通过整合多个来源的数据来分解卫生官方统计

Andreea L. Erciulescu
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引用次数: 0

摘要

分类统计有助于改进对社会的描述。然而,调查估计在较细的水平上比在较高的水平上受到更大的不确定性的影响,甚至在较细的水平上也常常得不到。行为风险因素监测系统(BRFSS)被认为是美国最主要的系统,通过电话调查从美国个人收集健康数据。在BRFSS的官方统计数据中,有两个相关的健康流行数量的州一级估计数:拥有私人医生的流行程度和拥有健康保险的流行程度。没有公布这些数量的县级BRFSS估计数。此外,美国还提供了对拥有医疗保险的流行程度的县级估计。小区域健康保险估算(SAHIE)计划。本文通过使用两个BRFSS流行变量之间的州一级关系,以及BRFSS和SAHIE健康保险覆盖率流行之间的县级桥梁,解决了州一级拥有私人医生的流行到县一级的分解问题。利用2018年公共使用数据,对患病率变量以及BRFSS和SAHIE量表进行了县级模型估计,提高了BRFSS公共使用数据的可用性。AMS学科分类:62D05, 62H10, 62J05, 62P99
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Disaggregating Health Official Statistics by Integrating Data From Multiple Sources
Disaggregated statistics help improve the description of the society. However, survey estimates are subject to larger uncertainty at finer levels than at higher levels, and often not even available at fine levels. The Behavioral Risk Factor Surveillance System (BRFSS) is considered the nation’s premier system collecting health data from individuals in the US using telephone surveys. Among the BRFSS official statistics, state-level estimates are available for two related health prevalence quantities: the prevalence of having a personal doctor and the prevalence of having health insurance coverage. No county-level BRFSS estimates are released for these quantities. In addition, county-level estimates for the prevalence of having health insurance coverage are also available from the US. Small Area Health Insurance Estimates (SAHIE) program. This article addresses the disaggregation of the state-level prevalence of having a personal doctor to the county level, by using the state-level relationship between the two BRFSS prevalence variables and the county-level bridge between the BRFSS and the SAHIE prevalence of having health insurance coverage. Using 2018 public-use data, county-level model estimates are produced for both prevalence variables and on both BRFSS and SAHIE scales, improving the usability of the BRFSS public-use data. AMS subject classifications: 62D05, 62H10, 62J05, 62P99
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