Project overview
A ‘Research Collaboration’ funded by NIHR involving five work packages (WPs) that include:
1. Undertaking a qualitative evidence synthesis and a consensus study (Delphi) to develop deeper understanding of what ‘burdensomeness’ and ‘complexity’ mean to people living with early-onset (by age 65) multiple long-term condition multimorbidity (MLTC-M), carers and healthcare professionals
2. Developing safe data environments and readiness for artificial intelligence (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 harmonising specified LTCs across birth cohorts and routine data.
3. In those safe data environments, using the WP1 burdensomeness/complexity indicators and applying AI methods to identify novel early-onset, burdensome MLTC-M clusters and match individuals in birth cohorts into routine data MLTC-M clusters, identify determinants of burdensome clusters using matched datasets, and model trajectories of long-term conditions (LTCs) and burden accrual.
4. Characterising clusters of early-life (pre-birth to 18 years) risk factors for early-onset, burdensome MLTC-M and sentinel conditions (the first LTC to occur in the lifecourse), defining population groups in early life at risk of future MLTC-M, identifying critical time points and targets for prevention, and modelling counterfactual prevention scenarios of interventions acting on combined risk factors at key timepoints
5. Engaging key stakeholders to prioritise 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
1. Undertaking a qualitative evidence synthesis and a consensus study (Delphi) to develop deeper understanding of what ‘burdensomeness’ and ‘complexity’ mean to people living with early-onset (by age 65) multiple long-term condition multimorbidity (MLTC-M), carers and healthcare professionals
2. Developing safe data environments and readiness for artificial intelligence (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 harmonising specified LTCs across birth cohorts and routine data.
3. In those safe data environments, using the WP1 burdensomeness/complexity indicators and applying AI methods to identify novel early-onset, burdensome MLTC-M clusters and match individuals in birth cohorts into routine data MLTC-M clusters, identify determinants of burdensome clusters using matched datasets, and model trajectories of long-term conditions (LTCs) and burden accrual.
4. Characterising clusters of early-life (pre-birth to 18 years) risk factors for early-onset, burdensome MLTC-M and sentinel conditions (the first LTC to occur in the lifecourse), defining population groups in early life at risk of future MLTC-M, identifying critical time points and targets for prevention, and modelling counterfactual prevention scenarios of interventions acting on combined risk factors at key timepoints
5. Engaging key stakeholders to prioritise 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
Staff
Lead researcher
Other researchers
