Computational simulation of intervention targets: elucidating coping style mechanisms in anxiety and depressive symptom networks in community populations.

BACKGROUND: Depressive and anxiety disorders represent globally significant mental health burdens, imposing substantial impacts on individuals, societies, and healthcare infrastructure. In China, nationally representative epidemiological studies report a lifetime prevalence of 7.6% for anxiety disorders and 6.6% for depressive disorders. Both conditions demonstrate age-dependent heterogeneity, with older populations exhibiting significantly higher susceptibility and elevated comorbidity. METHODS: The cross-sectional study of community populations constructed Ising network models for different age groups, and then applied the NodeIdentifyR algorithm (NIRA) to identify dual-pathway intervention targets — nodes projected to alleviate (treatment) or aggravate (prevention) symptoms. RESULTS: The analysis indicated a heterogeneous link between coping styles and anxiety and depression, with notable age differences observed. Further simulation of intervention outcomes suggested that age-specific coping profiles emerged as viable targets for clinical intervention. CONCLUSIONS: Using psychopathological network modeling, this study reveals age-related heterogeneity in the association patterns between coping styles and anxiety-depression symptoms, as well as in the corresponding intervention targets.