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

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. 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. 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. 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.