Evidence map›Paper›PMID 27584875›Full record

ArticlePreventing chronic disease2016

Chronic Disease Disparities by County Economic Status and Metropolitan Classification, Behavioral Risk Factor Surveillance System, 2013.

Kate M Shaw, Kristina A Theis, Shannon Self-Brown, Douglas W Roblin, Lawrence Barker

Abstract read
In one paragraph

Article in Preventing chronic disease, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers.

0numbers the graph read from it
0cells of the map it votes in
69citing papers in PubMed
–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

69 citing papers in PubMed.

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  11. The Dynamic Impact of Community Social Vulnerability on COVID-19 Outcomes: A Census Tract-level Analysis of Fulton County, Georgia.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2025
    Article
  12. Article
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  14. Telehealth for Chronic Disease Management Among Vulnerable Populations.Journal of racial and ethnic health disparities · 2024
    Article
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  16. Socioeconomic Determinants of Remote Patient Monitoring Implementation Among Rural and Urban Hospitals.Telemedicine journal and e-health : the official journal of the American Telemedicine Association · 2023
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9 more citing papers are in PubMed but not listed here.

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

5 authors.

Kate M ShawDivision of Global Migration and Quarantine, Centers for Disease Control and Prevention, 1600 Clifton Rd, MS E28, Chamblee, GA 30329-4027. Email: kmshaw@cdc.gov. Dr Shaw is also affiliated with Georgia State University School of Public Health, Atlanta, Georgia.
Kristina A TheisCenters for Disease Control and Prevention, Atlanta, Georgia.
Shannon Self-BrownGeorgia State University School of Public Health, Atlanta, Georgia.
Douglas W RoblinGeorgia State University School of Public Health, Atlanta, Georgia.
Lawrence BarkerCenters for Disease Control and Prevention, Atlanta, Georgia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionRacial/ethnic disparities have been studied extensively. However, the combined influence of geographic location and economic status on specific health outcomes is less well studied. This study's objective was to examine 1) the disparity in chronic disease prevalence in the United States by county economic status and metropolitan classification and 2) the social gradient by economic status. The association of hypertension, arthritis, and poor health with county economic status was also explored.

methodsWe used 2013 Behavioral Risk Factor Surveillance System data. County economic status was categorized by using data on unemployment, poverty, and per capita market income. While controlling for sociodemographics and other covariates, we used multivariable logistic regression to evaluate the relationship between economic status and hypertension, arthritis, and self-rated health.

resultsPrevalence of hypertension, arthritis, and poor health in the poorest counties was 9%, 13%, and 15% higher, respectively, than in the most affluent counties. After we controlled for covariates, poor counties still had a higher prevalence of the studied conditions.

conclusionWe found that residents of poor counties had a higher prevalence of poor health outcomes than affluent counties, even after we controlled for known risk factors. Further, the prevalence of poor health outcomes decreased as county economics improved. Findings suggest that poor counties would benefit from targeted public health interventions, better access to health care services, and improved food and built environments.

Indexed as

Behavioral Risk Factor Surveillance SystemHealth Status DisparitiesPoverty AreasAdolescentAdultAgedAge DistributionArthritisChronic DiseaseFemaleHealth BehaviorHumansHypertensionLogistic ModelsMaleMiddle Aged

Identifiers

PMID27584875
PMCPMC5008860

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.