Department of Health Economics and Health Services Research, Hamburg Center for Health Economics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany,
Department of Health Economics and Health Services Research, Hamburg Center for Health Economics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Gerontology. 2020;66(5):460-466. doi: 10.1159/000508723. Epub 2020 Jul 7.
There is a lack of studies investigating the link between time-varying factors associated with changes in frailty scores in very old age longitudinally. This is important because the level of frailty is associated with subsequent morbidity and mortality.
To examine time-dependent predictors of frailty among the oldest old using a longitudinal approach.
Longitudinal data were drawn from the multicentre prospective cohort study "Study on Needs, health service use, costs and health-related quality of life in a large sample of oldest-old primary care patients (85+)" (AgeQualiDe), covering primary care patients aged 85 years and over. Three waves were used (from follow-up, FU, wave 7 to FU wave 9 [with 10 months between each wave]; 1,301 observations in the analytical sample). Frailty was assessed using the Canadian Study of Health and Aging (CSHA) Clinical Frailty Scale (CFS). As explanatory variables, we included sociodemographic factors (marital status and age), social isolation as well as health-related variables (depression, dementia, and chronic diseases) in a regression analysis.
In total, 18.9% of the individuals were mildly frail, 12.4% of the individuals were moderately frail, and 0.4% of the individuals were severely frail at FU wave 7. Fixed effects regressions revealed that increases in frailty were associated with increases in age (β = 0.23, p < 0.001), and dementia (β = 0.84, p < 0.01), as well as increases in chronic conditions (β = 0.03, p = 0.058).
The study findings particularly emphasize the importance of changes in age, probably chronic conditions as well as dementia for frailty. Future research is required to elucidate the underlying mechanisms. Furthermore, future longitudinal studies based on panel regression models are required to confirm our findings.
目前缺乏研究调查与非常高龄人群的脆弱性评分随时间变化相关的时间变化因素之间的联系。这很重要，因为脆弱性水平与随后的发病率和死亡率有关。
使用纵向方法研究最年长人群中与脆弱性相关的时间依赖性预测因素。
这项纵向研究的数据来自多中心前瞻性队列研究“一项对大量最年长初级保健患者（85 岁以上）的需求、卫生服务使用、成本和健康相关生活质量的研究（AgeQualiDe）”，研究对象为 85 岁及以上的初级保健患者。共使用了 3 个波次（从随访、波 7 到随访波 9[每个波次之间相隔 10 个月]；分析样本中有 1301 个观察值）。使用加拿大健康与老龄化研究（CSHA）临床脆弱性量表（CFS）评估脆弱性。在回归分析中，作为解释变量，我们纳入了社会人口因素（婚姻状况和年龄）、社会孤立以及与健康相关的变量（抑郁、痴呆和慢性疾病）。
在随访波 7 时，总共有 18.9%的个体为轻度脆弱，12.4%的个体为中度脆弱，0.4%的个体为重度脆弱。固定效应回归显示，脆弱性增加与年龄增加（β=0.23，p<0.001）、痴呆（β=0.84，p<0.01）以及慢性疾病增加（β=0.03，p=0.058）有关。
研究结果特别强调了年龄变化、可能的慢性疾病以及痴呆对脆弱性的重要性。需要进一步的研究来阐明潜在的机制。此外，需要基于面板回归模型的未来纵向研究来验证我们的发现。
