title: Usefulness of a multiplying factor in predicting the final number of victims during a mass casualty incident.
abstract: OBJECTIVE
  Whenever a mass casualty incident (MCI) occurs, it is essential to anticipate the final number of victims to dispatch the adequate number of ambulances. In France, the custom is to multiply the initial number of prehospital victims by 2-4 to predict the final number. However, no one has yet validated this multiplying factor (MF) as a predictive tool. We aimed to build a statistical model to predict the final number of victims from their initial count.
  
  
  METHODS
  We observed retrospectively over 30 years of MCIs triggered in a large urban area. We considered three types of events: explosions, fires, and road traffic accidents. We collected the initial and final numbers of victims, with distinction between deaths, critical victims (T1), and delayed or minimal victims (T2-T3). The MF was calculated for each category of victims according to each type of event. Using a Poisson multivariate regression, we calculated the incidence risk ratio (IRR) of the final number of T1 as a function of the initial deaths and the initial T2-T3 counts, while controlling for potential confounding variables.
  
  
  RESULTS
  Sixty-eight MCIs were included. The final number of T1 increased with the initial incidence of deaths [IRR: 1.8 (1.4-2.2)], the initial number of T2-T3 being greater than 12 [IRR: 1.6 (1.3-2.1)], and the presence of one or more explosion [IRR: 1.4 (1.1-1.8)].
  
  
  CONCLUSIONS
  The MF seems to be an appealing decision-making tool to anticipate the need for ambulance resources. In explosive MCIs, we recommend multiplying T1 by 1.4 to estimate final count and the need for supplementary advanced life support teams.
authors.affiliation: aEmergency Department bOperational Planning Office, Fire Brigade of Paris, Paris cAP-HP, EMS (Samu92), University Poincare Hospital, Garches dCHU H Mondor, AP-HP, Créteil, France.
authors.forename: Olga
authors.initials: O
authors.lastname: Maurin
authors.forename: Michel
authors.initials: M
authors.lastname: Bignand
authors.forename: Daniel
authors.initials: D
authors.lastname: Jost
authors.forename: Stéphane
authors.initials: S
authors.lastname: Travers
authors.forename: Stéphane
authors.initials: S
authors.lastname: Raclot
authors.forename: Julie
authors.initials: J
authors.lastname: Trichereau
authors.forename: Olivier
authors.initials: O
authors.lastname: Bon
authors.forename: Benoit
authors.initials: B
authors.lastname: Frattini
authors.forename: Thomas
authors.initials: T
authors.lastname: Loeb
authors.forename: Eric
authors.initials: E
authors.lastname: Lecarpentier
authors.forename: René
authors.initials: R
authors.lastname: Noto
authors.forename: Gaëtan
authors.initials: G
authors.lastname: Poncelin de Raucourt
authors.forename: Jean-Pierre
authors.initials: JP
authors.lastname: Tourtier
mesh_terms: D004189:Disaster Planning / Q000379:methods; D004190:Disasters / Q000706:statistics & numerical data; D004632:Emergency Medical Services / Q000458:organization & administration / Q000706:statistics & numerical data; D005602:France / Q000453:epidemiology; D006801:Humans; D054527:Mass Casualty Incidents / Q000706:statistics & numerical data*; D015233:Models, Statistical; D016017:Odds Ratio; D012189:Retrospective Studies