The Multimorbidity PhD Programme for Health Professionals is the first in the UK to focus on multimorbidity, bringing together the clinical disciplines required to address this complex problem, spanning aetiology and mechanisms to epidemiological and applied health services research.
This programme aims to:
- Help tackle rising levels of multimorbidity
- Achieve internationally excellent research
- Catalyse development of urgently needed multimorbidity academic health professionals
Full details on this exciting new programme can be found on the link below:
The University of St Andrews projects are detailed below:
1. A mixed-methods study examining the reality and value of healthcare for people with multimorbidity during their last year of life
Prof Frances Quirk, St Andrews
Multimorbidity is common and results in high clinical and care needs. Healthcare demand rises sharply in the last year of life and is associated with high costs, but at questionable value to individuals. The evidence base for how people with advanced multimorbidity can be best supported by healthcare teams as they near the end of life is lacking.
The proposed mixed-methods PhD will encompass data linkage and qualitative studies. You will study a large decedent cohort in Fife, learning from their electronic healthcare records (EHR) about the multiple dimensions of healthcare accessed over the last year of life. In-depth demographic and clinical characterisation of multimorbidity within these patients will be undertaken, and patterns of healthcare use and outcomes will be explored. The focus of the qualitative study will be informed by the data linkage study findings and will involve in-depth interviews exploring the experiences of healthcare for people with multimorbidity near the end of life.
Understanding the scale and nature of multimorbidity towards the end of life, and the timing, nature, and value of healthcare currently accessed, is fundamental to a future evidence base for practice; informing person-centred care that offers value to individuals, as well as to the system.
2. Neurodevelopmental multimorbidity during the life course
Neurodevelopmental conditions (e.g. dyslexia and ADHD), psychiatric disorders (e.g. schizophrenia and depression) and mental health problems frequently co-occur and are caused by a complex interplay of multiple factors. This multidisciplinary project will use cutting-edge data science approaches applied to genomic and clinical data from large cohorts. The successful candidate will work with three experienced supervisors (Silvia Paracchini, Michelle Luciano and Judith Allardyce) based in two Scottish Universities (St Andrews and Edinburgh) and as part of large international teams.
While most conditions tend to be studied in isolation, this project aims to better elucidate the multimorbid cluster by studying the longitudinal trajectories of these conditions and understand their aetiology. The projects will combine the detailed analysis of clinical data to understand the spectrum of manifestations associated to clinical diagnoses at different ages with genetic analysis to identified shared risk factors. The success of the project will be enhanced by access to multiple resources ranging from clinical, longitudinal (e.g. ALSPAC) and multi-layers “omic” cohorts (e.g. UK Biobank). The ideal candidate will have an aptitude for data science and working with different datasets (e.g. genomic and clinical data) in large samples (e.g. > half million participants). The goal of the project is to improve our understanding of neurodevelopmental multimorbidities to develop better risk estimates and preventative interventions.
3. Avoiding social catastrophes in those with multimorbidity: an exploration of the use of routine healthcare data to predict the incidence of functionally significant major falls
Prof Peter D Donnelly, St Andrews
A key concern for older adults living with multimorbidity is the concept of frailty and the risk of subsequent falls. This study will explore data form health care records to identify multimorbidity in older patients and try to identify those at greatest risk of falls using machine learning techniques.
The fellow would also interview patients who had suffered falls to try and learn their “lived experience” and using the common themes from the interviews select models to best match what patients reported happening within their lives.
The fellow will develop knowledge and expertise in machine learning using routine healthcare and how to integrate this in a mixed methods approach with findings from interviews to help best answer key clinical questions.
4. Impact of data driven specialist pharmaceutical care in community settings for older adult psychiatry patients with functional or organic disease, multi-morbidities, and polypharmacy in primary care?
Prof Colin McCowan, St Andrews
Mental illness is linked to health inequalities, with patients diagnosed with mental health conditions commonly dying prematurely. One in three patients with multimorbidity also have an existing mental health condition. Multimorbidity presents major challenges to health care delivery, therefore continuity of care is particularly important to people with co-morbidities. Patients with mental illness and multi-morbidities however are less likely to receive continuity of care. In addition, multimorbid patients, who tend to be older and more susceptible to side effects from drugs, are frequently prescribed multiple medicines to manage their conditions. Having access to a cross-sector, integrated data reporting system could facilitate the identification and therefore prioritization of these patients with mental illness and multi-morbidities. This study investigates the use of data driven interventions in supporting NHS Fife pharmacists to manage medicines use in older adult psychiatry patients with functional or organic disease and multi-morbidities and quantifies the impact of this intervention on medicine concordance, hospital admissions and quality of life.
5. The impact of interprofessional collaborative practice on health and care outcomes in patients with multimorbidity
Multimorbidity (MM) is defined as the co-occurrence of two or more chronic conditions. Ineffective interprofessional collaboration (IPC) between different professions and specialist teams can lead to disorganized, fragmented and poor-quality care for patients with MM.
This study aims to develop the key components to assist healthcare professionals with effective IPC for patients with MM. A mixed methods study design will be used to:
- Investigate patients and health professional’s experiences, knowledge and perspectives of IPC and MM
- Design, pilot and evaluate a small-scale IPC in MM intervention in healthcare practice.
This is a unique opportunity to work with a supervision team with combined experience and expertise in IPC, multimorbidity, service improvement, and research using a mixture of research methods.