ACRC Fellows

Fellowship background

Mental health and social relationships are closely connected and are increasingly recognised as cornerstones of healthy ageing. Factors such as low mood, social isolation and loneliness have been linked to multiple health outcomes in later life, including cognitive ageing and multimorbidity. Yet despite this, the pathways connecting mental health and the social environment with health outcomes in later life remain unclear, with questions around causality, timing and underlying mechanisms.

My fellowship aims to address these gaps in knowledge through a multidisciplinary research programme focused on the interface between physical and mental health and the role of the social environment in healthy ageing. Leveraging epidemiological and causal inference approaches, I will work with multiple large-scale datasets – including electronic health records and birth cohort studies – to identify risk factors and pathways that could inform public health and clinical practice. In particular, this research could help to establish possible targets to prevent or delay adverse health outcomes in later life, identify those at higher risk earlier, and contribute to clinical management for people living with multiple health conditions in later life. 

My research background

I am an epidemiologist with a background in cognitive ageing and the mental and social health of older people. I currently hold an Alzheimer's Society postdoctoral fellowship investigating how life course mental health influences cognitive ageing and dementia risk. My research career began with a Psychology degree at the University of Edinburgh, followed by a MRC-funded PhD and a BRC bridging fellowship at UCL. My PhD and bridging fellowship investigated the epidemiology of late-life psychosis and its association with dementia using Swedish register data, in collaboration with researchers at the Karolinska Institute. Following this, I moved to the MRC Unit for Lifelong Health and Ageing at UCL, where I investigated links between social relationships, cognition and dementia using a multi-cohort approach as part of a large international consortium. I am delighted to be joining the University of Edinburgh as an ACRC fellow to contribute to ongoing research on the health of older people within the ACRC and across the university.

A systems approach to improving care experiences and outcomes in co-resident carer-care recipient dyads living with multiple long-term conditions in later life

Fellowship background

Increasing numbers of people in the UK live with multiple long-term conditions (MLTC). The challenges this poses to formal health and social care are well recognised, but the implications for care from family and friends (informal care) are less well understood. Informal care is the bedrock of formal care, especially when services are under extreme pressure. 

Informal carers, particularly older spouse carers, face increased health risks due to their own health issues, caregiving difficulties, and lack of tailored support. Addressing unmet needs of carer-care recipient pairs (or dyads) living with MLTC in later life is an urgent public health priority; however, existing specialist and community support services often focus narrowly on one side of the caregiving relationship, leading to gaps in tailored care and support. 

My research

My multidisciplinary and innovative research programme aims to investigate how reciprocal care relationships in people with MLTC are developed and sustained in later life and co-design a community-based dyadic intervention to improve care experiences and outcomes in carer-care recipient dyads. Initial work will synthesise existing evidence on the benefits of community-based dyadic interventions and use mixed methods, including qualitative longitudinal interviews with key stakeholders, quantitative modelling using UK national longitudinal data, and integrative analysis of findings, to better understand the experience of care and outcomes in carer-care recipient dyads living with MLTC. This improved understanding will be used in co-design activities with key stakeholders to co-create a community-based dyadic intervention prototype ready for feasibility testing and optimisation in future work. 

I am partially funded by The Vivensa Foundation.

Fellowship Background

Despite increased life expectancy, many older adults live with chronic conditions that can significantly reduce their quality of life. Improving the diagnosis and early prediction of common and often related conditions such as dementia, delirium, or stroke is essential to transforming healthcare in later life.

My research vision is to develop explainable Deep Learning methods to extract information from routine medical imaging, in a multi-modal setting with clinical records to predict the future advent of age-related brain conditions. This has the potential to (a) personalise care pathways to the individual, yielding better outcomes, (b) help those at-risk plan for a better quality of life despite their conditions and (c) contributes to the sustainability of care provision by enabling targeted interventions. On the discovery side, early prognosis (d) facilitates earlier inclusion in clinical trials, and – by following these individuals from an early stage – (e) can uncover the causal pathways and relationship between diseases, accelerating the search for cures. The cornerstone of my research will be the use of clinically acquired data within the confines of Trusted Research Environments to accelerate deployment in clinical workflows.

Central to my research philosophy is placing people at the heart of innovation. I will actively engage with stakeholders to ensure that the voices of practictioners and the wider public inform every stage of my research. This approach not only enriches the scientific process but ensures that the outcomes are directly aligned with the needs and preferences of those who will benefit most from our advancements.

My Research Background

I am a data/computer scientist with an interest in applying machine learning to health care. I hold a PhD in Data Science and an M.Sc. in Intelligent Robotics (University of Edinburgh), with a background in probabilistic modelling, deep learning, computer vision and time-series modelling. My mission is to leverage health data to improve lives, particularly by harnessing un-curated clinical sources that hold untapped potential. Throughout my career I have applied my skills across various disciplines including robotics, astronomy, transport planning, physics and behaviour science. In recent years, I have focused my efforts on medical imaging, contributing to the groundbreaking SCANDAN project which showcased the ability to do research at scale on routine clinical data. My various experiences have allowed me to cultivate a robust collaborative network and honed my ability to engage in productive dialogues with specialists from diverse backgrounds. Apart from my role within the ACRC, I am also a CHAI scholar and hold Associate status with the University of Dundee.

Outside my professional pursuits, I dedicate time to my budding family (including dealing with the tantrums of a two-year old toddler) and getting dirty/oily restoring heritage locomotives