Predicting Suicide in Counties: Creating a Quantitative Measure of Suicide Risk

Kate Mobley and Gita Taasoobshirazi
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Kate Mobley: School of Data Science and Analytics, Kennesaw State University, 3391 Town Point Dr. NW, Suite 2400, MD 9104, Kennesaw, GA 30144, USA
Gita Taasoobshirazi: School of Data Science and Analytics, Kennesaw State University, 3391 Town Point Dr. NW, Suite 2400, MD 9104, Kennesaw, GA 30144, USA

IJERPH, 2022, vol. 19, issue 13, 1-14

Abstract: Rising rates of suicide over the past two decades have increased the need for wide-ranging suicide prevention efforts. One approach is to target high-risk groups, which requires the identification of the characteristics of these population sub-groups. This suicidology study was conducted using large-scale, secondary data to answer the question: using the research on suicide, are there variables studied at the community level that are linked to suicide and are measurable using quantitative, demographic data that are already collected and updated? Data on deaths from suicide in U.S. counties for the years 2000, 2005, 2010 and 2015 were analyzed using multiple regression, longitudinal regression, and cluster analysis. Results indicated that the suicide rate in a county can be predicted by measuring the financial stability of the residents, the quality of mental health in the county, and the economic opportunity in the county. The results are further analyzed using two sociological theories, Social Strain Theory and the Theory of Anomie, and two psychological theories, the Shame Model and the Interpersonal Theory of Suicide.

Keywords: suicide; suicide prevention; quantitative analysis; public health; mental health (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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