Prediction of Sex-Specific Suicide Risk Using Machine Learning and Single-Payer Health Care Registry Data From Denmark

Abstract

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.

Design, setting, and participants: A case-cohort study nested within 8 national Danish health and social registries was conducted from January 1, 1995, through December 31, 2015. The source population was all persons born or residing in Denmark as of January 1, 1995. Data were analyzed from November 5, 2018, through May 13, 2019.

Exposures: Exposures included 1339 variables spanning domains of suicide risk factors.

Main outcomes and measures: Death from suicide from the Danish cause of death registry.

Results: A total of 14 103 individuals died by suicide between 1995 and 2015 (10 152 men [72.0%]; mean [SD] age, 43.5 [18.8] years and 3951 women [28.0%]; age, 47.6 [18.8] years). The comparison subcohort was a 5% random sample (n = 265 183) of living individuals in Denmark on January 1, 1995 (130 591 men [49.2%]; age, 37.4 [21.8] years and 134 592 women [50.8%]; age, 39.9 [23.4] years). With use of classification trees and random forests, sex-specific differences were noted in risk for suicide, with physical health more important to men's suicide risk than women's suicide risk. Psychiatric disorders and possibly associated medications were important to suicide risk, with specific results that may increase clarity in the literature. Generally, diagnoses and medications measured 48 months before suicide were more important indicators of suicide risk than when measured 6 months earlier. Individuals in the top 5% of predicted suicide risk appeared to account for 32.0% of all suicide cases in men and 53.4% of all cases in women.

Conclusions and relevance: Despite decades of research on suicide risk factors, understanding of suicide remains poor. In this study, the first to date to develop risk profiles for suicide based on data from a full population, apparent consistency with what is known about suicide risk was noted, as well as potentially important, understudied risk factors with evidence of unique suicide risk profiles among specific subpopulations.

Figure 1.. Classification Tree Depicting Suicide Predictors Among Men in Denmark, 1995-2015

Each shaded rectangle at the bottom (terminal node) represents the group of people with the characteristic profile in the branches above. Within the rectangles, n indicates the number of people who had the characteristic profile and suicide indicates the proportion of people in that bin who died by suicide. AD indicates adjustment disorder; RSS, reaction to severe stress. ^aDrugs used in addictive disorders. ^bPoisoning by adverse effect and underdosing of drugs, medicaments (a substance used for medical treatment), and biological substances. ^cReaction to severe stress and adjustment disorders.

Figure 2.. Classification Tree Depicting Suicide Predictors Among Women in Denmark, 1995- 2015

Each of the 2 columns of rectangles at the right (terminal node) represents the group of people with the characteristic profile in the branches at left. Within the rectangles, n indicates the number of people who had the characteristic profile and suicide indicates the proportion of people in that bin who died by suicide. MDD indicates major depressive disorder. ^aPoisoning, adverse effects, and underdosing of drugs, medicaments (a substance used for medical treatment), and biological substances. ^bDrugs used in addictive disorders. ^cβ-Lactam antibacterials, penicillins.

Figure 3.. Variable Importance of Suicide Predictors Among Men in Denmark From Split Sample Cross-Validation, 1995-2015

The blue dots represent the mean decrease in accuracy (MDA) value in fold 1 and the orange dots represent the MDA value in fold 2. The vertical line is the average of the MDA values of all predictors with nonzero MDA values in folds 1 and 2 (7.80). ^aPredictors that were in the top 30 predictors in folds 1 and 2 for men.

Figure 4.. Variable Importance of Suicide Predictors Among Women in Denmark From Split Sample Cross-Validation, 1995-2015

The blue dots represent the mean decrease in accuracy (MDA) value in fold 1 and the orange dots represent the MDA value in fold 2. The vertical line is the average of the MDA values of all predictors with nonzero MDA values in folds 1 and 2 (4.61). ^aPredictors that were in the top 30 predictors in folds 1 and 2 for women.