Are fast food restaurants an environmental risk factor for obesity?

Abstract

Eating at "fast food" restaurants has increased and is linked to obesity. This study examined whether living or working near "fast food" restaurants is associated with body weight.
A telephone survey of 1033 Minnesota residents assessed body height and weight, frequency of eating at restaurants, and work and home addresses. Proximity of home and work to restaurants was assessed by Global Index System (GIS) methodology.
Eating at "fast food" restaurants was positively associated with having children, a high fat diet and Body Mass Index (BMI). It was negatively associated with vegetable consumption and physical activity. Proximity of "fast food" restaurants to home or work was not associated with eating at "fast food" restaurants or with BMI. Proximity of "non-fast food" restaurants was not associated with BMI, but was associated with frequency of eating at those restaurants.
Failure to find relationships between proximity to "fast food" restaurants and obesity may be due to methodological weaknesses, e.g. the operational definition of "fast food" or "proximity", or homogeneity of restaurant proximity. Alternatively, the proliferation of "fast food" restaurants may not be a strong unique cause of obesity.

Full text

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BioMed Central
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International Journal of Behavioral
Nutrition and Physical Activity
Open Access
Research
Are fast food restaurants an environmental risk factor for obesity?
Robert W Jeffery*1, Judy Baxter1, Maureen McGuire2 and Jennifer Linde1
Address: 1Division of Epidemiology & Community Health, University of Minnesota School of Public Health, 1300 South 2nd Street, Suite 300,
Minneapolis, MN 55454-1015, USA and 2Guidant Corporation, Cardiac Rhythm Management Group, 4100 Hamline Ave., St. Paul, MN 55112,
USA
Email: Robert W Jeffery* - Jeffery@epi.umn.edu; Judy Baxter - baxter@epi.umn.edu; Maureen McGuire - maureen.mcguire@guidant.com;
Jennifer Linde - linde@epi.umn.edu
* Corresponding author
Abstract
Objective: Eating at "fast food" restaurants has increased and is linked to obesity. This study
examined whether living or working near "fast food" restaurants is associated with body weight.
Methods: A telephone survey of 1033 Minnesota residents assessed body height and weight,
frequency of eating at restaurants, and work and home addresses. Proximity of home and work to
restaurants was assessed by Global Index System (GIS) methodology.
Results: Eating at "fast food" restaurants was positively associated with having children, a high fat
diet and Body Mass Index (BMI). It was negatively associated with vegetable consumption and
physical activity. Proximity of "fast food" restaurants to home or work was not associated with
eating at "fast food" restaurants or with BMI. Proximity of "non-fast food" restaurants was not
associated with BMI, but was associated with frequency of eating at those restaurants.
Conclusion: Failure to find relationships between proximity to "fast food" restaurants and obesity
may be due to methodological weaknesses, e.g. the operational definition of "fast food" or
"proximity", or homogeneity of restaurant proximity. Alternatively, the proliferation of "fast food"
restaurants may not be a strong unique cause of obesity.
Introduction
Rapid increases in the prevalence of obesity in the US over
the last 20 to 30 years have been well documented and
their causes much discussed [1-4]. Because of the speed of
the change, it has been argued that the cause is more likely
to be environmental change, broadly conceptualized to
include physical and social factors, than to biological
change. Exploration of data on environmental trends has
identified a variety of potential environmental contribu-
tors. One trend that has attracted particular attention in
both the scientific and lay press is the dramatic increase in
eating away from home, and particularly at "fast food"
outlets. The scientific case for "fast food" restaurants as a
causal factor in obesity is based on several observations.
First, time trends in eating away from home roughly par-
allel the national time trends in obesity prevalence [5-10].
Second, although there is no clearly agreed upon defini-
tion of the concept, "fast food" outlets have been by far
the most rapidly expanding sector of the U.S. food distri-
bution system [9,11,12]. Third, cross-sectional and longi-
tudinal data on self-reported "fast food" restaurant use per
se and consumption of foods frequently sold at "fast
food" restaurants (e.g. hamburgers and French fries) have
been shown to be positively associated with body weight
Published: 25 January 2006
International Journal of Behavioral Nutrition and Physical Activity 2006, 3:2doi:10.1186/1479-5868-3-2
Received: 8 June 2005
Accepted: 25 January 2006
This article is available from: http://www.ijbnpa.org/content/3/1/2
© 2006 Jeffery et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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[13-17]. Finally, nutritional analysis of products sold in
"fast food" restaurants indicates that they are typically
high in energy density, which provides a plausible mech-
anism through which they might promote excess energy
intake [18-20].
Available data on fast food use and obesity are far from
conclusive, however. For example, the direction of causa-
tion is unclear, i.e., the menus and prices at "fast food" res-
taurants may result from the demands of an increasingly
obese population rather than being a direct cause of obes-
ity. It is also possible that a third variable, such as demo-
graphics and lifestyle characteristics (e.g. an aging
population with smaller families and a higher percent of
two income families), may cause both phenomena.
To date, ecological studies on relationships between obes-
ity and "fast food" restaurant exposure have largely
focused on aggregate rather than individual level analysis.
There is some evidence that there are more "fast food" res-
taurants in geographic areas in which obesity prevalence
is high (e.g. low income areas) [21-25]. A limited number
of studies have also reported positive associations
between aggregate measures of food outlet density in
defined geographic areas and aggregate measures of obes-
ity in those areas [22-26]. The only published study [27]
that we are aware of that assessed relationships between
exposure to "fast food" restaurants per se, and ease of
access to them at an individual level, however, found no
relationship. However, the sample was restricted to low-
income children under the age of 5 years. The present
investigation attempted to collect data that would bear
further on this issue. A sample of individuals from a Mid-
western state in the United States was identified and sur-
veyed by telephone to assess body weight and frequency
of patronage of "fast food" restaurant. The proximity of
their home and work addresses to "fast food" restaurants
and "non-fast food" restaurants was estimated using Glo-
bal Index Systems (GIS) methodology. The association
between these variables was examined. The overall
hypotheses guiding the investigation was that the proxim-
ity of "fast food" restaurants to individuals' homes and/or
work settings would be predictive of how often they ate at
those restaurants, and also would be predictive of body
mass index (BMI) as a measure of obesity. We also exam-
ined demographic and behavioral correlates of "fast food"
eating.
Methods
This research was approved by the Institutional Review
board of the University of Minnesota. Participants in this
investigation were 1,033 residents of the state of Minne-
sota identified in a random digit-dial telephone survey.
Individuals were told the survey was being conducted by
the University of Minnesota to identify patterns of eating
away from home and attitudes towards it. The survey took
approximately 10 minutes to complete. All adults over the
age of 18 were eligible to complete the survey. Survey
respondents were asked questions about their demo-
graphic characteristics, their height and weight, and their
eating habits with particular emphasis on frequency of
eating away from home. They were also asked for their
home and work addresses. The variables used in the cur-
rent analysis are reported height and weight from which
BMI was calculated (kg/m2), gender, education, marital
status, employment status, household size, number of
children, hours of TV watched per week, frequency of
reported dieting for weight control, days per week in
which individuals reported being physically active for 30
minutes or more, frequency of eating at "fast food" restau-
rants, and frequency of eating at other restaurants. All of
the above were assessed (see Table 2 for sample sizes
available for each item) with single item questions. Qual-
ity of diet was estimated using a series of questions
designed for this study asking people how often in the
past week they ate the following high-fat foods: tacos or
burritos; hamburgers of cheeseburgers; fried chicken or
fried fish; hot dogs, franks, or bratwurst; cold cuts or lunch
meats; pizza; fries or onion rings; potato chips, corn chips
or popcorn; ice cream of milk shakes; and doughnuts, pas-
tries, cake or cookies. A total fat score was calculated by
summing frequencies across items.
Ease of access to restaurants was calculated for each indi-
vidual using Geographical Index Systems (GIS) method-
ology. A company specializing in GIS analysis performed
the analysis under contract [Claritas, Inc.]. The company
used a file of home and work addresses provided by the
study in conjunction with public domain databases that
have comprehensive listings of food outlets categorized
by Standard Industrial Code (SIC). Home and work
addresses could not be geo-coded 10.5% and 5.4% of the
time, respectively. SIC codes are descriptive labels
attached to all businesses in the U.S. by the U.S. govern-
ment for statistical purposes. The SIC code for eating
places is (5812). The study contractor further subdivided
eating places into 19 sub-categories using "proprietary"
data available to the contractor. Three of these were con-
sidered "fast food" restaurants, quick service burger, quick
service roast beef, and quick service pizza parlor. Example
restaurants meeting these criteria include, McDonald's,
Long John Silver's, and Taco Bell. At present, there is no
agreed upon definition of "fast food" for research pur-
poses, and other investigators interested in this question
have often used all restaurants in analyses rather than "fast
food" due to this uncertainty [26]. In the present case we
conducted analyses using both the total number of restau-
rants and the number of "fast food" restaurants as defined
by our contractor within circles with radii of 0.5 miles, 1.0
miles and 2.0 miles with home and work addresses as the

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center of the circles. These distances were based on the
expectation that they could be reached by foot or motor
vehicle (more likely in the U.S.) in a short period of time.
Food outlet density was defined by the GIS, which calcu-
lated number of food establishments. Analyses are con-
ducted using all available data. When data were missing,
cases were dropped if all variables in the specific analysis
were not present. Two kinds of analyses were performed
on the data. Logistic regression analyses were first used to
identify variables associated with eating out at "fast food"
restaurants one or more times per week compared to
fewer times. The second set of analyses examined relation-
ships among body mass index, the number of times per
week people reported eating at "fast food" restaurants and
other restaurants, and the number of "fast food" or other
restaurants within different distances from their home
and work addresses. These analyses used linear regression
models controlling for age and education, both of which
are related to body weight and to place of residence. Initial
analyses were stratified by gender. If there were no differ-
ences in the relationship by gender, men and women were
included in the same analysis with gender as a covariate.
The numbers of restaurants at different distances were
highly correlated, and the analyses results were very simi-
lar for each distance. Analyses results reported here are for
the 2-mile radius.
Results
Table 1 presents descriptive statistics on the study partici-
pants. Approximately two-thirds were women. The distri-
bution of reported educational attainment was broad,
with approximately one quarter reporting high school
education or less and 40% reporting college education or
more. The majority of the respondents were married, and
most worked outside the home. The average age of
respondents was 46 years, average BMI was approximately
26 kg/m2, average family size was 2.6 individuals (one of
whom was a child), average reported TV hours watched
per week was 11.2, physical activity for 30 minutes or
more was reported on 3.6 days per week on average, and
participants mean reported frequency of dieting to control
weight was about midway on a 5 point scale anchored by
Always at one end and Never at the other. Reported fre-
quency of fast food eating was similar to that found in pre-
vious surveys in this population [13]. The achieved
sample was clearly older than the population average,
more female, and of higher education. It is believed, how-
ever, that there was a fairly broad representation of indi-
viduals in the geographic area.
Table 2: Factors Associated with Fast Food Eating* (≥ one time
per week)
N Odds
ratio
CI of odds
ratio
P
Married
Any children
Number of children
Household size
Work outside home
Any physical activity (yes)
Physical activity/week
TV (hr/wk)
Frequency of dieting
Fat summary score
Fruit
Vegetables
949
759
759
952
952
953
682
942
951
954
947
947
1.105
1.875
1.137
1.067
1.319
0.695
0.916
1.014
1.027
1.128
0.945
0.837
0.82–1.49
1.36–2.59
1.002–1.29
0.97–1.18
0.97–1.79
0.51–0.95
0.85–0.99
0.999–1.03
0.94–1.13
1.09–1.16
0.86–1.05
0.75–0.93
0.51
0.001
0.05
0.20
0.08
0.02
0.03
0.07
0.58
0.001
0.27
0.001
* Adjusted for age, education, and gender.
Table 1: Characteristics of Survey Respondents
Categorical VariablesN = 1033%
Gender (% female)
Education (%):
≤ High school
Vocational/Some college
≥ College
Marital status (% married)
Work outside home (% yes)
Eat fast food ≥ 1/wk (% yes)
1033
1030
68.6
24.7
33.4
41.9
68.5
63.5
51.4
1026
1031
1031
Continuous VariablesNMean SDMinimumMaximum
Age (y)
BMI
Household size (persons)
Children
TV (hr/wk)
Physical activity (30-min times/wk)
Frequency of dieting (1 = always, 5 = never)
958
985
1029
793
1021
713
1027
46.2
25.5
2.6
0.9
11.2
3.6
2.9
14.8
4.6
1.4
1.3
9.8
2.1
1.5
18.0
14.3
1.0
0.0
0.5
0.0
1.0
76.0
55.6
10.0
8.0
41.0
15.0
5.0

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Results of the GIS analysis indicated that on average, there
were 39 restaurants of all types within 2 miles of a home
address and 94 within 2 miles of a work address. Compa-
rable numbers for "fast food" restaurants were 15 and 32
for home and work site addresses, respectively. The seem-
ing high density of food establishments in this sample
may in part be to the fact that about 3/4 of the population
of Minnesota is urban. We do not know how these densi-
ties compare with those found elsewhere.
Table 2 shows the relationship between demographic and
behavioral characteristics and reported eating at "fast
food" restaurants at least once per week. Having children
in the home was associated with significantly higher rates
of reported eating at "fast food" restaurants, as was work-
ing outside the home and reporting a higher fat intake.
Vegetable intake and frequency of participating in physi-
cal activity were inversely related to frequency of reported
"fast food" restaurant use.
Table 3 reports the association between body mass index
and reported frequency of eating at restaurants. As has
been reported by others, we found a significant positive
association between BMI and frequency of reported eating
at "fast food" restaurants. We found no association
between BMI and frequency of reported eating at "non-
fast food" restaurants.
Table 4 shows the association between the number of res-
taurants within 2 miles of individuals' homes and the fre-
quency with which they reported eating at those
restaurants. Proximity of "fast food" restaurants to peo-
ple's home was not significantly related to the frequency
with which they reported eating at these restaurants. How-
ever, proximity of "non-fast food" restaurants was posi-
tively associated with frequency with which people
reported eating at those restaurants. Analyses examining
similar relationships for work rather than home
addresses, we found no relationship for either men or
women between reported restaurant eating frequency and
proximity of restaurants to work addresses.
The final analyses reported here examined the relation-
ships between body mass index and proximity of restau-
rants to home and work addresses. We found no
relationship between BMI and restaurant proximity to
home addresses for either women or men. For men only,
however, we found a significant inverse relationship
between BMI and restaurant proximity (both "fast" and
"non-fast" food). Men with more restaurants close to their
places of work were leaner. The relationships for men are
shown in Tables 5 and 6.
Discussion
The results of this investigation provide mixed evidence in
regard to the potential effects of a proliferation of conven-
ience food outlets on obesity in the US population. The
study showed that how often people report eating at "fast
food" restaurants is associated with higher weight and less
healthy eating habits. It also showed that working outside
the home and having children were associated with higher
frequencies of "fast food" restaurant patronage. We also
found that the accessibility of restaurants, defined as the
number of restaurants within a 2-mile radius of home
addresses, was predictive of frequency of reported overall
restaurant usage, although not frequency of reported "fast
food" restaurant usage. Number of restaurants near peo-
ple's homes was not associated with BMI. For men only,
the number of restaurants near to work addresses was
inversely associated with BMI. Overall, therefore, these
data seem consistent with the idea that geographic density
of restaurants may increase the likelihood that people will
eat away from home. We did not, however, find a strong
link between obesity itself and restaurant exposure varia-
bles.
Table 4: Associations of Reported Fast Food and Other Restaurant Use (per week) with Numbers of Restaurants Within 2 Miles of
Home*
Independent variableDependent variableN
β coefficientP value
Number of fast food restaurants
Number of other restaurants
Total number of restaurants
Frequency of fast food restaurant use/wk
Frequency of other restaurant use/wk
Frequency of any restaurant use/wk
911
911
913
0.301
-0.034
0.083
0.01
0.71
0.24
*Adjusted for age, education, and gender.
Address failure
10.5% of home addresses failed to match (of 1030)
5.4% of work addresses failed to match (of 278)
Table 3: Associations between BMI and Reported 'Fast' and
Other Food Restaurant Use*
Independent variable (times/wk
≥ 1 vs. none)
N P value
β coefficient
'Fast' food restaurant use
Other restaurant use
Total restaurant use
911
913
911
0.02
0.71
0.25
0.301
-0.034
0.084
*Adjusted for age, education, and gender.

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Given the amount of attention that has been focused on
the "fast food" restaurant as a toxic element in our envi-
ronment that promotes obesity, possible reasons for not
finding any relationship between ease of access to such
establishments and obesity in our population sample
merits additional comment. There are several possible rea-
sons why a real effect might not be detected in this data
set. These include the following. The availability of "fast
food" restaurant outlets may be relatively homogenous
across the U.S. environment or, alternatively, even though
different areas have different densities of restaurants, all
have enough access that physical access is not a limiting
factor governing restaurant patronage. If this is true,
addressing the question of whether the number of "fast
food" restaurants in an environment are associated with
obesity might require a wider range of exposure levels
than are possible in a limited geographic environment,
e.g. international comparisons.
A second possible reason for failing to find the expected
"fast food"/obesity relationship is that the ability to do
precise analysis of relationships between "fast food" expo-
sure and obesity may be limited by imprecision in the def-
inition of "fast food" and the definition of "exposure."
The prototypical "fast food" outlet is characterized by a
relatively limited menu and food preparation options,
quick service, paying for meals before they are received,
no wait staff, and the option to consume the meal at the
restaurant or take it out. However, there are wide varia-
tions in these elements in real world settings and which of
these, if any, are key is unknown. Some fast food restau-
rants are also more heavily advertised than others, a factor
which also was not taken into account in our definitions.
Similarly, defining proximity as a linear distance from a
place of residence or place of work may be too simplistic
a definition of exposure. Much more germane might be
access at particular points in time and space when a per-
son is in a particular need for something to eat (e.g. in
route to a child's sporting event). Thus, location of pri-
mary domicile or work site may be only one of many var-
iables related to "fast food" restaurant usage. Third, the
database available for GIS mapping of food outlets may
be sufficiently error prone to make identification of char-
acteristics of particular food outlets from SIC codes prob-
lematic. Are the codes up to date, do they actually capture
current conditions in every food outlet, who provides the
information for the categorization to begin with and how
accurate is it? Examination of these questions would
require significant additional effort and resources.
Lastly, it needs to be recognized that "fast food" restau-
rants may not in themselves make a major independent
contribution to obesity. If we live lifestyles which are con-
ducive to positive energy balance due in part to excess
energy intake, the existence of convenience food outlets
may be at least in part the consequence of the way in
which lifestyles affect consumer demands for food con-
venience, palatability and price (e.g. multiple family
breadwinners, long work hours and multiple overlapping
schedules of family members made possible by increasing
affluence) rather than a reaction to industry marketing
activities.
Conclusions
Reported eating at fast food restaurants was associated
with having children, with poorer eating and exercise hab-
its and with higher BMI. This study was unable to find a
relationship between any measure of restaurant proximity
and BMI.
Acknowledgements
This research was supported by Grant DK50456 from the National Insti-
tute of Diabetes and Digestive and Kidney Diseases. We wish to acknowl-
edge Claritas, Inc. for their assistance with GIS coding.
Table 6: Associations Between BMI and Numbers of Restaurants Within 2 Miles of Work in Men*
Independent variable Dependent variableN
β coefficientP value
Number of fast food restaurants
Number of other restaurants
Total number of restaurants
BMI
BMI
BMI
95
95
95
-0.029
-0.022
-0.005
0.008
0.01
0.01
*Adjusted for age and education (results for Females are NS)
Table 5: Associations Between BMI and Numbers of Restaurants Within 2 Miles of Home in Men*
Independent variable Dependent variableN
β coefficient P value
Number of fast food restaurants
Number of other restaurants
Total number of restaurants
BMI
BMI
BMI
305
305
305
-0.011
-0.008
0.002
0.20
0.27
0.26
*Adjusted for age and education (results for Females are NS)

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