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Job request: 17386

Organisation:
Bennett Institute
Workspace:
the-impact-of-covid-19-on-the-care-of-people-with-sickle-cell-disease-interactive
ID:
my5h6llcwvej53im
View repo View project.yaml

This page shows the technical details of what happened when the authorised researcher Andrew Brown 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: '3.0'

expectations:
  population_size: 1000

actions:

  generate_study_population_ethnicity_01GZ17N26M1KMZ5R42MCEDK1R4:
    run: cohortextractor:latest generate_cohort
      --study-definition study_definition_ethnicity
      --param end_date="2023-03-31"
      --output-dir output/01GZ17N26M1KMZ5R42MCEDK1R4 --output-format=feather
    outputs:
      highly_sensitive:
        cohort: output/01GZ17N26M1KMZ5R42MCEDK1R4/input_ethnicity.feather

  generate_study_population_weekly_01GZ17N26M1KMZ5R42MCEDK1R4:
    run: cohortextractor:latest generate_cohort
      --study-definition study_definition
      --param codelist_1_path="interactive_codelists/codelist_1.csv"
      --param codelist_1_type="medication"
      --param codelist_2_path="interactive_codelists/codelist_2.csv"
      --param codelist_2_type="event"
      --param codelist_1_frequency="weekly"
      --param time_value="None"
      --param time_ever="True"
      --param time_scale=""
      --param time_event="before"
      --param codelist_2_comparison_date="end_date"
      --param operator="AND"
      --param population="all"
      --param breakdowns=""
      --index-date_range="2023-04-10 to 2023-04-10 by week"
      --output-dir=output/01GZ17N26M1KMZ5R42MCEDK1R4
      --output-format=feather
      --output-file=output/01GZ17N26M1KMZ5R42MCEDK1R4/input_weekly_2023-04-10.feather
    outputs:
      highly_sensitive:
        cohort: output/01GZ17N26M1KMZ5R42MCEDK1R4/input_weekly_2023-04-10.feather

  generate_study_population_01GZ17N26M1KMZ5R42MCEDK1R4:
    run: cohortextractor:latest generate_cohort
      --study-definition study_definition
      --param codelist_1_path="interactive_codelists/codelist_1.csv"
      --param codelist_1_type="medication"
      --param codelist_2_path="interactive_codelists/codelist_2.csv"
      --param codelist_2_type="event"
      --param codelist_1_frequency="monthly"
      --param time_value="None"
      --param time_ever="True"
      --param time_scale=""
      --param time_event="before"
      --param codelist_2_comparison_date="end_date"
      --param operator="AND"
      --param population="all"
      --param breakdowns="sex,age,ethnicity,imd,region"
      --index-date-range="2019-09-01 to 2023-03-31 by month"
      --output-dir=output/01GZ17N26M1KMZ5R42MCEDK1R4
      --output-format=feather
    outputs:
      highly_sensitive:
        cohort: output/01GZ17N26M1KMZ5R42MCEDK1R4/input_*.feather

  join_cohorts_01GZ17N26M1KMZ5R42MCEDK1R4:
    run: >
      cohort-joiner:v0.0.38
        --lhs output/01GZ17N26M1KMZ5R42MCEDK1R4/input_20*.feather
        --rhs output/01GZ17N26M1KMZ5R42MCEDK1R4/input_ethnicity.feather
        --output-dir output/01GZ17N26M1KMZ5R42MCEDK1R4/joined
    needs: [generate_study_population_01GZ17N26M1KMZ5R42MCEDK1R4, generate_study_population_ethnicity_01GZ17N26M1KMZ5R42MCEDK1R4]
    outputs:
      highly_sensitive:
        cohort: output/01GZ17N26M1KMZ5R42MCEDK1R4/joined/input_20*.feather

  generate_measures_01GZ17N26M1KMZ5R42MCEDK1R4:
    run: >
      python:latest -m analysis.measures
        --breakdowns=sex
        --breakdowns=age
        --breakdowns=ethnicity
        --breakdowns=imd
        --breakdowns=region
        --input-dir="output/01GZ17N26M1KMZ5R42MCEDK1R4/joined"

    needs: [join_cohorts_01GZ17N26M1KMZ5R42MCEDK1R4]
    outputs:
      moderately_sensitive:
        measure: output/01GZ17N26M1KMZ5R42MCEDK1R4/joined/measure_all.csv
        decile_measure: output/01GZ17N26M1KMZ5R42MCEDK1R4/joined/measure_practice_rate_deciles.csv

  top_5_table_01GZ17N26M1KMZ5R42MCEDK1R4:
    run: >
      python:latest python analysis/top_5.py
      --codelist-1-path="interactive_codelists/codelist_1.csv"
      --codelist-2-path="interactive_codelists/codelist_2.csv"
      --output-dir="output/01GZ17N26M1KMZ5R42MCEDK1R4"
    needs: [generate_measures_01GZ17N26M1KMZ5R42MCEDK1R4]
    outputs:
      moderately_sensitive:
        tables: output/01GZ17N26M1KMZ5R42MCEDK1R4/joined/top_5*.csv

  plot_measure_01GZ17N26M1KMZ5R42MCEDK1R4:
    run: >
      python:latest python analysis/plot_measures.py
        --breakdowns=sex
        --breakdowns=age
        --breakdowns=ethnicity
        --breakdowns=imd
        --breakdowns=region
        --output-dir output/01GZ17N26M1KMZ5R42MCEDK1R4
    needs: [generate_measures_01GZ17N26M1KMZ5R42MCEDK1R4]
    outputs:
      moderately_sensitive:
        measure: output/01GZ17N26M1KMZ5R42MCEDK1R4/plot_measure*.png
        deciles: output/01GZ17N26M1KMZ5R42MCEDK1R4/deciles_chart.png

  event_counts_01GZ17N26M1KMZ5R42MCEDK1R4:
    run: >
      python:latest -m analysis.event_counts --input-dir="output/01GZ17N26M1KMZ5R42MCEDK1R4" --output-dir="output/01GZ17N26M1KMZ5R42MCEDK1R4"
    needs: [join_cohorts_01GZ17N26M1KMZ5R42MCEDK1R4, generate_study_population_weekly_01GZ17N26M1KMZ5R42MCEDK1R4]
    outputs:
      moderately_sensitive:
        measure: output/01GZ17N26M1KMZ5R42MCEDK1R4/event_counts.json

  generate_report_01GZ17N26M1KMZ5R42MCEDK1R4:
    run: >
      python:latest python analysis/render_report.py
      --output-dir="output/01GZ17N26M1KMZ5R42MCEDK1R4"
      --population="all"
      --breakdowns=sex
      --breakdowns=age
      --breakdowns=ethnicity
      --breakdowns=imd
      --breakdowns=region
      --codelist-1-name="Opioid-containing medicines (oral - excluding drugs for substance misuse) - dm+d"
      --codelist-2-name="Sickle (SPL-AtRiskv4) (SNOMED CT)"
      --codelist-1-link="opensafely/opioid-containing-medicines-oral-excluding-drugs-for-substance-misuse-dmd/6eb4a72e"
      --codelist-2-link="nhsd/sickle-spl-atriskv4-snomed-ct/7083ed37"
      --time-value="None"
      --time-scale=""
      --time-event="before"
      --start-date="2019-09-01"
      --end-date="2023-03-31"
      
      --time-ever
      
    needs: [event_counts_01GZ17N26M1KMZ5R42MCEDK1R4, top_5_table_01GZ17N26M1KMZ5R42MCEDK1R4, plot_measure_01GZ17N26M1KMZ5R42MCEDK1R4]
    outputs:
      moderately_sensitive:
        notebook: output/01GZ17N26M1KMZ5R42MCEDK1R4/report.html

Timeline

  • Created:

  • Started:

  • Finished:

  • Runtime: 09:01:08

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

Job information

Status
Failed
Backend
TPP
Workspace
the-impact-of-covid-19-on-the-care-of-people-with-sickle-cell-disease-interactive
Requested by
Andrew Brown
Branch
main
Force run dependencies
Yes
Git commit hash
7395891
Requested actions
  • run_all

Code comparison

Compare the code used in this Job Request