Title: The development and validation of population clusters for integrating health and social care: A mixed-methods study on Multiple Long-Term Conditions
Funder: Department of Health and Social Care

Abstract: Background: Multiple long-term conditions (MLTC-M) are increasingly prevalent and associated with high rates of morbidity, mortality and health-care expenditure. Strategies to tackle this have primarily focused on addressing biological aspects of disease but MLTC-M are the result of and associated with additional psycho-social, economic and environmental barriers. For example, poor cognitive function impacts self-management or medication adherence; financial constraints and lack of housing requiring social and psychological support, or physical limitations that impair access to health. A shift towards more personalised, holistic and integrated care could be an effective approach. This could be achieved by clustering heterogenous populations by health and social need, and then tailoring interventions to each homogenous cluster. Evidence is needed on how to generate clusters based on health and social need and to quantify the impact of clusters on long-term health and costs. Aim: To develop and validate population clusters that consider health and social care determinants and subsequent need for people with MLTC-M using data-driven AI methods compared to expert-driven approaches, followed by evaluation of cluster trajectories and their association with health outcomes and costs. Methods: A mixed-methods programme of work with parallel work streams as follows: WP 1.1: Semi-structured interviews exploring patient, carer and professional views on clinical and socio-economic factors influencing experiences of living with, or seeking care in MLTC-M. WP 1.2: Modified Delphi with stakeholders exploring systematic review evidence and WP 1.1 generated variables on health and social need. Feasibility of including these variables within existing UK databases, health and social care services and potential new data linkages will be examined WP 2: Expert driven segmentation alongside data-driven algorithms will be run on a national primary care database (CPRD) with validation in further databases (SAIL and Q-Research). Outputs will be compared, clusters characterised and trajectories over time examined to quantify associations with incident mortality, additional LT-C, worsening frailty, disease severity and ten-year health/social care cost. This WP will allow us to gain insights into differences in health and social care need between clusters, and outputs will provide signals on intervention development and recommendation on targeted individual-level service delivery. Dissemination: Findings will be published in high impact open-access journals; professional publications and shared on social media, blogs, stakeholder events, NICE, health and social care commissioners at a local level (CCG) and national level (NHSE), charities and through our PPIE collaborators.

Lay abstract: Background: An estimated 14 million people in England are living with multiple long-term health conditions (MLTC-M). Efforts to improve care mainly focus on biological markers of disease such as blood pressure or cholesterol, without adequately addressing other factors that contribute to good health. This may include social factors such as mobility, housing, finances or social isolation. A shift towards integrated care that considers the whole person and their environment is essential in addressing the complex, diverse and individual needs of people living with MLTC-M. One approach to delivering more personalised care is to 'cluster' or group people based on similarities in their medical and non-medical needs. This approach has been adopted in other countries but not in the UK due to uncertainty about how to develop clusters, and there is no evidence linking this approach to improved health or reduced costs. Aims: To generate evidence on how to cluster people by health and social need using machine learning, i.e. we use a computer system to help us to identify and group people with similar needs together To use this information to develop tailored approaches for clusters that join up health and social care, and improve the lives of people with MLTC-M Methods: We will ask patients, carers and professionals for views on what is important in MLTC-M when considering both medical and social need We will ask an expert panel for their views We will use millions of anonymised patient records to test machine learning and generate clusters that will be compared to those developed through patient/professional opinions to see which are better at predicting outcomes We will study clusters to learn what happens over time in terms of health/social costs, and to understand the profiles of people within each cluster We will use this information to develop tailored care for each cluster for people with MLTC-M PPIE: We have received and acted upon feedback from GP practice patient groups, patients and carers through Age UK/Diabetes UK and from public contributors in our ongoing integrated health and social care research. They have helped us to develop and prioritise the study question and improve its design. We have public contributors in our study team and on our steering panel who will continue to contribute to all aspects of design, data collection, analysis and sharing of results. Dissemination: We will make findings available and accessible to people with MLTC-M, and those who work and make decisions in health and social care. We will co-host events with our PPIE to share results and discuss implications, and publicise findings via publications in peer-reviewed journals, conference presentations, project website, blogs, social media and across our patient networks.

Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS;2.2 FACTORS RELATING TO PHYSICAL ENVIRONMENT;2.3 PSYCHOLOGICAL, SOCIAL AND ECONOMIC FACTORS;2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: GENERIC HEALTH RELEVANCE