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.