Association of latent class analysis-derived multimorbidity clusters with adverse health outcomes in patients with multiple long-term conditions: comparative results across three UK cohorts

Stefanie J. Krauth; Lewis Steell; Sayem Ahmed; Emma McIntosh; Grace Dibben; Peter Hanlon; Jim Lewsey; Barbara I. Nicholl; David McAllister; Susan M. Smith; Rachael A. Evans; Zahira Ahmed; Sarah Dean; Colin Greaves; Shaun Barber; Patrick Doherty; Nikki Gardiner; Tracy Ibbotson; Kate Jolly; Paula Ormandy; Sharon Simpson; Rod S Taylor; Sally Singh; Frances S Mair; Bhautesh Jani

doi:10.1016/j.eclinm.2024.102703

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Abstract

Background It remains unclear how to meaningfully classify people living with multimorbidity (multiple long-term conditions (MLTCs)), beyond counting the number of conditions.This paper aims to identify clusters of MLTCs in different age groups and associated risks of adverse health outcomes and service use.

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Krauth et al. (2024) studied this question.

synapsesocial.com/papers/68e62ad5b6db6435875bdd33 https://doi.org/10.1016/j.eclinm.2024.102703

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Association of latent class analysis-derived multimorbidity clusters with adverse health outcomes in patients with multiple long-term conditions: comparative results across three UK cohorts

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Abstract

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Association of latent class analysis-derived multimorbidity clusters with adverse health outcomes in patients with multiple long-term conditions: comparative results across three UK cohorts

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