The Impact of Reference Pricing of Nonsteroidal Anti‐Inflammatory Agents on the Use and Costs of Analgesic Drugs
Bibliographic record
Abstract
OBJECTIVE: To estimate the effect of reference pricing (RP) of nonsteroidal anti-inflammatory drugs (NSAIDs) on drug subsidy program and beneficiary expenditures on analgesic drugs. DATA SOURCES/STUDY SETTING: Monthly claims data from Pharmacare, the public drug subsidy program for seniors in British Columbia, Canada, over the period of February 1993 to June 2001. STUDY DESIGN: RP limits drug plan reimbursement of interchangeable medicines to a reference price, which is typically equal to the price of the lowest cost interchangeable drug; any cost above that is borne by the patient. Pharmacare introduced two different forms of RP to the NSAIDs, Type 1 in April 1994 and Type 2 in November 1995. Under Type 1 RP, generic and brand versions of the same NSAID are considered interchangeable, whereas under Type 2 RP different NSAIDs are considered interchangeable. We extrapolated average reimbursement per day of NSAID therapy over the months before RP to estimate what expenditures would have been without the policies. These counterfactual predictions were compared with actual values to estimate the impact of the policies; the estimated impacts on reimbursement rates were multiplied by the postpolicy volume of NSAIDS dispensed, which appeared unaffected by the policies, to estimate expenditure changes. PRINCIPAL FINDINGS: After Type 2 RP, program expenditures declined by $22.7 million (CAN), or $4 million (CAN), annually cutting expenditure by about half. Most savings accrued from the substitution of low-cost NSAIDs for more costly alternatives. About 20 percent of savings represented expenditures by seniors who elected to pay for partially reimbursed drugs. Type 1 RP produced one-quarter the savings of type 2 RP. CONCLUSIONS: Type 2 RP of NSAIDs achieved its goal of reducing drug expenditures and was more effective than Type 1 RP. The effects of RP on patient health and associated health care costs remain to be investigated.
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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 | 0.013 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".