Exploring vaccine preventable infections, outcomes and optimisation of vaccine uptake in people with multiple long-term conditions (MLTCs)
Project Title:
Exploring vaccine preventable infections, outcomes and optimisation of vaccine uptake in people with multiple long-term conditions (MLTCs) - a mixed methods study
Supervisors:
Dr Benjamin Parcell, Population Health and Genomics, University of Dundee
Prof Stephen McKenna, School of Computing, University of Dundee
Dr Suzanne Grant, Population Health and Genomics, University of Dundee
Dr Karen Barnett, Population Health and Genomics, University of Dundee
Summary:
Infection contributes to approximately one in five deaths globally, and people with multiple long-term conditions (MLTCs) are at increased risk. Immunisation is a key public health strategy to reduce infection and antimicrobial resistance (AMR), yet uptake remains suboptimal, with limited understanding of vaccine uptake and inequalities in people with MLTCs.
This mixed methods project aims to improve understanding of vaccine uptake, vaccine-preventable infections, and outcomes in people with MLTCs, and identify ways to optimise uptake. Objectives, which can be tailored to the fellow’s interests, include:
- Synthesising existing evidence on vaccine-preventable infections in patients with MLTCs.
- Quantifying vaccine uptake and outcomes, including effectiveness.
- Developing and evaluating artificial intelligence (AI) machine learning models for risk prediction, initially using XGBoost and neural additive models.
- Exploring patient and healthcare workers’ beliefs/experiences to identify key barriers/enablers to vaccine uptake.
- Co-designing solutions to improve practice
Methods will include literature review, statistical analysis of large healthcare datasets, use of AI methods, qualitative observations and focus group work with professional stakeholders and patient and public involvement and engagement (PPIE) groups.