一个贝叶斯阶层的小区域人口模型,用于计算数据源,来自美国社区调查的特定方法,人口估计计划和十年人口普查数据
Emily N Peterson1, Rachel C Nethery2, Tullia Padellini3
1Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University.
The annals of applied statistics
|September 26, 2024
概括
准确的小区域人口统计对流行病学至关重要. 一个新的贝叶斯人口模型整合了美国人口普查局的数据,以提供可靠的估计和不确定性,改善公共卫生研究.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 人口统计学 人口统计学
背景情况:
- 小区域人口计数对于流行病学研究至关重要,但往往缺乏质量评估.
- 美国人口普查局的数据来源 (十年人口普查,PEP,ACS) 有方法上的差异,导致不同的准确性和错误.
- 人口分母的差异可能会影响公共卫生疾病/死亡率计算.
研究的目的:
- 开发贝叶斯人口 (BPop) 模型,用于准确的年度小区域人口估计和不确定性量化.
- 整合多个美国人口普查局的数据来源,对其特定的方法和错误进行核算.
- 为小区域制作种族分层的人口估计,并预测多年来缺少数据的计数.
主要方法:
- 开发了一个贝叶斯人口 (BPop) 模型,整合了十年人口普查,人口估计计划 (PEP) 和美国社区调查 (ACS) 数据.
- 考虑到数据源特定的数据生成机制和错误.
- 专注于2006-2023年格鲁吉亚州159个县的黑人和白人人口,包括模拟和验证.
主要成果:
- 该BPop模型成功地融合了来自美国人口普查局多个来源的信息.
- 综合性,种族分层的小区域人口估计与相关的不确定性产生.
- 模型性能通过模拟和与现有数据源的比较来验证.
结论:
- BPop模型提供了一个强大的框架,可以改善小区域人口估计及其不确定性.
- 这种方法通过提供可靠的分母数据来提高公共卫生研究的准确性.
- 该模型可以适应各种空间细粒度和人口统计学子群体.
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