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.

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.

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.

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.

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.

Monitoring and benchmarking population diet quality globally: a step-wise approach

INFORMAS (International Network for Food and Obesity/non-communicable diseases Research, Monitoring and Action Support) aims to monitor and benchmark the healthiness of food environments globally. In order to assess the impact of food environments on population diets, it is necessary to monitor population diet quality between countries and over time. This paper reviews existing data sources suitable for monitoring population diet quality, and assesses their strengths and limitations. A step-wise framework is then proposed for monitoring population diet quality. Food balance sheets (FBaS), household budget and expenditure surveys (HBES) and food intake surveys are all suitable methods for assessing population diet quality. In the proposed 'minimal' approach, national trends of food and energy availability can be explored using FBaS. In the 'expanded' and 'optimal' approaches, the dietary share of ultra-processed products is measured as an indicator of energy-dense, nutrient-poor diets using HBES and food intake surveys, respectively. In addition, it is proposed that pre-defined diet quality indices are used to score diets, and some of those have been designed for application within all three monitoring approaches. However, in order to enhance the value of global efforts to monitor diet quality, data collection methods and diet quality indicators need further development work.

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.

Comparison of 4 established DASH diet indexes: examining associations of index scores and colorectal cancer

BACKGROUND

Multiple diet indexes have been developed to capture the Dietary Approaches to Stop Hypertension (DASH) dietary pattern and examine relations with health outcomes but have not been compared within the same study population to our knowledge.

OBJECTIVE

We compared 4 established DASH indexes and examined associations with colorectal cancer.

DESIGN

Scores were generated from a food-frequency questionnaire in the NIH-AARP Diet and Health Study (n = 491,841). Separate indexes defined by Dixon (7 food groups, saturated fat, and alcohol), Mellen (9 nutrients), Fung (7 food groups and sodium), and Günther (8 food groups) were used. HRs and 95% CIs for colorectal cancer were generated by using Cox proportional hazard models.

RESULTS

From 1995 through 2006, 6752 incident colorectal cancer cases were ascertained. In men, higher scores were associated with reduced colorectal cancer incidence by comparing highest to lowest quintiles for all indexes as follows: Dixon (HR: 0.77; 95% CI: 0.69, 0.87), Mellen (HR: 0.78; 95% CI: 0.71, 0.86), Fung (HR: 0.75; 95% CI: 0.68, 0.83), and Günther (HR: 0.81; 95% CI: 0.74, 0.90). Higher scores in women were inversely associated with colorectal cancer incidence by using methods defined by Mellen (HR: 0.79; 95% CI: 0.68, 0.91), Fung (HR: 0.84; 95% CI: 0.73, 0.96), and Günther (HR: 0.84; 95% CI: 0.73.0.97) but not Dixon (HR: 1.01; 95% CI: 0.80, 1.28).

CONCLUSION

The consistency in findings, particularly in men, suggests that all indexes capture an underlying construct inherent in the DASH dietary pattern, although the specific index used can affect results.

Update of the Healthy Eating Index: HEI-2010

The Healthy Eating Index (HEI) is a measure of diet quality in terms of conformance with federal dietary guidance. Publication of the 2010 Dietary Guidelines for Americans prompted an interagency working group to update the HEI. The HEI-2010 retains several features of the 2005 version: (a) it has 12 components, many unchanged, including nine adequacy and three moderation components; (b) it uses a density approach to set standards, eg, per 1,000 calories or as a percentage of calories; and (c) it employs least-restrictive standards; ie, those that are easiest to achieve among recommendations that vary by energy level, sex, and/or age. Changes to the index include: (a) the Greens and Beans component replaces Dark Green and Orange Vegetables and Legumes; (b) Seafood and Plant Proteins has been added to capture specific choices from the protein group; (c) Fatty Acids, a ratio of polyunsaturated and monounsaturated to saturated fatty acids, replaces Oils and Saturated Fat to acknowledge the recommendation to replace saturated fat with monounsaturated and polyunsaturated fatty acids; and (d) a moderation component, Refined Grains, replaces the adequacy component, Total Grains, to assess overconsumption. 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 US population and subpopulations, evaluate interventions, research dietary patterns, and evaluate various aspects of the food environment.

Knowledge of energy balance guidelines and associated clinical care practices: the U.S. National Survey of Energy Balance Related Care among Primary Care Physicians

OBJECTIVE

To assess primary care physicians' (PCPs) knowledge of energy balance related guidelines and the association with sociodemographic characteristics and clinical care practices.

METHOD

As part of the 2008 U.S. nationally representative National Survey of Energy Balance Related Care among Primary Care Physicians (EB-PCP), 1776 PCPs from four specialties who treated adults (n=1060) or children and adolescents (n=716) completed surveys on sociodemographic information, knowledge of energy balance guidelines, and clinical care practices.

RESULTS

EB-PCP response rate was 64.5%. For PCPs treating children, knowledge of guidelines for healthy BMI percentile, physical activity, and fruit and vegetables intake was 36.5%, 27.0%, and 62.9%, respectively. For PCPs treating adults, knowledge of guidelines for overweight, obesity, physical activity, and fruit and vegetables intake was 81.4%, 81.3%, 70.9%, and 63.5%, respectively. Generally, younger, female physicians were more likely to exhibit correct knowledge. Knowledge of weight-related guidelines was associated with assessment of body mass index (BMI) and use of BMI-for-age growth charts.

CONCLUSION

Knowledge of energy balance guidelines among PCPs treating children is low, among PCPs treating adults it appeared high for overweight and obesity-related clinical guidelines and moderate for physical activity and diet, and was mostly unrelated to clinical practices among all PCPs.

Income and race/ethnicity are associated with adherence to food-based dietary guidance among US adults and children

BACKGROUND

Income and race/ethnicity are associated with differences in dietary intakes that may contribute to health disparities among members of the US population.

OBJECTIVE

To examine alignment of intakes of food groups and energy from solid fats, added sugars, and alcohol with the 2005 Dietary Guidelines for Americans and MyPyramid, by family income and race/ethnicity.

DESIGN

Data from the National Health and Nutrition Examination Survey, a cross-sectional, nationally representative survey, for 2001-2004.

PARTICIPANTS/SETTING

Persons aged ≥2 years for whom reliable dietary intake data were available (n=16,338) were categorized by income (lowest, middle, and highest) and race/ethnicity (non-Hispanic white, non-Hispanic black, and Mexican American).

STATISTICAL ANALYSES PERFORMED

The National Cancer Institute method was used to estimate the proportions of adults and children in each income and race/ethnic group whose usual intakes met the recommendations.

RESULTS

Higher income was associated with greater adherence to recommendations for most food groups; the proportions meeting minimum recommendations among adults in the highest income group were double that observed for the lowest income group for total vegetables, milk, and oils. Fewer differences by income were apparent among children. Among the race/ethnic groups, the proportions meeting recommendations were generally lowest among non-Hispanic blacks. Marked differences were observed for milk-15% of non-Hispanic black children met the minimum recommendations compared with 42% of non-Hispanic white children and 35% of Mexican-American children; a similar pattern was evident for adults. One in five Mexican-American adults met the dry beans and peas recommendations compared with approximately 2% of non-Hispanic whites and non-Hispanic blacks. Most adults and children consumed excess energy from solid fats and added sugars irrespective of income and race/ethnicity.

CONCLUSIONS

The diets of some subpopulations, particularly individuals in lower-income households and non-Hispanic blacks, are especially poor in relation to dietary recommendations, supporting the need for comprehensive strategies to enable healthier dietary intake patterns.

The accuracy of the Goldberg method for classifying misreporters of energy intake on a food frequency questionnaire and 24-h recalls: comparison with doubly labeled water

BACKGROUND/OBJECTIVES

Adults often misreport dietary intake; the magnitude varies by the methods used to assess diet and classify participants. The objective was to quantify the accuracy of the Goldberg method for categorizing misreporters on a food frequency questionnaire (FFQ) and two 24-h recalls (24HRs).

SUBJECTS/METHODS

We compared the Goldberg method, which uses an equation to predict total energy expenditure (TEE), with a criterion method that uses doubly labeled water (DLW), in a study of 451 men and women. Underreporting was classified using recommended cut points and calculated values. Sensitivity and specificity, positive predictive value (PPV) and negative predictive value and the area under the receiver operating characteristic curve (AUC) were calculated. Predictive models of underreporting were contrasted for the Goldberg and DLW methods.

RESULTS

AUCs were 0.974 and 0.972 on the FFQ, and 0.961 and 0.938 on the 24HR for men and women, respectively. The sensitivity of the Goldberg method was higher for the FFQ (92%) than the 24HR (50%); specificity was higher for the 24HR (99%) than the FFQ (88%); PPV was high for the 24HR (92%) and FFQ (88%). Simulation studies indicate attenuation in odds ratio estimates and reduction of power in predictive models.

CONCLUSIONS

Although use of the Goldberg method may lead to bias and reduction in power in predictive models of underreporting, the method has high predictive value for both the FFQ and the 24HR. Thus, in the absence of objective measures of TEE or physical activity, the Goldberg method is a reasonable approach to characterize underreporting.

U.S. primary care physicians' diet-, physical activity-, and weight-related care of adult patients

BACKGROUND

Overweight and obesity are substantial problems in the U.S., but few national studies exist on primary care physicians' (PCPs') clinical practices regarding overweight and obesity.

PURPOSE

To profile diet, physical activity, and weight control practice patterns of PCPs who treat adults.

METHODS

A nationally representative survey of 1211 PCPs sampled from the American Medical Association's Masterfile was conducted in 2008 and analyzed in 2010. Outcomes included PCPs' assessment, counseling, referral, and follow-up of diet, physical activity, and weight control in adult patients with and without chronic disease and PCPs' use of pharmacologic treatments and surgical referrals for overweight and obesity.

RESULTS

The survey response rate was 64.5%. Half of PCPs (49%) reported recording BMI regularly. Fewer than 50% reported always providing specific guidance on diet, physical activity, or weight control. Regardless of patients' chronic disease status, <10% of PCPs always referred patients for further evaluation/management and <22% reported always systematically tracking patients over time concerning weight or weight-related behaviors. Overall, PCPs were more likely to counsel on physical activity than on diet or weight control (p's<0.05). More than 70% of PCPs reported ever using pharmacologic treatments to treat overweight and 86% had referred for obesity-related surgery.

CONCLUSIONS

PCPs' assessment and behavioral management of overweight and obesity in adults is at a low level relative to the magnitude of the problem in the U.S. Further research is needed to understand barriers to providing care and to improve physician engagement in tracking and managing healthy lifestyles in U.S. adults.

A NEW MULTIVARIATE MEASUREMENT ERROR MODEL WITH ZERO-INFLATED DIETARY DATA, AND ITS APPLICATION TO DIETARY ASSESSMENT

In the United States the preferred method of obtaining dietary intake data is the 24-hour dietary recall, yet the measure of most interest is usual or long-term average daily intake, which is impossible to measure. Thus, usual dietary intake is assessed with considerable measurement error. Also, diet represents numerous foods, nutrients and other components, each of which have distinctive attributes. Sometimes, it is useful to examine intake of these components separately, but increasingly nutritionists are interested in exploring them collectively to capture overall dietary patterns. Consumption of these components varies widely: some are consumed daily by almost everyone on every day, while others are episodically consumed so that 24-hour recall data are zero-inflated. In addition, they are often correlated with each other. Finally, it is often preferable to analyze the amount of a dietary component relative to the amount of energy (calories) in a diet because dietary recommendations often vary with energy level. The quest to understand overall dietary patterns of usual intake has to this point reached a standstill. There are no statistical methods or models available to model such complex multivariate data with its measurement error and zero inflation. This paper proposes the first such model, and it proposes the first workable solution to fit such a model. After describing the model, we use survey-weighted MCMC computations to fit the model, with uncertainty estimation coming from balanced repeated replication.The methodology is illustrated through an application to estimating the population distribution of the Healthy Eating Index-2005 (HEI-2005), a multi-component dietary quality index involving ratios of interrelated dietary components to energy, among children aged 2-8 in the United States. We pose a number of interesting questions about the HEI-2005 and provide answers that were not previously within the realm of possibility, and we indicate ways that our approach can be used to answer other questions of importance to nutritional science and public health.

A mixed-effects model approach for estimating the distribution of usual intake of nutrients: the NCI method

It is of interest to estimate the distribution of usual nutrient intake for a population from repeat 24-h dietary recall assessments. A mixed effects model and quantile estimation procedure, developed at the National Cancer Institute (NCI), may be used for this purpose. The model incorporates a Box-Cox parameter and covariates to estimate usual daily intake of nutrients; model parameters are estimated via quasi-Newton optimization of a likelihood approximated by the adaptive Gaussian quadrature. The parameter estimates are used in a Monte Carlo approach to generate empirical quantiles; standard errors are estimated by bootstrap. The NCI method is illustrated and compared with current estimation methods, including the individual mean and the semi-parametric method developed at the Iowa State University (ISU), using data from a random sample and computer simulations. Both the NCI and ISU methods for nutrients are superior to the distribution of individual means. For simple (no covariate) models, quantile estimates are similar between the NCI and ISU methods. The bootstrap approach used by the NCI method to estimate standard errors of quantiles appears preferable to Taylor linearization. One major advantage of the NCI method is its ability to provide estimates for subpopulations through the incorporation of covariates into the model. The NCI method may be used for estimating the distribution of usual nutrient intake for populations and subpopulations as part of a unified framework of estimation of usual intake of dietary constituents.

Fitting a bivariate measurement error model for episodically consumed dietary components

There has been great public health interest in estimating usual, i.e., long-term average, intake of episodically consumed dietary components that are not consumed daily by everyone, e.g., fish, red meat and whole grains. Short-term measurements of episodically consumed dietary components have zero-inflated skewed distributions. So-called two-part models have been developed for such data in order to correct for measurement error due to within-person variation and to estimate the distribution of usual intake of the dietary component in the univariate case. However, there is arguably much greater public health interest in the usual intake of an episodically consumed dietary component adjusted for energy (caloric) intake, e.g., ounces of whole grains per 1000 kilo-calories, which reflects usual dietary composition and adjusts for different total amounts of caloric intake. Because of this public health interest, it is important to have models to fit such data, and it is important that the model-fitting methods can be applied to all episodically consumed dietary components.We have recently developed a nonlinear mixed effects model (Kipnis, et al., 2010), and have fit it by maximum likelihood using nonlinear mixed effects programs and methodology (the SAS NLMIXED procedure). Maximum likelihood fitting of such a nonlinear mixed model is generally slow because of 3-dimensional adaptive Gaussian quadrature, and there are times when the programs either fail to converge or converge to models with a singular covariance matrix. For these reasons, we develop a Monte-Carlo (MCMC) computation of fitting this model, which allows for both frequentist and Bayesian inference. There are technical challenges to developing this solution because one of the covariance matrices in the model is patterned. Our main application is to the National Institutes of Health (NIH)-AARP Diet and Health Study, where we illustrate our methods for modeling the energy-adjusted usual intake of fish and whole grains. We demonstrate numerically that our methods lead to increased speed of computation, converge to reasonable solutions, and have the flexibility to be used in either a frequentist or a Bayesian manner.

Americans do not meet federal dietary recommendations

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.

A population's distribution of Healthy Eating Index-2005 component scores can be estimated when more than one 24-hour recall is available

The USDA's Healthy Eating Index-2005 (HEI-2005) is a tool to quantify the quality of diet consumed by individuals in the U.S. It comprises 12 components expressed as ratios of a food group or nutrient intake to energy intake. Components are scored on a scale from 0 to M, where M is 5, 10, or 20. Ideally, the HEI-2005 is calculated on the basis of the usual, or long-term average, dietary intake of an individual. In recent cycles of the NHANES, intake data have been collected via 24-h recalls for more than 1 d on most participants. We present here a statistical method to estimate a population's distribution of usual HEI-2005 component scores when >or=2 d of dietary information is available for a sample of individuals from the population. Distributions for the total population and for age-gender subgroups may be estimated. The method also yields an estimate of the population's mean total HEI-2005 score. Application of the method to NHANES data for 2001-2004 yielded estimated distributions for all 12 components; those of total vegetables (range 0-5), whole grains (range 0-5), and energy from solid fats, alcoholic beverages, and added sugars (range 0-20) are presented. The total population mean scores for these components were 3.21, 1.00, and 8.41, respectively. An estimated 30% of the total population had a score of <2.5 for total vegetables. This is the first time, to our knowledge, that estimated distributions of usual HEI-2005 component scores have been published.

Evaluating the food environment: application of the Healthy Eating Index-2005

BACKGROUND

The Healthy Eating Index-2005 (HEI-2005), a tool designed to evaluate concordance with the 2005 Dietary Guidelines, has been used to monitor the quality of foods consumed by Americans. Because the HEI-2005 is not tied to individual requirements and is scored on a per 1000 kcal basis, it can be used to assess the overall quality of any mix of foods.

PURPOSE

The goal of this paper is to examine whether the HEI-2005 can be applied to the food environment.

METHODS

Two examples were selected to examine the application of the HEI-2005 to the food environment: the dollar menu displayed at a fast-food restaurant (coded and linked to the MyPyramid Equivalents Database and the Food and Nutrient Database for Dietary Studies) to represent the community level and the 2005 U.S. Food Supply (measured with food availability data, loss-adjusted food availability data, nutrient availability data, and Salt Institute data) to represent the macro level.

RESULTS

The dollar menu and the 2005 U.S. Food Supply received 43.4 and 54.9 points, respectively (100 possible points). According to the HEI-2005, for the offerings at a local fast-food restaurant and the U.S. Food Supply to align with national dietary guidance, substantial shifts would be necessary: a concomitant addition of fruit, dark-green vegetables, orange vegetables, legumes, and nonfat milk; replacement of refined grains with whole grains; and reduction in foods and food products containing sodium, solid fats, and added sugars.

CONCLUSIONS

Because the HEI-2005 can be applied to both environmental- and individual-level data, it provides a useful metric for studies linking data across various levels of the socioecologic framework of dietary behavior. The present findings suggest that new dietary guidance could target not only individuals but also the architects of our food environment.

Healthfulness of the U.S. food supply: little improvement despite decades of dietary guidance

BACKGROUND

Every 5 years for the past several decades, the USDHHS and the U.S. Department of Agriculture have issued and updated the Dietary Guidelines for Americans, which form the basis of federal nutrition policy and have shown remarkable consistency across various editions among the major themes.

PURPOSE

This paper examines whether the U.S. food supply is sufficiently balanced to provide the recommended proportions of various foods and nutrients per the amount of energy, whether this balance has shifted over time, and which areas of the food supply may have changed more than others.

METHODS

The Healthy Eating Index-2005 (HEI-2005) was used to measure the dietary quality of the U.S. food supply, from 1970 to 2007. Sources of data were the USDA's food availability data, loss-adjusted food availability data, and nutrient availability data, and the U.S. Salt Institute's data on salt sold for human consumption.

RESULTS

Total HEI-2005 scores improved by about 10 points between 1970 and 2007, but they never achieved even 60 points on a scale from 0 to 100. Although meats and total grains were supplied generally in recommended proportions, total vegetables, total fruit, whole fruit, and milk were supplied in suboptimal proportions that changed very little over time. Saturated fat, sodium, and calories from solid fat, alcoholic beverages, and added sugars were supplied in varying degrees of unhealthy abundance over the years. Supplies of dark-green/orange vegetables and legumes and whole grains were entirely insufficient relative to recommendations, with virtually no change over time.

CONCLUSIONS

Deliberate efforts on the part of policymakers, the agriculture sector, and the food industry are necessary to provide a supply of foods consistent with nutrition recommendations and to make healthy choices available to all.

Modeling data with excess zeros and measurement error: application to evaluating relationships between episodically consumed foods and health outcomes

Dietary assessment of episodically consumed foods gives rise to nonnegative data that have excess zeros and measurement error. Tooze et al. (2006, Journal of the American Dietetic Association 106, 1575-1587) describe a general statistical approach (National Cancer Institute method) for modeling such food intakes reported on two or more 24-hour recalls (24HRs) and demonstrate its use to estimate the distribution of the food's usual intake in the general population. In this article, we propose an extension of this method to predict individual usual intake of such foods and to evaluate the relationships of usual intakes with health outcomes. Following the regression calibration approach for measurement error correction, individual usual intake is generally predicted as the conditional mean intake given 24HR-reported intake and other covariates in the health model. One feature of the proposed method is that additional covariates potentially related to usual intake may be used to increase the precision of estimates of usual intake and of diet-health outcome associations. Applying the method to data from the Eating at America's Table Study, we quantify the increased precision obtained from including reported frequency of intake on a food frequency questionnaire (FFQ) as a covariate in the calibration model. We then demonstrate the method in evaluating the linear relationship between log blood mercury levels and fish intake in women by using data from the National Health and Nutrition Examination Survey, and show increased precision when including the FFQ information. Finally, we present simulation results evaluating the performance of the proposed method in this context.

Comparing 3 dietary pattern methods--cluster analysis, factor analysis, and index analysis--With colorectal cancer risk: The NIH-AARP Diet and Health Study

The authors compared dietary pattern methods-cluster analysis, factor analysis, and index analysis-with colorectal cancer risk in the National Institutes of Health (NIH)-AARP Diet and Health Study (n = 492,306). Data from a 124-item food frequency questionnaire (1995-1996) were used to identify 4 clusters for men (3 clusters for women), 3 factors, and 4 indexes. Comparisons were made with adjusted relative risks and 95% confidence intervals, distributions of individuals in clusters by quintile of factor and index scores, and health behavior characteristics. During 5 years of follow-up through 2000, 3,110 colorectal cancer cases were ascertained. In men, the vegetables and fruits cluster, the fruits and vegetables factor, the fat-reduced/diet foods factor, and all indexes were associated with reduced risk; the meat and potatoes factor was associated with increased risk. In women, reduced risk was found with the Healthy Eating Index-2005 and increased risk with the meat and potatoes factor. For men, beneficial health characteristics were seen with all fruit/vegetable patterns, diet foods patterns, and indexes, while poorer health characteristics were found with meat patterns. For women, findings were similar except that poorer health characteristics were seen with diet foods patterns. Similarities were found across methods, suggesting basic qualities of healthy diets. Nonetheless, findings vary because each method answers a different question.

The population distribution of ratios of usual intakes of dietary components that are consumed every day can be estimated from repeated 24-hour recalls

Estimating the population distribution of the usual intake of a nutrient relative to that of another nutrient requires determination of individual-level ratios. If intake data are available on a per-day basis, as with 24-h dietary recalls, those ratios can be determined in 1 of 2 ways: as the usual ratio of intakes or the ratio of usual intakes. Each of these ratios has its own meaning and determination; the ratio of usual intakes is conceptually consistent with determinations obtained from FFQ data. We present a method for estimating the ratio of usual intakes that uses bivariate modeling of the 2 nutrient intakes in question. Application of the method to the NHANES data for the years 2001-2004 yielded estimated distributions for percent of usual energy intake from total fat, percent of usual energy intake from saturated fat, and usual sodium intake per 1000 kcal (4184 kJ) of usual energy intake. Distributions for both the total population and for age-gender subgroups were estimated. Approximately 60% of adults (>19 y) had a usual total fat intake that was within the recommended range of 20-35% of total energy, but only approximately 34% had a usual saturated fat intake <10% of total energy. The results changed only minimally when the other definition of usual intake, the usual ratio of intakes, was adopted.

Modeling data with excess zeros and measurement error: application to evaluating relationships between episodically consumed foods and health outcomes

Dietary assessment of episodically consumed foods gives rise to nonnegative data that have excess zeros and measurement error. Tooze et al. (2006, Journal of the American Dietetic Association 106, 1575-1587) describe a general statistical approach (National Cancer Institute method) for modeling such food intakes reported on two or more 24-hour recalls (24HRs) and demonstrate its use to estimate the distribution of the food's usual intake in the general population. In this article, we propose an extension of this method to predict individual usual intake of such foods and to evaluate the relationships of usual intakes with health outcomes. Following the regression calibration approach for measurement error correction, individual usual intake is generally predicted as the conditional mean intake given 24HR-reported intake and other covariates in the health model. One feature of the proposed method is that additional covariates potentially related to usual intake may be used to increase the precision of estimates of usual intake and of diet-health outcome associations. Applying the method to data from the Eating at America's Table Study, we quantify the increased precision obtained from including reported frequency of intake on a food frequency questionnaire (FFQ) as a covariate in the calibration model. We then demonstrate the method in evaluating the linear relationship between log blood mercury levels and fish intake in women by using data from the National Health and Nutrition Examination Survey, and show increased precision when including the FFQ information. Finally, we present simulation results evaluating the performance of the proposed method in this context.

Differences between food group reports of low-energy reporters and non-low-energy reporters on a food frequency questionnaire

BACKGROUND

Low-energy reporters (LERs) and non-LERs differ with respect to several characteristics, including self-reported intake of foods. Limited data exist regarding food intake difference between LERs and non-LERs identified using doubly labeled water (DLW).

OBJECTIVE

In the Observing Protein and Energy Nutrition Study (September 1999-March 2000), differences were examined between food group reports of LERs and non-LERs on a food frequency questionnaire (FFQ) (n=440).

DESIGN

LERs were identified using DLW. Responses of LERs (n=220) and non-LERs (n=220) for 43 food groups on the FFQ were examined in three ways: whether they reported consuming a food group (yes/no), how frequently they reported consuming it (times per day), and the reported portion size (small, medium, or large). Analyses were adjusted for total energy expenditure from DLW.

RESULTS

LERs, compared to non-LERs, were less likely to report consumption for one food group among women (soft drinks/regular). Among men, there was no difference between LERs and non-LERs with respect to reporting consumption of food groups. Reported mean daily frequency of consumption was lower among LERs compared with non-LERs for 23 food groups among women and 24 food groups among men (18 food groups were similar in men and women). In addition, reported mean portion sizes were smaller for LERs compared with non-LERs for six food groups among women and five food groups among men (three food groups were similar in men and women). Results varied minimally by sex and body mass index.

CONCLUSIONS

LERs, compared with non-LERs, were more likely to differ regarding their reported frequency of consumption of food groups than their reported consumption (yes/no) or portion size of food groups. Results did not vary greatly by sex or body mass index. It still remains unclear whether improvement in questionnaire design or additional tools or methods would lead to a decrease in differential reporting due to LER status on an FFQ.

Considerations for an obesity policy research agenda

The rise in obesity levels in the U.S. in the past several decades has been dramatic, with serious implications for public health and the economy. Experiences in tobacco control and other public health initiatives have shown that public policy may be a powerful tool to effect structural change to alter population-level behavior. In 2007, the National Cancer Institute convened a meeting to discuss priorities for a research agenda to inform obesity policy. Issues considered were how to define obesity policy research, key challenges and key partners in formulating and implementing an obesity policy research agenda, criteria by which to set research priorities, and specific research needs and questions. Themes that emerged were: (1) the embryonic nature of obesity policy research, (2) the need to study "natural experiments" resulting from policy-based efforts to address the obesity epidemic, (3) the importance of research focused beyond individual-level behavior change, (4) the need for economic research across several relevant policy areas, and (5) the overall urgency of taking action in the policy arena. Moving forward, timely evaluation of natural experiments is of especially high priority. A variety of policies intended to promote healthy weight in children and adults are being implemented in communities and at the state and national levels. Although some of these policies are supported by the findings of intervention research, additional research is needed to evaluate the implementation and quantify the impact of new policies designed to address obesity.

A population's mean Healthy Eating Index-2005 scores are best estimated by the score of the population ratio when one 24-hour recall is available

The USDA Healthy Eating Index-2005 (HEI-2005) is a tool to quantify and evaluate the quality of diet consumed by the U.S. population. It comprises 12 components, expressed as ratios of a food group or nutrient to energy intake. The components are scored on a scale from 0 to M, where M is 5, 10, or 20. Ideally, the HEI-2005 is calculated on the basis of the usual dietary intake of an individual. Intake data, collected via a 24-h recall, are often available for only 1 d for each individual. In this article, we examine how best to estimate a population's mean usual HEI-2005 component and total scores when 1 d of dietary information is available for a sample of individuals from the population. Three methods are considered: the mean of individual scores, the score of the mean of individual ratios, and the score of the ratio of total food group or nutrient intake to total energy intake, which we call the population ratio. We investigate via computer simulation which method is the least biased. The simulations are based on statistical modeling of the distributions of intakes reported by 738 women participating in the Eating at America's Table Study. The results show that overall, the score of the population ratio is the preferred method. We therefore recommend that the quality of the U.S. population's diet be assessed and monitored using this method.

Index-based dietary patterns and risk of colorectal cancer: the NIH-AARP Diet and Health Study

The authors compared how four indexes-the Healthy Eating Index-2005, Alternate Healthy Eating Index, Mediterranean Diet Score, and Recommended Food Score-are associated with colorectal cancer in the National Institutes of Health-AARP Diet and Health Study (n = 492,382). To calculate each score, they merged data from a 124-item food frequency questionnaire completed at study entry (1995-1996) with the MyPyramid Equivalents Database (version 1.0). Other variables included energy, nutrients, multivitamins, and alcohol. Models were stratified by sex and adjusted for age, ethnicity, education, body mass index, smoking, physical activity, and menopausal hormone therapy (in women). During 5 years of follow-up, 3,110 incident colorectal cancer cases were ascertained. Although the indexes differ in design, a similarly decreased risk of colorectal cancer was observed across all indexes for men when comparing the highest scores with the lowest: Healthy Eating Index-2005 (relative risk (RR) = 0.72, 95% confidence interval (CI): 0.62, 0.83); Alternate Healthy Eating Index (RR = 0.70, 95% CI: 0.61, 0.81); Mediterranean Diet Score (RR = 0.72, 95% CI: 0.63, 0.83); and Recommended Food Score (RR = 0.75, 95% CI: 0.65, 0.87). For women, a significantly decreased risk was found with the Healthy Eating Index-2005, although Alternate Healthy Eating Index results were similar. Index-based dietary patterns that are consistent with given dietary guidelines are associated with reduced risk.

A comparison of food-based recommendations and nutrient values of three food guides: USDA's MyPyramid, NHLBI's Dietary Approaches to Stop Hypertension Eating Plan, and Harvard's Healthy Eating Pyramid

The purpose of this research was to compare food-based recommendations and nutrient values of three food guides: the US Department of Agriculture's MyPyramid; the National Heart, Lung, and Blood Institute's Dietary Approaches to Stop Hypertension Eating Plan, and Harvard University's Healthy Eating Pyramid. Estimates of nutrient values associated with following each of the food guides at the 2,000-calorie level were made using a composite approach. This approach calculates population-weighted nutrient composites for each food group and subgroup, assuming average choices within food groups. Nutrient estimates were compared to the Dietary Reference Intakes and other goals and limits. Recommendations were similar regarding almost all food groups for both the type and amount of foods. Primary differences were seen in the types of vegetables and protein sources recommended and the amount of dairy products and total oil recommended. Overall nutrient values were also similar for most nutrients, except vitamin A, vitamin E, and calcium. These food guides were derived from different types of nutrition research, yet they share consistent messages: eat more fruits, vegetables, legumes, and whole grains; eat less added sugar and saturated fat; and emphasize plant oils.

Communicating healthy eating: lessons learned and future directions

Achieving and maintaining wide-scale positive dietary change is a complex and formidable endeavor, given the current food environment. Moreover, for positive change to occur, nutrition messages should be communicated in a scientifically precise, yet practical and motivating manner. This challenge was the impetus for the organization of a 2-day workshop hosted by the National Cancer Institute (NCI) and the Division of Nutrition Research Coordination (DNRC), both of the National Institutes of Health (NIH). The conference included communication, nutrition, and behavioral scientists, market researchers, media advocates, journalists, and public policy experts. Discussions regarding communication efforts and the best methods to craft, deliver, and evaluate the impact of nutrition messages illustrated both the challenges and the opportunities we face. During the discussions, important recommendations for nutrition communicators and interventionists emerged, based on existing knowledge from the communications field, lessons learned thus far, and noted gaps in our knowledge.

What can communication science tell us about promoting optimal dietary behavior?

Four of the 10 leading causes of death can be attributed to poor dietary behaviors. Nutrition professionals continue to struggle with the most effective ways to deliver nutrition messages that will result in changes in dietary behavior. On July 14-15, 2005, the National Cancer Institute and the Division of Nutrition Research Coordination, both of the National Institutes of Health, hosted a meeting to explore the state of the science concerning this issue. This paper provides an introduction to that meeting and the articles that resulted from it.

Most Americans eat much less than recommended amounts of fruits and vegetables

OBJECTIVE

To estimate the proportions of the population meeting recommendations for fruit and vegetable intake, we first estimated the usual intake distributions of total fruits and vegetables and then compared the results to the 5 A Day recommendation and to the recommendations for fruits and vegetables combined, found in the new US Department of Agriculture food guide, MyPyramid.

DESIGN/SUBJECTS

The primary dataset was created from one 24-hour recall from each of 8,070 respondents in the 1999-2000 National Health and Nutrition Examination Survey. Variances were estimated using one or two 24-hour recalls from 14,963 respondents in the 1994-1996 Continuing Survey of Food Intakes by Individuals.

STATISTICAL ANALYSIS

The statistical method developed at Iowa State University was used for estimating distributions of usual intake of dietary components that are consumed daily. It was modified to allow the adjustment of heterogeneous within-person variances using an external estimate of heterogeneity.

RESULTS

In 1999-2000, only 40% of Americans ate an average of five or more (1/2)-cup servings of fruits and vegetables per day. The proportions of sex-age groups meeting the new US Department of Agriculture recommendations ranged from 0.7% of boys aged 14 to 18 years, whose combined recommendation is 5 cups, to 48% of children aged 2 to 3 years, whose combined recommendation is 2 cups.

CONCLUSIONS

Americans need to consume more fruits and vegetables, especially dark green and orange vegetables and legumes. Nutritionists must help consumers realize that, for everyone older than age 3 years, the new recommendations for fruit and vegetable intakes are greater than the familiar five servings a day.

The Food Propensity Questionnaire: Concept, Development, and Validation for Use as a Covariate in a Model to Estimate Usual Food Intake

OBJECTIVE

Twenty-four-hour recalls capture rich information on food consumption, but suffer from inadequately measuring usual intakes of episodically consumed foods. We explore using food frequency questionnaire (FFQ) data as covariates in a statistical model to estimate individual usual intakes of episodically consumed foods and their distributions and describe the development of the Food Propensity Questionnaire, an FFQ introduced in the 2003-2004 National Health and Nutrition Examination Survey.

DESIGN

We analyzed data from 965 adult participants in the Eating at America's Table Study who completed four 24-hour recalls and an FFQ. We assessed whether or not increasing FFQ-reported frequency was associated with both number of 24-hour recall consumption days and amounts reported.

RESULTS

For 52 of 56 food groups (93%), and 218 of 230 individual foods (95%), there were significant monotonically increasing relationships between FFQ frequency and 24-hour recall probability of consumption. For 47 of 56 food groups (84%) and 55 of 230 (24%) individual foods, there were significant positive correlations between FFQ frequencies and consumption-day mean intake.

CONCLUSIONS

We found strong and consistent relationships between reported FFQ frequency of food and food-group consumption and probability of consumption on 24-hour recalls. This supports the premise that frequency data may offer important covariate information in supplementing multiple recalls for estimating usual intake of food groups.

Patterns of health behavior in U.S. adults

BACKGROUND

Associations between health-related behaviors are important for two reasons. First, disease prevention and health promotion depend on understanding both prevalence of health behaviors and associations among such behaviors. Second, behaviors may have synergistic effects on disease risk.

METHODS

We document patterns of adherence to recommendations concerning five behaviors (physical activity, tobacco use, alcohol consumption, fruit and vegetable consumption, and dietary fat intake) in U.S. adults (n = 15,425) using data from the Third National Health and Nutrition Examination Survey. Division of individuals into categories associated with adherence or nonadherence to lifestyle recommendations results in 32 patterns of adherence/nonadherence.

RESULTS

Proportions of U.S. adults with 21 of 32 behavior patterns characterized here deviated from proportions expected if health behaviors are independent of each other. The two extreme patterns, all adherence (5.9%) and all nonadherence (4.9%), were found in about double the proportion expected. Age, gender, race/ethnicity, education, and income were associated with a number of patterns, including the two extremes.

CONCLUSIONS

This analysis of behavior patterns highlights population subgroups of public health importance, provides a benchmark for studies of multivariate associations between health behaviors, and supports a multidimensional model of health behavior.

Evaluating the impact of population changes in diet, physical activity, and weight status on population risk for colon cancer (United States)

OBJECTIVE

To estimate the effects of observed population-level changes in risk factors on population risk and incidence of disease.

METHODS

Trends in a set of risk factors for colon cancer (vegetable intake, red meat intake, alcohol consumption, physical activity levels, and weight status) were modeled for the US adult population over the years 1975-1995 and combined with relative risk estimates from epidemiologic studies and a probability distribution for the induction period to estimate the percentage change in incidence rates from 1985 to 1995 due to the five risk factors. A sensitivity analysis was performed to account for imprecision related to estimates of trends in behavior and epidemiologic risk.

RESULTS

Increased vegetable intake and decreased intakes of red meat and alcohol reduced risk, while reduced physical activity and increased body mass index increased risk for colon cancer. When all five factors were considered together, change in the average population relative risk was small and the risk factors accounted for little of the recently observed decline in incidence.

CONCLUSIONS

Although these factors have the potential to greatly affect risk of colon cancer and incidence rates, little of that potential was realized since adverse trends neutralized what progress had been made in the areas of vegetables, red meat, and alcohol consumption.

Let the pyramid guide your food choices: capturing the total diet concept

This paper discusses how the guideline "Eat a variety of foods" became "Let the Pyramid guide your food choices," presents background information on the food guidance system upon which the Food Guide Pyramid is based and reviews methods that have been used to assess aspects of the total diet, i.e., the variety, moderation and proportionality, promoted by this guidance. The methods include measures of dietary variety, patterns based on Pyramid food group intakes and scoring methods comprised of multiple dietary components. Highlights of results from these methods include the following. Although approximately one third of the U.S. population eat at least some food from all Pyramid food groups, only approximately 1-3% eat the recommended number of servings from all food groups on a given day. Fruits are the most commonly omitted food group. Vegetables and meat are the groups most commonly met by adults, and dairy the most commonly met by youth. Intakes of specific types of vegetables (i.e., dark green, deep yellow) and of grains (i.e., whole grains) are well below that recommended; intakes of total fat and added sugars exceed current recommendations. Scoring methods show those diets of the majority of the population require improvement, and that diets improve with increases in education and income. This paper also discusses the limitations and strengths of these approaches, and concludes with suggestions to improve current food guidance and methods to assess the total diet.