Multimorbidity clusters and their associations with health-related quality of life in two UK cohorts

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

Abstract

Background: Identifying clusters of multiple long-term conditions (MLTCs), also known as multimorbidity, and their associated burden may facilitate the development of effective and cost-effective targeted healthcare strategies. This study aimed to identify clusters of MLTCs and their associations with long-term health-related quality of life (HRQoL) in two UK population-based cohorts. Methods: Age-stratified clusters of MLTCs were identified at baseline in UK Biobank (n = 502,363, 54.6% female) and UKHLS (n = 49,186, 54.8% female) using latent class analysis (LCA). LCA was applied to people who self-reported ≥ 2 LTCs (from n = 43 LTCs [UK Biobank], n = 13 LTCs [UKHLS]) at baseline, across four age-strata: 18–36, 37–54, 55–73, and 74 + years. Associations between MLTC clusters and HRQoL were investigated using tobit regression and compared to associations between MLTC counts and HRQoL. For HRQoL, we extracted EQ-5D index data from UK Biobank. In UKHLS, SF-12 data were extracted and mapped to EQ-5D index scores using a standard preference-based algorithm. HRQoL data were collected at median 5 (UKHLS) and 10 (UK Biobank) years follow-up. Analyses were adjusted for available sociodemographic and lifestyle covariates. Results: LCA identified 9 MLTC clusters in UK Biobank and 15 MLTC clusters in UKHLS. Clusters centred around pulmonary and cardiometabolic LTCs were common across all age groups. Hypertension was prominent across clusters in all ages, while depression featured in younger groups and painful conditions/arthritis were common in clusters from middle-age onwards. MLTC clusters showed different associations with HRQoL. In UK Biobank, clusters with high prevalence of painful conditions were consistently associated with the largest deficits in HRQoL. In UKHLS, clusters of cardiometabolic disease had the lowest HRQoL. Notably, negative associations between MLTC clusters containing painful conditions and HRQoL remained significant even after adjusting for number of LTCs. Conclusions: While higher LTC counts remain important, we have shown that MLTC cluster types also have an impact on HRQoL. Health service delivery planning and future intervention design and risk assessment of people with MLTCs should consider both LTC counts and MLTC clusters to better meet the needs of specific populations.

• Journal Article Type: Article
• Acceptance Date: Dec 9, 2024
• Online Publication Date: Jan 8, 2025
• Deposit Date: Jan 15, 2025
• Publicly Available Date: Jan 15, 2025
• Journal: BMC Medicine
• Electronic ISSN: 1741-7015
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 23
• Issue: 1
• Pages: 1
• DOI: https://doi.org/10.1186/s12916-024-03811-3
• Keywords: Quality of life, Multimorbidity, UK Biobank, Latent class analysis