Monitoring antimicrobial resistance in care homes through wastewater surveillance: a scoping review
Bibliographic record
Abstract
Background Antimicrobial resistance poses a growing threat, especially in care homes where older residents are particularly vulnerable due to frequent antibiotic use and co-morbidities. Following the COVID-19 pandemic, there has been a growing focus on wastewater surveillance for detecting and monitoring pathogens in healthcare settings. Aim This study followed the Joanna Briggs Institute scoping review framework to map the extent of available literature on wastewater-based epidemiological studies addressing antimicrobial resistance in care homes for older adults. Methods Six electronic databases (MEDLINE, Embase, Scopus, Web of Science, ProQuest, and Google Scholar) were searched from date of inception until 26 th August 2024. The search strategy employed variations of the keywords; ‘antimicrobial resistance,' ‘wastewater-based epidemiology,' and ‘care homes for older adults.' Studies were screened based on eligibility criteria, with data extracted by one researcher. Another researcher reviewed the charted data and resolved any queries. The search identified 83 studies, from which 11 studies, conducted between 2015 and 2024, were included. Findings The studies used grab or composite sampling, combined with culture-based methods for bacterial identification, antimicrobial susceptibility testing, and molecular techniques such as polymerase chain reaction and whole genome sequencing. Enterobacterales, including Escherichia coli and Klebsiella spp., were the most frequently detected, with high resistance rates, especially to some penicillins and cephalosporins. Conclusion Despite the small sample sizes reported in this review, wastewater-based epidemiology shows promise in monitoring antibiotic-resistant bacteria in care home wastewaters, offering insights into trends and genetic diversity, with the potential to inform public health strategies and antibiotic stewardship programmes.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".