Cited by 251Suicide and Self-Harm StudiesMachine Learning in HealthcareArtificial Intelligence in Healthcare

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Predictive Modeling and Concentration of the Risk of Suicide: Implications for Preventive Interventions in the US Department of Veterans Affairs

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McCarthy, John F., Bossarte, Robert M., Katz, Ira R., Thompson, Caitlin, Kemp, Janet, Hannemann, Claire M., Nielson, Christopher, Schoenbaum, Michael·2015·American Journal of Public Health

DOI 10.2105/ajph.2015.302737PMC PMC4539821

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Abstract

Objectives. The Veterans Health Administration (VHA) evaluated the use of predictive modeling to identify patients at risk for suicide and to supplement ongoing care with risk-stratified interventions. Methods. Suicide data came from the National Death Index. Predictors were measures from VHA clinical records incorporating patient-months from October 1, 2008, to September 30, 2011, for all suicide decedents and 1% of living patients, divided randomly into development and validation samples. We used data on all patients alive on September 30, 2010, to evaluate predictions of suicide risk over 1 year. Results. Modeling demonstrated that suicide rates were 82 and 60 times greater than the rate in the overall sample in the highest 0.01% stratum for calculated risk for the development and validation samples, respectively; 39 and 30 times greater in the highest 0.10%; 14 and 12 times greater in the highest 1.00%; and 6.3 and 5.7 times greater in the highest 5.00%. Conclusions. Predictive modeling can identify high-risk patients who were not identified on clinical grounds. VHA is developing modeling to enhance clinical care and to guide the delivery of preventive interventions.

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Authors: McCarthy, J. F., Bossarte, R. M., Katz, I. R., Thompson, C., Kemp, J., Hannemann, C. M., Nielson, C., & Schoenbaum, M.
Affiliations: United States Department of Veterans Affairs (US)
ORCID: Bossarte, Robert M.
Katz, Ira R.
Thompson, Caitlin
Schoenbaum, Michael
Venue: American Journal of Public Health
Publisher: American Public Health Association
Published: 2015-09
Volume: 105
Issue: 9
Pages: 1935-1942
ISSN: 0090-0036 (ISSN-L)
1541-0048
DOI: 10.2105/ajph.2015.302737
PMC: PMC4539821
OpenAlex: W1727999343
Cited by: 251
References: 34
Citations by year: 2026: 21 · 2025: 22 · 2024: 18 · 2023: 23 · 2022: 37 · 2021: 29 · 2020: 24 · 2019: 23 · 2018: 23 · 2017: 18 · 2016: 11 · 2015: 1
FWCI: 21.94
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: American Journal of Public Health (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Funders: U.S. Department of Veterans Affairs
Topics: Social Sciences › Psychology › Clinical Psychology › Suicide and Self-Harm Studies
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords: suicide risk prediction, suicide prevention, veterans, risk stratification, predictive modeling, Veterans Health Administration, medical records, National Death Index, high-risk patients
MeSH terms: Suicide Prevention, Female, Humans, Male, Predictive Value of Tests, Suicide, United States, United States Department of Veterans Affairs, Veterans, Risk Assessment

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Predictive Modeling and Concentration of the Risk of Suicide: Implications for Preventive Interventions in the US Department of Veterans Affairs

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