Global Burden of Disease analysis dataset of noncommunicable disease outcomes, risk factors, and SAS codes
This formatted dataset (wtedCVDRfsCov2017) 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 disease (NCD), body mass index (BMI), cardiovascular disease (CVD), and other health outcomes and associated dietary, metabolic, and other risk factors. The purpose of creating this 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 NCD, BMI, CVD, and other health outcomes with their associated risk factors. We compared the associations of GBD NCD outcomes and dietary risk factors with the EAT-Lancet Commission Planetary Health Diet recommendations. We derived a BMI multiple regression risk factor formula that satisfied all nine Bradford Hill causality criteria for epidemiology research. We found that animal products and added fats are negatively correlated with CVD early deaths worldwide but positively correlated with CVD early deaths with high quantities of animal foods and added fats. We interpret this as showing that optimal cardiovascular outcomes come with moderate (not low and not high) intakes of animal foods and added fats. For questions, please email email@example.com. Thanks.
Steps to reproduce
Download the above files for NCD, BMI and/or CVD analyses. Upload the dataset and SAS files into SAS software Note the descriptions of the files. Run the SAS files with the dataset Check the corresponding Excel files of Tables to see that they correspond with the SAS codes. The preprint for The EAT-Lancet Commission Planetary Health Diet compared with Institute of Health Metrics and Evaluation Global Burden of Disease ecological data analysis—Dietary risk factors correlated with noncommunicable disease early deaths: pending The preprint of "Artificial intelligence analytics applied to body mass index global burden of disease worldwide cohort data derives a multiple regression formula with population attributable fraction risk factor coefficients testable by all nine Bradford Hill causality criteria" : https://www.medrxiv.org/content/10.1101/2020.07.27.20162487v2 The preprint of Global Burden of Disease worldwide cohort analysis of dietary and other risk factors for cardiovascular diseases--lipid hypothesis versus fat-soluble vitamin hypothesis: https://www.medrxiv.org/content/10.1101/2021.04.17.21255675v6 For questions, email firstname.lastname@example.org