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1.  Fast-food menu offerings vary in dietary quality, but are consistently poor 
Public health nutrition  2013;17(4):924-931.
To evaluate five popular fast-food chains’ menus in relation to dietary guidance.
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.
Five popular fast-food chains in the USA.
Not applicable.
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.
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.
PMCID: PMC3883949  PMID: 23317511
Fast food; Dietary quality; Healthy Eating Index-2005; Dietary guidance; Food environment
2.  Healthfulness of the U.S. Food Supply 
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.
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.
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.
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 sub-optimal 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.
Deliberate efforts on the part of policymakers, agriculture and the food industry are necessary to provide a supply of foods consistent with nutrition recommendations and make healthy choices available to all.
PMCID: PMC2858769  PMID: 20153133
3.  Update of the Healthy Eating Index: HEI-2010 
Journal of the Academy of Nutrition and Dietetics  2013;113(4):10.1016/j.jand.2012.12.016.
The Healthy Eating Index (HEI) is a measure of diet quality in terms of conformance with federal dietary guidance. Publication of the Dietary Guidelines for Americans, 2010 prompted an interagency working group to update the HEI. The HEI-2010 retains several features of the 2005 version: (1) it has 12 components, many unchanged, including 9 adequacy and 3 moderation components; (2) it uses a density approach to set standards, e.g., per 1000 calories or as a percent of calories; and (3) it employs least-restrictive standards, i.e., those that are easiest to achieve among recommendations that vary by energy level, sex, and/or age. Changes to the index include: (1) Greens and Beans replaces Dark Green and Orange Vegetables and Legumes; (2) Seafood and Plant Proteins has been added to capture specific choices from the protein group; (3) Fatty Acids, a ratio of poly- and mono-unsaturated to saturated fatty acids, replaces Oils and Saturated Fat to acknowledge the recommendation to replace saturated fat with mono-and polyunsaturated fatty acids; and (4) a moderation component, Refined Grains, replaces the adequacy component, Total Grains, to assess over-consumption. The HEI-2010 captures the key recommendations of the 2010 Dietary Guidelines and, like earlier versions, will be used to assess the diet quality of the U.S. population and subpopulations, in evaluating interventions, in dietary patterns research, and to evaluate various aspects of the food environment.
PMCID: PMC3810369  PMID: 23415502
Healthy Eating Index; diet quality; diet assessment tool
4.  A mixed-effects model approach for estimating the distribution of usual intake of nutrients: The NCI method 
Statistics in medicine  2010;29(27):10.1002/sim.4063.
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.
PMCID: PMC3865776  PMID: 20862656
statistical distributions; diet surveys; nutrition assessment; mixed-effects model; nutrients; percentiles
5.  Income and race/ethnicity are associated with adherence to food-based dietary guidance among U.S. adults and children 
Income and race/ethnicity are associated with differences in dietary intakes that may contribute to health disparities among the United States population.
To examine alignment of intakes of food groups and energy from solid fats, added sugars, and alcohol with the 2005 Dietary Guidelines and MyPyramid, by family income and race/ethnicity.
Data from the National Health and Nutrition Examination Survey (NHANES), a cross-sectional nationally-representative survey, for 2001-2004.
Persons aged 2 years and older 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 (NHW), non-Hispanic Black (NHB), and Mexican-American (MA)).
Statistical analyses performed
The National Cancer Institute (NCI) method was used to estimate the proportions of adults and children in each income and race/ethnic group whose usual intakes met the recommendations.
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 NHB. Marked differences were observed for milk—15% of NHB children met the minimum recommendations compared to 42% of NHW children and 35% of MA children; a similar pattern was evident for adults. One in five Mexican American adults met the dry beans and peas recommendations compared to approximately 2% of NHW and NHB. Most adults and children consumed excess energy from solid fats and added sugars irrespective of income and race/ethnicity.
The diets of some subpopulations, particularly individuals in lower-income households and NHB, are especially poor in relation to dietary recommendations, supporting the need for comprehensive strategies to enable healthier dietary intake patterns.
PMCID: PMC3775640  PMID: 22709767
dietary guidance; income; race/ethnicity; disparities; usual dietary intakes; 24-hour recall; NHANES
6.  Differences between food group reports of low energy reporters and non-low energy reporters on a food frequency questionnaire 
Low-energy reporters (LERs) and non-LERs differ with respect to a number of characteristics, including self-reported intake of foods. Limited data exists investigating food intake differences with LERs identified using doubly labeled water (DLW).
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).
LERs were identified using DLW. LERs' (n=220) and non-LERs' (n=220) reports of 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/day), and the reported portion size (small, medium, or large). Analyses were adjusted for total energy expenditure from DLW.
LERs compared to non-LERs were less likely to report consumption for one food group among women (soft drinks/regular) and no food groups among men. Reported mean daily frequency of consumption was lower in LERs compared to non-LERs for 23 food groups among women and 24 food groups among men (18 food groups were similar in men and women). Additionally, reported mean portion sizes were smaller for LERs compared to non-LERs for 6 food groups among women and 5 food groups among men (3 food groups were similar in men and women). Results varied minimally by sex and body mass index (BMI).
LERs as compared to non-LERs were more likely to differ regarding their reported frequency of consumption of food groups than their reported consumption (yes/no) of the food groups or the food groups' reported portion sizes. Results did not vary greatly by sex or BMI. It still remains to be known 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.
PMCID: PMC3721508  PMID: 19559136
energy underreporting; dietary assessment; food frequency questionnaire; foods
7.  Knowledge of Energy Balance Guidelines and Associated Clinical Care Practices: The U.S. National Survey of Energy Balance Related Care among Primary Care Physicians 
Preventive Medicine  2012;55(1):28-33.
To assess primary care physicians’ (PCPs) knowledge of energy balance related guidelines and the association with sociodemographic characteristics and clinical care practices.
As part of the 2008 U.S. nationally representative National Survey of Energy Balance Related Care among Primary Care Physicians (EB-PCP), 1,776 PCPs from four specialties who treated adults (n=1,060) or children and adolescents (n=716) completed surveys on sociodemographic information, knowledge of energy balance guidelines, and clinical care practices.
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.
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.
PMCID: PMC3377834  PMID: 22609144
Energy balance; primary care; guidelines; physical activity; nutrition; body mass index; knowledge
8.  The accuracy of the Goldberg method for classifying misreporters of energy intake on a food frequency questionnaire and 24-hour recalls: Comparison with doubly labeled water 
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-hour recalls (24HR).
We compared the Goldberg method, which uses an equation to predict total energy expenditure (TEE), to a criterion method that uses doubly labeled water (DLW), in a study of 451 men and women. Underreporting was classified using recommended cutpoints and calculated values. Sensitivity and specificity, positive and negative predictive value (PPV and NPV), and the area under the receiver operating characteristic curve (AUC) were calculated. Predictive models of underreporting were contrasted for the Goldberg and DLW methods.
AUC 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.
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 characterizing underreporting.
PMCID: PMC3319469  PMID: 22127332
Diet; Diet Surveys; Energy Intake; Statistical Bias; Questionnaires/standards; Research Design
9.  Dietary Sources of Energy, Solid Fats, and Added Sugars Among Children and Adolescents in the United States 
The objective of this research was to identify top dietary sources of energy, solid fats, and added sugars among 2–18 year olds in the United States.
Data from the National Health and Nutrition Examination Survey (NHANES), a cross-sectional study, were used to examine food sources (percentage contribution and mean intake with standard errors) of total energy (2005–06) and calories from solid fats and added sugars (2003–04). Differences were investigated by age, sex, race/ethnicity, and family income, and the consumption of empty calories—defined as the sum of calories from solid fats and added sugars—was compared with the corresponding discretionary calorie allowance.
The top sources of energy for 2–18 year olds were grain desserts (138 kcal/day), pizza (136 kcal), and soda (118 kcal). Sugar-sweetened beverages (soda and fruit drinks combined) provided 173 kcal/day. Major contributors varied by age, sex, race/ethnicity, and income. Nearly 40% of total calories consumed (798 kcal/day of 2027 kcal) by 2–18 year olds were in the form of empty calories (433 kcal from solid fat and 365 kcal from added sugars). Consumption of empty calories far exceeded the corresponding discretionary calorie allowance for all sex-age groups (which range from 8–20%). Half of empty calories came from six foods: soda, fruit drinks, dairy desserts, grain desserts, pizza, and whole milk.
There is an overlap between the major sources of energy and empty calories: soda, grain desserts, pizza, and whole milk. The landscape of choices available to children and adolescents must change to provide fewer unhealthy foods and more healthy foods with fewer calories. Identifying top sources of energy and empty calories can provide targets for changes in the marketplace and food environment. However, product reformulation alone is not sufficient—the flow of empty calories into the food supply must be reduced.
PMCID: PMC3428130  PMID: 20869486
10.  U.S. Primary Care Physicians’ Diet, Physical Activity, and Weight-Related Care of Adult Patients 
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.
To profile diet, physical activity and weight control practice patterns of PCPs who treat adults.
A nationally representative survey of 1,211 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; PCPs’ use of pharmacologic treatments and surgical referrals for overweight and obesity.
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 (ps<0.05). More than 70% of PCPs reported ever using pharmacologic treatments to treat overweight and 86% had referred for obesity-related surgery.
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.
PMCID: PMC3142674  PMID: 21665061
11.  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.
PMCID: PMC3406506  PMID: 22848190
Bayesian approach; latent variables; measurement error; mixed effects models; nutritional epidemiology; zero-inflated data
The annals of applied statistics  2011;5(2B):1456-1487.
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.
PMCID: PMC3145332  PMID: 21804910
Bayesian methods; Dietary assessment; Latent variables; Measurement error; Mixed models; Nutritional epidemiology; Nutritional surveillance; Zero-Inflated Data
13.  Comparing 3 Dietary Pattern Methods—Cluster Analysis, Factor Analysis, and Index Analysis—With Colorectal Cancer Risk 
American Journal of Epidemiology  2009;171(4):479-487.
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.
PMCID: PMC2842201  PMID: 20026579
colorectal neoplasms; food habits; risk
14.  Modeling Data with Excess Zeros and Measurement Error: Application to Evaluating Relationships between Episodically Consumed Foods and Health Outcomes 
Biometrics  2009;65(4):1003-1010.
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.
PMCID: PMC2881223  PMID: 19302405
Dietary measurement error; Dietary survey; Episodically consumed foods; Excess zero models; Food frequency questionnaire; Fish; Individual usual intake; Mercury; Nonlinear mixed models; Regression calibration; 24-hour recall
15.  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/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.
PMCID: PMC2824162  PMID: 19211215
16.  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 available1 
The Journal of nutrition  2008;138(9):1725-1729.
The U.S. Department of Agriculture’s (USDA) Healthy Eating Index-2005 (HEI-2005) is a tool to quantify and evaluate the quality of diet consumed by the US 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-hour recall, are often available for only one day on each individual. In this paper, we examine how best to estimate a population’s mean usual HEI-2005 component and total scores when one day 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 US population’s diet be assessed and monitored using this method.
PMCID: PMC2581886  PMID: 18716176
17.  A new method for estimating the usual intake of episodically-consumed foods with application to their distribution 
We propose a new statistical method that uses information from two 24-hour recalls (24HRs) to estimate usual intake of episodically-consumed foods.
Statistical Analyses Performed
The method developed at the National Cancer Institute (NCI) accommodates the large number of non-consumption days that arise with foods by separating the probability of consumption from the consumption-day amount, using a two-part model. Covariates, such as sex, age, race, or information from a food frequency questionnaire (FFQ), may supplement the information from two or more 24HRs using correlated mixed model regression. The model allows for correlation between the probability of consuming a food on a single day and the consumption-day amount. Percentiles of the distribution of usual intake are computed from the estimated model parameters.
The Eating at America's Table Study (EATS) data are used to illustrate the method to estimate the distribution of usual intake for whole grains and dark green vegetables for men and women and the distribution of usual intakes of whole grains by educational level among men. A simulation study indicates that the NCI method leads to substantial improvement over existing methods for estimating the distribution of usual intake of foods.
The NCI method provides distinct advantages over previously proposed methods by accounting for the correlation between probability of consumption and amount consumed and by incorporating covariate information. Researchers interested in estimating the distribution of usual intakes of foods for a population or subpopulation are advised to work with a statistician and incorporate the NCI method in analyses.
PMCID: PMC2517157  PMID: 17000190
Usual intake; Episodically-consumed foods; statistical methods

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