2025-26 Project (Moore & Waterlow & Clark)

The prevalence of antibiotic resistance and antibiotic use in patients with bacterial infections over fifteen years (2010-2025) and interactions with demographic indicators and climate change globally

SUPERVISORY TEAM

Supervisor

Dr Catrin Moore at City St George’s
Email: camoore@sgul.ac.uk

Co-Supervisor

Professor Charlotte Clark at City St George’s
Email: chclark@sgul.ac.uk

PROJECT SUMMARY

Project Summary

We are currently entering a new era similar to the pre-antibiotic era with a lack of discovery of new antibiotics and none in the pipeline. While innovative solutions have been proposed, they remain to be implemented. Routine surgeries are problematic in many countries due to drug resistant infections and the lack of appropriate treatment. Antimicrobial resistance (AMR) is one of the top ten global threats killing up to an estimated 4.95 deaths in 2019. 

To treat patients effectively we need to compile all available data (clinical, microbiology, treatment and outcome) providing the best evidence to inform clinical treatment guidelines locally. We do not know how the age and sex of patients affects patients clinical outcomes nor how climate change is affecting patients in many countries. This work will model data from patients with urinary tract infections from a number of countries to begin to answer this question.

Project Key Words

Antimicrobial resistance, antimicrobial, age, sex, climate

MRC LID Themes

  1. Global Health
  2. Health Data Science
  3. Infectious Disease
  4. Translational and Implementation Research

Skills

MRC Core Skills

  1. Quantitative skills
  2. Interdisciplinary skills

Skills we expect a student to develop/acquire whilst pursuing this project

Analytical skills; quantitative and qualitative encompassing epidemiological design, statistical analysis and modelling

Routes

Which route/s are available with this project?

  • 1+4 = Yes
  • +4 = Yes

Possible Master’s programme options identified by supervisory team for 1+4 applicants:

  • LSHTM – MSc Climate Change & Planetary Health
  • LSHTM – MSc Control of Infectious Diseases
  • LSHTM – MSc Epidemiology
  • LSHTM – MSc Health Data Science
  • LSHTM – MSc Public Health

Full-time/Part-time Study

Is this project available for full-time study? Yes
Is this project available for part-time study? Yes

Location & Travel

Students funded through MRC LID are expected to work on site at their primary institution, meeting – at the minimum – the institutional research degree regulations and expectations. Students may also be required to travel for conferences (up to 3 over the duration of the studentship), and for any required training (for research degree study). Other travel expectations and opportunities highlighted by the supervisory team are noted below.

Primary location for duration of this research degree: City St George’s, London

Travel requirements for this project: None

Eligibility/Requirements

Particular prior educational requirements for a student undertaking this project

  • Minimum City St George’s institutional eligibility criteria for doctoral study.
  • Epidemiology background and a knowledge of AMR

Other useful information

  • Potential Industrial CASE (iCASE) conversion? = No

PROJECT IN MORE DETAIL

Scientific description of this research project

This PhD project expands a UKRI funded grant entitled The Comprehensive Understanding of Disease and AI Research (CURE). The CURE project is using human data as an exemplar to build a comprehensive data landscape on infectious diseases, mapping existing data and determining where gaps exist. This PhD will use this data to model several different variables and their interactions with AMR.   

1. Project objectives 
The importance of demographic information such as age and sex driving higher levels of antibiotic resistance are beginning to be described in Europe, the intersection between these demographic, behavioural (antibiotic use), clinical syndrome and climate change have yet to be examined. 

This project will examine the relationships between clinical syndrome, behaviour (ABU), demographic information, and climate change on the prevalence of antibiotic resistance globally over a fifteen-year period (2010-2025) in order to provide unique insights into future interventions, particularly in low- and middle-income countries where climate change will have the highest impact.   

2. Techniques to be used 
This expands current work that Dr Naomi Waterlow and Prof Gwen Knight have been doing to date on the intersection between AMR with age and sex and the work Prof Clark has been performing on environmental exposure and climate change and brings together a full range of expertise and experiences to fully explore interactions between clinical syndrome, behaviour (antimicrobial use), bacteria, demographic and climate influences on AMR which may provide unique insights into future interventions. The specific techniques include: epidemiology, statistics and data analysis, geographical information systems, multiple imputation and mathematical modelling.   

3. Confirmed availability of any required databases or specialist materials 
This PhD will use the datasets available from the CURE project which will be converted into a common data format to expand the data analysis and modelling. In addition to existing datasets within the partnership (WHO GLASS data, European CDC data, data from St George’s Hospital Trust), the CURE project will provide new datasets and more variables to determine the relationship between age, sex, climate change, AMR and AMU globally. We will acquire access to the CLEVER (Cohort for research into Living EnVironments and hEalth in children) an ADRUK funded electronic cohort up to 11 million children generated using linked administrative data.   

4. Potential risks to the project and plans for their mitigation 
Ethics will need to be obtained and will take some time to get in place, this will be prioritised at the beginning of the PhD. There may be a mismatch between data available in different countries, therefore we will prioritise the countries with the most complete data as exemplars/case studies for the PhD work.

Further reading

Relevant preprints and/or open access articles:
(DOI = Digital Object Identifier)

Additional information from the supervisory team

The supervisory team has provided a recording for prospective applicants who are interested in their project. This recording should be watched before any discussions begin with the supervisory team.

Moore-Waterlow-Clark recording

MRC LID LINKS

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