评估联系驱逐记录的方法::评估使用集成数据研究的可行性
J J Cutuli1,2,3, Mary Joan McDuffie2, Erin Nescott3
1Senior Research Scientist, Nemours Children's Health.
Delaware journal of public health
|August 25, 2023
概括
概率匹配改进了将驱逐数据与医疗补助和儿童庇护所记录联系起来. 这种方法创建了一个研究样本,用于对住房不稳定性和医疗保健准入的公共卫生研究.
科学领域:
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 整合不同的数据集对于了解人口健康至关重要.
- 驱逐,无家可归和获得医疗保健是健康的关键社会决定因素.
研究的目的:
- 评估将个人层面的驱逐,医疗补助和特拉华州的无家可归者庇护所数据集成的方法.
- 评估创建链接数据集的可行性,以研究被驱逐影响的儿童和青少年.
主要方法:
- 与直接和概率匹配进行了比较,以将驱逐与医疗补助记录联系起来.
- 将无家可归者庇护所使用数据纳入匹配过程中.
- 专注于儿童和青少年,与所有三种来源的数据联系在一起.
主要成果:
- 概率匹配给出了一个更高的匹配率 (22%) 比直接匹配 (14%) 驱逐到医疗补助数据.
- 无家可归者庇护所的数据显示,高匹配率 (75%) 与医疗补助数据.
- 一组216名儿童在所有三种数据源之间进行了链接,黑人儿童的比例显著过高 (75%).
结论:
- 将驱逐数据与健康和人力服务记录整合起来存在挑战.
- 概率匹配更有效地创建住房不稳定和医疗保健入学儿童的研究样本.
- 需要加强驱逐记录联系的策略,以进行全面的人口层面的研究.
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