Pharmaceutical Waste Management in Pharmacies in Zagreb
Notice bibliographique
Résumé
Aim The main goal was to study pharmaceutical waste (drugs that were unsold, as of their expiration date, and drugs disposed by patients in pharmacies) disposal by using data from pharmacies in the City of Zagreb, Republic of Croatia. Subsequently, we also tried to determine which drugs and their therapeutical groups were disposed of, the amount of prescription drugs and OTC (Over the Counter) drugs disposed, and the cost of their disposal. Methods The research included 51 of the overall 208 pharmacies in Zagreb (Agency for Medicinal Products and Medicinal Devices of Croatia, Publication. 2014.), which had a population of 795.505 inhabitants, almost a quarter of the whole population of Croatia (Croatian Bureau of Statistics – Republic of Croatia, Publication. 2014.). Pharmaceutical waste was collected in pharmacies from April 14 to May 14, 2014. After collection, the drugs were sorted using Anatomical Therapeutic Chemical Classification (ATC) into different therapeutical groups, noting them in an electronic formular for waste recording in each pharmacy (). We also used Register of Medicines as a source for prices of medicines (Lejla Bencaric, Register of Medicines in Croatia. Zagreb. 2014.) as well as internal data from companies whose core business was pharmaceutical waste disposal as the source for cost of its disposal. Results During the research, pharmacies collected 291.56 kilograms of pharmaceutical waste, containing 6289 medicine packings, including tablets and other formulations of drugs. 4549 (72.3%) of them were prescription drugs and 1740 (27.7%) were OTC drugs. Of all the pharmaceutical units, there were more prescribed medicines than OTCs (). The majority of the pharmaceutical waste drugs included Cardiovascular system (17.8%), Alimentary tract and metabolism (14.5%) and Nervous system (12.4%) according to ATC (). In the first group, the most prevalent drugs were the antihypertensives atenolol and amlodipine. In the second group, the leading drug was ranitidine, followed by insuline and metmorphine. Among the antibiotic drugs, amoxiciline in combination with clavulonic acid was the most reported. Among OTCs, acetylsalicylic acid was the most often evidented substance. Overall, results showed a strong correlation between drugs mostly prescribed by physicians and those evidented in pharmaceutical waste. We estimated the total cost of managing pharmaceutical waste to be 132,194 €. Conclusion Although the practice of disposing medicines through pharmacies is well regulated, its cost is high, especially when we know that most of it is consisted of prescribed drugs. This reflects patients’ non‐compliance or misunderstanding of directions in regard to drug use, showing us that the level of cooperation in drug treatment between patients and their physicians has room for improvement. Support or Funding Information This research had support from “Gradska ljekarna Zagreb”. Number of packages of prescription medicines (Rx) and OTCs per each pharmaceutical unit Unit Rx OTC 1 53 44 2 34 6 3 23 11 4 51 32 5 87 46 6 94 48 7 53 15 8 25 7 9 89 27 10 215 103 11 98 45 12 43 22 13 31 28 14 25 5 15 78 47 16 42 20 17 81 16 18 262 69 19 146 46 20 50 17 21 62 39 22 70 25 23 64 22 24 31 9
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».