Abstract
Exploring patterns and trends in suicide data is crucial to understanding the complex interplay of factors that affect mental health and suicide risk. This study delves into the global suicide landscape using the “Suicide Data with Continent" dataset from Kaggle, which aggregates comprehensive information on suicides from various countries and regions. Using multivariate regression analysis along with sophisticated data analysis and visualization techniques, including trend charts, maps, and pie charts through the Dash and Plotly libraries, we uncover continental patterns and trends related to suicide rates. The analysis, based on a dataset comprising 27.820 records spanning 32 years with details on demographics, socioeconomic indicators, and 6.748.420 recorded suicides, aims to illuminate the multifaceted nature of suicide phenomena. The findings not only foster a deeper understanding of global suicide dynamics, but also serve as a foundational analysis to inform future suicide prevention policies and programs, specifically laying the groundwork for an impending study in Colombia.
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References
Organization, W.H., et al.: Preventing suicide: a global imperative. World Health Organization (2014)
Wilson, D.: Revealed: countries with the highest (and lowest) suicide rates, 2024 - CEOWORLD magazine—ceoworld.biz. https://ceoworld.biz/2024/01/21/revealed-countries-with-the-highest-and-lowest-suicide-rates-2024/. Accessed 28 Mar 2024
New Study highlights drivers behind suicide in the Americas—paho.org. https://www.paho.org/en/news/23-2-2023-new-study-highlights-drivers-behind-suicide-americas. Accessed 28 Mar 2024
World Population by Country 2024 (Live)—worldpopulationreview.com. https://worldpopulationreview.com/. Accessed 28 Mar 2024
Klonsky, E.D.: The role of theory for understanding and preventing suicide (but not predicting it): a commentary on hjelmeland and knizek. Death Stud. (2019)
Organization, W.H., et al.: Live life: an implementation guide for suicide prevention in countries (2021)
Saxena, S., Setoya, Y.: World health organization’s comprehensive mental health action plan 2013–2020 (2014)
Rihmer, Z., Belsö, N., Kiss, K.: Strategies for suicide prevention. Curr. Opin. Psychiat. 15(1), 83–87 (2002)
Franklin, J.C., et al.: Risk factors for suicidal thoughts and behaviors: a meta-analysis of 50 years of research. Psychol. Bull. 143(2), 187 (2017)
Fonseka, T.M., Bhat, V., Kennedy, S.H.: The utility of artificial intelligence in suicide risk prediction and the management of suicidal behaviors. Aust. New Zealand J. Psychiat. 53(10), 954–964 (2019)
Kirlic, N., et al.: A machine learning analysis of risk and protective factors of suicidal thoughts and behaviors in college students. J. Am. Coll. Health 71(6), 1863–1872 (2023)
van Mens, K., et al.: Applying machine learning on health record data from general practitioners to predict suicidality. Internet Interv. 21, 100337 (2020)
Kim, S., Lee, H.-K., Lee, K.: Detecting suicidal risk using mmpi-2 based on machine learning algorithm. Sci. Rep. 11(1), 15310 (2021)
Hair, J., Black, W., Babin, B., Anderson, R., Tatham, R.: Multivariate data analysis. Cengage learning. Hampshire, United Kingdom (2019)
Kothari, C.: Research Methodology: Methods and Techniques. New age international, New Delhi (2004)
Ohlsson, L., et al.: Leaky gut biomarkers in depression and suicidal behavior. Acta Psychiatr. Scand. 139(2), 185–193 (2019)
Niederkrotenthaler, T., et al.: Association between suicide reporting in the media and suicide: systematic review and meta-analysis. Bmj 368 (2020)
[R] Suicide Rates (in-depth) - Stats & Insights—kaggle.com. https://www.kaggle.com/code/lmorgan95/r-suicide-rates-in-depth-stats-insights. Accessed 29 Mar 2024
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Rodríguez, E.A., Hernández-Hernández, G., Coronell, L.P., Calabria-Sarmiento, J.C., Escorcia-Gutierrez, J. (2024). Leveraging Global Suicide Statistics for Insightful Prevention Strategies: A Comprehensive Analysis. In: Saeed, K., Dvorský, J. (eds) Computer Information Systems and Industrial Management. CISIM 2024. Lecture Notes in Computer Science, vol 14902. Springer, Cham. https://doi.org/10.1007/978-3-031-71115-2_21
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DOI: https://doi.org/10.1007/978-3-031-71115-2_21
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