Abstract

SummaryBackgroundFew data are available on the supply and consumption of nutrients at the country level. To address this data gap, we aimed to create a database that provides information on availability (ie, supply) of 156 nutrients across 195 countries and territories from 1980 to 2013.MethodsWe matched 394 food and agricultural commodities from the Food and Agriculture Organization of the United Nations Supply and Utilization Accounts (SUAs) to food items in the United States Department of Agriculture Food Composition Database and obtained data on nutrient composition of the SUAs' food items. Then, after adjusting for inedible portion of each food item, we added the contributions of individual food items to the availability of each nutrient and estimated the national availability of macronutrients and micronutrients in each year. We validated our estimates by comparing our results with those of national nutrition surveys from three countries (the USA, South Korea, and Ecuador). Using dietary consumption data from the Global Burden of Disease study and two popular machine learning algorithms (Random Forest and XGBoost [extreme gradient boosting]), we developed predictive models to estimate the consumption of each nutrient based on their national availability.FindingsGlobally 2710 kcal (95% UI 2660–2770) were available per person per day in 2013. Carbohydrates were the major contributor to energy availability (70·5%), followed by fats (17·4%), and protein (10·5%). The energy availability and the contribution of macronutrients to total energy widely varied across levels of development. Countries at the higher level of development (high Socio-demographic Index countries) had more energy available per person per day (3270 kcal, 3220–3310); greater contributions from fats (26·0%) and proteins (11·9%) to total energy availability; and lower contributions from carbohydrate (54·8%). During 1980–2013, energy availability and the contributions of protein and fats to energy availability have increased globally and across levels of development while the contribution of carbohydrates to total energy availability has decreased. The supply of the micronutrients has also increased during the same period globally and across levels of development. Our validation analysis showed that, after accounting for waste at the retail and household level, our estimates of macronutrient availability were very close to the consumption data in nationally representative surveys. Our machine-learning models closely predicted the observed intake of nutrients with the out-of-sample correlation of greater than 0·8 between predicted and observed intake for the nutrients included in the analysis.InterpretationOur global nutrient database provides a picture of the supply of various nutrients at the country level and can be useful to assess the performance of national food systems in addressing the nutritional needs of their population.FundingBill & Melinda Gates Foundation.

Highlights

  • We matched 394 food and agricultural commodities from the Food and Agriculture Organization of the United Nations Supply and Utilization Accounts (SUAs) to food items in the United States Department of Agriculture Food Composition Database and obtained data on nutrient composition of the SUAs’ food items

  • This study, for the first time to our knowledge, uses data from 394 food items reported in the FAO’s Supply and Utilization Accounts and we evaluated the validity of our estimates by comparing them with consumption data from nationally representative nutrition surveys from three countries

  • Database construction We used SUAs from the FAO to estimate the availability of 156 nutrients in the 195 countries and territories included in the Global Burden of Disease study (GBD) 2016 across 33 years

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Summary

Introduction

We matched 394 food and agricultural commodities from the Food and Agriculture Organization of the United Nations Supply and Utilization Accounts (SUAs) to food items in the United States Department of Agriculture Food Composition Database and obtained data on nutrient composition of the SUAs’ food items. After adjusting for inedible portion of each food item, we added the contributions of individual food items to the availability of each nutrient and estimated the national availability of macronutrients and micronutrients in each year. We validated our estimates by comparing our results with those of national nutrition surveys from three countries (the USA, South Korea, and Ecuador). Using dietary consumption data from the Global Burden of Disease study and two popular machine learning algorithms (Random Forest and XGBoost [extreme gradient boosting]), we developed predictive models to estimate the consumption of each nutrient based on their national availabilit

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