关于社区性估计的研究笔记:美国人口普查局的新数据与对COVID-19过度死亡的应用
John Anders1,2, Craig Wesley Carpenter3,2, Katherine Ann Willyard2
1Department of Economics, Trinity University, San Antonio, TX, USA.
Demography
|May 23, 2024
概括
社区性估计 (CRE) 准确预测COVID-19死亡人数,优于其他脆弱性指标. CRE为评估社区灾害风险提供了更稳定,更强大的工具.
科学领域:
- 公共卫生 公共卫生
- 社会学 社会学 社会学
- 人口统计学 人口统计学
背景情况:
- 美国人口普查局开发了社区性估计 (CRE) 来衡量区域一级的脆弱性.
- 现有的脆弱性指数包括社会脆弱性指数 (SVI) 和国家风险指数 (NRI).
- 需要验证CRE对公共卫生危机的预测能力.
研究的目的:
- 为了验证美国人口普查局的社区性估计 (CRE) 使用COVID-19过剩死亡数据.
- 将CRE的预测准确度与人口因素,SVI,NRI和社会经济预测因素进行比较.
- 在COVID-19大流行期间评估CRE风险指标和社区级死亡率之间的关联.
主要方法:
- 使用行政微数据将社会保障局的死亡记录与CRE联系起来.
- 采用机器学习技术来分析预测能力.
- 与单一的人口分类 (种族,年龄),SVI,NRI和社会经济变量 (贫困,失业) 的CRE四分位数进行了比较.
主要成果:
- CRE四分位数比种族或年龄更稳定地预测了COVID-19过度死亡.
- 与SVI,NRI,贫困和失业相比,CRE对COVID-19过度死亡的预测能力更强.
- 在CRE风险指标中增加10个百分点与在最初的COVID-19爆发期间增加一个邻里死亡相关.
结论:
- 社区性估计 (CRE) 提供了更准确的预测,以预测社区对诸如流行病等灾害的脆弱性.
- 与传统的人口因素或现有指数 (SVI,NRI) 相比,CRE提供了更强大的脆弱性衡量标准.
- 这些发现支持CRE对公共卫生准备和应对计划的有用性.
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