Cancer drug expenditure in British Columbia and Saskatchewan: a trend analysis
Bibliographic record
Abstract
BACKGROUND: Expenditure on systemic therapy for cancer has been increasing quickly owing to population growth, increased use, both in the number of users and in prescription volume, and rising drug prices. Our objective was to describe trends in expenditure in British Columbia and Saskatchewan's cancer care systems and to elucidate these drivers of growth. METHODS: In this trend analysis, we obtained pharmacy dispensing records from the BC Cancer and Saskatchewan Cancer Agency pharmacies for all anticancer therapies dispensed in 2006-2013. We calculated total annual expenditure directly from the data and conducted a trend analysis of crude and standardized annual expenditure using generalized linear models. We estimated trends in the following components of total expenditure: cancer incidence, number of systemic therapy users per incident case, number of dispensed prescriptions per user and cost per prescription. Analysis was stratified by patient age group, cancer site and route of administration (oral or intravenous/other). RESULTS: Expenditure on systemic therapies, adjusted for population growth and aging, increased an average of 9.2% (95% confidence interval [CI] 7.2 to 11.2) per year in Saskatchewan and 6.4% (95% CI 5.3 to 7.6) per year in BC. Growth in expenditure on orally administered agents was more than 2 times higher than growth in expenditure on intravenous/other agents. Growth rates varied significantly by cancer site. In both provinces, rising cost per prescription was the largest contributor to overall growth. INTERPRETATION: Price is the primary driver of growth in systemic therapy expenditure in both BC and Saskatchewan. Understanding the mechanisms of expenditure growth may inform system planning and support policy-makers' efforts to manage rising costs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.021 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".