探索结构性种族主义与心理健康之间的关联:地理空间和机器学习分析
Fahimeh Mohebbi1, Amir Masoud Forati2, Lucas Torres3
1College of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, WI, United States.
JMIR public health and surveillance
|May 3, 2024
概括
结构性种族主义显著影响心理健康差异,不成比例地影响非洲裔美国人社区. 关键因素包括吸烟,贫困和缺乏医疗保险,需要有针对性的干预措施.
科学领域:
- 公共卫生 公共卫生
- 地理空间分析的研究.
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 结构性种族主义是精神健康差异的一个已知的驱动因素.
- 之前的研究集中在个人因素上,对它们作为结构性种族主义表现的集体影响的探索较少.
- 密尔沃基县的多样性为调查这些多因素影响提供了一个独特的环境.
研究的目的:
- 确定密尔沃基县结构性种族主义和心理健康差异之间的联系.
- 采用地理空间和深度学习技术进行全面分析.
- 量化结构性种族主义对心理健康不良患病率的影响.
主要方法:
- 编译了217个地理引用变量,最初不包括种族,以确定非种族决定因素.
- 使用基于树的方法 (随机森林) 和常规技术进行变量选择,解决多对线性.
- 应用地理加权随机森林和自组织地图,使用K-means集群来分析空间异质性和量化种族主义的影响.
主要成果:
- 十二个影响因素解释了95.11%的心理健康变化.
- 最主要的因素包括吸烟,贫困,睡眠不足,缺乏医疗保险,就业和年龄.
- 非洲裔美国人社区的高风险心理健康群体的可能性增加了2.23倍.
结论:
- 结构性种族主义显然塑造了心理健康差异,黑人社区承担了不成比例的负担.
- 综合的地理空间和深度学习方法有效地阐明了心理健康的复杂社会决定因素.
- 调查结果强调了有针对性的干预措施的必要性,该干预措施应对个人和系统因素,以减少心理健康不平等.
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