Purpose: To refine two subscales of the health-related quality of life comorbidity index (HRQoL-CI) into a single index measure. Methods: The 2010 and 2012 Medical Expenditure Panel Surveys were utilized as development and validation datasets, respectively. The least absolute shrinkage and selection operator was applied to select important comorbidity candidates associated with HRQoL. Exploratory factor analysis and confirmatory factor analysis (CFA) were used to assess dimensionality in comorbidity. Statistical weights were derived based on standardized factor loadings from CFA and regression coefficients from the model predicting HRQoL. Prediction errors and model R2 values were compared between HRQoL-CI and Charlson CI (CCI). Results: Eighteen comorbid conditions were identified. CFA models indicated that the second-order multidimensional comorbidity structure had a better fit to the data than did the first-order unidimensional structure. The predictive performance of the refined scale under a multidimensional structure utilizing statistical weights outperformed the original scale and CCI in terms of average prediction error and R2 in the prediction models (R2 values from refined scale model are 0.25, 0.30, and 0.28 versus those from CCI of 0.10, 0.09, and 0.06 for general health, SF-6D, and EQ-5D, respectively). Conclusion: The dimensionality of comorbidity and the weight scheme significantly improved the performance of the refined HRQoL-CI. The refined single HRQoL-CI measure appears to be an appropriate and valid instrument specific for risk adjustment in studies of HRQoL. Future research that validates the refined scales for different cultures, age groups, and healthcare settings is warranted.
- Comorbidity index
- Factor analysis
- Health-related Quality of life
- Least absolute shrinkage and selection operator
- Risk prediction model
ASJC Scopus subject areas
- Public Health, Environmental and Occupational Health