Bayesian Spatial Modeling of Anemia among children under 5 years in Guinea
Abstract Bacground: Anemia is a major public health problem in Africa with an increasing number of children under 5years getting infected. Guinea is one of the most affected countries. In 2018, the prevalence rate was 75% inchildren under 5 years. This study sought to identify the factors associated with anemia and to map spatialvariation of anemia across the eight (8) regions in Guinea for children under 5 years, which can provideguidance for control programs for the reduction of the disease.Methods: Data from the Guinea Multiple Indicator Cluster Survey (MICS5) 2016 was used for this study. Atotal of 2609 children under 5 years who had full covariate information were used in the analysis. Spatialbinomial logistic regression methodology was undertaken via Bayesian estimation based on Markov chainMonte Carlo (McMC) using WinBUGS software version 1.4. Results: Our findings revealed that 77% of children under 5 years in Guinea had anemia and the prevalence inthe regions ranged from 70.32% (Conakry) to 83.60% (N’Zerekore) across the country. After adjusting for nonspatial and spatial random effects in the model, older children (48–59 months) (OR: 0.47, CI [0.29 0.70]) were less likely to be anemic compared to those who are younger (0-11 months). Children whose mothers havecompleted secondary education or more had a reduced chance of anemia infection by 33% (OR: 0.67, CI [0.490.90]) and Children from household heads from Kissi ethnic group are less likely to have anemia than theircounterparts whose leader is from Soussou (OR: 0.48, CI [0.22 0.91]). Conclusion: The spatial analysis allowed the identification of high-risk areas as well as the identification ofsocio-economic and demographic factors associated with anemia among children under 5 years. Such ananalysis is important in helping policy makers and health practitioners in developing programs geared towardscontrol and management of anemia among children under 5 years in the country.