Whether insurance coverage for smoking-cessation medicines increases quitting rates is uncertain. In this paper we evaluate the overall effect of a new health plan pharmacy benefit on the use of pharmacotherapy, attempts to quit, and quitting rates. The presence of a smoking-cessation pharmacy benefit as implemented by these health plans produced no change in the use of bupropion, nicotine patches, or nicotine gum, nor did it result in higher rates of quitting smoking. Further studies are needed to test whether greater efforts to make smokers aware of insurance benefits or adding other types of cessation support might lead to any beneficial effects.

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The largest portion of the Medicaid undercount is caused by survey reporting error—that is, Medicaid recipients misreport their enrollment in health insurance coverage surveys. In this study, we sampled known Medicaid enrollees to learn how they respond to health insurance questions and to document correlates of accurate and inaccurate reports. We found that Medicaid enrollees are fairly accurate reporters of insurance status and type of coverage, but some do report being uninsured. Multivariate analyses point to the prominent role of program-related factors in the accuracy of reports. Our findings suggest that the Medicaid undercount should not undermine confidence in survey-based estimates of uninsurance.

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

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General population surveys of health insurance coverage are thought to undercount Medicaid enrollment, which may bias estimates of the uninsured. This article describes the results of an experiment undertaken in conjunction with a general population survey in Minnesota. Responses to health insurance questions by a known sample of public program enrollees are analyzed to determine possible reasons for the undercount and the amount of bias introduced in estimates of uninsured people. While public program enrollees often misreport the type of coverage they have, the impact on estimates of those without insurance is negligible. Restrictions to generalizing the finding beyond this study are discussed.

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This study examines whether reasonable standard errors for multivariate models can be calculated using the public use file of the Current Population Survey's Annual Social and Economic Supplement (CPS ASEC). We restrict our analysis to the 2003 CPS ASEC and model three dependent variables at the individual level. income, poverty, and health insurance coverage. We compare standard error estimates performed on the CPS ASEC public use file with those obtained from the Census Bureau's restricted internal data that include all the relevant sampling information needed to compute standard errors adjusted for the complex survey sample design. Our analysis shows that the multivariate standard error estimates derived from the public use CPS ASEC following our specification perform relatively well compared to the estimates derived from the internal Census Bureau file. However, it is essential that users of CPS ASEC data do not simply choose any available method since three of the methods commonly used for adjusting for the complex sample design produce substantially different estimates.