Skip to content
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
Underestimation of HIV prevalence in surveys when some people already know their status, and ways to reduce the bias
AIDS, Volume 27, No. 2, Year 2013
Notification
URL copied to clipboard!
Description
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 33000 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 Wolters Kluwer Health | Lippincott Williams & Wilkins.
Authors & Co-Authors
Floyd, Sian
Unknown Affiliation
Molesworth, Anna M.
Unknown Affiliation
Dube, Albert
Unknown Affiliation
Crampin, Amelia Catharine
Unknown Affiliation
Houben, Rein M.G.J.
Unknown Affiliation
Chihana, Menard
Unknown Affiliation
Price, Alison J.
Unknown Affiliation
Kayuni, Ndoliwe
Unknown Affiliation
Saul, Jacqueline
Unknown Affiliation
French, N.
Unknown Affiliation
Glynn, Judith R.
Unknown Affiliation
Statistics
Citations: 61
Authors: 11
Affiliations: 3
Identifiers
Doi:
10.1097/QAD.0b013e32835848ab
e-ISSN:
14735571
Research Areas
Health System And Policy
Infectious Diseases
Maternal And Child Health
Study Design
Cross Sectional Study
Cohort Study
Study Approach
Quantitative
Study Locations
Malawi
Participants Gender
Male
Female