title: Identification and validation of vertebral compression fractures using administrative claims data.
abstract: BACKGROUND
  Vertebral compression fractures (VCFs) are the most common type of osteoporotic fracture. Administrative claims data might be useful to identify VCFs, but this approach to case finding has received limited evaluation.
  
  
  METHODS
  Using the administrative claims databases of a large regional US health care organization, we identified adults with a claim with a VCF diagnosis code from January 2003 to June 2004 and excluded persons with malignancy. We examined the positive predictive values (PPV) of several claims algorithms to correctly identify any confirmed (prevalent or incident) VCF, and separately, incident VCFs.
  
  
  RESULTS
  A total of 259 persons were identified with a VCF suspected based on their administrative claims data. A claims algorithm that required a VCF diagnosis on any claim had a PPV to identify any confirmed VCF of 87% (95% confidence interval (CI), 82-91%). The PPV of this algorithm to identify a confirmed incident VCF was 46% (95% CI, 37-54%). An algorithm that required a spine imaging test followed by a physician visit with a VCF code within 10 days, or a hospitalization with a primary diagnosis code, had higher PPVs (PPV = 93%; 95% CI, 87-98% for any confirmed VCF; PPV = 61%; 95% CI, 49-74% for incident VCFs).
  
  
  CONCLUSIONS
  A simple case finding approach to identify VCFs using administrative claims data can identify prevalent VCFs with high accuracy but misclassified more than half of incident VCFs. A more complex claims algorithm may be used but still will result in some misclassification of incident VCFs.
authors.affiliation: Center for Education and Research on Therapeutics of Musculoskeletal Disorders, University of Alabama at Birmingham, Birmingham, Alabama 35294, USA. jcurtis@uab.edu
authors.forename: Jeffrey R
authors.initials: JR
authors.lastname: Curtis
authors.forename: Amy S
authors.initials: AS
authors.lastname: Mudano
authors.forename: Daniel H
authors.initials: DH
authors.lastname: Solomon
authors.forename: Juan
authors.initials: J
authors.lastname: Xi
authors.forename: Mary Elkins
authors.initials: ME
authors.lastname: Melton
authors.forename: Kenneth G
authors.initials: KG
authors.lastname: Saag
mesh_terms: D000368:Aged; D000465:Algorithms; D015897:Comorbidity; D016208:Databases, Factual; D003952:Diagnostic Imaging / Q000191:economics* / Q000379:methods / Q000706:statistics & numerical data; D005260:Female; D050815:Fractures, Compression / Q000145:classification / Q000175:diagnosis / Q000453:epidemiology*; D006801:Humans; D015994:Incidence; D007345:Insurance Claim Review; D008297:Male; D008875:Middle Aged; D010024:Osteoporosis / Q000188:drug therapy / Q000503:physiopathology; D011237:Predictive Value of Tests; D015995:Prevalence; D016103:Spinal Fractures / Q000145:classification / Q000175:diagnosis / Q000453:epidemiology*; D014481:United States / Q000453:epidemiology