Spatial-Temporal Modeling of Neighborhood Sociodemographic Characteristics and Food Stores
Am J Epidemiol.
Original Contribution Spatial-Temporal Modeling of Neighborhood Sociodemographic Characteristics and Food Stores
Archana P. Lamichhane
Joshua L. Warren
Initially submitted February 27, 2014; accepted for publication August 21, 2014. The literature on food stores, neighborhood poverty, and race/ethnicity is mixed and lacks methods of accounting for complex spatial and temporal clustering of food resources. We used quarterly data on supermarket and convenience store locations from Nielsen TDLinx (Nielsen Holdings N.V., New York, New York) spanning 7 years (2006-2012) and census tract-based neighborhood sociodemographic data from the American Community Survey (2006-2010) to assess associations between neighborhood sociodemographic characteristics and food store distributions in the Metropolitan Statistical Areas (MSAs) of 4 US cities (Birmingham, Alabama; Chicago, Illinois; Minneapolis, Minnesota; and San Francisco, California). We fitted a space-time Poisson regression model that accounted for the complex spatial-temporal correlation structure of store locations by introducing space-time random effects in an intrinsic conditionally autoregressive model within a Bayesian framework. After accounting for census tract-level area, population, their interaction, and spatial and temporal variability, census tract poverty was significantly and positively associated with increasing expected numbers of supermarkets among tracts in all 4 MSAs. A similar positive association was observed for convenience stores in Birmingham, Minneapolis, and San Francisco; in Chicago, a positive association was observed only for predominantly white and predominantly black tracts. Our findings suggest a positive association between greater numbers of food stores and higher neighborhood poverty, with implications for policy approaches related to food store access by neighborhood poverty. food availability; food stores; intrinsic conditionally autoregressive model; neighborhood characteristics; poverty; sociodemographic factors; spatial-temporal modeling; supermarkets Abbreviation: MSA, Metropolitan Statistical Area.
While neighborhood food environments are associated
with diet-related health outcomes, vulnerable subpopulations
may be particularly at risk (
), given observed differential
access to food resources (
) and associations with dietary
intake and weight (
). However, the evidence base is
3, 14, 15
), which might be an artifact of the
clustering of food stores and individual- and neighborhood-level
sociodemographic characteristics across geographic space.
The current literature base is largely cross-sectional in nature
and does not account for spatial and temporal patterning,
leaving the temporal and spatial dynamics of this relationship
New methodologies that account for spatial and temporal
clustering in neighborhood sociodemographic characteristics
and spatial distribution of food stores are needed.
Spatialtemporal modeling has become increasingly common in air
pollution research (
), to account for placement of
monitoring stations and assumptions about consistency of risk
across space. These models are applicable to studies of the food
environment, as there is clear spatial and temporal clustering
of food stores related to zoning, demand, and competition.
To this end, we used quarterly data on supermarket and
convenience store locations spanning a 7-year period (2006–
2012) and census tract–based sociodemographic data from
the same period to examine food store accessibility in 4
geographically and economically diverse US cities emblematic
of distinct historical development patterns. We used a
spacetime Poisson regression model implemented by Waller et al.
) to introduce space-time random effects using a
conditionally autoregressive model within a Bayesian framework.
We assessed whether neighborhood racial/ethnic composition
moderated the relationship between neighborhood poverty
and numbers of stores, accounting for spatial and temporal
correlation. To our knowledge, our study is the first to have
used these advanced Bayesian models to account for the
complex spatial-temporal correlation structure of food store
The geographic areas included in this study were the US
Census Metropolitan Statistical Areas (MSAs) of
Birmingham, Alabama; Chicago, Illinois; Minneapolis, Minnesota;
and San Francisco, California (
). Using unit boundaries
from the 2010 Census, each MSA was comprised of multiple
census tracts (Birmingham: 264 tracts; Chicago: 2,210 tracts;
Minneapolis: 772 tracts; San Francisco: 975 tracts).
Food store data
We used quarterly supermarket and convenience store data
from 2006 through the third quarter of 2012 (27 quarters)
obtained from Nielsen TDLinx (
), a commercial database of
stores selling consumer packaged goods in the United States
(Nielsen Holdings N.V., New York, New York). Nielsen
TDLinx uses official industry-standard definitions for food
store categories when available or its own rigorously
developed definitions of trade channels and subchannels supported
by trade associations and trade publications. We combined
TDLinx subchannel categories to form 3 food store groups:
1) supermarkets—defined as natural/gourmet food stores,
superettes, and conventional supermarkets and supercenters
(stores selling both food and nonfood items, including dry
grocery goods, canned goods, and perishable items) with
an annual sales volume greater than or equal to $1,000,000;
2) convenience stores—defined as conventional convenience
stores and gas stations/kiosks with a limited selection of
confectionary items, snacks, and beverages; and 3) other—
defined as warehouse stores, military commissaries, and
wholesale clubs. We performed extensive data-cleaning to correct
more than 20,000 spelling and address errors and formatting
problems and then geocoded all addresses using ArcGIS 10
software (Esri, Redlands, California) and StreetMap 2010
Premium (Esri) as the reference street network database,
finding higher reliability than locations provided by Nielsen.
Of 261,239 TDLinx data points, 96.8% (n = 252,996) were
successfully geocoded with ArcGIS and 0.3% (n = 723)
were located through Internet searches; Nielsen-provided
geocodes were used for the remaining 2.7% (n = 6,939). We
excluded 581 (0.2%) erroneous or unresolvable observations.
Census tract characteristics
We obtained census tract–level data on total population, total
area, percentage of the population living below the federal
poverty level, and race/ethnicity from the American Community
Survey (2006–2010) within each MSA (
measures were used, with tertiles of percentage of the
population living below the federal poverty level being used in
some analyses. We defined the racial/ethnic composition of
census tracts according to the method of Powell et al. (
predominantly white (≥70% of residents non-Hispanic white),
predominantly black (≥70% of residents non-Hispanic
black), predominantly Asian (≥70% of residents Asian/
Pacific Islander), predominantly Hispanic (≥70% of
residents Hispanic), or racially mixed (not meeting any of the
above criteria); racial/ethnic groups were combined into an
“other” category when the sample size was insufficient for
analysis. We ensured that sample sizes were adequate to fit
statistical models for white-versus-nonwhite comparisons,
combining racial/ethnic groups when necessary. To address
structural confounding (
), we ensured sufficient racial
diversity across levels of neighborhood poverty and did not
extrapolate outside of observed poverty rates for each racial
Descriptive analysis. Census tract characteristics and
numbers and densities of food stores (counts per 10,000
population) were compared across the 4 MSAs using analysis of
variance and χ2 tests for continuous variables and categorical
variables, respectively. We performed separate analyses to
compare densities of food stores according to census tract–
level poverty for each MSA, using SAS statistical software,
version 9.3 (SAS Institute, Inc., Cary, North Carolina).
Spatial-temporal Poisson regression analysis. Poisson
regression analyses were used to examine the associations
between neighborhood characteristics and separate quarterly
counts of supermarkets and convenience stores by store
type. The 4 MSAs were modeled separately due to the great
distances between cities and to allow for varying
relationships between store counts and sociodemographic
characteristics by city.
Because of spatial and temporal correlation in store counts
between census tracts, we introduced the statistical models
within a Bayesian framework which allowed for the efficient
fitting of advanced space-time models. We modeled counts
of a specific store type (dependent variable) at the census
tract level using a multivariable log-linear Poisson regression
model which accounted for variation in counts across space
and time, to assess whether neighborhood racial/ethnic
composition moderated the relationship between neighborhood
poverty and store counts. The model is given as
Yðsi; tÞjλðsi; tÞ ∼ Poissonfλðsi; tÞg;
lnfλðsi; tÞg ¼ xiT β þ β pþ1t þ β pþ2t2 þ ϕtðsiÞ þ θðsi; tÞ;
where Yðsi; tÞ is the store count in census tract si at time t and
λðsi; tÞ represents the expected count of the store type at the
same location and time. We assumed that the logarithm of the
expected count was a linear function of covariates and more
general error terms which control the observed counts in a
tract across time. Specifically, we allowed xi to be a vector
of tract-level covariates (including an intercept term) that
included the poverty level, racial/ethnic composition, area, and
population size of census tract si. Interactions between racial/
ethnic composition and poverty and between tract area and
population were also included as covariates. To control for
time, we included flexible linear and quadratic time
parameters, β pþ1 and β pþ2, that accounted for any broad temporal
changes in observed counts across all tracts. We allowed
for more local time adjustments (tract-specific) by modeling
the extra Poisson variability and allowing for changing
spatial relationships over time. Thus, we accounted both for
large-scale temporal changes that were similar across all
tracts and for small-scale changes that possibly varied
spatially. We introduced 2 additional terms into the usual
loglinear Poisson regression analysis. The form of our model
was originally introduced in the disease mapping setting
by Waller et al. (
) and is commonly used in the
spatialtemporal modeling of count data (
). The introduced
ϕtðsiÞ parameters account for spatial clustering of expected
counts at a specified time point, capturing the local clustering
trend and leading to similar expected counts in neighboring
census tracts. In contrast, the θðsi; tÞ parameters capture
region-wide heterogeneity over the entire study site of
interest. These parameters together represent the extra Poisson
variability contained in the data due to overdispersion caused
by the spatially and temporally correlated tract counts. Failing
to account for overdispersion in a Poisson regression can lead
to standard errors of parameter estimates which are
incorrectly too small and can result in misguided statistical
conclusions. Our approach of modeling the extra Poisson variability
in the form of the spatial-temporal and non–spatial-temporal
components allowed the probability of observing a zero
count to be higher in certain tracts/quarters if necessary.
Details on model fit are shown in the Web Appendix (available
Prior information. Specification of the Bayesian model
was completed by assigning prior distributions to the model
parameters. The introduced spatial-temporal parameters were
given a prior distribution allowing for a flexible
spatialtemporal relationship between expected counts of
neighboring census tracts across time. We used a nested (in time)
intrinsic conditionally autoregressive (ICAR) model (
specify the prior distribution, such that
ϕt ¼ fϕtðs1Þ; ::: ; ϕtðsmÞg ;
m ¼ number of census tracts in the specified MSA;
ϕt ∼ ICARðσϕ2t Þ;
ϕtðsiÞjϕtð siÞ ∼ N
X wij ϕtðsjÞ; wi t ;
j wiþ þ
where ϕtð siÞ¼ fϕtðs1Þ;::: ; ϕtðsi 1Þ;ϕtðsiþ1Þ; ::: ; ϕtðsmÞgT , wij
is equal to 1 if tracts si and sj are neighbors (touching borders)
and 0 otherwise, and wi+ is the number of neighbors of tract
si. Locations are not considered to be neighbors of
themselves, resulting in wii ¼ 0 for all i. In the proposed prior
distribution, the spatial parameters were nested within time, with
independence assumed between times. We allowed σ2 , the
unknown variance component at each time point, to change
across time. This allowed the spatial relationship to change
Spatial-Temporal Models of Neighborhood Food Stores 139
and allowed for the possibility of spatial-temporal interaction,
increasing the flexibility of the model.
The regression covariate parameters were given vague yet
proper prior distributions, such that βj ∼ Nð0; σβ2Þ with σ2 fixed
at a large value. The error terms that controlled the
regionwide heterogeneity at a specified time point were given
exchangeable prior distributions, such that θðsi; tÞ ∼ Nð0; σ2θt Þ.
These variance parameters were also allowed to change over
time, allowing for the possibility of space-time interaction
and a more flexible model in general. The variance
parameters were assigned independent inverse gamma prior
distributions, such that σϕ2 ∼ inverse gammað1:00; 0:35Þ and
σ2θt ∼ inverse gammað1:00; 0:35Þ. We selected “fair” priors,
as described by Banerjee et al. (
), which allowed the
extra Poisson variability to be equally partitioned between
the region-wide heterogeneity and spatial clustering
parameters a priori. Details regarding the transformation of variables
are included in the Web Appendix. All analyses were carried
out using R statistical software (R Foundation for Statistical
Computing, Vienna, Austria).
Chicago had the largest total number of tracts and total
area, and San Francisco had the smallest total area but the
largest population density (Table 1). San Francisco had the
highest median household income, and Birmingham was
the least affluent. Birmingham had the highest proportion
of tracts with at least 1 supermarket or convenience store in
comparison with the other MSAs.
There was variation in the proportion of tracts with at least
1 store by census tract poverty (Table 2). There was variation
in the density of supermarkets (Table 3) and convenience
stores (Table 4) by poverty across all years, with variation
in P values and number of tracts in each MSA. In general,
higher densities of supermarkets and convenience stores
were found in higher-poverty tracts.
Spatial-temporal Poisson regression analysis
Comparison of the expected predicted deviance values
from the basic and spatial-temporal Poisson regression
models showed smaller expected predicted deviances and
improved overall fit of the data for the latter models across all
MSA and store-type combinations (Web Table 1). Hence,
we present and discuss results from the space-time models.
All results are based on 15,000 draws from the posterior
distribution of the model parameters after a burn-in period of
15,000 draws. Posterior means and 95% credible intervals
for supermarket and convenience store models are shown in
Tables 5 and 6, respectively, and model results for the basic
Poisson regressions are shown in Web Tables 2 and 3. While
point estimates were similar in the space-time models and
standard models, the widths of the 95% credible intervals
differed as expected.
After accounting for tract-level area, population, area ×
population interaction, and spatial and temporal variability,
census tract poverty (empirically logit-transformed, corresponding
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Spatial-Temporal Models of Neighborhood Food Stores 141
to increasing poverty) was significantly positively associated
with an increase in the log of expected counts of
supermarkets (corresponding to increasing numbers) among tracts of
all different racial/ethnic compositions in all 4 MSAs
(interaction plots shown in Figure 1; interaction estimates
presented in Web Table 4). Therefore, the results suggest an
association between increased poverty and increased
numbers of supermarkets.
Comparison of expected numbers of supermarkets
according to combinations of census tract racial/ethnic composition
and poverty in Birmingham and Chicago suggested that
predominantly black tracts had the largest estimated increases
in supermarket counts with increasing tract-level poverty,
followed by tracts of “other” race/ethnicity and then
predominantly white tracts (Table 5). For instance, in the Birmingham
MSA, the slope was larger for the black (versus white) racial/
ethnic group. Every unit increase in the transformed poverty
variable was associated with a 0.28, 0.22, and 0.13 increase
in the log of expected supermarket counts in predominantly
black, “all other” racial composition, and predominantly white
census tracts, respectively, whereas there was not a statistically
significant difference between predominantly white and “other”
census tracts in Minneapolis and San Francisco (Table 5).
Figure 1 shows the interaction between poverty and race/
ethnicity for all 4 MSAs, including average tract area and
population specific to each MSA in the model. The interactions
between census tract racial composition and poverty were highly
statistically significant for Birmingham and Chicago, while no
significant interaction was observed between race/ethnicity and
poverty for supermarkets in Minneapolis and San Francisco
(albeit with increased uncertainty in San Francisco), as can
be seen from the different slopes for the racial/ethnic groups.
Similarly, census tract poverty was significantly positively
associated with an increase in the log of expected numbers
of convenience stores among tracts of all different racial/ethnic
compositions in Birmingham, Minneapolis, and San Francisco
(interaction plots shown in Figure 2; interaction estimates
presented in Web Table 5). In Birmingham, tracts of “other” race/
ethnic composition had the largest estimated increase in
convenience store counts with increasing tract-level poverty,
followed by predominantly black tracts and then predominantly
white tracts (increases in log expected convenience store
counts of 0.49, 0.28, and 0.13, respectively); predominantly
white tracts had the largest estimated increase in convenience
store counts as compared with tracts with “all other” racial
composition in Minneapolis and San Francisco (Table 6).
Chicago, which required a categorical poverty variable due to
an increasing and then decreasing expected store count with
increasing poverty levels for one of the racial/ethnic groups,
also showed statistically significant interaction between racial
composition and poverty, with estimated increasing numbers
of convenience stores from low-poverty tracts to
mediumpoverty tracts to high-poverty tracts for predominantly white
and predominantly black tracts (Figure 2). The largest
estimated black-white differences were observed at the lower end
of poverty, with decreasing differences as tract-level poverty
increased. Among the low-poverty census tracts, we observed
significantly more convenience stores in predominantly black
(vs. predominantly white) tracts; this association was reversed
for medium- and high-poverty tracts, with predominantly
white (vs. black) tracts having more estimated convenience
stores (Figure 2).
Our findings suggest variation in numbers of supermarkets
and convenience stores by neighborhood sociodemographic
characteristics in Birmingham, Chicago, Minneapolis, and
San Francisco from 2006 to 2012. Model evaluation
suggested spatial and temporal dependencies requiring an
approach to account for spatial-temporal clustering of food
stores over time. After accounting for tract area, population,
their interaction, and spatial and temporal variability,
tractlevel poverty was significantly and positively associated
with an increase in expected numbers of supermarkets
among tracts of all different racial/ethnic compositions in
all 4 MSAs. A similar positive association was observed
for convenience stores in Birmingham, Minneapolis, and
San Francisco; in Chicago, a positive association was
observed only for predominantly white and predominantly
black tracts. Thus, our findings suggest a positive association
between numbers of food stores and greater neighborhood
poverty, which could have implications for health,
particularly for residents of disadvantaged areas.
In contrast to several other studies (
6, 9, 28–30
), we found
greater numbers of supermarkets in high-poverty areas than in
MSAb and Year
Abbreviations: MSA, Metropolitan Statistical Area; SD, standard deviation.
a The convenience store category included conventional convenience stores and gas stations/kiosks.
b Numbers of census tracts (2010 census tract boundaries) falling within the MSAs: Birmingham, 264; Chicago,
2,210; Minneapolis, 772; San Francisco, 975.
c P value from regression analysis.
low-poverty areas. Our finding of a greater number of
convenience stores in high-poverty areas versus low-poverty areas is
similar to published literature (
6, 29, 31, 32
). Differences in
findings might relate to our modeling strategy, the quality of
the Nielsen TDLinx data, or our use of temporal data.
Previous studies examining the association between
neighborhood sociodemographic characteristics and food stores
have examined neighborhood poverty (
6, 9, 29
neighborhood race/ethnicity (
6, 9, 29, 30
) separately. Only a few
studies have examined interactions between neighborhood
poverty and race/ethnicity (
). Our findings suggest
heterogeneity in the positive association between census
tract poverty and numbers of food stores by census tract
race/ethnicity, even with overlapping 95% credible intervals
for supermarkets in 3 of the 4 cities. For supermarkets,
we found the strongest positive associations in Birmingham
and Chicago ( predominantly black race/ethnicity vs. “other”),
whereas for convenience stores we found comparatively
stronger positive associations in Minneapolis and San
Francisco ( predominantly white race/ethnicity vs. “other”),
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Birmingham (“other” race/ethnicity vs. white), and Chicago
(in the low-poverty stratum for black race/ethnicity vs.
“other”). Population density might influence this racial/
ethnic heterogeneity, as the higher-poverty census tracts
had higher population densities. Thus, it is possible that
population demand related to greater population density might
influence the observed distribution of food stores.
Despite increased minority participation in the urban
planning process in the past decade, policies in Birmingham,
Chicago, and San Francisco have led to inequitable
distribution of racial/ethnic minorities (albeit with variation) across
these cities (
). For example, racial diversity increased in
Chicago between 1980 and 2000 (37), and Minneapolis was
the least economically and racially diverse city in our sample
36, 38, 39
). There may be unmeasured macro-level factors
that influence the distributions of neighborhood poverty and
food store locations and bias results. Our analysis spanned the
period of the US financial and housing crises (2006–2012),
and it is possible that characteristics of the housing market
played a role in neighborhood poverty and the locations of
food stores. Our models can handle spatial correlation
between tracts and allow for varying relationships between
store counts and sociodemographic characteristics by city.
Our model testing suggested that accounting for spatial
and temporal dependencies is needed to contend with
clustering in food stores across time and geographic space.
However, in comparing the spatiotemporal model results
with those from the standard models, the estimates were
similar. We expected the point estimates from both models to be
similar, with the main differences occurring for the posterior
standard deviations and 95% credible interval widths (which
can be incorrectly and inaccurately small when
overdispersion is not addressed). Thus, we found that the 95% credible
intervals from the space-time models were wider for almost
all of the presented parameter estimates, and in a few
instances the parameter was no longer statistically significant
as a result. The fact that we did not observe major differences
between the spatiotemporal model results and those from the
standard models with respect to the point estimates provides
evidence that important spatially varying confounders were
not excluded from the analyses (
While there are many strengths of our approach, limitations
must be noted. First, like other investigators, we used census
tracts to assess the spatial availability of food stores, despite
lack of information about the relevant spatial units for local
food shopping (
). However, we used a full census of each
of the 4 MSAs and as such made no assumptions about the
context for individual food shopping. Further, we performed
sensitivity tests to consider whether census block groups
would be more appropriate, finding similarity in direction
and strength of estimated effects with tracts. Given the
cumbersome nature of the spatial-temporal models with large
numbers of smaller geographic units, we opted for census
tracts. In addition, the low temporal resolution of census
data is a limitation. Second, most studies have relied on
Dun & Bradstreet (Dun & Bradstreet, Inc., Short Hills,
New Jersey) and InfoUSA (Infogroup, Papillion, Nebraska)
data sources to characterize the retail food environment (
), although there is only moderate agreement between
these data sources and ground-level observations (
). In contrast, Nielsen TDLinx data are known to be of
higher quality (44) and are updated monthly, capturing changes
in store openings/closings, categorization, ownership, and
name (http://www.nielsen.com/us/en.html), and thus may be
a more accurate source of data on food store availability over
time, with the caveat that small and independent food outlets
may not be as well captured as larger stores (
). Our approach
of characterizing census tracts as predominately single-race
using a 70% cutpoint allowed us to examine racial/ethnic
composition in areas with sufficient concentrations, but
it did not allow study of more nuanced combinations in
racially/ethnically diverse tracts. Unmeasured confounding
and endogenicity of neighborhood poverty remain possible,
as we did not use causal models, though we allowed for the
possibility of spatial correlation between the regions. The
standard errors for the fixed effects were adjusted appropriately
with inclusion of the spatially correlated effects (
40, 51, 52
In conclusion, our findings suggest that there are greater
numbers of supermarkets and convenience stores in areas with
higher census tract–level poverty, after accounting for
tractlevel area, population, their interaction, and spatial and
temporal variability, which suggests potential to influence behavior to
the extent that availability of neighborhood food stores is
associated with dietary behaviors. The positive association between
poverty and supermarkets held true for census tracts of all
racial/ethnic compositions in all 4 MSAs, albeit with variation by
tract race/ethnicity. For convenience stores, there were
substantial racial/ethnic disparities as poverty level increased, with
higher numbers of convenience stores at high poverty levels
in predominantly nonwhite tracts versus predominantly white
tracts in Birmingham and in white tracts versus nonwhite tracts
in Minneapolis and San Francisco, and comparatively stronger
positive associations (black tracts vs. white tracts) at low
poverty levels in Chicago. Differences in the associations between
neighborhood poverty/race and food stores suggest variation in
access to unhealthy food options in poor and/or high-minority
), where residents are at disproportionate
risk for diet-related chronic diseases (
). The fact that the
associations vary over geographic space confirms the need for
context-specific analyses (14). Health-related policies
designed to reduce spatial inequalities in access to healthy
foods may need to be context-specific and to consider
neighborhood race/ethnicity and income level.
Author affiliations: Department of Nutrition, Gillings
School of Global Public Health, University of North Carolina
at Chapel Hill, Chapel Hill, North Carolina (Archana
P. Lamichhane, Pasquale Rummo, Penny Gordon-Larsen);
Department of Biostatistics, Gillings School of Global Public
Health, University of North Carolina at Chapel Hill, Chapel
Hill, North Carolina (Joshua L. Warren); Department of
Biostatistics, Yale School of Public Health, New Haven,
Connecticut (Joshua L. Warren); and Carolina Population Center,
University of North Carolina at Chapel Hill, Chapel Hill,
North Carolina (Marc Peterson).
This work was funded by the National Heart, Lung,
and Blood Institute (NHLBI) (grant R01 HL114091). The
Coronary Artery Risk Development in Young Adults
(CARDIA) Study, comprising the Metropolitan Statistical Areas
from which this study population was drawn, is supported by
contracts HHSN268201300025C, HHSN268201300026C,
HHSN268201300027C, HHSN268201300028C, HHSN268
201300029C, and HHSN268200900041C from the NHLBI,
the Intramural Research Program of the National Institute on
Aging (NIA), and an Intra-agency Agreement (AG0005)
between the NIA and the NHLBI. General support was received
from the Carolina Population Center, University of North
Carolina at Chapel Hill (grant R24HD050924); the Eunice
Kennedy Shriver National Institute of Child Health and Human
Development; the Nutrition Obesity Research Center,
University of North Carolina at Chapel Hill (grant P30DK56350 from
the National Institute for Diabetes and Digestive and Kidney
Diseases); and the Center for Environmental Health and
Susceptibility, University of North Carolina at Chapel Hill (grant
P30 ES010126 from the National Institute of Environmental
Health Sciences (NIEHS)). J.L.W. was supported by the
NIEHS (grant T32ES007018).
We acknowledge Tim Monbureau for statistical
programming and Kristin Walls, Sarah Alexanian, and Lori Delaney
for library and administrative assistance.
The National Institutes of Health had no role in the design
or conduct of the study; the collection, management,
analysis, or interpretation of the data; or the preparation, review, or
approval of the manuscript.
Drs. Joshua L. Warren and Penny Gordon-Larsen had full
access to all of the data in the study and take responsibility for
the integrity of the data and the accuracy of data analysis.
Conflict of interest: none declared.
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