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Comparison of Prediction Models for Use of Medical Resources at Urban Auto-racing Events

Published online by Cambridge University Press:  26 September 2014

Jose V. Nable ,
Asa M. Margolis ,
Benjamin J. Lawner ,
Jon Mark Hirshon ,
Alexander J. Perricone ,
Samuel M. Galvagno ,
Debra Lee ,
Michael G. Millin ,
Richard A. Bissell  and
Richard L. Alcorta
Jose V. Nable*
Affiliation:
Department of Emergency Medicine, University of Maryland School of Medicine, Baltimore, Maryland USA Department of Emergency Health Services, University of Maryland Baltimore County, Baltimore, Maryland USA Department of Emergency Medicine, MedStar Georgetown University Hospital, Georgetown University School of Medicine, Washington D.C. USA
Asa M. Margolis
Affiliation:
Department of Emergency Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland USA
Benjamin J. Lawner
Affiliation:
Department of Emergency Medicine, University of Maryland School of Medicine, Baltimore, Maryland USA Baltimore City Fire Department, Baltimore, Maryland USA
Jon Mark Hirshon
Affiliation:
Department of Emergency Medicine, University of Maryland School of Medicine, Baltimore, Maryland USA Charles McC. Mathias, Jr., National Study Center for Trauma and Emergency Medical Systems, University of Maryland School of Medicine, Baltimore, Maryland USA
Alexander J. Perricone
Affiliation:
Baltimore City Fire Department, Baltimore, Maryland USA
Samuel M. Galvagno
Affiliation:
Divisions of Critical Care and Trauma Anesthesiology, Department of Anesthesiology, University of Maryland School of Medicine, Baltimore, Maryland USA
Debra Lee
Affiliation:
Department of Emergency Medicine, University of Maryland School of Medicine, Baltimore, Maryland USA
Michael G. Millin
Affiliation:
Department of Emergency Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland USA
Richard A. Bissell
Affiliation:
Department of Emergency Health Services, University of Maryland Baltimore County, Baltimore, Maryland USA
Richard L. Alcorta
Affiliation:
Maryland Institute for Emergency Medical Services Systems, Baltimore, Maryland USA
*
Correspondence:Jose V. Nable, MD, NRP Department of Emergency Medicine University of Maryland School of Medicine 110 South Paca Street 6th Floor, Suite 200 Baltimore, Maryland 21201 USA E-mail [email protected]
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Abstract

Introduction

Predicting the number of patient encounters and transports during mass gatherings can be challenging. The nature of these events necessitates that proper resources are available to meet the needs that arise. Several prediction models to assist event planners in forecasting medical utilization have been proposed in the literature.

Hypothesis/Problem

The objective of this study was to determine the accuracy of the Arbon and Hartman models in predicting the number of patient encounters and transportations from the Baltimore Grand Prix (BGP), held in 2011 and 2012. It was hypothesized that the Arbon method, which utilizes regression model-derived equations to estimate, would be more accurate than the Hartman model, which categorizes events into only three discreet severity types.

Methods

This retrospective analysis of the BGP utilized data collected from an electronic patient tracker system. The actual number of patients evaluated and transported at the BGP was tabulated and compared to the numbers predicted by the two studied models. Several environmental features including weather, crowd attendance, and presence of alcohol were used in the Arbon and Hartman models.

Results

Approximately 130,000 spectators attended the first event, and approximately 131,000 attended the second. The number of patient encounters per day ranged from 19 to 57 in 2011, and the number of transports from the scene ranged from two to nine. In 2012, the number of patients ranged from 19 to 44 per day, and the number of transports to emergency departments ranged from four to nine. With the exception of one day in 2011, the Arbon model overpredicted the number of encounters. For both events, the Hartman model overpredicted the number of patient encounters. In regard to hospital transports, the Arbon model underpredicted the actual numbers whereas the Hartman model both overpredicted and underpredicted the number of transports from both events, varying by day.

Conclusions

These findings call attention to the need for the development of a versatile and accurate model that can more accurately predict the number of patient encounters and transports associated with mass-gathering events so that medical needs can be anticipated and sufficient resources can be provided.

NableJV , MargolisAM , LawnerBJ , HirshonJM , PerriconeAJ , GalvagnoSM , LeeD , MillinMG , BissellRA , AlcortaRL . Comparison of Prediction Models for Use of Medical Resources at Urban Auto-racing Events. Prehosp Disaster Med. 2014;29(6):1-6.

Keywords

auto-racingdisaster planningEmergency Medical Servicesmass-gathering events
Type
Original Research
Information
Prehospital and Disaster Medicine , Volume 29 , Issue 6 , December 2014 , pp. 608 - 613
Copyright
Copyright © World Association for Disaster and Emergency Medicine 2014 

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References

1. DeLorenzo, RA. Mass gathering medicine: a review. Prehosp Disaster Med. 1997;12(1):68-72.Google Scholar
2. Martin-Gill, C, Brady, WJ, Barlotta, K, et al. Hospital-based healthcare provider (nurse and physician) integration into an emergency medical services-managed mass-gathering event. Am J Emerg Med. 2007;25(1):15-22.Google Scholar
3. Milsten, AM, Maguire, BJ, Bissell, RA, Seaman, KG. Mass-gathering medical care: a review of the literature. Prehosp Disaster Med. 2002;17(3):151-162.Google Scholar
4. Hartman, N, Williamson, A, Sojka, B, et al. Predicting resource use at mass gatherings using a simplified stratification scoring model. Am J Emerg Med. 2009;27(3):337-343.Google Scholar
5. Varon, J, Fromm, RE, Chanin, K, Filbin, M, Vutpakdi, K. Critical illness at mass gathering is uncommon. J Emerg Med. 2003;25(4):409-413.Google Scholar
6. Moore, R, Williamson, K, Sochor, M, et al. Large-event medicine-event characteristics impacting medical need. Am J Emerg Med. 2011;29(9):1217-1221.CrossRefGoogle ScholarPubMed
7. Meites, E, Brown, JF. Ambulance need at mass gatherings. Prehosp Disaster Med. 2010;25(6):511-514.Google Scholar
8. Arbon, P, Bridgewater, F, Smith, C, et al. Mass gathering medicine: a predictive model for patient presentation and transport rates. Prehosp Disaster Med. 2001;16(3):150-158.Google Scholar
9. Bowdish, GE, Cordell, WH, Bock, HC, Vukov, LF. Using regression analysis to predict emergency patient volume at the Indianapolis 500 mile race. Ann Emerg Med. 1992;21(10):1200-1203.Google Scholar
10. Erickson, TB, Aks, SE, Koenigsberg, M, et al. Drug use patterns at major rock concert events. Ann Emerg Med. 1996;28(1):22-26.CrossRefGoogle ScholarPubMed
11. Richards, R, Richards, D, Whittaker, R. Method of predicting the number of casualties in the Sidney City-to-Surg Fun Runs. Med J Australia. 1984;141(12):805-808.CrossRefGoogle Scholar
12. Spaite, DW, Criss, EA, Valenzuela, TD, et al. A new model for prehospital medical care in large stadiums. Ann Emerg Med. 1998;17(8):825-828.CrossRefGoogle Scholar
13. Bock, HC, Cordell, WH, Hawk, AC, et al. Demographics of emergency medical care at the Indianapolis 500 Mile Race (1983-1990). Ann Emerg Med. 1992;21(10):69-74.Google Scholar
14. Stillman, J, Hale, JD, Burnett, J, et al. Old Farmer's Almanac. http://www.almanac.com. Accessed January 2, 2014.Google Scholar
15. Heat Index. National Weather Service Web site. http://www.srh.noaa.gov/epz/?n=wxcalc_heatindex. Accessed January 2, 2014.Google Scholar
16. Crowd estimates for Grand Prix vary. Baltimore Sun Web site. http://www.baltimoresun.com/sports/auto-racing/baltimore-grand-prix/bs-md-grand-prix-crowds-20110905,0,3778897.story#ixzz2YmuvCNfg. Accessed January 2, 2014.Google Scholar
17. Locoh-Donou, S, Guofen, Y, Welcher, M, et al. Mass-gathering medicine: a descriptive analysis of a range of mass-gathering event types. Am J Emerg Med. 2013;31(5):843-846.Google Scholar
18. Kade, KA, Brinsfield, K, Serino, RA, et al. Emergency medical consequence planning and management for national special security events after September 11: Boston 2004. Disaster Med Public Health Prep. 2008;2(3):166-173.CrossRefGoogle ScholarPubMed
19. Madzimbamuto, F, Madamombe, T. Traumatic asphyxia during stadium stampede. Cent Afr J Med. 2004;50(7):69-72.Google Scholar
20. Valesky, W, Silverberg, M, Gillett, B, et al. Assessment of hospital disaster preparedness for the 2010 FIFA World Cup using an internet-based, long distance tabletop drill. Prehosp Disaster Med. 2011;26(3):192-195.Google Scholar
21. Heiby, MJ, Barnhardt, W, Berry, T, et al. The impact of a mass gathering events with an on-site medical management team on municipal 911 emergency medical services. Am J Emerg Med. 2013;31(1):256-257.CrossRefGoogle Scholar
22. Grange, JT, Baumann, GW, Vaezazizi, R. On-site physicians reduce ambulance transports at mass gatherings. Prehosp Emerg Care. 2003;7(3):322-326.Google Scholar
23. Parrillo, SJ. Medical care at mass gatherings: considerations for physician involvement. Prehosp Disaster Med. 1995;10(4):273-275.Google Scholar
24. EMS Examination Task Force, et al. The core content of emergency medical services medicine. Prehosp Emerg Care. 2012;16(3):309-322.Google Scholar
25. Zeitz, KM, Zeitz, CJ, Arbon, P. Forecasting medical work at mass-gathering events: predictive model versus retrospective review. Prehosp Disaster Med. 2005;20(3):164-168.CrossRefGoogle ScholarPubMed