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      Estimating national and subnational nutrient intake distributions of global diets

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          ABSTRACT

          Background

          Access to high-quality dietary intake data is central to many nutrition, epidemiology, economic, environmental, and policy applications. When data on individual nutrient intakes are available, they have not been consistently disaggregated by sex and age groups, and their parameters and full distributions are often not publicly available.

          Objectives

          We sought to derive usual intake distributions for as many nutrients and population subgroups as possible, use these distributions to estimate nutrient intake inadequacy, compare these distributions and evaluate the implications of their shapes on the estimation of inadequacy, and make these distributions publicly available.

          Methods

          We compiled dietary data sets from 31 geographically diverse countries, modeled usual intake distributions for 32 micronutrients and 21 macronutrients, and disaggregated these distributions by sex and age groups. We compared the variability and skewness of the distributions and evaluated their similarity across countries, sex, and age groups. We estimated intake inadequacy for 16 nutrients based on a harmonized set of nutrient requirements and bioavailability estimates. Last, we created an R package—nutriR—to make these distributions freely available for users to apply in their own analyses.

          Results

          Usual intake distributions were rarely symmetric and differed widely in variability and skewness across nutrients and countries. Vitamin intake distributions were more variable and skewed and exhibited less similarity among countries than other nutrients. Inadequate intakes were high and geographically concentrated, as well as generally higher for females than males. We found that the shape of usual intake distributions strongly affects estimates of the prevalence of inadequate intakes.

          Conclusions

          The shape of nutrient intake distributions differs based on nutrient and subgroup and strongly influences estimates of nutrient intake inadequacy. This research represents an important contribution to the availability and application of dietary intake data for diverse subpopulations around the world.

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          Most cited references51

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          Health effects of dietary risks in 195 countries, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

          Summary Background Suboptimal diet is an important preventable risk factor for non-communicable diseases (NCDs); however, its impact on the burden of NCDs has not been systematically evaluated. This study aimed to evaluate the consumption of major foods and nutrients across 195 countries and to quantify the impact of their suboptimal intake on NCD mortality and morbidity. Methods By use of a comparative risk assessment approach, we estimated the proportion of disease-specific burden attributable to each dietary risk factor (also referred to as population attributable fraction) among adults aged 25 years or older. The main inputs to this analysis included the intake of each dietary factor, the effect size of the dietary factor on disease endpoint, and the level of intake associated with the lowest risk of mortality. Then, by use of disease-specific population attributable fractions, mortality, and disability-adjusted life-years (DALYs), we calculated the number of deaths and DALYs attributable to diet for each disease outcome. Findings In 2017, 11 million (95% uncertainty interval [UI] 10–12) deaths and 255 million (234–274) DALYs were attributable to dietary risk factors. High intake of sodium (3 million [1–5] deaths and 70 million [34–118] DALYs), low intake of whole grains (3 million [2–4] deaths and 82 million [59–109] DALYs), and low intake of fruits (2 million [1–4] deaths and 65 million [41–92] DALYs) were the leading dietary risk factors for deaths and DALYs globally and in many countries. Dietary data were from mixed sources and were not available for all countries, increasing the statistical uncertainty of our estimates. Interpretation This study provides a comprehensive picture of the potential impact of suboptimal diet on NCD mortality and morbidity, highlighting the need for improving diet across nations. Our findings will inform implementation of evidence-based dietary interventions and provide a platform for evaluation of their impact on human health annually. Funding Bill & Melinda Gates Foundation.
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            Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015

            Summary Background The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context. Methods We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors—the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI). Findings Between 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25%. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25% over the same period. All risks jointly evaluated in 2015 accounted for 57·8% (95% CI 56·6–58·8) of global deaths and 41·2% (39·8–42·8) of DALYs. In 2015, the ten largest contributors to global DALYs among Level 3 risks were high systolic blood pressure (211·8 million [192·7 million to 231·1 million] global DALYs), smoking (148·6 million [134·2 million to 163·1 million]), high fasting plasma glucose (143·1 million [125·1 million to 163·5 million]), high BMI (120·1 million [83·8 million to 158·4 million]), childhood undernutrition (113·3 million [103·9 million to 123·4 million]), ambient particulate matter (103·1 million [90·8 million to 115·1 million]), high total cholesterol (88·7 million [74·6 million to 105·7 million]), household air pollution (85·6 million [66·7 million to 106·1 million]), alcohol use (85·0 million [77·2 million to 93·0 million]), and diets high in sodium (83·0 million [49·3 million to 127·5 million]). From 1990 to 2015, attributable DALYs declined for micronutrient deficiencies, childhood undernutrition, unsafe sanitation and water, and household air pollution; reductions in risk-deleted DALY rates rather than reductions in exposure drove these declines. Rising exposure contributed to notable increases in attributable DALYs from high BMI, high fasting plasma glucose, occupational carcinogens, and drug use. Environmental risks and childhood undernutrition declined steadily with SDI; low physical activity, high BMI, and high fasting plasma glucose increased with SDI. In 119 countries, metabolic risks, such as high BMI and fasting plasma glucose, contributed the most attributable DALYs in 2015. Regionally, smoking still ranked among the leading five risk factors for attributable DALYs in 109 countries; childhood underweight and unsafe sex remained primary drivers of early death and disability in much of sub-Saharan Africa. Interpretation Declines in some key environmental risks have contributed to declines in critical infectious diseases. Some risks appear to be invariant to SDI. Increasing risks, including high BMI, high fasting plasma glucose, drug use, and some occupational exposures, contribute to rising burden from some conditions, but also provide opportunities for intervention. Some highly preventable risks, such as smoking, remain major causes of attributable DALYs, even as exposure is declining. Public policy makers need to pay attention to the risks that are increasingly major contributors to global burden. Funding Bill & Melinda Gates Foundation.
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              fitdistrplus: AnRPackage for Fitting Distributions

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                Author and article information

                Contributors
                Journal
                Am J Clin Nutr
                Am J Clin Nutr
                ajcn
                The American Journal of Clinical Nutrition
                Oxford University Press
                0002-9165
                1938-3207
                August 2022
                10 June 2022
                10 June 2022
                : 116
                : 2
                : 551-560
                Affiliations
                Department of Nutrition, Harvard T. H. Chan School of Public Health , Boston, MA, USA
                Bren School of Environmental Science and Management, University of California , Santa Barbara, Santa Barbara, CA, USA
                Marine Science Institute, University of California , Santa Barbara, Santa Barbara, CA, USA
                ARS Western Human Nutrition Research Center , USDA, Davis, CA, USA
                Nutrition and Health Research Center, National Institute of Public Health , Cuernavaca, Morelos, Mexico
                Department of Environmental Science and Policy, University of California , Davis, Davis, CA, USA
                Global Alliance for Improved Nutrition , Washington, DC, USA
                Division of Food Technology, National Food Institute, Technical University of Denmark , Lyngby, Denmark
                Department of Nutrition, Harvard T. H. Chan School of Public Health , Boston, MA, USA
                School of Oceanography, Shanghai Jiao Tong University , Shanghai, China
                Nutrition and Health Research Center, National Institute of Public Health , Cuernavaca, Morelos, Mexico
                Division of Food Technology, National Food Institute, Technical University of Denmark , Lyngby, Denmark
                Department of Nutrition, Federal University of Paraná , Curitiba, Brazil
                Centre for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment , Bilthoven, The Netherlands
                Department of Epidemiology and Public Health , Sciensano, Brussels, Belgium
                Department of Nutrition, Harvard T. H. Chan School of Public Health , Boston, MA, USA
                Institute for Statistics of the Federation of Bosnia and Herzegovina , Sarajevo, Bosnia and Herzegovina
                Harvard College , Cambridge, MA, USA
                Department of Nutrition, Harvard T. H. Chan School of Public Health , Boston, MA, USA
                Intake, Center for Dietary Assessment , FHI Solutions, Washington, DC, USA
                Department of Epidemiology and Public Health , Sciensano, Brussels, Belgium
                Trade and Markets Division, UN's Food and Agricultural Organization , Rome, Italy
                Department of Nutrition, Harvard T. H. Chan School of Public Health , Boston, MA, USA
                Department of Environmental Studies, The Porter School of the Environment and Earth Sciences , Tel Aviv University, Tel Aviv, Israel
                Department of Nutrition, Harvard T. H. Chan School of Public Health , Boston, MA, USA
                Betty and Gordon Moore Center for Science, Conservation International , Arlington, VA, USA
                Department of Nutrition, Harvard T. H. Chan School of Public Health , Boston, MA, USA
                Department of Environmental Health, Harvard T. H. Chan School of Public Health , Boston, MA, USA
                Department of Global Health and Population, Harvard T. H. Chan School of Public Health , Boston, MA, USA
                Author notes
                Address correspondence to SP (E-mail: spassarelli@ 123456hsph.harvard.edu ).
                Author information
                https://orcid.org/0000-0003-1044-3393
                https://orcid.org/0000-0002-0398-9825
                https://orcid.org/0000-0002-4400-3532
                https://orcid.org/0000-0002-5414-8671
                https://orcid.org/0000-0002-0412-2748
                https://orcid.org/0000-0002-4664-5547
                Article
                nqac108
                10.1093/ajcn/nqac108
                9348991
                35687422
                1add39e4-b8df-4f9d-9564-494fcffa3b25
                © The Author(s) 2022. Published by Oxford University Press on behalf of the American Society for Nutrition.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                : 08 February 2022
                : 05 April 2022
                : 19 April 2022
                : 04 August 2022
                Page count
                Pages: 10
                Funding
                Funded by: National Institutes of Health, DOI 10.13039/100000002;
                Award ID: 2T32DK007703-26
                Award ID: D43 TW010543
                Categories
                Original Research Communications
                AcademicSubjects/MED00060
                AcademicSubjects/MED00160
                Editor's Choice

                Nutrition & Dietetics
                nutrient,dietary data,intake,global health,methods,subgroup,distribution,epidemiology,nutrient intake,nutrition

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