American Journal of Epidemiology

American Journal of Epidemiology Advance Access originally published online on December 7, 2005
American Journal of Epidemiology 2006 163(3):279-288; doi:10.1093/aje/kwj031

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American Journal of Epidemiology Copyright © 2005 by the Johns Hopkins Bloomberg School of Public Health All rights reserved; printed in U.S.A.

Practice of Epidemiology

The National Cancer Institute Diet History Questionnaire: Validation of Pyramid Food Servings

Amy E. Millen¹, Douglas Midthune², Frances E. Thompson³, Victor Kipnis² and Amy F. Subar³

¹ Department of Social and Preventive Medicine, School of Public Health and Health Professions, State University of New York at Buffalo, Buffalo, NY
² Biometry Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, MD
³ Risk Factor Monitoring and Methods Branch, Applied Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD

Correspondence to Dr. Amy E. Millen, Department of Social and Preventive Medicine, Farber Hall, Room 270, State University of New York at Buffalo, 3435 Main Street (South Campus), Buffalo, NY 14214-8001 (e-mail: aemillen{at}buffalo.edu).

Received for publication June 1, 2005. Accepted for publication September 13, 2005.

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION References

 
The performance of the National Cancer Institute's food frequency questionnaire, the Diet History Questionnaire (DHQ), in estimating servings of 30 US Department of Agriculture Food Guide Pyramid food groups was evaluated in the Eating at America's Table Study (1997–1998), a nationally representative sample of men and women aged 20–79 years. Participants who completed four nonconsecutive, telephone-administered 24-hour dietary recalls (n = 1,301) were mailed a DHQ; 965 respondents completed both the 24-hour dietary recalls and the DHQ. The US Department of Agriculture's Pyramid Servings Database was used to estimate intakes of pyramid servings for both diet assessment tools. The correlation () between DHQ-reported intake and true intake and the attenuation factor () were estimated using a measurement error model with repeat 24-hour dietary recalls as the reference instrument. Correlations for energy-adjusted pyramid servings of foods ranged from 0.43 (other starchy vegetables) to 0.84 (milk) among women and from 0.42 (eggs) to 0.80 (total dairy food) among men. The mean and after energy adjustment were 0.62 and 0.60 for women and 0.63 and 0.66 for men, respectively. This food frequency questionnaire validation study of foods measured in pyramid servings allowed for a measure of food intake consistent with national dietary guidance.

data collection; food; nutrition assessment; nutrition surveys; questionnaires; validation studies [publication type]

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Abbreviations: CSFII, Continuing Survey of Food Intakes by Individuals; DHQ, Diet History Questionnaire; EATS, Eating at America's Table Study; FFQ, food frequency questionnaire; OPEN, Observing Protein and Energy Nutrition

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION References

 
The food frequency questionnaire (FFQ) is a practical approach to measuring dietary exposure in epidemiologic studies investigating relations between diet and disease. However, relative risk estimates derived from FFQs may be biased or attenuated because of substantial FFQ measurement error (1, 2).

Researchers at the National Cancer Institute developed a new cognitively based FFQ, the Diet History Questionnaire (DHQ), which is designed to minimize measurement error through improvements in questionnaire design, wording, layout, and database development (3). One unique feature of the DHQ database is the inclusion of the Pyramid Servings Database (4). The Pyramid Servings Database allows for the conversion of questionnaire responses into quantitative estimates, called "pyramid servings," of 30 different food groups in the US Department of Agriculture's original Food Guide Pyramid, issued in 1992 (5). The latest Food Guide Pyramid, introduced in April 2005, recommends intakes of foods in common household measures which are directly translated from the 1992 Pyramid Servings Database.

Pyramid servings represent a standardized classification scheme for estimating food intake consistent with dietary guidance. Having a pyramid servings database for an FFQ is an innovation in that component ingredients of disaggregated food mixtures are systematically assigned to the appropriate guidance-based food groups (6), thereby allowing for assessment of the full, continuous range of reported intakes of foods. In addition, it allows for the quantitative assessment of added sugar and discretionary fat from all food sources, previously unavailable from dietary databases. The Pyramid Servings Database provides an additional, potentially more precise metric, different from previously used metrics of frequencies, servings (derived by different methods), or grams, and thus warrants evaluation.

The DHQ's ability to assess nutrient intake was previously evaluated in the Eating at America's Table Study (EATS) (3). The purpose of this research was to validate the DHQ's ability to assess intake (in pyramid servings) of 30 US Department of Agriculture food groups and subgroups (4). We used the EATS data to compare intakes of pyramid servings between the DHQ and 24-hour dietary recalls (reference instrument). The standard measurement error model (7) for nutrients is not directly applicable to foods, because of the nonnegligible probability of zero intake on a given day for most foods. In this paper, we present a measurement error model for foods and show that under this model one can use the standard estimation procedures to obtain consistent estimates of the correlation with true intake and the attenuation factor, the multiplicative bias in the estimated log relative risk of disease due to measurement error in the DHQ.

Evaluation of FFQ-reported food intake is important, because nutritional epidemiologists are increasingly interested in the relations of foods and dietary patterns with disease outcomes (8). Additionally, because dietary recommendations are phrased in terms of pyramid servings or equivalent measures of food intake, directly estimating these from FFQs is advantageous for epidemiologic studies, since it will be possible to interpret findings in amounts and terms consistent with public health food recommendations in the new Food Guide Pyramid.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION References

 
Sample and study design
EATS, which began in August 1997, used random digit dialing techniques to obtain a nationally representative sample of persons aged 20–79 years, balanced by gender. A detailed description of the sampling process and methods used has been published elsewhere (3). There were 1,500 participants (n = 738 women, n = 762 men) who completed at least one of four nonconsecutive, telephone-administered 24-hour dietary recalls. Of these persons, 1,301 completed all four 24-hour dietary recalls and were eligible to receive the DHQ by mail. Participants were also randomized into two groups and were sent either the Block FFQ (9) or the Willett FFQ (10) by mail, in addition to the DHQ. Participants were asked to fill out the DHQ and the other mailed FFQ (either the Block FFQ or the Willett FFQ) 1 month apart. Participants were instructed to mail back the completed questionnaires. Of the 1,301 participants who completed all four 24-hour dietary recalls, 1,000 mailed back the DHQ.

Twenty-four-hour dietary recalls
The four 24-hour dietary recalls were scheduled to be administered 3 months apart (one per season) and collected between September 1997 and August 1998, using the multiple-pass method developed for the 1994–1996 Continuing Survey of Food Intakes by Individuals (CSFII) (11).

The 24-hour dietary recall data were coded using the Food Intake Analysis System (version 3.0), developed at the University of Texas, which uses food codes from the CSFII. For foods reported but not found in the CSFII database (n = 14), a food which provided the best match with regard to food description, total energy, and macronutrient content was chosen from the CSFII data files released through 1998 to approximate pyramid serving values.

Food frequency questionnaire
The DHQ, described previously (3), queries respondents about their frequency of intake of 124 separate food items and asks about portion sizes for most of these items by providing a choice of three ranges. For 44 of the 124 foods, 1–7 additional nested questions are asked about related factors such as seasonal intake, food type (e.g., low-fat, lean, diet, caffeine-free), fat use, or fat additions. The DHQ also includes additional questions about the use of low-fat foods.

Of the 1,301 DHQs sent to participants, 1,000 were returned. Thirty-five were deemed incomplete and excluded from further analysis because the respondents skipped two or more adjacent pages of the 36-page DHQ booklet. This left 519 women and 446 men for further analyses.

Pyramid Servings Database
We used the Pyramid Servings Database corresponding to the 1994–1996 CSFII (11), which provides the number of servings of each of the Food Guide Pyramid's food groups and the amounts of discretionary fat (in grams) and added sugar (in teaspoons) contained in 100 g of every food included in the survey. It utilizes a recipe file to disaggregate food mixtures into their component ingredients and assigns them to food groups. Pyramid servings were added to the DHQ database, in addition to the nutrient content of foods, on the basis of methods previously described (6). Since EATS' 24-hour dietary recalls and the DHQ both utilized the Pyramid Servings Database, pyramid servings of the 30 US Department of Agriculture major food groups and subgroups could be determined for both dietary measurement tools. To our knowledge, pyramid servings have not been added to either the Block or the Willett FFQ database, thereby precluding similar analyses of those tools.

Statistical analyses
We evaluate the ability of the DHQ to measure food group intakes in epidemiologic studies by estimating the correlation coefficient () and the attenuation factor () for each food group. is the correlation between reported intake (the DHQ) and estimated true usual intake (based on four 24-hour dietary recalls) and is estimated in this case from the deattenuated crude correlation between the DHQ and the four 24-hour dietary recalls. is the multiplicative factor by which an estimated log relative risk of disease would be biased because of measurement error in the DHQ. It is equivalent to the slope of the linear regression of the 24-hour dietary recall on the DHQ, and it usually falls between 0 and 1 in nutritional studies, thereby attenuating (biasing toward the null) estimates of relative risk. Values of close to 0 indicate maximum attenuation, while values close to 1 indicate minimum attenuation.

We estimated correlation coefficients and attenuation factors using the following measurement error model. For individual i, let p_i denote the probability of consuming an item from a given food group on any given day. Let A_i be the person's usual intake on a consumption day, measured on an appropriate scale. On the chosen scale, the person's overall true usual intake, T_i, can be represented as the product T_i = p_i A_i.

Error in the DHQ was allowed to include systematic bias correlated with true usual intake and within-person random variation as in the standard measurement error model (7). The 24-hour dietary recall (our reference instrument) was assumed to involve no misclassification of consumption days and to report zero intake only on a nonconsumption day. It was also assumed that, on an appropriate scale, the 24-hour dietary recall represents usual intake on a consumption day, plus additive within-person random error with mean zero uncorrelated with true intake and error in the DHQ. Because the repeat recalls were conducted approximately 3 months apart, it was assumed that random errors corresponding to repeat measurements for the same person were uncorrelated with each other.

Given the above assumptions, the measurement error model is

where, for person i, Q_i is the DHQ-reported intake, T_i is the true usual intake, ß₀ is the intercept and ß₁ is the slope of the linear regression of Q_i on T_i, and _i is random within-person error. F_ij is the jth repeat 24-hour dietary recall-reported intake, j = 1, ..., 4, and e_ij is the within-person random error for repeat measurement j. Within-person errors _i and e_ij are assumed to have means equal to zero and constant variances and to be independent of each other and of T_i and A_i. One can show that this measurement error model can be rewritten as

where _ij, j = 1, ..., 4, have mean zero and constant variance and are uncorrelated with true intake T_i, error in the DHQ, and each other. Therefore, the adapted measurement error model is similar to the standard model (7), with the exception that neither true usual intake nor random within-person error in the 24-hour dietary recall is normally distributed, even if the distributions of the usual amount on consumption days, A_i, and within-person error in the 24-hour dietary recall on consumption days, e_ij, are normal.

Prior to statistical analyses, we excluded observations from the 24-hour dietary recall and the DHQ that were determined to be outliers with respect to reported total energy intake measured on the log scale. For the remaining food group observations, data were transformed to a scale on which the 24-hour dietary recall had additive measurement error on days on which the participant consumed an item from the food group. Finally, we removed outliers among nonzero values for each food group and instrument, measured on the transformed scale. Outliers were defined as values outside the interval ranging from the 25th percentile of the distribution minus two times the interquartile range to the 75th percentile plus two times the interquartile range. We used the functional transformation method of Eckert et al. (12) to find the best power transformation for each food group. So that outliers would not unduly influence the choice of transformation, we first excluded outliers; for this step only, we used a more conservative definition of outliers, namely values outside the interval of the 25th and 75th percentiles, plus or minus three times the interquartile range. Final outlier exclusions across all food groups, instruments, and repeated applications of the 24-hour dietary recall ranged from 2 to 14 for men (mean = 7) and from 9 to 24 for women (mean = 13).

The measurement error model was applied to both unadjusted food intake and energy-adjusted food intake using the density method to adjust for energy (13). Parameters were estimated using maximum likelihood estimation, assuming that the random variables were normally distributed. Although this normality assumption is likely to be violated, at least with respect to such random variables as true usual intake and within-person random error for the 24-hour dietary recall, the estimated parameters will still be consistent (asymptotically unbiased). Since model-based estimates of standard errors may be incorrect, we calculated bootstrap standard errors, which do not depend on distributional assumptions (14). Since maximum likelihood estimation does not require every person to have complete data (i.e., four 24-hour dietary recalls and the DHQ), we included in the analyses all 1,500 study participants who completed at least one 24-hour dietary recall.

Because the adopted measurement error model assumes that DHQ-reported intake follows a continuous distribution, we excluded from the analysis three foods for which substantial percentages of respondents reported zero intake on the DHQ: yogurt, organ meat, and soy. Alcoholic beverages, for which rather large percentages of zero intake were also reported on the DHQ, were further analyzed for the DHQ consumers only.

	RESULTS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION References

 
The demographic profile of the participants during the progression of the study—from completion of at least one 24-hour dietary recall to randomization after completion of four 24-hour dietary recalls to completion of an FFQ—was previously reported (3). Participants completing an FFQ, as compared with those completing at least one 24-hour dietary recall, were significantly more likely to be female, older, and of White race/ethnicity. No educational differences were seen.

Table 1 shows the mean values and standard errors for daily intake of pyramid servings of the 30 food groups among 497 women and 436 men who completed all four 24-hour dietary recalls and the DHQ, after exclusion of 32 participants whose values for reported total energy intake were outliers. For women and men, reported intakes were greater (>15 percent difference) on the DHQ than on the 24-hour dietary recalls for other starchy vegetables, dark green vegetables, total fruit, citrus fruit/melon/berries, other fruit, and yogurt. For women, reported intakes were also greater for total vegetables, deep yellow vegetables, legumes, other vegetables, fish/other seafood, and milk. For men, reported intakes were also greater for nuts and seeds and alcohol. Reported intakes for men and women were lower (>15 percent difference) on the DHQ than on the 24-hour dietary recalls for total grains, nonwhole grains, poultry, organ meat, and eggs. For men, reported intakes were also lower for red meat/poultry/fish, soy, and cheese.

View this table: [in this window] [in a new window]   TABLE 1. Mean intakes and percentages of persons reporting no consumption in Food Guide Pyramid food groups among persons who completed four 24-hour dietary recalls and the Diet History Questionnaire, Eating at America's Table Study, 1997–1998

 
Table 2 shows the estimated correlation () between the DHQ and true usual intake and the attenuation factor () for the transformed food group data with and without energy adjustment, by gender. Adjusting for energy intake strengthened the correlation between truth and the DHQ for the majority of the food groups. Among women, energy-adjusted correlations ranged from 0.43 for servings of other starchy vegetables to 0.84 for servings of milk. Among men, energy-adjusted correlations ranged from 0.42 for servings of tomatoes and eggs to 0.80 for servings of total dairy food. The energy-adjusted correlation coefficients were greater than 0.50 for all food groups except white potatoes (men only), other starchy vegetables (women only), tomatoes, poultry (women only), and eggs. Energy-adjusted correlation coefficients were highest (>0.65) for whole grains (men only), total vegetables (women only), dark green vegetables, legumes (men only), total fruit, other fruit (men only), beef/pork/lamb, total dairy food, milk, discretionary fat (men only), added sugar, and alcohol. The mean across all energy-adjusted food groups was 0.62 for women and 0.63 for men (table 3).

View this table: [in this window] [in a new window]   TABLE 2. Deattenuated correlation coefficients () and attenuation factors () for correlation between the Diet History Questionnaire and true usual intake (based on four 24-hour dietary recalls) obtained using a measurement error model, unadjusted and adjusted for total energy intake, Eating at America's Table Study, 1997–1998

 

View this table: [in this window] [in a new window]   TABLE 3. Mean correlation and attenuation coefficients for correlation between the Diet History Questionnaire and four 24-hour dietary recalls, with and without adjustment for total energy intake, Eating at America's Table Study, 1997–1998

 
Energy adjustment primarily strengthened or left unchanged the attenuation coefficients. Among women, energy-adjusted attenuation coefficients ranged from 0.20 for other starchy vegetables to 0.92 for eggs (table 2). Among men, energy-adjusted attenuation coefficients ranged from 0.41 for other starchy vegetables to 1.32 for eggs. After energy adjustment, the attenuation coefficients were greater than 0.50 for all food groups except total grains (women only), nonwhole grains (women only), total vegetables (men only), white potatoes (men only), other starchy vegetables, dark green vegetables, tomatoes, legumes, poultry, fish/other seafood, franks/luncheon meats (women only), and discretionary fat. After energy adjustment, the mean was 0.60 for women and 0.66 for men (table 3).

	DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION References

 
We estimated the ability of the DHQ to assess food group intake as measured in pyramid servings for 30 US Department of Agriculture food groups. Validation of food intake poses challenges different from those of nutrient intake along several dimensions: 1) how to disentangle individual foods from mixtures; 2) how to combine the nutrient contributions of solid foods and less dense liquid foods; and 3) how to relate food intake to dietary guidance. The availability of the US Department of Agriculture's Pyramid Servings Database eloquently satisfies these challenges. To our knowledge, this is the first FFQ validation study of food groups measured in pyramid servings.

A further statistical challenge is the variability and nonnormal distribution of food intakes. Daily intakes of individual foods tend to be more variable than those for nutrients. Pyramid servings may somewhat reduce this variability for some foods, because the system disaggregates food mixtures into a smaller set of component ingredients. Therefore, one implication of using pyramid servings in future studies is a possible reduction in measurement error in assessing food intake. The distributions of pyramid servings are more similar to those of nutrients, both being represented by continuous variables in the database. This is unlike other methods of quantifying food intake, such as determining frequencies or grams, the latter of which takes into account frequency and portion size but generally does not disaggregate food mixtures.

The inclusion of a database with the ability to assess pyramid servings is a scientific advance which may allow researchers to more accurately assess intakes in different food groups and may help facilitate the communication of scientific findings to the public with reference to dietary guidance. We have shown here that the use of pyramid servings for FFQ databases is possible and that it has the potential to provide a standardized measurement of food intake from study to study.

The new Food Guide Pyramid, MyPyramid (http://www.mypyramid.gov/), makes recommendations based on common household measurements (i.e., cups, ounces, and teaspoons). Translating between household measurements and pyramid servings is relatively easy. This change is less a substantive one than an attempt to better communicate with consumers. For example, in MyPyramid, 1/4 cup of raisins is equivalent to 1/2 cup of fruit, and both are equivalent to one pyramid serving. There are, however, a few substantive changes, such as recommendations for the separation of oil from discretionary fat and the inclusion of the term "discretionary calories" to convey the idea that after food intake recommendations are met, people have a limited amount of calories to consume within food groups or as sugar, alcohol, or discretionary fat. At the time of this analysis, we used the current Pyramid Servings Database available to us. In the future, we will update the DHQ to the current system.

In our study, median intake estimates on the DHQ and the 24-hour dietary recalls differed for many pyramid food groups. There are several possible reasons for this. There may be a bias for people to report higher consumption of more socially desirable foods (e.g., fruits, vegetables, fish/other seafood, yogurt, milk) and lower consumption of less socially desirable foods (e.g., nonwhole grains, red meat/poultry/fish, organ meat, eggs, cheese) on the DHQ than on the 24-hour dietary recalls. In general, these findings were consistent with findings from previous studies (15–20). However, a few discrepancies did occur. For example, a few studies reported added fat consumption at a higher frequency on FFQs than on 24-hour dietary recalls (16–18). These inconsistencies may be partially explained by variation in the proportions of different types of fats consumed by country.

Another reason that reported intakes for some food groups differed between the DHQ and the 24-hour dietary recalls may be that there are differences in the nature of the two dietary assessment methods. Four random 24-hour dietary recalls are less likely to provide observations of intake for foods that are rarely consumed or only episodically consumed. The recalls may indicate lower levels of intake in comparison with an FFQ that queries about usual dietary intake over the past year.

For most food groups, the deattenuated correlation coefficients and attenuation factors were greater than 0.50 for both genders after energy adjustment; however, they were slightly stronger, on average, among men. Unlike mean and median values, correlation coefficients do not compare absolute intakes of foods between dietary assessment tools but rather compare how the different tools classify individuals with respect to food intake. Energy adjustment, on average, improved the correlation and attenuation coefficients. This suggests that adjusting for energy intake reduces measurement error in reporting of foods on the DHQ. Nearly all items on the DHQ contribute calories, and therefore total energy intake may serve as a good surrogate variable with which to adjust for measurement error in FFQs (21).

Attenuation factors are used to estimate the degree to which the log relative risk of disease would be attenuated due to measurement error in the DHQ. For example, if a true relative risk of 0.50 existed for a specific disease among women with a high adjusted total vegetable intake as compared with a low intake, this relative risk would be attenuated by measurement error to 0.85.

The attenuation and correlation coefficients among men were, on average, slightly higher than those among women. This may be explained by differential measurement error by gender. In the Observing Protein and Energy Nutrition (OPEN) Study, which also used the National Cancer Institute DHQ, Subar et al. (21) found that women underreported their total energy intake more than men did.

A number of FFQ-type food validation studies have been conducted among adults in Europe (15–18, 22–28), Asia (29–36), the United States (19, 20, 37, 38), Africa (39, 40), and South America (41). While it is recognized that differences in instruments and study samples used among these various studies may have caused differences in results, another factor deserving of attention is differences in analytical methods.

To our knowledge, our study is the first food validation study to use pyramid servings; no other studies reviewed in this manuscript measured food intake with this unit. Thus, it is possible that differences in study results could be explained by differences in the units used to express food intake: frequencies (the number of times a food is eaten within a specified time period (day, week, etc.), without reference to portion size) or servings (a count of the number of times a specific portion size of a food is eaten within a specified time period) (15, 19, 20, 29, 30, 37); grams (16–18, 22–28, 31–36, 38–40); percentage of total calories (41); or, as in this case, pyramid servings. Different studies may accentuate different metrics when assigning foods to specific food groups. For example, Flagg et al. (38) analyzed the sum of dairy food intake (milk + yogurt + cheese), combining liquid and solid dairy products, whereas Feskanich et al. (19) differentiated between the different types of dairy foods (i.e., milk, yogurt, cheese, cream cheese, etc.).

It appears that investigators in past FFQ validation studies considered added sugar to be the addition of raw sugar to prepared foods. In this study, pyramid servings of added sugar were based on all sources, including sugar added by food manufacturers to cereals, baked goods, etc. Similarly, pyramid servings of discretionary fats included fats added to foods during cooking and at the table, as well as fats exceeding those present in lean cuts of meat and poultry. The food groups "discretionary fats" and "added sugars" are unique additions to a foods database, because they express fat and sugar intake behavior as measures, within the dietary guidance concept. Individuals have choices in how to "spend" their fat and sugar allowances.

Finally, the search for consistency between studies is hampered by the presentation of Spearman correlation coefficients or unadjusted Pearson correlation coefficients (16–18, 22, 23, 26, 27, 29, 31–34, 39, 40), which are not directly comparable to deattenuated Pearson correlation coefficients, which correct for within-person variation in the reference instrument.

The results of this validation study may not be completely generalizable to other populations. Our sample consisted primarily of White, well-educated persons who were willing to complete four 24-hour dietary recalls followed by two FFQs. This is a limitation of many validation studies.

In this study, we assumed that error in the 24-hour dietary recalls included no misclassification of nonconsumption days and, for nonzero reported amounts, was unbiased and contained only within-person random error uncorrelated with errors in the DHQ. These assumptions may be unwarranted for self-reported reference instruments such as 24-hour dietary recalls and dietary records. The OPEN Study showed that there is reporting bias for energy and protein intake in both the DHQ and 24-hour dietary recalls, and that people systematically differ in their reporting accuracy (21). Therefore, all dietary reference instruments could involve systematic error at the individual level, and this error could be correlated with its counterpart in the DHQ. Thus, our results may reflect overestimation of the correlations with true intake and underestimation of true attenuation (42).

With respect to absolute energy and protein intakes, as the OPEN biomarker study showed, the DHQ has significant measurement error, especially in the direction of underreporting (21). This is probably true for many FFQs. However, after adjustment for total energy intake, protein intake showed far less measurement error in comparison with unadjusted values. This observation suggests that energy adjustment may minimize the effects of measurement error in FFQs (42), although the extent to which the results for protein can be extended to individual foods or food groups is unknown.

In summary, the results of this study demonstrate for the first time the use of a pyramid servings database for FFQs. The validity of the US Department of Agriculture Pyramid Servings Database was comparable or superior to that of previous FFQ food group validation studies using less precise food grouping methods. The use of a pyramid servings database in nutritional epidemiologic research provides substantial advantages in measuring consumption in individual food groups, as well as consumption of sugar and discretional fat. The inclusion of a pyramid servings database in the DHQ provides an innovative and advanced method of assessing food intake behavior for use in nutrition surveillance, assessment, or analysis of diet-disease associations. The DHQ is publicly available online at a National Cancer Institute website (http://riskfactor.cancer.gov/DHQ/).

	ACKNOWLEDGMENTS

 
Conflict of interest: none declared.

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