Using Bayesian Evidence Synthesis to Estimate Hepatitis C Prevalence Among Adults in New York City

• Activity Number: 637
• Type: Topic Contributed
• Date/Time: Thursday, August 13, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Hui Fen Tan* and Susanna M. Makela and Daliah Heller and Kevin Konty and Sharon Balter and Tian Zheng and James H. Stark
• Companies: Cornell University and Columbia University and City University of New York School of Public Health and and New York City Department of Health and Mental Hygiene and Columbia University and Pfizer Inc.
• Keywords: bayesian ; evidence synthesis ; hepatitis c ; disease prevalence estimation ; biased data ; incomplete data

Abstract

Existing methods to estimate the prevalence of hepatitis C (HCV) in New York City (NYC) are limited in scope and fail to assess hard to reach segments of the population who are at the highest risk including injection drug users (IDU). To address limitations in the current approaches, a Bayesian evidence synthesis model was employed to combine multiple sources of data in a systematic manner to generate unbiased prevalence estimates segmented by IDU and age groups. Data from 10 different sources directly or indirectly provided information on IDU population size and their associated HCV prevalence. Overall HCV prevalence in NYC among adults aged 20-59 years is 2.8% (95% credible interval: 2.66-2.98) which represents 134,791 chronic HCV cases. The model estimates that there are 27,465 current IDU of which 55% are HCV positive. Similarly, there are 120,762 ex-IDU and 59% have HCV. A notable trend in HCV positivity existed across age groups with adults aged 50-59 years having the largest proportion of positivity at 5.4% (N=54,486). As demonstrated the Bayesian evidence synthesis model is an effective method for estimating quantities of interest when data sources are biased or incomplete.