title: 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.
authors.affiliation: Department of Statistics, Rhodes University, Grahamstown, South Africa.|School of Mathematics, Statistics and Computer Science, University of Kwa-Zulu Natal, Pietermaritzburg, South Africa.
authors.forename: Amos
authors.initials: A
authors.lastname: Chinomona
authors.affiliation: School of Mathematics, Statistics and Computer Science, University of Kwa-Zulu Natal, Pietermaritzburg, South Africa.
authors.forename: Henry Godwell
authors.initials: HG
authors.lastname: Mwambi
mesh_terms: D000293:Adolescent; D000328:Adult; D005260:Female; D015658:HIV Infections / Q000453:epidemiology*; D006306:Health Surveys; D006801:Humans; D008297:Male; D008875:Middle Aged; D015995:Prevalence; D012044:Regression Analysis; D055815:Young Adult; D015030:Zimbabwe / Q000453:epidemiology