Evidence map›Paper›PMID 32793920›Full record

ArticlemedRxiv : the preprint server for health sciences2020

COVID-19 outcomes, risk factors and associations by race: a comprehensive analysis using electronic health records data in Michigan Medicine.

Tian Gu, Jasmine A Mack, Maxwell Salvatore, Swaraaj Prabhu Sankar, Thomas S Valley, Karandeep Singh, Brahmajee K Nallamothu, Sachin Kheterpal, Lynda Lisabeth, Lars G Fritsche and 1 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Tian GuDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI.
Jasmine A MackDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI.
Maxwell SalvatoreDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI.
Swaraaj Prabhu SankarRogel Cancer Center, University of Michigan Medicine, Ann Arbor, MI.
Thomas S ValleyDivision of Pulmonary and Critical Care Medicine and Department of Internal Medicine, University of Michigan, Ann Arbor, MI.
Karandeep SinghInstitute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI.
Brahmajee K NallamothuDivision of Cardiovascular Medicine and Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI.
Sachin KheterpalInstitute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI.
Lynda LisabethDepartment of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI.
Lars G FritscheDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI.
Bhramar MukherjeeDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI.

Funding

University of Michigan Training Program in Genomic ScienceT32HG000040 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Sebastian Zoellner · 1995 to 2026
$16.4M
NHGRI NIH HHS T32 HG000040
6 · The paper itself

Abstract

importanceBlacks/African-Americans are overrepresented in the number of COVID-19 infections, hospitalizations and deaths. Reasons for this disparity have not been well-characterized but may be due to underlying comorbidities or sociodemographic factors.

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

designA retrospective cohort study with comparative control groups.

settingPatients tested for COVID-19 at University of Michigan Medicine from March 10, 2020 to April 22, 2020.

participants5,698 tested patients and two sets of comparison groups who were not tested for COVID-19: randomly selected unmatched controls (n = 7,211) and frequency-matched controls by race, age, and sex (n = 13,351). Main Outcomes and Measures: We identified factors associated with testing and testing positive for COVID-19, being hospitalized, requiring intensive care unit (ICU) admission, and mortality (in/out-patient during the time frame). Factors included race/ethnicity, age, smoking, alcohol consumption, healthcare utilization, and residential-level socioeconomic characteristics (SES; i.e., education, unemployment, population density, and poverty rate). Medical comorbidities were defined from the International Classification of Diseases (ICD) codes, and were aggregated into a comorbidity score.

resultsOf 5,698 patients, (median age, 47 years; 38% male; mean BMI, 30.1), the majority were non-Hispanic Whites (NHW, 59.2%) and non-Hispanic Black/African-Americans (NHAA, 17.2%). Among 1,119 diagnosed, there were 41.2% NHW and 37.4% NHAA; 44.8% hospitalized, 20.6% admitted to ICU, and 3.8% died. Adjusting for age, sex, and SES, NHAA were 1.66 times more likely to be hospitalized (95% CI, 1.09-2.52; P=.02), 1.52 times more likely to enter ICU (95% CI, 0.92-2.52; P=.10). In addition to older age, male sex and obesity, high population density neighborhood (OR, 1.27 associated with one SD change [95% CI, 1.20-1.76]; P=.02) was associated with hospitalization. Pre-existing kidney disease led to 2.55 times higher risk of hospitalization (95% CI, 1.62-4.02; P<.001) in the overall population and 11.9 times higher mortality risk in NHAA (95% CI, 2.2-64.7, P=.004). CONCLUSIONS AND RELEVANCE: Pre-existing type II diabetes/kidney diseases and living in high population density areas were associated with high risk for COVID-19 susceptibility and poor prognosis. Association of risk factors with COVID-19 outcomes differed by race. NHAA patients were disproportionately affected by obesity and kidney disease.

Identifiers

PMID32793920
PMCPMC7418735

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.