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IMPORTANCE Suicide is a public health problem, with multiple causes that are poorly understood. The increased focus on combining health care data with machine-learning approaches in psychiatry may help advance the understanding of suicide risk.OBJECTIVE To examine sex-specific risk profiles for death from suicide using machine-learning methods and data from the population of Denmark.

Suicide is a major public health concern in the United States. Between 2000 and 2018, US suicide rates increased by 35%, contributing to the stagnation and subsequent decrease in US life expectancy. During 2019, suicide declined modestly, mostly owing to slight reductions in suicides among Whites. Suicide rates, however, continued to increase or remained stable among all other racial/ethnic groups, and little is known about recent suicide trends among other vulnerable groups. This article ( a) summarizes US suicide mortality trends over the twentieth and early twenty-first centuries, ( b) reviews potential group-level causes of increased suicide risk among subpopulations characterized by markers of vulnerability to suicide, and ( c) advocates for combining recent advances in population-based suicide prevention with a socially conscious perspective that captures the social, economic, and political contexts in which suicide risk unfolds over the life course of vulnerable individuals. Expected final online publication date for the Annual Review of Public Health, Volume 43 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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.

Background-It is unknown whether posttraumatic stress disorder (PTSD) is associated with incident infections. This study's objectives were to examine (1) the association between PTSD diagnosis and 28 types of infections and (2) the interaction between PTSD diagnosis and sex on the rate of infections. Methods-The study population consisted of a longitudinal nationwide cohort of all residents of Denmark who received a PTSD diagnosis between 1995 and 2011, and an age-and sex-matched general population comparison cohort. We fit Cox proportional hazards regression models to examine associations between PTSD diagnosis and infections. To account for multiple estimation, we adjusted the hazard ratios using semi-Bayes shrinkage. We calculated interaction contrasts to assess the presence of interaction between PTSD diagnosis and sex.Results-After semi-Bayes shrinkage, the hazard ratio (HR) for any type of infection was 1.8 [95% confidence interval (CI: 1.6, 2.0)], adjusting for marital status, non-psychiatric comorbidity, and diagnoses of substance abuse, substance dependence, and depression. The association between PTSD diagnosis and some infections (e.g., urinary tract infections) were stronger among women whereas other associations were stronger among men (e.g., skin infections).