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
General Practice and Primary Care, School of Health and Wellbeing, University of Glasgow, Glasgow, UK.
AGE Research Group, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK.
BMC Med. 2025 Jan 8;23(1):1. doi: 10.1186/s12916-024-03811-3.
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.
背景：识别多种长期疾病（MLTCs）集群，即共病，及其相关负担，可能有助于制定有效且具成本效益的针对性医疗保健策略。本研究旨在识别两个英国人群队列中的MLTCs集群及其与长期健康相关生活质量（HRQoL）的关联。 方法：在英国生物银行（n = 502363，54.6%为女性）和英国劳动力调查（UKHLS，n = 49186，54.8%为女性）中，使用潜在类别分析（LCA）在基线时确定按年龄分层的MLTCs集群。LCA应用于在基线时自我报告≥2种长期疾病（英国生物银行有43种长期疾病，UKHLS有13种长期疾病）的人群，分为四个年龄组：18 - 36岁、37 - 54岁、55 - 73岁和74岁及以上。使用托比特回归研究MLTCs集群与HRQoL之间的关联，并与MLTC计数与HRQoL之间的关联进行比较。对于HRQoL，我们从英国生物银行提取了EQ - 5D指数数据。在UKHLS中，提取了SF - 12数据，并使用基于标准偏好的算法将其映射到EQ - 5D指数得分。HRQoL数据在随访中位数5年（UKHLS）和10年（英国生物银行）时收集。分析针对可用的社会人口统计学和生活方式协变量进行了调整。 结果：LCA在英国生物银行中识别出9个MLTCs集群，在UKHLS中识别出15个MLTCs集群。以肺部和心脏代谢性长期疾病为中心的集群在所有年龄组中都很常见。高血压在所有年龄段的集群中都很突出，而抑郁症在较年轻组中较为常见，疼痛性疾病/关节炎在中年及以后的集群中很常见。MLTCs集群与HRQoL表现出不同的关联。在英国生物银行中，疼痛性疾病患病率高的集群始终与HRQoL方面最大的缺陷相关。在UKHLS中，心脏代谢疾病集群的HRQoL最低。值得注意的是，即使在调整长期疾病数量后，包含疼痛性疾病的MLTCs集群与HRQoL之间的负相关仍然显著。 结论：虽然更高的长期疾病计数仍然很重要，但我们已经表明MLTCs集群类型也会对HRQoL产生影响。针对MLTCs患者的医疗服务提供规划、未来干预设计和风险评估应同时考虑长期疾病计数和MLTCs集群，以更好地满足特定人群的需求。
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