Xu Richard Li, Wang Song, Wang Zewei, Zhang Yuhan, Xiao Yunyu, Pathak Jyotishman, Hodge David, Leng Yan, Watkins S Craig, Ding Ying, Peng Yifan
Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
Cockrell School of Engineering, The University of Texas at Austin, Austin, TX, USA.
Proc (IEEE Int Conf Healthc Inform). 2024 Jun;2024:189-199. doi: 10.1109/ichi61247.2024.00032. Epub 2024 Aug 22.
Social factors like family background, education level, financial status, and stress can impact public health outcomes, such as suicidal ideation. However, the analysis of social factors for suicide prevention has been limited by the lack of up-to-date suicide reporting data, variations in reporting practices, and small sample sizes. In this study, we analyzed 172,629 suicide incidents from 2014 to 2020 utilizing the National Violent Death Reporting System Restricted Access Database (NVDRS-RAD). Logistic regression models were developed to examine the relationships between demographics and suicide-related circumstances. Trends over time were assessed, and Latent Dirichlet Allocation (LDA) was used to identify common suicide-related social factors. Mental health, interpersonal relationships, mental health treatment and disclosure, and school/work-related stressors were identified as the main themes of suicide-related social factors. This study also identified systemic disparities across various population groups, particularly concerning Black individuals, young people aged under 24, healthcare practitioners, and those with limited education backgrounds, which shed light on potential directions for demographic-specific suicidal interventions.
家庭背景、教育水平、经济状况和压力等社会因素会影响公众健康结果，比如自杀念头。然而，由于缺乏最新的自杀报告数据、报告做法存在差异以及样本量较小，对预防自杀的社会因素分析受到了限制。在本研究中，我们利用国家暴力死亡报告系统受限访问数据库（NVDRS-RAD）分析了2014年至2020年期间的172,629起自杀事件。我们建立了逻辑回归模型来研究人口统计学与自杀相关情况之间的关系。评估了随时间的趋势，并使用潜在狄利克雷分配（LDA）来识别常见的自杀相关社会因素。心理健康、人际关系、心理健康治疗与披露以及与学校/工作相关的压力源被确定为自杀相关社会因素的主要主题。本研究还发现了不同人群之间的系统性差异，尤其是涉及黑人个体、24岁以下的年轻人、医疗从业者以及教育背景有限的人群，这为针对特定人群的自杀干预提供了潜在方向。
