Bayesian spatial modelling of malaria burden in two contrasted eco-epidemiological facies in Benin (West Africa): call for localized interventions - Barikissou Georgia Damien; Akoeugnigan Idelphonse Sode; Daniel Bocossa; Emmanuel Elanga-Ndille; Badirou Aguemon; Vincent Corbel; Marie-Claire Henry; Romain Lucas Glèlè Kakaï; Franck Remoué (BMC, 2022)
Abstract Background Despite a global decrease in malaria burden worldwide, malaria remains a major public health concern, especially in Benin children, the most vulnerable group. A better understanding of malaria's spatial and age-dependent characteristics can help provide durable disease control and elimination. This study aimed to analyze the spatial distribution of Plasmodium falciparum malaria infection and disease among children under five years of age in Benin, West Africa. Methods A cross-sectional epidemiological and clinical survey was conducted using parasitological examination and rapid diagnostic tests (RDT) in Benin. Interviews were done with 10,367 children from 72 villages across two health districts in Benin. The prevalence of infection and clinical cases was estimated according to age. A Bayesian spatial binomial model was used to estimate the prevalence of malaria infection, and clinical cases were adjusted for environmental and demographic covariates. It was implemented in R using Integrated Nested Laplace Approximations (INLA) and Stochastic Partial Differentiation Equations (SPDE) techniques. Results The prevalence of P. falciparum infection was moderate in the south (34.6%) of Benin and high in the northern region (77.5%). In the south, the prevalence of P. falciparum infection and clinical malaria cases were similar according to age. In northern Benin children under six months of age were less frequently infected than children aged 6-11, 12-23, 24-60 months, (p < 0.0001) and had the lowest risk of malaria cases compared to the other age groups (6-12), (13-23) and (24-60): OR = 3.66 [2.21-6.05], OR = 3.66 [2.21-6.04], and OR = 2.83 [1.77-4.54] respectively (p < 0.0001). Spatial model prediction showed more heterogeneity in the south than in the north but a higher risk of malaria infection and clinical cases in the north than in the south. Conclusion Integrated and periodic risk mapping of Plasmodium falciparum infection and clinical cases will make interventions more evidence-based by showing progress or a lack in malaria control.