How can we implement clinical prediction tools for dementia in care pathways particularly in communities experiencing socioeconomic deprivation?

Problem

Dementia is in increasingly common condition owed to population ageing. To reduce the health service, societal and personal impact of dementia there are calls to find ways to reduce one’s risk and delay the onset of dementia through the management of certain risk factors associated with a future dementia illness. Specifically, people living in areas of socioeconomic deprivation are at higher risk of cognitive problems yet often have less access to specialist diagnosis for dementia. Risk factors have been incorporated into dementia risk prediction tools which have been developed to identify those at the greatest risk to aid earlier diagnosis and stratification to interventions. However, none are currently being used with very few studies have incorporated stakeholder views in this area and to our knowledge this is the first study that aims to clarify any specific needs of underserved communities. The aim of this study was to identify with key stakeholders the barriers and facilitators in using a dementia risk prediction tool in primary care to guide care decisions and onward management and how this may differ in areas of social deprivation compared to areas of affluence.

Approach

Patients and primary healthcare professionals were recruited from three areas of England (Greater Manchester, Kent Sussex and Surrey and the North East and North Cumbria (NENC)) with a focus to involve practices and patients from areas of social deprivation. We involved practices from the Deep End Network in NENC. Patients were aged between 60 – 79. Semi-structured interviews were conducted, audio-recorded, anonymised and transcribed verbatim. Analysis used a middle-range implementation theory and an analytical framework, Normalization Process Theory.

Findings

Recruitment is ongoing and at present we have completed 35 interviews (24 staff and 11 patients) with several practices from areas of deprivation represented. Emerging themes include 1) Fear, 2) Trust, 3) Knowledge and 4) Attitudes to risk prediction and behaviour change. There is strong support for the implementation of dementia risk prediction in general practice. Amongst staff and patients there was support for healthier ageing resources, including brain health and modifiable risk factors for dementia and other conditions related to dementia. There is strong trust in primary care to implement such a tool. However, interviewees strongly believe patients have the right to refuse a prediction assessment. For people experiencing the greatest health inequities, there is strong support for these communities to be prioritised in the implementation of a dementia risk prediction tool.

Consequences

There is some positivity towards dementia risk identification. Although naturally barriers in implementation do exist, this could be attenuated through key stakeholder involvement, health prevention resources and support. Future studies looking to implement risk prediction tools need to co-produce care pathways with stakeholders particularly groups experiencing the greatest health inequities, to understand and develop implementation best practices and resources.