Race and sex disparities in prehospital recognition of acute stroke

Acad Emerg Med. 2015 Mar;22(3):264-72. doi: 10.1111/acem.12595. Epub 2015 Feb 25.

Abstract

Objectives: The objective of this study was to examine prehospital provider recognition of stroke by race and sex.

Methods: Diagnoses at emergency department (ED) and hospital discharge from a statewide database in California were linked to prehospital diagnoses from an electronic database from two counties in Northern California from January 2005 to December 2007 using probabilistic linkage. All patients 18 years and older, transported by ambulances (n = 309,866) within the two counties, and patients with hospital-based discharge diagnoses of stroke (n = 10,719) were included in the study. Logistic regression was used to analyze the independent association of race and sex with the correct prehospital diagnosis of stroke.

Results: There were 10,719 patients discharged with primary diagnoses of stroke. Of those, 3,787 (35%) were transported by emergency medical services providers. Overall, 32% of patients ultimately diagnosed with stroke were identified in the prehospital setting. Correct prehospital recognition of stroke was lower among Hispanic patients (odds ratio [OR] = 0.77, 95% confidence interval [CI] 0.61 to 0.96), Asians (OR = 0.66, 95% CI 0.55 to 0.80), and others (OR = 0.71, 95% CI = 0.53 to 0.94), when compared with non-Hispanic whites, and in women compared with men (OR = 0.82, 95% CI = 0.71 to 0.94). Specificity for recognizing stroke was lower in females than males (OR = 0.84, 95% CI = 0.78 to 0.90).

Conclusions: Significant disparities exist in prehospital stroke recognition.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Age Factors
  • Aged
  • Aged, 80 and over
  • Ambulances / statistics & numerical data*
  • California / epidemiology
  • Cross-Sectional Studies
  • Female
  • Healthcare Disparities / statistics & numerical data*
  • Hispanic or Latino
  • Humans
  • Insurance, Health
  • Logistic Models
  • Male
  • Middle Aged
  • Odds Ratio
  • Racial Groups / statistics & numerical data*
  • Retrospective Studies
  • Sensitivity and Specificity
  • Sex Factors
  • Stroke / diagnosis*
  • White People