Global Research on Antimicrobial Resistance (GRAM)
Global Research on Antimicrobial Resistance (GRAM) is the flagship project of the Oxford GBD (Global Burden of Disease) Group, and aims to provide robust, comprehensive and timely evidence of the burden of antimicrobial resistance (AMR) globally.
There is currently very little data on the incidence, prevalence or burden of AMR in low- and middle-income countries (LMICs), which limits our understanding of the impact that AMR is having on human and animal health, and inhibits the development and targeting of appropriate interventions.
The overall aim is to strengthen the evidence base on the current global burden of AMR, and how, where and why it is changing. This will provide the essential health intelligence to help drive awareness, support better surveillance, and prompt policy action to control AMR, including facilitating antimicrobial stewardship.
University of Oxford’s Big Data Institute and the Institute for Health Metrics and Evaluation
Wellcome Trust and The Bill and Melinda Gates Foundation
June 2016 - June 2021
- consolidate, review and analyse all available data and scientific information on AMR worldwide. Drawing on the literature, country surveillance systems, vital statistics and other data systems collecting causes of death, clinical records, and data from antimicrobial sensitivity testing.
- generate comparable AMR burden estimates for all clinical syndromes and pathogen-drug combinations, from 1990 to the present
- produce granular geospatial maps of AMR burden - as detailed as the data will allow - to enable policymakers and researchers to tailor interventions to the local level
- promote the widespread dissemination of the results to the public, the development community, academics and policymakers
- data collection from desk review or country visits, data preparation and management
- geospatial disease mapping of AMR
- dissemination of AMR mapping data through policy briefs, reports, infographics, conference and workshops
- collaboration and support of AMR data collection networks through workshops, the creation of data networks, and expansion of data users