diff --git a/data-raw/app/00_run_all.R b/data-raw/app/00_run_all.R
index cbd114b2..f79c3b7d 100644
--- a/data-raw/app/00_run_all.R
+++ b/data-raw/app/00_run_all.R
@@ -1,18 +1,20 @@
 Sys.time()
 # Load library and generate base geo data
+library(tictoc)
 source("data-raw/app/data_raw_helpers.R")
 source("data-raw/app/geo_data.R")
 
 # Define vars to retain in workflow
-keep_vars = c(ls(), 'keep_vars', 'get_metrics')
+keep_vars = c(ls(), 'keep_vars')
 
 # Run all scripts that generate an Rda file
-source("data-raw/app/01_headline_figures_df.R")
-source("data-raw/app/02_patients_age_gender_df.R")
-source("data-raw/app/03_patients_by_imd_df.R")
-source("data-raw/app/04_metrics_by_ch_type_df.R")
-source("data-raw/app/05_metrics_age_gender_df.R")
-source("data-raw/app/06_metrics_by_geo_and_ch_flag_df.R")
-source("data-raw/app/07_ch_flag_drug_df.R")
-source("data-raw/app/08_geo_ch_flag_drug_df.R")
-Sys.time()
\ No newline at end of file
+tic(); source("data-raw/app/01_headline_figures_df.R"); toc()
+tic(); source("data-raw/app/02_patients_age_gender_df.R"); toc()
+tic(); source("data-raw/app/03_patients_by_imd_df.R"); toc()
+tic(); source("data-raw/app/04_metrics_by_ch_type_85_split_df.R"); toc()
+tic(); source("data-raw/app/05_metrics_age_gender_df.R"); toc()
+tic(); source("data-raw/app/06_metrics_by_geo_and_ch_flag_df.R"); toc()
+tic(); source("data-raw/app/07_ch_flag_drug_df.R"); toc()
+tic(); source("data-raw/app/08_geo_ch_flag_drug_df.R"); toc()
+Sys.time()
+
diff --git a/data-raw/app/01_headline_figures_df.R b/data-raw/app/01_headline_figures_df.R
index 103025b2..e69e5dbe 100644
--- a/data-raw/app/01_headline_figures_df.R
+++ b/data-raw/app/01_headline_figures_df.R
@@ -8,6 +8,7 @@ con <- nhsbsaR::con_nhsbsa(database = "DALP")
 
 # Create a lazy table from year month dim table in DWCP
 data_db <- con %>%
+  #tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20230331"))
   tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20240331"))
 
 # Key findings used within analysis summary text
@@ -104,4 +105,10 @@ mod_headline_figures_df = rbind(annual_df, monthly_df)
 usethis::use_data(mod_headline_figures_df, overwrite = TRUE)
 
 # Disconnect from database
-DBI::dbDisconnect(con); rm(list = ls()); gc()
+DBI::dbDisconnect(con)
+
+# Remove vars specific to script
+remove_vars <- setdiff(ls(), keep_vars)
+
+# Remove objects and clean environment
+rm(list = remove_vars, remove_vars); gc()
\ No newline at end of file
diff --git a/data-raw/app/02_patients_age_gender_df.R b/data-raw/app/02_patients_age_gender_df.R
index 2bbbc946..3de3c2d4 100644
--- a/data-raw/app/02_patients_age_gender_df.R
+++ b/data-raw/app/02_patients_age_gender_df.R
@@ -1,5 +1,5 @@
-# Running time ~10 min
 
+# Running time ~10 min
 library(dplyr)
 library(dbplyr)
 devtools::load_all()
@@ -9,14 +9,14 @@ con <- nhsbsaR::con_nhsbsa(database = "DALP")
 
 # Item-level base table
 base_db <- con |>
-  tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20230331"))
+  #tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20230331"))
+  tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20240331"))
  
 # Add a dummy overall column
 base_db <- base_db |>
    mutate(OVERALL = "Overall")
  
 # Loop over each geography and aggregate using purrr's map function approach
-
 patients_by_fy_geo_age_gender_fun <- function(geography_name) {
   
   # Identify geography cols
@@ -59,6 +59,7 @@ patients_by_fy_geo_age_gender_fun <- function(geography_name) {
   
 }
 
+# Map function
 patients_by_fy_geo_age_gender_df <- purrr::map(
   names(geographies),
   patients_by_fy_geo_age_gender_fun
@@ -74,6 +75,7 @@ patients_by_fy_geo_age_gender_df <-
     #PCT_PATIENTS = janitor::round_half_up(PCT_PATIENTS, 1)
   )
 
+# Calculate patient proportions
 patients_by_fy_geo_age_gender_df <- patients_by_fy_geo_age_gender_df |>
   group_by(CH_FLAG, FY, GEOGRAPHY, SUB_GEOGRAPHY_CODE, SUB_GEOGRAPHY_NAME) |>
   mutate(
@@ -121,4 +123,9 @@ usethis::use_data(
 
 # Disconnect from database
 DBI::dbDisconnect(con)
-rm(list = ls()); gc()
+
+# Remove vars specific to script
+remove_vars <- setdiff(ls(), keep_vars)
+
+# Remove objects and clean environment
+rm(list = remove_vars, remove_vars); gc()
diff --git a/data-raw/app/03_patients_by_imd_df.R b/data-raw/app/03_patients_by_imd_df.R
index b5f2d098..960d8ab5 100644
--- a/data-raw/app/03_patients_by_imd_df.R
+++ b/data-raw/app/03_patients_by_imd_df.R
@@ -8,7 +8,8 @@ con <- nhsbsaR::con_nhsbsa(database = "DALP")
 
 # Create a lazy table from the item level base table
 fact_db <- con %>%
-  tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20230331"))
+  #tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20230331"))
+  tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20240331"))
 
 # Count care home patients in each decile
 mod_patients_by_imd_df <- fact_db %>%
@@ -37,4 +38,10 @@ mod_patients_by_imd_df <- fact_db %>%
 usethis::use_data(mod_patients_by_imd_df, overwrite = TRUE)
 
 # Disconnect
-DBI::dbDisconnect(con); rm(list = ls()); gc()
+DBI::dbDisconnect(con)
+
+# Remove vars specific to script
+remove_vars <- setdiff(ls(), keep_vars)
+
+# Remove objects and clean environment
+rm(list = remove_vars, remove_vars); gc()
diff --git a/data-raw/app/04_metrics_by_ch_type_85_split_df.R b/data-raw/app/04_metrics_by_ch_type_85_split_df.R
index 4dacdc7e..45ed56f2 100644
--- a/data-raw/app/04_metrics_by_ch_type_85_split_df.R
+++ b/data-raw/app/04_metrics_by_ch_type_85_split_df.R
@@ -1,11 +1,9 @@
 # Initial setup -----------------------------------------------------------
 
 # Expected run time ~35 minutes @parallel 24
-
 library(dplyr)
 library(dbplyr)
 library(tidyr)
-
 devtools::load_all()
 
 # Set up connection to DALP
@@ -17,7 +15,8 @@ con <- nhsbsaR::con_nhsbsa(database = "DALP")
 
 # Item-level base table
 base_db <- con %>%
-  tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20230331"))
+  #tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20230331"))
+  tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20240331"))
 
 # Initial manipulation to create CH_TYPE column, later to be grouped by
 init_db <- base_db %>%
@@ -48,6 +47,7 @@ init_db <- base_db %>%
     )
   ) 
 
+# Union both initi_db variants
 init_db <- init_db %>% 
   union(
     init_db %>% 
@@ -57,6 +57,7 @@ init_db <- init_db %>%
 
 ## Process ----------------------------------------------------------------
 
+# Get metrics
 metrics_by_ch_type_85_split_df <- get_metrics(
   init_db,
   first_grouping = c(
@@ -73,6 +74,7 @@ metrics_by_ch_type_85_split_df <- get_metrics(
   )
 )
 
+# Generate age band categories
 metrics_by_ch_type_85_split_df <- metrics_by_ch_type_85_split_df %>% 
   mutate(
     AGE_BAND = dplyr::case_match(
@@ -84,9 +86,16 @@ metrics_by_ch_type_85_split_df <- metrics_by_ch_type_85_split_df %>%
   ) %>% 
   dplyr::relocate(AGE_BAND, .after = CH_TYPE)
   
-## Save -------------------------------------------------------------------
+## Save ------------------------------------------------------------------------
 usethis::use_data(metrics_by_ch_type_85_split_df, overwrite = TRUE)
 
-# Cleanup -----------------------------------------------------------------
+# Cleanup ----------------------------------------------------------------------
+
+# Disconnect
 DBI::dbDisconnect(con)
-rm(list = ls())
+
+# Remove vars specific to script
+remove_vars <- setdiff(ls(), keep_vars)
+
+# Remove objects and clean environment
+rm(list = remove_vars, remove_vars); gc()
diff --git a/data-raw/app/05_metrics_age_gender_df.R b/data-raw/app/05_metrics_age_gender_df.R
index c80f90aa..052ade09 100644
--- a/data-raw/app/05_metrics_age_gender_df.R
+++ b/data-raw/app/05_metrics_age_gender_df.R
@@ -1,5 +1,6 @@
 # Running time ~35 min
 
+# Libraries and functions
 library(dplyr)
 library(dbplyr)
 devtools::load_all()
@@ -10,10 +11,11 @@ con <- nhsbsaR::con_nhsbsa(database = "DALP")
 
 # Item-level base table
 base_db <- con |>
-  tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20230331")) |>
+  #tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20230331"))
+  tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20240331")) %>% 
   filter(GENDER %in% c("Male", "Female"))
  
-
+# Get metrics
 metrics_by_age_gender_and_ch_flag_df <- get_metrics(
   base_db,
   first_grouping = c(
@@ -46,4 +48,9 @@ usethis::use_data(
 
 # Disconnect from database
 DBI::dbDisconnect(con)
-rm(list = ls()); gc()
+
+# Remove vars specific to script
+remove_vars <- setdiff(ls(), keep_vars)
+
+# Remove objects and clean environment
+rm(list = remove_vars, remove_vars); gc()
diff --git a/data-raw/app/06_metrics_by_geo_and_ch_flag_df.R b/data-raw/app/06_metrics_by_geo_and_ch_flag_df.R
index d7ee5847..68519755 100644
--- a/data-raw/app/06_metrics_by_geo_and_ch_flag_df.R
+++ b/data-raw/app/06_metrics_by_geo_and_ch_flag_df.R
@@ -1,28 +1,30 @@
-# Initial setup -----------------------------------------------------------
+# Initial setup ----------------------------------------------------------------
 
 # Expected run time ~40 minutes @parallel 24
 
+# Library and functions
 library(dplyr)
 library(dbplyr)
 library(stringr)
 library(glue)
 library(purrr)
-
 devtools::load_all()
 
 # Set up connection to DALP
 con <- nhsbsaR::con_nhsbsa(database = "DALP")
 
-# Data validation ---------------------------------------------------------
+# Data validation --------------------------------------------------------------
 
-## Setup ------------------------------------------------------------------
+## Setup -----------------------------------------------------------------------
 
+# Distinct postcode-related fields
 PCD <- con %>%
   tbl(from = "INT646_POSTCODE_LOOKUP") %>%
   select(ends_with("CODE"), ends_with("NAME")) %>%
   distinct() %>%
   collect()
 
+# function to transform fields by geography fields
 transform_PCD <- function(data, geography) {
   data %>%
     select(starts_with(glue("PCD_{geography}"))) %>%
@@ -33,19 +35,22 @@ transform_PCD <- function(data, geography) {
     filter(!is.na(SUB_GEOGRAPHY_NAME))
 }
 
+# Generate function output
 PCD_list <- list(
   REGION = PCD %>% transform_PCD("REGION"),
   ICB    = PCD %>% transform_PCD("ICB"),
   LAD    = PCD %>% transform_PCD("LAD")
 )
 
+# Define gis list
 GIS_list <- geo_data_validation
 
 # Check sub-geography codes and names match exactly between PCD and GIS; script
 # will stop if not
 
-## Check sub-geography codes ----------------------------------------------
+## Check sub-geography codes ---------------------------------------------------
 
+# Generate check
 check_sub_geo_codes <- list(
   in_GIS_only = map2(
     GIS_list,
@@ -59,6 +64,7 @@ check_sub_geo_codes <- list(
   )
 )
 
+# Stop if check fails
 stopifnot(
   "Some difference in geo codes: check `check_sub_geo_codes`"= {
     character(0) == check_sub_geo_codes %>%
@@ -67,8 +73,9 @@ stopifnot(
   }
 )
 
-## Check sub-geography names ----------------------------------------------
+## Check sub-geography names ---------------------------------------------------
 
+# Generate check
 check_sub_geo_names <- list(
   in_GIS_only = map2(
     GIS_list,
@@ -82,6 +89,7 @@ check_sub_geo_names <- list(
   )
 )
 
+# Stop if check fails
 stopifnot(
   "Some difference in geo names: check `check_sub_geo_names`"= {
     character(0) == check_sub_geo_names %>%
@@ -90,13 +98,14 @@ stopifnot(
   }
 )
 
-# Data prep ---------------------------------------------------------------
+# Data prep --------------------------------------------------------------------
 
-## Setup ------------------------------------------------------------------
+## Setup -----------------------------------------------------------------------
 
 # Item-level base table
 base_db <- con %>%
-  tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20230331"))
+  #tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20230331"))
+  tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20240331"))
 
 # Aggregate by a geography
 aggregate_by_geo <- function(geography_name) {
@@ -141,21 +150,28 @@ aggregate_by_geo <- function(geography_name) {
     rename(!!!geography_cols)
 }
 
-## Process ----------------------------------------------------------------
+## Process ---------------------------------------------------------------------
 metrics_by_geo_and_ch_flag_df <- names(geographies)[-1] %>% 
   map(aggregate_by_geo) %>%
   list_rbind()
 
-## Format -----------------------------------------------------------------
+## Format ----------------------------------------------------------------------
 metrics_by_geo_and_ch_flag_df <- metrics_by_geo_and_ch_flag_df %>%
   mutate(CH_FLAG = as.logical(CH_FLAG)) %>% 
   filter(!is.na(SUB_GEOGRAPHY_NAME)) %>% 
   format_data_raw("CH_FLAG") %>% 
   suppressWarnings() # We do not have Overall in this data
 
-## Save -------------------------------------------------------------------
+## Save ------------------------------------------------------------------------
 usethis::use_data(metrics_by_geo_and_ch_flag_df, overwrite = TRUE)
 
-# Cleanup -----------------------------------------------------------------
+# Cleanup ----------------------------------------------------------------------
+
+# Disconnect
 DBI::dbDisconnect(con)
-rm(list = ls())
+
+# Remove vars specific to script
+remove_vars <- setdiff(ls(), keep_vars)
+
+# Remove objects and clean environment
+rm(list = remove_vars, remove_vars); gc()
diff --git a/data-raw/app/07_ch_flag_drug_df.R b/data-raw/app/07_ch_flag_drug_df.R
index 17270a0e..6cc3792f 100644
--- a/data-raw/app/07_ch_flag_drug_df.R
+++ b/data-raw/app/07_ch_flag_drug_df.R
@@ -1,5 +1,4 @@
 
-
 # Library
 library(dplyr)
 library(dbplyr)
@@ -9,7 +8,8 @@ con <- nhsbsaR::con_nhsbsa(database = "DALP")
 
 # Create a lazy table from the item level base table
 fact_db <- con %>%
-  dplyr::tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20230331"))
+  #tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20230331"))
+  tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20240331"))
 
 # BNF columns
 bnf_cols = c(
@@ -91,7 +91,7 @@ get_geo_bnf_prop = function(index){
     BNF_PARENT = unique(df$BNF_PARENT),
     BNF_CHILD = unique(df$BNF_CHILD),
     METRIC = unique(df$METRIC)
-  ) %>%
+    ) %>%
     left_join(df) %>%
     mutate(VALUE = ifelse(is.na(VALUE), 0, VALUE))
 }
@@ -211,6 +211,12 @@ mod_ch_flag_drug_df %>% count(FY, CH_FLAG, BNF_PARENT)
 usethis::use_data(mod_ch_flag_drug_df, overwrite = TRUE)
 
 # Disconnect
-DBI::dbDisconnect(con); rm(list = ls()); gc()
+DBI::dbDisconnect(con)
+
+# Remove vars specific to script
+remove_vars <- setdiff(ls(), keep_vars)
+
+# Remove objects and clean environment
+rm(list = remove_vars, remove_vars); gc()
 
-#-------------------------------------------------------------------------------
+#-------------------------------------------------------------------------------
\ No newline at end of file
diff --git a/data-raw/app/08_geo_ch_flag_drug_df.R b/data-raw/app/08_geo_ch_flag_drug_df.R
index dab29663..ff66a34a 100644
--- a/data-raw/app/08_geo_ch_flag_drug_df.R
+++ b/data-raw/app/08_geo_ch_flag_drug_df.R
@@ -8,7 +8,8 @@ con <- nhsbsaR::con_nhsbsa(database = "DALP")
 
 # Create a lazy table from the item level base table
 fact_db <- con %>%
-  dplyr::tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20230331"))
+  #tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20230331"))
+  tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20240331"))
 
 # BNF columns
 bnf_cols = c(
diff --git a/data-raw/workflow/07_item_level_base.R b/data-raw/workflow/07_item_level_base.R
index 12c0278d..1f650783 100644
--- a/data-raw/workflow/07_item_level_base.R
+++ b/data-raw/workflow/07_item_level_base.R
@@ -199,7 +199,6 @@ presc_db = presc_db %>%
     PRESCRIBER_CODE = PRESCRIBER_LTST_CDE
     )
 
-
 # Process form fact
 form_db = form_db %>% 
   select(
diff --git a/data-raw/workflow/workflow_run_22_23.R b/data-raw/workflow/workflow_run_22_23.R
index 3c29c4d7..18893e33 100644
--- a/data-raw/workflow/workflow_run_22_23.R
+++ b/data-raw/workflow/workflow_run_22_23.R
@@ -39,7 +39,6 @@ create_care_home_address_match(
 # 6. Create postcode lookup table (latest available mappings) for joining in the next step: ~5 min
 # create_postcode_lookup() # Run once in first epoch script
 
-
 # 7. Join to fact table and get non ch-postcode records within time frame: ~9 hrs
 create_matched_prescription_base_table(
   match_data = "INT646_MATCH_20220401_20230331",