ArticleJournal of the International AIDS Society2018
Identification and validation of a multi-assay algorithm for cross-sectional HIV incidence estimation in populations with subtype C infection.
Article in Journal of the International AIDS Society, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Use of HIV Recency Assays for HIV Incidence Estimation and Other Surveillance Use Cases: Systematic Review.JMIR public health and surveillance · 2022Pooled it
- Evaluation of multi-assay algorithms for identifying individuals with recent HIV infection: HPTN 071 (PopART).PloS one · 2021Trial
- Prediction of recent HIV-1 infections using Shannon entropy analysis of HIV-1 group-specific antigen protein sequence.Virology journal · 2026Article
- An enhanced cross-sectional HIV incidence estimator that incorporates prior HIV test results.Statistics in medicine · 2024Article
- Study design approaches for future active-controlled HIV prevention trials.Statistical communications in infectious diseases · 2024Article
- Development of a multiassay algorithm (MAA) to identify recent HIV infection in newly diagnosed individuals in Indonesia.iScience · 2023Article
- Evaluation of multi-assay algorithms for cross-sectional HIV incidence estimation in settings with universal antiretroviral treatment.BMC infectious diseases · 2022Article
- Recent HIV Infection: Diagnosis and Public Health Implications.Diagnostics (Basel, Switzerland) · 2022Review
- Antiretroviral Drug Resistance in HIV Sequences From People Who Inject Drugs and Men Who Have Sex With Men Across 21 Cities in India.Open forum infectious diseases · 2022Article
- A systematic review of limiting antigen avidity enzyme immunoassay for detection of recent HIV-1 infection to expand supported applications.Journal of virus eradication · 2022Review
- Regression with interval-censored covariates: Application to cross-sectional incidence estimation.Biometrics · 2022Article
- Statistical considerations for cross-sectional HIV incidence estimation based on recency test.Statistics in medicine · 2022Article
- Validation of population-level HIV-1 incidence estimation by cross-sectional incidence assays in the HPTN 071 (PopART) trial.Journal of the International AIDS Society · 2021Article
- Temporal change in population-level prevalence of detectable HIV viraemia and its association with HIV incidence in key populations in India: a serial cross-sectional study.The lancet. HIV · 2021Article
- Undisclosed HIV status and antiretroviral therapy use among South African blood donors.Transfusion · 2021Article
- A top scoring pairs classifier for recent HIV infections.Statistics in medicine · 2021Article
- Impact of Early Antiretroviral Treatment Initiation on Performance of Cross-Sectional Incidence Assays.AIDS research and human retroviruses · 2020Article
- Modeling methods for estimating HIV incidence: a mathematical review.Theoretical biology & medical modelling · 2020Review
- Targeting the HIV Epidemic in South Africa: The Need for Testing and Linkage to Care in Emergency Departments.EClinicalMedicine · 2019Article
- Cross-sectional HIV incidence estimation in an evolving epidemic.Statistics in medicine · 2019Article
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Authors and funding
10 authors.
Funding
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.
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