Rheumatoid arthritis (RA) is a chronic, destructive, inflammatory arthritis that places significant burden on the individual and society. This thesis represents the most comprehensive effort to date to determine the accuracy of administrative data for detecting RA patients; and describes the development and validation of an administrative data algorithm to establish a province-wide RA database. Beginning with a systematic review to guide the conduct of this research, two independent, multicentre, retrospective chart abstraction studies were performed amongst two random samples of patients from rheumatology and primary care family physician practices, respectively. While a diagnosis by a rheumatologist remains the gold standard for establishing a RA diagnosis, the high prevalence of RA in rheumatology clinics can falsely elevate positive predictive values. It was therefore important we also perform a validation study in a primary care setting where prevalence of RA would more closely approximate that observed in the general population. The algorithm of [1 hospitalization RA code] OR [3 physician RA diagnosis codes (claims) with ≥1 by a specialist in a 2 year period)] demonstrated a high degree of accuracy in terms of minimizing both the number of false positives (moderately good PPV; 78%) and true negatives (high specificity: 100%). Moreover, this algorithm has excellent sensitivity at capturing contemporary RA patients under active rheumatology care (>96%). Application of this algorithm to Ontario health administrative data to establish the Ontario RA administrative Database (ORAD) identified 97,499 Ontarians with RA as of 2010, yielding a cumulative prevalence of (0.9%). Age/sex-standardized RA prevalence has doubled from 473 per 100,000 in 1996 to 784 per 100,000 in 2010, with approximately 50 new cases of RA emerging per 100,000 Ontarians each year. Our findings will inform future population-based research and will serve to improve arthritis surveillance activities across Canada and abroad.