A practical guide to structured data processing for antimicrobial resistance surveillance.
6 affiliations
- 1Sydney School of Public Health, Faculty of Medicine, The University of Sydney, Sydney, Australia.
- 2Sydney Infectious Diseases Institute, The University of Sydney, Camperdown, Australia.
- 3Columbia University, School of Public Health, New York, NY, USA.
- 4Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- 5Cambodia Oxford Medical Research Unit, Angkor Hospital for Children, Angkor, Cambodia.
- 6UNSW School of Women and Children's Health, UNSW, Sydney, Australia.
Abstract
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.
Related
- Antimicrobial Resistance (AMR) Surveillance: Ten Years On.
- Antimicrobial resistance of bacteria isolated from blood cultures at a tertiary hospital in Papua New Guinea: implications for antimicrobial stewardship and surveillance
- The Fleming Fund: Key Features of Design and Approach to Strengthening Antimicrobial Resistance Surveillance in Low- and Middle-Income Countries.
- Fleming Fund Implementation in Kenya: What Have We Learned About Strengthening One Health Antimicrobial Resistance Surveillance for Decision-making?
- Bacterial Mercury Resistance Reveals a Robust Species-Structured Human Antimicrobial Mobilome
BibTeX
@article{harrison2026practical,
title = {{A practical guide to structured data processing for antimicrobial resistance surveillance.}},
author = {Michelle Louise Harrison and Mengyao Tang and Alkim Ozaygen and Paul Turner and Thomas Snelling and Phoebe C M Williams and Yue Wu},
journal = {The Journal of antimicrobial chemotherapy},
year = {2026},
month = oct,
doi = {10.1093/jac/dkag357},
url = {https://europepmc.org/article/MED/42910529},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - A practical guide to structured data processing for antimicrobial resistance surveillance.
AU - Michelle Louise Harrison
AU - Mengyao Tang
AU - Alkim Ozaygen
AU - Paul Turner
AU - Thomas Snelling
AU - Phoebe C M Williams
AU - Yue Wu
PY - 2026
DA - 2026/10/01
JO - The Journal of antimicrobial chemotherapy
DO - 10.1093/jac/dkag357
UR - https://europepmc.org/article/MED/42910529
AB - 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.
SN - 0305-7453
SN - 1460-2091
ER -