A tool to enhance antimicrobial stewardship using similarity networks to identify antimicrobial resistance patterns across farms

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 sprea...

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Detalles Bibliográficos
Autores: Aguilar Vega, Cecilia, Scoglio, Caterina, Clavijo, María J., Robbins, Rebecca, Karriker, Locke, Liu, Xin, Martínez López, Beatriz
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/107577
Acceso en línea:https://hdl.handle.net/20.500.14352/107577
Access Level:acceso abierto
Palabra clave:636.09
Antimicrobial resistance
Infectious diseases
Veterinaria
3109 Ciencias Veterinarias
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oai_identifier_str oai:docta.ucm.es:20.500.14352/107577
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repository_id_str
spelling A tool to enhance antimicrobial stewardship using similarity networks to identify antimicrobial resistance patterns across farmsAguilar Vega, CeciliaScoglio, CaterinaClavijo, María J.Robbins, RebeccaKarriker, LockeLiu, XinMartínez López, Beatriz636.09Antimicrobial resistanceInfectious diseasesVeterinaria3109 Ciencias VeterinariasAntimicrobial 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.Universidad Complutense de Madrid20232023-02-2020232023-02-20journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/107577reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/1075772026-06-02T12:44:21Z
dc.title.none.fl_str_mv A tool to enhance antimicrobial stewardship using similarity networks to identify antimicrobial resistance patterns across farms
title A tool to enhance antimicrobial stewardship using similarity networks to identify antimicrobial resistance patterns across farms
spellingShingle A tool to enhance antimicrobial stewardship using similarity networks to identify antimicrobial resistance patterns across farms
Aguilar Vega, Cecilia
636.09
Antimicrobial resistance
Infectious diseases
Veterinaria
3109 Ciencias Veterinarias
title_short A tool to enhance antimicrobial stewardship using similarity networks to identify antimicrobial resistance patterns across farms
title_full A tool to enhance antimicrobial stewardship using similarity networks to identify antimicrobial resistance patterns across farms
title_fullStr A tool to enhance antimicrobial stewardship using similarity networks to identify antimicrobial resistance patterns across farms
title_full_unstemmed A tool to enhance antimicrobial stewardship using similarity networks to identify antimicrobial resistance patterns across farms
title_sort A tool to enhance antimicrobial stewardship using similarity networks to identify antimicrobial resistance patterns across farms
dc.creator.none.fl_str_mv Aguilar Vega, Cecilia
Scoglio, Caterina
Clavijo, María J.
Robbins, Rebecca
Karriker, Locke
Liu, Xin
Martínez López, Beatriz
author Aguilar Vega, Cecilia
author_facet Aguilar Vega, Cecilia
Scoglio, Caterina
Clavijo, María J.
Robbins, Rebecca
Karriker, Locke
Liu, Xin
Martínez López, Beatriz
author_role author
author2 Scoglio, Caterina
Clavijo, María J.
Robbins, Rebecca
Karriker, Locke
Liu, Xin
Martínez López, Beatriz
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv 636.09
Antimicrobial resistance
Infectious diseases
Veterinaria
3109 Ciencias Veterinarias
topic 636.09
Antimicrobial resistance
Infectious diseases
Veterinaria
3109 Ciencias Veterinarias
description 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.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-02-20
2023
2023-02-20
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/107577
url https://hdl.handle.net/20.500.14352/107577
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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