The University of Edinburgh School of Engineering, Edinburgh, UK.
Advanced Care Research Centre, University of Edinburgh, Edinburgh, UK.
J Epidemiol Community Health. 2024 Aug 25;78(10):609-615. doi: 10.1136/jech-2023-221829.
Frailty, a state of increased vulnerability to adverse health outcomes, has garnered significant attention in research and clinical practice. Existing constructs aggregate clinical features or health deficits into a single score. While simple and interpretable, this approach may overlook the complexity of frailty and not capture the full range of variation between individuals.
Exploratory factor analysis was used to infer latent dimensions of a frailty index constructed using survey data from the English Longitudinal Study of Ageing, wave 9. The dataset included 58 self-reported health deficits in a representative sample of community-dwelling adults aged 65+ (N=4971). Deficits encompassed chronic disease, general health status, mobility, independence with activities of daily living, psychological well-being, memory and cognition. Multiple linear regression examined associations with CASP-19 quality of life scores.
Factor analysis revealed four frailty subdimensions. Based on the component deficits with the highest loading values, these factors were labelled 'mobility impairment and physical morbidity', 'difficulties in daily activities', 'mental health' and 'disorientation in time'. The four subdimensions were a better predictor of quality of life than frailty index scores.
Distinct subdimensions of frailty can be identified from standard index scores. A decomposed approach to understanding frailty has a potential to provide a more nuanced understanding of an individual's state of health across multiple deficits.
衰弱是一种对健康不良后果易感性增加的状态,在研究和临床实践中受到了广泛关注。现有的构建方法将临床特征或健康缺陷综合为一个单一的分数。虽然这种方法简单且易于解释,但它可能忽略了衰弱的复杂性,无法捕捉个体之间的全部变化范围。
使用来自英国老龄化纵向研究第 9 波的调查数据,对使用衰弱指数构建的潜在维度进行探索性因子分析。该数据集包括了 65 岁及以上社区居住成年人的 58 种自我报告的健康缺陷(N=4971)。缺陷包括慢性病、一般健康状况、移动能力、日常生活活动的独立性、心理幸福感、记忆力和认知能力。多元线性回归分析了与 CASP-19 生活质量评分的关联。
因子分析显示出四个衰弱子维度。根据具有最高加载值的组成缺陷,这些因素被标记为“移动障碍和身体病态”、“日常活动困难”、“心理健康”和“时间定向障碍”。与衰弱指数评分相比,这四个子维度是生活质量的更好预测因素。
可以从标准指数评分中识别出衰弱的不同子维度。了解衰弱的分解方法有可能提供对个体在多个缺陷下健康状况的更细致的理解。