The EAT-Lancet Commission’s Planetary Health Diet Compared with the IHME GBD Ecological Data Analysis: data, Excel files, and SAS codes

An analysis database of Global Burden of Disease 2017 risk factor and health outcome data (GBD2017) originates from raw data files from the Institute of Health Metrics and Evaluation (IHME) affiliated with the University of Washington. We are volunteer collaborators with IHME and not employed by IHME or the University of Washington.

The population weighted GBD2017 data are on male and female cohorts ages 15-69 years including noncommunicable diseases (NCDs), body mass index (BMI), cardiovascular disease (CVD), and other health outcomes and associated dietary, metabolic, and other risk factors.
The purpose of creating this population-weighted, formatted database is to explore the univariate and multiple regression correlations of health outcomes with risk factors. Our research hypothesis is that we can successfully model NCDs, BMI, CVD, and other health outcomes with their attributable risks.

These Global Burden of disease data relate to the paper in Cureus: The EAT-Lancet Commission Planetary Health Diet compared with Institute of Health Metrics and Evaluation Global Burden of Disease Ecological Data Analysis: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10325883/
The data include the following:
1. The Excel files that accompanied the above SAS code to produce the tables
2. A text file to import the analysis database into SAS
3. The SAS code to format the analysis database to be used for analytics
4. SAS code for deriving Tables 11-19 of EAT Lancet v GBD data
5. Analysis database (wtedCVDRfsCov2017) of population weighted GBD2017 data that includes over 40 health risk factors, noncommunicable disease deaths/100k/year of male and female cohorts ages 15-69 years from 195 countries (the primary outcome variable that includes over 100 types of noncommunicable diseases) and over 20 individual noncommunicable diseases (e.g., ischemic heart disease, colon cancer, etc).

For questions, please email [e-mail].
Thanks.

本分析数据库源于华盛顿大学健康计量与评估研究所（IHME）提供的全球疾病负担2017年风险因素与健康结果数据（GBD2017）的原始数据文件。我方与IHME为志愿协作关系，非IHME或华盛顿大学雇员。该人口加权GBD2017数据涵盖了15至69岁的男性和女性队列，包括非传染性疾病（NCDs）、体质指数（BMI）、心血管疾病（CVD）及其他健康结果及其相关的饮食、代谢及其他风险因素。构建此人口加权、格式化数据库的目的是探讨健康结果与风险因素之间的单变量和多变量回归相关性。我们的研究假设是，我们能够成功地模拟NCDs、BMI、CVD及其他健康结果及其归因风险。

这些全球疾病负担数据与Cureus杂志上的论文《EAT-Lancet委员会行星健康饮食与健康计量与评估研究所全球疾病负担生态数据分析》相关：https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10325883/
数据包括以下内容：
1. 伴随上述SAS代码的Excel文件，用于生成表格
2. 用于将分析数据库导入SAS的文本文件
3. 用于格式化分析数据库以供分析的SAS代码
4. 用于从EAT Lancet与GBD数据中推导第11至19张表格的SAS代码
5. 包含超过40种健康风险因素、非传染性疾病死亡人数/每10万人/年（包括超过100种非传染性疾病的主要结果变量）及超过20种个别非传染性疾病（例如，缺血性心脏病、结肠癌等）的GBD2017年人口加权分析数据库（wtedCVDRfsCov2017）。

如有疑问，请发送电子邮件至[e-mail]。
感谢。