Strategies to reduce the burden of antibiotic resistance in China Antimicrobial resistance (AMR) and particularly resistance to antibiotics (ABR) has become one of the most complex public health challenges globally. Estimates have suggested that by 2050 AMR will be responsible for 10 million deaths, of which 4.73 million are in Asia, with an associated reduction of 2% to 3.5% in Gross Domestic Product (GDP) that will cost the world up to 100 trillion USD. Our collaborative research and training programme will bring together international experts at leading universities in China and the UK to tackle antibiotic resistance, the type of AMR that is the most pressing concern for human health. China is estimated to be the second largest consumer of antibiotics in the world, with widespread and often inessential use in both humans and livestock. Widespread consumption leads to antibiotic residues in water and soil that may exacerbate the development and transmission of resistance through organisms and chemicals in the environment. Studies have investigated the epidemiology and pattern of drug-resistant infections in China, but the size of the health and economic burdens caused by ABR on a national level and the role of the environment in the development and transmission of drug resistance are still unclear.    Most ABR research in China has focused on specific bacteria in hospital patients, selected food animals, or isolated determinants. Better evidence and broader understanding of environmental, community, economic and health care drivers and burdens of ABR based on a systems perspective that recognises interactions between these areas is urgently needed, as are evaluation tools to measure the effectiveness of different ABR-reducing intervention strategies. Due to a dense population, an intensive livestock breeding industry and massive antibiotic use, Eastern China is a key region for controlling antibiotic use and ABR. Our research aims to bridge these key evidence gaps and strengthen disciplinary and methodological research skills, through a set of closely linked projects that will generate the holistic knowledge which is needed to design, deliver and monitor targeted strategies to limit ABR in China and comparable settings. We will also establish sustainable partnerships with cross-disciplinary research expertise that is currently lacking in China and strengthen capacity in policy-relevant research. Since antibiotic resistant infections and their genetic components spread rapidly through international travel, research into ways of reducing the burden of ABR in China is important not only for populations in China and the wider Asian region, but globally.  Through three linked programmes of work based at three leading universities in China, supported by UK academics from a wide range of disciplines, we will: 1. Estimate the economic burden of AMR and determine the cost-effectiveness of potential intervention strategies  2. Design and evaluate a tailored intervention to modify antibiotic prescribing behaviour among health professionals and reduce antibiotic consumption among outpatients  3. Measure human exposure to antibiotics from environmental and livestock sources, estimate their health effects & develop tools for risk assessment and monitoring of environmental exposures to antibiotics and antibiotic-resistant genes 4. Gather evidence on current patterns of antibiotic use and the implementation of ABR-related policies and regulations at local, regional and national levels  5. Produce evidence-based recommendations on optimising antibiotic use, monitoring ABR and assessing the success of strategies to reduce ABR in China 6. Build cross-institutional and international collaborative groups to increase China's research capacity in a range of relevant disciplines and methodologies, as well as in the design and conduct of inter-disciplinary research. The Newton Fund builds research and innovation partnerships with developing countries across the world to promote the economic development and social welfare of the partner countries.