Journal of affective disorders · 2025
Patterns of co-occurring health risk behaviors and their associations with suicidality among U.S. adolescents: A latent class and decision tree analysis of YRBSS data (2011-2023).
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Zhu Jiajia, Hu Wenying, Yang Dongliang, Chen Mengyi, Li Jiajia, Zhu Jihua
Аннотация
BACKGROUND: Suicide rates among U.S. adolescents have risen in parallel with widespread engagement in co-occurring health risk behaviors (HRBs). Identifying co-occurrence patterns that predict suicidality may improve screening and intervention strategies.
METHODS: Using Youth Risk Behavior Surveillance System data (2011-2023; N = 53,350), latent class analysis (LCA) was used to classify adolescents based on six HRBs. Multivariable logistic regression examined associations with suicidal ideation, planning, and attempts. Classification and regression tree (CART) models explored interactions among behavioral, psychological, and demographic variables.
RESULTS: Five HRB patterns were identified: high-risk multi-behavior (9.5 %), diet and inactivity dominant (20.0 %), substance use-oriented (19.5 %), violence-dominant (9.9 %), and low-risk (41.1 %). Compared to the high-risk group, all other classes showed significantly lower odds of suicidality. The low-risk group had significantly lower odds of suicidal ideation (OR = 0.331, 95 % CI: 0.303-0.360), suicide planning (OR = 0.321, 95 % CI: 0.294-0.351), and suicide attempts (OR = 0.167, 95 % CI: 0.149-0.187). CART models identified depressive symptoms as the strongest predictor, followed by bullying and HRBs class membership; demographic factors had limited predictive value.
LIMITATIONS: Due to the cross-sectional nature of the data, causal inferences cannot be drawn.
CONCLUSIONS: Distinct patterns of HRBs are strongly associated with adolescent suicidality. Risk screening and interventions should incorporate behavioral profiling alongside mental health indicators to enhance targeting precision.
METHODS: Using Youth Risk Behavior Surveillance System data (2011-2023; N = 53,350), latent class analysis (LCA) was used to classify adolescents based on six HRBs. Multivariable logistic regression examined associations with suicidal ideation, planning, and attempts. Classification and regression tree (CART) models explored interactions among behavioral, psychological, and demographic variables.
RESULTS: Five HRB patterns were identified: high-risk multi-behavior (9.5 %), diet and inactivity dominant (20.0 %), substance use-oriented (19.5 %), violence-dominant (9.9 %), and low-risk (41.1 %). Compared to the high-risk group, all other classes showed significantly lower odds of suicidality. The low-risk group had significantly lower odds of suicidal ideation (OR = 0.331, 95 % CI: 0.303-0.360), suicide planning (OR = 0.321, 95 % CI: 0.294-0.351), and suicide attempts (OR = 0.167, 95 % CI: 0.149-0.187). CART models identified depressive symptoms as the strongest predictor, followed by bullying and HRBs class membership; demographic factors had limited predictive value.
LIMITATIONS: Due to the cross-sectional nature of the data, causal inferences cannot be drawn.
CONCLUSIONS: Distinct patterns of HRBs are strongly associated with adolescent suicidality. Risk screening and interventions should incorporate behavioral profiling alongside mental health indicators to enhance targeting precision.