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doi: 10.1097/QAD.0b013e32835848ab
Epidemiology and Social

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

Floyd, Siana; Molesworth, Annaa,b; Dube, Albertb; Crampin, Amelia C.a,b; Houben, Reina,b; Chihana, Menardb; Price, Alisona,b; Kayuni, Ndoliweb; Saul, Jacquelinea; French, Neila,b; Glynn, Judith R.a

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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 serosurveys 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-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.

Results: 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.

Conclusion: 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.

© 2013 Lippincott Williams & Wilkins, Inc.


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