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.

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.

Language and use of cancer screening services among border and non-border Hispanic Texas women.

OBJECTIVES: Compared to other groups, Mexican American women screen less frequently for cervical and breast cancer. The most significant barriers reported by previous researchers include not having a usual source of care, lacking health insurance and English-language difficulties. In this paper we document and examine the factors associated with disparities in cancer screening between border and non-border residents by language of interview (Spanish or English) among Texas Hispanic women. We hypothesize that, controlling for socioeconomic and demographic characteristics, border residents are more likely to utilize screening services than non-border residents because of the greater presence of bilingual services in border counties. DESIGN: We follow the framework of the Behavioral Model for Vulnerable Populations proposed by Gelberg et al. (Health Services Research, vol. 34, no. 6, pp. 1273-1302, 2000). This model conceptualizes use of health care as an outcome of the interplay of predisposing, enabling and need factors and recognizes that vulnerable groups face additional barriers to health care utilization. Data come from the 2000, 2002 and 2004 Texas Behavioral Risk Factor Surveillance surveys. RESULTS: Group differences in cancer screenings are explained largely by socioeconomic characteristics and structural barriers to access. The significance of language of interview and of border residence disappear after controlling for factors such as health insurance, income and a usual source of care. CONCLUSION: Women who selected to be interviewed in Spanish were less likely to report age-appropriate cancer examinations, health insurance and a regular health care provider than those who selected to be interviewed in English. Disparities in cancer screenings among vulnerable Hispanic populations could be reduced by promoting the establishment of a regular health care provider.