generated from opensafely/research-template
-
Notifications
You must be signed in to change notification settings - Fork 0
/
data_process_baseline.R
178 lines (142 loc) · 6.63 KB
/
data_process_baseline.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
################################################################
# This script defines exclusion criteria and extracts
# final study population
################################################################
# 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')
library('lubridate')
## Create directories
dir_create(here::here("output"), showWarnings = FALSE, recurse = TRUE)
dir_create(here::here("output", "cohort"), showWarnings = FALSE, recurse = TRUE)
dir_create(here::here("output", "descriptive"), showWarnings = FALSE, recurse = TRUE)
# Load functions
source(here::here("analysis", "custom_functions.R"))
end_date = as.Date("2023-02-04")
#######################################
# Prepare data
#######################################
# Read in and clean data
baseline <- read_feather(here::here("output", "input_baseline.feather")) %>%
mutate_at(c(vars(c(contains("_date")))), as.Date, format = "%Y-%m-%d") %>%
mutate(dob = as.Date(as.character(as.POSIXct(dob)), format = "%Y-%m-%d"),
# Set DOB to mid-month
dob = dob + 14,
# Flag for clinically vulnerable people
cv = immunosuppressed | chronic_kidney_disease | chronic_resp_disease |
diabetes | chronic_liver_disease | chronic_neuro_disease | asplenia |
chronic_heart_disease | sev_mental | sev_obesity | asthma,
# Determine earliest recorded date of flu vaccination
flu_vax_date = pmin(flu_vax_med_date, flu_vax_tpp_date,
flu_vax_clinical_date, na.rm = TRUE),
# Booster date (if received)
boost_date = case_when(
(!is.na(covid_vax_4_date) &
covid_vax_4_date >= as.Date("2022-09-05") &
covid_vax_4_date < end_date) ~ covid_vax_4_date,
(!is.na(covid_vax_3_date) & is.na(covid_vax_4_date) &
covid_vax_3_date >= as.Date("2022-09-05") &
covid_vax_3_date < end_date) ~ covid_vax_3_date,
TRUE ~ as.Date(NA)),
# Received booster anytime in 2022/23
booster = if_else(!is.na(boost_date), 1, 0, 0),
# If received fourth dose prior to second booster campaign
covid_vax4_early = if_else(covid_vax_4_date < as.Date("2022-09-05"), 1, 0, missing = 0),
# If received third dose prior to first booster campaign
covid_vax3_early = if_else(covid_vax_3_date < as.Date("2021-09-16"), 1, 0, missing = 0),
# Received another COVID vaccine in 3 months prior to start of campaign
covid_vax_recent = if_else((
(covid_vax_3_date >= as.Date("2022-07-15") &
covid_vax_3_date <= as.Date("2022-10-15")) |
(covid_vax_2_date >= as.Date("2022-07-15") &
covid_vax_2_date <= as.Date("2022-10-15")) |
(covid_vax_1_date >= as.Date("2022-07-15") &
covid_vax_1_date <= as.Date("2022-10-15"))
), 1, 0, 0),
# Received 2nd dose at least 3 months prior to start of campaign
covid_vax2 = if_else(covid_vax_2_date < as.Date("2022-07-15"), 1, 0, 0),
# Received 3rd dose at least 3 months prior to start of campaign
covid_vax3 = if_else(covid_vax_3_date < as.Date("2022-07-15"), 1, 0, 0),
# Flag for people prioritised for vaccine (including evidence of having received
# COVID vaccine before becoming available to general population)
vax_priority = cv | carehome | hscworker | covid_vax4_early |
covid_vax3_early
)
####################################################
# Overview of study population prior to exclusions
####################################################
total_pop_before_exclusions <- baseline %>%
group_by(age) %>%
mutate(total = n()) %>%
ungroup() %>%
group_by(age, total) %>%
summarise(
carehome = sum(carehome == 1),
immunosuppressed = sum(immunosuppressed == 1),
ckd = sum(chronic_kidney_disease == 1),
chronic_resp_disease = sum(chronic_resp_disease == 1),
asthma = sum(asthma == 1),
diabetes = sum(diabetes == 1),
asplenia = sum(asplenia == 1),
chronic_liver_disease = sum(chronic_liver_disease == 1),
chronic_neuro_disease = sum(chronic_neuro_disease == 1),
chronic_heart_disease = sum(chronic_heart_disease == 1),
sev_mental = sum(sev_mental == 1),
sev_obesity = sum(sev_obesity == 1),
cv = sum(cv == 1),
hscworker = sum(hscworker == 1),
endoflife = sum(endoflife == 1),
housebound = sum(housebound == 1),
covid_vax4_early = sum(covid_vax4_early == 1),
covid_vax3_early = sum(covid_vax3_early == 1),
covid_vax3 = sum(covid_vax3 == 1),
covid_vax2 = sum(covid_vax2 == 1),
covid_vax_recent = sum(covid_vax_recent == 1),
vax_priority = sum(vax_priority == 1)
) %>%
ungroup() %>%
# Redaction and rounding
mutate(across(-age, redact), across(-age, rounding))
# Save
write.csv(total_pop_before_exclusions,
here::here("output", "descriptive", "total_pop_before_exclusions.csv"),
row.names = FALSE)
#################################################
# Save final population after exclusions
#################################################
final <- baseline %>%
subset( # Exclude clinical priority groups
vax_priority == 0 &
# Include people who received both primary doses
covid_vax2 == 1 &
# Exclude if recently received COVID-19 vaccine
covid_vax_recent == 0 &
# Exclude if housebound
housebound == 0 &
# Exclude if at end of life
endoflife == 0) %>%
dplyr::select(!c(vax_priority, covid_vax_recent, endoflife,
cv, hscworker, carehome, housebound,
immunosuppressed, chronic_kidney_disease,
chronic_resp_disease, asthma, diabetes,
chronic_liver_disease, chronic_neuro_disease,
asplenia, chronic_heart_disease, sev_mental,
sev_obesity, flu_vax_tpp_date, flu_vax_med_date,
flu_vax_clinical_date
))
# Number of obs
print(paste0("Final population (n): ", nrow(final)))
#Save
write.csv(final,
here::here("output", "cohort", "cohort_final_sep.csv"),
row.names = FALSE)