Multimorbidity is defined as co-occurrence of 2 or more chronic conditions while Complex-multimorbidity is defined as having 4 or more conditions. It affects more than 25% of adults in England and is projected to increase from 10% to 17% within 2035. Currently, economic models of multimorbidity are just extensions of single disease models that fail to capture the entire range of conditions. Cost-effectiveness analysis on modifiable risk factors addressing multimorbidity are limited posing considerable challenges for resource allocation decision-making. This plan aims to detail out the steps for developing a cost-effectiveness model of interventions addressing multimorbidity profiles generated using latent class analysis and hierarchical clustering. An individual patient level microsimulation model will be followed for a cost-utility analysis. A set of priority biomarkers will be identified through review findings and machine learning techniques applied to the multimorbidity profiles. Interventions targeted at improving these biomarkers will be identified and assessed. The most cost-effectiveness interventions will be listed in a league table. Analysis of inequality will be done using individual’s socio-economic background including deprivation scores and ethnicity.
Tahsina et al. (2025) studied this question.