使用不同的集群方法分析多种健康的社会决定因素
Li Zhang1, Olivio J Clay2, Seung-Yup Lee3
1Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35233, USA.
International journal of environmental research and public health
|February 24, 2024
概括
分析健康的社会决定因素 (SDoH) 是复杂的. 这项研究使用了因子分析,聚类和潜在类分析来分组SDoH因素,为结果研究提供了方法.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 生物统计学 生物统计学
- 公共卫生 公共卫生
背景情况:
- 健康的社会决定因素 (SDoH) 在医疗保健中至关重要,但由于对线性而难以分析.
- 响应和评估患者资产,风险和经验的协议 (PRAPARE) 工具收集标准化的SDoH数据.
- 现有研究在分析复杂,相互关联的SDoH因素方面遇到了困难.
研究的目的:
- 评估和比较通过PRAPARE工具收集的三种统计方法来分析SDoH数据.
- 展示不同的统计方法如何解决结果研究中的SDoH对线性.
- 引导研究人员选择合适的方法来分析复杂的SDoH数据.
主要方法:
- 使用探索性因子分析 (FA),层次聚类和潜在类分析 (LCA).
- 分析了2380名患者的数据,并提供了完整的PRAPARE和社区级信息.
- 从每个方法中比较了已识别的SDoH集群和潜在类.
主要成果:
- 使用FA识别了三个复合SDoH集群.
- 通过等级集群发现了四个不同的集群.
- 揭示了使用LCA的患者的四个潜在类别.
- 根据所选择的统计方法证明了不同的结果.
结论:
- 多种统计方法可以有效地分析复杂的SDoH数据.
- 方法的选择取决于研究人员的具体结果和目标.
- 采用多样化的分析方法可以提高在医疗保健环境中对SDoH的理解.
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