Spatially Weighted Multinomial Modelling of Comorbidity of Anaemia and Malaria among Under-Five Children in Nigeria

Abstract

Children in sub-Saharan African countries, especially Nigeria, continue to suffer increased mortality owing to comorbidity of infections such as anaemia and malaria, which are known to epidemiologically overlap. In order to examine the risk factors and spatial patterns of comorbidity of anaemia and malaria using the 2021 Nigeria Malaria Indicator Survey (NMIS), a multinomial logit model was extended by incorporating a spatially weighted random effect. The impact of climatic variation on the childhood disease comorbidity was explored by weighting the spatial structured component based on the 2021 NMIS average cluster temperature of each state in Nigeria. A number of spatially weighted geo-additive models were fitted and compared using deviance information criterion. Inference was fully Bayesian, and an Intrinsic Conditional Autoregressive prior was used for structured random effects. Based on the map generated from the best-fitted model, which unveiled states that are more susceptible to the risk of disease comorbidity, the average temperature used as a weighting factor, however, has a strong relationship with the spatial pattern of disease comorbidity. States with low temperatures have a higher risk of comorbidity of anaemia and malaria compared to states with higher average temperatures. Area of residence, level of education of the mother, economic status of the mother, and owning mosquito-treated nets were identified as the significant risk factors associated with the disease comorbidity. Findings from this study will be helpful to policymakers and health authorities in their effort to combat the comorbidity of childhood anaemia and malaria, thereby reducing child mortality in Nigeria.