Diagnosis and treatment of urinary tract infections (UTI) in long term care facilities (LTCF) and preventing the pathways to antimicrobial resistance (AMR)

Urinary tract infections (UTI) are an important cause of morbidity and antibiotic use in older adults, with between one and two thirds of all antibiotic prescriptions in long term care facilities (LTCF) being for UTIs. Antimicrobial resistant (AMR) infections can have severe consequences for LTCF residents. So early detection of infections and appropriate infection prevention and control may help curb the spread of AMR. UTIs, for which there are no reliable diagnostic tests, are particularly challenging for many reasons in this vulnerable population. Many of the severe symptoms of UTI are ambiguous but can be deemed serious enough for hospitalisation. Consequently, there is potential for inappropriate use of antibiotics and increased hospital admissions.

This project aims to examine the variability in diagnosis of UTI across LTCF, how this leads to differential care for residents and the impact of this on antibiotic prescribing and subsequent hospitalisations. The project will investigate the interconnectedness of LTCF and hospitals, in the knowledge that transfers to and from hospital are an underestimated source of disease spread.

The student will develop a mathematical model simulating spread within and between hospital and LTCF settings. The project will explore scenarios to prevent such infections and the full impact of more accurate UTI diagnosis on LTCF cases and spread of infection, hospitalisation rates, antimicrobial usage, and ultimately AMR.

The student will develop quantitative methodological skills, including statistical and mathematical modelling, as well as advanced skills in handling and analysing large data. Through multidisciplinary, collaborative working necessary for model development and interpretation, the student will develop an in-depth (micro-)biological, epidemiological, and clinical knowledge of the high priority public health area of healthcare infections and AMR. The work of the PhD will be expected to directly inform evidence-based public health.

The student will be based at UKHSA where they will work with colleagues with multidisciplinary expertise, maximising the access to opportunities: both topic area (i.e. healthcare associated infections, HCAI) and methodological (modelling and analysis). Given the emphasis on data analysis and modelling in the project, they will join the HCAI & AMR Modelling and Evaluation Unit. They will work primarily across two divisions: HCAI, Fungal, AMR, AMU & Sepsis Division within which are epidemiologists, microbiologists, outbreak and infection prevention and control experts, clinicians and policy colleagues) and also Data and Analytical Sciences (within which is the Statistics, Modelling and Economics Department, with colleagues developing and applying sophisticated techniques to various aspects of infectious disease surveillance and mitigation). The HCAI & AMR Modelling and Evaluation Unit is a dynamic research environment with strong links to academia and has a track record of providing a stimulating and productive research environment for PhD students.

This project is in partnership with University College London (UCL) and with supervision from the Institute of Tropical Medicine in Antwerp, Belgium. The student will join Prof Shallcross’ research group at UCL which uses routine data combined with inter-disciplinary methods (qualitative research, basic science, coproduction) to develop interventions and policies to reduce the impact of infection and AMR on health and social care. The project presents an excellent opportunity for the student to benefit from the expertise of the Supervisors and the wider academic and professional environments in the academic, technical and clinical fields.

Supervisors:

Prof Julie V. Robotham (UKHSA), Prof Laura Shallcross (UCL), Dr Emily Agnew (UKHSA) and Dr Esther Van Kleef (ITM, Belgium)

Qualifications:

We invite applications from candidates who hold/or expect to gain a first or upper second-class honours degree (or equivalent), or a Master’s degree from a relevant discipline (such as Science/Mathematics/Statistics/Public Health/Epidemiology).

How to apply:

For more information or questions, please contact Emily.Agnew@ukhsa.gov.uk.

To apply please send the following, to Emily.Agnew@ukhsa.gov.uk:

1.     A copy of your CV.

2.     A personal statement, including your motivation for applying for a PhD studentship and your interest in this project in particular, outlining your relevant experience to-date and your career ambitions.

3.     Contact details for two professional/academic references.