Socioeconomic and Comorbid Factors Affecting Mortality and Length of Stay in COVID-19 Patients.

Background The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic exposed and exacerbated health disparities between socioeconomic groups. Our purpose was to determine if age, sex, race, insurance, and comorbidities predicted patients' length of stay (LOS) in the hospital and in-hospital mortality in patients diagnosed with coronavirus disease 2019 (COVID-19) during the early pandemic. Methods Utilizing retrospective, secondarily sourced electronic health record (EHR) data for patients who tested positive for COVID-19 from HCA Healthcare facilities, predictors of LOS and in-hospital mortality were assessed using regression. LOS and in-hospital mortality were assessed using logistic regression and negative binomial regression, respectively. All models included age, insurance status, and sex, while additional covariates were selected using the least absolute shrinkage and selection operator (LASSO) regression. LOS data were presented as incidence rate ratios (IRR), and in-hospital mortality was presented as odds ratios (OR), followed by their 95% confidence intervals (CI). Results There were 111,849 qualifying patient records from March 1, 2020, to August 23, 2020. After excluding those with missing data (n = 7), without clinically confirmed COVID-19 (n = 27,225), and those from a carceral environment (n = 1,861), there were 84,624 eligible patients. Compared to the population of the United States of America, our COVID-19 cohort had a larger proportion of African American