Evidence map›Paper›PMID 28228449›Full record

ArticleCirculation. Cardiovascular quality and outcomes2017

Neighborhood Differences in Post-Stroke Mortality.

Theresa L Osypuk, Amy Ehntholt, J Robin Moon, Paola Gilsanz, M Maria Glymour

Abstract read
In one paragraph

Article in Circulation. Cardiovascular quality and outcomes, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

  1. Article
  2. Article
  3. Observational
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. 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

5 authors.

Theresa L OsypukFrom the Division of Epidemiology & Community Health, University of Minnesota School of Public Health, Minneapolis (T.L.O.); Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA (A.E., P.G., M.M.G.); SBH Health System, Bronx Partners for Healthy Communities, NY (J.R.M.); and Department of Epidemiology & Biostatistics, University of California, San Francisco (P.G., M.M.G.). tosypuk@umn.edu.
Amy EhntholtFrom the Division of Epidemiology & Community Health, University of Minnesota School of Public Health, Minneapolis (T.L.O.); Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA (A.E., P.G., M.M.G.); SBH Health System, Bronx Partners for Healthy Communities, NY (J.R.M.); and Department of Epidemiology & Biostatistics, University of California, San Francisco (P.G., M.M.G.).
J Robin MoonFrom the Division of Epidemiology & Community Health, University of Minnesota School of Public Health, Minneapolis (T.L.O.); Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA (A.E., P.G., M.M.G.); SBH Health System, Bronx Partners for Healthy Communities, NY (J.R.M.); and Department of Epidemiology & Biostatistics, University of California, San Francisco (P.G., M.M.G.).
Paola GilsanzFrom the Division of Epidemiology & Community Health, University of Minnesota School of Public Health, Minneapolis (T.L.O.); Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA (A.E., P.G., M.M.G.); SBH Health System, Bronx Partners for Healthy Communities, NY (J.R.M.); and Department of Epidemiology & Biostatistics, University of California, San Francisco (P.G., M.M.G.).
M Maria GlymourFrom the Division of Epidemiology & Community Health, University of Minnesota School of Public Health, Minneapolis (T.L.O.); Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA (A.E., P.G., M.M.G.); SBH Health System, Bronx Partners for Healthy Communities, NY (J.R.M.); and Department of Epidemiology & Biostatistics, University of California, San Francisco (P.G., M.M.G.).

Funding

EPIDEMIOLOGY OF FUNCTIONAL STATUS IN ELDERLY HISPANICSR01AG012975 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI HAAN, MARY N · 1997 to 2015
$13.8M
Translational Epidemiology - Training for Research on Aging and Chronic diseaseT32AG049663 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Elizabeth Rose Mayeda, Mark J Pletcher · 2016 to 2026
$5.8M
Minnesota Population Center Science and Technical CoreP2CHD041023 · NICHD · UNIVERSITY OF MINNESOTA · PI THERESA LOUISE OSYPUK · 2016 to 2026
$4.3M
Effects of a housing policy experiment on youth behavioral problemsR01MD006064 · NIMHD · UNIVERSITY OF MINNESOTA · PI OSYPUK, THERESA LOUISE · 2010 to 2012
$1.1M
Effects of a neighborhood experiment on adolescent female psychological distressR21HD066312 · NICHD · NORTHEASTERN UNIVERSITY · PI OSYPUK, THERESA LOUISE · 2010 to 2011
$434k
Functional Impairment from Stroke: an Exploratory Study of DisparitiesR21AG034385 · NIA · HARVARD SCHOOL OF PUBLIC HEALTH · PI GLYMOUR, MEDELLENA MARIA · 2010 to 2011
$352k
Residential trajectories and adolescent health: results from a randomized trialR03HD080848 · NICHD · UNIVERSITY OF MINNESOTA · PI OSYPUK, THERESA LOUISE · 2014 to 2015
$164k
Short and long term depressive symptoms and arrhythmic pathways to strokeF31HL112613 · NHLBI · HARVARD SCHOOL OF PUBLIC HEALTH · PI GILSANZ, PAOLA · 2012 to 2013
$67k
American Heart Association-American Stroke Association 10SDG2640243NHLBI NIH HHS F31 HL112613NIA NIH HHS R01 AG012975NIA NIH HHS R21 AG034385NIA NIH HHS T32 AG049663NICHD NIH HHS P2C HD041023NICHD NIH HHS R03 HD080848NICHD NIH HHS R21 HD066312NIMHD NIH HHS R01 MD006064
6 · The paper itself

Abstract

backgroundPost-stroke mortality is higher among residents of disadvantaged neighborhoods, but it is not known whether neighborhood inequalities are specific to stroke survival or similar to mortality patterns in the general population. We hypothesized that neighborhood disadvantage would predict higher poststroke mortality, and neighborhood effects would be relatively larger for stroke patients than for individuals with no history of stroke. METHODS AND

resultsHealth and Retirement Study participants aged ≥50 years without stroke at baseline (n=15 560) were followed ≤12 years for incident stroke (1715 events over 159 286 person-years) and mortality (5325 deaths). Baseline neighborhood characteristics included objective measures based on census tracts (family income, poverty, deprivation, residential stability, and percent white, black, or foreign-born) and self-reported neighborhood social ties. Using Cox proportional hazard models, we compared neighborhood mortality effects for people with versus people without a history of stroke. Most neighborhood variables predicted mortality for both stroke patients and the general population in demographic-adjusted models. Neighborhood percent white predicted lower mortality for stroke survivors (hazard ratio, 0.75 for neighborhoods in highest 25th percentile versus below, 95% confidence interval, 0.62-0.91) more strongly than for stroke-free adults (hazard ratio, 0.92; 95% confidence interval, 0.83-1.02;

conclusionsNeighborhood characteristics predict mortality, but most effects are similar for individuals without stroke. Eliminating disparities in stroke survival may require addressing pathways that are not specific to traditional poststroke care.

Indexed as

Healthcare DisparitiesPoverty AreasResidence CharacteristicsAgedBlack or African AmericanFemaleHispanic or LatinoHumansIncidenceIncomeMaleMiddle AgedPrognosisProspective StudiesRisk AssessmentRisk Factorscommunitymortalityneighborhoodsocial supportsocioeconomic factorsstroke

Identifiers

PMID28228449
PMCPMC5328121

What OpenQuestion holds

Textmetadata
LicenceTDM
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.