Chen Li, Wang Zhangyi, Wu Qiaoyan, Tang Xiaochun
School of Nursing, Hunan Normal University & Affiliated Hengyang Central Hospital, Changsha, Hunan, China.
School of Nursing, Kiang Wu Nursing College of Macao, Macao, Macao SAR, China.
Front Public Health. 2025 May 9;13:1548256. doi: 10.3389/fpubh.2025.1548256. eCollection 2025.
As a result of the aging of the world population, a high number of older adults lose their children during their lifetime. Depression due to child bereavement is a significant psychological problem. Therefore, it is necessary to investigate the trajectories of depressive symptoms associated with child bereavement among older adults in China and determine the influencing factors.
In this study, data from the China Health and Retirement Longitudinal Study were used as the longitudinal data, and 284 women and 117 men aged over 60 years were included. A latent growth mixture model was used to identify trajectory patterns in depression due to child bereavement over time. Multivariate logistic regression analysis was used to determine the influencing factors.
Four trajectory patterns of depressive symptoms associated with child bereavement were identified: a low depression rapidly increasing group (12.0%), a high depression rapidly declining group (12.1%), a high depression slowly increasing group (23.1%), and a low depression stable group (52.8%). The findings of the multivariate logistic regression analysis showed that residence, sleep status, satisfaction with life, and self-report of health were related to the trajectory patterns of depressive symptoms among the participants.
This study revealed heterogeneity in changes in depressive symptoms among older adults with child bereavement in China. The government and medical institutions should consider these trajectory patterns of depression and adopt individualized support measures based on the characteristics of different groups.
由于世界人口老龄化,大量老年人在其一生中失去子女。因丧子导致的抑郁是一个重大的心理问题。因此,有必要调查中国老年人中与丧子相关的抑郁症状轨迹,并确定影响因素。
在本研究中,将中国健康与养老追踪调查的数据用作纵向数据,纳入了284名60岁以上的女性和117名60岁以上的男性。使用潜在增长混合模型来识别因丧子导致的抑郁随时间的轨迹模式。采用多因素逻辑回归分析来确定影响因素。
识别出与丧子相关的四种抑郁症状轨迹模式:低抑郁快速增加组(12.0%)、高抑郁快速下降组(12.1%)、高抑郁缓慢增加组(23.1%)和低抑郁稳定组(52.8%)。多因素逻辑回归分析结果表明,居住状况、睡眠状况、生活满意度和健康自评与参与者抑郁症状的轨迹模式有关。
本研究揭示了中国丧子老年人抑郁症状变化的异质性。政府和医疗机构应考虑这些抑郁轨迹模式,并根据不同群体的特征采取个性化的支持措施。