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

由SARS-CoV-2引起的2019冠状病毒病（COVID-19）是一种全球性大流行病。虽然大多数COVID-19 病例为轻度或中度，重度病例（约15%）需要住院治疗，危重病例（约5%）需要强化治疗 护理，许多死亡发生。男性、黑人和西班牙裔感染COVID-19的风险更大， 通过年龄、种族/民族和先前的潜在医学状况来预测预后。一个潜在的关键 尚未研究的因素是重度酒精使用或酒精使用障碍（Au/AUD）。Au/AUD可能增加风险 对于COVID-19感染和预后不良，通过不良的健康行为，通过酒精对 免疫系统，或间接影响，由于更普遍的潜在的医疗条件，预测 COVID-19预后不良。关于Au/AUD与COVID-19可能性的关系知之甚少 疫苗接种、感染或预后不良，并且如果这些关系被医学状况改变（例如， 高血压、肥胖症、糖尿病），空间定义的社会环境或暴露变量（例如，县 贫困或COVID-19发病率）或人口统计学特征（性别、年龄、种族/民族、贫困）。为了研究这个， 需要包括Au/AUD、人口统计学特征、空间标识符、诊断 治疗和死亡率信息。响应PA-20-195（并解决NOT-AA-20-011中的问题），我们 将利用退伍军人管理局（VA）的电子病历（EMR）系统为此目的。退伍军人管理局 每年治疗630万退伍军人VA患者有很高的COVID-19易感因素发生率，例如，男性，年龄较大 年龄慢性病VA共享数据资源识别COVID-19病例（现在N= 186，174， 9，299人死亡）。许多ICD-10-CM AUD或酒精筛查阳性（AUDIT-C）的VA患者提供了 关于COVID-19结果的可能性是否因Au/AUD状态而异的广泛数据。利用研究 我们建议进行一项为期两年的研究， Au/AUD与COVID-19疫苗接种、感染和预后的关系，以及这些关系如何 受人口统计学、医学、空间暴露特征的影响。目的1：确定Au/AUD的关系 COVID-19疫苗接种，感染，以及在感染者中，预后不良（例如，住院，ICU治疗， 死亡）。目的2：确定Au/AUD与COVID-19结局的相关性是否随时间、医疗条件 (e.g.,高血压、肥胖症、糖尿病）、空间暴露或人口统计学特征。01年，我们将 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，以确定 Au/AUD和COVID-19结局变量之前的医疗状况。2002年，我们将 国家死亡指数数据，以检查死亡原因，并扩大有关退伍军人的信息≥ 65岁， 医疗保险数据。Logistic回归将根据Au/AUD状态评估COVID-19结局的差异。之间 对于COVID-19患者，生存模型将确定至不良预后事件的时间是否相差Au/AUD。结果 将填补Au/AUD患者对COVID-19风险和预后知识的主要空白。

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.