Examining Multi-Level Correlates of Suicide by Merging NVDRS and ACS Data.

U.S. Census Bureau Center for Economic Studies research paper series Pub Date : 2017-01-01 Epub Date: 2017-03-01
David A Boulifard, Bernice A Pescosolido
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Abstract

This paper describes a novel database and an associated suicide event prediction model that surmount longstanding barriers in suicide risk factor research. The database comingles person-level records from the National Violent Death Reporting System (NVDRS) and the American Community Survey (ACS) to establish a case-control study sample that includes all identified suicide cases, while faithfully reflecting general population sociodemographics, in sixteen USA states during the years 2005-2011. It supports a statistical model of individual suicide risk that accommodates person-level factors and the moderation of these factors by their community rates. Named the United States Multi-Level Suicide Data Set (US-MSDS), the database was developed outside the RDC laboratory using publicly available ACS microdata, and reconstructed inside the laboratory using restricted access ACS microdata. Analyses of the latter version yielded findings that largely amplified but also extended those obtained from analyses of the former. This experience shows that the analytic precision achievable using restricted access ACS data can play an important role in conducting social research, although it also indicates that publicly available ACS data have considerable value in conducting preliminary analyses and preparing to use an RDC laboratory. The database development strategy may interest scientists investigating sociodemographic risk factors for other types of low-frequency mortality.

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通过合并 NVDRS 和 ACS 数据,研究自杀的多层次相关因素。
本文介绍了一个新型数据库和一个相关的自杀事件预测模型,它们克服了自杀风险因素研究中长期存在的障碍。该数据库将国家暴力死亡报告系统(NVDRS)和美国社区调查(ACS)中的个人层面记录结合起来,建立了一个病例对照研究样本,其中包括 2005-2011 年间美国 16 个州所有已确认的自杀病例,同时忠实反映了普通人群的社会人口统计数据。它支持个人自杀风险的统计模型,该模型考虑了个人层面的因素以及这些因素在社区中的比例。该数据库被命名为 "美国多层次自杀数据集"(US-MSDS),是在 RDC 实验室外利用公开的 ACS 微观数据开发的,并在实验室内利用限制访问的 ACS 微观数据进行了重建。对后一版本数据库的分析结果在很大程度上扩展了前一版本数据库的分析结果。这一经验表明,使用受限访问的 ACS 数据所能达到的分析精度在开展社会研究方面可以发挥重要作用,但同时也表明,公开的 ACS 数据在开展初步分析和准备使用 RDC 实验室方面也具有相当大的价值。研究其他类型低频死亡率的社会人口风险因素的科学家可能会对该数据库开发策略感兴趣。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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