Global Burden of Disease analysis dataset of noncommunicable disease outcomes, risk factors, and SAS codes

This formatted dataset (AnalysisDatabaseGBD) originates from raw data files from the Institute of Health Metrics and Evaluation (IHME) Global Burden of Disease Study (GBD2017) 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 preprint: The EAT-Lancet Commission Planetary Health Diet compared with Institute of Health Metrics and Evaluation Global Burden of Disease Ecological Data Analysis. The data include the following: 1. Analysis database 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). 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 1, 2, 3 and Supplementary Tables 5 and 6 5. SAS code for deriving the multiple regression formula in Table 4. 6. SAS code for deriving the multiple regression formula in Table 5 7. SAS code for deriving the multiple regression formula in Supplementary Table 7 8. SAS code for deriving the multiple regression formula in Supplementary Table 8 9. The Excel files that accompanied the above SAS code to produce the tables For questions, please email [e-mail]. Thanks.本格式化数据集（AnalysisDatabaseGBD）源自华盛顿大学附属健康指标与评估研究所（Institute of Health Metrics and Evaluation, IHME）发布的2017年全球疾病负担研究（Global Burden of Disease Study 2017, GBD2017）原始数据文件。我们为IHME的志愿协作伙伴，并非IHME或华盛顿大学的雇员。 本数据集采用人口加权的GBD2017数据，覆盖15-69岁男性与女性队列，涵盖非传染性疾病（noncommunicable diseases, NCDs）、身体质量指数（body mass index, BMI）、心血管疾病（cardiovascular disease, CVD）及其他健康结局，同时包含相关饮食、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，是探究健康结局与风险因素间的单变量及多元回归关联。本研究假设为：可成功构建非传染性疾病、BMI、CVD及其他健康结局与其归因风险的预测模型。 本全球疾病负担数据关联的预印本论文为：《EAT-柳叶刀委员会行星健康饮食与健康指标与评估研究所全球疾病负担生态数据分析的对比分析》。 本数据集包含以下内容： 1. 人口加权GBD2017数据分析数据库：涵盖全球195个国家15-69岁男性与女性队列的40余种健康风险因素、非传染性疾病年死亡率（单位：每10万人），核心结局变量包含100余种非传染性疾病，同时涵盖20余种单一非传染性疾病（如缺血性心脏病、结肠癌等）。 2. 用于将分析数据库导入SAS软件的文本文件 3. 用于格式化分析数据库以开展数据分析的SAS代码 4. 用于生成表1、表2、表3及补充表5、表6的SAS代码 5. 用于生成表4中多元回归公式的SAS代码 6. 用于生成表5中多元回归公式的SAS代码 7. 用于生成补充表7中多元回归公式的SAS代码 8. 用于生成补充表8中多元回归公式的SAS代码 9. 配合上述SAS代码生成表格的Excel文件 如有疑问，请致邮[e-mail]。 致谢。