# Epidemiologic and Machine Learning Approaches to Frame Suicide Prevention Strategies Among Juvenile Justice Youth - 2021

> **NIH NIH K01** · RESEARCH INST NATIONWIDE CHILDREN'S HOSP · 2024 · $123,172

## Abstract

PROJECT TITLE
Epidemiologic and Machine Learning Approaches to Frame Suicide Prevention Strategies Among Juvenile
Justice Youth
PROJECT SUMMARY / ABSTRACT
Suicide is the second leading cause of death among youth aged 10-24 years in the U.S. One population
found to have higher rates of suicidal behavior is youth incarcerated in the juvenile justice system. While
progress has been made to reduce suicide for youth within juvenile correctional facilities, minimal
consideration has been given to the risk for suicide in youth after incarceration. Previously incarcerated
youth face numerous challenges reintegrating back into the community which can increase their risk for
suicidal behavior. Estimates further suggest that 60% to 80% of youth involved in the justice system have
significant mental health issues, and time spent in the system can exacerbate these conditions. The unmet
need for mental health services by youth involved with the justice system is also a serious problem. Despite
the recognized risk in this vulnerable population, evidence-based suicide prevention strategies are not
integrated as part of routine reentry services for youth released from confinement. Even less is known about
successful approaches to implement these strategies in juvenile justice systems. To address this gap, the
proposed study uses innovative machine learning techniques to develop a risk prediction model
incorporating youth characteristics and contextual factors associated with confinement (violence,
victimization, segregation /isolation practices, health care services) to more accurately assess suicide risk in
youth following incarcerated. Guided by these findings and a structured implementation science framework,
this proposal will also conduct a pre-intervention assessment with key stakeholders to validate the utility of
the machine learning model to inform intervention selection. Consideration will be given to potential
facilitators and barriers to integrating the model into practice, and when, how, and where to intervene in the
juvenile justice process. Achieving the aims of this proposed study will provide targeted intervention
recommendations for suicide prevention among at-risk youth in the juvenile justice system. This proposal
will also support the training of Dr. Ruch, who is devoted to a research career to reduce suicide in youth
involved with justice system. Dr. Ruch’s training plan includes: (1) acquiring skills in machine learning and
forecast modeling techniques to more accurately identify suicide risk and inform targeted preventions for
youth in the justice system (2) enhancing knowledge of suicide prevention interventions, including health
service systems to understand how health care practices and policies may facilitate or impede intervention
for youth involved with the justice system and (3) strengthening skills in implementation science and
advanced qualitative research methods to bridge the gap between research and practice. This line of inqui...

## Key facts

- **NIH application ID:** 10852996
- **Project number:** 5K01MH127417-03
- **Recipient organization:** RESEARCH INST NATIONWIDE CHILDREN'S HOSP
- **Principal Investigator:** Donna Ruch
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $123,172
- **Award type:** 5
- **Project period:** 2022-06-16 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10852996

## Citation

> US National Institutes of Health, RePORTER application 10852996, Epidemiologic and Machine Learning Approaches to Frame Suicide Prevention Strategies Among Juvenile Justice Youth - 2021 (5K01MH127417-03). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10852996. Licensed CC0.

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