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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 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. The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and activities on the basis of race, color, national origin, sex, religion, age, disability, political beliefs, sexual orientation, or marital or family status. (Not all prohibited bases apply to all programs.) Persons with disabilities who require alternative means for communication of program information (Braille, large print, audiotape, etc.) should contact USDA's TARGET Center at (202) 720-2600 (voice and TDD). To file a complaint of discrimination, write USDA, Director, Office of Civil Rights, Room 326-W, Whitten Building, 14th and Independence Avenue, SW, Washington, DC 20250- 9410 or call (202) 720-5964 (voice and TDD). USDA is an equal opportunity provider and employer. 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 published semiannually by the Center for Nutrition Policy and Promotion, U.S. Department of Agriculture, Washington, DC. The Secretary of Agriculture has determined that publication of this periodical is necessary in the transaction of the public business required by law of the Department. This publication is not copyrighted. Thus, contents may be reprinted without permission, but credit to Family Economics and Nutrition Review would be appreciated. Use of commercial or trade names does not imply approval or constitute endorsement by USDA. Family Economics and Nutrition Review is indexed in the following databases: AGRICOLA, Ageline, Economic Literature Index, ERIC, Family Studies, PAIS, and Sociological Abstracts. Family Economics and Nutrition Review is for sale by the Superintendent of Documents. Sub_scription price is $13 per year ($18.20 for foreign addresses). Send subscription order and change of address to Superintendent of Documents, P.O. Box 371954, Pittsburgh, PA 15250-7954. (See subscription form on p. 72.) Original manuscripts are accepted for publication. (See "guidelines for submissions" on back inside cover.) Suggestions or comments concerning this publication should be addressed to Julia M. Dinkins, Editor, Family Economics and Nutrition Review, Center for Nutrition Policy and Promotion, USDA, 3101 Park Center Drive, Room 1034, Alexandria, VA 22302-1594. Family Economics and Nutrition Review is now available at www.cnpp.usda.gov. 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. 2003 Vol. 15 No. 2 13 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 of Agriculture. 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 References Adolescent nutrition: A springboard for health [Supplement]. (2002). Journal of the American Dietetic Association, 102(3). American Diabetes Association. (2000). Type 2 diabetes in children and adolescents. Diabetes Care, 23(3), 381-389. Contento, I., Balch, G.I., Bronner, Y.L., Lytle, L.A., Maloney, S.K., Olson, C.M., et al. (1995). The effectiveness of nutrition education and implications for nutrition education policy, programs, and research: A review of research. Journal of Nutrition Education, 27(6); special issue. Enns, C.W., Goldman, J.D., & Cook, A. (1997). Trends in food and nutrient intakes by adults: NFCS 1977-78, CSFll 1989-91, and CSFII1994-95. Family Economics and Nutrition Review, 10(4), 2-15. Enns, C.W., Mickle, S.J., & Goldman, J.D. (2002). Trends in food and nutrient intakes by children in the United States. Family Economics and Nutrition Review, 14(2), 56-68. Guenther, P.M. (1986). Beverages in the diets of American teenagers. Journal of the American Dietetic Association, 86(4), 493-499. Goldberg, G.R., Black, A.E., Jebb, S.A., Cole, T.J., Murgatroyd, P.R., Coward, W.A., et al. (1991). Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify underrecording. European Journal of Clinical Nutrition 45, 569-581. Harnack, L., Stang, J., & Story, M. (1999). Soft drink consumption among US children and adolescents: Nutritional consequences. Journal of the American Dietetic Association, 99(4), 436-441. Institute of Medicine. (2002). Dietary Reference Intakes for Energy, Carbohydrate, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids. Washington, DC: National Academic Press. 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 Vital and Health Statistics, No. 314). Hyattsville, MD: National Center for Health 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. SAS (Version 8.2) [computer software]. (1999). Cary, NC: SAS Institute. Schofield, W.N. (1985). Predicting basal metabolic rate, new standards and review of previous work. Human Nutrition Clinical Nutrition, 39C(Suppl. 1), 5-41. 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 Knowledge Survey I994-96. U.S. Department of Agriculture, Agricultural Research Service, Nationwide Food Surveys Rep. No. 96-1; NTIS No. PB98- 137268. Retrieved May 21, 2002, from http://www.barc.usda.gov/bhnrc/ foodsurvey/Dor.htrnl. 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, Year 1977-78. U.S. Department of Agriculture, Human Nutlition Information Service, Nationwide Food Consumption Survey 1977-78, Rep. I-1; NTIS No. 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:// www.barc.usda.gov/bhnrc/cnrg/tables.pdf. U.S. Department of Agriculture, Center for Nutrition Policy and Promotion. (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. U.S. Department of Health and Human Services. (2001). The Surgeon General's 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. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention. (1996). Physical Activity and Health: A Report of the Surgeon General. NTIS No. PB97-159149. Atlanta, GA. U.S. Department of Health and Human Services, National Center for Health Statistics. (2001). Table 70: Overweight children and adolescents 6-19 years of age, according to sex, age, race, and Hispanic origin: United States, selected years 1963-65 through 1988-94. In Health United States 2001 with Urban and Rural Health Chartbook. Retrieved June 25, 2002, from http://www.cdc.gov/nchs/data! hus/husOl.pdf. Weinsier, R.L., Hunter, G.R., Heini, A.F., Goran, M.I., & Sell, S.M. (1998). The etiology of obesity: Relative contribution of metabolic factors, diet, and physical activity. American Journal of Medicine 105(2), 145-150. 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. References Bowman, S.A. (1999). Diets of individuals based on energy intakes from added sugars. Family Economics and Nutrition Review, 12(2), 31-38. Casey, P.H., Szeto, K., Lensing, S., Gogle, M., & Weber, J. (2001). Children in food-insufficient, low-income families: Prevalence, health and nutrition status. Archives of Pediatrics and Adolescent Medicine, 155(4), 508-514. Certain, L.K., & Kahn, R.S. (2002). Prevalence, correlates, and trajectory of television viewing among infants and toddlers. Pediatrics, 109(4), 634-642. Crespo, C.J., Srnit, E., Troiano, R.P., Bartlett, S.J., Macera, C.A., & Anderson, R.E. (2001). Television watching, energy intake, and obesity in children: Results from the Third National Health and Nutrition Examination Survey, 1988-1994. Archives of Pediatrics and Adolescent Medicine, 155(3), 360-365. Dennison, B.A., Erb, T.A., & Jenkins, P.L. (2002). Television viewing and television in bedroom associated with overweight risk among low-income preschool children. Pediatrics, 109(6), 1028-1035. Dennison, B.A., Erb, T.A., & Jenkins, P.L. (2001). Predictors of dietary milk fat intake by preschool children. Preventive Medicine, 33(6), 536-542. Eisenmann, J.C., Bartes, R.T. , & Wang, M.Q. (2002). Physical activity, TV viewing, and weight in U.S. youth: 1999 Youth Risk Behavior Survey. Obesity Research, 10(5), 379-385. Fitzgibbon, M.L., Stolley, M.R., Dyer, A.R., Van Horn, L., & Kaufer-Christoffel, K. (2002). A community-based obesity prevention program for minority children: Rationale and study design for Hip-Hop to Health Jr. Preventive Medicine, 34(2), 289-297. Ludwig, D.S., Peterson, K.E., & Gortrnaker, S.L. (2001). Relationship between consumption of sugar sweetened drinks and childhood obesity: A prospective, 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). U.S. Department of Agriculture & 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). 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
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Title | Family Economics and Nutrition Review [Volume 15, Number 2] |
Date | 2003 |
Contributors (group) | Center for Nutrition Policy and Promotion (U.S.) |
Subject headings |
Home economics--United States--Periodicals Nutrition policy--United State--Periodicals |
Type | Text |
Format | Pamphlets |
Physical description | v. : $b ill. ; $c 28 cm. |
Publisher | Washington, D.C. : U.S. Dept. of Agriculture |
Language | en |
Contributing institution | Martha Blakeney Hodges Special Collections and University Archives, UNCG University Libraries |
Source collection | Government Documents Collection (UNCG University Libraries) |
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Additional rights information | NO COPYRIGHT - UNITED STATES. This item has been determined to be free of copyright restrictions in the United States. The user is responsible for determining actual copyright status for any reuse of the material. |
SUDOC number | A 77.245:15/2 |
Digital publisher | The University of North Carolina at Greensboro, University Libraries, PO Box 26170, Greensboro NC 27402-6170, 336.334.5482 |
Full-text | 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. The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and activities on the basis of race, color, national origin, sex, religion, age, disability, political beliefs, sexual orientation, or marital or family status. (Not all prohibited bases apply to all programs.) Persons with disabilities who require alternative means for communication of program information (Braille, large print, audiotape, etc.) should contact USDA's TARGET Center at (202) 720-2600 (voice and TDD). To file a complaint of discrimination, write USDA, Director, Office of Civil Rights, Room 326-W, Whitten Building, 14th and Independence Avenue, SW, Washington, DC 20250- 9410 or call (202) 720-5964 (voice and TDD). USDA is an equal opportunity provider and employer. 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 published semiannually by the Center for Nutrition Policy and Promotion, U.S. Department of Agriculture, Washington, DC. The Secretary of Agriculture has determined that publication of this periodical is necessary in the transaction of the public business required by law of the Department. This publication is not copyrighted. Thus, contents may be reprinted without permission, but credit to Family Economics and Nutrition Review would be appreciated. Use of commercial or trade names does not imply approval or constitute endorsement by USDA. Family Economics and Nutrition Review is indexed in the following databases: AGRICOLA, Ageline, Economic Literature Index, ERIC, Family Studies, PAIS, and Sociological Abstracts. Family Economics and Nutrition Review is for sale by the Superintendent of Documents. Sub_scription price is $13 per year ($18.20 for foreign addresses). Send subscription order and change of address to Superintendent of Documents, P.O. Box 371954, Pittsburgh, PA 15250-7954. (See subscription form on p. 72.) Original manuscripts are accepted for publication. (See "guidelines for submissions" on back inside cover.) Suggestions or comments concerning this publication should be addressed to Julia M. Dinkins, Editor, Family Economics and Nutrition Review, Center for Nutrition Policy and Promotion, USDA, 3101 Park Center Drive, Room 1034, Alexandria, VA 22302-1594. Family Economics and Nutrition Review is now available at www.cnpp.usda.gov. 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. 2003 Vol. 15 No. 2 13 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 of Agriculture. 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 References Adolescent nutrition: A springboard for health [Supplement]. (2002). Journal of the American Dietetic Association, 102(3). American Diabetes Association. (2000). Type 2 diabetes in children and adolescents. Diabetes Care, 23(3), 381-389. Contento, I., Balch, G.I., Bronner, Y.L., Lytle, L.A., Maloney, S.K., Olson, C.M., et al. (1995). The effectiveness of nutrition education and implications for nutrition education policy, programs, and research: A review of research. Journal of Nutrition Education, 27(6); special issue. Enns, C.W., Goldman, J.D., & Cook, A. (1997). Trends in food and nutrient intakes by adults: NFCS 1977-78, CSFll 1989-91, and CSFII1994-95. Family Economics and Nutrition Review, 10(4), 2-15. Enns, C.W., Mickle, S.J., & Goldman, J.D. (2002). Trends in food and nutrient intakes by children in the United States. Family Economics and Nutrition Review, 14(2), 56-68. Guenther, P.M. (1986). Beverages in the diets of American teenagers. Journal of the American Dietetic Association, 86(4), 493-499. Goldberg, G.R., Black, A.E., Jebb, S.A., Cole, T.J., Murgatroyd, P.R., Coward, W.A., et al. (1991). Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify underrecording. European Journal of Clinical Nutrition 45, 569-581. Harnack, L., Stang, J., & Story, M. (1999). Soft drink consumption among US children and adolescents: Nutritional consequences. Journal of the American Dietetic Association, 99(4), 436-441. Institute of Medicine. (2002). Dietary Reference Intakes for Energy, Carbohydrate, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids. Washington, DC: National Academic Press. 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 Vital and Health Statistics, No. 314). Hyattsville, MD: National Center for Health 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. SAS (Version 8.2) [computer software]. (1999). Cary, NC: SAS Institute. Schofield, W.N. (1985). Predicting basal metabolic rate, new standards and review of previous work. Human Nutrition Clinical Nutrition, 39C(Suppl. 1), 5-41. 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 Knowledge Survey I994-96. U.S. Department of Agriculture, Agricultural Research Service, Nationwide Food Surveys Rep. No. 96-1; NTIS No. PB98- 137268. Retrieved May 21, 2002, from http://www.barc.usda.gov/bhnrc/ foodsurvey/Dor.htrnl. 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, Year 1977-78. U.S. Department of Agriculture, Human Nutlition Information Service, Nationwide Food Consumption Survey 1977-78, Rep. I-1; NTIS No. 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:// www.barc.usda.gov/bhnrc/cnrg/tables.pdf. U.S. Department of Agriculture, Center for Nutrition Policy and Promotion. (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. U.S. Department of Health and Human Services. (2001). The Surgeon General's 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. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention. (1996). Physical Activity and Health: A Report of the Surgeon General. NTIS No. PB97-159149. Atlanta, GA. U.S. Department of Health and Human Services, National Center for Health Statistics. (2001). Table 70: Overweight children and adolescents 6-19 years of age, according to sex, age, race, and Hispanic origin: United States, selected years 1963-65 through 1988-94. In Health United States 2001 with Urban and Rural Health Chartbook. Retrieved June 25, 2002, from http://www.cdc.gov/nchs/data! hus/husOl.pdf. Weinsier, R.L., Hunter, G.R., Heini, A.F., Goran, M.I., & Sell, S.M. (1998). The etiology of obesity: Relative contribution of metabolic factors, diet, and physical activity. American Journal of Medicine 105(2), 145-150. 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. References Bowman, S.A. (1999). Diets of individuals based on energy intakes from added sugars. Family Economics and Nutrition Review, 12(2), 31-38. Casey, P.H., Szeto, K., Lensing, S., Gogle, M., & Weber, J. (2001). Children in food-insufficient, low-income families: Prevalence, health and nutrition status. Archives of Pediatrics and Adolescent Medicine, 155(4), 508-514. Certain, L.K., & Kahn, R.S. (2002). Prevalence, correlates, and trajectory of television viewing among infants and toddlers. Pediatrics, 109(4), 634-642. Crespo, C.J., Srnit, E., Troiano, R.P., Bartlett, S.J., Macera, C.A., & Anderson, R.E. (2001). Television watching, energy intake, and obesity in children: Results from the Third National Health and Nutrition Examination Survey, 1988-1994. Archives of Pediatrics and Adolescent Medicine, 155(3), 360-365. Dennison, B.A., Erb, T.A., & Jenkins, P.L. (2002). Television viewing and television in bedroom associated with overweight risk among low-income preschool children. Pediatrics, 109(6), 1028-1035. Dennison, B.A., Erb, T.A., & Jenkins, P.L. (2001). Predictors of dietary milk fat intake by preschool children. Preventive Medicine, 33(6), 536-542. Eisenmann, J.C., Bartes, R.T. , & Wang, M.Q. (2002). Physical activity, TV viewing, and weight in U.S. youth: 1999 Youth Risk Behavior Survey. Obesity Research, 10(5), 379-385. Fitzgibbon, M.L., Stolley, M.R., Dyer, A.R., Van Horn, L., & Kaufer-Christoffel, K. (2002). A community-based obesity prevention program for minority children: Rationale and study design for Hip-Hop to Health Jr. Preventive Medicine, 34(2), 289-297. Ludwig, D.S., Peterson, K.E., & Gortrnaker, S.L. (2001). Relationship between consumption of sugar sweetened drinks and childhood obesity: A prospective, 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). U.S. Department of Agriculture & 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). 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 |
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