Introduction
In today’s fast-paced world, the convenience of fast food is undeniable. A quick search for “Fast Food And Restaurants Near Me” readily reveals countless options, promising speedy meals at our fingertips. But with the rising rates of childhood obesity becoming a significant public health concern, it’s crucial to examine the impact of this easy access, especially on younger generations. Childhood obesity is a growing epidemic in the US, with millions of children and adolescents categorized as obese or at risk. This alarming trend carries serious health implications, including asthma, hypertension, type 2 diabetes, cardiovascular disease, and depression. The consumption of fast food among young people has dramatically increased over the decades, now constituting a significant portion of their daily caloric intake. While the availability of fast food is convenient, questions arise about its effect on health, particularly concerning the proximity of fast-food restaurants to places frequented by young people, such as schools.
While it seems intuitive that easy access to fast food might influence dietary habits and health, the scientific consensus has been unclear. Studies have shown a concentration of fast-food outlets near schools, suggesting increased access to less healthy food options for students. However, the direct link between this proximity and negative health outcomes like poor diet and obesity hasn’t been definitively established in all research. Some studies exploring the density of fast-food restaurants and health outcomes in young people have found no clear relationship. This article revisits this critical issue, drawing upon a comprehensive study conducted in California to shed light on the relationship between fast-food restaurant proximity to schools and adolescent health.
Methods: Examining the Link Between Fast Food Proximity and Health
This research delves into the relationship between the presence of fast-food restaurants near schools and the weight status and dietary habits of students. The study utilized data from the California Healthy Kids Survey (CHKS) spanning from 2002 to 2005. This extensive survey collected anonymous, individual-level responses from over 500,000 middle and high school students across California. The CHKS is a mandated survey by the California Department of Education, designed to assess health-risk behaviors in adolescents and provides a large, representative sample for analysis.
The primary health outcome measured was Body Mass Index (BMI), a widely accepted indicator of body fat calculated from weight and height. Researchers also examined overweight and obesity as binary outcomes. These were defined based on the Centers for Disease Control and Prevention (CDC) BMI-for-age percentiles, with the 85th percentile marking overweight and the 95th percentile indicating obesity.
Dietary habits were assessed through student reports of their food consumption in the 24 hours preceding the survey. This included whether they consumed vegetables, juice, fruit, soda, and fried potatoes, and the number of servings for each food type.
To quantify fast-food proximity, the study combined several datasets: a database of California schools with geographic coordinates, a 2003 database of California restaurants with coordinates, and a list of top limited-service restaurant brands from Technomic Inc., a food industry consulting firm.
A key indicator was created: “near a fast-food restaurant,” defined as attending a school within a half-mile radius of at least one restaurant from Technomic’s top limited-service list. This half-mile measure is consistent with previous research and represents a walkable distance of approximately 10 minutes. A comparison variable, “near other restaurant,” was also created to account for proximity to restaurants not classified as top limited-service, likely representing smaller chains or independent establishments. While these “other restaurants” might also cater to youth, the study primarily focused on the impact of well-known fast-food chains.
Statistical analysis employed multivariate regression models to determine the association between fast-food proximity and health outcomes. BMI was analyzed using ordinary least squares regression, while overweight and obesity, being binary outcomes, were analyzed using logistic regression, with results presented as adjusted odds ratios. These models controlled for a range of student-level characteristics (gender, age, grade, race/ethnicity, physical activity levels) and school-level factors (school type, proportion of students eligible for free/reduced-price meals, school enrollment, school location type, and county). Survey wave indicators were also included to account for time-related variations. Statistical software (Stata 10.0) was used to account for the complex survey design and potential clustering of outcomes within schools.
To ensure the robustness of findings, sensitivity analyses were conducted using alternative measures of proximity, such as distance to the nearest fast-food restaurant in varying proximity categories (within 0.25 miles, 0.25-0.5 miles, 0.5-0.75 miles) and the number of fast-food restaurants within a half-mile radius. Furthermore, control variables for proximity to other types of establishments like gas stations, motels, and grocery stores were added to isolate the specific effect of fast-food restaurants. Finally, a placebo outcome – past-month tobacco consumption – was examined to ensure the observed effects were specific to diet-related behaviors and not simply reflective of general environmental factors.
Results: Fast Food Proximity Linked to Increased Weight and Unhealthy Eating
The descriptive statistics from the CHKS data reveal important insights about the student population. The average BMI was within the healthy range according to CDC standards for adolescents. However, a significant portion of the sample was overweight (28%) or obese (12%). Over half of the students (55%) attended schools located near a fast-food restaurant (within a half-mile).
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 |
The regression analysis revealed a significant association between school proximity to fast-food restaurants and student weight status. Students attending schools near fast-food outlets were more likely to be overweight and obese compared to students at schools without nearby fast-food options. Specifically, the odds of being overweight were 1.06 times higher (95% CI = 1.02, 1.10) and the odds of being obese were 1.07 times higher (95% CI = 1.02, 1.12) for students with fast-food restaurants near their schools. Furthermore, these students had a significantly higher BMI, by 0.10 units (95% CI = 0.03, 0.16 kg/m2). While proximity to “other restaurants” also showed a statistically significant but smaller association with weight status, the effect was more pronounced for fast-food restaurants.
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 |
Further analysis explored the relationship between BMI and different proximity ranges to fast-food restaurants. Schools within a quarter-mile and between a quarter and half-mile of fast-food restaurants showed significant positive associations with student BMI. However, the association was not significant for schools located between a half-mile and three-quarters of a mile away. Measuring distance to the nearest fast-food restaurant also confirmed the initial finding: closer proximity was associated with higher BMI. Interestingly, the number of fast-food restaurants within a half-mile radius did not show a significant relationship with BMI, suggesting that simply being near a fast-food restaurant, rather than the density of restaurants, is the key factor.
In terms of dietary intake, students attending schools near fast-food restaurants reported less healthy eating habits. They were less likely to consume vegetables and juice on the day before the survey and reported fewer servings of vegetables, fruits, and juice overall.
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 |
Conversely, students near fast-food restaurants were more likely to report consuming soda on the previous day. While fried potato consumption was not significantly different overall, further analysis focusing specifically on “burger” fast-food establishments showed a higher likelihood of fried potato consumption among students near these restaurants.
To isolate the effect of fast-food restaurants, the study controlled for the presence of other common establishments near schools, such as gas stations, motels, and grocery stores. Proximity to these other types of businesses showed no significant association with student weight status. Crucially, the relationship between fast-food proximity and weight status remained significant even after controlling for these other establishments.
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 |
As a placebo test, the study examined past-month cigarette smoking. There was no significant association between fast-food restaurant proximity and smoking, suggesting that the observed effects are specific to diet and weight, rather than a general correlation with risky behaviors or environmental factors.
Subgroup analyses revealed that the association between fast-food proximity and BMI was stronger among Black students and students attending urban schools.
Discussion: Implications of Fast Food Accessibility on Adolescent Health
This study provides compelling evidence that the proximity of fast-food restaurants to schools is linked to increased overweight and obesity rates among adolescents. Students attending schools near fast-food outlets not only had higher BMIs and were more likely to be overweight or obese, but they also exhibited less healthy dietary patterns, consuming fewer fruits and vegetables and more soda. These findings remained robust even after controlling for various socioeconomic and school-level factors, and when compared to the proximity of other types of businesses. The lack of association with smoking further strengthens the argument that the observed effects are specific to diet-related outcomes.
Limitations
Despite the robust findings, certain limitations of the study should be considered. BMI calculation relied on self-reported height and weight, which could introduce some measurement error. However, research suggests that self-reported BMI is highly correlated with actual measurements. The CHKS, while comprehensive, is a school-based survey, and therefore excludes students who were absent, lacked parental consent, or had dropped out of school. While the study attempted to address potential bias from student absence and dropout, these factors could still influence the generalizability of the findings. Furthermore, soda intake, used as a measure of unhealthy consumption, did not differentiate between sugar-based and diet soda. While this might introduce some measurement error, it is likely to underestimate, rather than overestimate, the association between fast-food proximity and unhealthy dietary intake. The study also acknowledges the lack of data on certain school environment factors, such as school lunch policies and whether students are allowed to leave campus for lunch, which could moderate the observed relationships. Finally, the study is cross-sectional, meaning it captures a snapshot in time and cannot definitively establish causality. It’s possible that fast-food restaurants strategically locate near schools with a student population more inclined to consume fast food.
Conclusions and Policy Implications
Despite these limitations, this study offers valuable insights for informing school and public health policies. The findings suggest that limiting the proximity of fast-food restaurants to schools could be a viable strategy for reducing adolescent obesity rates and promoting healthier eating habits. Policy interventions could range from restricting commercial permits for fast-food restaurants near schools to implementing zoning regulations that encourage healthier food options in these zones. Schools and communities could also explore initiatives to provide adolescents with appealing and accessible alternatives to fast food, promoting healthier dietary choices. Given the substantial healthcare costs associated with obesity, addressing the environmental factors that contribute to unhealthy eating, such as the easy accessibility of fast food, is a crucial step towards improving adolescent health and well-being. Further research is needed to explore the causal mechanisms and to identify the most effective policy interventions for creating healthier food environments for young people.
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
[1] Must A, Strauss RS. Risks and consequences of childhood and adolescent obesity. Int J Obes Relat Metab Disord. 1999;23(suppl 2):S2-S11. PubMed
[2] Freedman DS, Dietz WH, Srinivasan SR, Berenson GS. The relation of overweight to cardiovascular risk factors among children and adolescents: the Bogalusa Heart Study. Pediatrics. 1999;103(6 pt 1):1175-1182. PubMed
[3] Nielsen SJ, Siega-Riz AM, Popkin BM. Trends in food sources of energy and nutrients among children and adolescents in the United States. Am J Prev Med. 2002;22(2):98-108. PubMed
[4] Flegal KM, Carroll MD, Ogden CL, Johnson CL. Prevalence and trends in obesity among US adults, 1999-2000. JAMA. 2002;288(14):1723-1727. PubMed
[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. PubMed
[6] Austin SB, Melly SJ, Sanchez BN, Patel A, Buka SL, Gortmaker SL. Clustering of fast-food restaurants around schools: a novel application of spatial statistics to the study of food environments. Am J Public Health. 2005;95(9):1575-1581. PubMed
[7] Davis AL, Bader HM, Bullock L, et al. The retail food environment and dietary intakes of adolescents. Environ Behav. 2006;38(1):59-81.
[8] Block JP, Scribner RA, DeSalvo KB. Fast food, race/ethnicity, and income: a geographic analysis. Am J Prev Med. 2004;27(3):211-217. PubMed
[9] MacDonald L, Nelson MC. Do fast-food restaurants contribute to obesity? Obesity. 2005;13(6):1171-1177. PubMed
[10] California Department of Education. California Healthy Kids Survey. Sacramento, CA: California Department of Education; 2002-2005.
[11] Centers for Disease Control and Prevention. BMI-for-age percentiles charts. Atlanta, GA: Centers for Disease Control and Prevention; 2000. Available at: http://www.cdc.gov/growthcharts/. Accessed January 17, 2007.
[12] Geographic Data Acquisitions. California Schools—Unified School Districts. Sacramento, CA: Geographic Data Acquisitions; 2003.
[13] Technomic. Technomic’s Top 100 Limited-Service Chain Restaurants. Chicago, IL: Technomic; 2003.
[14] Spencer EA, Appleby PN, Davey GK, Key TJ. Validity of self-reported height and weight in 4808 EPIC-Oxford participants. Eur J Clin Nutr. 2002;56(5):439-442. PubMed
[15] Blundell JE, Macdiarmid JI. Sugar as a nutrient and the fat-sparing effect of carbohydrate. Am J Clin Nutr. 1995;61(4 suppl):860S-869S. PubMed
[16] Story M, Kaphingst KM, French S, Holt K, Kreiger J, Lindquist R, et al. Creating healthy food and eating environments: policy and environmental approaches. Am J Prev Med. 2008;35(3):S290-S307.
[17] Woolf SH, Johnson RE, Fryer GE, Rust G, Satcher D,вилингетроваетре. The health impact of disparities in insurance coverage for the uninsured. Ann Fam Med. 2004;2(suppl 1):S18-S25.
