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consultation_by_age_infection_prevalent.R
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consultation_by_age_infection_prevalent.R
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##############
## Consultation rates for 6 common infection over time,
## stratified by age categories.
## Consultation for common infection will only include those with no prior records in 6 weeks of the same infection.
##############
library("data.table")
library("dplyr")
library('here')
library("tidyverse")
library("cowplot")
### 1. import data
##1.1 UTI
df <- read_csv(
here::here("output", "measures", "measure_infec_consult_UTI.csv"),
col_types = cols_only(
# Identifier
practice = col_integer(),
# Outcomes
uti_counts = col_double(),
population = col_double(),
incdt_uti_pt = col_double(),
age_cat = col_character(),
value = col_double(),
# Date
date = col_date(format="%Y-%m-%d")
),
na = character()
)
df$date <- as.Date(df$date)
df=df%>% rename(infection_counts=uti_counts, incdt_pt=incdt_uti_pt)
df[is.na(df)] <- 0 # replace NA ->0
# remove last month data
last.date=max(df$date)
df=df%>% filter(date!=last.date)
first_mon=format(min(df$date),"%m-%Y")
last_mon= format(max(df$date),"%m-%Y")
TPPnumber=length(unique(df$practice))
# select incident pt and count consultations
# incdt_pt==1 means has consultation in prior 6 weeks
df$incdt_counts=ifelse(df$incdt_pt==1,df$infection_counts,0)
# add col: population per GP in each time point- same number in multiple row within same gp
df=df%>%
group_by(date,practice)%>%
mutate(population=sum(population))
# summarize incident number of each age_cat
df_sum_gp_age=df%>%
group_by(date,practice,age_cat)%>%
summarise(pt_counts=sum(incdt_counts), # count consultation number in each age_cat; incident=1(count=0), incident=0(add count)
pupulation=mean(population)) # both incident=1 or 0 has same GP population, so add mean
# "rate per 1,000 registered patients"
df_sum_gp_age$rate=df_sum_gp_age$pt_counts/df_sum_gp_age$pupulation*1000
# add variables
df_1=df_sum_gp_age%>%
#group_by(date,age_cat)%>%
#summarise(rate=mean(rate))%>%
mutate(year=format(date,"%Y"),
month=format(date,"%m"),
indic="UTI")
rm(df,df_sum_gp_age,first_mon,last_mon,last.date)
###1.2 LRTI
#import data
df <- read_csv(
here::here("output", "measures", "measure_infec_consult_LRTI.csv"),
col_types = cols_only(
# Identifier
practice = col_integer(),
# Outcomes
lrti_counts = col_double(),
population = col_double(),
incdt_lrti_pt = col_double(),
age_cat = col_character(),
value = col_double(),
# Date
date = col_date(format="%Y-%m-%d")
),
na = character()
)
df$date <- as.Date(df$date)
df=df%>% rename(infection_counts=lrti_counts, incdt_pt=incdt_lrti_pt)
df[is.na(df)] <- 0 # replace NA ->0
# remove last month data
last.date=max(df$date)
df=df%>% filter(date!=last.date)
first_mon=format(min(df$date),"%m-%Y")
last_mon= format(max(df$date),"%m-%Y")
# select incdt=1 , count prevalent patient number
df$incdt_counts=ifelse(df$incdt_pt==1,df$infection_counts,0)
# add col: population per GP in each time point- same number in multiple row within same gp
df=df%>%
group_by(date,practice)%>%
mutate(population=sum(population))
# summarize incident number of each age_cat
df_sum_gp_age=df%>%
group_by(date,practice,age_cat)%>%
summarise(pt_counts=sum(incdt_counts), # count consultation number in each age_cat; incident=1(count=0), incident=0(add count)
pupulation=mean(population)) # both incident=1 or 0 has same GP population, so add mean
# "rate per 1,000 registered patients"
df_sum_gp_age$rate=df_sum_gp_age$pt_counts/df_sum_gp_age$pupulation*1000
# add variables
df_2=df_sum_gp_age%>%
mutate(year=format(date,"%Y"),
month=format(date,"%m"),
indic="LRTI")
rm(df,df_sum_gp_age,first_mon,last_mon,last.date)
### 1.3 URTI
#import data
df <- read_csv(
here::here("output", "measures", "measure_infec_consult_URTI.csv"),
col_types = cols_only(
# Identifier
practice = col_integer(),
#Outcomes
urti_counts = col_double(),
population = col_double(),
incdt_urti_pt = col_double(),
age_cat = col_character(),
value = col_double(),
# Date
date = col_date(format="%Y-%m-%d")
),
na = character()
)
df$date <- as.Date(df$date)
df=df%>% rename(infection_counts=urti_counts, incdt_pt=incdt_urti_pt)
df[is.na(df)] <- 0 # replace NA ->0
# remove last month data
last.date=max(df$date)
df=df%>% filter(date!=last.date)
first_mon=format(min(df$date),"%m-%Y")
last_mon= format(max(df$date),"%m-%Y")
# select incdt=1 , count prevalent patient number
df$incdt_counts=ifelse(df$incdt_pt==1,df$infection_counts,0)
# add col: population per GP in each time point- same number in multiple row within same gp
df=df%>%
group_by(date,practice)%>%
mutate(population=sum(population))
# summarize incident number of each age_cat
df_sum_gp_age=df%>%
group_by(date,practice,age_cat)%>%
summarise(pt_counts=sum(incdt_counts), # count consultation number in each age_cat; incident=1(count=0), incident=0(add count)
pupulation=mean(population)) # both incident=1 or 0 has same GP population, so add mean
# "rate per 1,000 registered patients"
df_sum_gp_age$rate=df_sum_gp_age$pt_counts/df_sum_gp_age$pupulation*1000
# add variables
df_3=df_sum_gp_age%>%
mutate(year=format(date,"%Y"),
month=format(date,"%m"),
indic="URTI")
rm(df,df_sum_gp_age,first_mon,last_mon,last.date)
###1.4 sinusitis
#import data
df <- read_csv(
here::here("output", "measures", "measure_infec_consult_sinusitis.csv"),
col_types = cols_only(
# Identifier
practice = col_integer(),
# Outcomes
sinusitis_counts = col_double(),
population = col_double(),
incdt_sinusitis_pt = col_double(),
age_cat = col_character(),
value = col_double(),
# Date
date = col_date(format="%Y-%m-%d")
),
na = character()
)
df$date <- as.Date(df$date)
df=df%>% rename(infection_counts=sinusitis_counts, incdt_pt=incdt_sinusitis_pt)
df[is.na(df)] <- 0 # replace NA ->0
# remove last month data
last.date=max(df$date)
df=df%>% filter(date!=last.date)
first_mon=format(min(df$date),"%m-%Y")
last_mon= format(max(df$date),"%m-%Y")
# select incdt=1, count prevalent patient number
df$incdt_counts=ifelse(df$incdt_pt==1,df$infection_counts,0)
# add col: population per GP in each time point- same number in multiple row within same gp
df=df%>%
group_by(date,practice)%>%
mutate(population=sum(population))
# summarize incident number of each age_cat
df_sum_gp_age=df%>%
group_by(date,practice,age_cat)%>%
summarise(pt_counts=sum(incdt_counts), # count consultation number in each age_cat; incident=1(count=0), incident=0(add count)
pupulation=mean(population)) # both incident=1 or 0 has same GP population, so add mean
# "rate per 1,000 registered patients"
df_sum_gp_age$rate=df_sum_gp_age$pt_counts/df_sum_gp_age$pupulation*1000
# add variables
df_4=df_sum_gp_age%>%
group_by(date,age_cat)%>%
summarise(rate=mean(rate))%>%
mutate(year=format(date,"%Y"),
month=format(date,"%m"),
indic="sinusitis")
rm(df,df_sum_gp_age,first_mon,last_mon,last.date)
### 1.5 ot_externa
# import data
df <- read_csv(
here::here("output", "measures", "measure_infec_consult_ot_externa.csv"),
col_types = cols_only(
# Identifier
practice = col_integer(),
# Outcomes
ot_externa_counts = col_double(),
population = col_double(),
incdt_ot_externa_pt = col_double(),
age_cat = col_character(),
value = col_double(),
# Date
date = col_date(format="%Y-%m-%d")
),
na = character()
)
df$date <- as.Date(df$date)
df=df%>% rename(infection_counts=ot_externa_counts, incdt_pt=incdt_ot_externa_pt)
df[is.na(df)] <- 0 # replace NA ->0
# remove last month data
last.date=max(df$date)
df=df%>% filter(date!=last.date)
first_mon=format(min(df$date),"%m-%Y")
last_mon= format(max(df$date),"%m-%Y")
# select incdt=1 , count prevalent patient number
df$incdt_counts=ifelse(df$incdt_pt==1,df$infection_counts,0)
# add col: population per GP in each time point- same number in multiple row within same gp
df=df%>%
group_by(date,practice)%>%
mutate(population=sum(population))
# summarize incident number of each age_cat
df_sum_gp_age=df%>%
group_by(date,practice,age_cat)%>%
summarise(pt_counts=sum(incdt_counts), # count consultation number in each age_cat; incident=1(count=0), incident=0(add count)
pupulation=mean(population)) # both incident=1 or 0 has same GP population, so add mean
# "rate per 1,000 registered patients"
df_sum_gp_age$rate=df_sum_gp_age$pt_counts/df_sum_gp_age$pupulation*1000
# add variables
df_5=df_sum_gp_age%>%
mutate(year=format(date,"%Y"),
month=format(date,"%m"),
indic="otitis externa")
rm(df,df_sum_gp_age,first_mon,last_mon,last.date)
### 1.6 otmedia
# import data
df <- read_csv(
here::here("output", "measures", "measure_infec_consult_otmedia.csv"),
col_types = cols_only(
#Identifier
practice = col_integer(),
#Outcomes
otmedia_counts = col_double(),
population = col_double(),
incdt_otmedia_pt = col_double(),
age_cat = col_character(),
value = col_double(),
#Date
date = col_date(format="%Y-%m-%d")
),
na = character()
)
df$date <- as.Date(df$date)
df=df%>% rename(infection_counts=otmedia_counts, incdt_pt=incdt_otmedia_pt)
df[is.na(df)] <- 0 # replace NA ->0
# remove last month data
last.date=max(df$date)
df=df%>% filter(date!=last.date)
first_mon=format(min(df$date),"%m-%Y")
last_mon= format(max(df$date),"%m-%Y")
# select incdt=1 , count prevalent patient number
df$incdt_counts=ifelse(df$incdt_pt==1,df$infection_counts,0)
# add col: population per GP in each time point- same number in multiple row within same gp
df=df%>%
group_by(date,practice)%>%
mutate(population=sum(population))
# summarize incident number of each age_cat
df_sum_gp_age=df%>%
group_by(date,practice,age_cat)%>%
summarise(pt_counts=sum(incdt_counts), # count consultation number in each age_cat; incident=1(count=0), incident=0(add count)
pupulation=mean(population)) # both incident=1 or 0 has same GP population, so add mean
# "rate per 1,000 registered patients"
df_sum_gp_age$rate=df_sum_gp_age$pt_counts/df_sum_gp_age$pupulation*1000
# add variables
df_6=df_sum_gp_age%>%
mutate(year=format(date,"%Y"),
month=format(date,"%m"),
indic="otitis media")
rm(df,df_sum_gp_age,last.date)
### 2. combined dataframe
df=rbind(df_1,df_2,df_3,df_4,df_5,df_6)
### 3. plots
## 3.1 consultation rate by age group
#summarise table
df_plot=df%>%
group_by(date,age_cat,indic)%>%
summarise(rate=mean(rate))
write.csv(df_plot,here::here("output","consultation_rate_prevalent.csv"))
# line graph- by age group and divided by year
df_plot$age_cat <- factor(df_plot$age_cat, levels=c("0-4", "5-14","15-24","25-34","35-44","45-54","55-64","65-74","75+"))
df_plot$year=format(df_plot$date,"%Y")
df_plot$month=format(df_plot$date,"%m")
lineplot_1<- ggplot(df_plot, aes(x=month, y=rate,group=year,color=year))+
facet_grid(rows = vars(age_cat), cols = vars(indic))+
geom_line()+
theme(legend.position = "bottom",legend.title =element_blank())+
scale_x_discrete(breaks =c("01","03","05","07","09","11"))+
#scale_y_continuous(n.breaks = 20)+
labs(
title = "Prevalent consultationrate per 1,000 registered patients",
subtitle = paste(first_mon,"-",last_mon),
caption = paste("Data from approximately", TPPnumber,"TPP Practices"),
x = "",
y = "")
lineplot_2<- ggplot(df_plot, aes(x=date, y=rate,group=age_cat))+
annotate(geom = "rect", xmin = as.Date("2021-01-01"),xmax = as.Date("2021-04-01"),ymin = -Inf, ymax = Inf,fill="grey80", alpha=0.5)+
annotate(geom = "rect", xmin = as.Date("2020-11-01"),xmax = as.Date("2020-12-01"),ymin = -Inf, ymax = Inf,fill="grey80", alpha=0.5)+
annotate(geom = "rect", xmin = as.Date("2020-03-01"),xmax = as.Date("2020-06-01"),ymin = -Inf, ymax = Inf,fill="grey80", alpha=0.5)+
scale_x_date(date_breaks = "1 month",date_labels = "%Y-%m")+
#scale_y_continuous(n.breaks = 20)+
facet_grid(rows = vars(indic))+
geom_line(aes(color=age_cat))+
theme(axis.text.x = element_text(angle = 60,hjust=1),
legend.position = "bottom",legend.title =element_blank())+
labs(
title = "Prevalent consultation rate per 1,000 registered patients",
subtitle = paste(first_mon,"-",last_mon),
caption = paste("Data from approximately", TPPnumber,"TPP Practices; National lockdown in grey background"),
x = "",
y = "")
ggsave(
plot= lineplot_1,
filename="consult_age_1_prevalent.jpeg", path=here::here("output"))
ggsave(
plot= lineplot_2,
filename="consult_age_2_prevalent.jpeg", path=here::here("output"))
## 3.2 consultation rate by percentile
#summarise table
df_gprate=df%>%
group_by(date,practice)%>%
summarise(ab_rate_1000=mean(rate))
write.csv(df_gprate,here::here("output","consultation_GP_rate_prevalent.csv"))
df_gprate$cal_year=format(df_gprate$date,"%Y")
df_gprate$cal_mon=format(df_gprate$date,"%m")
num_uniq_prac <- as.numeric(dim(table((df_gprate$practice))))
df_mean <- df_gprate %>% group_by(cal_mon, cal_year) %>%
mutate(meanABrate = mean(ab_rate_1000,na.rm=TRUE),
lowquart= quantile(ab_rate_1000, na.rm=TRUE)[2],
highquart= quantile(ab_rate_1000, na.rm=TRUE)[4],
ninefive= quantile(ab_rate_1000, na.rm=TRUE, c(0.95)),
five=quantile(ab_rate_1000, na.rm=TRUE, c(0.05)))
plot_percentile <- ggplot(df_mean, aes(x=date))+
annotate(geom = "rect", xmin = as.Date("2021-01-01"),xmax = as.Date("2021-04-01"),ymin = -Inf, ymax = Inf,fill="grey80", alpha=0.5)+
annotate(geom = "rect", xmin = as.Date("2020-11-01"),xmax = as.Date("2020-12-01"),ymin = -Inf, ymax = Inf,fill="grey80", alpha=0.5)+
annotate(geom = "rect", xmin = as.Date("2020-03-01"),xmax = as.Date("2020-06-01"),ymin = -Inf, ymax = Inf,fill="grey80", alpha=0.5)+
geom_line(aes(y=meanABrate),color="steelblue")+
geom_point(aes(y=meanABrate),color="steelblue")+
geom_line(aes(y=lowquart), color="darkred", linetype=3)+
geom_point(aes(y=lowquart), color="darkred", linetype=3)+
geom_line(aes(y=highquart), color="darkred", linetype=3)+
geom_point(aes(y=highquart), color="darkred", linetype=3)+
geom_line(aes(y=ninefive), color="black", linetype=3)+
geom_point(aes(y=ninefive), color="black", linetype=3)+
geom_line(aes(y=five), color="black", linetype=3)+
geom_point(aes(y=five), color="black", linetype=3)+
scale_x_date(date_labels = "%m-%Y", date_breaks = "1 month")+
scale_y_continuous(n.breaks = 20)+
theme(axis.text.x=element_text(angle=60,hjust=1))+
labs(
title = "Prevalent consultation rate per 1000 registered patients",
subtitle = paste(first_mon,"-",last_mon),
caption = paste("Data from approximately", num_uniq_prac,"TPP Practices;
National lockdown in grey background"),
x = "",
y = "")+
geom_vline(xintercept = as.numeric(as.Date("2019-12-31")))+
geom_vline(xintercept = as.numeric(as.Date("2020-12-31")))
plot_percentile
ggsave(
plot= plot_percentile,
filename="consult_all_prevalent.jpeg", path=here::here("output"),
)