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Forecasting Medical Work at Mass-Gathering Events: Predictive Model Versus Retrospective Review

Published online by Cambridge University Press:  28 June 2012

Kathryn M. Zeitz
Affiliation:
St John Ambulance Australia SA Inc, South Australia
Chris J. Zeitz
Affiliation:
St John Ambulance Australia SA Inc, South Australia University of Adelaide, Adelaide, Australia
Paul Arbon
Affiliation:
St John Ambulance Australia SA Inc, South Australia University of Canberra, Canberra, Australia
Corresponding
E-mail address:

Abstract

Introduction:

Mass-gathering events are dynamic and challenge traditional medical management systems. To improve the system for the provision of first aid at mass-gathering events, an evaluation of two models that assist in forecasting the number of patients presenting for first-aid services was conducted.

Method:

A prospective evaluation of a recurrent, mass-gathering event was undertaken comparing predicted patient presentations and ambulance transfers generated by a predictive model developed by Arbon et al and a retrospective review of seven years of historical, event data as described by Zeitz et al.

Results:

Patient presentation rate (per 1,000 patrons) for this event was 1.6 and the transport to hospital rate (per 1,000 patrons) was 0.07. The retrospective review closely predicted the actual overall attendance.Both methods forecast the number of patients presenting on a daily basis. The prediction proved to be more accurate, on a day-by-day basis, using the Zeitz method.

Conclusion:

The Arbon method is particularly useful for events where there is no or limited information about previous medical work. Retrospective review of data generated from specific events (Zeitz method) considers the unique and individual variability that can occur from event to event and is more accurate at predicting patient presentations when the data are available. Both methods have the potential to be used more frequently to adequately and efficiently plan for the resources required for specific events.

Type
Original Research
Copyright
Copyright © World Association for Disaster and Emergency Medicine 2005

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References

1.Parrillo, SJP: EMS and Mass Gatherings. In: eMedicine Instant Access to the Minds of Medicine, September 2004. Available at www.emedicine.com/emerg/topic812.htm. Accessed 20 October 2004.Google Scholar
2.Arbon, P: The development of a web-based algorithm for the predication of patient presentation rates at mass gatherings. Australian Journal of Emergency Management 2002;17(1):6064.Google Scholar
3.Arbon, P, Bridgewater, HG, Smith, C: Mass gathering medicine: A predictive model for patient presentation and transport rates. Prehosp Disast Med 2001;16(3):109116.CrossRefGoogle ScholarPubMed
4.Zeitz, KM, Zeitz, CJ, Schneider, D, Barret, D: Mass gathering events: Retrospective analysis of patient presentations over seven years. Prehosp Disast Med 2002;17(3):147150.CrossRefGoogle ScholarPubMed
5.Flabouris, A, Bridgewater, HG: An analysis of demand for first-aid care at a major public event. Prehosp Disast Med 1996;11(1):4854.CrossRefGoogle Scholar
6.Milsten, AM, Maguire, BJ, Bissel, RA, et al. : Mass-gathering medical care: A review of the literature. Prehosp Disast Med 2002;17(3):151162.CrossRefGoogle ScholarPubMed
7.Zeitz, KM, Zeitz, CJ: A review of work place injuries occurring at the Royal Adelaide Showgrounds during the 2002 Royal Adelaide Show. Workcover Corporation Grants Scheme Report, St John Adelaide. 2002.Google Scholar
8.Milsten, AM, Seaman, KG, Liu, P, et al. : Variables influencing medical usage rates, injury patterns, and levels of care for mass gatherings. Prehosp Disast Med 2003;18(4):334346.CrossRefGoogle ScholarPubMed
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