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Predictors of Demand for Emergency Prehospital Care: An Australian Study

Published online by Cambridge University Press:  28 June 2012

Michele J. Clark*
Affiliation:
The University of Queensland Medical SchoolHerston, Qld, Australia
Jennifer Purdie
Affiliation:
The University of Queensland Medical SchoolHerston, Qld, Australia
Gerry J. FitzGerald
Affiliation:
The University of Queensland Medical SchoolHerston, Qld, Australia
Noelene G. Bischoff
Affiliation:
The University of Queensland Medical SchoolHerston, Qld, Australia
Peter K. O'Rourke
Affiliation:
The University of Queensland Medical SchoolHerston, Qld, Australia
*
Department of Social and Preventive Medicine The University of Queensland Medical School Herston Road, Herston, Qld, Australia 4006. E-mail: M.Clark@spmed.uq.edu.au

Abstract

Introduction:

Determining the predictors of demand for emergency prehospital care can assist ambulance services in undertaking policy and planning activities.

Hypothesis:

Demand for prehospital care can be explained by demographic, health status, and economic determinants.

Methods:

The study used a cross-sectional design to investigate the association of demographic, health status, and insurance factors with the use of prehospital, ambulance care. Core data items including age, gender, marital status, country of origin, triage score, diagnosis, time of presentation, method of arrival, and patient disposition were collected for every patient who pre-sented at the Emergency Department of the study hospital over a four-month period. Ambulance usage was analysed using Poisson regression.

Results:

For the 10,229 patients surveyed, only a small number were triaged as having the highest level of urgent medical need (0.8%), but the majority of these used prehospital emergency medical care (90.2%). Predictors of ambulance use included age >65years (Prevalence Ratio [Prevalence Ratio] = 2.92; 95% confidence interval [ Confidence Interval]: 2.35–3.63), being married or in a defacto relationship (Prevalence Ratio = 0.69; 95% Confidence Interval: 0.60–0.79) or divorced, separated, or widowed (Prevalence Ratio = 0.83; 95% Confidence Interval: 0.70–0.98), triage score level 1 or 2 (Prevalence Ratio = 1.95; 95% Confidence Interval: 1.68–2.28), or triage score level 3 (Prevalence Ratio = 1.54; 95% Confidence Interval: 1.38–1.72), diagnosis involving either mental (Prevalence Ratio = 4.29; 95% Confidence Interval: 1.84–10.01), nervous (Prevalence Ratio = 2.74; 95% Confidence Interval: 1.19–6.31) or trauma (Prevalence Ratio = 2.33; 95% Confidence Interval: 1.03–5.27) conditions, and insurance status (Prevalence Ratio =1.54; 95% Confidence Interval: 1.40–1.71). Ethnicity, gender, and time of day were not associated with usage.

Conclusion:

Demand for ambulance services can be predicted by a number of demographic, medical status, and insurance variables. Age and triage levels are key influences on demand for ambulance services. Ambulance insurance status provides an economic incentive to use ambulance services regardless of the urgency of the medical condition.

Type
Original Research
Copyright
Copyright © World Association for Disaster and Emergency Medicine 1999

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