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Job request: 20812

Organisation:
Bennett Institute
Workspace:
opioids-covid-research
ID:
sqhx4xbpj64xirc2
View repo View project.yaml

This page shows the technical details of what happened when the authorised researcher Andrea Schaffer 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 
######################################
# This script defines the project pipeline - 
# it specifies the execution orders for all the code in this
# repo using a series of actions.
######################################


expectations:

  population_size: 10000

version: '3.0'

actions:

  generate_dataset_table:
    run: ehrql:v0 generate-dataset analysis/define_dataset_table.py 
      --output output/data/dataset_table.csv.gz
    outputs:
      highly_sensitive:
        cohort: output/data/dataset_table.csv.gz  

  # Measures - prevalent and new prescribing - overall
  measures_overall:
    run: ehrql:v0 generate-measures analysis/measures_overall.py 
      --output output/measures/measures_overall.csv
      --
      --start-date "2018-01-01"
      --intervals 54
    outputs:
      moderately_sensitive:
        measure_csv: output/measures/measures_overall.csv

  # Measures - prevalent prescribing - by demographic categories
  measures_demo_prev:
    run: ehrql:v0 generate-measures analysis/measures_demo_prev.py 
      --output output/measures/measures_demo_prev.csv
      --
      --start-date "2018-01-01"
      --intervals 54
    outputs:
      moderately_sensitive:
        measure_csv: output/measures/measures_demo_prev.csv
  
  # Measures - new prescribing - by demographic categories
  measures_demo_new:
    run: ehrql:v0 generate-measures analysis/measures_demo_new.py 
      --output output/measures/measures_demo_new.csv
      --
      --start-date "2018-01-01"
      --intervals 54
    outputs:
      moderately_sensitive:
        measure_csv: output/measures/measures_demo_new.csv

  # Measures - prevalent prescribing - by opioid type
  measures_type:
    run: ehrql:v0 generate-measures analysis/measures_type.py 
      --output output/measures/measures_type.csv
      --
      --start-date "2018-01-01"
      --intervals 54
    outputs:
      moderately_sensitive:
        measure_csv: output/measures/measures_type.csv 
  
  # # Measures - prevalent prescribing - checking overall counts
  # measures_test:
  #   run: ehrql:v0 generate-measures analysis/measures_test.py 
  #     --output output/measures/measures_test.csv
  #     --
  #     --start-date "2019-01-01"
  #     --intervals 42
  #   outputs:
  #     moderately_sensitive:
  #       measure_csv: output/measures/measures_test.csv 

  # # Measures - prevalent prescribing - checking overall counts
  # # measures_test_2:
  # #   run: ehrql:v0 generate-measures analysis/measures_test_2.py 
  # #     --output output/measures/measures_test_2.csv
  # #     --
  # #     --start-date "2019-01-01"
  # #     --intervals 42
  # #   outputs:
  # #     moderately_sensitive:
  # #       measure_csv: output/measures/measures_test_2.csv 

  # Measures - prevalent prescribing - checking overall counts
  measures_test_3:
    run: ehrql:v0 generate-measures analysis/measures_test_3.py 
      --output output/measures/measures_test_3.csv
      --
      --start-date "2019-01-01"
      --intervals 42
    outputs:
      moderately_sensitive:
        measure_csv: output/measures/measures_test_3.csv 

  # # Measures - prevalent prescribing - checking overall counts
  # measures_test_4:
  #   run: ehrql:v0 generate-measures analysis/measures_test_4.py 
  #     --output output/measures/measures_test_4.csv
  #     --
  #     --start-date "2019-01-01"
  #     --intervals 42
  #   outputs:
  #     moderately_sensitive:
  #       measure_csv: output/measures/measures_test_4.csv 

  ## Process time series data - overall prescribing 
  process_check_prefilled:
   run: r:latest analysis/process/process_check_prefilled.R
   needs: [measures_test_3]
   outputs:
      moderately_sensitive:
        timeseries_csv: output/timeseries/ts_prefilled*.csv

  # Measures - prevalent and new prescribing - in people in care home
  measures_carehome:
    run: ehrql:v0 generate-measures analysis/measures_carehome.py 
      --output output/measures/measures_carehome.csv
      --
      --start-date "2018-01-01"
      --intervals 54
    outputs:
      moderately_sensitive:
        measure_csv: output/measures/measures_carehome.csv
        
  ## Process time series data - overall prescribing 
  process_ts_overall:
   run: r:latest analysis/process/process_ts_overall.R
   needs: [measures_overall]
   outputs:
      moderately_sensitive:
        timeseries_csv: output/timeseries/ts_overall*.csv
 
  ## Process time series data - prescribing by demographics
  process_ts_demo:
   run: r:latest analysis/process/process_ts_demo.R
   needs: [measures_demo_prev, measures_demo_new]
   outputs:
      moderately_sensitive:
        timeseries_csv: output/timeseries/ts_demo*.csv
  
  ## Process time series data - prescribing by admin route
  process_ts_type:
   run: r:latest analysis/process/process_ts_type.R
   needs: [measures_type]
   outputs:
      moderately_sensitive:
        timeseries_csv: output/timeseries/ts_type*.csv
  
  ## Process time series data - prescribing to people in carehome
  process_ts_carehome:
   run: r:latest analysis/process/process_ts_carehome.R
   needs: [measures_carehome]
   outputs:
      moderately_sensitive:
        timeseries_csv: output/timeseries/ts_carehome*.csv

  ## Check time series
  figures_ts:
    run: r:latest analysis/descriptive/ts_figures.R
    needs: [process_ts_overall, process_ts_type, process_ts_carehome, process_ts_demo]
    outputs:
      moderately_sensitive:
        plots: output/descriptive/ts_plot*.png

  ## Results table 
  table:
    run: r:latest analysis/descriptive/table_stand.R
    needs: [generate_dataset_table]
    outputs:
      moderately_sensitive:
        table: output/tables/table_*.csv



  # OLD COHORTEXTRACTOR CODE

  # ## Cohort data
  # generate_study_population_1:
  #   run: cohortextractor:latest generate_cohort
  #     --study-definition study_definition
  #     --index-date-range "2018-01-01 to 2018-12-01 by month" 
  #     --output-dir=output 
  #     --output-format=csv
  #   outputs:
  #     highly_sensitive:
  #       cohort: output/input_*.csv

  # generate_study_population_2:
  #   run: cohortextractor:latest generate_cohort 
  #     --study-definition study_definition
  #     --index-date-range "2019-01-01 to 2019-12-01 by month" 
  #     --output-dir=output 
  #     --output-format=csv
  #   outputs:
  #     highly_sensitive:
  #       cohort: output/input*.csv

  # generate_study_population_3:
  #   run: cohortextractor:latest generate_cohort 
  #     --study-definition study_definition
  #     --index-date-range "2020-01-01 to 2020-12-01 by month" 
  #     --output-dir=output 
  #     --output-format=csv
  #   outputs:
  #     highly_sensitive:
  #       cohort: output/inpu*.csv

  # generate_study_population_4:
  #   run: cohortextractor:latest generate_cohort 
  #     --study-definition study_definition
  #     --index-date-range "2021-01-01 to 2021-12-01 by month" 
  #     --output-dir=output 
  #     --output-format=csv
  #   outputs:
  #     highly_sensitive:
  #       cohort: output/inp*.csv
  
  # generate_study_population_5:
  #   run: cohortextractor:latest generate_cohort 
  #     --study-definition study_definition
  #     --index-date-range "2022-01-01 to 2022-03-01 by month" 
  #     --output-dir=output 
  #     --output-format=csv
  #   outputs:
  #     highly_sensitive:
  #       cohort: output/in*.csv

  # ## Ethnicity      
  # generate_ethnicity_cohort:
  #   run: >
  #     cohortextractor:latest generate_cohort
  #       --study-definition study_definition_ethnicity
  #   outputs:
  #     highly_sensitive:
  #       cohort: output/input_ethnicity.csv


  # # Data processing ----
  
  # ## Add ethnicity
  # join_cohorts:
  #   run: >
  #     cohort-joiner:v0.0.48
  #       --lhs output/input_*.csv
  #       --rhs output/input_ethnicity.csv
  #       --output-dir output/data
  #   needs: [generate_study_population_1,  generate_study_population_2, 
  #     generate_study_population_5, generate_study_population_3, 
  #     generate_study_population_4, generate_ethnicity_cohort]
  #   outputs:
  #     highly_sensitive:
  #       cohort: output/data/input_*.csv 


  # ## Generate measures - full population
  # generate_measures:
  #   run: >
  #     cohortextractor:latest generate_measures 
  #       --study-definition study_definition
  #       --output-dir output/data
  #   needs: [join_cohorts]
  #   outputs:
  #     moderately_sensitive:
  #       measure_csv: output/data/measure_*.csv

Timeline

  • Created:

  • Started:

  • Finished:

  • Runtime: 00:00:10

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

Job information

Status
Failed
Backend
TPP
Workspace
opioids-covid-research
Requested by
Andrea Schaffer
Branch
main
Force run dependencies
No
Git commit hash
fb72431
Requested actions
  • process_check_prefilled

Code comparison

Compare the code used in this Job Request