Ethnic differences in body mass index trajectories from adolescence to adulthood: a focus on Hispanic and Asian subgroups in the United States.

Compared to whites, U.S. Hispanics have higher obesity rates; U.S. Asians have lower rates. However Hispanics and Asians are each comprised of several ethnic subgroups that differ with respect to country of origin, immigration history, and geographic distribution across the U.S. Among adolescents, ethnic differences in obesity have been previously reported, but no studies have examined longitudinal change in body mass index (BMI) by Hispanic and Asian subgroup category to understand when and why these disparities emerge, especially during the critical transition between adolescence and adulthood.Using nationally-representative, longitudinal data from 1355 Hispanics (Mexican, Puerto Rican, Cuban, Central/South American, Other Hispanic), 520 Asians (Chinese, Filipino, Other Asian), and 5061whites from the National Longitudinal Study of Adolescent Health (Waves II-IV: 1996-2009), we used linear mixed spline models to examine whether Hispanic and Asian adolescent subgroups shared the same BMI trajectories as whites as they aged into adulthood. We also investigated the role of social and behavioral factors in explaining race/ethnic differences.Among Hispanics, Mexican and Puerto Rican-origin individuals exhibited faster increases in BMI both in adolescence and in adulthood and these patterns were not attributable to the measured social and behavioral factors. There was also evidence of emerging disparities in Cuban males, and in Central/South Americans relative to whites. In contrast, Chinese, Filipino, and Other Asian adolescents had significantly lower BMI and slower BMI increases in adulthood compared to whites. In models adjusted for social and behavioral factors, Chinese-white and Other Asian-white differentials remained unexplained.Aggregate estimates of Hispanics and Asians mask important heterogeneity in BMI. A better understanding of weight dynamics early in the life course can inform how and when disparities emerge to better target prevention efforts.

Abstract licence: CC BY

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.