Evidence map›Paper›PMID 32875535›Full record

ArticleJournal of racial and ethnic health disparities2021

Racial, Economic, and Health Inequality and COVID-19 Infection in the United States.

Vida Abedi, Oluwaseyi Olulana, Venkatesh Avula, Durgesh Chaudhary, Ayesha Khan, Shima Shahjouei, Jiang Li, Ramin Zand

Abstract read
In one paragraph

Article in Journal of racial and ethnic health disparities, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 367 papers.

0numbers the graph read from it
0cells of the map it votes in
367citing 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

367 citing papers in PubMed.

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  20. Journaling During a Disaster: Challenges and Opportunities for Data Collection.Disaster medicine and public health preparedness · 2025
    Article

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

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Vida Abedi *Department of Molecular and Functional Genomics, Weis Center for Research, Geisinger Health System, Danville, PA, USA.
Oluwaseyi Olulana *Geisinger Commonwealth School of Medicine, Scranton, PA, USA.
Venkatesh AvulaDepartment of Molecular and Functional Genomics, Weis Center for Research, Geisinger Health System, Danville, PA, USA.
Durgesh ChaudharyGeisinger Neuroscience Institute, Geisinger Health System, Danville, PA, USA.
Ayesha KhanGeisinger Neuroscience Institute, Geisinger Health System, Danville, PA, USA.
Shima ShahjoueiGeisinger Neuroscience Institute, Geisinger Health System, Danville, PA, USA.
Jiang LiDepartment of Molecular and Functional Genomics, Weis Center for Research, Geisinger Health System, Danville, PA, USA.
Ramin ZandGeisinger Neuroscience Institute, Geisinger Health System, Danville, PA, USA. rzand@geisinger.edu.ORCID http://orcid.org/0000-0002-9477-0094

Funding

Electronic Health Record Data and Predictive Analytic Methods for HFR56HL116832 · NHLBI · CALIFORNIA PACIFIC MED CTR RES INSTITUTE · PI YAN, XIAOWEI SHERRY · 2018 to 2018
$761k
NHLBI NIH HHS R56 HL116832NHLBI NIH HHS R56HL116832
6 · The paper itself

Abstract

objectivesThere is preliminary evidence of racial and social economic disparities in the population infected by and dying from COVID-19. The goal of this study is to report the associations of COVID-19 with respect to race, health, and economic inequality in the United States.

methodsWe performed an ecological study of the associations between infection and mortality rate of COVID-19 and demographic, socioeconomic, and mobility variables from 369 counties (total population, 102,178,117 [median, 73,447; IQR, 30,761-256,098]) from the seven most affected states (Michigan, New York, New Jersey, Pennsylvania, California, Louisiana, Massachusetts).

resultsThe risk factors for infection and mortality are different. Our analysis shows that counties with more diverse demographics, higher population, education, income levels, and lower disability rates were at a higher risk of COVID-19 infection. However, counties with higher proportion with disability and poverty rates had a higher death rate. African Americans were more vulnerable to COVID-19 than other ethnic groups (1981 African American infected cases versus 658 Whites per million). Data on mobility changes corroborate the impact of social distancing.

conclusionOur study provides evidence of racial, economic, and health inequality in the population infected by and dying from COVID-19. These observations might be due to the workforce of essential services, poverty, and access to care. Counties in more urban areas are probably better equipped at providing care. The lower rate of infection, but a higher death rate in counties with higher poverty and disability could be due to lower levels of mobility, but a higher rate of comorbidities and health care access.

Indexed as

Health Status DisparitiesCOVID-19FemaleHumansMaleRacial GroupsRisk FactorsSocioeconomic FactorsUnited StatesCOVID-19Ecological-based studyEconomic inequalityHealthcare disparitiesHealth status disparitiesPopulation-based analysisRacial disparitySocioeconomic factorsUnited States

Identifiers

PMID32875535
PMCPMC7462354

What OpenQuestion holds

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Registered trials

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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.