Evidence map›Paper›PMID 37942643›Full record

ArticleStroke2023

Geographic Distribution of Social Service Resources for Stroke Survivors in Ohio Varies by Rurality.

Jeffrey J Wing, Jenna I Rajczyk, James F Burke

Abstract read
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In one paragraph

Article in Stroke, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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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

4 citing papers in PubMed.

  1. Article
  2. Socioeconomic and medical determinants of state-level subjective cognitive decline in the United States.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024
    Article
  3. Article
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Jeffrey J WingDivision of Epidemiology, College of Public Health (J.J.W., J.I.R.), The Ohio State University, Columbus.
Jenna I RajczykDivision of Epidemiology, College of Public Health (J.J.W., J.I.R.), The Ohio State University, Columbus.
James F BurkeDepartment of Neurology, Wexner Medical Center (J.F.B.), The Ohio State University, Columbus.ORCID 0000-0002-2296-1588

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBoth social service resources and stroke prevalence vary by geography, and health care resources are scarcer in rural areas. We assessed whether distributions of resources relevant to stroke survivors were clustered around areas of the highest stroke prevalence in Ohio and whether this is varied by rurality using an ecological study design.

methodsCensus tract (CT)-level self-reported stroke prevalence estimates (Centers for Disease Control and Prevention PLACES-2019 Behavioral Risk Factor Surveillance System) were linked with sociodemographic and rurality data (2019 American Community Survey) and geographic density of resources in Ohio (2020 findhelp data). Resources were grouped into categories: housing, in-home, financial, transportation, education, and therapy. Negative binomial regression models estimated the mean number of resources within 25 miles and 30 minutes of a CT centroid and quartiles of stroke prevalence for each resource group by rurality status (rural, urban, and suburban). Models were sequentially adjusted for total population and CT demographics.

resultsIn Ohio, stroke prevalence was 3.9% (0.4%-14.2%). The highest stroke prevalence quartile (versus lowest) was associated with fewer resources within 25 miles overall (resource ratio [RR], 0.57-0.98). The most pronounced disparities were in rural CT; rural CTs with the highest quartile stroke prevalence had fewer housing (RR, 0.49 [95% CI, 0.32-0.75]), in-home (RR, 0.31 [95% CI, 0.20-0.49]), and therapy (RR, 0.23 [95% CI, 0.13-0.43]) resources compared with those with the lowest quartile stroke prevalence (reference: mean, 1.2 housing, 5.1 in-home, and 4.9 therapy resources, respectively). Rural disparities no longer persisted after adjustment for federal poverty limit (rural: housing [RR, 0.69 (95% CI, 0.40-1.20)], in-home [RR, 0.65 (95% CI, 0.34-1.23)], and therapy [RR, 0.66 (95% CI, 0.33-1.32)]).

conclusionsStroke social service resources are inversely distributed relative to stroke prevalence in Ohio, particularly in rural areas. This inverse link in rural Ohio is likely explained by geographic differences in poverty. Stroke-specific resource-related interventions may be needed and should consider the roles of rurality and poverty.

Indexed as

Models, StatisticalStrokeHumansOhioRural PopulationSocial WorkUrban Populationepidemiologyhealth resourceshealth services researchprevalencestrokeUnited States

Identifiers

PMID37942643

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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.