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Identifying locations for public access defibrillators using mathematical optimization.

Timothy C Y Chan1, Heyse Li, Gerald Lebovic

  • 1Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada. tcychan@mie.utoronto.ca

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Summary
This summary is machine-generated.

Mathematical modeling can identify cardiac arrest clusters and optimize automated external defibrillator (AED) placement. This approach improves AED coverage and reduces response times, outperforming population-guided methods.

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Area of Science:

  • Public Health
  • Biomedical Engineering
  • Geographic Information Systems

Background:

  • Geospatial methods and mathematical optimization for cardiac arrest cluster identification and defibrillator deployment have not been previously studied.
  • Developing and testing such a method is crucial for improving emergency response.
  • Comparing this novel approach to population-guided methods is essential for validation.

Purpose of the Study:

  • To develop a geospatial method using mathematical optimization to identify cardiac arrest clusters.
  • To prioritize public locations for automated external defibrillator (AED) deployment.
  • To test the performance of this method against a population-guided approach.

Main Methods:

  • Geographically plotting all public location cardiac arrests and registered AED locations in Toronto.
  • Quantifying current AED coverage by analyzing cardiac arrests within 100m of registered AEDs.
  • Identifying cardiac arrest clusters lacking nearby AEDs and using mathematical optimization to compute coverage improvements.

Main Results:

  • Analysis of 1310 cardiac arrests and 1669 AEDs revealed only 23% coverage (304 arrests within 100m).
  • The average distance to the nearest AED was 281m.
  • Optimized deployment in 30 locations increased coverage to 32% and reduced average distance to 262m, outperforming population-guided methods.

Conclusions:

  • Geographic clusters of cardiac arrests can be readily identified and prioritized using mathematical modeling.
  • Optimized AED deployment significantly enhances cardiac arrest coverage and decreases proximity to the nearest AED.
  • Mathematical modeling offers a valuable tool to augment public AED deployment programs.