Finding What Is Inaccessible: Antimicrobial Resistance Language Use among the One Health Domains.

Area of Science:

• One Health
• Antimicrobial Resistance (AMR)
• Text Data Mining
• Natural Language Processing

Background:

• A One Health approach is crucial for combating antimicrobial resistance (AMR).
• Effective data sharing across human, animal, and environmental domains is essential for One Health success.
• Disparities in language use may impede interdisciplinary research and collaboration.

Purpose of the Study:

• To investigate differences in language use across the three One Health domains concerning AMR.
• To quantify the extent of disparate language usage within the One Health context.
• To identify potential barriers to effective interdisciplinary research caused by linguistic variation.

Main Methods:

• Analysis of 20,000 open-access articles related to AMR and One Health from the European PubMed Collection (1990-2019).
• Application of text data mining and natural language processing techniques to evaluate AMR key term frequency.
• Statistical analysis (2-way ANOVA) to determine significant differences in key term usage across One Health sub-specialties.

Main Results:

• Distinct AMR language usage was observed within each of the three One Health domains.
• Incongruent language usage across domains was identified, with significant differences in key term frequencies (p < 0.001).
• "Antibiotic resistance" and "AR" were used 18 times more frequently than "antimicrobial resistance" and "AMR" over the 29-year study period.

Conclusions:

• Disparate language use in AMR research across One Health domains weakens interdisciplinary research effectiveness.
• Linguistic discord creates accessibility issues for researchers using search engines, inhibiting collaboration.
• Authors publishing AMR research in the One Health context should use both "antimicrobial resistance" and "antibiotic resistance" terms, include "One Health," and prioritize open-access publication.