A Bayesian spatiotemporal statistical analysis of out-of-hospital cardiac arrests

Stefano Peluso, Antonietta Mira, Håvard Rue, Nicholas John Tierney, Claudio Benvenuti, Roberto Cianella, Maria Luce Caputo, Angelo Auricchio

Research output: Contribution to journalArticleResearchpeer-review

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.

Original languageEnglish
Number of pages15
JournalBiometrical Journal
DOIs
Publication statusAccepted/In press - 2020

Keywords

  • cardiac risk map
  • integrated nested Laplace approximation
  • temporal and spatial heterogeneity

Cite this

Peluso, S., Mira, A., Rue, H., Tierney, N. J., Benvenuti, C., Cianella, R., Caputo, M. L., & Auricchio, A. (Accepted/In press). A Bayesian spatiotemporal statistical analysis of out-of-hospital cardiac arrests. Biometrical Journal. https://doi.org/10.1002/bimj.201900166