Quantifying Components of Drug Expenditure Inflation: The British Columbia Seniors' Drug Benefit Plan
Bibliographic record
Abstract
OBJECTIVE: To quantify the relative and absolute importance of different factors contributing to increases in per capita prescription drug costs for a population of Canadian seniors. DATA SOURCES/STUDY SETTING: Data consist of every prescription claim from 1985 to 1999 for the British Columbia Pharmacare Plan A, a tax-financed public drug plan covering all community-dwelling British Columbians aged 65 and older. STUDY DESIGN: Changes in per capita prescription drug expenditures are attributed to changes to four components of expenditure inflation: (1) the pattern of exposure to drugs across therapeutic categories; (2) the mix of drugs used within therapeutic categories; (3) the rate of generic drug product selection; and (4) the prices of unchanged products. DATA COLLECTION/EXTRACTION METHODS: Data were extracted from administrative claims files housed at the UBC Centre for Health Services and Policy Research. PRINCIPAL FINDINGS: Changes in drug prices, the pattern of exposure to drugs across therapeutic categories, and the mix of drugs used within therapeutic categories all caused spending per capita to increase. Incentives for generic substitution and therapeutic reference pricing policies temporarily slowed the cost-increasing influence of changes in product selection by encouraging the use of generic drug products and/or cost-effective brand-name products within therapeutic categories. CONCLUSIONS: The results suggest that drug plans (and patients) would benefit from more concerted efforts to evaluate the relative cost-effectiveness of competing products within therapeutic categories of drugs.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".