Antimicrobial Resistance Surveillance: Data Harmonisation and Data Selection within Secondary Data Use.

Area of Science:

• Microbiology
• Public Health
• Veterinary Medicine

Background:

• Antimicrobial resistance (AMR) to last-resort antibiotics poses a significant global health threat.
• Effective surveillance and monitoring systems are essential for managing AMR on national and international scales.
• A One Health approach is necessary to comprehensively track AMR across human, animal, food, and environmental sectors.

Purpose of the Study:

• To develop a framework for a One Health surveillance system for antimicrobial resistance.
• To explore the integration of heterogeneous secondary data from various sources for AMR monitoring.
• To address the challenges in harmonizing diverse epidemiological, phenotypic, and molecular data.

Main Methods:

• Collected and pooled secondary data on carbapenem and colistin-resistant bacterial isolates from human, animal, food, and environmental sources.
• Integrated epidemiological metadata with phenotypic resistance and molecular data at the isolate level.
• Developed a generic strategy for data harmonization, including variable definition, categorization, and plausibility checks.

Main Results:

• Demonstrated the complexity of managing heterogeneous secondary data for AMR surveillance.
• Successfully applied a harmonization strategy to a pooled dataset, enabling preliminary descriptive analyses.
• Provided insights into the initial stages of developing an AMR surveillance program using existing data.

Conclusions:

• Data harmonization is a critical challenge in establishing integrated One Health AMR surveillance.
• A systematic approach to data management is vital for leveraging secondary data effectively.
• This study provides a foundation for developing robust AMR surveillance programs that incorporate diverse data streams.