The spatiotemporal distribution of substandard and falsified antimalarial medicines in Africa, 1996-2019

IntroductionIt is estimated that there were 263 million malaria cases and 597,000 deaths in 2023 globally, a substantial proportion of which are attributable to inadequate access to good-quality and efficacious antimalarials. Africa bears the highest malaria burden. Yet despite the high prevalence of substandard and falsified (SF) antimalarials in some African regions, their prevalence across space and time remains poorly understood. MethodsWe extracted data from the Infectious Disease Data Observatory (IDDO) Medicine Quality Scientific Literature Surveyor database on the prevalence of SF antimalarials. We used spatiotemporal modelling to estimate the proportion of antimalarials that were SF in each country and each year covered by the data. We constructed three different models; each included 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). ResultsWe extracted data from 76 random and convenience surveys conducted between 1996 and 2019, including 8213 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 prevalence of SF antimalarials occurred between 1996 and 2003, with higher predictions in West and Central Africa regions. Most countries had no clear temporal patterns, but we estimated a notable decline in reported SF prevalence in Kenya, Uganda, Tanzania, and Madagascar beginning around 2003. ConclusionThis study provides estimates of the prevalence of SF antimalarials in Africa at country level throughout time, improving our understanding of the heterogeneity in the burden of SF antimalarials across Africa. These estimates can inform targeted interventions to reduce the public health impact of SF medicines. However, we observed high levels of uncertainty throughout the study period in most countries, reflecting the sparsity of antimalarial quality surveillance data in Africa and implying that estimates should be interpreted with caution.