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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...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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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. |
format | Online Article Text |
id | pubmed-10449168 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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