Nicholas John Tierney1, H Jost Reinhold2, Antonietta Mira3, Martin Weiser4, Roman Burkart5, Claudio Benvenuti5, Angelo Auricchio6. 1. Department of Statistical Science, Mathematical Sciences, Science & Engineering Faculty, Queensland University of Technology, Brisbane, Queensland, Australia; ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), Brisbane, Queensland, Australia; Department of Econometrics and Business Statistics, Monash University, Melbourne, Victoria, Australia. 2. Data Science Center, Institute of Computational Science, Università della Svizzera italiana, Switzerland. 3. Data Science Center, Institute of Computational Science, Università della Svizzera italiana, Switzerland; Department of Science and High Technology, Università dell'Insubria, Italy. 4. Zuse Institute Berlin, Department of Numerical Mathematics, Berlin, Germany. 5. Fondazione Ticino Cuore, Lugano, Switzerland. 6. Fondazione Ticino Cuore, Lugano, Switzerland; Division of Cardiology, Fondazione Cardiocentro Ticino, Lugano, Switzerland; Center for Computational Medicine in Cardiology, Università della Svizzera Italiana, Lugano, Switzerland. Electronic address: angelo.auricchio@cardiocentro.org.
Abstract
BACKGROUND: Mathematical optimisation models have recently been applied to identify ideal Automatic External Defibrillator (AED) locations that maximise coverage of Out of Hospital Cardiac Arrest (OHCA). However, these fixed location models cannot relocate existing AEDs in a flexible way, and have nearly exclusively been applied to urban regions. We developed a flexible location model for AEDs, compared its performance to existing fixed location and population models, and explored how these perform across urban and rural regions. METHODS: Optimisation techniques were applied to AED deployment and OHCA coverage was assessed. A total of 2802 geolocated OHCAs occurred in Canton Ticino, Switzerland, from January 1st 2005 to December 31st 2015. RESULTS: There were 719 AEDs in Canton Ticino. 635 (23%) OHCA events occurred within 100 m of an AED, with 306 (31%) in urban, and 329 (18%) in rural areas. Median distance from OHCA events to the nearest AED was 224 m (168 m urban vs. 269 m rural). Flexible location models performed better than fixed location and population models, with the cost to deploy 20 new AEDs instead relocating 171 existing AEDs to new locations, improving OHCA coverage to 38%, compared to 26% using fixed models, and 24% with the population based model. CONCLUSIONS: Optimisation models for AEDs placement are superior to population models and should be strongly considered by communities when selecting areas for AED deployment. Compared to other models, flexible location models increase overall OHCA coverage, and decreases the distance to nearby AEDs, even in rural areas, while saving significant financial resources.
BACKGROUND: Mathematical optimisation models have recently been applied to identify ideal Automatic External Defibrillator (AED) locations that maximise coverage of Out of Hospital Cardiac Arrest (OHCA). However, these fixed location models cannot relocate existing AEDs in a flexible way, and have nearly exclusively been applied to urban regions. We developed a flexible location model for AEDs, compared its performance to existing fixed location and population models, and explored how these perform across urban and rural regions. METHODS: Optimisation techniques were applied to AED deployment and OHCA coverage was assessed. A total of 2802 geolocated OHCAs occurred in Canton Ticino, Switzerland, from January 1st 2005 to December 31st 2015. RESULTS: There were 719 AEDs in Canton Ticino. 635 (23%) OHCA events occurred within 100 m of an AED, with 306 (31%) in urban, and 329 (18%) in rural areas. Median distance from OHCA events to the nearest AED was 224 m (168 m urban vs. 269 m rural). Flexible location models performed better than fixed location and population models, with the cost to deploy 20 new AEDs instead relocating 171 existing AEDs to new locations, improving OHCA coverage to 38%, compared to 26% using fixed models, and 24% with the population based model. CONCLUSIONS: Optimisation models for AEDs placement are superior to population models and should be strongly considered by communities when selecting areas for AED deployment. Compared to other models, flexible location models increase overall OHCA coverage, and decreases the distance to nearby AEDs, even in rural areas, while saving significant financial resources.
Authors: Nicholas J Tierney; Antonietta Mira; H Jost Reinhold; Giuseppe Arbia; Samuel Clifford; Angelo Auricchio; Tiziano Moccetti; Stefano Peluso; Kerrie L Mengersen Journal: PLoS One Date: 2019-08-07 Impact factor: 3.240
Authors: Terry P Brown; Gavin D Perkins; Christopher M Smith; Charles D Deakin; Rachael Fothergill Journal: Resuscitation Date: 2021-10-29 Impact factor: 5.262
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