Background:
This is an exciting opportunity to be one of a four student cohort, working at the NIHR Health Protection Research Unit in Healthcare Associated Infection (HCAI) and Antimicrobial Resistance (AMR) at Imperial College London.
The Unit is a collaboration between Imperial College London and its partners, Public Health England, Imperial College Health Partners, Warwick University, and Cambridge Veterinary School.
The ethos of the Unit is one which embraces interdisciplinarity and multi-professional research to tackle AMR and HCAI, producing a vibrant and diverse research environment in which world-class facilities and access to international experts make the exploration of exciting and innovative research projects across the breadth of AMR possible.
The Unit’s Director, Professor Alison Holmes and our Theme Leads represent international expertise from the Faculty of Medicine, the Faculty of Engineering, the Dyson School of Design, the School of Public Health and the Centre for Molecular Bacteriology and Infection.
Depending on the choice of project, there are also opportunities to collaborate with two other HPRUs at Imperial; the HPRU in Respiratory Infection and the HPRU in Modelling Methodologies and Health Economics. Whichever project you choose, you will be working closely with our experienced research staff who include bench scientists, medics, epidemiologists, pharmacists and systems dynamics modellers. You will also be supported by the Unit’s dedicated administrative team who can assist students in adopting patient and public engagement in their projects and in applying for post-doctoral fellowships at the end of their studies.
Project
This is a 3 year PhD studentship with a stipend of £18,000 per annum. Fees are available at the home rate only.
SSI surveillance has been inadequate with existing surveillance and mandatory reporting insufficient for the clinical burden of surgical site infection. This project aims to develop an algorithm for automatic syndromic-level surveillance; produce standardised surveillance framework (i.e. minimal data sets) for adoption in low resource settings and perform economic evaluation for SSI surveillance.
This will be done by identifying surveillance priority (surgery category, patient population); linking electronic health records with national SSI surveillance data to perform predictive modelling of SSI; performing sensitivity analysis and comparing results with conventional surveillance approaches. Cost estimation of SSI surveillance at local and national level will be conduction with the potential to expand the work to include qualitative work on the perceived barriers to adoption, acceptability, role and value (economic, patient outcomes etc)
Objectives
Predictive modelling
- Embed predictive modelling alongside automatic surveillance within electronic health records for SSIs.
- Develop machine learning algorithms for risk prediction and surveillance alerts for healthcare associated infections and antimicrobial resistance transmission.
- anticipate signals/triggers in organisational performance in the context of complex systems to take remedial action.
Community based post-discharge surveillance
- Link procedure and prescribing data across secondary and primary care to identify SSI cases post-discharge.
- Develop primary care standardised diagnostic algorithm for SSI.
- Link to national surveillance (UK mandatory and voluntary SSI surveillance)
Eligibility criteria:
To apply you must have a minimum of an upper second class honours degree or equivalent, meet the College’s English language requirement and meet the criteria to be eligible for home fees.
To apply for this position please, use the enquiry form below to send a cover letter explaining why you are a suitable candidate for this PhD studentship, together with your CV and the names of two referees.
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