Impact of brand drug discount cards on private insurer, government and patient expenditures
Bibliographic record
Abstract
BACKGROUND: Brand discount cards have become a popular way for patients to reduce out-of-pocket spending on drugs; however, controversy exists over their potential to increase insurers' costs. We estimated the impact of brand discount cards on Canadian drug expenditures. METHODS: Using national claims-level pharmacy adjudication data, we performed a retrospective comparison of prescriptions filled using a brand discount card matched to equivalent generic prescriptions between September 2014 and September 2017. We investigated the impact on expenditures for 3 groups of prescriptions: those paid only through private insurance, those paid only through public insurance and those paid only out of pocket. RESULTS: We studied 2.82 million prescriptions for 89 different medications for which brand discount cards were used. Use of discount cards resulted in 46% higher private insurance expenditures than comparable generic prescriptions (+$23.09 per prescription, 95% confidence interval [CI] $22.97 to $23.21). Public insurance expenditures were only slightly higher with cards: an increase of 1.3% or $0.37 per prescription (95% CI $0.33 to $0.41). Finally, out-of-pocket transactions using a card resulted in mean patient savings of 7% or $3.49 per prescription (95% CI -$3.55 to -$3.43). The impact varied widely among medicines across all 3 analyses. INTERPRETATION: The use of brand discount cards increased costs to private insurers, had little impact on public insurers and resulted in mixed impacts for patients. These effects likely resulted from private insurers reimbursing brand drug prices even when generics were available and from discount cards being adjudicated after claims were sent to other insurers in most cases. Patients and their clinicians should recognize that discount cards have mixed impacts on out-of-pocket 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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".