The impact of OHIP+ pharmacare on use and costs of public drug plans among children and youth in Ontario: a time-series analysis
Bibliographic record
Abstract
BACKGROUND: In 2018, Ontario implemented a pharmacare program (Ontario Health Insurance Plan Plus [OHIP+]) to provide children and youth younger than 25 years with full coverage for prescription medications in the provincial formulary. We aimed to assess the use of public drug plans and costs of publicly covered prescriptions before and after the program's implementation and modification. METHODS: We conducted a population-based, interrupted time-series analysis using data on prescription drug claims, from the Canadian Institute for Health Information's National Prescription Drug Utilization Information System, for people younger than 25 years from January 2016 to October 2019 in Ontario, using British Columbia as the control. We assessed changes in the level and trend of publicly covered prescriptions and expenditures after the introduction of OHIP+ in January 2018 and after program modifications in April 2019. We also assessed plan use and expenditures for publicly covered prescriptions for diabetes and asthma. RESULTS: < 0.001). Similarly, total public drug expenditures increased by 254%, from $379 million in 2017 to $839 million in 2018, then reduced by 49% to $204 million in 2019. Monthly public plan expenditures increased by $115.94 (95% confidence interval [CI] $100.93 to $130.94) post-OHIP+ implementation and decreased by $99.97 (95% CI -$119.79 to -$80.15) per person per month after April 2019. INTERPRETATION: Adopting OHIP+ increased use of public drug plans and expenditures for publicly funded prescription medicines, and the program modification was associated with decreases in both outcomes. This study's findings can inform the national pharmacare debate; future research should investigate associations with health outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| 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".