Startseite Associations of social determinants of health and childhood obesity: a cross-sectional analysis of the 2021 National Survey of Children’s Health
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Associations of social determinants of health and childhood obesity: a cross-sectional analysis of the 2021 National Survey of Children’s Health

  • Kelsi Batioja EMAIL logo , Covenant Elenwo , Amy Hendrix-Dicken , Lamiaa Ali , Marianna S. Wetherill und Micah Hartwell
Veröffentlicht/Copyright: 9. Januar 2024

Abstract

Context

Childhood obesity is a growing health problem in the United States, with those affected having an increased likelihood of developing chronic diseases at a younger age. Social determinants of health (SDOH) are known to influence overall health. Families who are of low socioeconomic status (SES) have also been shown to be more likely to experience food insecurity.

Objectives

Our primary objective was to utilize the National Survey of Children’s Health (NSCH) 2021 data to determine the current associations between childhood obesity and SDOH. Secondarily, we estimated the prevalence of select SDOH among children with obesity.

Methods

We conducted a cross-sectional analysis of 2021 NSCH to extract data related to the SDOH domains. We extracted sociodemographic variables to utilize as controls and constructed logistic regression models to determine associations, via odds ratios, between SDOH and childhood obesity.

Results

Within the binary regression models, children with obesity (≥95th percentile) were more likely than children without obesity to experience SDOH in all domains. After controlling for sociodemographic variables, children with obesity were significantly more likely to experience food insecurity when compared to children without obesity (adjusted odds ratio [AOR]=1.39; 95 % confidence interval [CI]: 1.13–1.17).

Conclusions

In line with the current American Academy of Pediatrics (AAP) Clinical Practice Guidelines (CPG), improving policies for nutrition programs and addressing the lack of access to nutritious foods may alleviate some food insecurity. Ensuring that children have access to sufficient nutritious foods is critical in addressing childhood obesity and thus decreasing risk of chronic disease.

Childhood obesity is a major public health problem in the United States. According to the Centers for Disease Control and Prevention (CDC), childhood obesity affected approximately 14.7 million children and adolescents from 2017 to 2020, a prevalence of 19.7 % [1]. Pediatric body mass index (BMI) classification for overweight is assessed as being within the 85th to <95th percentile, and a child being ≥95th percentile is classified as having obesity [2]. Children who experience being overweight or having obesity are more likely to maintain obesity into adulthood [3], and they may also have an increased risk of developing chronic diseases such as diabetes, cardiovascular disease, and some forms of cancer at a younger age [3]. In addition to physical health conditions, children being overweight and having obesity have been associated with psychological effects such as depression, anxiety, low self-esteem, and decreased quality of life [4]. The cause of having obesity in childhood is multifactorial, with genetics, environmental factors, and family characteristics all playing a role in its development [5]. Although healthy diet and physical activity have been associated with a lower risk of developing obesity in childhood, and early establishment of healthy habits may be a protective factor [6], household, neighborhood, and societal factors – often termed as social determinants of health (SDOH) – may play a prominent role.

SDOH, which were first posited by Michael Marmot, are the conditions in which people are born, live, and work that play a significant role in morbidity and life expectancy [7]. SDOH can be broken up into five pillars: economic stability, education access and quality, healthcare access and quality, social and community context, and the neighborhood or built environment in which one lives [8]. Cumulatively, they are better termed “the cause of causes [7].” SDOH may influence health by mediating the availability of resources to maintain health, and by modifying the risk of exposure to environmental hazards and stress [9]. One example is socioeconomic status (SES), a component of the economic stability domain of SDOH. SES has been shown to be a powerful predictor of health outcomes, with poverty being a major cause of avoidable morbidity and mortality in the United States [10]. In children, the effects of lower SES are cumulative and progressive [11]. A study by Gable and Lutz [11] found that children of lower SES are more likely to be overweight and have poor health outcomes that may likely continue into adulthood. In addition, Singh et al. [12] found higher odds of obesity and overweight among children living in unsafe neighborhoods or poor housing conditions compared to children living in better conditions. Families who are of low SES have also been shown to be more likely to experience food insecurity [13]. According to the United States Department of Agriculture (USDA), food insecurity can be defined as “the limited or uncertain availability of nutritionally adequate and safe foods, or limited or uncertain ability to acquire acceptable foods in a socially acceptable way” [14]. One study found that children from food-insecure households were five times more likely to experience obesity than those from food-secure households [15]. Further, Au et al. [16] found that among schoolchildren 10–15 years of age, those living in food-insecure households had a higher BMI z score, waist circumference, and odds of experiencing obesity compared to those in food-secure households. Additionally, that same study found that children in food-insecure households consumed a significantly more amount of daily added sugar than those in food-secure households; however, both groups consumed well over the daily recommended amount [16]. Thus, identifying the associations between singular and intersectional domains of SDOH on childhood obesity may inform recommendations to address this growing health concern.

Because childhood obesity can have such profound effects on overall health well into adulthood, research into potential associations is paramount – especially in the midst of the COVID-19 pandemic. Childcare-related employment disruption increased by one-third in 2020 and was higher in low-income families and children from racial/ethnic-minority groups [17]. This led to loss of insurance coverage for children and unmet healthcare needs [17]. David et al. [18] found a strong correlation between individuals having a self-reported unmet SDOH need and being of non-White race/ethnicity. That same study also found that individuals with unmet SDOH needs delayed healthcare visits for significantly longer than individuals without unmet SDOH needs [18]. In addition to disrupted medical care, research has shown that levels of physical activity in children and adolescents during the COVID-19 pandemic dramatically decreased by 20 % [19] – which may have not only exacerbated obesity but also SDOH domains. Given the previous evidence of association between SDOH and childhood obesity, the American Academy of Pediatrics (AAP) included SDOH within the context of their 2021 Clinical Practice Guidelines (CPG) [2]. Specifically, the updated guidelines discuss the importance of providers regularly screening children for BMI, food insecurity, and SDOH. These regular screenings may help identify disparities in children and allow for earlier intervention by increasing access to food assistance and weight management programs [2]. Therefore, our primary objective was to utilize the National Survey of Children’s Health (NSCH) 2021 to determine the current associations between childhood obesity and SDOH given the publication of the new AAP CPG. As a secondary aim, we estimate the prevalence of select SDOH among children with obesity, which is needed to inform the development of equity-focused obesity treatment programs.

Methods

We utilized the 2021 NSCH data that are directed and funded by the Health Resources Service Administration (HRSA) Material and Child Health Bureau (MCHB) [20]. The NSCH collects and reports on multiple aspects of children’s lives – physical and mental health, school, neighborhood, social context, and the child’s family – in all 50 states and the District of Columbia [20]. The current NSCH survey is a combination of two surveys: the previous NSCH and the National Survey of Children and Special Health Care Needs (NS-CSHCN) [20]. Questionnaires are completed online or by mail by a primary caregiver of the child aged 0–17 years [20].

Eligibility

We included in our study responses from parents of children aged 10–17 years who answered the variable: “BMI CLASS – Body Mass Index, Percentage,” with the following responses: “Less than 5th percentile, 5th percentile to less than 85th percentile, 85th percentile to less than 95th percentile, and equal to or greater than 95th percentile.” Parents who refused to respond, had a missing response, or had children under 10 years and thus skipped the question, were excluded from the analysis. Whereas the United States Preventive Services Task Force (USPSTF) recommends screening for overweight and obesity in all children 6 years of age and older [21], the NSCH only collected data in children ages 10–17 years. This is in accordance with the Title V Maternal and Child Health national outcome measure assessing the percentage of children, age 10–17 years, who are overweight or obese (BMI at or above the 85th percentile) [22].

Measures

SDOH

The following questions from the NSCH were extracted as proxies to assess the five domains of SDOH. Current CPG outlined by the AAP recommend that children with obesity should be cared for in medical homes [2]. To assess healthcare access and quality in accordance with CPG recommendations, we looked at the questions: (1) “During the past 12 months, did this child see a doctor, nurse, or other healthcare professional for sick-child care, well-child check-ups, physical examinations, hospitalizations, or any other kind of medical care?”; and (2) “During the past 12 months, was there any time when this child needed healthcare but it was not received?,” which included healthcare, vision, dental, or mental healthcare. For analysis, both of the previous questions had responses of “Yes” or “No.” To address economic stability, we utilized data from the following question, (3) “Since this child was born, how often has it been very hard to cover the basics, like food or housing, on your family’s income?” with the responses “Never, Rarely, Somewhat often, and Very often”. For analysis, this question was collapsed into binary variables – Not difficult being the first two responses showing no difficulty in covering basics, and Difficult being the last two responses showing difficulty in covering basics due to family income. Food insecurity was determined by the answers to question (4) “Which of these statements best describes your household’s ability to afford the food you need during the past 12 months?,” with the responses being “We could always afford to eat good nutritious meals,” “we could always afford enough to eat but not always the kind of foods we should eat,” “sometimes we could not afford enough to eat,” and “often we could not afford enough to eat.” For analysis, this question was collapsed to a binary variable – Food secure being the first response category showing no limitations on access or quality of food, and Food insecure being any of the three latter responses demonstrating a lack of access to or quality of food choices. For neighborhood and school safety, we utilized the question, “To what extent do you agree with these statements about your neighborhood or community: (5) this child is safe in our neighborhood and (6) this child is safe at school?” Responses to these questions were “definitely agree, somewhat agree, somewhat disagree” and “definitely disagree” and collapsed into binary categories. These categories were classified as Safe being the agree response categories demonstrating school and/or neighborhood safety, and Unsafe being the disagree response categories showing that neighborhoods and/or schools experience a lack of safety. All respondents who did not answer one or more questions were excluded from all outcomes of interest. We additionally created a sum variable for all SDOH to estimate the cumulative impact of these determinants on obesity risk ranging from 0 to 5, with 5 indicating positive responses across all domains.

BMI classification

BMI classification was determined from parent-reported height and weight, calculated by the NSCH. BMI was then classified by the following: underweight (<5th percentile), normal weight (4th to 84th percentile), overweight (85th to 94th percentile), and having obesity (≥95th percentile).

Sociodemographic variables

Sociodemographic variables extracted from NSCH included age (10–17 years); sex (male or female); parent-reported race/ethnicity – Hispanic, non-Hispanic white, non-Hispanic Black, non-Hispanic Asian, non-Hispanic American Indian or Alaska Native (AI/AN, including non-Hispanic Native Hawaiian and Other Pacific Islander [NH/PI]), non-Hispanic Multi-Race, and non-Hispanic other; household income based on federal poverty level (FPL; 0–99 % FPL, 100–199 % FPL, 200–399 % FPL, 400 % or greater FPL); and education level of adult in household (less than high school, high school or GED, some college or technical school, college degree or higher). These variables were utilized as controls in our analysis.

Statistical analysis

For these analyses, BMI classification was dichotomized as having obesity (≥95th percentile) or not having obesity. We reported sample (n) and population estimates (N) by demographic characteristics among children with and without obesity. Next, we determined the prevalence of children experiencing SDOH by obesity status and utilized bivariate and multivariable logistic regression to determine associations, via odds ratios, between SDOH and child obesity. We then performed analysis utilizing a sum of SDOH experiences experienced by children. Statistical analyses were completed utilizing STATA 16.1 (StataCorp LLC., College Station, TX). Alpha was set at 0.05, therefore, p<0.05 was statistically significant. Confidence intervals (CI) were reported at 95 %.

Results

The 2021 NSCH survey had an overall response rate of 40.3 %. The number of children included in our sample who met the inclusion criteria was 20,091 (n), which represents a population estimate of 32, 028, 115 (N). Among these children, 3,053 were classified as having obesity with a weighted sample population estimate of 5,667,243 (17.59 %) being classified as obese.

Sociodemographics

Our sample primarily consisted of White children (n=13, 219, 49.78 %; Table 1), followed by Hispanic children (n=2,768, 26.69 %) and Black children (n=1,395, 13.28 %), and had slightly more male children (n=10,437, 51.41 %) than female. Approximately 61 % of the households in the sample were at or above the 200 % FPL, and 49.15 % (n=12,034) of children in our sample lived in a household where their parent or legal guardian is a college graduate.

Table 1:

Sociodemographics of children by obesity status (n=20,091, N=32,028,115) from the 2021 National Survey of Children’s Health (NSCH).

<95th percentile BMI n (%) ≥95th percentile BMI n (%) Total n (%)
Race/ethnicity
White 11,449 (86.47) 1770 (13.53) 13,219 (49.78)
Black or African American 1,088 (77.29) 307 (22.71) 1,395 (13.28)
American Indian or Alaska Native (AI/AN) 156 (75.39) 61 (24.61) 217 (0.63)
Asian 1,029 (92.37) 102 (7.63) 1,131 (4.4)
Two or more races 1,149 (82.35) 212 (17.65) 1,361 (5.22)
Hispanic 2,167 (75.92) 601 (24.08) 2,768 (26.69)
Federal poverty level
0–99 % FPL 1924 (75.17) 634 (24.83) 2,558 (18.34)
100–199 % FPL 2,628 (78.37) 632 (21.63) 3,260 (20.54)
200–399 % FPL 5,124 (81.27) 969 (18.73) 6,093 (29.71)
400 % + FPL 7,362 (90.34) 818 (9.66) 8,180 (31.40)
Sex
Male 8,577 (79.05) 1860 (20.95) 10,437 (51.41)
Female 8,461 (85.96) 1,193 (14.04) 9,654 (48.59)
Education level of reported adults
Less than high school 441 (74.96) 178 (25.04) 619 (10.38)
High school diploma or equivalent 2,128 (74.34) 680 (25.66) 2,808 (19.54)
Some college or technical school 3,705 (79.48) 925 (20.52) 4,630 (20.93)
College graduate or higher 10,764 (88.43) 1,270 (11.57) 12,034 (49.15)
Total
Total 17,038 (82.41) 3,053 (17.59) 20,091 (100)
  1. BMI, body mass index; FPL, federal poverty level.

Sociodemographics and childhood obesity

The highest prevalence of children identified as having obesity were AI/AN and NH/PI (n=61, 24.61 %; Table 1), followed by Hispanic children (n=601, 24.08 %) and Black children (n=307, 22.71 %). Children having obesity mostly lived in households with income 0–99 % FPL (n=634, 24.83 %). The majority of children identified as having obesity were male (n=1860, 20.95 %). Just over half of the children having obesity had a parent or legal guardian with a high school education or less.

Prevalence of children with obesity experiencing SDOH

Our results showed that children identified as having obesity without having received any medical care in the last 12 months was 34.21 % (n=916) compared to those classified as without obesity at 30.38 % (n=4,429; Table 2). Additionally, children having obesity had higher rates of needed healthcare in the last 12 months that were not received (n=195, 6.32 %) compared to children without obesity (n=739, 4.07 %). Children having obesity were more reported to be in a family who found it difficult to afford basic needs due to income (n=564, 17.46 %), as well as to live in a household that experiences food insecurity (n=1,172, 40.18 %) compared to children without obesity. Finally, the percentage of children with obesity living in an unsafe neighborhood was 6.14 % (n=144), and 2.58 % reported that they feel unsafe at the school in which they attend (n=100).

Table 2:

Prevalence and associations between a child having obesity and experiencing social determinants of health (SDOH) from 2021 National Survey of Children’s Health (NSCH).

BMI classification Total n (%) No n (%) Yes n (%) Binary model OR (95 % CI) Adjusted model AORa (95 % CI)
During the past 12 months, did this child receive any kind of medical care?
BMI<95th percentile 16,980 (100) 4,429 (30.38) 12,551 (69.62) 1 (Ref) 1 (Ref)
BMI≥95th percentile 3,038 (100) 916 (34.21) 2,122 (65.79) 0.84 (0.69–1.03) 1.09 (0.88–1.35)
During the past 12 months, was there a time when this child needed healthcare but it was not received?
BMI<95th percentile 16,993 (100) 16,254 (95.93) 739 (4.07) 1 (Ref) 1 (Ref)
BMI≥95th percentile 3,042 (100) 2,847 (93.68) 195 (6.32) 1.59 (1.07–2.38) 1.41 (0.9–2.20)
Experienced difficulty covering the basics on your family’s income ? b
BMI<95th percentile 16,717 (100) 15,058 (88.74) 1,659 (11.26) 1 (Ref) 1 (Ref)
BMI≥95th percentile 3,002 (100) 2,438 (82.54) 564 (17.46) 1.67 (1.32–2.10) 1.22 (0.94–1.57)
Experienced food insecurity? c
BMI<95th percentile 16,618 (100) 12,874 (73.73) 3,744 (26.27) 1 (Ref) 1 (Ref)
BMI≥95th percentile 2,983 (100) 1811 (59.82) 1,172 (40.18) 1.88 (1.57–2.27) 1.39 (1.13–1.70)
This child is safe in our neighborhood? d
BMI<95th percentile 16,565 (100) 16,104 (96.1) 461 (3.9) 1 (Ref) 1 (Ref)
BMI≥95th percentile 2,961 (100) 2,817 (93.87) 144 (6.14) 1.61 (1.01–2.58) 1.16 (0.73–1.85)
This child is safe at school? d
BMI<95th percentile 16,548 (100) 16,137 (97.63) 411 (2.37) 1 (Ref) 1 (Ref)
BMI≥95th percentile 2,957 (100) 2,857 (97.42) 100 (2.58) 1.09 (0.72–1.66) 0.9 (0.57–1.41)
  1. BMI, body mass index; CI, confidence interval; OR, odds ratio. a. model controlled for race/ethnicity, household income (%FPL), parental education, and child sex. b. Ability to afford household basics answers were collapsed into binary variables of Not difficult and Difficult. c. Ability to afford food answered were collapsed into binary variables of Food secure and Food insecure. d. Neighborhood and school safety answers were both collapsed into binary variables as Safe and Unsafe.

Regression analyses

For our binary model, our results revealed that children identified as having obesity were significantly more likely to experience having an unmet healthcare need (OR=1.59; 95 % CI: 1.07–2.38; Table 2), and familial difficulty affording basics due to insufficient income (OR=1.67; 95 % CI: 1.32–2.10), food insecurity (OR=1.88; 95 % CI: 1.57–2.27), and neighborhood safety (OR=1.61; 95 % CI: 1.01–2.58) compared to children without obesity. However, after adjusting for race/ethnicity, household income (% FPL), parental education, and child sex, food insecurity was the only result to remain statistically significant (AOR=1.39; 95 % CI: 1.13–1.7; Table 2). We found no significant associations between receiving medical care or school safety and childhood obesity.

Sum variable analysis

The majority of children in our sample were reported to have experienced 0 SDOH (n=13,163, 64.47 %) followed by experiencing one SDOH (n=3,897, 22.64 %; Figure 1). Compared to children without obesity, those with obesity were more likely to experience 1 SDOH (AOR: 1.63, 95 % CI: 1.32–2.01; Figure 1). The odds were more than twice as high for children with obesity to have experienced two or three domains of SDOH, and more than three times as high to experience four or more domains of SDOH compared to children without obesity.

Figure 1: 
Cumulative social determinants of health (SDOH) among children with and without obesity.
Figure 1:

Cumulative social determinants of health (SDOH) among children with and without obesity.

Discussion

Our results showed significant binary associations between multiple domains of SDOH (healthcare access, economic stability, food insecurity, and neighborhood safety) and childhood obesity; however, after controlling for race/ethnicity, parental education, household income, and child sex, only the SDOH domain of food insecurity was observed to be statistically significant. While adjusting for these control variables shows the strong relationship between food insecurity and obesity, low SES (which comprises most of the control variables) has been shown to have a bidirectional and generational relationships with SDOH [23], thus likely having indirect impacts on in the context of childhood obesity. Therefore, consideration for financial insecurity and the intersection of race and SES are important factors in any efforts to address childhood obesity and food insecurity.

In early 2023, the AAP released a revised “CPG for the Evaluation and Treatment of Children and Adolescents with Obesity [2],” which included SDOH domains such as SES, community-level factors, and food insecurity and are extensively discussed as risk factors to the development of childhood obesity [2]. Given the rising prevalence of childhood obesity in the United States, along with evidence supporting SDOH as predictors to the development of childhood obesity, it is imperative that clinical practice includes routine screening for, and intervention of, food insecurity in families [2]. As part of the 2023 GCP, the AAP recommends providers to utilize a toolkit developed in partnership between the AAP and the Food Research and Action Center titled “Screen and Intervene: A Toolkit for Pediatricians to Address Food Insecurity [2].” While pediatricians are the primary audience for the toolkit, all healthcare providers caring for children can utilize this toolkit as part of their clinical practice [24]. The toolkit includes education related to identifying children experiencing food insecurity, guidance on how to address the topic in a sensitive and culturally appropriate manner, and advice about how to connect families to appropriate community resources, as well as information regarding how to advocate for change at the policy level [24]. The “Hunger Vital Sign,” a validated two-question assessment, is also included as a tool to help providers identify families experiencing food insecurity [24].

Our findings further illuminate this linkage of food insecurity and childhood obesity, which concur alongside previous research. For example, Do et al. [25] found that adolescents living in food-insecure homes were more likely to be overweight or have obesity. Individuals experiencing food insecurity may tend to have more irregular eating patterns – consisting of underconsumption during food deprivation and overconsumption with adequate resources [26]. Additionally, consuming more high-calorie, low-cost foods due to their widespread availability may be a contributing factor to childhood obesity [26]. Further, implications on health and health outcomes can be related to food insecurity and childhood obesity. Food insecurity has been shown to influence long-term weight trajectories among children with obesity [27]. Tester et al. performed a longitudinal analysis examining food insecurity and weight change over time among low-income children [27]. They found that monthly change in BMI was significantly smaller for food-insecure children compared to children who were food-secure [27]. Similarly, other researchers have linked household food insecurity to poorer general and oral health, poorer academic performance, behavioral and cognitive problems, and depression [28]. For instance, children in food-insecure households have been shown to have increased odds of emotional eating, depressive symptoms, perceived stress, and household chaos and family functioning, compared to food-secure households [29]. However, other studies have found conflicting results. A systematic review of 13 articles found mixed evidence on the relationship between food insecurity and childhood obesity [30]. Another study of adolescents in the United States found a higher prevalence of adolescents having obesity in adolescents from food-insecure homes compared to those from food-secure homes; however, no association was found after adjusting for age, race, sex, poverty, and healthcare access [31]. Even with contradictory results, researchers agree that there is a relationship with complex underlying mechanisms [30].

Given the need for screening and appropriate management of SDOH such as food insecurity to decrease the likelihood of childhood obesity, it is critical that the reimbursement criteria be modified and adhered to because this continues to be a significant barrier to effective treatment strategies even after the passage of the Affordable Care Act [2]. Conversely, governmental programs aimed at improving food security have been shown to mitigate the associated health risks of being food insecure [32]. Ratcliffe et al. [33] utilized a bivariate probit model to show that participation in the Supplemental Nutrition Assistance Program (SNAP) reduces the likelihood of being food insecure or very food insecure by 31 and 20 %, respectively. SNAP also provided an additional 20 % decrease in food insufficiency among recipient families [33]. In terms of health benefits, adults with access to SNAP benefits in early childhood were shown to have lower risks of obesity, heart disease, and diabetes [34]. Similarly, mothers exposed to SNAP during pregnancy were found to have fewer low-birthweight babies [34]. Expanding access to supplemental nutrition programs – especially in early childhood – might be an important strategy for reducing rates of childhood obesity and the associated health outcomes.

As a part of the updated CPG for childhood obesity, the AAP subcommittee recommends that public health agencies, community organizations, healthcare systems, and community members increase community resources to help address SDOH while promoting healthy and active lifestyles [2]. Additionally, as previously noted, programs targeting household food insecurity may reduce children’s chronic and acute health problems. Emergency food systems in the United States, including food banks, are vital resources aimed at combating food insecurity. These are especially important for families designated ineligible for government assistance programs due to strict income-based eligibility rules [35]. Thus, nonprofit food banks and food pantries provide a solution for these ineligible groups that may be experiencing food insufficiency. Feeding America, a network of >200 food banks, serves those who are in need of nutritious foods and engages in advocacy to fight hunger [36]. The organization currently has over 60,000 food pantries and meal programs throughout the nation, serving 1 in 7 Americans annually [37]. Additionally, The Emergency Food Assistance Program (TEFAP) is a federal program that helps supplement the diets of low-income Americans by providing them with emergency food assistance at no cost [38]. Through funding from the USDA, organizations receiving TEFAP support – such as Feeding America – can keep their shelves stocked and increase access to nutritious foods among families experiencing food insecurity or insufficiency [39]. Therefore, continual funding for emergency food programs such as food banks and pantries might alleviate the risk of both food insecurity and childhood obesity, especially among low-income families who do not qualify for SNAP benefits.

Recommendations

Although programs such as SNAP and Women, Infants, and Children (WIC) have been widely utilized in the United States, these programs can be improved significantly through policy. For instance, even with the nearly 27 % increase in Fiscal Year 2021 SNAP benefits, the benefits will still not cover the average price of a meal in 21 % of counties in the United States [40]. As such, The State of Childhood Obesity, an initiative by the Robert Wood Johnson Family Foundation, has provided five policy recommendations related to SNAP [41]. These recommendations include: (1) broadening the program to cover more adults; (2) making sure benefits cover the cost of average meals in all counties; (3) improving eligibility and enrollment processes to eliminate barriers; (4) removing lifetime bans for felony drug convictions; and (5) continued authorization of the Gus Schumacher Nutrition Incentive Program [41]. Similarly, the group has provided policy recommendations for WIC, including extending benefits for mothers through the first 2 postpartum years, and for children under 6 years of age, among other recommendations [42].

In addition to changes to existing programs, concerns related to food access need to extend beyond meeting daily caloric intake to consider from where calories are coming. Beyond the USDA’s definition of food security, a standard definition of “nutrition security” is needed at the federal level to accommodate this new approach [43]. Additional policies should focus on increasing the feasibility of programs that not only increase access to healthy, nutritious foods, but also provide education and/or lifestyle modification guidance to increase the likelihood of consuming foods that are part of a balanced diet tailored to the individual [2]. Increasing access to nutritious meals at school by expanding free and reduced lunch programs should also be considered [44].

Renewed efforts should focus on the accessibility of fresh foods in both rural and urban food deserts where, given the lack of grocery stores, fast food and convenience stores are highly utilized [2, 45]. This problem is particularly concerning for mostly rural states, such as Oklahoma, where previous studies of two Tribal Nations found that individuals utilized convenience stores as a source for food and quick meals [45]. Previous interventions to provide healthy snack and meal options at convenience stores have been developed via Tribal/University partnerships and community participatory research [45, 46]. These interventions have been found to increase availability and purchasing of healthier foods, but not necessarily their consumption [46]. Osteopathic physicians in tribal areas may make a point in their practices to provide proper education to families in an attempt to increase healthier food intake when healthy foods are available. Tribal/University partnerships could expand to create more programs and healthy food options as interventions. For instance, the first tribally associated osteopathic medical school has been established at Oklahoma State University College of Osteopathic Medicine at the Cherokee Nation. Establishing a school relationship within the tribal community may help further establish and grow the partnership with the tribal communities. In the future, these relationships could allow further expansion of current programs and the establishment of new programs. Additional research should be undertaken to further expand these interventions, particularly with Tribal Nations given the rates of chronic health conditions, and food insecurity experienced by Indigenous peoples [46]. Given the high rates of obesity in AI/AN children and adults, it is vital that policy initiatives be enacted between federal and tribal governments for these populations. A way to the forced relocation of many Indigenous peoples from their ancestral homelands significantly disrupted tribal agricultural practices thus impacting traditional food systems [47]. Initiatives for these populations should be tailored to each individual sovereign nation given their unique cultural experiences and needs. Long-standing programs such as the Food Distribution Program on Indian Reservations (FDPIR) should be reassessed and modified to provide more nutritious and fresh foods [48]. This can be done by expanding programs such as the USDA’s Indigenous Food Sovereignty Initiative, which was created to increase accessibility to traditional foods and agriculture practices [49].

Limitations and Future Research

According to the NSCH codebook AI/AN, NH/PI, and ‘two or more races’ should be interpreted as weighted estimates because they are not independently controlled. However, our sample meets the provisions set by the NSCH, therefore our race subcategories are robust yearly estimates. Additionally, responses to a child’s current height and weight were not independently verified by the NSCH; however, the NSCH mitigates this by only surveying height and weight in children aged 10–17 years because a study revealed that parents tend to overestimate height and underestimate weight in children under 10 years old [50]. Finally, our study is cross-sectional in nature, thus our results should be interpreted as correlational rather than causal. In the future, research should focus on whether families in food-insecure households have improved access to nutritious foods for their children through assistance programs such as SNAP, USDA-funded programs, and nonprofit organizations. Additionally, childhood obesity rates in these families should be compared to those in food-insecure households who do not have such access to these resources. In clinical practice, children with mental health issues and physical limitations have been found to have the greatest need for support in addressing obesity [2]. Therefore, future research should focus on other aspects in food-insecure homes, such as stress and mental health issues that can lead to an unhealthy relationship with food and overeating, thus increasing the risk for having obesity.

Conclusions

Childhood obesity is an ongoing public health concern in the United States. Our study found that the food-insecurity domain of SDOH was significantly associated with childhood obesity. Given the poor health outcomes associated with childhood obesity and the current AAP CPG surrounding childhood obesity, ensuring that supplemental nutrition programs such as SNAP are reaching families experiencing food insecurity is increasingly crucial. USDA-funded programs, such as TEFAP and nonprofit agencies, are positioned to alleviate food insecurity and food insufficiency in affected households with children. Additionally, policy recommendations should provide nutrition education and lifestyle modification guidance to increase the likelihood of individuals consuming a balanced diet. Therefore, ensuring children have access to adequate amounts of nutritious foods is a critical step in addressing childhood obesity and preventing negative long-term health outcomes.


Corresponding author: Kelsi Batioja, BS, Oklahoma State University Center for Health Sciences, 1111 W 17th Street, Tulsa, OK 74107, USA, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: This study was secondary data analysis of a publicly available dataset, thus qualifying as non-human subject research. Therefore, it did not meet criteria for IRB approval or need for informed consent.

  3. Author contributions: Kelsi Batioja (study concept and design, first draft and revisions of manuscript, corresponding author), Covenant Elenwo (first draft and revisions of manuscript), Amy Hendrix-Dicken (first draft and revisions of manuscript), Lamiaa Ali (critical revisions for intellectual content and clinical accuracy), Marianna Wetherill (critical revisions for intellectual content), Micah Hartwell (study concept and design, data acquisition, data analysis and interpretations, critical revisions). All authors read and approved the final manuscript.

  4. Competing interests: Dr. Hartwell receives funding from the Human Resources Services Administration for research (U4AMC44250-01-02, PI: Audra Haney; R41MC45951 PI: Hartwell) which is outside of the current work. Amy Hendrix-Dicken owns limited stocks in Catalyst Pharmaceuticals, Johnson & Johnson, and AstraZeneca.

  5. Research funding: This study was not funded.

  6. Data availability: All data is publicly available at the website: https://www.census.gov/programs-surveys/nsch/data/datasets.html.

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Received: 2023-10-20
Accepted: 2023-12-04
Published Online: 2024-01-09

© 2023 the author(s), published by De Gruyter, Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 International License.

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