Prediction of sex-specific suicide risk using machine learning and single-payer healthcare registry data from Denmark
JAMA Jan 10, 2020
Gradus JL, Rosellini AJ, Horváth-Puhó E, et al. - Utilizing machine-learning methods and data from the population of Denmark, researchers studied sex-specific risk profiles for death from suicide. A case-cohort study was carried out between January 1, 1995, and December 31, 2015, in 8 Danish national health and social registries. In total, 14,103 people died by suicide between 1995 and 2015 (10,152 men [72.0%]; mean [SD] age, 43.5 [18.8] years and 3,951 women [28.0%]; age, 47.6 [18.8] years). The authors discovered that risk profiles for suicide were different for men and women in a general population sample of 265,183 persons who did not die as a result of suicide, with physical health more important for men than suicide risk for women. Findings suggest that psychotropic drugs and psychiatric disorders are relevant to suicide risk, and longer compared with shorter observation periods (eg, 48 vs 6 months prior to suicide) seemed more important for many diagnostic variables and prescriptions. Such results suggest agreement with what is known but potentially important about suicide risk, and recognized risk factors with evidence of unique suicide risk profiles in specific subpopulations.
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