Objective: extensive effort has been made to better define metabolic syndrome (MetS). Whether current definitions accurately diagnose MetS and predict risk of cardiovascular disease (CVD) or diabetes in diverse ethnic groups remains largely unknown. The objective of this study was to compare the prevalence of MetS and risk of CVD and diabetes among Asian American adults using two MetS definitions, one proposed by the Third Report of the National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (ATP III) and one by the International Diabetes Federation (IDF). Methods: we obtained a nationally representative sample of 2121 Asian American adults in the noninstitutionalized civilian population of the United States from the National Health and Nutrition Examination Survey (2011–2016). We computed age-adjusted, gender-specific MetS prevalence and each MetS component using ATP III and IDF definitions. Results: based on the IDF definition, MetS prevalence was 39.26% among Asian American men and 39.66% among Asian American women included in the study sample. Based on the ATP III definition, MetS prevalence in our sample was 39.38% among men and 36.11% among women. We found good concordance between the IDF and the ATP III definitions in identifying MetS in Asian American adults. Those with MetS defined only by the IDF definition had significantly higher body mass index (BMI) and waist circumference than those with MetS defined only by the ATP III definition. The IDF definition also better predicted elevated fasting insulin. Conclusions: the IDF definition is more pertinent than the ATP III definition for screening and estimating risk of CVD and diabetes in Asian American adults. Future studies should examine differences in MetS prevalence across Asian ethnic groups to facilitate the development of culturally tailored strategies improve MetS prevention and detection in Asian Americans.

(1) Background: Despite having consistently lower rates of obesity than other ethnic groups, Asian Americans (AAs) are more likely to be identified as metabolically obese, suggesting an ethnic-specific association between BMI and cardiometabolic outcomes. The goal of this study was to provide an estimate of metabolic syndrome (MetS) prevalence among AAs using national survey data and to compare this rate to that of non-Hispanic Whites (NHWs) over the BMI continuum. (2) Methods: Using the NHANES 2011–2016 data, we computed age-adjusted, gender-specific prevalence of MetS and its individual components for three BMI categories. Furthermore, we conducted multivariate binary logistic regression to examine the risk of MetS in AAs compared to NHWs, controlling for sociodemographic and lifestyle factors. The analysis sample consisted of 2121 AAs and 6318 NHWs. (3) Results: Among AAs, the prevalence of MetS and its components increased with higher BMI levels, with overall prevalence being 5.23% for BMI < 23, 38.23% for BMI of 23–27.4, and 77.68% for BMI ≥ 27.5 in men; and 18.61% for BMI < 23, 47.82% for BMI of 23–27.4, and 67.73% for BMI ≥ 27.5 in women. We also found that for those with a BMI > 23, AAs had a higher predicted risk of MetS than their NHW counterparts of the same BMI level, in both men and women. (4) Conclusions: Our findings support the use of lower BMI ranges for defining overweight and obesity in Asian populations, which would allow for earlier and more appropriate screening for MetS and may better facilitate prevention efforts.

Using nationally representative survey data, this research note examines the association between immigrant legal status and poverty in the United States. Our objective is to test whether estimates of this association vary depending on the method used to infer legal status in survey data, focusing on two approaches in particular: (1) inferring legal status using a logical imputation method that ignores the existence of legal-status survey questions (logical approach); and (2) defining legal status based on survey questions about legal status (survey approach). We show that the two methods yield contrasting conclusions. In models using the logical approach, among noncitizens, being a legal permanent resident (LPR) is counterintuitively associated with a significantly greater net probability of being below the poverty line compared with their noncitizen peers without LPR status. Conversely, using the survey approach to measure legal status, LPR status is associated with a lower net probability of living in poverty, which is in line with a growing body of qualitative and small-sample evidence. Consistent with simulation experiments carried out by Van Hook et al. (2015), the findings call for a more cautious approach to interpreting research results based on legal status imputations and for greater attention to potential biases introduced by various methodological approaches to inferring individuals’ legal status in survey data. Consequently, the approach used for measuring legal status has important implications for future research on immigration and legal status.

Research on the stratifying effects of migration status has increased sharply in the last two decades, although efforts have been hampered by the near absence of representative data that include detailed migration status measures. Researchers have developed various statistical and logical imputation methods that have produced widely varying estimates. In this article, we introduce a new indicator of migration status constructed from two federal surveys matched to the Social Security Administration's Numident file, a database that includes all citizens and legal residents of the United States. In models predicting poverty, our measure produces estimates comparable to those based on respondents’ own self-reports, in one federal survey, of their migration status. Both the administrative and survey-based measures produce poverty gradients that diverge from those produced by logic-based measures. Our findings contribute to mounting evidence of bias in the use of certain kinds of logic-based algorithms to impute migration status and demonstrate the promise of administrative record linkages in migration status research.