Getting to the root of the problem: A decision-tree analysis for suicide risk among young people experiencing homelessness

Abstract:

Objective : The assessment and prediction of suicide risk among young people experiencing
homelessness (YEH) has proven difficult. Although a large number of suicide risk factors have
been identified, there is limited guidance about their relative importance and the combinations of
factors (i.e., profiles) that heighten risk. Method : Using survey and social network methods, we
gathered information about 940 YEH and their relationships. We then used a machine learning
approach to construct Classification and Regression Tree models to predict suicidal ideation and
suicide attempts. Results : Thirteen variables were important correlates in the decision tree
models. This included prominent individual risk factors (e.g., trauma, depression), but over half
of them were social network factors (e.g., hard drug use). For suicidal ideation, the model had an
area under the receiver operating characteristic curve (AUC) value of 0.79, with Accuracy of
68%, Sensitivity of 48%, and Specificity of 73%. For suicide attempt, the model had an AUC
value of 0.86, with Accuracy of 71%, Sensitivity of 68%, and Specificity of 72%. Conclusions :
Effective suicide prevention programming should target the syndemic that threatens YEH (i.e.,
co-occurrence of trauma-depression-substance use-violence), including social norms in their
environments. With refinement, our decision trees may be useful aids for suicide risk screening
and guiding targeted intervention.