Longitudinal monitoring of sewershed resistomes in socioeconomically diverse urban neighborhoods
Bibliographic record
Abstract
BACKGROUND: Understanding factors associated with antimicrobial resistance (AMR) distribution across populations is a necessary step in planning mitigation measures. While associations between AMR and socioeconomic-status (SES), including employment and education have been increasingly recognized in low- and middle-income settings, connections are less clear in high-income countries where SES remains an important influence on other health outcomes. METHODS: We explored the relationship between SES and AMR in Calgary, Canada using spatially-resolved wastewater-based surveillance of resistomes detected by metagenomics across eight socio-economically diverse urban neighborhoods. Resistomes were established by shotgun-sequencing of wastewater pellets, and qPCR of targeted-AMR genes. SES status was established using 2021 Canadian census data. Conducting this comparison during the height of COVID-related international travel restrictions (Dec. 2020-Oct. 2021) allowed the hypotheses linking SES and AMR to be assessed with limited confounding. These were compared with sewage metagenomes from 244 cities around the world, linked with Human Development Index (HDI). RESULTS: Wastewater metagenomes from Calgary's socioeconomically diverse neighborhoods exhibit highly similar resistomes, with no quantitative differences (p > 0.05), low Bray-Curtis dissimilarity, and no significant correlations with SES. By comparison, dissimilarity is observed between globally-sourced resistomes (p < 0.05), underscoring the homogeneity of resistomes in Calgary's sub-populations. The analysis of globally-sourced resistomes alongside Calgary's resistome further reveals lower AMR burden in Calgary relative to other cities around the world. This is particularly pronounced for the most clinically-relevant AMR genes (e.g., beta-lactamases, macrolide-lincosamide-streptogramin). CONCLUSIONS: This work showcases the effectiveness of inclusive and comprehensive wastewater-based surveillance for exploring the interplay between SES and 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".