ArticleAmerican journal of epidemiology2013
Estimation of HIV incidence using multiple biomarkers.
Article in American journal of epidemiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
54 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
- Short Communication: Viral Genetic Linkage Analysis Among Black Men Who Have Sex With Men (HIV Prevention Trials Network 061).AIDS research and human retroviruses · 2019Trial
- Cross-sectional estimates revealed high HIV incidence in Botswana rural communities in the era of successful ART scale-up in 2013-2015.PloS one · 2018Trial
- Trial
- HIV-1 drug resistance and molecular transmission network analysis of newly diagnosed people living with HIV in Beijing, China: a retrospective study from 2015 to 2023.Virology journal · 2025Article
- Recent and Local HIV Infections among Newly Diagnosed Cases in Two Districts of Chongqing, China (2019-2021).AIDS and behavior · 2025Article
- 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
- 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
- Factors Associated With Newly HIV Infection and Transmitted Drug Resistance Among Men Who Have Sex With Men in Harbin, P.R. China.Frontiers in public health · 2022Article
- Recent HIV infection testing algorithms.The Indian journal of medical research · 2020Article
- Antibody avidity-based approach to estimate population-level incidence of hepatitis C.Journal of hepatology · 2020Article
- Design of a Multiplexed Analyte Biosensor using Digital Barcoded Particles and Impedance Spectroscopy.Scientific reports · 2020Article
- Modeling methods for estimating HIV incidence: a mathematical review.Theoretical biology & medical modelling · 2020Review
- Cross-sectional human immunodeficiency virus incidence estimation accounting for heterogeneity across communities.Biometrics · 2019Article
- Cross-sectional HIV incidence estimation in an evolving epidemic.Statistics in medicine · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The incidence of human immunodeficiency virus (HIV) is the rate at which new HIV infections occur in populations. The development of accurate, practical, and cost-effective approaches to estimation of HIV incidence is a priority among researchers in HIV surveillance because of limitations with existing methods. In this paper, we develop methods for estimating HIV incidence rates using multiple biomarkers in biological samples collected from a cross-sectional survey. An advantage of the method is that it does not require longitudinal follow-up of individuals. We use assays for BED, avidity, viral load, and CD4 cell count data from clade B samples collected in several US epidemiologic cohorts between 1987 and 2010. Considering issues of accuracy, cost, and implementation, we identify optimal multiassay algorithms for estimating incidence. We find that the multiple-biomarker approach to cross-sectional HIV incidence estimation corrects the significant deficiencies of currently available approaches and is a potentially powerful and practical tool for HIV surveillance.
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