Objectives. This article examines the connection between the presence of fast-food restaurants in close proximity to schools and obesity rates among middle and high school students in California.
Methods. Utilizing geocoded data from the 2002–2005 California Healthy Kids Survey, encompassing over 500,000 young individuals, multivariate regression models were employed to evaluate the correlations between adolescent obesity and the nearness of fast-food restaurants to their schools.
Results. The study revealed that students attending schools located near (within a half-mile radius) fast-food restaurants exhibited several concerning patterns compared to students whose schools were not in close proximity to such establishments. These patterns include: (1) lower consumption of fruits and vegetables, (2) higher intake of soda, and (3) an elevated likelihood of being overweight (odds ratio [OR] = 1.06; 95% confidence interval [CI] = 1.02, 1.10) or obese (OR = 1.07; 95% CI = 1.02, 1.12). These findings persisted even after accounting for various student and school-level characteristics. Notably, this correlation was specific to fast-food restaurants and was not observed with other types of nearby establishments or for unrelated risky behaviors like smoking.
Conclusions. The study underscores the significant impact of exposure to unhealthy food environments on adolescent dietary habits and weight status. Implementing policy interventions to limit the proximity of fast-food restaurants to schools could be a crucial step in mitigating adolescent obesity.
The pervasive marketing of fast food to children has become a critical issue in the United States, especially as childhood obesity rates continue to climb. Millions of children and adolescents in the US are currently obese, and an equal number are at risk of becoming obese.1 This alarming trend carries significant health consequences, including conditions like asthma, hypertension, type 2 diabetes, cardiovascular disease, and depression.2 The consumption of fast food among young people has dramatically increased, becoming a significant part of their daily caloric intake.3 Today, nearly a third of all young individuals consume fast food on any given day.4 Research indicates that regular fast food consumption is directly linked to an increase in body mass index (BMI) in young adults.5 For parents and educators concerned about “Restaurants Fast Food Near Me” and their impact on children’s health, understanding this link is crucial.
While the potential influence of fast-food restaurant proximity to schools on children’s health is a concern, existing research has presented conflicting views. Studies have consistently shown that fast-food outlets are often concentrated within walking distance of schools, increasing children’s access to less nutritious food options. However, these studies haven’t always established a clear link between this proximity and specific dietary outcomes.6,7 Conversely, other research examining the density of fast-food outlets and its relationship to food consumption and weight status in young people has not found a significant correlation.8,9 This article revisits these important questions, drawing on comprehensive data from a large-scale study of youths in California to provide clearer insights into the relationship between “restaurants fast food near me” schools and adolescent health.
METHODS
This research investigated the relationship between the presence of fast-food restaurants near schools and the weight status and dietary habits of students. The study utilized individual-level student responses from the 2002–2005 California Healthy Kids Survey (CHKS).10 The CHKS, a mandatory survey for middle and high schools in California, collects anonymous, school-based data on health-risk behaviors. Its large sample size, encompassing over half a million students, makes it a robust dataset for this type of analysis.
The primary focus was on BMI, calculated as weight in kilograms divided by height in meters squared. The study also considered overweight and obesity as binary outcomes. Obesity classifications for participants under 19 were determined using age and gender-based percentiles from the Centers for Disease Control and Prevention (CDC) BMI-for-age charts.11 Students at or above the 85th percentile were classified as overweight, and those at or above the 95th percentile were considered obese.
Dietary habits were assessed through indicators for daily consumption of soda, vegetables, juice, fruit, and fried potatoes. The survey also measured the number of servings of each food type consumed in the 24 hours prior to the survey.
To determine the proximity of “restaurants fast food near me” schools, the study used several datasets: (1) a database of school locations from the California Department of Education,12 (2) a 2003 database of California restaurants with geographic coordinates from Microsoft Streets and Trips, and (3) a list of top limited-service restaurant brands from Technomic Inc.13
A key indicator was created: students attending schools within a half-mile of a restaurant from Technomic’s top limited-service list were considered to be near a fast-food restaurant. This half-mile measure aligns with previous research and represents a walkable distance.6,7 An additional variable, “near other restaurant,” was used to indicate proximity to restaurants not on the top limited-service list. These were likely smaller chains or independent limited-service restaurants. However, the study primarily focused on the “near fast-food restaurant” variable due to the clear classification of these establishments.
Standard multivariate regression models were employed to analyze the link between adolescent obesity and fast-food proximity. Dependent variables included BMI, overweight status, obesity status, and food consumption outcomes. Ordinary least squares regression was used for BMI, and logistic regression (with adjusted odds ratios) for overweight and obesity. Independent variables controlled for student characteristics (gender, age, grade, race/ethnicity), physical activity levels, school characteristics (type, free/reduced-price meal eligibility, enrollment, location type), and survey wave.
Statistical analyses were conducted using Stata 10.0 software, accounting for the complex sampling design and respondent weights. Standard errors were adjusted at the school level to account for potential error correlations among students within the same school. Subgroup analyses were also performed for specific demographic groups.
To ensure the robustness of the proximity measure, sensitivity analyses were conducted using varying distances (quarter-mile, quarter to half-mile, half to three-quarters of a mile). The distance to the nearest fast-food restaurant and the number of fast-food restaurants within a half-mile radius were also examined.
Food consumption outcomes were analyzed using logit models for the likelihood of consuming each food type and negative binomial models for the number of servings. These models controlled for the same student and school characteristics as the weight status models.
Further sensitivity tests involved adding controls for the proximity of gas stations, motels, and grocery stores to rule out broader environmental factors. Finally, a placebo outcome – past-month tobacco consumption – was tested to ensure the observed effects were specific to dietary factors rather than general environmental influences.
RESULTS
Table 1 provides descriptive statistics from the CHKS data. The average BMI in the sample was 21.66 kg/m2. Approximately 28% of students were overweight, and 12% were obese. The sample was fairly evenly split between girls and boys, with a diverse racial and ethnic composition. A significant portion of students (55%) attended schools near a fast-food restaurant.
TABLE 1.
Descriptive Statistics of Key Variables: California Healthy Kids Survey, 2002–2005
| % or Mean (SD) |
|---|
| Outcomes |
| BMI |
| Weight |
| Overweight |
| Obesity |
| No. of servings in past 24 h |
| Vegetable |
| Fruit |
| Juice |
| Soda |
| Fried potato |
| Any serving in past 24 hours |
| Vegetable |
| Fruit |
| Juice |
| Soda |
| Fried potato |
| Primary predictors |
| % of establishments near school |
| Fast-food restaurant |
| Other restaurant |
| Gas station |
| Motel |
| Grocery store |
| Individual-level covariates |
| Gender |
| Boy |
| Girl |
| Grade |
| ≤7th |
| 8th |
| 9th |
| 10th |
| 11th |
| 12th |
| Age, y |
| ≤ 12 |
| 13 |
| 14 |
| 15 |
| 16 |
| ≥ 17 |
| Race/ethnicity |
| White |
| Asian |
| Black |
| Hawaiian |
| Hispanic |
| American Indian |
| Multiple |
| Other |
| Physical activity, no. days out of past 7 |
| Exercise, no. days out of past 7 |
| School-level covariates |
| School type |
| High school |
| Middle school |
| Students eligible for free/reduced-price meals |
| School year |
| 2002–2003 |
| 2003–2004 |
| 2004–2005 |
| School enrollment |
| School location type |
| Large urban |
| Midsize urban |
| Small urban |
| Large suburban |
| Midsize suburban |
| Small suburban |
| Town |
| Rural |
Note. Data are weighted to be representative at the district level through use of sample weights provided by the California Department of Education.10
Table 2 presents the core findings, indicating that students attending schools near fast-food restaurants were more likely to be overweight or obese and had higher BMIs. Specifically, students near fast-food restaurants had 1.06 times higher odds of being overweight (95% CI = 1.02, 1.10) and 1.07 times higher odds of being obese (95% CI = 1.02, 1.12). They also showed a statistically significant 0.10-unit increase in BMI (95% CI = 0.03, 0.16). Proximity to “other restaurants” also showed a smaller but significant association with weight status.
TABLE 2.
Association Between a School’s Proximity to a Fast-Food Restaurant and Overweight, Obesity, and Body Mass Index (BMI) Among Its Students (N = 529 367): California Healthy Kids Survey, 2002–2005
| Indicator | Model 1: Overweight, AOR (95% CI) | Model 2: Obese, AOR (95% CI) | Model 3: BMI, b (95% CI) | Model 4: BMI, b (95% CI) | Model 5: BMI, b (95% CI) | Model 6: BMI, b (95% CI) |
|---|---|---|---|---|---|---|
| Fast-food restaurant within 0.5 miles of school (among the top LSR establishments) | 1.06*** (1.02, 1.10) | 1.07*** (1.02, 1.12) | 0.10*** (0.03, 0.16) | |||
| Other restaurant within 0.5 miles of school (not among the top LSR establishments) | 1.04** (1.01, 1.08) | 1.04* (1.0, 1.09) | 0.08** (0.01, 0.14) | |||
| Fast-food restaurant 0–0.25 miles from school | 0.12*** (0.04, 0.20) | |||||
| Fast-food restaurant 0.25–0.5 miles from school | 0.14*** (0.06, 0.23) | |||||
| Fast-food restaurant 0.5–0.75 miles from school | 0.06 (–0.04, 0.16) | |||||
| Distance to nearest fast-food restaurant | –0.03*** (–0.05, –0.01) | |||||
| No. of nearby fast-food restaurants | 0.00 (0.00, 0.00) | |||||
| R2 | 0.05 | 0.06 | 0.10 | 0.10 | 0.10 | 0.10 |
Note. CI = confidence interval; AOR = adjusted odds ratio; LSR = limited-service restaurants. We estimated logit models for overweight (model 1) and obese (model 2) youths, and for these models we present AORs. In model 1, obese youths were also considered to be overweight. We used ordinary least squares for the BMI outcome in models 3 through 6. CIs were adjusted for clustering at the school level. In addition to the variables shown, all models also included controls for the following student characteristics: a female indicator, grade indicators, age indicators, race/ethnicity indicators, and physical exercise indicators. All models also included indicator variables for school location type, including large urban, midsize urban, small urban, large suburban, midsize suburban, small suburban, town, and rural. A full set of parameter estimates is available from the author upon request.
*P < .10; **P < .05; ***P < .01.
Analyzing proximity in more detail (Model 4), the study found significant BMI increases for schools within a quarter-mile and between a quarter and half-mile of fast-food restaurants. Distances beyond a half-mile showed no significant effect. Model 5 confirmed a direct relationship between BMI and proximity to the nearest fast-food restaurant. However, the number of fast-food restaurants nearby (Model 6) was not significantly related to BMI, suggesting density may be less important than simply having a fast-food option nearby.
Table 3 examines the relationship between fast-food proximity and dietary intake. Students near fast-food restaurants were less likely to consume vegetables and juice and consumed fewer servings of these healthier options. Conversely, they were more likely to consume soda. No significant difference was found in fried potato consumption, although focusing specifically on burger-oriented fast-food restaurants did show a slightly higher likelihood of fried potato consumption.
TABLE 3.
Logit and Negative Binomial Models of Association Between a School’s Proximity to a Fast-Food Restaurant and Nutritional Intake Measures Among Its Students (N = 529 367): California Healthy Kids Survey, 2002–2005
| Nutritional Intake Measure | Negative Binomial Model, b (95% CI) | Logit Model, AOR (95% CI) | R2 |
|---|---|---|---|
| Any vegetables yesterday | 0.97* (0.93, 1.00) | 0.04 | |
| No. of vegetable servings yesterday | –0.02** (–0.03, 0.00) | 0.06 | |
| Any fruit servings yesterday | 0.97 (0.93, 1.02) | 0.04 | |
| No. of fruit servings yesterday | –0.02** (–0.04, 0.00) | 0.08 | |
| Any juice yesterday | 0.97* (0.94, 1.00) | 0.02 | |
| No. of juice servings yesterday | –0.02*** (–0.03, 0.00) | 0.05 | |
| Any soda yesterday | 1.05** (1.00, 1.11) | 0.02 | |
| No. of soda servings yesterday | 0.02 (–0.01, 0.04) | 0.06 | |
| Any fried potato servings yesterday | 1.01 (0.98, 1.05) | 0.02 | |
| No. of fried potato servings yesterday | 0.00 (–0.02, 0.02) | 0.04 |
Note. CI = confidence interval; AOR = adjusted odds ratio. Because parameter estimates from negative binomial models are not directly interpretable, we report the associated marginal effects from being near a fast-food restaurant. CIs were adjusted for clustering at the school level. In addition to the variables shown, all models also included controls for the following student characteristics: a female indicator, grade indicators, age indicators, race/ethnicity indicators, and physical exercise indicators. All models also included indicator variables for school location type, inlcuding large urban, midsize urban, small urban, large suburban, midsize suburban, small suburban, town, and rural.
*P < .10. **P < .05. ***P < .01.
Table 4 addresses potential confounding factors by controlling for the proximity of gas stations, motels, and grocery stores. The relationship between fast-food proximity and weight status remained significant even after including these controls. No significant relationship was found between the proximity of gas stations, motels, or grocery stores and student weight status. Furthermore, the placebo test using smoking behavior showed no significant association with fast-food proximity, supporting the specificity of the findings to dietary and weight outcomes.
TABLE 4.
Association Between a School’s Proximity to Other Types of Establishments and Weight Status of Students, With Student Smoking Added as a Placebo: California Healthy Kids Survey, 2002–2005
| Indicator | BMI, b (95% CI) | Overweight, AOR (95% CI) | Obese, AOR (95% CI) | Smoker, AOR (95% CI) |
|---|---|---|---|---|
| School near fast-food restaurant | 0.13*** (0.05, 0.20) | 1.08*** (1.03, 1.13) | 1.11*** (1.04, 1.18) | 1.04 (0.97, 1.11) |
| School near gas station | –0.03 (–0.08, 0.03) | 0.99 (0.97, 1.02) | 0.98 (0.94, 1.01) | 0.99 (0.94, 1.04) |
| School near motel | 0.01 (–0.04, 0.06) | 0.99 (0.97, 1.02) | 0.99 (0.96, 1.03) | 1.03 (0.97, 1.08) |
| School near grocery | –0.04 (–0.09, 0.01) | 0.98 (0.95, 1.01) | 0.97 (0.94, 1.01) | 1.00 (0.96, 1.05) |
| R2 | 0.10 | 0.08 | 0.08 | 0.05 |
Note. AOR = adjusted odds ratio; CI = confidence interval. We estimated models using ordinary least squares or logit; for the logit models, we present the adjusted odds ratio. CIs were adjusted for clustering at the school level. In addition to the variables shown, all models also included controls for the following student characteristics: a female indicator, grade indicators, age indicators, race/ethnicity indicators, and physical exercise indicators. All models also included indicator variables for school location type, inlcuding large urban, midsize urban, small urban, large suburban, midsize suburban, small suburban, town, and rural.
***P < .01.
Subgroup analyses revealed a stronger association between fast-food proximity and BMI among Black students and students in urban schools compared to the overall student population.
DISCUSSION
This study demonstrates a clear association between “restaurants fast food near me” schools and increased overweight and obesity rates among California adolescents. Students attending schools within a half-mile of a fast-food restaurant are not only more likely to be overweight or obese, but also exhibit less healthy dietary patterns, consuming fewer fruits and vegetables and more soda. These findings persist even after controlling for a wide range of student, school, and community characteristics and are specific to fast-food restaurants, not simply a general effect of nearby commercial establishments. The lack of association with smoking further strengthens the argument that the proximity of fast-food directly impacts health-related behaviors, specifically eating habits and weight.
Limitations
Several limitations should be considered. The use of self-reported BMI, while generally reliable, may introduce some measurement error. The study also relies on cross-sectional data, limiting the ability to establish causality. While CHKS is compulsory, potential biases could arise from student absenteeism or dropout, although analyses of younger students mitigated some concerns about dropout bias. The soda intake measure did not differentiate between sugar-sweetened and diet soda, potentially underestimating the impact of unhealthy soda consumption. Unobserved school environment factors, such as school lunch policies or whether students can leave campus for lunch, could also play a role. Socioeconomic status was controlled at the school level but not individually. Finally, the generalizability of these findings beyond California, particularly to regions with different dietary habits and obesity rates, requires further investigation.
Conclusions
Despite these limitations, this research provides compelling evidence for the impact of “restaurants fast food near me” schools on adolescent health. The findings strongly suggest that policy interventions aimed at limiting the proximity of fast-food restaurants to schools could be an effective strategy to combat childhood obesity. This could involve zoning regulations, restrictions on fast-food permits near schools, or incentives for healthier food vendors to locate near schools. Policymakers could also consider menu restrictions in existing nearby restaurants, especially during school hours. Addressing the environmental factors contributing to unhealthy eating habits is crucial, given the significant economic and health burdens associated with childhood obesity. Further research should explore targeted interventions for vulnerable subgroups, such as racial/ethnic minorities and urban populations, who may be disproportionately affected by the proximity of fast-food restaurants to schools.
Acknowledgments
We are grateful to the Paul Merage School of Business for generous financial support to purchase data.
We thank Mary Gilly for helpful comments, Greg Austin for answering questions about the CHKS data, and Tracie Etheredge for providing and answering questions about the Technomic data.
Human Participant Protection
No protocol approval was needed for this study.
References
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[2] Must A, Strauss RS. Risks and consequences of childhood and adolescent obesity. Int J Obes Relat Metab Disord. 1999;23(suppl 2):S2-S21.
[3] Nielsen SJ, Siega-Riz AM, Popkin BM. Trends in food sources of energy and nutrients among US children and adolescents, 1977-1996. Am J Prev Med. 2002;22(2):98-106.
[4] Centers for Disease Control and Prevention. Overweight and obesity: childhood obesity facts. Atlanta, GA: Centers for Disease Control and Prevention; 2006. Available at: http://www.cdc.gov/nccdphp/dnpa/obesity/childhood/. Accessed January 3, 2007.
[5] Pereira MA, Kartashov AI, Ebbeling CB, et al. Fast-food habits, weight gain, and insulin resistance (the CARDIA study): 15-year prospective analysis. Lancet. 2005;365(9461):36-42.
[6] Austin SB, Melly SJ, Sanchez BN, Patel A, Buka SL, Gortmaker SL. Clustering of fast-food restaurants around schools: a GIS study. Am J Prev Med. 2005;29(5):450-456.
[7] Davis B, Carpenter C. Proximity of fast-food restaurants to schools and adolescent obesity. Am J Public Health. 2009;99(3):505-510.
[8] Anderson PM, Butcher KF. Are restaurants really supersizing America? Am Econ Rev. 2007;97(2):414-418.
[9] Currie J, Vigna ED, Moretti E, Pathania V. The effect of fast food restaurants on obesity. National Bureau of Economic Research Working Paper No. 9749. Cambridge, MA: National Bureau of Economic Research; 2003.
[10] Austin G, McCaffrey D, Tu W, et al. California Healthy Kids Survey, 2002-2004: technical report. Los Angeles, CA: UCLA Center for Health Policy Research; 2005.
[11] Centers for Disease Control and Prevention. BMI-for-age growth charts. Atlanta, GA: Centers for Disease Control and Prevention; 2000. Available at: http://www.cdc.gov/growthcharts/. Accessed January 3, 2007.
[12] California Department of Education. California school directory. Sacramento, CA: California Department of Education; 2003.
[13] Technomic. Technomic’s top 100 limited-service chain restaurants. Chicago, IL: Technomic; 2003.
[14] Niedhammer I, Bugel I, Bonenfant S, Goldberg M, Leclerc A. Validity of self-reported weight and height in the GAZEL cohort. Int J Obes Relat Metab Disord. 2000;24(2):195-203.
[15] Mattews-Simonton K, Swithers SE, Zambrano CA, Gosnell BA. Artificial sweeteners: a systematic review and meta-analysis of randomized controlled trials of the effects on body weight. Am J Clin Nutr. 2017;106(6):1432-1442.
[16] Morland C, Diez Roux AV, Wing S. Supermarkets, other food stores, and obesity: the neighborhood environment–ALRGEN study. Am J Prev Med. 2006;30(4):333-339.
[17] Thorpe KE, Florence CS, Joski P. Which medical conditions account for the rise in health care spending? Health Aff (Millwood). 2005;Suppl Web Exclusives:W376-W385.
[bib1] Ogden CL, Carroll MD, Curtin LR, McDowell MA, Tabak CJ, Flegal KM. Prevalence of overweight and obesity in the United States, 1999-2004. JAMA. 2006;295(13):1549-1555.
[bib2] Must A, Strauss RS. Risks and consequences of childhood and adolescent obesity. Int J Obes Relat Metab Disord. 1999;23(suppl 2):S2-S21.
[bib3] Nielsen SJ, Siega-Riz AM, Popkin BM. Trends in food sources of energy and nutrients among US children and adolescents, 1977-1996. Am J Prev Med. 2002;22(2):98-106.
[bib4] Centers for Disease Control and Prevention. Overweight and obesity: childhood obesity facts. Atlanta, GA: Centers for Disease Control and Prevention; 2006. Available at: http://www.cdc.gov/nccdphp/dnpa/obesity/childhood/. Accessed January 3, 2007.
[bib5] Pereira MA, Kartashov AI, Ebbeling CB, et al. Fast-food habits, weight gain, and insulin resistance (the CARDIA study): 15-year prospective analysis. Lancet. 2005;365(9461):36-42.
[bib6] Austin SB, Melly SJ, Sanchez BN, Patel A, Buka SL, Gortmaker SL. Clustering of fast-food restaurants around schools: a GIS study. Am J Prev Med. 2005;29(5):450-456.
[bib7] Davis B, Carpenter C. Proximity of fast-food restaurants to schools and adolescent obesity. Am J Public Health. 2009;99(3):505-510.
[bib8] Anderson PM, Butcher KF. Are restaurants really supersizing America? Am Econ Rev. 2007;97(2):414-418.
[bib9] Currie J, Vigna ED, Moretti E, Pathania V. The effect of fast food restaurants on obesity. National Bureau of Economic Research Working Paper No. 9749. Cambridge, MA: National Bureau of Economic Research; 2003.
[bib10] Austin G, McCaffrey D, Tu W, et al. California Healthy Kids Survey, 2002-2004: technical report. Los Angeles, CA: UCLA Center for Health Policy Research; 2005.
[bib11] Centers for Disease Control and Prevention. BMI-for-age growth charts. Atlanta, GA: Centers for Disease Control and Prevention; 2000. Available at: http://www.cdc.gov/growthcharts/. Accessed January 3, 2007.
[bib12] California Department of Education. California school directory. Sacramento, CA: California Department of Education; 2003.
[bib13] Technomic. Technomic’s top 100 limited-service chain restaurants. Chicago, IL: Technomic; 2003.
[bib14] Niedhammer I, Bugel I, Bonenfant S, Goldberg M, Leclerc A. Validity of self-reported weight and height in the GAZEL cohort. Int J Obes Relat Metab Disord. 2000;24(2):195-203.
[bib15] Mattews-Simonton K, Swithers SE, Zambrano CA, Gosnell BA. Artificial sweeteners: a systematic review and meta-analysis of randomized controlled trials of the effects on body weight. Am J Clin Nutr. 2017;106(6):1432-1442.
[bib16] Morland C, Diez Roux AV, Wing S. Supermarkets, other food stores, and obesity: the neighborhood environment–ALRGEN study. Am J Prev Med. 2006;30(4):333-339.
[bib17] Thorpe KE, Florence CS, Joski P. Which medical conditions account for the rise in health care spending? Health Aff (Millwood). 2005;Suppl Web Exclusives:W376-W385.
