BY-COVID - WP5 - Baseline Use Case: COVID-19 vaccine effectiveness assessment - Common Data Model Specification
Creators: name: Estupiñán-Romero, Francisco
name: Van Goethem, Nina
name: Meurisse, Marjan
name: González-Galindo, Javier
name: Bernal-Delgado, Enrique
Publisher: name: Zenodo
Year: 2022
Subjects: COVID-19
vaccines
comparative effectiveness
causal inference
international comparison
SARS-CoV-2
common data model
directed acyclic graph
synthetic data

description: This publication corresponds to the <strong>Common Data Model (CDM) specification</strong> of the Baseline Use Case proposed in T.5.2 (WP5) in the BY-COVID project on “<em>COVID-19 Vaccine(s) effectiveness in preventing SARS-CoV-2 infection.</em>” <strong>Research Question: “</strong><em>How effective have the SARS-CoV-2 vaccination programmes been in preventing SARS-CoV-2 infections?</em>” <strong>Intervention (exposure):</strong> COVID-19 vaccine(s) <strong>Outcome:</strong> SARS-CoV-2 infection <strong>Subgroup analysis:</strong> Vaccination schedule (<em>type of vaccine</em>) <strong>Study Design: </strong>An observational retrospective longitudinal study to assess the effectiveness of the COVID-19 vaccine(s) in preventing SARS-CoV-2 infections using routinely collected social, health and care data from several countries. A causal model was established using <strong>Directed Acyclic Graphs (DAGs)</strong> to map domain knowledge, theories and assumptions about the causal relationship between exposure and outcome. The DAG developed for the research question of interest is shown below. <strong>Cohort definition:</strong> All people, eligible to be vaccinated (from 5 to 115 years old, included) or with, at least, one dose of a SARS-CoV-2 vaccine (any of the available brands) having or not a previous SARS-CoV-2 infection. <strong>Inclusion criteria:</strong> All people vaccinated with at least one dose of the COVID-19 vaccine (any of the available brands) in an area of residence. Any person eligible to be vaccinated (from 5 to 115 years old, included) with a positive diagnosis (irrespective of the type of test) for SARS-CoV-2 infection (COVID-19) during the period of study. <strong>Exclusion criteria: </strong>People not eligible for the vaccine (from 0 to 4 years old, included) <strong>Study period:</strong> From the date of the first documented SARS-CoV-2 infection in each country to the most recent date in which data is available at the time of analysis. Roughly from 01-03-2020 to 30-06-2022, depending on the country. <strong>Files </strong>included in this publication: <strong><em>Causal model</em></strong> (responding to the research question) COVID-19 vaccine effectiveness causal model v.1.0.0 (HTML) - Interactive report showcasing the structural causal model (DAG) to answer the research question COVID-19 vaccine effectiveness causal model v.1.0.0 (QMD) - Quarto RMarkdown script to produce the structural causal model <strong><em>Common data model specification </em></strong>(following the causal model) COVID-19 vaccine effectiveness data model specification (XLXS) - Human-readable version (Excel) COVID-19 vaccine effectiveness data model specification dataspice (HTML) - Human-readable version (interactive report) COVID-19 vaccine effectiveness data model specification dataspice (JSON) - Machine-readable version <strong><em>Synthetic dataset </em></strong>(complying with the common data model specifications) COVID-19 vaccine effectiveness synthetic dataset (CSV) [UTF-8, pipe | separated, N~650,000 registries] COVID-19 vaccine effectiveness synthetic dataset EDA (HTML) - Interactive report of the exploratory data analysis (EDA) of the synthetic dataset COVID-19 vaccine effectiveness synthetic dataset EDA (JSON) - Machine-readable version of the exploratory data analysis (EDA) of the synthetic dataset COVID-19 vaccine effectiveness synthetic dataset generation script (IPYNB) - Jupyter notebook with Python scripting and commenting to generate the synthetic dataset
description: The scripts (software) accompanying the data model specification are offered "as-is" without warranty and disclaiming liability for damages resulting from using it. The software is released under the CC-BY-4.0 licence, which permits you to use the content for almost any purpose (but does not grant you any trademark permissions), so long as you note the license and give credit.