title: Disaster metrics: quantitative estimation of the number of ambulances required in trauma-related multiple casualty events.
abstract: BACKGROUND
  Estimating the number of ambulances needed in trauma-related Multiple Casualty Events (MCEs) is a challenging task.
  
  
  OBJECTIVE
  Emergency medical services (EMS) regions in the United States have varying "best practices" for the required number of ambulances in MCE, none of which is based on metric criteria. The objective of this study was to estimate the number of ambulances required to respond to the scene of trauma-related MCE in order to initiate treatment and complete the transport of critical (T1) and moderate (T2) patients. The proposed model takes into consideration the different transport times and capacities of receiving hospitals, the time interval from injury occurrence, the number of patients per ambulance, and the pre-designated time frame allowed from injury until the transfer care of T1 and T2 patients.
  
  
  METHODS
  The main theoretical framework for this model was based on prehospital time intervals described in the literature and used by EMS systems to evaluate operational and patient care issues. The North Atlantic Treaty Organization (NATO) triage categories (T1-T4) were used for simplicity.
  
  
  RESULTS
  The minimum number of ambulances required to respond to the scene of an MCE was modeled as being primarily dependent on the number of critical patients (T1) present at the scene any particular time. A robust quantitative model was also proposed to dynamically estimate the number of ambulances needed at any time during an MCE to treat, transport and transfer the care of T1 and T2 patients.
  
  
  CONCLUSIONS
  A new quantitative model for estimation of the number of ambulances needed during the prehospital response in trauma-related multiple casualty events has been proposed. Prospective studies of this model are needed to examine its validity and applicability.
authors.affiliation: Department of Emergency Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA. jbayram1@jhmi.edu
authors.forename: Jamil D
authors.initials: JD
authors.lastname: Bayram
authors.forename: Shawki
authors.initials: S
authors.lastname: Zuabi
authors.forename: Mazen J
authors.initials: MJ
authors.lastname: El Sayed
mesh_terms: D000552:Ambulances / Q000592:standards / Q000706:statistics & numerical data*; D004189:Disaster Planning / Q000379:methods / Q000706:statistics & numerical data*; D004632:Emergency Medical Services / Q000379:methods / Q000458:organization & administration* / Q000706:statistics & numerical data; D006801:Humans; D054527:Mass Casualty Incidents / Q000706:statistics & numerical data*; D015233:Models, Statistical; D020380:Needs Assessment; D055872:Surge Capacity / Q000592:standards / Q000706:statistics & numerical data; D013997:Time Factors; D014481:United States