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358 – Disparities and Other Important Issues in Health Policy Research
A Bayesian Meta-Analysis of the Effect of Alcohol Use on HCV-Treatment Outcomes with a Comparison of Resampling Methods to Assess Uncertainty in Parameter Estimates
Katherine Cauthen
Sandia National Laboratories
Gregory Lambert
Sandia National Laboratories
Patrick Finley
Sandia National Laboratories
David Ross
U.S. Department of Veterans Affairs
Maggie Chartier
U.S. Department of Veterans Affairs
Victoria J. Davey
U.S. Department of Veterans Affairs
Heavy alcohol use is associated with lower HCV treatment response rates in interferon-based therapies, and although health care providers recommend reducing or abstaining from alcohol use prior to treatment, many patients are not successful. This meta-analysis systematically summarizes the English-language literature up through June 30, 2014 on the relationship between alcohol use and HCV treatment outcomes, among patients who were not required to abstain from alcohol use in order to receive treatment. Seven articles studying 1,751 HCV-infected patients were identified. Log-ORs of HCV treatment response for heavy versus light alcohol use were calculated. We employed a Bayesian model to accommodate the small sample size. The summary estimate for the log-OR of HCV treatment response was -0.775 (CI -1.397, -0.236). We compare various resampling methods to assess the uncertainty in parameter estimates. Heterogeneity accounted for 60.28% of the variation among log-ORs, but meta-regression was unable to capture its sources. This meta-analysis confirms that heavy alcohol use is associated with decreased HCV treatment response compared to lighter levels of alcohol use.