Spatial modelling of the joint burden of malaria and anaemia co-morbidity in children: A Bayesian geoadditive perspective

Abstract

ABSTRACT Malaria infection, caused by plasmodium parasites, is a serious health challenge for children in the tropical regions. It becomes a serious life-threatening issue when the victim also suffers from anaemia because malaria parasite feeds on the iron particles present in the red blood cells. We consider a latent Gaussian model to jointly estimate the spatial patterns of co-morbidity from malaria and the different levels of anaemia among children under five years of age in Nigeria. The approach allows for response variables of different family of distribution to be jointly considered while accounting for metrical covariates as possible nonlinear effects and categorical variables as linear effects. Parameter estimation was through the integrated nested Laplace approximation. Our findings show similar spatial patterns of co-morbidity between malaria and severe anaemia and malaria and moderate anaemia but in the case of age of the child, the likelihoods of co-morbidity are similar for malaria and severe anaemia and malaria and mild anaemia. Urban residency, mother’s education, and household wealth index are consistently significant to the different forms of co-morbidity. Findings from the spatial effects avail decision-makers with location-specific evidence to prioritize and roll out interventions in a more judicious manner.