Public Health Nutrition: 11(12), 1248–1255
Food Stamp Program participation but not food insecurity is
associated with higher adult BMI in Massachusetts residents
living in low-income neighbourhoods
Amy L Webb1,*-, Andrew Schiff2,3, Douglas Currivan4and Eduardo Villamor1,5
1Department of Nutrition, Harvard School of Public Health, Boston, MA, USA:2Project Bread – Walk for
Hunger, Boston, MA, USA:3Rhode Island Community Food Bank, Providence, RI, USA:4RTI International,
Research Triangle Park, NC, USA:5Department of Epidemiology, Harvard School of Public Health, Boston,
Submitted 10 July 2007: Accepted 6 March 2008: First published online 8 May 2008
Objective: Food-insecure populations employ multiple strategies to ensure ade-
quate household food supplies. These strategies may increase the risk of over-
weight and obesity. However, existing literature reports conflicting associations
between these strategies and BMI. The objective of the present study was to
examine whether food insecurity and strategies for managing food insecurity are
associated with BMI in adults.
Design, setting and subjects: In 2005, RTI International and Project Bread con-
ducted a representative survey of 435 adult residents of low-income census tracts
in Massachusetts. Food insecurity was assessed using the US Department of
Agriculture’s eighteen-item Household Food Security Module.
Results: The prevalence of overweight and obesity was 51% and 25%, respec-
tively. After adjusting for age, sex, sociodemographic characteristics and food
insecurity, both participation in the Food Stamp Program (FSP) and participation
in any federal nutrition programme 12 months prior to the survey were each
associated with an approximate 3?0kg/m2higher adult BMI. In the subset of
current FSP participants (n 77), participation for $6 months was associated with
an 11?3kg/m2lower BMI compared with participation for ,6 months. Respon-
dents who consumed fast foods in the previous month had a mean BMI that was
2?4kg/m2higher than those who did not. Food insecurity was not associated with
BMI after adjustment for sociodemographic characteristics and FSP participation.
Conclusions: Participation in federal nutrition programmes and consumption of
fast food were each associated with higher adult BMI independent of food
insecurity and other sociodemographic factors. However, prolonged participation
in the FSP was associated with lower BMI.
Body mass index
Food Stamp Program
Household food insecurity occurs when the availability of
or ability to acquire safe and adequate food is limited or
uncertain(1). The percentage of US households reporting
food insecurity increased from 10?8% in 2002 to 11?4% in
2005(2). A 2005 survey of low-income census tracts in
Massachusetts reported that food insecurity in these areas
had increased from 20% in 2002 to 32% in 2005(3). By
comparison, approximately 7?1% of all Massachusetts
households were food-insecure in 2002–2004(2).
Strategies used by households to manage food inse-
curity may include reducing food intake, meal size or
meal frequency, relying on a limited number of low-cost,
energy-dense foods, or obtaining free or reduced-price
foods from emergency food sources(4). These strategies
may increase the risk of overweight and obesity by
promoting disordered eating habits(5)and reducing diet
diversity(6). Paradoxically, energy intakes may increase
due to the high energy density of many low-cost
foods(7,8). Some, but not all studies have found an asso-
ciation between food insecurity and overweight and
obesity(9–16). Multiple factors, including gender and ethnic
differences in study populations and differences in the
method of food insecurity assessment and study design,
likely contributed to inconsistent findings.
Low-income households may also manage food
insecurity by participating in government-sponsored
food assistance programmes such as the Food Stamp
y Correspondence address: Department of Anthropology, University of
Toronto, 100 St George Street, Toronto, Ontario, Canada, M5S 3G3.
*Corresponding author: Email email@example.com
r The Authors 2008
Program (FSP), the School Lunch Program and/or the
Special Supplemental Nutrition Program for Women,
Infants, and Children (WIC). Participation in the FSP was
associated with overweight and obesity in adult partici-
pants(14,17–19). However, in several studies, assessment of
the relationship between FSP participation and over-
weight independent of food security was not possible
and researchers could not rule out the possibility
that FSP participation was serving as a proxy for food
The aim of the present study was to examine whether
FSP participation is associated with BMI in adults,
independent of food security status. We analysed data
collected in a representative survey of low-income
examined associations between FSP participation, food
insecurity,sources of food
stores, charitable sources and fast-food restaurants) and
BMI, while accounting for potential sociodemographic
Between September and December of 2005, Project
Bread, a Massachusetts-based non-profit anti-hunger
organization, collaborated with RTI International to con-
duct a representative survey in qualified low-income
neighbourhoods in Massachusetts. The aim of the survey
was to assess household food security and food access,
grammes, self-reported health measures and perceived
social climate in respondents’ neighbourhoods.
To be eligible for participation in the survey, households
had to be located in one of the 216 Qualified Census
Tracts (QCT) in Massachusetts. As defined by the
Department of Housing and Urban Development, a QCT
is any census tract, or Census Bureau-designated geo-
graphic equivalent, in which at least 50% of households
have an income less than 60% of the area median gross
income. The QCT for this study were identified by the US
Census in 2000. List-assisted random-digit dialling (RDD)
techniques were used to generate a representative set
of telephone numbers from these QCT. The selected
numbers were stratified by those that were listed in the
telephone directory and those that were not listed. This
distinction was important for determining whether each
sampled telephone number was associated with a
household in one of the QCT. A total of 8187 unlisted
telephone numbers and 2032 listed telephone numbers
were randomly selected from the QCT. The unlisted
telephone numbers were screened to eliminate non-
working and business numbers. For households with
directory-listed numbers, zip code information was used
to ensure that households were within a QCT. House-
holds without directory-listed numbers were required to
provide their zip code when contacted to ensure that they
resided within a QCT. Households with children were
oversampled because the food security status of children
was of great interest. Only adults aged 18 years and over
were interviewed. In households with children, only an
adult who was responsible for the children was inter-
viewed. All interviews were conducted in English. A
total of 2819 households were contacted. Among these
2149 (76?2%) were eligible to participate in the survey.
Eligibility required matching the households’ zip code to
one of the 216 QCT in Massachusetts. Dividing the 465
completed interviews by the 2149 eligible sample units
produced a weighted response rate of 21?6%, based on
the American Association for Public Opinion Research
(AAPOR) RR3 formula(21).
An analysis weight was applied to each case. The initial
household sampling weight was calculated to be of equal
size to the sampling frame and the sample size. Since
households without children were sub-sampled, the
analysis weight (for households without children) was
multiplied by the inverse of the sub-sampling rate
(51?00/0?627). Households may have multiple residential
phone numbers (such as teen lines). To account for
multiple chances that these households could be reached,
the initial sampling weight was multiplied by the inverse
of the number of different residential telephone numbers.
The sampling weights were then adjusted for non-
response by the presence/absence of children in the
household. The non-response factor for households with
children (or households without children) is the ratio of
the sampling weight-sum for households with children
(or households without children) for which an interview
was attempted and the corresponding weight-sums for
households that completed the interviews. The analysis
weight is the product of the household sampling weight
and the non-response adjustment factor.
The survey included the US Department of Agriculture’s
(USDA) Household Food Security Module (HFSM) which
asks about conditions that characterize households
having difficulty meeting basic food needs(16). For
households with children, the full eighteen-item HFSM
scale was used to determine food security status. For
households without children, only the adult-specific ten-
item subset was used. Each question asks whether a given
condition occurred during the previous 12 months and
specifies lack of money or other resources as the reason
for the condition. Responses to the HFSM were scored
Food insecurity, food stamps and BMI in adults1249
according to established criteria to classify households as
either food-secure or food-insecure(16). By definition,
food-insecure households cannot buy enough food to
meet the basic food needs of household members
because of financial constraints. Households with and
without children that answered affirmatively to more than
three of the HFSM items were classified as food-insecure.
Some food-insecure households were further identified
as ‘food-insecure with hunger’ if they experienced pro-
longed periods without adequate food or more severe
instances of hunger. Households without children that
answered affirmatively to five or more items and house-
holds with children that answered affirmatively to seven
items were classified as food-insecure with hunger(16).
The survey included additional questions regarding
household income and other demographic character-
nutrition programmes (FSP, WIC and the free/reduced-
price school meals programme), use of free or low-cost
food from charitable sources (i.e. soup kitchens, church
or community outreach programmes, shelters, food
banks, friends, family) and use of supermarkets or other
store types for food purchases. The survey also included
questions related to health status, including self-reported
height and weight of the respondent, health coverage,
self-perceived health and indicators of social capital.
For the purposes of the survey, social capital referred to
civic engagement, community cohesion and functional
reciprocity among residents of a community.
The dependent variable was the adult respondent’s
current BMI (kg/m2) calculated using self-reported height
and weight. Predictor variables of interest included food
programmes including the FSP, WIC and school lunch
programme,and food sources.
SURVEYFREQ of the Statistical Analysis Systems statistical
software package version 9 (SAS Institute, Cary, NC,
with BMI in univariate analyses (P,0?05) or that were
considered to be potential confounders were included in
multivariate linear regression models. The final models
included BMI as a continuous outcome, the predictor of
interest, respondent age, and categorical variables for
race (white, Hispanic, black, other), sex, highest house-
hold education (not completed high school, high school
diploma, college or more), household income (ten
income brackets), household employment (all unem-
ployed, only part-time, at least one member full-time) and
place of birth (USA or not). Models in which food security
status was not the predictor of interest were additionally
adjusted for food security status. Models in which food
security status was the predictor of interest were adjusted
for FSP participation. For multivariate regression analyses,
food-insecure without hunger and food-insecure with
hunger were combined to create a binary food security
variable (food-secure v. food-insecure). The length of FSP
participation was defined dichotomously as ,6 months
or $6 months. Finally, we examined whether the asso-
ciations between FSP participation or food insecurity and
BMI were modified by sex or age, by testing interaction
terms with the likelihood ratio test.
Of the 465 households surveyed, 435 adult respondents
provided information on their height and weight and
were included for analysis of BMI. Forty-six per cent of
households had at least one child. Thirty-three per cent of
households were eligible for federal nutrition assistance
based on income alone. Additional characteristics of the
surveyed population are presented in Table 1. There were
no differences between households reporting height
and weight and those not reporting height and weight
with respect to education, income and age, food security
status, participation in the FSP or use of charitable food
sources. Ethnicity of the population included for analyses
differed significantly from those who did not report
height and weight. Among those not reporting height and
weight, 3% were Hispanic, 48% were African American
and 45% were white.
Fifty-one per cent of respondents were overweight
(BMI.25kg/m2), 25% were obese (BMI.30kg/m2) and
6% were morbidly obese (BMI.40kg/m2). Men, married
respondents, respondents in households with no full-time
employment and those with fair to poor self-reported
health scores had significantly higher mean BMI com-
pared with their respective reference groups (Table 1).
BMI did not differ by immigrant status, respondent age,
education, home ownership status, number of children,
social capital score, household size, income eligibility for
federal assistance, ethnicity, self-reported exercise in the
past month or health-care coverage (all P.0?05; data
available upon request).
Thirty per cent of households experienced food inse-
curity during the year preceding the survey and
approximately 45% of these experienced severe food
insecurity with hunger. Respondents classified as food-
insecure or food-insecure with hunger had significantly
higher BMI than those classified as food-secure (Table 2).
Mean BMI did not differ between food-insecure groups
with or without hunger. The proportion of respondents
with BMI.30kg/m2was significantly higher in those
who experienced foodinsecurity
food-secure respondents (P,0?05, Fig. 1). Among
food-insecure respondents, only those classified as food-
insecure with hunger reported BMI,19?5kg/m2(6%).
With respect to specific questions from the HFSM,
1250 AL Webb et al.
respondents whose food supplies did not last, who were
unable to afford balanced meals, cut meal sizes and ate
less than their perceived need had significantly higher
BMI than those who reported never having those
experiences (Table 2).
Forty-one per cent of households reported ever parti-
cipating in the FSP and 18% of surveyed households (n 77)
were participating at the time of the survey (Table 3).
Adult BMI was significantly higher in those who reported
their households ever participating in the FSP. However,
in analyses restricted to current FSP participants (n 77),
BMI was significantly lower in those respondents whose
households had participated in the programme for $6
months compared with those whose households had
participated for ,6 months. Those who reported house-
hold participation in the FSP, WIC and/or free/reduced-
price school meals during the 12 months prior to the
survey had significantly higher BMI than those who
reported no federal nutrition assistance. BMI was sig-
nificantly higher among those who obtained food from
charitable sources such as soup kitchens or food banks,
those who reported shopping at convenience stores and
those who consumed fast foods in the month prior to the
survey v. those who did not. BMI did not differ according
to use of supermarkets, ethnic grocery stores or farmer’s
markets (data available upon request).
Ever participating in the FSP and participation in any
federal nutrition assistance programme in the 12 months
prior to the survey remained significantly associated with
higher BMI after adjustment for sociodemographic factors
(Table 4). Among current FSP participants, participation
for 6 months or longer was associated with significantly
lower BMI compared with participation for less than
6 months. These associations remained statistically sig-
nificant after additional adjustment for food insecurity.
Eating fast food at least once in the month prior to the
survey remained significantly associated with higher BMI
after adjustment for sociodemographic characteristics and
food insecurity. The association between FSP participa-
tion and BMI was not significantly modified by sex or age
of the participant (P.0?05).
Neither food insecurity nor the individual components
of the HFSM were associated with BMI after adjustment
for sociodemographic characteristics and FSP participa-
tion. Similarly, the use of convenience stores and
obtaining food from charitable sources were not asso-
ciated with BMI after controlling for sociodemographic
characteristics and food insecurity.
Table 1 Mean BMI by sociodemographic characteristics for 435 adult residents of low-income neighbourhoods in Massachusetts who
responded to a household food security survey administered in 2005*
Weighted % BMI (kg/m2)
Respondent’s age (years)
Place of birth for respondent
Ethnicity of respondent
Marital status of respondent
Married or partnered
Self-reported health score of respondent
Highest education in household
Some high school
High school diploma or equivalent
College or more
At least one person in household works full time
*n 435; sum of weights5786900.
-From t test.
Food insecurity, food stamps and BMI in adults 1251
We examined whether participation in the FSP and other
strategies for managing food security were associated
with BMI independent of food security status in residents
of low-income communities. These factors may be
unique to residents of low-income communities and
could potentially increase their risk of overweight and
obesity. Ever participating in the FSP, participating in any
federal nutrition programme at some point during the
12 months prior to the survey and fast-food consumption
at least once in the month prior to the survey were each
associated with increased BMI independent of socio-
demographic factors and food insecurity. Conversely,
among current participants in the FSP, those who had
participated for $6 months had significantly lower BMI.
The positive association between household participation
in federal nutrition programmes and BMI in our popu-
lation is consistent with previous studies(14,17–19). In our
study, these associations were independent of socio-
demographic factors and food insecurity status.
The monthly distribution of FSP benefits has led some
researchers to suggest a ‘food stamp cycle’ hypoth-
esis(14,22). Households cycle between times of sufficient
and insufficient funds for food and, as a result, experience
disordered eating patterns that put them at risk for
obesity. A second hypothesis posits that participation
in nutrition assistance programmes may inadvertently
increase purchases of energy-dense foods by increasing a
household’s overall purchasing power. This hypothesis is
plausible if, even with the additional income provided by
Table 2 Mean BMI of 435 Massachusetts adult residents in low-income neighbourhoods by food security status and responses to specific
food security indicator questions from the US Household Food Security Module*
Weighted % BMI (kg/m2)
Food security status--
Food-insecure without hunger
Food-insecure with hunger
‘The food that (I/we) bought didn’t last, and (I/we) didn’t have
money to get more’
Sometimes or often true
‘(I/we) couldn’t afford to eat balanced meals’
Sometimes or often true
In the last 12 months, did you or other adults in your
household ever cut the size of your meals or skip meals
because there wasn’t enough money for food?
In the last 12 months, did you ever eat less than you felt
you should because there wasn’t enough money to buy food?
Proportion of respondents who reported that they changed
their eating habits in any wayy
*n 435; sum of weights5786900.
-From t test.
--In households with children, food security status was determined using the complete eighteen-item Household Food Security Module whereas in households
with no children the adult-specific subset was used to determine food security status.
ySummary variable included reducing meal size, eating less than perceived need, not eating when hungry and/or skipping meals.
Fig. 1 Categories of BMI ( , $40?0kg/m2;
39?9kg/m2;, $25?0 to 29?9kg/m2;
, ,19?5kg/m2) by food security status among 435 Massa-
responded to a household food security survey administered
in 2005 (P50?07)
, $30?0 to
, $19?5 to 24?9kg/m2;
1252 AL Webb et al.
Table 3 Mean BMI by participation in the Food Stamp Program (FSP) and sources of food for 435 adult residents of low-income
neighbourhoods in Massachusetts who responded to a household food security survey administered in 2005*
Weighted % BMI (kg/m2)
Respondent’s household had ever participated in the FSP
Among households who had ever participated in the FSP (n 175),
respondent’s household was currently participating in the FSP
Among those households currently enrolled in the FSP (n 77),
the length of time participating
Households participated in any federal nutrition assistance programme
during the 12 months prior to the survey--
Household obtained free or reduced-cost food from charitable sources
during the 12 months prior to the surveyy
In the month prior to the survey, the household shopped at a convenience store
In the month prior to the survey, respondent ate fast food at least once
*n 435; sum of weights5786900.
-From t test.
--Federal nutrition assistance programmes included the FSP, the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) and the free/
reduced-price school meals programme.
yCharitable sources included soup kitchens, food pantries, churches, friends and/or relatives, senior programmes and/or other sources of free or reduced-price
Table 4 Multivariate associations of food eating and acquisition habits and household food insecurity with respondent BMI (kg/m2) among
435 adult residents of low-income neighbourhoods in Massachusetts who responded to a household food security survey administered in
Model adjusted for
Model adjusted for
and food security status*
Difference in BMI95% CI Difference in BMI 95% CI
Household had ever participated in the FSP (yes v. no)
Among those ever participating (n 175), household was
currently participating in the FSP (yes v. no)
Among those currently participating (n 77), the household had
participated in the FSP for $6 months (yes v. no)
Household had participated in any federal nutrition assistance
programme in the 12 months prior to the survey (yes v. no)-
Household obtained food or food funds from a charitable source
during the 12 months preceding the survey (yes v. no)--
Household shopped at a convenience store in the month prior to
the survey (yes v. no)
Respondent ate fast food at least once during the month prior to
the survey (yes v. no)
Household experienced food insecurity in the 12 months prior to
210?3217?4, 23?3211?3217?5, 25?0
3?20?7, 5?73?21?9, 4?5
2?420?2, 4?92?220?4, 4?9
0?921?0, 2?80?921?0, 2?8
2?30?5, 4?22?40?7, 4?2
*Sociodemographic covariates in the final model included sex, age, race, household employment, household education, household income and place of birth.
-Federal nutrition assistance programmes included the Food Stamp Program (FSP), the Special Supplemental Nutrition Program for Women, Infants, and
Children (WIC) and the free/reduced-price school meals programme.
--Charitable food sources included soup kitchens, food pantries, churches, friends and/or relatives, senior programmes and/or other sources of free or reduced-
yA food-insecure household may or may not have experienced hunger. Final model included sex, age, race, household employment, household education,
household income, place of birth and participation in the FSP.
Food insecurity, food stamps and BMI in adults1253
food stamps, fruits, vegetables, lean meats and low-fat
dairy products remain too expensive for programme
reported that, in higher-income households, increasing
income was associated with small increases in expendi-
tures on fruits and vegetables(24). Conversely, in low-
income households (,130% of poverty line), purchases
of fruits and vegetables did not increase with income.
Rather, households allocated their increases in monthly
income to other foods, such as staples, and/or to
household needs, such as clothing, utilities or rent.
Similarly, Wilde et al.(25)reported a positive association
between FSP participation and increases in meat,
added sugars and total fats, while intakes of fruits and
vegetables did not change. These two studies suggest
that modest increases in income do not facilitate changes
in dietary intakes or food purchasing habits in low-
income populations, perhaps because extra funds are
allocated to household needs with higher priority, such
Of special interest was the observation that, in the
subgroup of current FSP participants (n 77), participation
for longer periods of time ($6 months) was associated
with lower BMI compared with shorter periods of parti-
cipation. This association remained significant after
adjustment for food insecurity and sociodemographic
characteristics of the household. It is plausible that long-
term participation allowed for gradual increases in
expenditures on healthier food items such as fruits and
vegetables or that increased exposure to nutrition edu-
cation offered through the FSP altered dietary habits.
However, information on food expenditures and nutrition
education exposure was not available to test these
We also found that greater fast-food intake was asso-
ciated with higher BMI. Bowman et al. reported that men
and women consumed more energy, total fat and satu-
rated fat on days that they consumed fast foods compared
with days they did not(26). Increased consumption of
fast foods may therefore contribute to weight gain if
compensatory reductions in the intake of other foods
or increases in physical activity are not practised. We are
unable to say, however, whether the association between
fast-food consumption and BMI is unique to low-income
populations because we did not collect information from
higher-income neighbourhoods that would facilitate such
Our study did not find food insecurity to be associated
with self-reported BMI as a continuous measure after
adjustment for sociodemographic factors and FSP parti-
cipation. Similarly, a longitudinal study in women
reported that food insecurity as measured by the HFSM
was not a predictor of subsequent, clinically significant
weight gain(9). Similarly, neither sex nor age significantly
modified the associations between food insecurity or FSP
participation on adult BMI.
froma USDA survey
Limitations and considerations
Owing to the cross-sectional nature of the present data,
we are unable to ascertain the temporal sequence of the
associations observed and are unable to address causality.
Second, it is possible that the use of reported rather than
measured heights and weights contributed to the lack of
association between food insecurity and BMI. Because
people tend to under-report their weight and over-report
their height, self-reported BMI may have been under-
estimated which would bias our estimates towards the
null(27). Populations at high risk of food insecurity may
not have been surveyed due to the sampling design,
including the homeless, those without land-line tele-
phones and households without an English speaker. The
survey did attempt to address the exclusion of house-
holds without a land-line telephone service. Each
respondent was questioned regarding interruptions in
telephone services (ITS) in the 12 months prior to the
survey; 15?3% reported ITS. Assuming that households
with a recent interruption were similar to households
who could not be contacted due to current lack of phone
service, sample weights that adjusted for non-telephone
coverage were developed. Comparisons of multiple
survey items using ITS-adjusted weights v. non-adjusted
weights indicated that no biases were present(3).
The weighted response rate for this survey was low
(21%) and thus the surveyed population may not be
generalizable. Indeed, survey respondents were more
educated, less likely to be Hispanic and less likely to have
been born outside the USA than respondents sampled
from these QCT during the 2000 US Census(3). However,
despite being low, the response rate for our survey is
consistent with the current downward trend in RDD
response rates. For example, the median response rate
for the state-wide Behavioral Risk Factors Surveillance
System (BRFSS) surveys has dropped by 12% over the
past several years(28,29). Although the median response
rate for the BRFSS surveys was 51?4% in 2006, state-wide
response rates were significantly lower in north-eastern
states like Massachusetts. In 2006, the response rate for
the Massachusetts BRFSS survey was 38?6% using the
Council of American Survey Research Organizations’
response rate formula, which generally produces a
higher response rate than the AAPOR calculation(21,30).
Additionally, administration of the survey in English may
have further contributed to ethnic discrepancies and a
lack of generalizability. By excluding households without
English speakers, the survey was less likely to capture
the situation of recent immigrants.
Because of their national reach and financial support,
government nutrition assistance programmes that target
low-income populations have the potential to serve as
vehicles for healthy diet and behaviour change by
increasing awareness of and economic and physical
access to healthy foods(18). Additional studies examining
1254AL Webb et al.
how and what aspects of nutrition assistance programmes Download full-text
influence the nutritional status and health of participants
are needed, specifically those that utilize prospective
approaches and monitor changes in weight, health out-
comes, food security status, and dietary and food pur-
chasing habits over time. Because the administration and
nutritional education components of programmes differ
by state, state-to-state comparisons of such data could
help identify specific implementation strategies that
would be most effective in promoting healthy habits.
Support for this project was provided in part by the
National Institutes of Health, grant T32 DK07703, and the
Massachusetts MSG/Nucleotides Class Action Settlement.
A.L.W. developed the analytic strategy, conducted the
statistical analyses and wrote the initial manuscript. A.S.
participated in the study design and survey development.
D.C. participated in survey development and statistical
analyses. E.V. contributed to the analytic strategy and data
analyses. All authors participated in the interpretation of
the data and in writing the final draft of the manuscript.
No authors had any conflicts of interest.
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