Spatio-temporal mapping of Madagascar's Malaria Indicator Survey results to assess Plasmodium falciparum endemicity trends between 2011 and 2016

Abstract

Background: Reliable measures of disease burden over time are necessary to evaluate the impact of interventions and assess sub-national trends in the distribution of infection. Three Malaria Indicator Surveys (MISs) have been conducted in Madagascar since 2011. They provide a valuable resource to assess changes in burden that is complementary to the country's routine case reporting system.

Methods: A Bayesian geostatistical spatio-temporal model was developed in an integrated nested Laplace approximation framework to map the prevalence of Plasmodium falciparum malaria infection among children from 6 to 59 months in age across Madagascar for 2011, 2013 and 2016 based on the MIS datasets. The model was informed by a suite of environmental and socio-demographic covariates known to influence infection prevalence. Spatio-temporal trends were quantified across the country.

Results: Despite a relatively small decrease between 2013 and 2016, the prevalence of malaria infection has increased substantially in all areas of Madagascar since 2011. In 2011, almost half (42.3%) of the country's population lived in areas of very low malaria risk (<1% parasite prevalence), but by 2016, this had dropped to only 26.7% of the population. Meanwhile, the population in high transmission areas (prevalence >20%) increased from only 2.2% in 2011 to 9.2% in 2016. A comparison of the model-based estimates with the raw MIS results indicates there was an underestimation of the situation in 2016, since the raw figures likely associated with survey timings were delayed until after the peak transmission season.

Conclusions: Malaria remains an important health problem in Madagascar. The monthly and annual prevalence maps developed here provide a way to evaluate the magnitude of change over time, taking into account variability in survey input data. These methods can contribute to monitoring sub-national trends of malaria prevalence in Madagascar as the country aims for geographically progressive elimination.

Keywords: Geostatistical model; Madagascar; Malaria Indicator Surveys; Map; Plasmodium falciparum.

Fig. 1

Malaria Indicator Survey screening sites for (a) 2011, (b) 2013 and (c) 2016. The coloured regions represent the country’s eight malaria ecozones [4]

Fig. 2

National-level mean monthly PfPR_6–59mo predictions, plotted alongside temporally variable predictor values. The box plot rectangles indicate the first to third quartiles (interquartile range), with the median shown as the dark line inside the box. Vertical lines correspond to the minimum and maximum values. Specified lags indicate the time points that were selected by the model as explanatory variables of PfPR_6–59mo. A time lag of 0 indicates that the covariate values in the concurrent month were predictive of PfPR_6–59mo, while a time lag of 3 indicates that the covariate value 3 months prior to the PfPR_6–59mo prediction was predictive of PfPR_6–59mo

Fig. 3

Predicted annual mean PfPR among children 6 to 59 months in age for 2011 (a), 2013 (b) and 2016 (c). d–f The corresponding map uncertainty (quantified as the prediction interquartile range). Values are mapped at 1 × 1 km pixel resolution. g–i National population breakdown by endemicity class, using population values based on WorldPop’s Whole Continent UN-adjusted Population Count datasets for Africa for 2010, 2015 and 2020. Estimates for 2011, 2013 and 2016 were created by linear interpolation of the bookending quinquennial rasters

Fig. 4

Box plots of predicted monthly PfPR_6–59mo by ecozone for 2011, 2013 and 2016. Ecozone extents are shown in Fig. 1 [4]

Fig. 5

Percentage changes in predicted PfPR among children 6 to 59 months old across the three MIS time points: a from 2011 to 2016 and b from 2013 to 2016. Histograms of pixel-level change c from 2011 to 2016 and d from 2013 to 2016. Positive % change indicates an increase in prevalence, while negative % change is a decrease