我们所有人的健康社会决定因素的分类:网络分析和可视化方法
Suresh K Bhavnani1,2, Weibin Zhang1, Daniel Bao1
1School of Public and Population Health, University of Texas Medical Branch, Galveston, TX, USA.
medRxiv : the preprint server for health sciences
|August 28, 2023
概括
健康的社会决定因素 (SDoH) 亚型显著影响健康结果. 识别这些复杂的SDoH模式可以指导针对性干预,以改善公共卫生和临床决策.
科学领域:
- 公共卫生和卫生服务研究研究
- 计算生物学和生物信息学
- 社会和经济的流行病学.
背景情况:
- 健康的社会决定因素 (SDoH) 占健康结果的30-55%,但大多数人经历多次,复杂的SDoH.
- 我们所有人研究计划提供了一个独特的数据集,整合了来自代表性不足的人群的各种健康信息.
- 了解SDoH的范围,反应和共发生成亚型对于开发有针对性的健康干预措施至关重要.
研究的目的:
- 为了确定与SDoH相关的调查问题在我们所有人的数据集中的范围和响应模式.
- 确定SDoH如何同时发生以形成不同的亚型,并评估它们与不良健康结果的关联.
主要方法:
- 一个专家小组分析了SDoH问题与健康人2030领域以及来自372,397名参与者的响应数据.
- 在12913名参与者中,双重模块化最大化从18个SDoH因子中确定了SDoH双重集群 (亚型).
- 通过使用多种数据类型,评估了已识别的SDoH亚型和结果 (抑郁症,延迟护理,急诊) 之间的关联.
主要成果:
- 在5个健康人2030领域中确定了110个SDoH问题,但注意到了显著的缺失和人口差异.
- 确定了四个显著和可复制的SDoH双 (亚型),显示出与不良健康结果的明显关联.
- "社会经济障碍"亚型 (低收入,粮食不安全等) 表明抑郁症的几率比率显著更高 (OR=4.2).
结论:
- 已识别的SDoH亚型表现出显著的集群,可复制性和与不良健康结果的关联,支持有针对性的干预措施.
- 这些数据驱动的子类型跨越多个SDoH领域,反映了现实世界的复杂性,并与已建立的SDoH概念模型保持一致.
- 开发的机器学习代码随着我们所有人的数据集的增长而可用于重复分析,从而使正在进行的SDoH亚型研究成为可能.
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