Autoimmune diseases following SARS-CoV-2 infection: Implications of COVID-19 vaccination. A cohort study of fifteen million people

This is the code and configuration for post-covid-autoimmune.

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The content has ONLY been made public to support the OpenSAFELY open science and transparency principles and to support the sharing of re-usable code for other subsequent users. No clinical, policy or safety conclusions must be drawn from the contents of this repository.

Repository navigation

• If you are interested in how we defined our code lists, look in the codelists folder.

• Analyses scripts are in the analysis directory:

  • If you are interested in how we defined our variables, we use study definition scripts to define three cohorts: pre-vaccination, vaccinated and unvaccinated. Study start dates (i.e., index) and end dates differ by cohort and are all described in the protocol. Hence, we have a study definition for each; these are written in python. Extracted data is then combined to create our final cohorts, in the preprocess data script.
  • This directory also contains all the R scripts that process, describe, and analyse the extracted data.

• The lib/ directory contains a list of active analyses.

• The project.yaml defines run-order and dependencies for all the analysis scripts. This file should not be edited directly. To make changes to the yaml, edit and run the create_project.R script which generates all the actions.

Manuscript

This manuscript is currently being drafted.

Code

The project.yaml defines project actions, run-order and dependencies for all analysis scripts. This file should not be edited directly. To make changes to the yaml, edit and run the create_project.R script instead. Project actions are then run securely using OpenSAFELY Jobs. Any published outputs from this project can be found at this link as well.

Below is a description of each action in the project.yaml. Arguments are denoted by {arg} in the action name.

• vax_eligibility_inputs
  • Runs metadates.R which creates metadata for aspects of the study design which are required for the generate_study_population actions.
  • Creates dataframes that contain dates for each phase of vaccination and conditions for defining JCVI vaccination groups.
• generate_study_population_{cohort}
  • There are three generate_study_population scripts that are run to create the three study populations: prevaccinated (prevax), vaccinated (vax) and electivively unvaccinated (unvax). These are study_definition_prevax.py, study_definition_vax.py, study_definition_unvax.py and study_definition_prelim.py.
  • These scripts are used to define JCVI groups, vaccine variables, variables used to apply eligibility criteria, outcome variables and covariate variables. Common variables used in all three scripts can be found in common_variables.py.
• preprocess data - {cohort}
  • Runs preprocess_data.R to apply dataframe tidying to input_{cohort}.rds (generated by generate_study_population_{cohort}.
  • Tidies vaccine variables, determines patient study index date, creates addtional variables (e.g. covid phenotype), tidies dataset and ensures all variables are in the correct format e.g. numeric, character etc.
• stage1_data_cleaning_{cohort}
  • Runs Stage1_data_cleaning.R.
  • Applies quality assurance rule and inclusion/exclusion criteria.
  • Outputted dataset is analysis ready.
• consort_{cohort}_midpoint6
  • Runs Stage1_data_cleaning.R.
  • Used to elaborate consort table, using study population size after each quality assurance, inclusion and exclusion criterion is applied.
• table1_{cohort}
  • Runs table1.R which calculates descriptive statistics for pre- and post-exposure events for all outcomes and subgroups.
  • Used for Table 1 in the manuscript.
• extendedtable1_{cohort}
  • Runs extendedtable1.R which calculates descriptive statistics for pre- and post-exposure events for all outcomes and subgroups.
  • Used for Extended Table 1 in the manuscript.
• table2_{cohort}
  • Runs table2.R which calculates pre- and post-exposure event counts and person days of follow-up for all outcomes and subgroups.
  • Used for Table 2 in the manuscript.
• venn - {cohort}
  • Runs venn.R.
  • Creates venn diagram data for all outcomes reporting where outcomes are sourced from i.e primary care, secondary care or deaths data.
• make_model_input-{name}
  • Runs make_model_input.R which prepares datasets for all the outcomes and subgroups.
  • Combines each outcome and subgroup in one formatted .rds file.
• describe_model_input-{name}
  • Runs describe_file.R which calculates counts, and descriptive statistics for the outcomes and covariates used in the cox models.
  • Combines all the information in one formatted .txt file.
• cox_ipw-{name}
  • Runs cox-ipw, a R reusable action for the OpenSAFELY framework.
  • Detailed descriptions of each arguments/options used to fit the cox models can be found in the README file.
• make_model_output
  • Runs make_model_output.R which combines all the R results in one formatted .csv file.

Creating the study population

In OpenSAFELY a study definition is a formal specification of the data that you want to extract from the OpenSAFELY database. This includes:

• the patient population (dataset rows).
• the variables (dataset columns).
• the expected distributions of these variables for use in dummy data.

Further details on creating the study population can be found in the OpenSAFELY documentation.

The contents of this repository MUST NOT be considered an accurate or valid representation of the study or its purpose. This repository may reflect an incomplete or incorrect analysis with no further ongoing work. The content has ONLY been made public to support the OpenSAFELY open science and transparency principles and to support the sharing of re-usable code for other subsequent users. No clinical, policy or safety conclusions must be drawn from the contents of this repository.

Output

Outputs follow OpenSAFELY naming conventions related to suppression rules by adding the suffix "_midpoint6". The suffix "_midpoint6_derived" means that the value(s) are derived from the midpoint6 values. Detailed information regarding naming conventions can be found here.

consort_*.csv

table1_*.csv

table2_*.csv

venn_*.csv

*model_output.csv

aerinput*.csv

About the OpenSAFELY framework

The OpenSAFELY framework is a Trusted Research Environment (TRE) for electronic health records research in the NHS, with a focus on public accountability and research quality. Read more at OpenSAFELY.org.

Licences

As standard, research projects have a MIT license.