title: Comparison of prediction models for use of medical resources at urban auto-racing events.
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.
  
  
  OBJECTIVE
  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 over predicted the number of encounters. For both events, the Hartman model over predicted the number of patient encounters. In regard to hospital transports, the Arbon model under predicted the actual numbers whereas the Hartman model both over predicted and under predicted 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.
authors.forename: Jose V
authors.initials: JV
authors.lastname: Nable
authors.forename: Asa M
authors.initials: AM
authors.lastname: Margolis
authors.forename: Benjamin J
authors.initials: BJ
authors.lastname: Lawner
authors.forename: Jon Mark
authors.initials: JM
authors.lastname: Hirshon
authors.forename: Alexander J
authors.initials: AJ
authors.lastname: Perricone
authors.forename: Samuel M
authors.initials: SM
authors.lastname: Galvagno
authors.forename: Debra
authors.initials: D
authors.lastname: Lee
authors.forename: Michael G
authors.initials: MG
authors.lastname: Millin
authors.forename: Richard A
authors.initials: RA
authors.lastname: Bissell
authors.forename: Richard L
authors.initials: RL
authors.lastname: Alcorta
mesh_terms: D000428:Alcohol Drinking / Q000453:epidemiology; D000846:Anniversaries and Special Events; D001334:Automobile Driving; D015142:Baltimore / Q000453:epidemiology; D003441:Crowding; D004189:Disaster Planning; D004632:Emergency Medical Services / Q000458:organization & administration*; D005260:Female; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D011237:Predictive Value of Tests; D012189:Retrospective Studies; D014187:Transportation of Patients; D014505:Urban Population; D014887:Weather