Add msoa_code to measures breakdown and create compute_deciles function

Author: viv3ckj

analysis/measures_definition_pf_breakdown.py

@@ -59,6 +59,7 @@
     otherwise="Missing",
 )
 
+msoa_code = addresses.for_patient_on(INTERVAL.start_date).msoa_code
 pharmacy_first_ids = select_events(clinical_events, codelist=pf_consultation_events_dict["pf_consultation_services_combined"]).consultation_id
 
 # # Select clinical events in interval date range
@@ -73,6 +74,7 @@
     "imd": imd_quintile,
     "region": latest_region,
     "ethnicity": ethnicity_combined,
+    "msoa_code": msoa_code
 }
 
 # Define the denominator as the number of patients registered

lib/functions/plot_measures.R

@@ -161,6 +161,17 @@ save_figure <- function(figure, width = 10, height = 6) {
   )
 }
 
+compute_deciles <- function(data, column, measure_column, date_column) {
+  decile_probs <- seq(10, 90, 10)
+  data %>%
+      reframe(
+        measure = first(measure_column),
+        date = first(date_column),
+        decile_values = quantile(column, decile_probs),
+        decile_labels = decile_probs
+      )
+}
+
 # Colour palettes
 gradient_palette <- c("#001F4D", "#0056B3", "#007BFF", "#66B3E2", "#A4D8E1", "grey")
 region_palette <- c("red", "navy", "#018701", "#ffa600ca", "purple", "brown", "#f4a5b2", "cyan", "green", "grey")

lib/functions/tidy_measures.R

@@ -32,7 +32,8 @@ pf_measures_groupby_dict <- list(
   sex = "Sex",
   imd = "IMD",
   region = "Region",
-  ethnicity = "Ethnicity"
+  ethnicity = "Ethnicity",
+  msoa_code = "MSOA"
 )
 
 pf_measures_ethnicity_list <- list(

reports/create_figures.Rmd

@@ -790,6 +790,12 @@ fig_pf_condition_provider_count <- plot_measures(
 )
 
 save_figure(fig_pf_condition_provider_count)
+
+df_measures_selected <- df_measures %>%
+  filter(measure_desc == "pharmacy_first_services") %>%
+  filter(group_by == "msoa_code")
+
+df_msoa_deciles <- compute_deciles(df_measures_selected, numerator, measure, interval_start)
 ```
 
 # References