U.S. Census Bureau, Washington, District of Columbia.
Humphrey School of Public Affairs, University of Minnesota, Minneapolis, Minnesota.
Health Serv Res. 2019 Oct;54(5):1099-1109. doi: 10.1111/1475-6773.13191. Epub 2019 Jul 9.
To measure the accuracy of survey-reported data on features and type of health insurance coverage.
Enrollment records from a private insurer were used as sample for primary survey data collection in spring of 2015 using the Current Population Survey health insurance module.
A reverse record check study where households with individuals enrolled in a range of public and private health insurance plans (including the marketplace) were administered a telephone survey that included questions about general source of coverage (eg, employer), program name (eg, Medicaid), portal, premium, and subsidies.
DATA COLLECTION/EXTRACTION METHODS: Survey data were matched back to enrollment records, which indicated coverage status at the time of the survey. Concordance between the records and survey data was assessed.
Correct reporting of general source of coverage ranged from 77.8 percent to 98.3 percent across coverage type, premium ranged from 91.6 percent to 96.4 percent, and subsidy ranged from 83.0 percent to 91.0 percent. Using a conceptual algorithm to categorize coverage type resulted in sensitivity of 98.3 percent for employer-sponsored enrollees, and 70.6 percent-77.6 percent for the other coverage types, while specificity ranged from 93.9 percent to 99.4 percent across coverage types.
Survey reports of features of coverage suggest they are viable items to include in an algorithm to categorize coverage type. Findings have implications beyond the CPS, particularly for marketplace enrollees.
衡量调查中关于健康保险覆盖范围的特征和类型的数据的准确性。
使用私人保险公司的参保记录作为样本,于 2015 年春季利用当前人口调查健康保险模块开展主要调查数据收集工作。
反向记录核对研究,针对参保范围广泛的公共和私人医疗保险计划(包括市场)的家庭(包括参保者)进行电话调查,内容包括关于一般保险来源(如雇主)、计划名称(如医疗补助)、入口、保费和补贴等方面的问题。
数据收集/提取方法:将调查数据与参保记录进行匹配,记录参保者在调查时的参保状况。评估记录与调查数据的一致性。
各种类型的参保者报告一般保险来源的准确率在 77.8%至 98.3%之间,保费准确率在 91.6%至 96.4%之间,补贴准确率在 83.0%至 91.0%之间。使用概念性算法对保险类型进行分类,结果显示雇主参保者的敏感度为 98.3%,其他保险类型的敏感度为 70.6%至 77.6%,而特异性在各种保险类型之间的范围为 93.9%至 99.4%。
调查对参保范围特征的报告表明,它们是一种可行的算法分类项目,可用于对保险类型进行分类。这一发现不仅对 CPS 有影响,特别是对市场参保者而言。