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Background Reliable estimates of the current and future disease burden of antimicrobial resistance (AMR) are essential to combat the global drug-resistant infection crisis [1, 2]. Yet, despite considerable global efforts, our understanding of the burden of resistant infections remains disturbingly sparse [3]. More reliable, detailed and dynamic information is essential to successfully address an apparent rise in resistant infections, enabling policymakers and healthcare providers to implement national AMR action plans, and efficiently allocate resources. Major, recent efforts to estimate AMR burden include the Global Burden of Disease (GBD) 2016 study [4], the Review on AMR [1], and a 2015 study conducted by the European Centre for Disease Prevention and Control (ECDC) [5]. However, these provide insufficiently precise or comprehensive estimates to inform effective policies. The figure of 700,000 annual deaths from resistant infections often cited from the Review on AMR suffers from methodological limitations and statistical uncertainty. GBD 2016 focuses exclusively on multi-drug-resistant and extensively drug-resistant tuberculosis, and covers only a fraction of global AMR burden. The ECDC publication focuses only on the European Union and European Economic Area, and depends on European Antimicrobial Resistance Surveillance Network (EARS-Net) data. Challenges in AMR surveillance More accurately measuring AMR burden requires a fully described scientific measurement framework [3]. The global scientific community is responding to this call through the Global Research on Antimicrobial Resistance (GRAM) project, led by the University of Washington’s Institute for Health Metrics and Evaluation, and the University of Oxford’s Big Data Institute. GRAM will be the first study to quantify the incidence, prevalence, excess mortality risk, and overall disease burden attributable to key antibiotic resistant bacteria at the global, national and – where possible – sub-national levels. It will enable direct comparison of AMR burden with other global health threats, and provide an invaluable evidence base to guide policy. The accuracy of AMR burden estimates depends on the quality and availability of input data [3]. Present global surveillance systems remain disconnected and underdeveloped, with recent recommendations from the United Nations highlighting the urgent need to strengthen AMR surveillance globally [2]. Only 70 countries have enrolled in the World Health Organization (WHO)’s Global Antimicrobial Resistance Surveillance System (GLASS); fewer than 50 reported AMR rates in its most recent call-out [6]. Data are self-reported, heterogeneous, and based on few isolates from a handful of surveillance sites. The development of GLASS is a step in the right direction, but it is at an early stage [7]. GLASS limitations reflect broader global data insufficiencies that hinder the quantification of AMR burden; many nations lack the laboratory and data management capacities to support effective surveillance [7]. Microbiology data may be available electronically, but clinical datasets are frequently archived in laboratory books and patient notes, creating barriers to analysis. Electronic data also suffers from entry error

Surveillance and monitoring of antimicrobial resistance: limitations and lessons from the GRAM project