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Is scientific evidence enough? Using expert opinion to fill gaps in data in antimicrobial resistance research

BACKGROUND: Antimicrobial Resistance (AMR) is a global problem with large health and economic consequences. Current gaps in quantitative data are a major limitation for creating models intended to simulate the drivers of AMR. As an intermediate step, expert knowledge and opinion could be utilized to...

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Autores principales: Cousins, Melanie, Parmley, E. Jane, Greer, Amy L., Neiterman, Elena, Lambraki, Irene A., Graells, Tiscar, Léger, Anaïs, Henriksson, Patrik J. G., Troell, Max, Wernli, Didier, Søgaard Jørgensen, Peter, Carson, Carolee A., Majowicz, Shannon E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10449168/
https://www.ncbi.nlm.nih.gov/pubmed/37616319
http://dx.doi.org/10.1371/journal.pone.0290464
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author Cousins, Melanie
Parmley, E. Jane
Greer, Amy L.
Neiterman, Elena
Lambraki, Irene A.
Graells, Tiscar
Léger, Anaïs
Henriksson, Patrik J. G.
Troell, Max
Wernli, Didier
Søgaard Jørgensen, Peter
Carson, Carolee A.
Majowicz, Shannon E.
author_facet Cousins, Melanie
Parmley, E. Jane
Greer, Amy L.
Neiterman, Elena
Lambraki, Irene A.
Graells, Tiscar
Léger, Anaïs
Henriksson, Patrik J. G.
Troell, Max
Wernli, Didier
Søgaard Jørgensen, Peter
Carson, Carolee A.
Majowicz, Shannon E.
author_sort Cousins, Melanie
collection PubMed
description BACKGROUND: Antimicrobial Resistance (AMR) is a global problem with large health and economic consequences. Current gaps in quantitative data are a major limitation for creating models intended to simulate the drivers of AMR. As an intermediate step, expert knowledge and opinion could be utilized to fill gaps in knowledge for areas of the system where quantitative data does not yet exist or are hard to quantify. Therefore, the objective of this study was to identify quantifiable data about the current state of the factors that drive AMR and the strengths and directions of relationships between the factors from statements made by a group of experts from the One Health system that drives AMR development and transmission in a European context. METHODS: This study builds upon previous work that developed a causal loop diagram of AMR using input from two workshops conducted in 2019 in Sweden with experts within the European food system context. A secondary analysis of the workshop transcripts was conducted to identify semi-quantitative data to parameterize drivers in a model of AMR. MAIN FINDINGS: Participants spoke about AMR by combining their personal experiences with professional expertise within their fields. The analysis of participants’ statements provided semi-quantitative data that can help inform a future of AMR emergence and transmission based on a causal loop diagram of AMR in a Swedish One Health system context. CONCLUSION: Using transcripts of a workshop including participants with diverse expertise across the system that drives AMR, we gained invaluable insight into the past, current, and potential future states of the major drivers of AMR, particularly where quantitative data are lacking.
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spelling pubmed-104491682023-08-25 Is scientific evidence enough? Using expert opinion to fill gaps in data in antimicrobial resistance research Cousins, Melanie Parmley, E. Jane Greer, Amy L. Neiterman, Elena Lambraki, Irene A. Graells, Tiscar Léger, Anaïs Henriksson, Patrik J. G. Troell, Max Wernli, Didier Søgaard Jørgensen, Peter Carson, Carolee A. Majowicz, Shannon E. PLoS One Research Article BACKGROUND: Antimicrobial Resistance (AMR) is a global problem with large health and economic consequences. Current gaps in quantitative data are a major limitation for creating models intended to simulate the drivers of AMR. As an intermediate step, expert knowledge and opinion could be utilized to fill gaps in knowledge for areas of the system where quantitative data does not yet exist or are hard to quantify. Therefore, the objective of this study was to identify quantifiable data about the current state of the factors that drive AMR and the strengths and directions of relationships between the factors from statements made by a group of experts from the One Health system that drives AMR development and transmission in a European context. METHODS: This study builds upon previous work that developed a causal loop diagram of AMR using input from two workshops conducted in 2019 in Sweden with experts within the European food system context. A secondary analysis of the workshop transcripts was conducted to identify semi-quantitative data to parameterize drivers in a model of AMR. MAIN FINDINGS: Participants spoke about AMR by combining their personal experiences with professional expertise within their fields. The analysis of participants’ statements provided semi-quantitative data that can help inform a future of AMR emergence and transmission based on a causal loop diagram of AMR in a Swedish One Health system context. CONCLUSION: Using transcripts of a workshop including participants with diverse expertise across the system that drives AMR, we gained invaluable insight into the past, current, and potential future states of the major drivers of AMR, particularly where quantitative data are lacking. Public Library of Science 2023-08-24 /pmc/articles/PMC10449168/ /pubmed/37616319 http://dx.doi.org/10.1371/journal.pone.0290464 Text en © 2023 Cousins et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Cousins, Melanie
Parmley, E. Jane
Greer, Amy L.
Neiterman, Elena
Lambraki, Irene A.
Graells, Tiscar
Léger, Anaïs
Henriksson, Patrik J. G.
Troell, Max
Wernli, Didier
Søgaard Jørgensen, Peter
Carson, Carolee A.
Majowicz, Shannon E.
Is scientific evidence enough? Using expert opinion to fill gaps in data in antimicrobial resistance research
title Is scientific evidence enough? Using expert opinion to fill gaps in data in antimicrobial resistance research
title_full Is scientific evidence enough? Using expert opinion to fill gaps in data in antimicrobial resistance research
title_fullStr Is scientific evidence enough? Using expert opinion to fill gaps in data in antimicrobial resistance research
title_full_unstemmed Is scientific evidence enough? Using expert opinion to fill gaps in data in antimicrobial resistance research
title_short Is scientific evidence enough? Using expert opinion to fill gaps in data in antimicrobial resistance research
title_sort is scientific evidence enough? using expert opinion to fill gaps in data in antimicrobial resistance research
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10449168/
https://www.ncbi.nlm.nih.gov/pubmed/37616319
http://dx.doi.org/10.1371/journal.pone.0290464
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