# Predicting Suicide Attempts in Youth with Child Protective Services Contact

> **NIH NIH F31** · JOHNS HOPKINS UNIVERSITY · 2020 · $40,245

## Abstract

The applicant, Mr. Geoffrey Kahn, proposes to identify key, modifiable predictors of attempted
suicide among children and youth who have had contact with Child Protective Services (CPS), and
quantify the impact of hypothetical preventive interventions that could be implemented for these
children. This research will be the foundation of the applicant’s doctoral dissertation, and will serve as one
component of a comprehensive training plan to prepare the applicant for a career in independent research. Mr.
Kahn will be mentored by a team of co-sponsors and consultants with expertise in suicide prevention,
advanced statistical methods, child welfare research, and the CPS system. He will complete coursework in
analysis of complex survey data and machine learning methods, participate in working groups on suicide
prevention, machine learning, and causal inference at the sponsoring institution, Johns Hopkins Bloomberg
School of Public Health, and present his findings to diverse audiences. The advanced training and focused
mentoring that Mr. Kahn will receive through this Fellowship will position him to conduct future, independent
research studies on the prevention of suicide, particularly suicide among youth.
 Suicide is the second leading cause of death among youth aged 15-24 years in the US. Suicide rates
have been increasing for nearly two decades, with that increase accelerating in recent years. Children who
have had contact with Child Protective Services are at increased risk for numerous mental health problems,
including suicidal ideation and attempts. The opioid epidemic is thought to be contributing to an increase in the
already substantial number of children entering the CPS system. Finally, suicide has been remarkably difficult
to predict, with recent meta-analyses showing that most currently identified risk factors do not predict future
suicide attempts or deaths significantly better than chance, and predictive accuracy has not improved over the
last 50 years of research. Suicide is likely caused by interaction among numerous risk factors, but most studies
have examined only single risk factors. New machine learning (ML) methods have been shown to out-perform
traditional regression in predictive models, and there are classes of ML algorithms that can identify complex
interactions among large numbers of variables. The applicant proposes a multi-part analysis of suicide
risk factors among youth with CPS contact. The proposed project will: 1) assess the role of CPS case
outcome on access to specialized mental health care and subsequent suicide attempt; 2) use machine
learning methods to develop and validate a predictive model for suicide attempts; and 3) apply causal
inference methods to quantify the impact of hypothetical interventions on the key risk/protective
factors identified in the study.
 The proposed project is consistent with NIMH’s strategic priorities, as it will elucidate complex groups of
risk factors which contribute to suicidal be...

## Key facts

- **NIH application ID:** 10003830
- **Project number:** 5F31MH120973-02
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Geoffrey Kahn
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $40,245
- **Award type:** 5
- **Project period:** 2019-08-16 → 2021-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10003830

## Citation

> US National Institutes of Health, RePORTER application 10003830, Predicting Suicide Attempts in Youth with Child Protective Services Contact (5F31MH120973-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10003830. Licensed CC0.

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