创新社区驱动的无家可归者清单和需求评估:HUD授权的时间点计数网络抽样方法
Zack W Almquist1, Ihsan Kahveci2, Mary Ashley Hazel3
1Departments of Sociology and Statistics, University of Washington, Seattle, Washington, USA.
American journal of epidemiology
|September 5, 2024
概括
这项研究引入了受访者驱动的抽样 (RDS),以更好地计算无家可归的人. 这种基于网络的方法提供了比传统的时间点计数 (PIT) 更准确的统计估计.
科学领域:
- 社会流行病学 社会流行病学
- 公共卫生方法 公共卫生方法
- 统计抽样 统计抽样
背景情况:
- 美国住房和城市发展部 (HUD) 要求对无家可归者进行时间点 (PIT) 计数.
- 目前的PIT计数依赖于基于志愿者的横截面数据收集,可能导致低估.
- 现有的方法经常使用方便抽样采访,限制代表性.
研究的目的:
- 提出和评估一种新的基于网络的受访者驱动抽样 (RDS) 方法,用于列举无家可归的无家可归人口.
- 开发一种统计估计方法,以产生更准确的无家可归人数.
- 为了比较新的RDS方法与传统PIT计数的有效性.
主要方法:
- 在无家可归者的RDS调查中开发了一种用于确定样本大小的功率分析.
- 在华盛顿州金县实施了大规模,人口代表的RDS调查,持续了三年 (2022,2023,2024).
- 应用了一种新的统计方法来估计无家可归者的数量,将结果与2020年PIT计数进行比较.
主要成果:
- 通过使用RDS.成功生成了无家可归个人的人口代表性样本.
- 新的估计方法为无家可归的人口提供了替代计数.
- 进行了RDS衍生估计和传统PIT计数之间的比较分析.
结论:
- 受访者驱动的抽样 (RDS) 提供了一种有前途的基于网络的方法,用于改善无家可归的无家可归人口的清单.
- 开发的统计估计方法为生成更准确的无家可归人数提供了有价值的工具.
- 未来的研究应该继续完善这些方法,以便在社会流行病学和公共卫生政策中得到更广泛的应用.
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