A tool to enhance antimicrobial stewardship using similarity networks to identify antimicrobial resistance patterns across farms
Date
2023-02-20
Authors
Aguilar‑Vega, Cecilia
Scoglio, Caterina
Clavijo, María J.
Robbins, Rebecca
Liu, Xin
Martínez‑López, Beatriz
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Nature Research
Abstract
Antimicrobial resistance (AMR) is one of the major challenges of the century and should be addressed with a One Health approach. This study aimed to develop a tool that can provide a better understanding of AMR patterns and improve management practices in swine production systems to reduce its spread between farms. We generated similarity networks based on the phenotypic AMR pattern for each farm with information on important bacterial pathogens for swine farming based on the Euclidean distance. We included seven pathogens: Actinobacillus suis, Bordetella bronchiseptica, Escherichia coli, Glaesserella parasuis, Pasteurella multocida, Salmonella spp., and Streptococcus suis; and up to seventeen antibiotics from ten classes. A threshold criterion was developed to reduce the density of the networks and generate communities based on their AMR profiles. A total of 479 farms were included in the study although not all bacteria information was available on each farm. We observed significant differences in the morphology, number of nodes and characteristics of pathogen networks, as well as in the number of communities and susceptibility profiles of the pathogens to different antimicrobial drugs. The methodology presented here could be a useful tool to improve health management, biosecurity measures and prioritize interventions to reduce AMR spread in swine farming.
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This article is published as Aguilar-Vega, Cecilia, Caterina Scoglio, María J. Clavijo, Rebecca Robbins, Locke Karriker, Xin Liu, and Beatriz Martínez-López. "A tool to enhance antimicrobial stewardship using similarity networks to identify antimicrobial resistance patterns across farms." Scientific Reports 13, no. 1 (2023): 2931. doi: https://doi.org/10.1038/s41598-023-29980-4.
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© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).