generated from opensafely/research-template
-
Notifications
You must be signed in to change notification settings - Fork 0
/
cumulative_vax_byage.R
205 lines (173 loc) · 8.05 KB
/
cumulative_vax_byage.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
################################################################
# This script:
# - Calculates cumulative uptake of booster dose/second booster
# COVID-19 vaccine by age
################################################################
# For running locally only #
# setwd("C:/Users/aschaffer/OneDrive - Nexus365/Documents/GitHub/vax-fourth-dose-RD")
# getwd()
# Import libraries #
library('tidyverse')
library('lubridate')
library('arrow')
library('here')
library('reshape2')
library('dplyr')
library('fs')
library('ggplot2')
library('RColorBrewer')
## Create directories
dir_create(here::here("output", "cumulative_rates"), showWarnings = FALSE, recurse = TRUE)
dir_create(here::here("output", "cohort"), showWarnings = FALSE, recurse = TRUE)
end_date = as.Date("2023-02-04")
#####################################################
# Functions ###
#####################################################
# Factorise ----
fct_case_when <- function(...) {
# uses dplyr::case_when but converts the output to a factor,
# with factors ordered as they appear in the case_when's ... argument
args <- as.list(match.call())
levels <- sapply(args[-1], function(f) f[[3]]) # extract RHS of formula
levels <- levels[!is.na(levels)]
factor(dplyr::case_when(...), levels=levels)
}
#####################################################
### Read in data ###
#####################################################
booster <- read_csv(here::here("output", "cohort", "cohort_final_sep.csv")) %>%
# Age at Nov 26
mutate(age_mos = (dob %--% "2022-11-26") %/% months(1),
age_yrs = (dob %--% "2022-11-26") %/% years(1)) %>%
# Exclude if died
subset(dod > as.Date("2022-11-26") | is.na(dod)) %>%
dplyr::select(c(age_mos, age_yrs, boost_date, booster))
#####################################################
### Cumulative vaccine update by 1-year age group ###
#####################################################
### Calculate cumulative proportion of people receiving booster dose
booster_age1_byday <- booster %>%
subset(age_yrs < 55 & age_yrs > 44) %>%
group_by(age_yrs) %>%
mutate(total = n()) %>% # Calculate denominator (total count per age category)
ungroup() %>%
subset(!is.na(boost_date)) %>% # Remove people with no booster vax
group_by(age_yrs, total, boost_date) %>%
summarise(boost_n = n()) %>% # Num vaccinated each day
ungroup() %>%
arrange(age_yrs, boost_date) %>%
group_by(age_yrs, total) %>%
mutate(boost_sum = cumsum(boost_n), # Cumulative num vaccinated each day
boost_sum = case_when(boost_sum > 7 ~ boost_sum), # Redaction
boost_sum = round(boost_sum / 5) * 5, # Rounding
total = round(total / 5) * 5, # Rounding
rate = boost_sum / total * 100) %>% # Cumulative % vaccinated each day
complete(boost_date = seq(min(as.Date("2022-09-03")),
max(end_date), by = '1 day')) %>%
fill(c(boost_sum, rate)) %>% # Create rows for days with zero vaccinations
ungroup() %>%
select(!boost_n) %>%
mutate(age_yrs = as.character(age_yrs))
# Save
booster_age1_byday <- booster_age1_byday %>% subset(boost_date >= as.Date("2022-09-06"))
write.csv(booster_age1_byday,
here::here("output", "cumulative_rates", "final_dose4_cum_byage1.csv"), row.names = FALSE)
### Plot cumulative booster dose over time
ggplot(subset(booster_age1_byday, boost_date >= as.Date("2022-09-05") |
boost_date < end_date)) +
geom_line(aes(x = boost_date, y = rate/100, group = age_yrs, col = age_yrs),
size = 1.25) +
geom_vline(aes(xintercept = as.Date("2022-10-14")), linetype = "longdash") +
scale_color_brewer(palette = "RdBu") +
scale_y_continuous(labels = scales::percent, limits = c(0, 1)) +
scale_x_continuous(breaks = c(as.Date("2022-09-01"), as.Date("2022-10-01"),
as.Date("2022-10-14"), as.Date("2022-11-01"),
as.Date("2022-12-01"), as.Date("2023-01-01"),
as.Date("2023-02-01")),
labels = c("Sep 1", "Oct 1", "Oct 14", "Nov 1", "Dec 1",
"Jan 1", "Feb 1")) +
xlab(NULL) + ylab("Received autumn booster") +
theme_bw() +
theme(panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1))
ggsave(here::here("output", "cumulative_rates", "plot_dose4_cum_age1.png"),
dpi = 300, units = "in", width = 6, height = 3.25)
######################################
### % vaccinated by age in months ###
######################################
booster_nov26 <- booster %>%
group_by(age_mos) %>%
mutate(boost_nov26 = if_else(boost_date <= as.Date("2022-11-26"), 1, 0, 0),
total = n()) %>%
ungroup() %>%
group_by(age_mos, total) %>%
summarise(n_boost = sum(boost_nov26)) %>%
mutate(n_boost = case_when(n_boost > 7 ~ n_boost), # Redaction
n_boost = round(n_boost / 5) * 5, # Rounding
total = round(total / 5) * 5, # Rounding
pcent_boost = n_boost / total * 100,
age_mos = as.numeric(age_mos))
# Save
write.csv(booster_nov26,
here::here("output", "cumulative_rates", "final_vax4_age_months.csv"), row.names = FALSE)
### Plot
ggplot(subset(booster_nov26, age_mos > 564 & age_mos < 636)) +
geom_vline(aes(xintercept = 50), linetype = "longdash") +
geom_point(aes(x = age_mos / 12, y = pcent_boost),
col = "dodgerblue3") +
scale_y_continuous(limits = c(0, 100)) +
scale_x_continuous(breaks = seq(47,53,1)) +
xlab(NULL) + ylab("Received second booster\nCOVID-19 vaccine (%)") +
theme_bw() +
theme(panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1))
ggsave(here::here("output", "cumulative_rates", "plot_dose4_age_months.png"),
dpi = 300, units = "in", width = 6, height = 3.25)
###################
##### Checks ######
###################
# # Plot of when people received each dose (see if it makes sense)
doses_by_day <- read_csv(here::here("output", "cohort", "cohort_final_sep.csv")) %>%
select(c(patient_id, covid_vax_1_date, covid_vax_2_date,
covid_vax_3_date, covid_vax_4_date)) %>%
melt(id = c("patient_id")) %>%
rename(vax = variable, date = value) %>%
mutate(vax = fct_case_when(
vax == "covid_vax_1_date" ~ "First dose",
vax == "covid_vax_2_date" ~ "Second dose",
vax == "covid_vax_3_date" ~ "Third dose",
vax == "covid_vax_4_date" ~ "Fourth dose"
)) %>%
subset(!is.na(date)) %>%
group_by(vax, date) %>%
summarise(vax_n = n()) %>%
group_by(vax) %>%
complete(date = seq(min(as.Date(date)),
max(end_date),
by = '1 day')) %>%
fill(vax_n) %>% # Create rows for days with zero vaccinations
ungroup() %>%
mutate(vax_n = case_when(vax_n > 7 ~ vax_n),
vax_n = round(vax_n / 5) * 5)
ggplot(doses_by_day, aes(x = date, y = vax_n)) +
geom_line(aes(col = vax)) +
geom_ribbon(aes(group = vax, fill = vax, ymax = vax_n), ymin = 0, alpha = 0.5) +
xlab("") + ylab("Received vaccine (n)") +
scale_colour_manual(values = c('#00496f', '#0f85a0', '#edd746', '#dd4124'))+
scale_fill_manual(values = c('#00496f', '#0f85a0', '#edd746', '#dd4124')) +
scale_x_continuous(breaks = c(as.Date("2021-01-01"), as.Date("2021-07-01"),
as.Date("2022-01-01"), as.Date("2022-07-01"),
as.Date("2023-01-01")),
labels = c("Jan 2021", "Jul 2021", "Jan 2022", "Jul 2022",
"Jan 2023")) +
theme_bw() +
theme(panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1))
ggsave(here::here("output", "cumulative_rates", "plot_all_doses_over_time.png"),
dpi = 300, units = "in", width = 6, height = 3.25)