Belouali Anas, Kitchen Christopher, Zirikly Ayah, Nestadt Paul, Wilcox Holly C, Kharrazi Hadi
Division of General Internal Medicine, Biomedical Informatics and Data Science (BIDS), Johns Hopkins School of Medicine, 2024 East Monument St. S 1-200, Baltimore, MD, 21205, USA.
Department of Health Policy and Management, Center for Population Health Information Technology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Sci Rep. 2025 Jul 2;15(1):23069. doi: 10.1038/s41598-025-07007-4.
Subtypes of suicide decedents have not been studied at a population level using linked clinical and public health surveillance records. Identifying suicide subtypes can help facilitate the development and deployment of population-level prevention strategies. This retrospective study uses the Maryland Suicide Data Warehouse (MSDW). The analyses included 848 individuals who died by suicide as well as 4,161 individuals who died by accident in the state of Maryland between January 1st, 2016, and December 31st, 2019. These individuals had electronic health records from Johns Hopkins Medical Institutes and statewide hospital discharge data. We employed deep embedded clustering and evaluated its performance against traditional clustering approaches. We evaluated different numbers of clusters (k = 2 to 10) and assessed clustering performance using stability metrics, achieving a cross-validated prediction strength of 0.94. We then performed cluster characterization and assessed cluster stability up to 1 year before suicide death. We identified four distinct suicide profiles. Profile 1 (23.2% of suicide cases) included older individuals with high comorbid conditions. Profile 2 (19.2%) was characterized by psychiatric illness, the highest healthcare utilization, and significant social needs. Profile 3 (25.4%) consisted of younger individuals with psychiatric illness, no recorded social needs, and the highest percentage of Medicaid patients. Profile 4 (32.2%) included less clinically engaged individuals with the fewest healthcare visits. Our findings show the effective use of clustering methods to identify meaningful and stable suicide decedent profiles, revealing significant demographic and clinical differences. The identified subtypes can inform population-level suicide prevention strategies.
尚未利用关联的临床和公共卫生监测记录在人群层面研究自杀死亡者的亚型。识别自杀亚型有助于推动人群层面预防策略的制定和实施。这项回顾性研究使用了马里兰州自杀数据仓库（MSDW）。分析纳入了2016年1月1日至2019年12月31日期间在马里兰州自杀死亡的848人以及意外死亡的4161人。这些人拥有来自约翰霍普金斯医学院的电子健康记录和全州范围的医院出院数据。我们采用了深度嵌入聚类，并将其性能与传统聚类方法进行了评估。我们评估了不同数量的聚类（k = 2至10），并使用稳定性指标评估聚类性能，交叉验证预测强度达到了0.94。然后我们进行了聚类特征分析，并评估了自杀死亡前长达1年的聚类稳定性。我们识别出了四种不同的自杀类型。类型1（占自杀案例的23.2%）包括患有多种共病的老年人。类型2（19.2%）的特征是患有精神疾病、医疗利用率最高且有重大社会需求。类型3（25.4%）由患有精神疾病的年轻人组成，没有记录在案的社会需求，且医疗补助患者比例最高。类型4（32.2%）包括临床参与度较低、就诊次数最少的个体。我们的研究结果表明聚类方法可有效用于识别有意义且稳定的自杀死亡者类型，揭示出显著的人口统计学和临床差异。所识别出的亚型可为人群层面的自杀预防策略提供参考。
