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Social vulnerability predictors of drug poisoning mortality: A machine learning analysis in the United States
The American Journal on Addictions ( IF 3.860 ) Pub Date : 2023-06-21 , DOI: 10.1111/ajad.13445
Moosa Tatar 1 , Mohammad R Faraji 2 , Katherine Keyes 3 , Fernando A Wilson 4, 5 , Mohammad S Jalali 6, 7
Affiliation  

Drug poisoning is a leading cause of unintentional deaths in the United States. Despite the growing literature, there are a few recent analyses of a wide range of community-level social vulnerability features contributing to drug poisoning mortality. Current studies on this topic face three limitations: often studying a limited subset of vulnerability features, focusing on small sample sizes, or solely including local data. To address this gap, we conducted a national-level analysis to study the impacts of several social vulnerability features in predicting drug mortality rates in the United States.

中文翻译:

药物中毒死亡率的社会脆弱性预测因素:美国的机器学习分析

药物中毒是美国意外死亡的主要原因。尽管文献不断增加,但最近有一些对导致药物中毒死亡率的社区层面的广泛社会脆弱性特征的分析。目前关于这一主题的研究面临三个局限性:通常研究漏洞特征的有限子集、关注小样本量或仅包含本地数据。为了解决这一差距,我们进行了一项国家级分析,以研究几种社会脆弱性特征对预测美国药物死亡率的影响。
更新日期:2023-06-21
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