Exploring vaccine preventable infections, outcomes and optimisation of vaccine uptake in people with multimorbidity - a mixed methods study
Supervisors:
Dr Benjamin Parcell, School of Medicine (University of Dundee)
Prof Stephen McKenna, School of Science and Engineering (University of Dundee)
Dr Suzanne Grant, School of Medicine (University of Dundee)
Dr Karen Barnett, School of Medicine (University of Dundee)
Summary:
Infection contributes to approximately one in five deaths globally. New evidence shows multimorbidity (MM) puts people at greater risk of death due to infection from a range of pathogens. Immunisation is an important public health strategy to reduce infection/antimicrobial resistance (AMR). Despite its evidence-base, vaccine uptake rates are suboptimal, with less known about vaccine uptake in patients with MM and how health inequalities relate to this.
The aim of this mixed methods project is to improve the understanding of vaccine preventable infections, uptake of vaccines and outcomes in multimorbid people and explore how vaccine uptake can be optimised. Objectives can be shaped by the fellows interest and include:
1. Synthesis of existing evidence on vaccine preventable infections in patients with MM.
2. Quantify vaccine uptake and outcomes including subsequent infections (vaccine effectiveness) in people with MM.
3. Examine artificial intelligence (AI) strategies - develop and evaluate machine learning models for risk prediction, initially using XGBoost, before investigating state-of-the-art, time-aware transformer models to better exploit temporal information in the electronic health record data.
4. Explore patient and healthcare workers’ beliefs/experiences to identify key barriers/enablers to vaccine uptake.
5. Use findings to co-design solutions for practice.
This will include literature reviews, statistical analysis of large healthcare datasets, use of AI methods, qualitative observations and focus group work with professional stakeholders and a Patient and Public Involvement (PPI) group.