The objective of this work is to examine the impact of socioeconomic and public health factors on childhood diseases and malnutrition in mentioned countries. The causes of child's illness or child's undernutrition are multiple. This work focuses on some risk factors which are assumed to cause the child's diseases and malnutrition as suggested by some previous works (see Kandala, 2001; Adebayo, 2002). Our analysis started with a large number of covariates including a set of bio-demographic and socioeconomic variables, such as current working status of mothers, place of residence, access to toilet facilities, etc (see chapter 2). The analyses are based on data from the 2003 household survey for Egypt and Nigeria for the Demographic and Health Surveys (DHS). More details about the data set are mentioned in the first chapter. The statistical analysis in this thesis is based on modern Bayesian approaches which allow a flexible framework for realistically complex models. These models allow us to analyze usual linear effects of categorical covariates, nonlinear effects of continuous covariates and the geographical effects within a unified semi-parametric Bayesian framework for modelling and inference. A first step of this work is to analyze the effects of the different types of covariates on response variables, diarrhea, fever, and cough which represent the child's diseases in our application. In this step, a Bayesian geoadditive logit model for binary response variables is used (see Fahrmeir and Lang, 2001). In a second step, we employ separate geoadditive probit models (instead of logit models used in the previous step) to the binary listed variables. Based on the results of the separate analyses, we applied geoadditive latent variable probit models (recently suggested by Raach, 2005; Raach and Fahrmeir, 2006) where the three observable disease variables are assumed to be indicators for the latent variable "health status" for the children. In this step, we also compared the results of the separate geoadditive probit models with the results of the latent variable models.

As a third step, we used geoadditive Gaussian regression and latent variable models to analyze the malnutrition status of children in both countries.

Finally, we used latent variable models for diseases and nutrition indicators together. In the final step, models with one as well as with two latent variables have been estimated using mixed indicators (binary indicators "health status", and continuous indicators "nutrition status") and the results are compared.