The EpiPen Problem: Analyzing Unethical Drug Price Increases and the Need for Greater Government Regulation
Bibliographic record
Abstract
In recent years, some pharmaceutical companies have started increasing the price of their existing drugs to exorbitant levels. Often, these drugs are medically necessary for patients, who are left to take on the high costs of the medicine. One recent example is Mylan, who raised the price of the EpiPen by four hundred percent, solely for the profit of its own company and to the detriment of consumers who rely on the EpiPen. Similar patterns of drug price increases have occurred in the past and will likely happen again in the future. This Comment will seek to identify the common elements of this pattern of increasing drug prices by looking at the behavior of corporations like Mylan and the way they operate, and it will assess current approaches to resolving this issue by looking at the roles of Congress, the Food and Drug Administration (FDA), and the Federal Trade Commission (FTC). The area of concern—apart from the way patients suffer from drug price increases—is that even after these companies are subjected to Congressional hearings to address their increasing drug prices, receive hefty fines from the FTC, experience bad press, and draw criticism about the issue of increasing drug prices, little change is made to resolve this problem. At the same time, industrialized nations around the world do not face the issue of increasing drug prices to the extent seen in the United States. Three countries—Canada, Switzerland, and France—have protected their citizens by structuring their healthcare system in a way that gives pharmaceutical companies little room to raise drug prices to high levels. These countries utilize approaches such as implementing a price ceiling, negotiating with pharmaceutical companies by looking at a drug’s therapeutic value, and setting a reassessment standard to periodically check on pharmaceutical companies. To this end, this Comment will look at these approaches in more detail and will analyze how they can be applied to the United States’ own healthcare system in a way that would prevent pharmaceutical companies from raising the prices of their drugs to unethical levels and, ultimately, lower the cost of prescription 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 | 0.028 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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".