ArticleThe Journal of infectious diseases2016
Use of Hepatitis C Virus (HCV) Immunoglobulin G Antibody Avidity as a Biomarker to Estimate the Population-Level Incidence of HCV Infection.
Article in The Journal of infectious diseases, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Biomarkers in Detection of Hepatitis C Virus Infection.Pathogens (Basel, Switzerland) · 2024Review
- Regression with interval-censored covariates: Application to cross-sectional incidence estimation.Biometrics · 2022Article
- Methods and indicators to validate country reductions in incidence of hepatitis C virus infection to elimination levels set by WHO.The lancet. Gastroenterology & hepatology · 2022Review
- Antibody avidity-based approach to estimate population-level incidence of hepatitis C.Journal of hepatology · 2020Article
- A comparison of two biological markers of recent hepatitis C virus (HCV) infection: implications for the monitoring of interventions and strategies to reduce HCV transmission among people who inject drugs.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2018Article
- Prevalence and mapping of hepatitis C infections among men who have sex with men in New York City.PloS one · 2018Article
- Age- and time-dependent prevalence and incidence of hepatitis C virus infection in drug users in France, 2004-2011: model-based estimation from two national cross-sectional serosurveys.Epidemiology and infection · 2017Article
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13 authors.
Funding
Abstract
backgroundSensitive methods are needed to estimate the population-level incidence of hepatitis C virus (HCV) infection.
methodsWe developed an HCV immunoglobulin G (IgG) antibody avidity assay by modifying the Ortho 3.0 HCV enzyme-linked immunoassay and tested 997 serum or plasma samples from 568 people who inject drugs enrolled in prospective cohort studies. Avidity-based testing algorithms were evaluated by their (1) mean duration of recent infection (MDRI), defined as the average time an individual is identified as having been recently infected, according to a given algorithm; (2) false-recent rate, defined as the proportion of samples collected >2 years after HCV seroconversion that were misclassified as recent; (3) sample sizes needed to estimate incidence; and (4) power to detect a reduction in incidence between serial cross-sectional surveys.
resultsA multiassay algorithm (defined as an avidity index of <30%, followed by HCV viremia detection) had an MDRI of 147 days (95% confidence interval [CI], 125-195 days), and the false-recent rates were 0.7% (95% CI, .2%-1.8%) and 7.6% (95% CI, 4.2%-12.3%) among human immunodeficiency virus (HIV)-negative and HIV-positive persons, respectively. In various simulated high-risk populations, this algorithm required <1000 individuals to estimate incidence (relative standard error, 30%) and had >80% power to detect a 50% reduction in incidence.
conclusionsAvidity-based algorithms have the capacity to accurately estimate HCV infection incidence and rapidly assess the impact of public health efforts among high-risk populations. Efforts to optimize this method should be prioritized.
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