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Record W2731324109 · doi:10.1371/journal.pone.0179384

Antimicrobial use surveillance in broiler chicken flocks in Canada, 2013-2015

2017· article· en· W2731324109 on OpenAlexafffundabout
Agnes Agunos, David Léger, Carolee A. Carson, Sheryl Gow, Angelina L Bosman, Rebecca Irwin, Richard J. Reid‐Smith

Bibliographic record

VenuePLoS ONE · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsPublic Health Agency of Canada
FundersAlberta Agriculture and ForestryCanadian Animal Health InstitutePublic Health AgencyPublic Health Agency of CanadaMinistry of Agriculture - Saskatchewan
KeywordsCoccidiostatsAntimicrobialFlockVirginiamycinBroilerAntibiotic resistanceVeterinary medicinePoultry farmingBacitracinBiologyBiotechnologyMedicineAntibioticsAnimal scienceMicrobiologyCoccidiosis

Abstract

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There is a paucity of data on the reason for and the quantity of antimicrobials used in broiler chickens in Canada. To address this, the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS) implemented surveillance of antimicrobial use (AMU) and antimicrobial resistance (AMR) in broiler chicken flocks in 2013. Shortly after this (2014), the poultry industry banned the preventive use of ceftiofur in broiler chickens. The objectives of this analysis were to describe antimicrobial use (AMU) in Canadian broiler chickens between 2013 and 2015 (n = 378 flocks), compare these results to other animal species in Canada, to highlight the utility of farm surveillance data to evaluate the impact of a policy change, and to explore how different antimicrobial use metrics might affect data interpretation and communication. The surveillance data indicated that the poultry industry policy resulted in lower antimicrobial use and resistance, and they successfully captured information on when, where, why, and how much antimicrobials were being used. The majority of antimicrobials were administered via the feed (95%). The relative frequency of antimicrobial classes used in broiler chickens differed from those used in swine or in food animal production in general. Coccidiostats were the most frequently used antimicrobial classes (53% of total kg). Excluding coccidiostats, the top three most frequently used antimicrobial classes were bacitracin (53% of flocks), virginiamycin (25%) and avilamycin (21%), mainly used for the prevention of necrotic enteritis. Depending on the AMU metric utilized, the relative rankings of the top antimicrobials changed; hence the choice of the AMU metric is an important consideration for any AMU reporting. When using milligrams/Population Correction Unit (mg/PCU) the top three antimicrobial classes used were bacitracins (76 mg/PCU), trimethoprim-sulfonamides (24 mg/PCU), and penicillins (15 mg/PCU), whereas when using a number of Defined Daily Doses in animals using Canadian standards /1,000 chicken-days at risk (nDDDvetCA/1,000 CD) the ranking was bacitracins (223 nDDDvetCA/1,000 CD), streptogramins (118 nDDDvetCA/1,000 CD), and trimethoprim-sulfonamides (87 nDDDvetCA/1,000 CD). The median animal treatment days in feed for one cycle (ATD/cycle) during the three-year study were 34 ATD/cycle; this was equal to the mean age of the flocks at pre-harvest sampling day (days at risk), indicating that the studied flocks except those that were raised without antibiotics and organic, were fed with medicated rations throughout the observation period. Overall, more than half (59%) of antimicrobials used in broiler chickens were in classes not used in human medicine, such as ionophores and chemical coccidiostats aimed to prevent coccidiosis. Compared to grower-finisher pigs and in production animal species (national sales data), the mg/PCU of antimicrobials used in broiler chickens was relatively lower. The findings of this paper highlighted the importance of farm-level AMU surveillance in measuring the impact of interventions to reduce antimicrobials in poultry.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.292

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.050
GPT teacher head0.246
Teacher spread0.195 · 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 designObservational
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

Citations56
Published2017
Admission routes3
Has abstractyes

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