title: Quantifying the impact of drug exposure misclassification due to restrictive drug coverage in administrative databases: a simulation cohort study.
abstract: OBJECTIVE
  Drug exposure misclassification may occur in administrative databases when individuals obtain nonreimbursed drugs by paying "out-of-pocket" or via alternative drug coverage plans. We examined the apparent association between oral antidiabetic therapy and mortality by simulating the effects of restrictive drug coverage policies.
  
  
  METHODS
  Population-based cohort study of 12,272 new patients using oral antidiabetic agents were identified using the administrative databases of Saskatchewan Health, 1991 to 1996. We randomly misclassified 0% [base case], 10%, 25%, and 50% of known patients taking metformin according to either overt drug exposure (e.g., metformin users switched to nonusers) or time of metformin initiation (e.g., delayed capture of exposure); thereby simulating the use of a "non-formulary" or "special authorization" policy, respectively. We also simulated an age-dependent coverage policy, mimicking a policy restricted to seniors.
  
  
  RESULTS
  Metformin use was associated with lower mortality compared with sulfonylurea use in the base case (adjusted hazard ratio [aHR] 0.88, 95% confidence interval [CI] 0.78-0.99) and the nonformulary simulations. The special authorization simulations demonstrated, however, an increasing relative mortality hazard of metformin versus sulfonylurea exposure: aHR 0.96, 95% CI 0.96-0.97 and aHR 1.34, 95% CI 1.31-1.37, for 10% and 50% delays in coverage capture respectively when 50% of metformin users were misclassified. Age-dependent drug coverage had a variable impact on mortality risk compared with the base-case cohort; however, a new-user simulation with a 1-year washout revealed consistent results to the base-case analysis.
  
  
  CONCLUSIONS
  Restrictive drug coverage policies may result in substantial drug exposure misclassification, potentially severely biasing the results of drug-outcome relationships using administrative databases.
authors.affiliation: School of Public Health, University of Alberta, Edmonton, AB, Canada.
authors.forename: John-Michael
authors.initials: JM
authors.lastname: Gamble
authors.forename: Finlay A
authors.initials: FA
authors.lastname: McAlister
authors.forename: Jeffrey A
authors.initials: JA
authors.lastname: Johnson
authors.forename: Dean T
authors.initials: DT
authors.lastname: Eurich
mesh_terms: D000367:Age Factors; D015331:Cohort Studies; D003198:Computer Simulation; D016208:Databases, Factual; D003920:Diabetes Mellitus / Q000188:drug therapy* / Q000401:mortality*; D006801:Humans; D007004:Hypoglycemic Agents / Q000008:administration & dosage / Q000191:economics / Q000627:therapeutic use*; D019458:Insurance Coverage / Q000706:statistics & numerical data*; D007356:Insurance, Pharmaceutical Services / Q000706:statistics & numerical data*; D008687:Metformin / Q000191:economics / Q000627:therapeutic use; D013453:Sulfonylurea Compounds / Q000191:economics / Q000627:therapeutic use
chemical_list: D007004:Hypoglycemic Agents; D013453:Sulfonylurea Compounds; D008687:Metformin