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000_define_covariates.do
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000_define_covariates.do
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version 16
/*==============================================================================
DO FILE NAME: define covariates
PROJECT: EIA OpenSAFELY project
DATE: 07/03/2022
AUTHOR: J Galloway / M Russell
adapted from C Rentsch
DESCRIPTION OF FILE: data management for EIA project
reformat variables
categorise variables
label variables
DATASETS USED: data in memory (from output/input.csv)
DATASETS CREATED: analysis files
OTHER OUTPUT: logfiles, printed to folder $Logdir
USER-INSTALLED ADO:
(place .ado file(s) in analysis folder)
==============================================================================*/
**Set filepathsdiabe
*global projectdir "C:\Users\k1754142\OneDrive\PhD Project\OpenSAFELY\Github Practice"
*global projectdir "C:\Users\Mark\OneDrive\PhD Project\OpenSAFELY\Github Practice"
global projectdir `c(pwd)'
capture mkdir "$projectdir/output/data"
capture mkdir "$projectdir/output/figures"
capture mkdir "$projectdir/output/tables"
global logdir "$projectdir/logs"
**Open a log file
cap log close
log using "$logdir/cleaning_dataset.log", replace
di "$projectdir"
di "$logdir"
import delimited "$projectdir/output/input.csv", clear
**Set Ado file path
adopath + "$projectdir/analysis/extra_ados"
**Set index dates ===========================================================*/
global year_preceding = "01/04/2018"
global start_date = "01/04/2019"
global appt_date = "01/04/2021"
global end_date = "01/04/2022"
**Rename variables (some are too long for Stata to handle) =======================================*/
rename chronic_respiratory_disease chronic_resp_disease
**Convert date strings to dates ====================================================*/
***Some dates are given with month/year only, so adding day 15 to enable them to be processed as dates
foreach var of varlist hba1c_mmol_per_mol_date ///
hba1c_percentage_date ///
creatinine_date ///
bmi_date_measured ///
abatacept_date ///
adalimumab_date ///
baricitinib_date ///
certolizumab_date ///
etanercept_date ///
golimumab_date ///
guselkumab_date ///
infliximab_date ///
ixekizumab_date ///
methotrexate_hcd_date ///
rituximab_date ///
sarilumab_date ///
secukinumab_date ///
tocilizumab_date ///
tofacitinib_date ///
upadacitinib_date ///
ustekinumab_date ///
{
capture confirm string variable `var'
if _rc!=0 {
assert `var'==.
rename `var' `var'_date
}
else {
replace `var' = `var' + "-15"
rename `var' `var'_dstr
replace `var'_dstr = " " if `var'_dstr == "-15"
gen `var'_date = date(`var'_dstr, "YMD")
order `var'_date, after(`var'_dstr)
drop `var'_dstr
}
format `var'_date %td
}
**Conversion for dates with day already included ====================================================*/
foreach var of varlist died_date_ons ///
eia_code_date ///
rheum_appt_date ///
rheum_appt2_date ///
rheum_appt3_date ///
ra_code_date ///
psa_code_date ///
anksp_code_date ///
undiff_code_date ///
last_gp_prerheum_date ///
last_gp_precode_date ///
last_gp_refrheum_date ///
last_gp_refcode_date ///
referral_rheum_prerheum ///
referral_rheum_precode ///
chronic_cardiac_disease ///
diabetes ///
hypertension ///
chronic_resp_disease ///
copd ///
chronic_liver_disease ///
stroke ///
lung_cancer ///
haem_cancer ///
other_cancer ///
esrf ///
organ_transplant ///
hydroxychloroquine_date ///
leflunomide_date ///
methotrexate_date ///
methotrexate_inj_date ///
sulfasalazine_date ///
{
capture confirm string variable `var'
if _rc!=0 {
assert `var'==.
rename `var' `var'_date
}
else {
rename `var' `var'_dstr
gen `var'_date = date(`var'_dstr, "YMD")
order `var'_date, after(`var'_dstr)
drop `var'_dstr
gen `var'_date15 = `var'_date+15
order `var'_date15, after(`var'_date)
drop `var'_date
rename `var'_date15 `var'_date
}
format `var'_date %td
}
**Rename variables with extra 'date' added to the end of variable names===========================================================*/
rename rheum_appt_date_date rheum_appt_date
rename rheum_appt2_date_date rheum_appt2_date
rename rheum_appt3_date_date rheum_appt3_date
rename eia_code_date_date eia_code_date
rename ra_code_date_date ra_code_date
rename psa_code_date_date psa_code_date
rename anksp_code_date_date anksp_code_date
rename undiff_code_date_date undiff_code_date
rename died_date_ons_date died_ons_date
rename last_gp_prerheum_date_date last_gp_prerheum_date
rename last_gp_refrheum_date_date last_gp_refrheum_date
rename last_gp_refcode_date_date last_gp_refcode_date
rename last_gp_precode_date_date last_gp_precode_date
rename hba1c_mmol_per_mol_date_date hba1c_mmol_per_mol_date
rename hba1c_percentage_date_date hba1c_percentage_date
rename creatinine_date_date creatinine_date
rename creatinine creatinine_value
rename bmi_date_measured_date bmi_date
rename bmi bmi_value
rename hydroxychloroquine_date_date hydroxychloroquine_date
rename leflunomide_date_date leflunomide_date
rename methotrexate_date_date methotrexate_date
rename methotrexate_inj_date_date methotrexate_inj_date
rename sulfasalazine_date_date sulfasalazine_date
rename abatacept_date_date abatacept_date
rename adalimumab_date_date adalimumab_date
rename baricitinib_date_date baricitinib_date
rename certolizumab_date_date certolizumab_date
rename etanercept_date_date etanercept_date
rename golimumab_date_date golimumab_date
rename guselkumab_date_date guselkumab_date
rename infliximab_date_date infliximab_date
rename ixekizumab_date_date ixekizumab_date
rename methotrexate_hcd_date_date methotrexate_hcd_date
rename rituximab_date_date rituximab_date
rename sarilumab_date_date sarilumab_date
rename secukinumab_date_date secukinumab_date
rename tocilizumab_date_date tocilizumab_date
rename tofacitinib_date_date tofacitinib_date
rename upadacitinib_date_date upadacitinib_date
rename ustekinumab_date_date ustekinumab_date
**Create binary indicator variables for relevant conditions ====================================================*/
foreach var of varlist eia_code_date ///
rheum_appt_date ///
rheum_appt2_date ///
rheum_appt3_date ///
ra_code_date ///
psa_code_date ///
anksp_code_date ///
undiff_code_date ///
died_ons_date ///
last_gp_prerheum_date ///
last_gp_precode_date ///
last_gp_refrheum_date ///
last_gp_refcode_date ///
referral_rheum_prerheum_date ///
referral_rheum_precode_date ///
chronic_cardiac_disease_date ///
diabetes_date ///
hypertension_date ///
chronic_resp_disease_date ///
copd_date ///
chronic_liver_disease_date ///
stroke_date ///
lung_cancer_date ///
haem_cancer_date ///
other_cancer_date ///
esrf_date ///
creatinine_date ///
organ_transplant_date ///
hydroxychloroquine_date ///
leflunomide_date ///
methotrexate_date ///
methotrexate_inj_date ///
sulfasalazine_date ///
abatacept_date ///
adalimumab_date ///
baricitinib_date ///
certolizumab_date ///
etanercept_date ///
golimumab_date ///
guselkumab_date ///
infliximab_date ///
ixekizumab_date ///
methotrexate_hcd_date ///
rituximab_date ///
sarilumab_date ///
secukinumab_date ///
tocilizumab_date ///
tofacitinib_date ///
upadacitinib_date ///
ustekinumab_date {
/*date ranges are applied in python, so presence of date indicates presence of
disease in the correct time frame*/
local newvar = substr("`var'", 1, length("`var'") - 5)
gen `newvar' = (`var'!=. )
order `newvar', after(`var')
}
**Create and label variables ===========================================================*/
**Demographics
***Sex
gen male = 1 if sex == "M"
replace male = 0 if sex == "F"
lab var male "Male"
lab define male 0 "No" 1 "Yes", modify
lab val male male
tab male, missing
***Ethnicity
replace ethnicity = .u if ethnicity == .
****rearrange in order of prevalence
recode ethnicity 2=6 /* mixed to 6 */
recode ethnicity 3=2 /* south asian to 2 */
recode ethnicity 4=3 /* black to 3 */
recode ethnicity 6=4 /* mixed to 4 */
recode ethnicity 5=4 /* other to 4 */
label define ethnicity 1 "White" ///
2 "Asian/Asian British" ///
3 "Black" ///
4 "Mixed/Other" ///
.u "Not known"
label values ethnicity ethnicity
lab var ethnicity "Ethnicity"
tab ethnicity, missing
***STP
rename stp stp_old
bysort stp_old: gen stp = 1 if _n==1
replace stp = sum(stp) //
drop stp_old
***Regions
encode region, gen(nuts_region)
tab region, missing
replace region="Not known" if region==""
gen region_nospace=region
replace region_nospace="SouthWest" if region=="South West"
replace region_nospace="EastMidlands" if region=="East Midlands"
replace region_nospace="East" if region=="East"
replace region_nospace="London" if region=="London"
replace region_nospace="NorthEast" if region=="North East"
replace region_nospace="NorthWest" if region=="North West"
replace region_nospace="SouthEast" if region=="South East"
replace region_nospace="WestMidlands" if region=="West Midlands"
replace region_nospace="YorkshireandTheHumber" if region=="Yorkshire and The Humber"
***IMD
*Reverse the order (so high is more deprived)
recode imd 5 = 1 4 = 2 3 = 3 2 = 4 1 = 5 0 = .u
label define imd 1 "1 least deprived" 2 "2" 3 "3" 4 "4" 5 "5 most deprived" .u "Not known"
label values imd imd
lab var imd "Index of multiple deprivation"
tab imd, missing
***Age variables
*Nb. works if ages 18 and over
*Create categorised age
drop if age<18 & age !=.
drop if age>109 & age !=.
drop if age==.
lab var age "Age"
recode age 18/39.9999 = 1 ///
40/49.9999 = 2 ///
50/59.9999 = 3 ///
60/69.9999 = 4 ///
70/79.9999 = 5 ///
80/max = 6, gen(agegroup)
label define agegroup 1 "18-39" ///
2 "40-49" ///
3 "50-59" ///
4 "60-69" ///
5 "70-79" ///
6 "80+"
label values agegroup agegroup
lab var agegroup "Age group"
tab agegroup, missing
*Create binary age
recode age min/69.999 = 0 ///
70/max = 1, gen(age70)
***Body Mass Index
*Recode strange values
replace bmi_value = . if bmi_value == 0
replace bmi_value = . if !inrange(bmi_value, 10, 80)
*Restrict to within 10 years of EIA diagnosis date and aged>16
gen bmi_time = (eia_code_date - bmi_date)/365.25
gen bmi_age = age - bmi_time
replace bmi_value = . if bmi_age < 16
replace bmi_value = . if bmi_time > 10 & bmi_time != .
*Set to missing if no date, and vice versa
replace bmi_value = . if bmi_date == .
replace bmi_date = . if bmi_value == .
replace bmi_time = . if bmi_value == .
replace bmi_age = . if bmi_value == .
*Create BMI categories
gen bmicat = .
recode bmicat . = 1 if bmi_value < 18.5
recode bmicat . = 2 if bmi_value < 25
recode bmicat . = 3 if bmi_value < 30
recode bmicat . = 4 if bmi_value < 35
recode bmicat . = 5 if bmi_value < 40
recode bmicat . = 6 if bmi_value < .
replace bmicat = .u if bmi_value >= .
label define bmicat 1 "Underweight (<18.5)" ///
2 "Normal (18.5-24.9)" ///
3 "Overweight (25-29.9)" ///
4 "Obese I (30-34.9)" ///
5 "Obese II (35-39.9)" ///
6 "Obese III (40+)" ///
.u "Not known"
label values bmicat bmicat
lab var bmicat "BMI"
tab bmicat, missing
*Create less granular categorisation
recode bmicat 1/3 .u = 1 4 = 2 5 = 3 6 = 4, gen(obese4cat)
label define obese4cat 1 "No record of obesity" ///
2 "Obese I (30-34.9)" ///
3 "Obese II (35-39.9)" ///
4 "Obese III (40+)"
label values obese4cat obese4cat
order obese4cat, after(bmicat)
***Smoking
label define smoke 1 "Never" 2 "Former" 3 "Current" .u "Not known"
gen smoke = 1 if smoking_status == "N"
replace smoke = 2 if smoking_status == "E"
replace smoke = 3 if smoking_status == "S"
replace smoke = .u if smoking_status == "M"
replace smoke = .u if smoking_status == ""
label values smoke smoke
lab var smoke "Smoking status"
drop smoking_status
tab smoke, missing
*Create non-missing 3-category variable for current smoking (assumes missing smoking is never smoking)
recode smoke .u = 1, gen(smoke_nomiss)
order smoke_nomiss, after(smoke)
label values smoke_nomiss smoke
**Clinical comorbidities
***eGFR
*Set implausible creatinine values to missing (Note: zero changed to missing)
replace creatinine_value = . if !inrange(creatinine_value, 20, 3000)
*Remove creatinine dates if no measurements, and vice versa
replace creatinine_value = . if creatinine_date == .
replace creatinine_date = . if creatinine_value == .
replace creatinine = . if creatinine_value == .
recode creatinine .=0
tab creatinine, missing
*Divide by 88.4 (to convert umol/l to mg/dl)
gen SCr_adj = creatinine_value/88.4
gen min = .
replace min = SCr_adj/0.7 if male==0
replace min = SCr_adj/0.9 if male==1
replace min = min^-0.329 if male==0
replace min = min^-0.411 if male==1
replace min = 1 if min<1
gen max=.
replace max=SCr_adj/0.7 if male==0
replace max=SCr_adj/0.9 if male==1
replace max=max^-1.209
replace max=1 if max>1
gen egfr=min*max*141
replace egfr=egfr*(0.993^age)
replace egfr=egfr*1.018 if male==0
label var egfr "egfr calculated using CKD-EPI formula with no ethnicity"
*Categorise into ckd stages
egen egfr_cat_all = cut(egfr), at(0, 15, 30, 45, 60, 5000)
recode egfr_cat_all 0 = 5 15 = 4 30 = 3 45 = 2 60 = 0, generate(ckd_egfr)
gen egfr_cat = .
recode egfr_cat . = 3 if egfr < 30
recode egfr_cat . = 2 if egfr < 60
recode egfr_cat . = 1 if egfr < .
replace egfr_cat = .u if egfr >= .
label define egfr_cat 1 ">=60" ///
2 "30-59" ///
3 "<30" ///
.u "Not known"
label values egfr_cat egfr_cat
lab var egfr_cat "eGFR"
tab egfr_cat, missing
*If missing eGFR, assume normal
gen egfr_cat_nomiss = egfr_cat
replace egfr_cat_nomiss = 1 if egfr_cat == .u
label define egfr_cat_nomiss 1 ">=60/not known" ///
2 "30-59" ///
3 "<30"
label values egfr_cat_nomiss egfr_cat_nomiss
lab var egfr_cat_nomiss "eGFR"
tab egfr_cat_nomiss, missing
gen egfr_date = creatinine_date
format egfr_date %td
*Add in end stage renal failure and create a single CKD variable
*Missing assumed to not have CKD
gen ckd = 0
replace ckd = 1 if ckd_egfr != . & ckd_egfr >= 1
replace ckd = 1 if esrf == 1
label define ckd 0 "No" 1 "Yes"
label values ckd ckd
label var ckd "Chronic kidney disease"
tab ckd, missing
*Create date (most recent measure prior to index)
gen temp1_ckd_date = creatinine_date if ckd_egfr >=1
gen temp2_ckd_date = esrf_date if esrf == 1
gen ckd_date = max(temp1_ckd_date,temp2_ckd_date)
format ckd_date %td
drop temp1_ckd_date temp2_ckd_date SCr_adj min max ckd_egfr egfr_cat_all
***HbA1c
*Set zero or negative to missing
replace hba1c_percentage = . if hba1c_percentage <= 0
replace hba1c_mmol_per_mol = . if hba1c_mmol_per_mol <= 0
*Change implausible values to missing
replace hba1c_percentage = . if !inrange(hba1c_percentage, 1, 20)
replace hba1c_mmol_per_mol = . if !inrange(hba1c_mmol_per_mol, 10, 200)
*Set most recent values of >24 months prior to EIA diagnosis date to missing
replace hba1c_percentage = . if (eia_code_date - hba1c_percentage_date) > 24*30 & hba1c_percentage_date != .
replace hba1c_mmol_per_mol = . if (eia_code_date - hba1c_mmol_per_mol_date) > 24*30 & hba1c_mmol_per_mol_date != .
*Clean up dates
replace hba1c_percentage_date = . if hba1c_percentage == .
replace hba1c_mmol_per_mol_date = . if hba1c_mmol_per_mol == .
*Express HbA1c as percentage
*Express all values as perecentage
noi summ hba1c_percentage hba1c_mmol_per_mol
gen hba1c_pct = hba1c_percentage
replace hba1c_pct = (hba1c_mmol_per_mol/10.929)+2.15 if hba1c_mmol_per_mol<.
*Valid % range between 0-20
replace hba1c_pct = . if !inrange(hba1c_pct, 1, 20)
replace hba1c_pct = round(hba1c_pct, 0.1)
*Categorise HbA1c and diabetes
*Group hba1c pct
gen hba1ccat = 0 if hba1c_pct < 6.5
replace hba1ccat = 1 if hba1c_pct >= 6.5 & hba1c_pct < 7.5
replace hba1ccat = 2 if hba1c_pct >= 7.5 & hba1c_pct < 8
replace hba1ccat = 3 if hba1c_pct >= 8 & hba1c_pct < 9
replace hba1ccat = 4 if hba1c_pct >= 9 & hba1c_pct !=.
label define hba1ccat 0 "<6.5%" 1">=6.5-7.4" 2">=7.5-7.9" 3">=8-8.9" 4">=9"
label values hba1ccat hba1ccat
tab hba1ccat, missing
*Express all values as mmol
gen hba1c_mmol = hba1c_mmol_per_mol
replace hba1c_mmol = (hba1c_percentage*10.929)-23.5 if hba1c_percentage<. & hba1c_mmol==.
*Group hba1c mmol
gen hba1ccatmm = 0 if hba1c_mmol < 58
replace hba1ccatmm = 1 if hba1c_mmol >= 58 & hba1c_mmol !=.
replace hba1ccatmm =.u if hba1ccatmm==.
label define hba1ccatmm 0 "HbA1c <58mmol/mol" 1 "HbA1c >=58mmol/mol" .u "Not known"
label values hba1ccatmm hba1ccatmm
lab var hba1ccatmm "HbA1c"
tab hba1ccatmm, missing
*Create diabetes, split by control/not (assumes missing = no diabetes)
gen diabcatm = 1 if diabetes==0
replace diabcatm = 2 if diabetes==1 & hba1ccatmm==0
replace diabcatm = 3 if diabetes==1 & hba1ccatmm==1
replace diabcatm = 4 if diabetes==1 & hba1ccatmm==.u
label define diabcatm 1 "No diabetes" ///
2 "Diabetes with HbA1c <58mmol/mol" ///
3 "Diabetes with HbA1c >58mmol/mol" ///
4 "Diabetes with no HbA1c measure"
label values diabcatm diabcatm
lab var diabcatm "Diabetes"
*Create cancer variable 'other cancer currently', includes carcinoma of the head and neck - need to confirm if this excludes NMSC
gen cancer =0
replace cancer =1 if lung_cancer ==1 | haem_cancer ==1 | other_cancer ==1
lab var cancer "Cancer"
lab define cancer 0 "No" 1 "Yes", modify
lab val cancer cancer
tab cancer, missing
*Create other comorbid variables
gen combined_cv_comorbid =1 if chronic_cardiac_disease ==1 | stroke==1
recode combined_cv_comorbid .=0
*Label variables
lab var hypertension "Hypertension"
lab define hypertension 0 "No" 1 "Yes", modify
lab val hypertension hypertension
lab var diabetes "Diabetes"
lab define diabetes 0 "No" 1 "Yes", modify
lab val diabetes diabetes
lab var stroke "Stroke"
lab define stroke 0 "No" 1 "Yes", modify
lab val stroke stroke
lab var chronic_resp_disease "Chronic respiratory disease"
lab define chronic_resp_disease 0 "No" 1 "Yes", modify
lab val chronic_resp_disease chronic_resp_disease
lab var copd "COPD"
lab define copd 0 "No" 1 "Yes", modify
lab val copd copd
lab var esrf "End-stage renal failure"
lab define esrf 0 "No" 1 "Yes", modify
lab val esrf esrf
lab var chronic_liver_disease "Chronic liver disease"
lab define chronic_liver_disease 0 "No" 1 "Yes", modify
lab val chronic_liver_disease chronic_liver_disease
lab var chronic_cardiac_disease "Chronic cardiac disease"
lab define chronic_cardiac_disease 0 "No" 1 "Yes", modify
lab val chronic_cardiac_disease chronic_cardiac_disease
lab var rheum_appt "Rheumatology appointment"
lab define rheum_appt 0 "No" 1 "Yes", modify
lab val rheum_appt rheum_appt
*Ensure everyone has EIA code=============================================================*/
**All patients should have EIA code
tab eia_code, missing
keep if eia_code==1
**Check first rheum appt date was before EIA code date==================================*/
**Rheumatology appt
tab rheum_appt, missing //proportion of patients with a rheum outpatient date in the 12 months before EIA code appeared in GP record; but, data only from April 2019 onwards
tab rheum_appt2, missing //proportion of patients with a rheum outpatient date in the 6 months before EIA code appeared in GP record; but, data only from April 2019 onwards
tab rheum_appt3, missing //proportion of patients with a rheum outpatient date in the 2 years before EIA code appeared in GP record; but, data only from April 2019 onwards
**Check timeframe of rheum appt relative to EIA code
tab rheum_appt if rheum_appt_date>eia_code_date & rheum_appt_date!=. //confirm proportion who had rheum appt (i.e. not missing) and appt after EIA code
tab rheum_appt if rheum_appt_date>(eia_code_date + 30) & rheum_appt_date!=. //confirm proportion who had rheum appt 30 days after EIA code
tab rheum_appt if rheum_appt_date>(eia_code_date + 60) & rheum_appt_date!=. //confirm proportion who had rheum appt 60 days after EIA code
replace rheum_appt=0 if rheum_appt_date>(eia_code_date + 60) & rheum_appt_date!=. //replace as missing those appts >60 days after EIA code
replace rheum_appt_date=. if rheum_appt_date>(eia_code_date + 60) & rheum_appt_date!=. //replace as missing those appts >60 days after EIA code
**Check timeframe of rheum appt relative to EIA code
replace rheum_appt2=0 if rheum_appt2_date>(eia_code_date + 60) & rheum_appt2_date!=. //replace as missing those appts >60 days after EIA code
replace rheum_appt2_date=. if rheum_appt2_date>(eia_code_date + 60) & rheum_appt2_date!=. //replace as missing those appts >60 days after EIA code_yea
replace rheum_appt3=0 if rheum_appt3_date>(eia_code_date + 60) & rheum_appt3_date!=. //replace as missing those appts >60 days after EIA code
replace rheum_appt3_date=. if rheum_appt3_date>(eia_code_date + 60) & rheum_appt3_date!=. //replace as missing those appts >60 days after EIA code
*Check first csDMARD/biologic was after rheum appt date=====================================================*/
**csDMARDs (not including high cost MTX; wouldn't be shared care)
gen csdmard=1 if hydroxychloroquine==1 | leflunomide==1 | methotrexate==1 | methotrexate_inj==1 | sulfasalazine==1
recode csdmard .=0
tab csdmard, missing
**csDMARDs (including high cost MTX)
gen csdmard_hcd=1 if hydroxychloroquine==1 | leflunomide==1 | methotrexate==1 | methotrexate_inj==1 | methotrexate_hcd==1 | sulfasalazine==1
recode csdmard_hcd .=0
tab csdmard_hcd, missing
**Date of first csDMARD script (not including high cost MTX prescriptions)
gen csdmard_date=min(hydroxychloroquine_date, leflunomide_date, methotrexate_date, methotrexate_inj_date, sulfasalazine_date)
format %td csdmard_date
**Date of first csDMARD script (including high cost MTX prescriptions)
gen csdmard_hcd_date=min(hydroxychloroquine_date, leflunomide_date, methotrexate_date, methotrexate_inj_date, methotrexate_hcd_date, sulfasalazine_date)
format %td csdmard_hcd_date
**Biologic use
gen biologic=1 if abatacept==1 | adalimumab==1 | baricitinib==1 | certolizumab==1 | etanercept==1 | golimumab==1 | guselkumab==1 | infliximab==1 | ixekizumab==1 | rituximab==1 | sarilumab==1 | secukinumab==1 | tocilizumab==1 | tofacitinib==1 | upadacitinib==1 | ustekinumab==1
recode biologic .=0
tab biologic, missing
**Date of first biologic script
gen biologic_date=min(abatacept_date, adalimumab_date, baricitinib_date, certolizumab_date, etanercept_date, golimumab_date, guselkumab_date, infliximab_date, ixekizumab_date, rituximab_date, sarilumab_date, secukinumab_date, tocilizumab_date, tofacitinib_date, upadacitinib_date, ustekinumab_date)
format %td biologic_date
**Exclude if first csdmard or biologic was before first rheum appt
tab csdmard if rheum_appt_date!=. & csdmard_date!=. & csdmard_date<rheum_appt_date
tab csdmard if rheum_appt_date!=. & csdmard_date!=. & (csdmard_date + 60)<rheum_appt_date
drop if rheum_appt_date!=. & csdmard_date!=. & (csdmard_date + 60)<rheum_appt_date //drop if first csDMARD more than 60 days before first rheum_appt_date
tab biologic if rheum_appt_date!=. & biologic_date!=. & biologic_date<rheum_appt_date
tab biologic if rheum_appt_date!=. & biologic_date!=. & (biologic_date + 60)<rheum_appt_date
drop if rheum_appt_date!=. & biologic_date!=. & (biologic_date + 60)<rheum_appt_date //drop if first biologic more than 60 days before first rheum_appt_date
*Generate diagnosis date===============================================================*/
*Use eia code date (in GP record) as diagnosis date
gen diagnosis_date=eia_code_date
format diagnosis_date %td
*Refine diagnostic window=============================================================*/
**Keep patients with diagnosis date was after 1st April 2019 and before 1st April 2022
keep if diagnosis_date>=date("$start_date", "DMY") & diagnosis_date!=.
tab eia_code, missing
keep if diagnosis_date<date("$end_date", "DMY") & diagnosis_date!=.
tab eia_code, missing
*Include only most recent EIA sub-diagnosis=============================================*/
replace ra_code =0 if psa_code_date > ra_code_date & psa_code_date !=.
replace ra_code =0 if anksp_code_date > ra_code_date & anksp_code_date !=.
replace ra_code =0 if undiff_code_date > ra_code_date & undiff_code_date !=.
replace psa_code =0 if ra_code_date >= psa_code_date & ra_code_date !=.
replace psa_code =0 if anksp_code_date > psa_code_date & anksp_code_date !=.
replace psa_code =0 if undiff_code_date > psa_code_date & undiff_code_date !=.
replace anksp_code =0 if psa_code_date >= anksp_code_date & psa_code_date !=.
replace anksp_code =0 if ra_code_date >= anksp_code_date & ra_code_date !=.
replace anksp_code =0 if undiff_code_date > anksp_code_date & undiff_code_date !=.
replace undiff_code =0 if ra_code_date >= undiff_code_date & ra_code_date !=.
replace undiff_code =0 if psa_code_date >= undiff_code_date & psa_code_date !=.
replace undiff_code =0 if anksp_code_date >= undiff_code_date & anksp_code_date !=.
gen eia_diagnosis=1 if ra_code==1
replace eia_diagnosis=2 if psa_code==1
replace eia_diagnosis=3 if anksp_code==1
replace eia_diagnosis=4 if undiff_code==1
lab define eia_diagnosis 1 "RA" 2 "PsA" 3 "AxSpA" 4 "Undiff IA", modify
lab val eia_diagnosis eia_diagnosis
tab eia_diagnosis, missing
drop if eia_diagnosis==. //should be none
decode eia_diagnosis, gen(eia_diag)
replace eia_diag="Undiff_IA" if eia_diagnosis==4
*Number of EIA diagnoses in 6-month time windows=========================================*/
**Month/Year of EIA code
gen year_diag=year(eia_code_date)
format year_diag %ty
gen month_diag=month(eia_code_date)
gen mo_year_diagn=ym(year_diag, month_diag)
format mo_year_diagn %tmMon-CCYY
generate str16 mo_year_diagn_s = strofreal(mo_year_diagn,"%tmCCYY!mNN")
lab var mo_year_diagn "Month/Year of Diagnosis"
lab var mo_year_diagn_s "Month/Year of Diagnosis"
**Month/Year of rheum appt
gen year_appt=year(rheum_appt_date) if rheum_appt_date!=.
format year_appt %ty
gen month_appt=month(rheum_appt_date) if rheum_appt_date!=.
gen mo_year_appt=ym(year_appt, month_appt)
format mo_year_appt %tmMon-CCYY
generate str16 mo_year_appt_s = strofreal(mo_year_appt,"%tmCCYY!mNN")
**Separate into 6-month time windows (for diagnosis date)
gen diagnosis_6m=1 if diagnosis_date>=td(01apr2019) & diagnosis_date<td(01oct2019)
replace diagnosis_6m=2 if diagnosis_date>=td(01oct2019) & diagnosis_date<td(01apr2020)
replace diagnosis_6m=3 if diagnosis_date>=td(01apr2020) & diagnosis_date<td(01oct2020)
replace diagnosis_6m=4 if diagnosis_date>=td(01oct2020) & diagnosis_date<td(01apr2021)
replace diagnosis_6m=5 if diagnosis_date>=td(01apr2021) & diagnosis_date<td(01oct2021)
replace diagnosis_6m=6 if diagnosis_date>=td(01oct2021) & diagnosis_date<td(01apr2022)
lab define diagnosis_6m 1 "Apr 2019-Oct 2019" 2 "Oct 2019-Apr 2020" 3 "Apr 2020-Oct 2020" 4 "Oct 2020-Apr 2021" 5 "Apr 2021-Oct 2021" 6 "Oct 2021-Apr 2022", modify
lab val diagnosis_6m diagnosis_6m
lab var diagnosis_6m "Time period for diagnosis"
tab diagnosis_6m, missing
bys eia_diagnosis: tab diagnosis_6m, missing
**Separate into 12-month time windows (for diagnosis date)
gen diagnosis_year=1 if diagnosis_date>=td(01apr2019) & diagnosis_date<td(01apr2020)
replace diagnosis_year=2 if diagnosis_date>=td(01apr2020) & diagnosis_date<td(01apr2021)
replace diagnosis_year=3 if diagnosis_date>=td(01apr2021) & diagnosis_date<td(01apr2022)
lab define diagnosis_year 1 "Apr 2019-Apr 2020" 2 "Apr 2020-Apr 2021" 3 "Apr 2021-Apr 2022", modify
lab val diagnosis_year diagnosis_year
lab var diagnosis_year "Year of diagnosis"
tab diagnosis_year, missing
bys eia_diagnosis: tab diagnosis_year, missing
**Separate into 6-month time windows (for appt date)
gen appt_6m=1 if rheum_appt_date>=td(01apr2019) & rheum_appt_date<td(01oct2019)
replace appt_6m=2 if rheum_appt_date>=td(01oct2019) & rheum_appt_date<td(01apr2020)
replace appt_6m=3 if rheum_appt_date>=td(01apr2020) & rheum_appt_date<td(01oct2020)
replace appt_6m=4 if rheum_appt_date>=td(01oct2020) & rheum_appt_date<td(01apr2021)
replace appt_6m=5 if rheum_appt_date>=td(01apr2021) & rheum_appt_date<td(01oct2021)
replace appt_6m=6 if rheum_appt_date>=td(01oct2021) & rheum_appt_date<td(01apr2022)
lab define appt_6m 1 "Apr 2019-Oct 2019" 2 "Oct 2019-Apr 2020" 3 "Apr 2020-Oct 2020" 4 "Oct 2020-Apr 2021" 5 "Apr 2021-Oct 2021" 6 "Oct 2021-Apr 2022", modify
lab val appt_6m appt_6m
lab var appt_6m "Time period for first rheumatology appt"
tab appt_6m, missing
bys eia_diagnosis: tab appt_6m, missing
**Separate into 12-month time windows (for appt date)
gen appt_year=1 if rheum_appt_date>=td(01apr2019) & rheum_appt_date<td(01apr2020)
replace appt_year=2 if rheum_appt_date>=td(01apr2020) & rheum_appt_date<td(01apr2021)
replace appt_year=3 if rheum_appt_date>=td(01apr2021) & rheum_appt_date<td(01apr2022)
lab define appt_year 1 "Apr 2019-Apr 2020" 2 "Apr 2020-Apr 2021" 3 "Apr 2021-Apr 2022", modify
lab val appt_year appt_year
lab var appt_year "Year of first rheumatology appt"
tab appt_year, missing
bys eia_diagnosis: tab appt_year, missing
*Define appointments and referrals======================================*/
**Proportion of patients with at least 6 or 12 months of GP registration after rheum appt (i.e. diagnosis date)
tab has_6m_follow_up
tab has_12m_follow_up
tab mo_year_diagn has_6m_follow_up
tab mo_year_diagn has_12m_follow_up
*For appt and csDMARD analyses, all patients must have 1) rheum appt 2) GP appt before rheum appt 3) 12m follow-up after rheum appt 4) 12m of registration after appt
gen has_6m_post_appt=1 if rheum_appt_date!=. & rheum_appt_date<td(01oct2021) & has_6m_follow_up==1 & last_gp_prerheum==1
recode has_6m_post_appt .=0
gen has_12m_post_appt=1 if rheum_appt_date!=. & rheum_appt_date<td(01apr2021) & has_12m_follow_up==1 & last_gp_prerheum==1
recode has_12m_post_appt .=0
lab var has_12m_post_appt "GP/rheum/registration < Apr 21"
lab define has_12m_post_appt 0 "No" 1 "Yes", modify
lab val has_12m_post_appt has_12m_post_appt
**Rheumatology appt
tab rheum_appt, missing //proportion of patients with a rheum outpatient date in the 12 months before EIA code appeared in GP record; but, data only from April 2019 onwards
tab rheum_appt2, missing //proportion of patients with a rheum outpatient date in the 6 months before EIA code appeared in GP record; but, data only from April 2019 onwards
tab rheum_appt3, missing //proportion of patients with a rheum outpatient date in the 2 years before EIA code appeared in GP record; but, data only from April 2019 onwards
*Gen rheum appt var only for those with 12m follow-up
gen rheum_appt_to21=rheum_appt if rheum_appt_date<td(01apr2021)
recode rheum_appt_to21 .=0
lab var rheum_appt_to21 "Rheumatology appt < Apr 21 "
lab define rheum_appt_to21 0 "No" 1 "Yes", modify
lab val rheum_appt_to21 rheum_appt_to21
**Check number of rheumatology appts in the year before EIA code
tabstat rheum_appt_count, stat (n mean sd p50 p25 p75)
bys diagnosis_year: tabstat rheum_appt_count, stat (n mean sd p50 p25 p75)
bys appt_year: tabstat rheum_appt_count, stat (n mean sd p50 p25 p75)
**Rheumatology referrals
tab referral_rheum_prerheum //last rheum referral in the 2 years before rheumatology outpatient (requires rheum appt to have been present)
tab referral_rheum_prerheum if rheum_appt!=0 & referral_rheum_prerheum_date<=rheum_appt_date //last rheum referral in the 2 years before rheumatology outpatient, assuming ref date before rheum appt date (should be accounted for by Python code)
tab referral_rheum_precode //last rheum referral in the 2 years before EIA code (could use if rheum appt missing)
gen referral_rheum_comb_date = referral_rheum_prerheum_date if referral_rheum_prerheum_date!=.
replace referral_rheum_comb_date = referral_rheum_precode_date if referral_rheum_prerheum_date==. & referral_rheum_precode_date!=.
format %td referral_rheum_comb_date
**GP appointments
tab last_gp_refrheum //proportion with last GP appointment in 2 years before rheum referral (pre-rheum appt); requires there to have been a rheum referral, before a rheum appt
gen last_gp_prerheum_to21=last_gp_prerheum if rheum_appt_date!=. & rheum_appt_date<td(01apr2021)
recode last_gp_prerheum_to21 .=0
lab var last_gp_prerheum_to21 "GP and rheum appt < Apr 21"
lab define last_gp_prerheum_to21 0 "No" 1 "Yes", modify
lab val last_gp_prerheum_to21 last_gp_prerheum_to21
gen all_appts=1 if last_gp_refrheum==1 & referral_rheum_prerheum==1 & rheum_appt==1 & last_gp_refrheum_date<=referral_rheum_prerheum_date & referral_rheum_prerheum_date<=rheum_appt_date
recode all_appts .=0
tab all_appts, missing //KEY - proportion who had a last gp appt, then rheum ref, then rheum appt
tab last_gp_refcode //last GP appointment before rheum ref (i.e. pre-eia code ref); requires there to have been a rheum referral before an EIA code (i.e. rheum appt could have been missing)
tab last_gp_prerheum //last GP appointment before rheum appt; requires there to have been a rheum appt before and EIA code
tab last_gp_precode //last GP appointment before EIA code
*Time to referral=============================================*/
**Time from last GP to rheum ref before rheum appt (i.e. if appts are present and in correct time order)
gen time_gp_rheum_ref_appt = (referral_rheum_prerheum_date - last_gp_refrheum_date) if referral_rheum_prerheum_date!=. & last_gp_refrheum_date!=. & rheum_appt_date!=. & referral_rheum_prerheum_date>=last_gp_refrheum_date & referral_rheum_prerheum_date<=rheum_appt_date
tabstat time_gp_rheum_ref_appt, stats (n mean p50 p25 p75) //all patients (should be same number as all_appts)
gen gp_ref_cat=1 if time_gp_rheum_ref_appt<=3 & time_gp_rheum_ref_appt!=.
replace gp_ref_cat=2 if time_gp_rheum_ref_appt>3 & time_gp_rheum_ref_appt<=7 & time_gp_rheum_ref_appt!=. & gp_ref_cat==.
replace gp_ref_cat=3 if time_gp_rheum_ref_appt>7 & time_gp_rheum_ref_appt!=. & gp_ref_cat==.
lab define gp_ref_cat 1 "Within 3 days" 2 "Between 3-7 days" 3 "More than 7 days", modify
lab val gp_ref_cat gp_ref_cat
lab var gp_ref_cat "Time to GP referral"
tab gp_ref_cat, missing
gen gp_ref_3d=1 if time_gp_rheum_ref_appt<=3 & time_gp_rheum_ref_appt!=.
replace gp_ref_3d=2 if time_gp_rheum_ref_appt>3 & time_gp_rheum_ref_appt!=.
lab define gp_ref_3d 1 "Within 3 days" 2 "More than 3 days", modify
lab val gp_ref_3d gp_ref_3d
lab var gp_ref_3d "Time to GP referral"
tab gp_ref_3d, missing
**Time from last GP to rheum ref before eia code (sensitivity analysis; includes those with no rheum appt)
gen time_gp_rheum_ref_code = (referral_rheum_precode_date - last_gp_refcode_date) if referral_rheum_precode_date!=. & last_gp_refcode_date!=. & referral_rheum_precode_date>=last_gp_refcode_date & referral_rheum_precode_date<=eia_code_date
tabstat time_gp_rheum_ref_code, stats (n mean p50 p25 p75)
**Time from last GP to rheum ref (combined - sensitivity analysis; includes those with no rheum appt)
gen time_gp_rheum_ref_comb = time_gp_rheum_ref_appt
replace time_gp_rheum_ref_comb = time_gp_rheum_ref_code if time_gp_rheum_ref_appt==. & time_gp_rheum_ref_code!=.
tabstat time_gp_rheum_ref_comb, stats (n mean p50 p25 p75)
*Time to appointment=============================================*/
**Time from last GP pre-rheum appt to first rheum appt (proxy measure for referral to appt delay)
gen time_gp_rheum_appt = (rheum_appt_date - last_gp_prerheum_date) if rheum_appt_date!=. & last_gp_prerheum_date!=. & rheum_appt_date>=last_gp_prerheum_date
tabstat time_gp_rheum_appt, stats (n mean p50 p25 p75)
**Time from rheum ref to rheum appt (i.e. if appts are present and in correct order)
gen time_ref_rheum_appt = (rheum_appt_date - referral_rheum_prerheum_date) if rheum_appt_date!=. & referral_rheum_prerheum_date!=. & referral_rheum_prerheum_date<=rheum_appt_date
tabstat time_ref_rheum_appt, stats (n mean p50 p25 p75)
gen gp_appt_cat=1 if time_gp_rheum_appt<=21 & time_gp_rheum_appt!=.
replace gp_appt_cat=2 if time_gp_rheum_appt>21 & time_gp_rheum_appt<=42 & time_gp_rheum_appt!=. & gp_appt_cat==.
replace gp_appt_cat=3 if time_gp_rheum_appt>42 & time_gp_rheum_appt!=. & gp_appt_cat==.
lab define gp_appt_cat 1 "Within 3 weeks" 2 "Between 3-6 weeks" 3 "More than 6 weeks", modify
lab val gp_appt_cat gp_appt_cat
lab var gp_appt_cat "Time to rheumatology assessment, overall"
tab gp_appt_cat, missing
gen gp_appt_cat_19=gp_appt_cat if appt_year==1
gen gp_appt_cat_20=gp_appt_cat if appt_year==2
lab define gp_appt_cat_19 1 "Within 3 weeks" 2 "Between 3-6 weeks" 3 "More than 6 weeks", modify
lab val gp_appt_cat_19 gp_appt_cat_19
lab var gp_appt_cat_19 "Time to rheumatology assessment, Apr 2019-2020"
lab define gp_appt_cat_20 1 "Within 3 weeks" 2 "Between 3-6 weeks" 3 "More than 6 weeks", modify
lab val gp_appt_cat_20 gp_appt_cat_20
lab var gp_appt_cat_20 "Time to rheumatology assessment, Apr 2020-2021"
gen gp_appt_3w=1 if time_gp_rheum_appt<=21 & time_gp_rheum_appt!=.
replace gp_appt_3w=2 if time_gp_rheum_appt>21 & time_gp_rheum_appt!=.
lab define gp_appt_3w 1 "Within 3 weeks" 2 "More than 3 weeks", modify
lab val gp_appt_3w gp_appt_3w
lab var gp_appt_3w "Time to rheumatology assessment, overall"
tab gp_appt_3w, missing
gen ref_appt_cat=1 if time_ref_rheum_appt<=21 & time_ref_rheum_appt!=.
replace ref_appt_cat=2 if time_ref_rheum_appt>21 & time_ref_rheum_appt<=42 & time_ref_rheum_appt!=. & ref_appt_cat==.
replace ref_appt_cat=3 if time_ref_rheum_appt>42 & time_ref_rheum_appt!=. & ref_appt_cat==.
lab define ref_appt_cat 1 "Within 3 weeks" 2 "Between 3-6 weeks" 3 "More than 6 weeks", modify
lab val ref_appt_cat ref_appt_cat
lab var ref_appt_cat "Time to rheumatology assessment"
tab ref_appt_cat, missing
gen ref_appt_3w=1 if time_ref_rheum_appt<=21 & time_ref_rheum_appt!=.
replace ref_appt_3w=2 if time_ref_rheum_appt>21 & time_ref_rheum_appt!=.
lab define ref_appt_3w 1 "Within 3 weeks" 2 "More than 3 weeks", modify
lab val ref_appt_3w ref_appt_3w
lab var ref_appt_3w "Time to rheumatology assessment"
tab ref_appt_3w, missing
**Time from rheum ref or last GP to rheum appt (combined - sensitivity analysis; includes those with no rheum ref)
gen time_refgp_rheum_appt = time_ref_rheum_appt
replace time_refgp_rheum_appt = time_gp_rheum_appt if time_ref_rheum_appt==. & time_gp_rheum_appt!=.
tabstat time_refgp_rheum_appt, stats (n mean p50 p25 p75)
*Time to EIA code==================================================*/
**Time from last GP pre-code to EIA code (sensitivity analysis; includes those with no rheum ref and/or no rheum appt)
gen time_gp_eia_code = (eia_code_date - last_gp_precode_date) if eia_code_date!=. & last_gp_precode_date!=. & eia_code_date>=last_gp_precode_date
tabstat time_gp_eia_code, stats (n mean p50 p25 p75)
**Time from last GP to EIA diagnosis (combined - sensitivity analysis; includes those with no rheum appt)
gen time_gp_eia_diag = time_gp_rheum_appt
replace time_gp_eia_diag = time_gp_eia_code if time_gp_rheum_appt==. & time_gp_eia_code!=.
tabstat time_gp_eia_diag, stats (n mean p50 p25 p75)
**Time from rheum ref to EIA code (sensitivity analysis; includes those with no rheum appt)
gen time_ref_rheum_eia = (eia_code_date - referral_rheum_precode_date) if eia_code_date!=. & referral_rheum_precode_date!=. & referral_rheum_precode_date<=eia_code_date
tabstat time_ref_rheum_eia, stats (n mean p50 p25 p75)
**Time from rheum ref to EIA diagnosis (combined - sensitivity analysis; includes those with no rheum appt)
gen time_ref_rheum_eia_comb = time_ref_rheum_appt
replace time_ref_rheum_eia_comb = time_ref_rheum_eia if time_ref_rheum_appt==. & time_ref_rheum_eia!=.
tabstat time_ref_rheum_eia_comb, stats (n mean p50 p25 p75)
**Time from rheum appt (6 month pre-) to EIA code
gen time_rheum_eia_code = (eia_code_date - rheum_appt_date) if eia_code_date!=. & rheum_appt_date!=.
tabstat time_rheum_eia_code, stats (n mean p50 p25 p75)
**Time from rheum appt (1 year pre-) to EIA code
gen time_rheum2_eia_code = (eia_code_date - rheum_appt2_date) if eia_code_date!=. & rheum_appt2_date!=.
tabstat time_rheum2_eia_code, stats (n mean p50 p25 p75)
**Time from rheum appt (2 years pre-) to EIA code
gen time_rheum3_eia_code = (eia_code_date - rheum_appt3_date) if eia_code_date!=. & rheum_appt3_date!=.
tabstat time_rheum3_eia_code, stats (n mean p50 p25 p75)
*Time from rheum appt to first csDMARD prescriptions on primary care record======================================================================*/
**Time to first csDMARD script for RA patients not including high cost MTX prescriptions; prescription must be within 12 months of diagnosis all csDMARDs below (could change in future analyses)==================*/
gen time_to_csdmard=(csdmard_date-rheum_appt_date) if csdmard==1 & rheum_appt_date!=. & (csdmard_date<=rheum_appt_date+365)
tabstat time_to_csdmard if ra_code==1, stats (n mean p50 p25 p75)
**Time to first csDMARD script for RA patients (including high cost MTX prescriptions)
gen time_to_csdmard_hcd=(csdmard_hcd_date-rheum_appt_date) if csdmard_hcd==1 & rheum_appt_date!=. & (csdmard_hcd_date<=rheum_appt_date+365)
tabstat time_to_csdmard_hcd if ra_code==1, stats (n mean p50 p25 p75)
**Time to first csDMARD script for PsA patients (not including high cost MTX prescriptions)
tabstat time_to_csdmard if psa_code==1, stats (n mean p50 p25 p75)
**Time to first csDMARD script for PsA patients (including high cost MTX prescriptions)
tabstat time_to_csdmard_hcd if psa_code==1, stats (n mean p50 p25 p75)
**Time to first csDMARD script for axSpA patients (not including high cost MTX prescriptions)
tabstat time_to_csdmard if anksp_code==1, stats (n mean p50 p25 p75)
**Time to first csDMARD script for Undiff IA patients (not including high cost MTX prescriptions)
tabstat time_to_csdmard if undiff_code==1, stats (n mean p50 p25 p75)
**csDMARD time categories (not including high cost MTX prescriptions)
gen csdmard_time=1 if time_to_csdmard<=90 & time_to_csdmard!=.
replace csdmard_time=2 if time_to_csdmard>90 & time_to_csdmard<=180 & time_to_csdmard!=.
replace csdmard_time=3 if time_to_csdmard>180 & time_to_csdmard<=365 & time_to_csdmard!=.
replace csdmard_time=4 if time_to_csdmard>365 | time_to_csdmard==.
lab define csdmard_time 1 "Within 3 months" 2 "3-6 months" 3 "6-12 months" 4 "No prescription within 12 months", modify
lab val csdmard_time csdmard_time
lab var csdmard_time "csDMARD in primary care, overall"
tab csdmard_time if ra_code==1, missing
tab csdmard_time if psa_code==1, missing
tab csdmard_time if anksp_code==1, missing
tab csdmard_time if undiff_code==1, missing
gen csdmard_time_19=csdmard_time if appt_year==1
recode csdmard_time_19 .=5
gen csdmard_time_20=csdmard_time if appt_year==2
recode csdmard_time_20 .=5
lab define csdmard_time_19 1 "Within 3 months" 2 "3-6 months" 3 "6-12 months" 4 "No prescription within 12 months" 5 "Outside 2019", modify
lab val csdmard_time_19 csdmard_time_19
lab var csdmard_time_19 "csDMARD in primary care, Apr 2019-2020"
lab define csdmard_time_20 1 "Within 3 months" 2 "3-6 months" 3 "6-12 months" 4 "No prescription within 12 months" 5 "Outside 2020", modify
lab val csdmard_time_20 csdmard_time_20
lab var csdmard_time_20 "csDMARD in primary care, Apr 2020-2021"
**csDMARD time categories - binary 6 months
gen csdmard_6m=1 if time_to_csdmard<=180 & time_to_csdmard!=.
replace csdmard_6m=0 if time_to_csdmard>180 | time_to_csdmard==.
lab define csdmard_6m 1 "Yes" 0 "No", modify
lab val csdmard_6m csdmard_6m
lab var csdmard_6m "csDMARD in primary care within 6 months"
tab csdmard_6m, missing
**csDMARD time categories (including high cost MTX prescriptions)
gen csdmard_hcd_time=1 if time_to_csdmard_hcd<=90 & time_to_csdmard_hcd!=.
replace csdmard_hcd_time=2 if time_to_csdmard_hcd>90 & time_to_csdmard_hcd<=180 & time_to_csdmard_hcd!=.
replace csdmard_hcd_time=3 if time_to_csdmard_hcd>180 & time_to_csdmard_hcd<=365 & time_to_csdmard_hcd!=.
replace csdmard_hcd_time=4 if time_to_csdmard_hcd>365 | time_to_csdmard_hcd==.