|dc.description.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.||en