Counts by Pharmacy First Service

analysis/report_measures.py

@@ -0,0 +1,69 @@
+from ehrql import INTERVAL, create_measures, months, codelist_from_csv
+from ehrql.tables.tpp import clinical_events, patients, practice_registrations
+
+measures = create_measures()
+
+# Dictionary of pharmacy first codes
+pharmacy_first_event_codes = {
+    # Community Pharmacy (CP) Blood Pressure (BP) Check Service (procedure)
+    "blood_pressure_service": ["1659111000000107"],
+    # Community Pharmacy (CP) Contraception Service (procedure)
+    "contraception_service": ["1659121000000101"],
+    # Community Pharmacist (CP) Consultation Service for minor illness (procedure)
+    "consultation_service": ["1577041000000109"],
+    # Pharmacy First service (qualifier value)
+    "pharmacy_first_service": ["983341000000102"],
+}
+
+registration = practice_registrations.for_patient_on(INTERVAL.end_date)
+
+# Select clinical events in interval date range
+selected_events = clinical_events.where(
+    clinical_events.date.is_on_or_between(INTERVAL.start_date, INTERVAL.end_date)
+)
+
+# Loop through each condition to create a measure
+for condition_name, codelist in pharmacy_first_event_codes.items():
+    condition_events = selected_events.where(
+        clinical_events.snomedct_code.is_in(codelist)
+    )
+
+    # Define the numerator as the count of events for the condition
+    numerator = condition_events.count_for_patient()
+
+
+    # Define the denominator as the number of patients registered
+    denominator = registration.exists_for_patient()
+
+    # Define the measure
+    measures.define_measure(
+        name=f"count_{condition_name}",
+        numerator=numerator,
+        denominator=denominator,
+        intervals=months(8).starting_on("2023-11-01")
+    )
+
+measures.configure_dummy_data(population_size=1000)
+
+# # Count pharmacy first codes
+# pharmacy_first_code_counts = {}
+
+# for code_desc, code in pharmacy_first_event_codes.items():
+#     count_codes_query = selected_events.where(
+#         selected_events.snomedct_code.is_in(code)
+#     ).count_for_patient()
+#     pharmacy_first_code_counts[f"count_{code_desc}"] = count_codes_query
+
+
+# for measures_name, code_counts in pharmacy_first_code_counts.items():
+#     measures.define_measure(
+#         name=measures_name,
+#         numerator=code_counts,
+#         group_by={
+#             "practice_region": registration.practice_nuts1_region_name
+#             },
+#         denominator=patients.exists_for_patient(),
+#         intervals=intervals,
+#     )
+
+

lib/functions/function_plot_measures.R

@@ -0,0 +1,31 @@
+plot_measures <- function(
+    data,
+    title = NULL,
+    x_label = NULL,
+    y_label = NULL) {
+
+  # Create plot
+  plot <- ggplot(
+    data,
+    aes(
+      x = interval_end,
+      y = numerator,
+      color = measure,
+      group = measure
+    )
+  ) +
+    geom_line() +
+    labs(
+      title = title,
+      x = x_label,
+      y = y_label,
+      color = "Condition"
+    ) +
+    # Setting the minimum y-value
+    ylim(y_min, NA) +
+    # Applying the minimal theme
+    theme_minimal()
+
+  # Return plot
+  plot
+}

lib/functions/sketch.R

@@ -0,0 +1,23 @@
+# In this file we're trying out some code
+library(tidyverse)
+library(ggplot2)
+library(gridExtra) # For arranging multiple plots
+
+# Load data
+df_measures <- readr::read_csv(
+  here::here("output", "report", "conditions_measures.csv")
+)
+
+# Develop plotting function 
+p <- ggplot(df_measures, aes(x = interval_end, y = numerator, color = measure, group = measure)) +
+  geom_line() +
+  labs(
+    title = paste("Number of Consultations for each Pharmacy First Clinical Condition per month"),
+    x = "Date",
+    y = "Number of Consultations",
+    color = "Condition"
+  ) +
+  ylim(0, NA)
+
+source(here::here("lib", "functions", "graph_func.R"))
+plot1_conditions <- plot_pharmacy_first_conditions(df_measures)

project.yaml

@@ -5,8 +5,10 @@ expectations:
   population_size: 1000
 
 actions:
-  generate_dataset:
-    run: ehrql:v1 generate-dataset analysis/dataset_definition.py --output output/dataset.csv.gz
+  generate_pf_measures:
+    run: >
+      ehrql:v1 generate-measures analysis/report_measures.py
+      --output output/report/conditions_measures.csv
     outputs:
-      highly_sensitive:
-        dataset: output/dataset.csv.gz
+      moderately_sensitive:
+        measure: output/report/conditions_measures.csv