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广义相加混合模型,用于识别基于理论且由数据驱动的公共脑健康、认知健康和心理健康策略。

Generalized additive mixed models to discern data-driven theoretically informed strategies for public brain, cognitive and mental health.

作者信息

Lammer Laurenz, Beyer Frauke, Riedel-Heller Steffi, Sacher Julia, Glaesmer Heide, Villringer Arno, Witte A Veronica

机构信息

Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103, Leipzig, Germany.

Clinic for Cognitive Neurology, University of Leipzig Medical Center, Leipzig, Germany.

出版信息

Eur J Epidemiol. 2025 Sep 15. doi: 10.1007/s10654-025-01296-9.

DOI:10.1007/s10654-025-01296-9
PMID:40947435
Abstract

Social isolation is recognized as a public health emergency. However, major guidelines provide vastly different recommendations on how to target it, and no strategy has been substantiated on firm theoretical or empirical grounds, yet. Rose's seminal The Strategy of Preventive Medicine provided a theoretical framework for such arbitrations between approaches. Therein, determining the shape of the relationship between risk factor and outcome is of paramount importance. However, quantitative approaches immediately applying this theory to evidence are still lacking. Thus, in this pre-registered analysis, we pursued a novel approach and employed generalized additive mixed models to model the shape of social isolation's Links to brain, cognitive and mental health outcomes in a well-characterised population-based sample. We derived brain measures from 3T MRIs, assessed cognitive functions with extensive neuropsychological testing and measured social isolation and mental health outcomes using established questionnaires. Overall, we studied over 10,000 (mean age 58a, 53% women) participants at baseline and over 5500 (mean age 64a, 53% women) at follow-up after ~ 6 years. The relationship of social contact with almost all outcomes was firmly linear and did not differ above and below the standard threshold for social isolation. Only for processing speed did we detect a steeper slope amongst socially isolated individuals. Hence, most of the health effects of social contact were observed in individuals that would not be categorised as socially isolated. Applying advanced statistical methods to a large and well-characterised dataset we provide evidence in support of a shift in focus away from individual-level and towards population-level preventive approaches.

摘要

社会隔离被视为一种公共卫生紧急情况。然而,主要指南对于如何应对这一问题给出了截然不同的建议,而且目前尚无基于坚实理论或实证依据的策略得到证实。罗斯的开创性著作《预防医学策略》为这些方法之间的权衡提供了一个理论框架。在该框架中,确定风险因素与结果之间关系的形式至关重要。然而,目前仍缺乏将这一理论立即应用于证据的定量方法。因此,在这项预先注册的分析中,我们采用了一种新颖的方法,运用广义相加混合模型来模拟在一个特征明确的基于人群的样本中,社会隔离与大脑、认知和心理健康结果之间联系的形式。我们从3T磁共振成像中获取大脑测量数据,通过广泛的神经心理学测试评估认知功能,并使用既定问卷测量社会隔离和心理健康结果。总体而言,我们在基线时研究了超过10000名参与者(平均年龄58岁,53%为女性),在约6年后的随访中研究了超过5500名参与者(平均年龄64岁,53%为女性)。社会接触与几乎所有结果之间的关系呈稳固的线性,且在社会隔离标准阈值上下并无差异。仅在处理速度方面,我们发现社会隔离个体中的斜率更陡。因此,社会接触对健康的大多数影响在那些不会被归类为社会隔离的个体中也能观察到。通过将先进的统计方法应用于一个大型且特征明确的数据集中,我们提供了证据,支持将重点从个体层面转向人群层面的预防方法。

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Impact of Loneliness and Social Isolation on Mental Health Outcomes Among Individuals With Rheumatic Diseases During the COVID-19 Pandemic.2019年冠状病毒病大流行期间,孤独和社会隔离对风湿病患者心理健康结局的影响。
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