, ETH Ethiopia, GAB Gabon, GHA Ghana, GIN Guinea, GMB Gambia, GNB Guinea-Bissau, GNQ Equatorial Guinea, KEN Kenya, LBR Liberia, LBY Libya, LSO Lesotho, MAR Morocco, MDG Madagascar, MLI Mali, MOZ Mozambique, MRT Mauritania, MWI Malawi, NAM Namibia, NER Niger, NGA Nigeria, RWA Rwanda, SDN Sudan, SEN Senegal, SLE Sierra Leone, SOM Somalia, SSD South Sudan, SWZ Swaziland, TCD Chad, TGO Togo, TUN Tunisia, TZA Tanzania, UGA Uganda, ZAF South Africa, ZMB Zambia, and ZWE Zimbabwe.

Figure 3. Estimated Number and Rate of Plasmodium falciparum Deaths in Sub-Saharan Africa in 2015.

Estimates are provided for each 5-km² grid cell within the previously defined limits of stable Plasmodium falciparum (Pf) transmission. Shown are the estimated number of deaths (Panels A, C, and E) and deaths per 10,000 population per year (Panels B, D, and F) per grid cell for children 4 years of age or younger (Panels A and B), those 5 to 14 years of age (Panels C and D), and persons 15 years of age or older (Panels E and F).

Figure 4. Age Pattern of Malaria Mortality in 2015.

Panel A shows the estimated percentage of all malaria deaths in 2015 that occurred in children 4 years of age or younger in each 5-km² pixel in sub-Saharan Africa. Panel B shows the relationship between estimated all-age death rates per pixel (x axis) and the estimated percentage of all malaria deaths in children 4 years of age or younger (y axis). Each pixel is plotted with the density of pixels indicated by the color shading (with highest density in red). The solid and dashed lines indicate the mean and 95% confidence intervals, respectively, of a locally estimated scatterplot-smoothed (loess) regression used here to visualize the central tendency of this relationship.

Figure 5. Identifying Regions with High Mortality and Low Malaria-Control Coverage.

Shown are areas with high mortality and low coverage of antimalarial treatment (Panel A) and areas with high mortality and low coverage of insecticide-treated bed nets (ITNs) (Panel B). Both maps show data for 2015. Data on bed nets were obtained from earlier geospatial mapping.