title: Validity of gout diagnoses in administrative data.
abstract: OBJECTIVE
  To determine the utility of using administrative data for epidemiologic studies of gout by examining the validity of gout diagnoses in claims data.
  
  
  METHODS
  From a population of approximately 800,000 members from 4 managed care plans, we identified patients who had at least 2 ambulatory claims for a diagnosis of gout between January 1, 1999 and December 31, 2003. From this group, a random sample of 200 patients was chosen for medical record review. Trained medical record reviewers abstracted gout-related clinical, laboratory, and radiologic data from the medical records. Two rheumatologists independently evaluated the abstracted information and assessed whether the gout diagnosis was probable/definite or unlikely/insufficient information. Discordant physician ratings were adjudicated by consensus. Based on record reviews, patients were also classified according to the American College of Rheumatology (ACR), Rome, and New York gout criteria and these results were compared with the physician global assessments.
  
  
  RESULTS
  There were 121 patients rated as having probable/definite gout by physician consensus, leading to a positive predictive value of >or=2 coded diagnoses of gout of 61% (95% confidence interval 53-67). There was low concordance between physician assessments and established gout criteria including ACR, Rome, and New York criteria (kappa = 0.17, 0.16, and 0.20, respectively).
  
  
  CONCLUSIONS
  Use of administrative data alone in epidemiologic and health services research on gout may lead to misclassification. Medical record reviews for validation of claims data may provide an inadequate gold standard to confirm gout diagnoses.
authors.affiliation: Meyers Primary Care Institute, University of Massachusetts Medical School, Fallon Foundation, and Fallon Community Health Plan, Worcester, Massachusetts 01655, USA. HarroldL@ummhc.org
authors.forename: Leslie R
authors.initials: LR
authors.lastname: Harrold
authors.forename: Kenneth G
authors.initials: KG
authors.lastname: Saag
authors.forename: Robert A
authors.initials: RA
authors.lastname: Yood
authors.forename: Ted R
authors.initials: TR
authors.lastname: Mikuls
authors.forename: Susan E
authors.initials: SE
authors.lastname: Andrade
authors.forename: Hassan
authors.initials: H
authors.lastname: Fouayzi
authors.forename: Judith
authors.initials: J
authors.lastname: Davis
authors.forename: K Arnold
authors.initials: KA
authors.lastname: Chan
authors.forename: Marsha A
authors.initials: MA
authors.lastname: Raebel
authors.forename: Ann
authors.initials: A
authors.lastname: Von Worley
authors.forename: Richard
authors.initials: R
authors.lastname: Platt
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D016001:Confidence Intervals; D019992:Databases as Topic; D003951:Diagnostic Errors; D005260:Female; D006073:Gout / Q000145:classification / Q000175:diagnosis* / Q000453:epidemiology*; D006279:Health Maintenance Organizations / Q000706:statistics & numerical data*; D006801:Humans; D007345:Insurance Claim Review / Q000706:statistics & numerical data*; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D014481:United States / Q000453:epidemiology