A systematic review of changing malaria disease burden in sub-Saharan Africa since 2000: comparing model predictions and empirical observations

Abstract

Background: The most widely used measures of declining burden of malaria across sub-Saharan Africa are predictions from geospatial models. These models apply spatiotemporal autocorrelations and covariates to parasite prevalence data and then use a function of parasite prevalence to predict clinical malaria incidence. We attempted to assess whether trends in malaria cases, based on local surveillance, were similar to those captured by Malaria Atlas Project (MAP) incidence surfaces.

Methods: We undertook a systematic review (PROSPERO International Prospective Register of Systematic Reviews; ID = CRD42019116834) to identify empirical data on clinical malaria in Africa since 2000, where reports covered at least 5 continuous years. The trends in empirical data were then compared with the trends of time-space matched clinical malaria incidence from MAP using the Spearman rank correlation. The correlations (rho) between changes in empirically observed and modelled estimates of clinical malaria were displayed by forest plots and examined by meta-regression.

Results: Sixty-seven articles met our inclusion criteria representing 124 sites from 24 African countries. The single most important factor explaining the correlation between empirical observations and modelled predictions was the slope of empirically observed data over time (rho = - 0.989; 95% CI - 0.998, - 0.939; p < 0.001), i.e. steeper declines were associated with a stronger correlation between empirical observations and modelled predictions. Factors such as quality of study, reported measure of malaria and endemicity were only slightly predictive of such correlations.

Conclusions: In many locations, both local surveillance data and modelled estimates showed declines in malaria burden and hence similar trends. However, there was a weak association between individual surveillance datasets and the modelled predictions where stalling in progress or resurgence of malaria burden was empirically observed. Surveillance data were patchy, indicating a need for improved surveillance to strengthen both empiric reporting and modelled predictions.

Keywords: Africa; Correlation; Incidence; Malaria; Malaria Atlas Project; Plasmodium falciparum; Prevalence; Systematic review.

Fig. 1

A summary flow of study selection process

Fig. 2

Assembled data included in the review by country, the number of sites and the sample size; dark grey indicates countries that reported national routine malaria case data, and the red dots indicate unique sites where data was identified

Fig. 3

Forest plot of the correlation between empirical malaria cases and MAP clinical incidence stratified by the slope of empirical cases. Blue squares represent the correlation of each study; the error bars through the blue boxes are the uncertainty intervals; the red diamonds show the overall pooled correlation and in each sub-group; the horizontal tips of the red diamonds are the uncertainty level; weights are computed as the inverse of within and between variance; references are listed alphabetically in the Additional file material