A Bayesian spatiotemporal statistical analysis of out‐of‐hospital cardiac arrests. Issue 4 (3rd February 2020)

• Record Type: Journal Article
• Authors: Peluso, Stefano; Mira, Antonietta; Rue, Håvard; Tierney, Nicholas John; Benvenuti, Claudio; Cianella, Roberto; Caputo, Maria Luce; Auricchio, Angelo
• Is Part Of: Biometrical journal. Volume 62:Issue 4(2020:Jul.)
• Page Start: 1105
• Page End: 1119
• Publication Date: 2020-02-03
• Subjects: cardiac risk map -- integrated nested Laplace approximation -- temporal and spatial heterogeneity; Biometry -- Periodicals; Medical statistics -- Periodicals; 570.15195
• DOI: 10.1002/bimj.201900166
• Languages: English
• ISSNs: 0323-3847

Abstract

Abstract: We propose a Bayesian spatiotemporal statistical model for predicting out‐of‐hospital cardiac arrests (OHCAs). Risk maps for Ticino, adjusted for demographic covariates, are built for explaining and forecasting the spatial distribution of OHCAs and their temporal dynamics. The occurrence intensity of the OHCA event in each area of interest, and the cardiac risk‐based clustering of municipalities are efficiently estimated, through a statistical model that decomposes OHCA intensity into overall intensity, demographic fixed effects, spatially structured and unstructured random effects, time polynomial dependence, and spatiotemporal random effect. In the studied geography, time evolution and dependence on demographic features are robust over different categories of OHCAs, but with variability in their spatial and spatiotemporal structure. Two main OHCA incidence‐based clusters of municipalities are identified.