Neurodegenerative 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-neurodegenerative.

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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 vacciantion 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

Variable	Description
Description	criterion applied to cohort
N_midpoint6	number of people in the cohort after criterion applied time
removed	number of people removed due to criterion being applied

table1_*.csv

Variable	Description
Characteristic	patient characteristic under consideration
Subcharacteristic	patient sub characteristic under consideration
N (%) midpoint6 derived	number of people with characteristic, alongside % of total
COVID-19 diagnoses midpoint6	number of people with characteristic and COVID-19

table2_*.csv

Variable	Description
name	unique identifier for analysis
cohort	cohort used for the analysis
exposure	exposure used for the analysis
outcome	outcome used for the analysis
analysis	string to identify whether this is the ‘main’ analysis or a subgroup
unexposed_person_days	number of person days before or without exposure in the analysis
unexposed_events_midpoint6	number of unexposed people with the outcome in the analysis
exposed_person_days	number of person days after exposure in the analysis
exposed_events_midpoint6	number of exposed people with the outcome in the analysis
total_person_days	number of person days in the analysis
total_events_midpoint6_derived	number of people with the outcome in the analysis
day0_events_midpoint6	number of people with the exposure and outcome on the same day
total_exposed_midpoint6	number of people with the exposure in the analysis
sample_size_midpoint6	number of people in the analysis

venn_*.csv

Variable	Description
outcome	outcome under consideration
only_snomed_midpoint6	outcome identified in primary care only
only_hes_midpoint6	outcome identified in secondary care only
only_death_midpoint6	outcome identified in death registry only
snomed_hes_midpoint6	outcome identified in primary and secondary care
snomed_death_midpoint6	outcome identified in primary care and death registry
hes_death_midpoint6	outcome identified in secondary care and death registry
snomed_hes_death_midpoint6	outcome identified in primary care, secondary care, and death registry
total_snomed_midpoint6	total outcomes identified in primary care
total_hes_midpoint6	total outcomes identified in secondary care
total_death_midpoint6	total outcomes identified in death registry
total_midpoint6_derived	total outcomes identified
cohort	cohort under consideration

*model_output.csv

Variable	Description
name	unique identifier for analysis
cohort	cohort used for the analysis
outcome	outcome used for the analysis
analysis	string to identify whether this is the ‘main’ analysis or a subgroup
error	captured error message if analysis did not run
model	string to identify whether the model adjustment
term	string to identify the term in the analysis
lnhr	log hazard ratio for the analysis
se_lnhr	standard error for the log hazard ratio for the analysis
hr	hazard ratio for the analysis
conf_low	lower confidence limit for the analysis
conf_high	higher confidence limit for the analysis
N_total_midpoint6	total number of people in the analysis
N_exposed_midpoint6	total number of people with the exposure in the analysis
N_events_midpoint6	total number of people with the outcome following exposure in the analysis
person_time_total	total person time included in the analysis
outcome_time_median	median time to outcome following exposure
strata_warning	string to identify strata variables that may cause model faults
surv_formula	survival formula for the analysis

aer_input_*.csv

Variable	Description
aer_sex	sex subgroup under consideration
aer_age	age subgroup under consideration
analysis	string to identify whether this is the ‘main’ analysis or a subgroup
cohort	cohort used for the analysis
outcome	outcome used for the analysis
unexposed_person_days	unexposed person days in the age/sex grouping
unexposed_events_midpoint6	number of events in unexposed people in the age/sex grouping
total_exposed_midpoint6	total number of people with the exposure in the age/sex grouping
sample_size_midpoint6	total number of people in the age/sex grouping

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.