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

Abstract

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.

Traumatic Brain Injury and Opioid Use: Additional Evidence Supporting the “Perfect Storm” of Cascading Vulnerabilities

“…As noted earlier, studies indicate that persons with TBI who engage in LTOT are at an increased risk for overdose (nonfatal and fatal) and suicide attempt 8,28–31. A recent study that used machine learning to reveal sex-specific predictors of suicide among persons with substance use disorders in Denmark revealed that prior poisoning (ie, overdose) was among the most important predictors of suicide death, particularly among women, and that this risk could extend for many years after the poisoning 54. Investigation of this dynamics among persons with TBI is warranted, as persons with TBI are at an elevated risk for suicide,33,34,55 and prior overdose may help identify those at even a greater risk of suicide.…”

Machine learning and the prediction of suicide in psychiatric populations: a systematic review

“…In women, interaction between recurrent major depression and other characteristics (e.g., poisoning, low income). Adams et al, [ 73 ] 15,953 patients with SUD (5745/10208) SUD Not specified Classification tree, RF (10-folds CV) 2563 social and demographic information, mental and physical health diagnoses, surgeries, medications, and poisonings Suicide attempts Classification tree AUC: 0.75 RF AUC: 0.77 Among men, most important variables: reaction to severe stress, adjustment disorders, drugs used, age 30+, and prior poisoning. Among women, most important predictors: prior poisonings and reaction to stress and adjustment disorders.…”

The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review

“…Furthermore, very few studies (n=5) ( 30 , 31 , 36 , 43 , 88 ) stratified their analyses by sex. This represents a significant gap in the literature, as the gender paradox in suicide (i.e., women tend to attempt suicide more often, while men tend to die by suicide more often) is well-known.…”