Date Published: August 7, 2019
Publisher: Public Library of Science
Author(s): Nicholas J. Tierney, Antonietta Mira, H. Jost Reinhold, Giuseppe Arbia, Samuel Clifford, Angelo Auricchio, Tiziano Moccetti, Stefano Peluso, Kerrie L. Mengersen, Tayyab Ikram Shah.
Floating catchment methods have recently been applied to identify priority regions for Automated External Defibrillator (AED) deployment, to aid in improving Out of Hospital Cardiac Arrest (OHCA) survival. This approach models access as a supply-to-demand ratio for each area, targeting areas with high demand and low supply for AED placement. These methods incorporate spatial covariates on OHCA occurrence, but do not provide precise AED locations, which are critical to the initial intent of such location analysis research. Exact AED locations can be determined using optimisation methods, but they do not incorporate known spatial risk factors for OHCA, such as income and demographics. Combining these two approaches would evaluate AED placement impact, describe drivers of OHCA occurrence, and identify areas that may not be appropriately covered by AED placement strategies. There are two aims in this paper. First, to develop geospatial models of OHCA that account for and display uncertainty. Second, to evaluate the AED placement methods using geospatial models of accessibility. We first identify communities with the greatest gap between demand and supply for allocating AEDs. We then use this information to evaluate models for precise AED location deployment.
Case study data set consisted of 2802 OHCA events and 719 AEDs. Spatial OHCA occurrence was described using a geospatial model, with possible spatial correlation accommodated by introducing a conditional autoregressive (CAR) prior on the municipality-level spatial random effect. This model was fit with Integrated Nested Laplacian Approximation (INLA), using covariates for population density, proportion male, proportion over 65 years, financial strength, and the proportion of land used for transport, commercial, buildings, recreation, and urban areas. Optimisation methods for AED locations were applied to find the top 100 AED placement locations. AED access was calculated for current access and 100 AED placements. Priority rankings were then given for each area based on their access score and predicted number of OHCA events.
Of the 2802 OHCA events, 64.28% occurred in rural areas, and 35.72% in urban areas. Additionally, over 70% of individuals were aged over 65. Supply of AEDs was less than demand in most areas. Priority regions for AED placement were identified, and access scores were evaluated for AED placement methodology by ranking the access scores and the predicted OHCA count. AED placement methodology placed AEDs in areas with the highest priority, but placed more AEDs in areas with more predicted OHCA events in each grid cell.
The methods in this paper incorporate OHCA spatial risk factors and OHCA coverage to identify spatial regions most in need of resources. These methods can be used to help understand how AED allocation methods affect OHCA accessibility, which is of significant practical value for communities when deciding AED placements.
To best serve the public, hospitals, medical centres, and emergency services should be in locations where they can serve the most people in need. Recent research has evaluated various techniques for evaluating or identifying locations of External Defibrillators (AEDs) [1, 2]. This is an idea upon which this paper builds: exploring how to evaluate, locate, and improve AED locations.
Among the 2802 OHCA cases in this study, over 70% of OHCA events occurred in those aged over 65 (Table 1). Additionally, the majority of events occurred in urban areas (Table 2).
This paper presents a novel modelling framework that jointly prioritizes regions for AED placement, evaluates AED placement methods, and identifies covariates important for predicting OHCA events. This reconciles a missed opportunity in past research by , which did not identify exact AED locations in priority regions, and in , which did not combine exact AED locations with relevant spatial information important in predicting OHCA occurrence [1, 15, 16]. Combining information from geospatial models and the precise facility locations gives a more complete picture of the impact of facility locations. The geospatial model revealed an increase of OHCA occurrence for the covariates: recreational and industrial areas, and the proportion of men, proportion of the population older than 65, and the population density. Additionally, the proportion of buildings, urban areas, and financial strengths are associated with lower counts of OHCA events.