Underestimation of HIV prevalence in surveys when some people already know their status, and ways to reduce the bias.

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Floyd, S ; Molesworth, A ; Dube, A ; Crampin, AC ; Houben, R ; Chihana, M ; Price, A ; Kayuni, N ; Saul, J ; French, N ; Glynn, JR (2013)

: To quantify refusal bias due to prior HIV testing, and its effect on HIV prevalence estimates, in general-population surveys.<br/> : Four annual, cross-sectional, house-to-house HIV serosurveys conducted during 2006-2010 within a demographic surveillance population of 33 000 in northern Malawi.<br/> : The effect of prior knowledge of HIV status on test acceptance in subsequent surveys was analysed. HIV prevalence was then estimated using ten adjustment methods, including age-standardization; multiple imputation of missing data; a conditional probability equations approach incorporating refusal bias; using longitudinal data on previous and subsequent HIV results; including self-reported HIV status; and including linked antiretroviral therapy clinic data.<br/> : HIV test acceptance was 55-65% in each serosurvey. By 2009/2010 79% of men and 85% of women had tested at least once. Known HIV-positive individuals were more likely to be absent, and refuse interviewing and testing. Using longitudinal data, and adjusting for refusal bias, the best estimate of HIV prevalence was 7% in men and 9% in women in 2008/2009. Estimates using multiple imputations were 4.8 and 6.4%, respectively. Using the conditional probability approach gave good estimates using the refusal risk ratio of HIV-positive to HIV-negative individuals observed in this study, but not when using the only previously published estimate of this ratio, even though this was also from Malawi.<br/> : As the proportion of the population who know their HIV-status increases, survey-based prevalence estimates become increasingly biased. As an adjustment method for cross-sectional data remains elusive, sources of data with high coverage, such as antenatal clinics surveillance, remain important.<br/>
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