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

Published: 6 April 2023| Version 10 | DOI: 10.17632/g6b39zxck4.10
Contributors:
David Cundiff,

Description

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 davidkcundiff@gmail.com. Thanks.

Files

Steps to reproduce

Download the above files for NCDs analyses. Upload the dataset and SAS files into SAS software Note the descriptions of the files. Run the SAS files with the analysis database. Check the corresponding Excel files of Tables to see that they correspond with the SAS codes. These data accompany a preprint titled The EAT-Lancet Commission Planetary Health Diet compared with Institute of Health Metrics and Evaluation Global Burden of Disease Ecological Data Analysis 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 davidkcundiff@gmail.com

Institutions

University of Washington

Categories

Health Sciences, Public Health, Population Health

Licence