title: Accuracy of Canadian health administrative databases in identifying patients with rheumatoid arthritis: a validation study using the medical records of rheumatologists.
abstract: OBJECTIVE
  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. Our aim was to validate administrative data algorithms to identify RA patients in Ontario, Canada.
  
  
  METHODS
  We performed a retrospective review of a random sample of 450 patients from 18 rheumatology clinics. Using rheumatologist-reported diagnosis as the reference standard, we tested and validated different combinations of physician billing, hospitalization, and pharmacy data.
  
  
  RESULTS
  One hundred forty-nine rheumatology patients were classified as having RA and 301 were classified as not having RA based on our reference standard definition (study RA prevalence 33%). Overall, algorithms that included physician billings had excellent sensitivity (range 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 medications did not significantly improve algorithm performance. The algorithm of "(1 hospitalization RA code ever) OR (3 physician RA diagnosis codes [claims] with ≥1 by a specialist in a 2-year period)" had a sensitivity of 97%, specificity of 85%, PPV of 76%, and negative predictive value 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.
  
  
  CONCLUSIONS
  We demonstrated 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 health care insurance.
authors.affiliation: University of Toronto, Toronto, Ontario, Canada.
authors.forename: Jessica
authors.initials: J
authors.lastname: Widdifield
authors.forename: Sasha
authors.initials: S
authors.lastname: Bernatsky
authors.forename: J Michael
authors.initials: JM
authors.lastname: Paterson
authors.forename: Karen
authors.initials: K
authors.lastname: Tu
authors.forename: Ryan
authors.initials: R
authors.lastname: Ng
authors.forename: J Carter
authors.initials: JC
authors.lastname: Thorne
authors.forename: Janet E
authors.initials: JE
authors.lastname: Pope
authors.forename: Claire
authors.initials: C
authors.lastname: Bombardier
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001172:Arthritis, Rheumatoid / Q000175:diagnosis* / Q000453:epidemiology; D057225:Data Mining / Q000706:statistics & numerical data*; D016208:Databases, Factual / Q000706:statistics & numerical data*; D011307:Drug Prescriptions / Q000706:statistics & numerical data; D005249:Fees and Charges / Q000706:statistics & numerical data; D005260:Female; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D016347:Medical Records Systems, Computerized / Q000706:statistics & numerical data*; D008875:Middle Aged; D009864:Ontario / Q000453:epidemiology; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012219:Rheumatology / Q000706:statistics & numerical data*; D018855:Single-Payer System / Q000706:statistics & numerical data*