How do vaccine-preventable acute respiratory infections affect frailty trajectories?
Dr Kate Mansfield at LSHTM
Professor Charlotte Warren-Gash at LSHTM
Frailty describes someone’s overall resilience and their capacity to recover quickly following health problems. Frailty typically affects old
er people, and reduces quality of life and increases risk of illness and death. Vaccine-preventable acute chest infections – including influenza, pneumonia, and COVID-19 – have short-term health implications, particularly in the vulnerable older population. However, it is less clear how short-term, often transient, chest infections may influence longer-term aging, including the progression of frailty, and we do not know whether vaccination against these infections alters risk of frailty.
The project will use very large, routinely collected, electronic health record datasets (from primary and secondary care) to investigate the relationship between vaccine-preventable acute respiratory infections and frailty trajectories. It will also investigate whether the receipt of vaccines against influenza, pneumonia, and COVID-19 influence frailty trajectories. Addressing these questions will improve our understanding of the potential for vaccinations to foster healthy ageing.
Project Key Words
frailty; infections; vaccines; electronic health records
MRC LID Themes
- Global Health = No
- Health Data Science = Yes
- Infectious Disease = Yes
- Translational and Implementation Research = No
MRC Core Skills
- Quantitative skills = Yes
- Interdisciplinary skills = No
- Whole organism physiology = No
Skills we expect a student to develop/acquire whilst pursuing this project
– Ability to conduct systematic reviews and meta-analyses.
– Experience of cleaning and analysing large linked electronic health record datasets.
– Experience of the strengths and limitations of using electronic health record data for epidemiological research.
– In depth knowledge of frailty indices and phenotypes.
– Experience of conducting cohort studies using various multivariable regression techniques.
– Causal inference skills.
– Experience of research ethics and data security.
– Advanced skills in statistical packages (e.g., Stata, R).
– Experience of quantitative bias analysis to explore systematic errors that may influence measures of associations.
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 Epidemiology
- LSHTM – MSc Health Data Science
- LSHTM – MSc Medical Statistics
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.
- Applicants must hold, or expect to obtain before the start of the PhD, a relevant MSc (Epidemiology, Health Data Science, Medical Statistics, or equivalent) awarded with good grades, or have a combination of relevant qualifications and experience demonstrating equivalent ability and attainment.
- This project can also be awarded as 1+4 (1-year MSc programme + 4-year PhD candidature). Through this route, a relevant BSc awarded with good grades is required.
Other useful information
- Potential CASE conversion? = No
PROJECT IN MORE DETAIL
Scientific description of this research project
Frailty describes someone’s overall resilience and their capacity to recover quickly following health problems. It typically affects older people, and is associated with reduced quality of life, and increased morbidity and mortality.
Influenza, pneumonia, and COVID-19 are common respiratory tract infections that are potentially preventable through vaccination. Small studies suggest that influenza and pneumonia can reduce physical function in the short term, and that pneumonia may reduce cognition. However, it is unclear how these acute infections may influence longer-term trajectories of frailty. We also do not know whether relevant vaccinations can reduce frailty development. Addressing these questions will improve understanding of the potential for vaccinations to foster healthy ageing.
The overall aim of the study will be to investigate how vaccine-preventable acute respiratory infections affect trajectories of frailty.
Specific objectives will be:
1. To conduct a systematic review evaluating the existing evidence of the association between vaccine-preventable acute respiratory infections (influenza, pneumonia, COVID-19) and development of frailty in older adults.
2. To investigate whether influenza, pneumonia, and COVID-19 are associated with frailty trajectories in electronic health records data using a matched cohort design.
3. To assess whether receipt of vaccines against influenza, pneumonia, and COVID-19 influence frailty trajectories after applying quantitative bias analysis.
TECHNIQUES TO BE USED
Objective 1: The student will initially conduct a systematic review to evaluate the existing evidence for any association between vaccine-preventable infections and incident frailty or functional decline.
Objective 2: The student will then conduct a matched cohort study to investigate whether influenza, pneumonia or COVID-19 infections or hospitalisations are associated with incident or worsening frailty. The study will use UK primary care electronic health record data from the Clinical Practice Research Datalink (CPRD) Aurum linked to hospital admissions data (Hospital Episode Statistics) and mortality records from the Office for National Statistics. The student will use the validated electronic frailty index (which is widely used in clinical practice) to define frailty. Data will be analysed using techniques including multivariable mixed effects ordinal logistic regression.
Objective 3: The student will also use electronic health record data from CPRD to investigate whether vaccine uptake is associated with the development of frailty over time. These analyses could use matched cohort or self-controlled case series designs, analysed using techniques such as Cox proportional hazards regression or conditional Poisson regression. The student will apply quantitative bias analysis techniques to improve robustness of findings.
CONFIRMED AVAILABILITY OF ANY REQUIRED DATABASES OR SPECIALIST MATERIALS
LSHTM has a multi-study licence to access linked CPRD data, which is renewed each year. The supervisory team is highly experienced at accessing and using these datasets and a wealth of helpful material is available through the Electronic Health Records research group (including a regularly updated intranet site).
POTENTIAL RISKS TO THE PROJECT AND PLANS FOR THEIR MITIGATION
Any delays in receiving full linked datasets will be mitigated through providing the student with access to a random sample of one million anonymised records on which to generate statistical code.
(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.