Spatial decision on allocating automated external defibrillators (AED) in communities by multi-criterion two-step floating catchment area (MC2SFCA)

Abstract

Background: The occurrence of out-of-hospital cardiac arrest (OHCA) is a critical life-threatening event which frequently warrants early defibrillation with an automated external defibrillator (AED). The optimization of allocating a limited number of AEDs in various types of communities is challenging. We aimed to propose a two-stage modeling framework including spatial accessibility evaluation and priority ranking to identify the highest gaps between demand and supply for allocating AEDs.

Methods: In this study, a total of 6135 OHCA patients were defined as demand, and the existing 476 publicly available AEDs locations and 51 emergency medical service (EMS) stations were defined as supply. To identify the demand for AEDs, Bayesian spatial analysis with the integrated nested Laplace approximation (INLA) method is applied to estimate the composite spatial risks from multiple factors. The population density, proportion of elderly people, and land use classifications are identified as risk factors. Then, the multi-criterion two-step floating catchment area (MC2SFCA) method is used to measure spatial accessibility of AEDs between the spatial risks and the supply of AEDs. Priority ranking is utilized for prioritizing deployment of AEDs among communities because of limited resources.

Results: Among 6135 OHCA patients, 56.85 % were older than 65 years old, and 79.04 % were in a residential area. The spatial distribution of OHCA incidents was found to be concentrated in the metropolitan area of Kaohsiung City, Taiwan. According to the posterior mean estimated by INLA, the spatial effects including population density and proportion of elderly people, and land use classifications are positively associated with the OHCA incidence. Utilizing the MC2SFCA for spatial accessibility, we found that supply of AEDs is less than demand in most areas, especially in rural areas. Under limited resources, we identify priority places for deploying AEDs based on transportation time to the nearest hospital and population size of the communities.

Conclusion: The proposed method will be beneficial for optimizing resource allocation while considering multiple local risks. The optimized deployment of AEDs can broaden EMS coverage and minimize the problems of the disparity in urban areas and the deficiency in rural areas.

Keywords: Basic statistical area; Bayesian analysis; Out-of-hospital cardiac arrest; Priority ranking; Zero-inflated Poisson model.

Fig. 1

Distribution of OHCA events in Kaohsiung City from 2011 to 2013

Fig. 2

Contour map of OHCA incidents on different categories of land use in Kaohsiung City

Fig. 3

Posterior density for fixed effects of the risk model; the spatial risk factors are the proportion of 65-and-older population, population density, and the percentages of housing, transformation and public categories in BSA

Fig. 4

Geographical distribution of risk adjusted by spatial risk factors including the proportion of 65-and-older population, population density, and the percentages of housing, transformation and public categories in BSA

Fig. 5

The residual map between the OHCA incidents and the estimated mean of posterior distribution derived from R-INLA

Fig. 6

Accessibility of onsite and dispatched AED services in Kaohsiung City by MC2SFCA

Fig. 7

Priority ranking for deployment of AEDs according to the population count in BSA with zero accessibility