databases and R code (10112016).rar

Objective: To illustrate the ability of hierarchical
Bayesian spatio-temporal models in capturing different geo-temporal structures
in order to explain hospital risk variations using three different conditions:
Percutaneous Coronary Intervention (PCI), Colectomy in Colorectal Cancer (CCC)
and Chronic Obstructive Pulmonary Disease (COPD). Research Design: This is an observational population-based
spatio-temporal study, from 2002 to 2013, with a two-level geographical
structure, Autonomous Communities (AC) and Health Care Areas (HA). Setting: The
Spanish National Health System, a quasi-federal structure with 17 regional
governments (AC) with full responsibility in planning and financing, and 203 HA
providing hospital and primary care to a defined population. Methods: A poisson-log normal mixed
model in the Bayesian framework was fitted using the INLA efficient estimation
procedure. Measures: The spatio-temporal hospitalization relative risks, the
evolution of their variation, and the relative contribution (fraction of
variation) of each of the model components (AC, HA, year and interaction AC-year).