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Job request: 5900

Organisation:
Bennett Institute
Workspace:
pincer-measures-emis
ID:
662laxknszbh4c2p
View repo View project.yaml

This page shows the technical details of what happened when the authorised researcher Louis Fisher requested one or more actions to be run against real patient data within a secure environment.

By cross-referencing the list of jobs with the pipeline section below, you can infer what security level the outputs were written to.

The output security levels are:

  • highly_sensitive
    • Researchers can never directly view these outputs
    • Researchers can only request code is run against them
  • moderately_sensitive
    • Can be viewed by an approved researcher by logging into a highly secure environment
    • These are the only outputs that can be requested for public release via a controlled output review service.

Jobs

Pipeline

Show project.yaml 
version: "3.0"

expectations:
  population_size: 5000

actions:
  generate_study_population_1:
    run: cohortextractor:latest generate_cohort --study-definition study_definition --index-date-range "2019-09-01 to 2020-05-01 by month" --output-format feather
    outputs:
      highly_sensitive:
        cohort: output/input_*.feather

  generate_study_population_2:
    run: cohortextractor:latest generate_cohort --study-definition study_definition --index-date-range "2020-06-01 to 2021-02-01 by month" --output-format feather
    outputs:
      highly_sensitive:
        cohort: output/input*.feather

  generate_study_population_3:
    run: cohortextractor:latest generate_cohort --study-definition study_definition --index-date-range "2021-03-01 to 2021-09-01 by month" --output-format csv.gz
    outputs:
      highly_sensitive:
        cohort: output/inpu*.csv.gz

  generate_study_definition_demographics_1:
    run: cohortextractor:latest generate_cohort --study-definition study_definition_demographics --index-date-range "2019-09-01 to 2020-05-01 by month" --output-format csv.gz
    outputs:
      highly_sensitive:
        cohort: output/input_demographics_*.csv.gz
  
  generate_study_definition_demographics_2:
    run: cohortextractor:latest generate_cohort --study-definition study_definition_demographics --index-date-range "2020-06-01 to 2021-02-01 by month" --output-format csv.gz
    outputs:
      highly_sensitive:
        cohort: output/input_demographics*.csv.gz

  generate_study_definition_demographics_3:
    run: cohortextractor:latest generate_cohort --study-definition study_definition_demographics --index-date-range "2021-03-01 to 2021-09-01 by month" --output-format csv.gz
    outputs:
      highly_sensitive:
        cohort: output/input_demographic*.csv.gz

  generate_study_population_region:
    run: cohortextractor:latest generate_cohort --study-definition study_definition_region --index-date-range "2019-09-01 to 2021-09-01 by month" --output-format csv.gz
    outputs:
      highly_sensitive:
        cohort: output/input_region*.csv.gz


  generate_study_population_ethnicity:
    run: cohortextractor:latest generate_cohort --study-definition study_definition_ethnicity --output-format feather
    outputs:
      highly_sensitive:
        cohort: output/input_ethnicity.feather
  
  

  # join_ethnicity_region:
  #   run: python:latest python analysis/join_ethnicity_region.py
  #   needs:
  #     [
  #       generate_study_population_1,
  #       generate_study_population_2,
  #       generate_study_population_3,
  #       generate_study_population_ethnicity,
  #     ]
  #   outputs:
  #     highly_sensitive:
  #       cohort: output/inp*.feather

  filter_population:
    run: python:latest python analysis/filter_population.py
    needs: [generate_study_population_1, generate_study_population_2, generate_study_population_3]
    outputs:
      highly_sensitive:
        cohort: output/input_filtered_*.feather

  generate_demographics:
    run: python:latest python analysis/demographics_summary.py
    needs:
      [
        filter_population,
        generate_study_population_ethnicity,
        generate_study_definition_demographics_1,
        generate_study_definition_demographics_2,
        generate_study_definition_demographics_3,
        generate_study_population_region
      ]
    outputs:
      moderately_sensitive:
        demographics: output/demographics_summary*.csv


  calculate_numerators:
    run: python:latest python analysis/calculate_numerators.py
    needs: [filter_population]
    outputs:
      highly_sensitive:
        cohort: output/indicator_e_f_*.feather

  # calculate_composite_indicators:
  #   run: python:latest python analysis/composite_indicators.py
  #   needs: [calculate_numerators, filter_population]
  #   outputs:
  #     moderately_sensitive:
  #       counts: output/*_composite_measure.csv

  generate_measures:
    run: cohortextractor:latest generate_measures --study-definition study_definition --output-dir=output
    needs: [filter_population]
    outputs:
      moderately_sensitive:
        measure_csv: output/measure_*_rate.csv

  generate_measures_additional:
    run: python:latest python analysis/calculate_measures.py
    needs: [calculate_numerators, filter_population]
    outputs:
      moderately_sensitive:
        measure_csv: output/measure*_rate.csv
       
  generate_measures_region:
    run: cohortextractor:latest generate_measures --study-definition study_definition_region --output-dir=output
    needs: [generate_study_population_region]
    outputs:
      moderately_sensitive:
        measure_csv: output/measure_msoa_rate.csv
        measure_csv_region: output/measure_region_rate.csv

  generate_region_counts:
    run: python:latest python analysis/check_region.py
    needs:
      [
        generate_study_population_region
      ]
    outputs:
      moderately_sensitive:
        region_count: output/combined_count.csv

  generate_summary_counts:
    run: python:latest python analysis/summary_statistics.py
    needs:
      [
        filter_population,
        generate_measures,
        generate_measures_additional,
        calculate_numerators,
      ]
    outputs:
      moderately_sensitive:
        patient_count: output/patient_count_*.json
        practice_count: output/practice_count_*.json
        summary: output/indicator_summary_statistics_*.json

  produce_stripped_measures:
    run: python:latest python analysis/stripped_measures.py
    needs:
      [
        generate_measures,
        generate_measures_additional
      ]
    outputs:
      moderately_sensitive:
        measures: output/measure_stripped_*.csv

  generate_plots:
    run: python:latest python analysis/plot_measures.py
    needs:
      [
        generate_measures,
        generate_measures_additional
      ]
    outputs:
      moderately_sensitive:
        counts: output/figures/plot_*.jpeg
        combined: output/figures/combined_plot_*.png
        medians: output/medians.json

  generate_notebook:
    run: jupyter:latest jupyter nbconvert /workspace/analysis/report.ipynb --execute --to html --template basic --output-dir=/workspace/output --ExecutePreprocessor.timeout=86400 --no-input
    needs: [generate_plots, generate_summary_counts]
    outputs:
      moderately_sensitive:
        notebook: output/report.html

  # generate_dem_notebook:
  #   run: jupyter:latest jupyter nbconvert /workspace/analysis/demographic_report.ipynb --execute --to html --template basic --output-dir=/workspace/output --ExecutePreprocessor.timeout=86400 --no-input
  #   needs: [generate_plots]
  #   outputs:
  #     moderately_sensitive:
  #       notebook: output/demographic_report.html

  # plot_Q1_comparisons:
  #   run: r:latest analysis/generate_demographic_slope_plot.R
  #   needs: [generate_plots]
  #   outputs:
  #     moderately_sensitive:
  #       plots: output/figures/SLOPE_*.png

  # run_tests:
  #   run: python:latest python -m pytest --junit-xml=output/pytest.xml --verbose
  #   outputs:
  #     moderately_sensitive:
  #       log: output/pytest.xml

  # test_population:
  #   run: python:latest python analysis/test_population.py
  #   needs: [filter_population]
  #   outputs:
  #     moderately_sensitive:
  #       counts: output/population_counts.csv
  #       count: output/patient_count_check.json

Timeline

  • Created:

  • Started:

  • Finished:

  • Runtime: 00:01:32

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

Job request

Status
Succeeded
Backend
EMIS
Workspace
pincer-measures-emis
Requested by
Louis Fisher
Branch
emis
Force run dependencies
No
Git commit hash
f5c2832
Requested actions
  • generate_demographics

Code comparison

Compare the code used in this job request