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美国社区调查中公共医疗保险报告的测量误差:来自记录链接的证据

Measurement Error in Public Health Insurance Reporting in the American Community Survey: Evidence from Record Linkage.

作者信息

Boudreaux Michel H, Call Kathleen Thiede, Turner Joanna, Fried Brett, O'Hara Brett

机构信息

Department of Health Services Administration, School of Public Health, University of Maryland, College Park, MD.

Division of Health Policy and Management and State Health Access Data Assistance Center, School of Public Health, University of Minnesota, Minneapolis, MN.

出版信息

Health Serv Res. 2015 Dec;50(6):1973-95. doi: 10.1111/1475-6773.12308. Epub 2015 Apr 12.

Abstract

OBJECTIVE

Examine measurement error to public health insurance in the American Community Survey (ACS).

DATA SOURCES/STUDY SETTING: The ACS and the Medicaid Statistical Information System (MSIS).

STUDY DESIGN

We tabulated the two data sources separately and then merged the data and examined health insurance reports among ACS cases known to be enrolled in Medicaid or expansion Children's Health Insurance Program (CHIP) benefits.

DATA COLLECTION/EXTRACTION METHODS: The two data sources were merged using protected identification keys. ACS respondents were considered enrolled if they had full benefit Medicaid or expansion CHIP coverage on the date of interview.

PRINCIPAL FINDINGS

On an aggregated basis, the ACS overcounts the MSIS. After merging the data, we estimate a false-negative rate in the 2009 ACS of 21.6 percent. The false-negative rate varies across states, demographic groups, and year. Of known Medicaid and expansion CHIP enrollees, 12.5 percent were coded to some other coverage and 9.1 percent were coded as uninsured.

CONCLUSIONS

The false-negative rate in the ACS is on par with other federal surveys. However, unlike other surveys, the ACS overcounts the MSIS on an aggregated basis. Future work is needed to disentangle the causes of the ACS overcount.

摘要

目的

在美国社区调查(ACS)中检查公共医疗保险的测量误差。

数据来源/研究背景:ACS和医疗补助统计信息系统(MSIS)。

研究设计

我们分别将两个数据源制成表格,然后合并数据,并检查已知参加医疗补助或扩大的儿童健康保险计划(CHIP)福利的ACS案例中的健康保险报告。

数据收集/提取方法:使用受保护的识别码合并两个数据源。如果ACS受访者在访谈日期拥有全额医疗补助或扩大的CHIP覆盖范围,则被视为已参保。

主要发现

总体而言,ACS对MSIS存在计数过高的情况。合并数据后,我们估计2009年ACS中的假阴性率为21.6%。假阴性率因州、人口群体和年份而异。在已知的医疗补助和扩大的CHIP参保者中,12.5%被编码为其他类型的保险,9.1%被编码为未参保。

结论

ACS中的假阴性率与其他联邦调查相当。然而,与其他调查不同的是,ACS总体上对MSIS存在计数过高的情况。需要开展进一步工作来理清ACS计数过高的原因。

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