Objective: To determine the magnitude and importance of declines in model performance associated with altering the data source and time frame from which comorbid conditions were identified in claims-based risk adjustment among persons with hip fracture.
Study design and setting: Medicare claims data were used to identify incident hip fracture cases in 1999. Three risk-adjustment instruments were evaluated: one by Iezzoni, the Charlson Index (Romano adaptation), and the Clinical Classification Software (CCS). Several implementation strategies, defined by altering data source (MedPar and/or Part B claims) and time frame (index hospitalization and/or 1-year preperiod), were assessed for each instrument. Logistic regression was used to predict 1-year mortality, and model performance was compared.
Results: Each instrument had modest ability to predict 1-year mortality after hip fracture. The CCS performed best overall (c=0.76), followed by the Iezzoni (c=0.73) and Charlson models (c=0.72). Although each instrument performed most favorably when applied to both inpatient and outpatient claims and when comorbidities were considered during the preperiod, varying data source and time frame had trivial effects on model performance.
Conclusion: The similar predictive ability of the three risk-adjustment instruments suggests that ease of implementation be a key consideration in choosing an approach for hip fracture populations.