Bayesion an Approach to Identify Predictors of Chilren Nutritional Status in Ethiopia

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Addis Abeba university


Child Mortality and Undernutrition are the two major public health problems in the developing world. The associated phenomena are household living style, type of residence and awareness in health and family planning programs. Ethiopia, in particular, suffers from worst forms of malnutrition due to access to health care and nutrition. This thesis explores the most dominant socio economic, demographic and environmental factors in children nutritional status. We used recently developed Bayesian structured additive models to flexibly model the effects of included covariates on the EDHS 2005 datasets of Ethiopia. Inference is fully Bayesian based on recent Markov chain Monte Carlo techniques. These models allow us to analyze usual linear effects of categorical covariates and nonlinear effects of continuous covariates within a unified semiparametric Bayesian framework for modeling and inference. Most of the socioeconomic, demographic and environmental determinants included in the study were found to be statistically significant with the exception of covariates household economic status and mother education



Children Nutritional Status i Ethiopia