Prediction of functional change and care outcomes: the Lothian Birth Cohort 1936 and the English Longitudinal Study of Ageing (ELSA)

The Advanced Care Research Centre at the University of Edinburgh is a new £20m interdisciplinary research collaboration aiming to transform later life with person centred integrated care

The vision of the ACRC is to play a vital role in addressing the Grand Challenge of ageing by transformational research that will support the functional ability of people in later life so they can contribute to their own welfare for longer. With fresh and diverse thinking across interdisciplinary perspectives our academy students will work to creatively embed deep understanding, data science, artificial intelligence, assistive technologies and robotics into systems of health and social care supporting the independence, dignity and quality-of-life of people living in their own homes and in supported care environments.

The ACRC Academy will equip future leaders to drive society’s response to the challenges of later life care provision; a problem which is growing in scale, complexity and urgency. Our alumni will become leaders in across a diverse range of pioneering and influential roles in the public, private and third sectors.  

Health informatics will be the cornerstone of future medical practice, the use of routine healthcare records to predict the likelihood of future morbidity or mortality, to stratify patients into risk categories, or to track the outcomes of interventions: #datasaveslives.

This PhD will make important new contributions to knowledge by identifying life course predictors of functional change and care outcomes in later life. The project will equip the successful clinical candidate (a trainee in a relevant specialty such as geriatric medicine, general medicine, or old age psychiatry) with cutting-edge skills in data science and risk prediction to perform and ultimately lead cutting-edge multi-disciplinary research that addresses major gaps in current knowledge in risk prediction for older people. It provides the opportunity to work with an interdisciplinary team of experts in cognitive ageing and analysis of complex datasets. 

The project will mainly use the Lothian Birth Cohort 1936, a deeply phenotyped community-dwelling cohort of older adults recruited for a study of cognitive ageing(n=1,091) with life-course data and 5 waves of follow-up from ages 70 – 82 years). The English Longitudinal Study of Ageing (ELSA), a more diverse group of people over a wider age range, with clear phenotyping, will be used for complementary analysis including benchmarking/generalization.  

The research will involve the extraction of key markers of functional change and frailty - e.g. falls, delirium, functional decline - and care outcomes - e.g. use of social care - from the Electronic Health Records(EHRs)of cohort participants, and linking these to already-collected data and other health and social care datasets. The candidate will work with the supervisory team to agree the main focus of the study, and the methodological approach.  

Clinical expertise is required to accurately interpret the EHRs, and there is potential for working closely across disciplines.

The candidate will gain skills in access and interpretation of EHRs, advanced statistical modelling in longitudinal and complex survey data information governance and risk prediction including through links with the SPS Research Training Centre. They will also engage with the LBC1936 cohort and gain experience in recruitment and retention to clinical studies of older people, and associated patient and public involvement/public engagement, and information governance, as well as understanding the differences between the and ELSA. The comparison of the model in the ELSA dataset(including its existing linkage to routine health data),and then potentially using data via the Scottish Centre for Administrative Data Research(SCADR)will provide training in the use of large datasets and techniques for managing missing data etc. There is also the potential for future projects to validate models with machine learning approaches  

The key objectives of the study are:

• identification of important geriatric medicine syndromes(e.g. falls, delirium, incontinence)and place of residence from electronic health records of LBC participants and linking this to previously-collected survey data and other dataset
• identification of socio-economic, demographic and biomedical predictors of transitions to care and care outcomes in different study groups(LBC and ELSA);
• validation of care prediction models in LBC1936 and the new linkage to EHR data using ELSA and to understanding methodological challenges such as attrition, non-response and generalisability to wider geographical contexts and cohorts

Eligibility:

- Full GMC registration or equivalent

- Evidence of academic track record