Obesity in Early Childhood: Examining the Relationship among Demographic,Behavioral,Nutritional,and Socioeconomic Factors

Abstract

Background:

The present study sought to explore the combined relationship of physical activity, screen time, consumption of sugar-sweetened beverages, infant feeding practices, and demographic factors with obesity in early childhood.

Methods:

The current study included cross-sectional Kindergarten Health Survey data collected annually from 2012 to 2016. The sample included 7814 kindergarten students, with a mean age of 5.02 [standard deviation = 0.33]. A Multinomial Logistic Regression using body mass index as the dependent variable and select demographic traits, dietary practices, sedentary behaviors, and physical activity indicators as independent variables was used to assess relationship of aforementioned factors to obesity.

Results:

Children were more likely to be obese if they were non-Caucasian, male, lived in rural areas, lived at or below the poverty level, had public insurance, or lived in single-parent households. Children who received less than 30 minutes of physical activity 3 or fewer days per week, had more than 2 hours of daily screen time, consumed any amount of soda, and consumed anything other than breast milk at 6 months of age also had a higher probability of being obese.

Conclusions:

The findings from this study suggest that investments in prevention strategies are needed to address the behavioral patterns and socioeconomic disparities before kindergarten. Efforts that increase access to nutritious food, physical activity, and overall family wellness and education, such as high-quality early childhood education, could be feasible prevention approaches.

Introduction

Obesity is a slow-moving pandemic spreading across the industrialized world, worsening a broad spectrum of health outcomes and sparking a creeping effect of diseases in patients at ever earlier ages. Despite a patchwork of public and private interventions, childhood obesity is rising. In 2016, obesity in youth aged 2–19 has risen epidemically to 18.5% in the United States.¹ Moreover, the prevalence of childhood obesity has surpassed the total number of overweight children, warranting serious consideration by stakeholders and public health professionals.²

Obesity increases the risks and complications of multiple cancers, musculoskeletal disorders, cardiovascular disease, diabetes, and depression, increasing the complexity, and cost of comorbid conditions.^3,4 Estimates place the annual fiscal cost of obesity in the United States at $663 billion⁵ by contributing to disease, disability, absenteeism, and loss of productivity.⁶ Obesity directly impacts quality of life, specifically in adolescents who are at a greater risk for hindered educational and social development outcomes.⁷ Moreover, children who are obese are more likely to suffer from obesity in adulthood.⁸

Existing literature on childhood obesity suggests that factors such as little to no physical activity,^9,10 excessive screen-time and sedentary time,^11–14 consumption of sugar-sweetened beverages (SSBs),^15,16 and not breastfeeding during infancy^17,18 increase the probability for obesity in children. Finally, there is an established link between obesity prevalence and several social determinants such as income, race, and built environment.^19–22

Although research shows that children as young as six who are obese experience slowed cognitive²³ and motor development,²⁴ few studies have sought to describe the effects of behavior, environment, and sociodemographic pressures on children who are obese between the ages of 4 and 6.^25,26 The purpose of this study was to explore the relationship of physical activity, screen time, consumption of SSBs, infant feeding practices, and demographic factors with obesity in children before entering kindergarten. By further determining which factors are most likely to influence obesity at an early age, efforts may be streamlined to target the most impactful interventions.

Methods

Kindergarten Health Survey (KHS)

The annual Kindergarten Health Survey (KHS) is a voluntary, cross-sectional survey distributed by elementary schools to the parents or guardians of incoming kindergarteners in Nevada. The KHS was developed as a collaborative partnership among community stakeholders with the purpose of assessing the health of students as they enter the K-12 school system. Questions on the KHS encompass aspects related to home environment, health behaviors, medical conditions, medical care, insurance status, and demographic information. Language utilized in the survey was developed through the adaptation of similar questions on existing validated surveys (e.g., Youth Risk Behavior Surveillance System). The survey is printed in both English and Spanish.

Study Design

An analysis was conducted using cross-sectional data from the KHS, a parent self-report survey, collected in the fall 2012 through the fall 2016. A total of 34,764 students were represented over that time period, of which 9271 students had body mass indices (BMIs) corresponding to the self-reported height and weight information on the survey and were included in this study. Those students who did not have both height and weight data, and students who had a height or weight that seemed improbable, were not included in the study. Details about the inclusion process for height and weight are available in the publically available annual report.²⁷ In this study, the 1457 students (15.7%) who were classified as underweight were excluded from the analysis resulting in a total of 7814 students. This study was reviewed and approved by the IRB at both UNLV and the Clark County School District.

Measures

Demographics

The KHS, a parent self-report survey, poses a variety of demographic questions to obtain characteristics of each assessed household. For the purpose of this study, questions regarding the child's sex, race/ethnicity, school district location, household income, if the respondent is in a single-parent household, and the residents’ medical insurance type were assessed. African American, Asian/Pacific Islander, and Native American participants were grouped in the “Other minorities” category to have enough data points as each subgroup did not contribute enough to the model to provide meaningful findings.

A poverty variable was developed by computing the total number of people in the home against the average household income. The poverty guidelines reported by the U.S. Department of Health and Human Services for each year of the survey were used as references to calculate whether a family was on, below, or above the poverty threshold based on the number of people in the household and their income. Household income was collected as a blocked range on the survey, and the number of people in the household was gathered by asking how many children and adults lived in the household. The calculated poverty threshold used the middle value for each blocked range choice as a reference point. For example, if a family of four selected an annual household income of $15,000–$24,999 in 2016, $20,000 was designated as the cutoff value putting this family below the poverty threshold of $24,300. For household medical insurance type, to develop one “public” insurance option, Nevada Check-up and Medicaid options were grouped together.

Body mass index

Respondents were asked to report the child's height and weight. These were then used to calculate BMI percentiles, paired with age and sex. BMI was calculated using the child's height and weight as [Weight (Pounds)/[Height (Inches)]² ∙ 703. The BMIs were then grouped by the number of children calculated to be underweight (<5th), normal weight (5–85th), overweight (85th to <95th), or obese (≥95th) based on respective percentiles as defined by the Centers for Disease Control and Prevention. The underweight category was not included in the analysis as this study aimed to compare behaviors and characteristics of children at a healthy weight to those categorized as overweight and obese. However, this category did comprise 15.7% of the study sample (n = 1457).

Dietary consumption

The KHS quantified the consumption of SSBs by asking respondents to answer, “During the past 7 days, how many times did your child drink a can, bottle, or glass of…” “Non-diet soda or pop?” This variable was recorded to reflect a clinical report from the Nutrition Committee of the American Academy of Pediatrics (AAP) stating that there is currently no recommendations of levels of intake for this age group.²⁸ Therefore, if a child was reported to consume any amount of nondiet soda at all in the past week, it was grouped into one consumption category, which was further supported by Han and Powell.²⁹

Starting in the 2012–2013 school year, respondents were asked to “Please check which best describes what your child drank at 6 months” with options to select “Breast Only,” “Breast & Formula,” “Formula Only,” “Other (e.g., food),” or “Not Sure.” Respondents could select multiple responses. Only breastfeeding behaviors at 6 months was used due to the AAP recommendations of exclusive breastfeeding of up to 6 months of age.²⁷ The categories of “Other,” “Not Sure,” and “Multiple” were recorded together as one category.

Sedentary behaviors

To determine activity level, the KHS included, “On an average school day, how many hours does your child watch TV? (circle one)” and similarly, “On an average school day, how many hours does your child play video or computer games? (Xbox, Nintendo, tablets, and/or the internet). (circle one).” Respondents were given the choices of “None,” “Less than one,” “1,” “2,” “3,” “4,” or “5+.” The AAP recommends that children do not spend more than 2 hours of screen time per day,²⁸ so for the purpose of this study, total screen time was recorded into two groups, those who had 2 hours or less of screen time a day and those who had more than 2 hours of screen time a day.

Physical activity

In years 2012–2015, the respondents were asked, “In general, how many days a week does your child do at least 30 minutes of physical activity? (circle one).” Options were provided for respondents to circle a number between 0 and 7 days. In 2016, the question was amended to reflect “60 minutes” as the new standard for physical activity instead of the “30 minutes” recommended in previous years. The number of days of the week circled was then grouped into three categories. If a child participated in physical activity 0–3 days a week, they were grouped into the “none” or “few days a week” category. If a child played 4–6 days a week, they were grouped into the “most days a week” category. The last category was “daily” physical activity if seven was circled. The present study included the timeframe for daily physical activity to be at least 30 minutes in the dataset.

Statistical Analysis

Statistical analysis was performed using IMB SPSS version 24. A Multinomial Logistic Regression was executed using BMI weight category as the dependent variable and all other measures as independent variables. To compare differences in proportion of each variable by weight category, contingency table analyses were performed. Significant variables were identified before inclusion in the model (Table 1). All independent variables were statistically significant (p < 0.001) with the exception of location which approached significance (p = 0.059), and therefore remained in the model. To examine multicollinearity, the Variance Inflation Factor was checked to confirm that all variables were younger than the age of three years. Reference values were healthy weight, female, urban, no single-parent household, Caucasian, above the poverty threshold, private health insurance, daily physical activity, 2 hours or less screen time, any soda consumption, and breast milk only.

Table 1.

Characteristics of Sample (n = 7814)

	Healthy weight (64.6%)	Overweight (12.8%)	Obese (22.6%)	Total (100%)	Likelihood ratio
Mean age (SD)^a	5.02 (.33)	5.06 (.37)	5.03 (0.29)	5.02 (0.33)
Sex					p = 0.000
Female	49.9%	56.7%	40.2%	48.6%
Male	50.1%	43.3%	59.8%	51.4%
Race/ethnicity
Caucasian	64.0%	57.4%	45.4%	59.0%	p = 0.000
Hispanic/Latino	12.6%	17.5%	23.9%	15.8%
Other minorities	8.2%	9.4%	12.8%	9.4%
Other/multiple	15.2%	15.7%	17.9%	15.9%
Location					p = 0.059
Rural	27.8%	31.5%	28.0%	28.3%
Urban	72.2%	68.5%	72.0%	71.7%
Child's poverty status					p = 0.000
Above poverty level	82.8%	78.5%	69.5%	79.2%
At or below poverty level	17.2%	21.5%	30.5%	20.8%
Single-parent household					p = 0.000
Yes	18.6%	22.8%	27.7%	21.2%
No	81.4%	77.2%	72.3%	78.8%
Type of medical insurance					p = 0.000
None/uninsured	6.6%	9.3%	8.8%	7.5%
Private	68.9%	63.3%	54.6%	64.9%
Public	18.7%	20.8%	29.5%	21.4%
Other/multiple	5.8%	6.6%	7.2%	6.2%
30 minutes of physical activity					p = 0.000
0–3 days a week	11.4%	12.8%	15.9%	12.6%
4–6 days a week	34.5%	37.4%	33.1%	34.5%
Daily	54.2%	49.8%	51.0%	52.9%
Daily screen time					p = 0.000
2 hours or less	52.4%	50.9%	42.4%	50.0%
More than 2 hours	47.6%	49.1%	57.6%	50.0%
Weekly soda consumption					p = 0.000
None	70.2%	67.4%	60.5%	67.6%
Any	29.8%	32.6%	39.5%	32.4%
Feeding practices at 6 months					p = 0.000
Breast only	20.9%	16.7%	13.6%	18.7%
Breast & formula	13.7%	16.0%	14.5%	14.2%
Formula only	30.4%	31.7%	35.0%	31.6%
Other (e.g., food), multiple, not sure	35.1%	35.6%	36.8%	35.5%

n = 7814 for all variables with the exception of mean age.

n = 2480.

SD, standard deviation.

Results

Table 1 illustrates a description of the sample of 7814 children entering kindergarten with a mean age of 5.02 [standard deviation = 0.33]. The sample consisted of a slightly higher percentage of male (51.4%) children, mostly Caucasian (59%), at a healthy weight (64.6%), who lived in nonsingle-parent households (78.8%), above the poverty threshold (79.2%), in urban areas (71.2%), and with private health insurance (64.9%). The current study assessed for associations between sociodemographic characteristics and childhood obesity, which are shown in Table 2. Children in the sample were more likely to be obese if they were non-Caucasian, male, lived in rural areas, lived at, or below the poverty level, had public insurance, or lived in single-parent households.

Table 2.

Associations between Body Mass Index Classification and Sociodemographic Characteristics in Reference to Healthy Weight

Variables	Overweight			Obese
Variables	OR	95% CI	p	OR	95% CI	p
Gender
Male	0.77	0.67–0.89	<0.001	1.49	1.33–1.67	<0.001
Female	1	1	1	1	1	1
Location
Rural	1.27	1.09–1.48	0.002	1.20	1.06–1.37	0.005
Urban	1	1	1	1	1	1
Single-parent household
Yes	1.20	1.00–1.43	0.045	1.27	1.10–1.46	0.001
No	1	1	1	1	1	1
Race/ethnicity
Hispanic/Latino	1.42	1.16–1.74	0.001	2.14	1.82–2.51	<0.001
Other minorities	1.22	0.95–1.56	0.128	1.82	1.51–2.20	<0.001
Other/multiple	1.15	0.94–1.40	0.168	1.52	1.30–1.78	<0.001
Caucasian	1	1	1	1	1	1
Poverty level
At or below poverty level	1.08	0.87–1.34	0.478	1.32	1.12–1.55	0.001
Above poverty level	1	1	1	1	1	1
Insurance status
None/uninsured	1.33	1.02–1.72	0.032	1.17	0.94–1.46	0.151
Public	1.02	0.82–1.26	0.893	1.27	1.08–1.50	0.004
Multiple/other	1.14	0.85–1.16	0.384	1.24	0.98–1.56	0.072
Private	1	1	1	1	1	1

The reference category is denoted by the number 1. Bold indicates a significance, any p < .05.

CI, confidence interval; OR, odds ratio.

Behavioral outcomes more associated with childhood obesity relative to healthy weight children in the sample are highlighted in Table 3. As shown, children who received less than 30 minutes of physical activity 3 or fewer days per week, had more than 2 hours of daily screen time, consumed any amount of soda, and consumed anything other than breast milk at 6 months of age are more likely to be obese than healthy weight children.

Table 3.

Associations between Body Mass Index Classification and Behavior in Reference to Healthy Weight

Variables	Overweight			Obese
Variables	OR	95% CI	p	OR	95% CI	p
30 minutes of physical activity
0–3 days a week	1.11	0.89–1.39	0.346	1.23	1.20–1.01	0.043
4–6 days a week	1.14	0.98–1.32	0.092	0.95	0.84–1.08	0.455
Daily	1	1	1	1	1	1
Daily screen time
More than 2 hours	1.03	0.90–1.19	0.666	1.23	1.10–1.38	<0.001
2 hours or less	1	1	1	1	1	1
Weekly soda consumption
Any	1.04	0.90–1.21	0.578	1.21	1.08–1.37	0.001
None	1	1	1	1	1	1
Feeding practices at 6 months
Breast milk & formula	1.41	1.11–1.80	0.005	1.44	1.17–1.77	<0.001
Formula only	1.23	1.00–1.51	0.051	1.44	1.21–1.72	<0.001
Not sure/other/multiple	1.22	0.99–1.49	0.057	1.38	1.16–1.64	<0.001
Breast milk only	1	1	1	1	1	1

The reference category is denoted by the number 1.

Discussion

It is estimated that in the United States, 13.9% of children between 2 and 5 years old experience obesity.¹ However, research on the relationships between childhood obesity and demographic traits or behavioral practices in this age range remains scarce. The present study aimed to bridge that gap by assessing how certain demographic traits, dietary practices, sedentary behaviors, and physical activity are associated with obesity in children who are about to enter kindergarten.

Just over 20% of the participants for this study were categorized as obese, well exceeding the national average. As more children continue to be classified into the obese category rather than overweight,² it is important to note that the current study found that statistically significant associations were only found between children who were healthy weight compared to children who were obese. No significant associations were found regarding the children who were overweight. While the current study cannot determine the cause of obesity, the results suggest that certain traits and behaviors increase the likelihood of being obese in young children. Addressing childhood obesity at the federal, state, and local levels may save the U.S. $663 billion annually⁵ by reducing expenses for disease, disability, absenteeism, and loss of productivity on people who have obesity.⁶ Even though Pearce et al. found that developmental risks for children aged 4 to 6 who were underweight or overweight do not significantly differ from those at a healthy weight,³⁰ obese children were found to be more vulnerable to health risks and poor social competencies than their peers with healthy weights.

The results of the present study reflect previous findings that show lower risks of obesity for children with adequate physical activity.^31,32 This strengthens the recommendations that young children need to participate in daily physical activity to reduce the risk of obesity and other associated chronic diseases.³³ Furthermore, Carson et al. state that children with higher rates of screen time tend to have less favorable body compositions, supporting the association between more than 2 hours of screen time and obesity in the current study.¹⁴ Similar to the results reported by Ludwig et al.,³⁶ the current study also found that children who consume any amount of soda, an SSB, were more likely to have obesity than children who consume none. Finally, Yan et al. showed that childhood obesity risk was reduced with duration of breastfeeding following a dose–response effect¹⁸ supporting the findings that infants exclusively breastfed for the first 6-months of life were less likely to be obese as incoming kindergarteners. Research suggests that exclusively breastfeeding and delaying weaning until at least 6 months provides protection for infants against obesity as they have lower BMIs until 5 years of age.³⁴ Once the infant is ready for complementary foods, those foods should be introduced along with breast milk, also known as complementary feeding, which aids in the acquisition of macro-and micronutrients that are vital for the infant to reach their optimal growth and development potential.³⁵ The foods selected during this time are very important as early feeding choices are determinants for obesity later in childhood.

Furthermore, the disparities between children who are categorized as either healthy weight or obese are generally documented across a widening gap in socioeconomic status (SES).³⁷ This was visible in the current sample as demographic factors associated with childhood obesity were seen among racial/ethnic minority groups, those having public health insurance, those living in single-parent households, those living below the poverty threshold, those living in rural areas, and among male children. The disparity of obesity in these groups emphasizes the need to develop interventions to establish lifelong health behaviors for the individual, family, and community.

Although there are few studies specific to children younger than six years of age, the current study further confirms that the behavior and social factors examined in the study are associated with obesity in children who are entering kindergarten. These findings are significant as Konnopka et al. found that on average, 11.68 years of potential life and 10.03 quality-adjusted life years are lost for overweight and obese individuals.³⁸

Limitations

There are a few limitations to consider for this study. First, it is cross sectional, therefore causation cannot be determined. Second, the method of data collection for the survey relies on voluntary participation of parents returning the survey, which may introduce bias as there may be differences between those who chose to return the survey and those who did not. Moreover, self-report surveys are also susceptible to recall bias and the consideration of social desirability by the respondent, which may cause untruthful responses.

With regard to SSBs, the only beverage assessed was soda consumption. There are many other forms of SSBs that were not accounted for in this study, such as juice or sweetened milk that may influence obesity in early childhood. In addition, actual sugar intake was not collected, therefore only allowing for the examination of a crude hypothesis. Finally, the question regarding breastfeeding does not distinguish whether the child was fed from the breast or fed breast milk from a bottle. Infants fed from the breast vs. through expressed breast milk may have better self-regulation of energy intake.³⁹ Future research distinguishing the mode of breast milk consumption could help assess its possible influences in childhood obesity.

Finally, studies report that food advertising and unhealthy food consumption while participating in sedentary screen time are potential factors that lead to weight gain.^40,41 However, researchers did not ask about the type of media children are exposed to, or if food and/or beverages are consumed during screen time. This may be helpful to assess along with whether there is a TV in the child's bedroom, which has shown to increase the likelihood of childhood obesity by potentially affecting sleep negatively.⁴²

Conclusion

The present study found that certain demographic traits, behaviors, and SES may negatively impact childhood weight status at a young age. Intervening within the early childhood context may mitigate some of the subsequent health issues associated with childhood obesity as the results presented concern children before entering kindergarten. This is consistent with findings by Taveras et al., highlighting the need to address obesity in early childhood and infancy to reduce disparities that may contribute to modifiable and preventable risk factors.⁴³ Taveras et al. found that addressing exclusive breastfeeding, sleep, and SSB consumption may result in positive outcomes if intervention and education occur in early childhood, especially among racial and ethnic groups associated with higher rates of childhood obesity in the sample.⁴³

Early childhood education and other programs that engage with these populations may be beneficial avenues to begin and sustain collaboration efforts. In 2018, roughly 84% of 5-year-old, 68% of 4-year-old, and 40% of 3-year-old children attended preprimary programs in the United States. Early childhood programs are optimal settings to provide access to healthy food, opportunity for active play, and education to parents about nutrition and healthy behaviors to practice at home. For some children, especially those more at-risk, the childcare environment may be the only opportunity to engage in structured, healthier behaviors,^45,46 thus, it is important to support families by providing adequate resources to replicate healthy behaviors in the home. In addition, access to early childhood education programs can link families with resources such as health insurance, food assistance, housing, and transportation, which can mitigate long-term health care costs that generally burden state and federal governments.⁴⁷ Stronger policies related to increasing access to quality early childhood programs and continuous support of evidence-based programming targeting healthy behaviors throughout K-12 can positively affect long-term health and economic well-being.

Footnotes

Acknowledgments

The authors thank Joshua Huebner, BA, for reviewing and providing comments on this article. The authors also thank all the school districts in Nevada, as well as the Nevada Department of Health and Human Services, Division of Public and Behavioral Health, Maternal, Child, and Adolescent Health for their participation and commitment to the implementation of the Kindergarten Health Survey.

Funding Information

Funding for the collection of the annual Kindergarten Health Survey was provided by the State of Nevada Division of Public and Behavioral Health, Maternal and Child Health Program.

Author Disclosure Statement

No competing financial interests exist.

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