Job request: 15992

Organisation:: DataLab
Workspace:: opensafely-internal-interactive
ID:: f2isoh6fcxfxciin

This page shows the technical details of what happened when authorised researcher Lucy B 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

Action:

generate_study_population_ethnicity_01GTM8RXTN1H66AGY6C1J7T01A

Status:

Status: Succeeded

Job identifier:

bu4zoveis4puiezg
Action:

generate_study_population_01GTM8RXTN1H66AGY6C1J7T01A

Status:

Status: Succeeded

Job identifier:

ltnvtomqstb6q6of
Action:

join_cohorts_01GTM8RXTN1H66AGY6C1J7T01A

Status:

Status: Succeeded

Job identifier:

lbgwvwphal7ea7yj
Action:

event_counts_01GTM8RXTN1H66AGY6C1J7T01A

Status:

Status: Succeeded

Job identifier:

vuggw5z5ab4hqdcq
Action:

generate_measures_01GTM8RXTN1H66AGY6C1J7T01A

Status:

Status: Succeeded

Job identifier:

qujvyrsgc5r2tokj
Action:

plot_measure_01GTM8RXTN1H66AGY6C1J7T01A

Status:

Status: Succeeded

Job identifier:

lrkljcsznkdaxsg5
Action:

deciles_chart_01GTM8RXTN1H66AGY6C1J7T01A

Status:

Status: Succeeded

Job identifier:

vykrf6odejn6gged
Action:

top_5_table_01GTM8RXTN1H66AGY6C1J7T01A

Status:

Status: Succeeded

Job identifier:

z3lyuhjbc6snl7ws
Action:

generate_report_01GTM8RXTN1H66AGY6C1J7T01A

Status:

Status: Succeeded

Job identifier:

kvko6iv7ac4qly3t

Pipeline

Show project.yaml

version: '3.0'

expectations:
  population_size: 1000

actions:

  generate_study_population_ethnicity_01GTM8RXTN1H66AGY6C1J7T01A:
    run: cohortextractor:latest generate_cohort
      --study-definition study_definition_ethnicity
      --param end_date="2023-02-22"
      --output-dir output/01GTM8RXTN1H66AGY6C1J7T01A --output-format=csv.gz
    outputs:
      highly_sensitive:
        cohort: output/01GTM8RXTN1H66AGY6C1J7T01A/input_ethnicity.csv.gz


  generate_study_population_01GTM8RXTN1H66AGY6C1J7T01A:
    run: cohortextractor:latest generate_cohort
      --study-definition study_definition
      --param codelist_1_path="codelists/codelist_1.csv"
      --param codelist_1_type="medication"
      --param codelist_2_path="codelists/codelist_2.csv"
      --param codelist_2_type="event"
      --param codelist_1_frequency="monthly"
      --param time_value="6"
      --param time_scale="months"
      --param time_event="before"
      --param codelist_2_comparison_date="event_1_date"
      --param operator="AND"
      --param population="adults"
      --param breakdowns="sex,age,imd"
      --index-date-range="2022-08-26 to 2023-02-22 by month"
      --output-dir=output/01GTM8RXTN1H66AGY6C1J7T01A
      --output-format=csv.gz
    outputs:
      highly_sensitive:
        cohort: output/01GTM8RXTN1H66AGY6C1J7T01A/input_*.csv.gz

  join_cohorts_01GTM8RXTN1H66AGY6C1J7T01A:
    run: >
      cohort-joiner:v0.0.38
        --lhs output/01GTM8RXTN1H66AGY6C1J7T01A/input_20*.csv.gz
        --rhs output/01GTM8RXTN1H66AGY6C1J7T01A/input_ethnicity.csv.gz
        --output-dir output/01GTM8RXTN1H66AGY6C1J7T01A/joined
    needs: [generate_study_population_01GTM8RXTN1H66AGY6C1J7T01A, generate_study_population_ethnicity_01GTM8RXTN1H66AGY6C1J7T01A]
    outputs:
      highly_sensitive:
        cohort: output/01GTM8RXTN1H66AGY6C1J7T01A/joined/input_20*.csv.gz

  generate_measures_01GTM8RXTN1H66AGY6C1J7T01A:
    run: >
      python:latest python analysis/measures.py
        --breakdowns="sex,age,imd"
        --input_dir="output/01GTM8RXTN1H66AGY6C1J7T01A/joined"
        --measure="med_review"

    needs: [join_cohorts_01GTM8RXTN1H66AGY6C1J7T01A]
    outputs:
      moderately_sensitive:
        measure: output/01GTM8RXTN1H66AGY6C1J7T01A/joined/measure*rate.csv
        decile_measure: output/01GTM8RXTN1H66AGY6C1J7T01A/joined/measure*rate_deciles.csv

  top_5_table_01GTM8RXTN1H66AGY6C1J7T01A:
    run: >
      python:latest python analysis/top_5.py
      --codelist-1-path="codelists/codelist_1.csv"
      --codelist-2-path="codelists/codelist_2.csv"
      --output-dir="output/01GTM8RXTN1H66AGY6C1J7T01A"
    needs: [generate_measures_01GTM8RXTN1H66AGY6C1J7T01A]
    outputs:
      moderately_sensitive:
        tables: output/01GTM8RXTN1H66AGY6C1J7T01A/joined/top_5*.csv

  deciles_chart_01GTM8RXTN1H66AGY6C1J7T01A:
    run: >
      deciles-charts:v0.0.33
        --input-files output/01GTM8RXTN1H66AGY6C1J7T01A/joined/measure_practice_rate_deciles.csv
        --output-dir output/01GTM8RXTN1H66AGY6C1J7T01A/joined
    config:
      show_outer_percentiles: true
      tables:
        output: true
      charts:
        output: true
    needs: [generate_measures_01GTM8RXTN1H66AGY6C1J7T01A]
    outputs:
      moderately_sensitive:
        deciles_charts: output/01GTM8RXTN1H66AGY6C1J7T01A/joined/deciles_*.*

  plot_measure_01GTM8RXTN1H66AGY6C1J7T01A:
    run: >
      python:latest python analysis/plot_measures.py
        --breakdowns="sex,age,imd"
        --output-dir output/01GTM8RXTN1H66AGY6C1J7T01A
    needs: [generate_measures_01GTM8RXTN1H66AGY6C1J7T01A]
    outputs:
      moderately_sensitive:
        measure: output/01GTM8RXTN1H66AGY6C1J7T01A/plot_measure*.png

  event_counts_01GTM8RXTN1H66AGY6C1J7T01A:
    run: >
      python:latest python analysis/event_counts.py --input_dir="output/01GTM8RXTN1H66AGY6C1J7T01A/joined" --output_dir="output/01GTM8RXTN1H66AGY6C1J7T01A"
    needs: [join_cohorts_01GTM8RXTN1H66AGY6C1J7T01A]
    outputs:
      moderately_sensitive:
        measure: output/01GTM8RXTN1H66AGY6C1J7T01A/event_counts.json

  generate_report_01GTM8RXTN1H66AGY6C1J7T01A:
    run: >
      python:latest python analysis/render_report.py
      --report-title="DMARDs & Medication Review - OpenSAFELY Service Restoration Observatory"
      --population="adults"
      --breakdowns="sex,age,imd"
      --codelist-1-name="DMARDs"
      --codelist-2-name="Medication Review - OpenSAFELY Service Restoration Observatory"
      --codelist-1-link="opensafely/dmards/2020-06-23"
      --codelist-2-link="opensafely/medication-review-opensafely-service-restoration-observatory/24b50f64"
      --time-value="6"
      --time-scale="months"
      --time-event="before"
      --start-date="2022-08-26"
      --end-date="2023-02-22"
      --num-practices=1000
      --request-id="01GTM8RXTN1H66AGY6C1J7T01A"
    needs: [event_counts_01GTM8RXTN1H66AGY6C1J7T01A, deciles_chart_01GTM8RXTN1H66AGY6C1J7T01A, top_5_table_01GTM8RXTN1H66AGY6C1J7T01A, plot_measure_01GTM8RXTN1H66AGY6C1J7T01A]
    outputs:
      moderately_sensitive:
        notebook: output/01GTM8RXTN1H66AGY6C1J7T01A/report.html

Timeline

Created: 6 months, 2 weeks ago 03 Mar 2023 17:37:07 UTC
Started: 6 months, 2 weeks ago 03 Mar 2023 17:36:49 UTC
Finished: 6 months, 2 weeks ago 03 Mar 2023 20:59:51 UTC
Runtime: 06:09:10

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

Job information

Status: Succeeded
Backend: TPP
Workspace: opensafely-internal-interactive
Requested by: Lucy B
Branch: main
Force run dependencies: Yes
Git commit hash: 7697e31
Requested actions: run_all