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Mobile food vendors in urban neighborhoods—Implications for diet and diet-related health by weather and season

Authors:
  • City University of New York City - Lehman College; City University of New York School of Public Health
Article

Mobile food vendors in urban neighborhoods—Implications for diet and diet-related health by weather and season

Mobile food vendors in urban neighborhoods—implications for diet and diet-related health
by weather and season
ABSTRACT (1191 characters – 1200-character limit)
This study describes mobile food vendors (street vendors) in Bronx, NY, considering
neighborhood-level correlations with demographic, diet, and diet-related health measures from
City data. Vendors offering exclusively “less-healthy” foods (e.g., chips, processed meats,
sweets) outnumbered vendors offering exclusively “healthier” foods (e.g., produce, whole grains,
nuts). Wet days and winter months reduced all vending on streets, but exclusively “less-healthy”
vending most. In summer, exclusively “less-healthy” vending per capita inversely correlated with
neighborhood-mean fruit-and-vegetable consumption and directly correlated with neighborhood-
mean BMI and prevalences of hypertension and hypercholesterolemia (Spearman correlations
0.90-1.00, p values 0.037 to <0.001). In winter, “less-healthy” vending per capita directly
correlated with proportions of Hispanic residents and those living in poverty (Spearman
correlations 0.90, p values 0.037). Mobile food vending may contribute negatively to urban food-
environment healthfulness overall, but exacerbation of demographic, diet, and diet-related health
disparities may vary by weather, season, and neighborhood characteristics.
KEYWORDS
Mobile food vendors; Street foods; Food environment; Urban, Neighborhood; Disparities
MANUSCRIPT: 2,077 words (2,000-word limit)
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INTRODUCTION
Most food-environment research to date has focused on proximity to and density of food stores
and restaurants.(Kirkpatrick et al., 2010; McKinnon et al., 2009) Few studies have examined
mobile food vending (e.g., roadside carts, trucks, and stands).(Abusabha et al., 2011; Lucan et
al., 2011; Sharkey et al., 2012; Tester et al., 2010, 2012; Tester et al., 2011; Valdez et al., 2012;
Widener et al., 2012) Studies in rural settings suggest that mobile vendors sell a limited range of
mostly prepared foods, refined sweets, and salty snacks.(Sharkey et al., 2012; Valdez et al., 2012)
Studies in urban settings suggest that mobile vendors offer various nutrient-poor, energy-dense
options (Tester et al., 2010) and tend to locate around schools in lower-income neighborhoods.
(Tester et al., 2011) Studies in both settings suggest that “healthier” options, like fruits and
vegetables, are available from at least some vendors.(Abusabha et al., 2011; Lucan et al., 2011;
Tester et al., 2010; Valdez et al., 2012; Widener et al., 2012)
Prior studies have generally not examined mobile vending on scales larger than just a few carts
or considered the possible shifting nutritional contributions of vendors related to their mobility.
Investigators in the current study sought to understand the variable contributions of mobile
vendors to neighborhood food environments for an entire urban county; to understand where,
when, and what vendors sell, and potential implications for community nutrition and health.
METHODS
With IRB approval, investigators conducted a primary assessment of mobile vending in the
Bronx, also performing neighborhood-level correlations with demographic, diet, and diet-related
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health measures from the City. Vending vehicle (e.g., cart, truck, stand), not person selling, was
the unit of observation and analysis.
Surveying streets
Two pairs of researchers systematically scanned streets for vending vehicles, assessing all streets
at least once. Investigators covered all 42mi2 of Bronx County, NY during usual business hours,
requiring a total of l40 weekdays summer-fall 2010.(Lucan et al., 2013)
Brief interviews
Investigators asked vendors about hours, days, months and locations for selling, and if weather is
a factor. Specific questions are available in another publication.(Lucan et al., 2013)
Direct observations
Investigators made observations about the vending vehicle, including unique identifier (e.g.,
permit number, license plate, distinctive features) and location (i.e., nearest street address or
street intersection). Investigators determined foods and beverages offered from displayed items,
signs, and menu boards, and clarified uncertainties by asking vendors.
Categorizing vending by items offered
Vending categories included: category A: fresh produce, i.e., fruit and/or vegetable stands and
carts; category B: ethnic foods, e.g., empanada stands, Chinese-food trucks, Halal carts; category
C: other prepared foods, e.g., hot-dog carts, barbeque vendors, cheesesteaks; category D: frozen
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novelty, e.g., ice-cream trucks, Italian-ice carts, snow-cone stands; and category E: other, e.g.,
honey-roasted nut, bagged-chips, and candy vendors.
Types of vending by healthfulness
Investigators categorized vending as “healthier” (offering only whole foods like fruits,
vegetables, unprocessed grains, unsweetened nuts), “less-healthy” (offering only processed or
prepared foods like bagged chips, preserved meats, assorted confections), or “mixed” (offering
both “healthier” and “less-healthy” items).
Neighborhood data
Information on neighborhoods came from the New York City Department of Health and Mental
Hygiene (DOHMH). DOHMH conducts a yearly, random-digit-dialed, Community Health
Survey of adults including various demographics and health-related questions
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included items about dietary intake (e.g., servings of fruits and vegetables consumed yesterday),
and diet-related health (e.g., ever told you have diabetes). Data were available for United
Hospital Fund “neighborhoods” (UHFs), dividing the Bronx into five regions having notable
demographic, population-density, and health differences. Details about survey response rates
(37% by landline, 46% by cell phone), stratified sampling design, survey weights and
adjustments for number of adults per household are available on the DOHMH website
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Weather and seasonality
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Vendors reported their months of operation and whether they suspended operations during
precipitation (i.e., rain or other). Based on reported months of operation investigators
characterized vendors as operating in summer (July-September), winter (January-March), or
both.
Data analysis and mapping
Investigators used Stata version 11 (Stata Corp LP, College Station, TX) to aggregate individual-
level DOHMH-survey responses to the level of the “neighborhood” (UHF), with sampling
weights reflecting the survey design. Analyses included frequencies and percentages of vendors
selling different items in neighborhoods by weather and season. Analyses also included
Spearman correlations between the number of “healthier”, “less-healthy” or “mixed” vendors per
number of residents in a neighborhood and neighborhood characteristics (i.e., demographic, diet,
and diet-related health measures). Investigators used ArcGIS software (version 9.3.1, ESRI,
Redlands, CA) to map seasonal variation in “healthier”, “less-healthy”, and “mixed” vending.
RESULTS
A total of 372 vending vehicles were identified. Fresh-produce vendors (vending category A),
totaling 84, were outnumbered more than 3:1 by other vendors (vending categories B-E) that
typically offered “less-healthy” items.
Seventy-two vendors were “in transit” (e.g., ice-cream trucks driving through neighborhoods),
precluding detailed assessment of the foods they offered. Of the 300 vendors assessed in detail, a
similar percentage offered “less-healthy” prepared food like hot dogs and fried rice as offered
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fruits or vegetables like apples or vegetable side dishes (29% vs. 31% respectively). Only one of
the vendors assessed in detail offered whole grain (brown rice) despite our study protocol’s
liberal inclusion of popcorn, whole-grain chips, and sweetened granola products in what could
have been counted as whole grain. A majority of vendors (59%) offered processed foods like
candies and salty snacks, including 15% of fresh-produce vendors (vending category A) who
sold pastries, cookies, and/or onion rings. Conversely, only 5% of non-produce vendors (vending
categories B-E) offered any fruit or vegetable (e.g., sliced melon and green salad).
Among vendors answering questions regarding weather (Table 1), all vendor types were less
numerous on rainy days; only 14% reported working irrespective of precipitation. Nearly 90% of
fresh-produce vendors and 95% of frozen-novelty vendors reported not coming out on wet days.
Overall, the preponderance of “less-healthy” vending decreased with precipitation such that the
proportion of vendors offering at least some healthier options (i.e., “healthier” + “mixed”
vending) increased in relative terms. A similar shift in vending proportions was seen with the
transition from summer to winter.(Appendix Table A1)
Figure 1 shows seasonal variation in vending type and geographic distribution by neighborhood.
A mix of vending across the Bronx in summer shifted to a southwest concentration of vending in
winter, with greater relative proportions of “healthier” and “mixed” vending types. More than
75% of all vendors located in the most southwestern neighborhood in winter, home to the poorest
Bronx communities with Hispanics representing 65% of populations.(United States Census
Bureau).
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Table 2 shows correlations by season between exclusively “less-healthy” vending per capita, and
mean neighborhood diet, diet-related health, and demographic characteristics. In summer, there
were generally strong correlations between exclusively “less-healthy” vending per capita and
neighborhood-mean characteristics. In winter, correlations were in the same direction but
generally smaller in magnitude. Exceptions were correlations with the proportions of Hispanic
residents and residents living below the federal poverty level; these correlations with per-capita
“less-healthy” vending became stronger in winter.
Similar correlation patterns were seen for any “less-healthy” (“less healthy” + “mixed”) vending
per capita and neighborhood-mean characteristics.(Appendix Table A2) There were no
meaningful correlations with per-capita “healthier” vending, either exclusively (Appendix Table
A3) or any (Appendix Table A4).
DISCUSSION
Mobile food vendors vary in both the items they offer and the consistency of their presence.
Overall, vendors offered “less-healthy” prepared and processed items over “healthier” whole
foods. However, the distribution of vendor types depended considerably on day-to-day weather,
seasonality, and neighborhood characteristics. Wet days and winter months substantially reduced
the total number of vendors on the street, but the amount of “less-healthy” vending most. During
winter, some neighborhood correlations with “less-healthy” vending were less severe, but poor
and Hispanic communities were disproportionately affected in both summer and winter.
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The current study is not the first to show disadvantaged populations might be further
disadvantaged by “less-healthy” mobile vending. A study from Nairobi showed street vendors in
the lowest-income areas sold the highest proportions of refined sugars and starches, and lowest
proportions of fruits, vegetables, legumes, and nuts.(Mwangi et al., 2002) A U.S. study showed
mobile vendors offered a relative paucity of “healthier” foods around schools in low-income
minority neighborhoods.(Tester et al., 2010) Another U.S. study showed the odds of purchasing
food from mobile vendors increased with food insecurity, and that “less-healthy” foods were
among the most-purchased items.(Sharkey et al., 2012) The current study is the first to
demonstrate potentially different implications of mobile vending by season (beyond winter
attrition in total vendor count (Valdez et al., 2012)).
Our study looked only at “mobile” vending in isolation from “fixed” food sources—e.g.,
restaurants and stores. The limited offerings of healthier foods in restaurants and stores in lower-
income and minority neighborhoods have been well documented,(Baker et al., 2006) particularly
in New York City.(Gordon et al., 2011; Horowitz et al., 2004; Neckerman et al., 2010) Mobile
vendors might improve healthy-food provision in such neighborhoods, both directly—through
initiatives like Green Carts, permitting street vendors to sell exclusively whole, fresh,
unprocessed produce (Leggat et al., 2012; Lucan et al., 2011)—and indirectly, by encouraging
adjacent stores to compete for fruit-and-vegetable business and provide fresh produce
themselves.(Leggat et al., 2012) Also, studies have suggested that when healthier options like
fruits and vegetables are available from mobile vendors, people purchase and consume more of
them irrespective of,(Abusabha et al., 2011; Jahn and Shavitz; Tester et al., 2010) even instead
of,(Tester et al., 2012) other less-healthful items that might be available.
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Our study had several strengths: (1) being the first countywide study of mobile food vendors in
the developed world, (2) using a multidimensional approach, as advocated by others,(Rose et al.,
2010; Widener et al., 2011) considering what items vendors offered, where vendors were located,
and when vendors sold, (3) considering not only “healthier” and “less-healthy” vendors, but also
“mixed” vendors—an incremental improvement on past food-environment research that bluntly
dichotomized food sources as “healthy”, like supermarkets, and “unhealthy”, like convenience
stores (Vernez Moudon et al., 2013) (ignoring that both can be sources of “healthier” and “less-
healthy” items), (4) modeling different scenarios by weather, season, and neighborhood for more
nuanced understanding than provided from prior studies, (5) showing important correlations
between vending healthfulness and diet, diet-related health, and demographic characteristics of
neighborhoods.
There were also limitations: (1) being cross-sectional, this study shows correlation, not causality,
(2) investigators were not able to speak with all identified vendors, but, crucially, interview
participation did not differ by whether vending was “healthier”, “mixed”, or “less-healthy” (data
not shown), (3) there is no way to verify if investigators identified all vendors since a great
majority of vendors had no permits (Lucan et al., 2013) and there is no adequate government
record or other list of vendors for enumeration, (4) investigators cannot comment on specific
sales or whether vendors’ customers are local residents, the locally employed who live
elsewhere, or transients; investigators can only assume, as others have,(Mwangi et al., 2002) that
the kinds of items vendors offered were those that local customers (residents and/or others)
tended to buy and consume, (5) results may not be generalizable to other communities.
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CONCLUSION
This study showed that mobile vendors provided healthy foods relatively infrequently. Mobile
vending might exacerbate demographic, diet, and diet-related health disparities in urban
neighborhoods, but the extent of their negative contributions to food-environment healthfulness
may vary by weather and season. From a positive standpoint, mobile vending could improve
healthy-food availability in neighborhoods (Abusabha et al., 2011; Jennings et al., 2012; Leggat
et al., 2012; Marx, 2012; Tester et al., 2012; Widener et al., 2012) and might provide a viable
alternative to more-resource-intensive strategies for food-environment modification focused on
‘fixed’ food sources (e.g., restricting fast-food development,(Babey et al., 2011; Keener, July
2009) attracting new grocers,(Babey et al., 2011; Centers for Disease Control, 2011; Institute of
Medicine, 2009; Keener, July 2009) redesigning small stores,(Bodor et al., 2010; Centers for
Disease Control, 2011; Dannefer et al., 2012; Gittelsohn et al., 2010; Institute of Medicine, 2009;
Keener, July 2009; O'Malley et al., 2013; Raja, 2008) or promoting supermarkets (Centers for
Disease Control, 2011; Giang et al., 2008; Institute of Medicine, 2009; Keener, July 2009;
Morland et al., 2002; Pothunkuchi, 2005)). Future research on mobile vending should explore
availability, quality, and price of mobile foods compared to foods from adjacent store-front
businesses, and determine customer demographics, purchasing, and consumption patterns. Until
the findings of such research are available, it is reasonable to conclude that mobile vendors are
part of broader food environments and should not be ignored in future food-environment
conceptualizations or studies.
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ACKNOWLEDGEMENTS
The authors would like to thank A. Hal Strelnick, MD, for project guidance; Nandini Deb, MA,
and Mahbooba Akhter Kabita for assistance with Bengali translation of interview questions;
Hope M. Spano and the Hispanic Center of Excellence at Albert Einstein College of Medicine
for intern coordination and financial support for data collection; Gustavo Hernandez for help
with data collection. This publication was made possible by the CTSA Grant UL1 RR025750 and
KL2 RR025749 and TL1 RR025748 from the National Center for Research Resources (NCRR).
Its contents are solely the responsibility of the authors and do not necessarily represent the
official view of the NCRR or NIH.
CONFICT OF INTEREST STATEMENT
None of the authors have any competing interests or conflicts to report. We have no commercial
associations or affiliations. This publication was made possible by the CTSA Grant UL1
RR025750 and KL2 RR025749 and TL1 RR025748 from the National Center for Research
Resources (NCRR). Research assistants received a stipend through the Bronx Center to Reduce
and Eliminate Racial and Ethnic Health Disparities (BxCREED).
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REFERENENCES
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0$ /&$ <$ =9$ )##$ 45$ 5#$ ;!$ %$ )$  ;
#(# "( # -  (( ( .( "#
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6( # 2# 6( . )((( - &(($ 6(# !(($ #
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A(#$ ;$ *#$ !$ 4#($ 20$ 2(($ !$ 6$ %)$  :( 
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5 5 5(#( +((#( / 6"( 2# 9## 6# $ .
A($ /$ 1#7#$ 1$ 1$ 2/$ 1#$ 1$ %#'#$ %$ %#$ +%$
0#7$ 8$ 1##$ 1$ !(7$ /!$  6 ##( - 0#(
2# 1 # ( # (( # ( #7 #
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A$ :$ 6(.2($ 9$ A#($ &%$ *#-#$ 4$ A##$ %$ =# 3$ A$
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+( - 9(($  4# A !(  6 :(
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/#$ 9$ 1#(C$ 9$ A :# = 5# )( - :# :( 4((
/($ !$ :#($ !$ 3($ ;$ 0#$ 1$ 4($ !$ 2#$ %$ 3$
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##(#"(( - -( # #" ( # ## - (#( 2# 6# $
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% ( #( # #   "(
(  <( 1# +#( # # ( <1
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4#$ 9$ *7$ 0$ &#$ :$ 9#$ 8$  6( 6 ;
& @7 :( A :# +((#( / <"# 2#
4#$ 1:$ 9#7$ !$ 1#7$ %$ /#$ 30$  A :# E"(
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Table 1. Variation in the number and distribution of mobile food vendors (N = 212a) by weather
Vending in fair weather only
(Vendors that reported not operating in precipitation)
Vending in all weather
(Vendors that reported operating even in precipitation)
Vending category
“Healthier”
N (%)b
“Mixed”
N (%)b
“Less-healthy”
N (%)b
Total
N (%)c
“Healthier”
N (%)b
“Mixed”
N (%)b
“Less-healthy”
N (%)b
Total
N (%)c
A. Fresh produce 51 (87.9) 7 (12.1) 0 (0.0) 58 (31.7) 5 (83.3) 1 (16.7) 0 (0.0) 6 (20.7)
B. Ethnic prepared 0 (0.0) 0 (0.0) 16 (100) 16 (8.7) 0 (0.0) 2 (28.6) 5 (71.4) 7 (24.1)
C. Other prepared 0 (0.0) 0 (0.0) 28 (100) 28 (15.3) 0 (0.0) 4 (36.4) 7 (63.6) 11 (37.9)
D. Frozen novelty 0 (0.0) 0 (0.0) 74 (100) 74 (40.4) 0 (0.0) 0 (0.0) 4 (100) 4 (13.8)
E. Other 0 (0.0) 2 (28.6) 5 (71.4) 7 (3.8) 0 (0.0) 0 (0.0) 1 (100) 1 (3.5)
Total 51 (27.9) 9 (4.92) 123 (67.2) 183 (100) 5 (17.2) 7 (24.1) 17 (58.6) 29 (100)
“Healthier” = offering only whole foods like fresh produce, unprocessed grains, or unsweetened nuts; “Less-healthy” = offering only
processed or prepared foods like bagged chips, preserved meats, and various confections; “Mixed” = offering a mix of “healthier” and
“less-healthy” food items.
a Data on whether vendors conducted business in all weather were not available for the total sample of 372 mobile food vendors.
Reasons included: vendor being in transit and not approachable for interview (n = 72); vendor refusal to answer questions for any of
a variety of reasons (Lucan et al., 2013) (n = 56); vendor being absent from open cart/truck/stand (n = 7); vendor having long line of
customers (n = 6); vendor not speaking English or Spanish well enough to communicate answers to bilingual investigators (n = 6);
vendor not being the owner of the cart/truck/stand and unsure how to answer questions (n = 4); vendor being unable to answer
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specific questions for any other reason (n = 9)
b Percentages are row percentages and may not sum to 100% due to rounding
c Percentages are column percentages and may not sum to 100% due to rounding
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Figure 1. Seasonal variation in geographic distribution of mobile food vendors reporting a
definitive yearly start date and yearly end date (N = 200)a
Summer = July - September; Winter = January - March. Definitions for “Healthier”, “Mixed”,
and “Less-Healthy” appear in the footnote to Table 2. United Hospital Fund neighborhoods
(UHFs) are used by the New York City Department of Health and Mental Hygiene to divide the
city into analyzable units; the Bronx has five UHFs with marked demographic and health
differences. The maps in the above figure show ¼-mile street-network “buffers” around each
vendor; other food-environment research has demonstrated that ¼ mile may be a particularly
relevant distance for accessing food,(Tester et al., 2010) especially for individuals without a car.
(Thornton et al., 2012) The map on the left shows locations for 196 vendors, the map on the
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right shows locations for 73 vendors; both maps may appear to show fewer locations due to
substantial overlap in areas of high vendor density. Specific reasons why data on seasonal
variation was not available for all identified vendors appear in footnote a of Appendix Table 1A.
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Table 2. Correlations by season between exclusively “less-healthy”a vending per capita, and
mean neighborhood diet, diet-related health, and demographic characteristics
Mean Neighborhood
Characteristicsb
Summer
rcp valued
Winter
rcp valued
Diet
Fruit and vegetable intake
(servings of fruits/vegetables eaten yesterday)
-0.80 0.104 -0.50 0.391
Diet-related health
Body mass index [BMI]
(reported weight [kg]/reported height2 [m2])
0.90 0.037 0.30 0.634
Prevalence of known diabetes
(ever been told you have diabetes)
0.40 0.505 0.20 0.747
Prevalence of known hypercholesterolemia
(ever been told you have high cholesterol)
0.90 0.037 0.80 0.104
Prevalence of known hypertension
(ever been told you have high blood pressure)
0.90 0.037 0.70 0.188
Demographics
Non-white proportion
(being any race other than “White”)
0.80 0.104 0.50 0.391
Hispanic proportion
(reporting “Hispanic” as ethnicity)
0.80 0.104 0.90 0.037
Proportion not graduating high school
(reporting less than full high-school education)
0.80 0.104 0.50 0.391
Proportion below 100% Federal Poverty Level
(calculated from household annual income)
0.80 0.104 0.90 0.037
a “Less-healthy” = offering only processed or prepared foods like bagged chips, preserved meats,
and various confections
b All “neighborhood” characteristics derived from the New York City Department of Health and
Mental Hygiene Community Health Survey for 2010 by aggregating individual data to the
level of the United Hospital Fund neighborhood
c r = Spearman correlation coefficient
d Nominal p-values (not adjusted for multiple comparisons)
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APPENDIX
Appendix Table A1. Seasonal variation in the number and distribution of mobile food vendors reporting a definitive yearly
start date and yearly end date (N = 200 a)
Vending in summer
(Vendors that reported operating July – Septemberb)
Vending in winter
(Vendors that reported operating in January – Marchb)
Vending category
“Healthier”
N (%)c
“Mixed”
N (%)c
“Less-healthy”
N (%)c
Total
N (%)d
“Healthier”
N (%)c
“Mixed”
N (%)c
“Less-healthy”
N (%)c
Total
N (%)d
A. Fresh produce 47 (85.5) 8 (14.6) 0 (0.0) 55 (28.1) 24 (100) 0 (0.0) 0 (0.0) 24 (32.9)
B. Ethnic prepared 0 (0.0) 2 (9.1) 20 (90.9) 22 (11.2) 0 (0.0) 2 (15.4) 11 (84.6) 13 (17.8)
C. Other prepared 0 (0.0) 4 (10.8) 33 (89.2) 37 (18.9) 0 (0.0) 4 (14.3) 24 (85.7) 28 (38.4)
D. Frozen novelty 0 (0.0) 0 (0.0) 75 (100) 75 (38.3) 0 (0.0) 0 (0.0) 2 (100) 2 (2.7)
E. Other 0 (0.0) 2 (28.6) 5 (71.4) 7 (3.6) 0 (0.0) 2 (33.3) 4 (66.7) 6 (8.2)
Total 47 (24.0) 16 (8.2) 133 (67.9) 196 (100) 24 (32.9) 8 (11.0) 41 (56.2) 73 (100)
“Healthier” = offering only whole foods like fresh produce, unprocessed grains, or unsweetened nuts; “Less-healthy” = offering only
processed or prepared foods like bagged chips, preserved meats, and various confections; “Mixed” = offering a mix of “healthier” and
“less-healthy” food items.
a Data on whether vendors conducted business in all weather were not available for the total sample of 372 mobile food vendors.
Reasons included: vendor being in transit and not approachable for interview (n = 72); vendor refusal to answer questions for any of
a variety of reasons (Lucan et al., 2013) (n = 56); vendor being absent from open cart/truck/stand (n = 7); vendor having long line of
customers (n = 6); vendor not speaking English or Spanish well enough to communicate answers to bilingual investigators (n = 6);
vendor not being the owner of the cart/truck/stand and unsure how to answer questions (n = 4); vendor being unable to answer
specific questions for any other reason (n = 21). A common reason for not having an answer was vendors being new to the business
and unsure exactly how far into the year they would continue selling.
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b Only 24% of vendors—for which yearly start dates and end dates could be confidently determined—sold year round (i.e., all 12
months)
c Percentages are row percentages and may not sum to 100% due to rounding
d Percentages are column percentages and may not sum to 100% due to rounding
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Appendix Table A2. Correlations by season between any “less-healthy” vending (“less-
healthy” or “mixed” vending)a per capita, and mean neighborhood diet, diet-related health,
and demographic characteristics
Mean Neighborhood
Characteristicsb
Summer
rcp valued
Winter
rcp valued
Diet
Fruit and vegetable intake
(servings of fruits/vegetables eaten yesterday)
-0.90 0.037 -0.50 0.391
Diet-related health
Body mass index [BMI]
(reported weight [kg]/reported height2 [m2])
0.80 0.104 0.30 0.629
Prevalence of known diabetes
(ever been told you have diabetes)
0.20 0.747 0.20 0.747
Prevalence of known hypercholesterolemia
(ever been told you have high cholesterol)
1.00 <0.001 0.80 0.104
Prevalence of known hypertension
(ever been told you have high blood pressure)
0.80 0.104 0.70 0.188
Demographics
Non-white proportion
(being any race other than “White”)
0.90 0.037 0.50 0.391
Hispanic proportion
(reporting “Hispanic” as ethnicity)
0.90 0.037 0.90 0.037
Proportion not graduating high school
(reporting less than full high-school education)
0.90 0.037 0.50 0.391
Proportion below 100% Federal Poverty Level
(calculated from household annual income)
0.90 0.037 0.90 0.037
Proportion of non-English speaking
(some other language used at home mostly)
0.70 0.188 0.70 0.188
a “Less-healthy” = offering only processed or prepared foods like bagged chips, preserved meats,
and various confections; “Mixed” = offering a mix of “healthier” and “less-healthy” food items
(“healthier” = offering only whole foods like fresh produce, unprocessed grains, or
unsweetened nuts)
b All “neighborhood” characteristics derived from the New York City Department of Health and
Mental Hygiene Community Health Survey for 2010 by aggregating individual data to the
level of the United Hospital Fund neighborhood
c r = Spearman correlation coefficient
d Nominal p-values (not adjusted for multiple comparisons)
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Appendix Table A3. Correlations by season between exclusively “healthier”a vending per
capita, and mean neighborhood diet, diet-related health, and demographic characteristics
Mean Neighborhood
Characteristicsb
Summer
rcp valued
Winter
rcp valued
Diet
Fruit and vegetable intake
(servings of fruits/vegetables eaten yesterday)
-0.10 0.873 -0.36 0.553
Diet-related health
Body mass index [BMI]
(reported weight [kg]/reported height2 [m2])
-0.20 0.747 0.154 0.805
Prevalence of known diabetes
(ever been told you have diabetes)
0.30 0.624 0.56 0.322
Prevalence of known hypercholesterolemia
(ever been told you have high cholesterol)
0.30 0.624 0.36 0.553
Prevalence of known hypertension
(ever been told you have high blood pressure)
0.30 0.624 0.41 0.493
Demographics
Non-white proportion
(being any race other than “White”)
0.10 0.873 0.36 0.553
Hispanic proportion
(reporting “Hispanic” as ethnicity)
0.60 0.285 0.62 0.269
Proportion not graduating high school
(reporting less than full high-school education)
0.10 0.873 0.36 0.553
Proportion below 100% Federal Poverty Level
(calculated from household annual income)
0.60 0.285 0.62 0.269
a “Healthier” = offering only whole foods like fresh produce, unprocessed grains, or unsweetened
nuts.
b All “neighborhood” characteristics derived from the New York City Department of Health and
Mental Hygiene Community Health Survey for 2010 by aggregating individual data to the
level of the United Hospital Fund neighborhood
c r = Spearman correlation coefficient
d Nominal p-values (not adjusted for multiple comparisons)
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Appendix Table A4. Correlations by season between any “healthier” vending (“healthier”
or “mixed” vending)a per capita, and mean neighborhood diet, diet-related health, and
demographic characteristics
Mean Neighborhood
Characteristicsb
Summer
rcp valued
Winter
rcp valued
Diet
Fruit and vegetable intake
(servings of fruits/vegetables eaten yesterday)
-0.10 0.873 -0.30 0.624
Diet-related health
Body mass index [BMI]
(reported weight [kg]/reported height2 [m2])
-0.30 0.624 1.00 0.873
Prevalence of known diabetes
(ever been told you have diabetes)
-0.30 0.624 0.60 0.285
Prevalence of known hypercholesterolemia
(ever been told you have high cholesterol)
0.30 0.624 0.40 0.505
Prevalence of known hypertension
(ever been told you have high blood pressure)
0.00 1.000 0.50 0.391
Demographics
Non-white proportion
(being any race other than “White”)
0.10 0.873 0.30 0.64
Hispanic proportion
(reporting “Hispanic” as ethnicity)
0.40 0.505 0.70 0.188
Proportion not graduating high school
(reporting less than full high-school education)
0.10 0.873 0.30 0.624
Proportion below 100% Federal Poverty Level
(calculated from household annual income)
0.40 0.505 0.70 0.188
a “Healthier” = offering only whole foods like fresh produce, unprocessed grains, or unsweetened
nuts; “Mixed” = offering a mix of “healthier” and “less-healthy” food items (“less-healthy” =
offering only processed or prepared foods like bagged chips, preserved meats, and various
confections)
b All “neighborhood” characteristics derived from the New York City Department of Health and
Mental Hygiene Community Health Survey for 2010 by aggregating individual data to the
level of the United Hospital Fund neighborhood
c r = Spearman correlation coefficient
d Nominal p-values (not adjusted for multiple comparisons)
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Background Consuming foods-away-from-home (FAFH) is ubiquitous; yet, it is unclear how it influences diet in diverse populations. Objective The study aimed to evaluate the association between frequency and type of consumption of foods away from home (FAFH) and diet quality. Design The study had a cross-sectional design. Participants self-reported the frequency of consuming FAFH “rarely’ (≤1/week) vs. ‘frequently’ (≥2 times/week) at various commercial establishments or non-commercial FAFH (i.e., friends or relatives’ homes). Participants /Setting: Participants were adults (ages 30-75y) from the Puerto Rico Assessment of Diet, Lifestyle, and Diseases (PRADLAD) study conducted in San Juan, Puerto Rico metro area (n=239) in 2015. Main outcome measures A validated food frequency questionnaire captured dietary intake. The Alternate Healthy Eating Index-2010 (AHEI) defined diet quality. Secondary outcomes included whether participants met Dietary Guidelines for Americans (DGA) recommendations for sodium, added sugars, saturated fat, dietary fiber, total energy, and alcohol. Statistical analyses performed Linear or logistic regression models adjusted for age, sex, employment, income, education, and food insufficiency tested differences in mean AHEI scores or odds (OR: 95%CI) of meeting (vs. not meeting) intake recommendations by FAFH type and frequency. Results Overall, 54.4% and 37.2% of participants reported consuming commercial FAFH and non-commercial FAFH ‘frequently’, respectively. Consuming FAFH ‘frequently’ (vs. ‘rarely’) was associated with lower mean AHEI scores for both commercial FAFH (57.92 vs. 63.58; p=0.001), and for non-commercial FAFH (56.22 vs. 62.32: p<0.001). Consuming commercial FAFH ‘frequently’ (vs. ‘rarely’) at any type of food establishment was associated with lower odds of meeting the dietary fiber DRI (OR: 0.43; CI: 0.23, 0.81). Consuming non-commercial FAFH ‘frequently’ was associated with lower odds of meeting recommendations for sodium (OR: 0.30; CI: 0.11, 0.79) and added sugars (OR: 0.41; CI: 0.18, 0.93). Conclusions Frequent consumption of FAFH is associated with lower diet quality and lower adherence to dietary recommendations in Puerto Rico. Future studies should explore if diet quality can be improved by prioritizing healthy at-home meals and reformulating the quality of commercial FAFH.
Conference Paper
Les recherches et les politiques publiques visant à améliorer le régime alimentaire des populations à faibles revenus se sont concentrés sur l'identification des déserts alimentaires, puis sur l'accessibilité objective et perçue à une alimentation saine, afin d'orienter l'action publique sur des domaines spécifiques. Malheureusement, ces actions n'ont eu qu'un succès marginal. Pour mieux comprendre cet échec, nous avons effectué une revue de littérature sur l'accessibilité objective et perçue. Le premier objectif de cet article est d'identifier les lacunes dans la littérature concernant les mesures utilisées pour l'accessibilité objective ou perçue à une alimentation saine, principalement le manque d'intérêt pour l'expérience vécue par la cible. Le second objectif est de questionner l'ajustement de ces construits qui proviennent principalement des Etats-Unis au contexte des villes françaises. En conclusion, nous proposons un agenda de recherche en marketing social afin de mieux cerner la complexité de l'accessibilité objective et perçue et leur lien avec les comportements alimentaires sains. Abstract: Past researches and public policy programs aiming to improve low-income population's diet have focused on the identification of food deserts, and then of objective and perceived accessibility to healthy food, to direct public action on specific areas. Unfortunately, these actions were only marginally successful. We conducted a literature review that focused on objective and perceived accessibility to better understand this failure. The first objective of this paper is to identify gaps in the literature in measures used either for objective or perceived accessibility to healthy food, focusing on the lack of interest in the target's own experience. The second objective is to question the adjustment of these constructs that come mainly from the USA in the context of French cities. In conclusion, we suggest an agenda of research in social marketing in order to better encapsulate the complexity of objective and perceived accessibility and their link to healthy food behavior.
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Past research on food-environment change has been limited in key ways: (1) considering only select storefront businesses; (2) presuming items sold based only on businesses category; (3) describing only ecological changes; (4) considering only multi-year intervals. The current study addressed past limitations by: (1) considering a full range of both storefront and non-storefront businesses; (2) focusing on items actually offered (both healthful and less-healthful varieties); (3) describing individual-business-level changes (openings, closings, changes in offerings); (4) evaluating changes within a single year. Using a longitudinal, matched-pair comparison of 119 street segments in the Bronx, NY (October 2016-August 2017), investigators assessed all businesses—food stores, restaurants, other storefront businesses (OSBs), street vendors—for healthful and less-healthful food/drink offerings. Changes were described for individual businesses, individual street segments, and for the area overall. Overall, the number (and percentage) of businesses offering any food/drink increased from 45 (41.7%) in 2016 to 49 (45.8%) in 2017; businesses newly opening or newly offering food/drink cumulatively exceeded those shutting down or ceasing food/drink sales. In 2016, OSBs (gyms, barber shops, laundromats, etc.) together with street vendors represented 20.0% and 27.3% of businesses offering healthful and less-healthful items, respectively; in 2017, the percentages were 31.0% and 37.0%. While the number of businesses offering healthful items increased, the number offering less-healthful items likewise increased and remained greater. If change in a full range of food/drink availability is not appreciated: food-environment studies may generate erroneous conclusions; communities may misdirect resources to address food-access disparities; and community residents may have increasing, but unrecognized, opportunities for unhealthful consumption.
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Objective Conceptualisations of ‘food deserts’ (areas lacking healthful food/drink) and ‘food swamps’ (areas overwhelm by less-healthful fare) may be both inaccurate and incomplete. Our objective was to more accurately and completely characterise food/drink availability in urban areas. Design Cross-sectional assessment of select healthful and less-healthful food/drink offerings from storefront businesses (stores, restaurants) and non-storefront businesses (street vendors). Setting Two areas of New York City: the Bronx (higher-poverty, mostly minority) and the Upper East Side (UES; wealthier, predominantly white). Participants All businesses on 63 street segments in the Bronx ( n 662) and on 46 street segments in the UES ( n 330). Results Greater percentages of businesses offered any , any healthful, and only less-healthful food/drink in the Bronx (42·0 %, 37·5 %, 4·4 %, respectively) than in the UES (30 %, 27·9 %, 2·1 %, respectively). Differences were driven mostly by businesses (e.g. newsstands, gyms, laundromats) not primarily focused on selling food/drink – ‘other storefront businesses’ (OSBs). OSBs accounted for 36·0 % of all food/drink-offering businesses in the Bronx (more numerous than restaurants or so-called ‘food stores’) and 18·2 % in the UES (more numerous than ‘food stores’). Differences also related to street vendors in both the Bronx and the UES. If street vendors and OSBs were not captured, the missed percentages of street segments offering food/drink would be 14·5 % in the Bronx and 21·9 % in the UES. Conclusions Of businesses offering food/drink in communities, OSBs and street vendors can represent substantial percentages. Focusing on only ‘food stores’ and restaurants may miss or mischaracterise ‘food deserts’, ‘food swamps’, and food/drink-source disparities between communities.
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Mobile food vendors (also known as street food vendors) may be important sources of food, particularly in minority and low-income communities. Unfortunately, there are no good data sources on where, when, or what vendors sell. The lack of a published assessment method may contribute to the relative exclusion of mobile food vendors from existing food-environment research. A goal of this study was to develop, pilot, and refine a method to assess mobile food vendors. Cross-sectional assessment of mobile food vendors through direct observations and brief interviews. Using printed maps, investigators canvassed all streets in Bronx County, NY (excluding highways but including entrance and exit ramps) in 2010, looking for mobile food vendors. For each vendor identified, researchers recorded a unique identifier, the vendor's location, and direct observations. Investigators also recorded vendors answers to where, when, and what they sold. Of 372 identified vendors, 38% did not answer brief-interview questions (19% were 'in transit', 15% refused; others were absent from their carts/trucks/stands or with customers). About 7% of vendors who ultimately answered questions were reluctant to engage with researchers. Some vendors expressed concerns about regulatory authority; only 34% of vendors had visible permits or licenses and many vendors had improvised illegitimate-appearing set-ups. The majority of vendors (75% of those responding) felt most comfortable speaking Spanish; 5% preferred other non-English languages. Nearly a third of vendors changed selling locations (streets, neighbourhoods, boroughs) day-to-day or even within a given day. There was considerable variability in times (hours, days, months) in which vendors reported doing business; for 86% of vendors, weather was a deciding factor. Mobile food vendors have a variable and fluid presence in an urban environment. Variability in hours and locations, having most comfort with languages other than English, and reluctance to interact with individuals gathering data are principal challenges to assessment. Strategies to address assessment challenges that emerged form this project may help make mobile-vendor assessments more routine in food-environment research.
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Given the emerging focus on improving food environments and food systems through planning, this article investigates racial disparities in neighborhood food environments. An empirical case of Erie County, New York tests the hypothesis that people belonging to different racial groups have access to different neighborhood food destinations. Using multiple methods—Gini coefficients and Poisson regression—we show that contrary to studies elsewhere in the country there are no food deserts in Erie County. However, like other studies, we find an absence of supermarkets in neighborhoods of color when compared to white neighborhoods. Nonetheless, our study reveals an extensive network of small grocery stores in neighborhoods of color. Rather than soliciting supermarkets, supporting small, high-quality grocery stores may be a more efficient strategy for ensuring access to healthful foods in minority neighborhoods.
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The paucity of accessible supermarkets is a continuing concern in inner-city communities. Based on a survey of planners in 32 communities, this article examines initiatives to encourage grocery retail investment, reasons for the existence or absence of initiatives, and factors in successful developments. This research shows that systematic, citywide grocery initiatives are rare, with such efforts limited to particular sites or developments. Reliance on private initiatives, absence of grassroots requests for action, and assignment of lower priority to grocery stores in commercial revitalization programs explain planner inaction. Successful initiatives are characterized by political leadership, competent public agency participation, and, often, partnerships with nonprofit agencies. This article also presents recommendations for community and economic development planners to increase grocery investment in underserved areas.
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Objective: To assess a county population’s exposure to different types of food sources reported to affect both diet quality and obesity rates. Design: Food permit records obtained from the local health department served to establish the full census of food stores and restaurants. Employing prior categorization schemes which classified the relative healthfulness of food sources based on establishment type (i.e. supermarkets v. convenience stores, or full-service v. fast-food restaurants), food establishments were assigned to the healthy, unhealthy or undetermined groups. Setting: King County, WA, USA. Subjects: Full census of food sources. Results: According to all categorization schemes, most food establishments in King County fell into the unhealthy and undetermined groups. Use of the food permit data showed that large stores, which included supermarkets as healthy food establishments, contained a sizeable number of bakery/delis, fish/meat, ethnic and standard quick-service restaurants and coffee shops, all food sources that, when housed in a separate venue or owned by a different business establishment, were classified as either unhealthy or of undetermined value to health. Conclusions: To fully assess the potential health effects of exposure to the extant food environment, future research would need to establish the health value of foods in many such common establishments as individually owned grocery stores and ethnic food stores and restaurants. Within-venue exposure to foods should also be investigated.
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The feasibility of working with neighborhood corner stores to increase the availability of fresh fruit and vegetables in low-income neighborhoods in New Orleans was assessed. Household interviews and 24-hour dietary recalls (n = 97), corner store customer intercept interviews (n = 60) and interviews with corner store operators (owners/managers) (n = 12) were conducted in three neighborhoods without supermarkets. Regional produce wholesalers were contacted by phone. Results indicated that the majority of neighborhood residents use supermarkets or super stores as their primary food source. Those who did shop at corner stores typically purchased prepared foods and/or beverages making up nearly one third of their daily energy intake. Most individuals would be likely to purchase fresh fruit and vegetables from the corner stores if these foods were offered. Store operators identified cost, infrastructure and lack of customer demand as major barriers to stocking more fresh produce. Produce wholesalers did not see much business opportunity in supplying fresh produce to neighborhood corner stores on a small scale. Increasing availability of fresh fruit and vegetables in corner stores may be more feasible with the addition of systems changes that provide incentives and make it easier for neighborhood corner stores to stock and sell fresh produce.
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America has a serious weight problem. Two-thirds of adults and nearly one-fifth of children in the United States are overweight, placing them at greater risk for heart disease, diabetes, and other chronic diseases including cancer and arthritis. Furthermore, obesity and its related health problems are placing a major strain on the U.S. health care system. Thus, this manual describes 24 recommended strategies by the Centers for Disease Control and Prevention (CDC) to encourage and support healthy eating and active living. In addition, a single measure is provided for each strategy to help communities track their progress over time. The 24 strategies and measures are divided into 6 categories that represent different aspects of the physical and food environments. Three appendices are included: (1) Project Work Groups; (2) Terms Used in This Manual; and (3) Useful Contacts for Data Collection. (Contains 77 resources.)
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Objectives: We assessed the effectiveness of an initiative to increase the stock and promotion of healthy foods in 55 corner stores in underserved neighborhoods. Methods: We evaluated the intervention through in-store observations and preintervention and postintervention surveys of all 55 store owners as well as surveys with customers at a subset of stores. Results: We observed an average of 4 changes on a 15-point criteria scale. The most common were placing refrigerated water at eye level, stocking canned fruit with no sugar added, offering a healthy sandwich, and identifying healthier items. Forty-six (84%) store owners completed both surveys. Owners reported increased sales of healthier items, but identified barriers including consumer demand and lack of space and refrigeration. The percentage of customers surveyed who purchased items for which we promoted a healthier option (low-sodium canned goods, low-fat milk, whole-grain bread, healthier snacks and sandwiches) increased from 5% to 16%. Conclusions: Corner stores are important vehicles for access to healthy foods. The approach described here achieved improvements in participating corner stores and in some consumer purchases and may be a useful model for other locales.
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Previous studies have provided mixed evidence with regards to associations between food store access and dietary outcomes. This study examines the most commonly applied measures of locational access to assess whether associations between supermarket access and fruit and vegetable consumption are affected by the choice of access measure and scale. Supermarket location data from Glasgow, UK (n = 119), and fruit and vegetable intake data from the 'Health and Well-Being' Survey (n = 1041) were used to compare various measures of locational access. These exposure variables included proximity estimates (with different points-of-origin used to vary levels of aggregation) and density measures using three approaches (Euclidean and road network buffers and Kernel density estimation) at distances ranging from 0.4 km to 5 km. Further analysis was conducted to assess the impact of using smaller buffer sizes for individuals who did not own a car. Associations between these multiple access measures and fruit and vegetable consumption were estimated using linear regression models. Levels of spatial aggregation did not impact on the proximity estimates. Counts of supermarkets within Euclidean buffers were associated with fruit and vegetable consumption at 1 km, 2 km and 3 km, and for our road network buffers at 2 km, 3 km, and 4 km. Kernel density estimates provided the strongest associations and were significant at a distance of 2 km, 3 km, 4 km and 5 km. Presence of a supermarket within 0.4 km of road network distance from where people lived was positively associated with fruit consumption amongst those without a car (coef. 0.657; s.e. 0.247; p0.008). The associations between locational access to supermarkets and individual-level dietary behaviour are sensitive to the method by which the food environment variable is captured. Care needs to be taken to ensure robust and conceptually appropriate measures of access are used and these should be grounded in a clear a priori reasoning.