Making Oneself "Fit": Community Perspectives on Disaggregated Race and Ethnicity Data Collection and Reporting in Healthcare Settings.

Patient race/ethnicity data collection in most U.S. health systems abide by federal standards, determined by the federal Office of Management and Budget. Yet, decades of research show that reliance on these categories alone limits understanding of within-group health disparities, systematically erasing key groups from health data. Because granular race/ethnicity data is complex and patients may be hesitant to disclose this personal information, it is important for health leaders to consider community perspectives when making decisions about race/ethnicity data procedures. As such, this study uses community focus groups to understand: (1) how individuals representing different racial/ethnic identities perceive the collection of race/ethnicity in healthcare settings; (2) differences in opinions between disaggregated race/ethnicity data collection instruments and those using federal standards; and (3) recommended practices for collecting race/ethnicity from patients. Participants self-selected into 13 focus groups and one key informant interview based on the race/ethnicity with which they most closely identified. Audio recordings from these groups were transcribed and evaluated using thematic content analysis. Among the 83 total participants in this study, there was a strong preference for more flexible and specific options for self-identifying race/ethnicity in healthcare settings. Participants also felt comfortable disclosing granular race/ethnicity to health providers but expressed discomfort with disclosing this information for other purposes. Recommendations for healthcare leaders include ensuring patients receive detailed communication about race/ethnicity data use and purpose, allowing multiple category selection, keeping the list of disaggregated response options short so as to not overwhelm patients, and providing a free text option to ensure inclusivity.

A Culturally Specific Community Supported Agriculture (CSA) Program to Improve Diet in Immigrant Communities in Brooklyn, New York.

Anti-Asian and anti-immigrant sentiment has surged in the country in the last 3 years. Food insecurity is also on the rise; in our local needs assessment of n = 1,270 Asian American adults in New York City, accessing food was cited as the number 1 priority among those who needed help. Finally, racial discrimination and food access are related to fear of being attacked-driving feelings of safety and therefore willingness to travel for food. To combat these narratives and leveraging pivots by our community partners, we implemented a community-supported agriculture pilot program (n = 38) to assess whether culturally appropriate food access can improve diet and foster cross-cultural learning among immigrant families in Brooklyn, NY. Over a 20-week period from June to October 2022, participants received Chinese-specific produce and nutrition education. Participants reported eating more and a greater variety of vegetables and had higher vegetable intake measured via skin carotenoid scores. This pilot may inform the adaptation of nutrition interventions to reduce inequities in chronic diseases in immigrant communities.

Substance use and treatment disparities among Asian Americans, Native Hawaiians, and Pacific Islanders: A systematic review.

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: 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.

Trends and disparities in prevalence of cardiometabolic diseases by food security status in the United States.

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: 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.

Profiles of a COVID-19 Syndemic: Anti-Asian Racism, Economic Challenges, and Mental and Physical Health.

BACKGROUND: During the COVID-19 pandemic, Asians/Asian Americans have experienced co-occurring threats of anti-Asian racism, economic challenges, and negative mental and physical health symptoms. OBJECTIVES: We examined the co-occurrence of COVID-19-related anti-Asian discrimination and collective racism, economic stressors, and mental and physical health challenges for Asians/Asian Americans during the COVID-19 pandemic. We also examined Asian/Asian American subgroups associated with these threats. METHODS: Nationally representative data from the 2021 Asian American and Native Hawaiian/Pacific Islander COVID-19 Needs Assessment Project (unweighted N = 3,508) were used to conduct a latent profile analysis to identify unique typologies of the co-occurrence of these threats. We also conducted chi-square analyses to investigate subgroup differences by latent profile. RESULTS: We identified five distinct latent profiles: multi-threat impact, low impact, collective racism, health challenges, and economic/health challenges. Forty percent of Asians/Asian Americans were in the multi-threat impact profile, indicating high levels across COVID-19-related threats. Subgroup analyses revealed significant differences in profile membership. East Asians, US-born Asians/Asian Americans, and those aged 25-44 seemed to be particularly affected by the proposed syndemic; results also differed by income. CONCLUSION: Asians/Asian Americans have experienced co-occurring and interrelated threats during COVID-19 that suggest the presence of a syndemic. Results from our study point to vulnerable Asian/Asian American subgroups and the need for targeted public health efforts to address racism, health challenges, and economic challenges in the context of COVID-19.

Associations Between Ultra-processed Food Consumption and Cardiometabolic Health Among Older US Adults: Comparing Older Asian Americans to Older Adults From Other Major Race-Ethnic Groups.

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.

Turning the Health Equity Lens to Diversity in Asian American Health Profiles.

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.

A qualitative study to inform the development of a subsidized community-supported agriculture program for Chinese Americans in Brooklyn, New York, U.S.

The objective of this study was to inform the development of a subsidized, culturally adapted Community-Supported Agriculture (CSA) program for the Chinese American (CA) community in Brooklyn, New York (NY), USA. We conducted interviews with CA adults to understand their eating and shopping behaviors, interests in CSAs, and recommendations for educational content to inform the development of a subsidized and culturally adapted CSA. We then conducted thematic analysis of those interviews and identified major themes. CA adult participants shared interest in a CSA primarily to support their own health, interact socially with farmers and other participants, and gain access to fresh, culturally appropriate produce. Major concerns for participation, especially among older adults, included language barriers, transportation, and mobility. The unique needs and motivations of CAs should be centered in the development of alternative food access interventions for this population. CA adult participants living in Brooklyn, NY may be interested in a subsidized culturally adapted CSA that includes health information about the produce and provides in-language support. Engaging communities in the development of a health program may be important to ensure accessibility and acceptability for CA populations.

Establishing the criterion validity of an adapted dietary screener for Asian Americans amongst Chinese American adults.

OBJECTIVE: To assess the criterion validity of a dietary screener questionnaire adapted for Asian Americans (ADSQ) compared to Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA-24) food diary data amongst Chinese American Adults (CHAs). The ADSQ incorporated example ethnic foods from six Asian American groups. Lessons learned with respect to translating the ADSQ from English into Simplified Chinese were also documented. DESIGN: Agreement between a two-day food diary (one weekend day and one weekday) and the ADSQ was assessed for vegetable, fruit, dairy, added sugar, fiber, calcium, and whole grain intake using paired t-tests to compare means and Spearman correlations to assess agreement between intake of food components. SETTING: Data were collected online and via phone interviews. PARTICIPANTS: Thirty-three CHAs aged 19-62 years (63.6% female). RESULTS: Mean differences were small for fruit, dairy, fiber, calcium, and whole grain intake, but were significantly different for vegetables and added sugar intake. Spearman correlations were < 0.5 and non-significant (p > 0.05) for all components. Both the ASA-24 and the ADSQ identified the same categories where CHAs intake is misaligned with dietary recommendations: whole grains, total fruit, and dairy. Difficulties were encountered in translating 13 out of 26 questions. CONCLUSIONS: The ADSQ may be a useful tool to identify intervention targets for improving dietary quality, but caution is warranted when interpreting vegetable and added sugar estimates. Differences in the English and Chinese languages underscore the need to take into account both literal translations and semantics in translating the ADSQ into other languages.

Relative validity of a Diet Risk Score (DRS) for Chinese American adults.

Objective: The objective of this study was to evaluate the relative validity of the nine-item Diet Risk Score (DRS) among Chinese American adults using Healthy Eating Index (HEI)-2015 scores. We provide insights into the application of the Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) for this population, and report on lessons learned from carrying out participant recruitment during the COVID-19 pandemic. Methods: Thirty-three Chinese American adults (mean age=40; 36% male) were recruited from the community and through ResearchMatch. Participants completed the DRS and two 24-hour food records, which were entered into the ASA 24-Hour Dietary Assessment Tool (ASA24) by community health workers (CHWs). HEI-2015 scores were calculated from each food record and an average score was obtained for each participant. One-way analysis of variance and Spearman correlations were used to compare total and component scores between the DRS and HEI-2015. Results: Mean HEI-2015 score was 56.7/100 (SD 10.6) and mean DRS score was 11.8/27 (SD 4.7), with higher scores reflecting better and worse diets, respectively. HEI-2015 and DRS scores were inversely correlated (r=-0.43, p<0.05). The strongest correlations were between HEI-2015 Total Vegetables and DRS Vegetables (r=-0.5, p<0.01), HEI-2015 Total Vegetables and Green Vegetables (r=-0.43, p=0.01) and HEI-2015 Seafood/Plant Protein and DRS Fish (r=-0.47, p<0.01). The inability to advertise and recruit for the study in person at community centres due to pandemic restrictions impeded the recruitment of less-acculturated individuals. A lack of cultural food items in the ASA24 database made it difficult to record dietary intake as reported by participants. Conclusion: The DRS can be a valuable tool for physicians to identify and reach Chinese Americans at risk of cardiometabolic disease.

A microbial causal mediation analytic tool for health disparity and applications in body mass index.

BACKGROUND: Emerging evidence suggests the potential mediating role of microbiome in health disparities. However, no analytic framework can be directly used to analyze microbiome as a mediator between health disparity and clinical outcome, due to the non-manipulable nature of the exposure and the unique structure of microbiome data, including high dimensionality, sparsity, and compositionality. METHODS: Considering the modifiable and quantitative features of the microbiome, we propose a microbial causal mediation model framework, SparseMCMM_HD, to uncover the mediating role of microbiome in health disparities, by depicting a plausible path from a non-manipulable exposure (e.g., ethnicity or region) to the outcome through the microbiome. The proposed SparseMCMM_HD rigorously defines and quantifies the manipulable disparity measure that would be eliminated by equalizing microbiome profiles between comparison and reference groups and innovatively and successfully extends the existing microbial mediation methods, which are originally proposed under potential outcome or counterfactual outcome study design, to address health disparities. RESULTS: Through three body mass index (BMI) studies selected from the curatedMetagenomicData 3.4.2 package and the American gut project: China vs. USA, China vs. UK, and Asian or Pacific Islander (API) vs. Caucasian, we exhibit the utility of the proposed SparseMCMM_HD framework for investigating the microbiome's contributions in health disparities. Specifically, BMI exhibits disparities and microbial community diversities are significantly distinctive between reference and comparison groups in all three applications. By employing SparseMCMM_HD, we illustrate that microbiome plays a crucial role in explaining the disparities in BMI between ethnicities or regions. 20.63%, 33.09%, and 25.71% of the overall disparity in BMI in China-USA, China-UK, and API-Caucasian comparisons, respectively, would be eliminated if the between-group microbiome profiles were equalized; and 15, 18, and 16 species are identified to play the mediating role respectively. CONCLUSIONS: The proposed SparseMCMM_HD is an effective and validated tool to elucidate the mediating role of microbiome in health disparity. Three BMI applications shed light on the utility of microbiome in reducing BMI disparity by manipulating microbial profiles. Video Abstract.

Identifying opportunities for collective action around community nutrition programming through participatory systems science.

PURPOSE: 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: 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.

Transportation and language access as crucial pillars for an immigrant-inclusive 21st-century food security program.

Though food insecurity has long been recognised to impact health, population-specific determinants of food insecurity have recently been studied systematically as an important public health concern. Indeed, while immigrant populations face particular challenges to equitable access to the food system, many of these concerns have not been systematically described. To this end, we critically review recent work that demonstrates the importance of transportation and language access as independent determinants of access to food for immigrant populations. Furthermore, we highlight proposals to mitigate barriers to access, including both academic and community-driven approaches to create overlapping institutional commitments for inclusive policymaking that meets the specific needs of diverse populations.

Methods for retrospectively improving race/ethnicity data quality: a scoping review.

Improving race and ethnicity (hereafter, race/ethnicity) data quality is imperative to ensure underserved populations are represented in data sets used to identify health disparities and inform health care policy. We performed a scoping review of methods that retrospectively improve race/ethnicity classification in secondary data sets. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, searches were conducted in the MEDLINE, Embase, and Web of Science Core Collection databases 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. Six main method types for improving race/ethnicity data 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%). Some form of validation evaluation was included in 86 articles (72%). We discuss the strengths and limitations of different method types and potential harms of identified methods. Innovative methods are needed to better identify racial/ethnic subgroups and further validation studies. Accurately collecting and reporting disaggregated data by race/ethnicity are critical to address the systematic missingness of relevant demographic data that can erroneously guide policymaking and hinder the effectiveness of health care practices and intervention.

Consumption of Ultraprocessed Foods and Body Fat Distribution Among U.S. Adults.

INTRODUCTION: 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.

A microbial causal mediation analytic tool for health disparity and applications in body mass index.

Background: Emerging evidence suggests the potential mediating role of microbiome in health disparities. However, no analytic framework is available to analyze microbiome as a mediator between health disparity and clinical outcome, due to the unique structure of microbiome data, including high dimensionality, sparsity, and compositionality. Methods: Considering the modifiable and quantitative features of microbiome, we propose a microbial causal mediation model framework, SparseMCMM_HD, to uncover the mediating role of microbiome in health disparities, by depicting a plausible path from a non-manipulable exposure (e.g. race or region) to a continuous outcome through microbiome. The proposed SparseMCMM_HD rigorously defines and quantifies the manipulable disparity measure that would be eliminated by equalizing microbiome profiles between comparison and reference groups. Moreover, two tests checking the impact of microbiome on health disparity are proposed. Results: Through three body mass index (BMI) studies selected from the curatedMetagenomicData 3.4.2 package and the American gut project: China vs. USA, China vs. UK, and Asian or Pacific Islander (API) vs. Caucasian, we exhibit the utility of the proposed SparseMCMM_HD framework for investigating microbiome’s contributions in health disparities. Specifically, BMI exhibits disparities and microbial community diversities are significantly distinctive between the reference and comparison groups in all three applications. By employing SparseMCMM_HD, we illustrate that microbiome plays a crucial role in explaining the disparities in BMI between races or regions. 11.99%, 12.90%, and 7.4% of the overall disparity in BMI in China-USA, China-UK, and API-Caucasian comparisons, respectively, would be eliminated if the between-group microbiome profiles were equalized; and 15, 21, and 12 species are identified to play the mediating role respectively. Conclusions: The proposed SparseMCMM_HD is an effective and validated tool to elucidate the mediating role of microbiome in health disparity. Three BMI applications shed light on the utility of microbiome in reducing BMI disparity by manipulating microbial profiles.

Trends and disparities in diabetes and prediabetes among adults in the United States, 1999-2018.

OBJECTIVE: The aims of the study were to describe the up-to-date trend of total, diagnosed, and undiagnosed diabetes and prediabetes, assess their associated disparities among population subgroups, and examine their relationship with sociodemographic factors among adults in the United States. STUDY DESIGN: This was a cross-sectional study from a nationally representative sample of US adults (aged ≥20 years) who participated in the National Health and Nutrition Examination Survey. METHODS: Diagnosed diabetes was defined as a self-reported previous diagnosis of diabetes by a physician or any other health professionals (other than during pregnancy). Undiagnosed diabetes was defined as elevated levels of fasting plasma glucose (≥126 mg/dL) or HbA1c (≥6.5%). Total diabetes included those who had either diagnosed or undiagnosed diabetes. Prediabetes was defined as an HbA1c level of 5.7%-6.4% or a fasting plasma glucose level of 100-125 mg/dL. All estimates were age standardized to the 2010 US census population for age groups 20-44, 45-64, and 65+ years. All analyses accounted for the complex survey design. Logistic regressions were used to conduct the analyses. RESULTS: A total of 21,600 (mean, 47.2 years [SD, 14.7]) individuals were analyzed. From 1999 to 2018, the age-standardized prevalence increased significantly from 9.17% to 14.7% (difference, 5.52%; 95% confidence interval [CI], 2.69%-8.35%; P-trend <0.001) for total diabetes, increased from 6.15% to 11.0% (difference, 4.79%; 95% CI, 2.27%-7.32%; P-trend<0.001) for diagnosed diabetes and remained stable from 3.01% to 3.73% (difference, 0.72%; 95% CI, -0.47% to 1.91%; P-trend = 0.19) for undiagnosed diabetes. The age-standardized prevalence of prediabetes increased significantly from 29.5% to 48.3% (difference, 18.8%; 95% CI, 13.3%-24.4%; P-trend<0.001). Disparities persisted with higher prevalence among adults with obesity and populations that have been marginalized, including racial and ethnic minorities, low income, less educated Americans, and those living in food-insecure household. CONCLUSIONS: The prevalence of diabetes and prediabetes increased significantly from 1999 to 2018 among US adults. There are substantial and persistent disparities among racial and ethnic minorities, populations experiencing socio-economic disadvantages, and adults with obesity.