Abstract
BackgroundTo assess the performance of BED-CEIA (BED) and AxSYM Avidity Index (Ax-AI) assays in estimating HIV incidence among female sex workers (FSW) in Kigali, Rwanda.Methodology and FindingsEight hundred FSW of unknown HIV status were HIV tested; HIV-positive women had BED and Ax-AI testing at baseline and ≥12 months later to estimate assay false-recent rates (FRR). STARHS-based HIV incidence was estimated using the McWalter/Welte formula, and adjusted with locally derived FRR and CD4 results. HIV incidence and local assay window periods were estimated from a prospective cohort of FSW. At baseline, 190 HIV-positive women were BED and Ax-AI tested; 23 were classified as recent infection (RI). Assay FRR with 95% confidence intervals were: 3.6% (1.2–8.1) (BED); 10.6% (6.1–17.0) (Ax-AI); and 2.1% (0.4–6.1) (BED/Ax-AI combined). After FRR-adjustment, incidence estimates by BED, Ax-AI, and BED/Ax-AI were: 5.5/100 person-years (95% CI 2.2–8.7); 7.7 (3.2–12.3); and 4.4 (1.4–7.3). After CD4-adjustment, BED, Ax-AI, and BED/Ax-AI incidence estimates were: 5.6 (2.6–8.6); 9.7 (5.0–14.4); and 4.7 (2.0–7.5). HIV incidence rates in the first and second 6 months of the cohort were 4.6 (1.6–7.7) and 2.2 (0.1–4.4).ConclusionsAdjusted incidence estimates by BED/Ax-AI combined were similar to incidence in the first 6 months of the cohort. Furthermore, false-recent rate on the combined BED/Ax-AI algorithm was low and substantially lower than for either assay alone. Improved assay specificity with time since seroconversion suggests that specificity would be higher in population-based testing where more individuals have long-term infection.
Highlights
The Serologic Testing Algorithm for Recent HIV Seroconversion (STARHS) offers a promising alternative to prospective measurement of HIV incidence, in developing countries where incidence rates may be high but are infrequently measured owing to limited resources[1,2,3,4]
Studies conducted in a range of populations and settings, reveal the tendency of STARHS assays, including the BED and AxSYM Avidity Index (Ax-AI), to misclassify certain individuals with long-term HIV infection as recently infected, inflating HIV incidence estimates relative to prospective cohort rates[5,7,8,9,10,11]
A number of strategies have been proposed for correcting assay misclassification, including statistical adjustment, assessment of CD4 count and antiretroviral therapy (ART) status among individuals tested in order to remove those with probable long-term infection (LTI) from ‘‘recent infection’’ (RI) classification by the assays prior to calculation of incidence, and use of a dual testing algorithm in which a second, different STARHS assay confirms the classification on an initial assay[8,9,12,13,14,15,16,17]
Summary
The Serologic Testing Algorithm for Recent HIV Seroconversion (STARHS) offers a promising alternative to prospective measurement of HIV incidence, in developing countries where incidence rates may be high but are infrequently measured owing to limited resources[1,2,3,4]. Two main STARHS assays are the BED capture enzyme immunoassay (BED)[5] and AxSYM Avidity Index method (Ax–AI)[6]. These and other STARHS assays exploit biologic properties of early HIV infection, such as development of HIV antibodies, to distinguish recent from long-term infections in cross-sectional samples of individuals testing HIV positive. Individuals who test HIV-positive in a cross-sectional survey can be followed in a ‘‘long-term infection cohort’’ with repeat STARHS testing $12 months later, in order to calculate assay false-recent rates (FRR); incidence estimates can be adjusted downward by applying the FRR to available statistical formulae[8,9,18]. To assess the performance of BED-CEIA (BED) and AxSYM Avidity Index (Ax-AI) assays in estimating HIV incidence among female sex workers (FSW) in Kigali, Rwanda
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