Alleviating Drug Shortages: The Role of Mandated Reporting Induced Operational Transparency
Bibliographic record
Abstract
The ongoing shortage of pharmaceutical drugs critically threatens public health. With increasing industry consolidation, operational disruptions at a firm can lead to a nationwide shortage of life-saving drugs. In 2012, the U.S. Food and Drug Administration mandated all manufacturers to report any manufacturing interruption that can potentially cause shortages. The goal of the mandate was to mitigate drug shortages by enhancing operational transparency in the pharmaceutical industry. Subsequently, other countries such as Canada have also begun mandating reporting of interruptions to alleviate drug shortages. We leverage the policy changes in the United States and Canada to understand the impact of mandated reporting induced operational transparency on alleviating the extent of drug shortages. Using the data on time-to-recovery for individual drug-shortage incident and annual-days-of-shortage for each drug, we find that the new policy alleviates drug shortages, but its effectiveness is contingent upon the prevailing level of competition in the product category. Although the intervention is not as impactful under a monopoly, the mandate is most effective under a duopoly, and its impact wanes as competition intensifies. In the absence of the mandate-induced transparency, competition does not necessarily alleviate shortages, but with the regulation, competition can relieve drug shortages. Our results potentially offer healthcare providers and policymakers the impetus to alleviate drug shortages by mandating interruption reporting and improving operational transparency. This paper was accepted by Charles Corbett, operations management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.144 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".