Longitudinal wastewater-based surveillance of vancomycin-resistant Enterococci in tertiary-care hospitals
Bibliographic record
Abstract
OBJECTIVE: Vancomycin-resistant Enterococcus (VRE) is an important cause of healthcare-associated infections. We adapted wastewater-based surveillance as a tool to longitudinally monitor VRE in hospitals through the detection of vancomycin resistance genes vanA and vanB. METHODS: Wastewater from four tertiary-care hospitals (three adult and one pediatric, totaling >2300 inpatient beds) and all three municipal wastewater treatment plants (WWTP) in Calgary, Canada (∼1.8 million) was sampled weekly (March to September 2022) and every other week (September 2022 to March 2023). Wastewater pellets were collected, DNA extracted, and vanA and vanB quantified by qPCR. vanA and vanB gene copies were assessed as raw (copies/mL) and normalized with three different fecal biomarkers - total bacterial 16S-rRNA, Bacteroides HF183 16S-rRNA, and human 18S-rRNA. Raw and normalized vanA and vanB abundance from each site was compared with clinically identified infections, vancomycin prescribing and hemodialysis services. RESULTS: The abundance of vanA was up to 1085-fold higher (p < 0.0001, Mann-Whitney) and vanB up to 32-fold higher (p < 0.01, Mann-Whitney) in adult hospitals compared to an aggregate municipal signal and exhibited significantly greater variation. Strong correlations between each method of fecal normalization and raw-measured vanA and vanB were observed, and no normalization method proved superior (Spearman's r = 0.50-0.96, p < 0.0001). vanA abundance was strongly correlated with hemodialysis provision (Spearman's r = 0.8357, p < 0.0001) but not vancomycin prescribing. CONCLUSIONS: Wastewater-based surveillance is a comprehensive tool capable of longitudinal real-time hospital surveillance for VRE with the potential to transform the ability of infection control and antimicrobial stewardship programs to dynamically track, understand, and mitigate nosocomial antimicrobial-resistant pathogens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 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".