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

Author

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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)

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.

Suggested Citation

  • Kate Mobley & Gita Taasoobshirazi, 2022. "Predicting Suicide in Counties: Creating a Quantitative Measure of Suicide Risk," IJERPH, MDPI, vol. 19(13), pages 1-14, July.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:13:p:8173-:d:855147
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    References listed on IDEAS

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