Standardizing multidrug resistance definitions and visualizations to support surveillance across One Health - Claudia Cobo Angel; Ava Glowney; Emma Lin; Abdolreza Mosaddegh; Kurtis Sobkowich; Zvonimir Poljak; J. Scott Weese; Casey L. Cazer (Journal of Global Antimicrobial Resistance, 2025)
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.