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National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Food and Nutrition Board; Committee to Review WIC Food Packages. Review of WIC Food Packages: Improving Balance and Choice: Final Report. Washington (DC): National Academies Press (US); 2017 May 1.
Review of WIC Food Packages: Improving Balance and Choice: Final Report.
Show detailsThis appendix presents the methodology and detailed results for the analyses of nutrient and food group intakes and diet quality of the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) participants and WIC-eligible subgroups using the National Health and Nutrition Examination Survey (NHANES).The WIC subgroup was defined as the subgroup of individuals reporting participation in WIC regardless of income level. Eligible non-WIC was defined as the subgroup of individuals with incomes less than or equal to 185 percent of the federal poverty level who did not report participation in WIC. Results presented in this appendix and summarized in Chapter 4 of this report update the methods and results that were presented in the phase I interim report. The tables presented in this appendix and the corresponding page numbers are listed below. These data, generated by Iowa State University, were checked by the committee members as well as by the staff. Data were also compared to the phase I results and to nationally representative data for reasonability.
TABLES
TABLE J-1a Dietary Reference Intakes Used for Assessing Nutrient Intakes of WIC-Eligible Subgroups
TABLE J-1b Dietary Reference Intakes Used for Assessing Nutrient Intakes of WIC-Eligible Subgroups
TABLE J-1c Macronutrient Intake Recommendations for WIC-Eligible Subgroups
TABLE J-2 Nutrients Selected for Intake Analysis
TABLE J-3 Food Groups for Analyses Based on Food Pattern Components in FPID and FPED
TABLE J-4 USDA Food Pattern Food Groups, Definitions, and Example Foods
TABLE J-5 Limitations to the NHANES Datasets Relevant to the Task and Resulting Subgroup Modification
TABLE J-6 NHANES Survey Years Applied for Each Analytical Subgroup
TABLE J-7 Sample Sizes for Subgroups of Women in the Combined NHANES 2005–2012 Dataset
TABLE J-8 NHANES Sample Sizes of Population Subgroups Selected for Nutrient and Food Intake Analyses: Phases I and II
TABLE J-9 Design Effects for Usual Intake of Specific Nutrients
TABLE J-10 Tasks Related to Infant Formula Requirements in the Food Packages and the Approach
TABLE J-11 Usual Intake Distributions of Energy Intake for Women Ages 19 to 50 Years
TABLE J-12 Usual Intake Distributions of Energy Intake for Infants Ages 0 to Less Than 12 Months
TABLE J-13 Usual Intake Distributions of Energy Intake for Children Ages 1 to Less Than 2 Years
TABLE J-14 Usual Intake Distributions of Energy Intake for Children Ages 2 to Less Than 5 Years
TABLE J-15 Distributions of Estimated Energy Requirements for Wome Ages 19 to 50 Years
TABLE J-16 Distributions of Estimated Energy Requirements for Infants Ages 0 to Less Than 12 Months
TABLE J-17 Distributions of Estimated Energy Requirements for Children Ages 1 to Less Than 2 Years
TABLE J-18 Distributions of Estimated Energy Requirements for Children Ages 2 to Less Than 5 Years
TABLE J-19 Usual Intake Distributions of Selected Macronutrients for Pregnant WIC-Participating Women Ages 19 to 50 Years, NHANES 2005–2012
TABLE J-20 Usual Intake Distributions of Selected Macronutrients for Pregnant Eligible Non-WIC-Participating Women Ages 19 to 50 Years, NHANES 2005–2012
TABLE J-21 Usual Intake Distributions of Selected Macronutrients for Breastfeeding WIC-Participating Women Ages 19 to 50 Years, NHANES 2005–2012
TABLE J-22 Usual Intake Distributions of Selected Macronutrients for Postpartum WIC-Participating Women Ages 19 to 50 Years, NHANES 2007–2012
TABLE J-23 Usual Intake Distributions of Selected Macronutrients for Nonpregnant, Postpartum, or Breastfeeding Eligible Non-WIC-Participating Women Ages 19 to 50 Years, NHANES 2005–2012
TABLE J-24 Usual Intake Distributions of Selected Macronutrients for WIC-Participating Infants Ages 0 to Less Than 6 Months, NHANES 2005–2008
TABLE J-25 Usual Intake Distributions of Selected Macronutrients for Eligible Non-WIC-Participating Infants Ages 0 to Less Than 6 Months, NHANES 2005–2008
TABLE J-26 Usual Intake Distributions of Selected Macronutrients for WIC-Participating Infants Ages 0 to Less Than 6 Months, NHANES 2011–2012
TABLE J-27 Usual Intake Distributions of Selected Macronutrients for Eligible Non-WIC-Participating Infants Ages 0 to Less Than 6 Months, NHANES 2011–2012
TABLE J-28 Usual Intake Distributions of Selected Macronutrients for WIC-Participating Infants Ages 6 to Less Than 12 Months, NHANES 2005–2008
TABLE J-29 Usual Intake Distributions of Selected Macronutrients for Eligible Non-WIC-Participating Infants Ages 6 to Less Than 12 Months, NHANES 2005–2008
TABLE J-30 Usual Intake Distributions of Selected Macronutrients for WIC-Participating Infants Ages 6 to Less Than 12 Months, NHANES 2011–2012
TABLE J-31 Usual Intake Distributions of Selected Macronutrients for Eligible Non-WIC-Participating Infants Ages 6 to Less Than 12 Months, NHANES 2011–2012
TABLE J-32 Usual Intake Distributions of Selected Macronutrients for WIC-Participating Children Ages 1 to Less Than 2 Years, NHANES 2005–2008
TABLE J-33 Usual Intake Distributions of Selected Macronutrients for Eligible Non-WIC-Participating Children Ages 1 to Less Than 2 Years, NHANES 2005–2008
TABLE J-34 Usual Intake Distributions of Selected Macronutrients for WIC-Participating Children Ages 1 to Less Than 2 Years, NHANES 2011–2012
TABLE J-35 Usual Intake Distributions of Selected Macronutrients for Eligible Non-WIC-Participating Children Ages 1 to Less Than 2 Years, NHANES 2011–2012
TABLE J-36 Usual Intake Distributions of Selected Macronutrients for WIC-Participating Children Ages 2 to Less Than 5 Years, NHANES 2005–2008
TABLE J-37 Usual Intake Distributions of Selected Macronutrients for Eligible Non-WIC-Participating Children Ages 2 to Less Than 5 Years, 2005–2008
TABLE J-38 Usual Intake Distributions of Selected Macronutrients for WIC-Participating Children Ages 2 to Less Than 5 Years, NHANES 2011–2012
TABLE J-39 Usual Intake Distributions of Selected Macronutrients for Eligible Non-WIC-Participating Children Ages 2 to Less Than 5 Years, 2011–2012
TABLE J-40 Usual Intake Distributions of Selected Micronutrients for Pregnant WIC-Participating Women Ages 19 to 50 Years, NHANES 2005–2012
TABLE J-41 Usual Intake Distributions of Selected Micronutrients for Pregnant Eligible Non-WIC-Participating Women Ages 19 to 50 Years, NHANES 2005–2012
TABLE J-42 Usual Intake Distributions of Selected Micronutrients for Breastfeeding WIC-Participating Women Ages 19 to 50 Years, NHANES 2005–2012
TABLE J-43 Usual Intake Distributions of Selected Micronutrients for Postpartum WIC-Participating Women Ages 19 to 50 Years, NHANES 2007–2012
TABLE J-44 Usual Intake Distributions of Selected Micronutrients for Nonpregnant, Postpartum, or Breastfeeding Non-WIC-Participating Women Ages 19 to 50 Years, NHANES 2005–2012
TABLE J-45 Usual Intake Distributions of Selected Micronutrients for WIC-Participating Infants Ages 0 to Less Than 6 Months, NHANES 2005–2008
TABLE J-46 Usual Intake Distributions of Selected Micronutrients for Eligible Non-WIC-Participating Infants Ages 0 to Less Than 6 Months, NHANES 2005–2008
TABLE J-47 Usual Intake Distributions of Selected Micronutrients for WIC-Participating Infants Ages 0 to Less Than 6 Months, NHANES 2011–2012
TABLE J-48 Usual Intake Distributions of Selected Micronutrients for Eligible Non-WIC-Participating Infants Ages 0 to Less Than 6 Months, NHANES 2011–2012
TABLE J-49 Usual Intake Distributions of Selected Micronutrients for WIC-Participating Infants Ages 6 to Less Than 12 Months, NHANES 2005–2008
TABLE J-50 Usual Intake Distributions of Selected Micronutrients for Eligible Non-WIC-Participating Infants Ages 6 to Less Than 12 Months, NHANES 2005–2008
TABLE J-51 Usual Intake Distributions of Selected Micronutrients for WIC-Participating Infants Ages 6 to Less Than 12 Months, NHANES 2011–2012
TABLE J-52 Usual Intake Distributions of Selected Micronutrients for Eligible Non-WIC-Participating Infants Ages 6 to Less Than 12 Months, NHANES 2011–2012
TABLE J-53 Usual Intake Distributions of Selected Micronutrients for WIC-Participating Children Ages 1 to Less Than 2 Years, NHANES 2005–2008
TABLE J-54 Usual Intake Distributions of Selected Micronutrients for Eligible Non-WIC-Participating Children Ages 1 to Less Than 2 Years, NHANES 2005–2008
TABLE J-55 Usual Intake Distributions of Selected Micronutrients for WIC-Participating Children Ages 1 to Less Than 2 Years, NHANES 2011–2012
TABLE J-56 Usual Intake Distributions of Selected Micronutrients for Eligible Non-WIC-Participating Children Ages 1 to Less Than 2 Years, NHANES 2011–2012
TABLE J-57 Usual Intake Distributions of Selected Micronutrients for WIC-Participating Children Ages 2 to Less Than 5 Years, NHANES 2005–2008
TABLE J-58 Usual Intake Distributions of Selected Micronutrients for Eligible Non-WIC-Participating Children Ages 2 to Less Than 5 Years, NHANES 2005–2008
TABLE J-59 Usual Intake Distributions of Selected Micronutrients for WIC-Participating Children Ages 2 to Less Than 5 Years, NHANES 2011–2012
TABLE J-60 Usual Intake Distributions of Selected Micronutrients for Eligible Non-WIC-Participating Children Ages 2 to Less Than 5 Years, NHANES 2011–2012
TABLE J-61 Distributions of Serum 25-Hydroxy Vitamin D of WIC Participants, NHANES 2005–2006
TABLE J-62 Food Group Intake Distributions of Pregnant WIC-Participating Women Ages 19 to 50 Years, NHANES 2005–2012
TABLE J-63 Food Group Intake Distributions of Pregnant, Eligible Non-WIC-Participating Women Ages 19 to 50 Years, NHANES 2005–2012
TABLE J-64 Food Group Intake Distributions of Breastfeeding, WIC-Participating Women Ages 19 to 50 Years, NHANES 2005–2012
TABLE J-65 Food Group Intake Distributions of Postpartum, WIC-Participating Women Ages 19 to 50 Years, NHANES 2005–2012
TABLE J-66 Food Group Intake Distributions of Nonpregnant, Postpartum, or Breastfeeding Non-WIC-Participating Women Ages 19 to 50 Years, NHANES 2005–2012
TABLE J-67 Food Group Intake Distributions of WIC-Participating Children Ages 1 to Less Than 2 Years, NHANES 2005–2008
TABLE J-68 Food Group Intake Distributions of Eligible Non-WIC-Participating Children Ages 1 to Less Than 2 Years, NHANES 2005–2008
TABLE J-69 Food Group Intake Distributions of WIC-Participating Children Ages 1 to Less Than 2 Years, NHANES 2011–2012
TABLE J-70 Food Group Intake Distributions of Eligible Non-WIC-Participating Children Ages 1 to Less Than 2 Years, NHANES 2011–2012
TABLE J-71 Food Group Intake Distributions of WIC-Participating Children Ages 2 to Less Than 5 Years, NHANES 2005–2008
TABLE J-72 Food Group Intake Distributions of Eligible Non-WIC-Participating Children Ages 2 to Less Than 5 Years, NHANES 2005–2008
TABLE J-73 Food Group Intake Distributions of WIC-Participating Children Ages 2 to Less Than 5 Years, NHANES 2011–2012
TABLE J-74 Food Group Intake Distributions of Eligible Non-WIC-Participating Children Ages 2 to Less Than 5 Years, NHANES 2011–2012
TABLE J-75 HEI–2010 Components and Scoring System
TABLE J-76 Summary of Mean HEI–2010 Scores for Women Ages 19 to 50 Years, NHANES 2005–2012
TABLE J-77 Summary of Mean HEI–2010 Scores for Children Ages 2 to Less Than 5 Years, NHANES 2011–2012
USING THE DIETARY REFERENCE INTAKES TO ASSESS NUTRIENT ADEQUACY
The committee used the Dietary Reference Intakes (DRIs) (defined in Box J-1 and presented in Tables J-1a through J-1c) to assess nutrient adequacy, which involved examining both inadequate and excessive intakes of nutrients. The methods applied in this report are generally the same as those used in the 2006 Institute of Medicine (IOM) report WIC Food Packages: Time for a Change and originally designed by Nusser et al. (1996) and Carriquiry (1999) (see Appendix C of IOM, 2006). Brief descriptions of the approaches are provided here, with modifications noted as appropriate. Nutrients analyzed for this report are listed in Table J-2.
Estimating Usual Intake Distributions
Assessing nutrient adequacy involves, first, estimating distributions of usual intake. The Iowa State University (ISU) method proposed by Nusser et al. (1996) and applied in the 2006 IOM report is generally accepted in the nutrition community, and several software packages are now available to generate the mean and variance of usual intake as well as percentiles of intake of the user's choosing. For this report, PC Software for Intake Distribution (PC-SIDE) was used to implement the ISU method (nutrients). To estimate the distribution of dietary components consumed episodically (food groups and subgroups), the Statistical Program for Age-adjusted Dietary Assessment (SPADE), a method similar to the National Cancer Institute method was implemented (Dekkers et al., 2014). These software packages are specifically designed for estimating the usual intake distributions of populations, and are not appropriate for application to individuals (IOM, 2000b).
Assessing the Prevalence of Inadequate Nutrient Intake with EARs
In all of the statistical analyses, intake data were weighted to population values by using survey weights associated with survey participants. Fractional jackknife replicate weights were used to estimate standard errors of estimated percentiles (Fuller, 2009). Usual nutrient intake distributions were estimated using methods that account for the statistical properties of the data (intra-individual variation and reported data that are not normally distributed (Nusser et al., 1996; IOM, 2000b). Beaton (1994) and Carriquiry (1999) suggested that the prevalence of inadequate intakes in the group can be estimated by the proportion of persons in the group whose usual intakes do not reach the Estimated Average Requirement (EAR) for the nutrient. This approach is known as the EAR cut-point method. To estimate the prevalence of inadequacy in a group that includes persons from subgroups that have different EARs, an approach proposed previously is used (IOM, 2000b). This approach consists of rescaling daily intakes for one of the population subgroups so they can be compared to the EAR of the other group (a similar rescaling was used in IOM, 2006). This approach was applied to two of the population subgroups of interest in this work: children ages 2 to less than 5 years (which requires DRIs for ages 1 to 3 years and ages 4 to 8 years), and women ages 19 to 50 years (which requires DRIs for ages 19 to 30 years and ages 31 to 50 years). The EAR cut-point method cannot be used to estimate the prevalence of iron inadequacy for women because requirements are not normally distributed, mostly because of menstrual losses of iron. However, because most of the women in the NHANES analytic sample were pregnant or breastfeeding, and the analytic sample was small, the cut-point method was implemented nonetheless.
Interpretation of intake differs for nutrients with Adequate Intakes (AIs) in that only limited inferences can be made about the prevalence of nutrient inadequacy. If a mean intake level is equal to or exceeds the AI, it is likely that the prevalence of inadequacy is low, but no conclusion can be drawn about the prevalence of inadequacy for a mean intake level that falls below the AI (IOM, 2000b). For this reason, in this report, means and usual intake distributions were determined for nutrients with an AI, but the prevalence of inadequacy could only be evaluated as low (the mean was above the AI) or unknown (the mean was below the AI). Because only AIs are available for infants ages 0 to less than 6 months, only this limited evaluation of inadequacy was possible for this age group.
Assessing the Prevalence of Excessive Intakes
Excessive intakes of micronutrients were assessed by comparing estimated usual nutrient intake distributions to the Tolerable Upper Intake Level (UL) for that nutrient, as described in the 2006 IOM report. Not all nutrients have ULs and, for four nutrients (folate, vitamin E, niacin, and magnesium), the UL is based on intake of supplements, fortificants, or pharmacological agents only (IOM, 1997, 1998, 2000a), not all of which were evaluated for this report. Thus, the prevalence of intakes exceeding the UL was determined only for retinol, vitamin C, vitamin B6, calcium, iron, phosphorous, zinc, copper, selenium, choline, and sodium in this report. Excess zinc intake was not considered of concern for formula-fed infants or children age 1 to less than 2 years because the method used to set the UL resulted in a narrow margin between the Recommended Dietary Allowance (RDA) and the UL (IOM, 2001). For other age groups, there exists no evidence for adverse effects from zinc naturally occurring in food (IOM, 2001), and the committee considers infant formula (and zinc provided therein) to be tightly regulated for safety by the U.S. Food and Drug Administration (FDA). Excess retinol intake was not considered of concern because of a similarly narrow margin between the UL and the RDA (IOM, 2001). Toxicity from excess consumption of retinol rarely occurs without supplemental intake (IOM, 2001). Finally, excess copper and selenium intake in children was not considered of concern because the UL is extrapolated down from adults (IOM, 2001). In addition, adult intakes of up to 12 mg of copper per day from food have not resulted in adverse effects (IOM, 2001).
Assessing the Prevalence of Inadequate and Excessive Consumption of Macronutrients
Acceptable macronutrient distribution ranges (AMDRs) for protein, fat, and carbohydrate intakes are expressed in terms of percent of total calories contributed by these macronutrients. However, for this report, protein intakes were evaluated relative to protein requirements in grams per kilogram of body weight per day (g/kg/d), rather than relative to the AMDR, as this assessment was considered more accurate when evaluating the adequacy of intakes of the WIC populations. Although the proportions of carbohydrate intakes above and below the AMDR were estimated, carbohydrate intakes below the AMDR are not considered of concern given lack of evidence for harm. Because the 2015–2020 Dietary Guidelines for Americans (DGA) emphasize saturated and not total fat (USDA/HHS, 2016), intakes of total fat relative to the AMDR were also not evaluated in this report. Added sugars and saturated fat do not have AMDRs, but as indicated in the DGA, the committee applied the guideline of not more than 10 percent of energy from each. Therefore, the upper limit for these nutrients varies depending on the overall kcal level that is appropriate for the individual (USDA/HHS, 2016).
Inadequacy or Excess: The Basis for Concern
The committee was tasked with developing nutrient intake adequacy estimates referenced to the DRIs. On a population level, inadequate or excessive intake of any nutrient is usually considered to be of concern when present in 2.5 percent or more of the population of interest (IOM, 2003). This percent should translate to an equivalent prevalence of impaired function or adverse effect. For example, a 5 percent prevalence of dietary iron inadequacy should translate to a 5 percent prevalence of low iron stores. For this report, a 5 percent threshold was applied (as in IOM, 2011a).This is a slightly relaxed standard, which accounts for some of the uncertainty in setting the EARs, as well as some of the generally accepted errors associated with dietary assessment. The same threshold was applied to proportions of the population with intakes above the UL. For nutrients with an AI, an assessment of adequacy cannot be made. Rather, it can only be stated that the mean usual intakes above the AI imply a low prevalence of inadequacy (IOM, 2000b). To be conservative, mean intakes below the AI were considered potentially indicative of inadequacy in this report. For saturated fat and added sugars, the percent of individuals with intakes exceeding 10 percent of energy were determined (as well as the distribution of intakes in gram amounts).
Special Case: Vitamin D
Evaluation of Vitamin D Adequacy
Both dietary intake and sun exposure contribute to an individual's vitamin D status. It is generally agreed that dietary intake of vitamin D is of limited value in the evaluation of vitamin D adequacy because the relationship between the two is nonlinear (IOM, 2011b). Further, the current U.S. Department of Agriculture (USDA) Food and Nutrient Composition Database does not separate vitamin D from 25-hydroxyvitamin D (25(OH)D) in foods. This results in an underestimate of the bioequivalent vitamin D in foods because 25(OH)D is four to five times more bioequivalent than is the parent form of vitamin D (Cashman, 2012; Cashman et al., 2012).
In contrast, serum 25(OH)D captures both total dietary intake of parent vitamin D and 25(OH)D and sun exposure and has been validated as a biomarker for assessing vitamin D adequacy (IOM, 2011b; Taylor et al., 2013). Data on adults ages 19 to 70 from NHANES 2005–2006 indicate that approximately 71 percent of the U.S. population consumes less than the EAR for dietary vitamin D, but the prevalence of inadequacy assessed by 25(OH)D is only about 19 percent (Taylor et al., 2013).
Vitamin D intake data are presented only for infants ages 0 to less than 12 months in this report because serum 25(OH)D data are not available for this group. Data on serum 25(OH)D were available for individuals ages 1 year and older for NHANES survey years 2005–2006. Current food package content of vitamin D is presented in this report to serve as a reference point for food package changes.
Evaluation of Serum Vitamin D Using NHANES
Estimation of usual serum vitamin D requires two observations. For some individuals only one observation was available. In these cases, the within-person variance in serum 25(OH)D from an earlier NHANES (2001–2002) was applied. By using this external estimate of the within-person variance, the serum 25(OH)D distribution could be adjusted as described in Jahns et al. (2005). Because there is no second day to permit estimation of the within-person variability for children, a value computed for the 2001–2002 NHANES (15 percent) was used to adjust the values. The EAR for serum 25(OH)D is 40 nmol/L for all groups.
Assessing Vitamin D Intake of Individuals Less Than 1 Year of Age
Vitamin D intake data are available for NHANES 2007–2012. Intake data are expressed in µg/d, but the EAR is given in international units (IU). The EAR in IUs was converted to µg by multiplying the amount in IUs by 0.025. For an EAR of 400 IU, the corresponding value in µg is 10.
Estimated Energy Requirements
Some of the analyses in this report used Estimated Energy Requirements (EERs) for the various WIC subgroups. A 2002 IOM committee developed equations to derive EERs that balance total energy expenditure at a level of physical activity consistent with health and support growth rates in children that are compatible with a healthy body size and composition (IOM, 2002/2005). In children, the EER was calculated based on an individual's age, body weight, height, and activity level. For adults, the EER was calculated based on age, gender, body weight, height, and physical activity level. The EER calculations applied in this report assumed a low physical activity level for women and children ages 2 to 5 years. The EER for pregnant and breastfeeding women also includes energy needs associated with the deposition of tissue or the secretion of milk. For pregnant women, the second trimester of pregnancy was assumed to cover all stages of pregnancy. For breastfeeding women, the EER assumed the first 6 months postpartum. Recent research suggested that the IOM (2002/2005) formula may overestimate energy needs for children (Butte et al., 2014), although this finding is yet to be validated broadly. Interpretations of data in this report were considered in light of these recent findings.
Evaluation of Food Group Intakes
Food group intakes can be compared to recommended food patterns for a specific energy level. Food patterns provided as part of the DGA represent a range of energy needs (USDA/HHS, 2016). For women, the food patterns selected were based on the EER (as described above) of WIC-participating subgroups, rounded to the nearest 100 kcal/d. For pregnant and breastfeeding women, this was 2,600 kcal; for postpartum women, this was 2,300 kcal. For women who were income eligible, but not pregnant, breastfeeding, or postpartum, this was 2,200 kcal. For children ages 2 to less than 5 years, the median EER was 1,517 kcals. A food pattern of 1,300 kcal was selected for this age group because (1) 1,500 kcal/d may reflect recent increases in body weights for young children and was considered too high for normal weight children in this age group, particularly in light of efforts to reduce and/or contain the prevalence of childhood obesity; and (2) the 1,300-kcal pattern was applied in both the previous WIC food package review (IOM, 2006) and the Child and Adult Care Food Program (CACFP) report (IOM, 2011a) and should similarly be appropriate for current WIC-participating children of the same ages. Although the 2,300-kcal patterns applied to postpartum women in the current report are somewhat higher than the EERs calculated for the IOM (2006) report (2,163), the patterns selected for this report correspond to the CACFP assumption of 2,400 kcal for women ages 19 to 29 years and 2,300 kcal for women ages 30 to 49 years. The calculated EERs for pregnant and breastfeeding women were approximately 2,600 kcal/d, which corresponds to an additional 300 kcal/d needed by these women relative to nonlactating postpartum women.
Because the food patterns are designed to ensure nutrient intakes that meet almost all of the RDAs, it would be ideal if almost everyone in a population reported usual diets that conformed to the food patterns. However, this goal is almost never achieved, so the committee chose a less restrictive approach in selecting foods group intakes that should be improved: If 50 percent or more of the population fell below the recommended level, then improving intake was considered a priority.
Datasets and Analytical Subgroups
The What We Eat in America Dataset
The primary source of data on food and nutrient intake of the U.S. population is the What We Eat in America (WWEIA) component of NHANES (USDA/ARS, 2005–2012). The WWEIA data used for this report were dietary intake data (foods and nutrients from food sources only, not dietary supplements) collected using the Automated Multiple-Pass Method (AMPM),1 and demographic information, including age, gender, and physiological status (e.g., pregnant, breastfeeding, or postpartum women [0–1 year after delivery]2). The only filter applied to create the analytic datasets was the indicator DR1DRSTZ (or DR2DRSTZ for day 2) that identified complete and reliable records. No outliers were removed. By and large, the published NHANES databases have few missing values, in particular for nutrient intake. The population survey weights were applied to all analyses, generating estimated intake values representative of the U.S. population, including by income categories. However, participation in programs such as WIC is not considered in the survey design (USDA/FNS, 2014). In addition, pregnant, breastfeeding, or postpartum women are not oversampled in most survey years (USDA/FNS, 2014), which results in small sample sizes for these physiological states, especially when narrowed to low-income participants.
Food intake data for each survey respondent were translated to USDA food group equivalent values using the Food Patterns Equivalent Database (FPED), a file that identifies the food group and subgroup categories associated with the DGA recommendations (USDA/ARS, 2014). A reasonability check was conducted to compare the output for this report to the nationally representative WWEIA data. Table J-3 presents the FPED component categories that are matched to the main components of the USDA food patterns. Table J-4 presents the definition of the food groups that make up the USDA food patterns, and lists example foods.
Utility of NHANES Datasets in WWEIA for Addressing the Task
The committee was tasked with assessing the nutrient and food group intakes of the WIC-eligible population, as well as low-income women who did not report being pregnant, breastfeeding, or postpartum. USDA's Food and Nutrition Service (USDA/FNS) also requested an evaluation of intakes before and after the 2009 food package changes, and an evaluation of WIC participants separate from eligible non-WIC participants.
USDA-FNS required full implementation of the 2007 (Interim Rule) food package changes by October 2009, and most states implemented the changes at some point between issuance of the 2007 Interim Rule and the October deadline (USDA/FNS, 2012). Given the complications with dividing the NHANES 2009–2010 dataset,3 the committee estimated prepackage-change intakes using NHANES 2005–2008, and postpackagechange intakes using NHANES 2011–2012, as sample sizes allowed.
The committee evaluated the population sizes to determine which combinations of individuals relevant to the task would allow adequately robust sample sizes. Oversampling of some NHANES population subsets has been discontinued (CDC, 2014), which was a concern for several of the WIC subgroups of interest because small subgroup sizes may result in statistically unreliable population-level estimates. The committee's initial goal was to analyze WIC participants4 and WIC-eligible nonparticipants in subgroups of women (ages 14 to 50 years, eligible by being pregnant, breastfeeding or postpartum), infants (formula fed or breastfed), and children (ages 1 to less than 2 years, and ages 2 to less than 5 years). These subgroups allow for comparison of nutrient and food intake of all individuals who participate in WIC as well as individuals who qualify but do not participate in the program. A third subgroup of women was included in the analyses: those who were low-income, but not WIC-eligible because they were not pregnant, breastfeeding, or postpartum. Inspection of the data in the survey years of interest (2005 through 2012) indicated that modification of these initially outlined population subgroups was required. Table J-5 details the limitations of NHANES for developing these initially designed population subsets and the modifications made to accommodate the limitations. Table J-6 details the survey years that were ultimately applied.
The committee first examined the subgroup sizes for women to determine the final analytical subgroups, as these were likely to be small. As shown in Table J-7, the sample sizes for eligible non-WIC-participating women that were breastfeeding or postpartum were 16 and 4, respectively. Therefore, these groups were not further examined.
Following careful consideration of these limitations and sample sizes, the committee designed the final population subgroups that would be analyzed for this report (see Table J-8). Subgroups identified as eligible, but non-WIC-participating reported incomes less than or equal to 185 percent of the poverty-to-income ratio (PIR) (based on PIR guidelines in HHS, 2015 and USDA/FNS, 2015). The WIC subgroups include only individuals reported as being on WIC in the NHANES survey (these individuals may or may not have a PIR less than or equal to 185 percent). There are two reasons for inclusion of any income level in the WIC group: (1) income could change within the certification period, but the individual remains in the program at the new income level, and (2) the objective is primarily to evaluate the effect of the food package, not the effect of income. Table J-8 also includes sample sizes for the 2005–2008 analysis, for which results are also presented later in this appendix.
Challenges with Dietary Intake Assessment of Breastfeeding Women
Inasmuch as NHANES samples women and children separately, no dyadic data are available for breastfeeding women and their infants. NHANES is able to identify which women are breastfeeding but not the intensity of their breastfeeding or, more directly, the amount of milk they are producing. The DRIs for breastfeeding women are for those who are exclusively breastfeeding. Therefore, when women produce less milk than exclusively breastfeeding women at that same duration of breastfeeding, their caloric needs will be overestimated. For this report, intakes of women coded as “breastfeeding” in NHANES were compared to the DRIs for breastfeeding women, and to a 2,600-kcal food pattern (which may be high for a woman who is minimally breastfeeding). Therefore, the proportion of breastfeeding women whose intakes are inadequate and the proportion with food-group intakes below that recommended will also be overestimated.
Limitations of Small Sample Sizes
As indicated in Table J-8 some of the sample sizes were small. The committee determined that means for subgroups other than women were adequately precise, despite sample sizes as small as 19. For example, to estimate mean usual intake of calcium for infants ages 0 to less than 6 months, a minimum sample size of about 20 infants is required to obtain an estimate that is no more than 20 mg below or above the true mean with 95 percent certainty. For zinc, a minimum of 12 infants is required to estimate the mean usual intake within 0.2 mg of the true value. This is because the estimated variance of usual intake tends to be small, in particular for infants and the design effect (DE) for infants is also small (below 2 for most nutrients). For quantities (i.e., “% Inadequacy”) other than means, the required sample sizes are significantly larger.
For women, some samples remained small and the variance of usual intakes tend to be large. Furthermore, in the case of women, estimated DEs tended to be larger than for children, especially in some of the subdomains. Table J-9 shows estimated DEs for seven nutrients, by subdomain. Note that the DE can be larger than 4 in some specific cases, but in general is between 1.8 and 2.5. To generate more robust nutrient intake estimates of the ratio of the within- to the between-person variance in intake, the method of Jahns et al. (2005) was applied. In this method, the variance ratio estimated from the subgroup intake data is combined with a ratio estimate obtained from the group of all women. To do this, an estimate of within-person variance (external variance) is generated using PC-SIDE to assess intake information of all low-income, pregnant, lactating, or postpartum women in all survey years. An internal ratio estimate is obtained separately for each subgroup. A new within- to between-person variance ratio is then computed as a weighted average of the external and internal variance ratio estimates. On average, the external variance was weighted by 100, and the internal variance was weighted by the number of women in the subgroup who provided 2 days of information. When this number is small (as in the case of pregnant or lactating women in 2011–2012), the external variance plays a larger role in the combined estimate. The resulting estimates are less subject to the large degree of variability in the within-person variance estimate that can be introduced by a small sample size. Both means and the “% Inadequacy” have improved reliability.
For the analysis of episodically consumed foods, small samples add enormous challenges. Neither the National Cancer Institute (NCI) method (Dekkers et al., 2014) nor SPADE (used here and described below) results in reliable estimates of distributions of usual food intake when the sample size is small and the proportion of zero consumption is large. In many cases, the programs fail to converge, and no estimation beyond the usual intake mean is possible. Further, neither of the two approaches (NCI or SPADE) permit combining an external and an internal within-person variance estimate when estimating the intake distribution, so the approach followed for nutrients (described above) cannot be implemented for foods. Consequently, with the small sample sizes that were available for women, and the large proportion of zero intakes observed for many of the food subgroups, estimates of the proportion of usual intakes of foods below recommendations are less reliable. Estimates of mean food intake are, however, considered precise with the sample sizes available for this report (Dekkers et al., 2014).
Methods for Evaluation of Food Group and Subgroup Intakes
Food group and subgroup intakes among WIC participating women, infants, and children were evaluated relative to the DGA recommended intakes or other dietary guidance as appropriate. To estimate the distribution of dietary components consumed episodically (food groups and subgroups), SPADE, a method similar to NCI, was implemented. Estimation of usual intake requires two observations; therefore, sample sizes are smaller for food intake compared to nutrient analyses. One consequence of the small sample sizes is that the standard error values are large.
Rationale for Not Conducting Statistical Comparisons
As stated previously, data generated include the subcategories of WIC participants and non-WIC participants as well as pre-2009 and post-2009. WIC participants were not statistically compared to nonparticipants because interpretation of any differences is complicated by the potential for underlying differences between the two groups or selection bias. These comparisons could also be affected by challenges with correct identification of survey respondents as participating in WIC.
Similarly, statistical comparisons of pre- to post-2009 intake data were considered inappropriate. For women and breastfed infants, small sample sizes required the committee to collapse multiple survey years (see Table J-6); therefore, presented results do not uniquely represent pre- or post-2009 intake data. For other subgroups, any detected differences before and after 2009 cannot necessarily be attributed to changes in the food packages.5 Additionally, the NHANES design is a repeated cross-sectional survey that does not allow for longitudinal analysis at any level (i.e., individual, state, or locality).
Tasks Specific to Infant Formulas
In addition to the science supporting functional ingredients in infant formulas, the IOM committee was asked to evaluate three additional aspects of infant formulas in the food packages: energy concentration, iron concentration, and volume provided. The three tasks and the evaluation approach are outlined in Table J-10.
Results of the Nutrient and Food Group Intake Analyses
In the tables that follow, results of the nutrient intake and food group intake analyses that were conducted for all analytical subgroups are presented.
DIET QUALITY OF WIC SUBGROUPS
The committee was tasked with evaluating the diet quality of WIC-eligible subpopulations using the Healthy Eating Index–2010 (HEI–2010) and one additional index of the committee's choosing. This appendix describes the methods applied in these analyses.
The Healthy Eating Index–2010
Because it is based on the DGA food patterns, which apply only to individuals ages 2 and older, the HEI–2010 was likewise applied only to individuals ages 2 year and older (Guenther et al., 2013). The HEI–2010 was designed to measure compliance with the key recommendations in the 2010 Dietary Guidelines for Americans (DGA). The HEI–2010 has not yet been updated to reflect the 2015–2020 DGA. The HEI–2010 covers 12 components as shown in Table J-75. Adequate consumption of all components except refined grains, sodium, and empty calories raises scores. Over-consumption of these three components lowers scores. A perfect overall score for the HEI–2010 is 100. Subscores for the components can be up to 20, with the ranges for each individual component being 0 to 5, 0 to 10, or 0 to 20. The HEI–2010 is the only metric in this report that applies the 2010 DGA as a point of comparison. Only data from the first 24-hour recall was used to calculate HEI–2010.6
Nutrient-Based Diet Quality Index
As described in the phase I report (NASEM, 2016), options for a second index were considered by the committee based on its evaluation of the literature on existing diet quality indexes other than the HEI–2010 and with consideration to three criteria: (1) the index can be applied to adults and children, (2) 24-hour recall data are applied, and (3) the index is based on a metric other than comparison to the DGA. After reviewing potential indexes, the committee determined that responding to the task would require an index that focuses mainly on nutrient content to provide a contrast to the food-group focus of the HEI–2010. However, the committee found that existing nutrient-based indexes could not be applied directly for two reasons. First, they could not be applied because they use daily values based on a 2,000-calorie diet as reference standards for nutrient intake rather than age-appropriate DRI values. Second, they do not necessarily include all of the nutrients and dietary components the committee was interested in assessing, based on current knowledge about nutrients of concern in the diets of young children and women of childbearing age (noted in the DGA) and the committee's assessment of the nutrient intakes of WIC-eligible populations. The committee developed an adapted nutrient-based diet quality (NBDQ) index based on the mean probability of adequacy for the nine shortfall nutrients, calculated for each individual (see Box 3-2).7
The index examined the following “positive” nutrients included in the DGA as shortfall nutrients and nutrients of concern:
- Potassium
- Dietary fiber
- Calcium
- Iron
- Vitamin C
- Folate
- Vitamin A
- Vitamin E
- Magnesium
The index is the mean percentage adequacy for these nine nutrients, calculated for each individual. Thus, the NBDQ can take on values between 0 and 100 for a person.
- For nutrients with an EAR: the percentage adequacy was calculated for each individual for each day. To do this, the method described in IOM (2000b) was applied using the DRI for assessment of intake of individuals and groups and z-scores were computed for each respondent as follows:
- a.
Usual intake at the individual level was first estimated as the best linear unbiased predictor (BLUP) of intake. The BLUP has the smallest prediction error variance among all linear predictors.
- b.
The difference between the individual's estimated usual intake of the nutrient and the EAR for the nutrient was then computed.
- c.
A z-score was computed as the ratio of the difference to the standard error of that difference.
- d.
Finally, the probability of observing a z-value that was at least as large as the one we observed for the individual was computed and multiplied by 100. These calculations were repeated for all the nutrients included in the index. The possible range is from 0 to 100.
- For the nutrients with an AI value (potassium and dietary fiber), reasonable intake ranges based on the AI were applied, to assign 0, 25, 50 and 100 percentage adequacy as follows:
- a.
Intake equal to or above the AI, percentage adequacy = 100
- b.
Intake below the AI but equal to or above 75 of the AI, percentage adequacy = 75
- c.
Intake below 75 percentage of the AI but equal to or above 50 percent of the AI, percentage adequacy = 50
- d.
Intake below 50 percent of the AI but equal to or above 25 percent of the AI, percentage adequacy = 25
- e.
Intake below 25 percent of the AI, percentage adequacy = 0
- The mean percentage adequacy for each individual was calculated by averaging the nutrient-wise percentage adequacy.
- The mean percentage adequacy for population subgroups was then calculated using individual survey weights.Initial descriptive statistics were generated to validate the index:
- a.
As a first step, the mean and standard deviation of the index were evaluated.
- b.
Second, the association of the index with energy intake was examined.
This approach is very similar to that published by Verger et al. (2012), except that the NBDQ includes only shortfall nutrients as defined by the 2015 DGA. When tracked with energy intake, the association between the NBDQ index and energy intake was not strong, which suggests that the index is a summary measure that predicts dietary quality beyond simply being a measure of overall energy intakes. NBDQ was applied to all subpopulations excluding infants.
Additional Adjustments for Within-Person Variance
One challenge with calculating the NBDQ is that within-person variance in intake must be estimated for each person although it is known that this within-person variance may not be constant across population subgroups or even across individuals within population subgroups. Given only 1 or 2 days of intake information for each person, it is not possible to estimate within-person variances at the individual level with confidence.
Instead of attempting to compute a within-person variance in intake for each person individually, a hierarchical model was applied to intake data. This allows for estimation of individual and subpopulation-level variances more precisely by using information across individuals and across groups. As a result, an individual's estimated within-person variance in intake is based not only on the individual's 2 days of data but also on the measurements taken on other individuals. The resulting estimate “shrinks” an individual's naïve estimate (based on the person's 2 days) toward the group's mean. In this light, the estimated variances are similar (in terms of methodology) to the estimated usual intakes that are obtained using the ISU (Nusser et al., 1996), the NCI (Dekkers et al., 2014), or other methods.
Once having these estimated within-person variances, the analysis proceeded as described in the 2000 IOM report (2000b) with two minor differences: first, the best linear unbiased predictor was used to estimate the person's usual intake of a nutrient, and second, the statistic was compared to a t-distribution to account for the fact that the within-person variances in intake are estimates.
For results of the NBDQ analysis, see Chapter 4, Tables 4-30 and 4-31.
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Footnotes
- 1
The AMPM is a computerized method for collecting interviewer-administered 24-hour dietary recalls. In NHANES it is applied in person for the first day and by telephone for the second day of data collection.
- 2
Women were selected from NHANES if coded as pregnant, breastfeeding, or if not breastfeeding, coded as 0 to less than 6 months postpartum. Some women reporting WIC participation did not report being pregnant, breastfeeding, or postpartum.
- 3
NHANES respondents are assigned weights specific to the 2-year datasets. Separation of a 2-year dataset requires recomputation of population weights, which was beyond the scope of this study. It also required knowledge of the location of the participant and the dates of the interviews. Both of these variables are unpublished to preserve privacy of participants.
- 4
Capturing WIC participation is dependent upon accurate reporting in NHANES. The committee's comparison of the weighted total number of recipients reporting WIC as well as extensive experience with reporting of programs like WIC suggest that WIC use is underreported. There is also a challenge in identifying the low-income group as eligible; the concept of income reported in NHANES does not correspond to state-level income requirements for eligibility. Some individuals may be income ineligible but may still legitimately participate in the program if adjunctively or automatically eligible due to participation in Medicaid, Temporary Assistance for Needy Families (TANF), or the Supplemental Nutrition Assistance Program (SNAP).
- 5
For example, as discussed in more depth in Chapter 2, adoption of the new food package in 2009 took place at the end of a recession and at a time when families were facing the worst labor market since the recession of the early 1980s. The American Recovery and Reinvestment Act of 2009 provided the funds necessary to increase the maximum benefit level of the Supplemental Nutrition Assistance Program (SNAP) by about 15 percent (EOPUS, 2014). Because SNAP recipients that meet age and physiological state requirements for WIC are automatically income eligible for WIC and, therefore, many WIC participants also receive SNAP benefits, food expenditures and consumption may have changed among those who were receiving both benefits.
- 6
The committee computed the distribution of HEI–2010 scores using the HEI–2010 Statistical Analysis System (SAS) macros that were posted by the National Cancer Institute (NCI, 2016). At the time these analyses were conducted, NCI had not yet updated the code to compute the HEI–2010; therefore, in cooperation with researchers at NCI, the macros were modified as appropriate. The updated macros now available through NCI are essentially identical to those used for the analyses in this report. The HEI–2010 SAS macros were used to implement the ratio method (Freedman et al., 2008), and provide a mean score with its standard error (SE) for each of the 12 HEI–2010 components as well as for the total score. The SE is computed using a Monte Carlo approach that permits accounting for the complex survey design of NHANES. For the time being, the NCI macros use only the first 24-hour recall for each person.
- 7
There are ample precedents for the use of a composite nutrient adequacy index. Mean adequacy ratios have been used for many years and have more recently been updated to reflect the DRIs. The NDBQ is essentially the same as the indexes used in several published studies (Foote et al., 2004; Murphy et al., 2006).
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