The Healthy Eating Index (HEI) is a measure of diet quality in terms of conformance with federal dietary guidance. Publication of the Dietary Guidelines for Americans, 2010 prompted an interagency working group to update the HEI. The HEI-2010 retains several features of the 2005 version: (1) it has 12 components, many unchanged, including 9 adequacy and 3 moderation components; (2) it uses a density approach to set standards, e.g., per 1000 calories or as a percent of calories; and (3) it employs least-restrictive standards, i.e., those that are easiest to achieve among recommendations that vary by energy level, sex, and/or age. Changes to the index include: (1) Greens and Beans replaces Dark Green and Orange Vegetables and Legumes; (2) Seafood and Plant Proteins has been added to capture specific choices from the protein group; (3) Fatty Acids, a ratio of poly- and mono-unsaturated to saturated fatty acids, replaces Oils and Saturated Fat to acknowledge the recommendation to replace saturated fat with mono-and polyunsaturated fatty acids; and (4) a moderation component, Refined Grains, replaces the adequacy component, Total Grains, to assess over-consumption. The HEI-2010 captures the key recommendations of the 2010 Dietary Guidelines and, like earlier versions, will be used to assess the diet quality of the U.S. population and subpopulations, in evaluating interventions, in dietary patterns research, and to evaluate various aspects of the food environment.

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The Healthy Eating Index (HEI), a measure of diet quality, was updated to reflect the 2010 Dietary Guidelines for Americans and the accompanying USDA Food Patterns. To assess the validity and reliability of the HEI-2010, exemplary menus were scored and 2 24-h dietary recalls from individuals aged ≥2 y from the 2003-2004 NHANES were used to estimate multivariate usual intake distributions and assess whether the HEI-2010 1) has a distribution wide enough to detect meaningful differences in diet quality among individuals, 2) distinguishes between groups with known differences in diet quality by using t tests, 3) measures diet quality independently of energy intake by using Pearson correlation coefficients, 4) has >1 underlying dimension by using principal components analysis (PCA), and 5) is internally consistent by calculating Cronbach's coefficient α. HEI-2010 scores were at or near the maximum levels for the exemplary menus. The distribution of scores among the population was wide (5th percentile = 31.7; 95th percentile = 70.4). As predicted, men's diet quality (mean HEI-2010 total score = 49.8) was poorer than women's (52.7), younger adults' diet quality (45.4) was poorer than older adults' (56.1), and smokers' diet quality (45.7) was poorer than nonsmokers' (53.3) (P < 0.01). Low correlations with energy were observed for HEI-2010 total and component scores (|r| ≤ 0.21). Cronbach's coefficient α was 0.68, supporting the reliability of the HEI-2010 total score as an indicator of overall diet quality. Nonetheless, PCA indicated multiple underlying dimensions, highlighting the fact that the component scores are equally as important as the total. A comparable reevaluation of the HEI-2005 yielded similar results. This study supports the validity and the reliability of both versions of the HEI.

Recent reports have asserted that, because of energy underreporting, dietary self-report data suffer from measurement error so great that findings that rely on them are of no value. This commentary considers the amassed evidence that shows that self-report dietary intake data can successfully be used to inform dietary guidance and public health policy. Topics discussed include what is known and what can be done about the measurement error inherent in data collected by using self-report dietary assessment instruments and the extent and magnitude of underreporting energy compared with other nutrients and food groups. Also discussed is the overall impact of energy underreporting on dietary surveillance and nutritional epidemiology. In conclusion, 7 specific recommendations for collecting, analyzing, and interpreting self-report dietary data are provided: (1) continue to collect self-report dietary intake data because they contain valuable, rich, and critical information about foods and beverages consumed by populations that can be used to inform nutrition policy and assess diet-disease associations; (2) do not use self-reported energy intake as a measure of true energy intake; (3) do use self-reported energy intake for energy adjustment of other self-reported dietary constituents to improve risk estimation in studies of diet-health associations; (4) acknowledge the limitations of self-report dietary data and analyze and interpret them appropriately; (5) design studies and conduct analyses that allow adjustment for measurement error; (6) design new epidemiologic studies to collect dietary data from both short-term (recalls or food records) and long-term (food-frequency questionnaires) instruments on the entire study population to allow for maximizing the strengths of each instrument; and (7) continue to develop, evaluate, and further expand methods of dietary assessment, including dietary biomarkers and methods using new technologies.

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Objective-We propose a new statistical method that uses information from two 24-hour recalls (24HRs) to estimate usual intake of episodically-consumed foods.Statistical Analyses Performed-The method developed at the National Cancer Institute (NCI) accommodates the large number of non-consumption days that arise with foods by separating the probability of consumption from the consumption-day amount, using a two-part model. Covariates, such as sex, age, race, or information from a food frequency questionnaire (FFQ), may supplement the information from two or more 24HRs using correlated mixed model regression. The model allows for correlation between the probability of consuming a food on a single day and the consumptionday amount. Percentiles of the distribution of usual intake are computed from the estimated model parameters. Results-TheEating at America's Table Study (EATS) data are used to illustrate the method to estimate the distribution of usual intake for whole grains and dark green vegetables for men and women and the distribution of usual intakes of whole grains by educational level among men. A simulation study indicates that the NCI method leads to substantial improvement over existing methods for estimating the distribution of usual intake of foods.Applications/Conclusions-The NCI method provides distinct advantages over previously proposed methods by accounting for the correlation between probability of consumption and amount consumed and by incorporating covariate information. Researchers interested in estimating the distribution of usual intakes of foods for a population or subpopulation are advised to work with a statistician and incorporate the NCI method in analyses. KeywordsUsual intake; Episodically-consumed foods; statistical methods When using dietary assessment among populations or individuals, investigators are often interested in capturing usual intakes -that is, long-term averages. The 24-hour dietary recall (24HR) provides rich details about dietary intake for a given day, but collecting more than two 24HRs per individual is impractical in large surveys such as the National Health and Nutrition Examination Survey (NHANES). Therefore, it is necessary to employ statistical methods to estimate usual dietary intake.Researchers are interested in estimating the usual intake of foods to assess compliance with food-based dietary recommendations and to relate food intake to health parameters. Unlike most nutrients, which are consumed daily, estimating usual intake of episodically-consumed foods presents the following unique challenges for statistical modeling (see the Glossary for a definition of "statistical modeling" and related terms):A. accounting for days without consumption of a particular food or food group; B. allowing for consumption-day amount data that are generally positively skewed and have extreme values in the upper tail of the intake distribution;C. distinguishing within-person variability, which consists of day-to-day variation in intake and random reporting errors, from between-...

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A longstanding goal of dietary surveillance has been to estimate the proportion of the population with intakes above or below a target, such as a recommended level of intake. However, until now, statistical methods for assessing the alignment of food intakes with recommendations have been lacking. The purposes of this study were to demonstrate the National Cancer Institute's method of estimating the distribution of usual intake of foods and determine the proportion of the U.S. population who does not meet federal dietary recommendations. Data were obtained from the 2001-2004 NHANES for 16,338 persons, aged 2 y and older. Quantities of foods reported on 24-h recalls were translated into amounts of various food groups using the MyPyramid Equivalents Database. Usual dietary intake distributions were modeled, accounting for sequence effect, weekend/weekday effect, sex, age, poverty income ratio, and race/ethnicity. The majority of the population did not meet recommendations for all of the nutrient-rich food groups, except total grains and meat and beans. Concomitantly, overconsumption of energy from solid fats, added sugars, and alcoholic beverages ("empty calories") was ubiquitous. Over 80% of persons age ≥ 71 y and over 90% of all other sex-age groups had intakes of empty calories that exceeded the discretionary calorie allowances. In conclusion, nearly the entire U.S. population consumes a diet that is not on par with recommendations. These findings add another piece to the rather disturbing picture that is emerging of a nation's diet in crisis.