| Literature DB >> 34897992 |
Ethan Klock1, Ethan Wilson2, Reinaldo E Fernandez1, Estelle Piwowar-Manning1, Ayana Moore3, Barry Kosloff4,5, Justin Bwalya4, Nomtha Bell-Mandla6, Anelet James6, Helen Ayles4,5, Peter Bock6, Deborah Donnell2, Sarah Fidler7, Richard Hayes8, Susan H Eshleman9, Oliver Laeyendecker1,10.
Abstract
INTRODUCTION: Cross-sectional incidence testing is used to estimate population-level HIV incidence and measure the impact of prevention interventions. There are limited data evaluating the accuracy of estimates in settings where antiretroviral therapy coverage and levels of viral suppression are high. Understanding cross-sectional incidence estimates in these settings is important as viral suppression can lead to false recent test results. We compared the accuracy of multi-assay algorithms (MAA) for incidence estimation to that observed in the community-randomized HPTN 071 (PopART) trial, where the majority of participants with HIV infection were virally suppressed.Entities:
Keywords: HPTN; PopART; cross-sectional incidence estimation; multi-assay algorithm; sub-Saharan Africa; validation study
Mesh:
Year: 2021 PMID: 34897992 PMCID: PMC8666582 DOI: 10.1002/jia2.25830
Source DB: PubMed Journal: J Int AIDS Soc ISSN: 1758-2652 Impact factor: 6.707
Participant demographics
| SC | HIV+ | HIV– | Follow‐up (years) | |
|---|---|---|---|---|
| Overall | 221 | 4627 | 15,845 | 16,463 |
| Study arm | ||||
| A | 76 | 1507 | 5217 | 6724 |
| B | 60 | 1616 | 5918 | 7534 |
| C | 85 | 1504 | 4710 | 6214 |
| Country | ||||
| South Africa | 89 | 1753 | 6845 | 7105 |
| Zambia | 132 | 2874 | 9000 | 9358 |
| Sex | ||||
| Female | 190 | 4048 | 11,106 | 11,575 |
| Male | 31 | 619 | 4739 | 4888 |
| 18–24 years old | ||||
| Female | 77 | 484 | 3441 | 3605 |
| Male | 11 | 50 | 1893 | 1951 |
Note: The table shows demographic characteristics of participants included in the study. Sex was assessed for all participants and for the subset of participants aged 18–24.
Abbreviations: Follow‐up, time between visits for uninfected participants and seroconverters, measured in years; HIV+, number of HIV seropositive individuals; HIV–, number of HIV seronegative individuals; SC, seroconverters.
Figure 1Comparison of cross‐sectional incidence estimates to observed incidence in HPTN 071 (PopART).
The figure shows a comparison of annual HIV incidence observed in the HPTN 071 (PopART) trial, and incidence estimated with three multi‐assay algorithms (MAAs). Abbreviations: LAg, limiting antigen avidity assay; MAA‐C, Clade C multi‐assay algorithm; Rapid, Asante rapid LAg test assay; VL, viral load.
Figure 2Comparison of cross‐sectional incidence estimates to observed incidence in HPTN 071 (PopART) by study arm, country, sex and sex among young persons.
The plots show observed incidence and incidence estimates for three multi‐assay algorithms (MAAs). The circles represent observed incidence based on longitudinal follow‐up. The sub‐analyses are presented by study arm (a); country (b); sex (c); and sex among young (aged 18–24) individuals (d); 95% confidence intervals are shown for each point estimate of incidence. Incidence estimates that differ significantly from the observed incidence are noted (* p<0.05; ** p<0.01). Abbreviations: LAg, limiting antigen avidity assay; MAA‐C, Clade C optimized multi‐assay algorithm; Rapid, Asante rapid LAg test assay; VL, viral load.