title: Prehospital intervention probability score: a novel method for determining necessity of emergency medical service units.
abstract: OBJECTIVE
  This article models use of emergency medical services (EMS) within a defined geographical area. Our goal was to develop an original quantitative method to delineate the need for EMS units within a geographical population.
  
  
  METHODS
  Use of the EMS system within 11 municipalities was analyzed in 2007. The geospatial distributions of interventions during this year were examined, as well as the population dynamics of the region. A statistical model to determine the probability of an individual within the call area requiring an intervention was proposed using weighted population statistics and the application of an intervention probability.
  
  
  RESULTS
  The observed interventional probability increased exponentially with age, notably after the age of 75. Areas with higher proportions of elderly residents had substantially higher rates of intervention and EMS use. Municipality H had the largest age-group of 20 to 24 years with an intervention probability of 0.34% (95% confidence interval [CI], 0.24-0.44), their more than 85-year age-group also had the largest intervention probability of 19.54% (95% CI, 15.60-23.48).
  
  
  CONCLUSIONS
  Contrary to current practice patterns of placing paramedic units in regions of greatest population density, we established a formula based on population vs intervention probability. We found the actual numbers of interventions performed are not dependent solely on population size but also are affected by the age of the population being served. This is particularly relevant to growing elderly communities. This determination will aid in the disbursement of limited prehospital resources in regions by improving availability of EMS personnel.
authors.affiliation: Department of Emergency Medicine, University of Medicine and Dentistry of New Jersey, Graduate School of Biological Sciences, Piscataway, New Jersey 08901, USA.
authors.forename: Denise
authors.initials: D
authors.lastname: Livingston
authors.forename: Andreia
authors.initials: A
authors.lastname: Marques-Baptista
authors.forename: Richard
authors.initials: R
authors.lastname: Brown
authors.forename: Junfeng
authors.initials: J
authors.lastname: Liu
authors.forename: Mark A
authors.initials: MA
authors.lastname: Merlin
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D002947:Cities / Q000453:epidemiology; D004630:Emergencies / Q000453:epidemiology*; D004632:Emergency Medical Services / Q000379:methods / Q000600:supply & distribution*; D004634:Emergency Medical Technicians / Q000600:supply & distribution; D006301:Health Services Needs and Demand; D006801:Humans; D008875:Middle Aged; D015233:Models, Statistical; D011156:Population Density; D011157:Population Dynamics; D011336:Probability; D055815:Young Adult