The development and validation of population clusters for integrating health and social care: A mixed-methods study on Multiple Long-Term Conditions

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.