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

COVID-19、酗酒和酒精使用障碍：一项针对退伍军人管理局患者的全国研究

• 批准号: 10596115
• 负责人: DEBORAH S HASIN
• 金额: $ 18.72万
• 依托单位: NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10596115
• 关键词: 2019-nCoV; Acute Respiratory Distress Syndrome; Address; Affect; Age; Alcohol consumption; Alcohols; Back; Behavioral; Big Data; Biological; Black Populations; Black race; COVID-19; COVID-19 prognosis; COVID-19 risk; COVID-19 severity; COVID-19 susceptibility; COVID-19 vaccination; Caring; Cause of Death; Cessation of life; Characteristics; Chronic; Computerized Medical Record; County; Data; Databases; Diabetes Mellitus; Diagnosis; Diagnostic; Disasters; Disease; Ethnic Origin; Event; Exploratory/Developmental Grant; Funding; Future; General Population; Health; Health behavior; Heavy Drinking; Hispanic; Hispanic Populations; Hospitalization; Hypertension; ICD-10-CM; Immune response; Immune system; Impairment; Individual; Infection; Intensive Care; Knowledge; Link; Logistic Regressions; Medical; Medicare; Minority; Misinformation; Modeling; National Institute on Alcohol Abuse and Alcoholism; Obesity; Outcome; Patients; Persons; Pneumonia; Policies; Poverty; Prevalence; Preventive; Process; Prognosis; Provider; Race; Research Infrastructure; Retrospective cohort; Risk; SARS-CoV-2 exposure; SARS-CoV-2 infection; SARS-CoV-2 negative; Scientist; Services; Social Environment; Statistical Data Interpretation; Stress; Testing; Time; United States Department of Veterans Affairs; Vaccinated; Vaccination; Vaccines; Veterans; Virus Diseases; Vital Status; alcohol effect; alcohol screening; alcohol use disorder; coping; cost; data repository; data resource; data sharing; data sharing networks; demographics; design; drinking; electronic medical record system; housing instability; human old age (65+); indexing; infection risk; interest; male; men; mortality; novel coronavirus; pandemic disease; residence; service providers; sex; sociodemographics; vaccine distribution; vaccine hesitancy

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.

由 SARS-CoV-2 引起的 2019 年冠状病毒病 (COVID-19) 是一种全球性流行病，而大多数 COVID-19 都是如此。 病例为轻度或中度，重症病例（~15%）需要住院治疗，重症病例（~5%）需要重症监护 男性、黑人和西班牙裔以及穷人感染 COVID-19 的风险更大。 预后是根据年龄、种族/民族和先前的潜在健康状况来预测的。 尚未研究的因素是大量饮酒或酒精使用障碍（AU/AUD）可能会增加风险。 对于因不良健康行为而导致的 COVID-19 感染和不良预后、酒精对身体的直接影响 免疫系统，或由于预测潜在医疗状况的普遍存在而产生的间接影响 对于 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，以及这些关系是否因医疗状况而改变（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 贫困率或 COVID-19 率）或人口特征（性别、年龄、种族/民族、贫困）。 需要大型数据库，包括 AU/AUD、人口特征、空间标识符、诊断、 响应 PA-20-195（并解决 NOT-AA-20-011 中的问题），我们 为此目的，将利用退伍军人管理局 (VA) 电子病历 (EMR) 系统。 每年治疗 630 万名退伍军人，其中有较高比例的 COVID-19 易感因素，例如男性、老年人。 VA 共享数据资源确定了 COVID-19 病例（目前 N=186,174 例，其中 9,299 例死亡）许多 VA 患者的 ICD-10-CM AUD 或酒精 (AUDIT-C) 筛查呈阳性。 利用研究，了解有关 COVID-19 结果的可能性是否因 AU/AUD 状态而异的大量数据。 针对现有项目中建立的基础设施，我们建议进行为期 2 年的研究，以全面解决 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、医疗、空间暴露特征的影响 目标 1：确定 AU/AUD 的关系。 COVID-19 疫苗接种、感染以及感染者的预后不良（例如住院、ICU 治疗、 目标 2：确定 AU/AUD 与 COVID-19 结果的关联是否随时间、医疗条件而变化 （例如高血压、肥胖、糖尿病）、空间暴露或人口特征。在第一年，我们将。 使用 12 个月的回顾期分析 EMR 诊断、治疗和生命状态死亡数据，以确定 在第 02 年，我们将纳入 AU/AUD 和 COVID-19 结果变量之前的医疗状况。 国家死亡指数数据用于检查死亡原因，并扩展 65 岁以上退伍军人的信息 医疗保险数据。Logistic 回归将按 AU/AUD 状态评估 COVID-19 结果的差异。 对于患有 COVID-19 的患者，生存模型将确定不良预后事件发生的时间是否因 AU/AUD 结果而异。 将填补 AU/AUD 患者对 COVID-19 风险和预后的主要知识空白。