Substandard and falsified medicine recalls in the legitimate supply chain: a systematic review of evidence
Bibliographic record
Abstract
OBJECTIVES: To compare international substandard and falsified (SF) medicine recall trends from published research papers based on governmental databases, summarise the extent of the problem in the legitimate supply chain, and identify ways to manage the issue. DESIGN: Systematic review of published academic evidence. DATA SOURCES: Drug recall data in published literature, obtained from official international government regulator databases in the USA, the UK, Canada, Sri Lanka, Zambia, Portugal, Nepal, Saudi Arabia, Argentina, Brazil, Chile, Cuba, Colombia, Mexico, Bolivia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras, Panama, Peru and Venezuela. ELIGIBILITY CRITERIA: A search for literature published between 2010 and 2024 was conducted using PubMed, MEDLINE and Embase. Included studies examined recall data of substandard and/or falsified medicines obtained through official government regulator websites. DATA EXTRACTION AND SYNTHESIS: Data were extracted using Excel files and synthesised using a thematic analysis approach. RESULTS: 13 research papers containing original data were included. Recall data were obtained from official regulatory databases in 23 different countries. Substandard medicines had significantly higher recall rates than falsified products, while parenteral drugs and tablets were the most recalled formulation types. The leading reasons for defective medicines were contamination, out-of-specification results, stability and packaging issues. India was identified as a common source of SF medicines in Zambia, Sri Lanka, Brazil and Nepal. Frequent recalls of anti-infective drugs were observed in countries with equatorial, tropical and subtropical climates, while high-income countries like Canada, Saudi Arabia and the UK faced issues with defective antihypertensive drugs. Interestingly, medicines affected by nitrosamines' contamination were recalled in all regions examined in 2018, but in other recall cases, there were disparities among recall action. CONCLUSIONS: There appeared to be similar international recall practices for some products like nitrosamines and not for others like rosiglitazone across the same time frame, which raises questions concerning international drug safety disparities. The requirement to align and globally strengthen regulatory frameworks was of emerging importance. Cooperation between regulatory authorities to create a harmonised approach to reporting medicine recalls and standardising the data included in a recall notification is proposed to facilitate a more accurate comparison of international trends surrounding recalled SF medicines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".