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

Abstract

Background: Malaria is a significant 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 subtropical areas of the world. Climate change is positively correlated with the geographical distribution of malaria vectors. There is substantial evidence of spatial and temporal differences in under-five 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 efforts.

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 variable 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 specified 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 association (LISA), and the points were mapped to display significant 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 significant predictors of malaria risk in children aged 6-59 months. Hot spot DHS clusters (enumeration areas) with a significantly high relative risk of malaria among children aged 6-59 months were repeatedly detected in the Ashanti region between 2014 and 2019.

Conclusion: The findings 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 five years.