A Machine-Learning-Imputed Global Atlas of Ultra-Processed Food Supply Shares and PIF-Based Burden Estimates for Non-Communicable Diseases

Abstract excerpt

No open atlas of ultra-processed food supply exists with documented predictive validity across countries, and the global non-communicable disease burden linked to such foods, estimated under counterfactual exposure scenarios with transparently decomposed uncertainty, has not been quantified within a single framework. We built a machine-learning-imputed atlas of the share of dietary energy from ultra-processed food...

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• Computational Drug Discovery Methods
• COVID-19 Clinical Research Studies
• Nutrition, Genetics, and Disease
• Nutritional Studies and Diet

Identifiers and source

Literature Corpus work

8cc821f5-0edb-5d69-8ea5-d188d83b87a5

DOI

10.64898/2026.08.01.26359471