Influence of antimicrobial consumption (AMC) on the detection of antimicrobial resistance genes (ARGs) in urban wastewater
Bibliographic record
Abstract
Background Antimicrobial resistance (AMR) is a global health threat, causing over 1.27 million deaths annually and linked to an additional 4.95 million. AMR transmission occurs beyond clinical settings, with wastewater serving as a sentinel of community-level spread. This study investigated how temporal changes in antimicrobial consumption (AMC) correlate with the prevalence of antimicrobial resistance genes (ARGs) in wastewater, using wastewater surveillance (WS) to monitor resistance trends in Quebec, Canada. Methodology AMC data (January 2019–May 2023) were obtained from the Institut National de Santé Publique du Québec (INSPQ) under a license from IQVIA Solutions Canada Inc. Wastewater samples (September 2020–September 2022) were obtained from three WWTPs and screened for 11 ARGs, including bla TEM , bla SHV , bla CTX-M , bla NDM , bla OXA-1/30 , qnr A, qnr B, mph E, and mef A. Analyses assessed temporal and spatial associations between AMC and ARGs. Results Total prescriptions declined from 537 to 392 per 1000 inhabitants between 2019 and 2020 (−27 %), likely due to the impact of the COVID-19 pandemic. This shift created a contrast that allowed us to better capture the signal of AMC through the noise in wastewater composition. β-lactams, macrolides, and fluoroquinolones were the most prescribed classes. ARGs were consistently detected in all 41 samples, with macrolide resistance genes being the most abundant. Strong correlations were observed between AMC and ARG prevalence in wastewater, particularly for β-lactams and fluoroquinolones (Spearman R = 0.80 and 0.81, p < 0.05). Spatial patterns showed uniform AMC but variable ARG levels. Conclusions Our study highlights the correlation between AMC and ARG. WS shows promise for real-time AMR monitoring.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".