Computational Simulation of Intervention Targets: Elucidating Coping Style Mechanisms in Anxiety and Depressive Symptom Networks Among Community-Based Populations

Background Depressive and anxiety disorders represent globally significant mental health burdens, imposing substantial impacts on individuals, societies, and healthcare infrastructure. In China, epidemiological data indicate prevalence rates of 6.8% for depression and 4.98% for anxiety, with both conditions demonstrating pronounced age-related heterogeneity and elevated comorbidity. Maladaptive coping mechanisms under stress constitute a core psychopathological pathway for these disorders. Methods This community-based cohort study employed network analysis and computational simulations to examine age-stratified effects of coping styles on symptomatology. Ising network models were constructed for discrete age cohorts, followed by application of the NodeIdentifyR algorithm (NIRA) to identify dual-pathway intervention targets—nodes projected to alleviate (treatment) or aggravate (prevention) symptoms. Results Analyses revealed that detrimental effects of negative coping styles substantially outweighed the protective capacity of positive coping strategies. Crucially, age-specific coping profiles emerged as viable targets for clinical intervention. Conclusions Age-tailored psychological interventions targeting identified coping mechanisms are imperative to mitigate symptom burden and improve outcomes across developmental stages in China. The findings underscore coping styles as critical modifiable factors for public health prioritization.