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01.12.2014 | Research article | Ausgabe 1/2014 Open Access

BMC Medical Research Methodology 1/2014

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

Zeitschrift:
BMC Medical Research Methodology > Ausgabe 1/2014
Autoren:
Berta Ibañez-Beroiz, Julián Librero, Enrique Bernal-Delgado, Sandra García-Armesto, Silvia Villanueva-Ferragud, Salvador Peiró
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​1471-2288-14-74) contains supplementary material, which is available to authorized users.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

BI and JL had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. BI, JL, EBD, SGA, SMF and SP, were responsible for the study concept, design and data acquisition. BI and JL carried out the data preparation and carried out the statistical analysis and BI, JL, SP and EBD drafted the manuscript. All authors participated in the analysis and interpretation of data, critical revision of the manuscript for important intellectual content, and all approved the final version submitted for publication.

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.
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