Multisystem Influences on Mental Health Risk Among Urban Older Adults: An Ecological Systems Theory Perspective.

Abstract

Introduction: Mental health risk among urban older adults has become an important public health and social policy issue in the context of rapid population aging in China. Existing studies have often focused on single factors and have lacked an integrated analysis of multi-level determinants within a unified theoretical framework. This study examined the associations of ecological system factors with mental health risk among urban older adults.

Methods: Data were drawn from the 2020 China Longitudinal Aging Social Survey (CLASS). Guided by Bronfenbrenner's ecological systems theory, this study constructed an analytical framework covering the micro-, meso-, exo-, macro-, and chrono-system levels. Ordered logistic regression, stepwise cumulative regression, bootstrap mediation analysis, and robustness tests were used to examine the associations of social support, activity participation, community environment, institutional environment, and risk events with mental health risk, as well as subgroup heterogeneity.

Results: Higher levels of social support and more frequent activity participation were associated with higher mental health risk. Better community and institutional environments were associated with lower mental health risk, whereas risk events were significantly associated with higher risk. Mediation analysis suggested that activity participation was indirectly associated with mental health risk through risk events, while the association between community environment and mental health risk was attenuated after risk events were taken into account. Heterogeneity analysis showed substantial differences across gender, age, income, and educational groups.

Discussion: Mental health risk among urban older adults was closely related to multi-level ecological factors. Policy efforts should focus on improving the quality of support, strengthening community services and institutional protection, and adopting stratified interventions for different groups. Given the cross-sectional design, the findings should be interpreted as associations rather than causal relationships.