Evaluating health facility access using Bayesian spatial models and location analysis methods.

Tierney, Nicholas J; Mira, Antonietta; Reinhold, H Jost; Arbia, Giuseppe; Clifford, Samuel; Auricchio, Angelo; Moccetti, Tiziano; Peluso, Stefano; Mengersen, Kerrie L · 2019 · DOAJ

DOI: 10.1371/journal.pone.0218310

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Summary

This study addresses the challenge of optimizing Automated External Defibrillator (AED) placement to improve Out-of-Hospital Cardiac Arrest (OHCA) survival rates. Existing methods for AED allocation typically fall into two categories: floating catchment methods, which identify priority regions based on supply-to-demand ratios but lack precise location recommendations, and mathematical optimization methods, which determine exact locations but ignore spatial risk factors like demographics and land use. The authors aim to bridge this gap by developing Bayesian geospatial models that account for uncertainty and spatial risk factors, then using these models to evaluate and refine precise AED placement strategies. The research utilizes data from the Ticino Registry of Cardiac Arrest in Switzerland, comprising 2,802 OHCA events and 719 existing AEDs recorded between 2005 and 2015. The study area was divided into a high-resolution grid (1100m x 1100m cells) to align point-level event data with municipality-level covariates, including population density, age, gender, financial strength, and land use. The authors employed a Zero-Inflated Poisson model with a conditional autoregressive prior to estimate expected OHCA counts per grid cell, accounting for spatial correlation and excess zeros. Accessibility was measured using a modified two-step floating catchment area method with an exponential decay function, defining adequate coverage as within 100 meters of an AED. Finally, optimization methods were applied to identify the top 100 optimal AED locations, which were then evaluated against the geospatial priority rankings. Results indicated that 64.28% of OHCA events occurred in rural areas, and over 70% of victims were aged over 65. The analysis revealed that AED supply was insufficient relative to demand in most areas. The combined methodology successfully identified priority regions for AED placement and evaluated the impact of specific location strategies. The optimization method placed AEDs in high-priority areas but tended to concentrate placements in grid cells with higher predicted OHCA counts. The study demonstrated that integrating spatial risk factors with precise location optimization provides a more comprehensive evaluation of AED accessibility than either method alone. The significance of this work lies in its practical application for public health planning. By combining Bayesian spatial modeling with location analysis, the proposed framework allows communities to understand how AED allocation affects accessibility while identifying specific drivers of OHCA occurrence. This approach enables more informed decisions regarding resource distribution, ensuring that AEDs are placed not just where historical events occurred, but where spatial risk factors and supply-demand gaps indicate the greatest need. The methods offer a robust tool for evaluating the impact of AED placement strategies on OHCA survival potential.

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