Evidence map›Paper›PMID 35027022›Full record

ArticleBMC public health2022

Association between county-level risk groups and COVID-19 outcomes in the United States: a socioecological study.

Sadiya S Khan, Amy E Krefman, Megan E McCabe, Lucia C Petito, Xiaoyun Yang, Kiarri N Kershaw, Lindsay R Pool, Norrina B Allen

Abstract read
In one paragraph

Article in BMC public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

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

  1. Pooled it
  2. Machine learning and probabilistic approaches for forecasting infectious disease transmission and cases.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2026
    Article
  3. Trends in long COVID among US adults, 2022-2024.Frontiers in public health · 2026
    Article
  4. Article
  5. Article
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  7. Article
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  10. Social-Economic Backgrounds to US County-Based COVID-19 Deaths: PLS-SEM Analysis.Journal of racial and ethnic health disparities · 2024
    Article
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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

8 authors.

Sadiya S KhanDivision of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, 680 N. Lake Shore Drive Ste. 1400, Chicago, IL, 60611, USA. s-khan-1@northwestern.edu.
Amy E KrefmanDepartment of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Megan E McCabeDepartment of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Lucia C PetitoDepartment of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Xiaoyun YangDepartment of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Kiarri N KershawDepartment of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Lindsay R PoolDepartment of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Norrina B AllenDepartment of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

Funding

The Claude D. Pepper Older Americans Independence Center (OAIC) at Northwestern UniversityP30AG059988 · NIA · NORTHWESTERN UNIVERSITY AT CHICAGO · PI ACKERMANN, RONALD T. · 2020 to 2024
$7.1M
NIA NIH HHS P30 AG059988
6 · The paper itself

Abstract

backgroundGeographic heterogeneity in COVID-19 outcomes in the United States is well-documented and has been linked with factors at the county level, including sociodemographic and health factors. Whether an integrated measure of place-based risk can classify counties at high risk for COVID-19 outcomes is not known.

methodsWe conducted an ecological nationwide analysis of 2,701 US counties from 1/21/20 to 2/17/21. County-level characteristics across multiple domains, including demographic, socioeconomic, healthcare access, physical environment, and health factor prevalence were harmonized and linked from a variety of sources. We performed latent class analysis to identify distinct groups of counties based on multiple sociodemographic, health, and environmental domains and examined the association with COVID-19 cases and deaths per 100,000 population.

resultsAnalysis of 25.9 million COVID-19 cases and 481,238 COVID-19 deaths revealed large between-county differences with widespread geographic dispersion, with the gap in cumulative cases and death rates between counties in the 90

conclusionsCounty-level COVID-19 cases and deaths had substantial disparities with heterogeneous geographic spread across the US. The approach to county-level risk characterization used in this study has the potential to provide novel insights into communicable disease patterns and disparities at the local level.

Indexed as

COVID-19HumansRisk FactorsRural PopulationSARS-CoV-2Social VulnerabilityUnited StatesCommunicable diseasesCounty-level healthCOVID-19Pandemic

Identifiers

PMID35027022
PMCPMC8756413

What OpenQuestion holds

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LicenceCC BY
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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.