Sex-Divergent Trends in Early-Onset Gastrointestinal Cancer in the United States, 1999 to 2019: analysis code and cached CDC WONDER data
Year: 2026
Type: software
Abstract: Analysis code and cached source data for the study Sex-Divergent Trends in Early-Onset Gastrointestinal Cancer in the United States, 1999 to 2019: Evidence Against a Purely Diagnostic Explanation. The archive contains three directories: code/ the complete R pipeline, 20 stages declared in pipeline.R. Rscript run_all.R reproduces every derived estimate, figure and table from the cached data. derived/ every derived estimate reported in the manuscript, as CSV. input_data/ the complete CDC WONDER server responses behind every reported number, cached at query time, so any value can be checked without re-querying. Incidence came from United States Cancer Statistics and deaths from the National Vital Statistics System, both retrieved through the CDC WONDER application programming interface (https://wonder.cdc.gov). Those data are publicly available and require no application or data use agreement, and as United States federal government output they are in the public domain. The MIT licence applies to the R code in this archive. No individual-level or identifiable data are included. All counts are aggregate, and cells below the CDC disclosure threshold of 16 are withheld at source. How to run. Unzip, then from the code/ directory run Rscript run_all.R under R 4.6.0 or later. The pipeline rebuilds every derived estimate, figure and table from the cached responses in input_data/, with no need to re-query CDC WONDER. Licence. The R code in this archive is released under the MIT licence. The cached CDC WONDER responses are United States federal government output and are in the public domain. How to cite. Nyambod FT, Msemakweli JG, Claris A, Onesphore M, Okpanachi V. Sex-Divergent Trends in Early-Onset Gastrointestinal Cancer in the United States, 1999 to 2019: analysis code and cached CDC WONDER data. Zenodo; 2026. doi:10.5281/zenodo.22086494
Institutions: Johns Hopkins University, University of Arizona
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7204133308