Using Short-Term Dietary Intake Data to Address Research Questions Related to Usual Dietary Intake among Populations and Subpopulations: Assumptions, Statistical Techniques, and Considerations.

Many research questions focused on characterizing usual, or long-term average, dietary intake of populations and subpopulations rely on short-term intake data. The objective of this paper is to review key assumptions, statistical techniques, and considerations underpinning the use of short-term dietary intake data to make inference about usual dietary intake. The focus is on measurement error and strategies to mitigate its effects on estimated characteristics of population-level usual intake, with attention to relevant analytic issues such as accounting for survey design. Key assumptions are that short-term assessments are subject to random error only (i.e., unbiased for individual usual intake) and that some aspects of the error structure apply to all respondents, allowing estimation of this error structure in data sets with only a few repeat measures per person. Under these assumptions, a single 24-hour dietary recall per person can be used to estimate group mean intake; and with as little as one repeat on a subsample and with more complex statistical techniques, other characteristics of distributions of usual intake, such as percentiles, can be estimated. Related considerations include the number of days of data available, skewness of intake distributions, whether the dietary components of interest are consumed nearly daily by nearly everyone or episodically, the number of correlated dietary components of interest, time-varying nuisance effects related to day of week and season, and variance estimation and inference. Appropriate application of assumptions and recommended statistical techniques allows researchers to address a range of research questions, though it is imperative to acknowledge systematic error (bias) in short-term data and its implications for conclusions.

Update of the Healthy Eating Index: HEI-2015.

The Healthy Eating Index (HEI) is a measure for assessing whether a set of foods aligns with the Dietary Guidelines for Americans (DGA). An updated HEI is released to correspond to each new edition of the DGA, and this article introduces the latest version, which reflects the 2015-2020 DGA. The HEI-2015 components are the same as in the HEI-2010, except Saturated Fat and Added Sugars replace Empty Calories, with the result being 13 components. The 2015-2020 DGA include explicit recommendations to limit intakes of both Added Sugars and Saturated Fats to <10% of energy. HEI-2015 does not account for excessive energy from alcohol within a separate component, but continues to account for all energy from alcohol within total energy (the denominator for most components). All other components remain the same as for HEI-2010, except for a change in the allocation of legumes. Previous versions of the HEI accounted for legumes in either the two vegetable or the two protein foods components, whereas HEI-2015 counts legumes toward all four components. Weighting approaches are similar to those of previous versions, and scoring standards were maintained, refined, or developed to increase consistency across components; better ensure face validity; follow precedent; cover a range of intakes; and, when applicable, ensure the DGA level corresponds to a score >7 out of 10. HEI-2015 component scores can be examined collectively using radar graphs to reveal a pattern of diet quality and summed to represent overall diet quality.

Applications of the Healthy Eating Index for Surveillance, Epidemiology, and Intervention Research: Considerations and Caveats.

The Healthy Eating Index (HEI) is a measure of diet quality that can be used to examine alignment of dietary patterns with the Dietary Guidelines for Americans. The HEI is made up of multiple adequacy and moderation components, most of which are expressed relative to energy intake (ie, as densities) for the purpose of calculating scores. Due to these characteristics and the complexity of dietary intake data more broadly, calculating and using HEI scores can involve unique statistical considerations and, depending on the particular application, intensive computational methods. The objective of this article is to review potential applications of the HEI, including those relevant to surveillance, epidemiology, and intervention research, and to summarize available guidance for appropriate analysis and interpretation. Steps in calculating HEI scores are reviewed and statistical methods described. Consideration of salient issues in the calculation and interpretation of scores can help researchers avoid common pitfalls and reviewers ensure that articles reporting on the use of the HEI include sufficient details such that the work is comprehensible and replicable, with the overall goal of contributing to knowledge on dietary patterns and health among Americans.

Evaluation of the Healthy Eating Index-2015.

BACKGROUND: The Healthy Eating Index (HEI), a diet quality index that measures alignment with the Dietary Guidelines for Americans, was updated with the 2015-2020 Dietary Guidelines for Americans. OBJECTIVE AND DESIGN: To evaluate the psychometric properties of the HEI-2015, eight questions were examined: five relevant to construct validity, two related to reliability, and one to assess criterion validity. DATA SOURCES: Three data sources were used: exemplary menus (n=4), National Health and Nutrition Examination Survey 2011-2012 (N=7,935), and the National Institutes of Health-AARP (formally known as the American Association of Retired Persons) Diet and Health Study (N=422,928). STATISTICAL ANALYSES: Exemplary menus: Scores were calculated using the population ratio method. National Health and Nutrition Examination Survey 2011-2012: Means and standard errors were estimated using the Markov Chain Monte Carlo approach. Analyses were stratified to compare groups (with t tests and analysis of variance). Principal components analysis examined the number of dimensions. Pearson correlations were estimated between components, energy, and Cronbach's coefficient alpha. National Institutes of Health-AARP Diet and Health Study: Adjusted Cox proportional hazards models were used to examine scores and mortality outcomes. RESULTS: For construct validity, the HEI-2015 yielded high scores for exemplary menus as four menus received high scores (87.8 to 100). The mean score for National Health and Nutrition Examination Survey was 56.6, and the first to 99th percentile were 32.6 to 81.2, respectively, supporting sufficient variation. Among smokers, the mean score was significantly lower than among nonsmokers (53.3 and 59.7, respectively) (P<0.01), demonstrating differentiation between groups. The correlation between diet quality and diet quantity was low (all <0.25) supporting these elements being independent. The components demonstrated multidimensionality when examined with a scree plot (at least four dimensions). For reliability, most of the intercorrelations among the components were low to moderate (0.01 to 0.49) with a few exceptions, and the standardized Cronbach's alpha was .67. For criterion validity, the highest vs the lowest quintile of HEI-2015 scores were associated with a 13% to 23% decreased risk of all-cause, cancer, and cardiovascular disease mortality. CONCLUSIONS: The results demonstrated evidence supportive of construct validity, reliability, and criterion validity. The HEI-2015 can be used to examine diet quality relative to the 2015-2020 Dietary Guidelines for Americans.

Assessing Dietary Outcomes in Intervention Studies: Pitfalls, Strategies, and Research Needs.

To inform strategies to improve the dietary intakes of populations, robust evaluations of interventions are required. This paper is drawn from a workshop held at the International Society of Behavioral Nutrition and Physical Activity 2017 Annual Meeting, and highlights considerations and research priorities relevant to measuring dietary outcomes within intervention studies. Self-reported dietary data are typically relied upon in such studies, and it is recognized that these data are affected by random and systematic error. Additionally, differential error between intervention and comparison groups or pre- and post-intervention can be elicited by the intervention itself, for example, by creating greater awareness of eating or drinking occasions or the desire to appear compliant. Differential reporting can render the results of trials incorrect or inconclusive by leading to biased estimates and reduced statistical power. The development of strategies to address intervention-related biases requires developing a better understanding of the situations and population groups in which interventions are likely to elicit differential reporting and the extent of the bias. Also needed are efforts to expand the feasibility and applications of biomarkers to address intervention-related biases. In the meantime, researchers are encouraged to consider the potential for differential biases in dietary reporting in a given study, to choose tools carefully and take steps to minimize and/or measure factors such as social desirability biases that might contribute to differential reporting, and to consider the implications of differential reporting for study results.

Extending Methods in Dietary Patterns Research.

The National Cancer Institute (NCI) and the National Institutes of Health (NIH) Office of Disease Prevention held a workshop titled, “Extending Methods in Dietary Patterns Research”, in May of 2016. The workshop’s goal was to articulate, refine, and prioritize methodological questions to advance the science of dietary patterns in epidemiological research. Although the focus was on how to improve methods for assessing the relationship between dietary patterns and cancer risk, many, if not all, of the discussions and conclusions are relevant for other health outcomes as well. Recognizing that dietary intake is both multidimensional (i.e., it is a complex, multi-layered exposure and behavior) and dynamic (i.e., it varies over time and the life course), workshop presenters and participants discussed methodological advances required to include these concepts in dietary patterns research. This commentary highlights key needs that were identified to extend methods in dietary patterns research by integrating multidimensionality and dynamism into how dietary patterns are measured and defined, and how relationships with dietary patterns and health outcomes are modeled.

American Diet Quality: Where It Is, Where It Is Heading, and What It Could Be.

BACKGROUND: Diet quality is critically important to the prevention of many types of chronic disease. The federal government provides recommendations for optimal diet quality through the Dietary Guidelines for Americans, and sets benchmarks for progress toward these recommendations through the Healthy People objectives. OBJECTIVE: This analysis estimated recent trends in American diet quality and compared those trends to the quality of diets that would meet the Healthy People 2020 objectives and the 2010 Dietary Guidelines for Americans in order to measure progress toward our national nutrition goals. DESIGN: This analysis used 24-hour recall data from the cross-sectional National Health and Nutrition Examination Survey, between the years of 1999-2000 and 2011-2012, to determine mean intakes of various dietary components for the US population over time. Mean intakes were estimated using the population ratio method, and diet quality was assessed using the Healthy Eating Index 2010 (HEI-2010). RESULTS: The mean HEI-2010 total score for the US population has increased from 49 in 1999-2000 to 59 in 2011-2012; continuing on that trajectory, it would reach a score of 65 by 2019-2020. A diet that meets the Healthy People 2020 objectives would receive a score of 74 and, by definition, a diet that meets the 2010 Dietary Guidelines for Americans would receive a score of 100. Trends in HEI-2010 component scores vary; all HEI-2010 component scores except sodium have increased over time. CONCLUSIONS: Diet quality is improving over time, but not quickly enough to meet all of the Healthy People 2020 objectives. Whole fruit and empty calories are the only HEI-2010 components on track to meet their respective Healthy People 2020 targets. Furthermore, the country falls short of the 2010 Dietary Guidelines for Americans by a large margin in nearly every component of diet quality assessed by the HEI-2010.

Diet Quality of Items Advertised in Supermarket Sales Circulars Compared to Diets of the US Population, as Assessed by the Healthy Eating Index-2010.

BACKGROUND: Supermarkets use sales circulars to highlight specific foods, usually at reduced prices. Resulting purchases help form the set of available foods within households from which individuals and families make choices about what to eat. OBJECTIVE: The purposes of this study were to determine how closely foods featured in weekly supermarket sales circulars conform to dietary guidance and how diet quality compares with that of the US population's intakes. DESIGN: Food and beverage items (n=9,149) in 52 weekly sales circulars from a small Midwestern grocery chain in 2009 were coded to obtain food group and nutrient and energy content. Healthy Eating Index-2010 (HEI-2010) total and component scores were calculated using algorithms developed by the National Cancer Institute. HEI-2010 scores for the US population aged 2+ years were estimated using data from the 2009-2010 National Health and Nutrition Examination Survey. HEI-2010 scores of circulars and population intakes were compared using Student's t tests. RESULTS: Mean total (42.8 of 100) HEI-2010 scores of circulars were lower than that of the US population (55.4; P<0.001). Among individual components, Total Protein Foods was the only one for which 100% of the maximum score was met by both circulars and the population. The scores were also similar between the circulars and population for Whole Grains (22%; P=0.81) and Seafood and Plant Proteins (70% to 74%; P=0.33). Circular scores were lower than those of the population for Total and Whole Fruits, Total Vegetables and Greens and Beans, Dairy, Sodium, and Empty Calories (P<0.001); they were higher only for Fatty Acids (P=0.006) and Refined Grains (P<0.001). CONCLUSIONS: HEI-2010 total scores for these sales circulars were even lower than US population scores, which have been shown repeatedly to reflect low diet quality. Supermarkets could support improvements in consumer diets by weekly featuring foods that are more in concordance with food and nutrient recommendations.

Addressing Current Criticism Regarding the Value of Self-Report Dietary Data.

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.

The National Cancer Institute's Dietary Assessment Primer: A Resource for Diet Research.

This monograph describes the National Cancer Institute's Dietary Assessment Primer, a web resource developed to help researchers choose the best available dietary assessment approach to achieve their research objective. All self-report instruments have error, but understanding the nature of that error can lead to better assessment, analysis, and interpretation of results. The Primer includes profiles of the major self-report dietary assessment instruments, including guidance on the best uses of each instrument; discussion of validation and measurement error generally and with respect to each instrument; guidance for choosing a dietary assessment approach for different research questions; and additional resources, such as a glossary, references, and overviews of specific/important issues in the field. This monograph also describes some future research needs in the field of dietary assessment.

The Dietary Patterns Methods Project: synthesis of findings across cohorts and relevance to dietary guidance.

The Dietary Patterns Methods Project (DPMP) was initiated in 2012 to strengthen research evidence on dietary indices, dietary patterns, and health for upcoming revisions of the Dietary Guidelines for Americans, given that the lack of consistent methodology has impeded development of consistent and reliable conclusions. DPMP investigators developed research questions and a standardized approach to index-based dietary analysis. This article presents a synthesis of findings across the cohorts. Standardized analyses were conducted in the NIH-AARP Diet and Health Study, the Multiethnic Cohort, and the Women's Health Initiative Observational Study (WHI-OS). Healthy Eating Index 2010, Alternative Healthy Eating Index 2010 (AHEI-2010), alternate Mediterranean Diet, and Dietary Approaches to Stop Hypertension (DASH) scores were examined across cohorts for correlations between pairs of indices; concordant classifications into index score quintiles; associations with all-cause, cardiovascular disease (CVD), and cancer mortality with the use of Cox proportional hazards models; and dietary intake of foods and nutrients corresponding to index quintiles. Across all cohorts in women and men, there was a high degree of correlation and consistent classifications between index pairs. Higher diet quality (top quintile) was significantly and consistently associated with an 11-28% reduced risk of death due to all causes, CVD, and cancer compared with the lowest quintile, independent of known confounders. This was true for all diet index-mortality associations, with the exception of AHEI-2010 and cancer mortality in WHI-OS women. In all cohorts, survival benefit was greater with a higher-quality diet, and relatively small intake differences distinguished the index quintiles. The reductions in mortality risk started at relatively lower levels of diet quality. Higher scores on each of the indices, signifying higher diet quality, were associated with marked reductions in mortality. Thus, the DPMP findings suggest that all 4 indices capture the essential components of a healthy diet.

The United States food supply is not consistent with dietary guidance: evidence from an evaluation using the Healthy Eating Index-2010.

The US food system is primarily an economic enterprise, with far-reaching health, environmental, and social effects. A key data source for evaluating the many effects of the food system, including the overall quality and extent to which it provides the basic elements of a healthful diet, is the Food Availability Data System. The objective of the present study was to update earlier research that evaluated the extent to which the US food supply aligns with the most recent federal dietary guidance, using the current Healthy Eating Index-2010 (HEI-2010) and food supply data extending through 2010. The HEI-2010 was applied to 40 years of food supply data (1970-2010) to examine trends in the overall food supply as well as specific components related to a healthy diet, such as fruits and vegetables. The HEI-2010 overall summary score hovered around half of optimal for all years evaluated, with an increase from 48 points in 1970 to 55 points (out of a possible 100 points) in 2010. Fluctuations in scores for most individual components did not lead to sustained trends. Our study continues to demonstrate sizable gaps between federal dietary guidance and the food supply. This disconnect is troublesome within a context of high rates of diet-related chronic diseases among the population and suggests the need for continual monitoring of the quality of the food supply. Moving toward a food system that is more conducive to healthy eating requires consideration of a range of factors that influence food supply and demand.

Higher diet quality is associated with decreased risk of all-cause, cardiovascular disease, and cancer mortality among older adults.

Increased attention in dietary research and guidance has been focused on dietary patterns, rather than on single nutrients or food groups, because dietary components are consumed in combination and correlated with one another. However, the collective body of research on the topic has been hampered by the lack of consistency in methods used. We examined the relationships between 4 indices--the Healthy Eating Index-2010 (HEI-2010), the Alternative Healthy Eating Index-2010 (AHEI-2010), the alternate Mediterranean Diet (aMED), and Dietary Approaches to Stop Hypertension (DASH)--and all-cause, cardiovascular disease (CVD), and cancer mortality in the NIH-AARP Diet and Health Study (n = 492,823). Data from a 124-item food-frequency questionnaire were used to calculate scores; adjusted HRs and 95% CIs were estimated. We documented 86,419 deaths, including 23,502 CVD- and 29,415 cancer-specific deaths, during 15 y of follow-up. Higher index scores were associated with a 12-28% decreased risk of all-cause, CVD, and cancer mortality. Specifically, comparing the highest with the lowest quintile scores, adjusted HRs for all-cause mortality for men were as follows: HEI-2010 HR: 0.78 (95% CI: 0.76, 0.80), AHEI-2010 HR: 0.76 (95% CI: 0.74, 0.78), aMED HR: 0.77 (95% CI: 0.75, 0.79), and DASH HR: 0.83 (95% CI: 0.80, 0.85); for women, these were HEI-2010 HR: 0.77 (95% CI: 0.74, 0.80), AHEI-2010 HR: 0.76 (95% CI: 0.74, 0.79), aMED HR: 0.76 (95% CI: 0.73, 0.79), and DASH HR: 0.78 (95% CI: 0.75, 0.81). Similarly, high adherence on each index was protective for CVD and cancer mortality examined separately. These findings indicate that multiple scores reflect core tenets of a healthy diet that may lower the risk of mortality outcomes, including federal guidance as operationalized in the HEI-2010, Harvard's Healthy Eating Plate as captured in the AHEI-2010, a Mediterranean diet as adapted in an Americanized aMED, and the DASH Eating Plan as included in the DASH score.

The Healthy Eating Index-2010 is a valid and reliable measure of diet quality according to the 2010 Dietary Guidelines for Americans.

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.

Fast-food menu offerings vary in dietary quality, but are consistently poor.

OBJECTIVE: To evaluate five popular fast-food chains' menus in relation to dietary guidance. DESIGN: Menus posted on chains' websites were coded using the Food and Nutrient Database for Dietary Studies and MyPyramid Equivalents Database to enable Healthy Eating Index-2005 (HEI-2005) scores to be assigned. Dollar or value and kids' menus and sets of items promoted as healthy or nutritious were also assessed. SETTING: Five popular fast-food chains in the USA. SUBJECTS: Not applicable. RESULTS: Full menus scored lower than 50 out of 100 possible points on the HEI-2005. Scores for Total Fruit, Whole Grains and Sodium were particularly dismal. Compared with full menus, scores on dollar or value menus were 3 points higher on average, whereas kids' menus scored 10 points higher on average. Three chains marketed subsets of items as healthy or nutritious; these scored 17 points higher on average compared with the full menus. No menu or subset of menu items received a score higher than 72 out of 100 points. CONCLUSIONS: The poor quality of fast-food menus is a concern in light of increasing away-from-home eating, aggressive marketing to children and minorities, and the tendency for fast-food restaurants to be located in low-income and minority areas. The addition of fruits, vegetables and legumes; replacement of refined with whole grains; and reformulation of offerings high in sodium, solid fats and added sugars are potential strategies to improve fast-food offerings. The HEI may be a useful metric for ongoing monitoring of fast-food menus.

Sugar-sweetened beverage consumption in the U.S.: novel assessment methodology.

BACKGROUND: Sugar-sweetened beverage (SSB) consumption has been linked with poor diet quality, weight gain, and increased risk for obesity, diabetes, and cardiovascular disease. Previous studies have been hampered by inconsistent definitions and a failure to capture all types of SSBs. PURPOSE: To comprehensively examine total SSB consumption in the U.S. using an all-encompassing definition that includes beverages calorically sweetened after purchase in addition to presweetened beverages. METHODS: Data from the 2005-2008 National Health and Nutrition Examination Survey (N=17,078) were analyzed in September 2012 and used to estimate calories (kilocalories) of added sugars from SSBs and to identify top sources of SSBs. RESULTS: On average, Americans aged ≥2 years consumed 171 kcal (8% of total kcal) per day from added sugars in SSBs; the top sources were soda, fruit drinks, tea, coffee, energy/sports drinks, and flavored milks. Male adolescents (aged 12-19 years) had the highest mean intakes (293 kcal/day; 12% of total kcal). CONCLUSIONS: Americans consume more calories from added sugars in beverages than previously reported. The methodology presented in this paper allows for more-comprehensive estimates than those previously used regarding the extent to which SSBs provide calories from added sugars.