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Objective: To evaluate the veracity of self-reports of month-level health insurance coverage in the Current Population Survey Annual Social and Economic Supplement (CPS). Data Sources and Study Setting: The CHIME (Comparing Health Insurance Measurement Error) study used health insurance enrollment records from a large regional Midwest insurer as sample for primary data collection in spring 2015. Study Design: A sample of individuals enrolled in a range of public and private coverage types (including Medicaid and marketplace) was administered the CPS health insurance module, which included questions about month-level coverage, by type, over a 17-18-month time span. Survey data was then matched to enrollment records covering that same time frame, and concordance between the records and self-reports was assessed. Data Collection/Extraction Methods: Sample was drawn by the insurer's informatics specialists and Census Bureau interviewers conducted the survey. Following data collection, updated enrollment records were matched to the survey data to produce a person-level file of coverage by type at the month-level. Principal Findings: For 91% of the overall sample, coverage status and type were reported accurately for at least 75% of observed months. Results varied somewhat by stability of coverage. Among those who were continuously covered throughout the 17-18 month observation period (which comprised 64% of the overall sample), that level of reporting accuracy was observed for 94% of the sample; for those who had censored spells (34% of the overall sample), the figure was 87%; and among those with gaps and/or changes according to the records (2% of the overall sample), for 82% of the group at least 75% of months were reported accurately. Conclusions: Findings suggest that reporting accuracy of month-level coverage in the CPS is high and that the survey could become a valuable new data source for studying the dynamics of coverage, including the Medicaid unwinding. KEYWORDS churn, CPS, health insurance, measurement error, Medicaid unwinding, validity, 1 | INTRODUCTION Month-level health insurance coverage data are critical for studying the dynamics of coverage over time associated with changes in life circumstances, the economy, and policy environments. The [...]

Objective: To examine factors associated with accurate reporting of private and public health insurance coverage. Data sources: Minnesota health plan enrollment records provided the sample for the Comparing Health Insurance Measurement Error (CHIME) study, a survey conducted in 2015 that randomly assigned enrollees to treatments that included health insurance questions from the American Community Survey (ACS) or the redesigned Current Population Survey Annual Social and Economic Supplement (CPS). Study design: Reverse record check study that compared CHIME study survey responses to enrollment records of coverage type (direct purchase on and off the Marketplace, Medicaid, or MinnesotaCare), service use, subsidy receipt, and duration of coverage from a major insurer. Data collection methods: Using matched enrollment and CHIME survey data and logistic regression, we examined correlates of accurate insurance type reporting, including characteristics of the insurance coverage, the covered individual, respondent, and family. Principal findings: Reporting accuracy across treatment and coverage type is high (77%-84%). As with past research, accurate reporting of public insurance is higher for people with characteristics consistent with eligibility for public insurance for both survey treatments. For the ACS treatment, reports of direct purchase insurance are more accurate for enrollees who receive a premium subsidy. Conclusions: Given the complexity of health insurance measurement and frequently changing policy environment, differences in reporting accuracy across treatments or coverage types are not surprising. Several results have important implications for data editing and modeling routines. First, adding premium and subsidy questions in federal surveys should prove useful given the finding that subsidy receipt is associated with reporting accuracy. Second, across both survey treatments, people whose opportunity structures (race, ethnicity, and income) match public program eligibility are accurate reporters of this coverage. This evidence supports using these commonly collected demographic variables in simulation, imputation, and editing routines. KEYWORDS American Community Survey, Current Population Survey, health insurance, Marketplace, Medicaid, reporting accuracy What is known on this topic * Identifying factors associated with accurate health insurance reporting may help refine models that account for measurement error and boost confidence in insurance coverage estimates. * Past research on correlates of reporting accuracy is limited to Medicaid, precedes the Affordable Care Act (ACA), and precedes the redesign of the Current Population Survey Annual Social and Economic Supplement (CPS). * Medicaid reporting accuracy varies by survey, characteristics of enrollees (e.g., age, administrative record reports of health care use, and duration of coverage), and respondent characteristics (e.g., income, education, and employment). What this study adds * Explores correlates of reporting accuracy among people with public and private coverage (including Marketplace coverage) after full implementation of the ACA. * Contrasts accuracy of reporting for the Comparing Health Insurance Measurement Error (CHIME) survey respondents receiving the health insurance questions series adapted from the American Community Survey (ACS) and the redesigned CPS. * We find that correlates of accurate reporting of public and subsidized Marketplace insurance are associated with program eligibility, which provides credibility to commonly applied data editing routines and simulation models., 1 | INTRODUCTION Errors in reporting health insurance coverage are well documented in validation studies, (1-4) as is variation in the magnitude of measurement error by coverage type (5-7) and [...]

Objective: To measure discordance between aggregate estimates of means-tested coverage from the American Community Survey (ACS) and administrative counts and examine the association of discordance with ACA Medicaid expansion. Data Sources: 2010-2016 ACS and counts of Medicaid and Children's Health Insurance Program enrollment from the Centers for Medicare & Medicaid Services. Study Design: State-by-year counts of means-tested coverage from the ACS were compared to administrative counts using percentage differences. Discordance was compared for states that did and did not adopt expansion using difference-in-differences. We then contrasted the effect of expansion on means-tested coverage estimated from the ACS with results from administrative data. Data Collection/Extraction: Survey and administrative data. Principal Findings: One year before expansion there was a 0.8 and 4 percent overcount in expansion and nonexpansion states, respectively. By 2016, there was a 10.64 percent undercount in expansion states vs a 0.02 percent undercount in nonexpansion states. The ACS suggests that expansion increased means-tested coverage in the full population by three percentage points, relative to five percentage points suggested by administrative records. Conclusions: Discordance between the ACS and administrative records has increased over time. The ACS underestimates the impact of Medicaid expansion, relative to administrative counts. KEYWORDS American Community Survey, Medicaid, 1| INTRODUCTION Federal surveys play an important role in evaluating the impacts of the Affordable Care Act (ACA). (1-11) Surveys have advantages over administrative data. They collect data from the [...]

Objective. To compare estimates of health coverage from the pre- and post- redesign of the Current Population Survey (CPS) Annual Social and Economic Supplement. Data Sources/Study Setting. The CPS 2013 Content Test. Study Design. A test of the old and new CPS in which the control panel was a subset of the CPS production cases interviewed by phone and the test panel was conducted in parallel (also by phone) with a sample that had already completed the final rotation of the CPS. Outcome variables tested include uninsured and coverage type by subgroup and calendar year versus point-in-time estimates. Data Collection/Extraction Methods. Census Bureau telephone interviewers. Principal Findings. The odds of having coverage in the past calendar year were higher under the new than the old CPS. Within the new CPS, calendar year estimates of coverage were higher than and distinct from point-in-time estimates. There were few statistically significant differences in coverage across demographic subgroups. Conclusions. The new method reduced presumed underreporting of past year coverage, and the integrated point-in-time/calendar-year series effectively generated distinct measures of each within the same questionnaire. Key Words. Insurance, redesign, experiment, measurement error, The U.S. Census Bureau's Current Population Survey Annual Social and Economic Supplement (CPS) is the most widely cited and used source of estimates on health insurance coverage (Blewett and Davern [...]