Race and ethnicity responses can change over time and across contexts – 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 U.S. and among all federally recognized race/ethnic groups. We use internal Census Bureau data from the 2000 and 2010 censuses in which responses have been linked at the individual level (N = 162 million). About 9.8 million people (6.1 percent) 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% changed). There were a variety of response change patterns, which we detail. In many race/Hispanic response groups, there is 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 race/ethnic categories. Researchers should think through and discuss the implications of race and Hispanic origin response change when designing analyses and interpreting results.

Race and ethnicity responses can change over time and across contexts -a component of population change not usually taken into account. To what extent do race and/or Hispanic origin responses change? Is change more common to/from some race/ethnic groups than others? Does the propensity to change responses vary by characteristics of the individual? To what extent do these changes affect researchers? We use internal Census Bureau data from the 2000 and 2010 censuses in which individuals' responses have been linked across years. Approximately 9.8 million people (about 6 percent) in our large, non-representative linked data have a different race and/or Hispanic origin response in 2010 than they did in 2000. Several groups experienced considerable fluidity in racial identification: American Indians and Alaska Natives, Native Hawaiians and Other Pacific Islanders, and multiple-race response groups, as well as Hispanics when reporting a race. In contrast, race and ethnic responses for single-race non-Hispanic whites, blacks, and Asians were relatively consistent over the decade, as were ethnicity responses by Hispanics. People who change their race and/or Hispanic origin response(s) are doing so in a wide variety of ways, as anticipated by previous research. For example, people's responses change from multiple races to a single race, from a single race to multiple races, from one single race to another, and some people add or drop a Hispanic response. The inflow of people to each race/Hispanic group is in many cases similar in size to the outflow from the same group, such that cross-sectional data would show a small net change. We find response changes across ages, sexes, regions, and response modes, with variation across groups. Researchers should consider the implications of changing race and Hispanic origin responses when conducting analyses and interpreting results.

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 Current Population Survey Annual Social and Economic Supplement (CPS ASEC) is an important source for estimates of the uninsured population. Previous research has shown that survey estimates produce an undercount of beneficiaries compared to Medicaid enrollment records. We extend past work by examining the Medicaid undercount in the 2007-2011 CPS ASEC compared to enrollment data from the Medicaid Statistical Information System for calendar years 2006-2010. By linking individuals across datasets, we analyze two types of response error regarding Medicaid enrollment – false negative error and false positive error. We use regression analysis to identify factors associated with these two types of response error in the 2011 CPS ASEC. We find that the Medicaid undercount was between 22 and 31 percent from 2007 to 2011. In 2011, the false negative rate was 40 percent, and 27 percent 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. We discuss implications for survey reports of health insurance coverage and for estimating the uninsured population.

Summary There have been many studies into the effectiveness of single interventions in pain, however, little is known of performance or outcome of pain clinics where treatment often consists of multiple, complex interventions. Many pain clinicians currently experience considerable difficulty in fulfilling the requirements of clinical governance and completing a personal portfolio. There is a clear and urgent need for a viable method of monitoring performance. This study describes a well‐developed computer‐based system – Pain Audit Collection System (PACS). PACS has been designed by pain clinicians through consensus and its success in uptake suggests that it is a viable method for outcome evaluation. An analysis is provided of outcome data in typical pain clinics. Further work is needed to investigate the utility of this data.