generated from opensafely/research-template
/
process_ts_carehome.R
119 lines (97 loc) · 5.31 KB
/
process_ts_carehome.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
######################################################
# This script:
# - imports measures data for prescribing to people
# in care home
# - cleans up data
# - applies rounding and redaction
# - saves processed dataset(s)
#######################################################
# For running locally only #
#setwd("C:/Users/aschaffer/OneDrive - Nexus365/Documents/GitHub/opioids-covid-research")
#getwd()
# Import libraries #
library('tidyverse')
library('lubridate')
library('arrow')
library('here')
library('reshape2')
library('dplyr')
library('fs')
## Custom functions
source(here("analysis", "lib", "custom_functions.R"))
# Create directory
dir_create(here::here("output", "timeseries"), showWarnings = FALSE, recurse = TRUE)
dir_create(here::here("output", "measures"), showWarnings = FALSE, recurse = TRUE)
###############################
# Clean up measures datasets #
###############################
# In carehome
carehome <- read_csv(here::here("output", "measures", "measures_carehome.csv")) %>%
filter(str_detect(measure, "carehome_age", negate = TRUE)) %>%
mutate(month = as.Date(interval_start, format="%Y-%m-%d"),
period = ifelse(month < as.Date("2020-03-01"), "Pre-COVID",
ifelse(month >= as.Date("2021-04-01"), "Recovery", "Lockdown"))) %>%
dplyr::select(!c(interval_start, interval_end, ratio, age_group, carehome)) %>%
pivot_wider(names_from = measure, values_from = c(numerator, denominator)) %>%
rename(opioid_any = numerator_opioid_any,
hi_opioid_any = numerator_hi_opioid_any,
opioid_new = numerator_opioid_new,
oral_opioid_any = numerator_oral_opioid,
trans_opioid_any = numerator_trans_opioid,
par_opioid_any = numerator_par_opioid,
pop_total = denominator_opioid_any,
pop_naive = denominator_opioid_new) %>%
dplyr::select(!c(denominator_hi_opioid_any, denominator_oral_opioid,
denominator_trans_opioid, denominator_par_opioid)) %>%
mutate(rate_opioid_any = (opioid_any / pop_total * 1000),
rate_hi_opioid_any = (hi_opioid_any / pop_total * 1000),
rate_opioid_new = (opioid_new / pop_total * 1000),
rate_oral_opioid_any = (oral_opioid_any / pop_total * 1000),
rate_trans_opioid_any = (trans_opioid_any / pop_total * 1000),
rate_par_opioid_any = (par_opioid_any / pop_total * 1000))
write.csv(carehome, file = here::here("output", "timeseries", "ts_carehome.csv"),
row.names = FALSE)
# In/not in carehome - sensitivity analysis
carehome_sens <- read_csv(here::here("output", "measures", "measures_carehome.csv")) %>%
filter(str_detect(measure, "_carehome_age", negate = FALSE)) %>%
mutate(month = as.Date(interval_start, format="%Y-%m-%d"),
period = ifelse(month < as.Date("2020-03-01"), "Pre-COVID",
ifelse(month >= as.Date("2021-04-01"), "Recovery", "Lockdown")),
carehome = if_else(carehome == T, "Yes", "No", "No"),
measure = substr(measure, 1, 10),
rate_opioid_any = (numerator / denominator * 1000)) %>%
rename(opioid_any = numerator, pop_total = denominator) %>%
dplyr::select(!c(interval_start, interval_end, ratio, measure))
write.csv(carehome_sens, file = here::here("output", "timeseries", "ts_carehome_sens.csv"),
row.names = FALSE)
###########################
# Rounding and redaction #
###########################
# In care home
carehome_round <- read_csv(here::here("output", "timeseries", "ts_carehome.csv")) %>%
mutate(opioid_any_round = rounding(opioid_any),
hi_opioid_any_round = rounding(hi_opioid_any),
opioid_new_round = rounding(opioid_new),
trans_opioid_any_round = rounding(trans_opioid_any),
par_opioid_any_round = rounding(par_opioid_any),
oral_opioid_any_round = rounding(oral_opioid_any),
pop_total_round = rounding(pop_total),
pop_naive_round = rounding(pop_naive),
rate_opioid_any_round = (opioid_any_round / pop_total_round * 1000),
rate_hi_opioid_any_round = (hi_opioid_any_round / pop_total_round * 1000),
rate_opioid_new_round = (opioid_new_round / pop_naive_round * 1000),
rate_trans_opioid_any_round = (trans_opioid_any_round / pop_total_round * 1000),
rate_par_opioid_any_round = (par_opioid_any_round / pop_total_round * 1000),
rate_oral_opioid_any_round = (oral_opioid_any_round / pop_total_round * 1000)) %>%
dplyr::select(!c(opioid_any, opioid_new, hi_opioid_any, trans_opioid_any,
par_opioid_any, oral_opioid_any, pop_naive, pop_total,
rate_opioid_any, rate_hi_opioid_any, rate_opioid_new,
rate_trans_opioid_any, rate_par_opioid_any, rate_oral_opioid_any))
write.csv(carehome_round, here::here("output", "timeseries", "ts_carehome_rounded.csv"), row.names = FALSE)
# By age and care home (sensitivity analysis)
carehome_sens_round <- read_csv(here::here("output", "timeseries", "ts_carehome_sens.csv")) %>%
mutate(opioid_any_round = rounding(opioid_any),
pop_total_round = rounding(pop_total),
rate_opioid_any_round = (opioid_any_round / pop_total_round * 1000)) %>%
dplyr::select(!c(opioid_any, pop_total, rate_opioid_any))
write.csv(carehome_sens_round, here::here("output", "timeseries", "ts_carehome_sens_rounded.csv"), row.names = FALSE)