Comparison of claims from high–drug cost beneficiaries in Ontario, Canada, and Australia: a cross-sectional analysis
Bibliographic record
Abstract
BACKGROUND: Globally, payers are struggling with rising drug costs, driven primarily by the increasing number of high-cost medications used by their beneficiaries. We aimed to compare the annual drug spending on claims from high-drug cost beneficiaries in the province of Ontario, Canada, and Australia. METHODS: We conducted a cross-sectional analysis of public drug claims in Ontario and Australia from fiscal years 2006 to 2017. We identified the total government costs for prescribed medications per beneficiary. During the study period, public drug coverage in Ontario was provided to all residents 65 years of age and older, those with financial needs, and those living in long-term care or in need of home care. Australia maintains a publicly funded, universal system covering all citizens. Based on annual spending, we divided beneficiaries into 4 cost groups, representing the top 1%, top 5%, top 10% and the remaining 90%. We reported the following for each cost group: medication cost and proportion of total government spending, number of unique drugs dispensed per person and the top 10 most costly drug classes. RESULTS: In Ontario and Australia, the top 1% of beneficiaries accounted for a large and increasing proportion of all government drug costs, growing from 12% ($405 946 197) to 24% ($1 345 977 248) in Ontario, and from 14% ($86 565 586) to 34% ($416 097 984) in Australia between 2006 and 2017. The most costly drug classes among high-drug cost beneficiaries in both jurisdictions were biologics and hepatitis C treatments. INTERPRETATION: In both Ontario and Australia, a small number of beneficiaries accounted for a large proportion of public drug spending, driven largely by the use of expensive medications. The current development of potential national pharmacare strategies in Canada must optimize the use of high-cost drugs to ensure the sustainability of the program.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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 teacher head, 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".