Net Health Plan Savings From Reference Pricing for Angiotensin-Converting Enzyme Inhibitors in Elderly British Columbia Residents
Bibliographic record
Abstract
BACKGROUND: Reference drug pricing (RP) is a cost-sharing strategy commonly used to control drug expenditures. Under RP, a benefit plan fully reimburses medications that are equally or less expensive than the reference price, and requires patients to pay the extra cost of therapeutically equivalent but higher priced drugs. Critics argued that drug plan savings are offset by administrative costs and increased spending on other health services. OBJECTIVE: We evaluated net healthcare savings in beneficiaries >or=65 years from the perspective of the British Columbia provincial health insurance system after it applied RP to angiotensin-converting enzyme (ACE) inhibitors in 1997. METHODS: We estimated savings in new users of antihypertensives after the start of RP plus associated administrative costs and savings from reductions in retail drug prices. Findings were integrated with earlier results on the consequences of RP on expenditures for drugs, physicians, and hospitalizations among all seniors who used ACE inhibitors before the introduction of RP. RESULTS: During the first year after the implementation of RP, savings for continuous users were CAN dollars 6.0 million. Savings for new users were dollars 0.2 million. Approximately five sixths thereof were achieved by utilization changes and one sixth by cost shifting to patients. There were no savings through drug price changes. Administering RP cost dollars 0.42 million. Overall net savings were estimated to be dollars 5.8 million during the first year after the start of RP. The magnitude of these savings is equal to 6% of all cardiovascular drug expenditures in seniors. After 10 years, approximately 50% of savings will be achieved by new users. CONCLUSION: We observed substantial net savings from RP for ACE inhibitors for the provincial health insurance system in British Columbia, although there were generous exemptions from the policy. In other jurisdictions, savings could be higher if drug prices decline after the start of reference pricing.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".