HIV Incidence in Rural South Africa: Comparison of Estimates from Longitudinal Surveillance and Cross-Sectional cBED Assay Testing

Abstract

BackgroundThe BED IgG-Capture Enzyme Immunoassay (cBED assay), a test of recent HIV infection, has been used to estimate HIV incidence in cross-sectional HIV surveys. However, there has been concern that the assay overestimates HIV incidence to an unknown extent because it falsely classifies some individuals with non-recent HIV infections as recently infected. We used data from a longitudinal HIV surveillance in rural South Africa to measure the fraction of people with non-recent HIV infection who are falsely classified as recently HIV-infected by the cBED assay (the long-term false-positive ratio (FPR)) and compared cBED assay-based HIV incidence estimates to longitudinally measured HIV incidence.Methodology/Principal FindingsWe measured the long-term FPR in individuals with two positive HIV tests (in the HIV surveillance, 2003–2006) more than 306 days apart (sample size n = 1,065). We implemented four different formulae to calculate HIV incidence using cBED assay testing (n = 11,755) and obtained confidence intervals (CIs) by directly calculating the central 95th percentile of incidence values. We observed 4,869 individuals over 7,685 person-years for longitudinal HIV incidence estimation. The long-term FPR was 0.0169 (95% CI 0.0100–0.0266). Using this FPR, the cross-sectional cBED-based HIV incidence estimates (per 100 people per year) varied between 3.03 (95% CI 2.44–3.63) and 3.19 (95% CI 2.57–3.82), depending on the incidence formula. Using a long-term FPR of 0.0560 based on previous studies, HIV incidence estimates varied between 0.65 (95% CI 0.00–1.32) and 0.71 (95% CI 0.00–1.43). The longitudinally measured HIV incidence was 3.09 per 100 people per year (95% CI 2.69–3.52), after adjustment to the sex-age distribution of the sample used in cBED assay-based estimation.Conclusions/SignificanceIn a rural community in South Africa with high HIV prevalence, the long-term FPR of the cBED assay is substantially lower than previous estimates. The cBED assay performs well in HIV incidence estimation if the locally measured long-term FPR is used, but significantly underestimates incidence when a FPR estimate based on previous studies in other settings is used.

Short Communication: Prioritizing Communities for HIV Prevention in sub-Saharan Africa

“…The addition of validated TRI to cross-sectional HIV surveys, 13 such as the DHS, would allow better prioritization for prevention of HIV acquisition than is currently possible. TRI are easy to apply.…”

“…In a systematic review, 16 we identified 39 published studies (up to March 2009) that estimated HIV incidence based on information obtained by applying the BED assay, the particular TRI used in the present study. Past applications of the BED assay included national and subnational HIV surveys of general populations in subSaharan Africa, 13,17,18 demonstrating that it is indeed feasible to add TRI to large-scale surveys in the region. Further studies are needed to estimate the cost-effectiveness of routinely adding TRI to HIV surveys in sub-Saharan Africa.…”

“…In each community, we measured the proportion of individuals with early-stage infection with the BED IgG-Capture Enzyme Immunoassay (BED assay), a test for recent HIV infection (TRI), which was previously validated in the surveillance area. 13 The BED assay defines early-stage infection as 153 days after seroconversion, i.e., approximately half a year after HIV infection. 14 We adjusted the proportion of individuals with early-stage infection for the long-term false-positive ratio of the BED assay in this community (1.69%).…”

“…14 We adjusted the proportion of individuals with early-stage infection for the long-term false-positive ratio of the BED assay in this community (1.69%). 13 We then used the BED assay information to estimate HIV incidence following a locally Standard errors are shown in parentheses. Standard errors of incidence and transmission probability point estimates are based on nonparametric bootstrapping with replacement over 1000 repetitions.…”

“…13 To identify individuals with late-stage disease, we linked the HIV data to information from a demographic surveillance. Individuals who died <15 months after the date of their HIV test were classified as late-stage HIV infection.…”