title: Identifying newly approved medications in Medicare claims data: a case study using tocilizumab.
abstract: BACKGROUND
  After U.S. licensure, parenterally administered medications are identified using non-specific drug codes. Accurately identifying these medications is critical to safety and effectiveness research. Methods to identify medications prior to assignment of specific drug codes have not been well described.
  
  
  OBJECTIVE
  To describe a generalized approach using non-specific drug codes to identify parenteral therapies in Medicare claims and to assess the ability of that approach to identify tocilizumab (TCZ), a new biologic agent approved in 2010.
  
  
  METHODS
  We used 2008-2010 Medicare data for a cohort of rheumatoid arthritis patients for algorithm development. Our algorithm classified non-specific drug codes based upon: 1) ICD9 codes; 2) unit values (i.e. dose); 3) codes for infusion/injection procedures; 4) expected versus observed total reimbursement amount and reimbursement per unit. We assessed algorithm performance by linking to an arthritis registry to examine external validity.
  
  
  RESULTS
  Of 472 803 claims with non-specific drug codes, 9762 claims satisfied the TCZ algorithm. 74.3% of 9762 claims were classified as TCZ by exact unit price or allowed amount, 4.4% by unique doses, 21.3% by diagnosis code and small deviation from unit price or allowed amount. The algorithm demonstrated good performance characteristics: sensitivity 94% (95% CI 80-99), specificity 100% (99-100) and PPV 97% (84-100).
  
  
  CONCLUSIONS
  Claims-based algorithms in Medicare or similar data systems can accurately identify newly approved biologics administered parenterally prior to the assignment of specific drug codes.
authors.affiliation: University of Alabama at Birmingham, Division of Clinical Immunology and Rheumatology, Birmingham, AL, USA.
authors.forename: Jeffrey R
authors.initials: JR
authors.lastname: Curtis
authors.forename: Fenglong
authors.initials: F
authors.lastname: Xie
authors.forename: Rui
authors.initials: R
authors.lastname: Chen
authors.forename: Lang
authors.initials: L
authors.lastname: Chen
authors.forename: Meredith L
authors.initials: ML
authors.lastname: Kilgore
authors.forename: James D
authors.initials: JD
authors.lastname: Lewis
authors.forename: Huifeng
authors.initials: H
authors.lastname: Yun
authors.forename: Jie
authors.initials: J
authors.lastname: Zhang
authors.forename: Nicole C
authors.initials: NC
authors.lastname: Wright
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Delzell
mesh_terms: D000465:Algorithms; D061067:Antibodies, Monoclonal, Humanized / Q000008:administration & dosage / Q000627:therapeutic use*; D001172:Arthritis, Rheumatoid / Q000188:drug therapy*; D059019:Clinical Coding; D015331:Cohort Studies; D016208:Databases, Factual; D017277:Drug Approval; D006801:Humans; D007267:Injections; D007349:Insurance, Health, Reimbursement / Q000706:statistics & numerical data; D006278:Medicare; D017891:Pharmacoepidemiology / Q000379:methods; D012042:Registries; D012680:Sensitivity and Specificity; D014481:United States
chemical_list: D061067:Antibodies, Monoclonal, Humanized; C502936:tocilizumab
keywords: Medicare; Part D; biologics; certolizumab; denosumab; linkage; pharmacoepidemiology; registry; rheumatoid arthritis; tocilizumab
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