Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts.
Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark.
JAMA Psychiatry. 2020 Jan 1;77(1):25-34. doi: 10.1001/jamapsychiatry.2019.2905.
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.
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 included 1339 variables spanning domains of suicide risk factors.
Death from suicide from the Danish cause of death registry.
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.
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.
自杀是一个公共卫生问题，其多种病因尚未得到充分理解。在精神病学中越来越重视将医疗保健数据与机器学习方法相结合，这可能有助于提高对自杀风险的认识。
使用机器学习方法和丹麦人口的健康数据，检查自杀死亡的性别特定风险特征。
设计、地点和参与者：这是一项嵌套在丹麦全国 8 个卫生和社会登记处内的病例对照研究，于 1995 年 1 月 1 日至 2015 年 12 月 31 日进行。来源人群是 1995 年 1 月 1 日或之前在丹麦出生或居住的所有人。数据于 2018 年 11 月 5 日至 2019 年 5 月 13 日进行分析。
暴露因素包括跨越自杀风险因素领域的 1339 个变量。
丹麦死因登记处的自杀死亡。
1995 年至 2015 年间共有 14103 人自杀（10152 名男性[72.0%]；平均[SD]年龄为 43.5[18.8]岁和 3951 名女性[28.0%]；年龄为 47.6[18.8]岁）。比较子队列是丹麦 1995 年 1 月 1 日的 5%随机样本（n=265183）（130591 名男性[49.2%]；年龄为 37.4[21.8]岁和 134592 名女性[50.8%]；年龄为 39.9[23.4]岁）。通过使用分类树和随机森林，注意到性别间在自杀风险方面存在差异，身体健康对男性自杀风险的重要性大于女性自杀风险。精神障碍和可能相关的药物治疗对自杀风险很重要，特定结果可能会增加文献中的清晰度。通常，自杀前 48 个月测量的诊断和药物治疗比自杀前 6 个月测量的诊断和药物治疗更能预测自杀风险。处于预测自杀风险最高的 5%的个体，在男性中似乎占所有自杀病例的 32.0%，在女性中占所有自杀病例的 53.4%。
尽管对自杀风险因素进行了几十年的研究，但对自杀的认识仍然很差。在这项迄今为止基于人群数据制定自杀风险特征的第一项研究中，注意到与已知的自杀风险之间存在明显的一致性，以及在特定亚人群中具有潜在的重要性，研究不足的风险因素具有独特的自杀风险特征的证据。
