Mapping emotional contagion: directional predictive patterns of depressive symptoms among middle-aged and older Chinese couples
Diyang Qu, Anni Zhu, Junkang Lin, Jiaxun Yu, Jinfeng Zhang, Jiasheng HuangAbstract
Background
China’s aging population faces increased prevalence in mid- and later life depression, with married middle-aged and older women vulnerable due to disproportionate caregiving and socioeconomic stress. Although emotional contagion between partners is well-documented, pathways through which depressive symptoms spread between these spouses remain unclear. This study examines the directional predictive patterns of depressive symptoms, wives to husbands specifically, within middle-aged and older Chinese couples.
Methods
Using the 2011–2013 longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS), we analyzed 2,578 heterosexual couples aged 45 years or older. Depressive symptoms were assessed at two time points using the 10-item Center for Epidemiological Studies-Depression scale (CES-D-10). Cross-lagged panel network (CLPN) modeling estimated temporal changes in predictive pathways of each spouse’s symptoms in the other’s, capturing intra-personal symptom interactions and inter-spousal predictive dynamics while comparing patterns between couples.
Results
Distinct symptom networks emerged for husbands and wives, with wives’ emotional states showing stronger cross-partner predictive associations, highlighting gender-specific patterns. Wives’ fearfulness emerged as a critical bridge symptom, significantly predicting increases in husbands’ depressive symptoms. Overall, significant cross-spousal spillover associations were observed; emotional contagion patterns were notably asymmetric, showing more pronounced symptom predictions from wives to husbands.
Conclusions
Findings indicate bidirectional associations of depressive symptoms with significant gender disparities in symptom dynamics among middle-aged and older couples, highlighting the necessity for gender-sensitive, relationship-focused research and interventions. Dyadic network analysis provides new insights into depression dynamics, advocating approaches addressing relational factors with individual symptoms in mental health strategies for China’s aging population.