Prevalence and Severity of Food Insecurity Before and During the Coronavirus Disease 2019 Pandemic Among Adults and Youth in Australia, Canada, Chile, Mexico, the United Kingdom, and the United States.

BACKGROUND: Disruptions from the coronavirus disease 2019 (COVID-19) pandemic potentially exacerbated food insecurity among adults and youth. OBJECTIVES: The objective was to examine changes in the prevalence and severity of food insecurity among adults and youth from before (2019) to during (2020) the pandemic in multiple countries. METHODS: Repeated cross-sectional data were collected among adults aged 18-100 y (n = 63,278) in 5 countries in November to December in 2018-2020 and among youth aged 10-17 y (n = 23,107) in 6 countries in November to December in 2019 and 2020. Food insecurity in the past year was captured using the Household Food Security Survey Module and the Child Food Insecurity Experiences Scale. Changes in the prevalence and severity of food insecurity were examined using logistic and generalized logit regression models, respectively. Models included age, gender, racial-ethnic identity, and other sociodemographic characteristics associated with food insecurity to adjust for possible sample differences across waves. Models were weighted to reflect each country's population. RESULTS: Adults [adjusted OR (AOR): 1.15; 95% CI: 1.02, 1.31] and youth (AOR: 1.43; 95% CI: 1.19, 1.71) in Mexico were more likely to live in food-insecure households in 2020 compared to 2019. Adults in Australia (AOR: 0.81; 95% CI: 0.72, 0.92) and Canada (AOR: 0.87; 95% CI: 0.77, 0.99) were less likely to live in food-insecure households in 2020. Trends in severity aligned with changes in prevalence, with some exceptions. Youth in Australia (AOR: 2.24; 95% CI: 1.65, 3.02) and the United States (AOR: 1.39; 95% CI: 1.04, 1.86) were more likely to have many compared with no experiences of food insecurity in 2020 compared to 2019. There was no evidence of change among adults and youth in the remaining countries. CONCLUSIONS: Except for Mexico, few changes in food insecurity among adults and youth were observed from before to during the COVID-19 pandemic. Action is needed to support households at risk of food insecurity.

Validation of Total Water Intake from the Automated Self-Administered 24-h Recall, 4-d Food Records, and a Food Frequency Questionnaire Using Doubly Labeled Water.

BACKGROUND: Although prior evidence indicates that water intake is important for health, the ability to accurately measure community-dwelling intake is limited. Only a few studies have evaluated self-reported water intake against an objective recovery biomarker. OBJECTIVES: The aim was to compare preformed water intakes (all sources including food) by multiple Automated Self-Administered 24-h recalls (ASA24s), food frequency questionnaires (FFQs), and 4-d food records (4DFRs) against a recovery biomarker, doubly labeled water (DLW), to assess measurement error. METHODS: Over 1 y, 1082 women and men (50%), aged 50 to 74 y, were asked to complete 6 ASA24s, 2 FFQs, 2 unweighted 4DFRs, and an administration of DLW (n = 686). Geometric means of water intake by self-report tools were compared with DLW. Attenuation factors and correlation coefficients between self-reported and the recovery biomarker (DLW) were estimated. RESULTS: Mean water intakes by DLW were 2777 mL/d (interquartile range, 2350 to 3331) in women and 3243 mL/d (interquartile range, 2720 to 3838) in men. Compared with DLW, water intake was underestimated by 18% to 31% on ASA24s and 43% to 44% on 4DFRs. Estimated geometric means from FFQs differed from DLW by -1% to +13%. For a single ASA24, FFQ, and 4DFR, attenuation factors were 0.28, 0.27, and 0.32 and correlation coefficients were 0.46, 0.48, and 0.49, respectively. Repeated use of 6 ASA24s, 2 FFQs, and 2 4DFRs improved attenuation factors to 0.43, 0.32, and 0.39 and correlation coefficients to 0.58, 0.53, and 0.54, respectively. CONCLUSIONS: FFQs may better estimate population means for usual water intake compared with ASA24 and 4DFR. Similar attenuation factors and correlation coefficients across all self-report tools indicate that researchers have 3 feasible options if the goal is understanding intake-disease relationships. The findings are useful for planning future nutrition studies that set policy priorities for populations and to understand the health impact of water. This trial was registered at clinicaltrials.gov as NCT03268577.

A narrative review of nutrient based indexes to assess diet quality and the proposed total nutrient index that reflects total dietary exposures.

A priori dietary indices provide a standardized, reproducible way to evaluate adherence to dietary recommendations across different populations. Existing nutrient-based indices were developed to reflect food/beverage intake; however, given the high prevalence of dietary supplement (DS) use and its potentially large contribution to nutrient intakes for those that use them, exposure classification without accounting for DS is incomplete. The purpose of this article is to review existing nutrient-based indices and describe the development of the Total Nutrient Index (TNI), an index developed to capture usual intakes from all sources of under-consumed micronutrients among the U.S. population. The TNI assesses U.S. adults' total nutrient intakes relative to recommended nutrient standards for eight under-consumed micronutrients identified by the Dietary Guidelines for Americans: calcium, magnesium, potassium, choline, and vitamins A, C, D, E. The TNI is scored from 0 to 100 (truncated at 100). The mean TNI score of U.S. adults (≥19 y; n = 9,954) based on dietary data from NHANES 2011-2014, was 75.4; the mean score for the index ignoring DS contributions was only 69.0 (t-test; p < 0.001). The TNI extends existing measures of diet quality by including nutrient intakes from all sources and was developed for research, monitoring, and policy purposes.Supplemental data for this article is available online at https://doi.org/10.1080/10408398.2021.1967872.

Measurement Error Affecting Web- and Paper-Based Dietary Assessment Instruments: Insights From the Multi-Cohort Eating and Activity Study for Understanding Reporting Error.

Few biomarker-based validation studies have examined error in online self-report dietary assessment instruments, and food records (FRs) have been considered less than food frequency questionnaires (FFQs) and 24-hour recalls (24HRs). We investigated measurement error in online and paper-based FFQs, online 24HRs, and paper-based FRs in 3 samples drawn primarily from 3 cohorts, comprising 1,393 women and 1,455 men aged 45-86 years. Data collection occurred from January 2011 to October 2013. Attenuation factors and correlation coefficients between reported and true usual intake for energy, protein, sodium, potassium, and respective densities were estimated using recovery biomarkers. Across studies, average attenuation factors for energy were 0.07, 0.07, and 0.19 for a single FFQ, 24HR, and FR, respectively. Correlation coefficients for energy were 0.24, 0.23, and 0.40, respectively. Excluding energy, the average attenuation factors across nutrients and studies were 0.22 for a single FFQ, 0.22 for a single 24HR, and 0.51 for a single FR. Corresponding correlation coefficients were 0.31, 0.34, and 0.53, respectively. For densities (nutrient expressed relative to energy), the average attenuation factors across studies were 0.37, 0.17, and 0.50, respectively. The findings support prior research suggesting different instruments have unique strengths that should be leveraged in epidemiologic research.

Advanced Dietary Analysis and Modeling: A Deep Dive into the National Cancer Institute Method.

BACKGROUND: The National Cancer Institute (NCI) method has been used widely by researchers to make inferences about usual dietary intake distributions of foods and nutrients based on a limited number of 24-h dietary recalls (24-HRs). Although the NCI method does not provide individual estimates of usual intake, it can be used to address many research questions, including modeling effects of nutrition interventions on population distributions of usual intake. Software for implementing the NCI method, and corresponding code examples, is publicly available in the form of SAS macros but little formal guidance exists for conducting advanced analyses. OBJECTIVES: We aim to present advanced techniques for working with NCI macros to conduct both basic and advanced dietary analyses and modeling. METHOD: We first present the 3 basic building blocks of analyses using the NCI method: 1) data set preparation, 2) application of the MIXTRAN macro to estimate parameters of the usual intake distribution, including effects of covariates, after transformation of 24-HRs to approximate normality, and 3) application of the DISTRIB macro to estimate the distribution of usual nutrient intake. Then, we illustrate how researchers can employ these building blocks to answer questions beyond typical descriptive analyses. RESULTS: Researchers can adapt the building blocks to: 1) account for factors such as demographic changes or nutrition interventions such as food fortification, 2) estimate the prevalence of dietary inadequacy via the full probability method, 3) incorporate nutrient intake from sources not always captured by 24-HRs, such as dietary supplements and human milk, and 4) carry out multiple subgroup analyses. This article describes the theoretical basis and operational guidance for these techniques. CONCLUSION: With this article as a detailed resource, researchers can leverage the basic NCI building blocks to investigate a wide range of questions about usual dietary intake distribution.

The Total Nutrient Index is a Useful Measure for Assessing Total Micronutrient Exposures Among US Adults.

BACKGROUND: Most dietary indices reflect foods and beverages and do not include exposures from dietary supplements (DS) that provide substantial amounts of micronutrients. A nutrient-based approach that captures total intake inclusive of DS can strengthen exposure assessment. OBJECTIVES: We examined the construct and criterion validity of the Total Nutrient Index (TNI) among US adults (≥19 years; nonpregnant or lactating). METHODS: The TNI includes 8 underconsumed micronutrients identified by the Dietary Guidelines for Americans: calcium; magnesium; potassium; choline; and vitamins A, C, D, and E. The TNI is expressed as a percentage of the RDA or Adequate Intake to compute micronutrient component scores; the mean of the component scores yields the TNI score, ranging from 0-100. Data from exemplary menus and the 2003-2006 (≥19 years; n = 8861) and 2011-2014 NHANES (≥19 years; n = 9954) were employed. Exemplary menus were used to determine whether the TNI yielded high scores from dietary sources (women, 31-50 years; men ≥ 70 years). TNI scores were correlated with Healthy Eating Index (HEI) 2015 overall and component scores for dairy, fruits, and vegetables; TNI component scores for vitamins A, C, D, and E were correlated with respective biomarker data. TNI scores were compared between groups with known differences in nutrient intake based on the literature. RESULTS: The TNI yielded high scores on exemplary menus (84.8-93.3/100) and was moderately correlated (r = 0.48) with the HEI-2015. Mean TNI scores were significantly different for DS users (83.5) compared with nonusers (67.1); nonsmokers (76.8) compared with smokers (70.3); and those living with food security (76.6) compared with food insecurity (69.1). Correlations of TNI vitamin component scores with available biomarkers ranged from 0.12 (α-tocopherol) to 0.36 (serum 25-hydroxyvitamin D), and were significantly higher than correlations obtained from the diet alone. CONCLUSIONS: The evaluation of validity supports that the TNI is a useful construct to assess total micronutrient exposures of underconsumed micronutrients among US adults.

Introduction to the SIMPLE Macro, a Tool to Increase the Accessibility of 24-Hour Dietary Recall Analysis and Modeling.

BACKGROUND: Information on long-term dietary intake is often required for research or program planning, but surveys routinely use short-term assessments such as 24-h recalls (24HRs). Methods to reduce the impact of within-person variation in 24HRs, such as the National Cancer Institute (NCI) method, typically require extensive training and skill. OBJECTIVES: We introduce the Simulating Intake of Micronutrients for Policy Learning and Engagement (SIMPLE) macro, a new tool to increase the accessibility of 24HR analysis. We explain the underlying theory behind the tool and provide examples of potential applications. METHODS: The SIMPLE macro connects the core NCI statistical code to estimate usual intake distributions and includes additional code to enable advanced analyses such as predictive modeling. The related SIMPLE-Iron macro applies the full probability method to estimate inadequate iron intake, and the SIMPLE-1D macro is used for descriptive or modeling analyses of data with a single 24HR per person. The macros and associated documentations are freely available. We analyzed data from the US National Health and Nutrition Examination Survey (NHANES) and the Cameroon National Micronutrient Survey to compare the SIMPLE macro to 1) the core NCI code using the Estimated Average Requirement cut point method, and 2) the IMAPP software for iron only, and to demonstrate the applications of the SIMPLE macro for estimating usual intake and predictive modeling. RESULTS: The SIMPLE macro generates identical results to the core NCI code. The SIMPLE-Iron macro also produces estimates of inadequate iron intake comparable to the IMAPP software. The examples demonstrate application of the SIMPLE macro to 1) descriptive analyses of nutrient intake from food and supplements (NHANES), and 2) analyses accounting for breast-milk nutrient intake and modeling fortification and supplementation programs (Cameroon). CONCLUSIONS: The SIMPLE macros may facilitate the analysis and modeling of dietary data to inform nutrition research, programs, and policy.

Examining the association between meal context and diet quality: an observational study of meal context in older adults.

BACKGROUND: Though a healthy diet is widely associated with reduced risks for chronic disease and mortality, older adults in the U.S. on average do not meet dietary recommendations. Given that few studies have examined the association between meal context on older adult diet quality, the aims of this study were (1) to compare the dietary quality of foods consumed in different meal contexts, as measured by the Healthy Eating Index 2015 (HEI-2015): meal location, the presence of others, and the use of electronic screens; and (2) to examine which components of the HEI-2015 drove differences in HEI-2015 total scores by meal context. METHODS: Interactive Diet and Activity Tracking in AARP study participants (50-74 years) completed the Automated Self-Administered 24-h Dietary Assessment tool (ASA24, version 2011) that included foods and beverages consumed and three meal contexts: "at home" versus "away from home," "alone" versus "with company," and "with screen time" versus "without screen time." A population ratio approach was used to estimate HEI-2015 total and component scores for all food items consumed by meal context. Mean HEI-2015 scores (range: 0-100) for the three meal context variables were compared using t-tests. Where there were significant differences in total scores, additional t-tests were used to explore which HEI-2015 components were the primary drivers. All tests were stratified by sex and adjusted for multiple comparisons. RESULTS: HEI-2015 scores were lower for meals consumed away vs. at home (mean difference (SE), males: - 8.23 (1.02); females: - 7.29 (0.93); both p < 0.0001) and for meals eaten with vs. without company (mean difference (SE), males: - 6.61 (1.06); females: - 7.34 (1.18); both p < 0.0001). There was no difference comparing with vs. without screen time. When HEI-2015 component scores were examined, fewer total fruits, whole grains, and dairy were consumed away from home or with company; more total vegetables and greens and beans, and less added sugars were consumed with company. CONCLUSIONS: Our findings suggest an association between the behavior cues of meal location and companions and dietary choices among older adults. Future studies can explore the individual and interactive effects of meal context on diet quality and subsequent health outcomes.

Comparison of 4 Methods to Assess the Prevalence of Use and Estimates of Nutrient Intakes from Dietary Supplements among US Adults.

BACKGROUND: Accurate and reliable methods to assess prevalence of use of and nutrient intakes from dietary supplements (DSs) are critical for research, clinical practice, and public health monitoring. NHANES has been the primary source of DS usage patterns using an in-home inventory with a frequency-based DS and Prescription Medicine Questionnaire (DSMQ), but little is known regarding DS information obtained from 24-h dietary recalls (24HRs). METHODS: The objectives of this analysis were to compare results from 4 different methods for measuring DS use constructed from two data collection instruments (i.e., DSMQ and 24HR) and to determine the most comprehensive method for measuring the prevalence of use and estimating nutrient intakes from DS for selected nutrients. NHANES 2011-2014 data from US adults (aged ≥19 y; n = 11,451) were used to examine the 4 combinations of methods constructed for measuring the prevalence of use of and amount of selected nutrients from DSs (i.e., riboflavin, vitamin D, folate, magnesium, calcium): 1) DSMQ, 2) 24HR day 1, 3) two 24HRs (i.e., mean), and 4) DSMQ or at least one 24HR. RESULTS: Half of US adults reported DS use on the DSMQ (52%) and on two 24HRs (mean of 49%), as compared with a lower prevalence of DS use when using a single 24HR (43%) and a higher (57%) prevalence when combining the DSMQ with at least one 24HR. Mean nutrient intake estimates were highest using 24HR day 1. Mean supplemental calcium from the DSMQ or at least one 24HR was 372 mg/d, but 464 mg/d on the 24HR only. For vitamin D, the estimated intakes per consumption day were higher on the DSMQ (46 µg) and the DSMQ or at least one 24HR (44 µg) than those on the 24HR day 1 (32 µg) or the mean 24HR (31 µg). Fewer products were also classed as a default or reasonable match on the DSMQ than on the 24HR. CONCLUSIONS: A higher prevalence of use of DSs is obtained using frequency-based methods, whereas higher amounts of nutrients are reported from a 24HR. The home inventory results in greater accuracy for products reported. Collectively, these findings suggest that combining the DSMQ with at least one 24HR (i.e., DSMQ or at least one 24HR) is the most comprehensive method for assessing the prevalence of and estimating usual intake from DSs in US adults.This trial was registered at clinicaltrials.gov as NCT03400436.

STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 2-More complex methods of adjustment and advanced topics.

We continue our review of issues related to measurement error and misclassification in epidemiology. We further describe methods of adjusting for biased estimation caused by measurement error in continuous covariates, covering likelihood methods, Bayesian methods, moment reconstruction, moment-adjusted imputation, and multiple imputation. We then describe which methods can also be used with misclassification of categorical covariates. Methods of adjusting estimation of distributions of continuous variables for measurement error are then reviewed. Illustrative examples are provided throughout these sections. We provide lists of available software for implementing these methods and also provide the code for implementing our examples in the Supporting Information. Next, we present several advanced topics, including data subject to both classical and Berkson error, modeling continuous exposures with measurement error, and categorical exposures with misclassification in the same model, variable selection when some of the variables are measured with error, adjusting analyses or design for error in an outcome variable, and categorizing continuous variables measured with error. Finally, we provide some advice for the often met situations where variables are known to be measured with substantial error, but there is only an external reference standard or partial (or no) information about the type or magnitude of the error.

STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 1-Basic theory and simple methods of adjustment.

Measurement error and misclassification of variables frequently occur in epidemiology and involve variables important to public health. Their presence can impact strongly on results of statistical analyses involving such variables. However, investigators commonly fail to pay attention to biases resulting from such mismeasurement. We provide, in two parts, an overview of the types of error that occur, their impacts on analytic results, and statistical methods to mitigate the biases that they cause. In this first part, we review different types of measurement error and misclassification, emphasizing the classical, linear, and Berkson models, and on the concepts of nondifferential and differential error. We describe the impacts of these types of error in covariates and in outcome variables on various analyses, including estimation and testing in regression models and estimating distributions. We outline types of ancillary studies required to provide information about such errors and discuss the implications of covariate measurement error for study design. Methods for ascertaining sample size requirements are outlined, both for ancillary studies designed to provide information about measurement error and for main studies where the exposure of interest is measured with error. We describe two of the simpler methods, regression calibration and simulation extrapolation (SIMEX), that adjust for bias in regression coefficients caused by measurement error in continuous covariates, and illustrate their use through examples drawn from the Observing Protein and Energy (OPEN) dietary validation study. Finally, we review software available for implementing these methods. The second part of the article deals with more advanced topics.

Older adults with obesity have higher risks of some micronutrient inadequacies and lower overall dietary quality compared to peers with a healthy weight, National Health and Nutrition Examination Surveys (NHANES), 2011-2014.

OBJECTIVE: To evaluate total usual intakes and biomarkers of micronutrients, overall dietary quality and related health characteristics of US older adults who were overweight or obese compared with a healthy weight. DESIGN: Cross-sectional study. SETTING: Two 24-h dietary recalls, nutritional biomarkers and objective and subjective health characteristic data were analysed from the National Health and Nutrition Examination Survey 2011-2014. We used the National Cancer Institute method to estimate distributions of total usual intakes from foods and dietary supplements for eleven micronutrients of potential concern and the Healthy Eating Index (HEI)-2015 score. PARTICIPANTS: Older adults aged ≥60 years (n 2969) were categorised by sex and body weight status, using standard BMI categories. Underweight individuals (n 47) were excluded due to small sample size. RESULTS: A greater percentage of obese older adults compared with their healthy-weight counterparts was at risk of inadequate Mg (both sexes), Ca, vitamin B6 and vitamin D (women only) intakes. The proportion of those with serum 25-hydroxyvitamin D < 40 nmol/l was higher in obese (12 %) than in healthy-weight older women (6 %). Mean overall HEI-2015 scores were 8·6 (men) and 7·1 (women) points lower in obese than in healthy-weight older adults. In addition, compared with healthy-weight counterparts, obese older adults were more likely to self-report fair/poor health, use ≥ 5 medications and have limitations in activities of daily living and cardio-metabolic risk factors; and obese older women were more likely to be food-insecure and have depression. CONCLUSIONS: Our findings suggest that obesity may coexist with micronutrient inadequacy in older adults, especially among women.

A New Statistical Method for Estimating Usual Intakes of Nearly-Daily Consumed Foods and Nutrients Through Use of Only One 24-hour Dietary Recall.

BACKGROUND: To estimate usual intake distributions of dietary components, collection of nonconsecutive repeated 24-h dietary recalls is recommended, but resource limitations sometimes restrict data collection to single-day dietary data per person. OBJECTIVES: We developed a new statistical method, the NCI 1-d method, which uses single-day dietary data and an external within-person to between-person variance ratio to estimate population distributions of usual intake of nearly-daily consumed foods and nutrients. METHODS: We used NHANES 2011-2014 data for men (n = 4938 and n = 4293 for the first and second 24-h recalls) to compare nutrient intake distributions of vitamin A, magnesium, folate, and vitamin E generated by the 1-d method (with use of only the first recall per person) with those from the NCI amount-only method (with use of all days of dietary intake per person). The within-person to between-person variance ratio from the amount-only model was used as the unbiased "external" estimate for the 1-d method. We also examined the effect of mis-specification of variance ratios on usual intake distributions. RESULTS: The amount-only and 1-d methods estimated statistically equivalent median (25p, 75p): 647 (459, 890) compared with 648 (461, 886) µg retinol activity equivalents/d, 338 (268, 420) compared with 334 (266, 417) mg magnesium/d, 595 (458, 762) compared with 589 (456, 758) µg dietary folate equivalents/d, and 9.7 (7.3, 12.6) compared with 9.6 (7.3, 12.7) mg vitamin E/d. As the external variance ratios increased from 25% to 200% of the unbiased ratios, the prevalence of inadequate intake ranged from 53% to 43% for vitamin A, 57% to 55% for magnesium, 16% to 2% for folate, and 70% to 73% for vitamin E. CONCLUSIONS: The 1-d method is a viable statistical method for estimating usual intakes of nearly-daily consumed dietary components when the variance ratio is unbiased. Results are sensitive to variance ratio selection, so researchers should still collect replicate data where possible.

School-Age Children Can Recall Some Foods and Beverages Consumed the Prior Day Using the Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) without Assistance.

BACKGROUND: Technological innovations allow for collection of 24-h recalls (24HRs) in a broader range of studies than previously possible. The web-based Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) has been shown to be feasible and to perform well in capturing true intake among adults. However, data to inform use with children are limited. OBJECTIVE: This observational feeding study was conducted to evaluate children's ability to accurately report a lunchtime meal using ASA24 without assistance. METHODS: The study was conducted among children (n = 100) aged 10-13 y within a school setting. Students were served an individual cheese pizza, baby carrots, ranch dip, yogurt, a cookie, and 1 choice of water, juice, or milk. Plate waste was collected and weighed. The next day, participants completed ASA24 and a sociodemographic questionnaire. Descriptive statistics were generated to determine match rates by food item and age, and linear regression analyses were conducted to examine associations between sociodemographic characteristics and accuracy of reported energy and nutrient intake. Associations between true and reported energy and nutrient intakes and portion sizes were assessed with use of t tests. RESULTS: Just under half (49%) of children fully completed ASA24 (median time, 41 min). Children reported an exact, close, or far match for 58% of all foods and beverages consumed, ranging from 29% for dip to 76% for pizza, but also reported some items not consumed as part of the study meal. Older children completed the recall in a shorter time than younger children (mean 31 among 13 y compared with 52 min among 10 y). Intakes of energy (39%), protein (33%), and sodium (78%) were significantly overestimated, whereas portion sizes for cookies (53%) and juice (69%) were underestimated. CONCLUSIONS: Children can report some foods and drinks consumed using ASA24, but our findings suggest challenges with independent completion, necessitating research to examine strategies, such as training and resources, to support data quality.

The Provision of Assistance Does Not Substantially Impact the Accuracy of 24-Hour Dietary Recalls Completed Using the Automated Self-Administered 24-H Dietary Assessment Tool among Women with Low Incomes.

Background: Evidence is lacking informing the use of the Automated Self-Administered 24-h Dietary Assessment Tool (ASA24) with populations characterized by low income. Objective: This study was conducted among women with low incomes to evaluate the accuracy of ASA24 recalls completed independently and with assistance. Methods: Three hundred and two women, aged ≥18 y and with incomes below the Supplemental Nutrition Assistance Program thresholds, served themselves from a buffet; amounts taken as well as plate waste were unobtrusively weighed to enable calculation of true intake for 3 meals. The following day, women completed ASA24-2016 independently (n = 148) or with assistance from a trained paraprofessional in a small group (n = 154). Regression modeling examined differences by condition in agreement between true and reported foods; energy, nutrient, and food group intakes; and portion sizes. Results: Participants who completed ASA24 independently and those who received assistance reported matches for 71.9% and 73.5% (P = 0.56) of items truly consumed, respectively. Exclusions (consumed but not reported) were highest for lunch (at which participants consumed approximately 2 times the number of distinct foods and beverages compared with breakfast and dinner). Commonly excluded foods were additions to main dishes (e.g., tomatoes in salad). On average, excluded foods contributed 43.6 g (46.2 kcal) and 40.1 g (43.2 kcal) among those in the independent and assisted conditions, respectively. Gaps between true and reported intake were different between conditions for folate and iron. Within conditions, significant gaps were observed for protein, vitamin D, and meat (both conditions); vitamin A, iron, and magnesium (independent); and folate, calcium, and vegetables (assisted). For foods and beverages for which matches were reported, no difference in the gap between true and reported portion sizes was observed by condition (P = 0.22). Conclusions: ASA24 performed relatively well among women with low incomes; however, accuracy was somewhat lower than previously observed among adults with a range of incomes. The provision of assistance did not significantly impact accuracy.

Best Practices for Dietary Supplement Assessment and Estimation of Total Usual Nutrient Intakes in Population-Level Research and Monitoring.

The use of dietary supplements (DS) is pervasive and can provide substantial amounts of micronutrients to those who use them. Therefore when characterizing dietary intakes, describing the prevalence of inadequacy or excess, or assessing relations between nutrients and health outcomes, it is critical to incorporate DS intakes to improve exposure estimates. Unfortunately, little is known about the best methods to assess DS, and the structure of measurement error in DS reporting. Several characteristics of nutrients from DS are salient to understand when comparing to those in foods. First, DS can be consumed daily or episodically, in bolus form and can deliver discrete and often very high doses of nutrients that are not limited by energy intakes. These characteristics contribute to bimodal distributions and distributions severely skewed to the right. Labels on DS often provide nutrient forms that differ from those found in conventional foods, and underestimate analytically derived values. Finally, the bioavailability of many nutrient-containing DS is not known and it may not be the same as the nutrients in a food matrix. Current methods to estimate usual intakes are not designed specifically to handle DS. Two temporal procedures are described to refer to the order that nutrient intakes are combined relative to usual intake procedures, referred to as a "shrinking" the distribution to remove random error. The "shrink then add" approach is preferable to the "add then shrink" approach when users and nonusers are combined for most research questions. Stratifying by DS before usual intake methods is another defensible option. This review describes how to incorporate nutrient intakes from DS to usual intakes from foods, and describes the available methods and fit-for-purpose of different analytical strategies to address research questions where total usual intakes are of interest at the group level for use in nutrition research and to inform policy decisions. Clinical Trial Registry: NCT03400436.

Estimating Usual Dietary In take From National Health and Nut rition Examination Survey Data Using the National Cancer Institute Method.

Dietary recommendations are intended to be met based on dietary intake over long periods, as associations between diet and health result from habitual intake, not a single eating occasion or day of intake. Measuring usual intake directly is impractical for large population-based surveys due to the respondent burden associated with reporting habitual intake over longer periods. Therefore, analytical techniques were developed to estimate usual intake using as few as 2 days of 24-hour dietary recall data. With National Health and Nutrition Examination Survey (NHANES) data, this report demonstrates how to estimate usual intake using the National Cancer Institute (NCI). This report demonstrates how to estimate the usual intake of nutrients consumed daily or episodically using NHANES data. Means, percentiles, and the percentages above or below specified Dietary Reference Intake (DRI) values for given day, within-person mean (WPM), and estimates of usual intake are presented. Consistent with previous analyses, mean intakes were similar across methods. However, the distributions estimated by nonusual intake methods were wider compared with the NCI Method, which can lead to misclassification of the percentage of the population above or below certain DRIs. Use of NHANES data to examine the proportion of the population at risk of insufficiency or excess of certain nutrients, with methods like given day and WPM that do not address within-person variation, may lead to biased estimates.

Development and Evaluation of the National Cancer Institute's Dietary Screener Questionnaire Scoring Algorithms.

Background: Methods for improving the utility of short dietary assessment instruments are needed.Objective: We sought to describe the development of the NHANES Dietary Screener Questionnaire (DSQ) and its scoring algorithms and performance.Methods: The 19-item DSQ assesses intakes of fruits and vegetables, whole grains, added sugars, dairy, fiber, and calcium. Two nonconsecutive 24-h dietary recalls and the DSQ were administered in NHANES 2009-2010 to respondents aged 2-69 y (n = 7588). The DSQ frequency responses, coupled with sex- and age-specific portion size information, were regressed on intake from 24-h recalls by using the National Cancer Institute usual intake method to obtain scoring algorithms to estimate mean and prevalences of reaching 2 a priori threshold levels. The resulting scoring algorithms were applied to the DSQ and compared with intakes estimated with the 24-h recall data only. The stability of the derived scoring algorithms was evaluated in repeated sampling. Finally, scoring algorithms were applied to screener data, and these estimates were compared with those from multiple 24-h recalls in 3 external studies.Results: The DSQ and its scoring algorithms produced estimates of mean intake and prevalence that agreed closely with those from multiple 24-h recalls. The scoring algorithms were stable in repeated sampling. Differences in the means were <2%; differences in prevalence were <16%. In other studies, agreement between screener and 24-h recall estimates in fruit and vegetable intake varied. For example, among men in 2 studies, estimates from the screener were significantly lower than the 24-h recall estimates (3.2 compared with 3.8 and 3.2 compared with 4.1). In the third study, agreement between the screener and 24-h recall estimates were close among both men (3.2 compared with 3.1) and women (2.6 compared with 2.5).Conclusions: This approach to developing scoring algorithms is an advance in the use of screeners. However, because these algorithms may not be generalizable to all studies, a pilot study in the proposed study population is advisable. Although more precise instruments such as 24-h dietary recalls are recommended in most research, the NHANES DSQ provides a less burdensome alternative when time and resources are constrained and interest is in a limited set of dietary factors.

Coffee Drinking Is Widespread in the United States, but Usual Intake Varies by Key Demographic and Lifestyle Factors.

BACKGROUND: Despite widespread popularity and possible health effects, the prevalence and distribution of coffee consumption in US adults are poorly characterized. OBJECTIVE: We sought to estimate usual daily coffee intakes from all coffee-containing beverages, including decaffeinated and regular coffee, among US adults according to demographic, socioeconomic, and health-related factors. METHODS: Dietary intake data from ≤2 nonconsecutive 24-h dietary recalls and a food-frequency questionnaire administered during the NHANES 2003-2006 were used to estimate the person-specific probability of consuming coffee on a particular day and the usual amount consumed on consumption days. Trends in population mean coffee consumption over time were evaluated by using multiple linear regression and 1-d 24-h recall data from NHANES 2003-2012. Analyses were weighted to be representative of the US adult population aged ≥20 y. RESULTS: An estimated 154 million adults, or 75% of the US population, aged ≥20 y reported drinking coffee; 49% reported drinking coffee daily. Prevalence did not vary by sex, education, income, or self-reported general health (all P ≥ 0.05) but did vary by age, race/ethnicity, smoking status, and alcohol drinking (all P < 0.05). Among coffee drinkers, the mean ± SE usual intake was 14.1 ± 0.5 fluid ounces/d (417 ± 15 mL/d). Mean usual intakes were higher in men than women, in older age groups than in those aged 20 to <30 y, in non-Hispanic whites than in non-Hispanic blacks or Hispanic/other races, in smokers than in never smokers, and in daily alcohol consumers than in nonconsumers (all P < 0.05). Population mean coffee consumption was stable from 2003 to 2012 (P-trend = 0.09). CONCLUSIONS: Coffee is widely consumed in the United States, with usual intakes varying by lifestyle and demographic factors, most notably by age. Longitudinal studies are needed to determine whether observed differences by age reflect birth cohort effects or changes in drinking patterns over the lifetime.

The Use of Digital Images in 24-Hour Recalls May Lead to Less Misestimation of Portion Size Compared with Traditional Interviewer-Administered Recalls.

BACKGROUND: The Automated Self-Administered 24-hour (ASA24) dietary recall system enhances the feasibility of collecting high-quality intake data in population-based studies. OBJECTIVE: The aim of this study was to assess the accuracy of portion size reporting in the ASA24 compared with interviewer-administered recalls. METHODS: True intake for 3 meals was ascertained in 81 adults aged 20-70 y from the Washington, DC area. Participants were randomly assigned to complete an unannounced ASA24 or an interviewer-administered Automated Multiple-Pass Method (AMPM) recall the following day. An adapted Bland-Altman approach was used to assess agreement between true and reported portion sizes. Linear regression was used to assess log-scale differences between true and reported portion sizes by recall mode. The proportions of reported portion sizes within 10% and 25% of truth were estimated. Analyses were conducted for all foods and drinks and predetermined categories. RESULTS: Mean differences between true and reported portion sizes were 3.7 g for the ASA24 and 11.8 g for the AMPM. According to the Bland-Altman-type plots, between 92% and 100% (depending on food or drink category and recall mode) of observations fell within the limits of agreement. After adjustment for multiple testing, the mean ratio of reported to true portion sizes was significantly >1 for the categories of all foods and drinks, all foods excluding liquids, amorphous or soft foods, and small pieces among AMPM respondents. Misestimation in the AMPM was significantly different from that in the ASA24 for all foods and drinks and for all foods excluding liquids. Small proportions of reported portions fell within 10% (16.2% for the ASA24 and 14.9% for the AMPM) and 25% (37.5% for the ASA24 and 33.2% for the AMPM) of truth. CONCLUSIONS: The results raise the possibility that digital images tailored to different types and formats of foods may facilitate improved estimation of amounts eaten but highlight the need for continued work in this aspect of dietary assessment. This trial was registered at clinicaltrials.gov as NCT00978406.