A machine learning analysis of suicidal ideation and suicide attempt among U.S. youth and young adults from multilevel, longitudinal survey data
ObjectivesTo investigate individual, interpersonal, health system, and community factors associated with suicidal ideation (SI) and attempts (SA).MethodsUtilizing nationally representative data from the National Longitudinal Study of Adolescent to Adult Health (7th-12th graders in 1994-95 followed &...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1511966/full |
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| author | Molly M. Jacobs Anne V. Kirby Jessica M. Kramer Nicole M. Marlow |
| author_facet | Molly M. Jacobs Anne V. Kirby Jessica M. Kramer Nicole M. Marlow |
| author_sort | Molly M. Jacobs |
| collection | DOAJ |
| description | ObjectivesTo investigate individual, interpersonal, health system, and community factors associated with suicidal ideation (SI) and attempts (SA).MethodsUtilizing nationally representative data from the National Longitudinal Study of Adolescent to Adult Health (7th-12th 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]).ResultsApproximately 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. Individual-level 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.ConclusionsThe 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. |
| format | Article |
| id | doaj-art-8d5b4bb88c6746e89683913f116e19e0 |
| institution | DOAJ |
| issn | 1664-0640 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Psychiatry |
| spelling | doaj-art-8d5b4bb88c6746e89683913f116e19e02025-08-20T03:11:07ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402025-02-011610.3389/fpsyt.2025.15119661511966A machine learning analysis of suicidal ideation and suicide attempt among U.S. youth and young adults from multilevel, longitudinal survey dataMolly M. Jacobs0Anne V. Kirby1Jessica M. Kramer2Nicole M. Marlow3Department of Health Services Research, Management and Policy, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United StatesDepartment of Occupational and Recreational Therapies, College of Health, University of Utah, Salt Lake City, UT, United StatesDepartment of Occupational Therapy, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United StatesDepartment of Health Services Research, Management and Policy, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United StatesObjectivesTo investigate individual, interpersonal, health system, and community factors associated with suicidal ideation (SI) and attempts (SA).MethodsUtilizing nationally representative data from the National Longitudinal Study of Adolescent to Adult Health (7th-12th 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]).ResultsApproximately 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. Individual-level 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.ConclusionsThe 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.https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1511966/fullsuicide attemptsuicidal ideationadolescents and young adultssocioecological frameworkmachine learninglongitudinal data |
| spellingShingle | Molly M. Jacobs Anne V. Kirby Jessica M. Kramer Nicole M. Marlow A machine learning analysis of suicidal ideation and suicide attempt among U.S. youth and young adults from multilevel, longitudinal survey data Frontiers in Psychiatry suicide attempt suicidal ideation adolescents and young adults socioecological framework machine learning longitudinal data |
| title | A machine learning analysis of suicidal ideation and suicide attempt among U.S. youth and young adults from multilevel, longitudinal survey data |
| title_full | A machine learning analysis of suicidal ideation and suicide attempt among U.S. youth and young adults from multilevel, longitudinal survey data |
| title_fullStr | A machine learning analysis of suicidal ideation and suicide attempt among U.S. youth and young adults from multilevel, longitudinal survey data |
| title_full_unstemmed | A machine learning analysis of suicidal ideation and suicide attempt among U.S. youth and young adults from multilevel, longitudinal survey data |
| title_short | A machine learning analysis of suicidal ideation and suicide attempt among U.S. youth and young adults from multilevel, longitudinal survey data |
| title_sort | machine learning analysis of suicidal ideation and suicide attempt among u s youth and young adults from multilevel longitudinal survey data |
| topic | suicide attempt suicidal ideation adolescents and young adults socioecological framework machine learning longitudinal data |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1511966/full |
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