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Leveraging Statistical Methods for an Analysis of Demographic Factors of Opioid Overdose Deaths

机译：利用统计方法分析阿片类药物过量死亡的人口因子

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Deaths from drug overdose including opioid overdose have been increasing at an alarming rate, and authorities still find tackling this problem an acute challenge. This paper applies Artificial Intelligence and statistical techniques to big data to identify the demographic and socio-economic factors that have led to the increasing number of drug overdose deaths in Allegheny County, Pennsylvania, United States. Using Artificial Intelligence software, we analyzed a dataset of over 3,500 patients alongside demographic and socioeconomic variables to gain detailed insights into the issue, insights that we can generalize to craft solutions to this problem in both domestic and global communities. Our findings revealed patterns ranging from possible psychological and behavioral factors and drug use on weekends, as well as a direct market supply effect on the number of deaths. These findings imply the need for authorities to offer educational workshops to individuals and their families about the dangers of the current drug epidemic and to design an effective policy for the oversight of drug market supply that includes taking firm action against violators.
机译：来自药物过量药物的死亡，包括阿片过量的药物过量，令人震惊的速度增加，当局仍然发现解决这个问题急性挑战。本文适用于大数据的人工智能和统计技术，以确定导致Allgheny县，宾夕法尼亚州，宾夕法尼亚州，宾夕法尼亚州，美国的人口统计和社会经济因素导致越来越多的药物过量死亡。使用人工智能软件，我们分析了一个超过3,500名患者的数据集，以及人口统计和社会经济变量，以获得对问题的详细洞察，我们可以概括为在国内和全球社区中的解决这个问题。我们的调查结果透露了各周末可能的心理和行为因素和药物使用的模式，以及对死亡人数的直接市场供应影响。这些发现意味着当局需要向个人及其家属提供有关目前药物流行的危险的教育研讨会，并为毒品市场供应制定有效政策，包括对违规者采取牢固的行动。

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