Costs and quality of hospitals in different health care systems: a multi-level approach with propensity score matching

Cross‐country comparisons of costs and quality between hospitals are often made at the macro level. The goal of this study was to explore methods to compare micro‐level data from hospitals in different health care systems. To do so, we developed a multi‐level framework in combination with a propensi...

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Bibliographic Details
Published in:Health economics Vol. 20; no. 1; pp. 85 - 100
Main Authors: Schreyögg, Jonas, Stargardt, Tom, Tiemann, Oliver
Format: Journal Article
Language:English
Published: Chichester, UK John Wiley & Sons, Ltd 01.01.2011
Wiley-Blackwell
Wiley Periodicals Inc
Series:Health Economics
Subjects:
acute myocardial infarction
Aged
Case studies
Comparative analysis
Data
Delivery of Health Care
Economic models
Germany - epidemiology
Health care
Health care expenditures
Health economics
Health services
Heart attacks
hospital costs
Hospital Costs - statistics & numerical data
Hospital Mortality - trends
Hospitals
Hospitals - standards
Humans
Justification
Male
Matching
Medical treatment
Middle Aged
Military hospitals
multilevel models
Myocardial infarction
Myocardial Infarction - drug therapy
Myocardial Infarction - economics
Patients
Propensity
Propensity Score
propensity score matching
Quality of care
Quality of Health Care
Studies
United States - epidemiology
Veterans
United States
Germany
United States > US
ISSN:1057-9230, 1099-1050, 1099-1050
Online Access:Get full text
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Summary:Cross‐country comparisons of costs and quality between hospitals are often made at the macro level. The goal of this study was to explore methods to compare micro‐level data from hospitals in different health care systems. To do so, we developed a multi‐level framework in combination with a propensity score matching technique using similarly structured data for patients receiving treatment for acute myocardial infarction in German and US Veterans Health Administration hospitals. Our case study shows important differences in results between multi‐level regressions based on matched and unmatched samples. We conclude that propensity score matching techniques are an appropriate way to deal with the usual baseline imbalances across the samples from different countries. Multi‐level models are recommendable to consider the clustered structure of the data when patient‐level data from different hospitals and health care systems are compared. The results provide an important justification for exploring new ways in performing health system comparisons. Copyright © 2010 John Wiley & Sons, Ltd.
Bibliography:ark:/67375/WNG-KZBRRDV1-8
ArticleID:HEC1568
istex:C66A558264A214EE185FE15E5C036E8F2E132DFC
This article was published online on 18 January 2010. An error was subsequently identified in the author name. This notice is included in the online and print versions to indicate that both have been corrected [13 July 2010].
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ISSN:1057-9230
1099-1050
1099-1050
DOI:10.1002/hec.1568