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Job request: 17378

Organisation:
Bennett Institute
Workspace:
the-impact-of-covid-19-on-the-care-of-people-with-sickle-cell-disease-interactive
ID:
oi5hkl4b4oa6hz2a
View repo View project.yaml

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

By cross-referencing the indicated Requested Actions with the Pipeline section below, you can infer what security level various outputs were written to. Outputs marked as highly_sensitive can never be viewed directly by a researcher; 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_01GZ0RDNT16YZ3MJQSJF8FHSYH:
    run: cohortextractor:latest generate_cohort
      --study-definition study_definition_ethnicity
      --param end_date="2023-03-31"
      --output-dir output/01GZ0RDNT16YZ3MJQSJF8FHSYH --output-format=feather
    outputs:
      highly_sensitive:
        cohort: output/01GZ0RDNT16YZ3MJQSJF8FHSYH/input_ethnicity.feather

  generate_study_population_weekly_01GZ0RDNT16YZ3MJQSJF8FHSYH:
    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/01GZ0RDNT16YZ3MJQSJF8FHSYH
      --output-format=feather
      --output-file=output/01GZ0RDNT16YZ3MJQSJF8FHSYH/input_weekly_2023-04-10.feather
    outputs:
      highly_sensitive:
        cohort: output/01GZ0RDNT16YZ3MJQSJF8FHSYH/input_weekly_2023-04-10.feather

  generate_study_population_01GZ0RDNT16YZ3MJQSJF8FHSYH:
    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/01GZ0RDNT16YZ3MJQSJF8FHSYH
      --output-format=feather
    outputs:
      highly_sensitive:
        cohort: output/01GZ0RDNT16YZ3MJQSJF8FHSYH/input_*.feather

  join_cohorts_01GZ0RDNT16YZ3MJQSJF8FHSYH:
    run: >
      cohort-joiner:v0.0.38
        --lhs output/01GZ0RDNT16YZ3MJQSJF8FHSYH/input_20*.feather
        --rhs output/01GZ0RDNT16YZ3MJQSJF8FHSYH/input_ethnicity.feather
        --output-dir output/01GZ0RDNT16YZ3MJQSJF8FHSYH/joined
    needs: [generate_study_population_01GZ0RDNT16YZ3MJQSJF8FHSYH, generate_study_population_ethnicity_01GZ0RDNT16YZ3MJQSJF8FHSYH]
    outputs:
      highly_sensitive:
        cohort: output/01GZ0RDNT16YZ3MJQSJF8FHSYH/joined/input_20*.feather

  generate_measures_01GZ0RDNT16YZ3MJQSJF8FHSYH:
    run: >
      python:latest -m analysis.measures
        --breakdowns=sex
        --breakdowns=age
        --breakdowns=ethnicity
        --breakdowns=imd
        --breakdowns=region
        --input-dir="output/01GZ0RDNT16YZ3MJQSJF8FHSYH/joined"

    needs: [join_cohorts_01GZ0RDNT16YZ3MJQSJF8FHSYH]
    outputs:
      moderately_sensitive:
        measure: output/01GZ0RDNT16YZ3MJQSJF8FHSYH/joined/measure_all.csv
        decile_measure: output/01GZ0RDNT16YZ3MJQSJF8FHSYH/joined/measure_practice_rate_deciles.csv

  top_5_table_01GZ0RDNT16YZ3MJQSJF8FHSYH:
    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/01GZ0RDNT16YZ3MJQSJF8FHSYH"
    needs: [generate_measures_01GZ0RDNT16YZ3MJQSJF8FHSYH]
    outputs:
      moderately_sensitive:
        tables: output/01GZ0RDNT16YZ3MJQSJF8FHSYH/joined/top_5*.csv

  plot_measure_01GZ0RDNT16YZ3MJQSJF8FHSYH:
    run: >
      python:latest python analysis/plot_measures.py
        --breakdowns=sex
        --breakdowns=age
        --breakdowns=ethnicity
        --breakdowns=imd
        --breakdowns=region
        --output-dir output/01GZ0RDNT16YZ3MJQSJF8FHSYH
    needs: [generate_measures_01GZ0RDNT16YZ3MJQSJF8FHSYH]
    outputs:
      moderately_sensitive:
        measure: output/01GZ0RDNT16YZ3MJQSJF8FHSYH/plot_measure*.png
        deciles: output/01GZ0RDNT16YZ3MJQSJF8FHSYH/deciles_chart.png

  event_counts_01GZ0RDNT16YZ3MJQSJF8FHSYH:
    run: >
      python:latest -m analysis.event_counts --input-dir="output/01GZ0RDNT16YZ3MJQSJF8FHSYH" --output-dir="output/01GZ0RDNT16YZ3MJQSJF8FHSYH"
    needs: [join_cohorts_01GZ0RDNT16YZ3MJQSJF8FHSYH, generate_study_population_weekly_01GZ0RDNT16YZ3MJQSJF8FHSYH]
    outputs:
      moderately_sensitive:
        measure: output/01GZ0RDNT16YZ3MJQSJF8FHSYH/event_counts.json

  generate_report_01GZ0RDNT16YZ3MJQSJF8FHSYH:
    run: >
      python:latest python analysis/render_report.py
      --output-dir="output/01GZ0RDNT16YZ3MJQSJF8FHSYH"
      --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_01GZ0RDNT16YZ3MJQSJF8FHSYH, top_5_table_01GZ0RDNT16YZ3MJQSJF8FHSYH, plot_measure_01GZ0RDNT16YZ3MJQSJF8FHSYH]
    outputs:
      moderately_sensitive:
        notebook: output/01GZ0RDNT16YZ3MJQSJF8FHSYH/report.html

Timeline

  • Created:

  • Started:

  • Finished:

  • Runtime: 02:07:34

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
Brian MacKenna (PHC)
Branch
main
Force run dependencies
Yes
Git commit hash
e014f11
Requested actions
  • run_all

Code comparison

Compare the code used in this Job Request