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Food_environment_does_not_pred.pdf
RESEARCH ARTICLE
Food environment does not predict self-
reported SSB consumption in New York City: A
cross sectional study
Ben R. Spoer 1,2☯
, Jonathan H. Cantor 2,3☯¤
, Pasquale E. Rummo 2☯
, Brian D. ElbelID 2,3☯*
1 Department of Social-Behavioral Studies, College of Global Public Health, New York University, New York
City, New York, United States of America, 2 Department of Population Health, New York University School of
Medicine, New York City, New York, United States of America, 3 Wagner School of Public Service, New York
University, New York City, New York, United States of America
☯ These authors contributed equally to this work. ¤ Current address: Department of Economics, Sociology and Statistics, RAND Corporation, Santa Monica, California, United States of America
Abstract
The purpose of this research was to examine whether the local food environment, specifi-
cally the distance to the nearest sugar sweetened beverage (SSB) vendor, a measure of
SSB availability and accessibility, was correlated with the likelihood of self-reported SSB
consumption among a sample of fast food consumers. As part of a broader SSB behavior
study in 2013–2014, respondents were surveyed outside of major chain fast food restau-
rants in New York City (NYC). Respondents were asked for the intersection closest to their
home and how frequently they consume SSBs. Comprehensive, administrative food outlet
databases were used to geo-locate the SSB vendor closest to the respondents’ home inter-
sections. We then used a logistic regression model to estimate the association between the
distance to the nearest SSB vendor (overall and by type) and the likelihood of daily SSB con-
sumption. Our results show that proximity to the nearest SSB vendor was not statistically
significantly associated with the likelihood of daily SSB consumption, regardless of type of
vendor. Our results are robust to alternative model specifications, including replacing the lin-
ear minimum distance measure with count of the total number of SSB vendors or presence
of a SSB vendor within a buffer around respondents’ home intersections. We conclude that
there is not a strong relationship between proximity to nearest SSB vendor, or proximity to a
specific type of SSB vendor, and frequency of self-reported SSB consumption among fast
food consumers in NYC. This suggests that policymakers focus on alternative strategies to
curtail SSB consumption, such as improving the within-store food environment or taxing
SSBs.
PLOS ONE | https://doi.org/10.1371/journal.pone.0196689 October 24, 2018 1 / 9
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OPEN ACCESS
Citation: Spoer BR, Cantor JH, Rummo PE, Elbel
BD (2018) Food environment does not predict self-
reported SSB consumption in New York City: A
cross sectional study. PLoS ONE 13(10):
e0196689. https://doi.org/10.1371/journal.
pone.0196689
Editor: Rosely Sichieri, State University of Rio de
janeiro, BRAZIL
Received: November 15, 2017
Accepted: April 17, 2018
Published: October 24, 2018
Copyright: © 2018 Spoer et al. This is an open access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: Data are available at
https://doi.org/10.3886/ICPSR37143.v1.
Funding: This study was funded by the National
Institute of Diabetes and Digestive and Kidney
Diseases of the National Institutes of Health
(R01DK097347) and (R01DK 099241, URL https://
www.niddk.nih.gov/), the National Institutes of
Health/National Heart, Lung, and Blood Institute
(grant R01 HL095935, URL https://www.nhlbi.nih.
gov/), the New York State Health Foundation (12-
01682 URL: https://nyshealthfoundation.org/), and
Introduction
According to data from the NYC Health and Nutrition Examination Survey, approximately
one-third of New York City (NYC) adults were obese in 2013–14 [1]. Frequent consumption
of sugar sweetened beverages (SSBs) has been associated with elevated risk of obesity.[2] In
2013, 23.3% of NYC adults reported consuming a SSB at least daily[3,4] and adult SSB drinkers
consumed 193 calories from SSBs daily.[5] Thus, one way to lower obesity prevalence is to
reduce the consumption of SSBs.
Local food environments are also associated with obesity[6], potentially via variation in
access to healthy (nutrient-rich, low energy density) and unhealthy (nutrient-poor, energy
dense) foods and beverages, among other factors.[7] Access to healthy or unhealthy foods and
beverages is driven in part by differential access to food outlets.[7] For example, closer proxim-
ity to less healthy restaurants (e.g. fast food) and food store locations (e.g. corner stores or
supermarkets) may increase unhealthy food and beverage consumption by increasing the
availability of unhealthy foods and beverages.
With this in mind, this research is concerned with two facets of the local food environment,
SSB accessibility and availability, as articulated in Caspi et. al. (2012).[7] Previous work sug-
gests high SSB accessibility and availability in NYC. Corner stores and fast food restaurants
represent a disproportionate percentage of the NYC food environment, and SSBs comprise a
substantial proportion of overall product purchases in corner stores in NYC.[8] This puts
NYC residents at an increased risk of high SSB consumption.
Few studies have examined the relationship between the local food environment and adult
SSB consumption.[9–11] In Los Angeles, one study found no relationship between SSB intake
and the distance between households and specific food outlet types.[11] However, no previous
studies have explored how the distance from participants’ home intersection to the nearest
food outlet is related to SSB consumption, nor does prior research focus on an area with ample
active and public transportation options, like NYC.
To address these gaps, we used cross-sectional data on frequency of self-reported SSB con-
sumption, spatially linked to comprehensive administrative neighborhood food resource data,
to examine the associations between proximity to the nearest SSB vendor from home (overall
and by vendor type) and self-reported SSB consumption frequency.
Methods
Study staff collected receipts and surveys from customers exiting chain fast food restaurants in
NYC during three rounds of data collection (January-April 2013, August-November 2013 and
January-June 2014). The five fast food chains that had the most locations in NYC, as well as
locations in New Jersey (which were used as a comparison group for a separate project) were
surveyed on weekdays during lunch (11:30am-2:30pm) and dinner hours (4:30pm-7:30pm).
Fast food chains were selected because they would best demonstrate the effect of the now
defunct NYC Soda Portion Cap Rule, which the data collection approach was designed to
examine. Customers were offered a $2 incentive to provide their receipt and fill out a survey.
Participants were eligible for the study if they were at least 18 years old, had a receipt from the
restaurant in question, and could communicate in English or Spanish. All participants pro-
vided verbal informed consent to participate. The study protocols were approved by the New
York University Medical Center Institutional Review Board. The full sample included over
6,000 participants, which provided over 90% power for the original study. The subsample ana-
lyzed in this research included 3,266 participants, and provides over 90% power to detect a
50% change in the odds of consuming an SSB at least once a day.[11]
Food environment does not predict self-reported SSB consumption in New York City
PLOS ONE | https://doi.org/10.1371/journal.pone.0196689 October 24, 2018 2 / 9
the Robert Wood Johnson Foundation (award
70823, URL https://www.rwjf.org/). All grants were
awarded to BDE. These funders played no role in
the study design, the data collection, analysis, or
interpretation, nor the writing of the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
Respondents were surveyed about demographic characteristics (gender, age, race/ethnicity,
education, and employment status), the intersection nearest their home, and their SSB pur-
chase and consumption. Specifically, respondents were asked how many times in the past
seven days they consumed a bottle or glass of regular (non-diet) soda, using a question adapted
from the National Health and Nutrition Examination Survey. In addition, data collectors
asked respondents “where do you buy regular soda most often?” Possible choices included
supermarkets, fast food restaurants and corner stores.
Based on the response to the SSB frequency measure, we created a dichotomous measure
for daily SSB consumption. To examine possible heterogeneity by type of restaurant or food
store, we created separate dichotomous measures (yes/no) for purchasing an SSB at least daily
from each type of SSB vendor, including fast food restaurants, wait service restaurants, bode-
gas, or supermarkets.
Using 2013–2014 licensing and inspection data from the New York State Department of
Agriculture and Markets, we defined corner stores as food vendors less than 2,000 square feet
(185.8 square meters) in area. Supermarkets were defined as locations greater than 5,000
square feet (464.5 square meters). Information on fast food restaurant locations was obtained
from the NYC Department of Health and Mental Hygiene Restaurant Grading data, via annual
inspections, also in 2013–2014. We attempted to geocode the home intersections for all NYC-
based survey respondents, with a 56% success rate. We then calculated the linear distance from
respondents’ home intersections to the nearest food outlet location using ArcGIS version 10.4.
We did the same for each type of food outlet. We assumed that each food outlet sold SSBs.
We used logistic regression models to examine the associations between the distance to
nearest food outlet location and (1) daily consumption (yes/no) of an SSB overall; and (2) daily
consumption (yes/no) of an SSB by food outlet type (i.e., corner store, fast food restaurant, or
supermarket). We controlled for the respondent’s age, gender, race/ethnicity (using dummies
for non-Hispanic white, non-Hispanic African American, Hispanic, and non-Hispanic other
race), education level, employment status, frequency of fast food restaurant visits in the past
week, the borough in which their home intersection was located, the restaurant chain where
the respondent was surveyed, and the survey round.
We ran alternative model specifications replacing the linear measure for minimum distance
with (1) a count of the number of food outlets within a network buffer; and (2) a dummy vari-
able for the presence of a food outlet within a network buffer. We separately ran an ordered
logistic regression model with our outcome specified as the frequency of SSB consumption in
the past 7 days (i.e., 0, 1–2 times per week, 3–4 times per week, 5–6 times per week, once a day,
2–3 times per day, 4–5 times per day, 6 or more per day). We also ran an alternative model
with the outcome specified as whether or not the survey respondent reported “Does not buy
soda” to the question “Where do you buy regular soda most often?” All regressions were esti-
mated using Stata version 13.
Results
The sample was split almost equally across gender (51% male), and was predominantly either
African American (39%) or Hispanic (37%) (Table 1). Over half of the sample had a high
school degree or more (60%) and was employed (65%). Respondents most commonly lived in
Manhattan (39%), followed by Brooklyn (26%), the Bronx (21%) and Queens (14%). The
mean distance to any SSB vendor was 213 feet (64.9 meters) (SD = 300 feet (91.44 meters)), the
nearest vendor was a fast food restaurant (353 feet (107.6 meters)), followed by a corner store
(372 feet (113.4 meters)), and finally a supermarket (1290 feet (393.2 meters)). Approximately
14% of geocoded survey respondents reported consuming a SSB at least daily. Additionally,
Food environment does not predict self-reported SSB consumption in New York City
PLOS ONE | https://doi.org/10.1371/journal.pone.0196689 October 24, 2018 3 / 9
7% of the sample reported purchasing a SSB at a corner store daily, 7% reported purchasing a
SSB at a supermarket daily, and, 4% reported purchasing a SSB at a fast food restaurant daily.
Full details regarding daily and weekly SSB consumption by the vendor type at which SSBs
were most frequently purchased are available in S1 Table.
Table 1. Demographic characteristic of study sample, overall and by exposure specification.
Demographic
characteristic
N (%) Linear measure for nearest store
distance (mean (SD))
Count of the number of stores in 264 feet (80.5
meters) (mean (SD))
Presence of a store in 264 feet
(80.5 meters)(%)
Total 3,266 212.7 feet
(64.8 meters)
(299.9 (91.4 m))
12.1
(12.3)
80.7%
Gender
Male 1,672
51.2%
207.0 (63.4 m)
(290.5 (88.5 m))
12.4
(12.3)
81.0%
Female 1,594
48.8%
218.6 (66.6 m)
(309.4 (94.3m))
11.7
(12.3)
80.4%
Race
White 557
17.1%
215.3 (65.6 m)
(300.0 (91.4 m))
13.7
(13.8)
78.6
African American / Black 1,257
38.5%
225.2 (68.6 m)
(322.2 (98.2 m))
10.3
(10.7)
80.0
Hispanic 1,196
36.6%
189.9 (57.9 m)
(253.1 (77.1 m))
12.9
(12.5)
83.0
American Indian, Asian
and Other
256
7.8%
252.1 (76.8 m)
(376.5 (114.8 m))
13.4
(13.8)
77.7
Age Group
18–24 680
20.8%
233.8
(331.0)
11.7
(12.5)
77.2%
25–39 1,153
35.3%
195.7
(282.4)
12.6
(12.8)
83.4%
40–49 569
17.4%
222.2
(306.9)
11.9
(12.0)
81.2%
50–64 628
19.2%
214.1
(291.8)
11.5
(11.6)
78.7%
65+ 236
7.2%
208.5
(290.6)
12.4
(11.8)
81.4%
Education Level
More than a high school
degree
1,975
60.5%
226.7 (69.1 m)
(310.2 (94.5 m))
12.2
(12.8)
78.9%
High school degree or less 1,291
39.5%
191.3 (58.3 m)
(282.3 (86.1 m))
11.8
(11.4)
83.4%
Employment Status
Not employed 1,159
35.5%
199.9 (60.93 m)
(268.8 (81.93 m))
11.8
(11.5)
82.1%
Employed 2,107
64.5%
219.7 (67.0 m)
(315.6 (96.2 m))
12.2
(12.7)
80.0%
Borough of the Intersection
Bronx 683
20.9%
221.1 (67.4 m)
(256.1 (78.1 m))
9.3
(9.7)
78.0%
Brooklyn 836
25.6%
222.4 (67.8 m)
(308.5 (94.0 m))
10.2
(11.3)
78.2%
Manhattan 1,280
39.2%
143.9 (43.9 m)
(192.3 (58.6 m))
15.3
(12.5)
89.7%
Queens 467
14.3%
371.8 (113.3 m)
(472.8 (114.1 m))
10.8
(14.6)
64.2%
https://doi.org/10.1371/journal.pone.0196689.t001
Food environment does not predict self-reported SSB consumption in New York City
PLOS ONE | https://doi.org/10.1371/journal.pone.0196689 October 24, 2018 4 / 9
In Table 2 we report results from models estimating the association between distance to
nearest SSB vendor and daily consumption of a SSB. There was no statistically significant effect
for distance to the closest SSB vendor on the odds of consuming a SSB daily (OR = 0.99, 95%
CI: 0.95, 1.02). We also report models that estimate the association between distance to nearest
type of SSB vendor and daily SSB purchase from a corner store, fast food restaurant, or super-
market. None of the estimates from these models was statistically significant.
Finally, we estimated an ordered logistic regression where the outcome was specified as the
frequency with which the respondent reported purchasing a SSB; and separately, we estimated
a logistic model predicting the likelihood of a respondent not purchasing a SSB in the past
week. Distance was not a statistically significant predictor in either specification (see S4 Table).
We also did not observe statistically significant associations between the count of the number
of food outlets within a network buffer and daily consumption of a SSB; nor between the pres-
ence of a food outlet within a network buffer and daily consumption of a SSB (S4 and S5
Tables).
Discussion
In this study of frequency of SSB consumption we did not find a correlation between proxim-
ity of SSB vendors and the frequency of self-reported SSB consumption among consumers
who frequent fast food restaurants. Our results also indicate that distance to the nearest SSB
vendor does not influence SSB consumption frequency, regardless of the type of vendor. These
results are consistent with a similar study, which did not find a correlation between distance to
SSB vendors and the frequency of SSB consumption in Los Angeles, California, another dense
urban area.[11]
Our findings may be due to the active transportation environment in NYC, which enables
consumers to access SSB vendors easily, including vendors that are far away from their homes.
This high transportation access environment may mean respondents do not need SSB vendors
near their homes in order to access SSB vendors. Finally, our survey did not include questions
about diet soda, which may have replaced SSBs for some consumers.
Our results indicate that fast food consumers in dense urban areas who purchase and con-
sume soda may be resilient to differences in SSB vendor proximity. With this in mind, limiting
availability and access to SSB vendors may not be an effective way to curb SSB consumption
for this population. Related to this finding, a recent policy intervention known as the NYC
Portion Cap Rule aimed to reduce SSB access and availability by limiting the size of SSBs sold
in NYC. The policy was met with substantial political and legal resistance that prevented its
passage into law, providing further evidence that interventions designed to limit SSB availabil-
ity and access in dense urban areas may not be feasible.[12]
Food environment interventions should instead target other dimensions of the food envi-
ronment. For example, SSB-taxes that affect SSB affordability are gaining popularity and have
shown promise with respect to their ability to curb SSB consumption, even in cities.[13–15]
Though these initiatives require substantial political will, they may be more feasible and effec-
tive than initiatives that limit SSB availability and accessibility. Additionally, further research
should be conducted in rural areas and areas that better resemble “food deserts”, where the
relationship between the food environment and SSB purchase and consumption may differ
compared to dense urban areas.
Our study has several limitations. We used a point of purchase sample of customers at fast
food restaurants, and we failed to collect a response rate for the survey, though a similar study
reports a rate of 60%.[16] Only 56% of cross-streets could be geocoded due to problems with
data quality, including misspelled or inaccurate responses to the cross-street item. These
Food environment does not predict self-reported SSB consumption in New York City
PLOS ONE | https://doi.org/10.1371/journal.pone.0196689 October 24, 2018 5 / 9
Table 2. Regression results predicting the purchase of a SSB once a day, overall and by food outlet type.
Consumes a SSB once
a day
Purchase a SSB once a day at
corner store
Purchase a SSB once a day at fast food
restaurants
Purchase a SSB once a day at
supermarket
Outcome percentage 14.1% 7.1% 3.9% 6.9%
Linear Measure for closest store distance (in 100
feet (30.48 meters))
0.99 0.98 0.99 0.99
(0.95,1.02) (0.95,1.02) (0.94,1.04) (0.98,1.01)
Gender
Male 1 1 1 1
Female 0.73�� 0.50��� 0.72 0.98
(0.59,0.90) (0.37,0.68) (0.48,1.07) (0.74,1.30)
Race
White 1 1 1 1
African American / Black 0.78 1.10 0.35�� 0.75
(0.56,1.09) (0.67,1.82) (0.18,0.68) (0.48,1.16)
Hispanic 0.89 1.08 0.93 0.78
(0.63,1.24) (0.65,1.79) (0.52,1.68) (0.50,1.21)
American Indian, Asian and Other 0.95 0.93 1.24 0.85
(0.59,1.51) (0.46,1.86) (0.58,2.66) (0.46,1.59)
Age Group
18–24 1 1 1 1
25–39 0.85 0.63�� 0.82 0.92
(0.64,1.12) (0.44,0.89) (0.50,1.35) (0.63,1.35)
40–49 0.92 0.58�� 0.90 1.23
(0.67,1.26) (0.38,0.87) (0.51,1.60) (0.81,1.87)
50–64 0.58�� 0.20��� 0.57+ 0.82
(0.41,0.81) (0.12,0.34) (0.31,1.06) (0.53,1.27)
65+ 0.25��� 0.10��� 0.23� 0.32��
(0.14,0.46) (0.03,0.28) (0.07,0.70) (0.14,0.73)
Education Level
More than a high school degree 1 1 1 1
High school degree or less 1.34� 1.58�� 1.03 1.18
(1.07,1.68) (1.15,2.16) (0.67,1.58) (0.87,1.60)
Employment Status
Not employed 1 1 1 1
Employed 0.78� 0.81 1.04 0.86
(0.62,0.99) (0.59,1.12) (0.68,1.61) (0.63,1.17)
Borough of the Intersection
Bronx 1 1 1 1
Brooklyn 0.71+ 0.69 0.44� 0.65+
(0.51,1.01) (0.43,1.10) (0.23,0.84) (0.41,1.03)
Manhattan 0.75+ 0.70+ 0.65+ 0.79
(0.55,1.01) (0.47,1.06) (0.40,1.08) (0.53,1.16)
Queens 0.89 1.04 0.39� 0.83
(0.60,1.30) (0.62,1.74) (0.18,0.84) (0.49,1.40)
Note: The outcome variable is the purchase or consumption of a SSB at least once a day. A corner store is defined as a food retailer that is less than 2,000 square feet
(185.8 square meters). A supermarket is defined as a food retailer that is over than 5,000 square feet (484.5 square meters). In parentheses are 95% confidence intervals.
Each column represents a separate multivariate logistic regression, adjusted for covariates. In the table we report the odds ratio for each of the covariates and the
confidence intervals in parentheses. Crude results are available in S3 Table.
p<0.10 = +.
p<0.05 = �.
p<0.01 = ��.
p<0.001 = ���.
https://doi.org/10.1371/journal.pone.0196689.t002
Food environment does not predict self-reported SSB consumption in New York City
PLOS ONE | https://doi.org/10.1371/journal.pone.0196689 October 24, 2018 6 / 9
misspellings and inaccuracies made it impossible for geocoding software to identify respon-
dents’ cross-streets. Respondents who could not be geocoded were more likely to have a high
school degree or less, more likely to be African American, and more likely to be employed;
thus, our results may not be generalizable to all fast food consumers. While we use verified
self-reported measures for soda consumption, these are not measures of actual consumption,
and one-item screeners may be biased. The short-survey format did not allow sufficient time
for 24-hour dietary recalls nor prospective SSB consumption measurements. Our study
assumed that all food vendors in NYC sell SSBs. Though we could not find research that char-
acterizes the entire NYC SSB environment, anecdotally we have not encountered a store where
this is not true.
Finally, though we expected our sample to consume SSBs more frequently than average,
instead we found low SSB consumption among our sample; according to the 2013 NYC
Department of Health and Mental Hygiene Community Health Survey, 23.3% of NYC resi-
dents consumed an SSB daily in 2013, compared to 14% in our sample.[3] The Community
Health Survey may provide a better estimate of SSB consumption among all NYC residents
because it utilizes a representative sample, which suggests that our results may not be general-
izable to all SSB consumers in NYC. Further, our results may not be generalizable outside of
urban areas with dense food environments.
This study also has important strengths. First, it utilizes a comprehensive database of food
vendors in NYC; though validity and reliability of these data are absent from the literature, we
are confident that these data capture a virtual census of NYC food retail outlets open at any
given time. Second, it builds on the current literature, replicating null findings from a related
study. Finally, it studies a population that is often invoked when considering policy tools to
address SSB consumption, and, in combination with previous literature, can be used to inform
obesity policy.
Conclusions
Our findings suggest that neither distance to different types of SSB vendors, presence of SSB
vendors, nor total number of SSB vendors have a substantial impact on SSB consumption.
Based on these results we conclude that limiting access and availability is likely not an effective
solution to decrease SSB consumption among those who frequent fast food restaurants in
dense urban areas. Alternatively, policymakers and public health practitioners may need to
focus on promoting healthy food purchases and consumption within food stores and restau-
rants, potentially via interventions such as the Healthy Bodegas initiative, or taxes on SSBs.
Supporting information
S1 Table. Frequency of SSB consumption by most frequent purchase location.
(XLSX)
S2 Table. Demographic characteristics by geocoded status.
(XLSX)
S3 Table. Crude regression results predicting the purchase of a SSB once a day, overall and
by food outlet type.
(XLSX)
S4 Table. Predicted frequency of soda consumption once a day with dummy variable for
distance buffers around respondents’ home intersections.
(XLSX)
Food environment does not predict self-reported SSB consumption in New York City
PLOS ONE | https://doi.org/10.1371/journal.pone.0196689 October 24, 2018 7 / 9
S5 Table. Predicted frequency of soda consumption once a day with buffer counts.
(XLSX)
Author Contributions
Conceptualization: Ben R. Spoer, Jonathan H. Cantor, Brian D. Elbel.
Data curation: Ben R. Spoer, Jonathan H. Cantor.
Formal analysis: Ben R. Spoer, Jonathan H. Cantor, Pasquale E. Rummo.
Investigation: Brian D. Elbel.
Methodology: Ben R. Spoer, Jonathan H. Cantor, Pasquale E. Rummo.
Resources: Brian D. Elbel.
Software: Ben R. Spoer.
Supervision: Ben R. Spoer, Brian D. Elbel.
Writing – original draft: Ben R. Spoer, Jonathan H. Cantor.
Writing – review & editing: Ben R. Spoer, Jonathan H. Cantor, Pasquale E. Rummo, Brian D.
Elbel.
References 1. NYC DOHMH. New York City Health and Nutrition Examination Survey. NYC HANES 2013–2014.
2014.
2. Malik V, Schulze M, Hu F. Intake of sugar-sweetened beverages and weight gain: A systematic review.
Am J Clin Nutr. 2006; 84(2):274–88. https://doi.org/10.1093/ajcn/84.1.274 PMID: 16895873
3. Kansagra SM, Kennelly MO, Nonas CA, Curtis CJ, Van Wye G, Goodman A, et al. Reducing sugary
drink consumption: New York City’s approach. Am J Public Health. 2015; 105(4):e61–4. https://doi.org/
10.2105/AJPH.2014.302497 PMID: 25713971
4. NYC DOHMH. Community Health Survey. New York City Community Health Survey. 2002.
5. Ruff RR, Akhund A, Adjoian T, Kansagra SM. Calorie Intake, Sugar-Sweetened Beverage Consump-
tion, and Obesity Among New York City Adults: Findings from a 2013 Population Study Using Dietary
Recalls. J Community Health. 2014; 39(6):1117–23. https://doi.org/10.1007/s10900-014-9865-3 PMID:
24671367
6. Cobb LK, Appel LJ, Franco M, Jones-Smith JC, Nur A, Anderson CAM. The relationship of the local
food environment with obesity: A systematic review of methods, study quality, and results. Obesity.
2015; 23(7):1331–44. https://doi.org/10.1002/oby.21118 PMID: 26096983
7. Caspi CE, Sorensen G, Subramanian S V, Kawachi I. The local food environment and diet: A systematic
review. Health Place. 2012 Sep; 18(5):1172–87. https://doi.org/10.1016/j.healthplace.2012.05.006
PMID: 22717379
8. Ruff RR, Akhund A, Adjoian T. Small Convenience Stores and the Local Food Environment: An Analy-
sis of Resident Shopping Behavior Using Multilevel Modeling. Am J Heal Promot. 2016 Jan; 30(3):172–
80.
9. Gase LN, DeFosset AR, Smith L V, Kuo T. The Association between Self-Reported Grocery Store
Access, Fruit and Vegetable Intake, Sugar-Sweetened Beverage Consumption, and Obesity in a
Racially Diverse, Low-Income Population. Front public Heal. 2014; 2(November):229.
10. Gustafson A, Lewis S, Moore K, Jilcott S. Food venue choice, consumer food environment, but not food
venue availability within daily travel patterns are associated with dietary intake among adults, Lexington
Kentucky 2011. Nutr J. 2013; 12(1):17.
11. Mejia N, Lightstone AS, Basurto-Davila R, Morales DM, Sturm R. Neighborhood Food Environment,
Diet, and Obesity Among Los Angeles County Adults, 2011. Prev Chronic Dis. 2015; 12(E143):1–10.
12. DA C, NW S. Reconsidering the politics of public health. JAMA [Internet]. 2013 Sep 11; 310(10):1025–
6. Available from: http://dx.doi.org/10.1001/jama.2013.110872 PMID: 23975269
Food environment does not predict self-reported SSB consumption in New York City
PLOS ONE | https://doi.org/10.1371/journal.pone.0196689 October 24, 2018 8 / 9
13. Sanger-Katz M. Soda tax passes in Philadelphia. Advocates ask: Who’s next. NY Times [Internet].
2016;
14. Colchero MA, Popkin BM, Rivera JA, Ng SW. Beverage purchases from stores in Mexico under the
excise tax on sugar sweetened beverages: observational study. BMJ [Internet]. 2016 Jan 6; 352. Avail-
able from: http://www.bmj.com/content/352/bmj.h6704.abstract
15. Falbe J, Thompson HR, Becker CM, Rojas N, McCulloch CE, Madsen KA. Impact of the Berkeley
Excise Tax on Sugar-Sweetened Beverage Consumption. Am J Public Health [Internet]. 2016 Aug 23;
106(10):1865–71. Available from: https://doi.org/10.2105/AJPH.2016.303362 PMID: 27552267
16. Dumanovsky T, Huang CY, Nonas CA, Matte TD, Bassett MT, Silver LD. Changes in energy content of
lunchtime purchases from fast food restaurants after introduction of calorie labelling: cross sectional
customer surveys. BMJ. 2011; 343:4464.
Food environment does not predict self-reported SSB consumption in New York City
PLOS ONE | https://doi.org/10.1371/journal.pone.0196689 October 24, 2018 9 / 9
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