Children’s healthy eating habits and parents’ socio-demographic characteristics in rural Texas,USA

Abstract

Objective:

Due to the disproportionately high rates of obesity within the US Hispanic community, there is a critical need to address this health disparity issue. The aim of this study is to examine the relationship between parents’ socio-demographic characteristics and their children’s food consumption.

Design:

Cross-sectional study.

Setting:

Participants were recruited from schools in a predominately Hispanic rural area of Texas, USA.

Method:

Parents (n = 298) of fourth grade (9–10 years old) children completed the survey. The independent variables were parents’ socio-demographic characteristics (e.g. ethnicity and income). The outcome variable was a Healthy Eating Index that refleting children’s frequencies of food consumption measured as daily frequency of consumption for healthy foods (e.g. skimmed milk), less healthy foods (e.g. potato) and unhealthy foods (e.g. Coke). We performed multiple linear regression.

Results:

Regression analysis shows that 13.7% variance of children’s food consumption could be predicted by their parents’ gender, ethnicity, marital status, education and income (R² = .137, p < 0.01). Parents’ ethnicity, education and income variables were strong predictors for children’s food consumption.

Conclusion:

Healthy eating can help reduce childhood obesity; however, we found children of US Hispanic parents ate less healthily. Culturally specific education programmes should be adopted for parents or families of Hispanic or Latino origin.

Keywords

Children healthy eating Hispanic/Latino health diet parents race/ethnicity

Introduction

Childhood overweight and obesity (CHO) continues to be an important public health concern worldwide (World Health Organization [WHO], 2016). In the USA, the growing epidemic of CHO has forced researchers to examine factors predicting obesity and obesogenic behaviours (Patrick et al., 2013). In the USA, CHO has dramatically increased among children and tripled among young people aged 10–19 years, in the previous three decades (Centers for Disease Control and Prevention [CDC], 2015a). Children, who are obese, are at increased risk of being overweight and obese as adults (Freedman et al., 2005). This trend has also coincided with the growth of adult obesity rates: more than 34.9% of the US adults are obese (CDC, 2015b).

Furthermore, CHO is associated with more than 20 chronic illnesses (e.g. heart disease, type 2 diabetes, cancers and hypertension) and has been attributed to negative health conditions linked to increased rates of mortality (CDC, 2015a; Freedman et al., 2001; Lobstein et al., 2004a). Not coincidently, the incidence of young people diagnosed with pre-hypertension and hypertension has mirrored the increase in CHO rates since the late 1980s (Din-Dzietham et al., 2007). Obese children, for example, are three times more likely to develop hypertension compared to their non-obese counterparts (Sorof and Daniels, 2002). Data from the National Health and Nutrition Examination Survey showed the average rates of high and low blood pressure among US children and adolescents aged 2–19 years increased by 1.4 mm Hg systolic and by 3.3 mm Hg diastolic, between 1988–1994 and 1999–2000 (Muntner et al., 2004).

Significant ethnic and racial disparities in obesity prevalence exist among children (CDC, 2015a). Studies have found that children of Hispanic background in the USA are at an increased risk for childhood and adulthood obesity (Ogden et al., 2016; Skinner et al., 2016). Presently, Hispanic children and adolescents hold the highest rate of CHO (CDC, 2015a). For example, Hernandez et al. (1998) found that approximately 17% of Hispanic preschool children were categorised as obese in comparison to 11% non-Hispanic Blacks, 4% non-Hispanic Whites and 3% non-Hispanic Asians. From 2007 to 2008, Hispanic boys, aged 2–19 years, were significantly more likely to be obese than non-Hispanic White boys (Ogden et al., 2014). In 2011–2012, CHO rates were higher among Hispanic children and adolescents (22.4%) than non-Hispanic Blacks (20.2%) and non-Hispanic Whites (14.1%, Ogden et al., 2014). Moreover, when examining chronic diseases among obese children, Hispanic children who are obese have higher rates of chronic diseases compared to their White counterparts (Muntner et al., 2004; Van Cleave et al., 2010). For example, a substantial increase in blood pressure rates, both diastolic and systolic, were detected in Hispanic boys, girls, compared to Whites in one study (Muntner et al., 2004).

CHO is influenced by multiple factors stemming from the individual, family and community levels. Researchers have stated explicitly that dietary behaviours, such as a high intake of sugary sweetened beverages (Della Torre et al., 2016; Field et al., 2014; Harrington, 2008) and fast-food consumption (Davis and Carpenter, 2009; French et al., 2001; Paeratakul et al., 2003) increase the risk of CHO. In addition, studies have also found that a low intake of fruit and vegetables contribute to higher levels of body mass index (BMI) among children (Epstein et al., 2001; Pesa and Turner, 2001; Veugelers and Fitzgerald, 2005). Compared to other groups, Hispanics have unhealthy dietary behaviours (high intake of sugary sweetened beverages, lower intake of fruit and vegetables and high consumption of fast-food), which increase their risk of CHO (Guerrero and Chung, 2016; Sorkin and Billimek, 2012; Zheng et al., 2015). A cross-sectional study conducted in a major southeastern city found that two out of every three foods Hispanic children consumed were unhealthy food items (e.g. pizza, chips, desserts, burgers or soda/juice, Wilson et al., 2009). Hispanic children were more likely than non-Hispanic White children to consume sugary sweetened beverages (38% vs 22.8%) and fast-food (78% vs 65.3%, Guerrero and Chung, 2016). Similarly, additional studies found that Hispanic children had higher intake of sugary sweetened beverages than non-Hispanic White children (Lasater et al., 2011; Richmond et al., 2013; Taveras et al., 2010).

Individuals living in obesogenic environments characterised by high poverty and lack of affordable fruit and vegetables have been found to have low intake of fruit and vegetables (Finkelstein et al., 2005; Lovasi et al., 2009). Children living below the federal poverty level are more likely to be obese compared to those who are above the federal poverty level (Singh and Kogan, 2010; State of Obesity, 2016). The effect of poverty on CHO has been traced to children as young as 2 years old (Klebanov et al., 2014). Family poverty is positively associated with increased risk for CHO. Toddlers living in poor neighbourhoods have a rapid increase of BMI compared to those in non-poor neighbourhoods (Klebanov et al., 2014). Furthermore, children residing in rural areas are at a more serious risk for obesity than those residing in non-rural areas (Conway et al., 2012; Davis et al., 2011; Johnson Iii and Johnson, 2015). Adults and children living in rural areas have a lower percentage in the consumption of fruit and vegetables in comparison to those residing in urban areas (Krishna et al., 2010; Lutfiyya et al., 2012).

Although several studies have been examining the dietary intake of Hispanic children, several research gaps exist. First, few study investigated obesity within the low-income Hispanic community residing in rural areas (Davis et al., 2011; Johnson Iii and Johnson, 2015). In 2010, 9.3% of the rural population was comprised by Hispanics (Housing Assistance Council, 2012). In addition, roughly 33% of Hispanic children reside in poverty and approximately 66% reside in low-income households (National Research on Hispanic Children & Families, 2015). Due to the disproportionately high rates of obesity within the Hispanic community, there is a need to address this gap in the existing research literature specific to the intersection of rurality, socioeconomic status and ethnicity. A second research gap is that healthy eating has been treated as a dichotomous rather than continuous outcome variable in most previous studies (Evans et al., 2009, 2014; Hiza et al., 2013; Santiago-Torres et al., 2014). Thus, these studies have been limited in their analyses of this variable. A third research gap is that many previous studies assessed associations between eating behaviours and socio-demographic characteristics by setting the socio-demographic variables as confounders (Drewnowski and Rehm, 2015; Kirkpatrick et al., 2012). However, the best socio-demographic variables set predicting the largest variance of the children’s healthy eating has not yet been identified.

To address the first research gap, we aimed to examine the relationship between parents’ socio-demographic characteristics and their children’s healthy eating behaviour in a predominately Hispanic rural area of Texas. To address the second research gap, we used a healthy eating index (HEI), which allows for the treatment of healthy eating as a quantifiable, continuous variable. To address the third research gap, we investigated the association between children’s HEI and parent’s socio-demographic variables using multiple regression analyses and entering the socio-demographic variables as the predictor set.

Methods

Data collection and study sample

Data for this study were derived from baseline data of a larger US National Institute of Health–funded investigation (Student Wellness Assessment and Advocacy Project [SWAAP]). The parent study utilised an intervention based on the Social Ecological Model (SEM) with a repeated cross sectional design (McLeroy et al., 1988). Since the purpose of this article was to investigate the relationship between children’s eating behaviours and parent’s socio-demographic characteristics, we narrowed the analyses to the selected variables. See other studies for full details about information on describing the intervention, reporting the effectiveness of the intervention and examining other variables that were not assessed in this article (Amuta et al., 2015; Chen et al., 2015; Lu et al., 2014).

Participants were parents of fourth-grader (9–10 years old) children recruited from seven schools across three school districts in a rural county located in southeast Texas. Invitations for study participation and consent forms (both in English and Spanish) were sent home through the students. Parents were asked to complete a self-administered paper–pencil questionnaire with 197 items assessing factors associated with CHO. Parents could choose to fill out either the English or Spanish version of the questionnaire based on their language preference. A total of 767 questionnaires were distributed and 298 were received, with a response rate of 38.9%. Upon completion of the questionnaires, each parent/child duo received a US$25 gift card. Data were collected twice: pre-intervention and post-intervention. Baseline data were used for this study to examine the independent factors that have an impact on parent’s dietary intake. The baseline data were collected in 2010. Information on data sampling and collection is described elsewhere (Amuta et al., 2015; Chen et al., 2015; Lu et al., 2014). This study was approved by the Institutional Review Board of Texas A&M University.

Measures

The self-administered paper–pencil questionnaire contains 197 items beginning with the questions asking for parents’ socio-demographic information. This study focused on socio-demographic items assessing parents’ gender, income, education level, Hispanic ethnicity and marital status. Parents were asked to indicate the frequency of their children’s foods consumption using 16 items (see Figure 1). Eight items (items 7, 10, 13, 14, 15, 16, 17 and 18) assessed children’s frequency of consuming healthy foods (e.g. skimmed milk and whole grain bread), five items (items 6, 12, 19, 20 and 21) assessed less healthy foods (e.g. fresh juice and potato) and three items (items 8, 9 and 11) assessed unhealthy foods (e.g. French fries and sweetened drinks). We classified foods as ‘less healthy’ because these foods either ranked between the ‘healthy foods’ and ‘unhealthy foods’ or there is some debate in the literature in terms of considering that type of food as healthy or unhealthy. For example, 100% fruit juice is healthier than sugar-sweetened drinks (e.g. carbonated drink); however, it is less healthily compared to whole fruit. Higher consumption of 100% juice is associated with higher obesity rate among children (Wojcicki and Heyman, 2012). Another example is that an unsalted plain baked potato with skin has only 160 calories; however, potatoes with mayonnaise and other dressings have high calories (Drayer, 2017). Also, potatoes have a high glycaemic index that will have greater impact on people’s blood sugar compared to other foods with lower glycaemic index (Foster-Powell et al., 2002; Ludwig et al., 1999). Thus, foods such as 100% juices and potatoes were classified as ‘less healthy’. Under each food item, there were seven possible response options ranged from ‘never or less than once per week’ to ‘4 or more times per day’, which were coded as continuous scales from 0 to 6. The Cronbach’s α for these 16 items measuring the frequency of children’s foods consumption was .76, indicating a good internal consistency. We adapted the HEI (Guenther et al., 2014) for the purposes of this study using: HEI = the average frequency of Healthy Food Consumptions – 0.5 × the average frequency of Less Healthy Food Consumptions Frequency – the average frequency of Unhealthy Food Consumptions Frequency. Higher HEI scores indicate healthier diets/food consumption patterns. The possible range of the HEI scale was from −6 (if the individual consumed unhealthy foods only and consumed each unhealthy food more than 4 times a day) to 6 (if the individual consumed healthy foods only and consumed each healthy food more than 4 times a day).

Figure 1.

Items asking children’s food consumption frequency.

Data analysis

Data analyses were conducted using SPSS 22.0, with a significance level set at α = .05. We conducted descriptive analysis for parental socio-demographic characteristics and children’s foods consumption frequency. Bivariate correlation was utilised to examine the association among these parental socio-demographic variables and children’s HEI scores. We also performed a multiple linear regression model using all-possible subsets analysis to choose the best set of parents’ socio-demographic variables to predict children’s HEI scores. All-possible-subsets analysis computes ‘the R² for each and every combination of predictors for each and every variable set size’ to identify the best predictor set (Guenther et al., 2014). All-possible-subsets analysis has multiple strengths over stepwise methods; therefore, researchers should use all-possible-subsets-analysis instead of stepwise methods (Thompson, 2006).

Results

Parental socio-demographic characteristics

Our sample size was 298. As shown in Table 1, the majority of the parents in the study were female (86.2 %). Half of the participants were of Hispanic origin (50.2%). Households earning US$40,000.00 or less were categorised as low income because the average household size in our study was 4.8 members. According to the Texas Department of Housing and Community Affairs, in Waller county, a household of four to five members earning US$44,940 or less can qualify for public housing (section 8, Affordable Housing Online, 2016). Most households (70.5%) had an annual income lower than US$40,000 and only 14.4% of them had an annual income higher than US$70,000. A large majority of the parents (89.9%) had education levels below a Bachelor’s degree. Most parents were married and lived with their spouses (68.5%), but some (29.5%) were single parents.

Table 1.

Parental socio-demographic characteristics.

Variables	Frequency	%
Gender
Male	38	12.9
Female	257	86.2
Missing	3	1.0
Ethnicity
Hispanic	143	50.2
Non-Hispanic	142	49.8
Income
Low (<US$40,000)	191	70.5
Medium (US$40,001–US$70,000)	41	15.1
High (>US$70,001)	39	14.4
Education
Below high school	89	32.1
High school/GED	80	28.9
Some college	80	28.9
Bachelor’s degree or higher	28	10.1
Marital status
Married	204	68.5
Single parent	88	29.5
Missing	6	2.0
Total	298	100.0

GED: General Educational Diploma.

Children’s foods consumption frequency

Parents were asked to identify how often their children consumed 16 types of food, representing three categories: unhealthy foods, less healthy foods and healthy foods. About 28.5% of the children had sugary sweetened beverages (e.g. Kool-Aid, carbonated and sports drinks) at least once every day. About 26.2% of the children had 100% fruit juices (e.g. orange, apple and tomato juice) at least once every day. About 43.3% of the children ate fruit at least once a day (Table 2). The children’s HEI scores ranged from −5.18 to 2.05 with a mean score of −1.02 (standard deviation [SD] = 1.15), indicating these children did not eat healthily on average.

Table 2.

Percentage of children consume one or more times per day.

Food category	Food item	%
Unhealthy	Sugar-sweetened drinks such as Kool-Aid, soda, cola, sports drinks or sugar-sweetened tea	28.5
	Artificially sweetened rinks such as diet cola, diet soda or Crystal light	9.7
	French fries, fried potatoes or potato chips	13.1
Less healthy	100% juice such as orange, apple or tomato	26.2
	Potatoes, NOT including French fries, fried potatoes or potato chips	8.3
	White rice	10.1
	White tortillas	10.3
	White bread	19.1
Healthy	Oatmeal	8.4
	Brown rice	7.7
	Whole wheat bread	18.8
	Corn tortillas	15.5
	Whole wheat tortillas	7.4
	Vegetables	24.2
	Fruit	43.3
	Soymilk	3.3

Bivariate correlations

We presented the correlation matrix among parental socio-demographic variables and children’s HEI scores in Table 3. Parents’ ethnicity, education and income variables were significantly and positively correlated with each other (p < .01). Also, parents’ ethnicity and education variables had significant negative associations with their children’s HEI scores (p < .05).

Table 3.

Bivariate relationships between variables.

Variables	1	2	3	4	5
1. Gender
2. Ethnicity	.097
3. Marital status	−.133*	.051
4. Education	−.085	.552**	.032
5. Income	−.153*	.192**	.292**	.396**
6. Healthy eating	−.079	−.293**	.016	−.146*	−.108

Correlation coefficient is significant at the .05 level (2-tailed); **Correlation coefficient is significant at the .01 level (2-tailed).

Multiple regression

As shown in Table 4, 13.7% of children’s HEI scores (R² = .137) could be predicted by their parents’ gender, ethnicity, marital status, education and income. Ethnicity was the best predictor among these variables (r_s = –.79189, p < .01). The next two strongest predictors were education (r_s = –.39459, p = .437) and income (r_s = –.29189, p = .268). The education and income variables should still be considered as good predictors because they had relatively high structure coefficients, despite statistically insignificant p values. Due to strong correlations among ethnicity, education and income variables (p < .01), the ethnicity variable took the predicting credits from the variables of education and income, so that their p values became insignificant. Parents’ ethnicity, education and income variables were strong predictors for children’s HEI scores. We found that Hispanic children tended to have lower HEI scores than non-Hispanic children (β = –.383, p < .001). In our sample, children having parents with higher education level tend to have lower HEI scores than those having parents with lower education level. Children coming from a family with higher income were more likely to have lower HEI scores than those from low-income family.

Table 4.

Multiple regression for healthy eating index by selected demographic variables.

Predictor	β	r_s	p
Gender	.049	−.21351	.505
Ethnicity	−.383	−.79189	< .001*
Marital status	.079	.043243	.288
Education	.07	−.39459	.437
Income	.09	−.29189	.268

Model R² = .137; adjusted R² = .113.

p value is significant at the .001 level.

Discussion

Traditionally, socioeconomic status has been viewed as a primary predictor of dietary patterns; poverty has been associated with poor food choice (Darmon and Drewnowski, 2008; Dubowitz et al., 2008; McKinnon et al., 2009). While this may be true, to some extent, giving primary consideration to variables of income and formal education overlooks the significance of potential mediators, like ethnicity and culture.

We found that Hispanic children tend to have lower HEI scores than non-Hispanic children. The literature also gives credence to the role of ethnicity in relationship to eating habits. Our results were consistent compared to findings in many previous studies. Many studies have looked at and discussed differences in eating habits by race (Airhihenbuwa et al., 1996; Caprio et al., 2008; Evans et al., 2009; Harley et al., 2014; Hiza et al., 2013). These findings have implications for policy makers and practitioners and should be considered when tailoring approaches and designing interventions. For example, Evans et al. (2009) found that Spanish-speaking Hispanics and Blacks were more likely than their English-speaking compatriots to use food as a means of calming their children. Hispanic parents also reported being less concerned about children overeating, which was incongruent with epidemiological data on CHO; however, Hispanic parents were also more likely to report concerns about their children being underweight than overweight, thus underestimating weight (Evans et al., 2009). Another example of racial/ethnic disparities in healthy eating can be found in Amuta et al.’s (2015) study which found lower fruit and vegetables consumption among Hispanic participants.

We found that children whose parents’ had higher levels of education and income tended to eat less healthily. While this finding may appear counterintuitive on its surface, recent studies have reported similar findings. For example, Evans et al. (2009) found that for every unit of increase in parental income among their rural participants (55% Hispanic), there was a decrease in the number of days children consumed fruit and vegetables during dinner. There have been attempts to explain differences in obesity patterns by analysing parental variables. Ziol-Guest et al. (2013) examined the relationship between parental employment and children’s body weight and found that children with highly educated mothers, working more hours per week, had significantly higher standardised BMI scores.

Several possibilities may explain the negative association between children’s HEI scores and parents’ income and education levels in our findings. First, the majority (86.2%) of the parents in our sample were mothers. Generally speaking, parents have different responsibilities in the family (Bott and Spillius, 2014). For example, many fathers typically spend time with children doing physical activity and sports training (Wheeler and Green, 2014). Many mothers within the Hispanic community are responsible for cooking and domestic chores (Lam et al., 2012). Second, most households (70.5%) in our study had an annual income lower than US$40,000 and only 14.4% of them had an annual income higher than US$70,000. We observed a negative association between children’s HEI scores and parents’ income and education because the majority of our participants were working mothers from low- to middle-income households. We believe that these mothers had to work to provide financial support to the family. Our findings were similar to studies reporting working mothers have less time for modelling or preparing healthy foods for their children (Beshara et al., 2010; Horning et al., 2017). Another possible explanation is that maladaptive dietary practices (e.g. binge eating) have been observed in middle-income minority ethnic populations, for reasons rooted in historical contexts and present day built environments (Airhihenbuwa et al., 1996; Powell et al., 2007; Spears et al., 2017). Higher income segments of minority populations are often overlooked in the literature, but as our findings suggest, these groups still encounter barriers to healthy eating behaviours. This phenomenon is still not fully understood and warrants further investigation.

Our study found that ethnicity is a stronger predictor of healthy eating than both education and income. These findings suggest the importance of examining sociocultural factors and the impact of acculturation within the Hispanic community. Recent studies have focused on the role of culture and attitudes towards specific eating habits, like fruit and vegetables consumption, with consideration of varying environmental factors (Evans et al., 2014; Hiza et al., 2013; Santiago-Torres et al., 2014). Accordingly, this study contributes to the literature through its nuanced investigation of healthy eating habits in relationship to ethnicity, providing insight into the patterns and practices of the study population. Our findings have implications for future studies and may inform the design of future interventions targeting ethnically diverse populations.

Study limitations

This study had a few limitations. A cross sectional study design with a relatively small sample size (n = 298) hinders our ability to infer causal relationships. Second, parents were asked to recall the dietary intake of their child, this method may have contributed to recall bias. The literature suggests, however, when comparing 4-day dietary survey methods to 4-day weighed inventories, that a 4-day repeated dietary recall was among the most appropriate methods for obtaining an accurate descriptive of an individual’s dietary intake (Holmes et al., 2008). Therefore, survey questions of future studies should conduct at least four 24-hour dietary intakes or conduct direct observation of participants’ food consumption to avoid recall bias. Third, our survey did not include a list of options for culturally appropriate foods/snacks; future studies should include culturally appropriate foods for target populations. Also, due to the limitation of using secondary data, we could not replicate Guenther and colleagues’ HEI measure; instead, we adapted their measure and created a HEI score that was feasible for our data. Fourth, we observed a relatively low response rate (38.9%), which may be due to the large number of survey questions (197 questions). In addition, the surveys were self-reported; therefore, the results might be biased compared to studies using objective measures. Finally, due to the nature of the questionnaire, we only received information about the frequency range of children’s food consumptions from parents. We assumed children and parents talked about school meals at home so parents knew about children’s food consumption in school. However, some parents could have provided inaccurate estimations about their children’s food consumption in school. Therefore, we did not have information about the exact number of times the children consumed the foods referenced. Despite these limitations, it is important to note that this study represents one of the few studies to examine parents’ ethnicity as a predictor for dietary intake.

Implications for research and practice

Although rates of CHO subsided between 2011 and 2014 in the USA (Ogden et al., 2016), CHO continues to have an impact in the Hispanic community (Freedman et al., 2006; Ogden et al., 2002). Ethnicity in our study was a stronger predictor of healthy eating than education and income, adding to evidence that food consumption patterns may be contributing to the high prevalence of overweight and obesity among Hispanic children in the USA (Hamner et al., 2017). Study results are supported by the existing literature demonstrating the association between consumption of high-caloric foods and increased risk of overweight and obesity among Hispanic children (Leea et al., 2011). In addition to foods with high-calorie content, consumption of sugar-sweetened beverages has also been demonstrated to increase risk of overweight and obesity (Keller and Bucher Della Torre, 2015; Ludwig et al., 2001).

Longstanding research indicates that the USA is both segregated in terms of socioeconomic status and race/ethnicity and the segregation contributes to disparities in health outcomes (Bower et al., 2014; Lichter et al., 2012). Our finding that ethnicity was a significant predictor of healthy eating provides further supporting evidence for the phenomena. Our participants came from the same geographic area. Higher rates of overweight and obesity among groups have been associated with geographic locations as disparities in access, quality and affordability of healthy foods are associated with neighbourhood socioeconomic status in the USA (Lee, 2012; Showell et al., 2016; Sturm and Datar, 2005). Health promotion activities and programmes aimed at reducing CHO among Hispanics should consider an ecological perspective of factors that drive differences in consumption patterns among the group, such as macro level factors, such as environment, culture and policy.

Footnotes

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Funding for the project leading to this article was made possible (in part) by grant 1P20MD0002295 from the US National Center on Minority Health and Health Disparities.

ORCID iD

Xuewei Chen

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