Changes in Thiazolidinedione Use and Outcomes Following Removal of a Prior-authorization Policy
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to assess the impact of removing prior-authorization restrictions on the use of thiazolidinediones (TZDs) and outcomes. METHODS: In a controlled interrupted time-series analysis, whereby new users of antidiabetic agents over 65 years of age in adjacent Canadian provinces with different TZD-related policies [Alberta (n = 16,653) and Saskatchewan (n = 6682)] were followed from January 2001 to December 2006. Prior authorization for TZDs was removed in Alberta (intervention province) in December 2003 (rosiglitazone) and February 2004 (pioglitazone); no policy changes occurred in Saskatchewan (control province). Adjusted differences in percent change between intervention and control provinces were used to estimate policy-attributable effects (PAE) on TZD use within 30 days and 1 year and patient outcomes (composite of all-cause mortality or hospitalization for acute coronary events or heart failure) within 1 year. RESULTS: Mean age was 75 years, 51% were female, and 206,055 antidiabetic prescriptions were dispensed during follow-up. TZD use within 30 days among new users of antidiabetic agents increased almost >10% in Alberta compared with Saskatchewan controls immediately following the policy change: PAE = 9.4%, 95% confidence interval, 7.3%-11.6%. Other less expensive antidiabetic drug use decreased to exactly the same extent, suggesting TZD substitution. Compared with the controls, in Alberta there were no changes in the primary (clinical) composite outcome at 1 year (PAE = 0.31%, 95% confidence interval, -2.8% to +3.4%). CONCLUSIONS: The removal of a prior-authorization policy for TZDs was associated with an immediate increase in TZD use but did not impact patient outcomes. In this case, the policy removal shifted drug use to a more expensive drug with less certain clinical benefit.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".