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Session: Mathematical and Measurement Advances to Enhance Nutritional Assessment (Poster Theater Flash Session 6)

(PTFS06-06-23) Discrepancies in Intake Estimates for Select Dietary Factors in Two Dietary Databases: Global Burden of Disease and Global Dietary Database

Sunday, July 23, 2023
4:25 PM – 4:29 PM ET
Location:  Fairfax

    Presenting Author(s)

  • Yutong Chen, MS, MPH (she/her/hers)

    PhD student
    Tufts University Friedman School of Nutrition Science and Policy
    boston, Massachusetts, United States

    Disclosure(s): No relevant financial relationship(s) with ineligible companies to disclose.

    • Co-Author(s)

  • BZ

    Bingjie Zhou, MS

    PhD Candidate
    Tufts University
    Boston, Massachusetts, United States

  • EN

    Elena N. Naumova, PhD

    Professor
    Tufts University
    Boston, Massachusetts, United States

Disclosure(s):
Yutong Chen, MS, MPH: No relevant financial relationship(s) with ineligible companies to disclose.
Bingjie Zhou, MS: No relevant financial relationship(s) with ineligible companies to disclose.
Objectives: To better understand the complexities of measuring dietary intakes and ramifications of using specific database for research and regulatory purposes, we need to know the degree of similarity of estimated dietary intakes across common data sources, and their reliability and validity. This study examines the similarities and discrepancies in 169 countries in 1990, 2000, 2010, and 2018 provided by the two most comprehensive dietary databases, Global Dietary Database (GDD) and the Global Burden Disease (GBD), using several metrics.

Methods: We identified 13 dietary factors (fruits, vegetables, red meat, processed meat, whole grains (WG), legumes and beans, nuts and seeds, dairy, sugar-sweetened beverages, fiber, polyunsaturated fatty acids, calcium, and sodium) with similar definitions in both databases. For these selected dietary factors, we further aligned records in terms of population age, intake units, and year. We compared the country-time-specific intakes using the differences in population distributions. We examined the similarities in the extremes using high-order moments for skewness, LS, kurtosis, LK, and the degree of agreement with non-parametric Spearman correlations, RGDD|GBD. For each dietary factor and each county-time pair we estimated the mean differences, ΔGDD|GBD, and created 4 metrics by normalizing each into a 0-1 scale: (ΔGDD|GBD –Δmin) / (Δmax –Δ<min), (1-R2GDD|GBD), and absolute differences in LS and LK, respectively. Using these metrics, we calculated an average score ranging from 0 to 1 (1 indicating highest discrepancies).

Results: Among all, processed meat intake means had the highest (158%) percentage difference (25.4 ± 23.9 vs 3.0 ± 4.0 grams/day, p< 0.01, respectively), with an average similarity score of 0.31. The lowest (8%) percentage difference was in vegetable intake means (162.8 ± 76.0 vs 150.4 ± 101.4 grams/day, p< 0.05, respectively), with average score of 0.33. Milk and WG intake estimates had the lowest and highest discrepancies (0.13 vs 0.37), respectively. Overall high values of LS (0.25 ± 0.1) and LK (0.17 ± 0.06) along with relatively low values of RGDD|GBD (0.41 ± 0.23) indicate the high degree of discrepancies for distributional extremes.

Conclusions: The high discrepancy between two major global databases calls for better harmonization of data collection protocols and model development.

Funding Sources: None