title: Utilizing Geographic Information Systems to Identify Clusters of Severe Sepsis Patients Presenting in the Out-of-Hospital Environment.
abstract: Understanding the geographic distribution of critical illness within a community may provide public health stakeholders with information that can be used to expedite access to specialized care. We hypothesized that severe sepsis patients transported by emergency medical services (EMS) exhibit geospatial clustering and that prehospital providers would recognize sepsis more frequently in patients transported from sepsis clusters. Retrospective review of a prospective, observational study of patients with severe sepsis transported to the emergency department (ED) by EMS and treated with early goal-directed therapy (EGDT).
  
  
  METHODS
  suspected infection, 2 or more criteria for systemic inflammation, and either systolic blood pressure <90 mmHg after a fluid bolus or lactate >4 mmol/liter.
  
  
  METHODS
  age <18 or need for immediate surgery. Patient location at the time of EMS activation was recorded. Analysis of the addresses identified clusters, defined as a location in which EMS transported more than one patient experiencing the above associated signs and symptoms of septic shock. Other data collected included self-reported patient location as private residence or chronic care facility. One hundred sixty severe sepsis patients transported by EMS were eligible for analysis, presenting from 125 locations. Ninety-one patients (57%) presented from a private residence and 69 (37%) from a chronic care facility. Fifty (31%) patients were transported from 15 locations, with 25 of those transported from just 4 locations. Cluster patients tended to be older, come from medical facilities, and were more likely to have sepsis recognized by prehospital providers. Results from this study demonstrate low pre-hospital recognition of sepsis, as well as geospatially clustered presentations, most notably from skilled nursing facilities. Community education, public health initiatives, and EMS interventions could be targeted in such clusters of cases in order to both improve sepsis recognition and potentially expedite time-sensitive interventions.
authors.forename: Allison C
authors.initials: AC
authors.lastname: Barrett
authors.forename: Jonathan R
authors.initials: JR
authors.lastname: Studnek
authors.forename: Michael A
authors.initials: MA
authors.lastname: Puskarich
authors.forename: Alan E
authors.initials: AE
authors.lastname: Jones
mesh_terms: D000328:Adult; D000368:Aged; D016000:Cluster Analysis; D004632:Emergency Medical Services / Q000706:statistics & numerical data*; D005260:Female; D040362:Geographic Information Systems; D006801:Humans; D008297:Male; D008875:Middle Aged; D012189:Retrospective Studies; D018805:Sepsis / Q000453:epidemiology*