Comparison of three comorbidity measures for predicting health service use in patients with osteoarthritis.

Published

Journal Article

OBJECTIVE: To compare the ability of 3 database-derived comorbidity scores, the Charlson Score, Elixhauser method, and RxRisk-V, in predicting health service use among individuals with osteoarthritis (OA). METHODS: The study population comprised 306 patients who were under care for OA in the Veterans Affairs (VA) health care system. Comorbidity scores were calculated using 1 year of data from VA inpatient and outpatient databases (Charlson Score, Elixhauser method), as well as pharmacy data (RxRisk-V). Model selection was used to identify the best comorbidity index for predicting 3 health service use variables: number of physician visits, number of prescriptions used, and hospitalization probability. Specifically, Akaike's Information Criterion (AIC) was used to determine the best model for each health service outcome variable. Model fit was also evaluated. RESULTS: All 3 comorbidity indices were significant predictors of each health service outcome (P < 0.01). However, based on AIC values, models using the RxRisk-V and Elixhauser indices as predictor variables were better than models using the Charlson Score. The model using the RxRisk-V index as a predictor was the best for the outcome of prescription medication use, and the model with the Elixhauser index was the best for the outcome of physician visits. CONCLUSION: The Rx-Risk-V and Elixhauser are suitable comorbidity measures for examining health services use among patients with OA. Both indices are derived from administrative databases and can efficiently capture comorbidity among large patient populations.

Full Text

Duke Authors

Cited Authors

  • Dominick, KL; Dudley, TK; Coffman, CJ; Bosworth, HB

Published Date

  • October 15, 2005

Published In

Volume / Issue

  • 53 / 5

Start / End Page

  • 666 - 672

PubMed ID

  • 16208675

Pubmed Central ID

  • 16208675

International Standard Serial Number (ISSN)

  • 0004-3591

Digital Object Identifier (DOI)

  • 10.1002/art.21440

Language

  • eng

Conference Location

  • United States