UNderstanding the Interplay between BURDEN of Treatment and Capacity in Multimorbidity (UNBURDEN)

Primary Supervisor 

Prof Frances MairGlasgow

Summary 

Managing multimorbidity is hard work for patients and their supporters.  Overwhelmed patients are less likely to adhere to treatments and are at risk of poorer outcomes. Existing models and measures of treatment burden assume an arithmetical relationship between workload and capacity but we do not know whether this is correct. We do not know what aspects of patient and caregiver capacity matter in everyday life and this research aims to address this key gap in knowledge.  We plan collaborative work with people with multimorbidity to: 

  • Identify, characterize, and understand key factors that influence capacity to self-manage multimorbidity (through qualitative interviews).  
  • Explore how the relationship between the person with multimorbidity and healthcare providers influences capacity (through quantitative data analysis of the Understanding Society dataset) 
  • Co-design with people with multimorbidity a checklist of capacity factors and/or potential interventions that will be workable and sustainable (through a “living lab” approach with patients, caregivers and health professionals) 

This study will use mixed methods so will involve development of skills in qualitative research methods (including approaches to data collection/analysis and living lab approaches) and quantitative data analysis, including use of statistical software (e.g. R).  The work should appeal to a broad range of health professionals. 

Kidney failure risk in multimorbidity

Primary Supervisor 

Prof Patrick MarkGlasgow 

Summary 

Chronic kidney disease affects 5-10% of the population, but treatment for kidney failure with dialysis or transplantation is rarely needed. Multimorbidity – the presence of two or more medical conditions – and frailty are very common in people with kidney disease. Our knowledge of how to manage people with kidney disease and multimorbidity is currently limited. This doctoral opportunity will use three datasets (UK Biobank, the Stockholm CREAtinine Measurements project (SCREAM) and Secure Anonymised Information Linkage (SAIL)) to answer the following questions: 

  • How do multimorbidity and frailty influence an individual’s risk of kidney failure? 
  • Can we develop tools to help clinicians manage people with kidney disease and multimorbidity better? 
  • Is UK health policy for risk assessment and clinical management of CKD appropriate for a contemporary population where multimorbidity and/or frailty are common? 

Supervisors: Prof Patrick Mark, Dr Bhautesh Jani, Dr Michael Sullivan 

Collaborator: Prof Juan Jesus Carrero (Karolinska Institute)

Understanding the impact of multimorbidity in minority ethnic populations.

Primary Supervisor 

Prof Kate O’DonnellGlasgow

Summary 

Ethnicity is likely to be a major determinant of multimorbidity but our current understanding is sparse. We know little about sociodemographic patterning, disease clusters, underpinning mechanisms or how minority ethnic communities recognise and live with multimorbidity. Much of the current work is from the US, which focus on different communities to those in the UK, in particular Hispanic and Latino populations.  

This project will contribute to our knowledge of multimorbidity in minority ethic populations, particularly in the UK. We propose a mixed methods approach comprising quantitative data analysis; qualitative exploration of the lived experience of multimorbidity in minority ethnic communities; and elucidation of the impact for policy and practice. Supervised by a multidisciplinary team, the successful applicant will be able to work with us to design a PhD that addresses key questions in this area, but which also gives the Fellow a PhD training that meets their career and development needs. This will include training in data science; social science methods; and the application of theory to address key questions of policy and practice. You will also have the opportunity to work with minority ethnic communities to develop research that addresses their needs. 

Achieving meaningful measurement of multimorbidity and its impacts among older adults living in care homes

Primary Supervisor 

Dr Terry QuinnGlasgow

Summary 

Older adults living in care homes are recognised to have complex physical and mental health needs. However, evaluating the scale and impact of multimorbidity in the care home sector is challenging. How the concept of multimorbidity is perceived and understood by those directly affected has also not been explored. These are key barriers when designing effective healthcare systems to support homes and residents and when implementing data collection tools to support evidence-based practice.   

This fellowship offers training in a portfolio of methods necessary for applied health and care research including evidence synthesis, individual participant data analysis, use of secondary data and participatory qualitative methods. It aims to identify meaningful ways of measuring multimorbidity and its impacts among older adults living in care homes. The project will bridge the gap between how measurement has taken place in research studies and how this is undertaken in practice, incorporating experience from residents, relatives and staff around measurement of multimorbidity and its impact. Ultimately this work will help create recommendations to inform practice and policy.

Developing a risk stratification tool to detect neurodevelopmental multimorbidity in children and adolescents

Primary Supervisor 

Dr Michael FlemingGlasgow 

Summary 

Children with single and multiple neurodevelopmental disorders exhibit poorer health, education, and employment outcomes however delays, of sometimes years, before neurodevelopmental conditions are diagnosed and managed cause preventable distress to the child, family, and teachers and lasting educational and social disadvantage. Developing a risk stratification tool will enable earlier detection and management reducing adversity on affected children and families.   

We will link together several Scotland-wide education and health databases to ascertain school children with conditions such as attention deficit hyperactivity disorder (ADHD), epilepsy, severe depression, autism, intellectual disabilities, dyslexia, dyscalculia, and language and speech disorders. A child having two or more of those conditions will be classified as having neurodevelopmental multimorbidity. 

We will determine risk factors associated with neurodevelopmental multimorbidity including maternal medication, maternal antecedents, pregnancy outcomes, early life hospitalisations, early life growth trajectories and development, early life injury/trauma, childhood medication for other chronic conditions, sociodemographic factors, and school progress. To explore development of a risk stratification tool we will randomly split the data into training, validation and test datasets and train and fine-tune several classifiers (e.g. logistic regression, linear discriminant analysis, support vector machines (SVM) and random forests) to predict the outcome of neurodevelopmental multimorbidity, using K-fold cross validation.  

Understanding multi-morbidity among people experiencing severe and multiple disadvantage (SMD) using mixed methods

Primary Supervisors

Dr Andrea WilliamsonGlasgow
Prof Sara MacDonald, Glasgow

Summary 

Severe and multiple disadvantage (SMD) is a shorthand term for a constellation of adversities which often overlap in the population, including homelessness, involvement in the criminal justice system, problem substance use, and mental ill-health. Though SMD is associated with profound health inequalities, there is little work to date on the scale of – and responses to – multi-morbidity among this population. Addressing this gap would help better design services and policies able to respond to the needs of people with SMD, in order mitigate the health inequalities they experience.  

This project would utilize a mixed-methods approach combining quantitative analysis of existing administrative data from health and non-health sources with qualitative work to explore perspectives from experts by experience in SMD and multi-morbidity, and the people who care for them. It would draw on conceptual frameworks from the multi-morbidity literature, such as candidacy and treatment burden, as well as those relating to social inclusion and health inequalities. Candidates would gain expertise in qualitative data collection and analysis, and working with linked administrative datasets, as well as in undertaking highly policy/practice relevant research and collaborating across multiple sectors for knowledge exchange. 

ICAMPH: Integrated CAre for Mental and Physical Health – Intervention co-design

Primary Supervisors

Dr David BlaneGlasgow
Prof Bridget Johnston, Glasgow

Summary 

Physical and mental health multimorbidity is two to three times more common in the most deprived populations compared with the least deprived and is associated with poor quality of life. Integrated collaborative care, a complex intervention that involves collaborative working between a patient’s GP or practice nurse and other members of the team to review progress towards physical and mental health goals (such as depression, anxiety, diet, exercise) and plan future care, has shown promising results, but has not been tested in Scotland.  

The aim of this fellowship is to co-develop an integrated collaborative care complex intervention involving link workers, financial advisors and mental health nurses targeting patients with physical and mental health multimorbidity, following the recently updated MRC-NIHR framework for developing and evaluating complex interventions. The Fellow will lead on shaping the project, but we propose four work packages (WPs): WP1 involves a scoping review of previous collaborative care interventions; WP2 involves patient and public involvement (PPI) and stakeholder engagement; WP3 involves co-development of collaborative care intervention; and WP4 will be feasibility testing of the intervention.  

Given the inter-disciplinary nature of this project, we would welcome applications from candidates from a range of health professional backgrounds.

The impact and experience of painful conditions and multimorbidity for people with intellectual disabilities

Primary Supervisor 

Dr Barbara NichollGlasgow 

Summary 

Adults with intellectual disabilities (ID) die on average 20 years younger than the general population and are known to experience significant inequalities in terms of quality of care and treatment within the healthcare system. Adults with ID report high levels of multimorbidity (presence of 2 or more long-term conditions (LTCs)) and painful conditions, yet very little is known about how these LTCs and pain impact on healthcare related outcomes, such as hospitalisations and mortality. This PhD project aims to investigate the impact of chronic pain on people with ID and multimorbidity using three distinct methods: systematic review of literature, analysis of a large primary care dataset linked to health outcomes of interest, and a qualitative study with people with ID and their carers. The findings will assist health and social care practitioners to better target services, support and interventions for patients with ID. In this PhD project you will have the opportunity to develop the research questions further and to align it to your particular professional interests.  

Using adverse events in clinical trials and routine data as a metric of multimorbidity

Primary Supervisors

Prof Francis Mair, Glasgow
Prof David McAllisterGlasgow

Summary 

Multimorbidity (where people have more than one condition) is perhaps the major challenge in evidence-based medicine. People with multimorbidity are very under-represented in randomised clinical trials, the bedrock methodology of evidence-based medicine. This problem is compounded by the fact that we lack good measures of multimorbidity in clinical trials, so the exact degree of representativeness is unknown. This makes it difficult for trialists to improve representativeness and for clinicians and guideline developers to assess the usefulness of particular trials when deciding about how to treat and prevent disease in people with multimorbidity. 

This PhD will involve applying clinical knowledge to existing datasets to address this problem. It will exploit routinely collected healthcare data and trial data reporting. The successful student will gain a deeper understanding of evidence-based medicine, as well as skills and knowledge in routinely collected (big) healthcare data. This will include skills in data wrangling and in conducting statistical analyses. 

This project would be suitable for a health professional with an understanding of a broad range of diseases. This might include doctors training in a medical specialty, general practice, geriatrics, clinical pharmacology or public health. It would also be suitable for a pharmacist with an interest in drug adverse events. Other health professionals able to demonstrate similar knowledge would also be considered for the studentship. 

Experimental medicine approaches linking brain and peripheral immune mechanisms mediating comorbid Depression in people with Rheumatoid Arthritis

Primary Supervisor 

Prof Jonathan CavanaghGlasgow 

Summary 

Depression comorbid with rheumatoid arthritis results in poor treatment adherence, worse treatment response, increased disability and increased mortality. Understanding the mechanisms underlying this has proved to be difficult but experimental medicine methodologies offer a way of resolving this. In these studies, we will explore the role of inflammation as a mechanism leveraging the highly specific “biologic” therapies and using state-of-the-art neuroimaging, biomarker technology and machine learning analysis methods. 

Specifically, the student will have the opportunity to join a cross-disciplinary group of clinicians and scientists in a series of experimental medicine studies. The studies use the latest neuroimaging and biomarker technologies to understand the mechanisms whereby systemic inflammation drives depression comorbid with enduring musculo-skeletal disorders such as rheumatoid arthritis. In this studentship there will be training opportunities in neuroimaging, biomarker technology and experimental clinical research as well as advanced machine learning analysis techniques.  

Quantifying the incremental costs and quality of life pathways of different trajectories of multimorbidity development

Primary Supervisor 

Prof Emma McIntoshGlasgow 

Summary 

Multimorbidity, presence of two or more chronic diseases is increasingly common and causes significant health burden. It is unclear what the incremental costs and  quality of life impacts are when going from a stage of having one chronic disease to having multiple chronic diseases, with different types of diseases. We will use two large population datasets from UK to understand this problem. We will use UK Biobank, a sample of approximately half million middle-aged participants recruited from the general population and SAIL, a representative sample of majority of population registered with primary care in Wales. In these two datasets, the focus of the analysis will be those with only one chronic disease. Over a period, we will quantify the incremental costs and quality of life (only in UK Biobank) associated with development of different types of multimorbidity based on type of chronic diseases.  

Leveraging social epidemiology and population genetics to identify causal risk factors for multimorbidity

Primary Supervisors

Dr Jan R. BoehnkeDundee
Dr Moneeza Siddiqui, Dundee

Summary 

Promoting health and wellbeing of populations is a global priority (e.g., Sustainable Development Goals). The WHO estimates that in developed countries at least 25% of the population live with two or more long-term conditions ("multimorbidity"). Multimorbidities pose a significant cost to healthcare services and adversely affect quality of life and the ability to participate in society. Since over 85% of people living with diabetes suffer from at least one other long-term health condition, addressing risks for the onset of diabetes and subsequent multimorbidity is critical to improving public health and reducing burden on the National Health Service.  

The PhD will investigate the potential of combining approaches from genetic and social epidemiology. This combination is currently under-utilised but promises to detect risk factors before they develop and deliver prediction models that not only consider an individual’s clinical and social characteristics, but also the interaction of their genes with these characteristics.  

Dundee is ideally placed to support this challenge- and curiosity-driven research as it is home to the GoDARTS and SHARE cohorts that have been an integral part of high-impact global diabetes genetics research. Infrastructure to facilitate such research is already in place to support the PhD candidate. Both supervisors are active researchers in the area and offer support within a research environment that ensures that the PhD candidate receives the required training and opportunities necessary for successful completion. 

Adverse Childhood Experiences: Understanding the impact on multimorbidity in adulthood, a focus on chronic pain, addiction and mental health

Primary Supervisor 

Prof Lesley A ColvinDundee 

Summary 

Multimorbidity is a global concern, associated with increased healthcare utilization, and is one of the biggest challenges facing the NHS.  One major contributory factor may be exposure to Adverse Childhood Experiences (ACEs), with long-term impact on physical and mental health.  ACEs can encompass neglect, abuse (physical, sexual, emotional), household dysfunction, and poor parental health. Around half of people have had at least one ACE, with exposure to more than one increased likelihood of significant multiple, health problems.  Both ACEs and multimorbidity have a disproportionate effect on the poorest in society contributing to persisting inequalities and social deprivation.  

This studentship will focus on ACEs and multi-morbidity in the context chronic pain, mental health and substance use. The studentship will benefit from the support of the infrastructure and strong interdisciplinary collaborative environment of the Consortium Against Pain Inequality (CAPE), led from the University of Dundee.  This is part of a wider UK intuitive where support for Early Career Researchers and collaborative working are part of the key aims of the Advanced Pain Discovery Platform ( see https://www.ukri.org/news/new-data-hub-and-research-into-chronic-pain/). The student will be based in the well-established Chronic Pain Research Group (see https://www.dundee.ac.uk/medicine/research/population-health-genomics/pain-research), with access to training in epidemiology, health data science, precision medicine, neuroscience, mixed methods research, pain medicine, primary care and translational research, depending on interests. 

Epidemiological investigation of periodontitis/diabetes interactions in Scotland... (Full title in description)

Title 

Epidemiological investigation of periodontitis/diabetes interactions in Scotland, and evaluation of a novel patient-clinician communication tool in management of diabetes and periodontitis. 

Primary Supervisor 

Prof Philip PreshawDundee 

Summary 

Periodontitis (advanced gum disease) and diabetes are common chronic non-communicable diseases which adversely affect each other: thus, diabetes increases the risk for periodontitis, and periodontitis increases risk of diabetes complications. Furthermore, periodontal treatment has been shown to result in improvements in diabetes control. In this exciting inter-disciplinary PhD project, national databases will be analysed to conduct epidemiological investigations of the relationships between diabetes and periodontitis at a population level, as well as investigating impact of periodontitis on other disease states, such as cardiovascular disease. Building on this, a novel patient-clinician information tool (the Diabetes Cross-Disciplinary Index, DXDI) will be utilized to further explore relationships between periodontitis and diabetes and other comorbidities at a population level. Finally, a qualitative interview study will investigate the perceived benefits of using DXDI for both people with diabetes and their healthcare providers (medical and dental clinicians). The post-holder will gain experiences in study design, epidemiological methodologies, data acquisition and analysis, statistics, clinical study design and conduct, good clinical practice, interview techniques and qualitative research methods.

Quantifying drug harm in polypharmacy, multimorbidity and frailty: pharmaco-epidemiological and machine learning approaches in large routine datasets

Primary Supervisor 

Prof Bruce GuthrieEdinburgh 

Summary 

Prescribed drugs have both high benefit and cause considerable harm in people with multimorbidity and polypharmacy, but our understanding of drug risks and harms is relatively narrow. For example, we have fairly good understanding of risks for some pairwise drug-drug interactions, but this is of limited relevance in polypharmacy where multiple drug-drug and drug-disease interactions are typically present. The student will choose an aspect of this complex problem to focus on, and develop analysis using and comparing pharmaco-epidemiological and machine learning methods. Examples of focus include examining the impact of shared adverse effects across multiple drugs, examining cumulative risk related to duration of treatment, and exploring how frailty and comorbidity mediate risk of harm. The student will have access to large, curated clinical datasets. The supervisors are two clinical academics interested in polypharmacy, prescribing safety and pharmaco-epidemiology, and a senior data scientist interested in developing and applying interpretable methods for analyzing the complexity of multimorbidity and polypharmacy. The supervisory team have excellent links with national guideline developers and medicine regulators. The student will therefore gain high-level expertise in applying complex quantitative methods in large datasets to address important clinical questions, and in translating findings into impact on understanding, policy and practice. 

Multimorbidity, delirium, and outcomes in hospitalised patients: a big data project using routine clinical and national audit data 

Primary Supervisor 

Prof Alasdair MacLullichEdinburgh 

Summary 

People with multimorbidity are at much higher risk of emergency conditions requiring hospital admission. Hospitalised people with multimorbidity have worse outcomes including higher mortality and reduced level of function leading to increasing dependency at home or new care home admission. Delirium is a serious sudden-onset neuropsychiatric syndrome that affects around 1 in 5 people in hospital. People with delirium have poor outcomes including 3-fold 30-day mortality risk, and functional and cognitive decline. Multimorbidity is a risk factor for delirium, but the degree of the risk including the potential role of particular disease clusters (e.g. nervous system disorders) is understudied.  

This project will examine two main questions: how multimorbidity influences delirium risk, and how delirium influences outcomes in people with multimorbidity. Two potential patient groups for study are medical emergencies and hip fracture patients. The student will have access to a uniquely strong combination of large scale data resources (>50000 patient records) to address these questions, including community and hospital data, national hip fracture audit data, and high quality clinical delirium ascertainment. The supervising team have expertise in analysing large datasets and in all clinical aspects of the project. The student will gain valuable experience of analysis of large healthcare datasets, and generating and sharing findings which have relevance for practice. 

Multimorbidity in socially excluded populations: developing a complex intervention to reduce stigma and enhance practitioner empathy in primary care settings

Primary Supervisor 

Prof Stewart MercerEdinburgh

Summary 

Groups that suffer from social exclusion, such as people experiencing homelessness (PEH), and people suffering from drug use disorders, commonly have high levels of complex needs including mental and physical multimorbidity. Such populations often ‘fail to engage’ with healthcare services, and perceived stigma plays an important role in this. Previous research has demonstrated the key importance of practitioner empathy in patient engagement and better health outcomes but this has not been investigated in socially excluded groups. The PhD will co-develop a primary care-based complex intervention to reduce stigma and enhance empathy towards patients suffering social exclusion with complex multimorbidity. The research will employ a mixed-methods approach, as well as systematic reviewing of the international literature. People with lived experience of social exclusion and primary care staff will be involved in the co-production of the intervention, which will be tested for feasibility. The supervisory team has extensive experience in primary care research, multimorbidity, deprivation, drug addiction, homelessness, and complex intervention development. They also have excellent links with policymakers, managers, and service providers and thus the student will gain from access to a range of expertise and advice regarding dissemination, translation of findings, and impact on policy and practice.  

Interventions to improve mental health and wellbeing among older people with multiple conditions: a mixed methods approach

Primary Supervisors

Dr Lucy StirlandEdinburgh
Prof Bruce Guthrie, Edinburgh
 

Summary 

Multimorbidity including physical and mental illnesses is common, especially among older people and those living in socioeconomic deprivation.[1] Research in this area has called for enhanced mental health support both to improve wellbeing and to prevent the onset of mental illness in people with multiple chronic conditions. However, evidence is lacking on what are the most effective and acceptable interventions, and what services are currently offered to patients. 

This PhD offers flexibility for the student to choose a specific area of focus, in terms of context (eg community services vs psychological interventions) and research questions and methods. All students will complete a systematic review and use existing routine data to understand the epidemiology of physical-mental health multimorbidity in older people in Scotland. Students will then choose which other aspects to focus on, including using more complex quantitative methods; service mapping and examination of inequalities in access; qualitative methods to understand older people and carers’ experience of wellbeing and what factors underpin or undermine it; and/or co-design methods co-produce potential interventions. Working closely with public partners will be an essential element of the research, and the student will gain experience in critical appraisal, data science and qualitative research methods matched to their future research interests. 

Cardiovascular risk prediction modelling in people with mental illness

Primary Supervisor 

Dr Caroline JacksonEdinburgh

Summary 

People diagnosed with severe mental illness (SMI) have a reduced life expectancy. This is largely due to natural causes of death of which cardiovascular disease is the most common. Robust cardiovascular risk scores are important for identifying high risk patients who would benefit most from treatment. Prediction of cardiovascular risk is usually based on age, sex, smoking status, hypertension and blood lipid profile, using cardiovascular risk scores. However, the available cardiovascular risk scores such as Framingham have been developed in populations without people with SMI and subsequently underestimate the risk in people with SMI which potentially leads to under treatment. Therefore tailored cardiovascular risk models for this vulnerable group or general population risk models with SMI status added to the predictors are necessary. Recently the QRISK3 model has been developed which is a general population score including SMI (not including depression) as a predictor. Before implementation in clinical practice external validation is required to assess performance in terms of discrimination and calibration in other settings. The aim of this PhD project is to evaluate the performance of QRISK3 in datasets from within and outside the UK and to expand SMI to include major depression.  

The impact of co-existing mental and physical multimorbidity on critical care pathways and outcomes

Primary Supervisor 

Dr Nazir LoneEdinburgh

Summary 

Critical care patients have a high prevalence of multimorbidity which is associated with poorer outcomes. In addition, 20% of patients have a mental health comorbidity. The impact of co-existing mental and physical multimorbidity in the context of critical illness is unclear. In particular, its impact on care pathways, interventions and outcomes have not been previously investigated. 

The overall aim of the studentship is to evaluate the impact of co-existing mental and physical multimorbidity on care pathways, acute care interventions, healthcare resource use and outcomes for critically ill patients in order to inform improvements in care quality.   

 During the studentship, a series of epidemiological analyses will be undertaken to determine associations between co-existing mental illness/physical multimorbidity and acute illness features as well as post-discharge recovery, underpinned by explicit causal frameworks. Subsequently, patterns in care pathways and how these relate to co-existing mental/physical multimorbidity will be explored using applied data science methods including unsupervised learning methods and spectral clustering. 

The studentship will provide training in epidemiology, statistics, causal inference, and applied machine learning methods. The student will benefit from the vibrant, academic environment in the Usher Institute and synergistic learning from other multimorbidity work undertaken by the team (https://edin.ac/3CqoERz).  

A mixed-methods study examining the reality and value of healthcare for people with multimorbidity during their last year of life

Primary Supervisor 

Prof Frances QuirkSt Andrews 

Summary 

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. 

Neurodevelopmental multimorbidity during the life course

Primary Supervisor 

Dr Silvia Parachinni, St Andrews 

Summary 

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. 

Avoiding social catastrophes in those with multimorbidity (Full title in description)

Title 

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 

Primary Supervisor 

Prof Peter D DonnellySt Andrews 

Summary 

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.  

Impact of data driven specialist pharmaceutical care in community settings for older adult psychiatry patients... (Full title in description)

Title 

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? 

Primary Supervisor 

Prof Colin McCowanSt Andrews 

Summary 

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. 

The impact of interprofessional collaborative practice on health and care outcomes in patients with multimorbidity

Primary Supervisors

Dr Veronica O’CarrollSt Andrews
Prof Frank Sullivan, St Andrews

Summary 

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.