The Advanced Care Research Centre (ACRC) is a new, multi-disciplinary, £20M research centre at the University of Edinburgh. The ACRC will lead society’s response to the grand challenge of an ageing population that is growing in size, longevity and needs through the pursuit of research intended to deliver “high‐quality data‐driven, personalised and affordable care to support the independence, dignity and quality‐of‐life of people living in their own homes and in supported care environments”.
The ACRC Academy is a dedicated Centre for Doctoral Training, co-located with the ACRC, whose students will deliver key aspects of the ACRC research agenda through a new doctoral-level research and training programme that will also equip them for careers across a wide range of pioneering and influential leadership roles in the public, private and third sectors.
The PhD with Integrated Study in Advanced Care is a novel, structured, thematic, cohort-based, programme of 48 months duration. Each PhD research project within the Academy has been devised by a supervisory team comprising academic staff from at least two of the three colleges within the University of Edinburgh. Each annual cohort of around twelve will include students with disciplinary backgrounds spanning from engineering and data science to humanities, social science, business and commerce, social work, medicine and related health and care professions. This unique level of diversity is a key attribute of our programme.
Frailty is a key concept in population ageing and geriatric clinical practice.
Whilst specific definitions and measures of frailty are contested, there is general agreement that frailty is a non-specific state reflecting age-related declines in multiple systems, leading to a range of adverse outcomes such as falls, fractures, hospitalisation, institutionalisation and mortality, and a version of this (the eFI) is already used by GPs across the UK to screen for frailty and target health and social care interventions in the community. Existing frailly indices are defined as a cumulative deficit model, i.e., they count the accrual of health issues or 'deficits' but do not distinguish deficits that underlie a frailty score. The goal of this project is to investigate novel definitions of frailty using statistical machine learning with the objective of conceptualising frailty as a, potentially complex and multidimensional, latent construct that varies across social, economic, demographic groups and birth cohorts, and assessing whether these bring improved capacity to predict adverse outcomes in survey (English Longitudinal Study of Ageing) and routine data (DataLoch).
Specifically, the project will:
- Explore and compare the structure of frailty in routine and administrative data,
- Define frailty as a (potentially multidimensional) latent construct based on probabilistic modelling of observed deficits, and/or based on encoder-decoder system that relates deficits to adverse outcomes,
- Explore the usefulness of existing and novel frailty measures in the context of categorising patients in the community and when admitted to hospital,
- Validate existing and novel frailty measures using similar survey and routine data across the world.
The project will be part of the ACRC workpackage on 'data-driven insight and prediction', and it will align with several other workpackages for finding frailty from text (enhancing the data infrastructure), for validating frailty in sensing platform (new technologies of care), and for validating frailty in practice (new models of care). The project will be supervised by an interdisciplinary team of academics from three colleges with expertise in machine learning (Sohan Seth, CSE), geriatric practices (Atul Anand, CMVM) and social policy (Alan Marshall, CAHSS).
Candidate Specification. We are specifically looking for applicants who will view their cutting-edge PhD research project in the context of the overall vision of the ACRC, who are keen to contribute to tackling a societal grand challenge and who can add unique value to – and derive great benefit from – training in a cohort comprising colleagues with a very diverse range of disciplines and backgrounds. We advise prospective candidates to engage in dialogue with the named project supervisor and/or the Director of the Academy prior to submitting an application.
Recruitment:
In order to fill the few remaining ACRC Academy places we are currently running a rolling recruitment process, with project adverts staying posted until recruitment is complete. Applications received from 8 May 2021 onwards will be reviewed on a twice weekly basis.
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