Recoded data and R package to data-mine associations between variables in a cohort of Mexican Covid-19 patients
Creators: name: Martínez, Octavio
Publisher: name: Zenodo
Year: 2023
Subjects: Covid19
Mexico
Statistical associations
Fisher's test

description: Contains recoded data for more than 25 millions of Covid-19 patients, originally published by the Mexican Government (Datos Covid19 México) between January 2020 and March 2023. A set of 26 selected variables for a total of 25,820,354 patients were recoded into an R binary file (File "<strong><em>come5.RData</em></strong>" which contains the data.frame "<strong><em>come5</em></strong>"). Names and other characteristics of the 26 variables can be seen in the R data.frame "<em><strong>vars.come5</strong></em>", which can be uploaded from the R file "<em><strong>vars.come5.RData</strong></em>". This upload also contains an R package named "<em><strong>CovidAssociations</strong></em>", which allows data mining of associations between pairs of variables in the cohort. Briefly, there are 3,899 results from different pairs of variables in 25 different sets of patients. <strong>Files and their use</strong> "CovidAssociations_1.0.tar.gz" - This file contains the R package CovidAssociations. After downloading that file to your working directory, you can install it by typing in the R command window <pre><code>install.packages("CovidAssociations_1.0.tar.gz", repos = NULL, type="source")</code></pre> After that type <pre><code>library(CovidAssociations)</code></pre> to make the package available. "CovidAssociations-manual.pdf" - Contains the PDF version of the package manual. "CovidAssocTutorial.pdf" - Contains the PDF version of a tutorial for the package. 4. "come5.RData" - This contains the data.frame with the recoded data. It is not necessary for the <em>CovidAssociations</em> package, but it is included if you want to perform further analyses. Having that file in your working directory you can load it and see some of their characteristics of the data.frame by typing in your R command window: <pre><code>load("come5.RData") # Loads the "come5" data.frame class(come5) # class (will be data.frame) dim(come5) # Number of rows (patients) and columns (variables) names(come5) # Names of the variables.</code></pre> 5. "vars.come5.RData" - Contains characteristics of the variables in the data.frame "come5". Having that file in your working directory you can load it and see some of their characteristics of the data frame by typing in your R command window: <pre><code>load("vars.come5.RData") # Loads the "vars.come5" data.frame class(vars.come5) # class (will be data.frame) dim(vars.come5) # Number of rows (patients) and columns (variables) names(vars.come5) # Names of the variables. head(vars.come5, 2) # Show the first two rows. table(vars.come5$v.class) # Classes of variables table(vars.come5$v.category) # Categories of variables</code></pre> <br> <strong>Note:</strong> I am preparing for submission a manuscript entitled "<strong>Associations between variables in a cohort of Mexican Covid-19 patients</strong>". As soon as this MS is submitted, I will add the reference here. <strong>ABSTRACT FOR THE MANUSCRIPT TO BE SUBMITTED</strong> <strong>Background:</strong> Here we analyze a vast cohort comprising over 25 million COVID-19 patients collected by the Mexican Government between 2020 and 2023. The dataset contains valuable information on attributes and comorbidities, enabling us to investigate clinically relevant associations. <strong>Objective:</strong> Our objective is to unravel the intricate network of relationships between variables within the entire cohort and specific patient subsets, with a particular focus on associations involving fatalities. <strong>Methods:</strong> We employ the Odds Ratio (OR), estimated from Fisher’s test on 2×2 contingency tables, as a measure of association between variable pairs. We compute a total of 3,899 such measures by examining all possible variable pairs within 25 patient groups. The results are ordered and presented as networks, where variables are depicted as nodes (vertices) and associations at a specific OR threshold are represented as links (edges). The recoded data, along with the results and data mining functions, are publicly accessible. <strong>Results:</strong> Our findings demonstrate that hospitalization, gender, and age significantly influence disease outcomes, as do comorbidities such as pneumonia, chronic renal problems, diabetes, and hypertension. Interestingly, we observe that the associations of comorbidities are diminished in pregnant women, suggesting a pro- tective effect of pregnancy against the detrimental impact of these comorbidities on COVID-19 patients. <strong>Conclusion:</strong> Our analysis of variables in Mexican COVID-19 patients reveals a complex network of as- sociations. By visualizing these associations as networks, we provide a clear and accessible representation that enhances our understanding of the factors contributing to fatalities in this population.