# Multi-site External Validation and Improvement of a Clinical Screening Tool for Future Firearm Violence

> **NIH ALLCDC R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2021 · $649,996

## Abstract

ABSTRACT
 Interventions in clinical settings, such as the emergency department (ED), are an opportunity for
interpersonal firearm violence prevention, particularly among youth, whom interpersonal firearm violence
disproportionately affects. A crucial prerequisite to successful clinical interventions is an accurate gauge of risk,
to ensure the judicious allocation of scarce resources; providing that missing prerequisite is the primary goal of
the proposed work. Machine learning methods, in contrast to traditional inferential statistical models, are
distinguished by their emphasis on prospective prediction, and have enhanced clinical prediction in several fields,
including heart disease, cancer diagnosis and outcomes, PTSD, suicide risk, and substance use, among others.
Yet, with the exception of the SAFETY score—developed by the current investigative team—machine learning
methods have not been leveraged to prospectively predict firearm violence. In this proposed work our research
objectives are two-fold: 1) Externally validate the SAFETY score by determining its ability to predict firearm
violence involvement within the next year on a new data set; and 2) Improve the SAFETY score by conducting
a comparative analysis of four powerful machine learning methods: elastic net penalized logistic regression,
random forests, support vector machines, and boosting (ensemble) methods. In this way, we are responding to
Objective One: Research to help inform the development of innovative and promising opportunities to enhance
safety and prevent firearm-related injuries, deaths, and crime. This approach is innovative because it builds upon
the only work to apply machine learning methods to firearm violence prediction, and it is a promising opportunity
to prevent firearm injuries because it will a) provide an explicit gauge of future firearm violence risk; and b)
characterize risk factor effects in terms of their prospective prediction ability, unlike any prior research. Thus this
research will both identify individuals in most need of intervention, and also point to potentially modifiable
predictive factors. Properly addressing this research question in a generalizable way requires a contemporary
data set with 1) a focus on a high-need, yet broad, study population; 2) comprehensive baseline measures that
provide a broad basis for prediction; and 3) geographic variability (Midwest, West Coast, and East Coast) that
enhances generalizability. Thus, we will recruit 1,500 youth age 18-24 from urban EDs in three broadly different
locales—Flint, Philadelphia, and Seattle—and administer a baseline survey covering several domains of
potential risk factors for future violence, and follow up with those youth at 6- and 12-months to ascertain the
primary outcome—firearm violence involvement (as victim or perpetrator)—as well as the secondary outcomes:
high-risk firearm behaviors, non-firearm violence, and violent injury. Because this work requires a prospective
longitudinal ...

## Key facts

- **NIH application ID:** 10268933
- **Project number:** 5R01CE003294-02
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Jason Elliott Goldstick
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** ALLCDC
- **Fiscal year:** 2021
- **Award amount:** $649,996
- **Award type:** 5
- **Project period:** 2020-09-30 → 2023-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10268933

## Citation

> US National Institutes of Health, RePORTER application 10268933, Multi-site External Validation and Improvement of a Clinical Screening Tool for Future Firearm Violence (5R01CE003294-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10268933. Licensed CC0.

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