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

David A Boulifard and Bernice A Pescosolido

Working Papers from U.S. Census Bureau, Center for Economic Studies

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.

Keywords: suicide risk; multi-level modeling; public use microdata areas; disclosure avoidance (search for similar items in EconPapers)
Pages: 20 pages
Date: 2017-01
New Economics Papers: this item is included in nep-hea
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https://www2.census.gov/ces/wp/2017/CES-WP-17-25.pdf First version, 2017 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:cen:wpaper:17-25

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