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<https://discovery.ucl.ac.uk/id/eprint/1476057> <http://purl.org/dc/terms/title> "Adjusting HIV prevalence estimates for non-participation: an application to demographic surveillance"^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/1476057> <http://purl.org/ontology/bibo/abstract> "Introduction: HIV testing is a cornerstone of efforts to combat the HIV epidemic, and testing conducted as part of surveillance\r\nprovides invaluable data on the spread of infection and the effectiveness of campaigns to reduce the transmission of HIV.\r\nHowever, participation in HIV testing can be low, and if respondents systematically select not to be tested because they know or\r\nsuspect they are HIV positive (and fear disclosure), standard approaches to deal with missing data will fail to remove selection\r\nbias. We implemented Heckman-type selection models, which can be used to adjust for missing data that are not missing at\r\nrandom, and established the extent of selection bias in a population-based HIV survey in an HIV hyperendemic community in\r\nrural South Africa. / Methods: We used data from a population-based HIV survey carried out in 2009 in rural KwaZulu-Natal, South Africa. In this\r\nsurvey, 5565 women (35%) and 2567 men (27%) provided blood for an HIV test. We accounted for missing data using interviewer\r\nidentity as a selection variable which predicted consent to HIV testing but was unlikely to be independently associated\r\nwith HIV status. Our approach involved using this selection variable to examine the HIV status of residents who would ordinarily\r\nrefuse to test, except that they were allocated a persuasive interviewer. Our copula model allows for flexibility when modelling\r\nthe dependence structure between HIV survey participation and HIV status. / Results: For women, our selection model generated an HIV prevalence estimate of 33% (95% CI 2740) for all people eligible to\r\nconsent to HIV testing in the survey. This estimate is higher than the estimate of 24% generated when only information from\r\nrespondents who participated in testing is used in the analysis, and the estimate of 27% when imputation analysis is used to\r\npredict missing data on HIV status. For men, we found an HIV prevalence of 25% (95% CI 1535) using the selection model,\r\ncompared to 16% among those who participated in testing, and 18% estimated with imputation. We provide new confidence\r\nintervals that correct for the fact that the relationship between testing and HIV status is unknown and requires estimation. / Conclusions: We confirm the feasibility and value of adopting selection models to account for missing data in population-based\r\nHIV surveys and surveillance systems. Elements of survey design, such as interviewer identity, present the opportunity to adopt\r\nthis approach in routine applications. Where non-participation is high, true confidence intervals are much wider than those\r\ngenerated by standard approaches to dealing with missing data suggest."^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/1476057> <http://purl.org/dc/terms/date> "2015-11-26" .
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