Accurate hepatitis C virus (HCV) incidence estimates are critical for monitoring progress towards HCV elimination goals, including an 80% reduction in HCV incidence by 2030. Moreover, incidence estimates can help guide prevention and treatment programming, particularly in the context of the US opioid epidemic.
An inexpensive, Genedia-based HCV IgG antibody avidity assay was evaluated as a platform to estimate cross-sectional, population-level primary HCV incidence using 1840 HCV antibody and RNA positive samples from 875 individuals enrolled in 5 cohort studies in the US and India. Using samples collected <2 years following HCV seroconversion, the mean duration of recent infection (MDRI) was calculated by fitting a maximum likelihood binomial regression model to the probability of appearing recent. Among samples collected ≥2 years post-HCV seroconversion, a subject-level false recent ratio (FRR) was calculated by estimating the probability of appearing recent using an exact binomial test. Factors associated with falsely appearing recent among samples collected ≥2 years post seroconversion were determined by Poisson regression with generalized estimating equations and robust variance estimators.
An avidity index cutoff of <40% resulted in an MDRI of 113 days (95%CI:84-146), and an FRR of 0.4% (95%CI:0.0-1.2), 4.6% (95%CI:2.2-8.3), and 9.5% (95%CI:3.6-19.6) among persons who were HIV-uninfected, HIV-infected, and HIV-infected with a CD4 count <200, respectively. No variation was seen between HCV genotypes 1 and 3. In hypothetical scenarios of high-risk settings, a sample size of <1000 individuals could reliably estimate primary HCV incidence.
This cross-sectional approach can estimate primary HCV incidence for the most common genotypes. This tool can serve as a valuable resource for program and policy planners seeking to monitor and reduce HCV burden.

Copyright © 2020. Published by Elsevier B.V.