The Journal of antimicrobial chemotherapy · 2026
A practical guide to structured data processing for antimicrobial resistance surveillance.
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Harrison Michelle Louise, Tang Mengyao, Ozaygen Alkim, Turner Paul, Snelling Thomas, Williams Phoebe C M, Wu Yue
Аннотация
BACKGROUND: Efforts have been made to standardize antimicrobial resistance (AMR) data collection globally. There remain challenges in improving the efficiency of data collection and processing to enable comparison of high-quality AMR data across settings. We aimed to design a causal analytic workflow to standardize data processing and provide structure for quality monitoring using a generic raw data structure.
METHODS: We identified challenges in processing AMR and infection surveillance data, including a high time burden, differing raw data structures, differences in the identification and management of likely contaminants and the laborious task of managing antimicrobial prescription and antimicrobial susceptibility testing (AST) data. Using two neonatal infection surveillance databases, we developed a standardized processing method to provide a generic data structure for robust cross-database comparisons.
RESULTS: We developed an eight-step process that integrated multidisciplinary input to streamline data processing. We implemented standardized scripts to convert AMR data from differing structures (wide and long) into a generic relational data structure representing four main concepts: 'persons', 'episodes', 'organisms' and 'specimens'. We implemented standardized algorithms to interpret and group antimicrobial and AST data and to categorize pathogens by degree of pathogenicity. This process led to the production of three data monitoring reports: a raw data report for data quality and validity checks, a monitoring report to identify key patterns in the data and an analytic report to answer key research questions.
CONCLUSIONS: More efficient, low-resource-dependent methods are essential for processing AMR data. This process will enable improved structure for reliable and comparative reporting of AMR across clinical sites globally.
METHODS: We identified challenges in processing AMR and infection surveillance data, including a high time burden, differing raw data structures, differences in the identification and management of likely contaminants and the laborious task of managing antimicrobial prescription and antimicrobial susceptibility testing (AST) data. Using two neonatal infection surveillance databases, we developed a standardized processing method to provide a generic data structure for robust cross-database comparisons.
RESULTS: We developed an eight-step process that integrated multidisciplinary input to streamline data processing. We implemented standardized scripts to convert AMR data from differing structures (wide and long) into a generic relational data structure representing four main concepts: 'persons', 'episodes', 'organisms' and 'specimens'. We implemented standardized algorithms to interpret and group antimicrobial and AST data and to categorize pathogens by degree of pathogenicity. This process led to the production of three data monitoring reports: a raw data report for data quality and validity checks, a monitoring report to identify key patterns in the data and an analytic report to answer key research questions.
CONCLUSIONS: More efficient, low-resource-dependent methods are essential for processing AMR data. This process will enable improved structure for reliable and comparative reporting of AMR across clinical sites globally.