The prevalence of Plasmodium falciparum in sub-Saharan Africa since 1900

Journal name:
Nature
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DOI:
doi:10.1038/nature24059
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Malaria transmission is influenced by climate, land use and deliberate interventions. Recent declines have been observed in malaria transmission. Here we show that the African continent has witnessed a long-term decline in the prevalence of Plasmodium falciparum from 40% prevalence in the period 1900–1929 to 24% prevalence in the period 2010–2015, a trend that has been interrupted by periods of rapidly increasing or decreasing transmission. The cycles and trend over the past 115 years are inconsistent with explanations in terms of climate or deliberate intervention alone. Previous global initiatives have had minor impacts on malaria transmission, and a historically unprecedented decline has been observed since 2000. However, there has been little change in the high transmission belt that covers large parts of West and Central Africa. Previous efforts to model the changing patterns of P. falciparum transmission intensity in Africa have been limited to the past 15 years1, 2 or have used maps drawn from historical expert opinions3. We provide quantitative data, from 50,424 surveys at 36,966 geocoded locations, that covers 115 years of malaria history in sub-Saharan Africa; inferring from these data to future trends, we would expect continued reductions in malaria transmission, punctuated with resurgences.

At a glance

Figures

  1. Changing spatial patterns of P. falciparum endemicity in sub-Saharan Africa since 1900.
    Figure 1: Changing spatial patterns of P. falciparum endemicity in sub-Saharan Africa since 1900.

    Predicted posterior predictions of age-standardized P. falciparum prevalence (PfPR2–10) per administrative unit on mainland sub-Saharan Africa and Madagascar, masked (white) according to biological- or control-related absence of transmission (see Methods and Supplementary Information 2.2) and the reported changing spatial extents of malaria transmission (see Methods, Supplementary Information 2.3, and Source Data).

  2. Summary and plausibility framework of P. falciparum transmission cycles in sub-Saharan Africa since 1900.
    Figure 2: Summary and plausibility framework of P. falciparum transmission cycles in sub-Saharan Africa since 1900.

    a, The median (dark green line) and 25–75% (medium green boundaries) and 2.5–97.5% (light green boundaries) interquartile credibility range of the posterior predictions of PfPR2–10 (see Source Data). b, Six periods of major intervention: (1) 1900–1949, restricted efforts through larval control (LC), environmental management (EM) and mass drug administration (MDA) using quinine (QN); (2) 1950–1969, launch of global malaria eradication programme (GMEP) in 1955, introduction of DDT and pilot elimination projects involving indoor residual house-spraying (IRS), accompanied later by MDA using chloroquine (CQ) and pyrimethamine (PYR); (3) 1970–1999, end of most vector control efforts, presumptive treatment of fevers with chloroquine, use of chloroquine in MDA to school children; (4) 2000–2004, the roll back malaria (RBM) initiative with insecticide-treated nets (ITN) for vulnerable children and pregnant women, expansion of intermittent presumptive treatment of malaria in pregnancy (IPTp) and failing first line treatment with sulfadoxine–pyrimethamine (SP) and/or chloroquine; (5) 2005–2010, distribution of long-lasting insecticide-treated nets (LLIN) on a large scale, IRS expansion, and switch from chloroquine or sulfadoxine–pyrimethamine to artemisinin-based combination therapy (ACT); (6) 2010–2015, increased IRS in many countries, scale-up of rapid diagnostic tests (RDTs), launch of the Global Technical Strategy (GTS) in 2012, which re-invigorated a global ambition for eradication and seasonal malaria chemoprevention (SMC) in West African countries. Vector resistance to organochlorines was detected in 1955 in Nigeria; organophosphate, carbamate and pyrethroid resistance was detected in the late 1980s and has expanded rapidly since the late 1990s20; chloroquine resistance was detected in 1978; sulfadoxine–pyrimethamine resistance was detected in 1953, with substantial clinical failure rates in 200012. c, Mean annual rainfall across the Sahara (green)10 and monthly minimum temperatures (blue)10; El Niño events leading to serious climate anomalies, including flooding in 1997–1998 in East Africa and drought in the Horn of Africa in 2014–2015 (red bars). Climate data from National Oceanic and Atmospheric Administration, US Department of Commerce (http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml).

  3. Availability of survey data over time.
    Extended Data Fig. 1: Availability of survey data over time.

    The temporal distribution of survey data per interval selected for analysis (number of surveys shown on top of bars).

  4. Spatial distribution of survey data.
    Extended Data Fig. 2: Spatial distribution of survey data.

    Location of 50,424 P. falciparum parasite surveys undertaken at 39,033 locations by time interval from 1900–1944 to 2010–2015.

  5. The spatial range of P. falciparum in Africa between 1900 and 1950.
    Extended Data Fig. 3: The spatial range of P. falciparum in Africa between 1900 and 1950.

    Light grey, absence of natural P. falciparum transmission; pink, natural extent of transmission; dark grey, countries not included in the analysis.

  6. Model convergence: Gelman–Rubin–Brooks plots demonstrating convergence during MCMC simulation for key model parameters.
    Extended Data Fig. 4: Model convergence: Gelman–Rubin–Brooks plots demonstrating convergence during MCMC simulation for key model parameters.

    Black line, ratio of within-chain variability to between-chain variability; dark grey line, within-chain variability (pooled); light grey line, between-chain variability (average).

  7. Model validation.
    Extended Data Fig. 5: Model validation.

    Predicted Pf PR2–10 versus observed PfPR2–10 for 100 randomly selected data points. Ninety-nine per cent of data points are within 95% credible interval (CI); Spearman rank correlation 0.46, P < 0.001 (two-sided test).

Main

Although short-term seasonal cycles are fundamental to malaria epidemiology, longer-term climate anomalies and shifting environmental and intervention landscapes also alter the likelihood of contact between mosquitoes and humans or the duration of host infection. The supra-seasonal, long-term cycles of transmission are poorly defined for P. falciparum malaria in Africa.

To provide an empirical basis for defining the long-term nature of malaria transmission cycles, we used data on the P. falciparum parasite rate (the proportion of persons positive for malaria infection among those examined). These data were derived from a repository assembled over the past 21 years (see Supplementary Information 1). To our knowledge, these data (available through the Harvard Dataverse, http://dx.doi.org/10.7910/DVN/Z29FR0) represent the largest repository assembled for any parasitic disease in Africa, derived from over 50,000 community-based surveys across sub-Saharan Africa since 19004 (Extended Data Figs 1, 2, Supplementary Information 1). We have used the space–time cube of data to leverage power from neighbouring areas and preceding data points in time5 in a conditional autoregressive spatial and temporal model, in order to compute a smoothed median estimate for approximately five-year intervals (since 1900) across 520 sub-national administrative polygons (Extended Data Fig. 3) and within the changing limits of P. falciparum transmission (Fig. 1).

Figure 1: Changing spatial patterns of P. falciparum endemicity in sub-Saharan Africa since 1900.
Changing spatial patterns of P. falciparum endemicity in sub-Saharan Africa since 1900.

Predicted posterior predictions of age-standardized P. falciparum prevalence (PfPR2–10) per administrative unit on mainland sub-Saharan Africa and Madagascar, masked (white) according to biological- or control-related absence of transmission (see Methods and Supplementary Information 2.2) and the reported changing spatial extents of malaria transmission (see Methods, Supplementary Information 2.3, and Source Data).

The median posterior predictions of P. falciparum prevalence provide a summary of several major cycles in the history of malaria transmission across the continent (Fig. 2). The impact of interventions and/or climate on these cycles can be assessed only by temporal plausibility rather than quantitative analysis. Between 1900 and 1944, efforts to control malaria focused on areas of European economic influence, primarily by targeting vector larvae or through mass quinine administration campaigns targeting the parasite itself6; we did not observe declines in transmission associated with these efforts.

Figure 2: Summary and plausibility framework of P. falciparum transmission cycles in sub-Saharan Africa since 1900.
Summary and plausibility framework of P. falciparum transmission cycles in sub-Saharan Africa since 1900.

a, The median (dark green line) and 25–75% (medium green boundaries) and 2.5–97.5% (light green boundaries) interquartile credibility range of the posterior predictions of PfPR2–10 (see Source Data). b, Six periods of major intervention: (1) 1900–1949, restricted efforts through larval control (LC), environmental management (EM) and mass drug administration (MDA) using quinine (QN); (2) 1950–1969, launch of global malaria eradication programme (GMEP) in 1955, introduction of DDT and pilot elimination projects involving indoor residual house-spraying (IRS), accompanied later by MDA using chloroquine (CQ) and pyrimethamine (PYR); (3) 1970–1999, end of most vector control efforts, presumptive treatment of fevers with chloroquine, use of chloroquine in MDA to school children; (4) 2000–2004, the roll back malaria (RBM) initiative with insecticide-treated nets (ITN) for vulnerable children and pregnant women, expansion of intermittent presumptive treatment of malaria in pregnancy (IPTp) and failing first line treatment with sulfadoxine–pyrimethamine (SP) and/or chloroquine; (5) 2005–2010, distribution of long-lasting insecticide-treated nets (LLIN) on a large scale, IRS expansion, and switch from chloroquine or sulfadoxine–pyrimethamine to artemisinin-based combination therapy (ACT); (6) 2010–2015, increased IRS in many countries, scale-up of rapid diagnostic tests (RDTs), launch of the Global Technical Strategy (GTS) in 2012, which re-invigorated a global ambition for eradication and seasonal malaria chemoprevention (SMC) in West African countries. Vector resistance to organochlorines was detected in 1955 in Nigeria; organophosphate, carbamate and pyrethroid resistance was detected in the late 1980s and has expanded rapidly since the late 1990s20; chloroquine resistance was detected in 1978; sulfadoxine–pyrimethamine resistance was detected in 1953, with substantial clinical failure rates in 200012. c, Mean annual rainfall across the Sahara (green)10 and monthly minimum temperatures (blue)10; El Niño events leading to serious climate anomalies, including flooding in 1997–1998 in East Africa and drought in the Horn of Africa in 2014–2015 (red bars). Climate data from National Oceanic and Atmospheric Administration, US Department of Commerce (http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml).

We observed two precipitous declines in infection prevalence, both of which followed rises in prevalence. One of these declines occurred between 1945 and 1949 and another between 2005 and 2009: dichlorodiphenyltrichloroethane (DDT) and chloroquine were introduced between 1945 and 1949, and the widespread introduction of insecticide-treated bed nets and artemisinin-based combination therapy occurred between 2005 and 2009 (Fig. 2). Indoor residual house-spraying with DDT was introduced through comparatively small projects during the 1950s and expanded in the 1960s only in southern Africa, Ethiopia, Sudan, Somalia and Madagascar. After successful trial projects, the expansion of the use of insecticide-treated bed nets to national scales took over a decade, reaching moderate levels of coverage in Africa by 20107.

The period from 1960 to 1984 was characterized by a slow decline in malaria prevalence across Africa (Fig. 2). Counter-intuitively, this coincided with a cessation of malaria elimination ambitions across much of sub-Saharan Africa8, with an emerging resistance among mosquitoes to organochloride insecticides9 and with a period in which malaria was integrated into broader health agendas that focused on the presumptive treatment of fevers with choroquine, a cheap, widely available and efficacious drug. This interval also included drought periods across much of the Sahel10 (Fig. 2), which rendered some areas unsuitable for malaria transmission11. Therefore, although several interventions may have influenced the observed trends, no single factor appears sufficient to account for them all.

Between 1985 and 2004, however, median malaria prevalence rose to levels similar to those witnessed fifty years earlier, before the introduction of DDT (Fig. 2). This period also saw a rapid expansion of chloroquine resistance across Africa12, climate anomalies connected with changes in Pacific Ocean sea surface temperatures10 (Fig. 2), and the failure of many national health agencies to prioritize the growing malaria epidemic because of a lack of international donor assistance13. Despite impressive gains in the coverage of effective interventions since 2005, the rate of reduction of malaria prevalence has slowed during the interval 2010–2015 (Fig. 2). Continued challenges to malaria control include difficulties in ensuring access to artemisinin-based combination therapies, the threat of drug resistance, rapidly emerging insecticide resistance and inadequate funding plans for replacing long-lasting insecticide-treated nets14.

There has been an overall decline in malaria transmission intensity over the past 115 years. Independent abiotic factors related to economic growth may have contributed to this overall decline, but the constant growth in sub-Saharan African GDP, urbanization and/or female education charted by the World Bank (World Development Indicators, accessed 8 April 2017)15 and United Nations (World Urbanization Prospects, accessed 8 April 2017)16 cannot account for the emergence of the 1985–2004 malaria epidemic. Conversely, minimum temperatures across sub-Saharan Africa have risen by over 1 °C since the 1970s10 (Fig. 2); the linear phenomena of global warming cannot explain the precipitous declines in malaria prevalence witnessed after 2004. The interplay among malaria, climate, effective or failing intervention, human settlement and development is inevitably complex. Our analysis highlights the fact that a focus on simple, single factors fails to adequately explain the cycles of parasite prevalence.

The reduction in malaria transmission intensity has not occurred equally between countries or within countries (Fig. 1), with more substantive declines and ‘shrinking of the map’ occurring at the margins of the historical range of P. falciparum transmission than in the heartland of Africa’s most efficient vector species, Anopheles gambiae sensu stricto and Anopheles coluzzii. This heartland forms a densely populated belt from West Africa through Central Africa toward Mozambique, and represents the most severely impacted area of the contemporary malaria-endemic world: it was ignored after 196017, 18 and risks being ignored today19. Our previous and current armoury of interventions has not eliminated malaria in this part of the world, and there is little indication that it will do so in the foreseeable future.

Although caution is required in predicting a complex future, if past trends remain consistent we would expect further reductions in the range and intensity of malaria transmission in Africa, punctuated with resurgences. We show the implausibility of simple explanations for temporal trends over the past 115 years, and therefore caution against using similar explanations for the trend of the past 15 years (for example, in ascribing this trend to human intervention alone). The unique malaria endemicity that prevails in Africa cannot be ignored in global efforts to eliminate P. falciparum, nor should we wait for future rises in malaria prevalence to re-galvanize interest in a parasite that remains entrenched across large parts of the continent.

Methods

No statistical methods were used to predetermine sample size.

Data Assembly

Over the past 21 years, we have sourced unpublished and published materials related to community-based malaria infection prevalence at European, United Nations and African national libraries, archives and ministry of health repositories. We undertook standard electronic data searches of peer-reviewed publications, and contacted malaria scientists, regional health research institutes, and government and non-government agencies involved in the delivery and monitoring of malaria interventions (Supplementary Information 1.3, 1.4). The minimum data requirements for the survey included the date and location, age range and numbers for participants examined, infection prevalence by species, and parasite detection method. A total of 50,424 parasite prevalence surveys were included4 (Extended Data Figs 1, 2, Supplementary Information 1.5).

Spatial limits and resolution of malaria predictions

We excluded previously endemic North African countries (Morocco, Algeria, Tunisia, Libya and Egypt), off-shore islands and countries where malaria has not been described (Western Sahara and Lesotho). Guided by the United Nations’ GAUL project (http://www.fao.org/geonetwork/srv/en/metadata.show?id=12691), we used current national and sub-national first-level administrative boundary units, with adaptations for the margins of natural P. falciparum risk and for disputed boundaries; we also dissolved the boundaries of small urban municipalities and ensured contiguous shapes between sub-national units. Rwanda, Burundi, Djibouti, Swaziland and The Gambia were treated as single polygons (Supplementary Information 2.1, 2.2; see Source Data of Fig. 1). The natural spatial limits of P. falciparum risk were derived from expert opinion, national maps and biological constraints (Supplementary Information 2.2; see Source Data of Fig. 1). The selection of 520 spatial polygons at the natural range of P. falciparum transmission is shown in Extended Data Fig. 3. Changing limits were mapped using data from national reports of malaria incidence from the 1960s onward (Supplementary Information 2.3; see Source Data of Fig. 1).

Statistical methods

We used a Bayesian hierarchical binomial model that simultaneously estimates stable spatial and temporal structured patterns and departures from these stable components5. The input data were as follows: observed number of children aged 2–10 years with P. falciparum (PfPRit) and total number of tested children aged 2–10 years (nit) for subnational region (i, from 1 to 520) and time periods (t = 1–16, corresponding to 1900–1929, 1930–1944, five-year periods from 1945–1949 to 2004–2009, and one six-year period, 2010–2015). The model was fitted using Markov chain Monte Carlo (MCMC) simulation using non-informative priors. Posterior distributions of parameters were obtained using WinBUGS software (Supplementary Information 3; see Source Data of Fig. 1). Gelman–Rubin statistics were used to assess model convergence (Extended Data Fig. 4). Output was validated using observed versus fitted PfPR2–10 from the full model (Extended Data Fig. 5).

Ethics statement

As the secondary use of aggregate survey data, our research centre considered the work to be non-human research for which individual informed consent was not applicable.

Code availability

The WINBUGs code for both the negative binomial and Poisson models are freely accessible at http://dx.doi.org/10.7910/DVN/Z29FR0. No restrictions apply to their use.

Data availability

The full database of survey data that support the findings of this study are available from the Harvard Dataverse (http://dx.doi.org/10.7910/DVN/Z29FR0) under a CC-BY 4.0 license. Source Data are available in the online version of the paper.

References

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Acknowledgements

We thank Á. Munoz and M. Thompson for advice on long-term climate data; M. Coetzee and J. Hemmingway for details of emerging insecticide resistance; E. Okiro, M. English and D. Zurovac for comments on earlier drafts of the paper; and the individuals and institutions who have helped to assemble malaria data from across Africa over the past 21 years (see Supplementary Information 5). The present study was supported by the International Development Research Centre, Canada (1996–1998) and the Wellcome Trust, UK (1996–1997: 048335) through the Mapping Malaria Risk in Africa (MARA/ARMA) project, and the Wellcome Trust through the Malaria Atlas Project (MAP) in 2005 (034694) and funding to R.W.S. as part of his Principal Fellowship since 2007 (079080 and 103602). A.M.N. acknowledges support from the Wellcome Trust as an Intermediary Fellow (095127); R.W.S., D.K., J.M., P.A., C.W.M., P.B. and A.M.N. acknowledge the support of the Wellcome Trust for the Kenya Major Overseas Programme (077092 and 203077). R.W.S. is grateful to the Department for International Development (UK) for their support of Strengthening the Use of Data for Malaria Decision Making in Africa (DFID Programme Code 203155), which provided support to D.K. and J.M.

Author information

Affiliations

  1. Kenya Medical Research Institute-Wellcome Trust Collaborative Programme, Nairobi, Kenya

    • Robert W. Snow,
    • David Kyalo,
    • Joseph Maina,
    • Punam Amratia,
    • Clara W. Mundia,
    • Philip Bejon &
    • Abdisalan M. Noor
  2. Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK

    • Robert W. Snow &
    • Philip Bejon
  3. Public Health Medicine, School of Nursing and Public Health, University of KwaZulu-Natal, Durban, South Africa

    • Benn Sartorius

Contributions

R.W.S. assembled the data, designed the experiment and wrote the paper; B.S. undertook the statistical analysis; P.B. provided support for data interpretation; A.M.N. provided support for data assembly and analysis; and D.K., J.M., P.A. and C.W.M. all provided assistance in locating survey reports, abstraction of data and geo-coding. All authors have access to the data and have reviewed the paper and Supplementary Information.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to:

Reviewer Information Nature thanks S. Dushoff, B. Greenwood, J. Gupta and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Author details

Extended data figures and tables

Extended Data Figures

  1. Extended Data Figure 1: Availability of survey data over time. (26 KB)

    The temporal distribution of survey data per interval selected for analysis (number of surveys shown on top of bars).

  2. Extended Data Figure 2: Spatial distribution of survey data. (530 KB)

    Location of 50,424 P. falciparum parasite surveys undertaken at 39,033 locations by time interval from 1900–1944 to 2010–2015.

  3. Extended Data Figure 3: The spatial range of P. falciparum in Africa between 1900 and 1950. (523 KB)

    Light grey, absence of natural P. falciparum transmission; pink, natural extent of transmission; dark grey, countries not included in the analysis.

  4. Extended Data Figure 4: Model convergence: Gelman–Rubin–Brooks plots demonstrating convergence during MCMC simulation for key model parameters. (168 KB)

    Black line, ratio of within-chain variability to between-chain variability; dark grey line, within-chain variability (pooled); light grey line, between-chain variability (average).

  5. Extended Data Figure 5: Model validation. (93 KB)

    Predicted Pf PR2–10 versus observed PfPR2–10 for 100 randomly selected data points. Ninety-nine per cent of data points are within 95% credible interval (CI); Spearman rank correlation 0.46, P < 0.001 (two-sided test).

Supplementary information

PDF files

  1. Supplementary Information (1.8 MB)

    This file provides detailed descriptions of large data assembly, changing margins of malaria risk, statistical handling of the data, Supplementary References, Acknowledgements and Supplementary Table 1.

  2. Reporting Summary (67 KB)

Excel files

  1. Supplementary Data (1.2 MB)

    Source data of model outputs per polygon 1900-2015. This file provides the model outputs per 520 administrative polygons in Africa for 16 prediction years since 1900.

  2. Supplementary Data (14 KB)

    Source data of median predictions of P. falciparum prevalance since 1900. This file contains the median and confidence range of all 520 polygon predictions of P. falciparum prevalence for 16 prediction years since 1900.

Zip files

  1. Supplementary Data (12.4 MB)

    Source data for GIS shape files of historical endemicity. This file provides margins and polygons of prediction of malaria at its historical extent.

  2. Supplementary Data (17.1 MB)

    Source data for GIS shape files of changing margins of malaria risk in Africa. This file shows how margins of transmission of malaria changed from 1900 to 2015.

Additional data