Substandard and falsified medicine recalls in the legitimate supply chain: a systematic review of evidence
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,017 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,005 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».