PURPOSE: Administrative databases that only capture records for benefit-approved prescriptions may underestimate exposure because they do not capture non-benefit prescriptions. Using a natural experiment, we illustrate the impact of automating a prior-authorization policy on the completeness of drug exposure. METHODS: Using Saskatchewan (Canada) databases, weekly counts of benefit-approved and total prescription records in 2006 for new users of antidiabetic agents were examined across four categories: thiazolidinediones (TZDs), metformin, glyburide, and insulin. On July 1, 2006, Saskatchewan's public drug plan implemented an automated, online-adjudicated, prior-authorization process for TZDs; previously, prior approval was paper based. No such policy changes occurred for other drugs. We estimated the effect of this policy change on drug exposure using interrupted time-series analyses. RESULTS: We examined 223 552 prescription records: 19% were for TZDs, 48% for metformin, 20% for glyburide, and 13% for insulin. Prior to automation, there were, on average, 571 benefit-approved TZD records per week; however, the number of benefit-approved TZD records increased immediately after the automated process was introduced by 240 prescriptions per week (95% CI 200-280, p < 0.001). The average proportion of TZD benefit-approved records was 73% before and increased to 93% immediately following policy change (20% absolute change, 95% CI 18.7-20.4%). No changes were observed for metformin, glyburide, or insulin (p > 0.1 for all). CONCLUSIONS: Automating prior authorization for TZDs immediately increased the proportion of captured TZD records, suggesting in our study that one-fifth of TZD exposure was previously misclassified. If replicable, this indicates that even subtle changes in reimbursement policy may affect the validity of drug exposure data.
PURPOSE: Administrative databases that only capture records for benefit-approved prescriptions may underestimate exposure because they do not capture non-benefit prescriptions. Using a natural experiment, we illustrate the impact of automating a prior-authorization policy on the completeness of drug exposure. METHODS: Using Saskatchewan (Canada) databases, weekly counts of benefit-approved and total prescription records in 2006 for new users of antidiabetic agents were examined across four categories: thiazolidinediones (TZDs), metformin, glyburide, and insulin. On July 1, 2006, Saskatchewan's public drug plan implemented an automated, online-adjudicated, prior-authorization process for TZDs; previously, prior approval was paper based. No such policy changes occurred for other drugs. We estimated the effect of this policy change on drug exposure using interrupted time-series analyses. RESULTS: We examined 223 552 prescription records: 19% were for TZDs, 48% for metformin, 20% for glyburide, and 13% for insulin. Prior to automation, there were, on average, 571 benefit-approved TZD records per week; however, the number of benefit-approved TZD records increased immediately after the automated process was introduced by 240 prescriptions per week (95% CI 200-280, p < 0.001). The average proportion of TZD benefit-approved records was 73% before and increased to 93% immediately following policy change (20% absolute change, 95% CI 18.7-20.4%). No changes were observed for metformin, glyburide, or insulin (p > 0.1 for all). CONCLUSIONS: Automating prior authorization for TZDs immediately increased the proportion of captured TZD records, suggesting in our study that one-fifth of TZD exposure was previously misclassified. If replicable, this indicates that even subtle changes in reimbursement policy may affect the validity of drug exposure data.