The NIHR HPRU in Healthcare Associated Infection and Antimicrobial Resistance was established at Oxford University in April 2014 and was renewed in April 2020 for a further 5 years.
Our Key collaborators are:
- The European Bioinformatics Institute
- University of Leeds
- Animal and Plant Health Agency
Our vision is to find better ways to manage and prevent threats from antimicrobial resistance and healthcare-associated infections, by detecting them faster, working out who needs protecting most and how this can be done.
We leverage the multidisciplinary breadth of our partnership to integrate powerful, increasingly rich types of data and models to identify the most efficient and cost-effective approaches for detection, surveillance, investigation and reduction of healthcare associated infection and antimicrobial resistance, including:
- Understanding better who is most at-risk from healthcare associated infection and antimicrobial resistance, particularly in terms of inequalities
- Developing and testing interventions to reduce their risks, and assessing how these can be targeted to at-risk populations
- Identifying the contexts in which healthcare associated infection and antimicrobial resistance proliferate, to manage and reduce their influence
- Improving software to exploit genetic data from millions of microorganisms, predict resistance to antibiotics, and identify transmission
We bring world-class Oxford researchers with experience in many different areas, including the Big Data Institute (BDI) and Social Sciences, together with patient representatives and highly experienced scientists from UKHSA, Leeds University, the Animal and Plant Health Agency and the European Bioinformatics Institute. We train junior researchers in the new methods needed to answer these questions and support them to become research leaders.
- Traditional and new ways of investigating diseases, e.g., using genetic data and looking for patterns in linked ‘big data’ from microbes and electronic records about patients’ health, regionally and nationally
- Robust statistical and economic methods
- Detailed modelling
- Models of behaviours around health choices
There are 4 broad themes:
Better understand who is affected by antimicrobial resistance and healthcare associated infections, and why, including the impact of inequalities and aging, and how we can monitor these conditions. We do this by combining many different types of ‘big data’ about patients and the microbes that make them ill.
Use this information to work out how we can reduce healthcare associated infections and antimicrobial resistance, in ways the NHS can afford and that target those at highest risk. We explore whether we should use drugs, tests for infections and vaccinations differently; or change the ways healthcare professionals interact with patients, empowering patients to manage their health and antibiotic use better.
Understand how healthcare associated and antimicrobial resistant infections can be affected by what happens on farms or in both the general and hospital environment.
Work out how to analyse and compare the genetic code of millions of microorganisms causing infections from across the world to find out more about they become resistant to antibiotics, cause epidemics and can be controlled.
Our outcomes include:
- Better use of routinely collected electronic health records to monitor and manage infections, reducing data collection burden for the NHS
- Better estimates of future trends in antibiotic use, AMR and HAIs, and when we need to change commonly-used antibiotics
- Recognition of important at-risk populations to target with future actions to prevent HAIs, and approaches to use antibiotics better
- Comparisons of the potential for AMR to spread through different routes, and strategies to reduce this
- New software solutions for managing and analysing large amounts of microorganism sequence data