分析社会因素,加强不同人群的自杀预防。

Richard Li Xu, Song Wang, Zewei Wang, Yuhan Zhang, Yunyu Xiao, Jyotishman Pathak, David Hodge, Yan Leng, S Craig Watkins, Ying Ding, Yifan Peng
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引用次数: 0

摘要

家庭背景、教育水平、经济状况和压力等社会因素会对自杀意念等公共卫生结果产生影响。然而,由于缺乏最新的自杀报告数据、报告方法的差异以及样本量较小,对预防自杀的社会因素的分析一直受到限制。在本研究中,我们利用国家暴力死亡报告系统限制访问数据库(NVDRS-RAD)分析了 2014 年至 2020 年期间的 172629 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。对随时间变化的趋势进行了评估,并使用 Latent Dirichlet Allocation (LDA) 来识别常见的自杀相关社会因素。心理健康、人际关系、心理健康治疗和披露以及与学校/工作相关的压力因素被确定为与自杀相关的社会因素的主要主题。这项研究还发现了不同人群中存在的系统性差异,尤其是黑人、24 岁以下的年轻人、医护人员和教育背景有限的人群,这为针对特定人群的自杀干预措施提供了潜在的方向。
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Analyzing Social Factors to Enhance Suicide Prevention Across Population Groups.

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.

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An average-case efficient two-stage algorithm for enumerating all longest common substrings of minimum length k between genome pairs. Analyzing Social Factors to Enhance Suicide Prevention Across Population Groups. Attention-based Imputation of Missing Values in Electronic Health Records Tabular Data. Developing a computational representation of human physical activity and exercise using open ontology-based approach: a Tai Chi use case. Evaluating Generative Models in Medical Imaging.
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