Bayesian Multinomial Ordinal Model to analyse the risk factors and spatial patterns of Childhood Anaemia in Tanzania

Abstract

Using the 2010 Tanzania Demographic and Health (TDHS) data, we fit a semi-parametric model that combines fixed effects, non-linear terms and spatial components in a unified framework. The spatial effect was modelled using a Markov random field prior. We simultaneously investigate the geographical variation and the risk factor on a polychotomous response of anaemia. We run several Bayesian models via Markov Chain Monte Carlo (MCMC) simulation techniques and the models were compared using Deviance Information Criteria (DIC). We found the risk factors associated with anaemia include place of residence, household poverty, childhood under-nutrition, and infectious diseases. We also evaluated non-linear relations of a mother’s age, body mass index, and hemoglobin level. Our method detects spatial effects that may not have been captured by the underlying factors and we produce predictive probability maps. Higher risk were found in the Eastern regions of Tanzania. The output of work highlights highly endemic regions that can assist government agency to target scarce health resource and effective policy direction.