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Mass Gathering Medicine: A Predictive Model for Patient Presentation and Transport Rates

Published online by Cambridge University Press:  28 June 2012

Paul Arbon
Affiliation:
St. John Ambulance, Australia School of Nursing and Midwifery, University of South Australia, Adelaide, South Australia
Franklin H.G. Bridgewater
Affiliation:
St. John Ambulance, Australia
Colleen Smith
Affiliation:
School of Nursing and Midwifery, University of South Australia, Adelaide, South Australia
Corresponding
E-mail address:

Abstract

Introduction:

This paper reports on research into the influence of environmental factors (including crowd size, temperature, humidity, and venue type) on the number of patients and the patient problems presenting to firstaid services at large, public events in Australia. Regression models were developed to predict rates of patient presentation and of transportation-to-a-hospital for future mass gatherings.

Objective:

To develop a data set and predictive model that can be applied across venues and types of mass gathering events that is not venue or event specific. Data collected will allow informed event planning for future mass gatherings for which health care services are required.

Methods:

Mass gatherings were defined as public events attended by in excess of 25,000 people. Over a period of 12 months, 201 mass gatherings attended by a combined audience in excess of 12 million people were surveyed through-out Australia. The survey was undertaken by St. John Ambulance Australia personnel. The researchers collected data on the incidence and type of patients presenting for treatment and on the environmental factors that may influence these presentations. A standard reporting format and definition of event geography was employed to overcome the event-specific nature of many previous surveys.

Results:

There are 11,956 patients in the sample. The patient presentation rate across all event types was 0.992/1,000 attendees, and the transportation-to-hospital rate was 0.027/1,000 persons in attendance. The rates of patient presentations declined slightly as crowd sizes increased. The weather (particularly the relative humidity) was related positively to an increase in the rates of presentations. Other factors that influenced the number and type of patients presenting were the mobility of the crowd, the availability of alcohol, the event being enclosed by a boundary, and the number of patient-care personnel on duty.

Three regression models were developed to predict presentation rates at future events.

Conclusions:

Several features of the event environment influence patient presentation rates, and that the prediction of patient load at these events is complex and multifactorial. The use of regression modeling and close attention to existing historical data for an event can improve planning and the provision of health care services at mass gatherings.

Type
Special Report
Copyright
Copyright © World Association for Disaster and Emergency Medicine 2001

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References

1. Rose, WD, Laird, SL, Prescott, JE et al. : Emergency medical services for collegiate football games: A six and one half year review. Prehospital and Disaster Medicine 1992;7:157159.CrossRefGoogle Scholar
2. De Lorenzo, RA: Mass gathering medicine: A review. Prehospital and Disaster Medicine 1997;12(1):6872.CrossRefGoogle ScholarPubMed
3. Donegan, D: Mass gathering medicine: A critical review. On–line accessed 15 December 2000: www.emermanconsulting.comGoogle Scholar
4. Sanders, AB, Criss, E, Steckl, P et al. : An analysis of medical care at mass gatherings. Annals of Emergency Medicine 1986;15: 515519.CrossRefGoogle ScholarPubMed
5. Franaszek, J: Medical care at mass gatherings. Annals of Emergency Medicine 1986;15:600601.CrossRefGoogle ScholarPubMed
6. Mitchell, JA, Barbera, MD: Mass gathering medical care: A twenty-five year review. Prehospital and Disaster Medicine 1997;12(4):7279.CrossRefGoogle Scholar
7. Weaver, WD, Sutherland, K, Wirkus, MJ et al. : Emergency medical care requirements for large public assemblies and a new strategy for managing cardiac arrest in this setting. Annals of Emergency Medicine 1989;18:155160.CrossRefGoogle Scholar
8. Leonard, RB, Petrilli, R, Noji, EK et al. : Provision for Emergency Medical Care for Crowds. American College of Emergency Physicians: Dallas 1990, pp 125.Google Scholar
9. Hnatow, DA, Gordon, DJ: Medical planning for mass gatherings: A retrospective review of the San Antonio Papal Mass. Prehospital and Disaster Medicine 1991;6:443450.CrossRefGoogle Scholar
10. Wassertheil, J, Keane, G, Fisher, N, Leditschke, JF: Cardiac arrest outcomes at the Melbourne Cricket Ground and Shrine of Remembrance using a tiered response strategy—A forerunner to public access defibrillation. Resuscitation 2000;44(2):97104.CrossRefGoogle ScholarPubMed
11. Bowdish, GE, Cordell, WH, Bock, HC et al. : Using regression analysis to predict emergency patient volume at the Indianapolis 500 mile race. Annals of Emergency Medicine 1992;21:12001203.CrossRefGoogle ScholarPubMed
12. Flabouris, A, Bridgewater, F: An analysis of demand for first aid care at a major public event. Prehospital and Disaster Medicine 1996;11:4851.CrossRefGoogle Scholar
13. Fulde, GWO, Forster, SL, Preis, Z: Open air rock concert: Organised disaster. Medical Journal of Australia 1992;157:820822.Google ScholarPubMed
14. Richards, R, Richards, D, Whittaker, R: Method of predicting the number of casualties in the Sydney City-to-Surf Fun Runs. Medical Journal of Australia 1984;141:805808.Google ScholarPubMed

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