Public Health Nutrition: 00(00), page 1 of 7
Healthy food availability and the association with BMI in
Sarah Stark Casagrande1,*†, Manuel Franco1,2, Joel Gittelsohn1, Alan B Zonderman3,
Michele K Evans3, Marie Fanelli Kuczmarski4and Tiffany L Gary-Webb1,‡
1Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA:2Centro Nacional Investigacio ´n
Cardiovascular, Calle de Melchor Fernandex Almagro, Madrid, Spain:3National Institute on Aging, Baltimore,
MD, USA:4College of Health Sciences, University of Delaware, Newark, DE, USA
Submitted 9 September 2010: Accepted 16 December 2010
Objective: To study the association between the availability of healthy foods and
BMI by neighbourhood race and socio-economic status (SES).
Design: Trained staff collected demographic information, height, weight and 24h
dietary recalls between 2004 and 2008. Healthy food availability was determined
in thirty-four census tracts of varying racial and SES composition using the
Nutrition Environment Measures Survey–Stores in 2007. Multilevel linear regres-
sion was used to estimate associations between healthy food availability and BMI.
Setting: Baltimore City, Maryland, USA.
Subjects: Adults aged 30–64 years (n 2616) who participated in the Healthy Aging
in Neighborhoods of Diversity across the Life Span study.
Results: Among individuals living in predominantly white neighbourhoods, high
availability of healthy foods was associated with significantly higher BMI com-
pared with individuals living in neighbourhoods with low availability of healthy
food after adjustment for demographic variables (b53?22, P50?001). Associa-
tions were attenuated but remained significant after controlling for dietary quality
Conclusions: Contrary to expectations, there was a positive association between
the availability of healthy food and higher BMI among individuals living in pre-
dominantly white neighbourhoods. This result could be due to individuals in
neighbourhoods with low healthy food availability travelling outside their
neighbourhood to obtain healthy food.
Healthy food availability
The prevalence of obesity in US adults has increased
significantly over the past several decades(1)and this
condition is known to increase the risk for many chronic
conditions including CVD(1–4). Given the high prevalence
of obesity, recent research has focused on the local food
environment, including the types of food stores and the
quality and availability of foods in a neighbourhood, and
their influence on health outcomes and behaviours. There
is evidence that dietary patterns differ across neighbour-
hoods and that these differences are not fully explained by
individual-level socio-economic characteristics(5–7). Data
have shown that supermarkets are more likely located
in wealthier neighbourhoods than in poorer neighbour-
hoods(8–12). Furthermore, the presence of supermarkets
and fewer fast-food restaurants has been associated with
less obesity and better dietary intake(13–17).
Despite demonstrated racial and socio-economic dis-
parities, few studies have assessed the association between
the availability of healthy food in neighbourhoods and
dietary intake or BMI by neighbourhood characteristics(18–20).
Therefore, the present study investigated the association
between the availability of healthy foods and BMI. It was
hypothesized that lower healthy food availability would be
associated with higher BMI. Moreover, since healthy food
availability has been shown to be associated with neigh-
bourhood characteristics, a secondary hypothesis was that
the association between neighbourhood healthy food avail-
ability and BMI would differ by neighbourhood race and
socio-economic status (SES).
Overview of the Healthy Aging in Neighborhoods
of Diversity across the Life Span study
The Healthy Aging in Neighborhoods of Diversity across the
Life Span (HANDLS) study is a multidisciplinary, prospective
SPublic Health Nutrition
y Present affiliation and address for correspondence: Social and Scientific
Systems, 8757 Georgia Avenue, Silver Spring, MD 20910, USA.
z Present affiliation: Columbia Mailman School of Public Health, 722
168th Street, New York, NY 10032, USA.
*Corresponding author: Email email@example.com
r The Authors 2011
epidemiological study set in Baltimore City and examines
the influence and interaction of race and SES on the
development of health disparities among minority and
lower-SES groups(21). The study design was stratified across
four factors: age, sex, race and SES. Baseline recruitment
included 2616 black and white adults aged 30–64 years
of middle and low SES, living in thirty-four census tracts
across Baltimore City. Data collection was implemented in
two stages by trained staff and physicians: (i) an in-home
household survey; and (ii) a physical examination and
medical history conducted in a mobile research vehicle
(MRV). Baseline data collection occurred from 2004 to 2008.
Inclusion criteria for participants included age 30–64 years
and the ability to give informed consent, perform at least
five measures and present valid picture identification.
Exclusion criteria included pregnancy, being within
6 months of active cancer treatment, and multi-ethnic
individuals who did not identify strongly with either the
black or white race. Survey and medical information is
confidential and approved by the National Institutes of
Health Institutional Review Board.
The neighbourhoods included in the HANDLS study
varied in terms of housing characteristics, green space
and residential v. commercial space. Most of the middle-SES
neighbourhoods were further removed from large com-
mercial areas and busy roads; they often included single-
family homes, duplexes and row homes that were well
maintained. The lower-SES neighbourhoods tended to be
bisected by commercial thoroughfares and more often had
abandoned homes and less maintained residences; some
neighbourhoods included government-assisted housing.
The majority of food stores were convenience or small
grocery stores that rarely sold fresh produce, whole wheat
bread or skimmed milk; whole milk, salty snacks, soda and
canned foods were typically available(22).
Individual-level household interview measures
Demographic measures from the HANDLS in-home ques-
tionnaire included self-reported age, sex, race, education,
income and general health status(23). Individual-level SES
was determined during the initial doorstep interview. Low
SES was defined as having a family income below 125% of
the poverty delimiter, which varies by household size. The
initial doorstep responder was asked about their household
size and then whether their household income was below
or above a specific number from the federal poverty level
table. Middle SES was defined as having a family income
equal to or greater than 125% of the poverty delimiter.
Participants reported on neighbourhood crime and on the
main mode of transportation used for travelling outside their
neighbourhood (e.g. car, walking).
Individual-level health behaviours and outcomes
Dietary intake was reported as an average of two 24h
dietary recalls taken during the in-home and MRV visits.
The data were collected by trained interviewers using the
US Department of Agriculture’s automated multiple-pass
method(24). Participants were asked to report all types
and amounts of foods and beverages consumed in the
past 24h. The 24h dietary recalls included consumption
on weekdays and weekends and over several seasons.
Dietary quality was evaluated using the Healthy Eating
Index-2005 (HEI) and selected HEI components; the HEI
has been validated and reflects the 2005 Dietary Guide-
lines for Americans(25,26). Higher HEI scores indicate a
diet of higher quality (total HEI range: 0–100). Medical
staff measured height and weight using standard mea-
surement tools to determine BMI (kg/m2).
Neighbourhood census measures
To characterize neighbourhoods beyond the collective of
individuals that live in them, data from the US Census were
used to determine neighbourhood race and SES. Neigh-
bourhoods were classified as predominantly black or white
if $60% of the residents were black or white, respec-
tively(10). Since only three tracts failed to meet these criteria
and the racial composition included few (,2%) non-blacks
or non-whites, these racially mixed tracts were classified by
the racial majority. Census tracts with $25% of residents
below the poverty threshold were categorized as low SES
and ,25% as middle SES. These cut-off points were
determined based on median values for census tract
percentage of poverty in the HANDLS study.
Healthy food availability
Implementation of the Nutrition Environment Measures
Survey–Stores instrument in Baltimore, MD
Data collected in 2006 as part of a previous study using the
Nutrition Environment Measures Survey–Stores (NEMS-S)
instrument(20)were used to determine healthy food avail-
ability in HANDLS census tracts. A total of 226 Baltimore
stores were assessed for the availability of eight food groups
and a healthy food availability index (HFAI) was calculated
for each store based on the items available (range: 0–27)(20).
Stores were categorized on the basis of Standard Industrial
Classification codes(27)as supermarkets (a chain store or
employs .50 personnel), grocery stores (stores with ,50
employees), convenience stores (food marts attached to gas
stations or 7–Eleven-type stores) or behind-glass stores
(food items displayed behind bullet-proof glass).
Results from the Baltimore Multi-Ethnic Study of Athero-
sclerosis (MESA) study indicated that a higher percentage of
predominantly black and lower-income neighbourhoods
were categorized in the lowest HFAI tertile. Furthermore,
supermarkets in predominantly black and lower-income
neighbourhoods had significantly lower HFAI scores;
findings were similar for grocery stores(20). Given the
policy implications of these main results and the known
inaccuracies of national business data(28), all food stores
in Baltimore City were characterized by type (e.g.
supermarket) in 2007 since the Baltimore MESA study
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2 S Stark Casagrande et al.
only assessed stores located in selected Baltimore City
census tracts. To characterize food stores, data collectors
compared Baltimore City information on food stores in
the area obtained from (i) InfoUSA, (ii) Baltimore area
phone books and (iii) Baltimore City Health Department
food license records. Data collectors visited each store,
verified the correct categorization and revised the list of
stores by adding stores omitted from the records and
removing stores that were closed upon visit.
HFAI scores were imputed for all stores in HANDLS
neighbourhoods using values from the Baltimore MESA
study; the imputation was based on the racial composi-
tion of the census tract and the store type for each food
store located in a HANDLS census tract. Thus, a super-
market located in a predominantly black neighbourhood
was assigned a lower score than a supermarket located in
a predominantly white neighbourhood; supermarkets
were assigned higher scores than grocery and con-
venience stores. Racial composition, rather than income,
was chosen for imputation based on the stronger trend in
HFAI scores in supermarkets and grocery stores.
Participant characteristics were stratified by tertiles of
neighbourhood healthy food availability. Mean BMI for
each healthy food availability tertile was calculated using
Linear regression coefficients (b) were estimated using
multilevel (random-effects) linear models with a random
intercept for each census tract. The main exposure vari-
able was the average HFAI in a census tract. The main
dependent variable was BMI. Dietary quality (total HEI),
main mode of transportation and perceived crime were
investigated as potential mediators in independent
regression models; adjustment for all three potential
mediators in the same model was also assessed.
All regression models were adjusted for potential con-
founders including age, sex, race, education, poverty status
and self-reported health. Each analysis was stratified by
neighbourhood race and SES. All regression analyses were
conducted using the STATA statistical software package ver-
sion 10?0 (StataCorp LP, College Station, TX, USA) and the
xtreg procedure. Participantswithmissingdata for the primary
outcome (BMI) were excluded from analyses and evaluated
for exclusion bias (n 874). There were no differences between
participants who were included and excluded from analysis
by race, poverty status, age, gender or education.
Individual-level characteristics by neighbourhood
healthy food availability
The average age of participants was 48 years and
56% were female (Table 1). More individuals above the
poverty threshold resided in neighbourhoods with high
healthy food availability (P50?010). Unexpectedly, a
higher proportion of individuals without a high school
healthy food availability (P,0?001). Overall, the mean
BMI of participants reflected unhealthy body weights
(BMI530kg/m2). Participants’ HEI scores were low com-
pared with national estimates(29). The mean HEI score was
49 (possible range: 0–100) for the total population. HEI
scores for total dietary intake, total fruit consumption and
total energy from saturated fat were higher (or better) for
individuals living in neighbourhoods with low healthy
food availability (P,0?001), with no difference for total
vegetable intake (P50?267).
Mean BMI by neighbourhood healthy food
BMI was higher in neighbourhoods with high healthy
food availability among individuals residing in pre-
dominantly white neighbourhoods (P,0?001; Table 2).
Conversely, mean BMI was lower in neighbourhoods
with high healthy food availability among individuals
residing in predominantly black (P50?017) and low-SES
Neighbourhood healthy food availability and the
association with BMI
Overall, there was no association between food avail-
ability in neighbourhoods and BMI after adjustment for
individual-level confounders (Table 3). Among indivi-
duals living in predominantly white neighbourhoods,
residing in neighbourhoods with medium or high food
availability was associated with significantly higher BMI
compared with individuals residing in neighbourhoods
with low food availability (b53?90, P,0?001; b53?22,
P50?001, respectively). After adjusting for dietary qual-
ity, associations were attenuated but remained significant
(b53?49, P50?003; b52?81, P50?012, respectively;
data not shown). Additional adjustment for perceived
crime and main mode of transportation did not further
attenuate or alter the significance of the findings.
Earlier research indicates that the types of food stores and
food availability in neighbourhoods are associated with
neighbourhood characteristics(8–12), dietary intake(19,30)
and obesity(13–16). Few studies have examined these
associations stratified by neighbourhood race and SES.
Contrary to the study hypothesis, greater healthy food
availability was associated with higher BMI among indi-
viduals living in predominantly white neighbourhoods
after adjustment for demographic variables and dietary
quality. One explanation for this unexpected finding
is that individuals living in neighbourhoods with low
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Food availability and association with BMI3
healthy food availability choose to travel outside their
neighbourhood to obtain healthy food. Indeed, indivi-
duals residing in neighbourhoods with low healthy food
availability reported more often using a car as the main
mode of transportation (83%) and reported virtually no
walking (1%) compared with individuals in this subgroup
residing in neighbourhoods with medium and high
healthy food availability (55%, 60% for car use and 7%, 8%
for walking, respectively; P,0?001). Furthermore, indivi-
duals in neighbourhoods with low healthy food availability
had better dietary quality (mean HEI score550) compared
with their counterparts residing in neighbourhoods with
medium and high healthy food availability (mean HEI
score547 and 48 respectively; P,0?001). Thus, in this
urban, predominantly white population, higher neighbour-
hood healthy food availability was not a marker for either
healthier diets or body weight.
Few studies have empirically assessed healthy food
availability and the association with health outcomes. A
cross-sectional study in twelve suburban/urban commu-
nities measured the availability of low-fat and high-fibre
products and found positive, significant correlations
between neighbourhood availability of these products
and self-reported healthfulness of individual diet(18).
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Table 2 BMI by neighbourhood healthy food availability and stratified by neighbourhood race and SES, Baltimore, MD, 2004–2008*
Neighbourhood healthy food availability
Total (n 2616)
Predominantly white (n 968)
Predominantly black (n 1648)
Middle (n 1451)
Low (n 1165)
29?87?7 30?88?1 29?77?70?038
SES, socio-economic status.
*Unadjusted mean values.
Table 1 Characteristics of HANDLS study participants stratified by neighbourhood healthy food availability, Baltimore, MD, 2004–2008
Healthy food availability
Overall (n 2616)Low (n 1410)Medium (n 475) High (n 731)
Age (years)48?19?2 48?69?1 47?89?2 47?49?30?013
Above poverty threshold
Health insurance, yes
,High school education
145455?6 78956?0 260 54?7 40555?50?896
Health outcomes and behaviours*
29?97?8 29?87?8 30?88?1 29?77?70?038
HANDLS, Healthy Aging in Neighborhoods of Diversity across the Life Span; HEI, Healthy Eating Index-2005.
-Total HEI, range 0 to 100; Total fruit, range 0 (0cups/1000kcal) to 5 ($0?8cups/1000kcal); Total vegetables, range 0 (0cups/1000kcal) to 5 ($1?1cups/
1000kcal); Saturated fat, range 0 ($15% of energy) to 10 (#7% of energy); 1000kcal54184kJ.
4S Stark Casagrande et al.
In another cross-sectional study, lower healthy food
availability, measured by the NEMS-S, was significantly
associated with poorer dietary patterns (fat and processed
meats pattern) in urban and suburban Baltimore(19). The
association became insignificant when adjusted for race;
higher neighbourhood healthy food availability was not
significantly associated with better dietary patterns (whole
grains and fruit pattern). The authors noted that healthy
food availability might be a proxy for neighbourhood
racial composition, given the strong correlation that was
documented between the two factors(20). Thus, the asso-
ciation between healthy food availability and diet quality
would be masked after controlling for race. With the
exception of individuals in predominantly white HANDLS
neighbourhoods, unadjusted results were insignificant
for BMI. This suggests that neighbourhood healthy food
availability, as assessed in the current study, may not be an
accurate measure to capture food consumption patterns in
this population. Information on the use of restaurants and
the location where participants most frequently shop for
food may begin to clarify the influence the neighbourhood
food environment has on health.
There may be several explanations for the lack of sig-
nificant results among individuals living in predominantly
black or low-SES neighbourhoods. Recent literature has
documented important implications and considerations
for measuring food availability in minority and low-
income neighbourhoods(31,32). Social constructs likely
play an important role for understanding neighbourhood
disorder and safety concerns that may impede the use of
local food stores, regardless of availability(32). Thus, the
availability of healthy foods would have little impact on
health outcomes in low-income, minority neighbour-
hoods. In predominantly black and low-SES HANDLS
neighbourhoods, individuals residing in neighbourhoods
with medium or high healthy food availability more often
reported seeing serious crime as a common occurrence
compared with their counterparts residing in neighbour-
hoods with low healthy food availability (P,0?001, data
not shown). Second, immigrant groups, particularly Asian
Americans in Baltimore City, have operated businesses in
low-income, black neighbourhoods for a number of
years(31,32). There may be language and cultural barriers
and feelings of discrimination by local food store owners
that reduce the use of these neighbourhood establish-
ments. Third, consumer interests need consideration
when assessing the effects of neighbourhood food
availability. Although foods of cultural preference would
be expected to be available in a neighbourhood, these
foods may be inadequately captured on standard surveys
(e.g. NEM-S). In addition, low-income consumers may
not be able to afford healthier fare such as fresh produce
and whole grains(33). Thus, if the measures of food
availability do not capture food relevant for the popula-
tion, the power to detect neighbourhood effects is
reduced. Finally, consumers residing in low-income,
minority neighbourhoods may often have concerns that
food quality, fresh or otherwise, is poor and choose to
purchase foods outside their neighbourhood(31).
The present study has several strengths. First, BMI was
objectively measured; this method, rather than self-report,
is preferred for large epidemiological studies. Second, a
systematic assessment of food stores was conducted in
Baltimore City. Since national business data may inaccu-
rately classify food stores(28), this method was a sig-
nificant improvement from previous studies. Finally, the
stratified sampling design allowed for associations to be
compared by neighbourhood characteristics.
Nevertheless, the study has some limitations. First, the
study was cross-sectional, which limited the ability to
make causal statements about observed associations.
Second, census tract boundaries were used to approx-
imate neighbourhoods, which created the potential for
measurement error when determining neighbourhood
food availability. If measurement error were present, it
would be expected to be non-differential; thus, results
would be biased towards the null. Third, no information
was available on where participants shopped. It was
assumed that the neighbourhood environment was most
influential on food procurement behaviours. Fourth, food
store data were collected in 2006–2007 while individual
baseline data were collected from 2004 to 2008. The
current analysis assumes that neighbourhood character-
istics and individual behaviours and health outcomes
were relatively stable during this time period. The time
point in the study represents the mid-point of the baseline
data collection years, which minimizes the magnitude of
this potential bias. Fifth, healthy food availability scores
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Table 3 Associations between healthy food availability and BMI (b, 95% CI), Baltimore, MD, 2004–2008*
Neighbourhood raceNeighbourhood SES
Overall White (n 10) Black (n 24) Middle (n 16) Low (n 18)
b 95% CIb95% CIb 95% CIb 95% CIb 95% CI
Healthy food availability
Ref., reference category.
*Adjusted for individual-level age, gender, race, poverty status, education and self-reported health (n 2541).
Food availability and association with BMI5
were imputed based on a previous study implemented in
Baltimore. Given that the characterization of food stores
was completed using the same procedures and in the
same geographic location as the current study, it is
assumed that these imputed values are solid estimates of
the true HFAI. Furthermore, a prior study suggests that
healthy food availability may be a proxy for neighbour-
hood racial composition(20); stratification by neighbour-
hood characteristics was a strategy used to circumvent
this issue and attempt to observe the independent effect
of healthy food availability.
one part of the built environment that may facilitate or
provide the opportunity for individuals to make healthier
choices and ultimately reduce BMI. Taken together with
previous work, it is likely that the influence of the food
environment operates differently across neighbourhoods
of varying characteristics. The mechanisms for these
associations deserve future investigation since neighbour-
hood food availability may partially account for racial and
SES disparities in obesity and dietary intake. Larger studies
with more variability in neighbourhood characteristics and
food availability will help to clarify these relationships in
the future. In addition, food pricing, location of employ-
ment and transportation patterns should be considered as
influential factors for obesity and dietary intake. The
potentially large public health impact that could be gained
from further investigation warrants continued exploration.
food availabilityis only
The current research was supported in part by the Intra-
mural Research Program of the National Institutes of
Health, National Institute on Aging. Data on healthy food
availability were supported by the Center for a Livable
Future at the Johns Hopkins Bloomberg School of Public
Health. T.L.G.-W. was funded by a grant from the National
Heart, Lung, and Blood Institute (K01-HL084700). The
authors have no conflicts of interest to disclose. S.S.C.
conceived the study design, performed data analyses and
led the writing. T.L.G.-W. contributed to the study origi-
nation, supervised the study and assisted with the writing.
J.G., A.B.Z. and M.K.E. contributed to the study origina-
tion and supervised the study. M.F. contributed the heal-
thy food availability data. M.F.K. assisted with the dietary
intake data. All authors helped to conceptualize ideas,
interpret findings and review drafts of the manuscript.
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