title: Validation of rheumatoid arthritis diagnoses in health care utilization data.
abstract: BACKGROUND
  Health care utilization databases have been increasingly used for studies of rheumatoid arthritis (RA). However, the accuracy of RA diagnoses in these data has been inconsistent.
  
  
  METHODS
  Using medical records and a standardized abstraction form, we examined the positive predictive value (PPV) of several algorithms to define RA diagnosis using claims data: A) at least two visits coded for RA (ICD-9, 714); B) at least three visits coded for RA; and C) at least two visits to a rheumatologist for RA. We also calculated the PPVs for the subgroups identified by these algorithms combined with pharmacy claims data for at least one disease-modifying anti-rheumatic drug (DMARD) prescription.
  
  
  RESULTS
  We invited 9,482 Medicare beneficiaries with pharmacy benefits in Pennsylvania to participate; 2% responded and consented for review of their medical records. There was no difference in characteristics between respondents and non-respondents. Using 'RA diagnosis per rheumatologists' as the gold standard, the PPVs were 55.7% for at least two claims coded for RA, 65.5% for at least three claims for RA, and 66.7% for at least two rheumatology claims for RA. The PPVs of these algorithms in patients with at least one DMARD prescription increased to 86.2%-88.9%. When fulfillment of 4 or more of the ACR RA criteria was used as the gold standard, the PPVs of the algorithms combined with at least one DMARD prescriptions were 55.6%-60.7%.
  
  
  CONCLUSIONS
  To accurately identify RA patients in health care utilization databases, algorithms that include both diagnosis codes and DMARD prescriptions are recommended.
authors.affiliation: Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital/Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA. skim62@partners.org
authors.forename: Seo Young
authors.initials: SY
authors.lastname: Kim
authors.forename: Amber
authors.initials: A
authors.lastname: Servi
authors.forename: Jennifer M
authors.initials: JM
authors.lastname: Polinski
authors.forename: Helen
authors.initials: H
authors.lastname: Mogun
authors.forename: Michael E
authors.initials: ME
authors.lastname: Weinblatt
authors.forename: Jeffrey N
authors.initials: JN
authors.lastname: Katz
authors.forename: Daniel H
authors.initials: DH
authors.lastname: Solomon
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D018501:Antirheumatic Agents / Q000627:therapeutic use; D001172:Arthritis, Rheumatoid / Q000175:diagnosis* / Q000188:drug therapy; D019992:Databases as Topic / Q000592:standards*; D016208:Databases, Factual / Q000592:standards*; D005260:Female; D006801:Humans; D008297:Male; D010414:Pennsylvania; D011237:Predictive Value of Tests; D012219:Rheumatology / Q000592:standards*
chemical_list: D018501:Antirheumatic Agents