HPTN Bibliographic Record
Patel EU, Cox AL, Mehta SH, Boon D, Mullis CE, Astemborski J, Osburn WO, Quinn J, Redd AD, Kirk GD, Thomas DL, Quinn TC, Laeyendecker O. Use of Hepatitis C Virus (HCV) Immunoglobulin G Antibody Avidity as a Biomarker to Estimate the Population-Level Incidence of HCV Infection. J Infect Dis. 2016, 214: 344-52.
BACKGROUND: Sensitive methods are needed to estimate the population-level incidence of hepatitis C virus (HCV) infection. METHODS: We 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. RESULTS: A 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. CONCLUSIONS: Avidity-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.