Geo additive Cox Models with Gaussian and Binomial Links for the Analysis of Wasting status of Nigerian Children

Abstract

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 krigingand thecontinuous covariates weight for height (wasting) that exhibit pronounced non-linear relationships with the response variable was analysed. 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. Wasting is higher among children in the urban areas, the more rich the parents the more prevalence of wasting. Mother’s education is inversely associated with child’s wasting. Sex of the child is not significant with wasting and severe wasting is prevalent in the Northern region of the country. The study builds a statistical model that will help various health agencies in the country in developing a framework, policies and programmes that will improve child health care.

Keywords: Wasting, Categorical data, Binomial,Gaussian, and Kriging