Predictors of demand for emergency prehospital care: an Australian study.
BACKGROUND
Determining the predictors of demand for emergency prehospital care can assist ambulance services in undertaking policy and planning activities.


OBJECTIVE
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 presented 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 > 65 years (Prevalence Ratio [PR] = 2.92; 95% confidence interval [CI]: 2.35-3.63), being married or in a de-facto relationship (PR = 0.69; 95% CI: 0.60-0.79) or divorced, separated, or widowed (PR = 0.83; 95% CI: 0.70-0.98), triage score level 1 or 2 (PR = 1.95; 95% CI: 1.68-2.28), or triage score level 3 (PR = 1.54; 95% CI: 1.38-1.72), diagnosis involving either mental (PR = 4.29; 95% CI: 1.84-10.01), nervous (PR = 2.74; 95% CI: 1.19-6.31) or trauma (PR = 2.33; 95% CI: 1.03-5.27) conditions, and insurance status (PR = 1.54; 95% CI: 1.40-1.71). Ethnicity, gender, and time of day were not associated with usage.


CONCLUSIONS
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.
Clark
M J
MJ
Department of Social and Preventive Medicine, University of Queensland Medical School, Herston, Australia. m.clark@spmed.uq.edu.au
Purdie
J
J
FitzGerald
G J
GJ
Bischoff
N G
NG
O'Rourke
P K
PK
D000293:Adolescent; D000328:Adult; D000368:Aged; D000552:Ambulances / Q000706:statistics & numerical data; D002648:Child; D002675:Child, Preschool; D003430:Cross-Sectional Studies; D004632:Emergency Medical Services / Q000706:statistics & numerical data / Q000639:trends*; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data; D005260:Female; D005544:Forecasting / Q000379:methods*; D006301:Health Services Needs and Demand / Q000706:statistics & numerical data / Q000639:trends*; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D008875:Middle Aged; D011446:Prospective Studies; D011793:Queensland