Evidence map›Paper›PMID 33978725›Full record

ArticleJAMA network open2021

Evaluation of Between-County Disparities in Premature Mortality Due to Stroke in the US.

Suhang Song, Gaoting Ma, Michael G Trisolini, Kenneth A Labresh, Sidney C Smith, Yinzi Jin, Zhi-Jie Zheng

Open access · goldAbstract read
In one paragraph

Article in JAMA network open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 18% 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Review
  6. DECODING STROKE DISPARITIES: ZIP CODES, COLOR CODES, AND EPIGENETIC CODES.Transactions of the American Clinical and Climatological Association · 2024
    Article
  7. Article
  8. 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 5 institutions in 2 countries.

Suhang SongTaub Institute for Research in Alzheimer's Disease and the Aging Brain, Columbia University, New York, New York.
Gaoting MaDepartment of Interventional Neuroradiology, Beijing Tiantan Hospital, Affiliated with Capital Medical University, Beijing, China.
Michael G TrisoliniRTI International, Research Triangle Park, North Carolina.
Kenneth A LabreshRTI International, Research Triangle Park, North Carolina.
Sidney C SmithDivision of Cardiology, School of Medicine, University of North Carolina at Chapel Hill.
Yinzi JinDepartment of Global Health, School of Public Health, Peking University, Beijing, China.
Zhi-Jie ZhengDepartment of Global Health, School of Public Health, Peking University, Beijing, China.
Peking University · CNRTI International · USCapital Medical University · CNColumbia University · USUniversity of North Carolina at Chapel Hill · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Identifying the factors associated with premature stroke mortality and measuring between-county disparities may provide insight into how to reduce variations and achieve more equitable health outcomes. Objective: To examine the between-county disparities in premature stroke mortality in the US, investigate county-level factors associated with mortality, and describe differences in mortality disparities by place of death and stroke subtype. Design, Setting, and Participants: This retrospective cross-sectional study linked the mortality and demographic data of US counties from the Centers for Disease Control and Prevention WONDER database to county-level characteristics from multiple databases. The outcome measure was county-level age-adjusted stroke mortality among adults aged 25 to 64 years in 2637 US counties from 1999 to 2018. This study was conducted from April 1, 2019, to October 31, 2020. Generalized linear Poisson regressions were fitted to investigate 4 sets of factors associated with county-level mortality: demographic composition, socioeconomic status, health care and environmental features, and population health. The Theil index score was calculated to assess the mortality disparities. Main Outcomes and Measures: Stroke mortality was measured as the number of deaths attributed to stroke in the data set. Out-of-stroke-unit death was defined as any death occurring in outpatient or emergency departments or at the pretransport location. Five stroke subtypes were included in the analysis. Results: Although mortality did not change substantially from 1999 to 2018 (from 12.62 to 11.81 per 100 000 population), the proportion of deaths occurring out of the stroke unit increased from 23.56% (4328 of 18 369) to 34.57% (6978 of 20 188). A large percentage of stroke of an uncertain cause was reported, with most deaths (55.20%) occurring out of the stroke unit. In the county with the highest premature stroke mortality, the incidence was 20.78 times as high as that in the county with the lowest mortality (65.04 vs 3.13 deaths per 100 000 population). The highest between-county disparities were found for stroke of uncertain cause. For out-of-stroke-unit death, county-level mortality was largely associated with demographic composition (31.6%) and health care and environmental features (25.8%). For in-hospital death, 29.8% of county-level mortality was associated with population health and 28.7% was associated with demographic composition. Conclusions and Relevance: These findings suggest that strategies addressing specific factors that underlie the mortality disparities among US counties, especially for out-of-stroke-unit death and stroke of uncertain cause, may be useful when tailored to the county-level context before implementing interventions for the neediest counties.

Indexed as

Mortality, PrematureAdultCross-Sectional StudiesEnvironmentFemaleHealth Status DisparitiesHospital MortalityHumansMaleMiddle AgedRetrospective StudiesSocioeconomic FactorsStrokeUnited States

Identifiers

PMID33978725
PMCPMC8116984
OpenAlexW3161662662

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.