Viv3ckj/update viz

analysis/measures_definition_pf_codes_conditions.py

@@ -4,14 +4,15 @@
     practice_registrations,
     patients,
     addresses,
+    ethnicity_from_sus,
 )
 from codelists import pharmacy_first_conditions_codelist, ethnicity_codelist
 
 measures = create_measures()
 measures.configure_dummy_data(population_size=1000)
 
 start_date = "2023-11-01"
-monthly_intervals = 8
+monthly_intervals = 9
 
 # Create dictionary of pharmacy first codes
 pharmacy_first_event_codes = {
@@ -27,23 +28,49 @@
 
 registration = practice_registrations.for_patient_on(INTERVAL.end_date)
 
-latest_ethnicity_category_num = (
+latest_ethnicity_from_codes_category_num = (
     clinical_events.where(clinical_events.snomedct_code.is_in(ethnicity_codelist))
     .where(clinical_events.date.is_on_or_before(INTERVAL.start_date))
     .sort_by(clinical_events.date)
     .last_for_patient()
     .snomedct_code.to_category(ethnicity_codelist)
 )
 
-latest_ethnicity_category_desc = case(
-    when(latest_ethnicity_category_num == "1").then("White"),
-    when(latest_ethnicity_category_num == "2").then("Mixed"),
-    when(latest_ethnicity_category_num == "3").then("Asian or Asian British"),
-    when(latest_ethnicity_category_num == "4").then("Black or Black British"),
-    when(latest_ethnicity_category_num == "5").then("Chinese or Other Ethnic Groups"),
-    when(latest_ethnicity_category_num.is_null()).then("Missing"),
+latest_ethnicity_from_codes = case(
+    when(latest_ethnicity_from_codes_category_num == "1").then("White"),
+    when(latest_ethnicity_from_codes_category_num == "2").then("Mixed"),
+    when(latest_ethnicity_from_codes_category_num == "3").then(
+        "Asian or Asian British"
+    ),
+    when(latest_ethnicity_from_codes_category_num == "4").then(
+        "Black or Black British"
+    ),
+    when(latest_ethnicity_from_codes_category_num == "5").then(
+        "Chinese or Other Ethnic Groups"
+    ),
 )
 
+ethnicity_from_sus = case(
+    when(ethnicity_from_sus.code.is_in(["A", "B", "C"])).then("White"),
+    when(ethnicity_from_sus.code.is_in(["D", "E", "F", "G"])).then("Mixed"),
+    when(ethnicity_from_sus.code.is_in(["H", "J", "K", "L"])).then(
+        "Asian or Asian British"
+    ),
+    when(ethnicity_from_sus.code.is_in(["M", "N", "P"])).then("Black or Black British"),
+    when(ethnicity_from_sus.code.is_in(["R", "S"])).then(
+        "Chinese or Other Ethnic Groups"
+    ),
+)
+
+ethnicity_combined = case(
+    when(latest_ethnicity_from_codes.is_not_null()).then(latest_ethnicity_from_codes),
+    when(latest_ethnicity_from_codes.is_null() & ethnicity_from_sus.is_not_null()).then(
+        ethnicity_from_sus
+    ),
+    otherwise="Missing",
+)
+
+
 # Age bands for age breakdown
 age = patients.age_on(INTERVAL.start_date)
 age_band = case(
@@ -59,11 +86,18 @@
 imd = addresses.for_patient_on(INTERVAL.start_date).imd_rounded
 max_imd = 32844
 imd_quintile = case(
-    when((imd >= 0) & (imd < int(max_imd * 1 / 5))).then("1"),
+    when((imd >= 0) & (imd < int(max_imd * 1 / 5))).then("1 (Most Deprived)"),
     when(imd < int(max_imd * 2 / 5)).then("2"),
     when(imd < int(max_imd * 3 / 5)).then("3"),
     when(imd < int(max_imd * 4 / 5)).then("4"),
-    when(imd <= max_imd).then("5"),
+    when(imd <= max_imd).then("5 (Least Deprived)"),
+    otherwise="Missing",
+)
+
+latest_region = case(
+    when(
+        registration.practice_nuts1_region_name.is_not_null()
+    ).then(registration.practice_nuts1_region_name),
     otherwise="Missing",
 )
 
@@ -77,8 +111,8 @@
     "age_band": age_band,
     "sex": patients.sex,
     "imd": imd_quintile,
-    "region": registration.practice_nuts1_region_name,
-    "ethnicity": latest_ethnicity_category_desc,
+    "region": latest_region,
+    "ethnicity": ethnicity_combined,
 }
 
 # Define the denominator as the number of patients registered

lib/functions/function_plot_measures.R

@@ -63,13 +63,17 @@ plot_measures <- function(
       color = guide_legend(nrow = guide_nrow)
     ) +
     labs(
+      title = title,
       x = x_label,
       y = y_label,
       colour = guide_label,
     ) +
     theme(
-      legend.position = legend_position
-    )
+      legend.position = legend_position,
+      plot.title = element_text(hjust = 0.5)
+    ) +
+
+    scale_colour_brewer(palette = "Set1")
 
   # Automatically change y scale depending selected value
   if (rlang::as_label(enquo(select_value)) %in% c("numerator", "denominator")) {

reports/pharmacy_first_report.Rmd

@@ -67,6 +67,34 @@ df_measures <- tidy_measures(
   pf_measures_name_mapping = pf_measures_name_mapping,
   pf_measures_groupby_dict = pf_measures_groupby_dict
 )
+
+df_measures$ethnicity <- factor(
+  df_measures$ethnicity,
+  levels = c("White", "Mixed", "Asian or Asian British",
+  "Black or Black British", "Chinese or Other Ethnic Groups",
+  "Missing"), 
+  ordered = TRUE
+)
+
+df_measures$age_band <- factor(
+  df_measures$age_band,
+  levels = c("0-19", "20-39", "40-59",
+  "60-79", "80+",
+  "Missing"), 
+  ordered = TRUE
+)
+
+df_measures$region <- factor(
+  df_measures$region,
+  levels = c("East", "East Midlands", "London",
+  "North East", "North West", "South East",
+  "South West", "West Midlands", "Yorkshire and The Humber",
+  "Missing"), 
+  ordered = TRUE
+)
+
+df_measures <- df_measures %>%
+  mutate(sex = factor(sex, levels = c("female", "male"), labels = c("Female", "Male")))
 ```
 
 # Background
@@ -93,7 +121,7 @@ Links to the codelist for each analysis can be found beneath the relevant sectio
 
 ### Total population
 
-```{r, message=FALSE, warning=FALSE, fig.height=4, fig.width=4}
+```{r, message=FALSE, warning=FALSE}
 # Select measures and breakdown
 df_measures_selected <- df_measures %>%
   filter(measure_desc == "clinical_service") %>%
@@ -108,6 +136,8 @@ plot_measures(
   guide_nrow = 1,
   facet_wrap = FALSE,
   facet_var = NULL,
+  title = "Number of consultations for each clinical service per month",
+  y_label = "Number of codes for consultations",
 )
 ```
 
@@ -127,7 +157,9 @@ plot_measures(
   colour_var = age_band,
   guide_nrow = 1,
   facet_wrap = TRUE,
-  facet_var = measure
+  facet_var = measure,
+  title = "Number of consultations for each clinical service by age band per month",
+  y_label = "Number of codes for consultations",
 )
 ```
 
@@ -148,6 +180,8 @@ plot_measures(
   guide_nrow = 1,
   facet_wrap = TRUE,
   facet_var = measure,
+  title = "Number of consultations for each clinical service by sex per month",
+  y_label = "Number of codes for consultations",
 )
 ```
 
@@ -168,6 +202,8 @@ plot_measures(
   guide_nrow = 1,
   facet_wrap = TRUE,
   facet_var = measure,
+  title = "Number of consultations for each clinical service by IMD per month",
+  y_label = "Number of codes for consultations",
 )
 ```
 
@@ -188,6 +224,8 @@ plot_measures(
   guide_nrow = 2,
   facet_wrap = TRUE,
   facet_var = measure,
+  title = "Number of consultations for each clinical service by region per month",
+  y_label = "Number of codes for consultations",
 )
 ```
 
@@ -210,6 +248,8 @@ plot_measures(
   guide_nrow = 2,
   facet_wrap = TRUE,
   facet_var = measure,
+  title = "Number of consultations for each clinical service by ethnicity per month",
+  y_label = "Number of codes for consultations",
 )
 ```
 
@@ -220,7 +260,7 @@ Here we show the number of consultations for each of the Pharmacy First Clinical
 
 ### Total population
 
-```{r, message=FALSE, warning=FALSE, fig.height=4, fig.width=4}
+```{r, message=FALSE, warning=FALSE}
 # Select measures and breakdown
 df_measures_selected <- df_measures %>%
   filter(measure_desc == "clinical_condition") %>%
@@ -234,6 +274,8 @@ plot_measures(
   guide_nrow = 1,
   facet_wrap = FALSE,
   facet_var = NULL,
+  title = "Number of consultations for each clinical condition per month",
+  y_label = "Number of codes for consultations",
 )
 ```
 
@@ -253,7 +295,9 @@ plot_measures(
   colour_var = age_band,
   guide_nrow = 1,
   facet_wrap = TRUE,
-  facet_var = measure
+  facet_var = measure,
+  title = "Number of consultations for each clinical condition by age band per month",
+  y_label = "Number of codes for consultations",
 )
 ```
 
@@ -274,6 +318,8 @@ plot_measures(
   guide_nrow = 1,
   facet_wrap = TRUE,
   facet_var = measure,
+  title = "Number of consultations for each clinical condition by sex per month",
+  y_label = "Number of codes for consultations",
 )
 ```
 
@@ -294,6 +340,8 @@ plot_measures(
   guide_nrow = 1,
   facet_wrap = TRUE,
   facet_var = measure,
+  title = "Number of consultations for each clinical condition by IMD per month",
+  y_label = "Number of codes for consultations",
 )
 ```
 
@@ -314,12 +362,14 @@ plot_measures(
   guide_nrow = 2,
   facet_wrap = TRUE,
   facet_var = measure,
+  title = "Number of consultations for each clinical condition by region per month",
+  y_label = "Number of codes for consultations",
 )
 ```
 
 ### Clinical Conditions by ethnicity
 
-```{r, message=FALSE, warning=FALSE}
+```{r, message=FALSE, warning=FALSE, fig.height=15, fig.width=8}
 
 # Select measures and breakdown
 df_measures_selected <- df_measures %>%
@@ -335,5 +385,7 @@ plot_measures(
   guide_nrow = 2,
   facet_wrap = TRUE,
   facet_var = measure,
+  title = "Number of consultations for each clinical condition by ethnicity per month",
+  y_label = "Number of codes for consultations",
 )
 ```