Addressing gaps in community-level antimicrobial resistance monitoring through wastewater surveillance
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is an escalating global health crisis, yet existing monitoring systems inadequately track AMR at the community level. Wastewater surveillance (WS) offers a practical solution by providing a scalable, non-invasive approach to monitor community-level AMR. This thesis contributes to the development of a national WS program in Canada, enhancing our capacity to detect and manage AMR. Central to this work was the advancement and validation of a wastewater-specific quantitative metagenomic (wqMeta) workflow, designed to enrich and quantify thousands of AMR gene families in diverse wastewater samples. A DNA extraction method was optimized, comparing two extraction kits—PowerMicrobiome (PMB) and MagNA Pure 96 (MP96). Processing 100 mL of wastewater with the PMB kit consistently yielded higher DNA concentrations and quality, enabling more effective downstream analyses. The wqMeta workflow, which normalized data by both total bacterial load and wastewater flow rates, outperformed the published qMeta method, which relied solely on bacterial load. The wqMeta approach closely mirrored quantitative PCR (qPCR) in its ability to quantify absolute AMR gene abundances, demonstrating superior accuracy and scalability. A nine-week pilot study across six wastewater treatment plants (WWTPs) in urban, rural, and remote communities in central Canada validated the workflow. Results revealed stable AMR concentrations over time, with significant spatial differences: urban sites exhibited higher AMR levels and gene diversity compared to remote sites, highlighting the influence of population density on AMR dissemination. This study underscores the potential of WS to bridge critical gaps in AMR monitoring and offers actionable insights for public health interventions. The findings demonstrate that WS, supported by advanced methodologies such as wqMeta, can provide real-time, population-wide AMR data. Implementing a national WS program would strengthen Canada’s ability to detect and respond to AMR trends, guiding evidence-based policy decisions to mitigate the growing threat of AMR.
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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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".