Ethnic/race differences in the attrition of older American survey respondents: implications for health-related research.

A large literature is devoted to the investigation of race/ethnic health disparities relying on data analysis. While the minority population is growing (U.S. Census Bureau 2008; Arias 2010), quite often past and current data have small samples of Mexican Americans, Latinos including Mexican Americans, blacks, and others, especially when stratified by basic demographic characteristics, such as age and gender (Dunlop et al. 2002). Representativeness of the participating respondents of the population of interest and nonrandom loss from the study are one of the biggest concerns leading to bias estimates and nongeneralizability of some results (Hausman and Wise 1979; Diehr and Johnson 2005). While representativeness of the sample is difficult to test due to lack of information about nonparticipants, attrition has been analyzed and addressed in some studies on health disparities. However, none of the studies have compared models of attrition across different ethnic and racial groups of aging populations. Using the Health and Retirement Study (HRS) 1992–2008, this study compares models of attrition for Latinos including Mexican American, Mexican American separately, black, and white elderly Americans. The main hypothesis is whether variables describing foreign birth, health insurance, and health status are associated with attrition and whether these associations differ across racial/ethnic groups of elderly. This study falls into the strand of literature about determinants of attrition among elderly individuals (Mihelic and Crimmins 1997; Van Beijsterveldt et al. 2002; Chatfield et al. 2005; Kapteyn et al. 2006; Tyas et al. 2006; Stimpson and Ray 2010). A recent literature review of attrition among elderly found that age, cognitive impairment, and poor health were associated with high attrition rates (Chatfield et al. 2005). However, none of the studies provided a comparison of determinants of attrition across different races and ethnicities. Such attrition is essential to the assessment of race/ethnic health disparities. Kapteyn et al. (2006) analyzed attrition in the HRS and found that being born outside of the United States, being Hispanic, being in poor/fair health, or having the onset of a health condition was associated with a higher probability of attrition than remaining in the HRS, depending on the type of model estimated. However, their analysis did not address the question of whether Hispanics and blacks with certain characteristics, for example, being in poor health, were more likely to attrite compared to similar whites. Rather than estimating a separate model for each race/ethnicity or interacting race/ethnicity with other covariates, the authors estimated a multinomial logit model for the pooled sample of whites, blacks, and Hispanics while introducing binary variables for each race/ethnicity category. This approach imposes a “common-effect” assumption when effects of other covariates on attrition are the same across different groups of elderly, which is relaxed in our study (Jha et al. 2007). Documenting differences in the attrition by race/ethnicity is also relevant to the literature on the “Hispanic paradox,” a concept that describes a mortality advantage among Hispanics compared to similar whites and African Americans (Markides and Coreil 1986). Among other explanations, some researchers have argued that this phenomenon is a result of the selective return migration to the country of origin by older people in poor health, the so-called salmon bias hypothesis (Markides and Eschbach 2005). However, we cannot test this hypothesis due to lack of data on migration. A competing risks model was estimated using a multinomial logit model when respondents of the study faced competing types of risks, such as dying, being lost from the study, and nonresponding in some years (Kapteyn et al. 2006). That is, when respondents entered the HRS in 1992, some of the participants were still present in 2008, the latest year available. However, some of the respondents died, others were in and out of the study during this period, while the rest of the respondents were lost from the study. Only one event/failure, such as death, nonresponse, or loss, can take place exclusively to the others with some probability, which defines a “competing risks” situation. The main assumption is the independence of risks of each event/failure type conditional on explanatory variables. The key explanatory variables are foreign birth, health insurance, and health status. Given the substantial financial and human resources dedicated to the issue of racial/ethnic health disparities, this study may assist in developing future studies on health disparities in aging populations. Moreover, differential attrition by race/ethnicity in panel surveys has significant negative implications for health research in general due to growing ethnical diversity of the U.S. population.