Bayesian spatiotemporal modelling and mapping of malaria risk among children under fve years of age in Ghana

Abstract

Background Malaria is a signifcant public health problem, particularly among children aged 6–59 months who bear the greatest burden of the disease. Malaria transmission is high and more pronounced in poor tropical and subtropi cal areas of the world. Climate change is positively correlated with the geographical distribution of malaria vectors. There is substantial evidence of spatial and temporal diferences in under-fve malaria risk. Thus, the study aimed to create intelligent maps of smooth relative risk of malaria in children under-5 years in Ghana that highlight high and low malaria burden in space and time to support malaria prevention, control, and elimination eforts. Method The study extracted and merged data on malaria among children aged 6–59 months from the 2014 Ghana Demographic and Health Surveys (GDHS), 2016 and 2019 Ghana Malaria Indicator Surveys (GMIS). The outcome vari able of interest was the count of children aged 6–59 months with a positive test on the rapid diagnostic test (RDT) result. Bayesian Hierarchical spatiotemporal models were specifed to estimate and map spatiotemporal variations in the relative risk of malaria. The existence of local clustering was assessed using the local indicator of spatial associa tion (LISA), and the points were mapped to display signifcant local clusters, hotpot, and cold spot communities. Results The number of positive malaria cases in children aged 6–59 months decreased marginally from 946.7 (36.4%) in 2014 to 603.6 (22.9) in 2019 DHS survey periods. Smooth relative risk of malaria among children aged 6–59 months has consistently increased in the Northern and Eastern regions between 2014 and 2019. Socioeconomic and climatic factors such as household size [Posterior Mean: -0.198 (95% CrI: 3.52, 80.95)], rural area [Posterior Mean: 1.739 (95% CrI: 0.581, 2.867)], rainfall [Posterior Mean: 0.003 (95% CrI: 0.001, 0.005)], and maximum temperature [Posterior Mean: -1.069 (95% CrI: -2.135, -0.009)] were all shown as statistically signifcant predictors of malaria risk in children aged 6–59 months. Hot spot DHS clusters (enumeration areas) with a signifcantly high relative risk of malaria among chil dren aged 6–59 months were repeatedly detected in the Ashanti region between 2014 and 2019. Conclusion The fndings of the study would provide policymakers with practical and insightful information for the equitable distribution of scarce health resources targeted at reducing the burden of malaria and its associated mortality among children under fve years.