Evidence map›Paper›PMID 33620758›Full record

ArticleAddiction (Abingdon, England)2021

Sex-Specific Risk Profiles for Suicide Among Persons with Substance Use Disorders in Denmark.

Rachel Sayko Adams, Tammy Jiang, Anthony J Rosellini, Erzsébet Horváth-Puhó, Amy E Street, Katherine M Keyes, Magdalena Cerdá, Timothy L Lash, Henrik Toft Sørensen, Jaimie L Gradus

Open access · goldAbstract read
In one paragraph

Article in Addiction (Abingdon, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 4 pooled it
3.1field-weighted citation impact, top 8% 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

10 citing papers in PubMed, 4 syntheses or guidelines pooled it, 21 citations in OpenAlex.

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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

10 authors at 6 institutions in 2 countries.

Rachel Sayko AdamsInstitute for Behavioral Health, Heller School for Social Policy and Management, Brandeis University, Waltham, MA, USA.ORCID 0000-0003-4450-0712
Tammy JiangDepartment of Epidemiology, Boston University School of Public Health, Boston, MA, USA.ORCID 0000-0002-6887-5706
Anthony J RoselliniCenter for Anxiety and Related Disorders, Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA.ORCID 0000-0002-8385-8621
Erzsébet Horváth-PuhóDepartment of Clinical Epidemiology, Aarhus University Hospital, Aarhus N, Denmark.ORCID 0000-0002-3594-2212
Amy E StreetNational Center for PTSD, VA Boston Healthcare System, Boston, MA, USA.ORCID 0000-0003-4554-9075
Katherine M KeyesDepartment of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA.ORCID 0000-0002-5624-021X
Magdalena CerdáCenter for Opioid Epidemiology and Policy, Department of Population Health, NYU Grossman School of Medicine, New York, NY, USA.ORCID 0000-0001-8441-7046
Timothy L LashDepartment of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA.ORCID 0000-0002-5240-5195
Henrik Toft SørensenDepartment of Epidemiology, Boston University School of Public Health, Boston, MA, USA.ORCID 0000-0003-4299-7040
Jaimie L GradusDepartment of Epidemiology, Boston University School of Public Health, Boston, MA, USA.ORCID 0000-0003-1459-5327
Boston University · USAarhus University Hospital · DKColumbia University · USEmory University · USNew York University · USVeterans Health Administration · US

Funding

Risk Profiles for Suicidal Behavior in the General PopulationR01MH109507 · NIMH · BOSTON UNIVERSITY MEDICAL CAMPUS · PI GRADUS, JAIMIE L. · 2016 to 2019
$1.3M
NIMH NIH HHS R01 MH109507NIMH NIH HHS R01MH109507
6 · The paper itself

Abstract

BACKGROUND AND

aimsPersons with substance use disorders (SUDs) are at elevated risk of suicide death. We identified novel risk factors and interactions that predict suicide among men and women with SUD using machine learning.

designCase-cohort study.

settingDenmark.

participantsThe sample was restricted to persons with their first SUD diagnosis during 1995 to 2015. Cases were persons who died by suicide in Denmark during 1995 to 2015 (n = 2774) and the comparison subcohort was a 5% random sample of individuals in Denmark on 1 January 1995 (n = 13 179). MEASUREMENTS: Suicide death was recorded in the Danish Cause of Death Registry. Predictors included social and demographic information, mental and physical health diagnoses, surgeries, medications, and poisonings.

findingsPersons among the highest risk for suicide, as identified by the classification trees, were men prescribed antidepressants in the 4 years before suicide and had a poisoning diagnosis in the 4 years before suicide; and women who were 30+ years old and had a poisoning diagnosis 4 years before and 12 months before suicide. Among men with SUD, the random forest identified five variables that were most important in predicting suicide; reaction to severe stress and adjustment disorders, drugs used to treat addictive disorders, age 30+ years, antidepressant use, and poisoning in the 4 prior years. Among women with SUD, the random forest found that the most important predictors of suicide were prior poisonings and reaction to severe stress and adjustment disorders. Individuals in the top 5% of predicted risk accounted for 15% of all suicide deaths among men and 24% of all suicides among women.

conclusionsIn Denmark, prior poisoning and comorbid psychiatric disorders may be among the most important indicators of suicide risk among persons with substance use disorders, particularly among women.

Indexed as

Substance-Related DisordersSuicideAdultCohort StudiesDenmarkFemaleHumansMaleRisk FactorsAlcohol-related disorders, Denmark, machine learning, poisoning, substance use disorder, suicide

Identifiers

PMID33620758
PMCPMC8459184
OpenAlexW3132542724

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.