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Characterizing Veteran suicide decedents that were not classified as high-suicide-risk

Published online by Cambridge University Press:  16 September 2024

Maxwell Levis Open the ORCID record for Maxwell Levis [Opens in a new window] ,
Monica Dimambro ,
Joshua Levy ,
Vincent Dufort ,
Abby Fraade ,
Max Winer  and
Brian Shiner
Maxwell Levis*
Affiliation:
White River Junction VA Medical Center, White River Junction, VT, USA Department of Psychiatry, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
Monica Dimambro
Affiliation:
White River Junction VA Medical Center, White River Junction, VT, USA
Joshua Levy
Affiliation:
Pathology and Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, CA, USA
Vincent Dufort
Affiliation:
White River Junction VA Medical Center, White River Junction, VT, USA
Abby Fraade
Affiliation:
Long Island University, Brooklyn, NY, USA
Max Winer
Affiliation:
White River Junction VA Medical Center, White River Junction, VT, USA
Brian Shiner
Affiliation:
White River Junction VA Medical Center, White River Junction, VT, USA Department of Psychiatry, Geisel School of Medicine at Dartmouth, Hanover, NH, USA National Center for PTSD Executive Division, White River Junction, VTS, USA
*
Corresponding author: Maxwell Levis; Email: [email protected]
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Abstract

Background

Although the Department of Veterans Affairs (VA) has made important suicide prevention advances, efforts primarily target high-risk patients with documented suicide risk, such as suicidal ideation, prior suicide attempts, and recent psychiatric hospitalization. Approximately 90% of VA patients that go on to die by suicide do not meet these high-risk criteria and therefore do not receive targeted suicide prevention services. In this study, we used national VA data to focus on patients that were not classified as high-risk, but died by suicide.

Methods

Our sample included all VA patients who died by suicide in 2017 or 2018. We determined whether patients were classified as high-risk using the VA's machine learning risk prediction algorithm. After excluding these patients, we used principal component analysis to identify moderate-risk and low-risk patients and investigated demographics, service-usage, diagnoses, and social determinants of health differences across high-, moderate-, and low-risk subgroups.

Results

High-risk (n = 452) patients tended to be younger, White, unmarried, homeless, and have more mental health diagnoses compared to moderate- (n = 2149) and low-risk (n = 2209) patients. Moderate- and low-risk patients tended to be older, married, Black, and Native American or Pacific Islander, and have more physical health diagnoses compared to high-risk patients. Low-risk patients had more missing data than higher-risk patients.

Conclusions

Study expands epidemiological understanding about non-high-risk suicide decedents, historically understudied and underserved populations. Findings raise concerns about reliance on machine learning risk prediction models that may be biased by relative underrepresentation of racial/ethnic minorities within health system.

Keywords

machine learning biasmachine learningsuicide preventionveterans and military
Type
Original Article
Information
Psychological Medicine , Volume 54 , Issue 11 , August 2024 , pp. 3135 - 3144
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Creative Commons
This is a work of the US Government and is not subject to copyright protection within the United States. Published by Cambridge University Press
Copyright
Copyright © US Department of Veterans Affairs, 2024

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