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

本数据集包含复现关联论文《10个国家中可行膳食转变可带来健康收益并伴随环境权衡》中所有图表所需的代码与作者生成的衍生结果。

CONTENTS:
代码部分：包含用于线性规划优化、比较风险评估（Comparative Risk Assessment, CRA）、蒙特卡洛不确定性分析、敏感性分析以及图表生成的Python脚本，同时提供用于环境配置的配置文件与requirements.txt文件。
衍生结果：涵盖10个国家（巴西、中国、埃及、埃塞俄比亚、印度、印度尼西亚、南非、英国、美国、越南）的国家级输出文件（CSV格式），包括规避的伤残调整生命年（Disability-Adjusted Life Years, DALYs）、膳食转变情况、环境影响变化（温室气体（Greenhouse Gas, GHG）、土地、水资源、富营养化）、成本分析、约束分解以及敏感性与不确定性汇总结果。
图表与表格：对应正文中补充/扩展数据材料的已渲染输出文件（适用格式为PDF/PNG），包含图形摘要（若存在）。

STUDY OVERVIEW:
本研究采用约束线性规划方法，识别在文化层面可行的膳食调整方案（相较于基线摄入量上下浮动50%的范围），以改善全球10个国家（共计41.5亿人口）的健康结局。健康结局与主要膳食转变汇总结果基于12个与全球疾病负担（Global Burden of Disease, GBD）对齐的膳食因素进行报告，而环境与成本结局则通过优化方案所对应的食品类别（食品篮子）数量变化计算得出。本研究采用联合国粮食及农业组织（Food and Agriculture Organization of the United Nations, FAO）GLEAM v3.0版本反刍动物产品的区域特定排放强度数据，以及Poore与Nemecek（2018）提出的其他食品的系数，经估算，优化后的膳食方案每年可规避3875万伤残调整生命年，并使人均年温室气体（GHG）排放量减少187千克二氧化碳当量（以人口加权平均值计）。作为对照，若采用全球平均系数则可得到人均年减排132千克二氧化碳当量的结果，即采用区域核算得到的气候影响估算值在数值上比全球平均方法高42%。

DATA SOURCES (not redistributed here; publicly available):
数据来源（本数据集未重新分发，均为公开可得资源）：
疾病负担数据：全球疾病负担（GBD，国际健康计量与评估协会IHME）
膳食摄入数据：2018年全球膳食数据库（Global Dietary Database, GDD）
环境系数数据：FAO GLEAM v3.0版本；Poore与Nemecek（2018）
食品供应数据：FAO食品平衡表
食品成本数据：FAO CoAHD

REPRODUCIBILITY:
可复现性说明：详见REPRODUCIBILITY.md文件，其中包含基于本数据集提供的代码与公开可用输入数据重新生成所有结果的分步指南。

LICENSE: CC BY 4.0
授权协议：知识共享署名4.0国际许可协议（CC BY 4.0）