Accuracy of Canadian health administrative databases in identifying patients with rheumatoid arthritis: a validation study using the medical records of rheumatologists

Goal — Health administrative data can be a valuable tool for disease surveillance and research. Few studies have rigorously evaluated the accuracy of administrative databases for identifying rheumatoid arthritis (RA) patients. The aim was to validate administrative data algorithms to identify RA patients in Ontario, Canada.

Methods — The researchers performed a retrospective review of a random sample of 450 patients from 18 rheumatology clinics. Using rheumatologist-reported diagnosis as the reference standard, the researchers tested and validated different combinations of physician billing, hospitalization and pharmacy data.

Results — 149 rheumatology patients were classified as having RA and 301 as not having RA based on the researchers’ reference standard definition (study RA prevalence: 33%). Overall, algorithms that included physician billings had excellent sensitivity (94–100%). Specificity and positive predictive value (PPV) were modest to excellent and increased when algorithms included multiple physician claims or specialist claims. The addition of RA drugs did not significantly improve algorithm performance. The algorithm of "(1 hospitalization RA code) OR (3 physician RA claims with ≤1 by a specialist in a 2-year period)" had a sensitivity of 97%, specificity of 85%, PPV of 76% and NPV of 98%. Most RA patients (84%) had an RA diagnosis code present in the administrative data within 1 year of a rheumatologist's documented diagnosis date.

Conclusion — The researchers demonstrate that administrative data can be used to identify RA patients with a high degree of accuracy. RA diagnosis date and disease duration are fairly well estimated from administrative data in jurisdictions of universal healthcare.