Trends in the Cost and Utilization of Publicly Dispensed Respiratory Inhalers in Ontario, Canada: A Repeated Cross‐Sectional Study
Bibliographic record
Abstract
PURPOSE: Several new respiratory inhalers have recently entered the market, including combination therapy products and generic alternatives. Therefore, we examined the cost and utilization of publicly dispensed respiratory inhalers in Ontario, Canada, and the impact of new market entrants on these trends. METHODS: We conducted a repeated cross-sectional study among provincial drug program beneficiaries dispensed a respiratory inhaler between January 1, 2003, and March 31, 2023. We estimated per-beneficiary spending on respiratory inhalers per quarter, defined as the cost (2022 Canadian dollars) of respiratory inhalers reimbursement divided by the number of beneficiaries dispensed a respiratory inhaler. Joinpoint regression models were used to characterize changes in the trend. RESULTS: Between Q1 of 2003 and Q1 of 2023, public payer spending rose 160% ($26 206 322 to $68 054 816), while the number of beneficiaries dispensed a respiratory inhaler increased 92% (155 893 to 299 418). Reimbursement of ICS/LABA inhalers accounted for half the cost ($33 844 484 in Q1 of 2023). The trend for per-beneficiary spending was marked by six joinpoints, with periods of increasing and decreasing quarterly costs. Between 2003 and 2015, per-beneficiary spending increased, reaching $248/beneficiary in Q1 of 2015. In Q2 of 2015, the trend decreased for the first time and continued to decline until Q2 of 2018 ($206/beneficiary). The trend increased again after Q3 of 2018 and reached a plateau in Q3 of 2019 ($216/beneficiary). CONCLUSIONS: Public formulary listing of combination therapy inhalers, namely LAMA/LABA in Q2 of 2015, coincided with a significant decline in per-beneficiary spending on respiratory inhalers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".