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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,142 @@ | ||
| #' Flag non-Scottish residents | ||
| #' | ||
| #' @details The variable keep flag can be in the range c(0:4) where | ||
| #' \itemize{ | ||
| #' \item{keep_flag = 0 when resident is Scottish} | ||
| #' \item{keep_flag = 1 when resident is not Scottish} | ||
| #' \item{keep_flag = 2 when the postcode is missing or a dummy, and the gpprac is missing} | ||
| #' \item{keep_flag = 3 when the gpprac is not English and the postcode is missing} | ||
| #' \item{keep_flag = 4 when the gpprac is not English and the postcode is a dummy} | ||
| #' } | ||
| #' The intention is to only keep the records where keep_flag = 0 | ||
| #' | ||
| #' @inheritParams add_hri_variables | ||
| #' | ||
| #' @return A data frame with the variable 'keep_flag' | ||
| flag_non_scottish_residents <- function( | ||
| data, | ||
| slf_pc_lookup) { | ||
| check_variables_exist(data, c("postcode", "gpprac")) | ||
|
|
||
| # Make a lookup of postcode areas, which consist of the first characters | ||
| # of the postcode | ||
| pc_areas <- slf_pc_lookup %>% | ||
| dplyr::mutate( | ||
| pc_area = stringr::str_match(postcode, "^[A-Z]{1,3}"), | ||
| scot_flag = TRUE | ||
| ) %>% | ||
| dplyr::distinct(pc_area, scot_flag) | ||
|
|
||
| # Create a flag, 'keep_flag', to determine whether individuals are Scottish | ||
| # residents or not | ||
| return_data <- data %>% | ||
| dplyr::mutate(pc_area = stringr::str_match(postcode, "^[A-Z]{1,3}")) %>% | ||
| dplyr::left_join(pc_areas, by = "pc_area") %>% | ||
| dplyr::mutate( | ||
| dummy_postcode = .data$postcode %in% c("BF010AA", "NF1 1AB", "NK010AA") | | ||
| stringr::str_sub(.data$postcode, 1, 4) %in% c("ZZ01", "ZZ61"), | ||
| eng_prac = .data$gpprac %in% c(99942, 99957, 99961, 99976, 99981, 99995, 99999), | ||
| scottish_resident = dplyr::case_when( | ||
| .data$scot_flag ~ 0L, | ||
| (is_missing(.data$postcode) | .data$dummy_postcode) & is.na(.data$gpprac) ~ 2L, | ||
| !.data$eng_prac & is_missing(.data$postcode) ~ 3L, | ||
| !.data$eng_prac & .data$dummy_postcode ~ 4L, | ||
| .default = 1L | ||
| ) | ||
| ) %>% | ||
| dplyr::select(-"dummy_postcode", -"eng_prac") | ||
|
|
||
| return(return_data) | ||
| } | ||
|
|
||
| #' Add HRI variables to an SLF Individual File | ||
| #' | ||
| #' @details Filters the dataset to only include Scottish residents, then | ||
| #' creates a lookup where HRIs are calculated at Scotland, Health Board, and | ||
| #' LCA level. Then joins on this lookup by chi/anon_chi. | ||
| #' | ||
| #' @param data An SLF individual file. | ||
| #' @param slf_pc_lookup The Source postcode lookup, defaults | ||
| #' to [get_slf_postcode_path()] read using [read_file()]. | ||
| #' | ||
| #' @return The individual file with HRI variables matched on | ||
| #' @export | ||
| add_hri_variables <- function( | ||
| data, | ||
| chi_variable = "chi", | ||
| slf_pc_lookup = read_file( | ||
| get_slf_postcode_path(), | ||
| col_select = "postcode" | ||
| )) { | ||
| hri_lookup <- data %>% | ||
| dplyr::select( | ||
| "year", | ||
| chi_variable, | ||
| "postcode", | ||
| "gpprac", | ||
| "lca", | ||
| "hbrescode", | ||
| "health_net_cost", | ||
| "acute_episodes", | ||
| "mat_episodes", | ||
| "mh_episodes", | ||
| "gls_episodes", | ||
| "op_newcons_attendances", | ||
| # op_newcons_dnas, | ||
| "ae_attendances", | ||
| "pis_paid_items", | ||
| "ooh_cases" | ||
| ) %>% | ||
| flag_non_scottish_residents(slf_pc_lookup = slf_pc_lookup) %>% | ||
| dplyr::filter(scottish_resident == 0L) %>% | ||
| # Scotland cost and proportion | ||
| dplyr::mutate( | ||
| scotland_cost = sum(health_net_cost), | ||
| scotland_pct = (health_net_cost / scotland_cost) * 100 | ||
| ) %>% | ||
| dplyr::arrange(dplyr::desc(health_net_cost)) %>% | ||
| dplyr::mutate(hri_scotp = cumsum(scotland_pct)) %>% | ||
| # Health Board | ||
| dplyr::group_by(hbrescode) %>% | ||
| dplyr::mutate( | ||
| hb_cost = sum(health_net_cost), | ||
| hb_pct = (health_net_cost / hb_cost) * 100 | ||
| ) %>% | ||
| dplyr::arrange(dplyr::desc(health_net_cost), .by_group = TRUE) %>% | ||
| dplyr::mutate(hri_hbp = cumsum(hb_pct)) %>% | ||
| dplyr::ungroup() %>% | ||
| # LCA | ||
| dplyr::group_by(lca) %>% | ||
| dplyr::mutate( | ||
| lca_cost = sum(health_net_cost), | ||
| lca_pct = (health_net_cost / lca_cost) * 100 | ||
| ) %>% | ||
| dplyr::arrange(dplyr::desc(health_net_cost), .by_group = TRUE) %>% | ||
| dplyr::mutate(hri_lcap = cumsum(lca_pct)) %>% | ||
| dplyr::ungroup() %>% | ||
| # Add HRI flags | ||
| dplyr::mutate( | ||
| hri_scot = hri_scotp <= 50.0, | ||
| hri_hb = hri_hbp <= 50.0, | ||
| hri_lca = hri_lcap <= 50.0, | ||
| # Deal with potential missing variables | ||
| hri_hb = dplyr::if_else(is_missing(hbrescode), FALSE, hri_hb), | ||
| hri_hbp = dplyr::if_else(is_missing(hbrescode), NA, hri_hbp), | ||
| hri_lca = dplyr::if_else(is_missing(lca), FALSE, hri_lca), | ||
| hri_lcap = dplyr::if_else(is_missing(lca), NA, hri_lcap) | ||
| ) %>% | ||
| # Select only required variables for the lookup | ||
| dplyr::select( | ||
| chi_variable, | ||
| "hri_scot", | ||
| "hri_scotp", | ||
| "hri_hb", | ||
| "hri_hbp", | ||
| "hri_lca", | ||
| "hri_lcap" | ||
| ) | ||
|
|
||
| return_data <- dplyr::left_join(data, hri_lookup, by = chi_variable) | ||
|
|
||
| return(return_data) | ||
| } |
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