Identifying prognosticators covariates of child nutritional status in ethiopia: A bayesian generalized additive modelling approach

Abstract

Malnutrition among children under age five is the major public health delinquent issue in the developing world, particularly in Ethiopia. This study aimed to figure out determinants of Ethiopian children malnutrition by applying Bayesian approach with Markov chain Monte Carlo (MCMC) techniques on the 2011 EDHS data. The preliminary analysis indicated that the overall prevalence of underweight among children in Ethiopia is found 36.4%. Bayesian generalized additive regression model applied to flexibly estimate effects of socio-economic, demographic, health and environmental covariates. The estimation result showed that covariates succeeding birth interval, gender of child, child by choice not by chance, vaccination and cough are significantly affect the children nutritional status in Ethiopia. The effect of child age, mother’s age at child birth, succeeding birth intervals, number of household member and birth order were also explored non-parametrically as determinants of children nutritional status. Based up on this biometric analysis, concerned governmental and non-governmental bodies should give emphasis on the significant covariates to improve the children nutritional status of the country.