Next Article in Journal
Physical Fitness, Physical Activity, Sedentary Behavior, or Diet—What Are the Correlates of Obesity in Polish School Children?
Next Article in Special Issue
Self-Reported Oil Spill Exposure and Pregnancy Complications: The GROWH Study
Previous Article in Journal
Association between Air Pollution and Emergency Room Visits for Atrial Fibrillation
Previous Article in Special Issue
Environmental Barriers to Participation of Preschool Children with and without Physical Disabilities
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Beyond Food Access: The Impact of Parent-, Home-, and Neighborhood-Level Factors on Children’s Diets

Department of Global Community Health and Behavioral Sciences, Tulane School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA
Mary Amelia Douglas Whited Community Women’s Health Education Center and Prevention Research Center (PRC), Tulane School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA
Louisiana Public Health Institute, New Orleans, LA 70112, USA
Children’s HealthSM, Children’s Medical Center, Dallas, TX 75235, USA
Sisters of Charity Foundation of Cleveland, Cleveland, OH 44115, USA
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2017, 14(6), 662;
Submission received: 5 May 2017 / Revised: 3 June 2017 / Accepted: 13 June 2017 / Published: 20 June 2017
(This article belongs to the Special Issue Environmental Influences on Maternal and Child Health)


Despite the growth in empirical research on neighborhood environmental characteristics and their influence on children’s diets, physical activity, and obesity, much remains to be learned, as few have examined the relationship between neighborhood food availability on dietary behavior in children, specifically. This analysis utilized data from a community-based, cross-sectional sample of children (n = 199) that was collected in New Orleans, Louisiana, in 2010. This dataset was linked to food environment data to assess the impact of neighborhood food access as well as household and parent factors on children’s diets. We observed a negligible impact of the neighborhood food environment on children’s diets, except with respect to fast food, with children who had access to fast food within 500 m around their home significantly less likely (OR = 0.35, 95% CI: 0.1, 0.8) to consume vegetables. Key parental and household factors did play a role in diet, including receipt of public assistance and cooking meals at home. Children receiving public assistance were 2.5 times (95% CI: 1.1, 5.4) more likely to consume fruit more than twice per day compared with children not receiving public assistance. Children whose family cooked dinner at home more than 5 times per week had significantly more consumption of fruit (64% vs. 58%) and vegetables (55% vs. 39%), but less soda (27% vs. 43%). Findings highlight the need for future research that focuses on the dynamic and complex relationships between built and social factors in the communities and homes of children that impact their diet in order to develop multilevel prevention approaches that address childhood obesity.

1. Introduction

Childhood obesity prevalence rates in the United States (U.S.) have remained stable between 2003 and 2004 and between 2009 and 2010, but the number of children who become obese at younger ages is increasing, and as many as 80% of obese children and adolescents will become obese adults [1]. Since the 1970s in the United States, this percentage has more than tripled [2], and 20% of school-aged children are obese [3]. While research has shown that it is difficult to predict trends for childhood obesity [3], one study estimates that, for adults, if obesity prevalence increases at the rate that it has between 1990 and 2008, there could be a 33% increase for obesity and a 130% increase for severe obesity by 2030 in the U.S. [4]. Obese and overweight children face numerous health problems that can extend into adulthood including sleep apnea, orthopedic complications, negative psychosocial interactions, type 2 diabetes, and hypertension; most of which were not previously common in children [5]. These are in addition to chronic diseases exacerbated by or seen as comorbidities with obesity such as heart disease, cancer, chronic kidney disease, asthma, and arthritis to name a few [6]. Experts agree that multilevel, comprehensive obesity prevention strategies are needed to understand and address this serious public health concern [7].
Despite the growth in empirical research on neighborhood environmental characteristics and their influence on children’s diets, physical activity, and obesity [8,9,10], much remains to be learned, as most work in childhood obesity has focused on the micro-environment (e.g., home and school influences) [11,12,13,14] and few have examined the relationship between neighborhood food availability on dietary behavior in children specifically [12,15,16,17,18]. Neighborhood contextual factors are likely to have an impact on an individual’s weight through direct or indirect influences on food intake and/or physical activity [13,14].
Neighborhood factors that influence child dietary behaviors need to be better understood to develop effective nutrition interventions. The physical availability of unhealthy food choices (e.g., density of fast food restaurants, and convenience stores) may be an important contributor to poor dietary habits and the risk of overweight [19,20,21]. Consumption of high-fat fast food may contribute to higher energy and fat intake, and lower intake of healthful nutrients [8,22], which could increase the risk of obesity because of massive portion sizes, high energy density, and palatability. Furthermore, the neighborhood nutrition environment may help to explain some of the racial, ethnic, and socioeconomic disparities in dietary patterns [14,23]. Fast food restaurants have been shown to be more prevalent in neighborhoods with a greater population of non-white individuals, where supermarkets are less prevalent [24,25,26]. Similarly, low-income neighborhoods have reportedly lower availability to healthy foods [27] and decreased energy adjusted intake of fruits, vegetables, and fish, with an increased intake of meat [28] than high-income neighborhoods.
While neighborhood level factors remain important, analyses of a U.S. national survey of children found that differential exposure to food outlets does not independently explain weight gain over time in elementary school-aged children and that variation in food outlet availability does not explain socioeconomic and racial/ethnic differences [29]. The placements of new supermarkets in underserved communities have shown to moderately improve residents’ perceptions of food accessibility, but do not lead to changes in body mass index, reported increase of fruit and vegetable intake [30,31], or the availability of healthful foods at home or in children’s dietary intake [16]. It may be crucial to consider other factors, beyond neighborhood food access, when trying to understand obesity risk among children.
The New Orleans, Louisiana (U.S.), area is rapidly changing and developing, making it an important city in which to examine the structure and significance of neighborhoods and health promotion activities. Children and families in New Orleans are undergoing alterations in their community environments and have been, and continue to be, exposed to heightened socioemotional stress, referred to as solastalgia, as a result of Hurricane Katrina [32,33]. Geographic food access has been shown to differ for black individuals in New Orleans, with fewer supermarkets (which have been associated with lower BMI (body mass index) and healthier food consumption) [34] and less availability of fresh fruits and vegetables in predominantly black or African-American neighborhoods [35]. Recent analyses indicate that this disparity increased after Katrina in 2007, and the disparity in access returned to pre-Katrina levels in 2009, but there remained a disproportionately unhealthy food environment for black individuals in New Orleans [35,36,37,38,39].
A multilevel, community-based study afforded the opportunity to examine the impact of local food access on diet among a cross-sectional sample of children, while taking into consideration multiple family, and individual level factors.

2. Materials and Methods

As part of a study to examine neighborhood influences on children’s health disparities, 199 children ages 4–14 years and their families were recruited from January–May 2010 through five local schools in an inner-city community in New Orleans, Louisiana. Schools who participated in the study served children from pre-kindergarten (Pre-K 4) to eighth grade. Data were collected at the level of the child, family/household, and neighborhood using a caregiver survey including the residential address of the child, which was geocoded. While children were recruited from schools within the same community, 87 census tracts were represented residentially.
Families were recruited through the schools via active consent with letters and a quantitative survey for caregivers sent home with the child and returned through the school. The caregiver survey assessed numerous factors, including obesogenic factors in the household, racial discrimination and child-specific data such as behavioral and emotional problems and other confounders (e.g., prenatal exposure and events). Families also reported if they were the recipients of food-related public assistance programs such as the Supplemental Nutrition Assistance Program (SNAP), formerly the Food Stamp Program, and Women, Infants, and Children (WIC). Children in this study came from unique households, with no sibling groups represented in the sample. Caregivers were also asked to provide the residential address of the child, which were geocoded to plot the child within their census tract. The project staff and school nurse were available (via phone or in person) to assist the caregivers in completing the quantitative survey. Additionally, as part of a larger study anthropometric measures, including height, weight, blood pressure, and waist circumference were collected on each child, at the school, by trained staff using standardized techniques. However, these were collected by partner group and anthropometric items were not available for this study.
A multilevel data system of children nested within their household (HH) and neighborhood was created. Sources for neighborhood data included the U.S. Census 2000, geographic data from ArcGIS, U.S. Census shapefiles, and food environment data from the Tulane Prevention Research Center (PRC). Definitions of neighborhood were based on the Census 2000 TIGER shapefile. Food store data collected included type of store (supermarket or small) based on the percentage of the stores total gross grocery sales [36], and 0.5-, 1.0-, and 2.0-mile distances from the child’s house to the store were calculated.
The primary outcomes of interest were consumption of food (fruit, vegetables, and sweets) and beverages (fruit juice, milk, and soda). Key dietary components were included based on common components in national surveys in the U.S. and balanced with respondent burden given the number of questions asked. Fruit juice, soda, and sweets were considered obesogenic factors in this study [40,41,42,43]. Parents reported on consumption of servings per day for each food and beverage group, and all consumption measures were coded to be dichotomous. Fruit, vegetables, juice, and milk were coded “1” if children consumed two or more servings per day, on average, and “0” if children consumed less than two servings per day. Sweets and soda were coded “1” if children consumed at least one serving per day, on average, and “0” if children consumed less than one serving per day.
The primary exposure of interest was a food store within a given perimeter of the child’s household (500 m for small food stores and 1000 m for supermarkets). Descriptive univariate, bivariate, and multivariate analyses were conducted. Based on previous food consumption literature, variables regarding age, sex, maternal marital status and education, number of children in the household, reception of public assistance, and dinners consumed at home were controlled for. Logistic regression models for individual food and beverage consumption outcomes were constructed using STATA 10.0 (STATA, College Station, TX, USA), with hierarchical models that accounted for census tract or neighborhood-level clustering of children living within the same neighborhood.

3. Results

Descriptive analysis on varied food and beverage consumption by individual, family, and neighborhood’s characteristics and bivariate analysis on association between independent variables and food and beverages consumption are presented in Table 1, except for race. The sample was 97% African-American or black with the remaining 3% being white or of other races. Among the 3% of children in the sample of races other than black, there were less than 5 children in each additional race category, so race is not presented and was not included in further analyses. Significant differences in food and beverage consumption were found across individual demographic variables, including child’s age, sex, and mother’s educational degree. Approximately 65% of sampled children aged 6 years old or younger consumed fruit more than twice per day compared with 60% of children aged 7–9 years old and 46% of children aged 10 or older. For juice consumption, over 80% of children 6 years old or younger had juice more than twice per day, while 70% of children aged 7–9 years old and 56% of children aged 10 or older had juice more than twice per day. The significant differences between fruit consumption and juice across children’s age groups were found in bivariate analysis. There were no significant differences in the consumption of vegetables, sweets, milk, or soda by age.
Differences in the consumption of fruit and sweets by sex were demonstrated in the bivariate analysis, with 62% of male children versus 50% of female children having access to fruit more than twice per day and 58% of male versus 70% of female children consuming sweets more than twice per day. No significant sex differences were shown in the consumption of vegetables, milk, and soda.
There was no significant difference in the frequency of any food or beverage consumption based on the mother’s marital status. However, the mother’s education level was found to significantly influence children’s consumption on sweets and juice, milk (although marginally, p < 0.10), and soda. Approximately 71% of children whose mothers had a high school education consumed sweets more than twice per day, compared with 67% of children whose mothers with less than a high school education, and 47% whose mothers had more than a high school education. Greater proportions of children with mothers who had lower education consumed juice, milk, and soda more than twice per day compared to those whose mothers had higher education.
Despite no significant finding on the relationship between the consumption of food or beverages and the number of children in family, significant associations were found between two other family environment-related variables and food or beverage consumption. Children who received public assistance had a significantly higher likelihood of consuming fruit, vegetables, and juice more than twice per day (p < 0.05) than children who did not receive any public assistance. Consistent with previous findings, children whose family cooked dinner at home more than 5 times per week had significantly more consumption of fruit (64% vs. 58%) and vegetables (55% vs. 39%), but less soda (27% vs. 43%) compared to children whose family cooked dinner at home less than 4 times per week.
With respect to the three neighborhood variables, access to a small store within 500 m and supermarket within 1000 m around residency were not significantly associated with children’s food and beverage consumption. However, children who had access to fast food within 500 m from their home were significantly less likely to consume fruit (43% vs. 59%) and vegetables (24% vs. 50%) than children with less fast food access.
Table 2 shows the multivariate analysis of the association between the neighborhood food environment and selected food and beverage consumption. Results reveal that children who had access to fast food within 500 m around their home were nearly three times less likely (OR = 0.35, 95% CI: 0.1, 0.8) to consume vegetables when controlling for individual demographic characteristics and family relevant variables. Cooking dinner at home and family receiving public assistance remained significantly associated with children’s vegetables consumption after controlling for other potential confounders. Compared with the children from families who cook dinner at home less than 4 times per week, the children from families who cook dinner more than 5 times per week were nearly two times more likely to consume vegetables (OR = 1.9, 95% CI: 1.1, 3.5). Children receiving any public assistance were also more than twice as likely to have access to vegetables compared to children not receiving any public assistance after controlling for other potential confounders (OR = 2.3, 95% CI: 1.2, 4.5). No significant relationships were observed for fruit and sweets consumption.
In addition, children aged 10–14 years old and children with high school educated mothers have lower likelihoods of drinking juice more than twice per day (OR = 0.2, 95% CI: 0.1, 0.5; OR = 0.4, 95% CI: 0.2, 0.8) than children aged 4–6 years old and children whose mothers have less than a high school education, respectively. Children receiving public assistance were 2.5 times (95% CI: 1.1, 5.4) more likely to consume fruit more than twice per day compared with children not receiving public assistance. Children whose mother had an education level greater than high school were less likely to drink milk more than twice per day compared to children with an education level lower than high school in the adjusted model (OR = 0.3, 95% CI: 0.1, 0.7). Regarding the consumption of soda, children with high school educated mothers, and those from a family who cooked dinner more than five times per week were less likely to drink soda more than once per day, compared with children with less educated mothers (OR = 0.4, 95% CI: 0.2, 0.8) and children from families who cook less than 4 times per week (OR = 0.5, 95% CI: 0.2, 0.9).

4. Discussion

The majority of research examining the relationship between multi-level factors related to childhood obesity have traditionally focused on the home and school. This study aimed to contribute to the limited body of literature focusing on this relationship at the neighborhood level. While significant differences in food and beverage consumption were found for certain individual and maternal demographic variables, at the neighborhood level, the presence of a fast food restaurant near the child’s home was the only neighborhood level exposure significantly associated with decreased vegetable consumption. Existing research that has examined the association between fast food availability and childhood obesity is mixed with significant associations found among adolescents [44], but not among preschool aged children [45]. The presence of a food store within a radius of the child’s household was not significantly correlated with consumption, and this is consistent with existing research that has documented an inverse or null association between the presence of supermarket and dietary behavior in children [12,15,16,17,18].
Although the relationship between the presence of a food store and consumption was not significant in these multivariable analyses, there seem to be two key factors affecting this relationship—maternal education and meals consumed at home. While the influence of the home environment on childhood obesity has been established, there are many factors that may be impacting this relationship [46]. An increase in the number of meals consumed away from home has been shown to be significantly associated with an increased energy intake of children [47]. Additionally, when children and adolescents consume meals away from home, they are more likely to consume sugar-sweetened beverages [48]. While experts agree that maternal education is an important factor to consider in regard to childhood obesity, some existing studies have not found the two to be significantly associated [49,50], while others suggest that the relationship between the two may be mediated by income [46]. Receipt of public assistance was also associated with an increase in vegetable intake, which contributes to mixed findings on the impact of both the Supplemental Nutrition Assistance Program (SNAP) and Women, Infants, and Children (WIC). Some studies have shown that participation in WIC resulted in increased nutrient intake for preschool children [51], but other studies have shown no difference in preschool children’s diet quality or weight status based on SNAP or WIC participation [52,53].
Excessive juice consumption was also a point of interest in this study. While younger children were more likely to consume juice daily than older children, it has been suggested that, as children age, they may be replacing beverages such as juice and milk with soft drinks [54], and although we did not see this pattern in this sample, it can be argued that, while juice and soft drinks are both obesogenic factors, soft drinks have even less nutritional value. Furthermore, children with mothers with less than a high school education were more likely to consume both juice and soda, and this association between maternal education and child feeding behaviors has been documented in the literature [55,56].
Further research is needed that captures the multiple levels that may influence dietary behavior in children and, in turn, childhood obesity, but research that examines the pathways through which these factors act is also needed. This was not possible in the current cross-sectional study. Identification of family level factors that mediate the relationships between neighborhood food access or level of food availability will also be crucial for identifying programs and policies to address the childhood obesity epidemic [56], particularly those that address and involve parents [57]. Mediators such as parent stress [58,59], parent employment [59,60], transportation [13,61], and cultural preferences and barriers are important to consider [62,63].
Limitations for this study include the cross-sectional nature of the design, the small sample size, and the lack of comprehensive dietary measures. Dietary behavior was based on parent report and may be subject to recall and misclassification bias, and a dichotomous categorization of the child’s neighborhood food environment may be too simplistic to characterize the effects of food availability on consumption [37]. Despite these limitations, this study sheds light on the complex relationship between neighborhood food availability and dietary behavior among children and within households.

5. Conclusions

Study findings reveal that parent, home, and neighborhood level factors may have an impact on children’s diets—particularly parent education, household receipt of public assistance, cooking meals at home, and neighborhood fast food access. The complex relationship between the built and social factors in both the communities and homes of children and their impact on obesity must be taken into account in developing multilevel prevention approaches. A more detailed, multi-level examination of the nutrition environment may help explain some of the racial, ethnic, and socioeconomic disparities in dietary patterns [64]. These results are important for continued work in New Orleans, where, although environmental food access has improved, a disproportionate number of underserved neighborhoods with unhealthy food environments still exist [39].


This study was supported by grants from the National Institutes of Health (NIH; 1R01ES020447 and P60AA009803-RC5 to Theall), Centers for Disease Control and Prevention (Prevention Research Center, Cooperative Agreement Number U48-DP001948), and the Frost Foundation (Mary Amelia Douglas Whited Community Women’s Health Education Center). This study was approved by the Tulane University Institutional Review Board. The authors thank all the participants who agreed to take part in this study as well as Emily Mabile, Frieda Brown, Kyle Esteves, Stacy Drury, and the project team. There are no conflicts of interest to report.

Author Contributions

Katherine P. Theall and Lauren Futrell Dunaway conceived and designed the experiments; Lauren Futrell Dunaway performed the experiments; Thomas Carton and Ping Ma analyzed the data; Adrienne R. Mundorf contributed reagents/materials/analysis tools; Lauren Futrell Dunaway, Kelsey Keel, and Katherine P. Theall wrote the paper.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Ogden, C.L.; Carroll, M.D.; Kit, B.K.; Flegal, K.M. Prevalence of childhood and adult obesity in the United States, 2011–2012. J. Am. Med. Assoc. 2014, 311, 806–814. [Google Scholar] [CrossRef] [PubMed]
  2. Fryar, C.D.; Carroll, M.D.; Ogden, C.L. Prevalence of Overweight and Obesity among Children and Adolescents: United States, 1963–1965 through 2011–2012; National Center for Health Statistics: Atlanta, GA, USA, 2014. [Google Scholar]
  3. Ogden, C.L.; Carroll, M.D.; Lawman, H.G.; Kruszon-Moran, D.; Kit, B.K.; Flegal, K.M. Trends in obesity prevalence among children and adolescents in the United States, 1988–1994 through 2013–2014. J. Am. Med. Assoc. 2016, 315, 2292–2299. [Google Scholar] [CrossRef] [PubMed]
  4. Finkelstein, E.A.; Khavjou, O.A.; Thompson, H.; Trogdon, J.G.; Pan, L.; Sherry, B.; Dietz, W. Obesity and severe obesity forecasts through 2030. Am. J. Prev. Med. 2012, 42, 563–570. [Google Scholar] [CrossRef] [PubMed]
  5. Kipke, M.D.; Iverson, E.; Moore, D.; Booker, C.; Ruelas, V.; Peters, A.L.; Kaufman, F. Food and park environments: Neighborhood-level risks for childhood obesity in east Los Angeles. J. Adolesc. Health 2007, 40, 325–333. [Google Scholar] [CrossRef] [PubMed]
  6. Mokdad, A.H.; Ford, E.S.; Bowman, B.A.; Dietz, W.H.; Vinicor, F.; Bales, V.S.; Marks, J.S. Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. J. Am. Med. Assoc. 2003, 289, 76–79. [Google Scholar] [CrossRef]
  7. Pratt, C.A.; Stevens, J.; Daniels, S. Childhood obesity prevention and treatment: Recommendations for future research. Am. J. Prev. Med. 2008, 35, 249–252. [Google Scholar] [CrossRef] [PubMed]
  8. Bowman, S.A.; Gortmaker, S.L.; Ebbeling, C.B.; Pereira, M.A.; Ludwig, D.S. Effects of fast-food consumption on energy intake and diet quality among children in a national household survey. Pediatrics 2004, 113, 112–118. [Google Scholar] [CrossRef] [PubMed]
  9. Gordon-Larsen, P.; McMurray, R.G.; Popkin, B.M. Determinants of adolescent physical activity and inactivity patterns. Pediatrics 2000, 105, e83–e90. [Google Scholar] [CrossRef] [PubMed]
  10. Sallis, J.F.; Prochaska, J.J.; Taylor, W.C. A review of correlates of physical activity of children and adolescents. Med. Sci. Sports Exerc. 2000, 32, 963–975. [Google Scholar] [CrossRef] [PubMed]
  11. Story, M.; Kaphingst, K.M.; Robinson-O’Brien, R.; Glanz, K. Creating healthy food and eating environments: Policy and environmental approaches. Annu. Rev. Public Health 2008, 29, 253–272. [Google Scholar] [CrossRef] [PubMed]
  12. An, R.; Sturm, R. School and residential neighborhood food environment and diet among California youth. Am. J. Prev. Med. 2012, 42, 129–135. [Google Scholar] [CrossRef] [PubMed]
  13. Sallis, J.F.; Glanz, K. The role of built environments in physical activity, eating, and obesity in childhood. Future Child. 2006, 16, 89–108. [Google Scholar] [CrossRef] [PubMed]
  14. Larson, N.I.; Story, M.T.; Nelson, M.C. Neighborhood environments: Disparities in access to healthy foods in the U.S. Am. J. Prev. Med. 2009, 36, 74–81. [Google Scholar] [CrossRef] [PubMed]
  15. Van Hulst, A.; Barnett, T.A.; Gauvin, L.; Daniel, M.; Kestens, Y.; Bird, M.; Gray-Donald, K.; Lambert, M. Associations between children’s diets and features of their residential and school neighbourhood food environments. Can. J. Public Health 2012, 103, 48–54. [Google Scholar]
  16. Elbel, B.; Moran, A.; Dixon, L.B.; Kiszko, K.; Cantor, J.; Abrams, C.; Mijanovich, T. Assessment of a government-subsidized supermarket in a high-need area on household food availability and children’s dietary intakes. Public Health Nutr. 2015, 18, 2881–2890. [Google Scholar] [CrossRef] [PubMed]
  17. Skidmore, P.; Welch, A.A.; van Sluijs, E.; Jones, A.P.; Harvey, I.; Harrison, F.; Griffin, S.; Cassidy, A. Impact of neighbourhood food environment on food consumption in children aged 9–10 years in the UK SPEEDY (sport, physical activity and eating behaviour: Environmental determinants in young people) study. Public Health Nutr. 2010, 13, 1022–1030. [Google Scholar] [CrossRef] [PubMed]
  18. Timperio, A.; Ball, K.; Roberts, R.; Campbell, K.; Andrianopoulos, N.; Crawford, D. Children’s fruit and vegetable intake: Associations with the neighbourhood food environment. Prev. Med. 2008, 46, 331–335. [Google Scholar] [CrossRef] [PubMed]
  19. Morland, K.; Roux, A.V.D.; Wing, S. Supermarkets, other food stores, and obesity: The atherosclerosis risk in communities study. Am. J. Prev. Med. 2006, 30, 333–339. [Google Scholar] [CrossRef] [PubMed]
  20. Jilcott, S.B.; Keyserling, T.; Crawford, T.; McGuirt, J.T.; Ammerman, A.S. Examining associations among obesity and per capita farmers’ markets, grocery stores/supermarkets, and supercenters in U.S. counties. J. Acad. Nutr. Diet. 2011, 111, 567–572. [Google Scholar] [CrossRef] [PubMed]
  21. Lopez, R.P. Neighborhood risk factors for obesity. Obesity 2007, 15, 2111–2119. [Google Scholar] [CrossRef] [PubMed]
  22. Paeratakul, S.; Ferdinand, D.P.; Champagne, C.M.; Ryan, D.H.; Bray, G.A. Fast-food consumption among U.S. adults and children: Dietary and nutrient intake profile. J. Acad. Nutr. Diet. 2003, 103, 1332–1338. [Google Scholar]
  23. Franco, M.; Diez-Roux, A.V.; Nettleton, J.A.; Lazo, M.; Brancati, F.; Caballero, B.; Glass, T.; Moore, L.V. Availability of healthy foods and dietary patterns: The multi-ethnic study of atherosclerosis. Am. J. Clin. Nutr. 2009, 89, 897–904. [Google Scholar] [CrossRef] [PubMed]
  24. Osganian, S.K.; Ebzery, M.K.; Montgomery, D.H.; Nicklas, T.A.; Evans, M.A.; Mitchell, P.D.; Lytle, L.A.; Snyder, M.P.; Stone, E.J.; Zive, M.M.; et al. Changes in the nutrient content of school lunches: Results from the CATCH eat smart food service intervention. Prev. Med. 1996, 25, 400–412. [Google Scholar] [CrossRef] [PubMed]
  25. Story, M.; Neumark-Sztainer, D.; French, S. Individual and environmental influences on adolescent eating behaviors. J. Acad. Nutr. Diet. 2002, 102, 40–51. [Google Scholar] [CrossRef]
  26. Cullen, K.W.; Baranowski, T.; Rittenberry, L.; Olvera, N. Social-environmental influences on children’s diets: Results from focus groups with African-, Euro- and Mexican-American children and their parents. Health Educ. Res. 2000, 15, 581–590. [Google Scholar] [CrossRef] [PubMed]
  27. Horowitz, C.R.; Colson, K.A.; Hebert, P.L.; Lancaster, K. Barriers to buying healthy foods for people with diabetes: Evidence of environmental disparities. Am. J. Public Health 2004, 94, 1549–1554. [Google Scholar] [CrossRef] [PubMed]
  28. Diez-Roux, A.V.; Nieto, F.J.; Caulfield, L.; Tyroler, H.A.; Watson, R.L.; Szklo, M. Neighbourhood differences in diet: The Atherosclerosis Risk in Communities (ARIC) Study. J. Epidemiol. Community Health 1999, 53, 55–63. [Google Scholar] [CrossRef] [PubMed]
  29. Lee, H. The role of local food availability in explaining obesity risk among young school-aged children. Soc. Sci. Med. 2012, 74, 1193–1203. [Google Scholar] [CrossRef] [PubMed]
  30. Cummins, S.; Flint, E.; Matthews, S.A. New neighborhood grocery store increased awareness of food access but did not alter dietary habits or obesity. Health Aff. 2014, 33, 283–291. [Google Scholar] [CrossRef] [PubMed]
  31. Dubowitz, T.; Ghosh-Dastidar, M.; Cohen, D.A.; Beckman, R.; Steiner, E.D.; Hunter, G.P.; Flórez, K.R.; Huang, C.; Vaughan, C.A.; Sloan, J.C.; et al. Diet and perceptions change with supermarket introduction in a food desert, but not because of supermarket use. Health Aff. 2015, 34, 1858–1868. [Google Scholar] [CrossRef] [PubMed]
  32. Scheeringa, M.S.; Zeanah, C.H. Reconsideration of harm’s way: Onsets and comorbidity patterns of disorders in preschool children and their caregivers following Hurricane Katrina. J. Clin. Child Adolesc. Psychol. 2008, 37, 508–518. [Google Scholar] [CrossRef] [PubMed]
  33. Albrecht, G.; Sartore, G.M.; Connor, L.; Higginbotham, N.; Freeman, S.; Kelly, B.; Stain, H.; Tonna, A.; Pollard, G. Solastalgia: The distress caused by environmental change. Austr. Psychol. 2007, 15, S95–S98. [Google Scholar] [CrossRef] [PubMed]
  34. Morland, K.; Wing, S.; Roux, A.D. The contextual effect of the local food environment on residents’ diets: The atherosclerosis risk in communities study. Am. J. Public Health 2002, 92, 1761–1768. [Google Scholar] [CrossRef] [PubMed]
  35. Bodor, J.N.; Rice, J.C.; Farley, T.A.; Swalm, C.M.; Rose, D. Disparities in food access: Does aggregate availability of key foods from other stores offset the relative lack of supermarkets in African-American neighborhoods? Prev. Med. 2010, 51, 63–67. [Google Scholar] [CrossRef] [PubMed]
  36. Rose, D.; Hutchinson, P.L.; Bodor, J.N.; Swalm, C.M.; Farley, T.A.; Cohen, D.A.; Rice, J.C. Neighborhood food environments and body mass index: The importance of in-store contents. Am. J. Prev. Med. 2009, 37, 214–219. [Google Scholar] [CrossRef] [PubMed]
  37. Rose, D.; Bodor, J.N.; Hutchinson, P.L.; Swalm, C.M. The importance of a multi-dimensional approach for studying the links between food access and consumption. J. Nutr. 2010, 140, 1170–1174. [Google Scholar] [CrossRef] [PubMed]
  38. Hutchinson, P.L.; Bodor, J.N.; Swalm, C.M.; Rice, J.C.; Rose, D. Neighbourhood food environments and obesity in southeast Louisiana. Health Place 2012, 18, 854–860. [Google Scholar] [CrossRef] [PubMed]
  39. Rose, D.; Bodor, J.N.; Rice, J.C.; Swalm, C.M.; Hutchinson, P.L. The effects of Hurricane Katrina on food access disparities in New Orleans. Am. J. Public Health 2011, 101, 482–484. [Google Scholar] [CrossRef] [PubMed]
  40. Malik, V.S.; Schulze, M.B.; Hu, F.B. Intake of sugar-sweetened beverages and weight gain: A systematic review. Am. J. Clin. Nutr. 2006, 84, 274–288. [Google Scholar] [PubMed]
  41. Wang, Y.C.; Bleich, S.N.; Gortmaker, S.L. Increasing caloric contribution from sugar-sweetened beverages and 100% fruit juices among U.S. children and adolescents, 1988–2004. Pediatrics 2008, 121, e1604–e1614. [Google Scholar] [CrossRef] [PubMed]
  42. Ludwig, D.S.; Peterson, K.E.; Gortmaker, S.L. Relation between consumption of sugar-sweetened drinks and childhood obesity: A prospective, observational analysis. Lancet 2001, 357, 505–508. [Google Scholar] [CrossRef]
  43. Malik, V.S.; Pan, A.; Willett, W.C.; Hu, F.B. Sugar-sweetened beverages and weight gain in children and adults: A systematic review and meta-analysis. Am. J. Clin. Nutr. 2013, 98, 1084–1102. [Google Scholar] [CrossRef] [PubMed]
  44. Davis, B.; Carpenter, C. Proximity of fast-food restaurants to schools and adolescent obesity. Am. J. Public Health 2009, 99, 505–510. [Google Scholar] [CrossRef] [PubMed]
  45. Burdette, H.L.; Whitaker, R.C. Neighborhood playgrounds, fast food restaurants, and crime: Relationships to overweight in low-income preschool children. Prev. Med. 2004, 38, 57–63. [Google Scholar] [CrossRef] [PubMed]
  46. Strauss, R.S.; Knight, J. Influence of the home environment on the development of obesity in children. Pediatrics 1999, 103, e85–e92. [Google Scholar] [CrossRef] [PubMed]
  47. Ogden, C.; Carroll, M.D.; Curtin, L.R.; Lamb, M.M.; Flegal, K.M. About childhood obesity. J. Am. Med. Assoc. 2010, 303, 242–249. [Google Scholar] [CrossRef] [PubMed]
  48. Gillis, L.J.; Bar-Or, O. Food away from home, sugar-sweetened drink consumption and juvenile obesity. J. Am. Coll. Nutr. 2003, 22, 539–545. [Google Scholar] [CrossRef] [PubMed]
  49. Reilly, J.J.; Armstrong, J.; Dorosty, A.R.; Emmett, P.M.; Ness, A.; Rogers, I.; Steer, C.; Sherriff, A. Early life risk factors for obesity in childhood: Cohort study. BMJ 2005, 330, 1357. [Google Scholar] [CrossRef] [PubMed]
  50. Gibson, L.Y.; Byrne, S.M.; Davis, E.A.; Blair, E.; Jacoby, P.; Zubrick, S.R. The role of family and maternal factors in childhood obesity. Med. J. Aust. 2007, 186, 591. [Google Scholar] [PubMed]
  51. Rose, D.; Habicht, J.P.; Devaney, B. Household participation in the food stamp and WIC programs increases the nutrient intakes of preschool children. J. Nutr. 1998, 128, 548–555. [Google Scholar] [PubMed]
  52. Leung, C.W.; Blumenthal, S.J.; Hoffnagle, E.E.; Jensen, H.H.; Foerster, S.B.; Nestle, M.; Cheung, L.W.Y.; Mozaffarian, D.; Willett, W.C. Associations of food stamp participation with dietary quality and obesity in children. Pediatrics 2013, 131, 463–472. [Google Scholar] [CrossRef] [PubMed]
  53. Ponza, M.; Devaney, B.; Ziegler, P.; Reidy, K.; Squatrito, C. Nutrient intakes and food choices of infants and toddlers participating in WIC. J. Acad. Nutr. Diet. 2004, 104, 71–79. [Google Scholar] [CrossRef] [PubMed]
  54. Harnack, L.; Stang, S.; Story, M. Soft drink consumption among U.S. children and adolescents: Nutritional consequences. J. Am. Diet. Assoc. 1999, 99, 436–441. [Google Scholar] [CrossRef]
  55. Hendricks, K.; Briefel, R.; Novak, T.; Ziegler, P. Maternal and child characteristics associated with infant and toddler feeding practices. J. Am. Diet. Assoc. 2006, 106, 135–148. [Google Scholar] [CrossRef] [PubMed]
  56. Vereecken, C.A.; Keukelier, E.; Maes, L. Influence of mother’s educational level on food parenting practices and food habits of young children. Appetite 2004, 43, 93–103. [Google Scholar] [CrossRef] [PubMed]
  57. Frieden, T.R.; Dietz, W.; Collins, J. Reducing childhood obesity through policy change: Acting now to prevent obesity. Health Aff. 2010, 29, 357–363. [Google Scholar] [CrossRef] [PubMed]
  58. Lindsay, A.C.; Sussner, K.M.; Kim, J.; Gortmaker, S. The role of parents in preventing childhood obesity. Future Child. 2006, 16, 169–186. [Google Scholar] [CrossRef] [PubMed]
  59. Parks, E.P.; Kumanyika, S.; Moore, R.H.; Stettler, N.; Wrotniak, B.H.; Kazak, A. Influence of stress in parents on child obesity and related behaviors. Pediatrics 2012, 130, e1096–e1104. [Google Scholar] [CrossRef] [PubMed]
  60. Bauer, K.W.; Hearst, M.O.; Escoto, K.; Berge, J.M.; Neumark-Sztainer, D. Parental employment and work-family stress: Associations with family food environments. Soc. Sci. Med. 2012, 75, 496–504. [Google Scholar] [CrossRef] [PubMed]
  61. Omar, M.A.; Coleman, G.; Hoerr, S. Healthy eating for rural low-income toddlers: Caregivers’ perceptions. J. Community Health Nurs. 2001, 18, 93–106. [Google Scholar] [CrossRef] [PubMed]
  62. Caprio, S.; Daniels, S.R.; Drewnowski, A.; Kaufman, F.R.; Palinkas, L.A.; Rosenbloom, A.L.; Schwimmer, J.B. Influence of race, ethnicity, and culture on childhood obesity: Implications for prevention and treatment. Obesity 2008, 16, 2566–2577. [Google Scholar] [CrossRef] [PubMed]
  63. Savage, J.S.; Fisher, J.O.; Birch, L.L. Parental influence on eating behavior: Conception to adolescence. J. Law Med. Ethics 2007, 35, 22–34. [Google Scholar] [CrossRef] [PubMed]
  64. Gordon-Larsen, P.; Adair, L.S.; Popkin, B.M. The relationship of ethnicity, socioeconomic factors, and overweight in U.S. adolescents. Obesity 2003, 11, 121–129. [Google Scholar]
Table 1. Food consumption by individual, family, and neighborhood characteristics (N = 199).
Table 1. Food consumption by individual, family, and neighborhood characteristics (N = 199).
Independent VariablesnFood %Beverages %
Fruit (2+/Day)Vegetables (2+/Day)Sweets (1+/Day)Juice (2+/Day)Milk (2+/Day)Soda (1+/Day)
I: Child: Age
 ≤67164.7% *44.1%64.7%80.9% ***52.9%38.2%
I: Child: Sex
 Male8862.4% *49.5%58.1% *66.7%49.5%35.5%
HH: Mother: Marital status
 Not married cohabiting15958.5%42.4%62.9%67.3%49.4%39.9%
 Married cohabiting4045.0%55.0%70.0%70.0%38.5%28.2%
HH: Mother: Education
 Less than high school5363.0%48.2%66.7% **81.5% **52.8% *54.7% ***
 High school (or GED )5253.0%40.4%71.0%63.0%51.0%33.0%
 More than high school9453.3%51.1%46.7%62.2%31.8%27.3%
HH: Number of children
HH: Public assistance
 No1350.7% **39.7% **62.0%62.7% **47.9%36.4%
HH: Cook dinner at home
 0–4 times/week12850.8% *39.2% **67.5%66.7%43.2%43.2% **
 5+ times/week7164.4%54.8%58.9%69.9%54.2%27.8%
N: Small store within 500 m
N: Supermarket within 1000 m
N: Fast food within 500 m 49.7% ***
 No14658.6% *24.3%64.2%68.5%49.4%36.9%
 Yes5343.2% 64.9%64.9%37.8%40.5%
Total 52.5%44.4%84.9%65.0%43.6%65.0%
*** p < 0.01; ** p < 0.05; * p < 0.1; I: Individual, HH: Household, N: Neighborhood.; GED: The General Education Development Test (GED) is a certificate in the U.S. equivalent to a high school diploma.
Table 2. Impact of the neighborhood food environment on food consumption: Multivariate model (adjusted odds ratios, 95% confidence interval) (N = 199).
Table 2. Impact of the neighborhood food environment on food consumption: Multivariate model (adjusted odds ratios, 95% confidence interval) (N = 199).
Independent VariablesFoodBeverages
Fruits (2+/Day) (55.8%)Vegetables (2+/Day) (45.0%)Sweets (1+/Day) (64.3%)Juice (2+/Day) (67.8%)Milk (2+/Day) (47.2%)Soda (1+/Day) (37.6%)
I: Child: Age
 7–90.6 (0.3, 1.4)0.8 (0.3, 1.7)1.0 (0.4, 2.2)0.4 (0.2, 1.0)0.8 (0.4, 1.7)0.5 (0.2, 1.1)
 10–140.4 (0.2, 0.9) **1.2 (0.6, 2.3)0.7 (0.4, 1.4)0.2 (0.1, 0.5) ****0.5 (0.3, 1.0)1.2 (0.6, 2.3)
I: Child: Sex
 Female0.7 (0.4, 1.3)0.8 (0.4, 1.5)1.5 (0.8, 2.7)1.5 (0.8, 2.8)0.8 (0.4, 1.4)1.1 (0.6, 2.1)
HH: Mother: Marital status
 Not married, cohabiting------
 Married, cohabiting0.8 (0.4, 1.7)2.0 (0.9, 4.1)2.1 (1.0, 4.6)2.3 (1.0, 5.2)0.8 (0.4, 1.7)0.8 (0.4, 1.8)
HH: Mother: Education
 Less than high school------
 High school (or GED)0.8 (0.4, 1.5)0.7 (0.4, 1.5)1.1 (0.5, 2.3)0.4 (0.2, 0.8) **0.9 (0.5, 1.7)0.4 (0.2, 0.8) ***
 More than high school0.6 (0.3, 1.5)1.1 (0.5, 2.6)0.4 (0.2, 1.0)0.3 (0.1, 0.8) **0.3 (0.1, 0.7) ***0.4 (0.2, 1.0)
HH: Number of children
 3+1.2 (0.6, 2.4)0.8 (0.4, 1.5)1.0 (0.5, 2.1)1.2 (0.6, 2.6)1.1 (0.6, 2.2)1.0 (0.5, 2.0)
HH: Public assistance
 Yes1.9 (1.0, 3.7)2.3 (1.2, 4.5) **1.7 (0.8, 3.4)2.5 (1.1, 5.4) **0.8 (0.4, 1.5)1.4 (0.7, 2.7)
HH: Cook dinner at home
 0–4 times/week------
 5+ times/week1.5 (0.8, 2.7)1.9 (1.1, 3.5) **0.7 (0.4, 1.3)1.0 (0.5, 1.9)1.7 (0.9, 3.1)0.5 (0.2, 0.9) **
N: Small store
N: Supermarket
N: Fast food
 Yes0.35 (0.1, 0.8) **
† Food access variables were not significant in bivariate models and because key exposures of interest, therefore not included in multivariate analyses presented. **** p < 0.001; *** p < 0.01; ** p < 0.05; I: Individual, HH: Household, N: Neighborhood.

Share and Cite

MDPI and ACS Style

Futrell Dunaway, L.; Carton, T.; Ma, P.; Mundorf, A.R.; Keel, K.; Theall, K.P. Beyond Food Access: The Impact of Parent-, Home-, and Neighborhood-Level Factors on Children’s Diets. Int. J. Environ. Res. Public Health 2017, 14, 662.

AMA Style

Futrell Dunaway L, Carton T, Ma P, Mundorf AR, Keel K, Theall KP. Beyond Food Access: The Impact of Parent-, Home-, and Neighborhood-Level Factors on Children’s Diets. International Journal of Environmental Research and Public Health. 2017; 14(6):662.

Chicago/Turabian Style

Futrell Dunaway, Lauren, Thomas Carton, Ping Ma, Adrienne R. Mundorf, Kelsey Keel, and Katherine P. Theall. 2017. "Beyond Food Access: The Impact of Parent-, Home-, and Neighborhood-Level Factors on Children’s Diets" International Journal of Environmental Research and Public Health 14, no. 6: 662.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop