Bayesian Regression Method with Gaussian and Binomial Links for the Analysis of Nigerian Children Nutritional Status (Stunting)

Abstract

Children's nutritional status is a reflection of their overall health. Malnutrition is associated with more than half of all children deaths worldwide. A study into geographical variability of nutritional status of children in Nigeria was observed from geo statistical mapping and a continuous covariates stunting (height for age) that exhibit pronounced non-linear relationships with the response variable was analysed. To properly account for stunting effects on child's age, sex, their place of resident, mothers' educational levels, parents' wealth index, regions and state of the child, kriging and additive models were merged using modified Cox model. The resulting Generalized Additive Mixed Model (GAMM) representation for the geo additive model allows for fitting and analysis using BayesX software. The Multiple Indicator Cluster Survey 3 (MICS3) data set contains several variables. Only those that are believed to be related to nutritional status were selected. All categorical covariates are effect coded. The child's age is assumed to be nonlinear; the state is spatial effect while other variables are parametric in nature.