Validating Health Insurance Coverage Survey Estimates: A Comparison of Self-Reported Coverage and Administrative Data Records

Davern, Michael; Call, Kathleen Thiede; Ziegenfuss, Jeanette; Davidson, Gestur; Beebe, Timothy J.; Blewett, Lynn A.

doi:10.1093/poq/nfn013

Cited by 47 publications

(58 citation statements)

References 8 publications

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“…This is similar to the rate of 6% Nelson et al (2000) found in their study of the BRFSS. However, the estimate from the MEPS-HC suggests less accuracy than that obtained by the Minnesota Tobacco Survey (Davern et al 2005), in which .6% of the privately insured did not report their coverage.…”

Section: Resultsmentioning

confidence: 65%

The Accuracy of Reported Insurance Status in the MEPS

Hill¹

2007

INQUIRY

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Estimates of the number of insured and uninsured Americans are watched by numerous policymakers and the public, yet studies find respondents do not report their insurance status perfectly. Using four sources of validation data, including surveys of employers and providers, this paper assesses the quality of respondents' reports of private insurance and uninsurance in the Medical Expenditure Panel Survey Household Component (MEPS-HC), a nationally representative household survey. Regression analysis is used to assess the extent to which factors are associated with disagreements in reported insurance status across sources.

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Section: Resultsmentioning

confidence: 65%

The Accuracy of Reported Insurance Status in the MEPS

Hill¹

2007

INQUIRY

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“…Dating back to the 1990s, previous research has shown that estimates of Medicaid coverage based on the Current Population Survey Annual Social and Economic Supplement (CPS ASEC) have consistently produced an undercount of beneficiaries when compared to actual Medicaid enrollment records Card et al 2004;Davern et al 2008;Davern et al 2009;Klerman et al 2005;Lewis et al 1998). We examine two hypotheses related to how respondents interpret questions about health insurance on the CPS ASEC: 1) whether they interpret the question as asking about their current coverage status or if they have difficulty remembering past coverage and 2) whether respondents confuse different programs or only report certain forms of insurance.…”

Section: Introductionmentioning

confidence: 99%

Response Error and the Medicaid Undercount in the Current Population Survey

Noon¹,

Fernández²,

Rastogi³

2017

Preprint

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The Current Population Survey Annual Social and Economic Supplement (CPS ASEC) is an important source for estimates of the uninsured population. Previous research has shown that survey estimates produce an undercount of beneficiaries compared to Medicaid enrollment records. We extend past work by examining the Medicaid undercount in the 2007-2011 CPS ASEC compared to enrollment data from the Medicaid Statistical Information System for calendar years 2006-2010. By linking individuals across datasets, we analyze two types of response error regarding Medicaid enrollment – false negative error and false positive error. We use regression analysis to identify factors associated with these two types of response error in the 2011 CPS ASEC. We find that the Medicaid undercount was between 22 and 31 percent from 2007 to 2011. In 2011, the false negative rate was 40 percent, and 27 percent of Medicaid reports in CPS ASEC were false positives. False negative error is associated with the duration of enrollment in Medicaid, enrollment in Medicare and private insurance, and Medicaid enrollment in the survey year. False positive error is associated with enrollment in Medicare and shared Medicaid coverage in the household. We discuss implications for survey reports of health insurance coverage and for estimating the uninsured population.

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“…A large segment of work in this area has focused on addressing the so-called “Medicaid undercount”, or the well-validated concern that estimates of Medicaid participation drawn from survey data sources are consistently lower than participation rates drawn from administrative data records (Call, Davidson, Davern, Blewett, & Nyman, 2008; Call, Davern, Klerman, & Lynch, 2012; Davern, Call, Ziegenfuss, Davidson, Beebe, & Blewett, 2008; Davern, Klerman, Baugh, Call, & Greenberg, 2009; Klerman, Ringel, & Roth, 2005). Studies have tended to use either an experimental approach, in which a random sample of survey respondents is drawn from administrative records and then survey respondents’ reports of program take-up are cross-checked with the administrative data, or a matching approach, in which administrative data records are identified and linked with respondents drawn from existing survey data sources and overlap between the two sources is examined (Call et al, 2008; Davern et al, 2008; 2009).…”

Section: Introductionmentioning

confidence: 99%

“…Studies have tended to use either an experimental approach, in which a random sample of survey respondents is drawn from administrative records and then survey respondents’ reports of program take-up are cross-checked with the administrative data, or a matching approach, in which administrative data records are identified and linked with respondents drawn from existing survey data sources and overlap between the two sources is examined (Call et al, 2008; Davern et al, 2008; 2009). National studies have found substantial underreporting of Medicaid take-up, such that approximately 42% of respondents identified in administrative data to be Medicaid recipients self-identify as non-recipients in national survey reports (e.g., Davern et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Can we trust parental reports of child care subsidy receipt?

Johnson

Herbst

2013

Children and Youth Services Review

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In recent years, research examining determinants and consequences of the means-tested child care subsidy program (the Child Care and Development Fund [CCDF]) has grown dramatically. To measure subsidy utilization, existing studies typically rely on parent-reported measures of subsidy receipt drawn from large surveys. As the research literature on child care subsidies has grown, however, so have concerns about the trustworthiness of parent-reported subsidy use. One way to assess the quality of parent-reported subsidy use is to examine its overlap with another subsidy receipt measure, drawn from a different source. The current paper uses the Fragile Families and Child Well-Being Study (FFCWS), the only existing survey data source that contains an alternate measure of subsidy receipt – based on child care provider report – which permits a comparison to parent-reported measures. We find evidence that increases our confidence in the trustworthiness of parents as accurate reporters of subsidy receipt. In recognition that neither data source reflects “true” subsidy receipt, however, we conclude with a discussion of limitations and steps for future research.

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Validating Health Insurance Coverage Survey Estimates: A Comparison of Self-Reported Coverage and Administrative Data Records

Cited by 47 publications

References 8 publications

The Accuracy of Reported Insurance Status in the MEPS

The Accuracy of Reported Insurance Status in the MEPS

Response Error and the Medicaid Undercount in the Current Population Survey

Can we trust parental reports of child care subsidy receipt?

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