Faculty Bibliography

BACKGROUND:The increasing relevance of substance use disorder (SUD) within the Asian American, Native Hawaiian, and Pacific Islander (AA&NH/PI) communities, particularly amidst rising anti-Asian hate incidents and the disproportionate health and economic challenges faced by the NH/PI community during the COVID-19 pandemic, underscores the urgency of understanding substance use patterns, treatment disparities, and outcomes. METHODS:Following PRISMA guidelines, 37 out of 231 studies met the search criteria. Study characteristics, study datasets, substance use rates, SUD rates, treatment disparities, treatment quality, completion rates, and analyses disaggregated by the most specific AA&NH/PI ethnic group reported were examined. RESULTS:Despite increased treatment admissions over the past two decades, AA&NH/PI remain underrepresented in treatment facilities and underutilize SUD care services. Treatment quality and completion rates are also lower among AA&NH/PI. Analyses that did not disaggregate AA and NHPI as distinct groups from each other or that presented aggregate data only within AA or NHPI as a whole were common, but available disaggregated analyses reveal variations in substance use and treatment disparities among ethnic groups. There is also a lack of research in exploring within-group disparities, including specific case of older adults and substance use. CONCLUSION/CONCLUSIONS:To address disparities in access to substance use treatment and improve outcomes for AA&NH/PI populations, targeted interventions and strategic data collection methods that capture diverse ethnic groups and languages are crucial. Acknowledging data bias and expanding data collection to encompass multiple languages are essential for fostering a more inclusive approach to addressing SUD among AA&NH/PI populations.

BACKGROUND:Previous studies have demonstrated the association between food security and cardiometabolic diseases (CMDs), yet none have investigated trends in prevalence of CMDs by food security status in the United States (US). METHODS:Serial cross-sectional analysis of the US nationally representative data from National Health and Nutrition Examination Survey (1999-2018) was conducted among adults aged 20 years or older. Food security status was defined by the US Household Food Security Survey Module (full, marginal, low, and very low food security). We estimated the age-adjusted prevalence of CMDs including obesity, hypertension, diabetes, and coronary heart disease by food security status. Racial and ethnic disparities in age-adjusted prevalence of CMDs by food security status were also assessed. RESULTS:A total of 49,738 participants were included in this analysis (weighted mean age 47.3 years; 51.3% women). From 1999 to 2018, the age-adjusted prevalence of CMDs was lower in full food secure group as compared with other groups. For example, trends in hypertension decreased from 49.7% (47.5-51.8%) to 45.9% (43.8-48.0%) (P-trend = 0.002) among the full and from 54.2% (49.9-58.5%) to 49.7% (46.8-52.6%) (P-trend = 0.02) among the marginal but remained stable among the low at 49.7% (47.9-51.6%) and among the very low at 51.1% (48.9-53.3%) (P-interaction = 0.02). Prevalence of diabetes increased from 8.85% (8.15-9.60%) to 12.2% (11.1-13.5%) among the full (P-trend < 0.001), from 16.5% (13.2-20.4%) to 20.9% (18.6-23.5%) (P-trend = 0.045) among the marginal and from 14.6% (11.1-19.0%) to 20.9% (18.8-23.3%) (P-trend = 0.001) among the low but remained stable at 18.8% (17.0-20.9) among the very low (P-trend = 0.35) (P-interaction = 0.03). Racial and ethnic differences in prevalence of CMD by food security status were observed. For example, among individuals with full food secure status, the prevalence of diabetes was 9.08% (95% CI, 8.60-9.59%) for non-Hispanic whites, 17.3% (95% CI, 16.4-18.2%) for non-Hispanic blacks, 16.1% (95% CI, 15.0-17.4%) for Hispanics and 14.9% (95% CI, 13.3-16.7%) for others. CONCLUSIONS AND RELEVANCE/CONCLUSIONS:Prevalence of CMDs was greatest among those experiencing food insecurity, and food insecurity disproportionately affected racial/ethnic minorities. Disparities in CMD prevalence by food security status persisted or worsened, especially among racial/ethnic minorities.

The monolithic misrepresentation of Asian American (AsAm) populations has maintained assumptions that AsAm people are not burdened by health disparities and social and economic inequities. However, the story is more nuanced. We critically review AsAm health research to present knowledge of AsAm health profiles from the past two decades and present findings and opportunities across three topical domains: (a) general descriptive knowledge, (b) factors affecting health care uptake, and (c) effective interventions. Much of the literature emphasized underutilization of health care services; low knowledge and awareness among AsAms about risk factors, prevention, diagnosis, and treatment; inadequate efforts to improve language access, provider-patient communication, and trust; and the critical roles of community- and faith-based organizations and leaders in health promotion initiatives. Future opportunities for AsAm health research will require adoption of and significant investment in community-engaged research infrastructure to increase representation, funding, and research innovation for AsAm communities. Expected final online publication date for the Annual Review of Public Health, Volume 45 is April 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Using data from the National Health and Nutrition Examination Survey (2001-2018; N = 19,602), this study examined whether ultra-processed food (UPF) consumption is associated with cardiometabolic health (obesity, hypertension, high cholesterol, and diabetes), among White, Black, Hispanic, and Asian Americans (AA) US adults 50 or older. Diet was assessed using 24 hour dietary recall. NOVA dietary classification system was used to calculate the percentage of caloric intake derived from UPFs. Cardiometabolic information was assessed through physical examination, blood tests, and self-reported medication information. A median of 54% (IQR: 40%, 68%) of caloric intake was attributed to UPFs and was lowest for AAs (34%, IQR: 20%, 49%) and highest for White adults (56%; IQR: 42, 69%). In multivariable adjusted models, UPF consumption was associated with greater odds of obesity, high cholesterol, and diabetes. UPF consumption is associated with poor cardiometabolic health among all US older adults. For AAs, UPFs may be particularly obesogenic.

Improving race/ethnicity data quality is imperative to ensuring underserved populations are represented in datasets used to identify health disparities and inform healthcare policy. We performed a scoping review of methods that retrospectively improve race/ethnicity classification in secondary datasets. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, searches were conducted in MEDLINE, Embase and Web of Science Core Collection in July 2022. A total of 2,441 abstracts were dually screened, 453 full-text articles were reviewed, and 120 articles were included. Study characteristics were extracted and described in a narrative analysis, including: method type used for race/ethnicity classification; races/ethnicities targeted for classification; publication year; method inputs; reference population (if applicable); target population; and whether the article included a validation process. Six main method types for improving race/ethnicity were identified: Expert Review (n=9; 8%), Name Lists (n = 27; 23%), Name Algorithms (n=55; 46%), Machine Learning (n=14; 12%), Data Linkage (n=9; 8%), and Other (n=6; 5%). The main racial/ethnic groups targeted for classification were Asian (n = 56; 47%) and White (n = 51; 43%). Eighty-six articles (72%) included some form of validation evaluation. We discuss the strengths and limitations of different method types and potential harms of identified methods. We recommend the need for innovative methods to better identify racial/ethnic subgroups and further validation studies. Accurately collecting and reporting disaggregated data by race/ethnicity is critical to address the systematic missingness of relevant demographic data that can erroneously guide policymaking and hinder the effectiveness of healthcare practices and intervention.

PURPOSE/OBJECTIVE:To apply principles of group model building (GMB), a participatory systems science approach, to identify barriers and opportunities for collective impact around nutrition programming to reduce cancer risk for immigrant communities in an urban environment. METHODS:We convened four in-person workshops applying GMB with nine community partners to generate causal loop diagrams (CLDs)-a visual representation of hypothesized causal relationships between variables and feedback structures within a system. GMB workshops prompted participants to collaboratively identify programmatic goals and challenges related to (1) community gardening, (2) nutrition education, (3) food assistance programs, and (4) community-supported agriculture. Participants then attended a plenary session to integrate findings from all workshops and identify cross-cutting ideas for collective action. RESULTS:Several multilevel barriers to nutrition programming emerged: (1) food policies center the diets and practices of White Americans and inhibit culturally tailored food guidelines and funding for culturally appropriate nutrition education; (2) the lack of culturally tailored nutrition education in communities is a missed opportunity for fostering pride in immigrant food culture and sustainment of traditional food practices; and (3) the limited availability of traditional ethnic produce in food assistance programs serving historically marginalized immigrant communities increases food waste and worsens food insecurity. CONCLUSION/CONCLUSIONS:Emergent themes coalesced around the need to embed cultural tailoring into all levels of the food system, while also considering other characteristics of communities being reached (e.g., language needs). These efforts require coordinated actions related to food policy and advocacy, to better institutionalize these practices within the nutrition space.

INTRODUCTION/BACKGROUND:The association between ultraprocessed food consumption and body composition and potential variations by sociodemographic factors is unclear. This study aims to examine the cross-sectional associations of ultraprocessed food consumption with imaging markers of body fat distribution in a nationally representative sample of U.S. adults, overall and by sociodemographic strata. METHODS:A total of 9,640 men and nonpregnant women aged 20-59 years were included from 4 cycles (2011-2012, 2013-2014, 2015-2016, 2017-2018) of the National Health and Nutrition Examination Survey with valid 24-hour dietary recalls and available whole-body dual-energy x-ray absorptiometry scans. Ultraprocessed foods were identified using the NOVA classification, with percentage energy from ultraprocessed food assessed in quintiles. Primary outcomes were absolute percentage fat (total, android, gynoid), and secondary ones were percentage fat (head, arm, leg, trunk), total abdominal fat (area, mass, volume), subcutaneous adipose tissue (area, mass, volume), and visceral adipose tissue (area, mass, volume). Multivariable-adjusted generalized linear regressions estimated independent relationships of ultraprocessed food intake with body composition overall and by sociodemographic subgroups. Analyses were conducted in September 2022 and January 2023. RESULTS:Ultraprocessed food consumption accounted for more than half (55.5%) of daily energy consumption in this sample. Adults in the highest quintile (>72.1% energy) had 1.60 higher total percentage fat (95% CI=0.94, 2.26), 2.08 higher android percentage fat (95% CI=1.26, 2.89), and 1.32 higher gynoid percentage fat (95% CI=0.71, 1.93) than those in the lowest quintile of ultraprocessed food consumption (<39.4% energy) (all p-trend<0.001). Consistent findings were observed for secondary outcomes. Associations of ultraprocessed food intake with total percentage fat, android percentage fat, and gynoid percentage fat varied by age, sex, race and ethnicity, education, and income. Among those in the highest quintile of ultraprocessed food consumption compared with the lowest quintile counterpart, total percentage fat was 1.85 (95% CI=0.86, 2.84) higher for non-Hispanic White adults and 1.57 (95% CI=0.68, 2.46) higher for Hispanic adults (p-trends<0.001), whereas no difference was observed among non-Hispanic Black adults (-0.22; 95% CI= -0.93, 1.36) (p-trend=0.47) and non-Hispanic Asian adults (0.93; 95% CI= -0.57, 2.42) (p-trend=0.04) (p-interaction=0.001). Associational patterns were similar for android percentage fat and gynoid percentage fat. CONCLUSIONS:In a national U.S. sample, higher intake of ultraprocessed food was associated with greater body fat, in particular android fat, and this relationship was most prominent in certain population subgroups. These cross-sectional findings call for prospective and interventional studies to assess the impact of ultraprocessed food on body composition in different populations.