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Introduction to AEDAn Automated External Defibrillator (AED) is a portable medical device that analyzes the heart's rhythm and, if necessary, delivers an electrical shock to help the heart re-establish an effective rhythm during sudden cardiac arrest (SCA). SCA occurs when the heart suddenly and unexpectedly stops beating, leading to a loss of blood flow to the brain and other vital organs. In such emergencies, time is of the essence, and using an AED, combined with Cardiopulmonary...
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Socioeconomically equitable public defibrillator placement using mathematical optimization.

K H Benjamin Leung1, Steven C Brooks2, Gareth R Clegg3

  • 1Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada.

Resuscitation
|July 16, 2021
PubMed
Summary
This summary is machine-generated.

Mathematical optimization improves automated external defibrillator (AED) placement, increasing coverage of out-of-hospital cardiac arrests (OHCAs). This strategy enhances equitable AED distribution, particularly in socioeconomically deprived areas.

Keywords:
Automated external defibrillatorsOut-of-hospital cardiac arrestScottish Index of Multiple DeprivationSocioeconomic factors

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Area of Science:

  • Public Health
  • Health Services Research
  • Spatial Analysis

Background:

  • Automated external defibrillators (AEDs) are crucial for out-of-hospital cardiac arrest (OHCA) survival.
  • Optimal placement of AEDs is essential to maximize their effectiveness.
  • Socioeconomic factors can influence access to and placement of emergency medical resources.

Purpose of the Study:

  • To evaluate if mathematical optimization can improve the alignment between AED locations and OHCA incidence.
  • To assess if optimization enhances AED coverage across different levels of socioeconomic deprivation.
  • To compare the impact of new AED placements versus relocation of existing AEDs.

Main Methods:

  • Utilized data on suspected OHCAs and registered AEDs in Scotland (2011-2017).
  • Mapped AED and OHCA data to socioeconomic deprivation levels using the Scottish Index of Multiple Deprivation (SIMD).
  • Applied mathematical optimization to determine optimal locations for additional AEDs and for relocating existing AEDs, analyzing coverage within 100m.

Main Results:

  • A significant disparity was found between existing AED distribution and OHCA locations across socioeconomic quintiles.
  • Optimization-guided AED placement significantly increased OHCA coverage compared to existing locations (P < 0.001).
  • Optimized placement improved OHCA coverage in deprived areas, aligning AED distribution with OHCA counts; relocating AEDs achieved coverage similar to doubling AED numbers.

Conclusions:

  • Mathematical optimization effectively aligns AED placement with OHCA distribution.
  • Optimization leads to more equitable AED coverage across socioeconomic deprivation levels.
  • Relocating existing AEDs using optimization is an efficient strategy for improving OHCA coverage.