Estimating HIV Prevalence in Zimbabwe Using Population-Based Survey Data

Abstract

Estimates of HIV prevalence computed using data obtained from sampling a subgroup of the national population may lack the representativeness of all the relevant domains of the population. These estimates are often computed on the assumption that HIV prevalence is uniform across all domains of the population. Use of appropriate statistical methods together with population-based survey data can enhance better estimation of national and subgroup level HIV prevalence and can provide improved explanations of the variation in HIV prevalence across different domains of the population. In this study we computed design-consistent estimates of HIV prevalence, and their respective 95% confidence intervals at both the national and subgroup levels. In addition, we provided a multivariable survey logistic regression model from a generalized linear modelling perspective for explaining the variation in HIV prevalence using demographic, socio-economic, socio-cultural and behavioural factors. Essentially, this study borrows from the proximate determinants conceptual framework which provides guiding principles upon which socio-economic and socio-cultural variables affect HIV prevalence through biological behavioural factors. We utilize the 2010–11 Zimbabwe Demographic and Health Survey (2010–11 ZDHS) data (which are population based) to estimate HIV prevalence in different categories of the population and for constructing the logistic regression model. It was established that HIV prevalence varies greatly with age, gender, marital status, place of residence, literacy level, belief on whether condom use can reduce the risk of contracting HIV and level of recent sexual activity whereas there was no marked variation in HIV prevalence with social status (measured using a wealth index), method of contraceptive and an individual’s level of education.

Socio‐behavioural characteristics and HIV: findings from a graphical modelling analysis of 29 sub‐Saharan African countries

“…One study showed that in poorer sub-Saharan African countries, wealthier individuals were more likely to be HIV positive, but in wealthier countries, poorer individuals were more likely to be HIV positive [32]. A study from Zimbabwe that used DHS data found that people who could read parts of a sentence had a higher risk of HIV infection than those who could not read at all, or who could read complete sentences [33]. We were slightly surprised by our results on the association between urbanity and variables with protective effects against HIV.…”

Socio-behavioural characteristics and HIV: findings from a graphical modelling analysis of 29 sub-Saharan African countries

“…One study showed that in poorer sub-Saharan African countries, wealthier individuals were more likely to be HIV positive, but in wealthier countries, poorer individuals were more likely to be HIV positive [32]. A study from Zimbabwe that used DHS data found that people who could read parts of a sentence had a higher risk of HIV infection than those who could not read at all, or who could read complete sentences [33].…”

Using Multiple Imputation and Inverse Probability Weighting to Adjust for Missing Data in HIV Prevalence Estimates: A Cross-Sectional Study in Mwanza, North Western Tanzania.

“…Multiple imputations (MI) involved imputing values for the missing HIV status, for those who did not attend the sero8 survey, based on age, sex, residence and marital status (12). We imputed 20 datasets (M=20) using the Markov Chain Monte Carlo (MCMC) algorithm with a binomial distribution replacing each missing HIV value with values consistent with that person's age, sex, residence and marital status.…”

“…Most researchers use conventional methods such as the complete case or available case analysis where the assumption is data are MCAR. The use of these methods in presence of missing data that are not MCAR results in loss of information and biased estimates of HIV prevalence (12). There has been development of statistical methods that can be applied to adjust for missing data when the missingness is not completely at random.…”