多病集群及其对老年人福祉的贡献:基于德国全国代表性样本的结果。

André Hajek, Razak M Gyasi, Karel Kostev, Pinar Soysal, Nicola Veronese, Lee Smith, Louis Jacob, Hans Oh, Supa Pengpid, Karl Peltzer, Hans-Helmut König
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引用次数: 0

摘要

目的:我们的目的是确定多病集群,特别是检查他们对德国最年长老人的福祉结果的贡献。方法:数据来自具有全国代表性的大型D80+研究,包括居住在德国的80岁及以上的社区居住和机构个人(n = 8773)。平均年龄85.6岁(SD: 4.1)。基于21种慢性疾病,进行潜在分类分析,以探索多发病(≥2种慢性疾病)群集。广泛使用的工具被用于量化幸福结果。结果:生活在德国的80岁及以上的人群中,大约90%的人患有多种疾病。确定了四种多病类:相对健康类(30.2%)、肌肉骨骼类(44.8%)、精神疾病类(8.6%)和高发病率类(16.4%)。作为精神障碍群体的一员,一直与幸福感下降(就生活满意度低、孤独感高、生活意义低的几率而言)有关,其次是高发病率群体的成员。结论:在德国老年人群中发现了4个多病聚集性人群。特别是属于精神障碍类始终与低幸福感相关,其次是属于高发病率类。这就强调,在未来进行纵向研究之前,有必要针对这些弱势群体作出努力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multimorbidity clusters and their contribution to well-being among the oldest old: Results based on a nationally representative sample in Germany.

Aim: Our aim was to identify multimorbidity clusters and, in particular, to examine their contribution to well-being outcomes among the oldest old in Germany.

Methods: Data were taken from the large nationally representative D80+ study including community-dwelling and institutionalized individuals aged 80 years and over residing in Germany (n = 8,773). The mean age was 85.6 years (SD: 4.1). Based on 21 chronic conditions, latent class analysis was carried out to explore multimorbidity (≥2 chronic conditions) clusters. Widely used tools were applied to quantify well-being outcomes.

Results: Approximately nine out of ten people aged 80 and over living in Germany were multimorbid. Four multimorbidity clusters were identified: relatively healthy class (30.2 %), musculoskeletal class (44.8 %), mental illness class (8.6 %), and high morbidity class (16.4 %). Being part of the mental disorders cluster was consistently linked to reduced well-being (in terms of low life satisfaction, high loneliness and lower odds of meaning in life), followed by membership in the high morbidity cluster.

Conclusions: Four multimorbidity clusters were detected among the oldest old in Germany. Particularly belonging to the mental disorders cluster is consistently associated with low well-being, followed by belonging to the high morbidity cluster. This stresses the need for efforts to target such vulnerable groups, pending future longitudinal research.

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