A pharmaceutical policy accident: collision of shareholder capitalism and Chinese state capitalism driving the shortage of an essential antibiotic
Bibliographic record
Abstract
Background: An explosion in a Chinese factory in 2016 caused a global shortage of essential broad-spectrum antibiotic piperacillin-tazobactam. Hitherto, no detailed, policy-relevant analysis has been conducted on this major shortage event. Thus, we aimed to (1) investigate causes; (2) describe supply chain challenges; and (3) uncover policy gaps to support possible mitigation actions. Methods: Applying an analytical framework for security of medical supply chains, we investigated the changing roles of Pfizer-led and Chinese API suppliers. We identified demand surge, capacity reduction and co-ordination failures. Triangulating between scientific literature, corporate, and regulatory documents, we analysed the impact of Western and Chinese policy contexts on supply chain resilience. Results: We uncovered 'red flags': geographically dispersed manufacturing failures due to complexity of sterile production; undetected supply chain concentration and interlinkages; and Chinese policy-led API supplier consolidation. We found these warning signals were ignored in the absence of a co-ordinated policy framework to identify and mitigate emerging global supply risks. Firstly, policy makers lacked visibility on growing 'volume dependency' in the chain. Secondly, national policy makers lacked a global view of supply risk. Thirdly, we show antibiotic API manufacturing economics were impacted by a number of non-pharmaceutical policy decisions (e.g. state aid, environmental standards, procurement rules) which contributed to supply chain vulnerability. Conclusions: Our findings suggest possible policy gaps in governance of supply chain resilience. Firstly, disclosure of API suppliers including degree of dependency may better pre-empt bottlenecks, facilitating priority setting for public investments in re-shoring where global API supply currently relies on few, or single plants; secondly, a whole-of-government approach may counter the potential impact of non-pharmaceutical policies on supply chain resilience. Our findings confirm suggestions from previous studies that international data sharing would be beneficial considering the global shortage effects which can emerge from a single point of failure.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".