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Enregistrement 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 sur OpenAlexfundno aff
Noelle Noyes

Notice bibliographique

RevueDigital Collections of Colorado (Colorado State University) · 2016
Typedissertation
Langueen
DomaineVeterinary
ThématiqueAnimal Behavior and Welfare Studies
Établissements canadiensnon disponible
Organismes subventionnairesBeef Cattle Research CouncilAlberta Beef ProducersColorado State University
Mots-clésProduction (economics)EpidemiologyResistance (ecology)AntimicrobialEcologyAntibiotic resistanceGeographyBiotechnologyBiologyMedicineEconomicsMicrobiologyMicroeconomics

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,041
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,030
Tête enseignante GPT0,260
Écart entre enseignants0,231 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2016
Routes d'admission1
Résumé présentoui

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