TY - JOUR
T1 - Sex-Specific Risk Profiles for Suicide Among Persons with Substance Use Disorders in Denmark
AU - Adams, Rachel Sayko
AU - Jiang, Tammy
AU - Rosellini, Anthony J.
AU - Horváth-Puhó, Erzsébet
AU - Street, Amy E.
AU - Keyes, Katherine M.
AU - Cerdá, Magdalena
AU - Lash, Timothy L.
AU - Sørensen, Henrik Toft
AU - Gradus, Jaimie L.
N1 - Funding Information:
This work was supported by National Institute of Mental Health (NIMH) grant R01MH109507 (PI: J.L.G.), and grant R248‐2017‐521 from the Lundbeck Foundation (PI: H.T.S.). The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The authors do not have any conflicts of interest to disclose. E.H.‐P. and H.T.S. contributed to the acquisition of data. E.H.‐P., A.J.R., and J.L.G. contributed to data analysis and all authors take responsibility for data interpretation.
Publisher Copyright:
© 2021 Society for the Study of Addiction
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021
Y1 - 2021
N2 - Background and Aims: Persons 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. Design: Case–cohort study. Setting: Denmark. Participants: The 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. Findings: Persons 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. Conclusions: In 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.
AB - Background and Aims: Persons 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. Design: Case–cohort study. Setting: Denmark. Participants: The 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. Findings: Persons 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. Conclusions: In 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.
KW - Alcohol-related disorders, Denmark, machine learning, poisoning, substance use disorder, suicide
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U2 - 10.1111/add.15455
DO - 10.1111/add.15455
M3 - Article
AN - SCOPUS:85102289421
JO - Addiction
JF - Addiction
SN - 0965-2140
ER -