心理健康的社会驱动因素:美国的一项使用机器学习的研究
Shichao Du1, Jie Yao2, Gordon C Shen3
1Department of Sociology, University at Albany, State University of New York, Albany, New York.
American journal of preventive medicine
|June 7, 2023
概括
社会驱动因素在美国人口普查地区显著影响心理健康. 机器学习确定吸烟,气候区和种族组成是影响抑郁和心理健康不良的关键因素.
科学领域:
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
- 社会流行病学 社会流行病学
背景情况:
- 社会决定因素显著影响人口的心理健康.
- 社会驱动因素的综合水平比较是可行的.
- 机器学习提供了一种新的方法来分析这些复杂的关系.
研究的目的:
- 识别和排名心理健康的社会驱动因素在美国的人口普查管道层面.
- 分析行为,环境和社会领域对心理健康结果的影响.
- 探索贫困和种族隔离如何缓解这些影响.
主要方法:
- 利用了来自38,379个美国人口普查区 (2021) 的数据.
- 提高机器学习 (2022年) 来分析两个心理健康指标 (抑郁症,心理健康不佳) 与三个社会驱动领域相比.
- 通过贫困和种族隔离分层对主要和亚样本分析中的主要驱动因素进行了审查.
主要成果:
- 社会驱动因素解释了超过90%的心理健康指标差异.
- 吸烟 (行为) 是抑郁症和心理健康不佳的常见驱动因素.
- 气候区域 (环境) 和种族组成 (社会) 作为显著的相关物体出现,根据人口普查区特征 (如贫困和种族隔离) 变化.
结论:
- 人口的心理健康深受当地社会驱动因素的影响.
- 人口普查通道层面的分析对于了解精神健康问题的上游原因至关重要.
- 基于这些细分分析的定制干预措施可以改善人口心理健康结果.
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