Using vignettes to rethink Latino-white disparities in self-rated health.

Researchers often rely on respondents' self-rated health (SRH) to measure social disparities in health, but recent studies suggest that systematically different reporting styles across groups can yield misleading conclusions about disparities in SRH. In this study, we test whether this finding extends to ethnic differences in self-assessments of health in particular domains. We document differences between US-born whites and four Latino subgroups in respondents' assessments of health in six health domains using data from the second wave of the Los Angeles Family and Neighborhood Survey (N = 1468). We use both conventional methods and an approach that uses vignettes to adjust for differential reporting styles. Our results suggest that despite consistent evidence from the literature that Latinos tend to rate their overall health more poorly than whites, and that Latino immigrants report worse SRH than US-born Latinos, this pattern is not true of self-reports in individual health domains. We find that at the bivariate level, US-born whites (and often US-born Mexicans) have significantly more pessimistic reporting styles than Latino immigrants. After adding controls, we find evidence of significantly different reporting styles for only one domain: US-born Mexicans and whites consistently interpret head pain more severely than the other Latino subgroups. Finally, we find that both before and after adjusting for differences in rating styles across groups, non-Mexican Latino immigrants report better social and physical functioning and less pain than other groups. Our findings underscore the advantages of domain-specific ratings when evaluating ethnic differences in self-assessments of health. We encourage researchers studying social disparities in health to consider respondents' self-assessments in a variety of domains, and to also investigate (when possible) potential biases in their findings due to different reporting styles. The anchoring vignettes approach we use is one potential method for overcoming biases due to different rating styles across groups.