COVID-19, heavy drinking and alcohol use disorders: a national study of Veterans Administration patients

由SARS-CoV-2引起的冠状病毒病2019(新冠肺炎)是一种全球性的大流行。而大多数新冠肺炎 病例为轻度或中度，重症(~15%)需要住院，危重(~5%)需要加强治疗 护理，而且会发生许多死亡。男性、黑人和西班牙裔感染新冠肺炎的风险更高，而穷人 预后是通过年龄、种族/民族和既往潜在的医疗条件来预测的。一个潜在的关键 尚未研究的因素是酗酒或酒精使用障碍(AU/AUD)。澳元/澳元可能会增加风险 对于新冠肺炎感染和预后不良的不良健康行为，受酒精的直接影响。 免疫系统，或由间接影响，由于更普遍的潜在医疗条件，预测 新冠肺炎预后不良。关于澳元/澳元与新冠肺炎发病可能性的关系，我们知之甚少 接种疫苗、感染或预后不良，并且如果这些关系被医疗条件改变(例如， 高血压、肥胖、糖尿病)、空间定义的社会环境或暴露变量(例如，县 贫困或新冠肺炎比率)或人口特征(性别、年龄、种族/族裔、贫困)。为了研究这一点， 需要大型数据库，包括AU/AUD、人口特征、空间识别符、诊断、 治疗和死亡信息。响应PA-20-195(并解决NOT-AA-20-011中的问题)，我们 将使用退伍军人管理局(退伍军人管理局)电子病历(EMR)系统来实现这一目的。退伍军人事务部 每年治疗630万名退伍军人。VA患者具有较高的新冠肺炎易感性因素，例如男性、年龄较大 年龄，慢性疾病。退伍军人事务部共享数据资源识别新冠肺炎案例(现在N=186,174，具有 9,299人死亡)。许多具有ICD-10-CM AUD或酒精阳性(AUD-C)筛查的VA患者提供 关于新冠肺炎结果的可能性是否因非盟/澳元状况而异的大量数据。利用一项研究 基础设施，我们建议进行为期两年的研究，以全面解决 AU/AUD与新冠肺炎接种、感染和预后的关系以及这些关系是如何 受人口统计、医疗、空间暴露特征的影响。目的1：确定AU/AUD的关系 对于新冠肺炎疫苗接种，感染，以及在那些感染的人中，预后较差(例如，住院，ICU治疗， 死亡)。目标2：确定AU/AUD与新冠肺炎预后的关联是否会随着时间、医疗条件的不同而变化 (例如，高血压、肥胖症、糖尿病)、空间暴露或人口特征。在公元01年，我们会 分析EMR诊断、治疗和生命状态死亡数据，使用12个月的回顾期间确定 Au/AUD和新冠肺炎结果变量之前的医疗条件。在公元02年，我们会把 国家死亡指数数据，以检查死因，并扩大关于65岁≥退伍军人的信息 医疗保险数据。Logistic回归将根据AU/AUD状态评估新冠肺炎结果的差异。其中 对于新冠肺炎患者，生存模型将决定预后不良事件发生的时间是否因AU/AUD而异。结果 将填补在AU/AUD患者中对新冠肺炎的风险和预后的主要知识空白。

Coronavirus Disease 2019 (COVID-19) caused by SARS-CoV-2 is a global pandemic. While most COVID-19 cases are mild or moderate, severe cases (~15%) require hospitalization, critical cases (~5%) require intensive care, and many deaths occur. Males, Blacks and Hispanics are at greater risk for COVID-19 infection, and poor prognosis is predicted by older age, race/ethnicity, and prior underlying medical conditions. A potentially critical factor not yet studied is heavy alcohol use or alcohol use disorder (AU/AUD). AU/AUD could increase the risk for COVID-19 infection and poor prognosis through poor health behaviors, by direct effects of alcohol on the immune system, or by indirect effects due to the greater prevalence of underlying medical conditions that predict poor COVID-19 prognosis. Little is known about the relationship of AU/AUD to the likelihood of COVID-19 vaccination, infection, or poor prognosis, and if these relationships are modified by medical conditions (e.g., hypertension, obesity, diabetes), spatially-defined socioenvironmental or exposure variables (e.g., county poverty or COVID-19 rates) or demographic characteristics (sex, age, race/ethnicity, poverty). To study this, large databases are needed that include AU/AUD, demographic characteristics, spatial identifiers, diagnostic, treatment and mortality information. Responding to PA-20-195 (and addressing issues in NOT-AA-20-011), we will utilize the Veterans Administration (VA) Electronic Medical Record (EMR) system for this purpose. The VA treats 6.3 million veterans a year. VA patients have high rates of COVID-19 vulnerability factors, e.g., male, older age, chronic medical conditions. A VA Shared Data Resource identifies COVID-19 cases (now N=186,174, with 9,299 deaths). The many VA patients with ICD-10-CM AUD or positive alcohol (AUDIT-C) screens provide extensive data on whether the likelihood of COVID-19 outcomes differ by AU/AUD status. Leveraging a research infrastructure established in an existing project, we propose a 2-year study to comprehensively address the relationship of AU/AUD to COVID-19 vaccination, infection and prognosis, and how these relationships are affected by demographic, medical, spatial exposure characteristics. Aim1: Determine the relationship of AU/AUD to COVID-19 vaccination, infection, and in those infected, poor prognosis (e.g., hospitalization, ICU treatment, death). Aim 2: Determine if associations of AU/AUD with COVID-19 outcomes vary over time, medical conditions (e.g., hypertension, obesity, diabetes), spatial exposures or demographic characteristics. In Year 01, we will analyze EMR diagnostic, treatment, and vital status death data, using a 12-month lookback period to determine AU/AUD and medical conditions that preceded COVID-19 outcome variables. In Year 02, we will incorporate National Death Index data to examine causes of death, and expand information on veterans ≥age 65 with Medicare data. Logistic regression will evaluate differences in COVID-19 outcomes by AU/AUD status. Among those with COVID-19, survival models will determine if time to poor prognosis events differs by AU/AUD. Results will fill major gaps in knowledge about the risks for and prognosis of COVID-19 among those with AU/AUD.