Modelling to support decisions about the geographic and demographic extensions of seasonal malaria chemoprevention in Benin

Abstract

AbstractBackgroundSeasonal malaria chemoprevention (SMC) has been implemented yearly in northern Benin since 2019 to reduce the malaria burden in children under 5 years of age. Its geographic scope was progressively extended until in 2022 two different extensions of SMC were considered: either demographic - children aged 5 to 10 in the currently targeted departments would also receive SMC, or geographic to children under 5 in new eligible departments to the south. As SMC had neither been implemented in the areas nor age groups suggested for expansion, modelling was used to compare the likely impact of both extensions.MethodsThe model OpenMalaria was calibrated to represent the history of malaria interventions and transmission risk in Benin. Currently planned future interventions and two scenarios for SMC extensions were simulated to inform where impact would be the highest.ResultsThe model predicted that between 2024 and 2026 the geographic extension of SMC would avert at least four times more severe malaria cases and five times more direct malaria deaths per targeted child than the demographic extension. However, numbers of severe cases averted per targeted child were similar between health zones eligible for geographic extension.ConclusionsThe geographic extension is more impactful and likely more cost-effective than the demographic extension, and will be implemented from 2024. Health zones were prioritised by availability of community health workers to deliver SMC. Mathematical modelling was a supportive tool to understand the relative impact of the different proposed SMC extensions and contributed to the decision-making process. Its integration significantly enhanced the utilisation of data for decision-making purposes. Rather than being used for forecasting, the model provided qualitative guidance that complemented other types of evidence.