Evidence map›Paper›PMID 36962752›Full record

ArticlePLOS global public health2022

The evolving roles of US political partisanship and social vulnerability in the COVID-19 pandemic from February 2020-February 2021.

Justin Kaashoek, Christian Testa, Jarvis T Chen, Lucas M Stolerman, Nancy Krieger, William P Hanage, Mauricio Santillana

Open access · goldFull text read
In one paragraph

Article in PLOS global public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.7field-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

14 citing papers in PubMed, 21 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Characterizing population-level changes in human behavior during the COVID-19 pandemic in the United States.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  6. Article
  7. Article
  8. Observational
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
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

7 authors at 3 institutions in 1 country.

Justin KaashoekHarvard College, Cambridge, Massachusetts, United States of America.ORCID https://orcid.org/0000-0002-9049-9698
Christian TestaDepartment of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.ORCID https://orcid.org/0000-0001-9103-5839
Jarvis T ChenDepartment of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
Lucas M StolermanMachine Intelligence Group for the Betterment of Health and the Environment, Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America.ORCID https://orcid.org/0000-0001-6313-0126
Nancy KriegerDepartment of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.ORCID https://orcid.org/0000-0002-4815-5947
William P HanageDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
Mauricio SantillanaMachine Intelligence Group for the Betterment of Health and the Environment, Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America.ORCID https://orcid.org/0000-0002-4206-418X
Harvard University · USNortheastern University · USOklahoma State University · US

Funding

Development of an Open-Source and Data-Driven Modeling Platform to Monitor and Forecast Disease ActivityR01GM130668 · NIGMS · NORTHEASTERN UNIVERSITY · PI SANTILLANA, MAURICIO · 2018 to 2022
$1.9M
NIGMS NIH HHS R01 GM130668
6 · The paper itself

Abstract

The COVID-19 pandemic has had intense, heterogeneous impacts on different communities and geographies in the United States. We explore county-level associations between COVID-19 attributed deaths and social, demographic, vulnerability, and political variables to develop a better understanding of the evolving roles these variables have played in relation to mortality. We focus on the role of political variables, as captured by support for either the Republican or Democratic presidential candidates in the 2020 elections and the stringency of state-wide governor mandates, during three non-overlapping time periods between February 2020 and February 2021. We find that during the first three months of the pandemic, Democratic-leaning and internationally-connected urban counties were affected. During subsequent months (between May and September 2020), Republican counties with high percentages of Hispanic and Black populations were most hardly hit. In the third time period -between October 2020 and February 2021- we find that Republican-leaning counties with loose mask mandates experienced up to 3 times higher death rates than Democratic-leaning counties, even after controlling for multiple social vulnerability factors. Some of these deaths could perhaps have been avoided given that the effectiveness of non-pharmaceutical interventions in preventing uncontrolled disease transmission, such as social distancing and wearing masks indoors, had been well-established at this point in time.

Identifiers

PMID36962752
PMCPMC10021880
OpenAlexW4311621852

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read11
identifiers read5
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.