美国心脏瘤病死亡率的地理模式和风险因素协会
Issam Motairek1, Weichuan Dong2, Pedro Rvo Salerno1
1Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, Ohio.
The American journal of cardiology
|June 29, 2023
概括
心脏瘤死亡率 (COM) 与各种社会环境因素有关. 机器学习识别了高风险县集群,揭示了青少年出生率和住房质量等关键驱动因素,以减少差距.
科学领域:
- 流行病学 流行病学
- 机器学习 机器学习
- 公共卫生 公共卫生
背景情况:
- 心脏瘤死亡率 (COM) 受复杂的社会经济,人口和环境因素的影响.
- 现有的脆弱性指标不足以捕捉这些暴露的复杂相互作用.
- 需要先进的分析方法来理解和解决COM差异.
研究的目的:
- 确定与美国各县的COM相关的高风险社会人口和环境因素.
- 利用机器学习和流行病学对COM驱动因素的新见解.
- 为减少COM差异提供有针对性的干预信息.
主要方法:
- 横截面研究设计,利用大量美国死者的数据集.
- 机器学习的应用,特别是分类和回归树 (CART),结合流行病学分析.
- 分析了2,717个县的987,009名死者,以确定社会环境集群.
主要成果:
- 确定与COM相关的9个不同的县社会环境集群.
- 在这些集群中观察到COM相对增加了64.1%.
- 确定了主要的因素:青少年出生率,1960年以前的住房 (漆),面积剥夺指数,家庭收入中位数,医院可用性和颗粒物空气污染暴露.
结论:
- 对心脏瘤死亡率的社会环境驱动因素的新见解.
- 机器学习在识别高风险人群中的实用性.
- 强调以社会环境数据为基础的有针对性的干预措施,以减少COM的健康差异.
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