A Bayesian Evidence Synthesis Approach to Estimate Disease Prevalence in Hard-To-Reach Populations: Hepatitis C in New York City
Sarah Tan, Susanna Makela, Daliah Heller, Kevin Konty, Sharon Balter,, Tian Zheng, James H. Stark

TL;DR
This paper presents a Bayesian evidence synthesis model that combines multiple data sources to accurately estimate hepatitis C prevalence among hard-to-reach populations in NYC, including injecting drug users and incarcerated individuals.
Contribution
It introduces a novel Bayesian multi-parameter evidence synthesis approach that incorporates age at first injecting drug use data, improving prevalence estimates for high-risk groups.
Findings
Estimated HCV prevalence in NYC adults aged 20-59 is 2.78%.
HCV prevalence is higher than previous estimates from surveys.
HCV transmission is increasing among young injecting adults.
Abstract
Existing methods to estimate the prevalence of chronic hepatitis C (HCV) in New York City (NYC) are limited in scope and fail to assess hard-to-reach subpopulations with highest risk such as injecting drug users (IDUs). To address these limitations, we employ a Bayesian multi-parameter evidence synthesis model to systematically combine multiple sources of data, account for bias in certain data sources, and provide unbiased HCV prevalence estimates with associated uncertainty. Our approach improves on previous estimates by explicitly accounting for injecting drug use and including data from high-risk subpopulations such as the incarcerated, and is more inclusive, utilizing ten NYC data sources. In addition, we derive two new equations to allow age at first injecting drug use data for former and current IDUs to be incorporated into the Bayesian evidence synthesis, a first for this type of…
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Taxonomy
TopicsLiver Disease Diagnosis and Treatment · Hepatitis C virus research · Alcohol Consumption and Health Effects
