Evaluating the Process to Develop the Dietary Guidelines for Americans, 2020-2025 Final Report (2023) / Chapter Skim
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2 The Process to Create the Dietary Guidelines for Americans Is Both Complicated and Complex: Background and Context for Task 3
2 The Process to Create the Dietary Guidelines for Americans Is Both Complicated and Complex: Background and Context for Task 3
Pages 25-56
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. This chapter begins with a discussion of factors that influence public trust in nutrition science (and by extension, in the DGA)
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. In this instance, the 2020 DGAC Scientific Report recommended limiting added sugars to 6% of daily calories but the 2020–2025 DGA retained the recommendation in the 2015-2020 DGA of 10% of daily calories.
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Enhancing public trust in clinical practice guidelines was emphasized by the Institute of Medicine (IOM) in a prior report, Clinical Practice Guidelines We Can Trust (IOM, 2011)
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. Although many of the issues described above, and the Pew survey itself, do not specifically address public trust in the DGA, they highlight the challenges the DGA face in gaining public trust regardless of their specific content.
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Given the erosion of trust in expertise in general and the public attention on COI in nutrition research, these issues are likely to remain pertinent to future DGA cycles. Therefore, assessment and transparent management of both individual and institutional COI will remain important for maintaining and increasing public trust in the DGA.
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and detailed in the timeline in Appendix B The 2017 report committee recommended a redesign of the DGA process that uses the full 5-year period to provide "continuity across 5-year cycles, with some activities spanning across DGA cycles," which it said would also assist with strategic planning, particularly for the systematic reviews to be conducted or updated (NASEM, 2017a)
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. The Dietary Guidelines Process Relies on Multiple Types of Complex Data Multiple types of data are gathered, assessed, and interpreted to create the DGAC Scientific Report (which informs the DGA)
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Dietary data are fundamental Data on dietary intake are used to assess current intakes and sources of guidance-based food groups and nutrients (e.g., sodium, added sugars) , identify nutrients of concern, characterize dietary exposures in the studies evaluated in systematic reviews, and model patterns of food intake that meet nutrient needs and reduce chronic disease risk.
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DGAC, in systematic reviews, food pattern modeling, and descriptive data analysis, makes the development of the DGA uniquely sensitive to the challenges with these data, which are discussed below. Dietary data are complex to collect and use "Usual" dietary intake (as opposed to intake on a given day)
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Further, in making inferences from WWEIA and other studies using short-term data, the potential implications of systematic error should be considered. The dietary data included in systematic reviews are primarily from cohort studies, which often use FFQs or brief screeners that aim to directly assess usual intake by asking about intake over long periods of time (e.g., past year or past 30 days)
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. These limitations contribute to measurement error when estimating dietary intake using selfreport tools and pose a barrier to accounting for heterogeneity in food composition (e.g., in food pattern modeling)
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. The 2020 DGAC Scientific Report includes both sibling studies and Mendelian randomization studies for some systematic reviews (DGAC, 2020; NASEM, 2022)
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Food pattern modeling estimated how changes in the amounts or types of foods and beverages in a food pattern would affect the ability of various population subgroups to meet their nutrient needs. NESR systematic reviews were used to answer specific questions about how diet and health are related (USDA/HHS, 2020)
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analyses of dietary intake data from NHANES WWEIA to deter mine the estimated proportion of energy from the top sources of added sugars; 2. systematic reviews of the relationship between added sugars con sumption and growth, body size, weight, cardiovascular disease, and type 2 diabetes; and 3.
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FIGURE 2-1 An example of integration of multiple types of data to inform the Dietary Guidelines Advisory Committee Scientific Report conclusions.
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The 2017 National Academies report noted that although recent editions of the DGAC Scientific Report have used conceptual frameworks, an analytic framework would be useful "to structure topic selection, synthesis, and interpretation of the evidence" (NASEM, 2017a)
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. An overarching analytic framework could also clarify how the DGAC weighs different types of evidence -- including additional inputs, such as prior DGAC Scientific Reports, prior editions of the DGA, and public comments submitted during the DGAC review period -- to arrive at its interpretations and conclusions (DGAC, 2020)
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Common computational models used in public health include agent-based models, social network analysis, and system dynamics models (Luke and Stamatakis, 2012) Complex adaptive systems: These consist of heterogeneous, interacting com ponents that can learn over time (Ahmed et al., 2005)
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, which recognizes that dietary intake is dynamic across the life cycle. Nonetheless, the 2017 National Academies report noted that methods such as the current approach to food pattern modeling are not capable of addressing the full extent of complexity of dietary intake and health or the variability in dietary patterns in the population (NASEM, 2017a)
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Computational methods and capabilities have continued to evolve since release of the 2017 National Academies report, enabling a greater range of approaches to data collection and study design that can better characterize the complex systems involved. At the same time, there has been exponential growth in the amount of data available on the systems
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and life stages requires a better understanding of the biological, behavioral, social environmental, and other complex systems interactions that affect dietary intake and the relationships between nutrition and various health outcomes (O'Sullivan et al., 2018)
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For example, systems approaches allow for a better connection of dietary intake patterns with more distal, future health outcomes. Systems approaches also hold promise for understanding how physical, social, historical, and political contexts shape individual behavior and health.
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Dynamics of the complex food environment underlying dietary intake in low-income groups: A systems map of associations extracted from a systematic umbrella literature review (Sawyer et al., 2021) Global food systems Use of the delta model to understand the food system and global nutrition (Smith et al., 2021)
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Systems approaches can support the DGA to reflect the complexity inherent in dietary intake and its relationships with various health outcomes over time, including heterogeneity across the population and changes over the life cycle. Application of systems approaches to the process itself can facilitate efficiencies to create a continuous learning process whereby each version of the DGA can build more seamlessly on the prior version and create transparency in how different types of data are integrated and interpreted.
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Application of systems science approaches and methods to the DGA process itself can facilitate efficiencies to create a continuous learning process whereby each version of the DGA can build more seamlessly on the prior version and create transparency in how different types of data are integrated and interpreted. Systems science approaches and methods would help to make the most of the data that are or could be used for the development of the DGA as well as to support transparency in the DGAC's interpretation of the evidence (NASEM, 2017a)
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2019. Evaluation of new technology-based tools for dietary intake assessment -- An ILSI Europe Dietary Intake and Exposure Task Force evaluation.
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Harvard: The Nutrition Source https://www.hsph.harvard.edu/ nutritionsource/2021/01/12/2020-dietary-guidelines/ (accessed September 22, 2022)
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2022. Using short-term dietary intake data to address research questions related to usual dietary intake among populations and subpopulations: Assumptions, statistical techniques, and considerations.
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2022. Strengthening research that a nswers nutrition questions of public health importance: Leveraging the experience of the USDA nutrition evidence systematic review team.
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2021. Dynamics of the complex food environment underlying dietary intake in low-income groups: A systems map of associations extracted from a system atic umbrella literature review.
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2021b. Food patterns equivalents database.
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