Ecotoxicological implications of increased antidepressant concentrations in the Laurentian Great Lakes Basin, 2018–2023
Bibliographic record
Abstract
Antidepressants are only partially metabolized and then eliminated in urine and feces. Since waste water treatment plants are not designed to remove pharmaceuticals, antidepressants and their metabolites eventually reach the environment. Antidepressants are among the most prescribed drugs in the world, and their prescription rates increased dramatically following the onset of the COVID-19 pandemic. Our aim was to compare their measured environmental concentrations (MECs) in surface water in the three years before and the three years after the pandemic onset. Nearly 1300 samples were collected from 67 sites in the Laurentian Great Lakes Basin, from streams and rivers. We developed a liquid chromatography-tandem mass spectrometry (LC-MS/MS) methodology to measure the MECs of 7 of the most frequently used antidepressants and 3 of their metabolites. Canadian antidepressant use data was also collected via the IQVIA MIDAS® database of estimated sales data for pharmaceutical drugs (2018–2021). We found that the median MECs for 9 of the 10 substances increased between 1.5- and 7.2-fold ( p < 0.05). The greatest median increases corresponded to fluvoxamine (4.8-fold) and 10-hydroxyamitriptyline (4.7-fold). Increases were concurrent with rising use rates post-COVID-onset. The highest concentrations corresponded to the metabolite O-desmethylvenlafaxine (3113.98 ng L −1 ) and its parent drug venlafaxine (699.59 ng L −1 ) in 2022. We collected and analyzed antidepressant surface water and ecotoxicological data to provide a comprehensive review to contextualize the LC-MS/MS data. We compared maximal MECs to ecotoxicological reference values and theorize a possible ecotoxicological impact when considering the overlap of maximal levels with ecotoxicological reference values cited in the scientific literature. We offer recommendations for next steps.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".