Alcohol Consumption & Mortality in the US 1950-2000

描述（由申请人提供）：本研究开发了改进的数据，然后进行了一系列时间序列、固定效应和随机效应分析，按原因对美国、个别州和五个地区的州进行了酒精相关死亡率分析。一个创新的特点是使用更准确地估计乙醇转换系数具体到年和州估计人均乙醇消费量从报告的葡萄酒，啤酒和烈酒销售数据。时间序列预测指标既包括对50年时间跨度内人均乙醇消费量的改进估计，也包括对三次持续约20年的人口调查系列的详细饮酒模式测量。调查数据包括五次全国酒精调查（1979-2000年）、1984年至今的年度州代表性行为风险因素监测系统调查和1985年至2000年的全国药物滥用家庭调查。结果测量包括肝硬化、胰腺炎、其他酒精相关癌症、心脏病（IHD）的年龄标准化和特定死亡率，所有明确与酒精相关的原因和亚类别，如精神病、依赖和多神经病变，所有外部原因和自杀、杀人、所有事故和机动车事故的亚类别，以及所有原因死亡率。该研究将在自回归综合移动平均（ARIMA）模型和美国各州的混合时间序列模型中开发和分析大多数结果的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之处在于，它将有代表性的人口调查数据与汇总数据相结合，以调查饮酒模式与人均消费量的影响。模式测量包括当前饮酒率、重度饮酒率（曾经和经常每次饮酒5杯以上）和消费集中度（如基尼指数）。各州吸烟率等一系列混杂变量将得到控制。另一个重要的目标是使用基于调查的饮酒措施来描述基于年龄、性别和种族的亚群体的消费模式，以及不能从总消费数据推断但在死亡率统计中确定的具体风险行为。在这些亚组队列分析中，考虑到饮酒和死亡率的变化，应该可以更精确地估计酒精消费如何影响死亡率，并有望获得有关非裔美国人和西班牙裔人口酒精相关死亡率的健康差异和时间过程的新信息。

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.