William J Meurer1, Deborah A Levine2, Kevin A Kerber3, Darin B Zahuranec4, James Burke4, Jonggyu Baek5, Brisa Sánchez6, Melinda A Smith7, Lewis B Morgenstern8, Lynda D Lisabeth9. 1. Department of Emergency Medicine, University of Michigan, Ann Arbor, MI; Department of Neurology, University of Michigan, Ann Arbor, MI; Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI; Institute of Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI; Stroke Program, University of Michigan, Ann Arbor, MI. 2. Institute of Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI; Stroke Program, University of Michigan, Ann Arbor, MI; Department of Internal Medicine, University of Michigan, Ann Arbor, MI. 3. Department of Neurology, University of Michigan, Ann Arbor, MI; Institute of Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI. 4. Department of Neurology, University of Michigan, Ann Arbor, MI; Institute of Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI; Stroke Program, University of Michigan, Ann Arbor, MI. 5. Department of Biostatistics, University of Michigan, Ann Arbor, MI. 6. Institute of Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI; Stroke Program, University of Michigan, Ann Arbor, MI; Department of Biostatistics, University of Michigan, Ann Arbor, MI. 7. Department of Neurology, University of Michigan, Ann Arbor, MI. 8. Department of Emergency Medicine, University of Michigan, Ann Arbor, MI; Department of Neurology, University of Michigan, Ann Arbor, MI; Institute of Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI; Stroke Program, University of Michigan, Ann Arbor, MI; Department of Epidemiology, University of Michigan, Ann Arbor, MI. 9. Department of Neurology, University of Michigan, Ann Arbor, MI; Institute of Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI; Stroke Program, University of Michigan, Ann Arbor, MI; Department of Epidemiology, University of Michigan, Ann Arbor, MI. Electronic address: llisabet@umich.edu.
Abstract
STUDY OBJECTIVE: Delay to hospital arrival limits acute stroke treatment. Use of emergency medical services (EMS) is key in ensuring timely stroke care. We aim to identify neighborhoods with low EMS use and to evaluate whether neighborhood-level factors are associated with EMS use. METHODS: We conducted a secondary analysis of data from the Brain Attack Surveillance in Corpus Christi project, a population-based stroke surveillance study of ischemic stroke and intracerebral hemorrhage cases presenting to emergency departments in Nueces County, TX. The primary outcome was arrival by EMS. The primary exposures were neighborhood resident age, poverty, and violent crime. We estimated the association of neighborhood-level factors with EMS use, using hierarchic logistic regression, controlling for individual factors (stroke severity, ethnicity, and age). RESULTS: During 2000 to 2009 there were 4,004 identified strokes, with EMS use data available for 3,474. Nearly half (49%) of stroke cases arrived by EMS. Adjusted stroke EMS use was lower in neighborhoods with higher family income (odds ratio [OR] 0.86; 95% confidence interval [CI] 0.75 to 0.97) and a larger percentage of older adults (OR 0.70; 95% CI 0.56 to 0.89). Individual factors associated with stroke EMS use included white race (OR 1.41; 95% CI 1.13 to 1.76) and older age (OR 1.36 per 10-year age increment; 95% CI 1.27 to 1.46). The proportion of neighborhood stroke cases arriving by EMS ranged from 17% to 71%. The fully adjusted model explained only 0.3% (95% CI 0% to 1.1%) of neighborhood EMS stroke use variance, indicating that individual factors are more strongly associated with stroke EMS use than neighborhood factors. CONCLUSION: Although some neighborhood-level factors were associated with EMS use, patient-level factors explained nearly all variability in stroke EMS use. In this community, strategies to increase EMS use should target individuals rather than specific neighborhoods.
STUDY OBJECTIVE: Delay to hospital arrival limits acute stroke treatment. Use of emergency medical services (EMS) is key in ensuring timely stroke care. We aim to identify neighborhoods with low EMS use and to evaluate whether neighborhood-level factors are associated with EMS use. METHODS: We conducted a secondary analysis of data from the Brain Attack Surveillance in Corpus Christi project, a population-based stroke surveillance study of ischemic stroke and intracerebral hemorrhage cases presenting to emergency departments in Nueces County, TX. The primary outcome was arrival by EMS. The primary exposures were neighborhood resident age, poverty, and violent crime. We estimated the association of neighborhood-level factors with EMS use, using hierarchic logistic regression, controlling for individual factors (stroke severity, ethnicity, and age). RESULTS: During 2000 to 2009 there were 4,004 identified strokes, with EMS use data available for 3,474. Nearly half (49%) of stroke cases arrived by EMS. Adjusted stroke EMS use was lower in neighborhoods with higher family income (odds ratio [OR] 0.86; 95% confidence interval [CI] 0.75 to 0.97) and a larger percentage of older adults (OR 0.70; 95% CI 0.56 to 0.89). Individual factors associated with stroke EMS use included white race (OR 1.41; 95% CI 1.13 to 1.76) and older age (OR 1.36 per 10-year age increment; 95% CI 1.27 to 1.46). The proportion of neighborhood stroke cases arriving by EMS ranged from 17% to 71%. The fully adjusted model explained only 0.3% (95% CI 0% to 1.1%) of neighborhood EMS stroke use variance, indicating that individual factors are more strongly associated with stroke EMS use than neighborhood factors. CONCLUSION: Although some neighborhood-level factors were associated with EMS use, patient-level factors explained nearly all variability in stroke EMS use. In this community, strategies to increase EMS use should target individuals rather than specific neighborhoods.
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