The Causal Analytic Workflow and A Practical Guide to Structured Data Processing for Antimicrobial Resistance Surveillance

Background: Efforts have been made to standardise Antimicrobial Resistance (AMR) data collection globally. There remain challenges in improving the efficiency of data collection and processing to facilitate the comparison of high-quality AMR data between institutions and across countries. We aimed to design a causal analytic workflow to standardise data processing and provide structure for quality data monitoring using a generic raw data structure. Methods: We identified challenges in the processing of AMR and infection surveillance data, including a high time burden to process data, differing raw data structures, differences in 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 (ACORN2 and NeoSEAP), we sought to develop a standardised processing method to provide a generic data structure for robust cross-database comparisons. Results: We developed an 8-step causal analytic process that integrated multidisciplinary input from data analysts, decision makers, domain experts, data collectors, and researchers to streamline data processing. We implemented standardised 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 standardised algorithms to interpret and group antimicrobial and AST data and to categorise 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 for identifying important patterns in the data, and an analytic report for answering key research questions. Conclusions: More efficient and low-resource dependent methods are essential in the processing of AMR data. This causal analytic process will allow improved structure for reliable, generalisable and comparative reporting of AMR across clinical sites globally.