Canadian Aquaculture: Supporting the need to develop sentinel surveillance programs for antimicrobial resistance among Canadian marine aquaculture facilities
Bibliographic record
Abstract
BACKGROUND AND AIM: Marine aquaculture is a rapidly expanding industry globally. Antimicrobial resistance (AMR) stemming from aquaculture is a public and occupational health concern but is poorly understood. Prescribed antibiotics are administered via fish-feed to treat infected fish, which also non-selectively exposes healthy stocks to the agents and increases antibiotic loads in surrounding environments. Consequential selection and amplification of AMR bacteria may create hotspots affecting aquaculture workers and their communities via consumption of contaminated fish and through direct or indirect occupational and environmental exposures to contaminants. Mitigative efforts have purportedly reduced antibiotic use but past and contemporary impacts on AMR exposures are unknown. This case study will identify regulatory and data gaps that currently hinder the implementation of sentinel surveillance strategies in Canada. METHODS: i) Profile antibiotic use by Canada’s marine aquaculture industry from 2016 to 2018 in comparison with other global industry leaders. ii) Comparative analysis of regulatory regime and surveillance differences across aquaculture-practicing leaders. RESULTS:Public reporting reveals Canadian marine aquaculture’s use of antibiotics was higher than reported in Norway, but lower than reported in Chile. There is no standardized monitoring for AMR via aquatic contaminants or identification of potential pathways from those contaminants to aquaculture workers and communities in Canada. This situation is somewhat different in other countries. Current data repositories are insufficient to elucidate the extent of human and environmental risk stemming from AMR emergence from within and around aquaculture sites. CONCLUSIONS:Canada should develop protective sentinel surveillance programs, but currently lacks the baseline studies required to stimulate evidence-based dialogue. Harmonization of data collection across jurisdictions can help define factors that increase selection pressure for the development of AMR in aquaculture settings. Furthermore, coordinated data management at the public health level can help flag health challenges potentially emerging from environmental exposures to AMR bacteria. KEYWORDS: Policy and practice, Pharmaceuticals, Occupational exposures
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".