Evidence map›Paper›PMID 35244703›Full record

ArticleJAMA network open2022

Assessment of Structural Barriers and Racial Group Disparities of COVID-19 Mortality With Spatial Analysis.

Qinyun Lin, Susan Paykin, Dylan Halpern, Aresha Martinez-Cardoso, Marynia Kolak

Erratum issuedAbstract read
In one paragraph

Article in JAMA network open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 36 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 1 pooled it
–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

36 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. COVID-19 Prevention Behaviors and Mistrust Among Black and Latino Public Housing Residents in NYC.Health education & behavior : the official publication of the Society for Public Health Education · 2026
    Trial
  3. Article
  4. Article
  5. Article
  6. Article
  7. Observational
  8. COVID-19 as a Mirror: Reflecting the Pandemic of Racism and the Historical Roots of Health Inequities.International journal of environmental research and public health · 2025
    Article
  9. Article
  10. Review
  11. Social Media and Digital Inequity: Reducing Health Inequities by Closing the Digital Divide.International journal of environmental research and public health · 2024
    Review
  12. Article
  13. Review
  14. Article
  15. Article
  16. Inequality in COVID-19 mortality in Quebec associated with neighbourhood-level vulnerability domains.Canadian journal of public health = Revue canadienne de sante publique · 2024
    Article
  17. Article
  18. Improving Pathways to Care for Ethnic Minority Communities.Advances in experimental medicine and biology · 2024
    Review
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Qinyun LinCenter for Spatial Data Science, The University of Chicago, Chicago.
Susan PaykinCenter for Spatial Data Science, The University of Chicago, Chicago.
Dylan HalpernCenter for Spatial Data Science, The University of Chicago, Chicago.
Aresha Martinez-CardosoDepartment of Public Health Sciences, The University of Chicago, Chicago.
Marynia KolakCenter for Spatial Data Science, The University of Chicago, Chicago.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

importanceAlthough social determinants of health (SDOH) are important factors in health inequities, they have not been explicitly associated with COVID-19 mortality rates across racial and ethnic groups and rural, suburban, and urban contexts.

objectivesTo explore the spatial and racial disparities in county-level COVID-19 mortality rates during the first year of the pandemic. DESIGN, SETTING, AND

participantsThis cross-sectional study analyzed data for all US counties in 50 states and the District of Columbia for the first full year of the COVID-19 pandemic (January 22, 2020, to February 28, 2021). Counties with a high concentration of a single racial and ethnic population and a high level of COVID-19 mortality rate were identified as concentrated longitudinal-impact counties. The SDOH that may be associated with mortality rate across these counties and in urban, suburban, and rural contexts were examined. The 3 largest racial and ethnic groups in the US were selected: Black or African American, Hispanic or Latinx, and non-Hispanic White populations. EXPOSURES: County-level characteristics and community health factors (eg, income inequality, uninsured rate, primary care physicians, preventable hospital stays, severe housing problems rate, and access to broadband internet) associated with COVID-19 mortality. MAIN OUTCOMES AND MEASURES: Data on county-level COVID-19 mortality rates (deaths per 100 000 population) reported by the US Centers for Disease Control and Prevention were analyzed. Four indexes were used to measure multiple dimensions of SDOH: socioeconomic advantage index, limited mobility index, urban core opportunity index, and mixed immigrant cohesion and accessibility index. Spatial regression models were used to examine the associations between SDOH and county-level COVID-19 mortality rate.

resultsOf the 3142 counties included in the study, 531 were identified as concentrated longitudinal-impact counties. Of these counties, 347 (11.0%) had a large Black or African American population compared with other counties, 198 (6.3%) had a large Hispanic or Latinx population compared with other counties, and 33 (1.1%) had a large non-Hispanic White population compared with other counties. A total of 489 254 COVID-19-related deaths were reported. Most concentrated longitudinal-impact counties with a large Black or African American population compared with other counties were spread across urban, suburban, and rural areas and experienced numerous disadvantages, including higher income inequality (297 of 347 [85.6%]) and more preventable hospital stays (281 of 347 [81.0%]). Most concentrated longitudinal-impact counties with a large Hispanic or Latinx population compared with other counties were located in urban areas (114 of 198 [57.6%]), and 130 (65.7%) of these counties had a high percentage of people who lacked health insurance. Most concentrated longitudinal-impact counties with a large non-Hispanic White population compared with other counties were in rural areas (23 of 33 [69.7%]), included a large group of older adults (26 of 33 [78.8%]), and had limited access to quality health care (24 of 33 [72.7%]). In urban areas, the mixed immigrant cohesion and accessibility index was inversely associated with COVID-19 mortality (coefficient [SE], -23.38 [6.06]; P < .001), indicating that mortality rates in urban areas were associated with immigrant communities with traditional family structures, multiple accessibility stressors, and housing overcrowding. Higher COVID-19 mortality rates were also associated with preventable hospital stays in rural areas (coefficient [SE], 0.008 [0.002]; P < .001) and higher socioeconomic status vulnerability in suburban areas (coefficient [SE], -21.60 [3.55]; P < .001). Across all community types, places with limited internet access had higher mortality rates, especially in urban areas (coefficient [SE], 5.83 [0.81]; P < .001). CONCLUSIONS AND RELEVANCE: This cross-sectional study found an association between different SDOH measures and COVID-19 mortality that varied across racial and ethnic groups and community types. Future research is needed that explores the different dimensions and regional patterns of SDOH to address health inequity and guide policies and programs.

Indexed as

Health Status DisparitiesRacial GroupsSpatial AnalysisCOVID-19Cross-Sectional StudiesDistrict of ColumbiaHumansRegression AnalysisSARS-CoV-2Social Determinants of Health

Identifiers

PMID35244703
PMCPMC8897755

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.