Evidence map›Paper›PMID 35442951›Full record

ArticlePloS one2022

Change in unemployment by social vulnerability among United States counties with rapid increases in COVID-19 incidence-July 1-October 31, 2020.

Shichao Tang, Libby Horter, Karin Bosh, Ahmed M Kassem, Emily B Kahn, Jessica N Ricaldi, Leah Zilversmit Pao, Gloria J Kang, Christa-Marie Singleton, Tiebin Liu and 2 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
3.1field-weighted citation impact, top 9% 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

6 citing papers in PubMed, 11 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 1 institution in 1 country.

Shichao TangCOVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.ORCID 0000-0001-5392-3106
Libby HorterCOVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.ORCID 0000-0003-2503-1690
Karin BoshCOVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.
Ahmed M KassemCOVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.ORCID 0000-0003-4053-7841
Emily B KahnCOVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.
Jessica N RicaldiCOVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.ORCID 0000-0003-0330-0554
Leah Zilversmit PaoCOVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.
Gloria J KangCOVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.
Christa-Marie SingletonCOVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.
Tiebin LiuCOVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.
Isabel ThomasCOVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.
Carol Y RaoCOVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.
Centers for Disease Control and Prevention · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveDuring the COVID-19 pandemic, the unemployment rate in the United States peaked at 14.8% in April 2020. We examined patterns in unemployment following this peak in counties with rapid increases in COVID-19 incidence.

methodWe used CDC aggregate county data to identify counties with rapid increases in COVID-19 incidence (rapid riser counties) during July 1-October 31, 2020. We used a linear regression model with fixed effect to calculate the change of unemployment rate difference in these counties, stratified by the county's social vulnerability (an indicator compiled by CDC) in the two months before the rapid riser index month compared to the index month plus one month after the index month.

resultsAmong the 585 (19% of U.S. counties) rapid riser counties identified, the unemployment rate gap between the most and least socially vulnerable counties widened by 0.40 percentage point (p<0.01) after experiencing a rapid rise in COVID-19 incidence. Driving the gap were counties with lower socioeconomic status, with a higher percentage of people in racial and ethnic minority groups, and with limited English proficiency.

conclusionThe widened unemployment gap after COVID-19 incidence rapid rise between the most and least socially vulnerable counties suggests that it may take longer for socially and economically disadvantaged communities to recover. Loss of income and benefits due to unemployment could hinder behaviors that prevent spread of COVID-19 (e.g., seeking healthcare) and could impede response efforts including testing and vaccination. Addressing the social needs within these vulnerable communities could help support public health response measures.

Indexed as

COVID-19EthnicityHumansIncidenceMinority GroupsPandemicsSocial VulnerabilityUnemploymentUnited States

Identifiers

PMID35442951
PMCPMC9020703
OpenAlexW4224297041

What OpenQuestion holds

Textmetadata
LicenceCC0
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.