Amos Chinomona|Henry Mwambi
Estimating HIV Prevalence in Zimbabwe Using Population-Based Survey Data
Rhodes University|University of KwaZulu-Natal|University of KwaZulu-Natal
estimates hiv prevalence computed using data obtained sampling subgroup national population may lack representativeness relevant domains population estimates often computed assumption hiv prevalence uniform across domains population use appropriate statistical methods together populationbased survey data enhance better estimation national subgroup level hiv prevalence provide improved explanations variation hiv prevalence across different domains population study computed designconsistent estimates hiv prevalence respective 95 confidence intervals national subgroup levels addition provided multivariable survey logistic regression model generalized linear modelling perspective explaining variation hiv prevalence using demographic socioeconomic sociocultural behavioural factors essentially study borrows proximate determinants conceptual framework provides guiding principles upon socioeconomic sociocultural variables affect hiv prevalence biological behavioural factors utilize 201011 zimbabwe demographic health survey 201011 zdhs data population based estimate hiv prevalence different categories population constructing logistic regression model established hiv prevalence varies greatly age gender marital status place residence literacy level belief whether condom use reduce risk contracting hiv level recent sexual activity whereas marked variation hiv prevalence social status measured using wealth index method contraceptive individuals level education