Job request: 17406

Organisation:: Bennett Institute
Workspace:: the-impact-of-covid-19-on-the-care-of-people-with-sickle-cell-disease-interactive
ID:: zth6f7odceffddgj

This page shows the technical details of what happened when the authorised researcher George Hickman 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.

Pipeline

Show project.yaml

version: '3.0'

expectations:
  population_size: 1000

actions:

  generate_study_population_ethnicity_01GZ17GWKP5NBK3SY82GJAE1XX:
    run: cohortextractor:latest generate_cohort
      --study-definition study_definition_ethnicity
      --param end_date="2023-03-31"
      --output-dir output/01GZ17GWKP5NBK3SY82GJAE1XX --output-format=feather
    outputs:
      highly_sensitive:
        cohort: output/01GZ17GWKP5NBK3SY82GJAE1XX/input_ethnicity.feather

  generate_study_population_weekly_01GZ17GWKP5NBK3SY82GJAE1XX:
    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/01GZ17GWKP5NBK3SY82GJAE1XX
      --output-format=feather
      --output-file=output/01GZ17GWKP5NBK3SY82GJAE1XX/input_weekly_2023-04-10.feather
    outputs:
      highly_sensitive:
        cohort: output/01GZ17GWKP5NBK3SY82GJAE1XX/input_weekly_2023-04-10.feather

  generate_study_population_01GZ17GWKP5NBK3SY82GJAE1XX:
    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/01GZ17GWKP5NBK3SY82GJAE1XX
      --output-format=feather
    outputs:
      highly_sensitive:
        cohort: output/01GZ17GWKP5NBK3SY82GJAE1XX/input_*.feather

  join_cohorts_01GZ17GWKP5NBK3SY82GJAE1XX:
    run: >
      cohort-joiner:v0.0.38
        --lhs output/01GZ17GWKP5NBK3SY82GJAE1XX/input_20*.feather
        --rhs output/01GZ17GWKP5NBK3SY82GJAE1XX/input_ethnicity.feather
        --output-dir output/01GZ17GWKP5NBK3SY82GJAE1XX/joined
    needs: [generate_study_population_01GZ17GWKP5NBK3SY82GJAE1XX, generate_study_population_ethnicity_01GZ17GWKP5NBK3SY82GJAE1XX]
    outputs:
      highly_sensitive:
        cohort: output/01GZ17GWKP5NBK3SY82GJAE1XX/joined/input_20*.feather

  generate_measures_01GZ17GWKP5NBK3SY82GJAE1XX:
    run: >
      python:latest -m analysis.measures
        --breakdowns=sex
        --breakdowns=age
        --breakdowns=ethnicity
        --breakdowns=imd
        --breakdowns=region
        --input-dir="output/01GZ17GWKP5NBK3SY82GJAE1XX/joined"

    needs: [join_cohorts_01GZ17GWKP5NBK3SY82GJAE1XX]
    outputs:
      moderately_sensitive:
        measure: output/01GZ17GWKP5NBK3SY82GJAE1XX/joined/measure_all.csv
        decile_measure: output/01GZ17GWKP5NBK3SY82GJAE1XX/joined/measure_practice_rate_deciles.csv

  top_5_table_01GZ17GWKP5NBK3SY82GJAE1XX:
    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/01GZ17GWKP5NBK3SY82GJAE1XX"
    needs: [generate_measures_01GZ17GWKP5NBK3SY82GJAE1XX]
    outputs:
      moderately_sensitive:
        tables: output/01GZ17GWKP5NBK3SY82GJAE1XX/joined/top_5*.csv

  plot_measure_01GZ17GWKP5NBK3SY82GJAE1XX:
    run: >
      python:latest python analysis/plot_measures.py
        --breakdowns=sex
        --breakdowns=age
        --breakdowns=ethnicity
        --breakdowns=imd
        --breakdowns=region
        --output-dir output/01GZ17GWKP5NBK3SY82GJAE1XX
    needs: [generate_measures_01GZ17GWKP5NBK3SY82GJAE1XX]
    outputs:
      moderately_sensitive:
        measure: output/01GZ17GWKP5NBK3SY82GJAE1XX/plot_measure*.png
        deciles: output/01GZ17GWKP5NBK3SY82GJAE1XX/deciles_chart.png

  event_counts_01GZ17GWKP5NBK3SY82GJAE1XX:
    run: >
      python:latest -m analysis.event_counts --input-dir="output/01GZ17GWKP5NBK3SY82GJAE1XX" --output-dir="output/01GZ17GWKP5NBK3SY82GJAE1XX"
    needs: [join_cohorts_01GZ17GWKP5NBK3SY82GJAE1XX, generate_study_population_weekly_01GZ17GWKP5NBK3SY82GJAE1XX]
    outputs:
      moderately_sensitive:
        measure: output/01GZ17GWKP5NBK3SY82GJAE1XX/event_counts.json

  generate_report_01GZ17GWKP5NBK3SY82GJAE1XX:
    run: >
      python:latest python analysis/render_report.py
      --output-dir="output/01GZ17GWKP5NBK3SY82GJAE1XX"
      --population="all"
      --breakdowns=sex
      --breakdowns=age
      --breakdowns=ethnicity
      --breakdowns=imd
      --breakdowns=region
      --codelist-1-name="Phenoxymethylpenicillin (oral preparations only)"
      --codelist-2-name="Sickle (SPL-AtRiskv4) (SNOMED CT)"
      --codelist-1-link="opensafely/phenoxymethylpenicillin-oral-preparations-only/14b427f8"
      --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_01GZ17GWKP5NBK3SY82GJAE1XX, top_5_table_01GZ17GWKP5NBK3SY82GJAE1XX, plot_measure_01GZ17GWKP5NBK3SY82GJAE1XX]
    outputs:
      moderately_sensitive:
        notebook: output/01GZ17GWKP5NBK3SY82GJAE1XX/report.html

Timeline

Created: 1 year, 6 months ago 28 Apr 2023 09:59:49 UTC
Started: 1 year, 6 months ago 28 Apr 2023 09:59:00 UTC
Finished: 1 year, 6 months ago 28 Apr 2023 09:59:00 UTC
Runtime:

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

Job information

Status: Failed
GitError: Error fetching commit 181dee2 from https://github.com/opensafely/the-impact-of-covid-19-on-the-care-of-people-with-sickle-cell-disease-interactive
Backend: TPP
Workspace: the-impact-of-covid-19-on-the-care-of-people-with-sickle-cell-disease-interactive
Requested by: George Hickman
Branch: main
Force run dependencies: No
Git commit hash: 181dee2
Requested actions: generate_study_population_01GZ17GWKP5NBK3SY82GJAE1XX

join_cohorts_01GZ17GWKP5NBK3SY82GJAE1XX

generate_measures_01GZ17GWKP5NBK3SY82GJAE1XX

top_5_table_01GZ17GWKP5NBK3SY82GJAE1XX

plot_measure_01GZ17GWKP5NBK3SY82GJAE1XX

event_counts_01GZ17GWKP5NBK3SY82GJAE1XX

generate_report_01GZ17GWKP5NBK3SY82GJAE1XX

Code comparison

Compare the code used in this Job Request