The health and economic effects of counterfeit drugs.
Bibliographic record
Abstract
BACKGROUND: Counterfeit drugs comprise an increasing percentage of the US drug market and even a larger percentage in less developed countries. Counterfeit drugs involve both lifesaving and lifestyle drugs. OBJECTIVE: To review the health and economic consequences of counterfeit drugs on the US public and on the healthcare system as a whole. METHOD: This comprehensive review of the literature encompassed a search of MEDLINE/PubMed, Google Scholar, and ProQuest using the keywords "counterfeit drugs," "counterfeit medicines," "fake drugs," and "fake medicines." A search of the various FiercePharma daily newsletter series on the healthcare market was also conducted. In addition, the US Food and Drug Administration and the World Health Organization websites were reviewed for additional information. DISCUSSION: The issue of counterfeit drugs has been growing in importance in the United States, with the supply of these counterfeit drugs coming from all over the world. Innovation is important to economic growth and US competitiveness in the global marketplace, and intellectual property protections provide the ability for society to prosper from innovation. Especially important in terms of innovation in healthcare are the pharmaceutical and biopharmaceutical industries. In addition to taking income from consumers and drug companies, counterfeit drugs also pose health hazards to patients, including death. The case of bevacizumab (Avastin) is presented as one recent example. Internet pharmacies, which are often the source of counterfeit drugs, often falsely portray themselves as Canadian, to enhance their consumer acceptance. Adding to the problems are drug shortages, which facilitate access for counterfeits. A long and convoluted supply chain also facilitates counterfeits. In addition, the wholesale market involving numerous firms is a convenient target for counterfeit drugs. Trafficking in counterfeits can be extremely profitable; detection of counterfeits is difficult, and the penalties are modest. CONCLUSION: Counterfeit drugs pose a public health hazard, waste consumer income, and reduce the incentive to engage in research and development and innovation. Stronger state licensure supervision of drug suppliers would be helpful. Technological approaches, such as the Radio Frequency Identification devices, should also be considered. Finally, counterfeit drugs may raise concerns among consumers about safety and reduce patient medication adherence.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".