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SynopsisBackgroundAntimicrobial resistance (AMR) surveillance of bloodstream infections is challenging in low- and middle-income countries (LMICs), limited laboratory capacity preventing routine patient-level susceptibility testing. Other specimen types could provide an effective approach to surveillance.ObjectivesOur study aims to systematically evaluate the relationship between resistance prevalence in non-sterile sites and bloodstream infections.MethodsAssociations between resistance rates in Escherichia coli and Staphylococcus aureus isolates from blood and other specimens were estimated in Oxfordshire, UK, 1998-2018, comparing proportions resistant in each calendar year using time series cross-correlations and across drug-years. We repeated analysis across publicly-available data from four high-income and 12 middle-income countries, and in three hospitals/programmes in LMICs.Results8102 E. coli bloodstream infections, 322087 E. coli urinary tract infections, 6952 S. aureus bloodstream infections and 112074 S. aureus non-sterile site cultures were included from Oxfordshire. Resistance trends over time in blood versus other specimens were strongly correlated (maximum cross-correlation 0.51-0.99, strongest associations in the same year for 18/27 pathogen-drug combinations). Resistance prevalence was broadly congruent across drug-years for each species. 276/312 (88%) species-drug-years had resistance prevalence in other specimen types within ±10% of that blood isolates. Results were similar across multiple countries and hospitals/programmes in high/middle/low income-settings.ConclusionsResistance in bloodstream and less invasive infections are strongly related over time, suggesting the latter could be a surveillance tool for AMR in LMICs. These infection sites are easier to sample and cheaper to obtain the necessary numbers of susceptibility tests, providing more cost-effective evidence for decisions including empiric antibiotic recommendations.

BackgroundReporting cumulative antimicrobial susceptibility testing data on a regular basis is crucial to inform antimicrobial resistance (AMR) action plans at local, national and global levels. However, analysing data and generating a report are time-consuming and often require trained personnel. We illustrate the development and utility of an offline, open-access and automated tool that can support the generation of AMR surveillance reports promptly at the local level.MethodsAn offline application to generate standardized AMR surveillance reports from routinely available microbiology and hospital data files was written in the R programming language. The application can be run by a double-click on the application file without any further user input. The data analysis procedure and report content were developed based on the recommendations of the World Health Organization Global Antimicrobial Resistance Surveillance System (WHO GLASS). The application was tested in Microsoft Windows 10 and 7 using open-access example data sets. We then independently tested the application in seven hospitals in Cambodia, Lao People’s Democratic Republic (PDR), Myanmar, Nepal, Thailand, the United Kingdom, and Vietnam.FindingsWe developed the AutoMated tool for Antimicrobial resistance Surveillance System (AMASS), which can support clinical microbiology laboratories to analyse their microbiology and hospital data files (in CSV or Excel format) onsite and promptly generate AMR surveillance reports (in PDF and Excel formats). The data files could be those exported from WHONET and/or other laboratory information systems. The automatically generated reports contain only summary data without patient identifiers. The AMASS application is downloadable from www.amass.website. The participating hospitals tested the application and deposited their AMR surveillance reports in an open-access data repository.InterpretationThe AMASS application can be a useful tool to support the generation and sharing of AMR surveillance reports.FundingMahidol Oxford Tropical Medicine Research Unit (MORU) is funded by the Wellcome Trust (Grant no. 106698/Z/14/Z). Oxford University Clinical Research Unit (OUCRU) is funded by the Wellcome Trust (Grant no. 106680/B/14/Z). The investigators are funded by the Wellcome Trust (CL is funded by a Training Research Fellowship [Grant no. 206736] and DL is funded by an Intermediate Training Fellowship [Grant no. 101103]). BSC is funded by the UK Medical Research Council and Department for International Development (Grant no. MR/K006924/1). The funder has no role in the design and conduct of the study, data collection, or analysis and interpretation of the data.