Comparison of different comorbidity measures in predicting the medical expenditures of stroke patients by utilizing the National Health Insurance Research Database

Yu Ying Lin, Shuen Fu Weng, Che-Ming Yang, Chiang Hsing Yang, Kuo-Cherh Huang

Research output: Contribution to journalArticle

Abstract

Objectives: This study compared the performance of different co-morbidity measures in predicting medical expenditures of stroke patients. Methods: Data were sourced from the Longitudinal Health Insurance Database 2005(LHID205), and the study population comprised all patients who were hospitalized due to stroke for the first time. Four co-morbidity measures were compared regarding the performance of predicting medical expenditures of subjects within 1 year after discharge: the Deyo-Charlson comorbidity index (CCI); Romano-CCI; D'Hoore-CCI; and Elixhauser method. The baseline model included patient age and gender, whether or not surgery was undertaken when hospitalized, and the length of stay. Two target years (2005 and 2008) of data were compared. The discriminatory power of the co-morbidity measures was assessed using the c-statistics derived from multiple logistic regression models. Results: All four co-morbidity measures significantly improved the predictive capacity of the baseline model. Furthermore, the Romano-CCI performed best in predicting medical expenditures of subjects within 1 year after discharge (c: 0.710-0.746). Conclusions: This study suggested that co-morbidity measures are significant predictors of medical expenditures of stroke patients, and the Romano-CCI performed best among the four co-morbidity measures in the research. When designing the payment schemes for stroke patients, the Taiwanese health authority ought to make adjustments in accordance with the burden of health care caused by co-morbidities.

Original languageEnglish
Pages (from-to)430-445
Number of pages16
JournalTaiwan Journal of Public Health
Volume35
Issue number4
DOIs
Publication statusPublished - Aug 1 2016

Fingerprint

National Health Programs
Health Expenditures
Comorbidity
Stroke
Databases
Morbidity
Research
Logistic Models
Health Insurance
Length of Stay
Delivery of Health Care
Health
Population

Keywords

  • Comorbidity indices
  • Medical expenditures
  • National Health Insurance Research Database
  • Stroke patients

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health

Cite this

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title = "Comparison of different comorbidity measures in predicting the medical expenditures of stroke patients by utilizing the National Health Insurance Research Database",
abstract = "Objectives: This study compared the performance of different co-morbidity measures in predicting medical expenditures of stroke patients. Methods: Data were sourced from the Longitudinal Health Insurance Database 2005(LHID205), and the study population comprised all patients who were hospitalized due to stroke for the first time. Four co-morbidity measures were compared regarding the performance of predicting medical expenditures of subjects within 1 year after discharge: the Deyo-Charlson comorbidity index (CCI); Romano-CCI; D'Hoore-CCI; and Elixhauser method. The baseline model included patient age and gender, whether or not surgery was undertaken when hospitalized, and the length of stay. Two target years (2005 and 2008) of data were compared. The discriminatory power of the co-morbidity measures was assessed using the c-statistics derived from multiple logistic regression models. Results: All four co-morbidity measures significantly improved the predictive capacity of the baseline model. Furthermore, the Romano-CCI performed best in predicting medical expenditures of subjects within 1 year after discharge (c: 0.710-0.746). Conclusions: This study suggested that co-morbidity measures are significant predictors of medical expenditures of stroke patients, and the Romano-CCI performed best among the four co-morbidity measures in the research. When designing the payment schemes for stroke patients, the Taiwanese health authority ought to make adjustments in accordance with the burden of health care caused by co-morbidities.",
keywords = "Comorbidity indices, Medical expenditures, National Health Insurance Research Database, Stroke patients",
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AU - Weng, Shuen Fu

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AU - Yang, Chiang Hsing

AU - Huang, Kuo-Cherh

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N2 - Objectives: This study compared the performance of different co-morbidity measures in predicting medical expenditures of stroke patients. Methods: Data were sourced from the Longitudinal Health Insurance Database 2005(LHID205), and the study population comprised all patients who were hospitalized due to stroke for the first time. Four co-morbidity measures were compared regarding the performance of predicting medical expenditures of subjects within 1 year after discharge: the Deyo-Charlson comorbidity index (CCI); Romano-CCI; D'Hoore-CCI; and Elixhauser method. The baseline model included patient age and gender, whether or not surgery was undertaken when hospitalized, and the length of stay. Two target years (2005 and 2008) of data were compared. The discriminatory power of the co-morbidity measures was assessed using the c-statistics derived from multiple logistic regression models. Results: All four co-morbidity measures significantly improved the predictive capacity of the baseline model. Furthermore, the Romano-CCI performed best in predicting medical expenditures of subjects within 1 year after discharge (c: 0.710-0.746). Conclusions: This study suggested that co-morbidity measures are significant predictors of medical expenditures of stroke patients, and the Romano-CCI performed best among the four co-morbidity measures in the research. When designing the payment schemes for stroke patients, the Taiwanese health authority ought to make adjustments in accordance with the burden of health care caused by co-morbidities.

AB - Objectives: This study compared the performance of different co-morbidity measures in predicting medical expenditures of stroke patients. Methods: Data were sourced from the Longitudinal Health Insurance Database 2005(LHID205), and the study population comprised all patients who were hospitalized due to stroke for the first time. Four co-morbidity measures were compared regarding the performance of predicting medical expenditures of subjects within 1 year after discharge: the Deyo-Charlson comorbidity index (CCI); Romano-CCI; D'Hoore-CCI; and Elixhauser method. The baseline model included patient age and gender, whether or not surgery was undertaken when hospitalized, and the length of stay. Two target years (2005 and 2008) of data were compared. The discriminatory power of the co-morbidity measures was assessed using the c-statistics derived from multiple logistic regression models. Results: All four co-morbidity measures significantly improved the predictive capacity of the baseline model. Furthermore, the Romano-CCI performed best in predicting medical expenditures of subjects within 1 year after discharge (c: 0.710-0.746). Conclusions: This study suggested that co-morbidity measures are significant predictors of medical expenditures of stroke patients, and the Romano-CCI performed best among the four co-morbidity measures in the research. When designing the payment schemes for stroke patients, the Taiwanese health authority ought to make adjustments in accordance with the burden of health care caused by co-morbidities.

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