Exploring Disparities in Dietary Quality Among Young Children Across Diverse Racial,Ethnic,and Immigrant Households

Abstract

Background:

Although racial, ethnic minorities, and immigrants are more likely to have poor diet-related health outcomes, few studies have compared children’s dietary quality across diverse households, which is the formative step to designing targeted interventions. The current study evaluates and compares the dietary quality of young children from diverse racial, ethnic, and immigrant households in Nebraska.

Methods:

Cross-sectional survey data were collected from adults living in Nebraska with at least one 2–6-year-old child residing in their household via an online survey regarding their federal assistance program participation and dietary quality of child(ren) residing in their household, measured using the short Healthy Eating Index (sHEI).

Results:

With nearly two-third participating in a federal assistance program, the sample includes respondents from diverse households (n = 1,277) including first-generation immigrant (n = 61), non-immigrant Hispanic (n = 538), non-immigrant non-Hispanic White (n = 509), non-immigrant non-Hispanic Black or African American (n = 120), and non-Hispanic American Indian or Native Hawaiian (n = 49). Based on analysis of covariance controlling for demographic variables, children from immigrant households had lower mean sHEI score 43.9 as compared to non-immigrant Hispanic 46.4, non-immigrant non-Hispanic White 47.1, non-immigrant non-Hispanic Black or African American 50.2, and non-Hispanic American Indian or Native Hawaiian 48.9. Racial/ethnic/immigrant household group differences were also observed for some sHEI component scores.

Conclusions:

Children from immigrant and non-immigrant Hispanic households had significantly lower sHEI scores on some subcomponents compared with other groups. Findings emphasize the need for additional research and culturally responsive multilevel nutrition interventions.

Exploring Disparities in Dietary Quality Among Young Children Across Nebraska

Racial, ethnic minoritized groups and immigrants face health disparities in the United States, related to obesity and associated diet-related chronic diseases such as cardiovascular disease and type 2 diabetes.^1,2 High rates of obesity are seen among Native American, Hispanic, and non-Hispanic Black populations—29.7%, 26.2%, and 24.8%, respectively, compared with 16.6% among non-Hispanic White population.^3,4 In addition, immigrants represent a vulnerable demographic, with one in four being overweight or obese.⁵ Sub-optimal dietary quality among U.S. children is a contributing factor to obesity and other diet-related disorders.⁶ National data from the Healthy Eating Index-2020 report a score of 54 out of 100 for children aged 2–18 years, reflecting particularly low consumption of vegetables and whole grains.⁷ Over 70% of U.S. preschoolers consume sugar-sweetened beverages weekly, 60% eat sweets, and 40% consume salty snacks four or more times weekly, while only 18% meet recommended intakes of fruits, vegetables, and milk.⁸ Recognizing the role of dietary quality in combating the obesity pandemic, particularly among children, is needed as early childhood is a critical period for developing eating behaviors that impact long-term health outcomes.^9,10

Existing research on the dietary quality of different racial, ethnic, and immigrant households with 2–6-year-olds in the United States is limited and presents mixed findings, indicating the need for further investigation. Studies have highlighted the prevalence of diets high in added sugars and saturated fats among young children and adults from diverse backgrounds, increasing their susceptibility to adverse health outcomes.^11–13 In many cases, the dietary quality of immigrants residing in the United States is poor compared with those residing in their country of origin.¹⁴ Additionally, immigrants in the United States who adapt to more western diets tend to have lower consumption of fruits and vegetables, higher intake of fats, and in general poor dietary quality.^15–17

Notably, considerable variations exist within and across minoritized groups in the United States. While some studies suggest that Hispanics exhibit higher dietary quality because of traditional food choices, others report lower dietary quality, particularly among preschool-aged children from Hispanic farmworker families.^18,19 Lower dietary quality has been observed in non-Hispanic Black children aged 6–11 years compared with non-Hispanic White children.²⁰ Research on the dietary quality of American Indian populations is limited, with most studies focusing on adults or pregnant women and indicating suboptimal dietary patterns.^21,22 A Canadian study examining diet quality among Indigenous children and youth aged 2–17 years found that Indigenous participants scored consistently lower on the Healthy Eating Index (HEI) compared with their non-Indigenous counterparts in both 2004 and 2015, highlighting persistent disparities. Research on Native Hawaiians, including children aged 6–13 years and caregivers, indicates lower consumption of essential food components and less nutrient-rich diets compared with non-Hispanic Whites.²³ Understanding these differences in dietary quality particularly among young children from diverse households is a formative step for developing nutrition policies and programs to address diet-related health conditions.

There is limited research comparing the dietary quality of children from diverse households in a single study, particularly in areas such as Nebraska, where unique demographic profiles may influence dietary behaviors and health outcomes. Nebraska’s immigrant population accounts for 7.2% of the total population, which is nearly half the national average of 13.7%.^24,25 In addition, racial and ethnic minoritized communities account for 21.8% of Nebraska’s population, compared with the national average of 24.7%. Within Nebraska, Hispanics represent the largest group at 11.4% (national average: 19.5%), followed by non-Hispanic Blacks or African Americans at 4.9% (national average: 13.7%), and American Indian or Alaska Native populations at 1.3% (national average: 1.3%).^26–28 One in three children in Nebraska is overweight or obese, which is higher than the national average of one in five, with elevated rates observed among American Indian, Hispanic, and Black or African American children, underscoring the urgent need to address dietary disparities and related health outcomes.^29,30 Therefore, the current study aims to evaluate and compare the dietary quality of 2–6-year-old children from diverse racial, ethnic, and immigrant households across Nebraska.

Methods

Participants and Recruitment

Data were drawn from a larger state-wide needs assessment, “Healthy People, Healthy State,” that surveyed 1316 Nebraska adults (aged 19 years or older) who had at least one 2–6-year-old child residing in their household. The Institutional Review Board at University of Nebraska-Lincoln approved all research procedures. Interested participants were asked to complete an informed consent form at the beginning of the survey. The current study focused on a subsample of 1,277 respondents with children from 2 to 6 years who were categorized as belonging to one of the selected five racial/ethnic/immigrant households: first-generation immigrant (n = 61), nonimmigrant Hispanic (n = 538), nonimmigrant non-Hispanic White (n = 509), nonimmigrant non-Hispanic Black or African American (n = 120), and non-Hispanic American Indian or Native Hawaiian (n = 49).

To ensure participation of diverse households, Nebraska Extension’s Supplemental Nutrition Assistance Program Education team and research team circulated flyers with QR code both digitally through online platforms and in print at selected sites such as Department of Health and Human Services offices and local public health offices, child care programs (Head Start, Educare), food pantries, the Supplemental Nutrition Program for Women, Infants, and Children (WIC) offices, ethnic grocery stores, local community parks, and nutrition education classes to capture hard-to-reach audiences.^18,31 Given the broad distribution of recruitment materials, an exact response rate cannot be calculated.

A 202-item survey adapted from previously validated measures was administered from 2023 to 2024 via Qualtrics and included questions on various constructs such as demography, participation in Food and Nutrition Assistance Programs, child(ren) participation in Head Start, and child dietary intake based on the short HEI (sHEI).^32,33 To ensure the integrity and security of the survey data, several key measures were implemented. The survey was available in three languages: English, Spanish, and Arabic. Spanish was chosen as one of the languages as Hispanics constitute the largest minoritized population in Nebraska.³⁴ Arabic was chosen as it is one of the five most spoken languages among language minority groups in Nebraska.³⁵ Interested participants were invited to share their email addresses for a chance to enter a $50 raffle.

Face Validity

Before launching the survey, cognitive interviews were conducted with three participants, who met specific inclusion criteria for face validity.³⁶ These criteria were being at least 19 years old, a resident of Nebraska, proficient in English, and able to provide information about food frequency. Using a structured approach, additional information was collected, and the survey was refined. Specifically, adjustments were made to the order of questions to improve flow and to the wording to improve understanding.

Measures

Dietary quality

Children’s diet quality was assessed using a food frequency questionnaire, sHEI, completed by an adult household member. The sHEI was modified for young children by restructuring questions and aligning scoring with age-specific serving size recommendations from the 2015–2020 guidelines. Scores range from 0 to 100, with higher scores indicating better adherence to USDA recommendations.³³ It includes 22 questions regarding usual frequency of intake of specific food components such as fruits, vegetables, green vegetables and beans, whole grains, dairy, total protein, seafood and plant protein, fatty acids, refined grains, sodium, added sugars, and saturated fats.³³

Sociodemographic characteristics

Respondents were asked to provide information on their gender, race, ethnicity, zip code of residence in Nebraska, place of birth, annual household income from all sources, highest level of schooling completed, and the number of people living in their household.³² In addition, they were asked if they had children between the ages of 2 and 6 years, and the gender of the child. If the respondent had more than one child in this age range, they were instructed to provide information for the oldest child only. Respondents were categorized as urban or rural residents by identifying counties from their zip codes and using Rural-Urban Continuum Codes to classify the counties as urban or rural.^37,38

Federal program participation

Respondents were asked about the programs they were participating in, such as Supplemental Nutrition Assistance Program (SNAP) (formerly Food Stamps), WIC, Gus Schumacher Nutrition Incentive Program, Temporary Assistance for Needy Families (TANF), and Medicaid. They were also asked if the child residing in their household is enrolled in Head Start.^39,40

Statistical Approach

Descriptive statistics were used to summarize sample characteristics, and χ² tests were performed to examine group differences among the five household groups based on respondents’ self-reported race, ethnicity, and immigration status. Analysis of covariance (ANCOVA) was performed to compare dietary quality overall and across 13 subcomponents among children from these five household groups. In the ANCOVA models, annual household income, education level, geographical location (rural or urban), and participation in federal assistance programs were included as covariates, as these are potential confounders commonly associated with dietary quality.^39–41 Child gender was excluded as a covariate because the diet quality scoring was adjusted for gender differences. Interpretation focused on main effect association of the racial/ethnic/immigrant group variable on dietary quality, controlling for covariates. For statistically significant omnibus group differences, pairwise comparisons were performed to identify which groups differed.⁴²

Analyses were conducted using IBM SPSS Statistics Version 27.0. Due to item-level missing data, the analytic sample ranged from 896 to 986, depending on the outcome. Statistical significance was set at α = 0.05. Because multiple tests were performed, p values were adjusted using the Benjamini–Hochberg correction.⁴³ Specifically, tests of the omnibus group associations on the individual subcomponents were adjusted for 13 comparisons, and within each outcome, pairwise tests were adjusted for 10 group comparisons. Adjusted R², partial eta-squared, and Cohen’s d (small effect sizes are defined as d = 0.2, medium as d = 0.5, and large as d = 0.8.) and the corresponding Benjamini–Hochberg corrected 95% confidence interval were calculated as measures of effect size and uncertainty.⁴⁴

Results

The final sample consisted of 1,277 participants with 42% from nonimmigrant Hispanic households, 40% from non-Hispanic White households, 9% from non-Hispanic Black or African American households, 5% first-generation immigrant households, and 5% from American Indian or Native Hawaiian households (Table 1). Significant group differences were observed in household location and program participation. Almost all immigrant households (96%) lived in urban areas, while nearly half (49%) of American Indian, Alaska Native, Native Hawaiian, or Other Pacific Islander (AI/AN/NH/OPI) households lived in rural settings. In addition, while 82% of nonimmigrant Hispanic households participated in at least one government food assistance program, >80% of AI/AN/NH/OPI households did not participate in any program. Furthermore, >70% of Black households reported an annual income under $39,999 compared with 24% of White households.

Table 1.

Frequencies (Percentages) of Demographic Characteristics and Program Participation by Caregiver Racial/Ethnic/Immigrant Group (n = 1277)

	Immigrant (n = 61)	Hispanic (NI) (n = 538)	White (NI, NH) (n = 509)	Black (NI, NH) (n = 120)	AI/AN or NH/OPI (NI, NH) (n = 49)	χ² (p)
Child gender
Male	45 (73.8%)	182 (34.2%)	333 (66.1%)	49 (41.2%)	30 (61.2%)	126.6 (<0.001)
Female	16 (26.2%)	349 (65.5%)	169 (33.5%)	69 (57.9%)	19 (38.8%)
Choose not to answer	0 (0%)	2 (0.4%)	2 (0.4%)	1 (0.8%)	0 (0%)
Education
High school diploma level or less	18 (29.5%)	67 (12.5%)	119 (23.4%)	63 (52.5%)	36 (73.5%)	179.7 (<0.001)
Some college or technical/associate/junior college	15 (24.6%)	283 (52.6%)	244 (47.9%)	45 (37.5%)	12 (24.5%)
Bachelor’s/grad/professional degree	28 (45.9%)	188 (34.9%)	146 (28.7%)	12 (10%)	1 (2.0%)
Annual Household Income
$0–$39,999	25 (43.9%)	107 (19.9%)	227 (46.2%)	85 (70.8%)	17 (34.7%)	246.4 (<0.001)
$40,000–$69,999	8 (14.0%)	111 (20.6%)	145 (29.5%)	28 (23.3%)	25 (51.0%)
$70,000 or more	24 (42.1%)	320 (59.5%)	119 (24.2%)	7 (5.8%)	7 (14.3%)
Location
Rural	2 (3.6%)	113 (31.9%)	102 (23.3%)	42 (36.5%)	23 (48.9%)	37.9 (<0.001)
Urban	53 (96.4%)	241 (68.1%)	335 (76.7%)	73 (63.5%)	24 (51.1%)	37.9 (<0.001)
Program participation
SNAP	8 (13.1%)	44 (8.2%)	186 (36.5%)	36 (30%)	4 (8.2%)	483.4 (<0.001)
SNAP + HS	3 (4.9%)	304 (56.5%)	35 (6.9%)	12 (10%)	0 (0.0%)
SNAP + WIC	6 (9.8%)	39 (7.3%)	44 (8.6%)	2 (1.7%)	4 (8.2%)
SNAP + WIC + HS	1 (1.6%)	31 (5.8%)	27 (5.3%)	5 (4.2%)	1 (2.0%)
Do not participate	43 (70.5%)	120 (22.3%)	217 (42.6%)	65 (54.2%)	40 (81.6%)

Frequencies may not sum to total sample size due to item-level missing data.

AI/AN, American Indian or Alaska Native; NH/OPI, Native Hawaiian or Other Pacific Islander; NI; Nonimmigrant; NH, Non-Hispanic.

Table 2 provides raw (unadjusted) descriptive statistics and covariate-adjusted estimates (ANCOVA marginal means and standard errors) summarizing child dietary quality outcomes by racial/ethnic/immigrant group. The mean sHEI score was 47 (SD = 9.6) out of a possible 100, indicating a low dietary quality.⁴⁵ ANCOVA tests of omnibus group differences in overall dietary quality and across the 13 subcomponents are also shown in Table 2. Effect sizes, confidence intervals, and statistical significance of the pairwise group comparisons associated with the significant omnibus associations are given in Table 3.

Table 2.

Descriptive Statistics and Model-Adjusted Estimates of the Dietary Quality Outcomes of Young Children (2–6-Years-Old) by Caregiver Racial/Ethnic/Immigrant Group

	Immigrant		Hispanic (NI)		White (NI, NH)		Black (NI, NH)		AI/AN or NH/OPI (NI, NH)		ANCOVA test of groupdifference^a
	n	M (SD)M_adj (SE)	n	M (SD) M_adj (SE)	n	M (SD) M_adj (SE)	n	M (SD) M_adj (SE)	n	M (SD) M_adj (SE)	ANCOVA test of groupdifference^a
Overall	51	45.3 (9.1) 43.9 (1.3)	354	44.1 (9.0) 46.4 (0.6)	419	49.1 (8.9) 47.1 (0.6)	115	51.2 (8.8) 50.2 (0.9)	47	49.6 (9.4) 48.9 (1.3)	F(4972) = 5.5, p < 0.001, η_p² = 0.02
Total fruits	49	6.3 (3) 5.6 (0.4)	353	3.4 (2.7) 4.3 (0.2)	413	5.0 (2.8) 4.6 (0.2)	113	6.3 (2.5) 6.1 (0.3)	47	6.1 (2.9) 5.3 (0.4)	F(4961) = 9.7, p < 0.013, η_p² = 0.04
Whole fruits	42	3.9 (1.8) 3.6 (0.3)	352	2.1 (1.7) 2.6 (0.1)	407	3.1 (1.7) 2.9 (0.1)	111	3.7 (1.6) 3.7 (0.2)	46	3.6 (1.7) 3.1 (0.3)	F(4944) = 8.2, p < 0.013, η_p ²= 0.03
Total veg.	41	2.8 (0.8) 2.7 (0.1)	350	2.4 (0.7) 2.6 (0.1)	397	2.9 (0.6) 2.8 (0.0)	111	2.8 (0.5) 2.8 (0.1)	46	2.9 (0.6) 2.8 (0.1)	F(4931) = 2.0, p = 0.126, η_p² = 0.01
Greens and beans	50	2.6 (2.5) 2.4 (0.3)	354	2.4 (2.5) 3.1 (0.2)	414	3.6 (2.2) 3.2 (0.1)	115	4.1 (1.9) 4.1 (0.2)	47	3.5 (2.3) 3.5 (0.4)	F(4966) = 5.7, p < 0.013, η_p² = 0.02
Whole grains	43	2.8 (1.5) 2.6 (0.3)	341	3.1 (2.1) 3.2 (0.1)	373	3.4 (1.8) 3.2 (0.1)	95	4.5 (1.8) 4.5 (0.2)	44	3.5 (1.9) 3.2 (0.3)	F(4882) = 10.6, p < 0.013, η_p² = 0.05
Dairy	51	4.9 (1.6) 4.6 (0.2)	350	4.1 (1.1) 4.1 (0.1)	406	4.3 (1.4) 4.1 (0.1)	114	5.0 (1.4) 4.8 (0.1)	47	4.3 (1.3) 3.9 (0.2)	F(4954) = 8.8, p < 0.013, η_p² = 0.04
Total proteins	51	4.6 (0.4) 4.5 (0.1)	353	4.8 (0.4) 4.6 (0.0)	412	4.7 (0.4) 4.6 (0.0)	113	4.7 (0.4) 4.7 (0.0)	47	4.6 (0.4) 4.6 (0.1)	F(4962) = 1.9, p = 0.121, η_p² = 0.01
Seafood & plant proteins	51	2.1 (1.7) 2.0 (0.2)	353	2.4 (1.5) 2.3 (0.1)	412	2.2 (1.7) 2.2 (0.1)	114	2.7 (1.7) 2.7 (0.2)	47	3.0 (1.6) 2.6 (0.3)	F(4963) = 2.7, p = 0.049, η_p² = 0.01
Fatty acids	44	4.3 (1.2) 4.0 (0.2)	349	3.6 (1.1) 3.7 (0.1)	400	4.6 (1.2) 4.2 (0.1)	111	4.0 (1.1) 3.7 (0.1)	43	4.2 (1.0) 4.1 (0.2)	F(4933) = 9.9, p < 0.013, η_p² = 0.04
Refined grains	44	5.1 (3.5) 5.2 (0.6)	352	5.3 (3.5) 5.4 (0.2)	400	6.1 (3.4) 5.9 (0.2)	109	5.4 (3.5) 5.2 (0.4)	46	4.2 (3.1) 4.7 (0.5)	F(4937) = 2.0, p = 0.129, η_p² = 0.01
Sodium	51	4.8 (1.8) 5.0 (0.2)	353	5.5 (1.4) 5.1 (0.1)	413	4.9 (1.8) 5.0 (0.1)	111	4.6 (1.8) 4.6 (0.2)	46	4.5 (1.8) 4.9 (0.3)	F(4960) = 1.7, p = 0.152, η_p² = 0.01
Added sugars	51	1.76 (2.6) 2.08 (0.4)	352	2.2 (2.5) 2.6 (0.2)	415	3.0 (2.5) 3.0 (0.2)	115	2.2 (2.6) 2.2 (0.3)	47	3.0 (2.7) 3.4 (0.4)	F(4966) = 3.7, p = 0.009, η_p² = 0.02
Sat. fat	50	2.73 (1.3) 2.80 (0.2)	354	3.0 (1.1) 3.2 (0.1)	413	3.0 (1.3) 3.0 (0.1)	112	3.0 (1.6) 3.1 (0.1)	47	3.2 (1.6) 3.2 (0.2)	F(4962) = 1.1, p = 0.366, η_p² = 0.00

p Values for the 13 subcomponents were adjusted for 13 comparisons using the Benjamini–Hochberg correction.

AI/AN, American Indian or Alaska Native; NH/OPI, Native Hawaiian or Other Pacific Islander; NI, Nonimmigrant; NH, Non-Hispanic; M_adj, Covariate-adjusted mean (ANCOVA marginal mean).

Table 3.

Cohen’s d and Benjamini–Hochberg Corrected 95% Confidence Intervals for Pairwise Comparisons Following Significant Omnibus Associations

Dietary component	Group	Hispanic (NI)	White (NI, NH)	Black (NI, NH)	AI/AN or NH/OPI (NI, NH)
Overall	Immigrant	−0.3 (−0.6, 0.1)	−0.4 (−0.7, −0.0)^*	−0.7 (−1.2, −0.2)^*	−0.5 (−1.0, −0.1)^*
	Hispanic (NI)		−0.1 (−0.2, 0.1)	−0.4 (−0.7, −0.1)^*	−0.3 (−0.6, 0.1)
	White (NI, NH)			−0.3 (−0.6, −0.1)^*	−0.2 (−0.5, 0.1)
	Black (NI, NH)				0.1 (−0.2, 0.5)
Total fruits	Immigrant	0.5 (0.1, 0.9)^*	0.4 (0.0, 0.7)^*	−0.2 (−0.5, 0.2)	0.1 (−0.3, 0.5)
	Hispanic (NI)		−0.1 (−0.3, 0.0)	−0.7 (−1.0, −0.4)^*	−0.4 (−0.8, −0.1)^*
	White (NI, NH)			−0.6 (−0.9, −0.3)^*	−0.3 (−0.6, 0.1)
	Black (NI, NH)				0.3 (−0.1, 0.7)
Whole fruits	Immigrant	0.6 (0.1, 1.1)^*	0.4 (0.1, 0.8)^*	−0.0 (−0.4, 0.3)	0.3 (−0.2, 0.7)
	Hispanic (NI)		−0.2 (−0.3, 0.0)	−0.6 (−0.9, −0.3)^*	−0.3 (−0.7, 0.0)
	White (NI, NH)			−0.5 (−0.8, −0.2)^*	−0.1 (−0.5, 0.2)
	Black (NI, NH)				0.3 (−0.1, 0.7)
Greens and beans	Immigrant	−0.3 (−0.6, 0.1)	−0.4 (−0.7, −0.0)^*	−0.8 (−1.3, −0.3)^*	−0.5 (−1.0, 0.0)^*
	Hispanic (NI)		−0.1 (−0.2, 0.1)	−0.4 (−0.7, −0.1)^*	−0.2 (−0.5, 0.1)
	White (NI, NH)			−0.4 (−0.7, −0.1)^*	−0.1 (−0.5, 0.2)
	Black (NI, NH)				0.3 (−0.1, 0.6)
Whole grains	Immigrant	−0.3 (−0.7, 0.0)	−0.4 (−0.7, −0.0)	−1.1 (−1.7, −0.6)^*	−0.4 (−0.9, 0.1)
	Hispanic (NI)		−0.0 (−0.2, 0.2)	−0.6 (−1.0, −0.3)^*	−0.0 (−0.3, 0.3)
	White (NI, NH)			−0.7 (−1.1, −0.4)^*	−0.0 (−0.3, 0.3)
	Black (NI, NH)				0.7 (0.2, 1.2)^*
Dairy	Immigrant	0.4 (0.1, 0.8)^*	0.3 (−0.0, 0.6)^*	−0.2 (−0.5, 0.2)	0.4 (−0.1, 0.9)^*
	Hispanic (NI)		−0.1 (−0.2, 0.1)	−0.7 (−1.0, −0.3)^*	0.1 (−0.2, 0.5)
	White (NI, NH)			−0.5 (−0.8, −0.2)^*	0.2 (−0.2, 0.5)
	Black (NI, NH)				0.7 (0.2, 1.2)^*
Seafood and plant proteins	Immigrant	−0.2 (−0.5, 0.1)	−0.1 (−0.4, 0.2)	−0.4 (−0.8, 0.0)	−0.3 (−0.8, 0.2)
	Hispanic (NI)		0.1 (−0.1, 0.2)	−0.2 (−0.5, 0.0)	−0.2 (−0.5, 0.2)
	White (NI, NH)			−0.3 (−0.6, 0.0)^*	−0.2 (−0.6, 0.1)
	Black (NI, NH)				0.1 (−0.3, 0.4)
Fatty acids	Immigrant	0.2 (−0.1, 0.6)	−0.2 (−0.6, 0.1)	0.3 (−0.1, 0.7)	−0.1 (−0.5, 0.4)
	Hispanic (NI)		−0.5 (−0.7, −0.2)^*	0.1 (−0.2, 0.3)	−0.3 (−0.7, 0.1)
	White (NI, NH)			0.5 (0.2, 0.8)^*	0.1 (−0.2, 0.5)
	Black (NI, NH)				−0.4 (−0.8, 0.1)
Added sugars	Immigrant	−0.2 (−0.5, 0.1)	−0.4 (−0.7, −0.0)	−0.1 (−0.4, 0.3)	−0.5 (−1.0, 0.0)
	Hispanic (NI)		−0.1 (−0.3, 0.0)	0.2 (−0.1, 0.4)	−0.3 (−0.7, 0.1)
	White (NI, NH)			0.3 (−0.0, 0.6)	−0.2 (−0.5, 0.2)
	Black (NI, NH)				−0.4 (−0.9, 0.0)

Negative (positive) values indicate that the row group had a lower (higher) mean score than the column group.

Significant difference (p < 0.05) after Benjamini–Hochberg adjustment.

AI/AN, American Indian or Alaska Native; NH/OPI, Native Hawaiian or Other Pacific Islander; NI, Nonimmigrant; NH, Non-Hispanic.

Controlling for education, income, geographic location, and program participation, there was a significant omnibus mean difference in children’s overall sHEI scores by racial/ethnic/immigrant group. Pairwise comparisons revealed that children of immigrant households had significantly lower dietary quality scores on average than children of non-Hispanic White, non-Hispanic Black, and non-Hispanic American Indian or Native Hawaiian households, with effect sizes ranging from moderately small (|d| = 0.36) to moderately large (|d| = 0.70). In contrast, children of non-Hispanic Black households had significantly higher dietary quality scores on average than all other groups except non-Hispanic American Indian or Native Hawaiian, with a similar range of effect sizes (|d| = 0.34–0.70). Children of immigrant households did not differ significantly in overall dietary quality scores compared with children from nonimmigrant Hispanic households.

When considering specific subcomponents of dietary quality, significant omnibus group differences were observed for total fruits, whole fruits, greens and beans, whole grains, dairy, seafood and plant proteins, fatty acids, and added sugars, but not for total vegetables, total proteins, refined grains, sodium, or saturated fats. For several subcomponent group differences, scores were higher for non-Hispanic Black children and lower for Hispanic children as compared with at least one other group. Specific patterns of differences are summarized below.

Children of non-Hispanic Black households had significantly higher mean total fruit and whole fruit scores than children of Hispanic and non-Hispanic White households (|d| = 0.45–0.69); significantly higher mean greens and beans scores than all other groups except non-Hispanic American Indian or Native Hawaiian (|d| = 0.39–0.79 for significant comparisons); significantly higher mean whole grains scores than all other groups (|d| = 0.63–1.11); significantly higher mean dairy scores than all other groups except immigrant group (|d| = 0.50–0.66 for significant comparisons); significantly higher mean seafood and plant protein scores than non-Hispanic White (|d| = 0.30); and significantly lower mean fatty acids scores than non-Hispanic White (|d| = 0.49).

Children from Hispanic households had significantly lower mean total fruit scores than children from immigrant and non-Hispanic American Indian or Native Hawaiian households (|d| = 0.40–0.51); significantly lower mean whole fruits and dairy scores than children of immigrant households (|d| = 0.42–0.59); and significantly lower mean fatty acids scores than children of non-Hispanic White households (|d| = 0.45). In addition, children of immigrant households had significantly lower mean greens and beans scores than children of non-Hispanic White and non-Hispanic American Indian or Native Hawaiian households (|d| = 0.36–0.47) but significantly higher mean dairy scores compared with children in these households (|d| = 0.30–0.43) and significantly higher mean total and whole fruits scores than children of non-Hispanic White households (|d| = 0.38–0.43).

Discussion

The findings revealed significant disparities in dietary quality across different racial, ethnic, and immigrant groups. Children from immigrant households had lower dietary quality compared with other groups; these differences reached medium effect sizes in comparisons with children from Black and American Indian or Native Hawaiian households. In addition, dietary quality subcomponent differences varied across groups, with medium effect sizes observed in several dietary components for children from Hispanic, Black, and White households.

Lower dietary quality among children from immigrant households partially aligns with previous studies, which have shown variability in dietary quality among different immigrant groups. For example, one study found that children of Hmong immigrants had lower dietary quality compared with White children, while no significant differences were observed for Somali/Ethiopian or Latino children.⁴⁶ The current study did not examine specific immigrant groups, but lower dietary quality for children from immigrant households could be attributed to several factors such as increased length of stay in the United States, reliance on fast food, positive perception of processed foods, controlling feeding practices, and challenges in enrolling for federal assistance programs.^46–51

Despite lower overall dietary quality, children of immigrant households scored better on fruit and dairy consumption, consistent with the previous findings where children of immigrant mothers had higher dietary quality as compared with their nonimmigrant counterparts in the United States.¹¹ These findings suggest that immigrants retain some healthy food traditions from their countries of origin. Previous research suggests that immigrants struggle to navigate food systems and access culturally appropriate ingredients.^52,53 Despite benefits of programs such as SNAP and WIC, almost three-fourth of immigrants did not participate in either of these food assistance programs. Barriers such as language, fear of immigration consequences, and misinformation may contribute to low participation.⁵⁴

Focus groups and community-based participatory research can provide a deeper understanding of the barriers immigrants face, while also serving as tools to engage communities, amplify their voices, and rebuild trust with authorities. Dietary quality and food parenting practices differ among children of immigrants from Africa, Latin America, and Asia compared with White children in the United States.⁴⁶ In a previous study, Hmong children with parents who migrated to the United States. Ten or more years ago had significantly lower dietary quality compared with White children (p < 0.001) and with the Hmong children with U.S.-born parents due to acculturation-related shifts in parenting.⁴⁶ Future studies should examine dietary quality across immigrant groups to inform tailored interventions. In addition, longitudinal studies are essential to track how changes over time in factors such as country of origin, generational differences, and acculturation levels influence children’s dietary quality, providing critical insights for addressing the long-term impacts on rising obesity rates.

In this study, children from Hispanic households scored low on dietary quality for the subcomponents discussed below. A medium effect size was observed for fatty acids when comparing Hispanic households with White households, aligning with research suggesting that Hispanic households in the Midwest region have poorer dietary quality compared with non-Hispanic White households.⁵⁵ Medium effect sizes for total fruits, whole fruits, and dairy indicate poorer dietary quality among children from Hispanic households compared with children from Black households. However, studies conducted at the national level have found that Hispanic children generally have better dietary quality than their non-Hispanic counterparts, but it was less pronounced for younger children.^19,20,55,56 These variations could be attributed to Hispanic households in the Midwest adopting a more traditional American diet.^55,57 Urban environments, where many Hispanic participants in our study resided, are linked to poorer dietary quality among Hispanic children compared with their rural counterparts, due to greater availability of sugar-sweetened beverages and energy-dense snacks⁵⁸ The variation in dietary quality within the Hispanic community underscores the need for future research to understand cultural and environmental factors influencing dietary disparities among Hispanic children. Addressing these disparities requires programs that go beyond translation to incorporate cultural differences within the Hispanic population, with training local community members called “promotores” playing a key role in developing and implementing healthy eating interventions.^59–63

Previous research indicates that children from non-Hispanic Black households are more likely to have suboptimal dietary quality.⁶⁴ In our study, dietary quality among children from Black households fell within the “needs improvement” range (scores between 51 and 80), which is comparatively better than national findings that report significantly lower dietary quality among non-Hispanic Black children compared with children of other races, more evident for older children.^20,45,65 This presents an opportunity to explore factors within these households that may be contributing to relatively better dietary quality and to identify ways to further support these efforts while addressing remaining challenges. While the previous research indicated differences in dietary quality were less pronounced in preschool-aged children, disparities tend to widen for older children. Such age-related variations in dietary disparities emphasize the importance of supporting households with young children to improve dietary quality in the long term.^20,45 These findings underscore the need for longitudinal studies to better understand how dietary disparities develop over time and to guide targeted interventions, while highlighting the critical role of household race, ethnicity, and immigration status in shaping these disparities among young children.

The limitations of this study must be acknowledged. First, adult-reported data may introduce inaccuracies in reporting children’s dietary intake, as they rely on adult perceptions rather than direct observations. Second, cross-sectional data limit the capacity to observe changes or trends in dietary acculturation, cultural food availability, and overall dietary quality over time. Although we controlled multiple confounding variables, as an observational study, we cannot make causal inferences about group differences. Third, our categorization of households does not capture heterogeneity within and between groups. We categorized participants as first-generation immigrants based on place of birth, potentially ignoring second and third generations and differences within immigrant experiences. We used participant-reported race, ethnicity, and immigration status to categorize households, and thus did not account for within-household variations. Fourth, the small sample size for immigrant households (n = 61) may limit generalizability, as this group may not fully represent all immigrant families in Nebraska. This may be due to barriers such as language differences or reluctance to participate in formal or official-looking surveys. Future research should replicate these findings in larger, more diverse samples to better understand dietary disparities among young children.

Conclusion

This study highlights the need for tailored interventions to address disparities in dietary quality among young children. Interventions should consider the unique challenges faced by different demographic groups and promote access to healthy, culturally appropriate foods. By doing so, policymakers and healthcare providers can work toward improving the overall health and well-being of children from diverse backgrounds. Targeted efforts are essential to bridge the gap in dietary quality of children from immigrant and Hispanic households.

Footnotes

Acknowledgments

The authors are grateful to the Nebraska Extension’s professionals working with Nutrition and Education Program (NEP) whose dedication facilitated participant recruitment and survey distribution across the state, highlighting the significance of this project for local communities.

Authors’ Contributions

N.B. and D.A.D.: Conceptualization, methodology, data curation, writing—original draft preparation. N.K. and T.P.: Data curation, writing—reviewing and editing. All other authors: Writing—reviewing and editing.

Author Disclosure Statement

The authors have no competing financial interests to declare.

Funding Information

This work was supported by the Betti and Richard Robinson Professorship and USDA’s Supplemental Nutrition Assistance Program — SNAP.

References

Azar

KMJ

, Chen

, Holland

, et al. Festival foods in the immigrant diet. J Immigr Minor Health, 2013; 15(5):953–960; doi: 10.1007/s10903-012-9705-4

Misra

, Khurana

. Obesity-related non-communicable diseases: South Asians vs White Caucasians. Int J Obes (Lond), 2011; 35(2):167–187; doi: 10.1038/ijo.2010.135

Bryan

, Afful

, Carroll

, et al. NHSR 158. National Health and Nutrition Examination Survey 2017–March 2020 Pre-Pandemic Data Files. National Center for Health Statistics: (U.S.); 2021; doi: 10.15620/cdc:106273

Bullock

, Sheff

, Moore

, et al. Obesity and overweight in American Indian and Alaska native children, 2006–2015. Am J Public Health, 2017; 107(9):1502–1507; doi: 10.2105/AJPH.2017.303904

Amiri

. Obesity and overweight prevalence in immigration: A meta-analysis. Obes Med, 2021; 22:100321; doi: 10.1016/j.obmed.2021.100321

Liu

, Micha

, Li

, et al. Trends in food sources and diet quality among US children and adults, 2003–2018. JAMA Netw Open, 2021; 4(4):e215262; doi: 10.1001/jamanetworkopen.2021.5262

U.S. Department of Agriculture, Food and Nutrition Service, Center for Nutrition Policy and Promotion. Average Healthy Eating Index-2020 scores for the U.S. Population—Total Ages 2 and Older and by Age Groups, WWEIA, NHANES 2017–2018. 2023.

Anderson

, Ramsden

, Kaye

. Diet qualities: Healthy and unhealthy aspects of diet quality in preschool children. Am J Clin Nutr, 2016; 103(6):1507–1513; doi: 10.3945/ajcn.115.128454

Scaglioni

, De Cosmi

, Ciappolino

, et al. Factors influencing children’s eating behaviors. Nutrients, 2018; 10(6):706; doi: 10.3390/nu10060706

10.

Wirt

, Collins

. Diet quality—what is it and does it matter? Public Health Nutr, 2009; 12(12):2473–2492; doi: 10.1017/S136898000900531X

11.

de Hoog

MLA

, Kleinman

, Gillman

, et al. Racial/ethnic and immigrant differences in early childhood diet quality. Public Health Nutr, 2014; 17(6):1308–1317; doi: 10.1017/S1368980013001183

12.

Stanaway

, Afshin

, Gakidou

, et al. Global, regional, and national comparative risk assessment of 84 behavioral, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017. The Lancet, 2018; 392(10159):1923–1994; doi: 10.1016/S0140-6736(18)32225-6

13.

Tao

M-H

, Liu

J-L

, Nguyen

U-S

. Trends in diet quality by race/ethnicity among adults in the United States for 2011–2018. Nutrients, 2022; 14(19):4178; doi: 10.3390/nu14194178

14.

Lopez-Cepero

, Valencia

, Jimenez

, et al. Comparison of dietary quality among Puerto Ricans living in Massachusetts and Puerto Rico. J Immigr Minor Health, 2017; 19(2):494–498; doi: 10.1007/s10903-016-0480-5

15.

Satia- Abouta

, Patterson

, Neuhouser

, et al. Dietary acculturation: Applications to nutrition research and dietetics. J Am Diet Assoc, 2002; 102(8):1105–1118; doi: 10.1016/S0002-8223(02)90247-6

16.

Van Hook

, Quiros

, Frisco

, et al. It is hard to swim upstream: Dietary acculturation among Mexican-origin children. Popul Res Policy Rev, 2016; 35(2):177–196; doi: 10.1007/s11113-015-9381-x

17.

Vargas

, Jurado

L-F

. Dietary acculturation among Filipino Americans. Int J Environ Res Public Health, 2015; 13(1):16; doi: 10.3390/ijerph13010016

18.

Quandt

, Trejo

, Suerken

, et al. Diet quality among preschool-age children of latino migrant and seasonal farmworkers in the United States. J Immigr Minor Health, 2016; 18(3):505–512; doi: 10.1007/s10903-015-0304-z

19.

, Cohen

, Greaney

, et al. Racial/ethnic disparities in US adolescents’ dietary quality and its modification by weight-related factors and physical activity. Int J Environ Res Public Health, 2019; 16(23):4803; doi: 10.3390/ijerph16234803

20.

Thomson

, Tussing-Humphreys

, Goodman

, et al. Diet quality in a nationally representative sample of American children by sociodemographic characteristics. Am J Clin Nutr, 2019; 109(1):127–138; doi: 10.1093/ajcn/nqy284

21.

Byker Shanks

, Ahmed

, Dupuis

, et al. Dietary quality varies among adults on the flathead nation of the confederated Salish and Kootenai Tribes in Montana. J Community Health, 2020; 45(2):388–399; doi: 10.1007/s10900-019-00753-3

22.

Ferranti

, Hartman

, Elliott

, et al. Diet quality of pregnant American Indian women in the Northern Plains. Prev Chronic Dis, 2019; 16:E53; doi: 10.5888/pcd16.180536

23.

Novotny

, Vijayadeva

, Grove

, et al. Dietary intake among native Hawaiian, Filipino, and White children and caregivers in Hawaii. Hawaii J Med Public Health, 2012; 71(12):353–358.

24.

Anonymous. State Demographics Data—NE. 2022. Available from: https://www.migrationpolicy.org/data/state-profiles/state/demographics/NE [Last accessed: July 11, 2024].

25.

Hahn

, Medina

. How Immigrants Have Dispersed Throughout the Country. 2024. Available from: https://www.census.gov/library/stories/2024/04/where-do-immigrants-live.html [Last accessed: November 27, 2024].

26.

Anonymous. Healthy People in States and Territories—Healthy People 2030 | Health.Gov. n.d. Available from: https://health.gov/healthypeople/tools-action/healthy-people-states-and-territories [Last accessed: September 19, 2024].

27.

Anonymous. United States of America. 2023.

28.

Anonymous. U.S. Census Bureau QuickFacts: United States. n.d. Available from: https://www.census.gov/quickfacts/fact/table/US# [Last accessed: January 9, 2025].

29.

Zhang

, Weiner

. Nebraska Minorities Disparity Facts Chart Book. Office of Health Disparities & Health Equity, 2021.

30.

CDC. About Obesity. 2024. Available from: https://www.cdc.gov/obesity/php/about/index.html [Last accessed: November 27, 2024].

31.

Peterson

, McNabb

, Maddali

, et al. Engaging communities to reach immigrant and minority populations: The Minnesota Immunization Networking Initiative (MINI), 2006-2017. Public Health Rep, 2019; 134(3):241–248; doi: 10.1177/0033354919834579

32.

Anonymous. NASIS | Bureau of Sociological Research. Nebraska; 2022. Available from: https://bosr.unl.edu/cas/sociology/bureau-of-sociological-research/nasis/ [Last accessed: April 26, 2024].

33.

Colby

, Zhou

, Allison

, et al. Development and validation of the short healthy eating index survey with a college population to assess dietary quality and intake. Nutrients, 2020; 12(9):2611; doi: 10.3390/nu12092611

34.

Kapustina

. Nebraska Health Disparities Report 2020. n.d.

35.

Anonymous. Nebraska Language and Limited English Proficiency Report, 2021.

36.

Willis

. Cognitive Interviewing. SAGE Publications, Inc.: Thousand Oaks, California; 2005; doi: 10.4135/9781412983655

37.

Dhingra

, Strine

, Holt

, et al. Rural-urban variations in psychological distress: Findings from the behavioral risk factor surveillance system, 2007. Int J Public Health, 2009; 54Suppl 1(1):16–22; doi: 10.1007/s00038-009-0002-5

38.

Anonymous. USDA ERS—Rural-Urban Continuum Codes. n.d. Available from: https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/ [Last accessed: May 30, 2024].

39.

Hingle

, Short

, Aflague

, et al. Food security is associated with higher diet quality among children of the US–Affiliated pacific region. J Nutr, 2023; 153(3):848–856; doi: 10.1016/j.tjnut.2023.01.015

40.

Knopp

. Characterizing the Dietary Quality of Family Meals of Head Start Participants. The Ohio State University; 2023.

41.

Kranz

, Findeis

, Shrestha

. Use of the Revised Children’s Diet Quality Index to assess preschooler’s diet quality, its sociodemographic predictors, and its association with body weight status. J Pediatr (Rio J), 2008; 84(1):26–34; doi: 10.2223/JPED.1745

42.

Hsu

. Multiple Comparisons: Theory and Methods. Chapman and Hall/CRC: New York; 1996; doi: 10.1201/b15074

43.

Benjamini

, Hochberg

. On the adaptive control of the false discovery rate in multiple testing with independent statistics. J Educ Behav Stat, 2000; 25(1):60–83; doi: 10.3102/10769986025001060

44.

Cohen

. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Routledge: New York; 2013; doi: 10.4324/9780203771587

45.

Acar Tek

, Yildiran

, Akbulut

, et al. Evaluation of dietary quality of adolescents using healthy eating index. Nutr Res Pract, 2011; 5(4):322–328; doi: 10.4162/nrp.2011.5.4.322

46.

Othman

, Fertig

, Trofholz

, et al. How time in the US and race/ethnicity shape food parenting practices and child diet quality. Appetite, 2022; 171:105870; doi: 10.1016/j.appet.2021.105870

47.

Bleiweiss-Sande

, Goldberg

, Evans

, et al. Perceptions of processed foods among low-income and immigrant parents. Health Educ Behav, 2020; 47(1):101–110; doi: 10.1177/1090198119885419

48.

Haley

, Kenney

, Gonzalez

, et al. Many Immigrant Families with Children Continued to Avoid Public Benefits in 2020, Despite Facing Hardships. 2021.

49.

Pelto

, Ocampo

, Garduño-Ortega

, et al. The nutrition benefits participation gap: Barriers to uptake of SNAP and WIC among Latinx American immigrant families. J Community Health, 2020; 45(3):488–491; doi: 10.1007/s10900-019-00765-z

50.

Berge

, Tate

, Trofholz

, et al. Examining variability in parent feeding practices within a low-income, racially/ethnically diverse, and immigrant population using ecological momentary assessment. Appetite, 2018; 127:110–118; doi: 10.1016/j.appet.2018.04.006

51.

Gustavsen

, Dong

, Nayga

, et al. Ethnic variation in Immigrants’ diets and food acculturation—United States 1999–2012. Agric Resour Econom Rev, 2021; 50(1):43–62; doi: 10.1017/age.2020.17

52.

Moffat

, Mohammed

, Newbold

. Cultural dimensions of food insecurity among immigrants and refugees. Hum Organ, 2017; 76(1):15–27; doi: 10.17730/0018-7259.76.1.15

53.

Mycek

, Hardison-Moody

, Bloom

, et al. Learning to eat the “right” way: Examining nutrition socialization from the perspective of immigrants and refugees. Food Cult Soc, 2020; 23(1):46–65; doi: 10.1080/15528014.2019.1700681

54.

Lacarte

. Explainer: Immigrants and the Use of Public Benefits in the United States. 2024. Available from: https://www.migrationpolicy.org/content/immigrants-public-benefits-us [Last accessed: November 27, 2024].

55.

Vadiveloo

, Perraud

, Parker

, et al. Geographic differences in the dietary quality of food purchases among participants in the nationally representative food acquisition and purchase survey (FoodAPS). Nutrients, 2019; 11(6):1233; doi: 10.3390/nu11061233

56.

Landry

, Asigbee

, Khazaee

, et al. Comparison of diet quality in low-income Hispanic and non-Hispanic Children. J Acad Nutr Diet, 2018; 118(9):A21; doi: 10.1016/j.jand.2018.06.258

57.

Yoshida

, Scribner

, Chen

, et al. Role of age and acculturation in diet quality among Mexican Americans—Findings from the national health and nutrition examination survey, 1999–2012. Prev Chronic Dis, 2017; 14:E59; doi: 10.5888/pcd14.170004

58.

Santiago-Torres

, Adams

, Carrel

, et al. home food availability, parental dietary intake, and familial eating habits influence the diet quality of urban Hispanic children. Child Obes, 2014; 10(5):408–415; doi: 10.1089/chi.2014.0051

59.

Brown

. Facing language, funding, and personnel barriers? How adult education principles and practices facilitate nutrition education for low-income Mexican American Audiences. In: Adult Education Research Conference; 2015.

60.

Martínez

. Adaptation of a Culturally Relevant Nutrition and Physical Activity Program for Low-Income, Mexican-Origin Parents with Young Children. 2015.

61.

Sánchez-Johnsen

, Escamilla

, Rodriguez

, et al. Latino community-based participatory research studies: A model for conducting bilingual translations. Hisp Health Care Int, 2015; 13(1):8–18; doi: 10.1891/1540-4153.13.1.8

62.

Rascón

, Garcia

, Nguyen-Rodriguez

, et al. Comprando Rico y Sano: Increasing latino nutrition knowledge, healthful diets, and food access through a national community-based intervention. Am J Health Promot, 2022; 36(5):876–880; doi: 10.1177/08901171211073956

63.

Sanchez

, Briant

, Wu-Georges

, et al. Eat healthy, be active community workshops implemented with rural Hispanic women. BMC Women’s Health, 2021; 21(1):24; doi: 10.1186/s12905-020-01157-5

64.

Kong

, Sanchez-Flack

, Fitzgibbon

, et al. Race/Ethnicity modifies the relationship between diet quality at the home- and individual-levels and weight status among African American and Hispanic/Latinx households with preschool-age children. Child Obes, 2023; doi: 10.1089/chi.2023.0100

65.

Hurley

, Oberlander

, Merry

, et al. The healthy eating index and youth healthy eating index are unique, nonredundant measures of diet quality among low-income, African American adolescents. J Nutr, 2009; 139(2):359–364; doi: 10.3945/jn.108.097113