Public-Health-Scotland · lizihao-anu · Oct 15, 2024 · Sep 18, 2024 · Sep 18, 2024 · Sep 18, 2024
diff --git a/NEWS.md b/NEWS.md
@@ -1,4 +1,4 @@
-# September 2024 Update - Unreleased 
+# September 2024 Update - released 13-Sep-24
 * New 24/25 files created
 * New NSU cohort for 23/24 available
 * New SPARRA scores calculated from April 24/25
@@ -75,7 +75,7 @@
  * Homelessness Flags.
 * Bug fixes: 
  * Blank `datazone` in A&E. This has been fixed and was due to PC8 postcode format matching onto SLF pc lookup. 
  * Large increase in preventable beddays. This was caused due to an SPSS vs R logic difference. Uses SPSS logic which 
    brings the difference down to `3.3%`. 
  * Issue with `locality` which showed `locality` in each row instead of its true `locality`. This has now been fixed. 
  * Duplicated CHI in the individual file. The issue was identified when trying to include HRIs. This has now been corrected. 
@@ -94,7 +94,7 @@
 * Removal of `keydate1_dateformat` and `keydate2_dateformat`.
 * `dd_responsible_lca` – This variable now uses CA2019 codes instead of the 2-digit ‘old’ LCA code.
 * Preventable beddays - not able to calculate these correctly. * Death fixes not included.
 * Variables not ordered in R like they used to be in SPSS.
 * End of HHG.
 * New variable `ch_postcode`.
 * rename of variables `cost_total_net_incdnas`, `ooh_outcome.1`, `ooh_outcome.2`, `ooh_outcome.3`, `ooh_outcome.4`, `totalnodncontacts`. 
@@ -155,7 +155,7 @@
 * Fixed a bug where CH costs was not referring to end of year. 
  * e.g. 2018 costs relates to 2017/18
 * The changes to Homelessness described in the March update have been properly implemented.
 * We now use [`{haven}`](https://haven.tidyverse.org/news/index.html) to compress the SPSS files which compresses them better than SPSS does 🤷
 ♂️
 * `cij_marker` is now a numeric instead of a string which changes empty strings to missing instead of blank using sysmis.
  * Check code of the form `cij_marker = "x"`. `x` now needs to be a numeric.

diff --git a/R/fill_geographies.R b/R/fill_geographies.R
@@ -191,7 +191,6 @@ fill_gpprac_geographies <- function(
 #'
 #' @return data with matched HSCP and LCA codes
 cascade_geographies <- function(data) {
-  # TODO rework this function into a series of smaller functions which operate on vectors
   # e.g. cascade_hscp_lca <- function(hscp, lca) {...}
   # Would take HSCP and populate any missing LCA using it
   data <- data %>%

diff --git a/R/process_extract_alarms_telecare.R b/R/process_extract_alarms_telecare.R
@@ -41,7 +41,6 @@ process_extract_alarms_telecare <- function(
       "smrtype",
       "chi",
       "dob",
-      # "person_id",
       "gender",
       "postcode",
       "sc_send_lca",

diff --git a/R/process_extract_care_home.R b/R/process_extract_care_home.R
@@ -115,7 +115,6 @@ process_extract_care_home <- function(
       "recid",
       "smrtype",
       "chi",
-      # "person_id",
       "dob",
       "gender",
       "postcode",

diff --git a/R/process_extract_gp_ooh.R b/R/process_extract_gp_ooh.R
@@ -88,7 +88,6 @@ process_extract_gp_ooh <- function(year,
     )
 
   # Keep the location descriptions as a lookup.
-  # TODO write the GP OoH lookup out using some functions
   location_lookup <- ooh_clean %>%
     dplyr::group_by(.data$location) %>%
     dplyr::summarise(

diff --git a/R/process_extract_home_care.R b/R/process_extract_home_care.R
@@ -95,8 +95,7 @@ process_extract_home_care <- function(
       tidyselect::starts_with("hc_cost_"),
       "cost_total_net",
       "hc_provider",
-      "hc_reablement",
-      # "person_id"
+      "hc_reablement"
     ) %>%
     slfhelper::get_anon_chi()
 

diff --git a/R/process_extract_homelessness.R b/R/process_extract_homelessness.R
@@ -154,7 +154,6 @@ process_extract_homelessness <- function(
     dplyr::rename(hl1_completeness = "pct_complete_all") %>%
     dplyr::mutate(hl1_completeness = round(.data$hl1_completeness, 1))
 
-  # TODO - Include person_id (from client_id)
   final_data <- hl1_data %>%
     dplyr::select(
       "year",

diff --git a/R/process_extract_sds.R b/R/process_extract_sds.R
@@ -41,7 +41,6 @@ process_extract_sds <- function(
       "smrtype",
       "chi",
       "dob",
-      # "person_id",
       "gender",
       "postcode",
       "sc_send_lca",

diff --git a/R/process_sc_all_alarms_telecare.R b/R/process_sc_all_alarms_telecare.R
@@ -87,11 +87,6 @@ process_sc_all_alarms_telecare <- function(
   # Replace social_care_id with latest if needed (assuming replace_sc_id_with_latest is a custom function)
   data <- replace_sc_id_with_latest(data)
 
-  # data$person_id <- paste0(
-  #   data$sending_location,
-  #   "-",
-  #   data$social_care_id
-  # )
 
   # Deal with episodes that have a package across quarters
   data[, pkg_count := seq_len(.N), by = list(
@@ -125,7 +120,6 @@ process_sc_all_alarms_telecare <- function(
     dob = data.table::last(dob),
     postcode = data.table::last(postcode),
     recid = data.table::last(recid),
-    # person_id = data.table::last(person_id),
     sc_send_lca = data.table::last(sc_send_lca)
   ), by = list(
     sending_location,

diff --git a/R/process_sc_all_care_home.R b/R/process_sc_all_care_home.R
@@ -47,7 +47,7 @@
      by = c("sending_location", "social_care_id")
    ) %>%
    replace_sc_id_with_latest() %>%
    dplyr::select(-latest_flag, -latest_sc_id)


  # cleaning and matching care home names
@@ -385,7 +385,6 @@
     )) %>%
     dplyr::select(
       "chi",
-      # "person_id",
       "gender",
       "dob",
       "postcode",

diff --git a/R/process_sc_all_sds.R b/R/process_sc_all_sds.R
@@ -128,11 +128,6 @@ process_sc_all_sds <- function(
       "SDS",
       convert_sc_sending_location_to_lca(sending_location)
     )]
-  # sds_full_clean_long$person_id <- paste0(
-  #   sds_full_clean_long$sending_location,
-  #   "-",
-  #   sds_full_clean_long$social_care_id
-  # )
 
   # Group, arrange and create flags for episodes
   sds_full_clean_long[,
@@ -176,7 +171,6 @@ process_sc_all_sds <- function(
     dob = data.table::last(dob),
     postcode = data.table::last(postcode),
     recid = data.table::last(recid),
-    # person_id = data.table::last(person_id),
     sc_send_lca = data.table::last(sc_send_lca)
   ), by = list(sending_location, social_care_id, smrtype, episode_counter)]
   rm(sds_full_clean_long)

diff --git a/_SPSS_archived/All_years/04-Social_Care/00-Social_Care_functions.R b/_SPSS_archived/All_years/04-Social_Care/00-Social_Care_functions.R
@@ -50,7 +50,6 @@ phs_db_connection <- function(dsn, username = Sys.getenv("USER")) {
 }
 
 
-# TODO- check R conversion for SC demog lookup. This may differ
 #' Social Care Demographic Lookup File Path
 #'
 #' @description Get the file path for the Social Care Demographic lookup file

diff --git a/_SPSS_archived/All_years/04-Social_Care/02-Home_care_data.R b/_SPSS_archived/All_years/04-Social_Care/02-Home_care_data.R
@@ -14,7 +14,6 @@ source("All_years/04-Social_Care/00-Social_Care_functions.R")
 sc_con <- phs_db_connection(dsn = "DVPROD")
 
 # Read demographic file
-# TODO replace the demographic file with R code
 demog_file <- read_demog_file(
   social_care_dir = social_care_dir,
   latest_update = latest_update()