<?xml version='1.0' encoding='UTF-8'?><xml><records><record><source-app name="HighWire" version="7.x">Drupal-HighWire</source-app><ref-type name="Journal Article">17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hassan, Nehal</style></author><author><style face="normal" font="default" size="100%">Slight, Robert</style></author><author><style face="normal" font="default" size="100%">Wang, Yanzhong</style></author><author><style face="normal" font="default" size="100%">Slight, Sarah P</style></author></authors><secondary-authors></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Primary healthcare utilisation among individuals with multimorbidity in deprived communities; a modelling study</style></title><secondary-title><style face="normal" font="default" size="100%">BJGP Open</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2026</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2026-05-08 00:00:00</style></date></pub-dates></dates><elocation-id><style  face="normal" font="default" size="100%">BJGPO.2026.0030</style></elocation-id><doi><style  face="normal" font="default" size="100%">10.3399/BJGPO.2026.0030</style></doi><volume><style face="normal" font="default" size="100%"></style></volume><issue><style face="normal" font="default" size="100%"></style></issue><abstract><style  face="normal" font="default" size="100%">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 &amp; 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 1 182 972 adults were registered with 40 general practices in the borough; 19.7% (n=170,128) were living with ≥2 LTCs, and over 65% (n=111,251) in the two most deprived quintiles. Ten clusters were developed and considered the most clinically appropriate. The Neuro-Psychiatric cluster was the largest, including 26.2% (n=44,492) of patients. Over 98% (n=23,306) of patients in the Autoimmune cluster were female, whereas 93.3% (n=41,516) of patients in the Neuro-psychiatric cluster were male. Most multimorbid patients in the Inflammatory (84.7%) and Mental Health (83.3%) clusters were aged between 18-50. Most patients in the Behavioral (90.3%) 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.</style></abstract></record></records></xml>