Family Economics and Nutrition Review [Volume 15, Number 2]

              Research Articles
3 Nutrient Intakes Among Dietary Supplement Users and Nonusers
in the Food Stamp Population
Jennifer Sheldon and David L. Pelletier
Trends in Food and Nutrient Intakes by Adolescents in the United States
Cecilia Wilkinson Enns, Sharon J. Mickle, and Joseph D. Goldman
Food Security, Dietary Choices, and Television-Viewing Status of
Preschool-Aged Children Living in Single-Parent or Two-Parent Households
Shanthy A. Bowman and Ellen W Harris
Center Reports
35 Expenditures on Children by Families, 2002
Mark Uno
Revision of USDA's Low-Cost, Moderate-Cost, and Liberal Food Plans
Andrea Carlson, Mark Uno, Shirley Gerrior, and P. Peter Basiotis
Insight 25: Report Card on the Diet Quality of Children Ages 2 to 9
Andrea Carlson, Mark Uno, Shirley Gerrior, and P. Peter Basiotis
55 Insight 26: Food Insufficiency and Prevalence of Overweight Among
Adult Women
P. Peter Basiotis and Mark Uno
Federal Studies • Journal Abstracts • Food Plans
• Consumer Prices • Poverty Thresholds
Ann M. Veneman, Secretary
U.S. Department of Agriculture
Eric M. Bost, Under Secretary
Food, Nutrition, and Consumer Services
Eric J, Hentges, Executive Director
Center for Nutrition Policy and Promotion
Steven N. Christensen, Deputy Director
Center for Nutrition Policy and Promotion
P. Peter Basiotis, Director
Nutrition Policy and Analysis Staff
Center for Nutrition Policy and Promotion
Mission Statement
To improve the health of Americans by developing and promoting dietary
guidance that links scientific research to the nutrition needs of consumers.
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Editor
Julia M. Dinkins
Associate Editor
David M. Herring
Managing Editor
Jane W. Fleming
Features Editor
Marklino
Peer Review Coordinator
Hazel Hiza
Family Economics and Nutrition Review is
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Research Articles
3 Nutrient Intakes Among Dietary Supplement Users and Nonusers
in the Food Stamp Population
Jennifer Sheldon and David L. Pelletier
15 Trends in Food and Nutrient Intakes by Adolescents in the United States
Cecilia Wilkinson Enns, Sharon J. Mickle, and Joseph D. Goldman
Research Brief
29 Food Security, Dietary Choices, and Television-Viewing Status of Preschool-Aged Children
Living in Single-Parent or Two-Parent Households
Shanthy A. Bowman and Ellen W Harris
Center Reports
35 Expenditures on Children by Families, 2002
Mark Uno
43 Revision of USDA's Low-Cost, Moderate-Cost, and Liberal Food Plans
Andrea Carlson, Mark Uno, Shirley Gerrior, and P Peter Basiotis
52 Insight 25: Report Card on the Diet Quality of Children Ages 2 to 9
Andrea Carlson, Mark Uno, Shirley Gerrior, and P Peter Basiotis
55 Insight 26: Food Insufficiency and Prevalence of Overweight Among Adult Women
P Peter Basiotis and Mark Uno
Regular Items
58 Federal Studies
66 Journal Abstracts
68 Official USDA Food Plans: Cost of Food at Home at Four Levels, U.S. Average,
December 2003
69 Consumer Prices
70 U.S. Poverty Thresholds and Related Statistics
71 Reviewers of Manuscripts for the 2003 Issues
Volume 15, Number 2
2003
PROPERTY OF THE
LIBRARY
APR 2 1 2004
University of North Carolina
at Greensboro
Front and Center
T he Center for Nutrition Policy and Promotion continues to link nutrition s~ience to t~e nutrition needs
of consumers. This issue of Family Economics and Nutrition Review provides the science on the
associations between nutrient intakes and dietary status of several segments of the U.S. population:
dietary supplement users and nonusers in the food stamp population, adolescents, and preschool-aged children.
Understanding the associations among supplement use, nutrient densities, and diet quality among subgroups
within a population informs policy. A long-term portrait of the intakes among U.S. adolescents leads to
recommendations regarding the intake of grains, vegetables, fruits , legumes, lean meats, dairy products, dietary
fat, physical activity levels, and effective nutrition education. A comparison among household types in which
preschool-aged children reside highlights the continuing need to address issues of food security, energy (kcal)
consumption, and sedentary activities that may place children at higher risks of being overweight or obese.
In addition to Family Economics and Nutrition Review, the Center uses a series of bulletins to inform consumers
of the connection between dietary guidance and nutritional well-being. In its latest issue of the bulletin Putting
the Guidelines into Practice, the Center suggests ways that consumers can "Get moving .. . For the health and
fun of it!" This bulletin helps consumers understand the benefits of physical activity, how much is needed, and
how to incorporate it into a busy lifestyle.
With its online dietary assessment tool-the Interactive Healthy Eating Index (IHEI)-the Center provides an
opportunity for consumers to input their daily food intakes and then receive a quick summary measure of the
quality of their diets. With USDA's release of the Interactive Physical Activity Tool (IPAT) this past December,
the Center combined two important aspects of healthful living: appropriate dietary intake and physical activity.
An enhancement to the IHEI, the IPAT allows users to input their daily activities and receive a physical activity
score in terms of current recommendations. In combination, the IHEI and the IPAT allow users to receive prompt,
accurate, and up-to-date information on diet quality and physical activity status.
From the research of Family Economics and Nutrition Review to the information of the consumer bulletins to the
interactive feedback of the complementary Web-based IHEI and IPAT, the Center's mission remains focused on
helping consumers link dietary guidance to lifelong dietary behaviors that can enhance their well-being.
Eric J. Hentges, PhD
Executive Director
Center for Nutrition Policy and Promotion
Jennifer Sheldon, BS
David L. Pelletier, PhD
Cornell University
2003 Vol. 15 No. 2
Research Articles
Nutrient Intakes Among Dietary
Supplement Users and Nonusers
in the Food Stamp Population
This study characterized the nutrient intakes of participants in the Food Stamp
Program (FSP) who used nutrient supplements, compared with those who did
not, and examined the variation in these relationships across different socio­demographic
subgroups. Dietary intakes from food sources for eight key
nutrients were examined from the 1994-96 Continuing Survey of Food Intakes
by Individuals. Two measures of overall diet quality were also included in the
analysis. Findings revealed that supplement use in FSP participants was
positively associated with nutrient densities for iron, calcium, fiber, folate,
vitamin A, and vitamin C and with overall diet quality. However, the direction
and magnitude of this association varied across age, gender, and ethnic groups
for iron, saturated fat, fiber, vitamin A, and one measure of overall diet quality
(Z-score). Thus, results show that supplement use is not uniformly associated
with more healthful diets among FSP participants.
The U.S. marketplace for dietary
supplements is large and
changing rapidly. National
surveys indicate that dietary supple­ments
are used by roughly 50 percent
of the U.S. population (Balluz,
Kieszak, Philen, & Mulinare, 2000;
Slesinsky, Subar, & Kahle, 1995).
Industry sources suggest that sales of
all forms of supplements combined-­including
nutrients, herbals, sports
products, and meal supplements-rose
from $8.6 billion in 1994 to $16 billion
in 2000 (Heasman & Mellentin, 2001).
During that same period, sales of
nutrient supplements, specifically,
rose from $3.9 billion to $6.1 billion.
This rise in consumption of dietary
supplements is only the beginning
of a much larger "functional foods
revolution" built upon the development
and marketing of a wide variety of
supplements, genetically engineered
foods, fortified foods, and conventional
foods with compositional properties
that are perceived or marketed as
having links to improved health,
performance, or well-being (Heasman
& Mellentin, 2001). The U.S. market
for functional foods is estimated to
tise from about $20 billion in 2000
to $50 billion by 2010 (Government
Accounting Office [GAO], 2000).
The rapid rise and high prevalence of
supplement use in the United States
stand in marked contrast to the views
and positions of professional and
scientific nutrition communities.
Organizations such as the American
Dietetic Association (ADA) (Hunt,
1996), the Dietary Guidelines for
Americans Advisory Committee (U.S.
Department of Agriculture [USDA] &
U.S. Department of Health and Human
Services [DHHS], 2000), and the
Food and Nutrition Board of the
Institute of Medicine (IOM, 1994)
have maintained that most individuals
can and should obtain all necessary
3
nutrients in adequate amounts from a
varied diet and that supplements are
needed only in special circumstances.
The position of the ADA regarding
supplementation is that
the best nutritional strategy for
promoting optimal health and
reducing the risk of chronic
disease is to obtain adequate
nutrients from a wide variety
of foods . Vitamin and mineral
supplementation is appropriate
when well-accepted, peer­reviewed,
scientific evidence
shows safety and effectiveness.
(Hunt, 1996, p. 73)
Notwithstanding the views of the ADA,
the Food and Drug Administration
(FDA), and other professional and
scientific bodies, Congress created
the Dietary Supplement Health and
Education Act in 1994 that has little
or no requirement for manufacturers
to demonstrate the safety and efficacy
of dietary supplements and is more
permissive than conventional foods
regarding the claims that marketers
can make about the benefits of these
products. In a recent report, the
GAO (2000) concluded that the
FDA's efforts and federal laws
provide limited assurances of
the safety of functional foods
and dietary supplements
[and] ... we also found that
agencies' efforts and federal
laws concerning health-related
claims on product labels and
in advertising provide limited
assistance to consumers in
making informed choices and
do little to protect them against
misleading and inaccurate
claims. (pp. 4-5)
While nutrient supplements taken in
moderation do not raise the same afety
concerns as do herbals and other
dietary supplements, they do raise
4
two other issues. One is their low
efficacy in individuals and populations
that do not suffer from nutrient
deficiencies (USDA, 1999). In such
cases, the exaggerated marketing
claims regarding their benefits may
mislead some consumers. While most
studies show that supplement use is
more common among Whites, women,
those with higher levels of education,
and those with higher incomes (USDA,
1999; Koplan, Annest, Layde, &
Rubin, 1986; Lyle, Mares-Perlman,
Klein, Klein, & Greger, 1998;
Pelletier & Kendall, 1997), usage
is not restricted to those groups. For
instance, analysis of the 1994-95
Continuing Survey of Food Intakes
by Individuals (CSFII) reveals that
supplements were used by 49 percent
of higher income individuals (greater
than 130 percent of the poverty line)
and 36 percent of lower income
individuals (USDA, 1999).
The second issue related to nutrient
supplements is whether they are used
as true supplements for an already
healthful diet or as a substitute for
such a diet. This is important because
of the wide range of health-promoting
substances contained in whole foods,
compared with supplements, which
still are far from being understood
fully. Most studies have shown that
supplement users, compared with
nonusers, tend to have higher vitamin
and mineral intakes from food (Koplan
et al., 1986; Looker, Sempos, Johnson,
& Yetley, 1998; Lyle et al., 1995),
suggesting a supplementing effect
rather than a substitutive effect. Those
studies have, however, assumed that
such a finding applies equally to
all consumers. The one study that
examined potential heterogeneity in
that relationship revealed that supple­ment
use is associated with more
healthful food intakes in some popu­lation
groups but also is associated
with less healthful food intakes in other
groups defined by sociodemographic
or attitudinal charactelistics (Pelletier
& Kendall, 1997).
The present study was initiated within
the context of a rapidly expanding
dietary supplement industry, a per­missive
set of laws and regulations,
continued uncertainty regarding safety
and efficacy, and questions concerning
the positive or negative relationships
between supplement use and the
quality of food intake. The specific
motivation for the study was the
proposal considered by Congress on
numerous occasions in the last decade
to permit the use of food stamps to
purchase nutrient supplements. This
proposal was included in a House bill
leading up to the welfare reform effort
in 1996 (H.R.l04-236) and more
recently in a Senate bill (S.l731)
leading up to the 2002 Farm bill. The
proposal has yet to be incorporated
into legislation on these and other
occasions.
An expert committee of the Life
Sciences Research Office (LSRO,
1998) and the USDA (1999) raised
a number of concerns regarding this
proposal, including evidence that
nutrient intakes of FSP participants
are similar to those of the general
population, that most FSP participants
can and do purchase supplements with
income other than food stamps, and
that administrative complications asso­ciated
with the proposed change are
considerable. In addition, the LSRO
report noted a lack of research-based
information concerning the relationship
between supplement use and dietary
intake among FSP participants.
This study examined the associations
between supplement use and nutrient
intakes from food among FSP partici­pants,
as well as the extent to which
these associations are uniform across
all sociodemographic subgroups of the
FSP population.
Family Economics and Nutrition Review
Methods
Data and Sample
The data used in this study were
derived from the 1994-96 CSFll. The
CSFII, a national survey of dietary
intake conducted by the USDA, is
weighted to reflect a nationally
representative sample of noninstitu­tionalized
persons living in the United
States (Tippett, Enns, & Moshfegh,
1999). The present study examined
the first recalled day for the 16,103
respondents who provided at least
1 day of dietary data. The focus of
this research was on nutrient intake
exclusively from food sources. As
defined by the 1994-96 CSFII, food
intake does not include vitamins,
minerals, or other supplements. Thus,
the nutrient intakes analyzed here
reflect these caveats.
Only 9,468 records were used in this
analysis. The respondents excluded
from the analysis were less than 18
years old; other than Hispanic, Black,
or White; and had missing records or
erroneous data. For the final sample,
886 were FSP participants and 8,582
were FSP nonparticipants.
Variables and Transformations
Much of the methodology used in
this study followed very closely the
methods of an earlier study by Pelletier
and Kendall (1997). The dietary data
used in this analysis were based on a
single 24-hour recall for each partici­pant.
To account for differences in
total energy intake, we used the 1-day
dietary recall nutrient data for the eight
key nutrients (total fat, saturated fat,
iron, calcium, fiber, folate, vitamin A,
and vitamin C), which were expressed
in proportion to total kilocalories
consumed and are referred to here as
nutrient densities. Such nutrient indices
are more indicative of overall diet
quality and make comparison among
records easier. Because of the
2003 Vol. 15 No.2
assumption that data are normally
distributed, which is implicit in many
standard statistical tests such as the
t and F tests as used in the present
analysis, various transformations were
used to ensure that individual nutrient
data represented a normal distribution.
A square root was used to transform
fiber and vitamin C intakes while a
natural log transformation was applied
to folate, calcium, iron, and vitamin A.
Because total fat and saturated fat data
were normally distributed, they were
not transformed.
In addition to the eight individual
nutrient density variables, we included
two additional variables in the regres­sion
to test the overall quality of each
respondent's diet. An average diet
score (index) was calculated from
the Z-score values of the eight key
nutrients. This average Z-score reflects
the quality of the diet with respect to
these key nutrients and, as such, may
provide different information than any
single nutrient considered alone. By
using the full dataset of 9,468 individ­uals
that included FSP participants
and nonparticipants, we were able to
calculate average intake values that
were representative of the entire U.S.
population. Subsequently, intake
values of smaller subgroups could
be compared with those of the whole
population. The sign of the Z-score
was reversed for total and saturated fat,
prior to summing across all nutrients,
to maintain consistency in the
interpretation of this index.
Another computed variable used to
measure overall diet quality was the
Healthy Eating Index (HEI). The HEI
was developed by the USDA's Center
for Nutrition Policy and Promotion to
assess and monitor the dietary status
of Americans in accordance with the
Food Guide Pyramid and the Dietary
Guidelines for Americans (Variyam,
Blaylock, Smallwood, & Basiotis,
1998). Each of the 10 components
of the HEI has a maximum score of
10 and a minimum score of 0. High
component scores indicate intakes
close to recommended ranges or
amounts; low component scores, less
compliance. The present analysis used
the five Food Guide Pyramid com­ponents
of the HEI, which reflect
how well each person incorporated
the desirable number of servings from
each of the five food groups on the
recalled day. These five components
were averaged together to achieve
a mean value for each person. It is
important to note that unlike the Z­score
index, the HEI was not adjusted
for energy intake or the quantity of
food intake on the day of the recall.
Sociodemographic variables consisted
of age, gender, education, employment
status, and ethnicity. Ethnicity was
coded as non-Hispanic Whites
("Whites"), non-Hispanic Blacks
("Blacks"), and anyone reporting
Hispanic origin ("Hispanic"). The
reference (omitted) groups in the
regression analyses were 50 years and
older (age), female (gender), less than
high school (education), unemployed
(employment status), and White
(ethnicity).
Nutrient supplement use was defined
based on the response to this question:
"How often, if at all, do you take any
vitamin supplement in pill or liquid
form?" Because of sample size con­siderations,
we defined users as those
reporting the use of any type of supple­ment
"every day or almost every day"
or "every so often," and we defined
nonusers (the reference group) as
those reporting "not at all."
Data Analysis
The relationships among dietary intake,
supplement use, and sociodemographic
characteristics in the population of FSP
participants were examined by using
multiple regressions.
5
... among FSP nonparticipants,
supplement use was more
common among Whites, women,
persons 50 years and older, and
those with a college degree or
more.
6
Table 1. Supplement use based on the various sociodemographic characteristics of
the U.S. population, CSFII1994-96
Non-food stamp Food stamp
Total sample recipients recipients
Variable (n = 9,468) (n = 8,582) (n = 886)
Percent users1
Ethnicity
White 51 52 40
Black 37 39 32
Hispanic 41 43 29
Gender
Female 55 57 41
Male 42 43 26
Age
18-49 years 47 48 43
50 years and older 52 53 33
Education
Less than high school 36 37 32
High school or some college 48 49 35
College degree or more 59 59 55
Employment status
Unemployed 48 49 35
Employed 49 51 36
1 Percentages are weighted. Some percentages may not total to 100 because of rounding.
• Main-effects models tested
whether the (generally) positive
association between supplement
use and dietary intake could
be accounted for by socio­demographic
variables. Each
nutrient and the two measures of
overall dietary quality were used
as a dependent variable in its own
model, and the association of
supplement use to the dependent
variable was observed before
and after adjusting for the set
of sociodemographic variables
(ethnicity, gender, age, education,
and employment status).
• Interaction models tested whether
the strength or direction of the
association was uniform across
ethnicity, gender, and age while
controlling for education and
employment status. This was
accomplished by testing the
significance of an entire block of
interactions between supplement
use and ethnicity, gender, and age
after controlling for the above­mentioned
variables. These
analyses included models with
only 2-way interaction terms and,
in separate runs, models with both
2-way and 3-way interaction
terms.
These statistical methods were
designed to permit a valid test of the
hypothesis that the strength or direction
of the association between supplement
use and nutrient density from food
among FSP participants is uniform
across groups defined by socio­demographic
characteristics. In this
study, such a test was obtained by
comparing the proportion of variance
explained by either the 2-way model
versus the main-effects model, the full
3-way model versus the main-effects
model, or the full 3-way model versus
the 2-way model. Because the table of
model coefficients is difficult to
interpret in the presence of higher
Family Economics and Nutrition Review
Table 2. Nutrient densities from the food consumed by supplement users and
nonusers participating in the Food Stamp Program
User Nonuser
Adjusted means1
Fat(% kcal) 33.3 33.6
Saturated fat (% kcal) 11 .0 11.3
Iron (mg/1 ,000 kcal)3" 7.4 6.7
Calcium (mg/1 ,000 kcal)3' 335.8 302.4
Fiber (g/1 ,000 kcal)2" 8.1 7.0
Folate (mcg/1 ,000 kcal)3' 116.2 101 .7
Vitamin A (RE/1 ,000 kcal)3' 328.8 271.1
Vitamin C (mg/1 ,000 kcal)2' 48.0 41.7
Z-score average4" 0.02 -0.15
HEI average .. 5.7 5.2
1Models for calculating adjusted means consist of age, gender, ethnicity, education, and employment status,
as well as a dummy variable to indicate supplement use.
2Square root transformation applied in regression; geometric means are shown for ease of interpretation.
3Naturallog transformation applied in regression; geometric means are shown for ease of interpretation.
4Z-scores were based on the total sample (n = 9,468), including FSP participants and nonparticipants.
'p < 0.05.
"p$0.001.
n = 309 users and 550 nonusers.
order interaction terms, graphs were
used to present differences in the
direction and magnitude of the
association of supplement use with
nutrient densities.
Although SUDAAN generates more
accurate variance estimates for surveys
with complex sample structures like
the CSFII, SAS was used to analyze
the data because they were better
suited for estimating the statistical
interactions involving supplement use.
Results
In the total CSFII sample1 and among
FSP nonparticipants, supplement use
was more common among Whites,
women, persons 50 years and older,
and those with a college degree or
more (table 1).
1Results for the total sample are shown for
comparison.
2003 Vol. 15 No. 2
Over half (51 to 59 percent) of those
in each socioeconomic group used
supplements. Similar patterns were
found among FSP participants, except
that supplement use was more common
in the younger age group (18 to 49
years). FSP participants had consis­tently
lower supplement use than did
nonparticipants in each of the socio­demographic
groups (40 to 55 percent
vs. 52 to 59 percent). Employment
status appeared to have little asso­ciation
with supplement use.
When age, gender, education,
employment status, and ethnicity
were controlled, results showed that
supplement users had statistically
higher vitamin and mineral densities
from food than did nonusers (table 2).
The density for each of these nutrients
was roughly 10 to 20 percent higher
in the diets of supplement users than
in the diets of nonusers. Also, in this
study, the two groups had very similar
densities of fat and saturated fat,
contrasting with the earlier study of
the general CSFII sample (1989-91)
that found significantly lower total
fat and saturated fat density among
supplement users (Pelletier & Kendall,
1997). Both measures of diet quality,
the Z-score average and the HEI
average, showed statistically more
healthful diets among supplement
users than among nonusers.
Regression coefficients for all the
variables in the main-effects models
(table 3) that were used to generate the
adjusted means in table 2 demonstrated
the more favorable nutrient profiles
for supplement users. In addition,
the results based on the main-effects
models revealed patterns among
various subgroups within the group
of FSP participants:
• Males, compared with females,
had significantly higher densities
of total fat, lower densities of
vitamin C, and lower Z-scores
for overall diet quality.
• Individuals less than 18 to 49 years
old, compared with those 50 years
old and over, had significantly
higher densities of saturated fat
and lower densities of iron, fiber,
folate, vitamins A and C, as well
as lower Z-scores.
• Hispanics, compared with Whites,
had higher densities of fiber, folate,
and vitamin C and higher Z-scores;
Blacks, compared with Whites, had
significantly lower densities of
calcium, folate, and vitamin A but
higher densities of vitamin C.
• Employed individuals, rather than
unemployed individuals, had
significantly lower densities
of iron and calcium and lower
Z-scores.
7
Table 3. Regression coefficients of the main-effects model for Food Stamp Program participants
Saturated Diet score HEI
Variable Total fat fat Iron Calcium Fiber Folate Vitamin A Vitamin C Z average average
Main Effects1
Intercept ***0.3336 ***0.1129 ***-4.8460 ***-0.8910 ***2.9970 ***-2.0116 ***-0.8367 ***0.2081 **0.1568 ***4.8784
Supplement user -0.0026 -0.0029 ***0.0928 *0.1048 ***0.1971 **0.1 332 **0.1930 *0.0150 ***0.1721 ***0.4375
Male **0.01876 0.0052 -0.0125 -0.0121 -0.0708 -0.0501 -0.0856 ***-0.0264 **-0.1141 ***0.5277
18-49 years 0.0078 *0.0070 ***-0.1271 -0.0399 ***-0.3877 ***-0.2306 ***-0.2797 **-0.0252 ***-0.2631 0.0839
Hispanic -0.0025 -0.0031 0.0529 -0.0315 ***0.2490 *0.1 309 0.1133 ***0.0525 ***0.1701 ***0.6401
Black -0.0037 -0.0041 0.0244 ***-0.2360 -0.1 040 *-0.0962 *-0.1718 **0.0205 -0.0780 0.0182
Employed 0.0055 -0.0009 **-0.0758 ***-0.1331 0.0111 -0.0800 -0.1260 -0.0093 *-0.1041 -0.0180
High school/
some college ***-0.0231 **-0.0090 0.0417 **-0.1 059 -0.0133 -0.0019 -0 .0630 0.0054 0.0404 0.1654
College or more *-0.0267 -0.0089 0.0152 0.0775 0.0869 0.0856 0.0315 **0.0463 *0.1805 *0.6052
R2 .0242 .0217 .0505 .0844 .0839 .0657 .0512 .0779 .1051 .0556
1Main effects are shown in relation to the reference (omitted) group within each variable: Female (Gender), 50 years and older (Age), White (Ethnicity), Unemployed
(Employment status), and Less than high school (Education).
*p s 0.05, •• p s 0.01' '"p s 0.001.
n = 859.
Table 4. Test of uniformity in the association between supplement use and nutrient intakes among Food Stamp Program
participants: 2-way and 3-way interaction models1
Total Saturated Diet score HEI
Variable fat fat Iron Calcium Fiber Folate Vitamin A Vitamin C Z average average
R2 for main-effects model .0242 .0217 .0505 .0844 .0839 .0657 .0512 .0779 .1051 .0556
R2 for 2-way model .0273 .0293 .0779 .0935 .10042 .0771 .0698 .0837 .1136 .0681
R2 for 3-way model .0371 .04304 .08823•4 .0946 .1020 .0836 .078o3 .0866 .12374 .0701
1Two-way models involved interaction terms between supplement use and ethnicity, age, or gender; 3-way models involved interaction terms between supplement use and
any two of these variables.
2-fwo-way versus main-effects model; R2 difference significant at p = .084 (fiber).
:lrhree-way versus main-effects model; R2 difference significant at p = .005 (iron) and p = .0458 (vitamin A).
4Three-way versus 2-day interaction model; R2 difference significant at p = .0375 (saturated fat), p = .0959 (iron), and p = .0890 (Z average).
n = 859.
• High school graduates tended
to have more healthful diets as
suggested by lower fat densities
and higher composite diet scores
than did non-high school graduates,
but the patterns of means and
statistical significance were not
consistent across all nutrients.
Overall, these results suggest a
complex and varying set of relation­ships
existing between socio-
8
demographic characteristics and
nutrient densities from food, even
before interaction terms were added
to the models.
To test for the uniformity of the
association between supplement use
and nutrient density from food across
major population groups, we sequent­ially
added interaction terms involving
the "user" variable to the main-effects
model (table 4). Two-way interactions
were first added, then blocks of
2-way and 3-way interactions were
added in sequence. The statistical
test of significance was based on the
F statistic for the R2 improvement,
as each block of interaction terms
was added to the model. Overall, the
test of uniformity in the association
between supplement use and nutrient
density was rejected for four of the
eight individual nutrients (saturated fat,
iron, fiber, and vitamin A) and for one
Family Economics and Nutrition Review
Figure 1. Percent difference in average Z-score between supplement users and
nonusers among Food Stamp Program participants, by ethnic and gender groups
(adjusted for employment status and education)
Average Z-score
40
30
20
10
0
-10
-14.2
-20
White Black
of the composite diet scores (Z-score).
Saturated fat, iron, vitamin A, and
the Z-score had significant 3-way
interactions; whereas, only fiber had
a significant 2-way interaction. The
test of uniformity in the relationship
between supplement use and nutrient
density could not be rejected for total
fat, calcium, folate, vitamin C, or the
HEI average. Overall, these results
suggest that, with respect to certain
nutrients and one of the composite diet
scores, the strength or direction of the
association between supplement use
and nutrient density was not uniform
across all subgroups within the sample
of FSP participants.
Based on the equations from the
above analyses, we generated a series
of predicted means to facilitate inter­pretation
of the interactions. These
predicted means revealed the magni­tude
and direction of the difference in
nutrient density among supplement
users versus nonusers across major
FSP subgroups. These differences are
summarized in figures 1 and 2. These
figures display the mean difference in
nutrient densities for supplement users
2003 Vol. 15 No. 2
Hispanic
versus nonusers in each socio­demographic
group, expressed as a
percentage of the mean for nonusers
in that group. This was done to aid
the interpretation of the regression
coefficients and to further standardize
the comparison across nutrients.
Figure 1 reveals that the basis for the
3-way interaction involving ethnicity,
gender, and supplement use is that
nutrient densities for Black females
do not show the same pattern as in the
other groups. As shown here for the
Average Z-score, five of the ethnicity x
gender groups had positive Difference
scores, indicating that in each of these
groups, supplement use was associated
with more healthful nutrient density
profiles. By contrast, Black females
had a negative Difference score,
indicating that supplement use in
that group was associated with a less
healthful nutrient profile. The patterns
for iron, vitamin A, and saturated fat
densities were similar (data not
shown).
Among older Whites and older
Hispanics, supplement use
was associated with more
healthful nutrient profiles for
iron, vitamin A, saturated fat,
and the composite Z-score.
However, this pattern was not
evident among older Blacks
where little or no association
existed between supplement
use and mean nutrient densities.
9
Figure 2. Percent difference in mean nutrient intakes between supplement users and nonusers among Food Stamp Program
participants, by ethnic and gender groups (adjusted for employment status and education)
Iron
25 15
20 10
c 15 cQ) 5
Q) .18-49 ~ 0
~ 10 Q)
(L -5 (L 1!!1 50 & older
5 -10
0 -15
-20
-5 -3.1 -25
White Black Hispanic White
Vitamin A
50,8 53.1
55 40
45 35
35 30
Q) 25
25 u 20 c 15 .18-49 c Q) ~ 15
~ 5 ~ 50 & older Q) 10 Q) :t::
(L -5 i:5 5
-15 0 -5
-25 -10
White Black Hispanic White
Figure 2 illustrates the basis for the
3-way interaction involving ethnicity,
age, and supplement use. In this case,
the relationships were more complex
than those shown in figure I . Among
older Whites and older Hispanics,
supplement use was associated with
more healthful nutrient profiles for
iron, vitamin A, saturated fat, and the
composite Z-score. However, this
pattern was not evident among older
Blacks where little or no association
existed between supplement use and
mean nutrient densities.
Among younger Whites and younger
Blacks, supplement use was associated
with a more healthful composite
Z-score (33.7 and 21.0 difference,
10
respectively); among younger
Hispanics, there was little or no
association (-5 difference). However,
in this case, the composite Z-score
obscured significant variation with
respect to individual nutrients. Thus,
the positive Z-score difference for
younger Blacks was a result of
supplement users, compared with
nonusers, having higher iron densities
and lower saturated fat densities.
Among younger Whites, the positive
Z-score difference was a result of
supplement users, compared with
nonusers, having higher iron and
vitamin A densities. Among younger
Hispanics, the near-zero ( -5) Z-score
difference was a result of supplement
users, compared with nonusers,
Saturated Fat
.18-49
~ 50 & older
Black Hispanic
Average Z-score
• 18-49
~ 50 & older
- 5
Black Hispanic
having higher iron density but lower
vitamin A.
While the above analyses pertaining to
the 3-way interactions were sufficient
to reject the hypothesis of uniformity
in the association between supplement
use and nutrient density from food,
they were not adequate for exploring
the social or behavioral basis for the
differences observed. Further insight
might be gained by testing more
complete models, including higher
level interactions with education,
geographic location of residence,
and other variables.
Family Economics and Nutrition Review
Discussion
There are two major findings from
our research. First, among FSP partici­pants,
supplement use is positively
associated with nutrient densities from
food for iron, calcium, fiber, folate,
vitamins A and C, and with two
composite diet quality scores (average
Z-score and average HEI). These
associations remain statistically
significant after accounting for age,
gender, ethnicity, education, and
employment status. In contrast to
findings in the general population
(Pelletier & Kendall, 1997), total fat
and saturated fat densities are not
significantly related to supplement use
among FSP participants. Second, while
these trends are evident for the FSP
population as a whole, the interaction
analysis reveals that the direction and
strength of the association between
supplement use and nutrient density
vary significantly across age, gender,
and ethnic groups for iron, saturated
fat, fiber, vitamin A, and Z-score
average. These findings are consistent
with the results of parallel statistical
analyses pertaining to the overall U.S.
population (Pelletier & Kendall, 1997)
and confirm the existence of significant
heterogeneity in the relationship
between supplement use and nutrient
densities from food.
The present study has a number of
strengths and limitations that should
be considered when interpreting these
findings. The strengths consist of the
following:
• the analysis focused on the FSP
participant population, which
is precisely the population of
interest in the policy proposals
considered by Congress;
2003 Vol. 15 No. 2
• the FSP sample was drawn from a
nationally representative survey
sample (CSFII) based on a
standardized survey methodology;
• the analysis was restricted to
nutrients of key public health
concern in the United States; and
• the analysis formally explored
statistical interactions, which few
other studies on this subject have
done.
The limitations of this study include
use of the following:
• a cross-sectional survey rather
than a longitudinal and/or
experimental design;
• a single dietary recall for each
subject, which is a poor measure
of usual intake for individuals;
• small sample sizes in some of
the cells used in the interaction
analysis; and
• a dichotomous variable (yes/no)
to measure supplement use,
which does not fully capture the
variation in usage related to type
of supplement, frequency,
regularity, and dosage.
In addition, the nutrient density
indices in this study are appropriate
for examining overall diet quality but
are not intended to indicate dietary
adequacy. The latter would require
comparison with Dietary Reference
Intakes or other external standards.
While it is important to acknowledge
the above limitations, in statistical
terms, the net effect of the problems
related to dietary recall, sample size,
and the dichotomous usage variable
is to reduce the power of this study
to find statistically significant asso­ciations
and interactions between
supplement use and nutrient density
from food. Thus, while these con­siderations
could have been invoked
as possible explanations for negative
findings (i.e., no statistically significant
interactions), they cannot be invoked as
an explanation for the positive fmdings
reported here. To the contrary, the
latter three methodological limitations
imply that the true (unobservable)
interactions may be larger in number
and stronger in magnitude than those
reported here.
Another methodological consideration
is that the present analysis is focused
on the mean nutrient densities of foods
consumed by various subgroups. From
a policy perspective, the greatest
concern may be with those individuals
at the lower end of the nutrient intake
distributions rather than with those
whose intakes are at the mean. Some
insight into this issue might be gained
in future studies by undertaking
distributional analyses of the larger
CSFII sample, which represents the
general population. In addition, future
studies should investigate whether
interactions of the type noted here, in
relation to nutrient density, may be due
to variation in energy intake, physical
activity, or other factors not measured
here.
Finally, it is important to reiterate
that the variations in nutrient density
documented here, and in a previous
study (Pelletier & Kendall, 1997), are
important not only in relation to the
particular nutrients studied but also
because they are assumed to reflect
systematic variations in patterns of
food intake among supplement users
and nonusers of different socio­demographic
groups. This is a sig­nificant
distinction, because chronic
disease tends to be associated more
closely with long-term patterns
11
of food intake than with the intake of
inclividual nutrients or supplements
( ational Research Council [NRC],
1989).
Policy Implications
This study highlights the pitfalls of
assuming that statistical averages
observed in the general population
can be applied to all of its subgroups.
This assumption is illustrated by one
of the claims made commonly by
representatives of the supplement
industry (Council for Responsible
Nutrition [CRN], 1998, 2002):
In general, supplement users
are healthy people who view
supplements as just one of
several approaches for
improving health. There is
no evidence that supplement
users rely on supplements as
a substitute for improving
dietary habits. In fact, surveys
show that supplement users
tend to have somewhat better
diets than [do] nonusers
(Koplan, 1986; Looker, 1988;
Hartz, 1988; Slesinsky, 1996).
This suggests that consumers
who use supplements are also
paying more attention to their
overall nutritional habits. Even
so, these consumers have
nutrient shortfalls in their
diets, and supplements can
help fill those gaps. (CRN,
2002, p. 14)
In contrast to these claims, a body of
research now exists which suggests
that in some U.S. sociodemographic
groups, supplement use is associated
with more healthful diets, and in some
groups, supplement use is associated
with less healthful diets. This pattern is
found in the general U.S. population
(Pelletier & Kendall, 1997) as well as
among participants in the FSP (present
12
study). In theory, however, these
patterns may exist either because
supplements are being used to sub­stitute
for healthful diets or because
supplement users are a self-selected
group. Although existing analyses of
national survey data are not adequate
for distinguishing between these two
explanations, qualitative research
with participants in the FSP reveals
a common belief that supplements
are intended to be a replacement or
substitute for food (Kraak et al., 2002).
The accumulated evidence highlights
a logical fallacy underlying one of the
common arguments for permitting the
use of food stamps to purchase nutrient
supplements. The logical fallacy is
that statistical averages observed from
cross-sectional survey data from the
general population apply equally to
all subgroups within the population
and, moreover, that such averages
can be used to predict the response
of the general population as well as
a low-income population (e.g., FSP
participants) to changes in policy. This
present study adds to the broader body
of evidence and rationales provided by
an expert committee (LSRO, 1998) and
a USDA report (1999), suggesting that
any potential benefits of permitting the
purchase of supplements with food
stamps are outweighed by the risks,
administrative complications, and
uncertainties. The repeated failure of
proposed legislation for changing FSP
policy regarding nutrient supplements
(e.g., H.R.l04-236 and S.l731)
suggests that policymakers may
agree with this assessment.
Acknowledgment
This research was funded through the
Food and Nutrition Research small
grants program sponsored by the
USDA Economic Research Service
and administered by the University
of California at Davis.
Family Economics and Nutrition Review
References
Balluz, L.S., Kieszak, S.M., Philen, R.M., & Mulinare, J. (2000). Vitamin and
mineral supplement use in the United States: Results from the Third National
Health and Nutrition Examination Survey. Archives of Family Medicine, 9,
258-262.
Council for Responsible Nutrition. (1998). The Benefits of Nutritional
Supplements. Washington, DC: Council for Responsible Nutrition.
Council for Responsible Nutrition. (2002). The Benefits of Nutritional
Supplements. Washington, DC: Council for Responsible Nutrition. Retrieved
from http://www.crnusa.org/benefits.htrnl.
Government Accounting Office (GAO). (2000). Improvements Needed in
Overseeing the Safety of Dietary Supplements and "Functional Foods."
Washington, DC: Government Accounting Office.
Hartz, S.C., Otradovec, C.L., McGandy, R.B., et al., (1988). Nutrient supplement
use by healthy elderly. Journal of American College Nutrition, 7, 119-128.
Heasman, M., & Mellentin, J. (2001). The Functional Foods Revolution. London:
Earthscan Publications, Ltd.
Hunt, J.R. (1996). Position of the American Dietetic Association: Vitamin and
mineral supplementation. Journal of the American Dietetic Association, 96,
73-77.
Institute of Medicine (10M). (1994). How Should the Recommended Dietary
Allowances Be Revised? Washington, DC: Food and Nutrition Board, Institute
of Medicine.
Kennedy, E.T., Ohls, J., Carlson, S., & Fleming, K. (1995). The Healthy Eating
Index: Design and applications. Journal of the American Dietetic Association,
95, 1103-1108.
Koplan, J.P., Annest, J.L., Layde, P.M., & Rubin, G.L. (1986). Nutrient intake and
supplementation in the United States (NHANES II). American Journal of Public
Health, 76, 287-289.
Kraak, V. , Pelletier, D.L., & Dollahite, J. (2002). Food, health, and nutrient
supplements: Beliefs among food stamp-eligible women and implications for food
stamp policy. Family Economics and Nutrition Review, 14(2), 21-35.
Life Sciences Research Office (LSRO). (1998). Analysis and Review of Available
Data and Expert Opinion on the Potential Value of Vitamin and Mineral
Supplements to Meet Nutrient Gaps Among Low-Income Individuals. Prepared
for U.S. Department of Agriculture. Washington, DC: Life Sciences Research
Office.
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Looker, A., Sempos, C.T., Johnson, C., & Yetley, E.A. (1998). Vitamin-mineral
supplement use: Association with dietary intake and iron status of adults. Journal
of the American Dietetic Association, 88, 808-814.
Lyle, B.J., Mares-Perlman, J.A., Klein, B.E., Klein, R., & Greger, J.L. (1998).
Supplement users differ from nonusers in demographic, lifestyle, dietary and
health characteristics. Journal of Nutrition, 128(12), 2355-2362.
National Research Council (NRC). (1989). Diet and Health: Implications for
Reducing Chronic Disease Risk. Washington, DC: National Academy Press.
Pelletier, D.L., & Kendall, A. (1997). Supplement use may not be associated with
better food intake in all population groups. Family Economics and Nutrition
Review, 10(4), 32-44.
Slesinski, M.J., Subar, A.F., & Kahle, L.L. (1996). Dietary intake of fat, fiber and
other nutrients is related to the use of vitamin and mineral supplements in the
United States: The 1992 National Health Interview Survey. Journal of Nutrition,
126, 3001-3008.
Tippett, K.S., Enns, C.W., & Moshfegh, A.J. (1999). Food consumption surveys
in the U.S. Department of Agriculture. Nutrition Today, 34(1), 33-47.
U.S. Department of Agriculture. (1999). The Use of Food Stamps to Purchase
Vitamin and Mineral Supplements. Washington, DC: Food and Nutrition Service,
U.S. Department of Agriculture. Retrieved from http://www.fns.usda.gov/oane/
MENU!Published/FSP/FILES/Program%20Designlvitamin.pdf.
U.S. Department of Agriculture, & U.S. Department of Health and Human
Services. (2000). Nutrition and Your Health: Dietary Guidelines for Americans
(5'h ed.) (Home and Garden Bulletin No. 232). Washington, DC: U.S. Department
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Variyam, J.N., Blaylock, J., Smallwood, D. , & Basiotis, P.P. (1998). USDA s
Healthy Eating Index and Nutrition Information (Technical Bulletin No. 1866).
U.S. Department of Agriculture, Economic Research Service.
14 Family Economics and Nutrition Review
Cecilia Wilkinson Enns, MS, RD
Sharon J. Mickle, 8S
Joseph D. Goldman, MA
U.S. Department of Agriculture
Agricultural Research Service
2003 Vol. 15 No.2
Trends in Food and Nutrient
Intakes by Adolescents in the
United States
Evaluations of dietary trends can show whether food habits are changing in
recommended directions. Trends in intakes among adolescents age 12 to 19
years were examined by using data from the Continuing Survey of Food Intakes
by Individuals (CSFII) 1994-96, the CSFII1989-91 , and the Nationwide Food
Consumption Survey 1977-78. Increases were seen in intakes of soft drinks, grain
mixtures, crackers/popcorn/pretzels/corn chips, fried potatoes, noncitrus juices/
nectars, lowfat milk, skim milk, cheese, candy, and fruit drinks/ades. Decreases in
intake were observed in whole milk and total milk, yeast breads/rolls, green beans,
corn/green peas/lima beans, beef, and pork. Lower percentages of calories from
fat were partly due to increased carbohydrate intakes. Adolescents had increases
in thiamin, niacin, vitamin 86, and iron and decreases in vitamin 812. Servings per
day from the food groups of the Food Guide Pyramid were used to discuss diet
quality in the most recent survey. For any given Pyramid group, less than one-half
of the adolescents consumed the recommended number of servings, and their
intakes of discretionary fat and added sugars were much higher than recom­mended.
Diets of adolescents still need to change in directions indicated by the
Dietary Guidelines for Americans, including increases in intakes of whole grains,
fruits, dark-green and deep-yellow vegetables, legumes, nonfat or lowfat dairy
products, and lean meats. Additionally, increases in physical activity should be
encouraged, as well as decreases in fats and added sugars. Effective nutrition
education efforts for adolescents should be supported at every level.
A s part of the National Nutrition
Monitoring and Related
Research Program, each of
the U.S. Department of Agriculture
(USDA) food and nutrient intake
surveys provides a snapshot of the
food choices made at a given time by
the population of the United States.
Information about trends in food and
nutrient intakes by adults age 20 years
and over and by children age 6 to
11 years has been published (Enns,
Goldman, & Cook, 1997; Eons, Mickle,
& Goldman, 2002). This article focuses
on trends in intakes by adolescents age
12 to 19 years.
To exarnjne whether adolescents' food
intakes have changed over time, we
compared nationally representative
estimates from the most recent USDA
survey of dietary intakes with similar
estimates from two previous USDA
surveys. The three surveys were the
Continuing Survey of Food Intakes by
Individuals (CSFII) 1994-96,1 CSFII
1 Although the most recent USDA dietary intake
survey encompassed the year 1998 as weU a
1994-96, data collection in 1998 only included
children under l 0 years of age. For that reason,
we identify the survey in this article as the CSFil
1994-96. The sampling weights constructed for
analysis of the CSFil 1994-96 data were used for
the present analysis.
15
1989-91, and the Nationwide Food
Consumption Survey (NFCS) 1977-78
(Tippett et al., 1995; USDA, 1983,
1999, 2000a). The estimates reported
in this study are of food intakes, the
percentages of individuals consuming
foods, and nutrient intakes for girls
and boys age 12 to 19 years dUiing all
three periods. In the discussion of diet
quality in the most recent survey, we
cite information on intakes stated in
terms of Food Guide Pyramid servings
(USDA, 2000b).
Design and Methods
The Three Surveys
The CSFII 1994-96 was the most recent
source of information on adolescents'
intakes in the evolving series of USDA
food and nutrient intake surveys that
also includes the two earlier surveys
(Tippett, Enns, & Moshfegh, 2000).
Differences among the three surveys
in sampling and methodology are
discussed briefly in the following
paragraphs. More information on
methods in the NFCS 1977-78 and the
CSFII 1989-91 is available elsewhere
(Tippett et al., 1995; USDA, 1983).
The target population covered all
50 States in 1994-96 versus the 48
conterminous States in 1977-78 and
1989-91. In 1989-91 and 1994-96,
the low-income population was over­sampled.
In 1977-78 and 1989-91, all
adolescents in sample households were
eligible for inclusion in the survey; in
1994-96, selected individuals within
each household were eligible. The
number of adolescents age 12 to 19
years and the all-individuals Day-1
response rate, respectively, for each
survey are 5,890 and 56.9 percent
(NFCS 1977-78), 1,627 and 57.6
percent (CSFII 1989-91), and 1,469
and 80.0 percent (CSFII 1994-96).
In 1977-78 and 1989-91, dietary data
were collected on 3 consecutive days
16
by using a 1-day dietary recall and a
2-day dietary record. In 1994-96, the
number of days was reduced to two,
partly to reduce respondent burden
(Tippett & Cypel, 1998). Both days
of CSFII 1994-96 dietary data were
collected with 1-day dietary recalls;
interviews were on nonconsecutive
days, 3 to 10 days apart, to ensure that
nutrient intakes on the 2 days would
be statistically uncorrelated. Between
the earlier surveys and the CSFII 1994-
96, the 1-day recall was modified to
include multiple passes through the
list of all foods and beverages recalled
by the respondent, with the goal of
improving the completeness of the
data collected (Tippett & Cypel, 1998).
The USDA Survey Nutrient Database
was updated on an ongoing basis to
incorporate additional nutrients and
improved nutrient values as well as to
reflect changes in foods on the market
(Tippett & Cypel, 1998; Tippett et al.,
1995; USDA, 1987, 1993).
Presentation of Estimates
Because the number of survey days
and the method of data collection on
Day 2 differed among the surveys,
tables comparing food and nutrient
intake estimates among the surveys
are based on only Day- I data collected
from each individual. Using these data
maximizes comparability among
surveys. One-day data are appropriate
for comparisons of group means. All
estimates are weighted to be nationally
representative.
Mean food intakes are presented "per
individual," meaning intakes include
those by both consumers and non­consumers
of the food group. To
calculate "per user" intakes of foods,
researchers may divide the mean intake
of a food group by the percentage of
individuals using that food group,
expressed as a decimal. Because only
selected food subgroups are presented,
subgroup intakes will not sum to the
food group total.2 Food mixtures were
not broken down; mixed foods reported
by respondents were grouped by their
main ingredient. 3 One effect of this
method of classifying food is the
inflation of some food groups or
subgroups (e.g., meat mixtures) and
deflation of others (e.g., sugars and
sweets) relative to the amounts they
would contain if all ingredients were
disaggregated.
Estimates based on a small number of
observations or on highly variable data
may tend to be less statistically reliable
than estimates based on larger sample
sizes or on less variable data. Standard
errors may be used to calculate a
measure of the relative variability of
an estimate called the coefficient of
variation, the ratio of the standard error
to the estimate itself. Because the CSFII
has a complex sample design, sampling
weights and procedures for specialized
standard error estimation were used in
computing the estimates and standard
errors (USDA, 2000a, documentation
section 5). SAS version 8.2 (1999)
and SUDAAN version 7.5.1 (Shah,
Barnwell, & Bieler, 1997) were used
for statistical calculations.
In the tables, we flagged estimates that
are potentially less reliable because of
factors such as small sample sizes or
large coefficients of variation. The
guidelines that were used for determin­ing
when a statistic may be less reliable
involve the use of a variance inflation
factor in the role of a broadly calculated
design effect. Those guidelines have
been described in detail elsewhere
(USDA, 1999, appendix B). The
2Readers interested in subgroups not included
here are directed to Tippett et al. (1995) and
USDA (1983, 1999).
3See "Table Notes" in Tippett et al. (1995) and
USDA (1983); see "Descriptions of Food
Groups" in USDA ( 1999).
Family Economics and Nutrition Review
variance inflation factors used in this
study were 1.19 (1977-78), 2.26
(1989-91), and 1.41 (1994-96).
Approximate t tests were performed
to determine whether food and nutrient
intakes and the percentages of indi­viduals
using foods were significantly
higher or lower in 1977-78 versus
1989-91, 1989-91 versus 1994-96,
and 1977-78 versus 1994-96. All
told, some 460 pairs of estimates
were compared. Because the analysis
involved such a large number of
comparisons, we used conservative
criteria for significance. When signifi­cant
differences are discussed in the
text, they may be referred to either as
"changes" (or values may be said to
have risen/fallen or to be higher/lower
in 1994-96 than in 1977 -78) or as
"trends."
The term "change" is used only if
intakes (or percentages using) in 1977-
78 and 1994-96 were different when
p was less than 0.001. The term "trend"
is used only if two criteria were met:
(1) mean intakes (or percentages using)
either rose or fell progressively from
one survey to the next (e.g., intake X
rose between 1977-78 and 1989-91 ,
then rose again between 1989-91 and
1994-96), and (2) p was less than 0.05
for both comparisons. For each trend,
the level of significance noted in the
tables ( < 0.05 or< 0.01) is the one
that is true of both the 1977-78 versus
1989-91 t test and the 1989-91 versus
1994-96 t test. For example, if the
1977-78 versus 1989-91 t test was
significant at p < 0.01 but the 1989-91
versus 1994-96 t test was significant
at p < 0.05, the latter level is shown in
the table.
2003 Vol. 15 No. 2
Results and Discussion
Beverages
Since the late 1970s, the overall picture
of beverage intakes by adolescents has
changed considerably. The diets of
both girls and boys age 12 to 19 had
decreasing trends over time in both
intakes of total fluid milk and the
percentages of individuals using fluid
milk (tables 1-4). Both girls' and boys'
diets had increasing trends in intakes
of soft drinks, and boys' diets also
had a trend to a higher percentage of
individuals using soft drinks. In 1977-
78 adolescents drank at least one and
one-halftimes as much fluid milk as
any other beverage, but by 1994-96
they drank about twice as much soft
drinks as milk. Adolescents' intake of
noncitrus juices and nectars-such as
apple juice, grape juice, and 100-
percent fruit juice blends-tripled
between 1977-78 and 1994-96,
although in the latter survey, they still
drank less noncitrus juices than soft
drinks, milk, or fruit drinks and ades.
Adolescents' intakes of fruit drinks
and ades, which contain little or no
fruit juice, doubled between 1977-78
and 1994-96.
The shift in beverage intakes is of
nutritional concern. Guenther (1986)
found negative associations between
intake of soft drinks and intakes of
milk, calcium, magnesium, riboflavin,
vitamin A, and vitamin C. Harnack,
Stang, and Story (1999), in an analysis
of CSFII 1994 data, reported a positive
association between consumption of
nondiet soft drinks and energy intake.
Wyshak (2000) found that high-school­age
girls who drink carbonated bever­ages
may have a higher risk of bone
fractures than is the case for girls who
do not diink carbonated beverages. In
a 19-month-long prospective study,
Ludwig, Peterson, and Gortmaker
(2001) observed an association between
consumption of sugar-sweetened drinks
Although the percentages of
adolescents drinking skim milk
more than doubled between
1977-78 and 1994-96, they still
remained low (7 to 9 percent) ....
17
and childhood obesity. Because the Table 1. Trends and changes in adolescent1 girls' mean intakes from selected food
studies by Guenther (1986), Harnack groups
et al. (1999), Wyshak (2000), and
Ludwig et al. (2001) were observa- Intake (grams)
Food group 1977-78 1989-91 1994-96 Change2 Trend3
tiona!, it cannot be inferred that the
relation hips between soft drinks and Grain products 215 261 306 +91
the negative outcomes described were Yeast breads and rolls 52 45 40 -12
causal. Further research is needed in Ready-to-eat cereals 11 15 17 +6
this area. Cakes, cookies, pastries, pies 34 26 37
Crackers, popcorn, pretzels, corn chips 5 8 15 + 11
Foods Mixtures mainly grain 59 100 132 +73
Overall, the intakes of grain products Vegetables 165 129 145
White potatoes 61 56 61 were about two-fifths higher in 1994-96 Fried white potatoes 18 31 31 +13
than in 1977-78 for girls and boys age Dark-green vegetables 6 5 9
12 to 19 years (tables 1 and 2). In all Deep-yellow vegetables 6 54 4
three surveys, the subgroup "mixtures Tomatoes 16 17 18
mainly grain"-grain-based mixtures Green beans 8 5 4 -5
such as pasta with sauce, rice dishes, Corn, green peas, lima beans 19 12 8 -11
and pizza-accounted for the largest Fruits 129 133 157
share (by weight) of grain products Citrus juices 53 68 67
eaten by adolescents. Teenage girls' Apples 20 11 13
Melons and berries 7 7 15 and boys' diets had increasing trends Noncitrus juices and nectars 12 19 35 +23 for both intakes and percentages using Milk and milk products 380 308 268 -112
grain mixtures (tables 3 and 4). Fluid milk 303 239 189 -114
Whole milk 166 97 67 -99
Increasing trends were observed in Lowfat milk 53 115 91 +38
adolescents' intakes of grain-based Skim milk 13 w 30 +17
snack foods from the group "crackers, Milk desserts 25 20 29
popcorn, pretzels, and com chips." Cheese 9 15 14 +5
Among boys, there were also trends Meat, poultry, and fish 186 152 158 -28
Beef 46 19 21 -25 toward lower intakes and percentages Pork 16 11 5 -10 consuming yeast breads and rolls; the Frankfurters, sausages, luncheon meats 17 15 15
decline in girls' intakes and percentages Chicken 21 20 19
using yeast breads and rolls could not Fish and shellfish 10 6 6
be classified as a trend. Yeast breads Mixtures mainly meat, poultry, fish 66 73 85
and rolls are common components in Eggs 18 12 13
sandwiches, and some sandwiches Legumes 19 13 14
(especially fast-food items) are cate- Fats and oils 11 10 10
gorized under "mixtures mainly meat, Sugars and sweets 22 23 31
Candy 5 6 12 +7 poultry, fish." Intake estimates for yeast Beverages 417 534 645 +228 breads and rolls would be higher if the Tea 89 87 92
breads and rolls from those sandwiches Fruit drinks and ades 72 87 134 +62
were included here. Carbonated soft drinks 208 324 396 +188
In 1994-96 only 35 percent of girls 112 to 19 years.
and 48 percent of boys consumed the 2Change =mean intakes in 1977-78 and 1994-96 are significantly different at p < 0.001.
number of servings of grain products 3-frend =mean intake rose or fell progressively from 1977-78 through 1989-91 to 1994-96.
recommended in the Food Guide
4Estimate is based on small sample size or coefficient of variation ~ 30 percent.
Pyramid based on their caloric intake .•. = trend significant at p < 0.05. = trend significant at p < 0.01.
(USDA, 2000b). Despite Pyramid
recommendations to choose "several
servings a day" of whole-grain foods
18 Family Economics and Nutrition Review
Table 2. Trends and changes in adolescent1 boys' mean intakes from selected food
groups
Intake (grams)
Food group 1977-78 1989-91 1994-96 Change2
Grain products 297 351 406
Yeast breads and rolls 77 65 54
Ready-to-eat cereals 18 25 29
Cakes, cookies, pastries, pies 48 45 49
Crackers, popcorn, pretzels, corn chips 6 9 19
Mixtures mainly grain 78 121 175
Vegetables 209 173 176
White potatoes 86 78 86
Fried white potatoes 27 35 44
Dark-green vegetables 8 9 6
Deep-yellow vegetables 8 4 6
Tomatoes 17 22 28
Green beans 12 64 34
Corn, green peas, lima beans 27 20 10
Fruits 143 157 174
Citrus juices 60 84 94
Apples 24 20 13
Melons and berries 7 64 114
Noncitrus juices and nectars 9 12 29
Milk and milk products 571 461 409
Fluid milk 472 376 303
Whole milk 257 145 100
Lowfat milk 88 197 157
Skim milk 17 224 40
Milk desserts 34 32 29
Cheese 11 13 19
Meat, poultry, and fish 257 221 250
Beef 64 34 30
Pork 24 12 12
Frankfurters, sausages, luncheon meats 26 27 28
Chicken 26 26 26
Fish and shellfish 9 7 8
Mixtures mainly meat, poultry, fish 94 103 135
Eggs 28 16 22
Legumes 28 27 17
Fats and oils 13 14 12
Sugars and sweets 32 29 35
Candy 5 8 13
Beverages 467 639 994
Tea 98 95 115
Fruit drinks and ades 98 104 205
Carbonated soft drinks 220 424 608
112 to 19 years.
2Change = mean intakes in 1977 · 78 and 1994-96 are significantly different at p < 0.001.
3Trend =mean intake rose or fell progressively from 1977-78 through 1989-91 to 1994-96.
4Estimate is based on small sample size or coefficient of variation ;::: 30 percent.
* = trend significant at p < 0.05.
" =trend significant at p < 0.01.
2003 Vol. 15 No. 2
+109
-23
+10
+14
+96
+17
+11
-9
-17
-11
+20
-162
-169
-157
+69
+8
-34
-12
+41
+8
+527
+107
+388
Trend3
(USDA, 1996), adolescents' intake
of whole grains in 1994-96 was only
about 1 serving per day.
Few trends were observed in adoles­cents'
intakes of vegetables. It is
important to remember that vegetables
are freq uently consumed as part of
meat mixtures and grain mixtures.
For adults in 1994, intakes of veg­etables
accounted for about 24 percent
and 28 percent (by weight) of grain
mixtures and meat mixtures, respec­tively
(Enns et al., 1997). If vegetables
account for a similar proportion of
grain and meat mixtures for adoles­cents
as for adults, then the observed
higher intakes of grain mixtures would
at least partially offset the lower
intakes of vegetables. Further research
is needed to clarify this issue. How­ever,
even when mixture ingredients
are separated into their respective
groups, 74 percent of adolescent girls
and 67 percent of adolescent boys had
diets that did not meet the Pyramid
recommendations for servings of
vegetables (USDA, 2000b). Despite
Pyramid recommendations to eat
both dark-green leafy vegetables
and legumes "several times a week,"
adolescents ate no more than one-fifth
of a serving from either category on
any given day.
Adolescents' intakes of fried white
potatoes were higher in 1994-96
than in 1977-78. The percentages
of adolescents using tomatoes rose
between 1977-78 and 1994-96, and
the increase qualified as a trend among
boys. Both girls and boys had lower
intakes and lower percentages using
the subgroups "green beans" and "corn,
green peas, and lima beans" in 1994-96
than in 1977-78. The decrease in the
percentage of boys using corn, green
peas, and lima beans met the definition
of a trend.
Aside from the observed changes in
intakes of noncitrus juices and nectars,
19
few changes occurred in fruit consump­tion.
Between 1977-78 and 1994-96,
the percentage using citrus juices and
apples fell among girls and both intakes
and percentages using apples fell
among boys. In 1994-96 only 18
percent of girls and 14 percent of boys
consumed the number of servings of
fruit recommended in the Food Guide
Pyramid based on their caloric intake
(USDA, 2000b).
Among milk and milk products sub­groups,
adolescents' intakes of some
high-fat items (e.g., whole milk)
decreased and others (e.g., cheese)
increased. Notably, milk intakes shifted
away from whole milk.4 Decreasing
trends were seen both in adolescents'
intakes of whole milk and in the
percentages of adolescents using
whole milk. Intakes of lower fat milks
(2%, 1%, and skim) by adolescents
surpassed those of whole milk in
1989-91. Although the percentages of
adolescents drinking skim milk more
than doubled between 1977-78 and
1994-96, they still remained low (7 to
9 percent), as did their intakes of skim
milk (30 to 40 grams [g], or about 1 to
1-113 fluid ounces). None of the shifts
in intakes of lower fat milks or percent­ages
using them qualified as a trend.
On the other hand, increasing trends in
the percentages of adolescents using
cheese were seen. Although cheese
intakes were higher in 1994-96 than in
1977-78, the increase did not qualify as
a trend. Because cheese is a common
4Another shift occurred that can be seen by
summing the milk subgroup intakes (whole,
lowfat, and skim) in a given survey and dividing
by the intake of total fluid milk. A greater
proportion of total fluid milk was allocated to a
specific fat level in later years than in I 977-78.
The increase may indicate a greater awareness
of the fat level of milk, because the ability to
classify fluid milk as whole, lowfat, or skim
depends on information provided by respon­dents.
Milk whose fat level was not specified
was included under total fluid milk but not in
any of the subgroups.
20
Table 3. Trends and changes in percentages of adolescent1 girls using items from
selected food groups
Percentage using
Food group 1977-78 1989-91 1994-96
Grain products 96 97 984
Yeast breads and rolls 75 65 61
Ready-to-eat cereals 29 28 30
Cakes, cookies, pastries, pies 40 30 41
Crackers, popcorn, pretzels, corn chips 16 20 31
Mixtures mainly grain 23 39 46
Vegetables 83 72 79
White potatoes 51 45 46
Fried white potatoes 28 32 35
Dark-green vegetables 5 6 7
Deep-yellow vegetables 7 7 11
Tomatoes 22 29 35
Green beans 10 7 4
Corn, green peas, lima beans 18 12 7
Fruits 50 44 46
Citrus juices 25 21 18
Apples 13 7 8
Melons and berries 3 3 6
Noncitrus juices and nectars 4 7 10
Milk and milk products 84 77 75
Fluid milk 72 60 50
Whole milk 42 29 18
Lowfat milk 13 27 24
Skim milk 4 4 9
Milk desserts 18 14 17
Cheese 19 29 36
Meat, poultry, and fish 92 81 80
Beef 33 18 22
Pork 21 14 11
Frankfurters, sausages, luncheon meats 27 27 25
Chicken 17 17 19
Fish and shellfish 9 6 6
Mixtures mainly meat, poultry, fish 32 35 34
Eggs 23 13 15
Legumes 11 9 11
Fats and oils 53 48 46
Sugars and sweets 47 44 46
Candy 9 12 24
Beverages 73 78 87
Tea 21 18 19
Fruit drinks and ades 19 21 27
Carbonated soft drinks 46 58 62
112 to 19 years.
2Change =percentages in 1977-78 and 1994-96 are significantly different at p < 0.001.
Jrrend =percentage rose or fell progressively from 1977-78 through 1989-91 to 1994-96.
4Estimate is based on small sample size or coefficient of variation ;:.: 30 percent.
• = trend significant at p < 0.05.
•• = trend significant at p < 0.01 .
Change2 Trend3
-15
+15
+23
+13
-6
-11
-7
-5
+6
-9
-22
-24
+ 11
+6
+17
-12
-11
-10
-8
+15
+14
+17
Family Economics and Nutrition Review
Table 4. Trends and changes in percentages of adolescent1 boys using items from
selected food groups
Percentage using
Food group 1977-78 1989-91 1994-96
Grain products 98 97 984
Yeast breads and rolls 81 71 63
Ready-to-eat cereals 37 35 33
Cakes, cookies, pastries, pies 45 39 41
Crackers, popcorn, pretzels, corn chips 15 20 27
Mixtures mainly grain 25 37 46
Vegetables 87 81 78
White potatoes 58 50 50
Fried white potatoes 34 37 39
Dark-green vegetables 6 6 4
Deep-yellow vegetables 8 8 8
Tomatoes 23 32 43
Green beans 12 6 3
Corn, green peas, lima beans 23 14 7
Fruits 50 44 45
Citrus juices 26 24 22
Apples 13 10 8
Melons and berries 3 3 4
Noncitrus juices and nectars 3 4 8
Milk and milk products 90 87 81
Fluid milk 82 72 60
Whole milk 50 31 23
Lowfat milk 16 39 31
Skim milk 3 5 7
Milk desserts 20 16 14
Cheese 19 27 37
Meat, poultry, and fish 96 90 87
Beef 37 26 24
Pork 27 14 16
Frankfurters, sausages, luncheon meats 32 35 32
Chicken 16 18 18
Fish and shellfish 7 5 5
Mixtures mainly meat, poultry, fish 37 36 38
Eggs 28 15 17
Legumes 12 11 11
Fats and oils 54 52 43
Sugars and sweets 53 41 47
Candy 8 14 21
Beverages 72 78 87
Tea 21 14 16
Fruit drinks and ades 20 18 28
Carbonated soft drinks 43 59 69
112to 19 years.
2Change =percentages in 1977-78 and 1994-96 are significantly different at p < 0.001 .
3Trend =percentage rose or fell progressively from 1977-78through 1989-91to 1994-96.
4Estimate is based on small sample size or coefficient of variation;:.: 30 percent.
* =trend significant at p < 0.05.
** =trend significant at p < 0.01.
2003 Vol. 15 No.2
Change2 Trend3
-19
+12
+21
-9
-9
+20
-9
-15
-5
+5
-9
-22
-27
+15
+4
-7
+18
-9
-13
-11
-11
+ 11
+13 **
+16
+8
+26
component in both grain and meat
mixtures, estimates for cheese would
be even higher if the cheese that was
an ingredient in these mixtures were
included here. In 1994-96 only 12
percent of girls and 30 percent of boys
consumed the number of servings of
dairy products recommended in the
Food Guide Pyramid based on their
age (USDA, 2000b).
The percentages of both girls and boys
using foods from the meat, poultry, and
fish group were lower in 1994-96 than
in 1977-78. Both intakes and percent­ages
of indi victuals using beef and pork
separately (i .e., not as part of a mixture)
fell. In all three surveys, intakes of
"mixtures mainly meat, poultry, fish"­such
as beef stew, hamburgers, chicken
pot pie, and tuna salad-accounted for
the largest share of intakes of total
meat, poultry, and fish. Percentages
of adolescents consuming eggs were
lower in 1994-96 than in 1977-78.
In 1994-96 only 22 percent of girls
and 44 percent of boys consumed the
number of servings of meat and meat
alternates recommended in the Food
Guide Pyramid based on their caloric
needs (USDA, 2000b). Cooked dry
beans (other than soybeans) and peas,
which may be tabulated under either
the vegetable group or the meat group,
were tabulated under the meat group for
that analysis; otherwise, the percentages
consuming the recommended number
of servings from the meat group would
have been even lower.
For both girls and boys, intakes and
percentages using candy increased
between 1977-78 and 1994-96.
However, the increases qualified as
trends only for the adolescent boys.
Fats, oils, and sugars are common
ingredients in foods; thus, the estimate
of intakes and percentages using fats,
oils, and sugars would be higher if the
amounts that were ingredients in other
foods were included here.
21
In 1994-96, intakes of discretionary fat Table 5. Trends and changes in adolescent1 girls' and boys' mean intakes of food
and added sugars5-items from the tip energy and selected nutrients and mean percentages of calories from protein, fat,
of the Pyramid-were much higher than and carbohydrate
recommended (USDA, 2000b). Among
adolescents, discretionary fat intake Intake
Food group 1977-78 1989-91 1994-96 Change2 Trend3
accounted for about 25 percent of
calories for girls and 26 percent for Girls
boys. In a diet that meets all other n=2,993 n=837 n=732
Pyramid recommendations, discretion-ary
fat intake would be expected to be Energy (kcal) 1,797 1,748 1,910
closer to 15 percent of calories (USDA, Protein (g) 70.6 66.0 65.3 -5.3
1996). In 1994-96, adolescent girls Fat (g) 80.0 67.4 69.3 -10.7
consumed 23 teaspoons of added Carbohydrate (g) 202.0 223.5 261.9 +59.9
sugars per day in a diet providing Protein (% kcal) 16.0 15.4 14.0 -2.0
Fat(% kcal) 39.3 33.8 32.2 -7.2 around 1,800 calories; adolescent Carbohydrate (% kcal) 45.4 51 .7 55.0 +9.6 ..
boys consumed 34 teaspoons of added Vitamin A (IU) 4,410 4,554 4,817
sugars per day in a diet providing Vitamin C (mg) 78 90 95
around 2,700 calories. The Pyramid Thiamin (mg) 1.23 1.39 1.44 +0.21
suggests that Americans try to limit Riboflavin (mg) 1.72 1.72 1.75
their added sugars to 6 teaspoons a Niacin (mg) 16.7 18.1 19.0 +2.3
day if they eat about 1,600 calories, Vitamin 86 (mg) 1.37 1.42 1.53 +0.16
12 teaspoons at 2,200 calories, or Vitamin 812 (,ug) 5.34 3.66 3.80 -1.54
18 teaspoons at 2,800 calories Calcium (mg) 784 797 771
Phosphorus (mg) 1,127 1,123 1,108 (USDA, 1996). Magnesium (mg) 213 216 223
Energy Out of Balance
Iron (mg) 10.3 11.9 13.8 +3.5
Over roughly the same period covered Boys
by the present analysis, the percentages n=2,897 n=790 n=737
of 12- to 19-year-old boys in the United
States who were overweight6 rose from Energy (kcal) 2,523 2,459 2,766 +243
Protein (g) 99.8 93.1 97.5 4.5 percent in 1976-80 to 11.3 percent Fat (g) 113.7 96.8 102.8 -10.8 in 1988-94; among adolescent girls, the Carbohydrate (g) 279.0 310.9 366.1 +87.0
increase was from 5.4 to 9.7 percent Protein (% kcal) 16.1 15.6 14.4 -1.7
(U.S. Department of Health and Human Fat (% kcal) 39.9 34.7 33.1 -6 .8
Services [DHHS], 2001). The increas- Carbohydrate (% kcal) 44.6 50.8 53.2 +8.5
ing prevalence of overweight is of Vitamin A (IU) 6,018 5,893 6,361
concern for many reasons, including Vitamin C (mg) 97 114 119
the increasing incidence and prevalence Thiamin (mg) 1.76 1.99 2.13 +0.36
of Type II diabetes mellitus among Riboflavin (mg) 2.51 2.49 2.58
Niacin (mg) 23.3 25.0 27.8 +4.4 overweight and obese adolescents Vitamin 86 (mg) 1.92 2.01 2.21 +0.29 (American Diabetes Association, Vitamin 812 (,ug) 7.50 5.89 5.85 -1 .65
2000). Overweight in adolescence is Calcium (mg) 1,145 1,145 1,145
also associated with high blood lipids, Phosphorus (mg) 1,608 1,598 1,633
Magnesium (mg) 301 299 311
5For definitions of discretionary fat and added
Iron (mg) 14.5 17.8 19.8 +5.3
sugars, see appendix D in Pyramid Servings 112 to 19 years.
table set I (USDA, 2000b). 2Change =mean intakes (or percentages) in 1977·78 and 1994-96 are significantly different at
60verweight is defined as body mass index
p < 0.001.
:l'frend =mean intake (or percentage) rose or fell progressively from 1977-78 through 1989·91 to 1994-96.
(BMI) at or above the sex- and age-specific ' =trend significant at p < 0.05.
95"' percentile BMI cutoff points reported in .. = trend significant at p < O.D1 .
the revised CDC Growth Charts: United States
(Kuczmarski et al., 2000).
22 Family Economics and Nutrition Review
hypertension, an increased likelihood
of overweight in adulthood, and various
other problems (DHHS, 2001).
In the face of increasing overweight,
one would expect to see either increas­ing
energy intake or decreasing energy
expenditure or both. In the present
analysis, no significant trends or
changes were seen in energy intakes
between 1977-78 and 1994-96 (table
5). Adolescent boys' energy intake was
over 200 kcal higher in 1994-96 than
in 1977-78 (2,766 kcal vs. 2,523 kcal).
Girls' energy intake was 1,910 kcal in
1994-96 and 1,797 kcal in 1977-78,
but no significant difference was found.
Findings of underreporting in surveys,
which are often but not always higher
among overweight respondents, might
lead one to speculate that the lack of a
trend in energy intake could be due to
increased underreporting over time as
a function of increased obesity. On the
other hand, methodological improve­ments
in the Agricultural Research
Service's 24-hour recall have addressed
several issues that are considered
important in obtaining complete intake
data (see "Design and Methods").
Using CSFII data, Krebs-Smith et al.
(2000) identified low-energy reporters
by first estimating basal metabolic rate
(BMR)7 based on self-reported body
weight, gender, and age and then
comparing the BMR estimates with
a cutofflevel.8 They found that the
percentage of adults who were low­energy
reporters was lower in 1994-96
(15 percent) than in 1989-91 (25
percent).
7BMR was estimated by using the formula
developed by Schofield (1985).
8Eighty percent of BMR was the cutoff level
used. That level was proposed by Goldberg et al.
(1991 ) as the lower limit of plausible energy
intake for a single individual with 2 days of
intake data and 99.7 percent confidence limits.
2003 Vol. 15 No. 2
They also found less undeiTeporting
among adolescents than among adults.
Only 9.5 percent of adolescents age 12
to 19 in 1994-96 were found to be low­energy
reporters (S.M. Krebs-Smith,
personal communication, March 8,
2002). Livingstone and Robson (2000)
have stated that determining whether
an adolescent's energy intake is
implausibly low should take into
account detailed information on the
adolescent's activity level; however,
such information is not available from
the three surveys in the present analysis.
Inactivity is probably a strong factor in
the increased prevalence of overweight
in the United States (DHHS, 2001;
Weinsier, Hunter, Heini, Goran, & Sell,
1998). In 1996 the Surgeon General
concluded that nearly half of American
youths 12 through 21 years of age are
not vigorously active on a regular basis,
that about one-tenth of them are not
active at all, and that physical activity
declines during adolescence (DHHS,
1996).
The Dietary Guidelines for Americans
recommend that adolescents engage
in at least 60 minutes of moderate
physical activity on most days of the
week, preferably daily (USDA &
DHHS, 2000). One strategy suggested
by the Dietary Guidelines to help
teens increase their activity is to limit
television watching. On any given day
in 1994-96, 32 percent of girls and 34
percent of boys age 12 to 19 watched
4 or more hours of television or videos,
29 percent of girls and 34 percent of
boys watched 2 to 3 hours, and 39
percent of girls and 33 percent of boys
watched 1 hour or less (unpublished
data).
Energy-Providing Nutrients
(Macronutrients)
Trends toward higher carbohydrate
intakes were evident among both
adolescent girls and boys. For girls,
carbohydrate intake was about 60 g per
For girls, carbohydrate intake
was about 60 g per day higher in
1994-96 than in 1977-78; for boys,
the intake was 87 g higher.
23
day higher in 1994-96 than in 1977-78;
for boys, the intake was 87 g higher.
For both girls and boys, protein and fat
intakes were lower in 1994-96 than in
1977-78, although the p value criterion
for a trend was not met.
These shifts in adolescents' macro­nutrient
intakes between 1977-78
and 1994-96 were reflected in trends
toward a lower proportion of food­energy
intake from fat and a higher
proportion from carbohydrate. Adoles­cents'
percentage of calories from
protein was also lower in 1994-96 than
in 1977-78, but the trend definition was
not met. The proportion of energy from
fat in adolescents' diets in 1994-96
(33 percent for girls and 32 percent
for boys) was still higher than what is
recommended by the Dietary Guide­lines
for Americans: 30 percent of
calories or less (USDA & DHHS,
2000). At 11 percent of calories for
girls and 12 percent of calories for boys
(unpublished data), saturated fat intakes
still exceeded the recommendation of
less than 10 percent of calories.
Although the shifts in the proportion
of energy intake from fat and carbohy­drate
appear to have brought the macro­nutrient
proportions in the average diet
nearer to the recommended levels, a
closer examination is less encouraging.
The observed decrease in the percent­age
of calories from fat is more due to
the increase in calories from carbohy­drate
than to the decrease in fat intake.
Fat intake decreased by almost I 00 kcal
for both girls and boys, but carbohy­drate
intake increased by about 240
kcal for girls and almost 350 kcal for
boys, based on estimates in table 5 that
were multiplied by Merrill and Watt's
(1973) general conversion factors
of 9 kcal/g for fat and 4 kcal/g for
carbohydrate.
24
Vitamins, Minerals, and Other
Dietary Components
Increasing trends were observed in
iron intakes for both adolescent girls
and boys (table 5). Boys' diets had an
increasing trend in niacin intake, and
crirls, diets had a higher intake that did
~ot meet the trend criteria. Addition­ally,
thiamin and vitamin B6 intakes for
adolescents were higher, and vitamin
B12 intakes were lower.
Mean dietary fiber intakes in 1994-96
were 13 g for girls and 17 g for boys
(unpublished data). The Institute of
Medicine (2002) has set the adequate
intake of total fiber (which equals
dietary fiber plus a minor amount of
functional fibers) at 26 g/day for girls
9 to 18 years, 31 g/day for boys 9 to 13
years, and 38 g/day for boys 14 to 18
years. Observed increases in carbohy­drate
intakes were paralleled neither
by significant increases in dietary fiber
intakes nor by increases in overall
intakes offiber-rich foods.
Summary and
Recommendations
The pattern of results seen for adoles­cents
echos many of the findings for
adults and children (Enns, Goldman, &
Cook, 1997; Enns, Mickle, & Goldman,
2002). Adolescents' food intakes
changed in various ways during the last
quarter of the 20th century. Adolescents'
diets exhibited trends not only toward
large increases in intakes of soft drinks
but also toward decreases in intakes
of total fluid milk that were driven by
decreases in whole milk. Some other
shifts were to higher intakes of grain
products (especially grain mixtures),
crackers/popcorn/pretzels/corn chips,
fried potatoes, noncitrus juices/nectars,
lowfat milk, skim milk, cheese, candy,
and fruit drinks/ades. Other shifts were
to lower intakes of yeast breads/rolls,
green beans, corn/green peas/lima
beans, beef, and pork.
Despite those shifts in intakes, most of
the take-home messages about how to
improve adolescents' diets remain the
same:
• Eat more whole grains.
• Eat more vegetables, especially
dark-green and deep-yellow
vegetables.
• Eat more fruits-both citrus and
noncitrus, with an emphasis on
whole fruits rather than juices.
• Eat more legumes.
• Shift to lean meats and meat
alternates.
• Drink more skim or 1% milk, or
eat more lowfat dairy products,
or include plenty of nondairy
souTces of calcium.
• Decrease the amount of fat used
in cooking.
The amount of discretionary fat and
added sugars in adolescents' diets is
much higher than is recommended by
the Food Guide Pyramid. Adolescents'
diets would benefit overall from
lowering intakes of "empty-calorie"
foods and beverages that are high in
fats and sugars but provide few other
nutrients. In addition, when choosing
among more nutrient-dense foods,
adolescents would do well to shift
toward items lower in fat and sugar.
Increases in intakes of foods high in
fiber and complex carbohydrate-such
as whole grains, vegetables, fruits other
than fruit juices, and legumes--could
lead to a diet lower in fat and added
sugars and higher in fiber and complex
carbohydrate. If such a change led to
a lower overall energy intake, weight
maintenance or loss would be made
easier. Because widespread inactivity
has been identified as a factor in the
national epidemic of overweight,
increased activity should be
Family Economics and Nutrition Review
encouraged. In a recent Call to Action,
the Surgeon General outlined key
actions to address overweight and
obesity (DHHS, 2001).
Educational efforts and interventions
successfully change dietary behavior
among adolescents, and factors leading
to the effectiveness of nutrition educa­tion
have been identified ("Adolescent
Nutrition," 2002; Contento et al.,
1995). Resources must be committed
on every level-national, State, local,
community, school, and family, as well
as in the health care system-to help
adolescents eat more healthfully and
become more active.
2003 Vol. 15 No. 2
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Krebs-Smith, S.M., Graubard, B.I., Kahle, L.L., Subar, A.F., Cleveland, L.E., &
Ballard-Barbash, R. (2000). Low energy reporters vs others: A comparison of
reported food intakes. European Journal of Clinical Nutrition, 54( 4 ), 281-287.
Kuczmarski, R.J., Ogden, C.L., Grummer-Strawn, L.M., Flegal, K.M., Guo, S.S.,
Wei, R., et al. (2000). CDC Growth Charts: United States (Advance Data from
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Statistics. Retrieved May 21, 2002, from http://www .cdc.gov/nchs/data/ad/
ad314.pdf.
Livingstone, M.B.E., & Robson, P.J. (2000). Measurement of dietary intake in
children. Proceedings of the Nutrition Society, 59(2), 279-293.
25
26
Ludwig, D.S., Peterson, K.E., & Gortmaker, S.L. (2001). Relation between
consumption of sugar-sweetened drinks and childhood obesity: A prospective,
observational analysis. Lancet, 357, 505-508.
Merrill, A.L., & Watt, B.K. (1973). Energy Value of Foods-Basis and
Derivation. U.S. Department of Agriculture, Agriculture Handbook No. 74, sl. rev.
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Shah, B.V., Barnwell, B.G., & Bieler, G.S. (1997). SUDAAN (Version 7.5.1)
[computer program]. Research Triangle Park, NC: Research Triangle Institute.
Tippett, K.S., & Cypel, Y.S. (Eds.). (1998). Design and Operation: The
Continuing Survey of Food Intakes by Individuals and the Diet and Health
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137268. Retrieved May 21, 2002, from http://www.barc.usda.gov/bhnrc/
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Tippett, K.S., Enns, C.W., & Moshfegh, A.J. (2000). Food consumption surveys in
the U.S. Department of Agriculture. In F.J. Francis (Ed.), Encyclopedia of Food
Science and Technology (2nd. ed., pp. 889-897). New York: Wiley.
Tippett, K.S., Mickle, S.J., Goldman, J.D., Sykes, K.E., Cook, D.A., Sebastian,
R.S., et al. (1995). Food and Nutrient Intakes by Individuals in the United States,
1 Day, 1989-91. U.S. Department of Agriculture, Agricultural Research Service,
Continuing Survey of Food Intakes by Individuals 1989-91, Nationwide Food
Surveys Rep. No. 91-2; NTIS No. PB95-272746.
U.S. Department of Agriculture. (1983). Food Intakes: Individuals in 48 States,
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PB91-103523.
U.S. Department of Agriculture. (1987). CSFIJ: Women 19-50 Years and Their
Children 1-5 Years, 4 Days, 1985. U.S. Department of Agriculture, Human
Nutrition Information Service, Nationwide Food Consumption Survey, Continuing
Survey of Food Intakes by Individuals, Rep. 85-4; NTIS No. PB88-110101.
U.S. Department of Agriculture. (1993). Food and Nutrient Intakes by Individuals
in the United States, 1 Day, 1987-88. U.S. Department of Agriculture, Human
Nutrition Information Service, Nationwide Food Consumption Survey,
Rep. 87-1-1; NTIS No. PB94-168325.
U.S. Department of Agriculture, Agricultural Research Service. (1999). Food and
Nutrient Intakes by Children 1994-96, 1998; ARS Food Surveys Research Group
Table Set 17. Retrieved May 21, 2002, from http://www.barc.usda.gov/bhnrc/
foodsurvey/pdf/scs_all.pdf.
Family Economics and Nutrition Review
U.S. Department of Agriculture, Agricultural Research Service. (2000a).
Continuing Survey of Food Intakes by Individuals 1994-96, 1998 [CD-ROM].
NTIS No. PB2000-500027.
U.S. Department of Agriculture, Agricultural Research Service. (2000b). Pyramid
Servings Intakes by U.S. Children and Adults: I994-96, 1998; ARS Community
Nutrition Research Group Table Set No. 1. Retrieved May 21, 2002, from http://
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(1996). The Food Guide Pyramid (sl. rev., Home and Garden Bulletin No. 252).
Washington, DC: U.S. Government Printing Office. Retrieved May 21, 2002,
from http://www. usda.gov/cnpp/Pubs/Pyramid/fdgdpyr l.pdf.
U.S. Department of Agriculture and U.S. Department of Health and Human
Services. (2000). Nutrition and Your Health: Dietary Guidelines for Americans
(5th ed., Home and Garden Bulletin No. 232). Washington, DC: U.S. Government
Printing Office. Retrieved May 21, 2002, from http://www.usda.gov/cnpp/
DietGd.pdf.
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Call to Action to Prevent and Decrease Overweight and Obesity. Rockville, MD:
Public Health Service, Office of the Surgeon General. Retrieved May 21, 2002,
from http://www.surgeongeneral.gov/topics/obesity/calltoaction/CalltoAction.pdf.
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Prevention. (1996). Physical Activity and Health: A Report of the Surgeon
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hus/husOl.pdf.
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etiology of obesity: Relative contribution of metabolic factors, diet, and physical
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Wyshak, G. (2000). Teenaged girls, carbonated beverage consumption, and bone
fractures. Archives of Pediatric and Adolescent Medicine, 154, 610-613.
2003 Vol. 15 No. 2 27
28 Family Economics and Nutrition Review
Shanthy A. Bowman, PhD
Ellen W. Harris, DrPH
Agricultural Research Service
U.S. Department of Agriculture
2003 Vol. 15 No. 2
Research Brief
Food Security, Dietary Choices,
and Television-Viewing Status of
Preschool-Aged Children Living
in Single-Parent or Two-Parent
Households
Over the past decades, the number of
U.S. single-parent households has
increased-particularly those headed
by females (U.S. Census Bureau, 2001).
In general, single-parent households
have a lower household income than
do other households and, consequently,
tend to spend Jess money on food. As a
result, single-parent households may be
food insecure (Casey, Szeto, Lensing,
Bogle, & Weber, 2001; Nord & Bickel,
2002).
In addition to changes in household
structure over these decades, the
prevalence of childhood overweight
and obesity also increased (Ogden,
Flegal, Carroll, & Johnson, 2002)­notably
among low-income groups
(Certain & Kahn, 2002)-and are a
concern for several reasons, including
their detrimental effects on children's
quality of life and the potential increase
in future health care costs. According
to the National Health and Nutrition
Examination Survey III (NHANES ill),
7.2 percent of 2- to 5-year-old children
were overweight between 1994 and
1998; according to Ogden and col­leagues
(2002), 10.4 percent were
overweight. Also, sedentary lifestyle
practices contribute to overweight
among children (Crespo et al., 2001).
Thus, we find that poor dietary intakes
that do not comply with expert recom­mendations,
combined with many hours
of television viewing, are among the
postulated reasons for the increase in
the prevalence of childhood overweight
and obesity in the United States
(Robinson, 1999).
The objectives of this study were to
compare food security and economic
status of households headed by females
only (single-parent) and households
headed by both a male and female
(two-parent) and to examine whether
children ages 2 to 5 in these households
had different patterns of dietary intakes
and television- and videotape-viewing
practices. The findings would show
whether children living in female­headed
households have dietary and
other behavioral charactetistics that
may promote childhood obesity.
Methods
We used data from the USDA's 1994-
96 Continuing Survey of Food Intakes
by Individuals (1994-96 CSFII) and the
1998 Supplemental Children's Survey
(1998 CSFII) (U.S. Department of
Agriculture [USDA], 2000). Both
surveys include nationally representa­tive
samples: the 1994-96 CSFII
includes persons of all ages, and the
1998 CSFII includes children from
birth to 9 years. In these two surveys,
dietary intake data are collected on
2 nonconsecutive days, 3 to 10 days
apart (Tippett & Cypel, 1998), via a
interviewer-administered 24-hour
29
recall that uses a multiple-pass tech­nique
to reduce underreporting. In the
surveys, interviews for children under
6 years old are conducted with the adult
household member (proxy) who is
responsible for preparing the child's
meals. Additionally, proxy interviews
are conducted for respondents who
cannot report for themselves because of
physical or mental limitations. For our
study, children were included if they
were 2 to 5 years old and had complete
food intake records on Day 1 of the
survey. The children resided in single­parent,
female-headed households or
two-parent households headed by both
a male and a female. The children
(n = 190) who lived in male-headed
households were excluded from this
study because of the small sample size.
Children's mean food and nutrient
intakes and television- and videotape­viewing
behaviors were analyzed, as
were household socioeconomic and
demographic characteristics. Nutrients
and food-group definitions in the
analysis were the same as those in the
1994-96 CSFII (see box). Households
that had enough of the kinds and
quantities of foods they wanted to
eat were considered "food secure";
households that either did not have
enough food to eat or did not always
have the kinds offoods they wanted to
eat were considered "food insecure."
Money spent by households on
groceries consisted of expenditures
on store-bought foods plus prepared
foods brought home from a grocery
store's soup or salad bar or deli.
Money spent on food away from home
consisted of expenditures on prepared
foods and beverages that were both
bought and eaten away from home
(e.g., food eaten at restaurants, fast­food
places, work or school cafeterias,
or foods and beverages from vending
machines). Money spent per person
per month for food was computed by
dividing the total money spent for food
30
Definitions of Added Sugars and Food Groups
Added sugars includes sugars used as ingredients in processed or prepared
foods, sugars eaten separately, and sugars added to foods at the table. Examples
of foods and bevera"es containing added sugars are baked goods such as cakes,
cookies, pastries and bread; dairy desserts; non-diet soft drinks; non-diet
flavored drinks; and candies, jams, jellies, and syrups. Added sugars do not
include sugars that are present naturally in foods, such as lactose in milk and
fructose in fruits.
Whole milk includes whole fluid milk, low sodium whole milk. and reconsti­tuted
whole dry milk.
Lowfat and skim milk includes lowfat ( 1% and 2%) milk, skim or nonfat milk,
lowfat or nonfat lactose-reduced fluid milk, and reconstituted lowfat and nonfat
dry milk.
Frankfurters and sausages includes frankfurters, sausages; luncheon meats
made from beef, pork, ham, veal, game, chicken, and turkey; and baby-food
meat sticks.
Melons and berries includes cantaloupe, honeydew melon, watermelon,
blueberries, blackberries, strawberries, raspberries, and cranberries.
Non-diet carbonated beverages and sweetened, fruit-flavored drinks
includes all carbonated soft drinks except unsweetened and sugar-free types; all
fruit drinks, fruit punches, fruit ades including those made from powdered mix
and frozen concentrates and excludes low-calorie and low-sugar types. Excludes
fruit juices.
by the household in a month by the total
number of individuals in the household.
No attempt was made to allocate money
differently among adults and children
within each household. For this study,
we discuss statistically significant
(p < 0.05) differences only.
The SUDAAN1 software package was
used to estimate percentages, means,
and standard errors and to compare
means of children living in households
headed by a female with those living in
households headed by both a male and
female. The SAS2 software package
1 SUDAAN for Solaris, release 8.0.1 , 2002,
Research Triangle Park, NC.
2SAS, release 8.2, 1999-2001, Cary, NC.
was used to estimate socioeconomic
and demographic characteristics of the
children living in these two households.
Results and Discussion
Of the 5,594 children included in this
study, 81 percent lived in two-parent
households and 19 percent lived in
female-headed households (table 1).
About half (53 percent) of all African­American
children lived in female­headed
households. Children living in
female-headed households were more
likely to live in low-income (4 of 10
below 130 percent of poverty level)
and urban (3 of 10) households, while
children living in two-parent house­holds
were more likely to live in
Family Economics and Nutrition Review
Table 1. Socioeconomic and demographic characteristics of children 2 to 5 years
1994·96, 98 CSFII '
Percentage of Percentage of
children in children living in
Characteristics total population 1 female-headed households2
Gender
Male
Female
Race/ethnicity
Caucasian
African American
All Hispanics
Non-Hispanic, other races
Household income (% of poverty)
Below 130%
131 to 350%
Above 350%
Urbanization
Urban
Suburban
Rural
Region
Northeast
Midwest
South
West
1n = 5,594.
2n = 999.
affluent suburban households. Com­pared
with other regions, the Western
region of the United States had the
lowest percentage of children living
in female-headed households, about
15 percent versus 20 percent.
The three indicators of food-security
status were strikingly different between
the two household types. While 74
percent of children in two-parent
households had enough of the kinds
of foods they wanted to eat, only 56
percent of children in female-headed
households were food secure (table 2).
Compared with children in two-parent
households, children in female-headed
households tended not to have the
kinds of food they wanted to eat
2003 Vol. 15 No. 2
51 .3 19.6
48.7 18.7
61 .8 10.3
16.2 53.2
16.3 20.2
5.7 15.2
31.4 44.5
43.7 10.2
24.9 3.1
32.2 30.0
47.8 12.2
20.0 18.4
19.2 20.3
23.7 20.3
33.6 21 .0
23.5 14.5
(37 percent vs. 24 percent) and not
enough food to eat (7 percent vs. 2
percent). Female-headed households
spent less money, per person, on
monthly groceries, compared with
two-parent households ($87 vs. $92).
In addition, these households spent less
money on foods purchased and eaten
away from home, including food from
fast-food places and restaurants ($17
per person vs. $26 per person). The
amount of money spent on fast-food
or carryout food brought into the house
was not different ($14 per person for
both household groups).
The children in female-headed house­holds
consumed more energy than did
children in male- and female-headed
Children from female-headed
households, compared with
those in male- and female-headed
households, consumed higher
amounts of high-fat foods such
as whole milk and frankfurters
and sausages, ate lower amounts
of relatively expensive fruits
such as melons and berries,
and drank more non-diet
carbonated beverages and
sweetened fruit-flavored drinks.
31
households (1,642 kcal vs. 1,577 kcal)
(table 3). Of these calories, higher
amounts and proportions were from
total fat and saturated fat. Whereas,
children in female-headed households
consumed 62 g of total fat (34 percent
of calories) and 23 g of saturated fat
(13 percent of calories), children in
two-parent households consumed 56 g
of total fat (32 percent of calories) a~d
21 g of saturated fat ( 12 percent of
calories). Thus, our results showed
that a smaller percentage of children
in female-headed households met the
recommendations of the Dietary
Guidelines for total fat and saturated
fat (USDA & DHHS, 2000).
Among the intake patterns that influ­enced
differences in nutrient status
were the following: Children from
female-headed households, compared
with those in male- and female-headed
households, consumed higher amounts
of high-fat foods such as whole milk
and frankfurters and sausages, ate
lower amounts of relatively expensive
fruits such as melons and benies,
and drank more non-diet carbonated
beverages and sweetened fruit-flavored
drinks.
For both household types, children's
consumption of added sugars far
exceeded the levels recommended
in the Food Guide Pyramid (USDA,
1996). The Food Guide Pyramid's
suggested levels of added sugars are
6, 12, and 18 teaspoons (24, 48, and
72 g) per 1,600, 2,200, and 2,800
calories of energy intakes per day.
Because of the increase in the preva­lence
of childhood obesity, reducing
intakes of foods and beverages that
contain high amounts of added sugars
and fat could help reduce intakes
of empty, extra calories during child­hood
(Ludwig, Peterson, & Gortmaker,
2001). Soft drinks and fruit-flavored
sugary drinks are the top sources
of added sugars in the U.S. diet
(Bowman, 1999).
32
Table 2. Food security status of and monthly expenditures by households with
children 2 to 5 years, 1994-96, 98 CSFII
Male- and female-headed Female-headed
Having enough of the kinds of food they want to eat*
Having enough but not always the kinds of food they
want to eat*
Sometimes or often not having enough to eat*
Household groceries*
Food bought and eaten away from home*
Fast-food or carryout food brought into home
*Statistically different at p < 0.05.
household household
74
24
2
Percent
56
37
7
Mean dollars per person per month
92 87
26 17
14 14
Table 3. Mean energy, selected nutrients, food intake status, and hours of
television- and videotape-viewing status of children 2 to 5 years, 1994-96, 98 CSFII
Male- and female-headed Female-headed
household household
Mean
Energy (kcal)* 1,577 1,642
Total fat (g)* 56 62
Saturated fat (g)* 21 23
Carbohydrate (g) 218 218
Added sugars (g) 62 62
Protein (g)* 56 59
Percent of total fat calories* 32 34
Percent of saturated fat calories* 12 13
Percent of children having 30"/o or less energy
from total taP* 40 32
Percent of children having 1 0"/o or less energy
from saturated fat1* 29 25
Whole milk (g)* 149 191
Lowfat and skim milk (g)* 188 114
Frankfurters and sausages (g)* 19 26
Melons and berries (g)* 14 7
Non-diet carbonated beverages and sweetened,
fruit-flavored drinks (g)* 203 227
Number of hours of television/videotapes viewed* 2.5 3.0
Percent of children who viewed more than 2 hours
of television/videotapes* 62 68
'Statistically different at p < 0.05.
1Recommendations of the USDA's Food Guide Pyramid (1996) and Dietary Guidelines for Americans (2000).
Family Economics and Nutrition Review
Differences were also seen in
television- and videotape-viewing
behaviors between the two household
groups. The children living in female,
single-parent households watched more
hours of television and videotapes,
compared with children living in
two-parent households (3.0 hours vs.
2.5 hours each day) . Additionally, a
higher percent of children in female­headed
households (68 percent vs. 62
percent) watched more than a total
of 2 hours per day. These fmdings are
important because television viewing
has been associated with weight status
in children (Dennison, Erb, & Jenkins,
2002; Eisenmann, Bartes, & Wang,
2002; Robinson, 1999; Saelens et al.,
2002).
Conclusions
Nutrition education for children
continues to be necessary, especially
for children living in female-headed
households. In particular, our study
demonstrated that children in these
households had higher energy and fat
intakes and watched more hours of
television and videotapes per day
than did children living in two-parent
households, thus placing themselves at
a higher risk for overweight or obesity.
Efforts should be made to encourage
lowfat food choices, especially in the
dairy and meat groups. In addition, we
observed that all children, regardless
of the household type, consumed a
lot of added sugars and drank a large
amount of fruit-flavored drinks and
non-diet carbonated beverages.
Encouraging children to drink water
or 100-percent juice, instead of
sweetened, fruit-flavored beverages,
would help reduce intakes of empty
calories.
Nutrition for caregivers also may be
beneficial because children's dietary
behaviors are patterned after their
2003 Vol. 15 No. 2
family's behaviors (Dennison et al.,
2001; Fitzgibbon, Stolley, Dyer,
Van Horn, & Kaufer-Christoffel, 2002;
Eisenmann et al., 2002). Adults who
prepare young children's food should
choose lean cuts of meat and adopt
lowfat food preparation techniques
such as removing skin from chicken,
trimming fat from meat, and encourag­ing
children to drink lowfat milk. These
practices would help reduce consump­tion
of both total and saturated fats.
Interventions should also aim at
reducing time spent viewing television
or videotapes. Encouraging children to
increase their physical activity may help
prevent or reduce obesity. Therefore,
early interventions with both children
and their caregivers are important for
preventing obesity later in life.
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sugars. Family Economics and Nutrition Review, 12(2), 31-38.
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food-insufficient, low-income families: Prevalence, health and nutrition status.
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observational analysis. Lancet, 357, 505-507.
33
Nord, M., & Bickel, G. (April, 2002). Measuring Children's Food Security in U.S.
Households, 1995-99. (Food Assistance and Nutrition Research Report No. 25).
Economic Research Service, U.S. Department of Agriculture.
Ogden, C.L., Flegal, K.M., Carroll, M.D., & Johnson, C.L. (2002). Prevalence and
trends in overweight among U.S. children and adolescents, 1999-2000. Journal of
the American Medical Association, 288(14), 1728-1732.
Robinson, T .. (1999). Reducing children's television viewing to prevent obesity.
Journal of the American Medical Association, 282(16), 1561-1567.
Saelens, B.E., Sallis, J.F., Nader, P.R., Broyles, S.L., Berry, C.C., & Taras, H.L.
(2002). Home environmental influences on children's television watching from
early to middle childhood. Journal of Developmental and Behavioral Pediatrics,
23(3), 127-132.
Tippett, K.S. , & Cypel, Y.S. (Eds.). (1998). Design and Operation: The Continuing
Survey of Food Intakes by Individuals and the Diet and Health Knowledge Survey,
1994-96. (NFS Report No. 96-1). U.S. Department of Agriculture, Agricultural
Research Service.
U.S. Census Bureau. (200 1). Statistical Abstract of the United States: 2001
(121st ed.). Washington, DC.
U.S. Department of Agriculture, Agricultural Research Service. (1998). Food and
Nutrient Intakes by Individuals in the United States, by Sex and Age, 1994-96.
(NFS Report No. 96-2).
U.S. Department of Agriculture, Agricultural Research Service. (2000). The
Continuing Survey of Food Intakes by Individuals, 1994-96, 1998. National
Technical Information Service. CD-ROM data.
U.S. Department of Agriculture, Center for Nutrition Policy and Promotion. (1996).
The Food Guide Pyramid. (Home and Garden Bulletin No. 252).
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(2000). Nutrition and Your Health: Dietary Guidelines for Americans (5th ed.)
(Home and Garden Bulletin No. 232).
34 Family Economics and Nutrition Review
Mark Lino, PhD
U.S. Department of Agriculture
Center for Nutrition Policy and Promotion
2003 Vol. 15 No.2
Center Reports
Expenditures on Children
by Families, 2002
This article presents the 2002 estimates of expenditures on children by husband­wife
and single-parent families. Data and methods used in calculating annual
child-rearing expenses are described. Estimates are provided by budgetary
component, age of the child, family income, and region of residence. For the
overall United States, estimates of child-rearing expenses ranged between $9,230
and $10,300 for a child in a two-child, husband-wife family in the middle-income
group.
C hild rearing is a costly endeavor.
Since 1960 the U.S. Department
of Agriculture (USDA) has
provided annual estimates offamily
expenditures on children from their
birth through age 17. USDA's annual
child-rearing expense estimates are
used in four major ways:
• To determine State child support
guidelines. The economic well­being
of millions of children is
affected by child support. Under the
Family Support Act of 1988, States
are required to have numeric child
support guidelines and to consider
the economic costs of raising a
child when establishing these
guidelines.
• To determine State foster care
payments. Many States use the
estimates to determine how much
to reimburse people with foster care
children. In 1999 about 581 ,000
children were in foster care (U.S.
Deprutment of Health and Human
Services, 2001).
figures to determine compensation
for the family .
• To educate anyone who is
considering when or whether to
have children. Knowing how much
it costs to raise a child until that
child reaches the age of maturity
may encourage teens to wait until
adulthood and be more prepared
financially to have children.
USDA Method for Estimating
Expenditures on Children by
Families1
USDA provides annual estimates of
expenditures on children from their
birth through age 17. These expendi­tures
on children, by husband-wife and
single-pru·ent families, are estimated
for the major budgetary components:
housing, food, transportation, clothing.
health care, child care/education, and
miscellaneous goods and services (see
box).
• To appraise damages arising from
personal injury or wrongful death
cases. For example, if a person with
children is hurt on a job such that
he or she cannot work, the courts
use the child-rearing expense
1 Expenditures on Children by Families, 2002
provides a more detailed description of the data
and methods. To obtain a copy, go to http://
www.cnpp.usda.gov, or you may contact USDA,
Center for Nutrition Policy and Promotion, 31 OJ
Park Center Drive, Room I 034, Alexandria, VA
22302 (telephone: 703-305-7600).
35
The most recently calculated child­rearing
expenses are based on 1990-92
Consumer Expenditure Survey (CE)
data, which are updated to 2002 dollars
by using the Consumer Price Index
(CPI). The CE, administered by the
Bureau of Labor Statistics, U.S.
Department of Labor, is the only
Federal survey of household expendi­tures
collected nationwide. It contains
information on sociodemographic ·
characteristics, income, and expendi­tures
of a nationally representative
sample of households. The sample used
to determine child-rearing expenses
consisted of 12,850 husband-wife
and 3,395 single-parent households,
weighted to reflect the U.S. population
of interest.
In determining child-rearing expenses,
USDA examines the intrahousehold
distribution of expenditures by using
data for each budgetary component.
In the CE, the data on these budgetary
components are child-specific (cloth­ing,
child care, and education) and
household-specific (housing, food,
transportation, health care, and miscel­laneous
goods and services). Multi­variate
analysis, used to estimate
household- and child-specific expendi­tures,
controlled for income level,
family size, age of the child, and
region of residence (when appropriate)
so that expenses could be determined
for families with these varying
characteristics.
Estimates of child-rearing expenses
are provided for three income levels,
which were determined by dividing the
sample of husband-wife families in the
overall United States into equal thirds.
For each income level, the estimates
are for the younger child in families
with two children. These younger
children were grouped in one of six
Categories of Household Expenditures
age categories: 0-2, 3-5, 6-8, 9-11,
12-14, or 15-17. Households with two
children were selected as the standard
because this was the average household
size in 1990-92. The focus is on the
younger child because the older child
may be over age 17.
Child-rearing estimates provided by the
USDA are based on CE interviews of
households with and without specific
expenses. For some families, expendi­tures
may be higher or lower than the
mean estimates, depending on whether
or not they incur a particular expense.
Calculation of child care and education
expenditures are examples, because
about 50 percent of husband-wife
families in the study spent no money
on these goods and services. Also, the
estimates cover only out-of-pocket
expenditures on children made by the
parents and not by others, such as
grandparents or friends.
Housing expenses: shelter (mortgage interest, property taxes, or rent; maintenance and repairs; and insurance), utilities
(gas, electricity, fuel, telephone, and water), and house furnishings and equipment (furniture, floor coverings, and major
and small appliances). For homeowners, housing expenses do not include mortgage principal payments; in the data set
used, such payments are considered to be part of savings.
Food expenses: food and nonalcoholic beverages purchased at grocery, convenience, and specialty stores, including
purchases with food stamps; dining at restaurants; and household expenditures on school meals.
Transportation expenses: the net outlay on the purchase of new and used vehicles, vehicle finance charges, gasoline and
motor oil, maintenance and repairs, insurance, and public transportation.
Clothing expenses: children's apparel such as diapers, shirts, pants, dresses, and suits; footwear; and clothing services
such as dry cleaning, alterations and repair, and storage.
Health care expenses: medical and dental services not covered by insurance, prescription drugs and medical supplies not
covered by insurance, and health insurance premiums not paid by the employer or other organizations.
Child care and education expenses: daycare tuition and supplies; babysitting; and elementary and high school tuition,
books. and supplies.
Miscellaneous expenses: personal care items, entertainment, and reading materials.
36 Family Economics and Nutrition Review
After estimating the various overall
. household and child-specific expendi­tures,
USDA allocated these total
amounts among family members (i.e.,
in a married-couple, two-child family,
the total amounts were allocated to the
husband, wife, older child, and younger
child). Because the expenditures for
clothing, child care, and education
are child-specific-and apply only to
children-allocations of these expenses
were made by dividing them equally
among the children. The CE does not
collect child-specific expenditures on
food and health care. Thus, to apportion
these budgetary components to a child
based on his or her age, USDA used
data from other Federal studies, which
show the shares of the household
budget spent on children's food and
health care.
Unlike food and health care, no
authoritative source exists for allocating
among family members the amount the
household spends on housing, transpor­tation,
and other miscellaneous goods
and services. The marginal cost and the
per capita methods are two common
approaches used to allocate these
expenses.
The marginal cost method measures
expenditures on children as the differ­ence
in expenses between couples
with children and equivalent childless
couples. Various equivalency measures,
yielding very different estimates of
expenditures on children, have been
proposed, but no standard measure has
been accepted by economists. Also,
the marginal cost approach assumes
that the difference in total expenditures
between couples with and without
children can be attributed solely to
the presence of children in a family.
This assumption is questionable,
especially because couples without
children often buy homes larger than
they need in anticipation of having
children. Comparing the expenditures
of these couples to those of similar
2003 Vol. 15 No. 2
couples with children could lead to
underestimating how much is spent
on meeting the lifetime needs-and
wants-of children.
For these reasons, USDA uses the
per capita method to allocate expenses
on housing, transportation, and miscel­laneous
goods and services in equal
proportions an1ong household
members. Although the per capita
method has its limitations, they are
considered less severe than those of
the marginal cost approach.
Because transportation expenses
resulting from work activities are not
directly related to the cost of raising a
child, these expenses were excluded
when determining children's
transportation expenses.
Expenditures on Children by
Husband-Wife Families
Child-Rearing Expenses and
Household Income Are Positively
Associated
In 2002, estimated average expenses on
children increased as income level rose
(fig. 1). Depending on the age of the
child, the annual expenses ranged from
$6,620 to $7,670 for families in the
lowest income group, from $9,230 to
$10,300 for families in the middle­income
group, and from $13,750 to
$14,950 for families in the highest
income group. The before-tax income
in 2002 for the lowest income group
was less than $39,700, between
$39,700 and $66,900 for the middle­income
group, and more than $66,900
for the highest income group.
On average, households in the lowest
income group spent 28 percent of their
before-tax income per year on a child;
those in the middle-income group,
18 percent; and those in the highest
group, 14 percent. The range in these
On average, households in the
lowest income group spent 28
percent of their before-tax income
per year on a child; those in the
middle-income group, 18 percent;
and those in the highest group,
14 percent.
37
percentages would be narrower if after­tax
income were considered, because a
greater percentage of income in higher
income households goes toward taxes.
On average, the amount spent on
children by families in the highest
income group was about twice the
amount spent by families in the lowest
income group. This amount varied
by budgetary component. In general,
expenses on a child for goods and
services considered to be necessities
(e.g., food and clothing) did not vary
as much as those considered to be
discretionary (e.g., miscellaneous
expenses) among households in the
three income groups.
Housing Is the Largest Expense
on a Child
Housing accounted for the largest
share of total