Characteristics of high–drug-cost beneficiaries of public drug plans in 9 Canadian provinces: a cross-sectional analysis
Bibliographic record
Abstract
BACKGROUND: Drugs are the fastest growing cost in the Canadian health care system, owing to the increasing number of high-cost drugs. The objective of this study was to examine the characteristics of high-drug-cost beneficiaries of public drug plans across Canada relative to other beneficiaries. METHODS: We conducted a cross-sectional study among public drug plan beneficiaries residing in all provinces except Quebec. We used the Canadian Institute for Health Information's National Prescription Drug Utilization Information System to identify all drugs dispensed to beneficiaries of public drug programs in 2016/17. We stratified the cohort into 2 groups: high-drug-cost beneficiaries (top 5% of beneficiaries based on annual costs) and other beneficiaries (remaining 95%). For each group, we reported total drug costs, prevalence of high-cost claims (> $1000), median number of drugs, proportion of beneficiaries aged 65 or more, the 10 most costly reimbursed medications and the 10 medications most commonly reimbursed. We reported estimates overall and by province. RESULTS: High-drug-cost beneficiaries accounted for nearly half (46.5%) of annual spending, with an average annual spend of $14 610 per beneficiary, compared to $1570 among other beneficiaries. The median number of drugs dispensed was higher among high-drug-cost beneficiaries than among other beneficiaries (13 [interquartile range (IQR) 7-19] v. 8 [IQR 4-13]), and a much larger proportion of high-drug-cost beneficiaries than other beneficiaries received at least 1 high-cost claim (40.9% v. 0.6%). Long-term medications were the most commonly used medications for both groups, whereas biologics and antivirals were the most costly medications for high-drug-cost beneficiaries. INTERPRETATION: High-drug-cost beneficiaries were characterized by the use of expensive medications and polypharmacy relative to other beneficiaries. Interventions and policies to help reduce spending need to consider both of these factors.
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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 | 0.001 |
| 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) | 0.002 | 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 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".