Evidence map›Paper›PMID 25390793›Full record

ArticleJAMA psychiatry2015

Predicting suicides after psychiatric hospitalization in US Army soldiers: the Army Study To Assess Risk and rEsilience in Servicemembers (Army STARRS).

Ronald C Kessler, Christopher H Warner, Christopher Ivany, Maria V Petukhova, Sherri Rose, Evelyn J Bromet, Millard Brown, Tianxi Cai, Lisa J Colpe, Kenneth L Cox and 17 more

Registry-linked trialOpen access · greenAbstract read
In one paragraph

Article in JAMA psychiatry, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07404787 (SAFEGUARD Phase 2 Pathfinding Study), which is not on this map. Cited by 204 papers, 13 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
204citing papers in PubMed, 13 pooled it
30.6field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT07404787 narecruitingnot on this mapstarted 2026, after this paper: background citation

SAFEGUARD Phase 2 Pathfinding Study

TypeinterventionalSponsorUniformed Services University of the Health SciencesRan2026 to 2029Enrolled1,500ConditionsSuicide, Suicide AttemptArmsPathfinding, Treatment As Usual (TAU)
3 · Its place in the literature

Who cites it

204 citing papers in PubMed, 13 syntheses or guidelines pooled it, 454 citations in OpenAlex.

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  6. Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations.International journal of environmental research and public health · 2020
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144 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

27 authors at 9 institutions in 2 countries.

Ronald C KesslerDepartment of Health Care Policy, Harvard Medical School, Boston, Massachusetts.
Christopher H WarnerDepartment of Behavioral Medicine, Blanchfield Army Community Hospital, Fort Campbell, Kentucky.
Christopher IvanyUS Army Office of the Surgeon General, Falls Church, Virginia.
Maria V PetukhovaDepartment of Health Care Policy, Harvard Medical School, Boston, Massachusetts.
Sherri RoseDepartment of Health Care Policy, Harvard Medical School, Boston, Massachusetts.
Evelyn J BrometDepartment of Psychiatry, Stony Brook University School of Medicine, Stony Brook, New York.
Millard BrownUS Army Office of the Surgeon General, Falls Church, Virginia.
Tianxi CaiDepartment of Biostatistics, Harvard School of Public Health, Boston, Massachusetts.
Lisa J ColpeNational Institute of Mental Health, Bethesda, Maryland.
Kenneth L CoxUS Army Public Health Command, Aberdeen Proving Ground, Maryland.
Carol S FullertonCenter for the Study of Traumatic Stress, Department of Psychiatry, Uniformed Services University of the Health Sciences, Bethesda, Maryland.
Stephen E GilmanDepartment of Social and Behavioral Sciences, Harvard School of Public Health, Boston, Massachusetts10Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts.
Michael J GruberDepartment of Health Care Policy, Harvard Medical School, Boston, Massachusetts.
Steven G HeeringaInstitute for Social Research, University of Michigan, Ann Arbor.
Lisa Lewandowski-RompsInstitute for Social Research, University of Michigan, Ann Arbor.
Junlong LiDepartment of Biostatistics, Harvard School of Public Health, Boston, Massachusetts.
Amy M Millikan-BellUS Army Public Health Command, Aberdeen Proving Ground, Maryland.
James A NaifehCenter for the Study of Traumatic Stress, Department of Psychiatry, Uniformed Services University of the Health Sciences, Bethesda, Maryland.
Matthew K NockDepartment of Psychology, Harvard University, Cambridge, Massachusetts.
Anthony J RoselliniDepartment of Health Care Policy, Harvard Medical School, Boston, Massachusetts.
Nancy A SampsonDepartment of Health Care Policy, Harvard Medical School, Boston, Massachusetts.
Michael SchoenbaumNational Institute of Mental Health, Bethesda, Maryland.
Murray B SteinDepartment of Psychiatry, University of California, San Diego, La Jolla14Deapartment of Family and Preventive Medicine, University of California, San Diego, La Jolla15Veterans Affairs San Diego Healthcare System, San Diego, California.
Simon WesselyKing's Centre for Military Health Research, King's College London, London, United Kingdom.
Alan M ZaslavskyDepartment of Health Care Policy, Harvard Medical School, Boston, Massachusetts.
Robert J UrsanoCenter for the Study of Traumatic Stress, Department of Psychiatry, Uniformed Services University of the Health Sciences, Bethesda, Maryland.
Army STARRS Collaborators
Harvard University · USUniformed Services University of the Health Sciences · USNational Institute of Mental Health · USUnited States Army Medical Command · USUniversity of Michigan–Ann Arbor · USBlanchfield Army Community Hospital · USKing's College London · GBStony Brook University · USUniversity of California, San Diego · US

Funding

Modifiable Risk and Protective Factors for Suicidal Behaviors in the US ArmyU01MH087981 · NIMH · HENRY M. JACKSON FDN FOR THE ADV MIL/MED · PI STEIN, MURRAY B., URSANO, ROBERT J. · 2009 to 2013
$65.0M
NIMH NIH HHS U01 MH087981NIMH NIH HHS U01MH087981
6 · The paper itself

Abstract

importanceThe US Army experienced a sharp increase in soldier suicides beginning in 2004. Administrative data reveal that among those at highest risk are soldiers in the 12 months after inpatient treatment of a psychiatric disorder.

objectiveTo develop an actuarial risk algorithm predicting suicide in the 12 months after US Army soldier inpatient treatment of a psychiatric disorder to target expanded posthospitalization care. DESIGN, SETTING, AND

participantsThere were 53,769 hospitalizations of active duty soldiers from January 1, 2004, through December 31, 2009, with International Classification of Diseases, Ninth Revision, Clinical Modification psychiatric admission diagnoses. Administrative data available before hospital discharge abstracted from a wide range of data systems (sociodemographic, US Army career, criminal justice, and medical or pharmacy) were used to predict suicides in the subsequent 12 months using machine learning methods (regression trees and penalized regressions) designed to evaluate cross-validated linear, nonlinear, and interactive predictive associations. MAIN OUTCOMES AND MEASURES: Suicides of soldiers hospitalized with psychiatric disorders in the 12 months after hospital discharge.

resultsSixty-eight soldiers died by suicide within 12 months of hospital discharge (12.0% of all US Army suicides), equivalent to 263.9 suicides per 100,000 person-years compared with 18.5 suicides per 100,000 person-years in the total US Army. The strongest predictors included sociodemographics (male sex [odds ratio (OR), 7.9; 95% CI, 1.9-32.6] and late age of enlistment [OR, 1.9; 95% CI, 1.0-3.5]), criminal offenses (verbal violence [OR, 2.2; 95% CI, 1.2-4.0] and weapons possession [OR, 5.6; 95% CI, 1.7-18.3]), prior suicidality [OR, 2.9; 95% CI, 1.7-4.9], aspects of prior psychiatric inpatient and outpatient treatment (eg, number of antidepressant prescriptions filled in the past 12 months [OR, 1.3; 95% CI, 1.1-1.7]), and disorders diagnosed during the focal hospitalizations (eg, nonaffective psychosis [OR, 2.9; 95% CI, 1.2-7.0]). A total of 52.9% of posthospitalization suicides occurred after the 5% of hospitalizations with highest predicted suicide risk (3824.1 suicides per 100,000 person-years). These highest-risk hospitalizations also accounted for significantly elevated proportions of several other adverse posthospitalization outcomes (unintentional injury deaths, suicide attempts, and subsequent hospitalizations). CONCLUSIONS AND RELEVANCE: The high concentration of risk of suicide and other adverse outcomes might justify targeting expanded posthospitalization interventions to soldiers classified as having highest posthospitalization suicide risk, although final determination requires careful consideration of intervention costs, comparative effectiveness, and possible adverse effects.

Indexed as

Mental DisordersSuicideSuicide PreventionAdultAftercareAlgorithmsDemographyFemaleHumansMaleMilitary PersonnelNeeds AssessmentPatient DischargePsychopathologyResilience, PsychologicalRisk

Identifiers

PMID25390793
PMCPMC4286426
OpenAlexW2121468917

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.