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Characteristics Associated With Racial/Ethnic Disparities in COVID-19 Outcomes in an Academic Health Care System

Educational Objective
To identify the key insights or developments described in this article
1 Credit CME
Key Points

Question  What sociodemographic and underlying health conditions are associated with COVID-19 outcomes and do they differ by race/ethnicity?

Findings  In this cohort study of 5698 patients tested for or diagnosed with COVID-19, high population density, type 2 diabetes, and kidney disease were associated with hospitalization, in addition to older age, male sex, and obesity. Adjusting for covariates, non-Hispanic Black patients were 1.72-fold more likely to be hospitalized than non-Hispanic White patients, while no significant race differences were observed in intensive care unit admission and mortality.

Meaning  These findings suggest that racial disparities existed in COVID-19 outcomes that cannot be explained after controlling for age, sex, socioeconomic status, and comorbidity score; therefore, targeted interventions to support high-risk populations are needed.


Importance  Black patients are overrepresented in the number of COVID-19 infections, hospitalizations, and deaths in the US. Reasons for this disparity may be due to underlying comorbidities or sociodemographic factors that require further exploration.

Objective  To systematically determine patient characteristics associated with racial/ethnic disparities in COVID-19 outcomes.

Design, Setting, and Participants  This retrospective cohort study used comparative groups of patients tested or treated for COVID-19 at the University of Michigan from March 10, 2020, to April 22, 2020, with an outcome update through July 28, 2020. A group of randomly selected untested individuals were included for comparison. Examined factors included race/ethnicity, age, smoking, alcohol consumption, comorbidities, body mass index (BMI; calculated as weight in kilograms divided by height in meters squared), and residential-level socioeconomic characteristics.

Exposure  In-house polymerase chain reaction (PCR) tests, commercial antibody tests, nasopharynx or oropharynx PCR deployed by the Michigan Department of Health and Human Services and reverse transcription–PCR tests performed in external labs.

Main Outcomes and Measures  The main outcomes were being tested for COVID-19, having test results positive for COVID-19 or being diagnosed with COVID-19, being hospitalized for COVID-19, requiring intensive care unit (ICU) admission for COVID-19, and COVID-19–related mortality (including inpatient and outpatient). Medical comorbidities were defined from the International Classification of Diseases, Ninth Revision, and International Classification of Diseases, Tenth Revision, codes and were aggregated into a comorbidity score. Associations with COVID-19 outcomes were examined using odds ratios (ORs).

Results  Of 5698 patients tested for COVID-19 (mean [SD] age, 47.4 [20.9] years; 2167 [38.0%] men; mean [SD] BMI, 30.0 [8.0]), most were non-Hispanic White (3740 patients [65.6%]) or non-Hispanic Black (1058 patients [18.6%]). The comparison group included 7168 individuals who were not tested (mean [SD] age, 43.1 [24.1] years; 3257 [45.4%] men; mean [SD] BMI, 28.5 [7.1]). Among 1139 patients diagnosed with COVID-19, 492 (43.2%) were White and 442 (38.8%) were Black; 523 (45.9%) were hospitalized, 283 (24.7%) were admitted to the ICU, and 88 (7.7%) died. Adjusting for age, sex, socioeconomic status, and comorbidity score, Black patients were more likely to be hospitalized compared with White patients (OR, 1.72 [95% CI, 1.15-2.58]; P = .009). In addition to older age, male sex, and obesity, living in densely populated areas was associated with increased risk of hospitalization (OR, 1.10 [95% CI, 1.01-1.19]; P = .02). In the overall population, higher risk of hospitalization was also observed in patients with preexisting type 2 diabetes (OR, 1.82 [95% CI, 1.25-2.64]; P = .02) and kidney disease (OR, 2.87 [95% CI, 1.87-4.42]; P < .001). Compared with White patients, obesity was associated with higher risk of having test results positive for COVID-19 among Black patients (White: OR, 1.37 [95% CI, 1.01-1.84]; P = .04. Black: OR, 3.11 [95% CI, 1.64-5.90]; P < .001; P for interaction = .02). Having any cancer was associated with higher risk of positive COVID-19 test results for Black patients (OR, 1.82 [95% CI, 1.19-2.78]; P = .005) but not White patients (OR, 1.08 [95% CI, 0.84-1.40]; P = .53; P for interaction = .04). Overall comorbidity burden was associated with higher risk of hospitalization in White patients (OR, 1.30 [95% CI, 1.11-1.53]; P = .001) but not in Black patients (OR, 0.99 [95% CI, 0.83-1.17]; P = .88; P for interaction = .02), as was type 2 diabetes (White: OR, 2.59 [95% CI, 1.49-4.48]; P < .001; Black: OR, 1.17 [95% CI, 0.66-2.06]; P = .59; P for interaction = .046). No statistically significant racial differences were found in ICU admission and mortality based on adjusted analysis.

Conclusions and Relevance  These findings suggest that preexisting type 2 diabetes or kidney diseases and living in high–population density areas were associated with higher risk for COVID-19 hospitalization. Associations of risk factors with COVID-19 outcomes differed by race.

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Article Information

Accepted for Publication: September 13, 2020.

Published: October 21, 2020. doi:10.1001/jamanetworkopen.2020.25197

Correction: This article was corrected on August 16, 2021, to fix errors in Table 2, the legend in Figure 2, and eTables 2, 3, 4, and 6 in the Supplement .

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Gu T et al. JAMA Network Open.

Corresponding Author: Bhramar Mukherjee, PhD, Department of Biostatistics, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109 (bhramar@umich.edu).

Author Contributions: Miss Gu and Dr Fritsche had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Fritsche and Mukherjee are co–senior authors.

Concept and design: Gu, Mack, Salvatore, Kheterpal, Lisabeth, Mukherjee.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Gu, Mack, Salvatore, Prabhu Sankar, Mukherjee.

Critical revision of the manuscript for important intellectual content: Gu, Mack, Salvatore, Valley, Singh, Nallamothu, Kheterpal, Lisabeth, Fritsche, Mukherjee.

Statistical analysis: Gu, Lisabeth, Fritsche, Mukherjee.

Obtained funding: Mukherjee.

Administrative, technical, or material support: Mack, Salvatore, Prabhu Sankar, Kheterpal, Fritsche, Mukherjee.

Supervision: Kheterpal, Mukherjee.

Conflict of Interest Disclosures: Dr Singh reported receiving salary support from Blue Cross Blue Shield of Michigan outside the submitted work. Dr Nallamothu reported serving as a principal investigator or coinvestigator on research grants from the National Institutes of Health (NIH), US Department of Veterans Affairs Health Services Research and Development, and the American Heart Association; receiving personal fees as Editor-in-Chief of Circulation: Cardiovascular Quality & Outcomes; being a co-inventor on a US Utility Patent Number US15/356,012 (US20170148158A1), held by the University of Michigan and licensed to AngioInsight; and holding ownership shares in and receiving consultancy fees from AngioInsight. Dr Lisabeth reported receiving personal fees from University of Michigan during the conduct of the study and grants from the NIH outside the submitted work. No other disclosures were reported.

Funding/Support: This study was funded by the University of Michigan Precision Health Initiative, University of Michigan Rogel Cancer Center, and Michigan Institute of Data Science. Dr Mukherjee’s research was funded by grant No. NSF DMS 1712933 from the National Science Foundation, and Dr Fritsche’s research was supported by grant No. CA 046592 from the National Cancer Institute, National Institutes of Health.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

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