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.
- Comorbidity indices
- Medical expenditures
- National Health Insurance Research Database
- Stroke patients
ASJC Scopus subject areas
- Public Health, Environmental and Occupational Health