How Social Context Changes Individual Risk of Suicide: Breaking through the Bifurcation in U.S. Research with Big Data
The U.S death rate from suicide is stubbornly consistent, promoting calls for research to provide novel directions for prevention and treatment. While previous research has established systematic patterning across psychological, social, and geographical levels, and multilevel influences have been theorized, U.S. research integrating insights across promising etiological streams has been largely blocked by the absence of large-scale data sets combining individual and contextual levels. This webinar presents findings from a project that addressed this bifurcation in research efforts by merging a number of well-known data sets and harmonizing key, available variables. The resulting United States Multi-Level Suicide Data Set (US-MSDS) provides the ability to see how individual level risk factors change depending on geographic residence. The analyses provide some novel findings that have critical implications for future research and programming.
Bernice A. Pescosolido, Ph.D. is Distinguished and Chancellor’s Professor of Sociology at Indiana University, Director of the Indiana Consortium for Mental Health Services Research (ICMHSR), and Co-Director of the Indiana University Network Science Institute (IUNI). Dr. Pescosolido has focused her research and teaching on social issues in health, illness, and healing. Her research agenda addresses how social networks connect individuals to their communities and to institutional structures, providing the “wires” through which people’s attitudes and actions are connected and shaped. In the area of suicide research, she has examined claims on the utility of official suicide statistics, the contemporary effects of religious affiliation, and the potential of a network translation of Durkheim’s theory.