DOI: 10.7910/dvn/eduln9
title: Global Datasets on Human Inadequate Nutrient Intakes

description: For extensive details, data dictionaries, and hyperlinks to associated articles, source materials, and GitHub repositories, please view the Full Description pdf in the attachments of this website. As a note, Simone Passarelli and Christopher Free are joint first authors and Ty Beal and Christopher Golden are joint senior authors for this publication.

Overview
This Dataverse page provides access to three related datasets that describe the prevalence of inadequate nutrient intakes for subnational age-sex groups in 185 countries globally. These datasets originated from three related papers with similar underlying methodologies, building off of each other to add new layers of information, including the role of fortification and protein in inadequate nutrient intakes. The variable names, subnational groups, and methodologies across the three papers have been harmonized, allowing users to select and download the dataset(s) that best meet their needs.

Dataset 1: Diet
The first dataset is from Passarelli et al. (2022), which estimated the prevalence of inadequate intakes for 15 micronutrients for 34 age-sex groups in 185 countries. These estimates did not include fortification and therefore represent the prevalence of inadequate intakes based on intrinsic nutrients in foods alone. The estimates were generated using estimates of mean micronutrient intakes from the Global Dietary Database and estimates in the variability of micronutrient intakes from Passarelli et al. (2022). Overall, inadequate intakes were described for nine vitamins (vitamin E, riboflavin, folate, vitamin C, vitamin B6, vitamin A, vitamin B12, thiamin, niacin) and six minerals (iodine, calcium, iron, zinc, selenium, and magnesium).

Dataset 2: Fortification
The second dataset is from Friesen et al. (in press), which used the methodology developed by Passarelli et al. (2022) to estimate the prevalence of inadequate intakes for 13 micronutrients in the same 34 age-sex groups in 185 countries accounting for current fortification programs as well as potential expanded fortification programs. Vitamin C and magnesium were excluded because they are not commonly fortified. The levels of current fortification were described using data from the Global Fortification Data Exchange (GFDx), which describes the fortification of five food vehicles (salt, oil, wheat flour, maize flour, rice) in nearly every country. Friesen et al. (in press) also estimated the prevalence of inadequate intake under improved fortification scenarios. Thus, results are presented for the following:

● Scenario 1-Current fortification: Evaluates the prevalence of inadequate intakes under current fortification programs.
● Scenario 2-Improved compliance: Evaluates the prevalence of inadequate intakes in a scenario in which compliance with current fortification programs increases to 90%.
● Scenario 3-Aligned standards: Evaluates the prevalence of inadequate intakes in a scenario in which country-specific fortification standards are increased to match international standards. 
● Scenario 4-Aligned standards with improved compliance: Evaluates the prevalence of inadequate intakes in a scenario in which country-specific fortification standards are increased to match international standards and compliance is increased to 90%.
● Scenario 5-Aligned standards, improved compliance, and expanded coverage: Evaluates the prevalence of inadequate intakes in a scenario in which country-specific fortification standards are increased to match international standards, compliance is increased to 90%, and more countries adopt fortification programs at international standards. 

Friesen et al. (in press) also estimated the prevalence of excessive intakes under the current and improved fortification programs.

Dataset 3: Protein

The third dataset is from De Nicola et al. (in preparation), which estimates the prevalence of inadequate dietary protein intake for 40 age–sex groups across 185 countries in 2018.

This dataset extends the distribution-based framework developed by Passarelli et al. (2024) by explicitly linking protein intake to modeled caloric intake distributions and physiological, body-weight–scaled protein requirements. Because protein intake is intrinsically coupled to energy intake, protein inadequacy is evaluated under both a counterfactual energy-adequate scenario and a set of realistic caloric intake scenarios.

Protein inadequacy is assessed relative to the Estimated Average Requirement (EAR), defined in grams per kilogram of body weight per day and converted to absolute daily requirements using country-, age-, and sex-specific body weight data. Intake distributions are reconstructed probabilistically using harmonized estimates of mean intake, variability, and distributional form.

The dataset includes results under three caloric intake scenarios (low, medium, and high), derived from FAO Food Balance Sheet data after adjusting for varying levels of downstream food waste. The medium scenario is calibrated such that global caloric inadequacy is approximately 10%, consistent with FAO benchmark estimates.

In addition, sensitivity analyses adjusting protein requirements for dietary protein quality are provided. Protein quality is proxied using the share of dietary protein derived from animal-source foods (ASFs). No adjustment is applied when ASF protein share is ≥40%; protein requirements are increased linearly by 0–15% when ASF share falls between 40% and 20%; and a full +15% increase is applied when ASF share is &lt;20%.

Nutrient reference values
Across the first two datasets, inadequate and excessive intakes are assessed using the harmonized nutrient reference values from Allen et al. (2020). For the third dataset (protein), inadequacy is assessed relative to the Estimated Average Requirement (EAR) defined by the U.S. Institute of Medicine (2005).

Dataset versions and file types
We provide two versions of each dataset: (1) a “full” version that includes all columns used for intermediate calculation; and (2) a “reduced” version that provides only the most important columns needed to describe subnational inadequate intake levels.

We provide each version as a CSV file for all users.

We also provide the GitHub repositories for each analysis with corresponding code: (1) baseline inadequate intake estimates; (2) fortification scenario estimates; and (3) protein estimates. See the PDF documentation attached for hyperlinks to Github repositories.


References
Passarelli S, Free CM, Allen LH, Batis C, Beal T, Biltoft-Jensen AP, Bromage S, Cao L, Castellanos-Guitiérrez A, Christensen T, Crispim SP, Dekkers A, De Ridder K, Kronsteiner-Gicevic S, Lee C, Li Y, Moursi M, Moyersoen I, Schmidhuber J, Shepon A, Viana DF, Golden CD (2022) Estimating national and sub-national habitual nutrient intake distributions of global diets. The American Journal of Clinical Nutrition 116(2): 551-560.

Passarelli S, Free CM, Shepon A, Beal T, Batis C, Golden CD (2024) Global estimation of dietary micronutrient inadequacies: a modeling analysis. The Lancet Global Health 12(10): e1590-e1599.

Friesen VM, Free CM, Adams KP, Bai Y, Costlow L, Dewey K, Masters WA, Mbuya MNN, Nordhagen S, Vasta F, Beal T. Impact of large-scale food fortification programs on micronutrient inadequacies and their implementation costs: a modelling analysis. In press at The Lancet Global Health.

De Nicola G, Beal T, Nordhagen S, Phillips SM, Passarelli S, Golden CD (2026) Estimating Protein Intake Inadequacy Worldwide. In preparation.