Rework measures definition and report

analysis/report_measures.py → analysis/measures_definition_pf_codes_conditions.py

@@ -1,13 +1,13 @@
 from ehrql import INTERVAL, create_measures, months, codelist_from_csv
-from ehrql.tables.tpp import clinical_events, patients, practice_registrations
+from ehrql.tables.tpp import clinical_events, practice_registrations
 
 measures = create_measures()
 measures.configure_dummy_data(population_size=1000)
 
 start_date = "2023-11-01"
 monthly_intervals = 8
 
-# Dictionary of pharmacy first codes
+# Create dictionary of pharmacy first codes
 pharmacy_first_event_codes = {
     # Community Pharmacy (CP) Blood Pressure (BP) Check Service (procedure)
     "blood_pressure_service": ["1659111000000107"],
@@ -19,7 +19,7 @@
     "pharmacy_first_service": ["983341000000102"],
 }
 
-# Import the codelist from CSV
+# Import pharmacy first conditions codelist
 pharmacy_first_conditions_codelist = codelist_from_csv(
     "codelists/user-chriswood-pharmacy-first-clinical-pathway-conditions.csv",
     column="code",
@@ -33,14 +33,7 @@
     clinical_events.date.is_on_or_between(INTERVAL.start_date, INTERVAL.end_date)
 )
 
-# Iterate through codelist, forming a dictionary
-pharmacy_first_conditions_codes = {}
-for codes, term in pharmacy_first_conditions_codelist.items():
-    normalised_term = term.lower().replace(" ", "_")
-    codes = [codes]
-    pharmacy_first_conditions_codes[normalised_term] = codes
-
-# Loop through each CLINICAL SERVICE to create a measure
+# Create measures for pharmacy first services
 for pharmacy_first_event, codelist in pharmacy_first_event_codes.items():
     condition_events = selected_events.where(
         clinical_events.snomedct_code.is_in(codelist)
@@ -59,7 +52,13 @@
         intervals=months(monthly_intervals).starting_on(start_date),
     )
 
-# Loop through each CLINICAL CONDITION to create a measure
+# Create measures for pharmacy first conditions
+pharmacy_first_conditions_codes = {}
+for codes, term in pharmacy_first_conditions_codelist.items():
+    normalised_term = term.lower().replace(" ", "_")
+    codes = [codes]
+    pharmacy_first_conditions_codes[normalised_term] = codes
+
 for condition_name, condition_code in pharmacy_first_conditions_codes.items():
     condition_events = selected_events.where(
         clinical_events.snomedct_code.is_in(condition_code)

project.yaml

@@ -7,8 +7,8 @@ expectations:
 actions:
   generate_pf_measures:
     run: >
-      ehrql:v1 generate-measures analysis/report_measures.py
-      --output output/report/conditions_measures.csv
+      ehrql:v1 generate-measures analysis/measures_definition_pf_codes_conditions.py
+      --output output/measures/pf_codes_conditions_measures.csv
     outputs:
       moderately_sensitive:
-        measure: output/report/conditions_measures.csv  
+        measure: output/measures/pf_codes_conditions_measures.csv  

reports/pharmacy_first_report.Rmd

@@ -0,0 +1,94 @@
+---
+title: "Pharmacy First"
+output:
+  html_document:
+    toc: true
+    toc_depth: 4
+date: "`r format(Sys.time(), '%d %B, %Y')`"
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = FALSE)
+library(tidyverse)
+library(here)
+library(readr)
+```
+
+```{r load-data, message=FALSE, warning=FALSE}
+# Load plotting function
+source(here::here("lib", "functions", "function_plot_measures.R"))
+# Load data
+df_measures <- readr::read_csv(
+  here::here("output", "measures", "pf_codes_conditions_measures.csv")
+)
+```
+
+```{r setup-dicts, message=FALSE, warning=FALSE}
+# Define the custom labels for clinical conditions
+pf_clinical_condition_dict <- c(
+  "count_acute_otitis_media" = "Acute Otitis Media",
+  "count_herpes_zoster" = "Herpes Zoster",
+  "count_acute_sinusitis" = "Acute Sinusitis",
+  "count_impetigo" = "Impetigo",
+  "count_infected_insect_bite" = "Infected Insect Bite",
+  "count_acute_pharyngitis" = "Acute Pharyngitis",
+  "count_uncomplicated_urinary_tract_infection" = "UTI"
+)
+
+# Define the custom labels for clinical services
+pf_clinical_service_dict <- c(
+  "count_blood_pressure_service" = "Blood Pressure Service",
+  "count_contraception_service" = "Contraception Service",
+  "count_consultation_service" = "Consultation Service",
+  "count_pharmacy_first_service" = "Pharmacy First Service"
+)
+```
+
+# Background
+
+Add background here.
+
+# Methods
+
+This study used data from OpenSAFELY-TPP, which covers 40% of the population of England. For a description of the representativeness of this sample, please see our manuscript [here](https://wellcomeopenresearch.org/articles/7-191/v1).
+Individuals were included if they were alive and registered at a TPP practice each month, across the study period.
+Patients were excluded if their listed age was not between 0 and 120 years.
+Counts represent patients with at least one clinical code of relevance in that month.
+Patients with more than one of the same clinical code in a month were only counted once. Rates divide the count by the included study population and multiply by 1,000 to achieve a rate per 1,000 registered patients.
+Counts <=7 have been redacted and all numbers rounded to the nearest 5 to avoid potential re-identification of individuals. The rates displayed were computed with these rounded counts.
+Our data relies on a relevant Pharmacy First code being added to a patient's GP record.
+The Pharmacy First service relies on [GP Connect - Update Record](https://digital.nhs.uk/services/gp-connect/gp-connect-in-your-organisation/gp-connect-update-record) to update a patient's GP record with consultation information from the community pharmacy.
+Following the launch of the Pharmacy First service, there has been a [Gradual roll-out of GP Connect - Update Record](https://cpe.org.uk/our-news/gp-connect-update-record-rollout-and-flow-of-information/) across the approved community pharmacy IT system suppliers.
+
+Links to the codelist for each analysis can be found beneath the relevant section.
+
+# Results
+
+## Clinical Services
+
+```{r, message=FALSE, warning=FALSE}
+plot_measures(df_measures,
+  title = "Number of consultations for each clinical service per month",
+  measure_names = names(pf_clinical_service_dict),
+  custom_labels = pf_clinical_service_dict,
+  y_label = "Number of codes for consultations",
+)
+```
+
+## Clinical Pathways
+
+This section focuses on the Clinical Pathways element of that Pharmacy First service.  
+
+## Clinical Condition
+
+This section focuses on the clinical conditions within the Clinical Pathways element of that Pharmacy First service:  
+Here we show the number of consultations for each of the Pharmacy First Clinical Pathways Clinical Conditions.
+
+```{r, message=FALSE, warning=FALSE}
+plot_measures(df_measures,
+  title = "Number of consultations for each clinical condition per month",
+  measure_names = names(pf_clinical_condition_dict),
+  custom_labels = pf_clinical_condition_dict,
+  y_label = "Number of codes for consultations",
+)
+```