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Purpose: Identify and examine the associations between health behaviors andincreased risk of adolescent suicide attempts, while controlling forsocioeconomic and demographic differences. Design: A data-driven analysis usingcross-sectional data. Setting: Communities in the state of Montana from 1999 to2017. Subjects: Selected 22,447 adolescents of whom 1,631 adolescents attemptedsuicide at least once. Measures: Overall 29 variables (predictors) accountingfor psychological behaviors, illegal substances consumption, daily activitiesat schools and demographic backgrounds, were considered. Analysis: A library ofmachine learning algorithms along with the traditionally-used logisticregression were used to model and predict suicide attempt risk. Modelperformances (goodness-of-fit and predictive accuracy) were measured usingaccuracy, precision, recall and F-score metrics. Results: The non-parametricBayesian tree ensemble model outperformed all other models, with 80.0% accuracyin goodness-of-fit (F-score:0.802) and 78.2% in predictive accuracy(F-score:0.785). Key health-behaviors identified include: being sad/hopeless,followed by safety concerns at school, physical fighting, inhalant usage,illegal drugs consumption at school, current cigarette usage, and having firstsex at an early age (below 15 years of age). Additionally, the minority groups(American Indian/Alaska Natives, Hispanics/Latinos), and females are also foundto be highly vulnerable to attempting suicides. Conclusion: Significantcontribution of this work is understanding the key health-behaviors and healthdisparities that lead to higher frequency of suicide attempts amongadolescents, while accounting for the non-linearity and complex interactionsamong the outcome and the exposure variables.

Health-behaviors associated with the growing risk of adolescent suicide attempts: A data-driven cross-sectional study