Data and Code for: Feasible dietary shifts yield health gains with environmental trade-offs in ten countries

This dataset provides the code and author-generated derived outputs required to reproduce all figures and tables in the associated manuscript: “Feasible dietary shifts yield health gains with environmental trade-offs in ten countries”.

CONTENTS:

Code: Python scripts for linear programming optimization, comparative risk assessment (CRA), Monte Carlo uncertainty analysis, sensitivity analyses, and figure/table generation. Includes configuration files and requirements.txt for environment setup.

Derived Results: Country-level outputs (CSV) for 10 countries (Brazil, China, Egypt, Ethiopia, India, Indonesia, South Africa, UK, USA, Vietnam), including DALYs averted, dietary transitions, environmental impact changes (GHG, land, water, eutrophication), cost analysis, constraint decomposition, and sensitivity/uncertainty summaries.

Figures & Tables: Rendered outputs (PDF/PNG where applicable) corresponding to the main text and supplementary/extended data materials, including the graphical abstract (if present).

STUDY OVERVIEW:
We applied constrained linear programming to identify culturally feasible dietary modifications (±50% of baseline intake) that improve health outcomes across ten countries (4.15 billion people). Health outcomes and the main dietary transition summaries are reported using 12 GBD-aligned dietary factors, while environmental and cost outcomes are computed from changes in food-category (food-basket) quantities implied by the optimized solution. Using FAO GLEAM v3.0 region-specific emission intensities for ruminant products and Poore & Nemecek (2018) coefficients for other foods, we estimate that optimized diets could avert 38.75 million DALYs annually and reduce greenhouse gas (GHG) emissions by 187 kg CO₂-eq per capita per year (population-weighted mean). For comparison, using global-average coefficients yields 132 kg CO₂-eq per capita per year, i.e., regional accounting produces climate estimates 42% larger in magnitude.

DATA SOURCES (not redistributed here; publicly available):

Disease burden: GBD (IHME)
Dietary intake: Global Dietary Database (GDD) 2018
Environmental coefficients: FAO GLEAM v3.0; Poore & Nemecek (2018)
Food supply: FAO Food Balance Sheets
Food costs: FAO CoAHD
REPRODUCIBILITY:
See REPRODUCIBILITY.md for step-by-step instructions to regenerate all outputs from the provided code and publicly available input data.

LICENSE: CC BY 4.0