Co-morbidity of childhood anaemia and malaria with a district-level spatial effect.

Abstract

Anaemia and malaria are the leading causes of sub-Saharan African childhood morbidity and mortality. This thesis aimed to explore the risk factors as well as the complex relationship between anaemia and malaria in young children across the districts or counties of four contiguous sub-Saharan African countries, namely Kenya, Malawi, Tanzania and Uganda. Nationally representative data from the Demographic and Health Surveys conducted in all four countries was used. The observed prevalence of anaemia and malaria was 52.5% and 19.7%, respectively, with a 15.1% prevalence of co-infection. Machine learning based exploratory classification methods were used to gain insight into the relationships and patterns among the explanatory variables and the two responses. The administrative districts are the level at which public health decisions are made within each of the countries. Accordingly, the best linear unbiased predictor (BLUP) ranking and selection approach was adopted to investigate the district-level spatial effects, while controlling for child-level, household-level and environmental factors. Further to the geoadditive model, a generalised additive mixed model with a spatial effect based on the geographical coordinates of the sampled clusters within the districts was applied. The relationship between the two diseases was further explored using joint modelling approaches: a bivariate copula geoadditive model and shared component model. The child’s age, mother’s education level, household wealth index and cluster altitude were found to be significantly associated with both the anaemia and malaria status of the child. The results of this study can help policy makers target the correct set of interventions or prevent the use of incorrect interventions for anaemia and malaria control and prevention. This aids in the targeted allocation of limited district health system resources within each of these countries.