Bayesion an Approach to Identify Predictors of Chilren Nutritional Status in Ethiopia
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Date
2009-06
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Addis Abeba university
Abstract
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
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Children Nutritional Status i Ethiopia