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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Vydané v:	Health economics Ročník 20; číslo 1; s. 85 - 100
Hlavní autori:	Schreyögg, Jonas, Stargardt, Tom, Tiemann, Oliver
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Chichester, UK John Wiley & Sons, Ltd 01.01.2011 Wiley-Blackwell Wiley Periodicals Inc
Edícia:	Health Economics
Predmet:	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
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Abstract	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.
AbstractList	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. [PUBLICATION ABSTRACT] 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 (C) 2010 John Wiley & Sons, Ltd. 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. 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. 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.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.
Author	Schreyögg, Jonas Stargardt, Tom Tiemann, Oliver
Author_xml	– sequence: 1 givenname: Jonas surname: Schreyögg fullname: Schreyögg, Jonas email: schreyoegg@bwl.lmu.de organization: Department for Health Services Management, Munich School of Management, Munich University, Munich, Germany – sequence: 2 givenname: Tom surname: Stargardt fullname: Stargardt, Tom organization: Department for Health Services Management, Munich School of Management, Munich University, Munich, Germany – sequence: 3 givenname: Oliver surname: Tiemann fullname: Tiemann, Oliver organization: Department for Health Services Management, Munich School of Management, Munich University, Munich, Germany
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Snippet	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... 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...
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SubjectTerms	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
Title	Costs and quality of hospitals in different health care systems: a multi-level approach with propensity score matching
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