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Concerning Limitations of Food-Environment Research: A Narrative Review and Commentary Framed around Obesity and Diet-Related Diseases in Youth


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... The limitations discussed in this article represent not only challenges for future food-environment research, but also concerning sources of error in the existing food- environment literature. If the error is random, its effect is to create noise, masking true associations when they actually exist. This possibility challenges the “null” findings of many studies that examined associations between food environments and diet or health outcomes. More worrisome, though, is that error could be systematic, and some evidence suggests that at least some of it is. The issue in this case is the potential for biased findings, creating associations that do not actually exist or exaggerating the magnitude of those that do. Either way, the Figure makes it clear that any food environment might produce very different, even opposite, findings depending on the methods used for assessment ...
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Concerning Limitations of Food-Environment
Research: A Narrative Review and Commentary
Framed around Obesity and Diet-Related
Diseases in Youth
Sean C. Lucan, MD, MPH, MS
Article history:
Accepted 15 August 2014
Food environment
Public health
Chronic disease
2212-2672/Copyright ª2014 by the Academy of Nutrition and Dietetics.
limitations of food-environment research (and rec-
ommendations to address them), it may be useful
to discuss the rationale for studying food environ-
ments in the rst place. Food environments are relevant to
diverse nutritional issues and health disparities. An especially
compelling argument for studying food environments is the
public health challenge of diet-related chronic diseases,
particularly in youth.
Diet-related chronic diseases (eg, obesity, diabetes, and
vascular diseases) are leading causes of disability and pre-
mature death in the United States.
Diseases that were once
considered adult-onsetnow appear earlier in the life
course, with preventable impairments affecting youth.
Over recent decades, young people have become more
with obesity early in life linked to later-life
chronic-disease risk,
and premature death.
Fortunately, if obese young people are able to transition to
normal weights as adults, they might escape chronic disease
risks as if they were never obese.
Unfortunately, such
transitions rarely occur; with advancing age and passing
generations, young people increasingly consume fewer
healthy whole foods such as fruits, vegetables, and whole
grains, and consume more unhealthy items, like rened
sweets (eg, candy, sugary drinks), simple starches (eg,
snacks chips), and various other rened and highly processed
There is little question that many factors inuence what
young people eat; individual, social, and cultural factors
are undoubtedly important.
Also important are physical
particularly the local environments in
which individuals can obtain foods and beverages: ie,
food environments.
Modifying individual, social, or
cultural factors may be quite difcult.
Modifying food
environmentskeeping individual, social, and cultural con-
texts in mindcould be a comparatively efcient strategy to
improve nutrition and health by making healthier eating the
Food environments include settings such as homes and
schools, but much of young peoples unhealthy food
consumption occurs away from these sites.
Thus, even
well-intentioned interventions directed at home or school
environments may be ineffective.
For instance, although
a state ban on all sugar-sweetened beverages in middle
schools reduced in-school access and purchasing of such
beverages, it did not reduce overall consumption.
A reason,
according to other research, may be that adolescents (even
from low-income households) will typically spend approxi-
mately $4 per day on items such as chips, candy, and soda
from outside sources.
Outside sources of food in environments around home and
school may be especially relevant for adolescents. Unfortu-
nately, such food environments, particularly in urban, low-
income, and minority communities, tend to offer mostly
less-healthy fast foods and convenience items with few
healthy alternatives.
This food-distribution reality is a
problem because some studies suggest that the greater the
density of and proximity to fast-food outlets and convenience
stores, the more likely adolescents are to consume fast foods
and soda,
have less healthy diets,
be/become over-
weight or obese,
and have features of metabolic syn-
Conversely, greater distance to convenience
or fast food
and closer proximity to supermar-
and restaurants serving vegetables
are associated
with higher produce consumption,
fewer purchases of
sugary beverages,
less fast-food intake,
overall healthier
and healthier weights.
Despite the associations noted above, some studies demon-
strate no consistent relationship between access to fast-food
restaurants or small stores on the one hand and dietary
or body weight on the other
; or between
supermarket access and produce consumption on one hand
ª2014 by the Academy of Nutrition and Dietetics. JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS 1
and diet quality on the other.
Some studies have even
generated counterintuitive ndings
: eg, that the odds of
consuming vegetables is greater the farther an individual
lives from a supermarket,
or that obesity rates are posi-
tively correlated with healthy food access and negatively
associated with fast-food exposure.
In a review of the literature from 2009, Larson and Story
concluded that the majority of food-environment studies
have methodological limitations which limit their credibility
to guide interventions and policy changes.
Although this
review was published 5 years ago, little has changed in the
landscape of food-environment research to date to suggest
much progress. Indeed, several common limitations remain
substantial problems for the eld.
The limitations described in the review that follows
involve problems of assessing physical access to food
sources in an environment. The review focuses specically
on measuring food-access issues relevant to young people
transitioning to adulthood, but many of the issues are
cross-cutting and generally relevant to other populations
and groups. For any groups, assessments of additional as-
pects of food environments also merit critique (eg, assess-
ments of items available in the home and in other settings
like work and school, and assessments of the placement,
prices, and promotion of items within surrounding retail
spaces); these additional considerations are beyond the
scope of this essentially geo-spatialefocused review. What
follows here are descriptions of ve common limitations in
food-source physical-access assessment, along with rec-
ommendations to address each.
Limitation 1: Inaccurate Datasets to Identify Food
The use of pre-existing datasets, like commercial business
lists, is exceedingly common in food-environment research.*
Such datasets were convenient, efcient, and appropriate for
early exploratory studies, and helped produce ndings that
called attention to possible associations between food envi-
ronments, individual diet, and downstream diet-related
health outcomes. Unfortunately, such datasets inadequately
reect actual food environments on the ground.
example, a study in one dense urban area showed that one of
the most commonly used business lists had a sensitivity of
only 39.3% overall (only 26.2% for general grocers) and a
positive predictive value of only 45.5% overall (only 32% for
specialty food stores) compared to direct observation.
if performance was twice as good in other settings (which
other validation studies suggest is not the case
), ndings
from research linking food environments to diet and diet-
related health outcomes relying solely on such business
lists would be in question.
Recommendation 1
Universally validating commercial business lists with
other sources of data or otherwise using two or more pre-
existing data sources for retail information (eg, telephone
or Internet directories, dining or shopping guides, various
government records, or multiple commercial business
may be a strategy for researchers to use moving
forward. This strategy would be appropriate when
geographic areas of interest are too large and/or too dense
with food sources to reasonably allow for direct observation
(eg, areas like an entire US state or a large urban county).
When discrepancies exist between datasets, direct ground-
truthing should be done to reconcile disagreements
if not possible, remote assessment using web-based or other
street-viewing applications
(but only if pilot-testing in
areas of interest demonstrates acceptable concordance with
direct observation). If even remote reconciliation is unfeasi-
ble (or ill-advised), at a minimum sensitivity analyses are in
order, modeling and reporting best and worst-case scenarios
of discrepancies to see whether conclusions change (as done
in validation studies reporting results by both exact/strict
and nonexact/lenient matching
). For smaller geographic
areas that are less dense with food sources (eg, areas like
some urban zip codes or rural counties), the gold standard
should probably be boots on the grounddirect assess-
Data from such primary collection may not
only be more complete, accurate, and applicable than that
from pre-existing retail sets, it might actually be more
economical as well given the considerable human and
monetary investment that could otherwise be required for
data purchasing/acquisition, proper data cleaning, and
dataset mergers and management.
Limitation 2: Categorizations of Food Sources Based
on Generalized Type
Most food-environment studies lump food sources of a
certain type together
(eg, as if every small store were the
same as every other small store in terms of varieties of foods
offered when demonstrably this is not the case
). For
example, supermarkets are usually considered as healthy
food sources even though they often sell plenty of highly
processed unhealthy fare.
Conversely, fast-food outlets
are usually considered as unhealthyfood sources even
though they often offer whole foods like green salads, sliced
fruit, and milk.
Recommendation 2
It is essential to not classify businesses based on name or
generalized type (eg, Pleasantville Grocery¼healthy)
without knowing anything about the foods and beverages
actually available. Distinctions of healthyand unhealthy”—
or preferably measures with greater gradation, like indexes or
numerical scores accounting for inevitable product mixes
should be based on what businesses actually offer. Compre-
hensive audits are not necessarily required, particularly for
studies at larger scales. Examining the availability (yes/no) of
a few select categories (eg, sugary beverages, salty snacks,
candy, fresh produce) may sufce for many purposes, with
assessments of test-retest performance and inter-rater
concordance to establish reliable tools and standardize
methods. Studies at larger scales may benet from remote-
assessment methods, for instance using Internet menus, cir-
culars, or other business advertisements (particularly for
chain stores and restaurants that have consistent offerings
across sites).
If actual assessments are not possible,
studies should again include sensitivity analyses (eg,
*Select list of 35 published studies available from the
author upon request.
modeling stores with unknown inventory as both having
candy [just as an example] and then as not having candy and
assessing whether reported results are robust to the uncer-
taintyan approach that is both novel and conservative). For
studies at smaller scales (as with Recommendation 1 dis-
cussed earlier) boots on the grounddirect assessmentsin
this case of select product categoriesshould probably be the
gold standard.
Limitation 3: Inclusion of Only a Limited Range of
Food Sources
Most studies of food environments have focused almost
exclusively on select stores (eg, supermarkets) and/or on
various kinds of restaurants (mostly fast-food outlets).
Such focus neglects alternative, often nonintuitive, food
sources such as gas stations, hardware stores, clothing
outlets, book sellers, general merchandisers, salons, phar-
macies, and other retailers offering food and/or drink.
also neglects impermanent sources of food that may also be
relevant, such as street vendors (ie, mobile food ven-
and farmersmarkets
). Certainly, the food
environment is much broader than just select food stores
and restaurants. Potential implications of including or
excluding certain types of food sources are illustrated in
the Figure.
Recommendation 3
Researchers should consider the totality of food sources in
their study areas of interest. Nonintuitive sources of highly
processed, prepackaged, convenience items could potentially
offset any healthy inuence of sources of whole fresh foods in
communities, and it is insufcient to focus on only major food
retailers when calories may be nearly ubiquitous across
diverse retailers. Recommendations for retail assessments
using pre-existing datasets, remote techniques, and sensi-
tivity analyses vs direct observationappear under Recom-
mendations 1 and 2 (discussed earlier). All retail assessments
should be as comprehensive as possible.
Limitation 4: Consideration of Food Sources in
Just as it is ill-advised to consider individual nutrients out of
the context of an overall foodand individual foods out of
the context of an overall dietso too it is ill-advised to
consider individual food sources out of the context of an
overall food environment. Unfortunately, most studies
consider only the effect of food sources X (eg, supermarkets)
and perhaps also the separate effect of food sources Y (eg,
fast-food outlets), but not how food sources X and Y interact.
A question such as: Are fruit carts around schools associated
with greater produce consumption regardless of whether
fast-food outlets are present?is just one of a type that
remains unanswered. This type of question can only be
addressed when a narrow focus on just fruit carts or just
fast-food outlets is expanded to consider a broader, poten-
tially interactive, big picture.
Recommendation 4
Because food sources do not operate in a vacuum, simulta-
neous consideration of food sources is imperative. At least a
few studies have made strides in this area, suggesting that it
is a ratio or the proportional contribution of multiple food
sources acting in concert that may matter more than any
one type of food source acting alone.
Future studies
should continue to explore the importance of proximity,
distribution, and density of multiple food sources relative to
one another, particularly keeping in mind Recommenda-
tions 1-3 (discussed earlier) for the greatest accuracy
and completeness in making individual food-source
Limitation 5: Problems with Dening Exposureto
Food Sources
Methodological choices matter when dening exposureor
accessto food sources.
One strategy commonly used to
dene exposurein food-environment studies is to use
administrative areas such as block groups, census tracts, or
zip codes (Figure, panel A).Such administrative areas may
be quite problematic for food-environment conclusions
though, because there could be highly uneven exposures
within administrative boundaries. For example, fast-food
outlets in a zip code might matter little to individuals living
in an area of the zip code far from where most of the fast-
food outlets are concentrated. Also, the boundaries of
administrative units might have little relation to the areas
where individuals actually engage with food (eg, ones
concept of neighborhoodmay be quite different than the
zip code where one lives).
A second strategy some studies use to dene food-source
exposure is to specify proximity or physical distance. Most
often, the method in this case is to draw a circular area with a
radius of linear or Euclidean distance as the crow iesfrom
a central point of interest (Figure, panel B).§However, linear
distances may be poor measures of actual exposure or
For instance, straight lines ignore possible
travel routes and barriers to transit like train tracks, rivers,
and divided highways.
A third strategy used in studies to dene food-source
exposure is to delineate unimpeded paths of an existing
street network to characterize proximity (Figure, panel C). A
problem here is often an exclusive focus on travel routes
around individualshomes, not around other points of po-
tential relevance (eg, around work or school).kAnother
problem with proximity by street network (or by Euclidean
distance) is the question of what length of travel might
be most relevant (eg,
mile, 1 mile); associations
obtained may be quite different depending on distances
Of the three strategies discussed, only exposure dened
by administrative area (Figure, panel A) inherently involves
Select list of 39 published studies available from the
author upon request.
Select list of 16 published studies available from the
author upon request.
Select list of 13 published studies available from the
author upon request.
Select list of 10 published studies available from the
author upon request.
(by denition) administrative boundaries or edges.Prox-
imity measures like Euclidean distance and street-network
areas (Figure, panels B and C) need not be bounded by
edges,but almost invariably are. Food-environment
studies generally only consider data in sample areas of in-
terest (eg, in county A), not data in areas directly adjacent
(eg, in bordering county B). This is a problem because any
assessment of a food environment in an area (regardless of
Limitations 1-4 discussed earlier) may be incorrect if edge
effects(ie, the effects of exposure across study-area
boundaries) are not considered. For instance, if there are
no supermarkets in a study area but there are plenty of
supermarkets just over the border (or edge) in an acces-
sible adjacent area, then the assessment of accessibility or
exposure for study individuals, considering only the study
area, may be completely wrong. In fact, one study demon-
strated that 37% of distance estimates for accessibility to
food retailers were wrong when edge effects were not
Finally, the exposure issues described earlier all relate to
strategies for dening xed and bounded geospatial areas,
but the experience of most individuals is probably not
xed or bounded. It may be more important to understand
how people navigate within local geographies to obtain
their food. Certainly, residential area may be important
(and other areas like those around school or work), but
proximity is not the only concern. Indeed, studies
have shown that other concerns may trump physical
proximity because individuals rarely shop at their nearest
For example, travel times may relate to food
and access to private vehicles or public
transportation may be modiers of the role local food en-
vironments play.
Recommendation 5
Research should consider potential travel routes
or actual
activity spacesas attempted in only a few studies to
The former is theoretically possible even
on larger scales (eg, states, countries), using geographic in-
formation systems (GIS) software packages. As with other is-
sues of uncertainty, sensitivity analyses could be done (eg,
modeling different possible travel paths between food sources
and home, school, and/or work locations). For smaller studies,
measuring actual activity spaces(ie, how individuals actually
travel in their daily routines) is possible using global posi-
tioning systems (GPS) or, somewhat less ideally, using partic-
ipant reports of travel routes and activities.
The limitations discussed in this article represent not only
challenges for future food-environment research, but also
Figure. Same food environment, three different strategies to measure exposure,three very different implications.
Not an
inherent property of the strategy (A, B, or C) used to dene exposure, but another consideration that highlights the different
ndings that might result from different assessments of exposure.
concerning sources of error in the existing food-
environment literature. If the error is random, its effect is
to create noise, masking true associations when they
actually exist. This possibility challenges the nullndings
of many studies that examined associations between food
environments and diet or health outcomes.More worri-
some, though, is that error could be systematic, and some
evidence suggests that at least some of it is.
issue in this case is the potential for biased ndings,
creating associations that do not actually exist or exagger-
ating the magnitude of those that do. Either way, the Figure
makes it clear that any food environment might produce
very different, even opposite, ndings depending on the
methods used for assessment.
Of additional concern is that the issues discussed in this
article represent only a sample of limitations in food-
environment studies. Other common limitations include
relying mostly on cross-sectional rather than longitudinal
designs (with a nontrivial potential for false-positive er-
) and problems with measurement on the outcome
sideof presumed associations: eg, assessing dietary intake,
such as fruit-and-vegetable consumption, through single-
item survey questions or assessing diet-related health out-
comes like body mass index using self-reported heights and
weights.#The take-away message from all of these limita-
tions is one of caution. Available research still does not allow
us to condently identify the ways in which food environ-
ments inuence diet
or diet-related health outcomes.
fact, our limited knowledge base challenges the develop-
ment of interventions and policies that would be of net
Future research needs to build on prior studies, improve
on past designs, and overcome the limitations of founda-
tional work in the eld. Although previous studies picked
low-hanging fruit and called attention to important areas
for investigation, there is still much hanging fruit to
collect (and probably some collected fruit that is past its
prime and ready to compost). Future picking will require
greater effort, resources, and investment than has become
the norm. The studies needed to advance the eld will
require higher-quality, more complete, and more nuanced
data on food sources, considering interactions between
food-source exposures and how people may navigate
through their lived spaces in more sophisticated ways.
Gains in these areas could help inform initiatives
and address unanswered questions about public health
nutritionquestions such as: Do supermarkets matter for
community nutrition when there is a high density of fast-
food exposure? Would adding a farmersmarket help? If
so, where? What if hot dog carts move in?
The path ahead is not an easy one. But generations of
young people growing up obese and unhealthy may be
depending on us to do better.
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S. C. Lucan is a family physician and public health researcher, Department of Family and Social Medicine, Albert Einstein College of Medicine,
Monteore Medical Center, Bronx, NY.
Address correspondence to: Sean C. Lucan, MD, MPH, MS, Department of Family and Social Medicine, Albert Einstein College of Medicine,
Monteore Medical Center, 1300 Morris Park Ave, Block Building, Room 410, Bronx, NY 10461. E-mail:
No potential conict of interest was reported by the author.
The author received no funding to support this manuscript.
The author has no acknowledgements for this article, but would like to recognize the many valuable contributions of other investigators who are
also engaged in food-environment research.
... Non-compliant establishments were checked via virtual conference, which involved checking the streets using tools such as Google Street View, which has the advantage of saving time and resources (Pliakas et al. 2017). Virtual conference can also be performed anywhere and anytime, with only one Internet connection as a prerequisite (Liese et al. 2010;Lucan 2015;Rundle et al. 2011). Thus, this virtual method was considered the fastest and most viable in the context of a metropolis. ...
... Regarding the construction of the database used in this study, the use of two secondary databases was an alternative to obtain a more complete and reliable database, considering that data collection through direct observation is unfeasible because of the large territorial extension (Liese et al. 2010;Lucan 2015). ...
This study aimed to describe the community food environment of a Brazilian metropolis using secondary data from 2015. To construct this database, the State and Municipal Secretariat were asked to provide information on 12 types of food sales establishment in the city of Belo Horizonte. Establishments that were not common between the two databases were checked using the Google Street View database. The final database consisted of establishments that were virtually checked as well as existing establishments in the two secondary databases. The establishments included in the final database were categorized as healthy, unhealthy, or mixed according to the predominance of food sales. Unhealthy establishments are the most available in the food environment. Lower income areas have a lower availability of all types of establishment, particularly healthy ones. As income increases, there is an increase in the availability of mixed and unhealthy establishments. Understanding the food environment of cities is important for decision makers to identify places where the implementation of public equipment for food safety and nutrition is necessary.
... 91 Important areas for future research evaluating the impact of the food environment on CVD risk factors include determining the role of an individual's experience within the food environment, the role of activity spaces, and the impact of a person's travel routes within nonresidential food environments on dietary behavior and other cardiovascular health markers. [92][93][94][95] ...
Social determinants of health (SDoH), which encompass the economic, social, environmental, and psychosocial factors that influence health, play a significant role in the development of cardiovascular disease (CVD) risk factors as well as CVD morbidity and mortality. The COVID-19 pandemic and the current social justice movement sparked by the death of George Floyd have laid bare long-existing health inequities in our society driven by SDoH. Despite a recent focus on these structural drivers of health disparities, the impact of SDoH on cardiovascular health and CVD outcomes remains understudied and incompletely understood. To further investigate the mechanisms connecting SDoH and CVD, and ultimately design targeted and effective interventions, it is important to foster interdisciplinary efforts that incorporate translational, epidemiological, and clinical research in examining SDoH-CVD relationships. This review aims to facilitate research coordination and intervention development by providing an evidence-based framework for SDoH rooted in the lived experiences of marginalized populations. Our framework highlights critical structural/socioeconomic, environmental, and psychosocial factors most strongly associated with CVD and explores several of the underlying biologic mechanisms connecting SDoH to CVD pathogenesis, including excess stress hormones, inflammation, immune cell function, and cellular aging. We present landmark studies and recent findings about SDoH in our framework, with careful consideration of the constructs and measures utilized. Finally, we provide a roadmap for future SDoH research focused on individual, clinical, and policy approaches directed towards developing multilevel community-engaged interventions to promote cardiovascular health.
... density, count) data as proxies for exposure. However, these measures may show different associations with dietary behaviours (23) . A common limitation of the literature is the assessment of only one or a limited selection of food outlets, usually fast food outlets or supermarkets, to assess the relationship between neighbourhood food outlet exposure and dietary intake or behaviours. ...
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Objective: Despite the increased attention on neighborhood food environments and dietary behaviors, studies focusing on adolescents are limited. This study aims to characterize typologies of food environments surrounding adolescents and their associations with fast food outlet visitation and snack food purchasing to/from school. Design: The number of food outlets (supermarket; green grocers; butcher/seafood/deli; bakeries; convenience stores; fast food/takeaways; café and restaurants) within a 1km buffer from home were determined using a Geographic Information System. Adolescents self-reported frequency of fast food outlet visitation and snack food purchasing to/from school. Latent Profile Analysis was conducted to identify typologies of the food environment. Cross-sectional multilevel logistic regression analyses were conducted to examine the relationships between food typologies, fast food outlet visitations and snack food purchasing to/from school. Setting: Melbourne, Australia. Participants: 410 adolescents (mean age= 15.5 (SD=1.5) years). Results: Four distinct typologies of food outlets were identified: 1) limited variety/low number; 2) some variety/low number; 3) high variety/medium number; 4) high variety/high number. Adolescents living in Typologies 1 and 2 had three times higher odds of visiting fast food outlets ≥1 per week (Typology 1: OR= 3.71, 95%CI 1.23, 11.19; Typology 2: OR= 3.65, 95% CI 1.21, 10.99) than those living in Typology 4. No evidence of association was found between typologies of the food environments and snack food purchasing behavior to/from school among adolescents. Conclusion: Local government could emphasize an overall balance of food outlets when designing neighborhoods to reduce propensity for fast food outlet visitation among adolescents.
... In addition, many studies have shown that the risk factors for diseases of the circulatory system and other chronic diseases are shifting to ever-younger age categories [4][5][6][7]. ...
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This article describes the design of a smart steering wheel intended for use in unobtrusive health and drowsiness monitoring. The aging population, cardiovascular disease, personalized medicine, and driver fatigue were significant motivations for developing a monitoring platform in cars because people spent much time in cars. The purpose was to create a unique, comprehensive monitoring system for the driver. The crucial parameters in health or drowsiness monitoring, such as heart rate, heart rate variability, and blood oxygenation, are measured by an electrocardiograph and oximeter integrated into the steering wheel. In addition, an inertial unit was integrated into the steering wheel to record and analyze the movement patterns performed by the driver while driving. The developed steering wheel was tested under laboratory and real-life conditions. The measured signals were verified by commercial devices to confirm data correctness and accuracy. The resulting signals show the applicability of the developed platform in further detecting specific cardiovascular diseases (especially atrial fibrillation) and drowsiness.
Objective To evaluate changes in the retail food environment profile in a Brazilian metropolis over a ten-year period. Design An ecological study was conducted in the city of Belo Horizonte, Minas Gerais, Brazil. The addresses of formal food establishments were geocoded and classified according to their sold-food profile. Density changes were analyzed according to neighborhood, population size, income level, and geospatial distribution. Setting 468 neighborhoods in the city of Belo Horizonte, Minas Gerais, Brazil Participants 83,752 formal food establishments registered for operation in any one or more of those years: 2008, 2011, 2015, and 2018 Results There was an increase in unhealthy establishments (154%), followed by mixed (51%) and healthy establishments (32%), during the period evaluated, in addition to an increase in density according to income categories. There was a higher proportion of unhealthy establishments in relation to healthy establishments, indicating worsening of the community food environment over time. Conclusions Over the course of ten years, changes in the neighborhood's food environment were unfavorable for adequate access to healthy foods in lower-income neighborhoods. The findings reinforce the need for interventions aimed at increasing the availability of businesses that offer healthy food in the city.
This study investigates drivers of food acquisition practices in the food environment of peri-urban Hyderabad, India. We used a multi-method qualitative methodology that included in-depth interviews (n = 18) and an innovative qualitative geographical information systems (Q-GIS) approach, featuring participatory photo mapping and follow-up graphic-elicitation interviews (n = 22). Secondary data from eight focus group discussions (n = 94) was used to corroborate findings related to fruits and vegetables. Thematic analysis identified three primary drivers of food acquisition practices among adults: 1) Food prices and affordability; 2) Vendor and product properties, including (a) quality and freshness, and (b) adulteration and contamination; and 3) Social capital. Drivers of food acquisition and consumption among children and adolescents were a key concern for our participants, and included food availability and accessibility, desirability, and convenience. Findings reveal a need for targeted interventions in external and personal food environments to improve diets, nutrition, and health in this setting.
Objective: This study aimed to identify and describe healthful and youth-oriented qualities of the restaurant food environment around high schools. Methods: Using direct observation data from 58 restaurants located within a half-mile (804.5 meters) of all high schools in a single district, two index measures of the restaurant food environment were created: healthfulness index and youth-oriented index. Wilcoxon signed-rank order was used to examine differences in restaurant features according to index scores. Results: Mean healthfulness score was 8.9 (range = 2-14, max = 19) and mean youth-oriented score was 5.5 (range = 0-11, max = 12). Differences were found in signed-rank order of healthfulness and youth-oriented index restaurant scores (p = 0.02). Conclusion: Results suggest that restaurants have room for improvement in offering customers a healthful environment, some restaurants are more likely to appeal to youth, and that youth-oriented restaurants were different than restaurants with high healthfulness scores. Further qualitative exploration of food environment features will help contextualize the influence of restaurants on youth eating behaviors.
Objective To examine the association between economic residential segregation and food environment. Design Ecological. Food stores categorized according to the NOVA classification were geocoded, and absolute availability was calculated for each neighborhood. Segregation was measured using local G i * statistic, a measure of the standard deviation (SD) between the economic composition of a neighborhood (the proportion of heads of households in neighborhoods earn monthly income of 0 to 3 minimum wages) and larger metropolitan area, weighted by the economic composition of surrounding neighborhoods. Segregation was categorized as high [most segregated], medium [integrated], and low [less segregated or integrated]. A proportional odds models were used to model the association between segregation and food environment. Setting Belo Horizonte, Brazil. Participants Food stores. Results After adjustment for covariates, neighborhoods characterized by high economic segregation had fewer food stores overall compared to neighborhoods characterized by low segregation [OR=0.56;CI95%=0.45-0.69]. In addition, high segregated neighborhoods were 49% (OR=0.51;95%CI=0.42–0.61) and 45% (OR=0.55;95% CI=0.45–0.67) less likely to have a high number of food stores that predominantly marketed ultra-processed foods and mixed food stores, respectively, as compared to their counterparts. Conclusions Economic segregation is associated with differences in the distribution of food stores. Both low and high segregation territories should be prioritized by public policies to ensure healthy and adequate nutrition as a right for all communities. The former must continue to be protected from access to unhealthy commercial food outlets while the latter must be the locus of actions that limit the availability of unhealthy commercial food store.
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Results: The majority of 4 th - 6 th grade urban students shopped in corners stores either in the morning (57.4%) or in the afternoon (58.5%). Nearly half (44.8%) reported shopping and purchasing in both the morning and the afternoon. Children reported spending approximately $2.00 per corner store visit. Approximately two-thirds of children reported that they walked to or from school. Children who walked to school frequented corner stores more than those using other commuting methods. Relative weight status was not related to corner store or commuting patterns. Conclusion: Many low-income children purchase food at corner stores before and/or after school making corner stores an important target for public health nutrition. While many children walk to school, those are more likely to frequent corner stores. Neither corner store nor commuting pattern was associated with relative weight.
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Objectives: Among studies of the built environment, few examine neighbourhood food environments in relation to children's diets. We examined the associations of residential and school neighbourhood access to different types of food establishments with children's diets. Methods: Data from QUALITY (Quebec Adipose and Lifestyle Investigation in Youth), an ongoing study on the natural history of obesity in 630 Quebec youth aged 8-10 years with a parental history of obesity, were analyzed (n=512). Three 24-hour diet recalls were used to assess dietary intake of vegetables and fruit, and sugar-sweetened beverages. Questionnaires were used to determine the frequency of eating/snacking out and consumption of delivered/take-out foods. We characterized residential and school neighbourhood food environments by means of a Geographic Information System. Variables included distance to the nearest supermarket, fast-food restaurant and convenience store, and densities of each food establishment type computed for 1 km network buffers around each child's residence and school. Retail Food Environment indices were also computed. Multivariable logistic regressions (residential access) and generalized estimating equations (school access) were used for analysis. Results: Residential and school neighbourhood access to supermarkets was not associated with children's diets. Residing in neighbourhoods with lower access to fast-food restaurants and convenience stores was associated with a lower likelihood of eating and snacking out. Children attending schools in neighbourhoods with a higher number of unhealthful relative to healthful food establishments scored most poorly on dietary outcomes. Conclusions: Further investigations are needed to inform policies aimed at shaping neighbourhood-level food purchasing opportunities, particularly for access to fast-food restaurants and convenience stores.
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Research on the impact of the built environment on obesity and access to healthful foods often fails to incorporate information about how individuals interact with their environment. A sample of 198 low-income WIC recipients from two urban neighborhoods were interviewed about where they do their food shopping and surveys were conducted of food stores in their neighborhoods to assess the availability of healthful foods. Results indicate that participants rarely shop at the closest supermarket, traveling on average 1.58 miles for non-WIC food shopping and 1.07 miles for WIC shopping. Findings suggest that access to healthful foods is not synonymous with geographic proximity.
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Few studies have assessed how people's perceptions of their neighborhood environment compare with objective measures or how self-reported and objective neighborhood measures relate to consumption of fruits and vegetables. A telephone survey of 4,399 residents of Philadelphia, Pennsylvania, provided data on individuals, their households, their neighborhoods (self-defined), their food-environment perceptions, and their fruit-and-vegetable consumption. Other data on neighborhoods (census tracts) or "extended neighborhoods" (census tracts plus 1-quarter-mile buffers) came from the US Census Bureau, the Philadelphia Police Department, the Southeastern Pennsylvania Transportation Authority, and the federal Supplemental Nutrition Assistance Program. Mixed-effects multilevel logistic regression models examined associations between food-environment perceptions, fruit-and-vegetable consumption, and individual, household, and neighborhood characteristics. Perceptions of neighborhood food environments (supermarket accessibility, produce availability, and grocery quality) were strongly associated with each other but not consistently or significantly associated with objective neighborhood measures or self-reported fruit-and-vegetable consumption. We found racial and educational disparities in fruit-and-vegetable consumption, even after adjusting for food-environment perceptions and individual, household, and neighborhood characteristics. Having a supermarket in the extended neighborhood was associated with better perceived supermarket access (adjusted odds ratio for having a conventional supermarket, 2.04 [95% CI, 1.68-2.46]; adjusted odds ratio for having a limited-assortment supermarket, 1.28 [95% CI, 1.02-1.59]) but not increased fruit-and-vegetable consumption. Models showed some counterintuitive associations with neighborhood crime and public transportation. We found limited association between objective and self-reported neighborhood measures. Sociodemographic differences in individual fruit-and-vegetable consumption were evident regardless of neighborhood environment. Adding supermarkets to urban neighborhoods might improve residents' perceptions of supermarket accessibility but might not increase their fruit-and-vegetable consumption.
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Objectives: We examined whether supermarket choice, conceptualized as a proxy for underlying personal factors, would better predict access to supermarkets and fruit and vegetable consumption than mere physical proximity. Methods: The Seattle Obesity Study geocoded respondents' home addresses and locations of their primary supermarkets. Primary supermarkets were stratified into low, medium, and high cost according to the market basket cost of 100 foods. Data on fruit and vegetable consumption were obtained during telephone surveys. Linear regressions examined associations between physical proximity to primary supermarkets, supermarket choice, and fruit and vegetable consumption. Descriptive analyses examined whether supermarket choice outweighed physical proximity among lower-income and vulnerable groups. Results: Only one third of the respondents shopped at their nearest supermarket for their primary food supply. Those who shopped at low-cost supermarkets were more likely to travel beyond their nearest supermarket. Fruit and vegetable consumption was not associated with physical distance but, with supermarket choice, after adjusting for covariates. Conclusions: Mere physical distance may not be the most salient variable to reflect access to supermarkets, particularly among those who shop by car. Studies on food environments need to focus beyond neighborhood geographic boundaries to capture actual food shopping behaviors.
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Understanding which physical environmental factors affect adult obesity, and how best to influence them, is important for public health and urban planning. Previous attempts to summarise the literature have not systematically assessed the methodological quality of included studies, or accounted for environmental differences between continents or the ways in which environmental characteristics were measured. We have conducted an updated review of the scientific literature on associations of physical environmental factors with adult weight status, stratified by continent and mode of measurement, accompanied by a detailed risk-of-bias assessment. Five databases were systematically searched for studies published between 1995 and 2013. Two factors, urban sprawl and land use mix, were found consistently associated with weight status, although only in North America. With the exception of urban sprawl and land use mix in the US the results of the current review confirm that the available research does not allow robust identification of ways in which that physical environment influences adult weight status, even after taking into account methodological quality.
Background. The aim of this study was to determine overall trends of total energy intake by food location and food type in diets of adolescents and young adults.
... In states that banned all SSBs, fewer students reported in-school SSB access (prevalence difference, –14.9; 95% CI, –23.6 to –6.1) or purchasing (–7.3; –11.0 to –3.5), adjusted for race/ethnicity, poverty status, locale, state obesity prevalence, and state clustering. ...
Conference Paper
Researchers often examine the types of food stores and restaurants located within neighborhoods to describe the food environments of individuals. Most Americans, however, travel daily outside their neighborhoods. This paper describes a study conducted in Lexington, KY that collected global positioning system (GPS) data to investigate food environments within participants' activity spaces, or the spaces in which they conduct daily activities. Furthermore, it explores the influence of GPS tracking duration on these activity-based food environment assessments. One-hundred twenty-one residents of a census tract carried a GPS data logger for three weekdays and answered a survey regarding diet and food shopping. Thirteen later participated in GPS tracking for a full week. Food locations were obtained from the county health department to characterize the food environment within each participant's activity space, defined as a half-mile buffer around their GPS track. Statistical analysis included t-tests, ANOVA, and pairwise correlations to compare food environment measures and participant characteristics. The main findings of this study indicated several statistically significant relationships among participants' diets, food purchases, and activity-based food environments. GPS tracking duration, however, significantly influenced activity space size and measures of the food environment. Seven-day activity spaces were larger (10.14 vs. 20.45 sq mi, p<0.0001), and contained lower densities of full-service (8.69 vs. 6.42 per sq mi, p=0.01) and limited-service restaurants (12.43 vs. 9.25, p=0.01), small groceries (1.59 vs. 0.88, p=0.01), and convenience stores (2.31 vs. 1.81, p=0.01). Proportional values were more similar. Future studies should carefully consider the effects of GPS tracking duration.