Do prior authorization policies discourage first-line antipsychotic use in patients newly discharged from a hospitalization for schizophrenia in Saskatchewan?
Bibliographic record
Abstract
BACKGROUND: Drug benefit providers can decrease prescribing of specific medications through prior authorization policies. In Saskatchewan, certain second generation antipsychotics (SGAs) are recognized as first-line agents to manage schizophrenia; but, require prior authorization because their coverage is restricted in other conditions. We aimed to determine if the need for prior-authorization substantially diminishes prescribing of first-line SGAs in comparison to unrestricted agents. OBJECTIVES: To conduct an ecological comparison of SGA prescribing with changes in prior- authorization policies between 1997 and 2005 using health-administrative databases in Saskatchewan, Canada. METHOD: Eligible subjects were discharged from hospital with a first-time primary diagnosis of schizophrenia between 1997 and 2005. SGAs dispensed within 7 days of discharge were used to estimate prescribing preferences for olanzapine and quetiapine relative to risperidone. Percentages of SGA use were age and sex standardized to the 2000 cohort. RESULTS: Out of 1,277 eligible patients, 521 (41%) received 564 SGA dispensations within 7-days of hospital discharge. Between 1997 and 1998, risperidone was the only SGA covered for first-line use and made up 72.6% (82/113) of SGA use while olanzapine made up 27.4% (31/113) for a crude preference ratio of 0.38 (27.4/72.6). Risperidone use decreased to 65.8% in 1999-2002 and to 47.4% in 2003-2005 as a percentage of SGA dispensations. Correspondingly, the preference ratios for olanzapine and quetiapine increased from 0.40 to 0.57 and from 0.12 to 0.54 in these respective periods. CONCLUSIONS: The requirement for prior-authorization does not appear to substantially diminish prescribing of first-line SGAs for the treatment of schizophrenia in Saskatchewan, Canada.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".