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Background <p>Suicidality is a major public health concern worldwide. Evidence on the prevalence and risk factors of suicidality amongst unaccompanied young refugees (UYRs), a population already at risk for mental health disorders, is scarce.</p> Methods <p>Given the complexity of individual risk factor constellations influencing suicidality, machine learning (ML) methods offer a statistical approach that can detect complex relations within the data. Four ML classifiers, (logistic regression (LR), random forest (RF), support vector machines (SVM), and extreme gradient boosting (XGB)) were trained on a dataset of <i>n</i> = 623 UYRs (M<sub>age</sub>=16.77, SD = 1.34, range: 12–21), retrieved from the large-scale randomized controlled trial <i>Better Care</i> to predict suicidal ideation. Features used in the classifiers were age, gender, asylum status, having contact with the family, and whether parents are alive as well as clinically elevated post-traumatic stress symptoms (PTSS), depressive symptoms and past suicide attempts. The classifiers were then tested on the independent dataset of <i>n</i> = 94 UYRs (M<sub>age</sub>=16.31, SD = 2.03, range: 5–21) retrieved from the screening tool <i>porta</i> project to examine their predictive performance.</p> Results <p>The prevalence of past-week suicidal ideation in the combined sample of <i>N</i> = 717 was 18.13%. All classifiers yielded good predictive performance (accuracy 0.734–0.840, sensitivity 0.857, AUC 0.853–0.880). The most relevant features were past suicide attempts, PTSS and depressive symptoms as risk factors, and having a living mother as protective factor.</p> Conclusions <p>Suicidal ideation is prevalent amongst UYRs, and using ML approaches, the classifiers were able to classify roughly 85% of the cases with suicidal ideation in the past week correctly as suicidal. Building on the findings of this study, screening for suicidality could be further improved by implementing ML classifiers in the assessment to highlight potential at risk cases early, and suitable interventions be developed.</p>

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Prevalence and risk factors of suicidal ideation amongst unaccompanied young refugees: a machine learning approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月12日

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  • Jacob Keller,
  • Jenny Eglinsky,
  • Maike Garbade,
  • Elisa Pfeiffer,
  • Paul L. Plener,
  • Rita Rosner,
  • Thorsten Sukale,
  • Cedric Sachser

摘要

Background

Suicidality is a major public health concern worldwide. Evidence on the prevalence and risk factors of suicidality amongst unaccompanied young refugees (UYRs), a population already at risk for mental health disorders, is scarce.

Methods

Given the complexity of individual risk factor constellations influencing suicidality, machine learning (ML) methods offer a statistical approach that can detect complex relations within the data. Four ML classifiers, (logistic regression (LR), random forest (RF), support vector machines (SVM), and extreme gradient boosting (XGB)) were trained on a dataset of n = 623 UYRs (Mage=16.77, SD = 1.34, range: 12–21), retrieved from the large-scale randomized controlled trial Better Care to predict suicidal ideation. Features used in the classifiers were age, gender, asylum status, having contact with the family, and whether parents are alive as well as clinically elevated post-traumatic stress symptoms (PTSS), depressive symptoms and past suicide attempts. The classifiers were then tested on the independent dataset of n = 94 UYRs (Mage=16.31, SD = 2.03, range: 5–21) retrieved from the screening tool porta project to examine their predictive performance.

Results

The prevalence of past-week suicidal ideation in the combined sample of N = 717 was 18.13%. All classifiers yielded good predictive performance (accuracy 0.734–0.840, sensitivity 0.857, AUC 0.853–0.880). The most relevant features were past suicide attempts, PTSS and depressive symptoms as risk factors, and having a living mother as protective factor.

Conclusions

Suicidal ideation is prevalent amongst UYRs, and using ML approaches, the classifiers were able to classify roughly 85% of the cases with suicidal ideation in the past week correctly as suicidal. Building on the findings of this study, screening for suicidality could be further improved by implementing ML classifiers in the assessment to highlight potential at risk cases early, and suitable interventions be developed.