Open Access Research article

Joint spatial modeling to identify shared patterns among chronic related potentially preventable hospitalizations

Berta Ibañez-Beroiz1*, Julián Librero2, Enrique Bernal-Delgado3, Sandra García-Armesto3, Silvia Villanueva-Ferragud4 and Salvador Peiró2

Author Affiliations

1 NavarraBiomed – Fundación Miguel Servet - Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC), C/Irunlarrea s/n 31008, Pamplona, Spain

2 Centro Superior de Investigación en Salud Pública (CSISP-FISABIO) - Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC), Valencia, Spain

3 Instituto Aragonés de Ciencias de la Salud. IIS Aragón - Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC), Zaragoza, Spain

4 European Commission, DG HEALTH & CONSUMERS (SANCO), Health Technology and Science Policy Officer, Brussels, Belgium

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BMC Medical Research Methodology 2014, 14:74  doi:10.1186/1471-2288-14-74

Published: 4 June 2014

Abstract

Background

Rates of Potentially Preventable Hospitalizations (PPH) are used to evaluate access of territorially delimited populations to high quality ambulatory care. A common geographic pattern of several PPH would reflect the performance of healthcare providers. This study is aimed at modeling jointly the geographical variation in six chronic PPH conditions in one Spanish Autonomous Community for describing common and discrepant patterns, and to assess the relative weight of the common pattern on each condition.

Methods

Data on the 39,970 PPH hospital admissions for diabetes short term complications, chronic obstructive pulmonary disease (COPD), congestive heart failure, dehydration, angina admission and adult asthma, between 2007 and 2009 were extracted from the Hospital Discharge Administrative Databases and assigned to one of the 240 Basic Health Zones. Rates and Standardized Hospitalization Ratios per geographic unit were estimated. The spatial analysis was carried out jointly for PPH conditions using Shared Component Models (SCM).

Results

The component shared by the six PPH conditions explained about the 36% of the variability of each PPH condition, ranging from the 25.9 for dehydration to 58.7 for COPD. The geographical pattern found in the latent common component identifies territorial clusters with particularly high risk. The specific risk pattern that each isolated PPH does not share with the common pattern for all six conditions show many non-significant areas for most PPH, but with some exceptions.

Conclusions

The geographical distribution of the risk of the PPH conditions is captured in a 36% by a unique latent pattern. The SCM modeling may be useful to evaluate healthcare system performance.

Keywords:
Potentially preventable hospitalizations; Small-area analysis; Bayes theorem; Geographic information systems