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

. 2020 Oct 1;3(10):e2025197. doi: 10.1001/jamanetworkopen.2020.25197. Characteristics Associated With Racial/Ethnic Disparities in COVID-19 Outcomes in an Academic Health Care System

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

Tian Gu et al. JAMA Netw Open. 2020.

. 2020 Oct 1;3(10):e2025197. doi: 10.1001/jamanetworkopen.2020.25197. Authors Tian Gu  1 Jasmine A Mack  1 Maxwell Salvatore  1 Swaraaj Prabhu Sankar  2   3 Thomas S Valley  4   5 Karandeep Singh  5   6 Brahmajee K Nallamothu  7 Sachin Kheterpal  5   8 Lynda Lisabeth  9 Lars G Fritsche  1   2   10 Bhramar Mukherjee  1   2   9 Affiliations

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Erratum in Abstract

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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Conflict of interest statement

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.

Figures

Figure 1.. Coronavirus Disease 2019 Outcomes by…

Figure 1.. Coronavirus Disease 2019 Outcomes by Race/Ethnicity

Abbreviations: ICU, intensive care unit; OR, odds…

Figure 1.. Coronavirus Disease 2019 Outcomes by Race/Ethnicity

Abbreviations: ICU, intensive care unit; OR, odds ratio. aχ2 test P < .001, comparing the proportion between White and Black patients. bLogistic regression with Firth correction. cMultivariable logistic regression with adjustment 1 (ie, age, sex, race/ethnicity; having test results positive for coronavirus disease 2019 also adjusted for population density). dMultivariable logistic regression with adjustment 2 (adjustment 1 + Neighborhood Disadvantage Index). eMultivariable logistic regression with adjustment 3 (adjustment 2 + comorbidity score).

Figure 2.. Coronavirus Disease 2019 Susceptibility White…

Figure 2.. Coronavirus Disease 2019 Susceptibility White and Black Patients

The results were from model…

Figure 2.. Coronavirus Disease 2019 Susceptibility White and Black Patients

The results were from model logit P(YCOVID = 1|X, Covariate) = β0 + βXX + βRaceRace + βintX × Race + βcovCovariate, in which Covariate = age + sex + NDI (+ comorbidity score in demographic and socioeconomic status models). aReference: age 18 to younger than 35 years. bReference: women. cReference: body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) 18.5 to less than 25. dReference: never smoker. eReference: no alcohol consumption. fPer 1000 persons per square mile. gFrom 2010 census data.

Figure 3.. Coronavirus Disease 2019 Outcomes for…

Figure 3.. Coronavirus Disease 2019 Outcomes for White and Black Patients

The results were from…

Figure 3.. Coronavirus Disease 2019 Outcomes for White and Black Patients

The results were from model logit P(YCOVID = 1|X, Covariate) = β0 + βXX + βRaceRace + βintX × Race + βcovCovariate, in which YCOVID = Yhospitalization (A) or YCOVID = YICU (B) and Covariate = age + sex + NDI (+ comorbidity score in demographic and socioeconomic status models). aReference: age 18 to younger than 35 years. bReference: women. cReference: BMI 18.5 to less than 25. dReference: never smoker. eReference: no alcohol consumption. fPer 1000 persons per square mile. gFrom 2010 census data.

Update of Similar articles Cited by References
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