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Job request: 17336

Organisation:
Bennett Institute
Workspace:
the-impact-of-covid-19-on-the-care-of-people-with-sickle-cell-disease-interactive
ID:
jxlajgt6yogvr7vc
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_01GYWBE8A7S9RMVPHATGD5JSN8:
    run: cohortextractor:latest generate_cohort
      --study-definition study_definition_ethnicity
      --param end_date="2023-03-31"
      --output-dir output/01GYWBE8A7S9RMVPHATGD5JSN8 --output-format=csv.gz
    outputs:
      highly_sensitive:
        cohort: output/01GYWBE8A7S9RMVPHATGD5JSN8/input_ethnicity.csv.gz

  generate_study_population_weekly_01GYWBE8A7S9RMVPHATGD5JSN8:
    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="10"
      --param time_ever="False"
      --param time_scale="years"
      --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/01GYWBE8A7S9RMVPHATGD5JSN8
      --output-format=csv.gz
      --output-file=output/01GYWBE8A7S9RMVPHATGD5JSN8/input_weekly_2023-04-10.csv.gz
    outputs:
      highly_sensitive:
        cohort: output/01GYWBE8A7S9RMVPHATGD5JSN8/input_weekly_2023-04-10.csv.gz

  generate_study_population_01GYWBE8A7S9RMVPHATGD5JSN8:
    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="10"
      --param time_ever="False"
      --param time_scale="years"
      --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/01GYWBE8A7S9RMVPHATGD5JSN8
      --output-format=csv.gz
    outputs:
      highly_sensitive:
        cohort: output/01GYWBE8A7S9RMVPHATGD5JSN8/input_*.csv.gz

  join_cohorts_01GYWBE8A7S9RMVPHATGD5JSN8:
    run: >
      cohort-joiner:v0.0.38
        --lhs output/01GYWBE8A7S9RMVPHATGD5JSN8/input_20*.csv.gz
        --rhs output/01GYWBE8A7S9RMVPHATGD5JSN8/input_ethnicity.csv.gz
        --output-dir output/01GYWBE8A7S9RMVPHATGD5JSN8/joined
    needs: [generate_study_population_01GYWBE8A7S9RMVPHATGD5JSN8, generate_study_population_ethnicity_01GYWBE8A7S9RMVPHATGD5JSN8]
    outputs:
      highly_sensitive:
        cohort: output/01GYWBE8A7S9RMVPHATGD5JSN8/joined/input_20*.csv.gz

  generate_measures_01GYWBE8A7S9RMVPHATGD5JSN8:
    run: >
      python:latest -m analysis.measures
        --breakdowns=sex
        --breakdowns=age
        --breakdowns=ethnicity
        --breakdowns=imd
        --breakdowns=region
        --input-dir="output/01GYWBE8A7S9RMVPHATGD5JSN8/joined"

    needs: [join_cohorts_01GYWBE8A7S9RMVPHATGD5JSN8]
    outputs:
      moderately_sensitive:
        measure: output/01GYWBE8A7S9RMVPHATGD5JSN8/joined/measure_all.csv
        decile_measure: output/01GYWBE8A7S9RMVPHATGD5JSN8/joined/measure_practice_rate_deciles.csv

  top_5_table_01GYWBE8A7S9RMVPHATGD5JSN8:
    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/01GYWBE8A7S9RMVPHATGD5JSN8"
    needs: [generate_measures_01GYWBE8A7S9RMVPHATGD5JSN8]
    outputs:
      moderately_sensitive:
        tables: output/01GYWBE8A7S9RMVPHATGD5JSN8/joined/top_5*.csv

  plot_measure_01GYWBE8A7S9RMVPHATGD5JSN8:
    run: >
      python:latest python analysis/plot_measures.py
        --breakdowns=sex
        --breakdowns=age
        --breakdowns=ethnicity
        --breakdowns=imd
        --breakdowns=region
        --output-dir output/01GYWBE8A7S9RMVPHATGD5JSN8
    needs: [generate_measures_01GYWBE8A7S9RMVPHATGD5JSN8]
    outputs:
      moderately_sensitive:
        measure: output/01GYWBE8A7S9RMVPHATGD5JSN8/plot_measure*.png
        deciles: output/01GYWBE8A7S9RMVPHATGD5JSN8/deciles_chart.png

  event_counts_01GYWBE8A7S9RMVPHATGD5JSN8:
    run: >
      python:latest python analysis/event_counts.py --input-dir="output/01GYWBE8A7S9RMVPHATGD5JSN8" --output-dir="output/01GYWBE8A7S9RMVPHATGD5JSN8"
    needs: [join_cohorts_01GYWBE8A7S9RMVPHATGD5JSN8, generate_study_population_weekly_01GYWBE8A7S9RMVPHATGD5JSN8]
    outputs:
      moderately_sensitive:
        measure: output/01GYWBE8A7S9RMVPHATGD5JSN8/event_counts.json

  generate_report_01GYWBE8A7S9RMVPHATGD5JSN8:
    run: >
      python:latest python analysis/render_report.py
      --output-dir="output/01GYWBE8A7S9RMVPHATGD5JSN8"
      --population="all"
      --breakdowns=sex
      --breakdowns=age
      --breakdowns=ethnicity
      --breakdowns=imd
      --breakdowns=region
      --codelist-1-name="Non-high dose long acting opioids (OpenPrescribing) -  dm+d"
      --codelist-2-name="Sickle (SPL-AtRiskv4) (SNOMED CT)"
      --codelist-1-link="opensafely/non-high-dose-long-acting-opioids-openprescribing-dmd/39e300ce"
      --codelist-2-link="nhsd/sickle-spl-atriskv4-snomed-ct/7083ed37"
      --time-value="10"
      --time-scale="years"
      --time-event="before"
      --start-date="2019-09-01"
      --end-date="2023-03-31"
    needs: [event_counts_01GYWBE8A7S9RMVPHATGD5JSN8, top_5_table_01GYWBE8A7S9RMVPHATGD5JSN8, plot_measure_01GYWBE8A7S9RMVPHATGD5JSN8]
    outputs:
      moderately_sensitive:
        notebook: output/01GYWBE8A7S9RMVPHATGD5JSN8/report.html

Timeline

  • Created:

  • Started:

  • Finished:

  • Runtime: 26:10:27

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

Job information

Status
Succeeded
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
d08ac0e
Requested actions
  • run_all

Code comparison

Compare the code used in this Job Request