Patient-specific predictors of ambulance use

Ann Emerg Med. 1997 Apr;29(4):484-91. doi: 10.1016/s0196-0644(97)70221-x.

Abstract

Study objective: To determine patient-specific socioeconomic and health status characteristics for patients arriving by ambulance at an emergency department.

Methods: Ambulance use among adult ED patients presenting with abdominal pain, chest pain, head trauma, or shortness of breath was studied at five urban teaching hospitals in the north-eastern United States. Cross-sectional analysis within a prospective cohort study of 4,979 consecutive patients was performed using an interval sequence subset of 2,315 patients (84% of those eligible) to whom questionnaires were administered. Ambulance use (21% of surveyed patients; 26% of all patients) was analyzed with logistic regression.

Results: Predictors of ambulance use included age greater than 65 years (odds ratio [OR], 1.95; 95% confidence interval [CI], 1.34 to 2.82); clinical severity (OR, 3.11; 95% CI, 2.27 to 4.25); poverty (OR, 1.40; 95% CI, 1.08 to 1.83); physical function (OR, 1.05; 95% CI, 1.02 to 1.09 for each point of worsening function on a 12-point physical function scale); and various types of health insurance coverage. Race, sex, education, Medicaid coverage, frequency of ED use, living arrangements, and primary physician availability were not predictive in multivariate analysis of surveyed patients.

Conclusion: Ambulance use varies by age, clinical severity, income, patient-specific characteristics of physical function, and type of health insurance. Medicaid coverage and frequent ED use are not predictive of increased ambulance use.

Publication types

  • Multicenter Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Acute Disease
  • Adolescent
  • Adult
  • Age Factors
  • Aged
  • Ambulances / statistics & numerical data*
  • Cohort Studies
  • Cross-Sectional Studies
  • Demography
  • Female
  • Health Services Research
  • Health Status Indicators
  • Hospitals, Teaching
  • Humans
  • Insurance, Health
  • Logistic Models
  • Male
  • Middle Aged
  • Multivariate Analysis
  • Predictive Value of Tests
  • Prospective Studies
  • Socioeconomic Factors
  • Transportation of Patients / statistics & numerical data*
  • United States