Pharmaceuticals in drinking water: a scoping review to raise pharmacists’ public health and environmental awareness on contamination in groundwater, surface water, and other sources
Bibliographic record
Abstract
OBJECTIVE: To summarize knowledge on medications and medication-disinfection byproducts-compounds formed when pharmaceutical contaminants react with disinfectants-found in drinking water and its sources (effluents, surface water, groundwater), aiming to raise awareness and empower pharmacists to implement best practices for improving public health and reducing environmental impact. METHODS: A scoping review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. Data were retrieved from Medline and Embase between 2005 and 2023 using keywords related to drinking water, pharmaceutical waste, and water purification. Articles had to focus on human medication use and originate from North America and Europe. Data on types of medications, concentrations found in drinking water or its sources, and types of byproducts were extracted, and a narrative synthesis was written. KEY FINDINGS: Forty-five articles were included. Among pre-defined classes, antihypertensives, analgesics, antibiotics, and psychotropic medications were most frequently found. The most commonly identified medications included carbamazepine, diclofenac, ibuprofen, and acetaminophen/paracetamol. Traces of medications were present in many water sources, with higher concentrations near pharmaceutical industries. Some medications, like carbamazepine, persist in the environment for extended periods. Although not necessarily found in drinking water, medication-disinfection byproducts can be toxic, and further evidence is required to assess their risk. CONCLUSIONS: Many medications are found in drinking water and its sources, highlighting the need for pharmacists to consider their public health impact. Best practices, such as prescribing only when necessary, deprescribing, social/green prescribing, and opting for environmentally friendly alternatives, should be enforced.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".