为了更好的数据分类:以人为中心的方法来理解AANHPI社会人口统计多样性在资源有限的时间
Lu Dong1,2, Jaimie Shaff1, Douglas Yeung1
1RAND, Santa Monica, California, United States of America.
PloS one
|November 19, 2025
概括
对亚洲,夏威夷原住民和其他太平洋岛民 (AANHPI) 社区的分类数据揭示了重要的健康差异. 隐性类别分析确定了不同的子组,对于有针对性的干预和减少健康不平等至关重要.
科学领域:
- 公共卫生 公共卫生
- 健康差距 研究 研究 研究 研究
- 社会的社会学和社会学.
背景情况:
- 卫生不平等导致美国每年损失数十亿美元,需要对数据进行分类,以便有效地分配资源.
- 亚洲,夏威夷原住民和其他太平洋岛民 (AANHPI) 社区的综合数据可能会掩盖这个多样化的人口中的关键健康挑战.
- 了解人口健康需要详细的数据来识别和解决差异.
研究的目的:
- 使用以人为中心的分析方法,在AANHPI人群中确定有意义的子组.
- 证明隐性类分析 (LCA) 在揭示聚合人口数据中的异质性的实用性.
- 通过强调子组特定的健康需求,为有针对性的公共卫生干预提供信息.
主要方法:
- 隐性类分析 (LCA) 用于识别AANHPI群体内的子组.
- 第1项研究分析了扩大AAPI调查 (N=1026名AANHPI成年人) 的数据.
- 第二项研究利用了2023年全国健康态度调查 (N=318名AANHPI受访者),收集了社会人口统计数据.
主要成果:
- 研究1确定了基于收入,教育,语言和世代地位的AANHPI样本中的四个潜在类别.
- 在研究1中,阶级特征揭示了年龄,婚姻状况和就业方面的差异.
- 第二项研究确定了两个阶级 (高社会经济地位和低社会经济地位),年龄分布,房屋所有权和社区幸福感不同.
结论:
- 隐性类分析是一种可行且有价值的方法,用于识别聚合种群中的子组.
- 调查结果强调了AANHPI群体的异质性,以及对公共卫生数据分类的关键需求.
- 这些见解对于制定有针对性的干预措施和优化资源配置以有效解决健康差异至关重要.
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