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Existing methods to estimate the prevalence of chronic hepatitis C (HCV) inNew York City (NYC) are limited in scope and fail to assess hard-to-reachsubpopulations with highest risk such as injecting drug users (IDUs). Toaddress these limitations, we employ a Bayesian multi-parameter evidencesynthesis model to systematically combine multiple sources of data, account forbias in certain data sources, and provide unbiased HCV prevalence estimateswith associated uncertainty. Our approach improves on previous estimates byexplicitly accounting for injecting drug use and including data from high-risksubpopulations such as the incarcerated, and is more inclusive, utilizing tenNYC data sources. In addition, we derive two new equations to allow age atfirst injecting drug use data for former and current IDUs to be incorporatedinto the Bayesian evidence synthesis, a first for this type of model. Ourestimated overall HCV prevalence as of 2012 among NYC adults aged 20-59 yearsis 2.78% (95% CI 2.61-2.94%), which represents between 124,900 and 140,000chronic HCV cases. These estimates suggest that HCV prevalence in NYC is higherthan previously indicated from household surveys (2.2%) and the surveillancesystem (2.37%), and that HCV transmission is increasing among young injectingadults in NYC. An ancillary benefit from our results is an estimate of currentIDUs aged 20-59 in NYC: 0.58% or 27,600 individuals.

A Bayesian Evidence Synthesis Approach to Estimate Disease Prevalence in Hard-To-Reach Populations: Hepatitis C in New York City