Evidence map›Paper›PMID 34469440›Full record

ArticlePloS one2021

Racial and ethnic differentials in COVID-19-related job exposures by occupational standing in the US.

Noreen Goldman, Anne R Pebley, Keunbok Lee, Theresa Andrasfay, Boriana Pratt

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 107 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
107citing papers in PubMed, 1 pooled it
14.3field-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

107 citing papers in PubMed, 1 synthesis or guideline pooled it, 181 citations in OpenAlex.

  1. Pooled it
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  6. The Latino health experience: Past and future.Proceedings of the National Academy of Sciences of the United States of America · 2026
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47 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 1 country.

Noreen GoldmanOffice of Population Research, School of Public and International Affairs, Princeton University, Princeton, New Jersey, United States of America.ORCID 0000-0003-2865-9491
Anne R PebleyCalifornia Center for Population Research, Fielding School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America.
Keunbok LeeCalifornia Center for Population Research, University of California Los Angeles, Los Angeles, California, United States of America.ORCID 0000-0002-7359-3674
Theresa AndrasfayLeonard Davis School of Gerontology, University of Southern California, Los Angeles, California, United States of America.ORCID 0000-0003-3885-8718
Boriana PrattOffice of Population Research, Princeton University, Princeton, New Jersey, United States of America.
Princeton University · USUniversity of California, Los Angeles · USUniversity of Southern California · US

Funding

RESEARCH TRAINING IN GERONTOLOGYT32AG000037 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI JENNIFER A AILSHIRE, EILEEN M CRIMMINS · 1985 to 2026
$13.2M
Scientific and Technical CoreP2CHD041022 · NICHD · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI HIRAM BELTRAN-SANCHEZ · 2016 to 2026
$6.9M
Social disparities in physical functioning by race, ethnicity, and immigration statusR01AG061094 · NIA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI PEBLEY, ANNE R · 2019 to 2023
$1.7M
NIA NIH HHS R01 AG061094NIA NIH HHS T32 AG000037NICHD NIH HHS P2C HD041022
6 · The paper itself

Abstract

Researchers and journalists have argued that work-related factors may be partly responsible for disproportionate COVID-19 infection and death rates among vulnerable groups. We evaluate these issues by describing racial and ethnic differences in the likelihood of work-related exposure to COVID-19. We extend previous studies by considering 12 racial and ethnic groups and five types of potential occupational exposure to the virus: exposure to infection, physical proximity to others, face-to-face discussions, interactions with external customers and the public, and working indoors. Most importantly, we stratify our results by occupational standing, defined as the proportion of workers within each occupation with at least some college education. This measure serves as a proxy for whether workplaces and workers employ COVID-19-related risk reduction strategies. We use the 2018 American Community Survey to identify recent workers by occupation, and link 409 occupations to information on work context from the Occupational Information Network to identify potential COVID-related risk factors. We then examine the racial/ethnic distribution of all frontline workers and frontline workers at highest potential risk of COVID-19, by occupational standing and by sex. The results indicate that, contrary to expectation, White frontline workers are often overrepresented in high-risk jobs while Black and Latino frontline workers are generally underrepresented in these jobs. However, disaggregation of the results by occupational standing shows that, in contrast to Whites and several Asian groups, Latino and Black frontline workers are overrepresented in lower standing occupations overall and in lower standing occupations associated with high risk, and thus may be less likely to have adequate COVID-19 protections. Our findings suggest that greater work exposures likely contribute to a higher prevalence of COVID-19 among Latino and Black adults and underscore the need for measures to reduce potential exposure for workers in low standing occupations and for the development of programs outside the workplace.

Indexed as

OccupationsRacial GroupsSARS-CoV-2AdultCOVID-19EthnicityFemaleHumansMaleOccupational ExposureRisk FactorsSocioeconomic FactorsUnited StatesWorkplace

Identifiers

PMID34469440
PMCPMC8409606
OpenAlexW3196893569

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.