Antimicrobial resistance: what happens with age and by sex?
Dr Gwenan Knight at LSHTM
Dr Catrin Moore at SGUL
Dr Naomi Waterlow at LSHTM
Antimicrobial resistance (AMR) is a growing global problem for people of all ages, that requires innovative, cross-disciplinary solutions. However, most AMR research and data presentation ignores variation by age and sex, presenting instead “resistance to drug X in bacteria Y in country Z”. This is despite the huge changes in infection risk, comorbidities, antibiotic and healthcare exposure that happen over the life course.
In this project, we will use data analysis paired with mathematical modelling to explore the dynamics of resistance gene accumulation and shuffling. Pairing microbiology data with clinical patterns, we will ask what mechanisms and rates would allow us to explain the patterns we see by age and sex in resistance combinations and hence optimise intervention design.
Project Key Words
Antimicrobial resistance, ageing, mathematical modelling, MRSA, resistance diversity, intervention impact
MRC LID Themes
- Global Health = Yes
- Health Data Science = Yes
- Infectious Disease = Yes
- Translational and Implementation Research = Yes
MRC Core Skills
- Quantitative skills = Yes
- Interdisciplinary skills = Yes
- Whole organism physiology = No
Skills we expect a student to develop/acquire whilst pursuing this project
Awareness of antimicrobial resistance evolution complexity; analytical skills; mathematical modelling; statistical data analysis
Which route/s is this project available for?
- 1+4 = Yes
- +4 = Yes
Possible Master’s programme options identified by supervisory team for 1+4 applicants:
- LSHTM – MSc Demography & Health
- LSHTM – MSc Epidemiology
- LSHTM – MSc Health Data Science
Is this project available for full-time study? Yes
Is this project available for part-time study? Yes
Particular prior educational requirements for a student undertaking this project
- LSHTM’s standard institutional eligibility criteria for doctoral study.
- Quantitative background (some mathematical training with ideally some experience in use of R).
Other useful information
- Potential CASE conversion? = No
PROJECT IN MORE DETAIL
Scientific description of this research project
Antimicrobial resistance (AMR) is a leading cause of morbidity and mortality across the life course of humans. However, most AMR research and data presentation ignores variation by age and sex, presenting national or syndrome based resistant prevalence indices. This is despite the huge changes in infection risk, comorbidities, antibiotic and healthcare exposure that happen over the life course. Our work at a population level shows the stark importance of including age in the analysis of AMR dynamics, with different trends of proportion resistant by bacteria-antibiotic combination.
This project will be nested within an MRC CDA fellowship that has begun to address these patterns. One initial hypothesis was that resistance would increase with age – we have not found this for many single bacteria-antibiotic combinations. This PhD would test whether there is an accumulation affect with age: those bacteria that cause infections in older individuals that are resistant are resistant to more antibiotics than those in younger individuals. Our previous research has also explored rates of resistance gene movement in a key AMR bacteria (Staphylococcus aureus)– pairing this rapid shuffling with the age patterns is a key knowledge gap for AMR.
The objectives of this project will be to:
– Quantify the variation in number and type of antibiotic combinations as a patient ages and statistically test for age and antibiotic trends
– Determine the transmission and evolution rates of resistant gene movement that explain multilevel data (microbiology and ecological patterns)
– Develop tools to support clinicians to account for age in empiric prescribing decision making and model potential impact on infection burden
The techniques to be used will be:
– Data analysis and regression techniques
– Mathematical transmission dynamic modelling to account for potential mechanisms driving the patterns by age and sex
– Evolutionary mathematical models to explore resistance gene transfer building on laboratory work in S. aureus to account for ecological patterns seen.
– Clinically co-designed software development
– EARS-NET isolate database with antibiograms, age and sex (3.5million isolates) across Europe for bloodstream infections
– Several active hospital collaborations will provide patient level information linked to isolate resistance profiles
– Mathematical modelling training and support, and computing cluster within the Centre for Mathematical Modelling of Infectious Diseases (CMMID) at LSHTM
– Ongoing research on MRSA resistance movement and hence data on rates
Potential risks :
– We have access to the EARS-NET data for the main fellowship but have had to anonymise countries for publication. The risk would be that patterns we find in resistant accumulation can be explained by country-level factors that we may find difficult to publish. However, we can work with the ECDC to explore publishing options.
– Existing laboratory data may not provide exactly the data and hence parameters required for this project. Whilst no laboratory work is proposed in this project, there is the possibility that placement in collaborative labs in SGUL could be done and the experiments (co-culture transfer of resistant work) with S. aureus are relatively cheap.
– New supervisory team and hence rhythms of working have not been established. However, GK and NW work on GK’s fellowship on this topic and have a history of successful collaboration. GK and CM have been long term colleagues. All work in London so in-person meetings and discussion will support PhD supervision.
(Relevant preprints and/or open access articles)
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.