Logistic Regression Modelling for Complex Survey Data with an Application for Bed Net Use in Mozambique

Abstract

Logistic Regression Models have been widely used in many areas of research, namely in health sciences, to study risk factors associated to diseases. Many population based surveys, such as Demographic and Health Survey (DHS), are constructed assuming complex sampling, i.e., probabilistic, stratified and multistage sampling, with unequal weights in the observations; this complex design must be taken into account in order to have reliable results. However, this very relevant issue usually is not well analyzed in the literature. The aim of the study is to specify the logistic regression model with complex sample design, and to demonstrate how to estimate it using the R software survey package. More specifically, we used Mozambique Demographic Health and Survey data 2011 (MDHS 2011) to illustrate how to correct for the effect of sample design in the particular case of estimating the risk factors associated to the probability of using mosquito bed nets. Our results show that in the presence of complex sampling, appropriate methods must be used both in descriptive and inferential statistics.