Skip to content

Job request: 24952

Organisation:
NHS England
Workspace:
adhd-pre-and-post-covid
ID:
i6m42wlye4xfit2n

This page shows the technical details of what happened when the authorised researcher Kin Quan 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

  • Action:
    Context_txt
    Status:
    Status: Succeeded
    Job identifier:
    bd36jzcxg4jgyclw

Pipeline

Show project.yaml
version: '3.0'

# Ignore this`expectation` block. It is required but not used, and will be removed in future versions.
expectations:
  population_size: 5000

actions:
#   generate_dataset:
#     run: > 
#       ehrql:v1 generate-dataset analysis/dataset_definition_dx_to_med_time.py
#       --output output/adhd_dataset.csv.gz
#     outputs:
#       highly_sensitive:
#         adhd_dataset: output/adhd_dataset.csv.gz

#   generate_output_table:
#     run: python:v2 python analysis/calculate_time_diff_from_dx_to_med.py
#     needs: [generate_dataset]
#     outputs:
#       moderately_sensitive:
#         table1: output/adhd_dia_med_gap_weeks.csv
  
#   generate_adhd_prevalence:
#     run: > 
#       ehrql:v1 generate-measures analysis/measures_definition.py 
#       --output output/adhd_prevalence.csv
#     outputs:
#       moderately_sensitive:
#         measure: output/adhd_prevalence.csv

#   thin_slice_medication: 
#     run: > 
#       ehrql:v1 generate-measures analysis/medication_thin_slice_measures.py
#       --output output/thin_slice_medication_measures.csv 
#     outputs:
#       moderately_sensitive:
#         measure: output/thin_slice_medication_measures.csv

  Table_2_Prevalence_of_ADHD_Diagnosis:
    run: > 
      ehrql:v1 generate-measures analysis/Table_2_Prevalence_of_ADHD_Diagnosis.py
      --output output/Table_2_Prevalence_of_ADHD_Diagnosis.csv
    outputs:
      moderately_sensitive:
        measure: output/Table_2_Prevalence_of_ADHD_Diagnosis.csv

  # Table_2_Prevalence_of_ADHD_Remission:
  #   run: > 
  #     ehrql:v1 generate-measures analysis/Table_2_Prevalence_of_ADHD_Remission.py 
  #     --output output/Table_2_Prevalence_of_ADHD_Remission.csv
  #   outputs:
  #     moderately_sensitive:
  #       measure: output/Table_2_Prevalence_of_ADHD_Remission.csv

  Patient_table_3_rolling_6month_incident:
    run: > 
      ehrql:v1 generate-dataset analysis/Patient_table_3_rolling_6month_incident.py
      --output output/Patient_table_3_rolling_6month_incident.csv.gz
    outputs:
      highly_sensitive:
        measure: output/Patient_table_3_rolling_6month_incident.csv.gz

  Table_3_rolling_6_month_medication:
    run: python:v2 python analysis/Table_3_rolling_6_month_medication.py
    needs: [Patient_table_3_rolling_6month_incident]
    outputs:
      moderately_sensitive:
        table1: output/Table_3_rolling_6_month_medication.csv

  Table_3_with_ADHD_that_are_prescribed_ADHD_medication:
    run: > 
      ehrql:v1 generate-measures analysis/Table_3_with_ADHD_that_are_prescribed_ADHD_medication.py
      --output output/Table_3_with_ADHD_that_are_prescribed_ADHD_medication.csv 
    outputs:
      moderately_sensitive:
        measure: output/Table_3_with_ADHD_that_are_prescribed_ADHD_medication.csv
  
  Table_3_without_ADHD_that_are_prescribed_ADHD_medication:
    run: > 
      ehrql:v1 generate-measures analysis/Table_3_without_ADHD_that_are_prescribed_ADHD_medication.py
      --output output/Table_3_without_ADHD_that_are_prescribed_ADHD_medication.csv 
    outputs:
      moderately_sensitive:
        measure: output/Table_3_without_ADHD_that_are_prescribed_ADHD_medication.csv

  Table_3_are_prescribed_ADHD_medication_in_ADHD_group:
    run: > 
      ehrql:v1 generate-measures analysis/Table_3_are_prescribed_ADHD_medication_in_ADHD_group.py
      --output output/Table_3_are_prescribed_ADHD_medication_in_ADHD_group.csv 
    outputs:
      moderately_sensitive:
        measure: output/Table_3_are_prescribed_ADHD_medication_in_ADHD_group.csv

  Table_3_percentage_of_people_with_ADHD_then_have_had_meds_in_the_last_6_months:
    run: > 
      ehrql:v1 generate-measures analysis/Table_3_percentage_of_people_with_ADHD_then_have_had_meds_in_the_last_6_months.py
      --output output/Table_3_percentage_of_people_with_ADHD_then_have_had_meds_in_the_last_6_months.csv 
    outputs:
      moderately_sensitive:
        measure: output/Table_3_percentage_of_people_with_ADHD_then_have_had_meds_in_the_last_6_months.csv

  # Table_3_with_ADHD_that_are_NOT_prescribed_ADHD_medication:
  #   run: > 
  #     ehrql:v1 generate-measures analysis/Table_3_with_ADHD_that_are_NOT_prescribed_ADHD_medication.py
  #     --output output/Table_3_with_ADHD_that_are_NOT_prescribed_ADHD_medication.csv 
  #   outputs:
  #     moderately_sensitive:
  #       measure: output/Table_3_with_ADHD_that_are_NOT_prescribed_ADHD_medication.csv

  # Table_3_NOT_prescribed_ADHD_medication_in_ADHD_group:
  #   run: > 
  #     ehrql:v1 generate-measures analysis/Table_3_NOT_prescribed_ADHD_medication_in_ADHD_group.py
  #     --output output/Table_3_NOT_prescribed_ADHD_medication_in_ADHD_group.csv 
  #   outputs:
  #     moderately_sensitive:
  #       measure: output/Table_3_NOT_prescribed_ADHD_medication_in_ADHD_group.csv

  Patient_table_5_dia_to_med:
    run: > 
      ehrql:v1 generate-dataset analysis/Patient_table_5_dia_to_med.py
      --output output/Patient_table_5_dia_to_med.csv.gz
    outputs:
      highly_sensitive:
        measure: output/Patient_table_5_dia_to_med.csv.gz

  Table_5_time_from_diagnosis_to_treatment:
    run: python:v2 python analysis/Table_5_time_from_diagnosis_to_treatment.py
    needs: [Patient_table_5_dia_to_med]
    outputs:
      moderately_sensitive:
        table1: output/Table_5_time_from_diagnosis_to_treatment.csv

  Context_txt:
    run: python:v2 analysis/Context_doc.py
    outputs:
      moderately_sensitive:
        table1: output/context.txt

Timeline

  • Created:

  • Started:

  • Finished:

  • Runtime: 00:00:14

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

Job information

Status
Succeeded
Backend
TPP
Requested by
Kin Quan
Branch
main
Force run dependencies
Yes
Git commit hash
bfd5ff6
Requested actions
  • Context_txt

Code comparison

Compare the code used in this Job Request