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

Award Number: K01MH127417
ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP
PERIOD OF PERFORMANCE START DATE: 06/16/2022
PERIOD OF PERFORMANCE END DATE: 05/31/2026

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Epidemiologic and Machine Learning Approaches to Frame Suicide Prevention Strategies Among Juvenile Justice Youth - 2021 - 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 inquiry will further the field of youth suicide research by introducing innovative technological approaches for suicide prevention in a significantly high-risk and underserved population, while also generating new insights about the distinct implementation challenges in the potentially resource constrained juvenile justice system.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2025 ( Subtotal = $121,841 )
2025	2025	RESEARCH INSTITUTE AT NATIONWIDE CHILDREN'S HOSPITAL	700 CHILDRENS DR	COLUMBUS	OH	43205	FRANKLIN	USA	Mental Health Research Grants	000	4	5/27/2025	NON-COMPETING CONTINUATION	$121,841
														Subtotal = $121,841

Issue Date FY: 2024 ( Subtotal = $123,172 )
2024	2024	RESEARCH INSTITUTE AT NATIONWIDE CHILDREN'S HOSPITAL	700 CHILDRENS DR	COLUMBUS	OH	43205	FRANKLIN	USA	Mental Health Research Grants	000	3	5/8/2024	NON-COMPETING CONTINUATION	$123,172
														Subtotal = $123,172

Issue Date FY: 2023 ( Subtotal = $124,464 )
2023	2023	RESEARCH INSTITUTE AT NATIONWIDE CHILDREN'S HOSPITAL	700 CHILDRENS DR	COLUMBUS	OH	43205	FRANKLIN	USA	Mental Health Research Grants	000	2	6/1/2023	NON-COMPETING CONTINUATION	$124,464
														Subtotal = $124,464

Issue Date FY: 2022 ( Subtotal = $125,717 )
2022	2022	RESEARCH INSTITUTE AT NATIONWIDE CHILDREN'S HOSPITAL	700 CHILDRENS DR	COLUMBUS	OH	43205	FRANKLIN	USA	Mental Health Research Grants	000	1	6/16/2022	NEW	$125,717
														Subtotal = $125,717

Grand Total All Awards = $495,194

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

Award Number: K01MH127417

ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP

PERIOD OF PERFORMANCE START DATE: 06/16/2022

PERIOD OF PERFORMANCE END DATE: 05/31/2026

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