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Quantifying the impact of drug exposure misclassification due to restrictive drug coverage in administrative
John-Michael Gamble1, Finlay A McAlister, Jeffrey A Johnson
1School of Public Health, University of Alberta, Edmonton, AB, Canada.
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.
Conclusion:
Restrictive drug coverage policies may result in substantial drug exposure misclassification, potentially severely biasing the results of drug-outcome relationships using administrative databases.
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