Pharmaceutical Waste Management in Pharmacies in Zagreb
Bibliographic record
Abstract
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
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".