Optimizing Spatiotemporal Defibrillator Access
Is it possible to improve access to automated external defibrillators (AEDs) in out-of-hospital cardiac arrest (OHCA), accounting for both spatial and temporal accessibility, compared to spatial accessibility alone?
Using the Toronto Regional RescuNET cardiac arrest database, the authors identified public-location OHCAs in Toronto, Canada, and obtained a list of registered AEDs from Toronto emergency medical services. They quantified coverage loss due to limited temporal access by comparing the number of OHCAs that occurred within 100 meters of a registered AED (assumed 24/7 coverage) with the number that occurred both within 100 meters of a registered AED and when the AED was available (actual coverage). Then they developed a spatiotemporal optimization model that determined AED locations to maximize OHCA actual coverage.
There were 2,440 public OHCAs and 737 registered AED locations. A total of 451 OHCAs were covered by registered AEDs under assumed 24/7 coverage, and 354 OHCAs under actual coverage, representing a coverage loss of 21.5% (p < 0.001). Using the spatiotemporal model to optimize AED deployment, a 25.3% relative increase in actual coverage was achieved over the spatial-only approach (p < 0.001).
One in five OHCAs occurred near an inaccessible AED at the time of the OHCA. Potential AED use was significantly improved with a spatiotemporal optimization model.
Bystander AED use during OHCA depends on a multitude of factors; among them availability of the AED itself. Survival of the OHCA is highly variable according to location and time, but generally does not exceed 10%. A significant proportion of OHCAs occur close to a public AED that is inaccessible at the time of the arrest, due to the limited hours of operation of the businesses that house them. This innovative paper addresses this seemingly obvious concept of temporal availability of AEDs, layered on top of their spatial availability. The authors propose a spatiotemporal model to offset the greatest loss of access to AEDs, at night and during the weekends, when survival is the lowest. While it was developed for Toronto, this model may be translatable to other cities, and further enhanced with mobile apps and public education.
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