title: Utilization of emergency medical services in a large urban area: description of call types and temporal trends.
abstract: BACKGROUND
  Emergency medical services (EMS) systems are used by the public for a range of medically related problems.
  
  
  OBJECTIVE
  To understand and analyze the patterns of EMS utilization and trends over time in a large urban EMS system so that we may better direct efforts toward improving those services.
  
  
  METHODS
  The 63 call type designations from all New York City (NYC) 9-1-1 EMS calls between 1999 and 2007 were obtained and grouped into 10 broad and 30 specific medical categories. Aggregated numbers of total EMS calls and individual categories were divided by NYC resident population estimates to determine utilization rates. Temporal trends were evaluated for statistical significance with Spearman's rho (ρ).
  
  
  RESULTS
  There were 9,916,904 EMS calls between 1999 and 2007, with an average of 1,101,878 calls/year. Utilization rates increased from 129.5 to 141.9 calls/1,000 residents/year over the study period (average annual rise of 1.16%). Among all medical/surgical call types (excluding trauma), there was an average annual increase of 1.8%/year. The most substantial increases were among "psychiatric/drug related" (+5.6%/year), "generalized illness" (+3.2%/year), and "environmental related" calls (+2.9%/year). The largest decrease was among "respiratory" calls (-1.2%/year), specifically for "asthma" (-5.0%/year). For trauma call types, there was an annual average decrease of 0.4%/year, with the category of "violence related" calls having the greatest decline (-3.3%/year).
  
  
  CONCLUSIONS
  There was an increase in overall EMS utilization rates, though not all call types rose uniformly. Rather, a number of significant trends were identified reflecting either changing medical needs or changing patterns of EMS utilization in NYC's population.
authors.affiliation: Office of Medical Affairs, The New York City Fire Department, Brooklyn, New York 11201, USA.
authors.forename: Kevin G
authors.initials: KG
authors.lastname: Munjal
authors.forename: Robert A
authors.initials: RA
authors.lastname: Silverman
authors.forename: John
authors.initials: J
authors.lastname: Freese
authors.forename: James D
authors.initials: JD
authors.lastname: Braun
authors.forename: Bradley J
authors.initials: BJ
authors.lastname: Kaufman
authors.forename: Douglas
authors.initials: D
authors.lastname: Isaacs
authors.forename: Andrew
authors.initials: A
authors.lastname: Werner
authors.forename: Mayris
authors.initials: M
authors.lastname: Webber
authors.forename: Charles B
authors.initials: CB
authors.lastname: Hall
authors.forename: David J
authors.initials: DJ
authors.lastname: Prezant
mesh_terms: D000465:Algorithms; D004631:Emergency Medical Service Communication Systems / Q000706:statistics & numerical data*; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005260:Female; D006297:Health Services Accessibility; D006801:Humans; D008297:Male; D009519:New York City; D011634:Public Health; D012189:Retrospective Studies; D013223:Statistics as Topic; D018709:Statistics, Nonparametric; D013997:Time Factors; D014218:Triage / Q000706:statistics & numerical data*; D014505:Urban Population / Q000706:statistics & numerical data*