Middle-aged people's depressive symptoms have negative impact on children and adolescents' mental health, family harmony, and the whole social mentality. Identifying the developmental trajectories of depressive symptoms can help to design effective prevention programs. Previous studies have reported 3 to 5 developmental trajectories of depressive symptoms among adolescents, early adults, and the elderly. However, whether this pattern can be generalized to Chinese middle adulthood needs to be studied further, based on the findings that the prevalence and influence factors associated with depressive symptoms are culture-dependent.
To date, studies on the developmental trajectories of depressive symptoms among middle adulthood are far from consistent. In addition, many cross-sectional studies have proved that depressive symptoms were also affected by gender, residence, the level of education, and other factors. Nonetheless, there are few longitudinal studies conducted to explore the risk factors for developmental trajectories. Therefore, the aim of the present longitudinal study is to examine developmental patterns of depressive symptoms in Chinese middle adulthood. In addition, we also examined whether the developmental patterns would differ by gender, residence, and the level of education.
Participants were 10654 middle adulthood (53.4% females; Mage = 43.2 years, SD = 4.4 years) from the China Family Panel Studies (CFPS) project conducted by China Social Science Survey Center of Peking University, recruited from 25 provinces in Mainland China. This study adopted longitudinal design at 3 times over the course of 6 years. The data were collected in 2012 for the first time, four years later for the second time, and two years later for the third time. Longitudinal data on depressive symptoms were measured by the short version of the Center for Epidemiologic Studies Depression Scale (CES-D), and the internal consistency reliability of the three measures was between .75 and .79.
The growth mixture modeling was used to explore the developmental trajectories, while logistic regression was used to examine the effects of gender, residence, and the level of education. The data were analyzed using SPSS18.0 and Mplus17.4, including descriptive analysis, correlation analysis, logistic regression, latent growth curve model, and growth mixture modeling. The results showed that: (1) The developmental trajectories of depressive symptoms among Chinese middle adulthood were identified with three different patterns: consistently low group (87%), low-sharp increasing group (5%), and moderate-slow decreasing group (8%). (2) Predictors of developmental trajectories with greater symptom burden included female gender, rural resident, and lower education, a larger percentage of the low-sharp increasing group, and moderate-slow decreasing group were females, rural residents and those with lower level of education.
The study made contributions to the knowledge on the development of middle adulthood's depressive symptoms in China. It was the first study to examine the developmental trajectories and risk factors of depressive symptoms among Chinese middle adulthood. Our findings have certain guiding significance for the improvement of middle adulthood's depressive symptoms.
Key words
middle adulthood /
depressive symptoms /
growth mixture modeling
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References
[1] 侯金芹, 陈祉妍. (2016). 青少年抑郁情绪的发展轨迹: 界定亚群组及其影响因素. 心理学报, 48(8), 957-968.
[2] 黄垣成, 赵清玲, 李彩娜. (2021). 青少年早期抑郁和自伤的联合发展轨迹: 人际因素的作用. 心理学报, 53(5), 515-526.
[3] 江光荣, 李丹阳, 任志洪, 闫玉朋, 伍新春, 朱旭, 张琳. (2021). 中国国民心理健康素养的现状与特点. 心理学报, 53(2), 182-198.
[4] 金家飞, 徐姗, 王艳霞. (2014). 角色压力、工作家庭冲突和心理抑郁的中美比较——社会支持的调节作用. 心理学报, 46(8), 1144-1160.
[5] 黎志华, 谢玮玉, 尹霞云, 刘文俐, 周先进. (2018). 大学生抑郁的发展轨迹: 基于潜变量混合增长模型的分析. 中国临床心理学杂志, 26(4), 711-715.
[6] 林崇德. (2018). 发展心理学. 人民教育出版社..
[7] 石智雷, 杨宇泽. (2020). 高学历的人更容易抑郁吗?——教育对成年人抑郁情绪的影响. 北京师范大学学报(社会科学版), 2, 148-160.
[8] 王济川, 王小倩, 姜宝法. (2011). 结构方程模型: 方法与应用. 高等教育出版社.
[9] 王孟成, 毕向阳, 叶浩生. (2014). 增长混合模型: 分析不同类别个体发展趋势. 社会学研究, 29(4), 220-241.
[10] 王孟成, 毕向阳. (2018). 潜变量建模与Mplus应用·进阶篇. 重庆大学出版社..
[11] 袁加锦, 汪宇, 鞠恩霞, 李红. (2010). 情绪加工的性别差异及神经机制. 心理科学进展, 18(12), 1899-1908.
[12] Butler, A. B., & Skattebo, A. (2004). What is acceptable for women may not be for men: The effect of family conflicts with work on job-performance ratings. Journal of Occupational and Organizational Psychology, 77(4), 553-564.
[13] Chen Y., Bennett D., Clarke R., Guo Y., Yu C., Bian Z., & Chen Z. (2017). Patterns and correlates of major depression in Chinese adults: A cross-sectional study of 0.5 million men and women. Psychological Medicine, 47(5), 958-970.
[14] Clarke P., Marshall V., House J., & Lantz P. (2011). The social structuring of mental health over the adult life course: Advancing theory in the sociology of aging. Social Forces, 89(4), 1287-1313.
[15] de la Torre-Luque A., de la Fuente J., Prina M., Sanchez-Niubo A., Haro J. M., & Ayuso-Mateos J. L. (2019). Long-term trajectories of depressive symptoms in old age: Relationships with sociodemographic and health-related factors. Journal of Affective Disorders, 246, 329-337.
[16] Edgerton J. D., Shaw S., & Roberts L. W. (2019). An exploration of depression symptom trajectories, and their predictors, in a Canadian sample of emerging adults. Emerging Adulthood, 7(5), 352-362.
[17] Ellis R. E. R., Seal M. L., Simmons J. G., Whittle S., Schwartz O. S., Byrne M. L., & Allen N. B. (2017). Longitudinal trajectories of depression symptoms in adolescence: Psychosocial risk factors and outcomes. Child Psychiatry and Human Development, 48(4), 554-571.
[18] Harding J. F., Morris P. A., & Hughes D. (2015). The relationship between maternal education and children's academic outcomes: A theoretical framework. Journal of Marriage and Family, 77(1), 60-76.
[19] Hu Y. Y., Li P., & Martikainen P. (2019). Rural-urban disparities in age trajectories of depression caseness in later life: The China health and retirement longitudinal study. PLoS ONE, 14(4), Article e0215907.
[20] Hybels C. F., Landerman L. R., & Blazer D. G. (2013). Latent subtypes of depression in a community sample of older adults: Can depression clusters predict future depression trajectories? Journal of Psychiatric Research, 47(10), 1288-1297.
[21] Jones J. W., Ledermann T., & Fauth E. B. (2018). Self-rated health and depressive symptoms in older adults: A growth mixture modeling approach. Archives of Gerontology and Geriatrics, 79, 137-144.
[22] Kim E. Y., Kim S. H., Ha K., Lee H. J., Yoon D. H., & Ahn Y. M. (2015). Depression trajectories and the association with metabolic adversities among the middle-aged adults. Journal of Affective Disorders, 188, 14-21.
[23] Lee, J. (2020). Trajectories of depression between 30s and 50s: Latent growth modeling. Issues in Mental Health Nursing, 41(7), 624-636.
[24] Lim H. J., Cheng Y. Z., Kabir R., & Thorpe L. (2021). Trajectories of depression and their predictors in a population-based study of Korean older adults. The International Journal of Aging and Human Development, 93(3), 834-853.
[25] Little, R. J. A. (1988). A test of missing completely at random for multivariate data with missing values. Journal of the American Statistical Association, 83(404), 1198-1202.
[26] Liu C. L., Wei Y., Ling Y., Huebner E. S., Zeng Y. F., & Yang Q. (2020). Identifying trajectories of Chinese high school students' depressive symptoms: An application of latent growth mixture modeling. Applied Research in Quality of Life, 15(3), 775-789.
[27] Lubke, G., & Muthén, B. O. (2007). Performance of factor mixture models as a function of model size, covariate effects, and class-specific parameters. Structural Equation Modeling: A Multidisciplinary Journal, 14(1), 26-47.
[28] Melchior M., Chastang J. F., Head J., Goldberg M., Zins M., Nabi H., & Younès N. (2013). Socioeconomic position predicts long-term depression trajectory: A 13-year follow-up of the GAZEL cohort study. Molecular Psychiatry, 18(1), 112-121.
[29] Musliner K. L., Munk-Olsen T., Eaton W. W., & Zandi P. P. (2016). Heterogeneity in long-term trajectories of depressive symptoms: Patterns, predictors and outcomes. Journal of Affective Disorders, 192, 199-211.
[30] Ni Y. H., Tein J. Y., Zhang M. Q., Yang Y. W., & Wu G. T. (2017). Changes in depression among older adults in China: A latent transition analysis. Journal of Affective Disorders, 209, 3-9.
[31] Radloff, L. S. (1977). The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385-401.
[32] Roberts B. W., Walton K. E., & Viechtbauer W. (2006). Patterns of mean-level change in personality traits across the life course: A meta-analysis of longitudinal studies. Psychological Bulletin, 132(1), 1-25.
[33] Salk R. H., Hyde J. S., & Abramson L. Y. (2017). Gender differences in depression in representative national samples: Meta-analyses of diagnoses and symptoms. Psychological Bulletin, 143(8), 783-822.
[34] Stordal E., Mykletun A., & Dahl A. A. (2003). The association between age and depression in the general population: A multivariate examination. Acta Psychiatrica Scandinavica, 107(2), 132-141.
[35] Sun N., Hua C. L., Qiu X., & Brown J. S. (2022). Urban and rural differences in trajectories of depressive symptoms in later life in the United States. Journal of Applied Gerontology, 41(1), 148-157.
[36] Wood W.,& Eagly, A. H. (2012). Advances in experimental social psychology. Academic Press..