The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs. 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). 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. 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. 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. Measurement of comorbidity is important in the context of health services research, epidemiologic studies, and clinical trials. Comorbidity assessment can also play an important role in health care planning and policy making. Because osteoarthritis (OA) and other rheumatic diseases are more prevalent among older adults (1, 2), additional chronic illnesses (i.e., comorbidities) are common in these patient groups. Adequate assessment of comorbidities is therefore essential for characterizing the overall health status in this patient population. There are many methods and scales available for assessing comorbidity, including patients' self reports, medical record abstraction, and administrative databases. There has been growing interest in the use of administrative databases for comorbidity assessment, since large databases are increasingly available in many