Adolescent Interplay of Adversity and Risk: An Ising Network Analysis of the 2023 Youth Risk Behavior Survey

Adolescents may experience multiple early adversities alongside mental and behavioral challenges whose inter-relationships are not well captured by traditional conceptual models. Network analysis examines the conditional associations among indicators, offering new insights into how these experiences and behaviors cluster and interconnect. Drawing on a nationally representative sample of U.S. adolescents, we estimated an Ising network of 26 items from the 2023 national Youth Risk Behavior Survey (YBRS, N = 20,103) across five domains: adverse childhood experiences (ACEs), victimization, suicidal behaviors, substance use, and violence. We examined node and bridge centrality and assessed network invariance through the Network Comparison Test by sex (female vs. male), sexual orientation (heterosexual vs. sexual minorities and bisexuals separately), and race/ethnicity, comparing White respondents to Black/African American, Hispanic/Latino, Asian, American Indian/Alaska Native, and Multiracial respondents. The network was stable with substantial overall connectivity. Three variables showed the highest centrality (as measured by node strength z-scores): suicidal ideation (1.8), caregiver emotional abuse (1.8), and hopelessness (1.7). The strongest between-domain connectors (bridge strength z-scores) were hopelessness (2.2), physical fighting (2.0), and gun carrying (1.6). Hopelessness emerged as the most important variable in the network. Caregiver emotional abuse was the most relevant node within the ACEs. Physical fights and gun carrying were key in connecting violence to other domains. Exploratory subgroup comparisons suggested broadly similar network structure and global strength across sex, sexual orientation, and most racial/ethnic comparisons, with the strongest evidence of structural differences for Black/African American versus White respondents. These findings offer new directions for future research by identifying potential indicators of risk or cascade processes that may inform hypotheses about whether the observed associations reflect causal pathways.