# Project 3: Suicide Risk Identification in Jails using Data Linkage and Automation

> **NIH NIH P50** · MICHIGAN STATE UNIVERSITY · 2022 · $279,630

## Abstract

Abstract Project #3
Although professional and accreditation standards exist to guide identification of suicide risk, few jails effectively
screen for such risk at booking (Intercept 2). Given that individuals booking into jails may be less forthcoming in
reporting thoughts and behaviors to correctional officers, current identification practices are insufficient. It may
be possible to enhance identification methods in jails, replicating a method developed in community health
systems. Using a general population sample from seven health systems, the Mental Health Research Network
developed a suicide risk model to predict suicide attempts/deaths using electronic health records and insurance
claims data. Claims records were used to create the model that resulted in a risk score that could be available
for medical personnel, alerting them to the possibility of heightened suicide risk. Replicating this validation, using
a jail population with integrated Medicaid claims data, could result in a similar identification process for justice-
involved individuals available at jail intake (booking) that could assist in detecting who among those entering jail
could be at risk for suicide attempts and suicide deaths. Risk identified through the model will be compared to
the practice-as-usual identification within the jail. Because there is no standardized process for identification of
suicide risk within jails, each jail’s screening process will be assessed separately. This proposal would leverage
three geographically and demographically diverse jails in one state, increasing the generalizability of the findings.
Aim 1. Validate the suicide risk model with a jail population sample (three jails; on all of those who enter during
a specific length of time), using Medicaid claims and vital record data. Aim 2. Compare the risk flag to the current
suicide risk identification process (e.g. practice as usual) within 3 diverse jails. Aim 3. Evaluate implementation
factors to inform the design of a future hybrid trial and integration within jails, working with state Medicaid and
the Department of Health and Human Services. Improved suicide risk identification in jails could decrease the
adverse impacts that suicide has on those who are detained, family members, correctional staff, the institution
and community (i.e. liability, costs). Our long-term goal of this research targets jail systems by
implementing an automated ‘suicide risk flag’ – derived from prior health records, resulting in improved
detection at intake that would lead to intervention to reduce suicide attempts and suicide deaths within
the jail. The assembled team has experience with development of the model, familiarity and experience
implementing screening tools within jails, and integrating and analyzing jail and Medicaid data. The project
leverages an established partnership between the team and criminal justice system. This project will inform an
R01 hybrid effectiveness-implementation trial to assess wh...

## Key facts

- **NIH application ID:** 10441875
- **Project number:** 1P50MH127512-01A1
- **Recipient organization:** MICHIGAN STATE UNIVERSITY
- **Principal Investigator:** Brian Kenneth Ahmedani
- **Activity code:** P50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $279,630
- **Award type:** 1
- **Project period:** 2022-08-22 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10441875

## Citation

> US National Institutes of Health, RePORTER application 10441875, Project 3: Suicide Risk Identification in Jails using Data Linkage and Automation (1P50MH127512-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10441875. Licensed CC0.

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