[Unknown type: UNSPECIFIED]
Abstract
Background: multiple long-term health conditions (Multimorbidity) (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 also the result of and associated with additional psycho-social, economic and environmental barriers. A shift towards more personalised, holistic and integrated care could be effective. This could be made more efficient by identifying groups of the population based on their health and social need. These will, in turn, contribute to evidence-based solutions supporting delivery of interventions tailored to address the needs pertinent to each 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.
Objective: to develop and validate population clusters that consider determinants of health and social care need for people with MLTC-M using data-driven ML(machine-learning) methods compared to expert-driven approaches within primary care national databases, 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 including; 1) Qualitative semi-structured interview study exploring patient, carer and professional views on clinical and socio-economic factors influencing experiences of living with, or seeking care in MLTC-M, 2) Modified Delphi with relevant stakeholders to generate variables on health and social (wider) determinants and to examine feasibility of including these variables within existing primary care databases and 3) Cohort study with expert driven segmentation, alongside data-driven algorithms. Outputs will be compared, clusters characterised and trajectories over time examined, to quantify associations with mortality, additional long-term conditions, worsening frailty, disease severity and ten-year health/social care costs.
Results: the study will commence in October 2021 and is expected to be completed by October 2023.
Conclusions: by studying MLTC-M clusters we will assess how more personalised care can be developed, accurate costs provided, better understand the personal and medical profiles and environment of individuals within each cluster. 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. Clinical Trial: Not applicable
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