Improving Models of Alcohol Consumption Mismeasurement and Burden of Disease

项目概要/摘要 酒精是许多疾病的一个促成因素，包括许多癌症，有证据表明， 酒精引起的疾病会带来巨大的医疗和经济成本， 发病率。然而，目前的证据基础有两个弱点。首先，估计酒精含量- 可归因的疾病发病率需要准确测量酒精暴露的流行程度， 研究人员用来衡量酒精暴露流行率的监测数据来源可能是 容易出错。其次，大多数估计酒精所致疾病发病率和相应 医疗和经济成本的重点是事件的情况下，可以避免的费用，如果没有人在 这是一个不合理的酒精减少目标。 这项研究将解决这两个弱点，首先开发一个新的，贝叶斯方法估计 从自我报告的调查数据中了解酒精暴露的流行程度，然后将这种方法应用于一个新的 研究对疾病负担的潜在影响以及在以下情况下可能发生的相应经济成本： 在不同的亚组中实现了酒精使用的适度减少（例如，基于饮酒 频率和人口统计学）。因此，这项研究将产生有价值的方法进步和新的 证据，将更好地告知如何最好地采取公共卫生战略，以减少负担和成本， 与饮酒有关的疾病。

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.