Multilevel Analysis of Factors Associated with Child Mortality in Uganda

The purpose of this paper is to examine the effect of factors associated with child mortality in Uganda. The Demographic and Health Survey data set (2006) is used to investigate these factors. A random effects regression model and logistic regression model were fitted to establish the significant factors affecting child mortality. The paper considers two types of factors which are maternal Factors (Education level, age of mother, wealth of household) and child factors (birth order, weight of child, Sex of a child, and duration of breastfeeding). Sex, birth weight, Education level, age of mother and household wealth were found to be important predictors of child mortality. However, controlling for mother level factors in model II, the within childhood characteristics were highly correlated. From an explicit multilevel analytic framework, the study therefore demonstrates that individual (child) and mother level characteristics are independent predictors of child mortality; and that there is significant variation in odds of reporting child mortality, even after controlling for effects of both child and mother-level characteristics. The p - values in the random effects model were small compared to the p – values of a standard logistic model. The results of random effects model are more statistically significant than those of a standard logistic regression model. Therefore, the random effects regression model is recommended as an appropriate alternative to standard logistic regression in order to account for variations due to a hierarchical structure