Antibody avidity-based approach to estimate population-level incidence of hepatitis C

Accurate 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 1,840 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, an individual-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 cut-off of <40% resulted in an MDRI of 113 days (95% CI 84-146), and FRRs 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 individuals who were HIV-uninfected, HIV-infected, and HIV-infected with a CD4 count <200/μl, respectively. No variation was seen between HCV genotypes 1 and 3. In hypothetical scenarios of high-risk settings, a sample size of <1,000 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.

Determining the rate of new hepatitis C virus (HCV) infections in a population is critical to monitoring progress toward HCV elimination and to appropriately guide control efforts. However, since HCV infections are most often initially asymptomatic, it is difficult to estimate the rate of new HCV infections without following HCV-uninfected people over time and repeatedly testing them for HCV infection. Here, we present a novel, resource-efficient method to estimate the rate of new HCV infections in a population using data from a single timepoint.