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

Abstract

BackgroundReliable 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.MethodsA 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.ResultsDespite 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.ConclusionsMalaria 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.Electronic supplementary materialThe online version of this article (10.1186/s12916-018-1060-4) contains supplementary material, which is available to authorized users.

Plasmodium falciparum Parasitemia and Band Sensitivity of the SD Bioline Malaria Ag P.f/Pan Rapid Diagnostic Test in Madagascar

“…23,24 A policy shift toward their routine use across the island began in 2007, and these now represent the only method of malaria diagnosis outside referral hospitals. 25 In this country of heterogeneous malaria epidemiology, P. falciparum predominates 25,26 ; however, clinical P. vivax malaria exists alongside P. falciparum. 18,19 Given the country's current focus on malaria elimination, 27 correct interpretation of different RDT results, obtained with a PfHRP2/ pan-pLDH combination RDT, is therefore important from both epidemiological surveillance and treatment standpoints.…”

Novel Insights into Malaria Vector Surveillance in Madagascar Using a Quadrant Enabled Screen Trap (QUEST) and Bloodmeal Detection Assay for Regional Transmission (BLOODART)

“…Wild-caught mosquitoes were collected by Case Western Reserve University and Madagascar NMCP entomologists in December 2017 (six nights) and April 2018 (five nights), corresponding to either side of the rainy season (December to March [33]). A peak in clinical malaria has been observed in April-May for the Tsiromandidy Health District [34].…”

Mapping malaria seasonality in Madagascar using health facility data

“…These were temporally aligned with the health facility data and included the Climate Hazards Group Infrared Precipitation and Station data (CHIRPS), enhanced vegetation index (EVI), daytime land surface temperature (LST_day), diurnal difference in land surface temperature (LST_delta), night-time land surface temperature (LST_night), tasselled cap brightness (TCB), tasselled cap wetness (TCW), and the temperature suitability indices for Plasmodium falciparum and Plasmodium vivax (TSI_Pf and TSI_Pv). Details on the sources of these data are available elsewhere [19,20]. To relate the observed seasonal patterns to the potential driving factors, monthly medians of these environmental data were derived and standardised.…”

“…To relate the observed seasonal patterns to the potential driving factors, monthly medians of these environmental data were derived and standardised. One to 3 month lags were included for each covariate to allow for delayed and accumulated responses to these environmental variables [19].…”

“…In this paper, we build on previous attempts and map malaria seasonality in Madagascar, a country of marked ecological and epidemiological diversity that is struggling to meet targets for malaria burden reductions and hence where further information for optimising interventions would be valuable [1,19]. We demonstrate how common descriptors of seasonality can be made comparable across locations to facilitiate modelling and support policy decisions, a topic of interest for not just modellers but also the wider public health community.…”