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

Sian Floyd, Anna Molesworth, Albert Dube, Amelia C Crampin, Rein Houben, Menard Chihana, Alison Price, Ndoliwe Kayuni, Jacqueline Saul, Neil French, Judith R Glynn

Research output: Contribution to journalArticlepeer-review

Abstract

OBJECTIVE:: To quantify refusal bias due to prior HIV-testing, and its effect on HIV prevalence estimates, in general-population surveys. DESIGN:: Four annual, cross-sectional, house-to-house HIV sero-surveys conducted during 2006-2010 within a demographic surveillance population of 33,000 in northern Malawi. METHODS:: 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-standardisation; 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. RESULTS:: HIV test acceptance was 55-65% in each sero-survey. By 2009/10 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 2008/9. Estimates using multiple imputation 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. CONCLUSION:: As the proportion of the population who know their HIV-status increases, survey-based prevalence estimates become increasingly biased. Since an adjustment method for cross-sectional data remains elusive, sources of data with high coverage, such as ANC surveillance, remain important.
Original languageEnglish
Pages (from-to)233-42
JournalAIDS
Volume27
Issue number2
DOIs
Publication statusPublished - 2012

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