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老年人社会参与轨迹及其预测因素:基于 2002 年至 2018 年 CLHLS 队列研究。

Trajectories of Social Participation and Its Predictors in Older Adults: Based on the CLHLS Cohorts from 2002 to 2018.

机构信息

Xiangya School of Nursing, Central South University, Changsha 410013, China.

Xiangya-Oceanwide Health Management Research Institute, Central South University, Changsha 410013, China.

出版信息

Int J Environ Res Public Health. 2023 Feb 27;20(5):4260. doi: 10.3390/ijerph20054260.

Abstract

Social participation is a key factor in achieving active aging. This study aimed to explore the trajectories and predictors of social participation changes among older adults in China. The data used in this study are from the ongoing national longitudinal study CLHLS. A total of 2492 older adults from the cohort study were included. Group-based trajectory models (GBTM) were used to identify potential heterogeneity in longitudinal changes over time and investigate associations between baseline predictors and trajectories for different cohort members using logistic regression. Four different trajectories of social participation were reported in older adults, namely, stable (8.9%), slow decline (15.7%), lower score with decline (42.2%), and higher score with decline (9.5%). On multivariate analyses, age, years of schooling, pension, mental health, cognitive function, instrumental activities of daily living, and initial social participation scores significantly impact the rate of change in social participation over time. Four trajectories of social participation were identified in the Chinese elderly population. Management of mental health, physical function, and cognitive function appear to be important in maintaining the long-term social participation of older people in the community. Early identification of factors influencing the rapid decline in social participation and timely interventions can maintain or improve social participation levels in older adults.

摘要

社会参与是实现积极老龄化的关键因素。本研究旨在探讨中国老年人社会参与变化的轨迹和预测因素。本研究使用的数据来自正在进行的国家纵向研究 CLHLS。共纳入了来自队列研究的 2492 名老年人。使用基于群组的轨迹模型(GBTM)来识别随时间纵向变化的潜在异质性,并使用逻辑回归调查基线预测因素与不同队列成员轨迹之间的关联。报告了老年人社会参与的四种不同轨迹,分别为稳定(8.9%)、缓慢下降(15.7%)、下降伴随评分较低(42.2%)和下降伴随评分较高(9.5%)。在多变量分析中,年龄、受教育年限、养老金、心理健康、认知功能、日常生活活动能力和初始社会参与评分显著影响社会参与随时间的变化率。在中国老年人群中确定了四种社会参与轨迹。管理心理健康、身体功能和认知功能似乎对于维持社区老年人的长期社会参与非常重要。早期识别影响社会参与快速下降的因素并及时进行干预,可以维持或提高老年人的社会参与水平。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eae5/10001875/5722d1fb7053/ijerph-20-04260-g001.jpg

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