• Olga Maurin, Michel Bignand, Daniel Jost, Stéphane Travers, Stéphane Raclot, Julie Trichereau, Olivier Bon, Benoit Frattini, Thomas Loeb, Eric Lecarpentier, René Noto, Gaëtan Poncelin de Raucourt, and Jean-Pierre Tourtier.
• 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.
• Eur J Emerg Med. 2017 Oct 1; 24 (5): 377-381.

ObjectiveWhenever 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.MethodsWe 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.ResultsSixty-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)].ConclusionThe 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.

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