In the United States (US), limited English proficiency is associated with a higher risk of obesity and diabetes. “Intersectionality”, or the interconnected nature of social categorizations, such as race/ethnicity and… Click to show full abstract
In the United States (US), limited English proficiency is associated with a higher risk of obesity and diabetes. “Intersectionality”, or the interconnected nature of social categorizations, such as race/ethnicity and gender, creates interdependent systems of disadvantage, which impact health and create complex health inequities. How these patterns are associated with language-based health inequities is not well understood. The study objective was to assess the potential for race/ethnicity, gender, and socioeconomic status to jointly moderate the association between primary language (English/Spanish) and having obesity and diabetes. Using the 2018 Behavioral Risk Factor Surveillance System (n = 431,045), weighted generalized linear models with a logistic link were used to estimate the associations between primary language (English/Spanish) and obesity and diabetes status, adjusting for confounders using stratification for the intersections of gender and race/ethnicity (White, Black, Other). Respondents whose primary language was Spanish were 11.6% more likely to have obesity (95% CI 7.4%, 15.9%) and 15.1% more likely to have diabetes (95% CI 10.1%, 20.3%) compared to English speakers. Compared to English speakers, Spanish speakers were more likely to have both obesity (p < 0.001) and diabetes (p < 0.001) among White females. Spanish speakers were also more likely to have obesity among males and females of other races/ethnicities (p < 0.001 for both), and White females (p = 0.042). Among males of other racial/ethnic classifications, Spanish speakers were less likely to have both obesity (p = 0.011) and diabetes (p = 0.005) than English speakers. Health promotion efforts need to recognize these differences and critical systems–change efforts designed to fundamentally transform underlying conditions that lead to health inequities should also consider these critical sociodemographic factors to maximize their effectiveness.
               
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