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Managing antimicrobial resistance as a diffuse environmental pollutant

  • Full or part time
  • Application Deadline
    Thursday, January 10, 2019
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

About This PhD Project

Project Description

Summary: This project will use environmental economic approaches to inform the rational management
of antibiotics in global agriculture. The work will focus on a marginal abatement cost method
previously applied in other areas of environmental pollution.

Background: This project will consider the challenge of managing antimicrobial resistance (AMR), using frameworks and instruments developed in the context of other environmental pressures e.g. greenhouse gas emissions. Overuse of antimicrobial (AM) medicines in humans and animals is soaring due to insufficient regulation, with consequent positive selection pressure for bacterial AMR, thereby posing increased health threats to all of us. In the European Union alone, an estimated 25000 people die each year due to infections caused by resistant bacteria, resulting in economic losses estimated at €1.5 billion in extra health care costs and productivity losses. AMR has been called the quintessential One Health challenge with some determinate but other biologically indeterminate factors driving selection for resistance in the environment. In this sense, AMR can be considered a particularly complex diffuse pollutant. At the very least, we should be reducing AM medicine use as a precautionary approach, and there is potential to learn from experience of the management of diffuse environmental pollution. Comparison with greenhouse gas emissions (GHG) mitigation (or abatement) is instructive. Both are ‘wicked problems’ characterised by market and institutional failures in terms of costs being externalised and the absence of any global regulatory or governance architecture. There are multiple human and animal sources of AM “pollution”, further complicated by complex interactions, pooling and persistence in the environment. There are multiple potential entry points to modify clinical and veterinary uses of medicines, and for detection and diagnosis of resistance. Many interventions complement or interact in unanticipated ways. There is an obvious human dimension to AM medicine use, raising unresolved behavioural challenges. Finally, there are political economy dimensions, pitting public and private sector interests, and those in developed and developing countries. Numerous national and international strategies and action plans have aimed to improve stewardship of medically important drugs and to reduce their use, especially in food-producing animals, but few of these suggest clear frameworks to evaluate interventions.
This project will consider these challenges with a particular focus on the application of a marginal abatement cost curve (MACC) approach to AMR to the reduction of AM use in agriculture. A MACC framework will illustrate the technical, economic and behavioural feasibility of key interventions as well as providing a novel focal framework for global AMR research and policy. The thesis will also consider different voluntary, mandatory and market-based instruments, all previously tested in the contexts of agri-environmental and climate change policies. This climate change policy experience considers similar supply chain benchmarking challenges, and offers theoretical and methodological lessons for AMR management.

Key research questions: 1) What metrics can be used to express effectiveness of AMR reduction? 2) What farm and supply chain measures are effective in reducing the use of antimicrobials/antibiotics in UK/global agriculture? 3) What are the direct costs/benefits of each measure including co-effects? Which measures are most cost-effective as shown by the MACC? 4) Which market-based and policy instruments apply to each measure?

Training: A comprehensive training programme will be provided comprising both specialist scientific training and generic transferable and professional skills. The latter includes modelling, programming, geo-processing tools and introduction to epidemiology; e.g. ArcGIS and data statistical, using R scripts. Phase two of the project will required training in behavioural modelling.

Requirements This award is likely suit a student seeking to expand their expertise in the ‘One Health’ field with specific emphasis on big data analysis and farm level modelling. Applicants from a quantitative disciplinary background (masters equivalent in maths, engineering, physics, biology, medical or veterinary science, economics and agricultural sciences) aiming to develop cross-disciplinary skills are particularly welcome. This student is also relevant to students from the current UoE Masters degrees in Geosciences (e.g. Ecological Economics, Ecosystem Services Environmental Sustainability, Environment and Development, Integrated Resource Management, GIS).

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