Medicaid Undercount and Bias to Estimates of Uninsurance: New Estimates and Existing Evidence

Abstract

Objective. To examine whether known Medicaid enrollees misreport their health insurance coverage in surveys and the extent to which misreports of lack of coverage bias estimates of uninsurance. Data Source. Primary survey data from the Medicaid Undercount Experiment. Study Design. Analyze new data from surveys of Medicaid enrollees in California, Florida, and Pennsylvania and summarize existing research examining bias in coverage estimates due to misreports among Medicaid enrollees. Data Collection Method. Subjects were randomly drawn from Medicaid administrative records and were surveyed by telephone. Principal Findings and Conclusions. Cumulative evidence shows that a small percentage of Medicaid enrollees mistakenly report being uninsured, resulting in modest upward bias in estimates of uninsurance. A somewhat larger percentage of enrollees report having some other type of coverage than no coverage, biasing Medicaid enrollment estimates downward but not biasing estimates of uninsurance significantly upward. Implications for policy makers' confidence in survey estimates of coverage are discussed.Key Words. Validation study, health insurance coverage, survey and administrative data, Medicaid undercount There is consensus among researchers that population surveys of health insurance coverage undercount the number of individuals enrolled in Medicaid

Reasons For The Wide Variation In Medicaid Participation Rates Among States Hold Lessons For Coverage Expansion In 2014

“…Only 4.6 percent of Medicaid enrollees think that they are uninsured, and we used this figure to adjust our take-up estimates. 33 At a population level, this approach is unbiased. However, in identifying predictors of take-up, underreporting could be correlated with certain independent variables, which could lead to spurious findings regarding predictors of take-up.…”

The Effect of the 2014 Medicaid Expansion on Insurance Coverage for Newly Eligible Childless Adults

“…The NHIS has several featuresincluding a point-in-time coverage question and the use of state-specific plan names-that should mitigate limitations identified in other surveys (Cantor et al, 2007;Klerman et al, 2009). Previous audit studies suggested that survey reporting error may have a limited impact on estimates of the uninsurance rate in our setting (Call, Davidson, et al, 2008). However, we were also interested in the source of health insurance, and reporting error may have a larger impact on estimates of take-up and crowd-out.…”

Misclassification in Binary Choice Models

“…Several papers have examined misreporting in surveys and have found high rates of misclassification in binary variables such as participation in welfare programs (Marquis and Moore, 1990;Meyer, Mok and Sullivan, 2009;Meyer, Goerge and Mittag, 2014), Medicaid enrollment (Call et al, 2008;Davern et al, 2009a,b) and education (Black, Sanders and Taylor, 2003). Bound, Brown and Mathiowetz (2001) provide an overview of misreporting in survey data.…”