Bayesian generalized linear model for identifying predictors of child nutritional status in Ethiopia

Abstract

Background: In Ethiopia, child malnutrition is one of the most serious public health problem and the highest in the world. The overall prevalence of underweight among children under five years old in Ethiopia was 25% and 7% are severely and moderate underweight in 2014 respectively. Malnutrition in sub-Saharan Africa contributes to high rates of childhood morbidity and mortality. However, little information on the nutritional status of children is available from informal settlements. The primary aim of this study was then to determine the determinants of children malnutrition in Ethiopia using Bayesian generalized linear model. The overall prevalence of underweight among children in Ethiopia was . Methods: Data was obtained from 2011 Ethiopian Demographic and Health Survey (EDHS). Bayesian Generalized regression model was used to identify the effects of selected socioeconomic, demographic, health and environmental covariates. Bayesian approach with Markov Chain Monte Carlo (MCMC) technique was used. Results: The analysis result revealed that out of the 11, 654 number cases examined in this study 32.8% of male children were underweight and 33.6% of female children were underweight. It was found that the covariates succeeding birth interval, sex of child, child by choice not by chance, vaccination and cough were the most important determinants of children nutritional status in Ethiopia. Conclusion: Inference is the fully Bayesian and classical generalized linear model based on recent Markov chain Monte Carlo techniques.Some of the socioeconomic, demographic and environmental determinants included in the study were found to be statistically significant. The result was suggested that for reducing childhood malnutrition, due emphasis should be given in improving the knowledge and practice of parents on appropriate young child feeding practice and frequent growth monitoring together with appropriate and timely interventions.