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Applying spatio-temporal models to assess variations across health care areas and regions: Lessons from the decentralized Spanish National Health System
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 Chroni...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5293276/ https://www.ncbi.nlm.nih.gov/pubmed/28166233 http://dx.doi.org/10.1371/journal.pone.0170480 |
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author | Librero, Julián Ibañez, Berta Martínez-Lizaga, Natalia Peiró, Salvador Bernal-Delgado, Enrique |
author_facet | Librero, Julián Ibañez, Berta Martínez-Lizaga, Natalia Peiró, Salvador Bernal-Delgado, Enrique |
author_sort | Librero, Julián |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5293276 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-52932762017-02-17 Applying spatio-temporal models to assess variations across health care areas and regions: Lessons from the decentralized Spanish National Health System Librero, Julián Ibañez, Berta Martínez-Lizaga, Natalia Peiró, Salvador Bernal-Delgado, Enrique PLoS One Research Article 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. Public Library of Science 2017-02-06 /pmc/articles/PMC5293276/ /pubmed/28166233 http://dx.doi.org/10.1371/journal.pone.0170480 Text en © 2017 Librero et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Librero, Julián Ibañez, Berta Martínez-Lizaga, Natalia Peiró, Salvador Bernal-Delgado, Enrique Applying spatio-temporal models to assess variations across health care areas and regions: Lessons from the decentralized Spanish National Health System |
title | Applying spatio-temporal models to assess variations across health care areas and regions: Lessons from the decentralized Spanish National Health System |
title_full | Applying spatio-temporal models to assess variations across health care areas and regions: Lessons from the decentralized Spanish National Health System |
title_fullStr | Applying spatio-temporal models to assess variations across health care areas and regions: Lessons from the decentralized Spanish National Health System |
title_full_unstemmed | Applying spatio-temporal models to assess variations across health care areas and regions: Lessons from the decentralized Spanish National Health System |
title_short | Applying spatio-temporal models to assess variations across health care areas and regions: Lessons from the decentralized Spanish National Health System |
title_sort | applying spatio-temporal models to assess variations across health care areas and regions: lessons from the decentralized spanish national health system |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5293276/ https://www.ncbi.nlm.nih.gov/pubmed/28166233 http://dx.doi.org/10.1371/journal.pone.0170480 |
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