
Faculty, Staff and Student Publications
Publication Date
5-12-2022
Journal
JMIR Public Health and Surveillance
Abstract
BACKGROUND: Since the initial COVID-19 cases were identified in the United States in February 2020, the United States has experienced a high incidence of the disease. Understanding the risk factors for severe outcomes identifies the most vulnerable populations and helps in decision-making.
OBJECTIVE: This study aims to assess the factors associated with COVID-19-related deaths from a large, national, individual-level data set.
METHODS: A cohort study was conducted using data from the Optum de-identified COVID-19 electronic health record (EHR) data set; 1,271,033 adult participants were observed from February 1, 2020, to August 31, 2020, until their deaths due to COVID-19, deaths due to other reasons, or the end of the study. Cox proportional hazards models were constructed to evaluate the risks for each patient characteristic.
RESULTS: A total of 1,271,033 participants (age: mean 52.6, SD 17.9 years; male: 507,574/1,271,033, 39.93%) were included in the study, and 3315 (0.26%) deaths were attributed to COVID-19. Factors associated with COVID-19-related death included older age (80 vs 50-59 years old: hazard ratio [HR] 13.28, 95% CI 11.46-15.39), male sex (HR 1.68, 95% CI 1.57-1.80), obesity (BMI 40 vs/m
CONCLUSIONS: This is one of the largest national cohort studies in the United States; we identified several patient characteristics associated with COVID-19-related deaths, and the results can serve as the basis for policy making. The study also offered directions for future studies, including the effect of other socioeconomic factors on the increased risk for minority groups.
Keywords
Adult, Black or African American, COVID-19, Cohort Studies, Humans, Male, Middle Aged, SARS-CoV-2, United States, White People
DOI
10.2196/29343
PMID
35377319
PMCID
PMC9132142
PubMedCentral® Posted Date
5-12-2022
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Clinical Epidemiology Commons, Community Health and Preventive Medicine Commons, COVID-19 Commons, Data Science Commons