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Job request: 24922

Organisation:
King's College London
Workspace:
inflammatory_rheum
ID:
eoyucpftmp3k6wwf
View repo View project.yaml

This page shows the technical details of what happened when the authorised researcher Mark Russell requested one or more actions to be run against real patient data in the project, within a secure environment.

By cross-referencing the list of jobs with the pipeline section below, you can infer what security level various outputs were written to. Researchers can never directly view outputs marked as highly_sensitive ; they can only request that code runs against them. Outputs marked as moderately_sensitive can be viewed by an approved researcher by logging into a highly secure environment. Only outputs marked as moderately_sensitive can be requested for release to the public, via a controlled output review service.

Jobs

Pipeline

Show project.yaml 
version: '4.0'

actions:
             
  generate_dataset_incidence:
    run: ehrql:v1 generate-dataset analysis/dataset_definition_incidence.py --output output/dataset_incidence.csv
    outputs:
      highly_sensitive:
        cohort: output/dataset_incidence.csv 

  generate_dataset_eia:
    run: ehrql:v1 generate-dataset analysis/dataset_definition_eia.py --output output/dataset_eia.csv
    needs: [generate_dataset_incidence]
    outputs:
      highly_sensitive:
        cohort: output/dataset_eia.csv       

  generate_measures_2016:
    run: ehrql:v1 generate-measures analysis/dataset_definition_incidence_measures.py
      --output output/measures/measures_incidence_2016.csv
      --
      --start-date "2016-04-01"
      --intervals 12
    needs: [generate_dataset_incidence]
    outputs:
      moderately_sensitive:
        measure_csv: output/measures/measures_incidence_2016.csv

  generate_measures_2017:
    run: ehrql:v1 generate-measures analysis/dataset_definition_incidence_measures.py
      --output output/measures/measures_incidence_2017.csv
      --
      --start-date "2017-04-01"
      --intervals 12
    needs: [generate_dataset_incidence]
    outputs:
      moderately_sensitive:
        measure_csv: output/measures/measures_incidence_2017.csv             

  generate_measures_2018:
    run: ehrql:v1 generate-measures analysis/dataset_definition_incidence_measures.py
      --output output/measures/measures_incidence_2018.csv
      --
      --start-date "2018-04-01"
      --intervals 12
    needs: [generate_dataset_incidence]
    outputs:
      moderately_sensitive:
        measure_csv: output/measures/measures_incidence_2018.csv

  generate_measures_2019:
    run: ehrql:v1 generate-measures analysis/dataset_definition_incidence_measures.py
      --output output/measures/measures_incidence_2019.csv
      --
      --start-date "2019-04-01"
      --intervals 12
    needs: [generate_dataset_incidence]
    outputs:
      moderately_sensitive:
        measure_csv: output/measures/measures_incidence_2019.csv

  generate_measures_2020:
    run: ehrql:v1 generate-measures analysis/dataset_definition_incidence_measures.py
      --output output/measures/measures_incidence_2020.csv
      --
      --start-date "2020-04-01"
      --intervals 12
    needs: [generate_dataset_incidence]
    outputs:
      moderately_sensitive:
        measure_csv: output/measures/measures_incidence_2020.csv

  generate_measures_2021:
    run: ehrql:v1 generate-measures analysis/dataset_definition_incidence_measures.py
      --output output/measures/measures_incidence_2021.csv
      --
      --start-date "2021-04-01"
      --intervals 12
    needs: [generate_dataset_incidence]
    outputs:
      moderately_sensitive:
        measure_csv: output/measures/measures_incidence_2021.csv

  generate_measures_2022:
    run: ehrql:v1 generate-measures analysis/dataset_definition_incidence_measures.py
      --output output/measures/measures_incidence_2022.csv
      --
      --start-date "2022-04-01"
      --intervals 12
    needs: [generate_dataset_incidence]
    outputs:
      moderately_sensitive:
        measure_csv: output/measures/measures_incidence_2022.csv

  generate_measures_2023:
    run: ehrql:v1 generate-measures analysis/dataset_definition_incidence_measures.py
      --output output/measures/measures_incidence_2023.csv
      --
      --start-date "2023-04-01"
      --intervals 12
    needs: [generate_dataset_incidence]
    outputs:
      moderately_sensitive:
        measure_csv: output/measures/measures_incidence_2023.csv

  generate_measures_2024:
    run: ehrql:v1 generate-measures analysis/dataset_definition_incidence_measures.py
      --output output/measures/measures_incidence_2024.csv
      --
      --start-date "2024-04-01"
      --intervals 12
    needs: [generate_dataset_incidence]
    outputs:
      moderately_sensitive:
        measure_csv: output/measures/measures_incidence_2024.csv

  incidence_disease:
    run: stata-mp:latest analysis/001_disease_incidence.do
    needs: [generate_dataset_incidence, generate_measures_2016, generate_measures_2017, generate_measures_2018, generate_measures_2019, generate_measures_2020, generate_measures_2021, generate_measures_2022, generate_measures_2023, generate_measures_2024]
    outputs:
      moderately_sensitive:
        log1: logs/incidence_disease.log   
        table1: output/tables/baseline_table_rounded.csv
        table2: output/tables/incidence_count_*.csv
        table3: output/tables/incidence_count_p_*.csv
        table4: output/tables/incidence_rates_rounded.csv
        figure1: output/figures/count_inc_*.svg      
        figure2: output/figures/count_inc_p_*.svg
        figure3: output/figures/inc_rate_*.svg

  run_sarima:
    run: r:latest analysis/200_sarima.R
    needs: [incidence_disease]
    outputs:
      moderately_sensitive:
        log1: logs/sarima_log.txt   
        figure1: output/figures/raw_pre_covid_*.svg
        figure2: output/figures/differenced_pre_covid_*.svg
        figure3: output/figures/seasonal_pre_covid_*.svg
        figure4: output/figures/raw_acf_*.svg
        figure5: output/figures/differenced_acf_*.svg
        figure6: output/figures/seasonal_acf_*.svg
        figure7: output/figures/auto_residuals_*.svg
        figure8: output/figures/obs_pred_*.svg
        table1: output/tables/change_incidence_byyear.csv
        table2: output/tables/values_*.csv

  eia_cleaning:
    run: stata-mp:latest analysis/100_eia_cleaning.do
    needs: [generate_dataset_incidence, generate_dataset_eia]
    outputs:
      highly_sensitive:
        log1: logs/eia_dataset.log   
        data1: output/data/file_eia_all.dta

Timeline

  • Created:

  • Started:

  • Finished:

  • Runtime: 00:27:31

These timestamps are generated and stored using the UTC timezone on the TPP backend.

Job information

Status
Succeeded
Backend
TPP
Workspace
inflammatory_rheum
Requested by
Mark Russell
Branch
main
Force run dependencies
No
Git commit hash
ec6b6dc
Requested actions
  • generate_dataset_eia
  • eia_cleaning

Code comparison

Compare the code used in this Job Request