Evaluating health facility access using Bayesian spatial models and location analysis methods
| Autor: | Antonietta Mira, Stefano Peluso, Nicholas Tierney, H. Jost Reinhold, Kerrie Mengersen, Angelo Auricchio, Samuel Clifford, Giuseppe Arbia, Tiziano Moccetti |
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| Přispěvatelé: | Tierney, N, Mira, A, Reinhold, J, Arbia, G, Clifford, S, Auricchio, A, Moccetti, T, Peluso, S, Kerrie, M, University of Zurich, Tierney, Nicholas J |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Male
Computer science Economics Epidemiology Ambulances Social Sciences Transportation 030204 cardiovascular system & hematology 0302 clinical medicine Bayesian Model Health facility Medicine and Health Sciences Child Geographic Areas Statistical Data Aged 80 and over Multidisciplinary Geography Statistics Architectural Accessibility Middle Aged Settore SECS-S/01 - STATISTICA Child Preschool Physical Sciences Medicine Engineering and Technology Female Research Article Urban Areas Adult Optimization Bayesian Models Adolescent Science 610 Medicine & health 1100 General Agricultural and Biological Sciences Spatial Models Out of hospital cardiac arrest 11171 Cardiocentro Ticino 03 medical and health sciences Young Adult Population Metrics 1300 General Biochemistry Genetics and Molecular Biology Humans Operations management Automated external defibrillator Aged Population Density 1000 Multidisciplinary Spatial Analysis Models Statistical Population Biology Infant Newborn Infant Biology and Life Sciences 030208 emergency & critical care medicine Bayes Theorem Rural Areas Medical Risk Factors Earth Sciences Health Facilities Rural area Out-of-Hospital Cardiac Arrest Mathematics Finance Defibrillators |
| Zdroj: | PLoS ONE PLoS ONE, Vol 14, Iss 8, p e0218310 (2019) |
| ISSN: | 1932-6203 |
| Popis: | BackgroundFloating 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.MethodsCase 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.ResultsOf 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.ConclusionThe 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. |
| Databáze: | OpenAIRE |
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