Abstract
BackgroundSelection bias is common in clinic-based HIV surveillance. Clinics located in HIV hotspots are often the first to be chosen and monitored, while clinics in less prevalent areas are added to the surveillance system later on. Consequently, the estimated HIV prevalence based on clinic data is substantially distorted, with markedly higher HIV prevalence in the earlier periods and trends that reveal much more dramatic declines than actually occur.MethodsUsing simulations, we compare and contrast the performance of the various approaches and models for handling selection bias in clinic-based HIV surveillance. In particular, we compare the application of complete-case analysis and multiple imputation (MI). Several models are considered for each of the approaches. We demonstrate the application of the methods through sentinel surveillance data collected between 2002 and 2008 from India.ResultsSimulations suggested that selection bias, if not handled properly, can lead to biased estimates of HIV prevalence trends and inaccurate evaluation of program impact. Complete-case analysis and MI differed considerably in their ability to handle selection bias. In scenarios where HIV prevalence remained constant over time (i.e. β = 0), the estimated derived from MI tended to be biased downward. Depending on the imputation model used, the estimated bias ranged from −1.883 to −0.048 in logit prevalence. Furthermore, as the level of selection bias intensified, the extent of bias also increased. In contrast, the estimates yielded by complete-case analysis were relatively unbiased and stable across the various scenarios. The estimated bias ranged from −0.002 to 0.002 in logit prevalence.ConclusionsGiven that selection bias is common in clinic-based HIV surveillance, when analyzing data from such sources appropriate adjustment methods need to be applied. The results in this paper suggest that indiscriminant application of imputation models can lead to biased results.
Highlights
Selection bias is common in clinic-based HIV surveillance
Given the difficulties in tracking HIV incidence, in many low-and-middle-income countries, the extent of the HIV epidemic in the general population has primarily been monitored via clinic-based HIV surveillance programs that measure prevalence among women receiving antenatal care (ANC) [3]
Previous studies have highlighted the problem of selection bias associated with ANC-based surveillance [4,5,6]; that is, the level of HIV prevalence from ANC sites has been shown to be markedly higher than corresponding population-based surveys, as women attending ANC are at higher risk of HIV than the general population
Summary
Selection bias is common in clinic-based HIV surveillance. Clinics located in HIV hotspots are often the first to be chosen and monitored, while clinics in less prevalent areas are added to the surveillance system later on. Given the difficulties in tracking HIV incidence, in many low-and-middle-income countries, the extent of the HIV epidemic in the general population has primarily been monitored via clinic-based HIV surveillance programs that measure prevalence among women receiving antenatal care (ANC) [3]. Previous studies have highlighted the problem of selection bias associated with ANC-based surveillance [4,5,6]; that is, the level of HIV prevalence from ANC sites has been shown to be markedly higher than corresponding population-based surveys, as women attending ANC are at higher risk of HIV than the general population Recognition of this issue led to revisions by UNAIDS of trends in HIV prevalence in 2003, and corrections for this bias are included as part of the Epidemic Projection Package (EPP) [7]. Corrections for this bias revised the total number of people living with HIV worldwide down from the previously estimated 40 million to 35 million in 2001 [8]
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