Applying spatio-temporal models to assess variations across health care areas and regions: Lessons from the decentralized Spanish National Health System
Resumen: 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). Results Following PCI-CCC-CODP order, the three conditions show differences in the initial hospitalization rates (from 4 to 21 per 10, 000 person-years) and in their trends (upward, inverted V shape, downward). Most of the risk variation is captured by phenomena occurring at the HA level (fraction variance: 51.6, 54.7 and 56.9%). At AC level, the risk of PCI hospitalization follow a heterogeneous ascending dynamic (interaction AC-year: 17.7%), whereas in COPD the AC role is more homogenous and important (37%). Conclusions In a system where the decisions loci are differentiated, the spatio-temporal modeling allows to assess the dynamic relative role of different levels of decision and their influence on health outcomes.
Idioma: Inglés
DOI: 10.1371/journal.pone.0170480
Año: 2017
Publicado en: PloS one 12, 2 (2017), 0170480 [12 pp]
ISSN: 1932-6203

Factor impacto JCR: 2.766 (2017)
Categ. JCR: MULTIDISCIPLINARY SCIENCES rank: 15 / 64 = 0.234 (2017) - Q1 - T1
Factor impacto SCIMAGO: 1.164 - Agricultural and Biological Sciences (miscellaneous) (Q1) - Medicine (miscellaneous) (Q1) - Biochemistry, Genetics and Molecular Biology (miscellaneous) (Q1)

Financiación: info:eu-repo/grantAgreement/ES/ISCIII/PI08-90255
Financiación: info:eu-repo/grantAgreement/ES/ISCIII/PI10-00494
Financiación: info:eu-repo/grantAgreement/ES/ISCIII/PI14-00786
Tipo y forma: Article (Published version)

Creative Commons You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.


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