Bu Feifei, Fancourt Daisy
Department of Behavioural Science and Health, Institute of Epidemiology and Health Care, University College London, London, United Kingdom.
JAMA Netw Open. 2024 Dec 2;7(12):e2451580. doi: 10.1001/jamanetworkopen.2024.51580.
IMPORTANCE: Issues related to social connection are increasingly recognized as a global public health priority. However, there is a lack of a holistic understanding of social connection and its health impacts given that most empirical research focuses on a single or few individual concepts of social connection. OBJECTIVE: To explore patterns of social connection and their associations with health and well-being outcomes. DESIGN, SETTING, AND PARTICIPANTS: This cohort study included participants aged 50 years and older from the fourth wave of the English Longitudinal Study of Aging (2008-2009). Machine learning cluster analysis and regression analysis were used. The analyses were performed from January to July 2024. EXPOSURE: Social connection clusters informed by the cluster analysis. MAIN OUTCOMES AND MEASURES: This study considered outcomes related to mental health (depression), hedonic (life satisfaction, pleasure) and eudaimonic (self-realization) well-being, general health (self-reported health), and health behavior (moderate or vigorous physical activity). Key confounders, identified using directed acyclic graphs, including age, sex, ethnicity, education, social class, and wealth, were controlled for. RESULTS: Among 7706 participants aged 50 years and older (mean [SD] age, 64.7 [9.6] years; 4248 [55.1%] female; 7536 [97.8%] White), 5 clusters were identified, including disconnected (974 [12.6%]), gapped structure/poor function (1109 [14.4%]), gapped structure/high function (1582 [20.5%]), poor function/mixed quality (1501 [19.5%]), and highly connected (2540 [33.0%]). All clusters had poorer outcomes compared with the highly connected cluster (eg, depression among individuals in disconnected vs highly connected clusters: odds ratio [OR], 2.73; 95% CI, 2.24 to 3.33), many of which persisted after controlling for baseline outcome (eg, depression among individuals in disconnected vs highly connected clusters: OR, 1.95; 95% CI, 1.57 to 2.43). The difference was smallest between the highly connected and gapped structure/high function clusters across most outcomes (eg, depression among individuals in gapped structure/high function vs highly connected: OR, 1.34; 95% CI, 1.10-1.64; after controlling for baseline outcome: OR, 1.28; 95% CI, 1.03-1.59). CONCLUSIONS AND RELEVANCE: This cohort study highlights the importance of considering multidimensional measures of social connection and understanding the nuance of its heterogenous patterns. Understanding the typologies of social connection has substantial implications for exploring modifiable risk factors for social disconnection and for understanding the mechanisms linking social connection to health-related outcomes.
重要性:与社会联系相关的问题日益被视为全球公共卫生的优先事项。然而,鉴于大多数实证研究聚焦于社会联系的单个或少数几个个体概念,目前缺乏对社会联系及其对健康影响的全面理解。 目的:探讨社会联系模式及其与健康和幸福结果的关联。 设计、背景和参与者:这项队列研究纳入了英国老龄化纵向研究第四波(2008 - 2009年)中50岁及以上的参与者。使用了机器学习聚类分析和回归分析。分析于2024年1月至7月进行。 暴露因素:由聚类分析得出的社会联系类别。 主要结局和测量指标:本研究考虑了与心理健康(抑郁)、享乐(生活满意度、愉悦感)和自我实现幸福、总体健康(自我报告的健康状况)以及健康行为(适度或剧烈体育活动)相关的结局。使用有向无环图确定的关键混杂因素,包括年龄、性别、种族、教育程度、社会阶层和财富,均得到了控制。 结果:在7706名50岁及以上的参与者中(平均[标准差]年龄为64.7[9.6]岁;4248名[55.1%]为女性;7536名[97.8%]为白人),识别出了5个类别,包括孤立型(974名[12.6%])、结构有差距/功能较差型(1109名[14.4%])、结构有差距/功能较高型(1582名[20.5%])、功能较差/质量混合型(1501名[19.5%])和高度连接型(2540名[33.0%])。与高度连接型类别相比,所有类别都有更差的结局(例如,孤立型与高度连接型类别中个体的抑郁情况:比值比[OR]为2.73;95%置信区间[CI]为2.24至3.33),其中许多结局在控制基线结局后仍然存在(例如,孤立型与高度连接型类别中个体的抑郁情况:OR为1.95;95%CI为1.57至2.43)。在大多数结局方面,高度连接型和结构有差距/功能较高型类别之间的差异最小(例如,结构有差距/功能较高型与高度连接型个体的抑郁情况:OR为1.34;95%CI为1.10 - 1.64;控制基线结局后:OR为1.28;95%CI为1.03 - 1.59)。 结论与意义:这项队列研究强调了考虑社会联系的多维测量以及理解其异质模式细微差别的重要性。理解社会联系的类型对于探索可改变的社会孤立风险因素以及理解将社会联系与健康相关结局联系起来的机制具有重要意义。
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