[19]), which was present in 16.1% of this sample. The Elixhauser method and Charlson Score include indicators for peptic ulcer disease, which can be a side effect of NSAID use (20). Peptic ulcer disease was present among ∼3% of this sample, according to both the Elixhauser and Charlson measures. The RxRisk-V contains a broader, more general indicator for gastric acid disorders, which was present in a much larger proportion of the sample (35.6%). The mean ± SD number of physician visits during the followup year was 26.4 ± 28.3 and the mean number of prescription drugs was 10.4 ± 10.2; 14.8% of the sample were hospitalized. Relationships of the comorbidity indices to health service use variables are presented in Table 2. In the negative binomial regression models, all summary comorbidity indices were significantly associated with the number of physician visits (P < 0.001) and the number of prescription drugs (P < 0.001). Individual indicators for all disease categories in the original Elixhauser method are not presented, but 8 of the 30 diseases were significantly associated with the number of physician visits (P < 0.05) and 7 of the 30 diseases were significantly associated with the number of prescription drugs (P < 0.05). In the logistic regression model, all summary comorbidity indices were significantly associated with probability of hospitalization (P < 0.01). However, the logistic regression model for hospitalization using individual disease indicators defined by the Elixhauser method did not converge, due to small sample sizes in some of the categories. For the number of physician visits outcome, the models with the Elixhauser total score and individual indicators were the best models based on AIC values and were clearly better than models with either the RxRisk-V or Charlson Score as a predictor variable. Both of these models also had acceptable model fit according to deviance values (deviance/degrees of freedom [df] = 1.14–1.26) (21). For the number of prescription drugs outcome, the model with the RxRisk-V score was clearly the best model based on AIC values. This model also had acceptable model fit according to the deviance value (deviance/df = 1.21). For the dichotomous hospitalization outcome, no best model was identified based on AIC values. The c statistic for these logistic regression models ranged from 0.65 to 0.67. This study compared 3 comorbidity measures, the Charlson Score, RxRisk-V, and Elixhauser method, among a group of patients with ICD-9-CM codes for OA. Although these measures have been validated previously, it is important to examine the performance of comorbidity measures in the context of specific settings and health outcomes. In this case, we were interested in comparing these measures among patients with OA and examining their ability to predict health service use variables of particular relevance to this patient group (physician visits, prescription drug use, and hospitalization). Results of this study showed that all 3 comorbidity measures were significant predictors of health service use among individuals with OA. However, there were some differences in their ability to predict specific health service variables. Among the 3 measures, the Elixhauser method (both the total score and the individual indicators) provided the best fit for the model predicting physician visits. The RxRisk-V was associated with the next best fit, followed by the Charlson Score. The RxRisk-V provided a substantially better fit than the other measures among