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A farm-to-fork quantitative risk assessment model for Salmonella Heidelberg resistant to third-generation cephalosporins in broiler chickens in Canada

2020· article· en· W3006128137 on OpenAlexafffundabout
Lucie Collineau, Brennan Chapman, Xu Bao, Branavan Sivapathasundaram, Carolee A. Carson, Aamir Fazil, Richard J. Reid‐Smith, Ben A. Smith

Bibliographic record

VenueInternational Journal of Food Microbiology · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsPublic Health OntarioUniversity of TorontoUniversity of GuelphPublic Health Agency of Canada
FundersGovernment of Canada
KeywordsCeftiofurSalmonellaEnvironmental healthBroilerCephalosporinAntibiotic resistanceRisk assessmentFlockVeterinary medicineAntimicrobialMedicinePoultry farmingBiotechnologyAntibioticsBiologyAnimal scienceMicrobiology

Abstract

fetched live from OpenAlex

Salmonella Heidelberg resistant to ceftiofur (a third-generation cephalosporin antimicrobial agent) in broiler chicken products pose a risk to public health in Canada. The objective of this study was to assess the extent of that risk and to evaluate the effect of intervention measures along the agri-food chain. A stochastic farm-to-fork quantitative microbial risk assessment model was developed following the Codex Alimentarius Guidelines for Risk Analysis of Foodborne Antimicrobial Resistance. Different scenarios were analyzed to assess the individual relative effects of 18 possible interventions in comparison to a baseline scenario. The baseline scenario represented the first year of on-farm antimicrobial use surveillance in the Canadian broiler industry and the year before an industry-imposed ban on the preventive use of antimicrobials of very high importance to human health (2013), where 31.3% of broiler flocks consisted of birds to which ceftiofur was administered. The baseline scenario predicted an average probability of illness of 1.1 per 100,000 servings (SE: 0.064 per 100,000), corresponding to an average of 22,000 human infections (SE: 1900) with ceftiofur-resistant S. Heidelberg per year, which is likely an overestimation. This risk was reduced by 90% or 20% when two separate scenarios designed to capture the effect of withdrawing preventive ceftiofur use from poultry production were simulated using different approaches; data used for the former scenario were confounded by other potential concomitant control measures (e.g. Salmonella vaccination programme), so the true effect likely lies somewhere between the two estimates. A theoretical 'worst case' scenario where all flocks had birds exposed to ceftiofur increased the risk by 107%. A 50% reduction in the probability of human prior exposure to antimicrobials, which has a selective and competitive effect for Salmonella spp. following ingestion of contaminated products, reduced the risk by 65%. Other promising measures that could be considered for further risk management included improved cleaning and disinfection between broiler flocks on farm (risk reduction by 26%), exclusive use of air chilling (risk reduction by 34%), and the improvement of meat storage and preparation conditions, e.g., no temperature abuse at retail (risk reduction by 88%). These findings showed the importance of a structured approach to assessing and potentially implementing effective interventions to reduce the risk associated with ceftiofur-resistant S. Heidelberg at different steps along the agri-food chain. Major data gaps included information on concentrations of resistant bacteria, cross contamination at processing and how ceftiofur-resistant S. Heidelberg behave in comparison with susceptible ones, e.g., in terms of growth and survival ability, as well as pathogenicity and virulence.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.332

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.057
GPT teacher head0.284
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations62
Published2020
Admission routes3
Has abstractyes

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