Statistical methods for analysing complex survey data : an application to HIV/AIDS in Ethiopia.

Abstract

The HIV/AIDS pandemic is currently the most challenging public health matter that

faces third world countries, especially those in Sub-Saharan Africa. Ethiopia, in East

Africa, with a generalised and highly heterogeneous epidemic, is no exception, with

HIV/AIDS affecting most sectors of the economy. The first case of HIV in Ethiopia

was reported in 1984. Since then, HIV/AIDS has become a major public health con

cern, leading the Government of Ethiopia to declare a public health emergency in

2002. In 2011, the adult HIV/AIDS prevalence in Ethiopia was estimated at 1.5%.

Approximately 1.2 million Ethiopians were living with HIV/AIDS in 2010.

Surveys are an important and popular tool for collecting data. Analytical use of survey

data especially health survey data has become very common, with a focus on the association of particular outcome variables with explanatory variables at the population

level. In this study we used the data from the 2005 Ethiopian Demographic and Health

Survey, (EDHS 2005), and identified key demographic, socioeconomic, sociocultural,

behavioral and proximate determinants of HIV/AIDS risk factor. Usually most survey

analysts ignore the complex survey design issues like clustering, stratification and unequal probability of selection (weights). This study deals with complex survey design

and takes the design aspect into account, because failure to do so leads to bias parameters estimates and standard error, wide confidence intervals and statistical tests

will be incorrect.

In this study, three statistical approaches were used to analyse the complex survey

data. The first approach was a survey logistic regression used to model the binary

outcome (HIV serostatus) and set of explanatory variables (the dependence of the

HIV risk factors). The difference between survey logistic regression and the ordinary

logistic regression is that survey logistic regression approach takes the study design

into account during analysis. The second approach was a multilevel logistic regression

model, that assumed that the data structure in the population was hierarchical, and

that individual within household was selected from clusters that were randomly selected

from a national sampling frame. We considered a three-level model for our analysis.

This second approach considered the results from Frequentist and a Bayesian multilevel

models. Bayesian methods can provide accurate estimates of the parameters and the

uncertainty associated with them. The third approach used was a Spatial models

approach where model parameters were estimated under the Integrated Nested Laplace

Approximation (INLA) paradigm.