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Nutrition Science?Policy
Disparities in obesity prevalence due to variation in the retail
food environment: three testable hypotheses
Paula B Ford and David A Dzewaltowski
Although the overall population in the United States has experienced a dramatic
increase in obesity in the past 25 years, ethnic/racial minorities, and socio­
economically disadvantaged populations have a greater prevalence of obesity, as
compared to white, and/or economically advantaged populations. Disparities in
obesity are unlikely to be predominantly due to individual psychosocial or biological
di?erences, and they may re?ect di?erences in the built or social environment. The
retail food environment is a critical aspect of the built environment that can
contribute to observed disparities. This paper reviews the literature on retail food
environments in the United States and proposes interrelated hypotheses that
geographic, racial, ethnic, and socioeconomic disparities in obesity within the
United States are the result of disparities in the retail food environment. The ?ndings
of this literature review suggest that poor-quality retail food environments in
disadvantaged areas, in conjunction with limited individual economic resources,
contribute to increased risk of obesity within racial and ethnic minorities and
socioeconomically disadvantaged populations.
© 2008 International Life Sciences Institute
INTRODUCTION
Prevalence of overweight (BMI 225 kg/m2) and obesity
(BMI 230 kg/m2) has increased dramatically in the
United States in the past 25 years, with recent surveys
reporting approximately 23% of adults categorized as
obese.1 Among children and adolescents, the prevalence
of overweight has increased even more dramatically,
having almost tripled since 1980.2 While most interna­
tional obesity rates are not as high as those reported in the
United States, similar trends have been reported in other
industrialized countries.2,3
Although overweight and obesity has increased
across almost all racial, ethnic and socioeconomic levels,
there are signi?cant disparities within the overall US
population, with higher BMIs associated with socioeco­
nomic disadvantage and non-white race and ethnicity.2,4–6
Employing multivariate regression techniques on
reported height and weight data from the 2000 National
Health Interview Study, Denney et al.4 identi?ed dispari­
ties in relative risks associated with overweight and
obesity that persisted even after controlling for sex, age,
marital status, region, family income, education, employ­
ment, smoking, biking/walking habits, and weekly vigor­
ous activities. The relative risk ratios and 95% con?dence
intervals (95% CIs) for overweight among various racial/
ethnic groups were as follows: 1.60 (95% CI, 1.44–1.76)
for non-Hispanic blacks; 2.14 (95% CI, 1.32–3.47) for
Native Americans; 0.5 (95% CI, 0.40–0.61) for Asian
Americans; 1.21 (95% CI, 0.93–1.58) for Puerto Ricans;
1.54 (95% CI, 1.36–1.76) for Mexican Americans; and
1.57 (95% CI, 2.16–2.45) for Cuban Americans. It is
important to note, however, that when strati?ed by sex,
disparities by race and ethnicity are more consistently
observed among women, as compared to men.4,7–9
Disparities in obesity prevalence by race and ethnicity
A?liations: PB Ford is with the Department of Human Nutrition and the North Central Sustainable Agriculture Research and Education
(SARE) Program, Kansas State University, Manhattan, Kansas, USA. DA Dzewaltowski is with the Department of Kinesiology and
Community Health Institute, Kansas State University, Manhattan, Kansas, USA.
Correspondence: PB Ford, Department of Human Nutrition, 4A Edwards Hall, Kansas State University, Manhattan, KS 66506-4810, USA.
E-mail: pford@ksu.edu; Phone: +1-785-532-5328, Fax: +1-785-532-6532.
Key words: disparities, food access, food environment, neighborhood e?ects, obesity
216
doi:10.1111/j.1753-4887.2008.00026.x
Nutrition Reviews® Vol. 66(4):216–228
that persist even after controlling for socioeconomic
position have been reported elsewhere.5,6,10–12
Low socioeconomic status (SES) has also been inde­
pendently associated with increased risk for obesity in
industrialized countries, particularly in women. In a
recently published review of the literature on SES and
obesity, McLaren9 identi?ed inverse associations between
SES and obesity among women in 63% of cross-sectional
studies conducted in industrialized countries. In contrast,
the pattern of association between SES and obesity was
less consistent among men in industrialized countries,
with a general pattern of non-signi?cance or curvilinear­
ity with most socioeconomic indicators (income, material
possessions, and occupation) and an inverse association
with other socioeconomic indicators (education).
The central proximal causes for racial, ethnic, and
socioeconomic disparities in the prevalence of obesity
have traditionally been attributed to individual di?er­
ences in health behaviors in?uencing calorie balance. Spe­
ci?cally, health behavior research in this area has found
racial/ethnic, and socioeconomic di?erences in physical
activity,13 fresh fruit and vegetable consumption,14 and
dietary fat intake.15 However, social ecological theory sug­
gests that individual health decisions are determined by
multiple levels of in?uence, including institutional, com­
munity, and broader physical, economic, and cultural
environmental levels.16 Recent attention to the contribu­
tion of built environments to obesity (“obesogenic envi­
ronments”) has led to the development of several
frameworks for empirically describing retail food envi­
ronments with respect to the availability, accessibility
and pricing of foods associated with healthy eating
behaviors.17–21 These models identify environmental vari­
ables hypothesized to in?uence eating behaviors at the
contextual level, a critical prerequisite for systematically
examining nutrition environments using multilevel
models that include information gathered at both the
individual level and the environmental level.
The present report proposes three hypotheses that
can serve as a framework for empirically testing the asso­
ciation between neighborhood retail food environments
and obesity, and for examining the role environmental
disparities may play in the prevalence of obesity among
di?erent racial/ethnic and socioeconomic groups within
the United States. The proposed hypotheses to be tested
include: 1) geographic di?erences in the access and avail­
ability of foods result in disparities in the retail food envi­
ronment; 2) neighborhoods of low SES with high
concentrations of racial/ethnic minorities have limited
accessibility to and availability of healthy foods (poor­
quality retail food environment), as compared to neigh­
borhoods of relatively high SES and low concentrations of
ethnic/racial minorities; and 3) individuals exposed to
poor-quality retail food environments are more likely to
Nutrition Reviews® Vol. 66(4):216–228
have diets that include foods of low nutritional quality
and high caloric density and to have higher rates of
obesity, as compared to individuals exposed to highquality food environments.
To provide preliminary evidence to test these
hypotheses, a PubMed (National Library of Medicine,
Bethesda, Maryland) search was conducted for the period
1992–2007 using the search terms “food environment”,
“nutrition environment”, “food access”, “food availabil­
ity”, and “obesity”. Studies found through the electronic
search were supplemented with others that were brought
to our attention through the literature review. Abstracts
of selected papers were screened and the study was
included in the review if it was conducted in the United
States and included a characterization of the retail food
environment. Of the 13 studies included in the review, six
employed an ecological research design, four used a
cross-sectional approach, and three were multilevel
studies. The studies are organized and discussed by
hypothesis, and summarized in Tables 1–3.
HYPOTHESIS 1
Geographic di?erences in the access and availability
of foods result in disparities in the retail food
environment
The question of whether food environments di?er geo­
graphically has been addressed by several investigations
in a host of disciplines.22–25 It is important to note,
however, that di?erences in the retail food environment
do not always represent disparities. Consistent with the
de?nition of health disparities as outlined by Braveman,26
disparities in the food environment refer to avoidable
di?erences in the access and availability of healthful foods
that systematically place socially disadvantaged groups
at a further disadvantage for achieving healthy diets.
Although it has been well documented that there are
regional variations associated with food preference and
price among ethnic groups and by region, disparities in
retail food environments across neighborhoods are not
well understood. However, observational measures of the
quality of retail food environments, as characterized by
availability, accessibility, and pricing, provide a useful
method for comparing food environments between
neighborhoods. A selective summary of recent research
examining geographic di?erences in retail food environ­
ments using observational measures is presented in
Table 1.
First introduced as a concept to examine disparities
in food access and pricing in the United Kingdom, the
term “food desert” has been used to describe areas with
limited access to retail grocery stores.27 Early research on
food deserts was primarily concerned with exploring the
217
Table 1 Summary of studies related to hypothesis 1 – geographic di?erences in the access and availability of
foods result in disparities in the retail food environment.
Reference
Location/setting
Food environment
Key ?ndings
measure/method
Morris et al.
National (direct observation
Store type
a) Average food costs 20% higher in small/
(1992)72
in rural areas)
medium grocery stores as compared to
supermarkets
Market basket
b) Fruit and vegetable availability limited
in small/medium grocery stores
c) 32% of residents in persistently poor
rural counties redeemed food stamps at
small/medium grocery stores as
compared to 20% redemption rates in
small/medium grocery stores
Chung et al.
Minneapolis, MN (urban)
Store type
a) Chain stores prices signi?cantly lower
(1993)44
with greater variety of foods available
as compared to convenience and small
grocery stores
Market basket
b) Chain stores less prevalent in urban
core areas
c) Gap between urban core and suburban
TFP basket signi?cant and due primarily
to presence of chain stores (chain stores
$16 price reduction) with net impact of
poverty to increase price of basket by
approximately 3%
Horowitz et al. New York City – paired
Market basket
a) 18% of grocery stores in low SES
(2004)49
comparison: East Harlem (low
neighborhoods stocked foods
SES, high ethnic minority pop.)
associated with recommended diet, as
and Upper East Side (high SES
compared to 58% of grocery stores in
and low ethnic minority pop.)
high SES neighborhoods
b) Only 9% of low SES bodegas carried
recommended foods as compared to
48% of high SES bodegas
Block et al.
Chicago – paired comparison
Market basket, including a) A?uent neighborhoods had more chain
(2006)43
quality characteristics
grocery stores and supermarkets, while
(participatory, direct
less a?uent neighborhoods had more
observation)
“low-cost” retail grocery chains
1. Austin (low SES, high ethnic
b) Price di?erentials between
minority pop.)
neighborhoods not signi?cant when
controlling for store type
2. Oak Park (high SES and low
c) Produce in Austin neighborhood rated
ethnic minority pop.)
as lower quality as compared to
produce in Oak Park
impact of retail ?ight from the urban core, but it has since
been extended to include rural areas that have experi­
enced reductions in populations and concomitant
reductions in the retail sector, including small-town
supermarkets.28–30 Research in this area examined the
availability of supermarkets by store type (supermarket
chain versus small grocer or convenience store) and
pricing di?erentials among stores.27,31 Of the four studies
identi?ed in this review (Table 1), there is relatively con­
sistent evidence that the quality of the retail food envi­
ronment (as measured by access and availability of
healthy foods) varies geographically, and that low-quality
food environments are associated with neighborhood
218
deprivation. This contrasts with recently reported food­
environment studies from the United Kingdom in which
the association between the quality of the food environ­
ment and the sociodemographic structure of the neigh­
borhood is mixed,32 casting some doubt on the existence
of “food deserts” within the United Kingdom.30,33,34 While
some of the variance associated with the relationship
between retail food environment and neighborhood
demographics in the United States and the United
Kingdom can be linked with di?erent patterns of residen­
tial segregation among countries, additional sources of
variance may be associated with Modi?able Areal Unit
Problems (MAUP) in which both scale and zoning in?u­
Nutrition Reviews® Vol. 66(4):216–228
Nutrition Reviews® Vol. 66(4):216–228
219
New York City – East Harlem (low SES,
high ethnic minority pop.) and
Upper East Side (high SES and low
ethnic minority pop.)
Detroit, MI
North Carolina
(n = 75 census tracts)
Horowitz et al.
(2004)49
Zenk et al.
(2005)25
and Zenk
et al. (2006)52
Moore et al.
(2006)48
Maryland
(n = 276 census tracts)
New York
(n = 334 census tracts)
Location/setting
Reference
Cross-sectional design, Poisson
regression, and multilevel analysis
Cross-sectional design, chi-square and
spatial regression, geographic
information systems (GIS)
Research design, methods,
and analysis
Ecological design, direct observation of
food environment measures
Store type
c) Price and availability of fruits and
vegetables
b) Distance to supermarket
a) Store type
Price and availability of core foods
needed for diabetic diet
Store type
Outcome variable
a) 18% of grocery stores in East Harlem stocked
foods associated with recommended diet, as
compared to 58% of grocery stores in the Upper
East Side
b) Only 9% of East Harlem bodegas carried
recommended foods as compared to 48% of
Upper East Side bodegas
a) Quality of fresh produce lower in predominantly
African American low SES (AA-low SES)
communities as compared to racially
heterogeneous middle-income communities
(RH-mid SES), even after adjusting for store type
b) 97% of AA-low SES live within 1 mile of > 8 liquor
stores, as compared to 87.9% in RH-low SES,
59.3% AA-mid SES, and 0% RH-mid SES
c) Selection (#) and price of produce did not vary
signi?cantly by store type or neighborhood
d) Within lowest SES group, African American
neighborhoods have 2.7 fewer supermarkets
within 3-mile radius as compared to white
neighborhoods
e) Within lowest SES group, African Americans
resided 1.1 miles further from supermarket as
compared to white residents
f) Interaction between race/ethnicity signi?cant and
inclusion of interaction term improved spatial
regression model ?t (c2 = 15.83, p < 0) a) Minority and racially mixed neighborhoods, after adjusting for population ratio, had more grocery stores and fewer supermarkets than white neighborhoods (African American tracts SR = 0.5; 95% CI 0.3–0.7; mixed tracts SR = 0.7, 95% CI 0.5–0.9) b) Lower income neighborhoods had half as many supermarkets as compared to a?uent neighborhoods (SR = 0.5; 95% CI 0.3–0.8) Neighborhood-level variables: income, race/ethnicity Neighborhood variables: average income and racial composition Model adjusted for confounders, including population density Individual-level variables: income and race/ethnicity Neighborhood-level SES. Race- average income and racial composition Individual-level variables: income, race/ethnicity Key ?ndings Explanatory variables Table 2 Summary of studies related to hypothesis 2 – neighborhoods of low SES with high concentrations of racial/ethnic minorities have limited accessibility and availability of healthy foods (poor-quality retail food environment). 220 Nutrition Reviews® Vol. 66(4):216–228 Location/setting St. Louis, MO (n = 220 census tracts) National Brooklyn, NY Reference Baker et al. (2006)47 Powell et al. (2006)40 Morland et al. (2007)53 Table 2 Continued Cross-sectional design, direct observation, Poisson regression Ecological design, multivariate analysis Research design, methods, and analysis Ecological design, direct observation of food environments, spatial clustering statistics a) Neighborhood racial segregation b) Neighborhood confounders: population density and neighborhood wealth (median house value) b) Availability of fresh, canned, frozen and prepared produce b) Regional/other confounders: population density, region, degree of urbanization a) Neighborhood variables: income, race/ethnicity a) Neighborhood variables: % below poverty level and race/ethnicity at census tract Explanatory variables a) Store type a) Store type a) Supermarket audit tool and creation of z score Outcome variable a) Spatial clustering of supermarkets (unadjusted and without including quality ranking) was not signi?cant (p < 0.50); however, clustering by race/ ethnicity was observed b) Spatial clustering of supermarkets using quality scores (z score from audit) was signi?cant (p < 0.01; p < 0.03) with supermarkets in highest two quality tertiles clustered in census tracts with >75% white and <10% below poverty a) Low-income neighborhoods had 25% fewer supermarkets as compared to middle-income neighborhoods (p < 0.01) b) After controlling for income and other covariates, the availability of supermarkets in African-American neighborhoods was only 48% of white neighborhoods (p < 0.01). c) Hispanic neighborhoods have 32% as many supermarkets as compared to non-Hispanic neighborhoods (p < 0.01) a) Prevalence of supermarket varied by neighborhood composition, with white, racially mixed, and black areas having 0.33, 0.27, and 0.0 supermarkets per census tract, respectively b) 64% of fresh produce surveyed had a higher presence in predominantly white areas, as compared to 31% in racially mixed and 5% in predominantly black areas Key ?ndings Nutrition Reviews® Vol. 66(4):216–228 221 Location setting North Carolina, Maryland, New York (n = 10,623) North Carolina, Maryland, New York (n = 10,623) Reference Morland et al. (2002)58 Morland et al. (2006)42 Cross-sectional design, multilevel analysis, geographic information systems Research design and method of analysis Cross-sectional design, multilevel analysis, geographic information systems b) Neighborhood-level variables: store type, race/ethnicity, income b) Hypertension c) Other CVD risk factors a) Individual-level variables: income, race/ethnicity b) Neighborhood variables: store type, SES, race/ethnicity Explanatory and confounding variables a) Individual-level variables: income, educational attainment, region, race/ ethnicity a) Body mass index Outcome variable Fruit and vegetable intake Key ?ndings a) After adjusting for income and education, ?ve times as many supermarkets were available in neighborhoods with >75%
white population; only 8% of African
Americans lived in census tract with
supermarket (p < 0.01) b) African Americans living in the same census tract as a supermarket were more likely to meet the dietary guidelines for fruit and vegetable consumption (RR = 1.32; 95% CI 1.40–1.80). This e?ect did not extend to whites (RR = 1.11; 95% CI 0.93–1.32) c) Relationship between residence in same tract as supermarket exhibited dose-response e?ect, with ... Purchase answer to see full attachment

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