The US Food and Drug Administration's updated nutrition labeling requirements will include added sugars starting in July 2018, but no measure currently exists to identify the added sugar content of products and what it represents among purchases. Beverages are one of the first targets for reducing added sugar consumption, and hence are the focus here. Our goal was to estimate trends in added sugars in nonalcoholic packaged beverage products available in the United States and to estimate amounts of added sugars obtained from these beverages given the purchases of US households overall and by subpopulations. On the basis of nutrition label data from multiple sources, we used a stepwise approach to derive the added sugar content of 160,713 beverage products recorded as purchased by US households in 2007-2012 (345,193 observations from 110,539 unique households). We estimated the amounts of added sugars obtained from packaged beverages US households reported buying in 2007-2008, 2009-2010, and 2011-2012, overall and by subpopulations based on household composition, race/ethnicity, and income. The key outcomes are added sugars in terms of per capita grams per day and the percentage of calories from packaged beverages. Packaged beverages alone account for per capita consumption of 12 g/d of added sugars purchased by US households in 2007-2012, representing 32-48% of calories from packaged beverages. Whereas the absolute amount of added sugars from beverages has not changed meaningfully over time, the relative contribution of added sugars to calories from beverages has increased. Non-Hispanic black households and low-income households obtain both higher absolute and relative amounts of added sugars from beverages than non-Hispanic white households and high-income households (all < 0.01). These results provide measures of added sugars from packaged beverages at both the product level and the population level in the United States and can be used for comparisons after the revised nutrition labels are implemented and for future monitoring.

In order to monitor nutritional changes in the US food supply and assess potential impact on individual dietary intake, an approach was developed to enhance existing standard food composition tables with time-varying product- and brand-specific information for barcoded packaged foods. A “Crosswalk” was formed between barcoded products and USDA foodcodes in a time-specific manner, such that sales-weighted average nutritional profiles were generated for each foodcode based on corresponding products (275,000 to 350,000 per 2-year cycle). This Crosswalk-enhanced food composition table was applied to dietary intake data from the National Health and Nutrition Examination Survey (cycles 2007–2008, 2009–2010, and 2011–2012). Total energy density of foods consumed by Americans from stores/vending was stable over time and differed by <5 kcal/100g using the Crosswalk-enhanced vs standard database. However, changes in the energy density of food groups were found utilizing the Crosswalk that were not detected using the standard database. Likewise, significant declines in energy intake from beverages among children (288±7.3 to 258±6.8 kcal/d) were found using the Crosswalk-enhanced database but were non-significant using the standard database. The Crosswalk approach can potentially augment national nutrition surveys by utilizing commercial food purchase and nutrient databases to capture changes in the nutrient content of packaged foods.

South Africa (SA) is facing a rising prevalence of obesity and diet-related chronic diseases. The government is seeking to develop effective, evidence-based policy measures to address this. A well-designed, fit-for-purpose nutrient profiling model (NPM) can aid policy development. The aim of this study was to develop a fit-for-purpose NPM in SA. Steps included: (1) determining the purpose and target population; (2) selecting appropriate nutrients and other food components to include; (3) selecting a suitable NPM type, criteria and base; and (4) selecting appropriate numbers and thresholds. As part of the evaluation, the nutritional composition of packaged foods containing nutritional information (n = 6747) in the SA food supply chain was analyzed, a literature review was undertaken and various NPMs were evaluated. Our findings indicated that it is most appropriate to adapt an NPM and underpin regulation with a restrictive NPM that limits unhealthy food components. The Chile 2019 NPM was identified as suitable to adapt, and total sugar, saturated fat, sodium and non-sugar sweetener were identified as appropriate to restrict. This NPM has the potential to underpin restrictive policies, such as front-of-package labelling and child-directed marketing regulations in SA. These policies will support the fight against obesity and NCDs in the country.

BackgroundDespite well‐documented racial and ethnic disparities in dietary intake among Americans, most studies examine diet inequalities using only a single standard food composition table (FCT) for all populations. Further, although the majority of Americans' energy intake comes from grocery stores, racial/ethnic disparities in the nutrient content of dietary intake from stores have not been examined.ObjectiveWe aimed to determine whether the fat, sugar, and sodium content of store‐bought foods consumed by Americans differ by race/ethnicity and whether disparities are underestimated using a single standard food composition table compared with using population‐specific food composition data based on the brands and products that each racial/ethnic group buys at the grocery store.MethodsThis study used 24‐hour dietary recall data for a nationally representative US sample of 23,062 children and adults from the 2007–2012 National Health and Nutrition Examination Survey (NHANES). We developed a separate FCT for each racial/ethnic group by first linking Nutrition Facts Panel data from barcoded foods (168,077 to 193,195 per survey) to codes for packaged foods consumed from stores by NHANES participants. We then determined sales‐weighted mean nutrient content for each food reported in NHANES, with weighting derived from food purchases by all households (standard FCT) or by non‐Hispanic (NH) white, NH black, or Hispanic households (3 population‐specific FCTs) in the nationwide Nielsen Homescan Consumer Panel. Survey‐weighted mean nutrient content per 100g for total intake from stores and for store‐bought foods (excluding beverages) were determined for each racial/ethnic group using the standard FCT and population‐specific FCTs.ResultsThe nutrient content of dietary intake from stores was significantly higher for NH blacks compared with NH whites for total fat (+11.3%), saturated fat (+6.6%), sugar (+14.5%), and sodium (+9.6%) content per 100g. Store‐bought foods consumed by NH blacks were significantly higher in total fat (+9.4%), saturated fat (+4.7%), and sodium (+7.8%) content compared with foods consumed by NH whites. However, total fat and saturated fat content of dietary intake from stores was significantly lower (−5.9% and −5.5%, respectively) for Hispanics compared with NH whites. Store‐bought foods consumed by Hispanics were significantly lower in total fat (−9.8%), saturated fat (−12.8%), sugar (−17.2%), and sodium (−4.7%) content compared with foods consumed by NH whites. Black‐white disparities in total fat and sugar content were significantly underestimated (P<0.01) and Hispanic‐white disparities in total fat and saturated fat content were significantly overestimated (P<0.01) using standard compared with population‐specific food composition data.ConclusionsIn our study, the nutrient profile of dietary intake from stores was less healthful for NH blacks and more healthful for Hispanics compared with NH whites. Our findings suggest that population‐specific food composition data may enhance the accuracy of estimated racial/ethnic dietary disparities.Support or Funding InformationThis work was supported by the Robert Wood Johnson Foundation (67506, 68793, 70017, 71837), NIH (R01DK098072, DK56350) and the Carolina Population Center and its NIH center grant (P2C HD050924).

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