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07_eol_WP3_MAIHDA.R
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07_eol_WP3_MAIHDA.R
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##############################################################
# MAIHDA analysis
# Author: Miranda & Sophie
# Date: 07/12/23 # nolint # nolint: commented_code_linter.
# Initial aim: Running descriptive statistics
# for potential stratum variables to determine whether group sizes are sufficient
##############################################################
# Note: Patients with no IMD are excluded from the analysis as are patients aged 0-24. Analysis focuses on patients who die at home, with a cancer diagnosis.
# Load packages
library(tidyverse)
library(lubridate)
library(dplyr)
library(data.table)
# Create folder structure
fs::dir_create("output", "os_reports", "WP3")
# Code settings
startdate <- dmy("01-09-2022")
enddate <- dmy("31-08-2023")
# Prepare data
df <- read_csv(file = here::here("output", "os_reports", "input_os_reports.csv.gz")) %>%
mutate(dod_ons = as_date(dod_ons)
, study_month = floor_date(dod_ons, unit = "month")
, pod_ons_new = case_when(pod_ons == "Elsewhere"
| pod_ons == "Other communal establishment" ~ "Elsewhere/other"
, TRUE ~ as.character(pod_ons))
, cod_ons_3 = str_sub(cod_ons, 1, 3)
, cod_ons_4 = str_sub(cod_ons, 1, 5)
, codgrp = case_when(cod_ons_4 %in% c("U071", "U072") ~ "Covid-19"
, cod_ons_3 >= "J09" & cod_ons_3 <= "J18" ~ "Flu and pneumonia"
, (cod_ons_3 >= "J00" & cod_ons_3 <= "J08") | (cod_ons_3 >= "J19" & cod_ons_3 <= "J99") ~ "Other respiratory diseases"
, cod_ons_3 %in% c("F01", "F03", "G30") ~ "Dementia and Alzheimer's disease"
, cod_ons_3 >= "I00" & cod_ons_3 <= "I99" ~ "Circulatory diseases"
, cod_ons_3 >= "C00" & cod_ons_3 <= "C99" ~ "Cancer"
, TRUE ~ "All other causes")) %>%
filter(study_month >= startdate & study_month <= enddate & imd_quintile >=1 & age_band != "0-24")
# Counts of places of death by ethnicity for all deaths + cancer only - not for release
deaths_ethnicity_place_all_raw <- df %>%
group_by(ethnicity_Combined, pod_ons_new) %>%
summarise(count = n())
fwrite(deaths_ethnicity_place_all_raw, here::here("output", "os_reports", "WP3", "deaths_ethnicity_place_all_raw.csv"))
deaths_ethnicity_place_cancer_raw <- df %>%
filter(codgrp == "Cancer") %>%
group_by(ethnicity_Combined, pod_ons_new) %>%
summarise(count = n())
fwrite(deaths_ethnicity_place_cancer_raw, here::here("output", "os_reports", "WP3", "deaths_ethnicity_place_cancer_raw.csv"))
# Descriptive analysis to inform modelling - counts of age_band / sex / ethnicity and imd_rounded
cols_of_interest <- c("n");
count_by_sex <- df %>%
count(sex) %>%
dplyr::mutate(across(.cols = all_of(cols_of_interest), .fns = ~ replace(.x, (. <= 7 & . > 0), NA))) %>%
dplyr::mutate(across(.cols = all_of(cols_of_interest), .fns = ~ .x %>% `/`(5) %>% round()*5));
fwrite(count_by_sex, here::here("output", "os_reports", "WP3", "count_by_sex.csv"))
count_by_age_band <- df %>%
count(age_band) %>%
dplyr::mutate(across(.cols = all_of(cols_of_interest), .fns = ~ replace(.x, (. <= 7 & . > 0), NA))) %>%
dplyr::mutate(across(.cols = all_of(cols_of_interest), .fns = ~ .x %>% `/`(5) %>% round()*5));
fwrite(count_by_age_band, here::here("output", "os_reports", "WP3", "count_by_age_band.csv"))
count_by_sex_age_band <- df %>%
count(sex, age_band) %>%
dplyr::mutate(across(.cols = all_of(cols_of_interest), .fns = ~ replace(.x, (. <= 7 & . > 0), NA))) %>%
dplyr::mutate(across(.cols = all_of(cols_of_interest), .fns = ~ .x %>% `/`(5) %>% round()*5));
fwrite(count_by_sex_age_band, here::here("output", "os_reports", "WP3", "count_by_sex_age_band.csv"))
count_by_ethnicity <- df %>%
count(ethnicity_Combined) %>%
dplyr::mutate(across(.cols = all_of(cols_of_interest), .fns = ~ replace(.x, (. <= 7 & . > 0), NA))) %>%
dplyr::mutate(across(.cols = all_of(cols_of_interest), .fns = ~ .x %>% `/`(5) %>% round()*5));
fwrite(count_by_ethnicity, here::here("output", "os_reports", "WP3", "count_by_ethnicity.csv"))
count_by_imd_quintile <- df %>%
count(imd_quintile) %>%
dplyr::mutate(across(.cols = all_of(cols_of_interest), .fns = ~ replace(.x, (. <= 7 & . > 0), NA))) %>%
dplyr::mutate(across(.cols = all_of(cols_of_interest), .fns = ~ .x %>% `/`(5) %>% round()*5));
fwrite(count_by_imd_quintile, here::here("output", "os_reports", "WP3", "count_by_imd_quintile.csv"))
count_by_group <- df %>%
count(sex, age_band, ethnicity_Combined, imd_quintile) %>%
dplyr::mutate(across(.cols = all_of(cols_of_interest), .fns = ~ replace(.x, (. <= 7 & . > 0), NA))) %>%
dplyr::mutate(across(.cols = all_of(cols_of_interest), .fns = ~ .x %>% `/`(5) %>% round()*5));
fwrite(count_by_group, here::here("output", "os_reports", "WP3", "count_by_group.csv"))
# Descriptive analysis to inform modelling for cancer deaths at home - counts of age_band / sex / ethnicity and imd_rounded ------
cancer_count_by_sex <- df %>%
filter(codgrp == "Cancer"
& pod_ons_new == "Home") %>%
group_by(sex) %>%
count(sex) %>%
dplyr::mutate(across(.cols = all_of(cols_of_interest), .fns = ~ replace(.x, (. <= 7 & . > 0), NA))) %>%
dplyr::mutate(across(.cols = all_of(cols_of_interest), .fns = ~ .x %>% `/`(5) %>% round()*5));
fwrite(cancer_count_by_sex, here::here("output", "os_reports", "WP3", "cancer_count_by_sex.csv"))
cancer_count_by_age_band <- df %>%
filter(codgrp == "Cancer"
& pod_ons_new == "Home") %>%
group_by(age_band) %>%
count(age_band) %>%
dplyr::mutate(across(.cols = all_of(cols_of_interest), .fns = ~ replace(.x, (. <= 7 & . > 0), NA))) %>%
dplyr::mutate(across(.cols = all_of(cols_of_interest), .fns = ~ .x %>% `/`(5) %>% round()*5));
fwrite(cancer_count_by_age_band, here::here("output", "os_reports", "WP3", "cancer_count_by_age_band.csv"))
cancer_count_by_sex_age_band <- df %>%
filter(codgrp == "Cancer"
& pod_ons_new == "Home") %>%
group_by(sex, age_band) %>%
count(sex, age_band) %>%
dplyr::mutate(across(.cols = all_of(cols_of_interest), .fns = ~ replace(.x, (. <= 7 & . > 0), NA))) %>%
dplyr::mutate(across(.cols = all_of(cols_of_interest), .fns = ~ .x %>% `/`(5) %>% round()*5));
fwrite(cancer_count_by_sex_age_band, here::here("output", "os_reports", "WP3", "cancer_count_by_sex_age_band.csv"))
cancer_count_by_ethnicity <- df %>%
filter(codgrp == "Cancer"
& pod_ons_new == "Home") %>%
group_by(ethnicity_Combined) %>%
count(ethnicity_Combined) %>%
dplyr::mutate(across(.cols = all_of(cols_of_interest), .fns = ~ replace(.x, (. <= 7 & . > 0), NA))) %>%
dplyr::mutate(across(.cols = all_of(cols_of_interest), .fns = ~ .x %>% `/`(5) %>% round()*5));
fwrite(cancer_count_by_ethnicity, here::here("output", "os_reports", "WP3", "cancer_count_by_ethnicity.csv"))
cancer_count_by_imd_quintile <- df %>%
filter(codgrp == "Cancer"
& pod_ons_new == "Home") %>%
group_by(imd_quintile) %>%
count(imd_quintile) %>%
dplyr::mutate(across(.cols = all_of(cols_of_interest), .fns = ~ replace(.x, (. <= 7 & . > 0), NA))) %>%
dplyr::mutate(across(.cols = all_of(cols_of_interest), .fns = ~ .x %>% `/`(5) %>% round()*5));
fwrite(cancer_count_by_imd_quintile, here::here("output", "os_reports", "WP3", "cancer_count_by_imd_quintile.csv"))
cancer_count_by_group <- df %>%
filter(codgrp == "Cancer"
& pod_ons_new == "Home") %>%
group_by(sex, age_band, imd_quintile, ethnicity_Combined) %>%
count(sex, age_band, imd_quintile, ethnicity_Combined) %>%
dplyr::mutate(across(.cols = all_of(cols_of_interest), .fns = ~ replace(.x, (. <= 7 & . > 0), NA))) %>%
dplyr::mutate(across(.cols = all_of(cols_of_interest), .fns = ~ .x %>% `/`(5) %>% round()*5));
fwrite(cancer_count_by_group, here::here("output", "os_reports", "WP3", "cancer_count_by_group.csv"))