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descriptive_coding_script.R
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descriptive_coding_script.R
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# Script to generate descriptive stats from long covid coding
# LIBRARIES
library(tidyverse)
library(lubridate)
library(ggalluvial)
library(janitor)
#FUNCTIONS
#function to generate frequency tables
generate_freq_tables <- function(cohort_df, grouping_var){
grouping_var_name = names(cohort_df %>% select({{ grouping_var}} ))
cohort_df %>%
group_by({{ grouping_var }}) %>%
summarise(total_patients = n(),
acute_covid = sum(!is.na(diag_acute_covid)),
ongoing_covid = sum(!is.na(diag_ongoing_covid)),
post_covid = sum(!is.na(diag_post_covid)),
refer_post_covid_clinic = sum(!is.na(referral_pc_clinic)),
refer_yourcovidrecovery_website_only = sum(!is.na(referral_yourcovidrecovery_website_only)),
refer_yourcovidrecovery_website_program = sum(!is.na(referral_yourcovidrecovery_website_program)),
) %>%
rename("Group" = {{ grouping_var }}) %>%
mutate("Demographic" = grouping_var_name) %>%
adorn_totals("row") %>%
select(Demographic,
everything()) %>%
mutate(acute_covid_rate_per_100000 = round(acute_covid/total_patients * 100000, 1),
acute_covid_rate_CI_lower = round(crude_rate_normal_approx(acute_covid, total_patients, "lower") * 100000, 1),
acute_covid_rate_CI_upper = round(crude_rate_normal_approx(acute_covid, total_patients, "upper") * 100000, 1),
ongoing_covid_rate_per_100000 = round(ongoing_covid/total_patients * 100000,1),
ongoing_covid_rate_CI_lower = round(crude_rate_normal_approx(ongoing_covid, total_patients, "lower")*100000, 1),
ongoing_covid_rate_CI_upper = round(crude_rate_normal_approx(ongoing_covid, total_patients, "upper")*100000, 1),
post_covid_rate_per_100000 = round(post_covid/total_patients * 100000, 1),
post_covid_rate_CI_lower = round(crude_rate_normal_approx(post_covid, total_patients, "lower") * 100000, 1),
post_covid_rate_CI_upper = round(crude_rate_normal_approx(post_covid, total_patients, "upper") * 100000, 1),
refer_post_covid_clinic_rate_per_100000 = round(refer_post_covid_clinic/total_patients * 100000, 1),
refer_post_covid_clinic_rate_CI_lower = round(crude_rate_normal_approx(refer_post_covid_clinic, total_patients, "lower") * 100000, 1),
refer_post_covid_clinic_rate_CI_upper = round(crude_rate_normal_approx(refer_post_covid_clinic, total_patients, "upper") * 100000, 1),
refer_yourcovidrecovery_website_only_per_100000 = round(refer_yourcovidrecovery_website_only / total_patients * 100000, 1),
refer_yourcovidrecovery_website_only_CI_lower = round(crude_rate_normal_approx(refer_yourcovidrecovery_website_only, total_patients, "lower") * 100000, 1),
refer_yourcovidrecovery_website_only_CI_upper = round(crude_rate_normal_approx(refer_yourcovidrecovery_website_only, total_patients, "upper") * 100000, 1),
refer_yourcovidrecovery_website_program_rate_per_100000 = round(refer_yourcovidrecovery_website_program / total_patients * 100000, 1),
refer_yourcovidrecovery_website_program_rate_CI_lower = round(crude_rate_normal_approx(refer_yourcovidrecovery_website_program, total_patients, "lower") * 100000, 1),
refer_yourcovidrecovery_website_program_rate_CI_upper = round(crude_rate_normal_approx(refer_yourcovidrecovery_website_program, total_patients, "upper") * 100000, 1)
) %>%
ungroup()
}
crude_rate_normal_approx <- function(num, denom, upper_or_lower) {
upper <- num/denom + 1.96*sqrt(num)/denom
lower <- num/denom - 1.96*sqrt(num)/denom
return(
if (upper_or_lower == "upper"){
upper
} else if (upper_or_lower == "lower") {
lower
} else "error"
)
}
#REUSED VARIABLES
#demographic_variables
demo_vars <- c('sex', 'region', 'imd', 'ethnicity', 'age_group')
# Load cohort of all patients
cohort <- read_csv(file = "output/input_all.csv",
col_types = cols(patient_id = col_number(),
age_group = col_factor(levels = c("0-17","18-24", "25-34", "35-44", "45-54", "55-69", "70-79", "80+")),
msoa = col_factor(),
sex = col_factor(),
imd = col_factor(levels = c("1 (Most Deprived)", "2", "3", "4", "5 (Least Deprived)", "Unknown")),
ethnicity = col_factor(),
.default = col_date())
)
#Read in MSOA lookup
#https://geoportal.statistics.gov.uk/datasets/fe6c55f0924b4734adf1cf7104a0173e_0/explore?showTable=true
MSOA_Region_Lookup <- read_csv("analysis/MSOA_Region_Lookup.csv")
cohort <- cohort %>%
left_join(MSOA_Region_Lookup,
by = c("msoa" = "MSOA11CD")) %>%
rename("region" = "RGN11NM")
rm(MSOA_Region_Lookup)
#Table 1 Cohort
Table_1 <- demo_vars %>%
map(~generate_freq_tables(grouping_var = .data[[.x]],
cohort_df = cohort)) %>%
bind_rows() %>%
filter(across(where(is.numeric), ~ . >6)) %>%
group_by(Demographic) %>%
select(Demographic, Group, N = total_patients)
write_csv(Table_1, "output/Table_1.csv")
#Fig 1 Line Graph of code counts
Fig_1 <- cohort %>%
select(-diag_acute_covid, -diag_any_lc_diag) %>%
pivot_longer(cols = starts_with('referral')|starts_with('diag'), names_to = "code", names_repair = "minimal") %>%
group_by(code, month = floor_date(value, unit = "month")) %>%
summarise(n= n()) %>%
filter(!is.na(month), n > 10)
write_csv(Fig_1, "output/Fig_1_numbers.csv")
Fig_1 %>%
ggplot(aes(x= month, y= n, color = code)) +
geom_line()+
theme_minimal() +
labs(title= "Fig. 1 - Counts of long covid diagnosis and referral codes over time") +
scale_colour_hue(labels = c("Ongoing symptomatic COVID-19 Diagnosis Code",
"Post-COVID-19 Syndrome Diagnosis Code",
"Referral to Post-Covid Clinic Code",
"Referral to yourcovidrecovery.nhs.uk Website Code",
"Referral to yourcovidrecovery.nhs.uk Online Program Code")) +
theme(legend.position = "bottom", legend.direction = "vertical", legend.title = element_blank())
ggsave("output/Fig_1.png", width = 10, height = 7, units = "in")
#Table 2 referral_diag_table
Table_2 <- cohort %>%
group_by("Diagnosis" = case_when(!is.na(diag_any_lc_diag) ~ "Ongoing symptomatic or Post-COVID-19 syndrome diagnosis coded", TRUE ~ "No Diagnosis Coded"),
) %>%
summarise(n = n(),
`Referral to yourcovidrecovery website` = sum(!is.na(referral_yourcovidrecovery_website_only)),
`Referral to yourcovidrecovery program` = sum(!is.na(referral_yourcovidrecovery_website_program)),
`Referral to Post-COVID clinic` = sum(!is.na(referral_pc_clinic))) %>%
arrange(desc(Diagnosis))
write_csv(Table_2, "output/Table_2.csv")
#Table 3 demographic splits
Table_3 <- demo_vars %>%
map(~generate_freq_tables(grouping_var = .data[[.x]],
cohort_df = cohort)) %>%
bind_rows() %>%
filter(across(where(is.integer), ~ . >6)) %>%
group_by(Demographic) %>%
mutate(acute_covid_percentage = ifelse(Group == "Total", NA, round(acute_covid / sum(acute_covid) * 100, 1)),
ongoing_covid_percentage = ifelse(Group == "Total", NA, round(ongoing_covid / sum(ongoing_covid) * 100, 1)),
post_covid_percentage = ifelse(Group == "Total", NA, round(post_covid / sum(post_covid) * 100, 1)),
refer_yourcovidrecovery_website_only_percentage = ifelse(Group == "Total", NA, round(refer_yourcovidrecovery_website_only / sum(refer_yourcovidrecovery_website_only) * 100, 1)),
refer_yourcovidrecovery_website_program_percentage = ifelse(Group == "Total", NA, round(refer_yourcovidrecovery_website_program / sum(refer_yourcovidrecovery_website_program) * 100, 1)),
refer_post_covid_clinic_percentage = ifelse(Group == "Total", NA, round(refer_post_covid_clinic / sum(refer_post_covid_clinic) * 100, 1))
) %>%
select(Demographic, Group, total_patients, starts_with("acute_"), starts_with("ongoing_"), starts_with("post_"), starts_with("refer_post"), starts_with("refer_your"), everything())
write_csv(Table_3, "output/Table_3.csv")
#alluvial datasets
#Ongoing to self-care / pc
Fig_2 <- cohort %>%
filter(!is.na(diag_ongoing_covid)) %>%
mutate("has_diag_og_covid" = factor(case_when(!is.na(diag_ongoing_covid) ~ "Ongoing Covid", TRUE ~ "No Ongoing Covid"), ordered = TRUE),
"referral_yourcovidrecovery_website_only" = factor(case_when(!is.na(referral_yourcovidrecovery_website_only) ~ "YCR (website only)", TRUE ~ "No Signpost"), ordered = TRUE),
"has_diag_post_covid" = factor(case_when(!is.na(diag_post_covid) ~ "Post-COVID-19", TRUE ~ "No Post-COVID-19"), ordered = TRUE),
#"referral_yourcovidrecovery_program" = factor(case_when(!is.na(referral_yourcovidrecovery_website_program) ~ "YCR (website program)", TRUE ~ "No Referral"), ordered = TRUE),
# "referral_pc_clinic" = factor(case_when(!is.na(referral_pc_clinic) ~ "Post Covid Clinic", TRUE ~ "No Referral"), ordered = TRUE)
) %>%
group_by(has_diag_og_covid,
referral_yourcovidrecovery_website_only,
has_diag_post_covid,
# referral_yourcovidrecovery_program,
# referral_pc_clinic
) %>%
summarise(freq = n()) %>%
filter(freq > 6)
#alluvial graph - og destinations
ggplot(as.data.frame(Fig_2), aes(y=freq,
axis1=has_diag_og_covid,
axis2=referral_yourcovidrecovery_website_only,
axis3=has_diag_post_covid,
# axis4=referral_yourcovidrecovery_program,
# axis5=referral_pc_clinic
)) +
geom_alluvium(aes(fill = referral_yourcovidrecovery_website_only), aes.bind = TRUE) +
geom_stratum(width = 1/6, fill = "black", color = "grey") +
geom_label(stat = "stratum", aes(label = after_stat(stratum))) +
scale_x_discrete(limits = c("Ongoing Symptomatic Diagnosis",
"Yourcovidrecovery website",
"Post-COVID-19 Syndrome"
#"Yourcovidrecovery program",
#"Post-COVID-19 Clinic"
),
expand = c(0.05, 0.05)) +
scale_y_continuous(limits = c(0, sum(!is.na(cohort$diag_ongoing_covid))), expand = c(0.005, 0.005)) +
ggtitle("Fig. 2 - Patient flow from ongoing covid to referral destinations") +
theme_minimal() +
theme(legend.position = "bottom", legend.title = element_blank())
ggsave("output/Fig_2.png", width = 10, height = 7, units = "in")
write_csv(Fig_2, "output/Fig_2_numbers.csv")
#Post covid to self-care / pc
Fig_3 <- cohort %>%
filter(!is.na(diag_post_covid)) %>%
mutate("has_diag_post_covid" = case_when(!is.na(diag_post_covid) ~ "Post Covid", TRUE ~ "No Post Covid"),
"referral_yourcovidrecovery_program" = case_when(!is.na(referral_yourcovidrecovery_website_program) ~ "YCR Website Program", TRUE ~ "No Referral"),
"referral_pc_clinic" = case_when(!is.na(referral_pc_clinic) ~ "Post Covid Clinic", TRUE ~ "No Referral")
) %>%
group_by(has_diag_post_covid,
referral_yourcovidrecovery_program,
referral_pc_clinic
) %>%
summarise(freq = n()) %>%
filter(freq > 6)
#alluvial graph - pc destinations
ggplot(as.data.frame(Fig_3), aes(y=freq,
axis1=has_diag_post_covid,
axis2=referral_yourcovidrecovery_program,
axis3=referral_pc_clinic)) +
geom_alluvium(aes(fill = referral_yourcovidrecovery_program)) +
geom_stratum(width = 1/12, fill = "black", color = "grey") +
geom_label(stat = "stratum", aes(label = after_stat(stratum))) +
scale_x_discrete(limits = c("Post-COVID-19 Syndrome",
"Yourcovidrecovery program",
"Post-COVID-19 Clinic"),
expand = c(0.05, 0.05)) +
scale_y_continuous(limits = c(0, sum(!is.na(cohort$diag_post_covid))), expand = c(0.005, 0.005)) +
ggtitle("Fig. 3 - Patient flow from Post-COVID-19 syndrome diagnosis code") +
theme_minimal() +
theme(legend.position = "bottom", legend.title = element_blank())
ggsave("output/Fig_3.png", width = 10, height = 7, units = "in")
write_csv(Fig_3, "output/Fig_3_numbers.csv")