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. 2019 Sep 10;22(Suppl 1):e12944. doi: 10.1111/obr.12944

Fast‐food restaurant, unhealthy eating, and childhood obesity: A systematic review and meta‐analysis

Peng Jia 1,2,, Miyang Luo 3,4,2, Yamei Li 5, Ju‐Sheng Zheng 6, Qian Xiao 7,8,2, Jiayou Luo 5,2,
PMCID: PMC7988557  PMID: 31507064

Summary

Excessive access to fast‐food restaurants (FFRs) in the neighbourhood is thought to be a risk factor for childhood obesity by discouraging healthful dietary behaviours while encouraging the exposure to unhealthful food venues and hence the compensatory intake of unhealthy food option. A literature search was conducted in the PubMed, Web of Science, and Embase for articles published until 1 January 2019 that analysed the association between access to FFRs and weight‐related behaviours and outcomes among children aged younger than 18. Sixteen cohort studies and 71 cross‐sectional studies conducted in 14 countries were identified. While higher FFR access was not associated with weight‐related behaviours (eg, dietary quality score and frequency of food consumption) in most studies, it was commonly associated with more fast‐food consumption. Despite that, insignificant results were observed for all meta‐analyses conducted by different measures of FFR access in the neighbourhood and weight‐related outcomes, although 17 of 39 studies reported positive associations when using overweight/obesity as the outcome. This systematic review and meta‐analysis revealed a rather mixed relationship between FFR access and weight‐related behaviours/outcomes among children and adolescents.

Keywords: dietary behaviour, fast food, food environment, obesity

1. INTRODUCTION

Obesity is a major risk factor for global mortality, with an estimated 2.8 million people died of obesity‐related causes each year. 1 Obesity can lead to a variety of health consequences, including heart disease, stroke, diabetes mellitus, hypertension, dyslipidaemia, breathing disorders, and certain types of cancer. 2 In 2016, the World Health Organization (WHO) reported that 39% of adults had obesity, whereas the obesity rate in 1975 was only around 3% in men and 6% in women. 1 However, a considerable proportion of adult obesity stem from childhood, which, therefore, is a critical period to prevent obesity. 3 , 4 Childhood obesity is one of the most serious global public health problems in the 21st century. The prevalence of overweight and obesity in children and adolescents has risen dramatically from 4% in 1975 to over 18% in 2016. 5 Childhood obesity can also lead to a range of health problems, including high blood pressure, high total cholesterol, and impaired glucose tolerance. Prevention of childhood obesity requires high priority in public health practices.

It is widely accepted that some environmental factors in the neighbourhood may interact with personal characteristics to affect individual weight status. 6 , 7 Fast‐food restaurants (FFRs) are one of such environmental factors, which are defined as food venues primarily engaged in providing food services (except snack and non‐alcoholic beverage bars) where patrons generally order or select items and pay before eating. They allow convenient consumption of fast food that typically contains high levels of calories, saturated fat, trans‐fat, sugar, simple carbohydrates, and sodium, sold at a relatively low price. FFRs can be categorized using Standard Industrial Classification (SIC) code (5812002) or North America Industry Classification System (NAICS) code (722513) under the category of limited‐service restaurants. 8 With the rapid development of international fast‐food chains, the consumption of fast food has risen dramatically over the past few decades along with the increasing obesity rates globally. 9 , 10 Moreover, fast food has been more popular among children and adolescents, partly due to easy availability, taste, and marketing strategies. 11 Some studies showed that FFR access may potentially lead to greater risk of being overweight and obesity, especially in children and adolescents. 12 However, compared with the evidence suggesting an association between fast‐food consumption and weight gain, 13 the association between FFR access and childhood obesity was less clear.

This systematic review comprehensively investigated the association between FFR access and weight‐related behaviours and weight status. We tested our hypothesis that the greater FFR access was associated with higher levels of unhealthful food intake and weight gains among children and adolescents. Studies that use a full range of measures of FFR access in the neighbourhood and examine multiple weight‐related behaviours and outcomes were included. Furthermore, meta‐analyses were conducted to quantify the association between FFR access and childhood obesity.

2. METHODS

A systematic review and meta‐analysis were conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses.

2.1. Study selection criteria

Studies that met all of the following criteria were included in the review: (a) study subjects (children and adolescents aged younger than 18); (b) study outcomes (weight‐related behaviours [eg, diet, physical activity, and sedentary behaviour] and/or outcomes [eg, overweight and obesity measured by body mass index (BMI, kg/m2), waist circumference, waist‐to‐hip ratio, and body fat]); (c) article types (peer‐reviewed original research); (d) time of publication (earlier than 1 January 2019); and (5) language (articles written in English).

Studies that met any of the following criteria were excluded from the review: (1) studies that incorporated no measures of FFR access or weight‐related behaviours/outcomes; (2) computer‐based simulation studies without the inclusion of human participants; (3) controlled experiments conducted in manipulated rather than naturalistic settings; (4) articles not written in English; or (5) letters, editorials, study/review protocols, or review articles.

2.2. Search strategy

A keyword search was performed in three electronic bibliographic databases: PubMed, Web of Science, and Embase. The search strategy included all possible combinations of keywords from the three groups related to fast‐food restaurant, children, and weight‐related behaviours or outcomes. The specific search strategy is provided in Appendix A.

Two reviewers (M.L. and Y.L.) independently conducted the title and abstract screening and identified potentially relevant articles for the full‐text review. Inter‐rater agreement was assessed by using the Cohen kappa (κ = 0.8). Discrepancies were compiled by M.L. and screened by a third reviewer (P.J.). M.L., Y.L., and PJ. jointly determined the list of articles for the full‐text review through discussion. Then M.L. and Y.L. independently reviewed the full texts of all articles in the list and determined the final pool of articles included in the review. Inter‐rater agreement was again assessed by the Cohen kappa (κ = 0.9). [Correction added on 14 January 2021, after first online publication: the title of Section 2.2 has been amended to ‘Search Strategy’.]

2.3. Data extraction

Two reviewers (M.L. and Y.L.) independently extracted data from each included study, and discrepancies were resolved by the third reviewer (P.J.). A standardized data extraction form was used to collect methodological and outcome variables from each selected study, including authors, year of publication, study area and scale, sample size and characteristics, statistical models, and age at baseline, follow‐up years, number of repeated measures, and attrition rate for cohort studies, as well as measures of FFR access, weight‐related behaviours/outcomes, and their association.

2.4. Meta‐analysis

A meta‐analysis was performed to estimate the pooled effect size of FFR access on each weight‐related behaviour and outcomes. Weight‐related outcomes included BMI, BMI percentile, BMI z‐score, overweight, and obesity. Overweight was defined as BMI at or above the 85th percentile, and obesity was defined as BMI at or above the 95th percentile based on references mentioned in each article. Several studies were excluded from the meta‐analysis due to the following reasons: neither standard error nor confidence interval (CI) was reported; effect size was unable to be transformed into a standardized coefficient (ie, beta coefficient) due to limited information reported; the unit of effect size was inconsistent with others; and less than two studies reported the same outcome variable.

Effect sizes were reported by mean differences for continuous outcomes (ie, BMI and BMI percentile) and odds ratios for categorical variables (ie, overweight and obesity). Study heterogeneity was assessed by using the I 2 index. The level of heterogeneity represented by I 2 was interpreted as modest (I 2 ≤ 25%), moderate (25% < I 2 ≤ 50%), substantial (50% < I 2 ≤ 75%), or considerable (I 2 > 75%). Q tests were also conducted, where P < .1 indicates the presence of heterogeneity across studies. A random‐effect model was used to pool the estimates from individual studies because of the varying population and criteria used to define outcomes. Publication bias was assessed by a visual inspection of the funnel plot and Begg's and Egger's tests. All meta‐analyses were performed by the “meta” and “metagen” packages using R version 4.3‐2. 14 All analyses used two‐sided tests, and P < .05 was considered statistically significant except for the evaluation of heterogeneity (P < .1).

2.5. Study quality assessment

The quality of included studies was assessed using the National Institutes of Health's Quality Assessment Tool for Observational Cohort and Cross‐Sectional Studies. 15 This assessment tool rates each study based on 14 criteria (Table S2). For each criterion, a score of one was assigned if “yes” was the response, whereas a score of zero was assigned otherwise (ie, an answer of “no,” “not applicable,” “not reported,” or “cannot determine”). A study‐specific global score ranging from zero to 14 was calculated by summing up scores across all criteria. The study quality assessment helped measure the strength of scientific evidence but was not used to determine the inclusion of studies.

3. RESULTS

3.1. Study selection

A total of 1441 articles were identified through the keyword search, of which 66 were non‐duplicated articles (Figure 1). After title and abstract screening, 575 articles were excluded. The full texts of the remaining 111 articles were reviewed against the study selection criteria, and 24 articles were further excluded. The remaining 87 studies that examined the association between FFR access and children's weight‐related behaviours and/or outcomes were included in this review.

FIGURE 1.

FIGURE 1

Flowchart of study inclusion and exclusion

3.2. Study characteristics

Eighty‐seven included studies consisted of 16 cohort studies and 71 cross‐sectional studies and were published during 2004 to 2018 (Table 1). Most of these studies were conducted in the United States (n = 45); one international comparison study was conducted in the United States, Canada, and Scotland; the remaining studies were conducted in Canada (n = 13), the United Kingdom (n = 10), China (n = 4), Australia (n = 3), South Korea (n = 3), Denmark (n = 2), Brazil (n = 1), France (n = 1), Germany (n = 1), Ireland (n = 1), New Zealand (n = 1), and Sweden (n = 1). Twenty of these studies were conducted at a national level, and 26, 32, and nine studies were conducted at state, city, and county levels, respectively. The sample size of included studies ranged widely from 78 to 3 003 288, and school children/adolescents were the most common study population (n = 54).

TABLE 1.

Basic characteristics of 87 included studies

Author (Year)[ref] a Study Area [Scale] b Sample Size Sample Age (Years, range, and/or mean ± SD) c Sample Characteristics Statistical Models
Longitudinal studies
Chen (2016) 27 Arkansas, USA [S] 21 639 in 2003–2004 School children (followed up from academic year 2003/2004 to 2009/2010 with seven repeated measures) Growth curve model and cox regression
Chen (2016) 28 USA [N] 7090 11 in 2004 School children (followed up from 2004 to 2007 with two repeated measures) Multilevel linear regression
Fraser (2012) 29 Avon, UK [CT] 4022 13 y Adolescents (followed up from 13 to 15 y with two repeated measures and an attrition rate of 21.2%) Linear and logistic regression
Ghenadenik (2018) 30 Montreal, Canada [C] 391 8‐10 in 2005‐2008 Children with a parental history of obesity (followed up from 2005‐2008 to 2008‐2011 with two repeated measures and an attrition rate of 37.9%) Multivariate linear regression
Green (2018) 31 Leeds, UK [C] 746 11‐12 in 2005 Secondary school students (followed up from 2005 to 2010 with three repeated measures) Multilevel linear regression
Hamano (2017) 32 Sweden [N] 944 487 0‐14 in 2005 Entire Swedish population (followed up from 2005 to 2010 with two repeated measures) Multilevel logistic regression
Khan (2012) 33 USA [N] 11 700 in 2004 School children (followed up from 2004 to 2007 with two repeated measures) Multilevel linear regression
Lee (2012) 34 USA [N] 7710 6.2 ± 0.4 in 1999 School children (follow up from 1999 to 2004 with four repeated measures and an attrition rate of 43.0%) Multilevel linear regression
Leung (2011) 35 California, USA [CT4] 353 6‐7 in 2005 Girls (followed up from 2005 to 2008 with three repeated measures and an attrition rate of 20.5%) Generalized linear and logistic regression
Pearce (2018) 36 Gloucestershire, UK [CT] 1577 in 2006‐2007 School children (followed up from 2006/2007 to 2012/2013 with two repeated measures and an attrition rate of 34.4%) Multivariate logistic regression
Powell (2009) 37 USA [N] 5215 12‐17 (15.5 ± 1.7) in 1997 Adolescents living at home (followed up from 1997 to 2000 with four repeated measures) Multilevel linear regression
Shier (2012) 38 USA [N] 6260 in 2004 School children (followed up from 2004 to 2007 with two repeated measures) Multilevel linear regression
Smith (2013) 39 London, UK [C] 757 11‐12 in 2001 Secondary school students (followed up from 2001 to 2005 with two repeated measures and an attrition rate of 45.2%) Generalized linear regression
Sturm (2005) 40 USA [N] 6918 6.2 ± 0.4 in 1999 Elementary school children (followed up from 1999 to 2002 with three repeated measures and an attrition rate of 42.4%) Multilevel linear regression
Van Hulst (2015) 41 Quebec, Canada [S] 512 8‐10 in 2005‐2008 Children with a parental history of obesity (followed up from 2005‐2008 to 2008‐2011 with two repeated measures and an attrition rate of 9.8%) Multivariate linear regression
Wang (2012) 42 China [N] 185 6‐18 in 2004 School‐age children (followed up from 2004 to 2006 with two repeated measures and an attrition rate of 19%) Multilevel linear regression
Cross‐sectional studies
Alviola (2014) 43 Arkansas, USA [S] 942 public schools in 2008‐2009 Children in kindergarten, grades 2, 4, 6, 8, and 10 Multivariate linear regression
An (2012) 44 California, USA [S] 13 462 8226 aged 5‐11 and 5236 aged 12‐17 in 2005 and 2007 Measured in 2005 and 2007 Negative binomial regression
Bader (2013) 45 New York City, USA [C] 94 348 ≥13 y in 2007‐2008 Public high school students Generalized multilevel linear regression
Baek (2014) 46 California, USA [S] 926 018 in 2007 Grades 5, 7, and 9 students Multilevel linear regression
Barrett (2017) 47 Hampshire, UK [CT] 1173 6 y between 2007 and 2014 NA Multilevel linear regression
Burdette (2004) 48 Cincinnati, Ohio, USA [C] 7020 36‐59 months in 1998‐2001 Low‐income preschool children Multivariate logistic regression
Carroll‐Scott (2013) 49 New Haven, USA [C] 1048 10.9 ± 0.8 in 2009 Grades 5 and 6 students Multilevel linear regression
Casey (2012) 50 Bas‐Rhin, France [S] 3327 12.0 ± 0.6 in 2001 Middle‐school first‐level students Multilevel logistic regression
Cetateanu (2014) 51 UK [N] 3 003 288 4‐5 and 10‐11 in 2007‐2010 School Reception and Grade 6 children Multivariate linear regression
Chiang (2011) 52 Taiwan [S] 2283 6‐13 in 2001‐2002 Elementary school children

Multivariate

linear regression

Choo (2017) 20 Seoul, South Korea [C] 126 9‐12 in 2015 Elementary school children at Grades 4 to 6

Multivariate

logistic regression

Clark (2014) 53 Otago, New Zealand [S] 664 15‐18 in 2011 Grades 11‐13 adolescents Generalized estimating equation
Correa (2018) 54 Florianopolis, Brazil [C] 2195 7‐14 in 2012‐2013 School children Multivariate logistic regression
Crawford (2008) 55 Melbourne, Australia [C] 380 137 aged 8‐9 and 243 aged 13‐15 in 2004 Schoolchildren Linear and logistic regression
Cutumisu (2017) 56 Quebec, Canada [S] 26 655 in 2010‐2011 Secondary school children Multilevel logistic regression
Davis (2009) 57 California, USA [S] 529 367 in 2002‐2005 Middle and high school students Linear and logistic regression
Dwicaksono (2018) 58 New York, USA [S] 680 school districts in 2010‐2012 School‐aged children Multivariate linear regression
Fiechtner (2013) 59 Massachusetts, USA [S] 438 2‐6.9 in 2006‐2009 Overweight and obese preschool‐age children Multivariate linear regression
Fiechtner (2015) 60 Massachusetts, USA [S] 49 770 4‐18 in 2011‐2012 Paediatric patients Multivariate linear regression
Forsyth (2012) 61

Minneapolis/St.

Paul, USA [C]

2724 14.5 ± 2.0 in 2009‐2010 Adolescents in secondary schools Multilevel linear regression
Fraser (2010) 62 Leeds, West Yorkshire, UK [C] 33 594 3‐14 in 1998‐2006 NA Generalized estimating equation
Galvez (2009) 63 New York, USA [C] 323 6‐8 in 2004 NA Multivariate logistic regression
Gilliland (2012) 64 London, UK [C] 891 10‐14 y Grades 6‐8 students Multilevel linear regression
Gorski Findling (2018) 65 USA [N] 3748 2‐18 in 2012‐2013 NA Logistic regression
He (2012) 66 London, Ontario, Canada [C] 782 11‐13 in 2006‐2007 Grades 7 and 8 students

Multilevel logistic

regression

He (2012) 67 London, Ontario, Canada [C] 632 11‐14 in 2006‐2007 Grades 7 and 8 students Multilevel linear regression
Hearst (2012) 68 Minneapolis and St Paul, MN, USA [C2] 634 10.8‐17.7 in 2007‐2008 Adolescents

Multilevel linear

regression

Heroux (2012) 69 Canada, Scotland, and the USA [N3] 26 778 13‐15 in 2009‐2010 Students

Multilevel logistic

regression

Ho (2010) 70 Hong Kong, China [S] 24 796 14.5 ± 0.11 in 2006‐2007 Secondary school students Logistic regression
Hobin (2013) 71 Ontario, Canada [S] 21 754 in 2005‐2006 Grades 9 to 12 students in secondary schools

Multilevel linear

regression

Howard (2011) 72 California, USA [S] 879 public schools in 2007 Grade 9 students in public schools Multivariate linear regression
Jago (2007) 73 Houston, USA [C] 204 10‐14 in 2003 Boy scouts Multivariate linear regression
Jilcott (2011) 74 Pitt County, USA [CT] 744 8‐18 (12.9 ± 2.5) in 2007‐2008 Paediatric patients Generalized linear regression
Joo (2015) 75 Suwon, Hwaseong, and Osan, Korea [C3] 243 in 2012 Grades 6 and 8 students Chi‐square test and t test
Kelly (2018) 76 Ireland [N] 5344 in 2010 Post‐primary school students Logistic regression
Kepper (2016) 77 Louisiana, USA [S] 78 2‐5 (2.9 ± 0.7) y Pre‐school children Multivariate linear regression
Koleilat (2012) 78 Los Angeles, USA [CT] 266 ZIP codes 3‐4 in 2008 Children who participated in the WIC programme ANOVA
Lakes (2016) 79 Berlin, Germany [C] 28 159 5‐6 in 2012 Preschool children Multivariate linear regression
Lamichhane (2012) 80 South Carolina, USA [S] 359 14.5 ± 2.9 in 2001‐2005 Youth with diabetes Generalized estimating equation
Lamichhane (2012) 81 South Carolina, USA [S] 845 11.7 ± 4.7 in 2001‐2006 Youth with diabetes Generalized estimating equation
Langellier (2012) 82 Los Angeles, USA [CT] 1694 schools in 2008‐2009 Grades 5, 7, and 9 students Multilevel linear regression
Larsen (2015) 83 Toronto, Canada [C] 943 11.02 ± 9.63 in 2010‐2011 Grades 5 and 6 students Logistic regression
Laska (2010) 84 Minneapolis, USA [C] 349 11‐18 (15.4 ± 1.7) in 2006‐2007 Adolescents Multilevel linear regression
Laxer (2014) 85 Canada [N] 6099 11‐15 in 2009‐2010 Grades 6‐10 students

Multilevel logistic

regression

Le (2016) 86 Saskatoon, Canada [C] 1221 10‐14 in 2011 Elementary school students Logistic regression
Leatherdale (2011) 87 Ontario, Canada [S] 1207 Grades 5‐8 in 2007‐2008 School children at grades 5‐8 Multilevel logistic regression
Li (2011) 88 Xi'an, China [C] 1792 11‐17 in 2004 Junior high students Multilevel linear regression
Liu (2007) 89 Marion, Indiana, USA [CT] 7334 3‐18 in 2000 Children for routine well‐child care Logistic regression
Longacre (2012) 90 New Hampshire and Vermont, USA [S2] 1547 12‐18 in 2007‐2008 Grades 7‐11 students Poisson regression
Mellor (2011) 91 Virginia, USA [S] 2023 11.4 ± 1.7 in 2006 Grades 3, 6, and 7 students Linear and logistic regression
Miller (2014) 92 Perth, Australia [C] 1850 5‐15 in 2005‐2010 NA Logistic regression
Ohri‐Vachaspati (2015) 93 New Jersey, USA [S] 560 3‐18 years in 2009‐2010 NA Multivariate logistic regression
Oreskovic (2009) 94 Massachusetts, USA [S] 6680 2‐18 in 2006

Children from a Partners

HealthCare outpatient affiliate

Multilevel logistic regression
Oreskovic (2009) 95 Massachusetts, USA [S] 21 008 2‐18 in 2006

Children from a Partners

HealthCare outpatient affiliate

Multilevel logistic regression
Pabayo (2012) 96 Edmonton, Canada [C] 1760 4‐5 in 2005‐2007 Pre‐school children Multivariate logistic regression
Park (2013) 97 Seoul, South Korea [C] 939 12.1 ± 1.8 in 2011 Grades 4‐9 students Generalized estimating equation
Powell (2009) 98 USA [N] 6594 6‐17 (12.0 ± 3.2) in 1998, 2000, and 2002 NA Multilevel linear regression
Powell (2011) 99 USA [N] 1134 12‐18 (14.8 ± 1.9) in 1997 and 2002‐2003 (measured in 1997 and 2002‐2003) Multivariate linear regression
Salois (2012) 100 USA [N] 2192 counties 2‐4 in 2007‐2009 Low‐income preschool children Multivariate linear regression
Sanchez (2012) 101 California, USA [S] 926 018 in 2007 Grades 5, 7, and 9 students Multilevel logistic regression
Seliske (2009) 102 Canada [N] 7281 11‐16 in 2005‐2006 Grades 6‐10 students Multilevel logistic regression
Shareck (2018) 103 London, UK [C] 3089 13‐15 in 2014 Year 9 students in secondary schools Poisson regression
Shier (2016) 104 USA [N] 903 12‐13 in 2013 Children in military families Multivariate linear regression
Svastisalee (2012) 105 Denmark [N] 6034 11‐15 in 2006 Grades 5, 7, and 9 students Multilevel logistic regression
Svastisalee (2016) 106 Denmark [N] 4642 11‐15 in 2010 Grades 5, 7, and 9 students Multilevel logistic regression
Tang (2014) 107 New Jersey, USA [C4] 12 954 13.5 ± 3.5 in 2008‐2009 Middle and high school students in low‐income communities Multilevel linear regression
Timperio (2008) 108 Melbourne and Geelong, Australia [C2] 801 340 aged 5‐6 and 461 aged 10‐12 in 2002‐2003 School children Logistic regression
Van Hulst (2012) 109 Quebec, Canada [C] 512 8‐10 in 2005‐2008 Grades 2‐5 students Logistic regression and generalized estimating equation
Wall (2012) 110 Minneapolis/St. Paul, USA [C] 2682 14.5 ± 2.0 in 2009‐2010 Public middle and high school students Multivariate linear regression
Wasserman (2014) 111 Kansas, USA [C2] 12 118 4‐12 in 2008‐2009 Elementary school children Multilevel linear regression
Williams (2015) 112 Berkshire, UK [CT] 16 956 4‐5 y and 10‐11 y in 2010‐2011 Primary school children Multilevel linear and logistic regression

Abbreviation: NA, not available.

a

Studies included in meta‐analyses are in bold.

b

Study area: [N], national; [S], state (eg, in the United States) or equivalent unit (eg, province in China and Canada); [Sn], n states or equivalent units; [CT], county or equivalent unit; [CTn], n counties or equivalent units; [C], city; [Cn], n cities.

c

Sample age: age in baseline year for longitudinal studies or mean age in survey year for cross‐sectional studies.

3.3. Measures of FFR access

Access to FFRs was usually measured by the presence (n = 20), number (n = 50), and density (n = 23) of FFRs within various types of buffer zones and the distance to the nearest FFR (n = 28; Table S1). Other measures included the proportion of FFRs over all restaurants or food venues (n = 4) and weighted accessibility score (n = 3), which was calculated from measures of number and distance in the neighbourhood. Most of these measures used road‐network (n = 36) or straight‐line distance (n = 34) around the centroid of home (n = 56) and/or school (n = 34). Some studies also focused on home‐school travel route (n = 4) and community child centre (n = 1). Among studies that used measures within road‐network–based buffer zones, the radii of buffer zones ranged from 0.16 to 3.0 km, and the most commonly used ones were 0.8 (n = 13), 0.4 (n = 9), and 1.6 km (n = 7). In contrast, among studies that measured FFR access within straight‐line buffer zones, a greater range of radii, from 0.16 to 8.0 km, was observed, and the commonly used ones included 1.0 (n = 13), 0.8 (n = 8), and 1.6 km (n = 6). Other measures of buffer zones included 5 to 20 minutes' walking time (n = 3), postal zone (n = 4), and census tract (n = 5).

3.4. Measures of weight‐related behaviours and outcomes

Of the 87 included studies, 35 studies have used weight‐related behaviours as outcome variables, which were usually measured by food consumption (n = 33) and physical activity (n = 3). Measures of food consumption varied a lot across studies, including food consumption frequency (n = 23), dietary quality scores calculated based on food frequency questionnaire (n = 8), average daily nutrition intake (n = 2), food purchasing frequency (n = 2), and place having lunch (n = 1). Measures of physical activity included the time of exercise and time of sedentary behaviours.

Weight‐related outcomes were used in 61 studies, where measures included overweight/obesity (n = 37), BMI z‐score (n = 15), BMI (n = 10), BMI percentile (n = 6), BMI standard deviation score (SDS) (n = 2), waist circumference SDS (n = 1), height z‐score (n = 1), weight z‐score (n = 1), waist circumference z‐score (n = 1), waist circumference (n = 1), triceps skinfold thickness (TSF) z‐score (n = 1), waist‐height ratio (n = 1), weight gain (n = 1), and body fat percentage (n = 1). The BMI references used included the US Centers for Disease Control and Prevention (CDC) growth charts (n = 32), WHO growth reference (n = 3), UK BMI reference (n = 2), 2012 guidelines of Korea CDC (n = 1), and reference values of Kromeyer‐Haushild (n = 1). Most of weight‐related outcomes were objectively measured (n = 49), while some were self‐ or parent‐reported (n = 12).

3.5. FFR access and weight‐related behaviours

In terms of dietary behaviours related to FFR access, the most commonly studied types of food included fast food, fruit and vegetable, juice, and sugar‐sweetened beverages. Of the 14 studies analysing the frequency of fast‐food consumption, 11 studies reported a positive association between FFR access and fast‐food consumption, with this association observed only among boys in two studies; such association varied in rural (positive) and urban (negative) areas in one study, and the other two studies reported a null association. Nine of 14 studies examining FFR access and fruit/vegetable consumption reported a null association between them, while three reported a negative association and two reported a positive association. Two of five studies focusing on FFR access and juice consumption reported a negative association and the other three studies reported a null association. For the consumption of sugar‐sweetened beverages, five of eight studies reported null associations while three reported a positive association. Seven studies using a dietary quality score as the outcome variable reported positive (n = 2), negative (n = 3), and null (n = 2) associations with FFR access. No associations were found between FFR access and nutritional intake. In terms of physical (in)activity, most associations were not significant for both exercise (n = 2) and sedentary behaviours (n = 2), whereas one study reported a positive association for exercise.

3.6. FFR access and weight status

When using continuous weight measures as outcome variables (ie, BMI and BMI percentile/z‐score/SDS), the majority of the included cohort studies reported non‐significant associations with FFR access and weight‐related outcomes (n = 9), with only one study reporting a positive association. Half of the cohort studies using overweight/obesity as outcome variables reported a positive association (n = 3), while the other half reported non‐significant associations (n = 3). Twenty‐four cross‐sectional studies used BMI‐related continuous measures as weight‐related outcomes, where 19 studies reported null associations, nine studies reported a positive association, and two studies reported a negative association. Overweight/obesity was used as outcome variables in 33 cross‐sectional studies, where 26 studies reported null associations, and positive and negative associations were reported in 11 and four studies, respectively. In addition, the majority of the studies (n = 8) conducting stratified analyses showed different associations with FFR access among stratified subgroups, including gender (n = 5), age (n = 4), income (n = 1), ethnicity (n = 1), and grades (n = 1).

3.7. Meta‐analyses between FFR access and weight status

We conducted separate meta‐analyses for different measures of FFR access (i.e., presence of FFRs, density of FFRs, and distance to the nearest FFR) and outcomes (i.e., overweight/obesity, obesity, BMI, BMI percentile, and BMI z‐score) (Table 2). Although most studies suggested a positive association between FFR access and weight‐related outcomes, none of the meta‐analyses demonstrated significant results. For instance, the pooled odds ratio for the presence of FFRs and overweight/obesity was 1.01 (95% CI, 0.97‐1.05) based on 13 included studies (Figure 2). The pooled odds ratio for the number of FFRs and overweight/obesity was 1.00 (95%CI, 0.99‐1.01) based on 15 included studies (Figure 3). The pooled beta coefficient for the density of FFRs and BMI z‐score was 0.0006 (95% CI, −0.0015 to 0.0027) based on eight included studies. The meta‐analysis for the density of FFRs and BMI showed a negative but not significant association (β = −0.0275; 95% CI, −0.3132 to 0.2582)3.

TABLE 2.

Meta‐analyses of associations between access to fast‐food restaurants (FFRs) and weight status

Author (Year)[ref] Study Design 1 Study Area [Scale] 2 Sample Size FFR Measures Weight‐related Outcomes Estimated Effect Pooled Effect Size (95% CI) I 2 Index
Presence of FFRs and overweight/obesity (N = 13)
Hamano (2017) 32 LO Sweden [N] 944 487 Presence of FFRs within 1‐km straight‐line buffer around home Hospital or out‐patient diagnosis of childhood obesity

OR (95% CI)

0.99 (0.94‐1.05)

OR (95% CI)

1.01 (0.97‐1.05) random

50%
Leung (2011) 35 LO California, USA [CT4] 353 Presence of FFRs within 0.4‐km road‐network buffers around home Overweight/obesity (BMI percentile ≥85th on the 2000 US CDC growth charts)

OR (95% CI)

0.82 (0.28‐2.44)

Correa (2018) 54 CS Florianopolis, Brazil [C] 2195 Presence of FFRs within 0.4‐km straight‐line buffer around home Overweight/Obesity (BMI z‐score > +1SD based on the 2007 WHO growth reference, equivalent to BMI ≥25 kg/m2 in adults)

OR (95% CI)

1.00 (0.75‐1.34)

Crawford (2008) 55 CS Melbourne, Australia [C] 380 Presence of FFRs within 2‐km straight‐line buffer around home Overweight/obesity based on IOTF cut‐offs, equivalent to BMI ≥25 kg/m2 in adults

OR (95% CI)

8‐ to 9‐year‐old boys: 1.52 (0.84‐2.76); 8‐ to 9‐year‐old girls: 0.48 (0.06‐3.60); 13‐ to 15‐year‐old boys: 0.63 (0.19‐2.10); 13‐ to 15‐year‐old girls: 0.19 (0.09‐0.41)

0.56 (0.17‐1.84) random

Davis (2009) 57 CS California, USA [S] 529 367 Presence of FFRs within 0.8 km from school Overweight/obesity (BMI ≥85th percentile) based on the US CDC growth charts

OR (95% CI)

1.06 (1.02‐1.10)

Heroux (2012) 69 CS Canada, Scotland, and the USA [N3] 26 778 Presence of FFRs within 1‐km straight‐line buffer around school Overweight/obesity based on IOTF cut‐offs, equivalent to BMI ≥25 kg/m2 in adults

OR (95% CI)

Canadian youth (n = 11 945): 0.92 (0.83‐1.03); Scottish youth (n = 4697): 0.94 (0.74‐1.20); US youth (n = 4928): 1.08 (0.96‐1.21)

0.98 (0.88‐1.10) random

Mellor (2011) 91 CS Virginia, USA [S] 2023 Presence of FFRs within 0.16‐/0.4‐/0.8‐/1.6‐km road‐network buffers around home Obesity (BMI ≥95th percentile) based on the 2000 US CDC growth charts

OR (95% CI)

0.16‐km buffer zone: 3.83 (0.94‐15.63); 0.4‐km buffer zone: 1.05 (0.67‐1.65); 0.8‐km buffer zone: 1.19 (0.80‐1.77); 1.6‐km buffer zone: 0.94 (0.64‐1.39)

1.09 (0.86‐1.38) fixed

Miller (2014) 92 CS Perth, Australia [C] 1850 Presence of FFRs within 0.8‐km road‐network buffer around home Overweight/obesity (BMI ≥85th percentile) based on the 2000 US CDC growth charts

OR (95% CI)

0.691 (0.529‐0.903)

Ohri‐Vachaspati (2015) 93 CS New Jersey, USA [S] 560 Presence of FFRs within 0.4‐km road‐network buffer around home Overweight/obesity (BMI ≥85th percentile) based on the 2000 US CDC growth charts

OR (95% CI)

0.67 (0.38‐1.20)

Oreskovic (2009) 95 CS Massachusetts, USA [S] 21 008 Presence of FFRs within 0.4‐km road‐network buffer around home Overweight/obesity (BMI ≥85th percentile) based on the 2000 US CDC growth charts

OR (95% CI)

1.05 (0.98‐1.12)

Seliske (2009) 102 CS Canada [N] 7281 Presence of FFRs within 1‐km straight‐line buffer around school Overweight/obesity based on IOTF cut‐offs, equivalent to BMI ≥25 kg/m2 in adults

OR (95% CI)

0.83 (0.70‐0.98)

Shier (2016) 104 CS USA [N] 903 Parent‐perceived presence of FFRs within 20‐min walk from home Overweight/obesity (BMI ≥85th percentile)

β (SE)

0.020 (0.030)

OR (95% CI)

1.02 (0.96‐1.08)

Tang (2014) 107 CS New Jersey, USA [C4] 12 954 Presence of FFRs within 0.4‐km road‐network buffer around school Overweight/obesity (BMI percentile ≥85th)

β (95% CI)

0.03 (−0.004 to 0.06)

OR (95% CI)

1.03 (1.00‐1.07)

Presence of FFRs and obesity (N = 4)
Hamano (2017) 32 LO Sweden [N] 944 487 Presence of FFRs within 1‐km straight‐line buffer around home Hospital or out‐patient diagnosis of childhood obesity

OR (95% CI)

0.99 (0.94‐1.05)

OR (95% CI)

1.04 (0.99‐1.09) random

42%
Davis (2009) 57 CS California, USA [S] 529 367 Presence of FFRs within 0.8 km from school Obesity (BMI ≥95th percentile) based on the US CDC growth charts

OR (95% CI)

1.07 (1.02‐1.12)

Mellor (2011) 91 CS Virginia, USA [S] 2023 Presence of FFRs within 0.16/0.4/0.8/1.6‐km road‐network buffers around home Obesity (BMI ≥95th percentile) based on the 2000 US CDC growth charts

OR (95% CI)

0.16‐km buffer zone: 3.83 (0.94‐15.63); 0.4‐km buffer zone: 1.05 (0.67‐1.65); 0.8‐km buffer zone: 1.19 (0.80‐1.77); 1.6‐km buffer zone: 0.94 (0.64‐1.39)

1.09 (0.86‐1.38) fixed

Oreskovic (2009) 95 CS Massachusetts, USA [S] 21 008 Presence of FFRs within 0.4‐km road‐network buffer around home Obesity (BMI ≥95th percentile) based on the 2000 US CDC growth charts

OR (95% CI)

1.06 (0.98‐1.14)

Number of FFRs and overweight/obesity (N = 15)
Bader (2013) 45 CS New York, USA [C] 94 348 Number of FFRs in the residential census tract Overweight/obesity (BMI ≥85th percentile) based on the 2011 US CDC growth charts

OR (95% CI)

0.972 (0.957‐0.988)

OR (95% CI)

1.00 (0.99‐1.01) random

89%
Choo (2017) 20 CS Seoul, South Korea [C] 126 Number of Western FFRs within 0.2‐km straight‐line buffer around community child centre Obesity (BMI ≥95th percentile or BMI >25 kg/m2) based on the 2012 guidelines of Korea Centers for Disease Control and Prevention

OR (95% CI)

0.87 (0.608‐1.245)

Crawford (2008) 55 CS Melbourne, Australia [C] 380 Number of FFRs within 2‐km straight‐line buffer around home Overweight/obesity based on IOTF cut‐offs, equivalent to BMI ≥25 kg/m2 in adults

OR (95% CI)

8‐ to 9‐year‐old boys: 0.96 (0.84‐1.10); 8‐ to 9‐year‐old girls: 0.82 (0.63‐1.08); 13‐ to 15‐year‐old boys: 0.91 (0.78‐1.06); 13‐ to 15‐year‐old girls: 0.86 (0.74‐0.99)

0.90 (0.83‐0.98) fixed

Fraser (2010) 62 CS Leeds, West Yorkshire, UK [C] 33 594 Number of FFRs in residential super‐output area (SOA) Overweight/obesity (BMI ≥85th percentile)

OR (95% CI)

1.01 (1.002‐1.02)

Gorski Findling (2018) 65 CS USA [N] 3748 Number of FFRs within 1.6‐km straight‐line buffer around home Overweight/obesity (BMI ≥85th percentile) based on the 2000 US CDC growth charts

OR (95% CI)

0.99 (0.96‐1.02)

Larsen (2015) 83 CS Toronto, Canada [C] 943 Number of FFRs within 1‐km road‐network buffer around home Overweight and obesity based on IOTF cut‐offs, equivalent to BMI ≥25 kg/m2 in adults

OR (95% CI)

0.978 (0.953‐1.003)

Leatherdale (2011) 87 CS Ontario, Canada [S] 1207 Number of FFRs within 1‐km straight‐line buffer around school Obesity (BMI percentile ≥95th based on the 2000 US CDC growth charts)

OR (95% CI)

0.96 (0.82‐1.13)

Mellor (2011) 91 CS Virginia, USA [S] 2023 Number of FFRs within 0.16‐/0.4‐/0.8‐/1.6‐km road‐network buffer around home Obesity (BMI ≥95th percentile) based on the 2000 US CDC growth charts

OR (95% CI)

0.16‐km buffer zone: 3.07 (0.75‐12.59); 0.4‐km buffer zone: 1.04 (0.92‐1.19); 0.8‐km buffer zone: 0.97 (0.89‐1.06); 1.6‐km buffer zone: 0.98 (0.94‐1.03)

0.98 (0.95‐1.02) fixed

Miller (2014) 92 CS Perth, Australia [C] 1850 Number of FFRs within 0.8‐/3‐km road‐network buffer around home Overweight/obesity (BMI ≥85th percentile) based on the 2000 US CDC growth charts

OR (95% CI)

0.8‐km buffer zone: 0.961 (0.919‐1.006); 3‐km buffer zone: 0.993 (0.988‐0.999)

0.98 (0.96‐1.01) random

Oreskovic (2009) 94 CS Massachusetts, USA [S] 6680 Number of FFRs within 0.4‐km road‐network buffer around home Overweight (BMI ≥85th percentile) based on the 2000 US CDC growth charts

OR (95% CI)

high‐income towns: 1.09 (0.82‐1.26) low‐income towns: 1.09 (1.07‐1.11)

1.09 (1.07‐1.11) fixed

Oreskovic (2009) 95 CS Massachusetts, USA [S] 21 008 Number of FFRs within 0.4‐km road‐network buffer around home Overweight/obesity (BMI ≥85th percentile) based on the 2000 US CDC growth charts

OR (95% CI)

0.98 (0.96‐1.01)

Park (2013) 97 CS Seoul, South Korea [C] 939 Number of FFRs within 0.5‐km straight‐line buffer around school Overweight/obesity (BMI ≥85th percentile) based on the 2007 Korean National Growth Charts

OR (95% CI)

0.83 (0.72‐0.96)

Shier (2016) 104 CS USA [N] 903 Number of FFRs within 3.2‐km straight‐line buffer around home Overweight/obesity (BMI ≥85th percentile)

β (SE)

0.000 (0.002)

OR (95% CI)

1.00 (0.9961‐1.0039)

Tang (2014) 107 CS New Jersey, USA [C4] 12 954 Number of FFRs within 0.4‐km straight‐line buffer around school Overweight/obesity (BMI percentile ≥85th)

β (95% CI)

0.0001 (−0.004 to 0.005)

OR (95% CI)

1.0001 (0.9960‐1.0042)

Wasserman (2014) 111 CS Kansas, USA [C2] 12 118 Number of FFRs within 0.8‐km straight‐line buffer around school Overweight (BMI ≥85th percentile)

OR (95% CI)

1.02 (0.98‐1.08)

Number of FFRs and obesity (N = 8)
Choo (2017) 20 CS Seoul, South Korea [C] 126 Number of Western FFRs within 0.2‐km straight‐line buffer around community child centre Obesity (BMI ≥95th percentile or BMI >25 kg/m2) based on the 2012 guidelines of Korea Centers for Disease Control and Prevention

OR (95% CI)

0.87 (0.608‐1.245)

OR (95% CI)

1.02 (0.98‐1.07) random

90%
Fraser (2010) 62 CS Leeds, West Yorkshire, UK [C] 33 594 Number of FFRs in residential super‐output area (SOA) Obesity (BMI ≥95th percentile)

OR (95% CI)

1.01 (1.002‐1.02)

Leatherdale (2011) 87 CS Ontario, Canada [S] 1207 Number of FFRs within 1‐km straight‐line buffer around school Obesity (BMI percentile ≥95th based on the 2000 US CDC growth charts)

OR (95% CI)

0.96 (0.82‐1.13)

Mellor (2011) 91 CS Virginia, USA [S] 2023 Number of FFRs within 0.16‐/0.4‐/0.8‐/1.6‐km road‐network buffer around home Obesity (BMI ≥95th percentile) based on the 2000 US CDC growth charts

OR (95% CI)

0.16‐km buffer zone: 3.07 (0.75‐12.59); 0.4‐km buffer zone: 1.04 (0.92‐1.19); 0.8‐km buffer zone: 0.97 (0.89‐1.06); 1.6‐km buffer zone: 0.98 (0.94‐1.03)

0.98 (0.95‐1.02) fixed

Oreskovic (2009) 94 CS Massachusetts, USA [S] 6680 Number of FFRs within 0.4‐km road‐network buffer around home Obesity (BMI ≥95th percentile) based on the 2000 US CDC growth charts

OR (95% CI)

high‐income towns: 0.95 (0.72‐1.25); low‐income towns: 1.13 (1.10‐1.16)

1.13 (1.10‐1.16) fixed

Oreskovic (2009) 95 CS Massachusetts, USA [S] 21 008 Number of FFRs within 0.4‐km road‐network buffer around home Obesity (BMI ≥95th percentile) based on the 2000 US CDC growth charts

OR (95% CI)

0.99 (0.96‐1.02)

Park (2013) 97 CS Seoul, South Korea [C] 939 Number of FFRs within 0.5‐km straight‐line buffer around school Obesity (BMI ≥95th percentile) based on the 2007 Korean National Growth Charts

OR (95% CI)

1.15 (0.94‐1.39)

Wasserman (2014) 111 CS Kansas, USA [C2] 12 118 Number of FFRs within 0.8‐km straight‐line buffer around school Obesity (BMI ≥95th percentile)

OR (95% CI)

1.02 (0.97‐1.08)

Distance (km) to the nearest FFR and overweight/obesity (N = 6)
Choo (2017) 20 CS Seoul, South Korea [C] 126 Road‐network distance (m) to the closest Western FFR around community child centre Obesity (BMI ≥95th percentile or BMI >25 kg/m2) based on the 2012 guidelines of Korea Centers for Disease Control and Prevention

OR (95% CI)

1.00 (0.984‐1.008)

OR (95% CI)

0.98 (0.95‐1.01) random

19%
Crawford (2008) 55 CS Melbourne, Australia [C] 380 Road‐network distance (km) to the nearest FFR from home Overweight/obesity based on IOTF cut‐offs, equivalent to BMI ≥25 kg/m2 in adults

OR (95% CI)

8‐ to 9‐year‐old boys: 0.99 (0.86‐1.15); 8‐ to 9‐year‐old girls: 1.02 (0.83‐1.25); 13‐ to 15‐year‐old boys: 1.08 (0.89‐1.30); 13‐ to 15‐year‐old girls: 1.18 (0.96‐1.45)

1.05 (0.96‐1.15) fixed

Larsen (2015) 83 CS Toronto, Canada [C] 943 Road‐network distance (km) to the nearest FFR from home Overweight and obesity based on IOTF cut‐offs, equivalent to BMI ≥25 kg/m2 in adults

OR (95% CI)

1.261 (0.871‐1.825)

Miller (2014) 92 CS Perth, Australia [C] 1850 Road‐network distance (m) to the nearest FFR from home Overweight/obesity (BMI ≥85th percentile) based on the 2000 US CDC growth charts

OR (95% CI)

1.000 (1.000‐1.000)

Oreskovic (2009) 94 CS Massachusetts, USA [S] 6680 Road‐network distance (km) to the nearest FFR from home Overweight (BMI ≥85th percentile) based on the 2000 US CDC growth charts

OR (95% CI)

high‐income towns: 0.93 (0.86‐1.00); low‐income towns: 0.97 (0.92‐1.03)

0.96 (0.92‐1.00) fixed

Oreskovic (2009) 95 CS Massachusetts, USA [S] 21 008 Road‐network distance (km) to the nearest FFR from home Overweight (BMI ≥85th percentile) based on the 2000 US CDC growth charts

OR (95% CI)

0.98 (0.95‐1.00)

Distance (km) to the nearest FFR and obesity (N = 3)
Choo (2017) 20 CS Seoul, South Korea [C] 126 Road‐network distance (m) to the closest Western FFR around community child centre Obesity (BMI ≥95th percentile or BMI >25 kg/m2) based on the 2012 guidelines of Korea Centers for Disease Control and Prevention

OR (95% CI)

1.00 (0.984‐1.008)

OR (95% CI)

0.93 (0.84‐1.02) random

65%
Oreskovic (2009) 94 CS Massachusetts, USA [S] 6680 Road‐network distance (km) to the nearest FFR from home Obesity (BMI ≥95th percentile) based on the 2000 US CDC growth charts

OR (95% CI)

high‐income towns: 0.93 (0.82‐1.04); low‐income towns: 0.83 (0.75‐0.91)

0.87 (0.80‐0.94) fixed

Oreskovic (2009) 95 CS Massachusetts, USA [S] 21 008 Road‐network distance (km) to the nearest FFR from home Obesity (BMI ≥95th percentile) based on the 2000 US CDC growth charts

OR (95% CI)

0.97 (0.94‐1.01)

Number of FFRs and BMI percentile (N = 2)
An (2012) 44 CS California, USA [S] 13 462 Number of FFRs within 0.8‐km straight‐line buffer around school Parent‐reported BMI percentile based on the 2000 US CDC growth charts

β (SE)

5‐11 y: −0.0009 (0.0019); 12‐17 y: −0.0025 (0.0022)

−0.0016 (0.0014) fixed

β (95% CI) 0.0990 (‐0.2124, 0.4104) Random 57%
Wasserman (2014) 111 CS Kansas, USA [C2] 12 118 Number of FFRs within 0.8‐km straight‐line buffer around school Measured BMI percentile based on the 2000 US CDC growth charts

β (SE)

0.35 (0.23)

Distance to nearest FFR and BMI z‐score (N = 2)
Crawford (2008) 55 CS Melbourne, Australia [C] 380 Road‐network distance (km) to the nearest FFR from home Measured BMI z‐score based on the 2000 US CDC growth charts

β (95% CI)

8‐ to 9‐year‐old boys: 0.05 (0.00‐0.10); 8‐ to 9‐year‐old girls: −0.04 (−0.13 to 0.05); 13‐ to 15‐year‐old boys: 0.04 (−0.06 to 0.13); 13‐ to 15‐year‐old girls: 0.03 (−0.03 to 0.09)

0.0304 (−0.0029 to 0.0637) fixed

β (95% CI) 0.0316 (0.0098‐0.0534) fixed/random 0
Lamichhane (2012) 81 CS South Carolina, USA [S] 845 Road‐network distance (mile) to the nearest FFR from home Measured BMI z‐score based on the 2000 US CDC growth charts

β (95% CI)

0.052 (0.007‐0.098) km: 0.0325 (0.0044‐0.0613)

Presence of FFRs and BMI z‐score (N = 6)
Ghenadenik (2018) 30 LO Montreal, Canada [C] 391 Presence of FFRs in residential street segment Measured BMI z‐score based on the 2010 US CDC growth charts

β (SE)

0.105 (0.185)

β (95% CI)

0.0276 (−0.0205 to 0.0757) random

27%
Crawford (2008) 55 CS Melbourne, Australia [C] 380 Presence of FFRs within 2‐km straight‐line buffer around home Measured BMI z‐score based on the 2000 US CDC growth charts

β (95% CI)

8‐ to 9‐year‐old boys: −0.02 (−0.23 to 0.25); 8‐ to 9‐year‐old girls: −0.01 (−1.11 to 1.09); 13‐ to 15‐year‐old boys: −0.49 (−0.95 to −0.03); 13‐ to 15‐year‐old girls: −0.35 (−0.69 to −0.02)

−0.1573 (−0.3220 to 0.0073) fixed

Gilliland (2012) 64 CS London, UK [C] 891 Presence of FFRs within 0.5‐km road‐network buffer around home Self‐reported BMI z‐score based on the WHO growth charts

β (SE)

0.012 (0.121)

Tang (2014) 107 CS New Jersey, USA [C4] 12 954 Presence of FFRs within 0.4‐km road‐network buffer around school BMI z‐score based on the 2000 US CDC growth charts

β (95% CI)

0.07 (−0.01 to 0.15)

Wall (2012) 110 CS Minneapolis/St Paul, USA [C] 2682 Presence of FFRs within 1.2‐km road‐network buffer around home Measured BMI z‐score based on the 2000 US CDC growth charts

β (SE)

boys: 0.095 (0.078); girls: 0.045 (0.060)

0.0636 (0.0476) fixed

Williams (2015) 112 CS Berkshire, UK [CT] 16 956 Presence of FFRs within 0.8‐km road‐network buffer around school Measured BMI z‐score based on the IOTF reference curves

β (95% CI)

0.02 (−0.02 to 0.06)

Number of FFRs and BMI z‐score (N = 8)
Chen (2016) 27 LO Arkansas, USA [S] 21 639 Number of FFRs along the most direct street route from home to school within 50‐m buffer on either side of the street Measured BMI z‐score based on the 2000 US CDC growth charts

β (95% CI)

0.0001 (−0.0004 to 0.0007)

β (95% CI)

0.0006 (−0.0015 to 0.0027) random

41%
Green (2018) 31 LO Leeds, UK [C] 746 Number of FFRs within 1‐km straight‐line buffer around home Measured BMI SDS based on the UK 1990 growth charts

β (95% CI)

−0.017 (−0.035 to 0.002)

Baek (2014) 46 CS California, USA [S] 926 018 Number of FFRs within 0.4‐/0.8‐/1.2‐km straight‐line buffer around school Measured BMI z‐score based on the 2000 US CDC growth charts

β (SE)

0.4‐km buffer zone: 1.14 × 10−3 (3.73 × 10−3); 0.8‐km buffer zone: 1.12 × 10−3 (1.97 × 10−3); 1.2‐km buffer zone: 1.72 × 10−3 (1.15 × 10−3)

0.0015 (0.0010) fixed

Crawford (2008) 55 CS Melbourne, Australia [C] 380 Number of FFRs within 2‐km straight‐line buffer around home Measured BMI z‐score based on the 2000 US CDC growth charts

β (95% CI)

8‐ to 9‐year‐old boys: −0.01 (−0.05 to 0.04); 8‐ to 9‐year‐old girls: −0.02 (−0.15 to 0.11); 13‐ to 15‐year‐old boys: −0.07 (−0.14 to 0.01); 13‐ to 15‐year‐old girls: −0.03 (−0.08 to 0.02)

−0.0262 (−0.0540 to 0.0017) fixed

Fraser (2010) 62 CS Leeds, West Yorkshire, UK [C] 33 594 Number of FFRs in residential super‐output area (SOA) Measured BMI SDS based on the UK1990 BMI reference

β (95% CI)

0.004 (−0.007 to 0.01)

Lamichhane (2012) 81 CS South Carolina, USA [S] 845 Number of FFRs within 1.6‐km road‐network buffer around home Measured BMI z‐score based on the 2000 US CDC growth charts

β (95% CI)

0.002 (−0.027 to 0.031)

Shier (2016) 104 CS USA [N] 903 Number of FFRs within 3.2‐km straight‐line buffer around home BMI z‐score based on the 2000 US CDC growth charts

β (SE)

−0.001 (0.004)

Tang (2014) 107 CS New Jersey, USA [C4] 12 954 Number of FFRs within 0.4‐km straight‐line buffer around school BMI z‐score based on the 2000 US CDC growth charts

β (95% CI)

0.01 (−0.002 to 0.02)

Presence of FFRs and BMI (N = 3)
Davis (2009) 57 CS California, USA [S] 529 367 Presence of FFRs within 0.8 km from school BMI

β (95% CI)

0.10 (0.03‐0.16)

β (95% CI)

0.2888 (−0.0942 to 0.6719) random

52%
Li (2011) 88 CS Xi'an, China [C] 1792 Presence of FFRs within 10‐min walk around school reported by school doctors Measured BMI

β (95% CI)

0.7 (0.1‐1.2)

Mellor (2011) 91 CS Virginia, USA [S] 2023 Presence of FFRs within 0.8‐km road‐network buffer around home Measured BMI

β (95% CI)

0.35 (−0.42 to 1.13)

Presence of FFRs and BMI (N = 2)
Davis (2009) 57 CS California, USA [S] 529 367 Presence of FFRs within 0.4 km from school BMI

β (95% CI)

0.12 (0.04‐0.20)

β (95% CI)

0.2420 (−0.2555 to 0.7395) random

37%
Mellor (2011) 91 CS Virginia, USA [S] 2023 Presence of FFRs within 0.4‐km road‐network buffer around home Measured BMI

β (95% CI)

0.77 (−0.24 to 1.78)

Density of FFRs and BMI (N = 2)
Powell (2009) 37 LO USA [N] 5215 Density of FFRs per 10 000 capita Self‐reported BMI

β (SE)

0.1215 (0.1164)

β (95% CI)

−0.0275 (−0.3132 to 0.2582) random

70%
Powell (2009) 98 CS USA [N] 6594 Density of FFRs per 10 000 capita Mother‐reported BMI

β (SE)

−0.1701 (0.1081)

Number of FFRs and BMI (N = 2)
Davis (2009) 57 CS California, USA [S] 529 367 Number of FFRs within 0.8‐km road‐network buffer around school BMI

β (95% CI)

0.00 (0.00‐0.00)

β (95% CI)

0.004 (−0.15 to 0.16)

NA
Mellor (2011) 91 CS Virginia, USA [S] 2023 Number of FFRs within 0.8‐km road‐network buffer around home Measured BMI

β (95% CI)

0.004 (−0.15 to 0.16)

Presence of FFRs and school overweight rates (N = 2)
Howard (2011) 72 CS California, USA [S] 879 public schools Presence of FFRs within 0.8‐km road‐network buffer around school School overweight rates based on criterion‐referenced gender‐, age‐, and test‐specific cut‐offs established by a national advisory panel

β (95% CI)

−0.04 (−1.18 to 1.10)

β (95% CI)

0.1767 (−0.5830 to 0.9365) fixed/random

0
Langellier (2012) 82 CS Los Angeles, USA [CT] 1694 schools Presence of FFRs within 0.8‐km road‐network buffer around school School overweight rates based on the sex‐ and age‐specific cut‐offs defined by the Physical Fitness Testing programme in 2009

β (SE)

0.35 (0.52)

1

Study design: LO, longitudinal; CS, cross‐sectional;

2

Study area: [N], national; [S], state (eg, in the United States) or equivalent unit (eg, province in China and Canada); [Sn], n states or equivalent units; [CT], county or equivalent unit; [CTn], n counties or equivalent units; [C], city; [Cn], n cities.

FIGURE 2.

FIGURE 2

Meta‐analysis of associations between presence of fast‐food restaurants in the neighbourhood and childhood overweight/obesity [Colour figure can be viewed at wileyonlinelibrary.com]

FIGURE 3.

FIGURE 3

Meta‐analysis of associations between density of fast‐food restaurants in the neighbourhood and childhood overweight/obesity [Colour figure can be viewed at wileyonlinelibrary.com]

3.8. Study quality assessment

Table S2 reported criterion‐specific and global ratings from the study quality assessment. The included studies scored 10.1 of 14 on average, with a range from seven to 14.

4. DISCUSSION

In this review, 87 studies focusing on FFR access and childhood obesity were selected and based on which meta‐analyses were conducted. This pool was composed of studies with various study designs (ie, 16 cohort studies and 71 cross‐sectional studies) and study locations (ie, 14 different countries). Weight‐related behaviours and outcomes were used as outcome variables in 35 and 61 studies, respectively. Although FFR access was positively associated with fast‐food consumption in the majority of studies focusing on that relationship, its associations with other weight‐related behaviours, including frequency of food consumption, dietary quality score, and physical activity, were either mixed or not significant. For the association between FFR access and weight‐related outcomes, no associations were reported in most studies when using BMI‐related continuous measures; when using overweight/obesity measures, about half of cohort studies and one‐third of cross‐sectional studies reported a positive association. No significant results were observed in separate meta‐analyses between various measures of FFR access and body weight.

One previous review on local food environment and obesity in North America has covered the association between FFR access and childhood obesity, where the majority of studies found no associations, with half of the remaining studies reporting a positive association and the other half reporting a negative association. 16 Also, the availability of fast food was more consistently associated with higher risk of obesity in low‐income populations. Likewise, with mixed findings from the included studies (some evidence supporting a positive association but others suggesting a null association), this review also cannot provide a clear‐cut answer for the association between FFR access and childhood obesity. 16 Some parts of our hypothesis on the association between FFR access, unhealthy food consumption, and childhood obesity have been supported. For example, 10 of 14 included studies supported the link between FFR access and increased fast‐food consumption; a systematic review also supported the association between fast‐food consumption and increased caloric intake. 13 However, the association between fast‐food consumption and childhood obesity may be subject to many confounding factors, especially at the population level, as childhood obesity may be attributed to other factors including genetic susceptibility, prenatal and early life factors, consumption of other unhealthy or healthy food, physical activity, and family factors. 17 Therefore, the association between fast‐food consumption and childhood obesity is less easier to access without careful control for confounders. 18 More modern technologies used in spatial lifecourse epidemiologic research, such as location‐based mobile services, ecological momentary assessment, and things of Internet, need to be applied to collect more data on food acquisition and consumption and other behaviours that may result in and prevent obesity. 6 , 19 In addition, many studies have reported different associations when stratified by factors including age, gender, ethnicity, and income. Thus, it is also possible that the association between FFR access and childhood obesity only applies to certain subgroups and is not significant at the population level.

The results of the included studies may be influenced by several factors. First, the variation of food served in different FFRs may to some extent affect study results. For instance, one study analysed both Korean and western FFRs and reported that fast‐food consumption was significantly associated with access to Korean FFRs instead of access to western FFRs, which may be explained by the higher density of Korean FFRs than of western FFRs in the study area. 20 It is also important to note that many FFRs have started to change its unhealthy public image by providing relatively healthy food choices, like salad and sugar‐free beverages. Furthermore, some countries have taken measures to control excessive fast‐food eating by providing nutrition information (ie, calorie intake) on the menu. The actual effects of these measures on obesity prevention among children and adolescents are expected to be examined and adjusted in future studies. This also implies that definitions of the FFR and the fast food sold in those venues need to be made clear in each specific context. Second, variations in measures of FFR access were observed among the included studies. Although the measures used were either the number of FFRs or the distance to the nearest FFR, the definition of the neighbourhood area based on which measures were taken can be quite different, where the radii of buffer zones could range from 0.16 to 8 km. The administrative or postal zone boundaries were used where individual addresses were not available. 21 We suggested that the measurement in one study should be conducted at multiple scales for better comparability with other studies, 8 also to better present the results with the Modifiable Areal Unit Problem overcome. 22 , 23 Moreover, measuring the access to FFRs has been more challenging and complicated with the development of transportation and communication. Nowadays, it becomes increasingly convenient to commute in different parts of a city, and an increasing number of people, especially the young, have started to choose ordering food online. Thus, the definition and delineation of the area in which people have convenient access to certain food venues may need to change accordingly. 24 The advanced spatial and location‐based applications may aid in measuring the realistic access to fast food. 25 Third, different from most types of food venues, FFRs appear more frequently along the highways (eg, in rest areas and near gas stations), so fast‐food purchasing may happen in neither residential areas nor school neighbourhoods. Therefore, to comprehensively examine the effects of FFRs on childhood obesity needs the coordination of multiple stakeholders in the real world to track movement patterns of patrons and flows of fast food.

This systematic review, especially meta‐analyses, was limited by the number of studies available. We need to analyse separately for different combinations of exposure and outcomes, as it is not feasible to seamlessly synthesize different measures of access to FFR, as well as different measures of weight‐related behaviours and outcomes. Also, it is not feasible to conduct meta‐analyses for combinations with insufficient studies. We also noticed that the majority of the included studies were cross‐sectional studies, which has prevented us from examining effects of FFR access on individual weight status over time. Future studies were needed to focus on longitudinal associations to investigate their causality. 26

5. CONCLUSIONS

This systematic review revealed a rather mixed association between FFR access and weight‐related behaviours/outcomes among children and adolescents. Methods of defining and measuring FFR access need to be improved to better estimate individuals' exposure to FFRs. Also, research on pathways from FFR access to childhood obesity is needed to allow multiple stakeholders to design effective interventions and policies for prevention of childhood obesity.

CONFLICT OF INTEREST

No conflict of interest was declared.

Supporting information

Figure S1. Funnel plot for meta‐analysis of associations between presence of fast‐food restaurants in the neighborhood and childhood overweight/obesity

Figure S2. Funnel plot for meta‐analysis of associations between density of fast‐food restaurants in the neighborhood and childhood overweight/obesity

Table S1. Measures of access to fast‐food restaurants (FFRs), weight‐related behaviors, and body‐weight status in the included studies (see [ref] in the main text)

Table S2. Study quality assessment (see [ref] in the main text)

ACKNOWLEDGEMENTS

We thank the International Institute of Spatial Lifecourse Epidemiology (ISLE) and the National Natural Science Foundation of China (81872641) for research support. [Correction added on 8 February 2021, after first online publication: Acknowledgements have been revised.]

APPENDIX A. SEARCH STRATEGY

The search strategy includes all possible combinations of keywords in the title/abstract from the following three groups:

  1. “fast‐food restaurant*,” “fast‐food restaurant*,” “fastfood restaurant*,” “fast food outlet*,” “fast‐food outlet*,” “fastfood outlet*,” “takeaway restaurant*,” “take‐away restaurant*,” “take away restaurant*,” “takeaway outlet*,” “take‐away outlet*,” “take away outlet*,” “takeout restaurant*,” “take‐out restaurant*,” “take out restaurant*,” “takeout outlet*,” “take‐out outlet*,” “take out outlet*,” “limited‐service restaurant*,” “limited service restaurant,” “quick‐service restaurant,” “quick service restaurant*”;

  2. “child*,” “juvenile*,” “pubescent*,” “pubert*,” “adolescen*,” “youth*,” “teen*,” “kid*,” “young*,” “youngster*,” “minor*,” “student*,” “pupil*,” “pediatric*,” “preschooler*,” “pre‐schooler*,” “schoolchild*,” “school‐child*,” “school child*,” “schoolage*,” “school‐age*,” “school age*”;

  3. “diet*,” “diet behavio*,” “dietary behavio*,” “eating*,” “eating behavio*,” “food*,” “food intak*,” “food consum*,” “energy intak*,” “energy consum*,” “energy balance,” “calorie*,” “caloric intak*,” “physical activit*,” “physical exercis*,” “exercis*,” “body activit*,” “body mass index,” “BMI,” “weight,” “weight status,” “weight‐related behavio*,” “weight‐related health,” “overweight,” “obese,” “obesity,” “adiposity,” “abdominal overweight,” “abdominal obesity,” “central overweight,” “central obesity,” “central adiposity,” “waist circumference,” “waist to hip,” “waist‐to‐hip,” “waist to height,” “waist‐to‐height,” “waist to stature,” “waist‐to‐stature,” “fatness,” “body fat,” “excess fat,” “excess weight,” “overnutrition,” “over‐nutrition,” “over nutrition.”

Jia P, Luo M, Li Y, Zheng J‐S, Xiao Q, Luo J. Fast‐food restaurant, unhealthy eating, and childhood obesity: A systematic review and meta‐analysis. Obesity Reviews. 2021;22(S1):e12944. 10.1111/obr.12944

Peng Jia and Miyang Luo have equal contribution.

[Correction added on 14 January 2021, after first online publication: Peng Jia's affiliations have been updated.]

[Correction added on 8 February 2021, after first online publication: Funding Information has been revised.]

Contributor Information

Peng Jia, Email: jiapengff@hotmail.com.

Jiayou Luo, Email: jiayouluo@csu.edu.cn.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1. Funnel plot for meta‐analysis of associations between presence of fast‐food restaurants in the neighborhood and childhood overweight/obesity

Figure S2. Funnel plot for meta‐analysis of associations between density of fast‐food restaurants in the neighborhood and childhood overweight/obesity

Table S1. Measures of access to fast‐food restaurants (FFRs), weight‐related behaviors, and body‐weight status in the included studies (see [ref] in the main text)

Table S2. Study quality assessment (see [ref] in the main text)


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