Citation

Fulginiti A, Segal A, Wilson J, Hill C, Tambe M, Castro C, Rice E. J. Soc. Social Work Res. 2022; 13(2): 327-352.

Copyright

(Copyright © 2022, Society for Social Work and Research, Publisher University of Chicago Press)

DOI

10.1086/715211

PMID

unavailable

Abstract

OBJECTIVE: Although many suicide risk factors have been identified, there is limited guidance about their relative importance and the combinations of factors that heighten risk among young people experiencing homelessness (YEH). We sought to use decision-tree (DT) analyses to better understand and predict suicidal ideation and suicide attempts among YEH.

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 DT models; this included prominent individual risk factors (e.g., trauma, depression), but more than half 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, and vio-lence), including social norms in their environments. With refinement, our decision trees may be useful for suicide risk screening and guiding targeted intervention. © 2022 Society for Social Work and Research. All rights reserved.

Language: en

Keywords

suicide; decision tree; machine learning; homeless