Abstract

In primary care, patients’ electronic health records can be searched to identify information that indicates whether an individual is at increased risk or at the early stages of a specific condition, such as diabetes or stroke. The “risk prediction tool” is usually developed using a mathematical equation or “algorithm” incorporating scores for different risk factors such as age, sex, and other health conditions.

Dementia is now classed as a major public health issue following research showing at least 12 risk factors for the illness. Numbers of people with dementia will triple by 2050. Despite dementia being a leading cause of disability and death, there is still no suitable tool to alert clinicians to those people at increased risk of dementia and who might benefit from early intervention to delay or prevent it. Also, we know that those from underserved communities, such as certain minorities and those on low incomes, are less likely to be identified as at risk of dementia at an early stage when preventive measures can be offered to reduce their risk. However, we currently do not know how to accurately calculate dementia risk in such communities and what approaches maybe welcomed and acceptable and, if so, how best to roll it out in primary care.

Aims

This project aims to explore approaches to reduce the risk or impact of dementia in underserved communities firstly to determine whether such ‘tools’ would be welcomed by patients and the public and secondly, whether it is possible to create such approaches. The project will do this by:

reviewing previous and current research to summarise what has been learnt about predicting future dementia and people’s attitudes to identifying individuals at higher risk of dementia
talking to key groups of people from minority and low-income communities on the findings of this research, and to determine their views on risk factors and the use of models to predict dementia risk
exploring whether we can use routinely collected patient information from GP health records as the basis for developing and testing a new approach to assessing dementia risk, including their relevance to underserved communities.
Finally, with these key groups of people, a risk prediction “algorithm” and strategy for implementation into general practice and other community settings will be co-designed.

The results will provide the foundation for the development of a new primary care computer-based tool for assessing dementia risk that is acceptable to diverse groups in the UK, with the aim of early intervention to delay onset. The work program will also increase engagement of underserved groups in dementia research. This new knowledge is essential to the development of dementia prevention and risk reduction strategies and will help to target existing treatments more efficiently to those at highest risk of dementia.