TY - JOUR
T1 - A Bayesian spatio-temporal statistical analysis of Out-of-Hospital Cardiac Arrests
AU - Peluso, Stefano
AU - Mira, Antonietta
AU - Rue, Haavard
AU - Tierney, Nicholas John
AU - Benvenuti, Claudio
AU - Cianella, Roberto
AU - Caputo, Maria Luce
AU - Auricchio, Angelo
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: Financial support from Fondazione Fratelli Agostino Enrico Rocca is acknowledged. Antonietta Mira was partially supported by SNF grant 105218 166504. The authors thank the Swiss Cardiology Foundation.
PY - 2020/2/3
Y1 - 2020/2/3
N2 - We propose a Bayesian spatio-temporal statistical model for predicting Out-of-Hospital Cardiac Arrests (OHCA). 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 spatio-temporal 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 spatio-temporal structure. Two main OHCA incidence-based clusters of municipalities are identified.
AB - We propose a Bayesian spatio-temporal statistical model for predicting Out-of-Hospital Cardiac Arrests (OHCA). 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 spatio-temporal 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 spatio-temporal structure. Two main OHCA incidence-based clusters of municipalities are identified.
UR - http://hdl.handle.net/10754/661091
UR - https://onlinelibrary.wiley.com/doi/abs/10.1002/bimj.201900166
UR - http://www.scopus.com/inward/record.url?scp=85078888707&partnerID=8YFLogxK
U2 - 10.1002/bimj.201900166
DO - 10.1002/bimj.201900166
M3 - Article
C2 - 32011763
SN - 0323-3847
JO - Biometrical Journal
JF - Biometrical Journal
ER -