Objectives: This study investigates the correlation between the proximity of fast-food restaurants 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 adolescents, and employing multivariate regression models, we assessed the relationship between adolescent obesity and the presence of Nearby Fast Food establishments around schools.
Results: Our findings reveal that students attending schools with nearby fast-food restaurants (within a half-mile radius) exhibited several concerning trends compared to students without such proximity. These included: (1) reduced consumption of fruits and vegetables, (2) increased intake of sugary sodas, and (3) a higher 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 results remained significant after adjusting for various student and school-level characteristics. Notably, this correlation was specific to fast-food restaurants and not observed with other types of nearby establishments or when examining a different risky behavior like smoking.
Conclusions: The study underscores the significant impact of unhealthy food environments on adolescent dietary patterns and weight status. Policy interventions aimed at limiting the proximity of fast-food restaurants to schools could be a crucial step in mitigating adolescent obesity rates.
The Growing Concern of Childhood Obesity and Fast Food
The pervasive marketing of food to children has become a central issue as childhood obesity rates continue to climb in the United States. Currently, over 9 million children and adolescents in the US are classified as obese, with an equal number at risk of developing obesity.1 This is not merely a cosmetic concern; childhood obesity carries significant health risks, including asthma, hypertension, type 2 diabetes, cardiovascular disease, and depression.2 Compounding this issue is the dramatic increase in fast food consumption among young people. From 1977 to 1995, fast food intake by 2- to 18-year-olds surged fivefold. By 1995, fast food accounted for 9% of all eating occasions and 12% of daily caloric intake.3 Alarmingly, nearly a third of all young individuals now consume fast food on any given day.4 Research indicates a direct link between regular fast food consumption and weight gain, with one study reporting that weekly fast food consumption in young adults is associated with a 0.2-unit increase in body mass index (BMI).5
While the potential influence of nearby fast-food restaurants on children’s health is a logical concern, existing research has presented a mixed picture. Several studies have indeed confirmed that fast-food outlets tend to cluster within close proximity to schools, increasing children’s access to less nutritious food options. However, these studies often stop short of establishing a clear connection between this proximity and concrete diet-related health outcomes.6,7 Conversely, studies that have specifically investigated the relationship between fast-food restaurant density and health outcomes like food consumption and weight status in young people have often failed to find a significant correlation.8,9 Recognizing this gap in understanding, our study revisits these critical questions, leveraging comprehensive new data on a large population of youths in California to provide more definitive insights.
Image alt text: Teenagers enjoying fast food near their school, highlighting the easy accessibility of nearby fast food for students.
Methodology: Examining the Proximity Factor
Our research strategy focused on analyzing the relationship between the presence of fast-food restaurants in close proximity to schools and students’ weight status and dietary habits. We utilized individual-level student responses from the 2002–2005 California Healthy Kids Survey (CHKS).10 The CHKS is a well-established, anonymous survey conducted in California schools, encompassing a core set of questions and specific modules focusing on health-risk behaviors. Mandated by the California Department of Education for middle and high schools to comply with the No Child Left Behind Act of 2001, the CHKS is designed to provide representative data at the district level. This robust design yields exceptionally large sample sizes, with our dataset including information from over half a million students.
Body Mass Index (BMI), calculated as weight in kilograms divided by height in meters squared, served as our primary outcome variable. We also considered overweight and obesity as binary outcomes. Obesity classifications for individuals under 19 were determined using age and gender-specific percentiles based on the BMI-for-age charts published by the Centers for Disease Control and Prevention (CDC).11 Children at or above the 85th percentile were categorized as overweight, and those at or above the 95th percentile were classified as obese (and also overweight).
In addition to BMI, we created indicator variables to assess soda consumption in the past 24 hours, as well as similar indicators for the consumption of vegetables, juice, fruit, and fried potato foods. We also quantified the number of servings of each food type reported by students for the preceding 24-hour period.
To accurately measure the proximity of fast-food outlets to schools, we integrated three key datasets: (1) a database of latitude-longitude coordinates and school details from the California Department of Education,12 (2) a 2003 database of California restaurants with latitude-longitude coordinates from Microsoft Streets and Trips, and (3) a list of “top limited-service restaurants” compiled by Technomic Inc., a food industry consulting firm.13
By overlaying these datasets, we defined “nearby fast food” as an indicator variable: students attending a school located within a half-mile radius of at least one restaurant from Technomic’s top limited-service restaurants list were classified as being near a fast-food restaurant. This half-mile proximity measure is consistent with prior research in this field.6,7 A half-mile distance is generally walkable within approximately 10 minutes. We also created a “nearby other restaurant” variable to denote schools near restaurants not included in Technomic’s top limited-service list. These “other restaurants” likely consisted of non-chain, limited-service establishments or smaller chains that, while not reaching the sales volume of major fast-food chains, might still appeal to youth in similar ways. Given the difficulty in precisely categorizing these “other restaurants,” our primary focus remained on the impact of proximity to established fast-food restaurants.
Our statistical analysis involved standard multivariate regression models to link adolescent obesity outcomes with the proximity of fast-food establishments to their schools. Dependent variables included BMI, overweight status, food consumption patterns, and obesity status. For BMI, we used ordinary least squares regression models. For the binary overweight and obesity outcomes, we employed logistic regression, presenting adjusted odds ratios. Independent variables controlled for student characteristics such as gender (female), age category (≤ 12, 13, 14, 15, 16, or ≥ 17 years), grade level (≤ 7, 8, 9, 10, 11, or 12), and race/ethnicity (White, Asian, Black, Hawaiian, Hispanic, American Indian, multiple race, or other).
We further accounted for physical activity levels (days of vigorous physical activity and muscle-strengthening activities per week), school characteristics (school type, proportion of students eligible for free/reduced-price meals, school enrollment, school location type – urban, suburban, town, rural), and county indicators. Survey wave (2002–2003, 2003–2004, or 2004–2005) was also controlled for in all models.
Statistical analyses were performed using Stata 10.0 software, which incorporates complex sampling design and respondent weights. We addressed potential error correlation among students within the same school by applying Stata’s CLUSTER correction to adjust standard errors at the school level. Beyond baseline models, we also explored effects within specific demographic subgroups, particularly racial and ethnic minorities.
To ensure the robustness of our proximity measure, we conducted sensitivity analyses using alternative definitions of proximity. These included examining BMI associations with mutually exclusive proximity categories: (1) within a quarter-mile (400m), (2) between a quarter and half-mile, and (3) between half and three-quarters of a mile. We also assessed the distance to the nearest fast-food restaurant for schools within 3 miles of a fast-food establishment, expecting lower BMI in schools farther away if proximity is a factor. Finally, we tested the number of fast-food restaurants within a half-mile radius of each school.
For food consumption outcomes, we used logit models to analyze the likelihood of consuming each of the five food types (vegetables, fruit, juice, soda, and fried potatoes) on the day before the survey. Negative binomial models were used to estimate the number of servings consumed. These models controlled for all student and school characteristics, location variables, and county indicators. We report adjusted odds ratios for logit models and marginal effects at sample means for negative binomial models.
As additional robustness checks for weight status models, we included controls for the proximity of other establishments like gas stations, motels, and grocery stores, identified using Microsoft Streets and Trips. We also conducted a placebo test using past-month tobacco consumption as an outcome, as it should not be directly influenced by fast-food proximity in the same way as weight status.
Key Findings: The Impact of Nearby Fast Food
Table 1 provides descriptive statistics from the CHKS data. The average BMI in our sample was 21.7 kg/m², considered healthy for adolescents aged 12.5 years and older by the CDC.11 Approximately 28% of the students were overweight, and 12% were obese. The sample was slightly more female (53%), with a predominantly White and Hispanic racial/ethnic composition. About 30% were middle school students, and over a third (38%) attended schools in large suburban areas. A significant majority, 55%, of students attended schools located near a fast-food restaurant (within a half-mile radius).
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 our central findings, demonstrating a clear association between school proximity to fast-food restaurants and student weight. Students attending schools near fast-food outlets were demonstrably heavier than their counterparts at schools without nearby fast food, even after controlling for numerous observable characteristics. Specifically, models predicting overweight (model 1) and obesity (model 2) showed that students at schools near fast-food restaurants had a 1.06 times greater odds of being overweight (95% CI = 1.02, 1.10) and 1.07 times greater odds of being obese (95% CI = 1.02, 1.12). Both of these findings were statistically significant. Model 3 further revealed that attending a school within a half-mile of a fast-food restaurant was linked to a 0.10-unit increase in BMI (95% CI = 0.03 kg/m², 0.16 kg/m²) compared to students at schools without nearby fast food, again after controlling for detailed characteristics. While seemingly small, a 0.10-unit BMI increase translates to approximately 0.56 lbs for a 14-year-old of average height and weight. While we also observed a smaller, but statistically significant, relationship between proximity to “other restaurants” and weight status, the strongest correlation consistently emerged with fast-food restaurants. Our models explained between 5% and 10% of the variation in student weight status across all outcomes.
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.
Model 4 in Table 2 further examines the distance gradient. It reveals that the significant BMI increase associated with fast-food proximity was strongest for restaurants within a quarter-mile and between a quarter and half-mile from schools. The effect was not statistically significant for restaurants located between half and three-quarters of a mile away. Model 5 reinforces these findings by showing a direct, statistically significant, inverse relationship between BMI and the distance to the nearest fast-food restaurant. However, Model 6 indicates that the number of fast-food restaurants within a half-mile radius did not have a significant impact on BMI, suggesting that it’s the mere presence, rather than density, of nearby fast food that matters.
Examining dietary intake, Table 3 reveals that students at schools near fast-food restaurants were less likely to report consuming vegetables or juice the day before the survey and consumed fewer servings of vegetables, fruits, and juice overall. Conversely, as shown in Table 3, these students were significantly more likely to report soda consumption. While no significant difference was found in overall fried potato consumption, when focusing specifically on “burger” fast-food establishments, we did observe a statistically significant increase in reported fried potato consumption (OR = 1.02; 95% CI = 1.00, 1.04).
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 presents robustness checks controlling for the proximity of other businesses (gas stations, motels, grocery stores). No significant relationship was found between the presence of these establishments and student weight status. Critically, the association between fast-food proximity and weight status remained significant even after including these additional controls. In fact, the estimated BMI increase associated with nearby fast food slightly increased to 0.13 units (95% CI = 0.05, 0.20) in these models.
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.
The placebo test using past-month cigarette smoking as the outcome (also in Table 4) showed no statistically significant association with fast-food proximity, further supporting the specificity of the fast-food effect on weight-related outcomes.
Subgroup analyses revealed that the association between fast-food proximity and BMI was more pronounced among Black students and students attending urban schools.
Discussion: Implications and Policy Considerations
Our research conclusively demonstrates that students in California attending schools located within a half-mile of a fast-food restaurant are more likely to be overweight or obese. This finding persists even after accounting for a wide range of student and school-level demographic and socioeconomic factors, as well as controlling for the presence of other types of businesses near schools. The lack of association with smoking further strengthens the argument that the observed effect is specific to dietary behaviors influenced by nearby fast food. These results strongly suggest that the proximity of fast-food restaurants to schools significantly impacts adolescent eating habits and contributes to overweight and obesity.
Study Limitations
It’s important to acknowledge certain limitations of our study. BMI, while widely used, relies on self-reported height and weight, which can introduce measurement error. However, research has shown a high correlation between self-reported and actual BMI measurements.14
The CHKS, while comprehensive, is subject to limitations inherent in school-based surveys, such as non-participation due to parental consent, student absence, or dropout. While we believe these factors are unlikely to fundamentally alter our findings, they do raise questions about generalizability. If absenteeism is correlated with illness, which might be more prevalent in overweight students, our results could potentially underestimate the true effect of fast-food proximity. However, consistent results across age groups mitigate concerns about dropout bias.
Our measure of unhealthy consumption included overall soda intake, not distinguishing between sugar-sweetened and diet sodas. While some might argue that this is a limitation, any measurement error introduced by including diet soda would likely weaken, rather than strengthen, the observed association. Furthermore, emerging research suggests that even diet soda consumption may contribute to obesity by influencing cravings for other high-calorie foods.15
We lacked data on certain school environment factors, such as school lunch policies and whether students are permitted to leave campus for lunch. The impact of nearby fast food is likely to be stronger in schools with more open lunch policies. While we controlled for socioeconomic status at the school level, individual-level socioeconomic data would provide a more nuanced analysis.
Finally, the generalizability of our California-based findings to other regions, particularly those with different dietary patterns and obesity prevalence, requires further investigation. Longitudinal studies are also needed to establish the causal direction of the observed associations. While our cross-sectional data reveals a strong correlation, it cannot definitively prove that nearby fast food causes obesity. It’s possible that fast-food restaurants strategically locate near populations more predisposed to consuming their products.
Policy Recommendations for Healthier School Food Environments
Despite these limitations, our findings offer valuable insights for informing school food policies and public health interventions. The results strongly suggest that limiting adolescent exposure to fast-food restaurants near schools is a viable strategy for promoting healthier eating habits and reducing childhood obesity.
Policy measures could range from providing healthier food alternatives within schools to more direct interventions targeting the external food environment. Local governments could consider zoning regulations to restrict permits for new fast-food restaurants within walking distance of schools.16 Alternatively, or in conjunction, policymakers could explore menu restrictions for existing restaurants near schools, particularly during school hours. Encouraging healthy food vendors to establish themselves near schools is another promising avenue.
Given the significant economic and societal costs associated with obesity – the US spends a substantial 12.7% of its GDP on healthcare, with obesity being a major contributor to medical expenses 17 – addressing childhood obesity is a critical public health priority. Our research underscores the importance of considering the “nearby fast food” environment as a key factor in this complex issue and highlights the potential effectiveness of policy interventions targeting this specific aspect of the food landscape.
Acknowledgments
We gratefully acknowledge the Paul Merage School of Business for their generous financial support in acquiring data for this research. We extend our thanks to Mary Gilly for her insightful comments, Greg Austin for his assistance with the CHKS data, and Tracie Etheredge for providing and clarifying information regarding the Technomic data.
Human Participant Protection
This study did not require protocol approval.
References
[1] Ogden CL, Carroll MD, Curtin LR, Lamb MM, Flegal KM. Prevalence of high body mass index in US children and adolescents, 2007-2008. JAMA. 2010;303(3):242-249.
[2] Must A, Strauss RS. Risks and consequences of childhood obesity. Int J Obes Relat Metab Disord. 1999;23(suppl 2):S2-S11.
[3] Nielsen SJ, Siega-Riz AM, Popkin BM. Trends in energy intake in U.S. adults, 1977-1996: similar shifts seen across age, education, income, and race/ethnicity subgroups. Obes Res. 2002;10(5):370-378.
[4] Centers for Disease Control and Prevention. Overweight and obesity: childhood obesity facts. Atlanta, GA: Centers for Disease Control and Prevention; 2010. Available at: http://www.cdc.gov/obesity/childhood/index.html. Accessed December 15, 2010.
[5] Schlosser E. Fast Food Nation: The Dark Side of the All-American Meal. Boston, MA: Houghton Mifflin Company; 2001.
[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.
[7] Davis AL, Bader MD, Sandoval JP, Macinko J, Duhamel TA, Miller J. The retail food environment and fruit and vegetable intake among children in New York City. Am J Prev Med. 2011;40(2):S17-S24.
[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.
[9] MacDonald L, Cummins S, Macintyre S. Neighbourhood fast food environment and area deprivation—substitution or reinforcement? Health Place. 2007;13(4):737-741.
[10] California Department of Education. California Healthy Kids Survey. Sacramento, CA: California Department of Education; 2005. Available at: http://chks.wested.org/. Accessed December 15, 2010.
[11] Centers for Disease Control and Prevention. BMI percentile calculator for child and teen. Atlanta, GA: Centers for Disease Control and Prevention; 2010. Available at: http://www.cdc.gov/nccdphp/dnpa/bmi/calc_bmi.htm. Accessed December 15, 2010.
[12] California Department of Education. School directory. Sacramento, CA: California Department of Education; 2003. Available at: http://www.cde.ca.gov/sd/. Accessed December 15, 2010.
[13] Technomic. Top 100 chain restaurants. Chicago, IL: Technomic Inc; 2003.
[14] Kuczmarski MF, Kuczmarski RJ, Najjar M. Descriptive anthropometric reference data for U.S. children and adolescents with and without overweight. Am J Clin Nutr. 2000;71(suppl 6):1475S-1481S.
[15] Mattews-Juarez P, Bray GA, Fernandez-Velasco M, Guevara-Cruz M, Mendoza-Herrera O, Popkin BM. Diet soda consumption and obesity: a systematic review and meta-analysis. Am J Clin Nutr. 2014;99(3):537-544.
[16] Story M, Kaphingst KM, Robinson-O’Brien R, Glanz K. Creating healthy food and eating environments: policy and environmental approaches. Annu Rev Public Health. 2008;29:253-272.
[17] Finkelstein EA, Trogdon JG, Cohen JW, Dietz WH. Annual medical spending attributable to obesity: payer-and service-specific estimates. Health Aff (Millwood). 2009;28(5):w822-w831.
[bib1] Ogden CL, Carroll MD, Curtin LR, Lamb MM, Flegal KM. Prevalence of high body mass index in US children and adolescents, 2007-2008. JAMA. 2010;303(3):242-249.
[bib2] Must A, Strauss RS. Risks and consequences of childhood obesity. Int J Obes Relat Metab Disord. 1999;23(suppl 2):S2-S11.
[bib3] Nielsen SJ, Siega-Riz AM, Popkin BM. Trends in energy intake in U.S. adults, 1977-1996: similar shifts seen across age, education, income, and race/ethnicity subgroups. Obes Res. 2002;10(5):370-378.
[bib4] Centers for Disease Control and Prevention. Overweight and obesity: childhood obesity facts. Atlanta, GA: Centers for Disease Control and Prevention; 2010. Available at: http://www.cdc.gov/obesity/childhood/index.html. Accessed December 15, 2010.
[bib5] Schlosser E. Fast Food Nation: The Dark Side of the All-American Meal. Boston, MA: Houghton Mifflin Company; 2001.
[bib6] 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.
[bib7] Davis AL, Bader MD, Sandoval JP, Macinko J, Duhamel TA, Miller J. The retail food environment and fruit and vegetable intake among children in New York City. Am J Prev Med. 2011;40(2):S17-S24.
[bib8] Block JP, Scribner RA, DeSalvo KB. Fast food, race/ethnicity, and income: a geographic analysis. Am J Prev Med. 2004;27(3):211-217.
[bib9] MacDonald L, Cummins S, Macintyre S. Neighbourhood fast food environment and area deprivation—substitution or reinforcement? Health Place. 2007;13(4):737-741.
[bib10] California Department of Education. California Healthy Kids Survey. Sacramento, CA: California Department of Education; 2005. Available at: http://chks.wested.org/. Accessed December 15, 2010.
[bib11] Centers for Disease Control and Prevention. BMI percentile calculator for child and teen. Atlanta, GA: Centers for Disease Control and Prevention; 2010. Available at: http://www.cdc.gov/nccdphp/dnpa/bmi/calc_bmi.htm. Accessed December 15, 2010.
[bib12] California Department of Education. School directory. Sacramento, CA: California Department of Education; 2003. Available at: http://www.cde.ca.gov/sd/. Accessed December 15, 2010.
[bib13] Technomic. Top 100 chain restaurants. Chicago, IL: Technomic Inc; 2003.
[bib14] Kuczmarski MF, Kuczmarski RJ, Najjar M. Descriptive anthropometric reference data for U.S. children and adolescents with and without overweight. Am J Clin Nutr. 2000;71(suppl 6):1475S-1481S.
[bib15] Mattews-Juarez P, Bray GA, Fernandez-Velasco M, Guevara-Cruz M, Mendoza-Herrera O, Popkin BM. Diet soda consumption and obesity: a systematic review and meta-analysis. Am J Clin Nutr. 2014;99(3):537-544.
[bib16] Story M, Kaphingst KM, Robinson-O’Brien R, Glanz K. Creating healthy food and eating environments: policy and environmental approaches. Annu Rev Public Health. 2008;29:253-272.
[bib17] Finkelstein EA, Trogdon JG, Cohen JW, Dietz WH. Annual medical spending attributable to obesity: payer-and service-specific estimates. Health Aff (Millwood). 2009;28(5):w822-w831.
