title: A Geographic Simulation Model for the Treatment of Trauma Patients in Disasters.
abstract: BACKGROUND
  Though the US civilian trauma care system plays a critical role in disaster response, there is currently no systems-based strategy that enables hospital emergency management and local and regional emergency planners to quantify, and potentially prepare for, surges in trauma care demand that accompany mass-casualty disasters.
  
  
  OBJECTIVE
  A proof-of-concept model that estimates the geographic distributions of patients, trauma center resource usage, and mortality rates for varying disaster sizes, in and around the 25 largest US cities, is presented. The model was designed to be scalable, and its inputs can be modified depending on the planning assumptions of different locales and for different types of mass-casualty events.
  
  
  METHODS
  To demonstrate the model's potential application to real-life planning scenarios, sample disaster responses for 25 major US cities were investigated using a hybrid of geographic information systems and dynamic simulation-optimization. In each city, a simulated, fast-onset disaster epicenter, such as might occur with a bombing, was located randomly within one mile of its population center. Patients then were assigned and transported, in simulation, via the new model to Level 1, 2, and 3 trauma centers, in and around each city, over a 48-hour period for disaster scenario sizes of 100, 500, 5000, and 10,000 casualties.
  
  
  RESULTS
  Across all 25 cities, total mean mortality rates ranged from 26.3% in the smallest disaster scenario to 41.9% in the largest. Out-of-hospital mortality rates increased (from 21.3% to 38.5%) while in-hospital mortality rates decreased (from 5.0% to 3.4%) as disaster scenario sizes increased. The mean number of trauma centers involved ranged from 3.0 in the smallest disaster scenario to 63.4 in the largest. Cities that were less geographically isolated with more concentrated trauma centers in their surrounding regions had lower total and out-of-hospital mortality rates. The nine US cities listed as being the most likely targets of terrorist attacks involved, on average, more trauma centers and had lower mortality rates compared with the remaining 16 cities.
  
  
  CONCLUSIONS
  The disaster response simulation model discussed here may offer insights to emergency planners and health systems in more realistically planning for mass-casualty events. Longer wait and transport times needed to distribute high numbers of patients to distant trauma centers in fast-onset disasters may create predictable increases in mortality and trauma center resource consumption. The results of the modeled scenarios indicate the need for a systems-based approach to trauma care management during disasters, since the local trauma center network was often too small to provide adequate care for the projected patient surge. Simulation of out-of-hospital resources that might be called upon during disasters, as well as guidance in the appropriate execution of mutual aid agreements and prevention of over-response, could be of value to preparedness planners and emergency response leaders. Study assumptions and limitations are discussed. Carr BG , Walsh L , Williams JC , Pryor JP , Branas CC . A geographic simulation model for the treatment of trauma patients in disasters. Prehosp Disaster Med. 2016;31(4):413-421.
authors.affiliation: 1Office of the Assistant Secretary for Preparedness and Response,US Department of Health and Human Services,Washington,DCUSA.
authors.forename: Brendan G
authors.initials: BG
authors.lastname: Carr
authors.affiliation: 3GAP Solutions, Inc.; in support of the Office of the Assistant Secretary for Preparedness and Response,US Department of Health and Human Services;Herndon,VirginiaUSA.
authors.forename: Lauren
authors.initials: L
authors.lastname: Walsh
authors.affiliation: 4Johns Hopkins University,Whiting School of Engineering,Baltimore,MarylandUSA.
authors.forename: Justin C
authors.initials: JC
authors.lastname: Williams
authors.affiliation: 5University of Pennsylvania,Perelman School of Medicine,Philadelphia,PennsylvaniaUSA.
authors.forename: John P
authors.initials: JP
authors.lastname: Pryor
authors.affiliation: 5University of Pennsylvania,Perelman School of Medicine,Philadelphia,PennsylvaniaUSA.
authors.forename: Charles C
authors.initials: CC
authors.lastname: Branas
mesh_terms: D057191:Capacity Building / Q000379:methods / Q000458:organization & administration / Q000706:statistics & numerical data*; D003198:Computer Simulation; D004189:Disaster Planning / Q000458:organization & administration / Q000592:standards / Q000706:statistics & numerical data*; D004636:Emergency Service, Hospital / Q000458:organization & administration / Q000592:standards / Q000706:statistics & numerical data*; D040362:Geographic Information Systems; D006801:Humans; D054527:Mass Casualty Incidents; D008962:Models, Theoretical; D014193:Trauma Centers / Q000458:organization & administration / Q000592:standards / Q000706:statistics & numerical data*; D014218:Triage / Q000379:methods / Q000592:standards*; D014481:United States / Q000453:epidemiology; D014947:Wounds and Injuries / Q000453:epidemiology* / Q000401:mortality / Q000628:therapy
keywords: EMS Emergency Medical Services; computer models; disaster planning; health care systems; mass-casualty incidents