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ORIGINAL RESEARCH article

Front. Psychiatry
Sec. Public Mental Health
Volume 16 - 2025 | doi: 10.3389/fpsyt.2025.1511966

A Machine Learning Analysis of Suicidal Ideation and Suicide Attempt among U.S. Youth and Young Adults from Multilevel, Longitudinal Survey Data

Provisionally accepted
Molly Marie Jacobs Molly Marie Jacobs 1Anne Kirby Anne Kirby 2Jessica Kramer Jessica Kramer 1Nicole Marguerite Marlow Nicole Marguerite Marlow 1*
  • 1 University of Florida, Gainesville, United States
  • 2 The University of Utah, Salt Lake City, Utah, United States

The final, formatted version of the article will be published soon.

    Objectives: To investigate individual, interpersonal, health system, and community factors associated with suicidal ideation (SI) and attempts (SA).Methods: Utilizing nationally representative data from the National Longitudinal Study of Adolescent to Adult Health (7 th -12 th graders in 1994-95 followed >20 years until 2016-18, N=18,375), least absolute shrinkage selector operator (LASSO) regression determined multilevel predictors of SA and SI. Models comprised full and diagnosis subgroups (ADD/ADHD, depression, PTSD, anxiety, learning disabilities [LD]).Results: Approximately 2.48% and 8.97% reported SA and SI, respectively. Over 25% had depression, and 20.98% anxiety, 6.42% PTSD, 4.55% ADD/ADHD, and 2.50% LD.LASSO regression identified 20 and 21 factors associated with SA and SI. Individuallevel factors associated with SI and SA included educational attainment, substance use, ADD/ADHD, depression, anxiety, and PTSD. Interpersonal-level factors included social support, household size, and parental education, while health system-level factors comprised health care receipt, health insurance, and counseling. The strongest associations were among individual-level factors followed by interpersonal and health system factors.The distinct factors associated with SI and SA across diagnostic subgroups highlight the importance of targeted, subgroup-specific suicide prevention interventions. These findings emphasize the value of precise, data-driven approaches for suicide prevention among diverse populations and individuals with disabilities across the life-course.

    Keywords: suicide attempt, Suicidal Ideation, Adolescents and young adults, Socioecological framework, machine learning, longitudinal data

    Received: 15 Oct 2024; Accepted: 28 Jan 2025.

    Copyright: © 2025 Jacobs, Kirby, Kramer and Marlow. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Nicole Marguerite Marlow, University of Florida, Gainesville, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.