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

itemsetdataset

promptsetdataset

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 exlusion of covid vaccines records

Flowchart_criteria_for_all_vaccines

Flowchart of the exlusion of all vaccines records

D3_clean_all_vaccines

This dataset contains the records of all the curated doses of all vaccines in the instance listed in Table 4 of the SAP, including the curated covid vaccines. It is obtained from the original conceptsets datastes by replicating each vaccination record as many times as the indicators that it is used for, see the first example in the tab Example: a record with Vacco Id DIP-HIB-PER-POL-TET is replicated 3 times, once per the indicator DPT, once per the indicator HiB, and once for the indicator Pol. Then, each record is labelled with various exclusion citeria, most importantly, records with dats 30 daya apart form a previous record as marked as 'duplicates'. In the next step, all the record labelled as 'removed row' will be removed

D3_all_vaccines_curated

This dataset contains the records of all the curated doses of all roots of indicators in the instance listed in Table 7 of the SAP. Doses of covid vaccines are included. It is obtained by D3_clean_all_vaccines by excluding records that are duplicates or of bad quality, and after appending the curated covid vaccines

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

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_TD_all

contains the time-dependent evolution of the binary variable condition. Only changes of status are recorded, with date 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_events_complete

contains the time-dependent evolution of all events. Only changes of status are recorded, with date when the condition changes and period end; 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_covariates_complete

D3_study_population_target_cohorts

contains the list of persons in the study population, with study entry and exit dates, and entry and exit from all the cohorts for incidence/prevalence and for coverage

D4_counts_persontime_yearly

contains the persontime observed each year in all the strata from start to end of study

D4_counts_persontime_yearly_aggregated

contains the persontime observed each year in all the strata from start to end of study

D4_count_events_point_prevalence

NA

D4_events_point_prevalence_aggregated

NA

D4_count_events_period_prevalence

NA

D4_events_period_prevalence_aggregated

NA

D4_monthly_prevalence_vaccination_cohort

for each study cohort and each indicator: persons in the cohort at entry and monthly, monthly prevalence of use of vaccine, and cumulative prevalence of use of vaccine

D5_IR_yearly

NA

D5_IR_yearly_std

NA

D5_Pre_point_background

Point prevalence estimates will be calculated at the start of each year: the numerator is the persons with the disease in the year prior, denominator is all persons present at the start of each calendar year.

D5_Pre_period_background

Point prevalence estimates will be calculated at the start of each year: the numerator is the persons with the disease in the year prior, denominator is all persons present at the start of each calendar year.

D5_Pre_point_background_std

Point prevalence estimates will be calculated at the start of each year: the numerator is the persons with the disease in the year prior, denominator is all persons present at the start of each calendar year.

D5_Pre_period_background_std

period prevalence estimates will be calculated at the start of each year: the numerator is the persons with the disease in the year prior, denominator is all persons present at the start of each calendar year.

D5_vaccine_coverage_cohorts

for each study cohort and each indicator: monthly prevalence and estimated monthly prevalence with inverse-probability weighting (Braeye et al, Vaccine. 2020)

Table_1_flowchart

Flowchart

Table_2_code_counts

count of codes for each AESI or NCO

Table_3_population

characteristics at baseline