Investigating the feasibility and potential of combining industry AMR monitoring systems: a comparison with WHO GLASS

Background Efforts to estimate the global burden of antimicrobial resistance (AMR) have highlighted gaps in existing surveillance systems. Data gathered from hospital networks globally by pharmaceutical industries to monitor antibiotic efficacy in different bacteria represent an additional source to track the temporal evolution of AMR. Here, we analysed available industry monitoring systems to assess to which extent combining them could help fill the gaps in our current understanding of AMR levels and trends. Methods We analysed six industry monitoring systems (ATLAS, GEARS, SIDERO-WT, KEYSTONE, DREAM, and SOAR) obtained from the Vivli platform and reviewed their respective isolates collection and analysis protocols. Using the R software, we designed a pipeline to harmonise and combine these into a single dataset. We assessed the reliability of resistance estimates from these sources by comparing the combined dataset to the publicly available subset of WHO GLASS for shared bacteria-antibiotic-country-year combinations. Results Combined, the industry monitoring systems cover 18 years (4 years for GLASS), 85 countries (71), 412 bacterial species (8), and 75 antibiotics (25). Although all industry systems followed a similar centralised testing approach, the criteria for isolate collection were unclear (patients selection, associated sampling periods...). For E.coli , K. pneumoniae and S. aureus , at least 65% of comparable resistance proportions were within 0.1 of the corresponding estimate in GLASS. We did not identify systemic bias towards resistance in industry systems compared to GLASS. Conclusions Combining industry monitoring systems can substantially strengthen our knowledge of global AMR burden across bacterial species and countries. High agreement values for available comparisons with GLASS suggest that data for other bacteria-antibiotic-country-year combinations only present in industry systems could complement GLASS, particularly for Priority Pathogens currently not covered. This valuable information on resistance levels could help clinicians and stakeholders prioritize testing and select appropriate antibiotics in settings with limited surveillance data. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study used ONLY openly available human data that can be obtained at: https://searchamr.vivli.org/ (industry monitoring systems) and https://github.com/qleclerc/GLASS2022 (GLASS data). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data used in this study are available online at: https://searchamr.vivli.org/ (industry monitoring systems) and https://github.com/qleclerc/GLASS2022 (GLASS data). All code and data produced are available online at https://github.com/qleclerc/AMR\_data\_prize. [https://github.com/qleclerc/AMR\_data\_prize][1] [1]: https://github.com/qleclerc/AMR_data_prize