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101_baseline_tables.do
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101_baseline_tables.do
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/*==============================================================================
DO FILE NAME: baseline_tables.do
PROJECT: COVID collateral IMD
DATE: 17 February 2023
AUTHOR: R Costello
adapted from R Mathur and K Wing
DESCRIPTION OF FILE: Produce a table of baseline characteristics for 3 years (2019, 2020, 2021)
DATASETS USED: output/measures/tables/input_tables_*
DATASETS CREATED: None
OTHER OUTPUT: Results in csv: baseline_table*.csv
Log file: logs/table1_descriptives
USER-INSTALLED ADO:
(place .ado file(s) in analysis folder)
Notes:
Table 1 population is people who are in the study population on 1st January 2019, 2020 & 2021
==============================================================================*/
adopath + ./analysis/ado
capture log close
log using ./logs/table1_descriptives.log, replace
cap mkdir ./output/tables
* Create baseline tables for 3 years
forvalues i=2019/2021 {
* Import csv file
import delimited ./output/input_static_`i'-01-01.csv, clear
*update variable with missing so that 0 is shown as unknown (just for this table)
*(1) IMD
replace imd=6 if imd==0
* Create age categories
egen age_cat = cut(age), at(18, 40, 60, 80, 120) icodes
label define age 0 "18 - 40 years" 1 "41 - 60 years" 2 "61 - 80 years" 3 ">80 years"
label values age_cat age
bys age_cat: sum age
*generate a binary rural urban (with missing assigned to urban)
generate urban_rural_bin=.
replace urban_rural_bin=1 if urban_rural<=4|urban_rural==.
replace urban_rural_bin=0 if urban_rural>4 & urban_rural!=.
label define urban_rural_bin 0 "Rural" 1 "Urban"
label values urban_rural_bin urban_rural_bin
safetab urban_rural_bin urban_rural, miss
label var urban_rural_bin "Rural-Urban"
preserve
* Create baseline table
table1_mc, vars(age_cat cat \ sex cat \ imd cat \ urban_rural_bin cat \ ///
has_t1_diabetes cat \ has_t2_diabetes cat \ has_asthma cat \ has_copd cat ) clear
export delimited using ./output/tables/baseline_table_`i'.csv
* Rounding numbers in table to nearest 5
destring _columna_1, gen(n) force
destring _columnb_1, gen(percent) ignore("-" "%" "(" ")") force
gen rounded_n = round(n, 5)
keep factor level rounded_n percent
export delimited using ./output/tables/baseline_table_`i'_rounded.csv
restore
preserve
* table by migration status
table1_mc, vars(age_cat cat \ sex cat \ imd cat \ urban_rural_bin cat \ ///
has_t1_diabetes cat \ has_t2_diabetes cat \ has_asthma cat \ has_copd cat ) by(migration_status) clear
export delimited using ./output/tables/baseline_table_migration_`i'.csv
destring _columna_1, gen(n1) force
destring _columna_0, gen(n0) force
destring _columnb_1, gen(percent1) ignore("-" "%" "(" ")") force
destring _columnb_0, gen(percent0) ignore("-" "%" "(" ")") force
gen rounded_n1 = round(n1, 5)
gen rounded_n0 = round(n0, 5)
keep factor level rounded_n0 percent0 rounded_n1 percent1
export delimited using ./output/tables/baseline_table_migration_`i'_rounded.csv
restore
gen imd_1 = (imd==1)
gen imd_2 = (imd==2)
gen imd_3 = (imd==3)
gen imd_4 = (imd==4)
gen imd_5 = (imd==5)
tempfile tempfile
preserve
keep if imd==1
table1_mc, vars(age_cat cat \ sex cat \ imd cat \ urban_rural_bin cat \ ///
has_t1_diabetes cat \ has_t2_diabetes cat \ has_asthma cat \ has_copd cat) clear
save `tempfile', replace
restore
forvalues j=2/5 {
preserve
keep if imd==`j'
table1_mc, vars(age_cat cat \ sex cat \ imd cat \ urban_rural_bin cat \ ///
has_t1_diabetes cat \ has_t2_diabetes cat \ has_asthma cat \ has_copd cat) clear
append using `tempfile'
save `tempfile', replace
restore
}
use `tempfile', clear
export delimited using ./output/tables/baseline_table_imd`i'.csv
destring _columna_1, gen(n) force
destring _columnb_1, gen(percent) ignore("-" "%" "(" ")") force
gen rounded_n = round(n, 5)
keep factor level rounded_n percent
export delimited using ./output/tables/baseline_table_imd`i'_rounded.csv
}
* Close log file
log close