Viv3ckj/statistics

analysis/measures_definition_pf_breakdown.py

@@ -37,7 +37,6 @@
     when(age.is_null()).then("Missing"),
 )
 
-
 # IMD groupings for IMD breakdown
 imd = addresses.for_patient_on(INTERVAL.start_date).imd_rounded
 max_imd = 32844
@@ -68,7 +67,6 @@
     clinical_events.date.is_on_or_between(INTERVAL.start_date, INTERVAL.end_date)
 ).where(clinical_events.consultation_id.is_in(pharmacy_first_ids))
 
-
 # Breakdown metrics to be produced as graphs
 breakdown_metrics = {
     "age_band": age_band,
@@ -150,4 +148,4 @@
             denominator=pf_condition_denominators[condition_name],
             group_by={breakdown: variable},
             intervals=months(monthly_intervals).starting_on(start_date),
-        )
+        )

analysis/measures_definition_pf_descriptive_stats.py

@@ -0,0 +1,66 @@
+from ehrql import INTERVAL, create_measures, months
+from ehrql.tables.tpp import (
+    practice_registrations,
+    patients,
+)
+
+from pf_dataset import pharmacy_first_event_codes
+from measures_definition_pf_medications import pharmacy_first_med_codes
+from measures_definition_pf_breakdown import pharmacy_first_conditions_codes, selected_events
+
+measures = create_measures()
+measures.configure_dummy_data(population_size=1000)
+
+start_date = "2024-02-01"
+monthly_intervals = 9
+
+registration = practice_registrations.for_patient_on(INTERVAL.end_date)
+
+# Loop through all codes in each sublist of the dictionary to flatten the list ready for is_in commands to be used and have a list of pf_condition codes
+pf_condition_codelist = [code for sublist in pharmacy_first_conditions_codes.values() for code in sublist]
+
+# Create variable which contains boolean values of whether pharmacy first event exists for patient
+has_pf_consultation = selected_events.where(
+    selected_events.snomedct_code.is_in(
+        pharmacy_first_event_codes["combined_pf_service"]
+    )
+).exists_for_patient()
+
+# PF consultations with PF clinical condition
+has_pf_condition = selected_events.where(
+    selected_events.snomedct_code.is_in(
+        pf_condition_codelist
+    )
+).exists_for_patient()
+
+# PF consultations with prescribed PF medication
+has_pf_medication = selected_events.where(
+    selected_events.snomedct_code.is_in(
+        pharmacy_first_med_codes
+    )
+).exists_for_patient()
+
+# Define the denominator as the number of patients registered
+denominator = registration.exists_for_patient() & patients.sex.is_in(["male", "female"]) & has_pf_consultation
+measures.define_defaults(
+    denominator = denominator)
+
+# Measures for PF consultations with PF medication
+measures.define_measure(
+    name="count_pfmed_status",
+    numerator=has_pf_medication,
+    intervals=months(monthly_intervals).starting_on(start_date),
+)
+# Measures for PF consultations with PF condition
+measures.define_measure(
+    name="count_pfcondition_status",
+    numerator=has_pf_condition,
+    intervals=months(monthly_intervals).starting_on(start_date),
+)
+
+# Measures for PF consultations with both PF medication and condition
+measures.define_measure(
+    name="count_pfmed_and_pfcondition_status",
+    numerator=has_pf_condition & has_pf_medication,
+    intervals=months(monthly_intervals).starting_on(start_date),
+)

dummy_tables/clinical_events.csv

@@ -1,33 +1,12 @@
 patient_id,consultation_id,date,snomedct_code
 1,1,2023-12-01,1577041000000109
+1,1,2023-12-01,15805002
 2,2,2023-12-01,1577041000000109
+2,2,2023-12-01,100
 3,3,2023-12-01,1577041000000109
+3,3,2023-12-01,15805002
 4,4,2023-12-01,1577041000000109
-5,5,2023-12-01,1577041000000109
-6,6,2023-12-01,1577041000000109
-7,7,2023-12-01,1577041000000109
-8,8,2023-12-01,1577041000000109
-9,9,2023-12-01,1577041000000109
-10,10,2023-12-01,1577041000000109
-11,11,2023-12-01,1577041000000109
-12,12,2023-12-01,1577041000000109
-13,13,2023-12-01,1577041000000109
-14,14,2023-12-01,1577041000000109
-15,15,2023-12-01,1577041000000109
-16,16,2023-12-01,1577041000000109
-17,17,2023-12-01,1577041000000109
-18,18,2023-12-01,1577041000000109
-19,19,2023-12-01,1577041000000109
-20,20,2023-12-01,1577041000000109
-21,21,2023-12-01,1577041000000109
-22,22,2023-12-01,1577041000000109
-23,23,2023-12-01,1577041000000109
-24,24,2023-12-01,1577041000000109
-25,25,2023-12-01,1577041000000109
-26,26,2023-12-01,1577041000000109
-27,27,2023-12-01,1577041000000109
-28,28,2023-12-01,1577041000000109
-29,29,2023-12-01,1577041000000109
-30,30,2023-12-01,1577041000000109
-31,31,2023-12-01,1577041000000109
-32,32,2023-12-01,1000000000000000
+4,4,2023-12-01,15805002
+4,4,2023-12-01,39692111000001101
+5,5,2023-12-01,100000
+5,5,2023-12-01,15805002

project.yaml

@@ -30,6 +30,15 @@ actions:
       moderately_sensitive:
         measure: output/measures/pf_medications_measures.csv
 
+  generate_pf_statistics_measures:
+    run: >
+      ehrql:v1 generate-measures analysis/measures_definition_pf_descriptive_stats.py
+      --dummy-tables dummy_tables
+      --output output/measures/pf_descriptive_stats_measures.csv
+    outputs:
+      moderately_sensitive:
+        measure: output/measures/pf_descriptive_stats_measures.csv
+
   generate_pf_dataset_definition:
     run: >
       ehrql:v1