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 C...