统计数据整合模型,以架起卫生官方统计的桥梁

Q3 Decision Sciences
Andreea L. Erciulescu
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引用次数: 0

摘要

美国至少有两个医疗保险覆盖范围估计来源:行为风险因素监测系统(BRFSS)和小面积医疗保险估计(SAHIE)计划。本文使用多级模型对BRFSS和SAHIE数据进行了集成,该模型考虑了可用数据的不同聚合水平和数据所受的不同误差。通过从两个来源和跨地理区域借用信息,改善了BRFSS或SAHIE可获得的初始州级估计的不确定性。县一级的模型估计是在BRFSS和SAHIE两个尺度上产生的,提高了BRFSS公共使用数据的可用性,并激发了对除医疗保险覆盖范围外的BRFSS数量估计的可能扩展。该应用程序使用2018年的公共使用数据。简要讨论了小面积估计模型和测量误差模型的并行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Statistical data integration models to bridge health official statistics
There are at least two sources of health insurance coverage estimates in the United States: the Behavioral Risk Factor Surveillance System (BRFSS) and the Small Area Health Insurance Estimates (SAHIE) program. This paper addresses the integration of BRFSS and SAHIE data using multilevel models that account for the different levels of aggregation at which data are available and for the different errors to which data are subject to. The uncertainty in the initial state-level estimates available from BRFSS and SAHIE is improved by borrowing information from both sources and across geographies. County-level model estimates are produced on both BRFSS and SAHIE scales, improving the usability of the BRFSS public-use data and inspiring possible extensions to estimation of BRFSS quantities other than health insurance coverage. The application uses 2018 public-use data. Parallels to small area estimation models and measurement error models are briefly discussed.
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来源期刊
Statistical Journal of the IAOS
Statistical Journal of the IAOS Economics, Econometrics and Finance-Economics and Econometrics
CiteScore
1.30
自引率
0.00%
发文量
116
期刊介绍: This is the flagship journal of the International Association for Official Statistics and is expected to be widely circulated and subscribed to by individuals and institutions in all parts of the world. The main aim of the Journal is to support the IAOS mission by publishing articles to promote the understanding and advancement of official statistics and to foster the development of effective and efficient official statistical services on a global basis. Papers are expected to be of wide interest to readers. Such papers may or may not contain strictly original material. All papers are refereed.
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