Abstract
IntroductionCross‐sectional methods can be used to estimate HIV incidence for surveillance and prevention studies. We evaluated assays and multi‐assay algorithms (MAAs) for incidence estimation in subtype C settings.MethodsWe analysed samples from individuals with subtype C infection with known duration of infection (2442 samples from 278 adults; 0.1 to 9.9 years after seroconversion). MAAs included 1‐4 of the following assays: Limiting Antigen Avidity assay (LAg‐Avidity), BioRad‐Avidity assay, CD4 cell count and viral load (VL). We evaluated 23,400 MAAs with different assays and assay cutoffs. We identified the MAA with the largest mean window period, where the upper 95% confidence interval (CI) of the shadow was <1 year. This MAA was compared to the LAg‐Avidity and BioRad‐Avidity assays alone, a widely used LAg algorithm (LAg‐Avidity <1.5 OD‐n + VL >1000 copies/mL), and two MAAs previously optimized for subtype B settings. We compared these cross‐sectional incidence estimates to observed incidence in an independent longitudinal cohort.ResultsThe optimal MAA was LAg‐Avidity <2.8 OD‐n + BioRad‐Avidity <95% + VL >400 copies/mL. This MAA had a mean window period of 248 days (95% CI: 218, 284), a shadow of 306 days (95% CI: 255, 359), and provided the most accurate and precise incidence estimate for the independent cohort. The widely used LAg algorithm had a shorter mean window period (142 days, 95% CI: 118, 167), a longer shadow (410 days, 95% CI; 318, 491), and a less accurate and precise incidence estimate for the independent cohort.ConclusionsAn optimal MAA was identified for cross‐sectional HIV incidence in subtype C settings. The performance of this MAA is superior to a testing algorithm currently used for global HIV surveillance.
Highlights
Cross-sectional methods can be used to estimate HIV incidence for surveillance and prevention studies
We used a large set of subtype C samples from individuals with known duration of infection to identify an optimal multi-assay algorithms (MAAs) for HIV incidence estimation in subtype C settings
The optimal subtype C MAA, which included the LAg-Avidity assay, the BioRad-Avidity assay, and viral load, had a mean window period of 248 days. This is >100 days longer than the mean window period for a LAg algorithm (LAg-Avidity assay 1000), which is widely used for incidence estimation in surveillance and other studies
Summary
Accurate methods for estimating HIV incidence are critical for HIV surveillance and for evaluating the effectiveness of HIV prevention efforts [1]. Multi-assay algorithms (MAAs) that combine serologic and non-serologic assays, such as viral load (VL) and CD4 cell count, have been identified that provide accurate incidence estimates in subtype B settings [8,9,10]. These MAAs have been used to estimate HIV incidence in clinical trials and cohort studies [10,11,12,13]. We compared the performance of this MAA to five other testing algorithms (the LAg-Avidity assay alone, the BioRad-Avidity assay alone, a widely-used LAg algorithm, and two MAAs previously optimized for subtype B settings)
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