Evidence map›Paper›PMID 28374665›Full record

ArticlePsychological medicine2017

Using self-report surveys at the beginning of service to develop multi-outcome risk models for new soldiers in the U.S. Army.

A J Rosellini, M B Stein, D M Benedek, P D Bliese, W T Chiu, I Hwang, J Monahan, M K Nock, M V Petukhova, N A Sampson and 4 more

Open access · greenAbstract read
In one paragraph

Article in Psychological medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 pooled it
7.2field-weighted citation impact, top 4% 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 19 citations in OpenAlex.

  1. Pooled it
  2. Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations.International journal of environmental research and public health · 2020
    Pooled it
  3. Review
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  5. SCORE2 Outperforms Pol-SCORE in Detecting Increased Cardiovascular Risk.Pathophysiology : the official journal of the International Society for Pathophysiology · 2025
    Article
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  10. Predicting correlated outcomes from molecular data.Bioinformatics (Oxford, England) · 2021
    Article
  11. Review
  12. Article
  13. Article
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

14 authors at 6 institutions in 1 country.

A J RoselliniDepartment of Health Care Policy,Harvard Medical School,Boston, Massachusetts,USA.
M B SteinDepartments of Psychiatry and Family Medicine & Public Health,University of California San Diego,La Jolla, California,USA.
D M BenedekDepartment of Psychiatry,Center for the Study of Traumatic Stress, Uniformed Services University School of Medicine,Bethesda, MD,USA.
P D BlieseDarla Moore School of Business,University of South Carolina,Columbia, South Carolina,USA.
W T ChiuDepartment of Health Care Policy,Harvard Medical School,Boston, Massachusetts,USA.
I HwangDepartment of Health Care Policy,Harvard Medical School,Boston, Massachusetts,USA.
J MonahanSchool of Law,University of Virginia,Charlottesville, VA,USA.
M K NockDepartment of Psychology,Harvard University,Cambridge, Massachusetts,USA.
M V PetukhovaDepartment of Health Care Policy,Harvard Medical School,Boston, Massachusetts,USA.
N A SampsonDepartment of Health Care Policy,Harvard Medical School,Boston, Massachusetts,USA.
A E StreetNational Center for PTSD, VA Boston Healthcare System,Boston, Massachusetts,USA.
A M ZaslavskyDepartment of Health Care Policy,Harvard Medical School,Boston, Massachusetts,USA.
R J UrsanoDepartment of Psychiatry,Center for the Study of Traumatic Stress, Uniformed Services University School of Medicine,Bethesda, MD,USA.
R C KesslerDepartment of Health Care Policy,Harvard Medical School,Boston, Massachusetts,USA.
Harvard University · USUniformed Services University of the Health Sciences · USUniversity of California, San Diego · USUniversity of South Carolina · USUniversity of Virginia · USVA Boston Healthcare System · 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 MH087981
6 · The paper itself

Abstract

backgroundThe U.S. Army uses universal preventives interventions for several negative outcomes (e.g. suicide, violence, sexual assault) with especially high risks in the early years of service. More intensive interventions exist, but would be cost-effective only if targeted at high-risk soldiers. We report results of efforts to develop models for such targeting from self-report surveys administered at the beginning of Army service.

methods21 832 new soldiers completed a self-administered questionnaire (SAQ) in 2011-2012 and consented to link administrative data to SAQ responses. Penalized regression models were developed for 12 administratively-recorded outcomes occurring by December 2013: suicide attempt, mental hospitalization, positive drug test, traumatic brain injury (TBI), other severe injury, several types of violence perpetration and victimization, demotion, and attrition.

resultsThe best-performing models were for TBI (AUC = 0.80), major physical violence perpetration (AUC = 0.78), sexual assault perpetration (AUC = 0.78), and suicide attempt (AUC = 0.74). Although predicted risk scores were significantly correlated across outcomes, prediction was not improved by including risk scores for other outcomes in models. Of particular note: 40.5% of suicide attempts occurred among the 10% of new soldiers with highest predicted risk, 57.2% of male sexual assault perpetrations among the 15% with highest predicted risk, and 35.5% of female sexual assault victimizations among the 10% with highest predicted risk.

conclusionsData collected at the beginning of service in self-report surveys could be used to develop risk models that define small proportions of new soldiers accounting for high proportions of negative outcomes over the first few years of service.

Indexed as

Models, StatisticalSelf ReportAdolescentAdultCrime VictimsFemaleFollow-Up StudiesHealth SurveysHumansMaleMental DisordersMilitary PersonnelPhysical AbusePrognosisRisk AssessmentSex OffensesArmydisciplinary problemsmental healthmilitarypredictive modelingrisk assessmentviolence

Identifiers

PMID28374665
PMCPMC5679702
OpenAlexW2604470482

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.