Assessing global dietary habits: a comparison of national estimates from the FAO and the Global Dietary Database1234
Del Gobbo, Liana C
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CitationDel Gobbo, Liana C, Shahab Khatibzadeh, Fumiaki Imamura, Renata Micha, Peilin Shi, Matthew Smith, Samuel S Myers, and Dariush Mozaffarian. 2015. “Assessing global dietary habits: a comparison of national estimates from the FAO and the Global Dietary Database1234.” The American Journal of Clinical Nutrition 101 (5): 1038-1046. doi:10.3945/ajcn.114.087403. http://dx.doi.org/10.3945/ajcn.114.087403.
AbstractBackground: Accurate data on dietary habits are crucial for understanding impacts on disease and informing policy priorities. Nation-specific food balance sheets from the United Nations FAO provided the only available global dietary estimates but with uncertain validity. Objectives: We investigated how FAO estimates compared with nationally representative, individual-based dietary surveys from the Global Dietary Database (GDD) and developed calibration equations to improve the validity of FAO data to estimate dietary intakes. Design: FAO estimates were matched to GDD data for 113 countries across the following 9 major dietary metrics for 30 y of data (1980–2009): fruit, vegetables, beans and legumes, nuts and seeds, whole grains, red and processed meats, fish and seafood, milk, and total energy. Both absolute and percentage differences in FAO and GDD mean estimates were evaluated. Linear regression was used to evaluate whether FAO estimates predicted GDD dietary intakes and whether this prediction varied according to age, sex, region, and time. Calibration equations were developed to adjust FAO estimates to approximate national dietary surveys validated by using randomly split data sets. Results: For most food groups, FAO estimates substantially overestimated individual-based dietary intakes by 74.5% (vegetables) and 270% (whole grains) while underestimating beans and legumes (−50%) and nuts and seeds (−29%) (P < 0.05 for each). In multivariate regressions, these overestimations and underestimations for each dietary factor further varied by age, sex, region, and time (P < 0.001 for each). Split–data set calibration models, which accounted for country-level covariates and other sources of heterogeneity, effectively adjusted FAO estimates to approximate estimates from national survey data (r = 0.47–0.80) with small SEs of prediction (generally 1–5 g/d). Conclusions: For all food groups and total energy, FAO estimates substantially exceeded or underestimated individual-based national surveys of individual intakes with significant variation depending on age, sex, region, and time. Calibration models effectively adjusted the comprehensive, widely accessible FAO data to facilitate a more-accurate estimation of individual-level dietary intakes nationally and by age and sex.
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