Title: Multidisciplinary Ecosystem to study Lifecourse Determinants and Prevention of Early-onset Burdensome Multimorbidity (MELD-B)
Funder: Department of Health and Social Care

Abstract: Background Multimorbidity occurs earlier in the lifecourse among people from more disadvantaged backgrounds. Wider determinants such as education and work influence development of long-term physical and mental health conditions (LTCs) across the lifecourse. Sequence of accrual of conditions varies considerably and influences outcomes. Sentinel conditions (the first LTC to occur in the lifecourse) may play an important role. Understanding how wider determinants, sentinel conditions and LTC accrual sequence affect risk of early-onset burdensome/complex multiple LTC multimorbidity (MLTC-M) is vital to inform when and how to deploy prevention interventions. Data at sufficient scale linking whole lifecourse events, including wider determinants, with clinical information is lacking.Through our Development Award ( MELD ) we established the necessary environment, principles, systems, methods and team to use artificial intelligence (AI) techniques to study lifecourse causes of early-onset MLTC-M. The aim for our Research Collaboration is to safely deliver an Artificial Intelligence (AI)-enhanced epidemiological analytic system in which optimal lifecourse time points and targets for prevention of early-onset, burdensome MLTC-M are identified through multidisciplinary synthesis and analysis of birth cohorts and electronic health records, and disseminated to key stakeholders. Methods - five Work-Packages (WP): Through qualitative evidence synthesis and a consensus study we will develop deeper understanding of what burdensomeness and complexity mean to people living with early-onset (by age 65) MLTC-M, carers and healthcare professionals, and produce a suite of burdensomeness/complexity indicators for use as clustering domains in routine healthcare data.(WP1&ndash;lead Fraser) We will provide the safe data environment and readiness for AI analyses across large, representative routine healthcare datasets (Secure Anonymised Information Linkage (SAIL) and Clinical Practice Research Datalink (CPRD)) and birth cohorts (National Child Development Study (NCDS), Aberdeen Children of the 1950s (ACONF), 1970 British Cohort Study (BCS70)), then harmonise specified LTCs across birth cohorts and routine data.(WP2&ndash;lead Boniface) Using the WP1 burdensomeness/complexity indicators, we will apply AI methods to identify novel early-onset, burdensome MLTC-M clusters and sentinel conditions in routine data, develop and apply semi-supervised learning to match individuals in birth cohorts into routine data MLTC-M clusters, identify determinants of burdensome clusters using matched datasets, and model trajectories of LTC and burden accrual.(WP3&ndash;lead Hoyle) We will characterise clusters of early-life (pre-birth to 18 years) risk factors for early-onset, burdensome MLTC-M and sentinel conditions, define population groups in early life at risk of future MLTC-M, identify critical time points and targets for prevention, and model counterfactual prevention scenarios of interventions acting on combined risk factors at key timepoints.(WP4&ndash;lead Alwan). We will investigate the influence of sentinel conditions and sequence of determinant and condition accrual on development of early-onset, burdensome MLTC-M clusters and compare AI and causal inference modelling for potential preventable moments across the lifecourse (WP3/4). Patient and public involvement is embedded throughout. Timeline: 30 months from April 2022 Impact and Dissemination We will identify and engage key stakeholders to explore timepoints and targets to prevent/delay specified sentinel conditions and early-onset, burdensome MLTC-M. Partnering with our PPI Advisory Board, and maintaining stakeholder engagement, we will co-produce public health implementation recommendations based on our findings.(WP5(cross-cutting)&ndash;lead Wilkinson).

Lay abstract: Background to the research A growing number of people are living with several long-term health conditions like diabetes, heart disease, depression or dementia. We call this multimorbidity. Many things throughout a person s life influence the chances of developing health conditions. This includes their biology (e.g.age, ethnicity), things that happen to them (e.g.infections, accidents), behaviours (e.g.smoking, diet) and broader experiences (e.g. the environment people grew up in, their education, work, income). People from more disadvantaged backgrounds and/or certain ethnicities are more likely to develop multimorbidity and to develop it earlier. The impact (or burden ) of multimorbidity, and the order that people develop conditions, also vary. We don t know what all the possible opportunities are to prevent burdensome multi-morbidity. Aims of the research We aim to understand more about what things influence the way people develop early multimorbidity over their lifetime and the burden this has for them. By early we mean before age 65. This will identify key time points in a person s life where prevention efforts should be targeted or strengthened to reduce the risk of that person developing burdensome multimorbidity. Design and methods used To understand what burdensome means for people with multimorbidity and how it could be prevented or reduced, we will ask patients, carers and multimorbidity experts their views and carefully summarise previous research on this issue. Ideally, we would study very large numbers of people from birth to death. However, not enough of this type of data is available. Therefore, we will use Artificial Intelligence (AI) methods (using computers to learn patterns from data) to help us connect information and knowledge from two very big General Practitioner datasets with information from three birth cohorts &ndash; research studies of people all born in the same year (e.g. 1970) and followed throughout their lives. We will also use AI methods to help us understand the order in which people develop conditions and how they group together to become burdensome . We will have experts and members of the public in a People, Policy and Impact group identify opportunities to prevent burdensome multimorbidity. Patient and public involvement A diverse ten-person Patient and Public Advisory Board will oversee the project. Members of the Board will actively engage with each element of the project and be asked to discuss results as they emerge. This will ensure that the experiences and priorities of people living with multimorbidity will always be considered. Their input will ensure all decisions consider the experiences and priorities of people with multimorbidity. Dissemination Our People, Policy and Impact group will share our learning and influence policy and practice on preventing burdensome multimorbidity by co-producing public health advice. We will share findings through academic channels, a website and social media.

Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS;2.2 FACTORS RELATING TO PHYSICAL ENVIRONMENT;2.3 PSYCHOLOGICAL, SOCIAL AND ECONOMIC FACTORS
Health categories: GENERIC HEALTH RELEVANCE