In:
Journal of the International AIDS Society, Wiley, Vol. 21, No. 2 ( 2018-02)
Abstract:
Cross‐sectional methods can be used to estimate HIV incidence for surveillance and prevention studies. We evaluated assays and multi‐assay algorithms ( MAA s) for incidence estimation in subtype C settings. Methods We 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). MAA s included 1‐4 of the following assays: Limiting Antigen Avidity assay ( LA g‐Avidity), BioRad‐Avidity assay, CD 4 cell count and viral load ( VL ). We evaluated 23,400 MAA s 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 LA g‐Avidity and BioRad‐Avidity assays alone, a widely used LA g algorithm ( LA g‐Avidity 〈 1.5 OD ‐n + VL 〉 1000 copies/ mL ), and two MAA s previously optimized for subtype B settings. We compared these cross‐sectional incidence estimates to observed incidence in an independent longitudinal cohort. Results The optimal MAA was LA g‐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 LA g 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. Conclusions An 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.
Type of Medium:
Online Resource
ISSN:
1758-2652
,
1758-2652
DOI:
10.1002/jia2.2018.21.issue-2
Language:
English
Publisher:
Wiley
Publication Date:
2018
detail.hit.zdb_id:
2467110-1
