The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Predictive Modeling and Concentration of the Risk of Suicide: Implications for Preventive Interventions in the US Department of Veterans Affairs
Open accessAbstract
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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“Suicide prediction models based on computational analyses of electronic health records may help identify individuals with the highest risk for suicide at time of discharge, the subgroup that would potentially benefit most from intensive treatment and follow-up (237, 238).”
From A brain-centric model of suicidal behavior (2020) · cites this paper as [238]
In the citing paper
“patients with high suicide risk also have significantly elevated risk of other negative outcomes, such as other types of death by external cause, suicide attempts, severe and permanently disabling injuries, and repeat psychiatric hospitalizations (20, 88).”
From Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System (2020) · cites this paper as [88]
In the citing paper
“These veterans were found to account for approximately 2% of all suicide deaths in VHA.”
From Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System (2020) · cites this paper as [88]
In the citing paper
“The Reach Vet model attempted to isolate the 0.1% of all VHA users with highest suicide risk.”
From Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System (2020) · cites this paper as [88]
In the citing paper
“the VHA Reach Vet model (88), which is used to target preventive interventions to the roughly 35,000 VHA patients each year considered to be at highest suicide risk, is based on a 30-day time horizon, which we showed clearly to be suboptimal in the current application to inpatients.”
From Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System (2020) · cites this paper as [88]
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Metadata
- 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)
- 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
- 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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