Multimorbidity clusters and their associations with health-related quality of life in two UK cohorts
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 associa...
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2025-01-01
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| author | Lewis Steell Stefanie J. Krauth Sayem Ahmed Grace O. Dibben Emma McIntosh Peter Hanlon Jim Lewsey Barbara I. Nicholl David A. McAllister Susan M. Smith Rachael Evans Zahira Ahmed Sarah Dean Colin Greaves Shaun Barber Patrick Doherty Nikki Gardiner Tracy Ibbotson Kate Jolly Paula Ormandy Sharon A. Simpson Rod S. Taylor Sally J. Singh Frances S. Mair Bhautesh D. Jani the PERFORM research team |
| author_facet | Lewis Steell Stefanie J. Krauth Sayem Ahmed Grace O. Dibben Emma McIntosh Peter Hanlon Jim Lewsey Barbara I. Nicholl David A. McAllister Susan M. Smith Rachael Evans Zahira Ahmed Sarah Dean Colin Greaves Shaun Barber Patrick Doherty Nikki Gardiner Tracy Ibbotson Kate Jolly Paula Ormandy Sharon A. Simpson Rod S. Taylor Sally J. Singh Frances S. Mair Bhautesh D. Jani the PERFORM research team |
| author_sort | Lewis Steell |
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| description | 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. |
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| institution | Kabale University |
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| spelling | doaj-art-a418fa6e1cab48f9b2ef7d242e63aedf2025-01-12T12:26:51ZengBMCBMC Medicine1741-70152025-01-0123111710.1186/s12916-024-03811-3Multimorbidity clusters and their associations with health-related quality of life in two UK cohortsLewis Steell0Stefanie J. Krauth1Sayem Ahmed2Grace O. Dibben3Emma McIntosh4Peter Hanlon5Jim Lewsey6Barbara I. Nicholl7David A. McAllister8Susan M. Smith9Rachael Evans10Zahira Ahmed11Sarah Dean12Colin Greaves13Shaun Barber14Patrick Doherty15Nikki Gardiner16Tracy Ibbotson17Kate Jolly18Paula Ormandy19Sharon A. Simpson20Rod S. Taylor21Sally J. Singh22Frances S. Mair23Bhautesh D. Jani24the PERFORM research teamGeneral Practice and Primary Care, School of Health and Wellbeing, University of GlasgowGeneral Practice and Primary Care, School of Health and Wellbeing, University of GlasgowHealth Economics and Health Technology Assessment, School of Health and Wellbeing, University of GlasgowMRC/CSO Social & Public Health Sciences Unit, School of Health and Wellbeing, University of GlasgowHealth Economics and Health Technology Assessment, School of Health and Wellbeing, University of GlasgowGeneral Practice and Primary Care, School of Health and Wellbeing, University of GlasgowHealth Economics and Health Technology Assessment, School of Health and Wellbeing, University of GlasgowGeneral Practice and Primary Care, School of Health and Wellbeing, University of GlasgowHealth Economics and Health Technology Assessment, School of Health and Wellbeing, University of GlasgowDiscipline of Public Health and Primary Care, Trinity College DublinDepartment of Respiratory Sciences, University of LeicesterDepartment of Respiratory Sciences, University of LeicesterUniversity of Exeter Medical SchoolSchool of Sport, Exercise and Rehabilitation Sciences, University of BirminghamClinical Trials Unit, University of LeicesterDepartment of Health Science, University of YorkDepartment of Cardiopulmonary Rehabilitation, University Hospitals of Leicester NHS TrustGeneral Practice and Primary Care, School of Health and Wellbeing, University of GlasgowInstitute of Applied Health Research, University of BirminghamSchool of Health and Society, University of SalfordMRC/CSO Social & Public Health Sciences Unit, School of Health and Wellbeing, University of GlasgowMRC/CSO Social & Public Health Sciences Unit, School of Health and Wellbeing, University of GlasgowDiscipline of Public Health and Primary Care, Trinity College DublinGeneral Practice and Primary Care, School of Health and Wellbeing, University of GlasgowGeneral Practice and Primary Care, School of Health and Wellbeing, University of GlasgowAbstract 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.https://doi.org/10.1186/s12916-024-03811-3MultimorbidityQuality of lifeLatent class analysisUK Biobank |
| spellingShingle | Lewis Steell Stefanie J. Krauth Sayem Ahmed Grace O. Dibben Emma McIntosh Peter Hanlon Jim Lewsey Barbara I. Nicholl David A. McAllister Susan M. Smith Rachael Evans Zahira Ahmed Sarah Dean Colin Greaves Shaun Barber Patrick Doherty Nikki Gardiner Tracy Ibbotson Kate Jolly Paula Ormandy Sharon A. Simpson Rod S. Taylor Sally J. Singh Frances S. Mair Bhautesh D. Jani the PERFORM research team Multimorbidity clusters and their associations with health-related quality of life in two UK cohorts BMC Medicine Multimorbidity Quality of life Latent class analysis UK Biobank |
| title | Multimorbidity clusters and their associations with health-related quality of life in two UK cohorts |
| title_full | Multimorbidity clusters and their associations with health-related quality of life in two UK cohorts |
| title_fullStr | Multimorbidity clusters and their associations with health-related quality of life in two UK cohorts |
| title_full_unstemmed | Multimorbidity clusters and their associations with health-related quality of life in two UK cohorts |
| title_short | Multimorbidity clusters and their associations with health-related quality of life in two UK cohorts |
| title_sort | multimorbidity clusters and their associations with health related quality of life in two uk cohorts |
| topic | Multimorbidity Quality of life Latent class analysis UK Biobank |
| url | https://doi.org/10.1186/s12916-024-03811-3 |
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