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Record W2892089731 · doi:10.25675/3.019954

The epidemiology and ecology of antimicrobial use and resistance in North American beef production systems

2016· dissertation· en· W2892089731 on OpenAlexfundno aff
Noelle Noyes

Bibliographic record

VenueDigital Collections of Colorado (Colorado State University) · 2016
Typedissertation
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsnot available
FundersBeef Cattle Research CouncilAlberta Beef ProducersColorado State University
KeywordsProduction (economics)EpidemiologyResistance (ecology)AntimicrobialEcologyAntibiotic resistanceGeographyBiotechnologyBiologyMedicineEconomicsMicrobiologyMicroeconomics

Abstract

fetched live from OpenAlex

Antimicrobial resistance (AMR) is a critical public health issue (1), and analysis of historical Escherichia coli isolates reveals that AMR has been increasing steadily since the introduction of antimicrobial drugs (AMDs) (2). Meat production systems are thought to contribute to the problem by harboring a reservoir of AMR that interfaces with humans either through persistence in the food chain or dissemination of wastes into the environment (3–6). Antimicrobial use (AMU) in food producing animals is often cited as a driver of AMR in humans, but it is extremely challenging to design and execute studies that can be used to infer causality between the two. As a result, producers and policy makers alike have relatively little high-quality evidence on which to base informed and rational decisions with regard to AMU and other production management practices. The four studies presented in this doctoral thesis attempt to overcome some of the obstacles that currently impede inferential analysis regarding AMU practices and AMR. The first two studies stem from a project in which detailed AMU and AMR data were collected throughout the feeding period for over 5,000 individual cattle across 300 pens. The unprecedented collection of prospective data from such a large number of uniquely identified commercial cattle enabled us to achieve a much more robust level of causal inference compared to many previous studies. The last two studies employed shotgun metagenomics to interrogate the entire AMR potential (the “resistome”) of a given sample, enabling novel insight into the longitudinal, microbe-level genetic ecology of AMR within beef production systems. Because AMR develops and is maintained within the genetic context a microbial population, the resistome-microbiome approach contributes a critical and long-lacking piece to the overall puzzle of AMR within beef production. Thus, while each study in this dissertation approaches the research question of AMR from a slightly different angle, all of them provide crucial and novel information to our scientific understanding of AMU and AMR in beef production. The 4 studies also complement one another through investigation of different aspects of AMU and AMR across nearly the entire beef production system. The first study not only investigates AMU-AMR associations within Mannheimia haemolytica, but also examines how these associations affect respiratory-related morbidity and mortality outcomes in commercial cattle. As such, this study is focused on the animal health and economic dimensions of AMU and AMR in a critically important respiratory pathogen. The second study investigates within-feedlot AMU-AMR associations in non-type-specific Escherichia coli, a widely used “indicator” species for AMR, and compares different analytical methods for analyzing the types of data collected as part of ongoing surveillance of AMR in livestock production. Therefore, this study focuses on the public health and regulatory dimension of AMU-AMR in feedlot beef production. The third study tracks AMR in cattle production effluents such as feces, soil and water, thus encompassing the environmental dissemination routes that may play a role in the transmission of AMR from livestock to humans. And finally the fourth study tracks AMR in cattle and their environments from feedlot entry through slaughter and fabrication, thereby delving into the food supply dimension of beef production. Importantly, all 4 studies were conducted in commercial beef feedlot operations, and samples are collected from commercial cattle and their environments. All 4 studies are strictly observational; the participating operations did not alter their production practices and cattle were not managed differently for any of the studies. While this approach may add complexity to the interpretation of study findings, it has the distinct advantage of enabling insight into AMU-AMR dynamics on operations that constitute an integral part of the fabric of our society. The AMU and other production practices utilized on these operations were not contrived, and therefore the external validity of the study findings are more widely applicable than those gleaned from research animals and herds. The findings of the 4 studies in this dissertation are therefore novel, complementary and highly relevant to the societal, political and scientific debate surrounding AMU and AMR in beef production.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.238

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.030
GPT teacher head0.260
Teacher spread0.231 · 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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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