Medicaid expansion and the Medicaid undercount in the American Community Survey.

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.

Response error and the Medicaid undercount in the current population survey.

OBJECTIVE: To measure the Medicaid undercount and analyze response error in the 2007-2011 Current Population Survey Annual Social and Economic Supplement (CPS ASEC). DATA SOURCES/STUDY SETTING: Medicaid Statistical Information System (MSIS) 2006-2010 enrollment data linked to the 2007-2011 CPS ASEC person records. STUDY DESIGN: By linking individuals across datasets, we analyze false-negative error and false-positive error in reports of Medicaid enrollment. We use regression analysis to identify factors associated with response error in the 2011 CPS ASEC. PRINCIPAL FINDINGS: We find that the Medicaid undercount in the CPS ASEC ranged between 22 and 31% from 2007 to 2011. In 2011, the false-negative rate was 40%, and 27% 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. CONCLUSIONS: Survey estimates of Medicaid enrollment and estimates of the uninsured population are affected by both false-positive response error and false-negative response error, and these response errors are non-random.

The Errors in Reporting Medicare Coverage: A Comparison of Survey Data and Administrative Records.

Objectives: We examine survey reporting of Medicare coverage of the older population by evaluating discordance between survey responses and administrative records. Method: We link data from the 2014 Current Population Survey Annual Social and Economic Supplement (CPS ASEC) and 2014 Medicare Enrollment Database to evaluate the extent to which individuals misreport Medicare coverage in the CPS ASEC. Using regression analyses, we assess factors associated with misreporting. Results: We find the CPS ASEC undercounts the population aged 65 years and older with Medicare by 4.5%. Misreporting of Medicare coverage is associated with citizenship status, immigration year of entry, employment, coverage of other household members, and imputation of Medicare responses. Adjusting for misreporting, Medicare coverage among older individuals increases from 93.4% to 95.6%. Discussion: The CPS ASEC underestimates Medicare coverage for the older population. Administrative records may be useful to evaluate and improve survey imputation of Medicare coverage when missing.

America's Churning Races: Race and Ethnicity Response Changes Between Census 2000 and the 2010 Census.

A person's racial or ethnic self-identification can change over time and across contexts, which is a component of population change not usually considered in studies that use race and ethnicity as variables. To facilitate incorporation of this aspect of population change, we show patterns and directions of individual-level race and Hispanic response change throughout the United States and among all federally recognized race/ethnic groups. We use internal U.S. Census Bureau data from the 2000 and 2010 censuses in which responses have been linked at the individual level (N = 162 million). Approximately 9.8 million people (6.1 %) in our data have a different race and/or Hispanic-origin response in 2010 than they did in 2000. Race response change was especially common among those reported as American Indian, Alaska Native, Native Hawaiian, Other Pacific Islander, in a multiple-race response group, or Hispanic. People reported as non-Hispanic white, black, or Asian in 2000 usually had the same response in 2010 (3 %, 6 %, and 9 % of responses changed, respectively). Hispanic/non-Hispanic ethnicity responses were also usually consistent (13 % and 1 %, respectively, changed). We found a variety of response change patterns, which we detail. In many race/Hispanic response groups, we see population churn in the form of large countervailing flows of response changes that are hidden in cross-sectional data. We find that response changes happen across ages, sexes, regions, and response modes, with interesting variation across racial/ethnic categories. Researchers should address the implications of race and Hispanic-origin response change when designing analyses and interpreting results.