Patterns of US Citizenship Status vs. Diet Quality among Adults of African Descent

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Jeffery, Xuejing Duan, Azam Ardakani, Sapna Batheja, Gifty Stevinson, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4183130/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jul, 2025 Read the published version in BMC Nutrition → Version 1 posted 15 You are reading this latest preprint version Abstract Introduction : With the substantial growth rate of the Black immigrant population in the US, the impact of colonization on the diets of African Americans and disproportionately high rates of chronic diseases in the Black Community, studies should place more emphasis on ethnicity when investigating nutrition-related risk factors. This study examined the relationship between variations in citizenship level and diet quality among adult subjects of African descent. Methods : We analyzed data from 1,198 African American adults in the NHANES 2015-2016. A 'citizenship level' scale was developed using principal component analysis, incorporating years lived in the US, country of birth, and citizenship status. Diet quality was assessed using HEI scores. Associations between citizenship level, gender, age, income, and categorized diet quality (poor, moderate, or good) were examined using a Monte Carlo simulation of Fisher's exact tests. Linear regression models were employed to examine the relationship between citizenship level and continuous HEI scores, adjusting for gender, age, and income, with stratified analyses conducted for each demographic subgroup. Results : Fisher's exact tests revealed that the level of US citizenship, gender, age, and income level were all significantly associated with the categorical level of diet quality (HEI). Regression analysis demonstrated that a lower level of US citizenship was significantly associated with higher HEI scores, indicative of better diet quality ( p < 0.001), even after adjusting for gender, age, and income. This relationship persisted across most demographic subgroups but appeared stronger among males and those in the lowest income bracket. The relationship was also significant for younger adults but not for adults aged 65 and older. Conclusions : Studies suggest a need for more inclusive culturally tailored nutrition interventions. To minimize the impact of colonization and US assimilation on lifestyle practices and chronic diseases, future studies should consider interventions that utilize traditional dietary patterns of the African diaspora as a tool to improve the quality of life among people of African descent. African American Acculturation Black Immigrant Healthy Eating Index Figures Figure 1 BACKGROUND Acculturation and Health Risks among Africans and Descendants of Africans in the US Acculturation was historically rooted in subdisciplines of sociology, namely, anthropology and archaeology, where interactions between two culturally different groups were observed [ 14 ]. Subsequently, it was embraced by the field of psychology [ 14 ]. In studies concerning Black health, often the nuance of acculturation is not accounted for when considering the diverse population in the Black community. There have been contradictions in measuring the relationship between acculturation and health, which researchers have attributed to the broad variation in how acculturation is conceptualized due to unidimensional, bidimensional, and multidimensional constructs [ 14 ]. The most traditional method for measuring acculturation has been unidimensional scales based on years spent in the host country and the predominant language(s) spoken. Bidimensional and multidimensional scales factor attitudes, beliefs, values, and behaviors in addition to language(s) spoken and time spent in the host country. Fox, Thayer, and Wadhwa (2017) [ 14 ] proposed the adoption of comprehensive bidimensional and multidimensional acculturation scales, which capture a broader spectrum of lifestyles and experiences. Moreover, they suggest tracking acculturation measures with health biomarkers and health behaviors in addition to the traditional use of self-reported health status [ 14 ]. These precautions are potential methods for addressing inconsistencies and enhancing our understanding. Generational Eating Patterns of African Americans vs. Immigrants The heterogeneity of the Black population is revealed through the variety of cultures and lifestyles, including food preferences and diet quality. Most of the research investigating dietary intake among minoritized immigrant populations has been reported for Hispanic and Asian cohorts, with limited information regarding dietary acculturation among African Americans [ 3 ], who were not regarded as having their own unique culture in mainstream America considering their US-born citizenship status. Without a clear understanding or defining factor for Black culture, investigating their diet trends justifies the larger need to identify this unique sub-population in areas outside of the deficit narrative. African Americans possess their own heritage and diverse identities due to the admixed influences of [enslaved] African, European, and Native American ancestors [ 29 , 46 , 51 ]. The heritage of African Americans is often embedded in their culinary practices as well. Historical accounts of “soul food” are rooted in culinary traditions among Africans, Europeans, and Native Americans. Enslaved Africans contributed their knowledge of cultivating crops such as rice, yams, peanuts, kola nuts, okra, collard greens, black-eyed peas, other beans, watermelon, and spices/seasonings [ 26 ]; European settlers introduced pork, cattle, spinach, radishes, wheat flour bread/other flour-based foods (i.e., pasta, pie crusts), dairy creams and cheeses, fritters and deep-fried chicken [ 38 ]; and Native Americans hunted and cultivated tomatoes, peppers, corn/corn-based foods (i.e., grits, cornbread), fish, wild game, beans, green beans, potatoes, pumpkin, squash, and mustard and turnip greens [ 26 , 39 ]. Several studies have suggested that maintenance of traditional diets among black immigrants contributed to higher diet quality than did their counterparts with eating behaviors that patterned the standard American diet [ 3 , 19 ]. One study on a sample of Haitian Americans reported that they had significantly greater diet quality based on the Alternative Healthy Eating Index (AHEI) and Healthy Eating Index (HEI) scores compared with US-born participants [ 19 ]. Another study suggested that foreign-born status was associated with higher AHEI and Dietary Approaches to Stop Hypertension (DASH) scores and higher intakes of certain nutrients and food types among foreign-born black subjects than among US-born black subjects [ 3 ]. A series of interviews with black female subjects grouped by ethnicity (US-born, African-born, and Caribbean/Latin American-born) revealed that both African and Caribbean-born subjects were more likely to be college-educated with higher household incomes than were US-born subjects [ 4 ]. Furthermore, African and Caribbean-born participants expressed commonalities in the use of fresh herbs and spices, fruits, vegetables, and fish as well as the wide availability of their foods at ethnic markets, except for tropical fruits [ 4 ]. However, black immigrants are also susceptible to the “immigrant health paradox”, which postulates that more years of exposure to environmental stressors such as racial tension and unhealthy lifestyle practices in a dominant culture can diminish health status over time [ 8 , 17 ]. One study revealed that African immigrants who reported even a moderate change in dietary habits since arriving in the US categorized themselves as having poorer self-rated health than those with low dietary change [ 35 ]. Eating patterns among African Americans have been more closely tied to environmental factors than other ethnicities of African descent [ 4 , 10 , 15 ]. Early research on the shifts in household dietary patterns among African Americans was attributed to social and environmental adaptations such as seasonality of specific foods (pork, chicken, cabbage, apples, hominy, rice, and beans), region of residence, and changes in availability reflected in the Great Migration of many African Americans from the American South to the North [ 10 ]. A lower socioeconomic status was associated with a lower intake of fruits, vegetables, whole grains, and fiber and a higher intake of sugar-sweetened beverages, processed meats, and sodium [ 15 ]. Perceived barriers to dietary patterns with high nutrient density among African Americans in an urban community were cost, limited grocery store and restaurant availability, limited knowledge of preparation methods, and higher waste/spoilage rates of fresh produce [ 47 ]. We analyzed the associations between demographic factors/citizenship status and diet quality among adults of African descent in this study. Purpose and Objectives of the Study The impact of acculturation or citizenship level on health and the risk of chronic diseases are less defined among African American, African, and Afro-Caribbean immigrants in the US than among other racial/ethnic groups. Furthermore, most studies on eating patterns among individuals of African descent have not stratified the samples based on years of residence/assimilation in the US. Therefore, this preliminary analysis aims to measure the relationship between variations in citizenship level and diet quality among adults of self-identified and non-Hispanic African descent from the National Health and Nutrition Examination Survey (NHANES) 2015–2016. Our research questions consist of the following: Are there socioeconomic and demographic factors associated with diet quality among adults of African descent in the US? Is level of citizenship associated with diet quality among adults of African descent in the US? METHODS Subjects We analyzed data from a cross-sectional sample of adults of African descent from the NHANES 2015–2016. The initial data consisted of 1495 subjects. Adults were excluded based on age (younger than 20 years) and ethnicity (Hispanic). After the extraction procedures, our final data set consisted of 1198 adults who self-identified as Black/African American. We used univariate analyses to measure descriptive statistics and group mean values of the adult samples by age group. These included sex, age, country of birth, citizenship status, years spent in the US, income level, and mean diet quality scores (Table 1 ). This study was approved by the Institutional Review Board of the University of the District of Columbia due to its publicly accessible data by the US Centers for Disease Control and Prevention (CDC). Citizenship Level We developed a unidimensional scale termed the ‘Level of Citizenship’ to assess citizenship status. The Level of Citizenship Scale comprises three components: country of birth, citizenship status, and duration of residence in the United States. Based on these elements, we classified the level of citizenship into four distinct categories for adults: high, moderately high, moderately low, and low citizenship. The criteria used to categorize subjects into these four citizenship levels are outlined in Table 1 . Table 1 Level of citizenship categorized by birthplace, citizenship status, and length of time living in the United States among adults of African descent. Level of Citizenship Value Description Score High US-born, citizen, living in the US ≥ 20 years or entire life 7 Moderately High Foreign-born, citizen or noncitizen, living in the US 10–19 + years 3–6 Moderately Low Foreign-born, citizen or noncitizen, living in the US 6–9 years 2 Low Foreign-born, noncitizen, living in the US 0–5 years 1 To determine the level of citizenship variable, we summed the scores for each component, which resulted in a total score ranging from 1 to 7 for adults. Lower scores indicated lower levels of citizenship, while higher scores denoted higher levels of citizenship. This composite variable allowed us to quantify gradations in citizenship status in this US population sample for use in regression analyses. Diet Quality The Healthy Eating Index (HEI) was used to measure the diet quality of the adult samples. The HEI is a quantifiable qualitative assessment of the nutrient density of food intake based on a scale of 1 to 100 [ 54 ]. The HEI was developed by the Center for Nutrition Policy and Promotion (CNPP) of the USDA to assess how well an individual’s consumed food groups align with the Dietary Guidelines for Americans [ 18 ]. We calculated HEIs for this sample of subjects. The HEI scores were based on the adequacy components of food groups that are advisable and moderation components of food groups with the recommended limits of the 2020–2025 Dietary Guidelines for Americans [ 53 ]. Poor diet quality is defined as an HEI score of 0 to 49, moderate diet quality is defined as an HEI score of 50 to 79, and good diet quality is defined as an HEI score of 80 to 100 [ 54 ]. HEI scores were calculated based on two-day records of dietary intake reported by each subject. Data Analysis We analyzed the data using Python software. Principal component analysis (PCA) was conducted to develop the ‘Level of Citizenship’ scale. The internal reliability of the Citizenship Scale was examined using Cronbach’s alpha [ 49 ]. Monte Carlo simulation of Fisher’s exact test was used to analyze associations between the level of citizenship, gender, age, income, and diet quality (HEI) categorized as poor, moderate, or good per HEI score. Multiple linear regression models were constructed to investigate the relationship between the level of citizenship and diet quality as measured by the continuous HEI score, adjusting for gender, age, and income. A full model was generated first, followed by stratified linear regression models split by gender, age group, and income level to determine whether the association between citizenship and diet quality varied across these sample subgroups. All analyses were two-sided with statistical significance set at 0.05. RESULTS Subjects The study sample consisted of 1198 adults of African descent from the NHANES 2015–2016 survey. There were slightly more females (n = 635, 53.0%) than males (n = 563, 47.0%). The sample was predominantly born in the US (n = 1044, 87.1%) and had US citizenship status (n = 1136, 94.8%). Most participants had resided in the US for more than 20 years (n = 1126, 94.0%). Approximately one-third of the participants were young adults aged 20–39 years. In terms of socioeconomic status, 30.5% reported household incomes under $ 25,000, while 24.3% had incomes of $ 65,000 or greater. The demographic characteristics of the participants are presented in Table 2 . Principal component analysis (PCA) was conducted on citizenship variables, including country of birth, citizenship status, and length of time in the US. The resulting scale had a Cronbach's alpha of 0.77, indicating acceptable reliability [ 49 ]. The level of citizenship was categorized as high for 87.4% of participants, moderately high for 10.9%, moderately low for 1.1%, and low for 0.6%. Table 2 Sample characteristics: Descriptive results of AA adults aged 20 years and older for the NHANES data cycle 2015–2016 Demographics Number (%) Mean (SD) Sex Male 563 (47.0) Female 635 (53.0) Age (years) 48.52 (17.0) Age Group : 20–39 400 (33.4) 40–64 565 (47.2) 65 and above 233 (19.4) Income Level : $ 0–24,999 365 (30.5) $ 25,000–44,999 239 (19.9) $ 45,000 - $ 64,999 156 (13.0) ≥ $ 65,000 291 (24.3) Missing 147 (12.3) Level of Citizenship High 1044 (87.4) Moderately High 130 (10.9) Moderately Low 13 (1.1) Low 7 (0.6) Country of Birth Born in US 1044 (87.1) Born in another country 154 (12.9) US Citizenship Status US Citizen 1136 (94.8) Non-US Citizen 62 (5.2) Length of Time in US Less than 1 year 0 1 to 5 years 7 (0.6) 5 to 9 years 13 (1.09) 10 to14 years 23 (1.92) 15 to 19 years 26 (2.17) More than 20 1126 (94.0) Missing 3 (0.3) The HEI scores for adults were calculated based on two days of dietary intake data from the NHANES 2015–2016. The mean HEI score was 48.74 ± 13.68 (n = 1060) for day one and 45.51 ± 13.46 (n = 610) for day two. The combined average HEI score for adults across both days was 48.64 ± 12.7. For participants without day two HEI data, only the day one score was used to calculate their average. Based on recommended HEI cutoffs, 57.9% of adults were categorized as having poor diet quality (HEI scores 0–49), 40.0% as having moderate diet quality (HEI 50–79), and 2.1% as having good diet quality (HEI 80–100). The specific HEI scores and diet quality category distributions are presented in Table 3 . Most adults demonstrated poor compliance with dietary recommendations, with approximately 2% meeting the criteria for good diet quality based on the Healthy Eating Index. Table 3 Diet quality and HEI categories of AA adults aged ≥ 20 years according to the NHANES data cycle 2015–2016 Demographics Number (%) N Mean (SD) HEI-Day 1 1060 48.74 (13.68) HEI-Day 2 610 45.51 (13.46) Diet Quality (HEI) 1060 48.64 (12.7) HEI Category 0 to 49 (Low) 592 (57.9) 50 to 79 (Moderate) 409 (40.0) 80 to 100 (High) 21 (2.1) Table 3 shows the results from the Monte Carlo simulation of Fisher's exact test examining associations between the level of citizenship, gender, age, income, and diet quality categorized as poor, moderate, or good based on HEI scores. A significant association was found between the level of citizenship and diet quality ( p < 0.001). Among adults with high levels of US citizenship, 61.0% had poor diet quality. In contrast, in the moderately high and moderately low citizenship groups, only 33.9% and 33.3%, respectively, had poor diet quality, while 63.3% and 55.6%, respectively, had moderate diet quality. Additionally, the high-citizenship group had poorer diet quality (1.9%) than the moderately high group (2.8%). These results demonstrate that an increased duration of US residency and citizenship is associated with lower diet quality based on HEI standards. Additionally, a significant association was found between sex and diet quality ( p = 0.008). A greater proportion of males had poor diet quality (62.9%) than females (53.6%). In contrast, more females attained moderate (43.9%) and good (2.6%) diet quality than males did (35.6% and 1.5%, respectively). Furthermore, diet quality also significantly differed by age group ( p < 0.001). Young adults aged 20–39 years had the highest percentage of poor diet (67.5%), while older adults aged 65 + years had the lowest percentage (48.7%). The 65 + age group had the highest percentage of good diet quality (3.2%), compared to just 1.5% of young adults. Finally, diet quality was significantly associated with income level ( p < 0.001). A lower income of less than $ 25,000 was associated with a greater percentage of individuals with poor diet quality (64.7% and 60.6%), while a higher income of more than $ 65,000 was associated with the lowest percentage of individuals with poor diet quality (46.6%). Furthermore, only 0.3% of the individuals in the lowest income bracket achieved good diet quality, while 5.6% of those in the highest income bracket achieved good diet quality. In addition, the associations between high US citizenship/residency duration, male sex, younger age, lower income level, and poorer compliance with HEI diet recommendations are visually elucidated in Fig. 1 . Table 3 Monte Carlo simulation of Fisher's exact test for associations of sociodemographic factors with diet quality Diet Quality - Healthy Eating Index Poor Moderate Good P value N (%) N (%) N (%) Level of Citizenship < .001 High 547 (61.0) 333 (37.1) 17 (1.9) Moderately High 37 (33.9) 69 (63.3) 3 (2.8) Moderately Low 3 (33.3) 5 (55.6) 1 (11.1) Low 3 (75.0) 1 (25.0) 0 (0.0) Sex 0.008 Male 299 (62.9) 169 (35.6) 7 (1.5) Female 293 (53.6) 240 (43.9) 14 (2.6) < .001 Age Group : 20–39 228 (67.5) 105 (31.1) 5 (1.5) 40–64 272 (54.9) 213 (43.0) 10 (2.0) 65 and above 92 (48.7) 91 (48.1) 6 (3.2) Income Level : < .001 $ 0–24,999 194 (64.7) 105 (35.0) 1 (0.3) $ 25,000–44,999 132 (60.6) 83 (38.1) 3 (1.4) $ 45,000 - $ 64,999 84 (59.6) 56 (39.7) 1 (0.7) ≥ $ 65,000 117 (46.6) 120 (47.8) 14 (5.6) N = Number of subjects % = proportion of subjects within each subcategory of sex, age group, and income level. We conducted linear regression analyses to examine the relationship between the level of citizenship and diet quality as measured by the continuous HEI score while adjusting for gender, age, and income level. The analyses were also stratified by sex, age group, and income level to determine whether the association between citizenship and diet quality differed across these sample subgroups. In the full sample adjusted model, level of citizenship ( p < 0.001), gender ( p < 0.01), age ( p < 0.001), and income ( p < 0.001) were all significantly associated with HEI scores (see Table 4 ). Those with lower citizenship levels tended to have higher HEI scores, indicating superior diet quality, after adjusting for demographic factors. When stratified by gender, the association between lower citizenship and higher HEI scores remained significant for both males ( p < 0.001) and females ( p < 0.01). The citizenship coefficient was slightly stronger among males, suggesting greater disparity in diet quality by citizenship level among men. Based on analyses by age group, we found significant citizenship-HEI associations in the younger 20–39 ( p < 0.001) and 40–64 ( p < 0.001) age strata but not among the 65 + years age group ( p = 0.29). The directionality was consistent with lower citizenship associated with superior diet quality. Finally, when stratified by income, lower citizenship level persisted as a significant predictor of higher HEI scores in all income brackets except for $ 45,000– $ 64,999 ( p = 0.24). However, the strength of the association was slightly stronger in the lowest income bracket ( $ 0–24,999) than in the highest income bracket (> $ 65,000). In conclusion, lower levels of US citizenship and shorter US residency durations were associated with superior diet quality per HEI metric across gender, age, and income subgroups. The detailed coefficients and p values for each model can be found in Table 4 . Table 4 Stratified Linear Regression Models for Citizenship Levels Predicting Healthy Eating Index Scores, Adjusted for Covariates Coefficient Full Model Male Model Female Model 20 to 39 Model 40 to 64 Model 65 and above Model $ 0–24,999 Model $ 25–44,999 Model $ 45,000 - $ 64,999 Model > $ 65,000 Model Level of Citizenship -2.83*** (0.43) -3.26*** (0.58) -2.29** (0.65) -2.20*** (0.56) -3.51*** (0.71) -2.77 (1.91) -3.72*** (0.67) -2.56*** (0.76) -1.40 (1.20) -2.85** (1.06) Gender 2.62*** (0.79) 2.23 (1.29) 2.03 (1.15) 4.90* (2.06) 1.81 (1.23) 1.69 (1.61) 3.49 (1.87) 4.10* (1.70) Age 0.18*** (0.02) 0.15*** (0.03) 0.21*** (0.03) 0.18*** (0.03) 0.19*** (0.05) 0.05 (0.06) 0.26*** (0.06) Income Level 1.84*** (0.32) 1.45** (0.46) 2.22*** (0.45) 1.63** (0.56) 2.01*** (0.47) 1.87* (0.89) Note: *** p < 0.001, ** p < 0.01, * p < 0.05 DISCUSSION Racial heterogeneity and nutritional status An advantage of heterogeneous groupings of black/African American subjects in health studies is that they can account for genetic diversity, social determinants, regional residence, US nativity, ethnicity, and acculturation [ 1 , 6 , 25 , 31 , 51 , 56 ]. In this study, we stratified the age groups of adult subjects of African descent by US nativity/citizenship level in our regression models and found that the level of US citizenship based on citizenship status and years of US residence influenced diet quality scores among adults of African descent after controlling for confounding variables. We observed similar trends in which high US-born and moderately high citizenship levels were associated with lower diet quality. These findings are consistent with prior studies that reported poorer dietary patterns [ 3 , 4 , 19 ] and a greater prevalence of chronic diseases/risk factors among US-born residents and US-born offspring of immigrants of African descent [ 8 , 17 ]. Social Determinants and Diet Quality Sex, age, and income significantly influenced the diet quality scores. Lower HEI scores were observed for male subjects, the 20–39 age group, and subjects with the lowest income level than for female subjects, the 65 + age group, and subjects with the highest income level (p < 0.001), respectively. Deterioration of healthy eating patterns among African Americans in the US can be traced back to circumstantial dietary adaptations to colonization after the transatlantic slave trade, which subjected them to limited rations of food access in addition to the adoption of some culinary preferences of Europeans [ 23 , 40 ]. Consequently, legislation of the Dawes Act of 1887 displaced Indigenous populations, with whom many African Americans intermingled and shared mixed ancestry, from land that they were accustomed to using for hunting/gathering, growing edible crops, and fishing [ 28 , 32 ]. Other factors included decreased access to farmland for fresh crop production following the Great Migration of a multitude of US Black individuals from the American South to the North and other urban communities for greater economic opportunity [ 11 ]. Furthermore, there were higher rates of economic hardship among black farmers who were denied business loans by the USDA to aid in their ability to maintain crop production and distribution [ 9 ]. At present, environmental factors such as education, income, housing, urbanization, technological advances, and neighborhood food access have remained influential determinants of dietary patterns among the entire US population. In general, Americans do not meet the recommendations for fruit, vegetable, and whole grain intake based on the USDA Dietary Guidelines for Americans [ 20 , 24 , 30 ]. However, Black/African Americans are disproportionately affected by disparities in education, median income, employment, single-parent households, healthcare access/treatment by providers, and residential proximity to full-service grocery chains [ 12 ]. Acculturation/Citizenship Level and Diet Quality Despite the small sample of non-US-born subjects of African descent, we found significant associations between levels of US citizenship and diet quality scores among this cohort. A greater percentage of adults with high-US-born and moderately high levels of US citizenship had poor diet quality/HEI scores than did adults with low-foreign-born and moderately low levels of US citizenship. In addition, a substantially small subset of adults within all groups had good diet quality. Our findings among these subjects of African descent align with studies concerning the “Immigrant Health Paradox” and the impact of assimilation into the US and adaptation to the typical American diet among Asian and Hispanic groups [ 50 ]. A distinctive aspect of this study is our investigation into patterns and influences of acculturation/US nativity and diet among individuals of African ancestry within groups that have not been widely studied in this area of research. Due to the divergent origins of African Americans and Black immigrants in the US, there are several nuances to the mere definition of acculturation among this population. To that end, researchers developed and validated the African American Acculturation Scale (AAS), which accounts for cultural distinctions between African Americans and mainstream Caucasian Americans based on traditional norms associated with eight dimensions of African American culture, including health beliefs and eating patterns [ 22 ]. In addition, bidimensional and multidimensional methods of measuring acculturation can be applied to African, Afro-Caribbean, and Afro-Latino subgroups. Traditional eating patterns of the African diaspora could serve as one of many tools for altering the trajectory of health among US Black individuals. One study evaluated quantifiable measures of colon cancer risk and disproportionate rates among African Americans, where African Americans and rural Africans underwent a two-week diet exchange to a traditional rural African diet high in fiber and a typical American diet high in animal protein and fat, respectively [ 36 ]. Investigators observed significant reductions in biomarkers of inflammation indicative of colon cancer risk among African Americans who switched to the traditional African diet, whereas a rise in inflammatory biomarkers of colon cancer risk was noted among rural Africans who transitioned to a diet high in animal protein and fat [ 36 ]. Last, with the inclusion of dairy in the USDA food guide, another factor to account for when measuring adherence to US Dietary Guidelines among individuals of African, Asian, and Indigenous ancestry is the approximate 68% prevalence of lactase malabsorption among these racial and ethnic groups as well as a cultural tendency to consume fewer servings of milk compared to individuals of European ancestry [ 48 ]. Therefore, future studies that include HEI instrumentation should consider the use of other modified HEI tools if warranted. One study that investigated a large cohort of subjects from the NHANES revealed that neither the HEI nor the AHEI was more beneficial than the other in demonstrating how well diet intake can predict diabetes management and other health indicators [ 2 ]. Interestingly, there has been a notable increase in cardiometabolic risk among the West African cohort of Black immigrants since the 1990s [ 7 , 34 , 46 ]. This paradigm shift from traditional African diets low in fat/sugars and high in fiber, herbs, and spices has been attributed to westernized assimilation to diets high in fat, salt, and sugar and low in fiber, especially in urban areas of West African countries [ 27 , 34 ]. This transition has been influenced by urbanization, improvements in socioeconomic status, and technological innovations [ 27 ]. Comparatively, a twin-city study of African immigrants in Minnesota revealed a very low incidence of CVD risk factors and increased self-reported protective lifestyle behaviors among subjects from East African countries and the opposite trend among immigrants from West African countries [ 45 ]. At the systemic level, there has been a substantial increase in the availability of energy-dense and animal protein foods combined with a minuscule increase in the availability of fruits and vegetables in higher-income countries of West Africa, such as Ghana [ 13 , 16 ]. Limitations The diet quality measures that were assessed via two-day food records were based on self-reported information, and some participants did not complete day two of their food records. The research design of this study was not experimental but observational. Therefore, we could determine the association between our tested variables of US citizenship level and diet quality, but not causality. Due to the small frequency of subjects who reported African descent in the NHANES, especially foreign-born subjects, we could not stratify the results into categories of US-born, Caribbean-born, and African-born US residents who identified as non-Hispanic Black/African American in this smaller preliminary sample of our extended analysis plan. Instead, we developed an ordinal scoring system to measure the level of US citizenship for our regression model based on country of birth, US citizenship (yes or no), and number of years residing in the US. However, of the four ordinal levels of citizenship, the highest and lowest were US-born and foreign-born residents, respectively. This method is a parallel alternative considering research findings concerning relationships between time-based exposures to Westernized countries and deteriorated actual and self-rated health. These data were limited to a unidimensional approach for measuring acculturation, which does not take beliefs, values, or other cultural customs into account. Therefore, we did not approach this study from the traditional lens of the acculturation construct but rather from the US citizenship level. The most common unidimensional parameters for acculturation, including non-English native languages, are not applicable to African Americans, who represented most of the subjects in this sample. Most of these subjects of African descent were born in the US, the dominant culture in this study, and English is their primary language [ 44 , 52 ]. English is also a commonly spoken language among black immigrants from British-colonized countries in Africa [ 5 ] and the Caribbean [ 57 ]. In addition, there was an inadequate sample of non-Hispanic subjects of African descent who reported speaking a second language to add it as a measure of acculturation/citizenship. Conversely, various acculturation studies among Hispanic and Asian populations include language as a parameter. Considering that many African and Caribbean countries are British-colonized and that English is the predominant language spoken in the US, English is the dominant language of most non-Hispanic Black individuals in the US [ 5 , 57 ]. Multiple sources of data suggest that the poorest health outcomes of obesity and noncommunicable diseases in the US are among the American South population, irrespective of race/ethnicity [ 33 , 42 , 55 ]. Therefore, it is imperative to consider regional differences in the eating patterns and food culture of any racial/ethnic community as well. Conclusion and Future Directions This sample is limited to data collected before any COVID-19 pandemic-related influences. In our longitudinal follow-up paper, we will oversample the limited frequencies of subjects of African descent through a combined analysis of extended cohort years of data sets from 2015–2024, including regional variations in diet quality among African Americans and Black Immigrants (African, Afro-Caribbean, and other countries). If the sample size is sufficient, we will stratify comparisons between US regions such as the southern, northern, northeastern, midwestern, and western coasts. Future studies should continue exploring interventions that utilize digital technology to adapt to modernization and emphasize culturally diverse models for healthy eating as an applied approach to mitigating health inequities among people of color. Additional considerations should emphasize relationships and trust building that could improve the recruitment of participants, as one qualitative study noted distrust and not having culturally centered models for healthy eating as barriers [ 21 ]. To date, the Physician’s Committee for Responsible Medicine (PCRM) developed the African Heritage Power Plate [ 41 ], a plant-based version of the USDA MyPlate inspired by the African Heritage food pyramid and food guides of Oldways Health through the Heritage initiative [ 37 ]. These models emphasize the inclusion of culturally tailored ancestral traditions that are compatible with healthy eating patterns of the 2020–2025 Dietary Guidelines for Americans [ 53 ]. Thus far, one community-based intervention that utilized the Oldways ”A Taste of African Heritage” cooking program with 586 participants over five years revealed statistically significant improvements in fruit/vegetable intake, weight, waist circumference, and systolic blood pressure [ 43 ]. In closing, unfavorable social determinants, discrimination, homogenous groupings, the acculturation of black immigrants, centuries of colonization and food injustice among African Americans, and Eurocentric criteria for models of healthy eating have perpetuated disproportionately higher rates of diet-related chronic diseases and risk factors among the US Black population. Researchers and healthcare professionals must be cognizant of these factors among all subgroups of Black individuals in the US and be trained to deliver culturally tailored interventions that are cardioprotective and sustainable. Declarations Ethics approval and consent to participate: Not applicable. Consent for Publication: Not applicable. Competing Interests: The authors declare that they have no competing interests. Availability of Data and Materials: The raw data spreadsheets and analysis results are available in the Supplementary Material. Funding: This study was supported by Hatch funds provided by the National Institute of Food and Agriculture of the USDA under grant numbers NE1939 for Multistate and 1012814. Author contributions: Conceptualization and design of the study were performed by T.J. Background was written by T.J., S.B., and G.S. Methods were written by T.J and X.D. Statistical analyses were performed by X.D. and A.A. Tables were prepared by X.D. and A.A. The results were written by T.J. X.D., and A.A. Discussion/Conclusion was written by T.J. and G.S. All (T.J., X.D., A.A., S.B., L.M., and G.S.) reviewed, edited, and approved this draft. 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M., Kim, V., Nguyen, M. H., Curammeng, E. R., Paredes, A. D., Teranishi, R. T. The Racial Heterogeneity Project: Implications for educational research, practice, and policy. 2017. https://www.act.org/content/dam/act/unsecured/documents/R1641-racial-heterogeneity-project-2017-06.pdf Norgaard, K. M., Reed, R., & Van Horn, C. A continuing legacy: Institutional racism, hunger, and nutritional justice on the Klamath. MIT Press; 2011. Oates, G. R., Jackson, B. E., Partridge, E. E., Singh, K. P., Fouad, M. N., Bae, S. Sociodemographic patterns of chronic disease: How the Mid-South region compares to the rest of the country. American Journal of Preventive Medicine , 2017, 52(1S1): S31-S39. doi 10.1016/j.amepre.2016.09.004. PMID: 27989290; PMCID: PMC5171223. Obichi, C. C., & Dee, V. Acculturation, Cultural Beliefs, and Cardiovascular Disease Risk Levels Among Nigerian, Ghanaian and Cameroonian Immigrants in the United States. Journal of Immigrant and Minority Health , 2022, 24 (5), 1154–1160. https://doi.org/10.1007/s10903-021-01321-5 Okafor, M. C., Carter-Pokras, O. D., & Zhan, M. Greater dietary acculturation (dietary change) is associated with poorer current self-rated health among African immigrant adults. Journal of Nutrition Education and Behavior , 2014, 46 (4), 226–235. https://doi.org/10.1016/j.jneb.2013.11.015 O'Keefe, S. J., Li, J. V., Lahti, L., Ou, J., Carbonero, F., Mohammed, K., Posma, J. M., Kinross, J., Wahl, E., Ruder, E., Vipperla, K., Naidoo, V., Mtshali, L., Tims, S., Puylaert, P. G., DeLany, J., Krasinskas, A., Benefiel, A. C., Kaseb, H. O., Newton, K., … Zoetendal, E. G. Fat, fibre and cancer risk in African Americans and rural Africans. Nature Communications , 2015, 6 , 6342. https://doi.org/10.1038/ncomms7342 Oldways: Cultural Food Traditions. 2023. https://oldwayspt.org/ Opie, F. D. Hog and Hominy: Soul Food from Africa to America . Columbia University Press. Pp. 18; 2013. Park, S., Hongu, N., & Daily, J. (Native American foods: History, culture, and influence on modern diets. Journal of Ethnic Foods, 2016, 3(3). doi: 10.1016/j.jef.2016.08.001 Pass, M.B., & Bullock, S.K. Black Health and Wellness: Past and Present Implications of the “Slave Diet” for African Americans. Black History Bulletin, 2021, 84 (2), 12-15. doi:10.1353/bhb.2021.0002. Physician’s Committee for Responsible Medicine. African Americans: Healthy Communities. 2023. https://www.pcrm.org/good-nutrition/healthy-communities/african-americans Raghupathi, W., & Raghupathi, V. An empirical study of chronic diseases in the United States: A visual analytics approach. International Journal of Environmental Research and Public Health , 2018, 15 (3), 431. https://doi.org/10.3390/ijerph15030431 Reicks, M., Gold, A., Tran, N., & LeBlanc, K. Impacts of A Taste of African Heritage: A Culinary Heritage Cooking Course. 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Supplementary Files NHANES201516CodingFormAA.docx NHANESVariablesAAAdultsCleaned.xlsx Cite Share Download PDF Status: Published Journal Publication published 07 Jul, 2025 Read the published version in BMC Nutrition → Version 1 posted Editorial decision: Revision requested 20 Aug, 2024 Reviews received at journal 31 Jul, 2024 Reviews received at journal 26 Jul, 2024 Reviewers agreed at journal 20 Jul, 2024 Reviews received at journal 18 Jul, 2024 Reviewers agreed at journal 18 Jul, 2024 Reviewers agreed at journal 18 Jul, 2024 Reviews received at journal 28 Jun, 2024 Reviewers agreed at journal 07 Jun, 2024 Reviewers agreed at journal 06 Jun, 2024 Reviewers invited by journal 04 Jun, 2024 Editor invited by journal 22 May, 2024 Submission checks completed at journal 17 Apr, 2024 Editor assigned by journal 17 Apr, 2024 First submitted to journal 28 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4183130","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":292207615,"identity":"1321a931-8bd6-469c-98a2-79ced9125c8b","order_by":0,"name":"Tia D. 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Subsequently, it was embraced by the field of psychology [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In studies concerning Black health, often the nuance of acculturation is not accounted for when considering the diverse population in the Black community. There have been contradictions in measuring the relationship between acculturation and health, which researchers have attributed to the broad variation in how acculturation is conceptualized due to unidimensional, bidimensional, and multidimensional constructs [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The most traditional method for measuring acculturation has been unidimensional scales based on years spent in the host country and the predominant language(s) spoken. Bidimensional and multidimensional scales factor attitudes, beliefs, values, and behaviors in addition to language(s) spoken and time spent in the host country. Fox, Thayer, and Wadhwa (2017) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] proposed the adoption of comprehensive bidimensional and multidimensional acculturation scales, which capture a broader spectrum of lifestyles and experiences. Moreover, they suggest tracking acculturation measures with health biomarkers and health behaviors in addition to the traditional use of self-reported health status [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These precautions are potential methods for addressing inconsistencies and enhancing our understanding.\u003c/p\u003e \u003cp\u003eGenerational Eating Patterns of African Americans vs. Immigrants\u003c/p\u003e \u003cp\u003eThe heterogeneity of the Black population is revealed through the variety of cultures and lifestyles, including food preferences and diet quality. Most of the research investigating dietary intake among minoritized immigrant populations has been reported for Hispanic and Asian cohorts, with limited information regarding dietary acculturation among African Americans [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], who were not regarded as having their own unique culture in mainstream America considering their US-born citizenship status. Without a clear understanding or defining factor for Black culture, investigating their diet trends justifies the larger need to identify this unique sub-population in areas outside of the deficit narrative. African Americans possess their own heritage and diverse identities due to the admixed influences of [enslaved] African, European, and Native American ancestors [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The heritage of African Americans is often embedded in their culinary practices as well. Historical accounts of \u0026ldquo;soul food\u0026rdquo; are rooted in culinary traditions among Africans, Europeans, and Native Americans. Enslaved Africans contributed their knowledge of cultivating crops such as rice, yams, peanuts, kola nuts, okra, collard greens, black-eyed peas, other beans, watermelon, and spices/seasonings [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]; European settlers introduced pork, cattle, spinach, radishes, wheat flour bread/other flour-based foods (i.e., pasta, pie crusts), dairy creams and cheeses, fritters and deep-fried chicken [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]; and Native Americans hunted and cultivated tomatoes, peppers, corn/corn-based foods (i.e., grits, cornbread), fish, wild game, beans, green beans, potatoes, pumpkin, squash, and mustard and turnip greens [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral studies have suggested that maintenance of traditional diets among black immigrants contributed to higher diet quality than did their counterparts with eating behaviors that patterned the standard American diet [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. One study on a sample of Haitian Americans reported that they had significantly greater diet quality based on the Alternative Healthy Eating Index (AHEI) and Healthy Eating Index (HEI) scores compared with US-born participants [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Another study suggested that foreign-born status was associated with higher AHEI and Dietary Approaches to Stop Hypertension (DASH) scores and higher intakes of certain nutrients and food types among foreign-born black subjects than among US-born black subjects [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. A series of interviews with black female subjects grouped by ethnicity (US-born, African-born, and Caribbean/Latin American-born) revealed that both African and Caribbean-born subjects were more likely to be college-educated with higher household incomes than were US-born subjects [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Furthermore, African and Caribbean-born participants expressed commonalities in the use of fresh herbs and spices, fruits, vegetables, and fish as well as the wide availability of their foods at ethnic markets, except for tropical fruits [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, black immigrants are also susceptible to the \u0026ldquo;immigrant health paradox\u0026rdquo;, which postulates that more years of exposure to environmental stressors such as racial tension and unhealthy lifestyle practices in a dominant culture can diminish health status over time [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. One study revealed that African immigrants who reported even a moderate change in dietary habits since arriving in the US categorized themselves as having poorer self-rated health than those with low dietary change [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEating patterns among African Americans have been more closely tied to environmental factors than other ethnicities of African descent [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Early research on the shifts in household dietary patterns among African Americans was attributed to social and environmental adaptations such as seasonality of specific foods (pork, chicken, cabbage, apples, hominy, rice, and beans), region of residence, and changes in availability reflected in the Great Migration of many African Americans from the American South to the North [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A lower socioeconomic status was associated with a lower intake of fruits, vegetables, whole grains, and fiber and a higher intake of sugar-sweetened beverages, processed meats, and sodium [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Perceived barriers to dietary patterns with high nutrient density among African Americans in an urban community were cost, limited grocery store and restaurant availability, limited knowledge of preparation methods, and higher waste/spoilage rates of fresh produce [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. We analyzed the associations between demographic factors/citizenship status and diet quality among adults of African descent in this study.\u003c/p\u003e \u003cp\u003ePurpose and Objectives of the Study\u003c/p\u003e \u003cp\u003eThe impact of acculturation or citizenship level on health and the risk of chronic diseases are less defined among African American, African, and Afro-Caribbean immigrants in the US than among other racial/ethnic groups. Furthermore, most studies on eating patterns among individuals of African descent have not stratified the samples based on years of residence/assimilation in the US. Therefore, this preliminary analysis aims to measure the relationship between variations in citizenship level and diet quality among adults of self-identified and non-Hispanic African descent from the National Health and Nutrition Examination Survey (NHANES) 2015\u0026ndash;2016. Our research questions consist of the following:\u003c/p\u003e \u003cp\u003eAre there socioeconomic and demographic factors associated with diet quality among adults of African descent in the US?\u003c/p\u003e \u003cp\u003eIs level of citizenship associated with diet quality among adults of African descent in the US?\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eSubjects\u003c/p\u003e \u003cp\u003eWe analyzed data from a cross-sectional sample of adults of African descent from the NHANES 2015\u0026ndash;2016. The initial data consisted of 1495 subjects. Adults were excluded based on age (younger than 20 years) and ethnicity (Hispanic). After the extraction procedures, our final data set consisted of 1198 adults who self-identified as Black/African American. We used univariate analyses to measure descriptive statistics and group mean values of the adult samples by age group. These included sex, age, country of birth, citizenship status, years spent in the US, income level, and mean diet quality scores (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This study was approved by the Institutional Review Board of the University of the District of Columbia due to its publicly accessible data by the US Centers for Disease Control and Prevention (CDC).\u003c/p\u003e \u003cp\u003eCitizenship Level\u003c/p\u003e \u003cp\u003eWe developed a unidimensional scale termed the \u0026lsquo;Level of Citizenship\u0026rsquo; to assess citizenship status. The Level of Citizenship Scale comprises three components: country of birth, citizenship status, and duration of residence in the United States. Based on these elements, we classified the level of citizenship into four distinct categories for adults: high, moderately high, moderately low, and low citizenship. The criteria used to categorize subjects into these four citizenship levels are outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLevel of citizenship categorized by birthplace, citizenship status, and length of time living in the United States among adults of African descent.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel of Citizenship\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue Description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUS-born, citizen, living in the US\u0026thinsp;\u0026ge;\u0026thinsp;20 years or entire life\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForeign-born, citizen or noncitizen, living in the US 10\u0026ndash;19\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u0026ndash;6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForeign-born, citizen or noncitizen, living in the US 6\u0026ndash;9 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForeign-born, noncitizen, living in the US 0\u0026ndash;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo determine the level of citizenship variable, we summed the scores for each component, which resulted in a total score ranging from 1 to 7 for adults. Lower scores indicated lower levels of citizenship, while higher scores denoted higher levels of citizenship. This composite variable allowed us to quantify gradations in citizenship status in this US population sample for use in regression analyses.\u003c/p\u003e \u003cp\u003eDiet Quality\u003c/p\u003e \u003cp\u003eThe Healthy Eating Index (HEI) was used to measure the diet quality of the adult samples. The HEI is a quantifiable qualitative assessment of the nutrient density of food intake based on a scale of 1 to 100 [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The HEI was developed by the Center for Nutrition Policy and Promotion (CNPP) of the USDA to assess how well an individual\u0026rsquo;s consumed food groups align with the Dietary Guidelines for Americans [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. We calculated HEIs for this sample of subjects. The HEI scores were based on the adequacy components of food groups that are advisable and moderation components of food groups with the recommended limits of the 2020\u0026ndash;2025 Dietary Guidelines for Americans [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Poor diet quality is defined as an HEI score of 0 to 49, moderate diet quality is defined as an HEI score of 50 to 79, and good diet quality is defined as an HEI score of 80 to 100 [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. HEI scores were calculated based on two-day records of dietary intake reported by each subject.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eWe analyzed the data using Python software. Principal component analysis (PCA) was conducted to develop the \u0026lsquo;Level of Citizenship\u0026rsquo; scale. The internal reliability of the Citizenship Scale was examined using Cronbach\u0026rsquo;s alpha [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Monte Carlo simulation of Fisher\u0026rsquo;s exact test was used to analyze associations between the level of citizenship, gender, age, income, and diet quality (HEI) categorized as poor, moderate, or good per HEI score.\u003c/p\u003e \u003cp\u003eMultiple linear regression models were constructed to investigate the relationship between the level of citizenship and diet quality as measured by the continuous HEI score, adjusting for gender, age, and income. A full model was generated first, followed by stratified linear regression models split by gender, age group, and income level to determine whether the association between citizenship and diet quality varied across these sample subgroups. All analyses were two-sided with statistical significance set at 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eSubjects\u003c/p\u003e \u003cp\u003eThe study sample consisted of 1198 adults of African descent from the NHANES 2015\u0026ndash;2016 survey. There were slightly more females (n\u0026thinsp;=\u0026thinsp;635, 53.0%) than males (n\u0026thinsp;=\u0026thinsp;563, 47.0%). The sample was predominantly born in the US (n\u0026thinsp;=\u0026thinsp;1044, 87.1%) and had US citizenship status (n\u0026thinsp;=\u0026thinsp;1136, 94.8%). Most participants had resided in the US for more than 20 years (n\u0026thinsp;=\u0026thinsp;1126, 94.0%). Approximately one-third of the participants were young adults aged 20\u0026ndash;39 years. In terms of socioeconomic status, 30.5% reported household incomes under \u003cspan\u003e$\u003c/span\u003e25,000, while 24.3% had incomes of \u003cspan\u003e$\u003c/span\u003e65,000 or greater. The demographic characteristics of the participants are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003ePrincipal component analysis (PCA) was conducted on citizenship variables, including country of birth, citizenship status, and length of time in the US. The resulting scale had a Cronbach's alpha of 0.77, indicating acceptable reliability [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The level of citizenship was categorized as high for 87.4% of participants, moderately high for 10.9%, moderately low for 1.1%, and low for 0.6%.\u003c/p\u003e \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSample characteristics: Descriptive results of AA adults aged 20 years and older for the NHANES data cycle 2015\u0026ndash;2016\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003eDemographics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 29.5082%;\"\u003e\n \u003cp\u003eNumber (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 29.5082%;\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\n \u003cp\u003e563 (47.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\n \u003cp\u003e635 (53.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e48.52 (17.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group\u003c/strong\u003e:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003e20\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\n \u003cp\u003e400 (33.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003e40\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\n \u003cp\u003e565 (47.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003e65 and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\n \u003cp\u003e233 (19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome Level\u003c/strong\u003e:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e0\u0026ndash;24,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\n \u003cp\u003e365 (30.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e25,000\u0026ndash;44,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\n \u003cp\u003e239 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e45,000 - \u003cspan\u003e$\u003c/span\u003e64,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\n \u003cp\u003e156 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003e\u0026ge;\u003cspan\u003e$\u003c/span\u003e65,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\n \u003cp\u003e291 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\n \u003cp\u003e147 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel of Citizenship\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\n \u003cp\u003e1044 (87.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003eModerately High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\n \u003cp\u003e130 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003eModerately Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\n \u003cp\u003e13 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\n \u003cp\u003e7 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry of Birth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003eBorn in US\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\n \u003cp\u003e1044 (87.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003eBorn in another country\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\n \u003cp\u003e154 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUS Citizenship Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003eUS Citizen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\n \u003cp\u003e1136 (94.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003eNon-US Citizen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\n \u003cp\u003e62 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLength of Time in US\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003eLess than 1 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003e1 to 5 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\n \u003cp\u003e7 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003e5 to 9 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\n \u003cp\u003e13 (1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003e10 to14 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\n \u003cp\u003e23 (1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003e15 to 19 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\n \u003cp\u003e26 (2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003eMore than 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\n \u003cp\u003e1126 (94.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 50.8197%;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 29.5082%;\"\u003e\n \u003cp\u003e3 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\u003cp\u003eThe HEI scores for adults were calculated based on two days of dietary intake data from the NHANES 2015\u0026ndash;2016. The mean HEI score was 48.74\u0026thinsp;\u0026plusmn;\u0026thinsp;13.68 (n\u0026thinsp;=\u0026thinsp;1060) for day one and 45.51\u0026thinsp;\u0026plusmn;\u0026thinsp;13.46 (n\u0026thinsp;=\u0026thinsp;610) for day two. The combined average HEI score for adults across both days was 48.64\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7. For participants without day two HEI data, only the day one score was used to calculate their average. Based on recommended HEI cutoffs, 57.9% of adults were categorized as having poor diet quality (HEI scores 0\u0026ndash;49), 40.0% as having moderate diet quality (HEI 50\u0026ndash;79), and 2.1% as having good diet quality (HEI 80\u0026ndash;100). The specific HEI scores and diet quality category distributions are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Most adults demonstrated poor compliance with dietary recommendations, with approximately 2% meeting the criteria for good diet quality based on the Healthy Eating Index.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiet quality and HEI categories of AA adults aged\u0026thinsp;\u0026ge;\u0026thinsp;20 years according to the NHANES data cycle 2015\u0026ndash;2016\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHEI-Day 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.74 (13.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHEI-Day 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.51 (13.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiet Quality (HEI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.64 (12.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHEI Category\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 to 49 (Low)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e592 (57.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50 to 79 (Moderate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e409 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80 to 100 (High)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the results from the Monte Carlo simulation of Fisher's exact test examining associations between the level of citizenship, gender, age, income, and diet quality categorized as poor, moderate, or good based on HEI scores.\u003c/p\u003e \u003cp\u003eA significant association was found between the level of citizenship and diet quality (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among adults with high levels of US citizenship, 61.0% had poor diet quality. In contrast, in the moderately high and moderately low citizenship groups, only 33.9% and 33.3%, respectively, had poor diet quality, while 63.3% and 55.6%, respectively, had moderate diet quality. Additionally, the high-citizenship group had poorer diet quality (1.9%) than the moderately high group (2.8%). These results demonstrate that an increased duration of US residency and citizenship is associated with lower diet quality based on HEI standards.\u003c/p\u003e \u003cp\u003eAdditionally, a significant association was found between sex and diet quality (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008). A greater proportion of males had poor diet quality (62.9%) than females (53.6%). In contrast, more females attained moderate (43.9%) and good (2.6%) diet quality than males did (35.6% and 1.5%, respectively).\u003c/p\u003e \u003cp\u003eFurthermore, diet quality also significantly differed by age group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Young adults aged 20\u0026ndash;39 years had the highest percentage of poor diet (67.5%), while older adults aged 65\u0026thinsp;+\u0026thinsp;years had the lowest percentage (48.7%). The 65\u0026thinsp;+\u0026thinsp;age group had the highest percentage of good diet quality (3.2%), compared to just 1.5% of young adults.\u003c/p\u003e \u003cp\u003eFinally, diet quality was significantly associated with income level (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A lower income of less than \u003cspan\u003e$\u003c/span\u003e25,000 was associated with a greater percentage of individuals with poor diet quality (64.7% and 60.6%), while a higher income of more than \u003cspan\u003e$\u003c/span\u003e65,000 was associated with the lowest percentage of individuals with poor diet quality (46.6%). Furthermore, only 0.3% of the individuals in the lowest income bracket achieved good diet quality, while 5.6% of those in the highest income bracket achieved good diet quality.\u003c/p\u003e \u003cp\u003eIn addition, the associations between high US citizenship/residency duration, male sex, younger age, lower income level, and poorer compliance with HEI diet recommendations are visually elucidated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eMonte Carlo simulation of Fisher's exact test for associations of sociodemographic factors with diet quality\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eDiet Quality - Healthy Eating Index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLevel of Citizenship\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e547 (61.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e333 (37.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (33.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (63.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e299 (62.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e169 (35.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e293 (53.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e240 (43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge Group\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e228 (67.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105 (31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e272 (54.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e213 (43.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65 and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92 (48.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91 (48.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome Level\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e0\u0026ndash;24,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e194 (64.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105 (35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e25,000\u0026ndash;44,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132 (60.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83 (38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e45,000 - \u003cspan\u003e$\u003c/span\u003e64,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84 (59.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u003cspan\u003e$\u003c/span\u003e65,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117 (46.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120 (47.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eN\u0026thinsp;=\u0026thinsp;Number of subjects\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e% = proportion of subjects within each subcategory of sex, age group, and income level.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe conducted linear regression analyses to examine the relationship between the level of citizenship and diet quality as measured by the continuous HEI score while adjusting for gender, age, and income level. The analyses were also stratified by sex, age group, and income level to determine whether the association between citizenship and diet quality differed across these sample subgroups.\u003c/p\u003e \u003cp\u003eIn the full sample adjusted model, level of citizenship (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), gender (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), age (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and income (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were all significantly associated with HEI scores (see Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Those with lower citizenship levels tended to have higher HEI scores, indicating superior diet quality, after adjusting for demographic factors.\u003c/p\u003e \u003cp\u003eWhen stratified by gender, the association between lower citizenship and higher HEI scores remained significant for both males (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and females (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The citizenship coefficient was slightly stronger among males, suggesting greater disparity in diet quality by citizenship level among men.\u003c/p\u003e \u003cp\u003eBased on analyses by age group, we found significant citizenship-HEI associations in the younger 20\u0026ndash;39 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 40\u0026ndash;64 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) age strata but not among the 65\u0026thinsp;+\u0026thinsp;years age group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.29). The directionality was consistent with lower citizenship associated with superior diet quality.\u003c/p\u003e \u003cp\u003eFinally, when stratified by income, lower citizenship level persisted as a significant predictor of higher HEI scores in all income brackets except for \u003cspan\u003e$\u003c/span\u003e45,000\u0026ndash;\u003cspan\u003e$\u003c/span\u003e64,999 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.24). However, the strength of the association was slightly stronger in the lowest income bracket (\u003cspan\u003e$\u003c/span\u003e0\u0026ndash;24,999) than in the highest income bracket (\u0026gt;\u003cspan\u003e$\u003c/span\u003e65,000).\u003c/p\u003e \u003cp\u003eIn conclusion, lower levels of US citizenship and shorter US residency durations were associated with superior diet quality per HEI metric across gender, age, and income subgroups. The detailed coefficients and p values for each model can be found in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eStratified Linear Regression Models for Citizenship Levels Predicting Healthy Eating Index Scores, Adjusted for Covariates\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFull Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemale Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 to 39\u003c/p\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40 to 64\u003c/p\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65 and above Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e0\u0026ndash;24,999\u003c/p\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e25\u0026ndash;44,999\u003c/p\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e45,000 - \u003cspan\u003e$\u003c/span\u003e64,999\u003c/p\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026gt;\u003cspan\u003e$\u003c/span\u003e65,000\u003c/p\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel of Citizenship\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.83*** (0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.26*** (0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.29** (0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.20*** (0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.51*** (0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.77 (1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-3.72*** (0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-2.56*** (0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-1.40 (1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-2.85** (1.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.62*** (0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.23 (1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.03 (1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.90* (2.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.81 (1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.69 (1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.49 (1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4.10* (1.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.18*** (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15*** (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21*** (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.18*** (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.19*** (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.05 (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.26*** (0.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.84*** (0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.45** (0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.22*** (0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.63** (0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.01*** (0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.87* (0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003cp\u003eNote: *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eRacial heterogeneity and nutritional status\u003c/p\u003e \u003cp\u003eAn advantage of heterogeneous groupings of black/African American subjects in health studies is that they can account for genetic diversity, social determinants, regional residence, US nativity, ethnicity, and acculturation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. In this study, we stratified the age groups of adult subjects of African descent by US nativity/citizenship level in our regression models and found that the level of US citizenship based on citizenship status and years of US residence influenced diet quality scores among adults of African descent after controlling for confounding variables. We observed similar trends in which high US-born and moderately high citizenship levels were associated with lower diet quality. These findings are consistent with prior studies that reported poorer dietary patterns [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and a greater prevalence of chronic diseases/risk factors among US-born residents and US-born offspring of immigrants of African descent [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSocial Determinants and Diet Quality\u003c/p\u003e \u003cp\u003eSex, age, and income significantly influenced the diet quality scores. Lower HEI scores were observed for male subjects, the 20–39 age group, and subjects with the lowest income level than for female subjects, the 65 + age group, and subjects with the highest income level (p \u0026lt; 0.001), respectively.\u003c/p\u003e \u003cp\u003eDeterioration of healthy eating patterns among African Americans in the US can be traced back to circumstantial dietary adaptations to colonization after the transatlantic slave trade, which subjected them to limited rations of food access in addition to the adoption of some culinary preferences of Europeans [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Consequently, legislation of the Dawes Act of 1887 displaced Indigenous populations, with whom many African Americans intermingled and shared mixed ancestry, from land that they were accustomed to using for hunting/gathering, growing edible crops, and fishing [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Other factors included decreased access to farmland for fresh crop production following the Great Migration of a multitude of US Black individuals from the American South to the North and other urban communities for greater economic opportunity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, there were higher rates of economic hardship among black farmers who were denied business loans by the USDA to aid in their ability to maintain crop production and distribution [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. At present, environmental factors such as education, income, housing, urbanization, technological advances, and neighborhood food access have remained influential determinants of dietary patterns among the entire US population. In general, Americans do not meet the recommendations for fruit, vegetable, and whole grain intake based on the USDA Dietary Guidelines for Americans [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, Black/African Americans are disproportionately affected by disparities in education, median income, employment, single-parent households, healthcare access/treatment by providers, and residential proximity to full-service grocery chains [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAcculturation/Citizenship Level and Diet Quality\u003c/p\u003e \u003cp\u003eDespite the small sample of non-US-born subjects of African descent, we found significant associations between levels of US citizenship and diet quality scores among this cohort. A greater percentage of adults with high-US-born and moderately high levels of US citizenship had poor diet quality/HEI scores than did adults with low-foreign-born and moderately low levels of US citizenship. In addition, a substantially small subset of adults within all groups had good diet quality. Our findings among these subjects of African descent align with studies concerning the “Immigrant Health Paradox” and the impact of assimilation into the US and adaptation to the typical American diet among Asian and Hispanic groups [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA distinctive aspect of this study is our investigation into patterns and influences of acculturation/US nativity and diet among individuals of African ancestry within groups that have not been widely studied in this area of research. Due to the divergent origins of African Americans and Black immigrants in the US, there are several nuances to the mere definition of acculturation among this population. To that end, researchers developed and validated the African American Acculturation Scale (AAS), which accounts for cultural distinctions between African Americans and mainstream Caucasian Americans based on traditional norms associated with eight dimensions of African American culture, including health beliefs and eating patterns [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In addition, bidimensional and multidimensional methods of measuring acculturation can be applied to African, Afro-Caribbean, and Afro-Latino subgroups.\u003c/p\u003e \u003cp\u003eTraditional eating patterns of the African diaspora could serve as one of many tools for altering the trajectory of health among US Black individuals. One study evaluated quantifiable measures of colon cancer risk and disproportionate rates among African Americans, where African Americans and rural Africans underwent a two-week diet exchange to a traditional rural African diet high in fiber and a typical American diet high in animal protein and fat, respectively [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Investigators observed significant reductions in biomarkers of inflammation indicative of colon cancer risk among African Americans who switched to the traditional African diet, whereas a rise in inflammatory biomarkers of colon cancer risk was noted among rural Africans who transitioned to a diet high in animal protein and fat [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLast, with the inclusion of dairy in the USDA food guide, another factor to account for when measuring adherence to US Dietary Guidelines among individuals of African, Asian, and Indigenous ancestry is the approximate 68% prevalence of lactase malabsorption among these racial and ethnic groups as well as a cultural tendency to consume fewer servings of milk compared to individuals of European ancestry [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Therefore, future studies that include HEI instrumentation should consider the use of other modified HEI tools if warranted. One study that investigated a large cohort of subjects from the NHANES revealed that neither the HEI nor the AHEI was more beneficial than the other in demonstrating how well diet intake can predict diabetes management and other health indicators [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInterestingly, there has been a notable increase in cardiometabolic risk among the West African cohort of Black immigrants since the 1990s [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. This paradigm shift from traditional African diets low in fat/sugars and high in fiber, herbs, and spices has been attributed to westernized assimilation to diets high in fat, salt, and sugar and low in fiber, especially in urban areas of West African countries [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This transition has been influenced by urbanization, improvements in socioeconomic status, and technological innovations [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Comparatively, a twin-city study of African immigrants in Minnesota revealed a very low incidence of CVD risk factors and increased self-reported protective lifestyle behaviors among subjects from East African countries and the opposite trend among immigrants from West African countries [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. At the systemic level, there has been a substantial increase in the availability of energy-dense and animal protein foods combined with a minuscule increase in the availability of fruits and vegetables in higher-income countries of West Africa, such as Ghana [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLimitations\u003c/p\u003e \u003cp\u003e The diet quality measures that were assessed via two-day food records were based on self-reported information, and some participants did not complete day two of their food records. The research design of this study was not experimental but observational. Therefore, we could determine the association between our tested variables of US citizenship level and diet quality, but not causality. Due to the small frequency of subjects who reported African descent in the NHANES, especially foreign-born subjects, we could not stratify the results into categories of US-born, Caribbean-born, and African-born US residents who identified as non-Hispanic Black/African American in this smaller preliminary sample of our extended analysis plan. Instead, we developed an ordinal scoring system to measure the level of US citizenship for our regression model based on country of birth, US citizenship (yes or no), and number of years residing in the US. However, of the four ordinal levels of citizenship, the highest and lowest were US-born and foreign-born residents, respectively. This method is a parallel alternative considering research findings concerning relationships between time-based exposures to Westernized countries and deteriorated actual and self-rated health.\u003c/p\u003e \u003cp\u003eThese data were limited to a unidimensional approach for measuring acculturation, which does not take beliefs, values, or other cultural customs into account. Therefore, we did not approach this study from the traditional lens of the acculturation construct but rather from the US citizenship level. The most common unidimensional parameters for acculturation, including non-English native languages, are not applicable to African Americans, who represented most of the subjects in this sample. Most of these subjects of African descent were born in the US, the dominant culture in this study, and English is their primary language [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. English is also a commonly spoken language among black immigrants from British-colonized countries in Africa [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and the Caribbean [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. In addition, there was an inadequate sample of non-Hispanic subjects of African descent who reported speaking a second language to add it as a measure of acculturation/citizenship. Conversely, various acculturation studies among Hispanic and Asian populations include language as a parameter. Considering that many African and Caribbean countries are British-colonized and that English is the predominant language spoken in the US, English is the dominant language of most non-Hispanic Black individuals in the US [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMultiple sources of data suggest that the poorest health outcomes of obesity and noncommunicable diseases in the US are among the American South population, irrespective of race/ethnicity [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Therefore, it is imperative to consider regional differences in the eating patterns and food culture of any racial/ethnic community as well.\u003c/p\u003e "},{"header":"Conclusion and Future Directions","content":"\u003cp\u003eThis sample is limited to data collected before any COVID-19 pandemic-related influences. In our longitudinal follow-up paper, we will oversample the limited frequencies of subjects of African descent through a combined analysis of extended cohort years of data sets from 2015–2024, including regional variations in diet quality among African Americans and Black Immigrants (African, Afro-Caribbean, and other countries). If the sample size is sufficient, we will stratify comparisons between US regions such as the southern, northern, northeastern, midwestern, and western coasts. Future studies should continue exploring interventions that utilize digital technology to adapt to modernization and emphasize culturally diverse models for healthy eating as an applied approach to mitigating health inequities among people of color. Additional considerations should emphasize relationships and trust building that could improve the recruitment of participants, as one qualitative study noted distrust and not having culturally centered models for healthy eating as barriers [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo date, the Physician’s Committee for Responsible Medicine (PCRM) developed the African Heritage Power Plate [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], a plant-based version of the USDA MyPlate inspired by the African Heritage food pyramid and food guides of Oldways Health through the Heritage initiative [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. These models emphasize the inclusion of culturally tailored ancestral traditions that are compatible with healthy eating patterns of the 2020–2025 Dietary Guidelines for Americans [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Thus far, one community-based intervention that utilized the Oldways ”A Taste of African Heritage” cooking program with 586 participants over five years revealed statistically significant improvements in fruit/vegetable intake, weight, waist circumference, and systolic blood pressure [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In closing, unfavorable social determinants, discrimination, homogenous groupings, the acculturation of black immigrants, centuries of colonization and food injustice among African Americans, and Eurocentric criteria for models of healthy eating have perpetuated disproportionately higher rates of diet-related chronic diseases and risk factors among the US Black population. Researchers and healthcare professionals must be cognizant of these factors among all subgroups of Black individuals in the US and be trained to deliver culturally tailored interventions that are cardioprotective and sustainable.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data spreadsheets and analysis results are available in the Supplementary Material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Hatch funds provided by the National Institute of Food and Agriculture of the USDA under grant numbers NE1939 for Multistate and 1012814.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization and design of the study were performed by T.J. Background was written by T.J., S.B., and G.S. Methods were written by T.J and X.D. Statistical analyses were performed by X.D. and A.A. Tables were prepared by X.D. and A.A. The results were written by T.J. X.D., and A.A. Discussion/Conclusion was written by T.J. and G.S. All (T.J., X.D., A.A., S.B., L.M., and G.S.) reviewed, edited, and approved this draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank our colleagues at the University of the District of Columbia for providing us with a platform to promote diversity, equity, and inclusion in nutrition/health-related research and practice.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgyemang C, Bhopal R, Bruijnzeels M.Negro, Black, Black African, African Caribbean, African American or what? 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DOI: http://dx.doi.org/10.15585/mmwr.mm6529a3\u003c/li\u003e\n\u003cli\u003eWilson, J. F., Weale, M. E., Smith, A. C., Gratrix, F., Fletcher, B., Thomas, M. G., Bradman, N., \u0026amp; Goldstein, D. B. Population genetic structure of variable drug response. \u003cem\u003eNature Genetics\u003c/em\u003e, 2001, \u003cem\u003e29\u003c/em\u003e(3), 265\u0026ndash;269. https://doi.org/10.1038/ng761\u003c/li\u003e\n\u003cli\u003eZong, J., \u0026amp; Batalova, J. Caribbean immigrants in the United States. 2019. https://www.migrationpolicy.org/article/caribbean-immigrants-united-states-2017.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutn","sideBox":"Learn more about [BMC Nutrition](http://bmcnutr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nutn/default.aspx","title":"BMC Nutrition","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"African American, Acculturation, Black Immigrant, Healthy Eating Index","lastPublishedDoi":"10.21203/rs.3.rs-4183130/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4183130/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e: With the substantial growth rate of the Black immigrant population in the US, the impact of colonization on the diets of African Americans and disproportionately high rates of chronic diseases in the Black Community, studies should place more emphasis on ethnicity when investigating nutrition-related risk factors. This study examined the relationship between variations in citizenship level and diet quality among adult subjects of African descent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We analyzed data from 1,198 African American adults in the NHANES 2015-2016. A 'citizenship level' scale was developed using principal component analysis, incorporating years lived in the US, country of birth, and citizenship status. Diet quality was assessed using HEI scores. Associations between citizenship level, gender, age, income, and categorized diet quality (poor, moderate, or good) were examined using a Monte Carlo simulation of Fisher's exact tests. Linear regression models were employed to examine the relationship between citizenship level and continuous HEI scores, adjusting for gender, age, and income, with stratified analyses conducted for each demographic subgroup.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Fisher's exact tests revealed that the level of US citizenship, gender, age, and income level were all significantly associated with the categorical level of diet quality (HEI). Regression analysis demonstrated that a lower level of US citizenship was significantly associated with higher HEI scores, indicative of better diet quality (\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001), even after adjusting for gender, age, and income. This relationship persisted across most demographic subgroups but appeared stronger among males and those in the lowest income bracket. The relationship was also significant for younger adults but not for adults aged 65 and older.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: Studies suggest a need for more inclusive culturally tailored nutrition interventions. To minimize the impact of colonization and US assimilation on lifestyle practices and chronic diseases, future studies should consider interventions that utilize traditional dietary patterns of the African diaspora as a tool to improve the quality of life among people of African descent.\u003c/p\u003e","manuscriptTitle":"Patterns of US Citizenship Status vs. Diet Quality among Adults of African Descent","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-22 04:31:49","doi":"10.21203/rs.3.rs-4183130/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-20T17:12:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-31T09:00:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-26T12:08:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"294329845618063895486415533201477201658","date":"2024-07-20T18:58:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-18T12:43:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61611620944247249299586722324730265622","date":"2024-07-18T08:55:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"35982170547328335457841276017619345587","date":"2024-07-18T07:42:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-28T19:17:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"55393352292451602085591839136852363820","date":"2024-06-07T22:30:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"37335776814080026131518238087266809205","date":"2024-06-06T10:24:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-04T22:11:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-22T10:44:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-17T07:30:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-17T07:30:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nutrition","date":"2024-03-28T14:59:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutn","sideBox":"Learn more about [BMC Nutrition](http://bmcnutr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nutn/default.aspx","title":"BMC Nutrition","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bc6d5e58-5269-4658-ab15-4195b0fad75d","owner":[],"postedDate":"April 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-14T16:05:35+00:00","versionOfRecord":{"articleIdentity":"rs-4183130","link":"https://doi.org/10.1186/s40795-025-01108-z","journal":{"identity":"bmc-nutrition","isVorOnly":false,"title":"BMC Nutrition"},"publishedOn":"2025-07-07 15:57:31","publishedOnDateReadable":"July 7th, 2025"},"versionCreatedAt":"2024-04-22 04:31:49","video":"","vorDoi":"10.1186/s40795-025-01108-z","vorDoiUrl":"https://doi.org/10.1186/s40795-025-01108-z","workflowStages":[]},"version":"v1","identity":"rs-4183130","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4183130","identity":"rs-4183130","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}