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Nutrition Department, University of California, Davis, CA 95616-8669;
*
Max Planck Institute for Demographic Research, Rostock, Germany;
Nutrition Department, Pennsylvania State University, University Park, PA 16802; and
**
Cancer Research Center of Hawaii, University of Hawaii, Honolulu, HI 96813
3To whom correspondence and reprint requests should be addressed. E-mail: mstownsend{at}ucdavis.edu.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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KEY WORDS: • overweight • obesity • food security • food insecurity • food insufficiency • humans
|
INTRODUCTION |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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, making it a concern to nutritionists,
legislators and other policy makers (2)
. Among these
food-insecure households, 34.9% showed evidence of moderate or
severe hunger (1)
. Some in the United States, particularly
policy makers, have doubted the existence of food insecurity in lower
income population groups because of the high rates of overweight seen
in these groups (3)
. Definitions and examples of key words
used in this paper appear in Figure 1
.
|
5
6)
. Although
poverty in the United States rarely leads to clinical manifestations of
malnutrition, poverty was a significant predictor of hunger and food
insecurity (7)
. In addition, adults from low income
families were more likely to be overweight than other adults
(8
9
10)
. The prevalence of obesity [defined as body mass
index
(BMI)4
30 kg/m2] increased from 12% in 1991 to
17.9% in 1998 (11)
.
Because overweight is usually thought to be associated with excessive
food intake (9)
, and hunger with an inadequate food supply
(1
,5
,7)
, thinking in terms of excess body weight and an
inadequate food supply in the same individual connotes a paradox
(12)
. Consequently, it would be easy to understand why
policy makers and politicians might discredit the possibility of
insufficient food supplies in impoverished families with overweight
members.
The suggestion of a relationship between hunger and obesity in the
United States was first proposed in a case study in 1994
(12)
. Dietz suggested that "food choices or physiologic
adaptations in response to episodic food shortages could cause
increased body fat." He recommended confirmation of this hypothesis
with research examining the relationship of overweight and food
insecurity in large cross-sectional and prospective studies
(12)
.
Although individuals with poor food security might be expected to have
reduced food intake, and thus reduced body fat and less likelihood of
being overweight, these associations have not been adequately studied.
To our knowledge, only one study examined the relationship and that was
in a group of 193 women in rural New York State (13)
.
Those researchers suggested that at least some of the effects of lower
income on higher adiposity were mediated through food insecurity. They
proposed that, in the food insecure, the influence on body weight was
composed of two opposing influences, i.e., the first, promotion of
weight gain and the second, weight loss. First, food insecurity
influenced weight gain by causing disordered eating patterns. Second,
food insecurity affected weight status and promoted weight loss. The
first pathway predominated in the mildly food insecure, whereas the
second pathway predominated in the severely food insecure
(13)
.
Because being overweight is usually associated with a plentiful food
supply and being underweight with hunger (12)
, we suggest
that this paradigm requires reexamination. The purpose of this paper is
to examine the relationship between food insecurity and overweight
status.
The following questions are addressed: Is there overweight in the United States among the food insecure? What is the prevalence of overweight among this food-insecure group? Is there more overweight among the food insecure than the general population? Is there more overweight among food-insecure low income women? Is there more overweight among food-insecure food stamp recipients? Is food insecurity a predictor of overweight?
To our knowledge, this study is the first to explore the food insecurity/overweight relationship using a nationally representative sample of the U.S. population.
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SUBJECTS AND METHODS |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Sample and sampling frame.
The CSFII employed a stratified multistage probability design to obtain
representative samples of U.S. households (14)
. The
surveys consist of partial probability samples of households in the 48
contiguous states. Institutionalized and homeless persons were not
included. Data from the 1994, 1995 and 1996 CSFII were combined for
this study, to yield a sufficient sample of women who
self-identified as food insecure. A final sample was generated to
meet the following criteria: 20 y old, reported height and weight
available, income data available, nonpregnant and nonlactating. The
final sample included 4537 women and 5004 men.
Conceptual model.
The conceptual model that guided this study was theory-informed and
is shown in Figure 2
. The variables in the model are of interest because of the hypothesized
relationships to food insecurity or BMI. Bronfenbrennner’s
Ecology of Human Development (15
,16)
guided the
conceptualization with its emphasis on all factors affecting one
another in a child’s life. Applied to this research, all factors,
including food insecurity, influence body weight. Included in the model
are two demographic (age and ethnicity), three socioeconomic
(education, income and occupation), two government assistance (welfare
status and food stamps), three environmental (household size,
urbanization and region of country) and five lifestyle (vigorous
exercise, television time, percentage of dietary energy as fat,
percentage of dietary energy as saturated fat and total energy
intake) variables. According to this model, food insecurity influences
overweight directly as well as indirectly through lifestyle factors.
Furthermore, food insecurity is influenced by age, income, education,
occupation, household size, welfare status and food stamp status.
Ethnicity, region and urbanization variables were included in the
model to ensure that these did not confound any relationships examined.
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The independent variable, food insecurity, was based on one question
with four response elements as shown in Table 1
. Sample size for each response category, sum of weights, percentage
overweight and the mean income as a percentage of poverty are provided
in the table. In addition, an identifier term for each response
category is noted. This self-reported hunger measure was found to
be valid (2
,17
,18)
and reliable (2)
.
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The dependent variable was BMI, which was computed as weight (kg)
divided by height squared (m2) and adjusted as described
below. The rationale for classification of BMI categories was an
adaptation of criteria recommended in the consensus statement of the
1985 NIH Development Conference on the Health Implications of Obesity
(19)
. The overweight cut-off points were defined as
27.3 kg/m2 for women and 27.8 kg/m2 for men and
coincided with the 85th percentile for overweight from the second
National Health and Nutrition Examination Survey. New federal
guidelines for the classification of overweight and obesity in adults
were published in June 1998 by the National Heart, Lung and Blood
Institute (20)
. Use of these guidelines dramatically
increased the prevalence of overweight as defined by BMI of 25.0
kg/m2. We have chosen to use the earlier criteria for this
study to identify a subset of individuals who are clearly overweight.
The obesity cut-off point, 30 kg/m2, was not defined as
the outcome measure, because the numbers of obese men and women in the
moderately and severely insecure categories were too low to be able to
estimate associations with confidence. Furthermore, the
self-reported weights and heights were adjusted to better estimate
measured weights and measured heights using the equations developed by
Michael Rowland (21)
. Separate equations for men and women
were applied to the self-reported data to adjust for underreporting
of weight and overestimation of height, as follows:
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where height is in inches, weight in pounds and age in years.
Other independent variables.
The income variable, based on household income as a
percentage of poverty level for the corresponding years of the survey
(22
23
24)
, was divided into four ordinal categories. The
first group, 0–185% of poverty, was intended to capture adults
receiving government aid including the Women, Infants and Children’s
Supplemental Nutrition Program (WIC) serving families to 185% of
poverty. An ethnicity variable was created as follows:
Caucasian, African American, Hispanic, Asian American, Native American
and other non-Hispanic ethnicities. The last three categories were
collapsed into the other category for the regression
analyses (Table 3)
.
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4 persons in the
household. A four-category ordinal variable for vigorous
exercise was created in response to the question "How often
do you exercise vigorously enough to work up a sweat?" The
television/video variable was based on the question
"How many hours did you watch television or videotapes yesterday?"
which was asked on two occasions. Table 2
gives a complete list of all variable categories.
|
. The dietary fat
and dietary saturated fat variables represented percentages
of energy intake from fat and saturated fat and were divided into
approximate quartiles. The energy variable was total energy
intake in kJ. Because energy needs were confounded by height, the
influence of height was removed by regressing energy on height, and
residuals were calculated for each subject. The residuals were then
divided into quartiles, i.e., the quartile cut-off values are
-1615, -239.3 and + 1282.0 kJ. Statistical analysis.
Differences among food-insecurity categories with respect to
overweight prevalence and mean incomes were examined with ANOVA and
Tukey’s test for pairwise differences using a significance level of
P < 0.05 (Table 1)
. Contingency tables were
calculated to assess the bivariate associations between food security
and the various demographic and dietary variables using the
2 test (Table 2)
.
Multivariate modeling was approached in three ways. In the first two, parsimonious models were sought using the General Linear Model procedure in SAS (Statistical Analysis System, Release 7.0 for Windows; SAS Institute, Cary, NC) to determine which variables best predict overweight. Stepwise techniques were used to develop a categorical model using the sample weights and random cluster effect described below. Initially, all main effects were entered into the model, and nonsignificant variables were removed in a backward stepwise fashion. Then, all two-way interactions of the significant main effects were added to the model and nonsignificant interactions were removed in a backward fashion. Similarly, all three- and four-way interactions were added to the model and removed in a backward fashion. In the final models, variables that did not make a meaningful contribution to explaining the variance in overweight were removed. The level of significance was 0.05 based on type III sum of squares.
Because food insecurity was closely related to income, we performed another analysis adjusting for income as a continuous, rather than as a categorical variable.
In the third approach, further analysis was conducted with a logistic
regression model to predict the probability of being overweight
(Table 3
). Significant variables from the previous model were chosen as initial
predictors. All variable categories were entered as dummy variables to
magnify the prediction capabilities of the model for each variable
category. Nonsignificant interaction terms were removed to create the
final model. The level of significance was 0.05. Because group
proportions of overweight were generally between 0.2 and 0.8, the
General Linear Model procedure to obtain adjusted means was performed.
A weighting variable was applied to all analyses to adjust for the
intentional oversampling of some groups and the nonresponse of some
individuals—variable wt3d1, provided in the data files
(26)
. Additionally, 86 sample clusters were identified
(based on CSFII variables varstrat and varunit,
which provided geographic information for the surveyed households) and
were included in the multivariate models as random effects to avoid the
problem of cluster differences being misidentified as differences due
to other demographic variables. Where necessary, cluster was nested
within factors such as region and urbanization; however, most variables
such as age and ethnic group had several variable categories
represented within each cluster. Analyses were repeated using the
SurveyReg procedure of SAS 8.1, which adjusts for the clustered sample
design. The findings were essentially unchanged and thus we have chosen
to present the results from the GLM procedure.
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RESULTS |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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, but no relationship was observed for men
(P = 0.44, n = 4970). This observation
was consistent with studies that have examined socioeconomic status and
overweight by gender (8
,10)
. Because there was a gender
x food insecurity interaction with this dataset, the remaining
analyses focused on women.
Each food insecurity response category is shown in Table 1
by
prevalence of adjusted overweight and by mean household income stated
as a percentage of poverty level. Of the 966 women (915 weighted)
reporting mild food insecurity, 41% were overweight compared with 34%
of the food-secure population (P < 0.05). The
moderate food insecurity category of 86 women at 52% overweight was
significantly different from the food secure. Food security was related
to income with a dose-response effect for three categories (Table 1)
. The food secure had a higher income than the mildly and moderately
insecure groups (P < 0.0001). Furthermore, the mildly
insecure had a higher income than the moderately insecure (P
< 0.0001).
Food insecurity was related to a number of independent variables in bivariate analyses (not shown in table) as follows: income (P < 0.001), education (P < 0.001), occupation (P < 0.001), region of the country (P = 0.002), urbanization (P = 0.009), ethnicity (P < 0.001), age (P < 0.001), household size (P < 0.001), welfare status (P < 0.001), food stamps (P < 0.001), total energy intake (P = 0.003) and television viewing (P = 0.002). Food insecurity was not significantly related to energy from dietary fat or saturated fat (P > 0.05).
Prevalence of overweight for 11 variables is shown in Table 2
. The
prevalence of overweight was highest for those in the lowest income
category (43.8%), with an educational level of 
11th grade (49.8%),
who ate a diet 38.1% in fat energy (38.3%), who rarely/never
exercised vigorously (41.2%) and who watched television >4 h/d
(46.3%). The majority of African Americans and Native Americans, and
food stamp recipients reported being overweight (57.1, 64.5 and 51.8%,
respectively). Among the lowest income group, the prevalence of
overweight among the food secure was 41.1%, the mildly insecure,
48.3%, and the moderately insecure, 51.5%. The prevalence of
overweight among the mildly and moderately insecure groups was
significantly higher than for the food secure. The fourth category of
food insecurity was not included in these analyses because only 11
women reported they "often did not have enough to eat."
Because the low income category included a broad range of incomes, 0–185% of poverty, we examined individuals at the lower end of that group, i.e., recipients of food stamps. Among the food stamp population, rates of overweight for the secure, mildly insecure and moderately insecure were 48.4, 53.7 and 68.3%, respectively, and exhibited a positive linear relationship similar to that of the low income group. The prevalence of overweight among the moderately insecure food stamp recipients was significantly higher than for the mildly insecure and the food secure.
For most variables, trends were seen across the food insecurity
categories for the prevalence of overweight (Table 2)
. A
dose-response effect was seen for 26 of the 31 variable categories
with data for three variable categories of food security. For these
variable categories, the trend in overweight increased with the degree
of insecurity. The prevalence of overweight for the mildly insecure was
greater than for the food secure for 38 of the 40 variable categories
with data.
Because the three multivariate models provided support for the same
inferences regarding the overweight/food insecurity relationship, the
first two models are reported briefly (data not shown), whereas the
third model, the logistic regression, is reported in detail (Table 3)
.
In the first model, the 15 variables in the conceptual framework (Fig. 2)
were entered into the ANOVA model as independent variables along
with food insecurity. Food insecurity continued to be important in
predicting overweight (P < 0.01). Thus, food
insecurity was a contributor to overweight over and above the effect of
income. In the second model, the mean BMI for the food secure was
significantly different from the mean BMI for the food insecure when
controlling for the same demographic and lifestyle variables
(P < 0.0001).
Because income and food insecurity were correlated, the same analysis was repeated with income as a continuous variable. The food insecurity variable continued to be significant (P < 0.0001) when the model was adjusted for income as a continuous variable.
In the third multivariate model, logistic regression was used to
predict the probability of a woman being overweight (Table 3)
. The six
significant main effects remaining in the final ANOVA model were
entered into the logistic regression model. Although income was not a
significant main effect, it was included in the model as a continuous
variable. Women at the intercept had the following baseline
characteristics: Caucasian race, food secure, 20–34 y old, college
educated, watched television/video <1 h/d and vigorously exercised
5–7 times a week. Mildly insecure women were 30% more likely to be
overweight than those who were food secure [odds ratio (OR) 1.3,
P = 0.005]. African-American race emerged as the
greatest single predictor of overweight (OR 2.3, P < 0.0001). Other variable categories that were significant predictors of
overweight but with smaller OR were Hispanic ethnicity, high school
education or less, >35 y old, television/video viewing >1 h/d,
receiving food stamps and exercising 4 times per month.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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who had sufficient food but worried about
having enough, were counted among the food insecure in this
study. Worrying that food will run out can be considered food
insecurity, but not food insufficiency, and we wished
to examine the full range of food insecurity in our
analyses.
These results confirm that food insecurity for women was related to
overweight in this study. Using a large national sample, a paradox
emerged. The prevalence of overweight was lower among the two extremes
of food insecurity (Table 1)
, i.e., the food secure and the severely
insecure, although likely for two very different reasons. Among
food-secure women, food intake may be voluntarily
restricted to prevent weight gain or maintain weight (27)
.
Among the severely food insecure, food intake may be
involuntarily restricted due to insufficient resources to
access food (2)
. The mildly food insecure had a higher
mean BMI than women who self-identified as food secure. In
addition, overweight occurred among mild and moderate levels of food
insecurity, a finding similar to that of the study of 193 women by
Frongillo and colleagues (13)
. Taken together, these
results suggest that overweight is related to involuntary, temporary
food restriction.
One possible explanation for the high prevalence of overweight
among food stamp recipients involves a food acquisition cycle
(28)
. Abundant food supplies may be available the first 3
wk of the month, followed by 1 wk without food stamps or money when
food selection is limited. Then, when money and food stamps are
restored at the first of the food stamp month, food-insecure
families may overeat highly palatable and rich foods. This cycle may
synchronize with food stamp distribution, suggesting a "food stamp
cycle" hypothesis. Furthermore, this behavior could be reminiscent of
binge eating, also known as disinhibition in the psychology literature
(27
,39)
. Binge eating can result in weight gain
(27
,29
,31
,37
,38)
. Thus, overeating by food-insecure
families when palatable food is plentiful, i.e., when food stamps or
money for food is available, followed by a short period of involuntary
food restriction, followed by overeating, could be a pattern that
results in gradual weight gain over time.
Although the "food stamp cycle" hypothesis has yet to be tested, a
limited number of human and animal studies provide evidence for it.
These studies show that food deprivation in humans
(30
31
32
33)
and animals (34
35
36)
and food
restriction in children (12
,37
38
39)
produce a tendency
toward binge eating behaviors when a plentiful food supply is
available. If food deprivation/restriction occurs among
food-insecure food stamp recipients, it is probably not long term,
but episodic. We suggest that externally imposed food restriction,
i.e., involuntary, as would occur when a family runs out of food stamps
and money at the end of the month, may lead during time of plenty to
overeating, binge eating and disregard for internal satiety cues.
Future research should include an exploration of this hypothesis.
The finding of a gender difference in the food insecurity/overweight
relationship is noteworthy and two possible explanations are offered.
Women may be more sensitive to the social pressures to be thin than men
and therefore, may have a lower threshold for detecting an
overweight/food insecurity relationship. Yet at extremes, male
conscientious objectors overate after severe food restriction in the
well-known Minnesota study conducted during World War II
(31
,32)
. Only a few of the food-insecure respondents
in the CSFII dataset were severely insecure. Another explanation might
be that food-insecure women were often heads of households with
children, whereas men reporting food insecurity were often alone.
Consequently, the gender comparison might be inappropriate.
Limitations and alternate interpretations.
Although this study sample was representative of the adult U.S. population, a number of study limitations should be considered. First, because of the cross-sectional design, any inferences regarding cause and effect must be made with caution and should be considered preliminary. Use of secondary data presented certain difficulties. Analyses were limited to the topics, wording of questions and variables in the survey instrument. For example, variables of interest such as parity, marital status, disordered eating patterns including disinhibition and family medical history were not available in the dataset.
Validation studies of all CSFII items have not been reported, making
interpretation of some results problematic. For example, it was not
known how respondents define "vigorous exercise to work up a
sweat." All data were self-reported, introducing a variety of
social response biases. In the case of the self-reported heights
and weights, however, these biases were reduced by a correction factor
(21)
. In addition, the homeless, who were more likely to
be food insecure, were not sampled. Systematic error may have occurred
in the four response elements to the food insecurity question (Table 1)
by categorizing women to groups incorrectly. In the future, this error
will be minimized by the replacement of the CSFII items with the
18-item Core Food Security Module, which carefully identifies the
severity of the food insecurity (1
,40)
. Another concern is
that food-insecure women may be fearful of answering honestly
because honest responses might be perceived as justification for
removal of children from their care. Last, it is feasible that the food
insecurity/overweight relationship could be attributable entirely, or
in part, to variables not in the model, such as psychosocial factors,
e.g., knowledge about maintaining a normal body weight, attitudes about
body weight, perceived control of body weight, social support, health
awareness and/or health beliefs.
The data for the severely insecure were problematic. Assumptions of normalcy were not valid for the 11 women in this category of food insecurity. It is very likely that these women have health issues overriding those of food such as mental illness and drug and alcohol abuse. Although results are provided for this category of food insecurity, they should be interpreted with extreme caution.
This study demonstrated that overweight exists among the food insecure. Moreover, it was more prevalent among the food insecure than the food secure and among insecure food stamp recipients than among other food stamp recipients. After controlling for relevant variables in multivariate models, food insecurity continued to be significantly and independently related to overweight status.
Given that the rates of both obesity (11)
and food
insecurity (1
,3)
are on the rise, this is an important
topic for further investigation. The finding that food insecurity had
unexpected and paradoxical consequences in this study, i.e., higher
rates of overweight, and consequently, the potential for increased
incidence of obesity-related chronic diseases, must be addressed.
In addition, there are public policy implications for USDA’s food
assistance and poverty programs, particularly the food stamp program.
According to Dietz (12)
, confirmation of these findings
would suggest that the prevalence of obesity among low income groups
may require increased food supplementation in the form of food stamps
to achieve a more uniform pattern of food intake. Consequently,
elaboration of the food insecurity/overweight relationship would allow
for better intervention designs.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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2 Funded in part by a grant from USDA, Economic Research Service, Contract/Grant # U.S. Department of Agriculture
43–3AEM-6–8010.
4 Abbreviations used: BMI, body mass index; CSFII, Continuing Survey of Food Intakes by Individuals; OR, odds ratio.
Manuscript received September 21, 2000. Initial review completed October 24, 2000. Revision accepted March 22, 2001.
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