与美国中风死亡率相关的县级社会环境因素:一项跨部门研究
Pedro R V O Salerno1, Issam Motairek1, Weichuan Dong2
1Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA.
Angiology
|April 3, 2024
概括
机器学习确定了与中风死亡率相关的关键社会人口统计学和环境因素. 这些因素包括收入,吸烟,住房,体力活动和糖尿病,突出了中风风险的复杂驱动因素.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 数据科学数据科学数据科学
背景情况:
- 脑卒中死亡率在美国具有显著的地理差异.
- 健康的社会人口和环境决定因素 (SEDH) 与中风风险有关.
- 了解这些复杂的关系对于有针对性的公共卫生干预至关重要.
研究的目的:
- 探索与美国县级中风死亡率相关的社会人口和环境健康决定因素 (SEDH).
- 通过机器学习确定特定的县级集群和关键的SEDH因素,这些因素有助于中风死亡率.
主要方法:
- 来自美国2397个县的15岁以上的死亡人数 (2016-2020年) 的横截面分析.
- 分析了54个县级SEDH变量与年龄调整的中风死亡率相关的分析.
- 应用分类和回归树 (CART) 用于集群识别和随机森林用于变量重要性.
主要成果:
- 确定了10个不同的县集群,显示中风死亡率相对增加了77.5%.
- 确定了八个关键的SEDH因素:家庭收入中位数,出生体重低,目前的吸烟者,严重的住房问题,获得运动,身体不活动,糖尿病和过度饮酒.
- 这些因素指导了县集群的分类,这些县集群具有不同的中风死亡风险.
结论:
- 社会人口统计学和环境暴露与中风死亡率有着复杂,多方面的关系.
- 机器学习有效地解构了这些关系,揭示了中风的社会环境驱动因素.
- 这些发现支持开发针对性的干预措施,以根据已识别的SEDH因素减轻中风风险.
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