Association between a publicly funded universal drug program and antipsychotic and antidepressant medication dispensing to children
Bibliographic record
Abstract
BACKGROUND: The prescribing of antidepressants and antipsychotics to children has increased worldwide, but little is known about how changes in drug funding policy influence the practice. In 2018, Ontario introduced a universal pharmacare program (OHIP+) for children and youth, amending it in April 2019 to cover only those without private insurance. We examined the association of these policy changes with antipsychotic and antidepressant medication prescribing. METHODS: We conducted a population-based study of antidepressant and antipsychotic medication dispensing to children ≤ 18 years old between September 1, 2014, and February 29, 2020. We obtained dispensing data from the IQVIA Geographic Prescription Monitor database, and used interventional autoregressive integrated moving average models to examine whether the implementation of OHIP + and its subsequent revision were associated with changes in dispensing. RESULTS: The implementation of OHIP + was not associated with changes in the rate of antidepressants (-19.3 units per 1,000 population; 95% confidence interval [CI]: -41.7 to 3.1) or antipsychotics (+ 1.0 unit per 1,000 population; 95% CI: -5.4 to 7.5) dispensed. Similarly, subsequent changes to the program restricting coverage to children without private insurance were not associated with antidepressant (0.3 units per 1,000; 95% CI: -7.4 to 7.9) or antipsychotic (1.0 units per 1,000; 95% CI: -0.9 to 2.9) dispensing trends. CONCLUSION: Implementation of a publicly-funded pharmacare program did not influence trends in antidepressant or antipsychotic medication dispensing among children.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".