Critical factors determining access to acute stroke care

Neurology. 1998 Aug;51(2):427-32. doi: 10.1212/wnl.51.2.427.

Abstract

Objective: Our objective was to assess gender, ethnic, and access-to-care factors critical in delay time (DT) for presentation to the hospital for acute stroke.

Background: Little information is available on the effect of gender, ethnicity, and access issues on DT.

Design: Demographic, access-to-care, and DT information was obtained from emergency department (ED) documentation of stroke patients admitted from July 1995 through June 1997 at Hermann Hospital, Houston, TX. Univariate and multivariate regression analyses were performed.

Results: Of the 241 eligible patients, 126 were African American (AA), 82 were non-Hispanic white (NHW), and 33 were Hispanic American (HA). Median DT from symptom onset to presentation to the ED was 222 minutes for AAs, 280 minutes for HAs, and 230 minutes for NHWs. A multivariate regression model estimated DT to ED arrival decreased with ambulance transport (p = 0.003) and increased in patients with a primary care physician (p = 0.145) and in women (p = 0.052). DT to see an ED physician after hospital arrival decreased with ambulance transport (p < 0.001), hemorrhage patients (p = 0.006), and worse stroke severity (p = 0.038), and increased in women (p = 0.041). DT to see a neurologist decreased with hemorrhage (p = 0.002) and ambulance arrival (p = 0.010). Neurologists saw patients within 3 hours of symptom onset in 34% of NHWs, 28% of AAs, and 18% of HAs.

Conclusion: Gender and access-to-care issues may be important determinants of delay in acute stroke care. Less than 20% of HAs presented to the ED within 3 hours of symptom onset.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Acute Disease
  • Aged
  • Aged, 80 and over
  • Cerebrovascular Disorders / therapy*
  • Critical Care*
  • Emergency Medical Services*
  • Ethnicity*
  • Female
  • Health Services Accessibility*
  • Humans
  • Logistic Models
  • Male
  • Middle Aged
  • Regression Analysis
  • Retrospective Studies
  • Risk Factors
  • Sex Distribution
  • Software
  • Time Factors