the models predicting prescription medications. The Elixhauser total score provided the next best fit, followed by the Charlson Score. The Elixhauser total score was the strongest predictor of hospitalization, but the differences in predictive ability among the models were small, indicating no clear advantage for any of the indices. In general, these results suggest that the RxRisk-V and Elixhauser method may be preferable to the Charlson Score when predicting overall health service use (especially physician visits and prescription medication use). Although the Charlson Score has been widely validated and commonly used, it was originally developed to predict mortality. In this study, the Charlson Score was a significant predictor of all health service use outcomes. However, it was not as strong a predictor of overall physician visits and prescription medication use, which are often of interest in the context of chronic, long-term diseases such as arthritis. This finding is important because it indicates that the Charlson Score may not provide as comprehensive of a comorbidity adjustment in models predicting these 2 health service use outcomes. Results also showed that the RxRisk-V was a somewhat stronger predictor of prescription medication use than the Elixhauser method, and the converse was true in models predicting physician visits. This is not surprising because the RxRisk-V is a pharmacy-based measure, and the Elixhauser measure is based on diagnosis codes from physician visits. However, results indicate that the RxRisk-V and Elixhauser method are each suitable for examining both physician visits and prescription medications. There are strengths and limitations to both pharmacy-based and diagnosis-based comorbidity indices that should be considered when selecting and using a measure. One strength of diagnosis-based measures is that in some cases they can identify specific diseases more accurately than pharmacy-based measures. For example, some medications are used to treat both hypertension and congestive heart failure, 2 conditions with very different levels of severity. In addition, physicians may prescribe medications for “off label” uses, leading to misclassification of a disease. Whereas pharmacy-based measures do not detect the indication for the prescription, diagnosis-based measures identify the specific disease being treated. A diagnosis-based measure could also identify comorbid rheumatic diseases (i.e., OA and RA). However, diagnosis-based measures (including the Elixhauser method and Charlson Score) typically treat rheumatic diseases broadly and do not identify separate conditions. Therefore the overall burden of rheumatic conditions is typically not reflected in diagnosis-based measures. The main limitations of diagnosis code-based indices are coding errors, omissions, and biases (4). Coding errors can be made by clinicians or at the point of data entry. Coding biases occur when some ICD-9-CM codes are used preferentially because of insurance reimbursement rules. Pharmacy-based comorbidity measures are not as subject to coding errors as are diagnosis-based measures. However, pharmacy-based comorbidity measures may fail to comprehensively assess diseases if patients choose not to fill prescriptions, if prescriptions are obtained from multiple health care sources, or if medications are available without a prescription. We restricted our study to a 10% random sample because of the time burden of extracting pharmacy data from local electronic medical