D3_PERSONS

Contains the cleaned version of PERSONS, where birth date and death date are reconstituted as dates

D3_events_DEATH

Contains the deaths observed in the study population

D3_output_spells_category

Contains the spells exited from CreateSpells, i.e., all the continuous spells of observation period of each person, stratified per op_meaning. op_meaning is by default the same for all observation periods, and is set in 05_subpopulations_restricting_meanings for those data sources where the analysis is conducted on subpopulations having different sets of data banks

conceptsetdataset

These are multiple datasets, one per each conceptset, which is a value in the list c(conceptsets_exact_matching, conceptsets_children_matching), set in 07_algorithms. Each conceptset dataset is named after the conceptset. Each conceptset is associated to a list of codes. The dataset is obtained by retrieving records from the CDM bearing a code that match one of the codes in the codelist. The matching can be exact (for the conceptsets in conceptsets_exact_matching) or per ascendant (for conceptsets in conceptsets_children_matching) . Records are retrieved from the EVENTS table, but also from other tables which may bear a record, such as PROCEDURES or VACCINES

D3_clean_vaccines

This dataset contains all the records of a COVID vaccine, including their imputation and modifications and exclusion criteria. Exclusion criteria must be applied before using the variable in the next steps

D3_vaccines_curated

This dataset contains only the records of a COVID vaccine that enter the study

Flowchart_criteria_for_doses

Flowchart of the exclusion of covid vaccines records

D3_clean_spells

Contains the spells exited from CreateSpells plus their binary variables that are to be used for cleaning purposes version; spells that fall outside the interval between birth and death are cut, and op_start_date that start before the baby is 60 days are recasted to birth (to be checked with DAPs)

D3_selection_criteria_from_PERSONS_to_study_population

Contains the exclusion criteria to go from PERSONS to the study population

D4_study_population

Contains the list of persons in the study population, with study entry and exit dates

Flowchart_exclusion_criteria

Flowchart of the exclusion of PERSONS from D3_PERSONS to the study population

D3_events_OUTCOMESIM_simple

Contains the outcomes observed in the study population, including negative outcomes but excluding covid and complex algorithms

D3_events_OUTCOMECOMPL_complex

Contains the outcomes observed in the study population, including only complex algorithms

D3_events_ALL_OUTCOMES

Contains the outcomes observed in the study population, including negative outcomes but excluding covid

D3_covid_episodes

Contains the episodes of COVID observed in all persons in the study population. Each episode is separated from the next by at least 60 days

D3_covid_episodes_description

Contains the description of the episodes of COVID observed in all persons in the study population. Each episode is separated from the next by at least 60 days

QC_covid_episodes

Occurrence of components of covid, per meaning, to all persons in the study population

D3_covid_severity_components_hospitalisation

Contains the episodes of COVID in D3_covid_episodes that had level of severity 'intensive care unit (ICU)'

QC_covid_severity_components_hospitalisation

Occurrence of components of covid hospitalisation, per meaning, to all persons in the study population

D3_covid_severity_components_ICU

Contains the episodes of COVID in D3_covid_episodes that had level of severity 'hospitalised'

QC_covid_severity_components_ICU

Occurrence of components of covid ICU, per meaning, to all persons in the study population

D3_covid_severity_components_death

Contains the episodes of COVID in D3_covid_episodes that had level of severity 'death'

QC_covid_severity_components_death

Occurrence of components of covid death, per meaning, to all persons in the study population

D3_TD_variable_COVID

Contains the time-dependent evolution of the categorical variable COVID. Occurrences are recorded with their level of severity. The date is the date of the first information about the infection, information about severity may accrue across time

D3_covariates_ALL

Contains the covariates observed in the study populationin two points in time: at baseline, and at first vaccination (if any)

QC_all_components_OUTCOME

Occurrence of components of the outcome OUTCOME during 2019 (and to be dropped, during one year of lookback), per meaning, to all persons in the study population at 1/1/2019 or entering during 2019

D3_Total_study_population

Study population with entry and exit dates, date of birth, gender, dates and type of vaccinations

D3_study_population_by_dose

This is a time-dependent dataset, reporting status of vaccination in periods of time: each person is observed as many times as the vaccines they have received + the time since baseline. Moreover, date of first covid infection ever is stored

D3_study_population_by_window_and_dose

Time period after each dose (each week, from 1 to 4)

D3_study_population_SCRI

Contains the baseline characteristics of the study population, for the cohort study

D3_study_population_cohort

Contains the baseline characteristics of the study population, for the cohort study

D3_TD_variable_condition

Contains the time-dependent evolution of the binary variable condition. Only changes of status are recorded, with date of when the condition changes; the components of the condition last 365 days if they are diagnosis, and 90 days if they are drug proxies; unique spells are created when the algorithm is 1 (if either a dianosis or a drug proxy is active), and the algorithm is reverted to values 0 whenever no component is active

D3_TD_variable_comedication

Contains the time-dependent evolution of the binary variable comedication. Only changes of status are recorded, with date of when the condition changes; a recording of the medication lasts 90 days and the algorithm is reverted to values 0 whenever no drug records active

D3_TD_variable_NUMBER_CONDITIONS

Contains the time-dependent evolution of the ordinal variable NUMBER_CONDITIONS, which counts the number of comorbidities in a list of 9 associated with covid severity (see specifications in the Data Model tab). Only changes of status are recorded, with date of change

D4_count_events_windows

Contains the counts of the AESIs observed within a specific time window (28 days) after each vaccine

D4_count_events_windows_aggregated

Contains the counts of the AESIs observed within a specific time window (28 days) after each vaccine

D4_persontime_monthly

Contains the persontime observed each month in all the strata of vaccination, per dose and brand, and per previous exposure to COVID, including non vaccinated, from start to end of study

D4_persontime_monthly_aggregated

Contains the persontime observed each month in all the strata of vaccination, per dose and brand, and per previous exposure to COVID, including non vaccinated, from start to end of study

D4_persontime_background

Contains the persontime before vaccination, stratified per calendar month and past covid, and the occurrence of the AESIs

D4_persontime_background_aggregated

Contains the persontime before vaccination, stratified per calendar month and past covid, and the occurrence of the AESIs

D5_IR_background

Contains the persontime observed each month in all the strata of vaccination, per dose and brand, and per previous exposure to COVID, including non vaccinated, from start to end of study

D5_IR_background_std

Background rates of AESIs, per presence or absence of history of COVID, age and sex, month and year

Table 1

Flowchart

Table 2

Characteristics at baseline

Table 3

Characteristics at baseline

Table 4

Characteristics at first vaccination

Table 5

Distance between vaccination doses

Table 6

Background rates