Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015

Abstract

Since the year 2000, a concerted campaign against malaria has led to unprecedented levels of intervention coverage across sub-Saharan Africa. Understanding the effect of this control effort is vital to inform future control planning. However, the effect of malaria interventions across the varied epidemiological settings of Africa remains poorly understood owing to the absence of reliable surveillance data and the simplistic approaches underlying current disease estimates. Here we link a large database of malaria field surveys with detailed reconstructions of changing intervention coverage to directly evaluate trends from 2000 to 2015, and quantify the attributable effect of malaria disease control efforts. We found that Plasmodium falciparum infection prevalence in endemic Africa halved and the incidence of clinical disease fell by 40% between 2000 and 2015. We estimate that interventions have averted 663 (542–753 credible interval) million clinical cases since 2000. Insecticide-treated nets, the most widespread intervention, were by far the largest contributor (68% of cases averted). Although still below target levels, current malaria interventions have substantially reduced malaria disease incidence across the continent. Increasing access to these interventions, and maintaining their effectiveness in the face of insecticide and drug resistance, should form a cornerstone of post-2015 control strategies.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Changes in infection prevalence 2000–2015.
Figure 2: Changing endemicity and effect of interventions 2000–2015.

References

  1. World Health Organization. World Malaria Report 2014 (World Health Organization, 2014)

  2. Roll Back Malaria Partnership/World Health Organization. Global Malaria Action Plan 1 (2000–2015) (World Health Organization, 2008)

  3. World Health Organization. Global Technical Strategy for Malaria 2016–2030 (World Health Organization, 2015)

  4. Roll Back Malaria Partnership/World Health Organization. Action and Investment to Defeat Malaria 2016–2030 (World Health Organization on behalf of the Roll Back Malaria Partnership Secretariat, 2015)

  5. Rowe, A. K. et al. Caution is required when using health facility-based data to evaluate the health impact of malaria control efforts in Africa. Malar. J. 8, 209 (2009)

    Article  Google Scholar 

  6. Chizema-Kawesha, E. et al. Scaling up malaria control in Zambia: progress and impact 2005–2008. Am. J. Trop. Med. Hyg. 83, 480–488 (2010)

    Article  Google Scholar 

  7. Lim, S. S. et al. Net benefits: a multicountry analysis of observational data examining associations between insecticide-treated mosquito nets and health outcomes. PLoS Med. 8, e1001091 (2011)

    Article  ADS  Google Scholar 

  8. Lengeler, C. Insecticide-treated bed nets and curtains for preventing malaria. Cochrane Database Syst. Rev. 2, CD000363 (2004)

    Google Scholar 

  9. Giardina, F. et al. Effects of vector-control interventions on changes in risk of malaria parasitaemia in sub-Saharan Africa: a spatial and temporal analysis. Lancet Glob. Health 2, e601–e615 (2014)

    Article  Google Scholar 

  10. Hay, S. I. et al. A world malaria map: Plasmodium falciparum endemicity in 2007. PLoS Med. 6, e1000048 (2009)

    Article  Google Scholar 

  11. Gething, P. W. et al. A new world malaria map: Plasmodium falciparum endemicity in 2010. Malar. J. 10, 378 (2011)

    Article  Google Scholar 

  12. Noor, A. M. et al. The changing risk of Plasmodium falciparum malaria infection in Africa: 2000–10: a spatial and temporal analysis of transmission intensity. Lancet 383, 1739–1747 (2014)

    Article  Google Scholar 

  13. Patil, A. P. et al. Defining the relationship between Plasmodium falciparum parasite rate and clinical disease: statistical models for disease burden estimation. Malar. J. 8, 186 (2009)

    Article  Google Scholar 

  14. Griffin, J. T., Ferguson, N. M. & Ghani, A. C. Estimates of the changing age-burden of Plasmodium falciparum malaria disease in sub-Saharan Africa. Nat. Commun. 5, 3136 (2014)

    Article  ADS  Google Scholar 

  15. Smith, T. et al. Ensemble modeling of the likely public health impact of a pre-erythrocytic malaria vaccine. PLoS Med. 9, e1001157 (2012)

    Article  Google Scholar 

  16. Hay, S. I. et al. Estimating the global clinical burden of Plasmodium falciparum malaria in 2007. PLoS Med. 7, e1000290 (2010)

    Article  Google Scholar 

  17. Diggle, P. & Ribeiro, P. Model-based Geostatistics (Springer, 2007)

    MATH  Google Scholar 

  18. Weiss, D. J. et al. Re-examining environmental correlates of Plasmodium falciparum malaria endemicity: a data-intensive variable selection approach. Malar. J. 14, 68 (2015)

    Article  Google Scholar 

  19. Smith, D. L., Guerra, C. A., Snow, R. W. & Hay, S. I. Standardizing estimates of the Plasmodium falciparum parasite rate. Malar. J. 6, 131 (2007)

    Article  Google Scholar 

  20. Cameron, E. et al. Defining the relationship between infection prevalence and clinical incidence of Plasmodium falciparum malaria. Nat. Commun. 6, 8170 (2015)

    Article  ADS  Google Scholar 

  21. Wenger, E. A. & Eckhoff, P. A. A mathematical model of the impact of present and future malaria vaccines. Malar. J. 12, 126 (2013)

    Article  Google Scholar 

  22. Battle, K. E. et al. Global database of Plasmodium falciparum and P. vivax incidence records from 1985–2013. Sci. Data 2, 150012 (2015)

    Article  Google Scholar 

  23. WorldPop. Gridded population distributions. http://www.worldpop.org.uk (2015)

  24. Cibulskis, R. E., Aregawi, M., Williams, R., Otten, M. & Dye, C. Worldwide incidence of malaria in 2009: estimates, time trends, and a critique of methods. PLoS Med. 8, e1001142 (2011)

    Article  Google Scholar 

  25. Liu, L. et al. Global, regional, and national causes of child mortality in 2000–13, with projections to inform post-2015 priorities: an updated systematic analysis. Lancet 385, 430–440 (2015)

    Article  Google Scholar 

  26. GBD 2013 Mortality and Causes of Death Collaborators. Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 385, 117–171 (2015)

Download references

Acknowledgements

The authors acknowledge assistance from M. Renshaw in providing information from the Roll Back Malaria Harmonization Working Group Programmatic Gap Analysis and other guidance in the interpretation of our results. We thank members of the Roll Back Malaria Monitoring and Evaluation Reference Group and the World Health Organization Surveillance Monitoring and Evaluation Technical expert Group for their feedback and suggestions. We thank C. Burgert of the DHS (Demographic and Health Surveys) Program for her assistance with DHS Survey access and interpretation. P.W.G. is a Career Development Fellow (no. K00669X) jointly funded by the UK Medical Research Council (MRC) and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement and receives support from the Bill and Melinda Gates Foundation (BMGF; nos OPP1068048, OPP1106023). These grants also support E.C., S.B., B.M., U.D., D.J.W., D.B. and A.H. The Swiss TPH component was supported through the project no. OPP1032350 funded by the BMGF. D.L.S. is funded by the BMGF (OPP1110495). S.I.H. is funded by a Senior Research Fellowship from the Wellcome Trust (no. 095066), which also supports K.E.B., and grants from the BMGF (nos. OPP1119467, OPP1106023 and OPP1093011). S.I.H. and D.L.S. also acknowledge funding support from the RAPIDD program of the Science & Technology Directorate, Department of Homeland Security, and the Fogarty International Center, National Institutes of Health. J.T.G. is funded by an MRC Fellowship (no. G1002284). E.A.W. and P.A.E. are funded by the Global Good Fund.

Author information

Authors and Affiliations

Authors

Contributions

Conceived of and designed the research: P.W.G. and S.B. Drafted the manuscript: P.W.G. and S.B. Drafted the Supplementary Information: S.B., D.J.W., E.C., D.B., U.D., B.M. Prepared data: S.B., D.J.W., B.M., U.D., K.B., C.L.M., A.H., A.B., J.Y., T.P.E. Conducted the analyses: S.B., D.J.W., E.C., D.B., C.A.F., M.L., R.E.C. Supported the analyses: P.A.E., E.A.W., O.B., M.A.P., T.A.S., J.T.G., C.A.F., M.L., F.L., D.L.S. Supported interpretation and policy contextualization: S.B., A.B., T.P.E., J.Y., C.A.F., M.L., J.M.C., C.L.J.M., D.L.S., S.I.H., R.E.C., P.W.G. All authors discussed the results and contributed to the revision of the final manuscript.

Corresponding author

Correspondence to P. W. Gething.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Schematic overview of main input data, model components, and outputs.

Each component is detailed in the Supplementary Information.

Extended Data Figure 2 Fitted function representing effect of ITNs.

Curves illustrate the predicted effect of ITNs as a function of coverage (five example coverage levels are shown, specified as mean coverage over preceding 4-year period) and baseline transmission. The baseline PfPR is shown on the horizontal axis and the suppressed PfPR given the ITN coverage level shown on the vertical axis. The diagonal line (representing zero ITN effect) is shown in black, and parameter uncertainty around each ITN effect line is illustrated by the semi-transparent envelopes. Results shown are derived from a Bayesian geostatistical model fitted to n = 27,573 PfPR survey points; n = 24,868 ITN survey points; n = 96 national survey reports of ACT coverage; n = 688 country-year reports on ITN, ACT and IRS distribution by national programs; and n = 20 environmental and socioeconomic covariate grids.

Extended Data Figure 3 Changing incidence rate by country, 2000–2015.

Estimated country-level rates of all-age clinical incidence are shown for 2000 and 2015. For Sudan and South Sudan, we used the post-2011 borders throughout the time period to allow comparability. Results shown are derived from a Bayesian geostatistical model fitted to n = 27,573 PfPR survey points; n = 24,868 ITN survey points; n = 96 national survey reports of ACT coverage; n = 688 country-year reports on ITN, ACT and IRS distribution by national programs; n = 20 environmental and socioeconomic covariate grids; and n = 30 active-case detection studies reporting P. falciparum clinical incidence.

Extended Data Figure 4 Decline in infection prevalence attributable to main malaria control interventions.

ad, Each map shows absolute decline in PfPR2–10 between 2000 and 2015 within areas of stable transmission attributable to the combined effect of ITNs, ACTs, and IRS (a); and the individual effect of ITNs (b); ACTs (c); and IRS (d). Note that the colour scaling differs between the panels. Results shown in all panels are derived from a Bayesian geostatistical model fitted to n = 27,573 PfPR survey points; n = 24,868 ITN survey points; n = 96 national survey reports of ACT coverage; n = 688 country-year reports on ITN, ACT and IRS distribution by national programs; and n = 20 environmental and socioeconomic covariate grids. Maps in this figure are available from the Malaria Atlas Project (http://www.map.ox.ac.uk/) under the Creative Commons Attribution 3.0 Unported License.

Supplementary information

Supplementary Information

This file contains Supplementary Text and Data, Supplementary Figures 1-5, Supplementary Tables 1-4 and Supplementary References. (PDF 13325 kb)

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bhatt, S., Weiss, D., Cameron, E. et al. The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature 526, 207–211 (2015). https://doi.org/10.1038/nature15535

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature15535

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing