PROJECT SUMMARY/ABSTRACT Alcohol is a contributing factor for a number of diseases, including many cancers, and evidence suggests that there are substantial medical and economic costs associated with alcohol-attributable disease incidence. However, the current evidence base suffers from two weaknesses. First, estimating alcohol- attributable disease incidence requires accurate measurements of the prevalence of alcohol exposures, and the surveillance data sources researchers use to measure the prevalence of alcohol exposures are likely error prone. Second, most studies that estimate alcohol-attributable disease incidence and corresponding medical and economic costs focus on the incident cases and costs that could be avoided if no one in the population consumed alcohol, which is an unreasonable alcohol reduction target. This study will address these two weaknesses by first developing a new, Bayesian approach for estimating the prevalence of alcohol exposures from self-report survey data and then applying this approach to a novel study of the potential impacts on burden of illness and corresponding economic costs that could occur if modest reductions in alcohol use were achieved across different subgroups (e.g., based on drinking frequencies and demographics). Thus, this study will produce valuable methodological advances and new evidence that will better inform how best to adopt public health strategies for reducing the burden and cost of illness associated with alcohol use.