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. 2015 Oct 8;526(7572):207-211.
doi: 10.1038/nature15535. Epub 2015 Sep 16.

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

S Bhatt #  1 , D J Weiss #  1 , E Cameron #  1 , D Bisanzio  1 , B Mappin  1 , U Dalrymple  1 , K Battle  1 , C L Moyes  1 , A Henry  1 , P A Eckhoff  2 , E A Wenger  2 , O Briët  3   4 , M A Penny  3   4 , T A Smith  3   4 , A Bennett  5 , J Yukich  6 , T P Eisele  6 , J T Griffin  7 , C A Fergus  8 , M Lynch  8 , F Lindgren  9 , J M Cohen  10 , C L J Murray  11 , D L Smith  1   11   12   13 , S I Hay  11   13   14 , R E Cibulskis  8 , P W Gething  1
Affiliations

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

S Bhatt et al. Nature. .

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.

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Figures

Extended Data Figure 1
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
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
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
Extended Data Figure 4. Decline in infection prevalence attributable to main malaria control interventions
a–d, 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.
Figure 1
Figure 1. Changes in infection prevalence 2000–2015
a, PfPR2-10 for the year 2000 predicted at 5×5 km resolution. b, PfPR2-10 for the year 2015 predicted at 5×5 km resolution. c, Absolute reduction in PfPR2-10 from 2000 to 2015. d, Smoothed density plot showing the relative distribution of endemic populations by PfPR2-10 in the years 2000 (red line) and 2015 (blue line). The frequencies on the vertical axis have been scaled to make the densities visually comparable. The classical endemicity categories are shown for reference in green shades. 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 a–c are available from the Malaria Atlas Project (http://www.map.ox.ac.uk/) under the CreativeCommons Attribution 3.0 Unported License.
Figure 2
Figure 2. Changing endemicity and effect of interventions 2000–2015
a, Predicted time series of population-weighted mean PfPR2-10 across endemic Africa. The red line shows the actual prediction and the black line a ‘counterfactual’ prediction in a scenario without coverage by ITNs, ACTs, or IRS. The coloured regions indicate the relative contribution of each intervention in reducing PfPR2-10 throughout the period. b, The predicted cumulative number of clinical cases averted by interventions at the end of each year, with the specific contribution of each intervention distinguished. Results shown in both 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. Panel b additionally incorporates data from n = 30 active-case detection studies reporting P. falciparum clinical incidence.
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