Primary healthcare utilisation among individuals with multimorbidity in deprived communities; a modelling study

BACKGROUND: Almost one in four adults in England have two or more long-term health conditions (LTCs). Patients living in deprived areas develop multimorbidity seven years earlier than those in the least deprived areas; this puts significant pressure on our healthcare system. Some conditions share similar characteristics and can commonly occur together. AIM: We conducted a modelling study to cluster patients based on shared characteristics and understand the healthcare utilisation of these different multimorbidity clusters. DESIGN & SETTING: A modelling study using routinely collected clinical data from general practices in a highly deprived London borough (IMD quintiles 1-2). METHOD: We analysed a large database of demographic and healthcare records. Adults (≥18 years) registered between 2018 and 2022 with at least two long-term conditions (LTCs) were included. Latent class analysis was used to identify patient clusters, adjusting for four covariates. RESULTS: ) clusters were between 50-90; this cluster demonstrated the highest likelihood of healthcare utilization. CONCLUSION: Individual-based clustering can provide an in-depth understanding of clinical profiles and healthcare utilization of multimorbid patients living in deprived regions.