Antimicrobial stewardship in British Columbia farmed finfish: linking antimicrobial use and resistance in the context of Yellow Mouth disease
Bibliographic record
Abstract
Antimicrobial resistance (AMR) and antimicrobial use (AMU) are important management tools for diseases such as yellow mouth (YM). There are concerns about the potential impacts of AMR on the environment, fish stock, and the potential for transmission to humans. As such, AMR in the marine environment poses a unique One Health threat that requires the attention of researchers in public health, veterinary medicine, and environmental science. The close relationship between industry and multiple levels of government in British Columbia (BC) aquaculture necessitates collaboration between these entities to find a meaningful and lasting solution. The majority of AMU in BC is attributed to YM, caused by the bacterium Tenacibaculum maritimum. Understandings of this bacterium, pathogenesis and risk factors, have only recently being explored. It is vital to evaluate this important disease in the context of the unique marine environment to identify strategies for reducing AMU. The objectives of this thesis were to: 1) describe the antimicrobial susceptibilities of a historical provincial-level collection of bacterial isolates from farmed BC salmonids; 2) evaluate the relationship between AMR and AMU in BC finfish aquaculture using retrospective, province-level surveillance data provided by the BC Government; 3) assess the factors associated with YM outbreaks and requiring antimicrobial treatment using industry data for farmed Atlantic salmon in BC; and 4) to assess the practicality of using Cox proportional hazards models for this purpose. To address objective 1, this thesis presents historical AMR characteristics for the BC finfish industry, prevalence, and trends over time using AMR data from the BC Animal Health Centre (AHC) for the years 2007-2018 (Chapter 2). Chapter 3 (objective 2) presents the analysis of historical, provincial-level AMR and AMU data using logistic regression models. The outcome variables for our regression models were oxytetracycline, florfenicol, and grouped potentiated sulfonamide resistance. Objectives 3 and 4 were addressed by applying survival analysis models to industry-provided data for BC Atlantic salmon, consisting of daily environmental and biological variables such as temperature and fish density, for the first 120 days after pen placement in the ocean. Daily measurements evaluation of the effects of different variables on the risk (hazard) of breaking out with YM over time (Chapter 4). Of the 1,237 unique bacterial isolates isolated between 2007-2018 from the BC AHC (Chapter 2), most (n=1,042) were from Atlantic salmon, with 69 unique bacterial species, dominated by Aeromonas salmonicida (n=174), Aliivibrio wodanis (n=84), and Yersinia ruckeri (n=79). Resistance to all tested antimicrobials was detected, albeit at low levels. Sparse distribution of resistance data prevented assessment of chronological trends, however, there was concerning multi-class drug resistance in some isolates. Logistic regression analysis (Chapter 3) identified that AMR to one drug was associated with resistance to any one other drug. We did not find any significant association between resistance and the use of any given antimicrobial except for sulfonamide use in the sulfonamide resistance model. The survival analysis (Chapter 4) identified many environmental, biological, and managerial factors that are potentially associated with AMU for YM outbreaks. Of the 321 pen placements at sea, 292 had AMU for YM within the first 120 days. The average time until treatment was 30 days. Ongoing challenges with violation of Cox proportional hazards model assumptions forced the use of a simplified model including only the time-varying covariates, 10m depth temperature and salinity using restricted cubic splines, but these proved to be a challenge, with model syntax and software precluding interpretation. The results of this thesis provide important baseline data and contribute to the increasingly pertinent body of literature on AMR and AMU data in the unique marine aquaculture environment. Despite the relatively low level of AMR, the association between resistance to different antimicrobials highlights the potential for co-selection for AMR and multidrug resistance. These results should be considered in the context of potential threats to One Health and public health that require further study. We present evidence that environmental factors are important risk factors for the development of YM, a leading cause of AMU in BC, and encourage and guide future research on aquaculture-based survival analysis. The results of this thesis have both scientific and industry applications that we hope will be utilized by both entities during this transformative time in BC aquaculture.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".