Standardizing multidrug resistance definitions and visualizations to support surveillance across One Health
Objective: This study aimed to understand the current use of visualizations for multidrug resistance (MDR) data across the One Health spectrum and the visualization preferences and definitions of MDR used by antimicrobial resistance experts, with emphasis on the animal health sector of One Health, w...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Journal of Global Antimicrobial Resistance |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213716525000785 |
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| author | Claudia Cobo Angel Ava Glowney Emma Lin Abdolreza Mosaddegh Kurtis Sobkowich Zvonimir Poljak J. Scott Weese Casey L. Cazer |
| author_facet | Claudia Cobo Angel Ava Glowney Emma Lin Abdolreza Mosaddegh Kurtis Sobkowich Zvonimir Poljak J. Scott Weese Casey L. Cazer |
| author_sort | Claudia Cobo Angel |
| collection | DOAJ |
| description | Objective: This study aimed to understand the current use of visualizations for multidrug resistance (MDR) data across the One Health spectrum and the visualization preferences and definitions of MDR used by antimicrobial resistance experts, with emphasis on the animal health sector of One Health, which lacks standardized MDR definitions. Methods: A rapid scoping review was conducted to synthesize current approaches to visualize MDR. Six databases and grey literature were searched with antimicrobial, resistance, surveillance, and figure or dashboard terms. An active machine learning model was used for the initial screening of references. An online survey was distributed to self-identified antimicrobial resistance experts, including questions about respondents’ country of employment, job position, definitions of MDR, and preferences for MDR metrics and visualizations. Results: Bar charts, visual antibiograms, heat maps, and network graphs were the most common visualizations employed in peer-reviewed publications, websites, and reports. Survey respondents preferred simplistic visualizations, such as line graphs and heat maps. Respondents used a variety of MDR definitions, although resistance to three or more antimicrobial categories was the most common. Some respondents advocated for the exclusion of intrinsic resistance in the definition, while others argued for its inclusion. Conclusions: Despite historic proposals for standardizing international definitions of MDR, a lack of consensus remains. Respondents also expressed different preferences for MDR visualizations. Some visualizations currently in use, such as network graphs, are complex and may be challenging to interpret. Harmonization of MDR definitions and optimization of visualizations are essential to facilitate comparisons across populations and studies. |
| format | Article |
| id | doaj-art-ebb7be2eaf2649d9ad1989d4dc1cf56c |
| institution | Kabale University |
| issn | 2213-7165 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Global Antimicrobial Resistance |
| spelling | doaj-art-ebb7be2eaf2649d9ad1989d4dc1cf56c2025-08-20T03:30:32ZengElsevierJournal of Global Antimicrobial Resistance2213-71652025-06-014317317910.1016/j.jgar.2025.04.006Standardizing multidrug resistance definitions and visualizations to support surveillance across One HealthClaudia Cobo Angel0Ava Glowney1Emma Lin2Abdolreza Mosaddegh3Kurtis Sobkowich4Zvonimir Poljak5J. Scott Weese6Casey L. Cazer7Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USADepartment of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USADepartment of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USADepartment of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USADepartment of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, Ontario, CanadaDepartment of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, Ontario, CanadaDepartment of Pathobiology, Ontario Veterinary College, University of Guelph, Guelph, Ontario, CanadaDepartment of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA; Department of Public and Ecosystem Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA; Corresponding author. Mailing address: 968 Campus Rd, Ithaca, NY 14853, USA.Objective: This study aimed to understand the current use of visualizations for multidrug resistance (MDR) data across the One Health spectrum and the visualization preferences and definitions of MDR used by antimicrobial resistance experts, with emphasis on the animal health sector of One Health, which lacks standardized MDR definitions. Methods: A rapid scoping review was conducted to synthesize current approaches to visualize MDR. Six databases and grey literature were searched with antimicrobial, resistance, surveillance, and figure or dashboard terms. An active machine learning model was used for the initial screening of references. An online survey was distributed to self-identified antimicrobial resistance experts, including questions about respondents’ country of employment, job position, definitions of MDR, and preferences for MDR metrics and visualizations. Results: Bar charts, visual antibiograms, heat maps, and network graphs were the most common visualizations employed in peer-reviewed publications, websites, and reports. Survey respondents preferred simplistic visualizations, such as line graphs and heat maps. Respondents used a variety of MDR definitions, although resistance to three or more antimicrobial categories was the most common. Some respondents advocated for the exclusion of intrinsic resistance in the definition, while others argued for its inclusion. Conclusions: Despite historic proposals for standardizing international definitions of MDR, a lack of consensus remains. Respondents also expressed different preferences for MDR visualizations. Some visualizations currently in use, such as network graphs, are complex and may be challenging to interpret. Harmonization of MDR definitions and optimization of visualizations are essential to facilitate comparisons across populations and studies.http://www.sciencedirect.com/science/article/pii/S2213716525000785Multidrug resistanceSurveillanceVisualizationDefinitionScoping review |
| spellingShingle | Claudia Cobo Angel Ava Glowney Emma Lin Abdolreza Mosaddegh Kurtis Sobkowich Zvonimir Poljak J. Scott Weese Casey L. Cazer Standardizing multidrug resistance definitions and visualizations to support surveillance across One Health Journal of Global Antimicrobial Resistance Multidrug resistance Surveillance Visualization Definition Scoping review |
| title | Standardizing multidrug resistance definitions and visualizations to support surveillance across One Health |
| title_full | Standardizing multidrug resistance definitions and visualizations to support surveillance across One Health |
| title_fullStr | Standardizing multidrug resistance definitions and visualizations to support surveillance across One Health |
| title_full_unstemmed | Standardizing multidrug resistance definitions and visualizations to support surveillance across One Health |
| title_short | Standardizing multidrug resistance definitions and visualizations to support surveillance across One Health |
| title_sort | standardizing multidrug resistance definitions and visualizations to support surveillance across one health |
| topic | Multidrug resistance Surveillance Visualization Definition Scoping review |
| url | http://www.sciencedirect.com/science/article/pii/S2213716525000785 |
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