Identifying the Subgroups of Depression Trajectories Among the Middle-aged and Older Chinese Individuals with Chronic Diseases: an 8-year Follow-up Study Based on CHARLS

Overview

Journal Front Public Health

Specialty Public Health

Date 2024 Oct 31

PMID 39478749

Authors

Jiaxing Pei

Mei Hu

Qiang Lu

Pengfei Zhou

Yijing Shang

Huiwang Zhang

Xiaoguang Yang

Yunming Li

Affiliations

Soon will be listed here.

Abstract

Background: Prior studies have demonstrated a prevalent occurrence of depression among the middle-aged and older Chinese individuals with chronic diseases. Nevertheless, there is limited research on the specific subgroups of depression trajectories within this population and the factors influencing these subgroups.

Objective: To explore the changing trajectory and influencing factors of depression in the middle-aged and older individuals with chronic disease in China, and provide the data reference for the health management of the older adult population in China.

Methods: A longitudinal cohort study was conducted using the data from the China Health and Retirement Longitudinal Study (CHARLS) in 2011, 2013, 2015, 2018, and 2020. A total of 2,178 participants with complete data were included. The level of depression was evaluated using the Center for Epidemiologic Studies Depression Scale (CESD-10). The Latent Class Mixed Models (LCMM) were employed to estimate trajectories of depressive symptoms. The Kruskal-Wallis test and the Pearson test were used to determine the significant factors affecting trajectory grouping. Subsequently, the multinomial logistic regression model was utilized to perform a multifactorial analysis of the variables impacting the trajectory subgroup of change in depressive symptoms.

Results: The LCMM-analysis revealed three distinct subgroups of depression trajectories: the "Low stable group" comprising 36.7% of the sample, the "Medium growth group" comprising 34.4% of the sample, and the "High growth group" comprising 28.9% of the sample. Among the baseline characteristics of different depression trajectory subgroups, there were significant differences in gender, residence, education, marital status, social activity participation, number of chronic diseases, smoking status, BMI, midday napping (minutes) and nighttime sleep duration (hours). Through multiple logistic regression analysis, our findings demonstrate that among the middle-aged and older Chinese individuals with chronic diseases, the following individuals should be the key groups for the prevention and treatment of depressive symptoms: Those who are young, female, residing in rural areas, having primary school education and below, being single, not participating in social activities, suffering from multiple chronic diseases, and having shorter naps and sleeping at night.

Conclusion: There is heterogeneity in the subgroups of depression trajectories among the Chinese middle-aged and older individuals with chronic diseases. The focus should be on the distinct characteristics of various trajectories of depression within the realm of health management.

Citing Articles

Association of TyG Index and TG/HDL-C Ratio with Trajectories of Depressive Symptoms: Evidence from China Health and Retirement Longitudinal Study.

Guo T, Zou Q, Wang Q, Zhang Y, Zhong X, Lin H Nutrients. 2025; 16(24.

PMID: 39770920 PMC: 11676214. DOI: 10.3390/nu16244300.

References

Feng Z, Li Q, Zhou L, Chen Z, Yin W . The relationship between depressive symptoms and activity of daily living disability among the elderly: results from the China Health and Retirement Longitudinal Study (CHARLS). Public Health. 2021; 198:75-81. DOI: 10.1016/j.puhe.2021.06.023. View

Badiya P, Siddabattuni S, Dey D, Javvaji S, Nayak S, Hiremath A . Identification of clinical and psychosocial characteristics associated with perinatal depression in the south Indian population. Gen Hosp Psychiatry. 2020; 66:161-170. DOI: 10.1016/j.genhosppsych.2020.08.002. View

Kennedy B, Berger S, Brunet A, Campisi J, Cuervo A, Epel E . Geroscience: linking aging to chronic disease. Cell. 2014; 159(4):709-13. PMC: 4852871. DOI: 10.1016/j.cell.2014.10.039. View

Wang K, Hu L, Wang L, Shu H, Wang Y, Yuan Y . Midday Napping, Nighttime Sleep, and Mortality: Prospective Cohort Evidence in China. Biomed Environ Sci. 2023; 36(8):702-714. DOI: 10.3967/bes2023.073. View

Li H, Liu X, Zheng Q, Zeng S, Luo X . Gender differences and determinants of late-life depression in China: A cross-sectional study based on CHARLS. J Affect Disord. 2022; 309:178-185. DOI: 10.1016/j.jad.2022.04.059. View

Moussavi S, Chatterji S, Verdes E, Tandon A, Patel V, Ustun B . Depression, chronic diseases, and decrements in health: results from the World Health Surveys. Lancet. 2007; 370(9590):851-8. DOI: 10.1016/S0140-6736(07)61415-9. View

Su B, Li D, Xie J, Wang Y, Wu X, Li J . Chronic Disease in China: Geographic and Socioeconomic Determinants Among Persons Aged 60 and Older. J Am Med Dir Assoc. 2022; 24(2):206-212.e5. DOI: 10.1016/j.jamda.2022.10.002. View

Lu N, Wu B, Pei Y, Peng C . Social capital, perceived neighborhood environment, and depressive symptoms among older adults in rural China: the role of self-rated health. Int Psychogeriatr. 2021; 34(8):691-701. DOI: 10.1017/S1041610221000958. View

Wu P, Lee M, Huang T . Effectiveness of physical activity on patients with depression and Parkinson's disease: A systematic review. PLoS One. 2017; 12(7):e0181515. PMC: 5531507. DOI: 10.1371/journal.pone.0181515. View

10.

You R, Li W, Ni L, Peng B . Study on the trajectory of depression among middle-aged and elderly disabled people in China: Based on group-based trajectory model. SSM Popul Health. 2023; 24:101510. PMC: 10509349. DOI: 10.1016/j.ssmph.2023.101510. View

11.

Kandola A, Ashdown-Franks G, Hendrikse J, Sabiston C, Stubbs B . Physical activity and depression: Towards understanding the antidepressant mechanisms of physical activity. Neurosci Biobehav Rev. 2019; 107:525-539. DOI: 10.1016/j.neubiorev.2019.09.040. View

12.

Lorant V, Deliege D, Eaton W, Robert A, Philippot P, Ansseau M . Socioeconomic inequalities in depression: a meta-analysis. Am J Epidemiol. 2003; 157(2):98-112. DOI: 10.1093/aje/kwf182. View

13.

Hammen C . Risk Factors for Depression: An Autobiographical Review. Annu Rev Clin Psychol. 2018; 14:1-28. DOI: 10.1146/annurev-clinpsy-050817-084811. View

14.

Pearce M, Garcia L, Abbas A, Strain T, Schuch F, Golubic R . Association Between Physical Activity and Risk of Depression: A Systematic Review and Meta-analysis. JAMA Psychiatry. 2022; 79(6):550-559. PMC: 9008579. DOI: 10.1001/jamapsychiatry.2022.0609. View

15.

Nguena Nguefack H, Page M, Katz J, Choiniere M, Vanasse A, Dorais M . Trajectory Modelling Techniques Useful to Epidemiological Research: A Comparative Narrative Review of Approaches. Clin Epidemiol. 2020; 12:1205-1222. PMC: 7608582. DOI: 10.2147/CLEP.S265287. View

16.

Fang J, Wen Z, Ouyang J, Huang G, Liu X . The reciprocal longitudinal relationship between body mass index and subjective well-being in China. Soc Sci Med. 2022; 297:114829. DOI: 10.1016/j.socscimed.2022.114829. View

17.

Wang Y, Jiang G, Wang L, Chen M, Yang K, Wen K . Association of the depressive scores, depressive symptoms, and conversion patterns of depressive symptoms with the risk of new-onset chronic diseases and multimorbidity in the middle-aged and elderly Chinese population. EClinicalMedicine. 2022; 52:101603. PMC: 9358433. DOI: 10.1016/j.eclinm.2022.101603. View

18.

Lim G, Tam W, Lu Y, Ho C, Zhang M, Ho R . Prevalence of Depression in the Community from 30 Countries between 1994 and 2014. Sci Rep. 2018; 8(1):2861. PMC: 5809481. DOI: 10.1038/s41598-018-21243-x. View

19.

Liu M, Zhang Y, Yang S, Wu Q, Ye Z, Zhou C . Bidirectional relations between depression symptoms and chronic kidney disease. J Affect Disord. 2022; 311:224-230. DOI: 10.1016/j.jad.2022.05.104. View

20.

Eckstrom E, Neukam S, Kalin L, Wright J . Physical Activity and Healthy Aging. Clin Geriatr Med. 2020; 36(4):671-683. DOI: 10.1016/j.cger.2020.06.009. View