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

  • Jose V. Nable (a1) (a2) (a3), Asa M. Margolis (a4), Benjamin J. Lawner (a1) (a5), Jon Mark Hirshon (a1) (a6), Alexander J. Perricone (a5), Samuel M. Galvagno (a7), Debra Lee (a1), Michael G. Millin (a4), Richard A. Bissell (a2) and Richard L. Alcorta (a8)...

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.

Nable JV , Margolis AM , Lawner BJ , Hirshon JM , Perricone AJ , Galvagno SM , Lee D , Millin MG , Bissell RA , Alcorta RL . Comparison of Prediction Models for Use of Medical Resources at Urban Auto-racing Events. Prehosp Disaster Med. 2014;29(6):1-6 .

Copyright

Corresponding author

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 jnable@umem.org

References

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1. DeLorenzo, RA. Mass gathering medicine: a review. Prehosp Disaster Med. 1997;12(1):68-72.
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.
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.
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.
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.
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.
7. Meites, E, Brown, JF. Ambulance need at mass gatherings. Prehosp Disaster Med. 2010;25(6):511-514.
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.
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.
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.
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.
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.
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.
14. Stillman, J, Hale, JD, Burnett, J, et al. Old Farmer's Almanac. http://www.almanac.com. Accessed January 2, 2014.
15. Heat Index. National Weather Service Web site. http://www.srh.noaa.gov/epz/?n=wxcalc_heatindex. Accessed January 2, 2014.
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.
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.
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.
19. Madzimbamuto, F, Madamombe, T. Traumatic asphyxia during stadium stampede. Cent Afr J Med. 2004;50(7):69-72.
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.
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.
22. Grange, JT, Baumann, GW, Vaezazizi, R. On-site physicians reduce ambulance transports at mass gatherings. Prehosp Emerg Care. 2003;7(3):322-326.
23. Parrillo, SJ. Medical care at mass gatherings: considerations for physician involvement. Prehosp Disaster Med. 1995;10(4):273-275.
24. EMS Examination Task Force, et al. The core content of emergency medical services medicine. Prehosp Emerg Care. 2012;16(3):309-322.
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.

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