Evidence map›Paper›PMID 33506402›Full record

ArticleJournal of general internal medicine2021

Impact of Race and Socioeconomic Status on Outcomes in Patients Hospitalized with COVID-19.

Daniel Quan, Lucía Luna Wong, Anita Shallal, Raghav Madan, Abel Hamdan, Heaveen Ahdi, Amir Daneshvar, Manasi Mahajan, Mohamed Nasereldin, Meredith Van Harn and 2 more

Open access · hybridAbstract read
In one paragraph

Article in Journal of general internal medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
69citing papers in PubMed, 1 pooled it
9.0field-weighted citation impact, top 1% of its field
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

69 citing papers in PubMed, 1 synthesis or guideline pooled it, 118 citations in OpenAlex.

  1. Obesity as an independent risk factor for COVID-19 severity and mortality.The Cochrane database of systematic reviews · 2023
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  18. Cluster Analysis reveals Socioeconomic Disparities among Elective Spine Surgery Patients.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024
    Article
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  20. Article

9 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors at 2 institutions in 1 country.

Daniel QuanWayne State University School of Medicine, Detroit, MI, USA.
Lucía Luna WongWayne State University School of Medicine, Detroit, MI, USA.
Anita ShallalDepartment of Infectious Disease, Henry Ford Hospital, Detroit, MI, USA.
Raghav MadanWayne State University School of Medicine, Detroit, MI, USA.
Abel HamdanWayne State University School of Medicine, Detroit, MI, USA.
Heaveen AhdiWayne State University School of Medicine, Detroit, MI, USA.
Amir DaneshvarWayne State University School of Medicine, Detroit, MI, USA.
Manasi MahajanWayne State University School of Medicine, Detroit, MI, USA.
Mohamed NasereldinWayne State University School of Medicine, Detroit, MI, USA.
Meredith Van HarnDepartment of Public Health Sciences, Henry Ford Hospital, Detroit, MI, USA.
Ijeoma Nnodim OparaDepartment of Internal Medicine, Internal Medicine-Pediatrics Section, Wayne State University School of Medicine, Detroit, MI, USA.
Marcus ZervosGlobal Affairs Professor of Medicine, Assistant Dean Wayne State University School of Medicine, MI, Detroit, USA. mzervos1@hfhs.org.
Wayne State University · USHenry Ford Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe impact of race and socioeconomic status on clinical outcomes has not been quantified in patients hospitalized with coronavirus disease 2019 (COVID-19).

objectiveTo evaluate the association between patient sociodemographics and neighborhood disadvantage with frequencies of death, invasive mechanical ventilation (IMV), and intensive care unit (ICU) admission in patients hospitalized with COVID-19.

designRetrospective cohort study.

settingFour hospitals in an integrated health system serving southeast Michigan.

participantsAdult patients admitted to the hospital with a COVID-19 diagnosis confirmed by polymerase chain reaction. MAIN MEASURES: Patient sociodemographics, comorbidities, and clinical outcomes were collected. Neighborhood socioeconomic variables were obtained at the census tract level from the 2018 American Community Survey. Relationships between neighborhood median income and clinical outcomes were evaluated using multivariate logistic regression models, controlling for patient age, sex, race, Charlson Comorbidity Index, obesity, smoking status, and living environment. KEY

resultsBlack patients lived in significantly poorer neighborhoods than White patients (median income: $34,758 (24,531-56,095) vs. $63,317 (49,850-85,776), p < 0.001) and were more likely to have Medicaid insurance (19.4% vs. 11.2%, p < 0.001). Patients from neighborhoods with lower median income were significantly more likely to require IMV (lowest quartile: 25.4%, highest quartile: 16.0%, p < 0.001) and ICU admission (35.2%, 19.9%, p < 0.001). After adjusting for age, sex, race, and comorbidities, higher neighborhood income ($10,000 increase) remained a significant negative predictor for IMV (OR: 0.95 (95% CI 0.91, 0.99), p = 0.02) and ICU admission (OR: 0.92 (95% CI 0.89, 0.96), p < 0.001).

conclusionsNeighborhood disadvantage, which is closely associated with race, is a predictor of poor clinical outcomes in COVID-19. Measures of neighborhood disadvantage should be used to inform policies that aim to reduce COVID-19 disparities in the Black community.

Indexed as

COVID-19COVID-19 TestingAdultHumansMichiganRetrospective StudiesSARS-CoV-2Social ClassUnited StatesCOVID-19disadvantagedisparitiesracesocioeconomic status

Identifiers

PMID33506402
PMCPMC7840076
OpenAlexW3124831727

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.