The unintended (and costly) effects due to the introduction of an unrestricted reimbursement policy for atypical antipsychotic medications in a Canadian public prescription drug program: 1996/97 to 2005/06.
Bibliographic record
Abstract
BACKGROUND: Due to the increasing costs of pharmaceuticals, drug benefit programs often implement various policies that limit availability of drugs. These policies can have unforeseen consequences. OBJECTIVES: To examine the utilization and expenditures for antipsychotic medications in a provincial government community-based drug program over a 10-year period when atypical antipsychotics were introduced and multiple reimbursement policy changes with respect to these agents were employed. METHODS: Retrospective analysis of the Newfoundland and Labrador Prescription Drug Program (NLPDP) claims database from 1996/97 to 2005/06. Antipsychotic medication utilization and expenditure were measured and effects of changes in reimbursement policies examined. Excess expenditure was measured by subtracting the actual from modelled expenditure under different policies. RESULTS: Between 1996/97 and 2005/06, the number of prescriptions for antipsychotic medications increased by 75% and expenditures by more than 720% to $7.2 million (peaking at $7.9 million in 2003/04), with atypical agents making up 96% of the total. Expenditure for antipsychotic medications grew by an annual average rate of 26.3%. At the same time, the number of people enrolled in the drug program declined by an annual average rate of 1.13%. The total excess amount of money spent was $266,195 per 1,000 beneficiaries during unlimited access to atypical agents. CONCLUSION: There has been a substantial, unintentional, increase in the prescribing of atypical antipsychotics each year in Newfoundland and Labrador over the 10 years, likely due to off-label use following the unrestricted and partial restrictive access policies for these medications. Perhaps restricted access for recognized usage should be enforced.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".