Alcohol Consumption & Mortality in the US 1950-2000

描述(由申请人提供)：这项研究开发了改进的数据，然后进行了美国、个别州和分成五个地区的州的酒精相关死亡率的时间序列、固定和随机影响分析。一项创新功能是使用更准确地估计特定年份和州的乙醇转换系数，根据报告的葡萄酒、啤酒和烈性酒销售数据估计人均乙醇消费量。时间序列预测指标既包括对50年内人均酒精消费量的改进估计，也包括针对几个目标的详细饮酒模式测量，这些测量来自三个人口调查系列，持续了大约20年。调查数据包括五次全国酒精调查(1979-2000年)、1984年至今具有国家代表性的年度行为危险因素监测系统调查和1985年至2000年全国家庭药物滥用调查。结果衡量标准包括：肝硬变、胰腺炎、其他与酒精有关的癌症、心脏病、所有明确与酒精有关的原因以及精神病、依赖和多发性神经病等子类别、所有外部原因和自杀、凶杀、所有事故和机动车事故以及所有原因死亡率的年龄标准化死亡率和特定死亡率。这项研究将开发和分析美国各州自回归综合移动平均(ARIMA)模型和合并时间序列模型中大多数结果的50年时间序列(几个更具体原因的33年序列)。这项工作的一个独特特点是，它将具有代表性的人口调查数据与汇总数据结合起来，调查饮酒模式与人均消费的影响。模式衡量标准包括当前饮酒率、重度饮酒率(曾经和经常饮用5杯以上的饮料)和消费集中度(例如基尼指数)。一些令人困惑的变量，如州吸烟率将得到控制。一个重要的额外目标是使用基于调查的饮酒措施来表征基于年龄、性别和种族的亚群的消费模式，这些特定的危险行为无法从总消费数据中推断出来，但可以在死亡率统计中确定。考虑到这些亚组中饮酒和死亡率的差异，队列分析应该在估计饮酒如何影响死亡率方面提供更高的精确度，并有望获得关于非裔美国人和西班牙裔人口与酒精相关的死亡率的健康差异和时间进程的新信息。

DESCRIPTION (provided by applicant): This study develops improved data for, and then conducts, a sequence of time-series, fixed and random effects analyses of alcohol-related mortality by cause for the US, individual states, and states grouped into five regions. An innovative feature is the use of more accurately estimated ethanol conversion factors specific to year and state to estimate per capita ethanol consumption from reported wine, beer and spirits sales data. Time series predictors include both these improved estimates of per capita ethanol consumption over a 50-year time span and, for several aims, detailed drinking pattern measures from three population survey series extending over approximately 20 years. Survey data include 5 waves (1979-2000) of the National Alcohol Survey, and the annual state-representative Behavioral Risk Factor Surveillance System surveys from 1984 to present and the National Household Survey on Drug Abuse surveys, 1985 to 2000. Outcome measures include age-standardized and specific mortality rates for liver cirrhosis, pancreatitis, other alcohol related cancers, heart disease (IHD), all explicitly alcohol-related causes and sub-categories such as psychosis, dependence and polyneuropathy, all external causes and the subcategories of suicide, homicide, all accidents and motor vehicle accidents, and all cause mortality. The study will develop and analyze 50-year time series for most outcomes (a 33-year series for several more specific causes) in Auto-Regressive Integrated Moving-Average (ARIMA) models and in pooled time-series models of the US states. A unique feature of this work is that it combines representative population survey data with aggregate data to investigate the effect of drinking patterns in conjunction with per capita consumption. Pattern measures include rates of current drinking, heavy drinking (ever and frequently consuming 5+ drinks per occasion), and concentration of consumption (e.g. the Gini index). A number of confounding variables such as state smoking rates will be controlled. An important additional aim is to use the survey-based drinking measures to characterize consumption patterns of age-, gender-, and ethnicity-based subgroups, specific risk behaviors that cannot be inferred from aggregate consumption data but are identified in mortality statistics. Accounting for variation both in drinking and mortality rates in these subgroup cohort analyses should provide much greater precision in estimating how alcohol consumption influences mortality and is expected to yield new information on health disparities in, and the time course of, alcohol-related mortality for African American and Hispanic populations.