Abstract / Summary
Introduction: In 2024, an estimated 282 million malaria cases and 610,000 deaths occurred globally, many attributable to inadequate access to quality-assured and efficacious antimalarials. Africa bears the greatest burden of malaria, yet the spatial and temporal distribution of substandard and falsified (SF) antimalarials across the continent remains poorly understood. Methods: We extracted data from the Infectious Disease Data Observatory (IDDO) Medicine Quality Scientific Literature Surveyor database on SF antimalarial prevalence. We applied spatiotemporal modelling to estimate the proportion of antimalarials that were SF by country and year. We constructed three different models with identical spatial structures and covariates but different specifications of the temporal trends. We modelled spatial effects using the Besag-York-Mollie model (BYM). We fitted the models using integrated nested Laplace approximation (INLA) and compared models' predictive ability using the widely applicable information criterion (WAIC). Results: We extracted data from 76 random and convenience surveys conducted between 1996 and 2019, including 8,213 antimalarial samples in total. The model with the best predictive performance included an interaction between space and time (WAIC=499.430), suggesting that the highest SF prevalence occurred between 1996 and 2003, with higher predictions in West and Central Africa. Most countries had no clear temporal patterns, but a notable decline was estimated in Kenya, Uganda, Tanzania, and Madagascar beginning around 2003. Conclusion: This study provides country-level, time-varying estimates of SF antimalarial prevalence, improving understanding of spatial and temporal heterogeneity in Africa. Although these findings can inform targeted interventions, considerable uncertainty reflects sparse surveillance data and warrants cautious interpretation. Keywords: Substandard; falsified; antimalarials; spatiotemporal analysis; Africa