100_baseline_characteristics.do

version 16

/*==============================================================================
DO FILE NAME:			baseline tables
PROJECT:				EIA OpenSAFELY project
DATE: 					07/03/2022
AUTHOR:					J Galloway / M Russell
						adapted from C Rentsch										
DESCRIPTION OF FILE:	baseline tables
DATASETS USED:			main data file
DATASETS CREATED: 		tables
OTHER OUTPUT: 			logfiles, printed to folder $Logdir
USER-INSTALLED ADO: 	 
  (place .ado file(s) in analysis folder)						
==============================================================================*/

**Set filepaths
*global projectdir "C:\Users\k1754142\OneDrive\PhD Project\OpenSAFELY\Github Practice"
global projectdir `c(pwd)'
di "$projectdir"

capture mkdir "$projectdir/output/tables"
capture mkdir "$projectdir/output/figures"

global logdir "$projectdir/logs"
di "$logdir"

**Open a log file
cap log close
log using "$logdir/descriptive_tables.log", replace

**Set Ado file path
adopath + "$projectdir/analysis/extra_ados"

**Use cleaned data from previous step
use "$projectdir/output/data/file_eia_all.dta", clear

set scheme plotplainblind

**Set index dates ===========================================================*/
global year_preceding = "01/04/2018"
global start_date = "01/04/2019"
global appt_date = "01/04/2021"
global end_date = "01/10/2022"

*Descriptive statistics======================================================================*/

**Total number of patients with diagnosis date after 1st April 2019 and before end date
tab eia_code

**Verify that all diagnoses were in study windows
tab mo_year_diagn, missing
tab diagnosis_6m, missing
tab diagnosis_year, missing

**EIA sub-diagnosis (most recent code)
tab eia_diagnosis, missing
bys eia_diagnosis: tab diagnosis_6m, missing
bys eia_diagnosis: tab diagnosis_year, missing

*Diagnostic incidence by year, by disease
preserve
recode ra_code 0=.
recode psa_code 0=.
recode anksp_code 0=.
recode undiff_code 0=.
collapse (count) total_diag=eia_code ra_diag=ra_code psa_diag=psa_code axspa_diag=anksp_code undiff_diag=undiff_code, by(diagnosis_year) 
**Round to nearest 5
foreach var of varlist *_diag {
	gen `var'_round=round(`var', 5)
	drop `var'
}
**Generate incidences by year (baseline population 17,683,500)
foreach var of varlist *_diag_round {
	gen incidence_`var'=((`var'/17683500)*10000)
}
export delimited using "$projectdir/output/tables/diag_count_byyear.csv", replace
restore

*Diagnostic incidence by year, by disease; female patients
preserve
keep if male==0
recode ra_code 0=.
recode psa_code 0=.
recode anksp_code 0=.
recode undiff_code 0=.
collapse (count) total_diag=eia_code ra_diag=ra_code psa_diag=psa_code axspa_diag=anksp_code undiff_diag=undiff_code, by(diagnosis_year) 
**Round to nearest 5
foreach var of varlist *_diag {
	gen `var'_round=round(`var', 5)
	drop `var'
}
**Generate incidences by year (baseline female population 8,866,535)
foreach var of varlist *_diag_round {
	gen incidence_`var'=((`var'/8866535)*10000)
}
export delimited using "$projectdir/output/tables/diag_count_byyear_female.csv", replace
restore

*Diagnostic incidence by year, by disease; male patients
preserve
keep if male==1
recode ra_code 0=.
recode psa_code 0=.
recode anksp_code 0=.
recode undiff_code 0=.
collapse (count) total_diag=eia_code ra_diag=ra_code psa_diag=psa_code axspa_diag=anksp_code undiff_diag=undiff_code, by(diagnosis_year) 
**Round to nearest 5
foreach var of varlist *_diag {
	gen `var'_round=round(`var', 5)
	drop `var'
}
**Generate incidences by year (baseline male population 8,816,965)
foreach var of varlist *_diag_round {
	gen incidence_`var'=((`var'/8816965)*10000)
}
export delimited using "$projectdir/output/tables/diag_count_byyear_male.csv", replace
restore

*Graph of diagnoses by month, by disease
preserve
recode ra_code 0=.
recode psa_code 0=.
recode anksp_code 0=.
recode undiff_code 0=.
collapse (count) total_diag=eia_code ra_diag=ra_code psa_diag=psa_code axspa_diag=anksp_code undiff_diag=undiff_code, by(mo_year_diagn) 
**Round to nearest 5
foreach var of varlist *_diag {
	gen `var'_round=round(`var', 5)
	drop `var'
}
**Generate incidences by month (baseline population 17,683,500)
foreach var of varlist *_diag_round {
	gen incidence_`var'=((`var'/17683500)*10000)
}
export delimited using "$projectdir/output/tables/diag_count_bymonth.csv", replace

twoway connected incidence_total_diag_round mo_year_diagn, ytitle("Monthly incidence of IA diagnoses per 10,000 population", size(small)) || connected incidence_ra_diag_round mo_year_diagn, color(sky) || connected incidence_psa_diag_round mo_year_diagn, color(red) || connected incidence_axspa_diag_round mo_year_diagn, color(green) || connected incidence_undiff_diag_round mo_year_diagn, color(gold) xline(722) yscale(range(0(0.1)0.6)) ylabel(0 "0" 0.1 "0.1" 0.2 "0.2" 0.3 "0.3" 0.4 "0.4" 0.5 "0.5" 0.6 "0.6", nogrid) xtitle("Date of diagnosis", size(small) margin(medsmall)) xlabel(711 "Apr 2019" 717 "Oct 2019" 723 "Apr 2020" 729 "Oct 2020" 735 "Apr 2021" 741 "Oct 2021" 747 "Apr 2022" 753 "Oct 2022", nogrid ) title("", size(small)) name(incidence_twoway, replace) legend(region(fcolor(white%0)) order(1 "Total EIA diagnoses" 2 "RA" 3 "PsA" 4 "AxSpA" 5 "Undifferentiated IA")) saving("$projectdir/output/figures/incidence_twoway_rounded.gph", replace)
	graph export "$projectdir/output/figures/incidence_twoway_rounded.svg", replace
	
restore	

*Graph of diagnoses by month, by disease; female patients
preserve
keep if male==0
recode ra_code 0=.
recode psa_code 0=.
recode anksp_code 0=.
recode undiff_code 0=.
collapse (count) total_diag=eia_code ra_diag=ra_code psa_diag=psa_code axspa_diag=anksp_code undiff_diag=undiff_code, by(mo_year_diagn) 
**Round to nearest 5
foreach var of varlist *_diag {
	gen `var'_round=round(`var', 5)
	drop `var'
}
**Generate incidences by month (baseline female population 8,866,535)
foreach var of varlist *_diag_round {
	gen incidence_`var'=((`var'/8866535)*10000)
}
export delimited using "$projectdir/output/tables/diag_count_bymonth_female.csv", replace

twoway connected incidence_total_diag_round mo_year_diagn, ytitle("Monthly incidence of IA diagnoses per 10,000 female population", size(small)) || connected incidence_ra_diag_round mo_year_diagn, color(sky) || connected incidence_psa_diag_round mo_year_diagn, color(red) || connected incidence_axspa_diag_round mo_year_diagn, color(green) || connected incidence_undiff_diag_round mo_year_diagn, color(gold) xline(722) yscale(range(0(0.1)0.8)) ylabel(0 "0" 0.1 "0.1" 0.2 "0.2" 0.3 "0.3" 0.4 "0.4" 0.5 "0.5" 0.6 "0.6" 0.7 "0.7" 0.8 "0.8", nogrid) xtitle("Date of diagnosis", size(small) margin(medsmall)) xlabel(711 "Apr 2019" 717 "Oct 2019" 723 "Apr 2020" 729 "Oct 2020" 735 "Apr 2021" 741 "Oct 2021" 747 "Apr 2022" 753 "Oct 2022", nogrid ) title("", size(small)) name(incidence_twoway_rounded_female, replace) legend(region(fcolor(white%0)) order(1 "Total EIA diagnoses" 2 "RA" 3 "PsA" 4 "AxSpA" 5 "Undifferentiated IA")) saving("$projectdir/output/figures/incidence_twoway_rounded_female.gph", replace)
	graph export "$projectdir/output/figures/incidence_twoway_rounded_female.svg", replace	
	
restore	

*Graph of diagnoses by month, by disease; male patients
preserve
keep if male==1
recode ra_code 0=.
recode psa_code 0=.
recode anksp_code 0=.
recode undiff_code 0=.
collapse (count) total_diag=eia_code ra_diag=ra_code psa_diag=psa_code axspa_diag=anksp_code undiff_diag=undiff_code, by(mo_year_diagn) 
**Round to nearest 5
foreach var of varlist *_diag {
	gen `var'_round=round(`var', 5)
	drop `var'
}
**Generate incidences by month (baseline male population 8,816,965)
foreach var of varlist *_diag_round {
	gen incidence_`var'=((`var'/8816965)*10000)
}
export delimited using "$projectdir/output/tables/diag_count_bymonth_male.csv", replace

twoway connected incidence_total_diag_round mo_year_diagn, ytitle("Monthly incidence of IA diagnoses per 10,000 male population", size(small)) || connected incidence_ra_diag_round mo_year_diagn, color(sky) || connected incidence_psa_diag_round mo_year_diagn, color(red) || connected incidence_axspa_diag_round mo_year_diagn, color(green) || connected incidence_undiff_diag_round mo_year_diagn, color(gold) xline(722) yscale(range(0(0.1)0.8)) ylabel(0 "0" 0.1 "0.1" 0.2 "0.2" 0.3 "0.3" 0.4 "0.4" 0.5 "0.5" 0.6 "0.6" 0.7 "0.7" 0.8 "0.8", nogrid) xtitle("Date of diagnosis", size(small) margin(medsmall)) xlabel(711 "Apr 2019" 717 "Oct 2019" 723 "Apr 2020" 729 "Oct 2020" 735 "Apr 2021" 741 "Oct 2021" 747 "Apr 2022" 753 "Oct 2022", nogrid ) title("", size(small)) name(incidence_twoway_rounded_male, replace) legend(region(fcolor(white%0)) order(1 "Total EIA diagnoses" 2 "RA" 3 "PsA" 4 "AxSpA" 5 "Undifferentiated IA")) saving("$projectdir/output/figures/incidence_twoway_rounded_male.gph", replace)
	graph export "$projectdir/output/figures/incidence_twoway_rounded_male.svg", replace	
	
restore

*Incidence of rheumatology diagnoses, by ethnicity
preserve
gen total=1 if ethnicity!=.u
gen white=1 if ethnicity==1
gen asian=1 if ethnicity==2
gen black=1 if ethnicity==3
gen mixed=1 if ethnicity==4
collapse (count) total_diag=total white_diag=white asian_diag=asian black_diag=black mixed_diag=mixed, by(diagnosis_year) 
**Round to nearest 5
foreach var of varlist *_diag {
	gen `var'_round=round(`var', 5)
	drop `var'
}
**Generate incidences by year
gen incidence_total=((total_diag_round/13892705)*10000) //all non-missing ethnicities
gen incidence_white=((white_diag_round/12025695)*10000)
gen incidence_asian=((asian_diag_round/1029955)*10000)
gen incidence_black=((black_diag_round/343885)*10000)
gen incidence_mixed=((mixed_diag_round/493170)*10000)
export delimited using "$projectdir/output/tables/diag_count_byyear_ethn.csv", replace

restore

*Incidence of rheumatology diagnoses, by imd quintile
preserve
gen imd_all=1 if imd!=.u
gen imd_1=1 if imd==1
gen imd_2=1 if imd==2
gen imd_3=1 if imd==3
gen imd_4=1 if imd==4
gen imd_5=1 if imd==5
collapse (count) imd_all_diag=imd_all imd_1_diag=imd_1 imd_2_diag=imd_2 imd_3_diag=imd_3 imd_4_diag=imd_4 imd_5_diag=imd_5, by(diagnosis_year) 
**Round to nearest 5
foreach var of varlist *_diag {
	gen `var'_round=round(`var', 5)
	drop `var'
}
**Generate incidences by year
gen incidence_imd_all=((imd_all_diag_round/17415045)*10000) //all non-missing imds
gen incidence_imd_1=((imd_1_diag_round/3285410)*10000)
gen incidence_imd_2=((imd_2_diag_round/3557860)*10000)
gen incidence_imd_3=((imd_3_diag_round/3762515)*10000)
gen incidence_imd_4=((imd_4_diag_round/3448770)*10000)
gen incidence_imd_5=((imd_5_diag_round/3360490)*10000)
export delimited using "$projectdir/output/tables/diag_count_byyear_imd.csv", replace

restore

*Graph of rheumatology appointments by month, by disease
preserve
keep if rheum_appt_date!=.
recode ra_code 0=.
recode psa_code 0=.
recode anksp_code 0=.
recode undiff_code 0=.
collapse (count) total_diag=eia_code ra_diag=ra_code psa_diag=psa_code axspa_diag=anksp_code undiff_diag=undiff_code, by(mo_year_appt)
export delimited using "$projectdir/output/tables/appt_count_bymonth.csv", replace 

twoway connected total_diag mo_year_appt, ytitle("Number of new diagnoses per month", size(medsmall)) || connected ra_diag mo_year_appt, color(sky) || connected psa_diag mo_year_appt, color(red) || connected axspa_diag mo_year_appt, color(green) || connected undiff_diag mo_year_appt, color(gold) xline(722) ylabel(, nogrid) xtitle("Date of first rheumatology appointment", size(medsmall) margin(medsmall)) xlabel(711 "Apr 2019" 717 "Oct 2019" 723 "Apr 2020" 729 "Oct 2020" 735 "Apr 2021" 741 "Oct 2021" 747 "Apr 2022" 753 "Oct 2022", nogrid ) title("", size(small)) name(incidence_twoway_appt, replace) legend(region(fcolor(white%0)) order(1 "Total EIA diagnoses" 2 "RA" 3 "PsA" 4 "AxSpA" 5 "Undiff IA")) saving("$projectdir/output/figures/incidence_twoway_appt.gph", replace)
	graph export "$projectdir/output/figures/incidence_twoway_appt.svg", replace
	
restore

**For first rheumatology appt date
tab mo_year_appt, missing
tab appt_6m, missing
tab appt_year, missing
bys eia_diagnosis: tab appt_6m, missing
bys eia_diagnosis: tab appt_year, missing

**Demographics
tabstat age, stats (n mean sd)
eststo X: estpost tabstat age, stat(n mean sd) by(eia_diagnosis) 
esttab X using "$projectdir/output/tables/stats_by_diagnosis.csv", cells("count Mean(fmt(1)) SD(fmt(1))") collabels("Count" "Mean" "SD") replace plain nomtitle noobs
bys eia_diagnosis: tabstat age, stats (n mean sd)
tab agegroup, missing
tab male, missing
tab ethnicity, missing
tab imd, missing
tab region, missing

**Comorbidities
tab smoke, missing
tabstat bmi_value, stats (n mean p50 p25 p75)
tab bmicat, missing
tabstat creatinine_value, stats (n mean p50 p25 p75)
tabstat egfr, stats (n mean p50 p25 p75)
tab egfr_cat, missing
tab egfr_cat_nomiss, missing
tab esrf, missing
tab ckd, missing //combination of creatinine and esrf codes
tab diabetes, missing 
tabstat hba1c_percentage, stats (n mean p50 p25 p75)
tabstat hba1c_mmol_per_mol, stats (n mean p50 p25 p75)
tabstat hba1c_pct, stats (n mean p50 p25 p75) //with conversion of mmol values
tab hba1ccat, missing
tabstat hba1c_mmol, stats (n mean p50 p25 p75) //with conversion of % values
tab hba1ccatmm, missing
tab diabcatm, missing //on basis of converted %
tab cancer, missing //lung, haem or other cancer
tab hypertension, missing
tab stroke, missing
tab chronic_resp_disease, missing
tab copd, missing
tab chronic_liver_disease, missing
tab chronic_cardiac_disease, missing

/*Tables=====================================================================================*/
*Baseline table by eia diagnosis
table1_mc, by(eia_diagnosis) total(before) onecol nospacelowpercent iqrmiddle(",")  ///
	vars(agegroup cat %5.1f \ ///
		 male bin %5.1f \ ///
		 ethnicity cat %5.1f \ ///
		 imd cat %5.1f \ ///
		 bmicat cat %5.1f \ ///
		 smoke cat %5.1f \ ///
		 hypertension bin %5.1f \ ///
		 diabcatm cat %5.1f \ ///
		 chronic_cardiac_disease bin %5.1f \ /// 
		 stroke bin %5.1f \ ///
		 cancer bin %5.1f \ ///
		 chronic_resp_disease bin  %5.1f \ ///
		 chronic_liver_disease bin %5.1f \ ///
		 ckd cat %5.1f \ ///
		 ) saving("$projectdir/output/tables/baseline_bydiagnosis.xls", replace)

*Baseline table by year of diagnosis
table1_mc, by(diagnosis_year) total(before) onecol nospacelowpercent iqrmiddle(",")  ///
	vars(agegroup cat %5.1f \ ///
		 male bin %5.1f \ ///
		 ethnicity cat %5.1f \ ///
		 imd cat %5.1f \ ///
		 bmicat cat %5.1f \ ///
		 smoke cat %5.1f \ ///
		 hypertension bin %5.1f \ ///
		 diabcatm cat %5.1f \ ///
		 chronic_cardiac_disease bin %5.1f \ /// 
		 stroke bin %5.1f \ ///
		 cancer bin %5.1f \ ///
		 chronic_resp_disease bin  %5.1f \ ///
		 chronic_liver_disease bin %5.1f \ ///
		 ckd cat %5.1f \ ///
		 ) saving("$projectdir/output/tables/baseline_byyear.xls", replace)

*Referral and appointment performance==============================================================================*/

**Rheumatology appt 
tab rheum_appt, missing //proportion of patients with an rheum outpatient date (with first attendance option selected) in the 12 months before EIA code appeared in GP record; data only April 2019 onwards
tab rheum_appt_any, missing //proportion of patients with a rheum outpatient date (without first attendance option selected) in the 6 months before EIA code appeared in GP record; data only April 2019 onwards
tab rheum_appt2, missing //proportion of patients with a rheum outpatient date (without first attendance option selected) in the 6 months before EIA code appeared in GP record; data only April 2019 onwards
tab rheum_appt3, missing //proportion of patients with a rheum outpatient date (without first attendance option selected) in the 2 years before EIA code appeared in GP record; data only April 2019 onwards

**First rheum appt over time
tab rheum_appt if diagnosis_date>=td(01apr2019) & diagnosis_date<td(01oct2019), missing  
tab rheum_appt_any if diagnosis_date>=td(01apr2019) & diagnosis_date<td(01oct2019), missing 

tab rheum_appt if diagnosis_date>=td(01oct2019) & diagnosis_date<td(01apr2020), missing  
tab rheum_appt_any if diagnosis_date>=td(01oct2019) & diagnosis_date<td(01apr2020), missing

tab rheum_appt if diagnosis_date>=td(01apr2020) & diagnosis_date<td(01oct2020), missing  
tab rheum_appt_any if diagnosis_date>=td(01apr2020) & diagnosis_date<td(01oct2020), missing 

tab rheum_appt if diagnosis_date>=td(01oct2020) & diagnosis_date<td(01apr2021), missing  
tab rheum_appt_any if diagnosis_date>=td(01oct2020) & diagnosis_date<td(01apr2021), missing

tab rheum_appt if diagnosis_date>=td(01apr2021) & diagnosis_date<td(01oct2021), missing  
tab rheum_appt_any if diagnosis_date>=td(01apr2021) & diagnosis_date<td(01oct2021), missing 

tab rheum_appt if diagnosis_date>=td(01oct2021) & diagnosis_date<td(01apr2022), missing  
tab rheum_appt_any if diagnosis_date>=td(01oct2021) & diagnosis_date<td(01apr2022), missing

tab rheum_appt if diagnosis_date>=td(01apr2022) & diagnosis_date<td(01oct2022), missing  
tab rheum_appt_any if diagnosis_date>=td(01apr2022) & diagnosis_date<td(01oct2022), missing

**By year
tab rheum_appt if diagnosis_date>=td(01apr2019) & diagnosis_date<td(01apr2020), missing  
tab rheum_appt_any if diagnosis_date>=td(01apr2019) & diagnosis_date<td(01apr2020), missing 

tab rheum_appt if diagnosis_date>=td(01apr2020) & diagnosis_date<td(01apr2021), missing  
tab rheum_appt_any if diagnosis_date>=td(01apr2020) & diagnosis_date<td(01apr2021), missing 

tab rheum_appt if diagnosis_date>=td(01apr2021) & diagnosis_date<td(01apr2022), missing  
tab rheum_appt_any if diagnosis_date>=td(01apr2021) & diagnosis_date<td(01apr2022), missing 

tab rheum_appt if diagnosis_date>=td(01apr2022) & diagnosis_date<td(01apr2023), missing  
tab rheum_appt_any if diagnosis_date>=td(01apr2022) & diagnosis_date<td(01apr2023), missing 

**Check if above criteria are picking up the same appt
tabstat time_rheum_eia_code, stats (n p50 p25 p75) //using 12 months pre-EIA code
tabstat time_rheum2_eia_code, stats (n p50 p25 p75) //using 6 months pre-EIA code
tabstat time_rheum3_eia_code, stats (n p50 p25 p75) //using 2 years pre-EIA code

**By region
bys nuts_region: tab rheum_appt if nuts_region!=. //check proportion by region

**Rheumatology referrals (Nb. low capture of coded rheumatology referrals at present, therefore last GP appt used as proxy of referral date currently - see below)
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 
tab referral_rheum_precode //last rheum referral in the 2 years before EIA code

**GP appointments
tab last_gp_refrheum //proportion with last GP appointment in year before rheum referral (pre-rheum appt); requires there to have been a rheum referral
tab all_appts, missing //proportion who had a last gp appt, then rheum ref, then rheum appt
tab last_gp_refcode //last GP appointment before rheum ref (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

**Number with rheumatology appt and >12 months of follow-up
tab rheum_appt if rheum_appt_date<(date("$end_date", "DMY")-365), missing
tab rheum_appt_to21

**Number with GP appointment prior to rheum appointment and >12 months of follow-up
tab last_gp_prerheum if rheum_appt_date!=. & rheum_appt_date<(date("$end_date", "DMY")-365), missing 
tab last_gp_prerheum_to21

**Number with GP appointment prior to rheum appointment and >12 months of follow-up and >12m of continuous registration after appointment
tab last_gp_prerheum if rheum_appt_date!=. & rheum_appt_date<(date("$end_date", "DMY")-365) & has_12m_follow_up==1, missing 

**As above
tab has_12m_post_appt, missing

**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 if appt_year!=., stat (n mean sd p50 p25 p75)

**Check medium used for rheumatology appointment
tab rheum_appt_medium, missing
tab rheum_appt_medium if has_12m_post_appt==1, missing
bys appt_year: tab rheum_appt_medium if has_12m_post_appt==1, missing

*Time to rheum referral (see notes above)=============================================*/

*Restrict all analyses below to patients with rheum appt, GP appt and 12m follow-up and registration
keep if has_12m_post_appt==1

**Time from last GP to rheum ref before rheum appt (i.e. if appts are present and in correct order)
tabstat time_gp_rheum_ref_appt, stats (n mean p50 p25 p75) //all patients (should be same number as all appts)
bys eia_diagnosis: tabstat time_gp_rheum_ref_appt, stats (n mean p50 p25 p75) //by eia diagnosis
bys appt_6m: tabstat time_gp_rheum_ref_appt, stats (n mean p50 p25 p75) //by diagnosis year
bys appt_year: tabstat time_gp_rheum_ref_appt, stats (n mean p50 p25 p75) //by diagnosis year
bys nuts_region: tabstat time_gp_rheum_ref_appt if nuts_region!=., stats (n mean p50 p25 p75) //by region

**Time from GP to rheum ref categorised
tab gp_ref_cat, missing
tab gp_ref_cat
bys eia_diagnosis: tab gp_ref_cat, missing
bys eia_diagnosis: tab gp_ref_cat
bys appt_6m: tab gp_ref_cat, missing
bys appt_6m: tab gp_ref_cat
bys appt_year: tab gp_ref_cat, missing
bys appt_year: tab gp_ref_cat
bys nuts_region: tab gp_ref_cat if nuts_region!=., missing
bys nuts_region: tab gp_ref_cat if nuts_region!=.

/*
tab gp_ref_3d, missing
tab gp_ref_3d
bys eia_diagnosis: tab gp_ref_3d, missing
bys eia_diagnosis: tab gp_ref_3d
bys appt_6m: tab gp_ref_3d, missing
bys appt_6m: tab gp_ref_3d
bys nuts_region: tab gp_ref_3d if nuts_region!=., missing
bys nuts_region: tab gp_ref_3d if nuts_region!=.
*/

**Time from last GP to rheum ref before eia code (sensitivity analysis; includes those with no rheum appt)
tabstat time_gp_rheum_ref_code, stats (n mean p50 p25 p75)
bys eia_diagnosis: 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)
tabstat time_gp_rheum_ref_comb, stats (n mean p50 p25 p75)
bys eia_diagnosis: tabstat time_gp_rheum_ref_comb, stats (n mean p50 p25 p75)

*Time to rheum appointment=============================================*/

**Time from last GP pre-rheum appt to first rheum appt (proxy measure for referral to appt delay)
tabstat time_gp_rheum_appt, stats (n mean p50 p25 p75)
bys eia_diagnosis: tabstat time_gp_rheum_appt, stats (n mean p50 p25 p75)
bys appt_6m: tabstat time_gp_rheum_appt, stats (n mean p50 p25 p75) //by diagnosis year
bys appt_year: tabstat time_gp_rheum_appt, stats (n mean p50 p25 p75) //by diagnosis year
bys nuts_region: tabstat time_gp_rheum_appt if nuts_region!=., stats (n mean p50 p25 p75) //by region

**Time from rheum ref to rheum appt (i.e. if appts are present and in correct time order)
tabstat time_ref_rheum_appt, stats (n mean p50 p25 p75)
bys eia_diagnosis: tabstat time_ref_rheum_appt, stats (n mean p50 p25 p75)
bys appt_6m: tabstat time_ref_rheum_appt, stats (n mean p50 p25 p75) //all patients by diagnosis year
bys appt_year: tabstat time_ref_rheum_appt, stats (n mean p50 p25 p75) //all patients by diagnosis year
bys nuts_region: tabstat time_ref_rheum_appt if nuts_region!=., stats (n mean p50 p25 p75) //by region

**Time from last GP to rheum appt categorised
tab gp_appt_cat, missing
tab gp_appt_cat
bys eia_diagnosis: tab gp_appt_cat, missing
bys eia_diagnosis: tab gp_appt_cat
bys appt_6m: tab gp_appt_cat, missing
bys appt_6m: tab gp_appt_cat
bys appt_year: tab gp_appt_cat, missing
bys appt_year: tab gp_appt_cat
bys nuts_region: tab gp_appt_cat if nuts_region!=., missing
bys nuts_region: tab gp_appt_cat if nuts_region!=.

**Below is what is used for ITSA models (on a month by month basis)
tab gp_appt_3w
bys eia_diagnosis: tab gp_appt_3w
bys appt_year: tab gp_appt_3w 
bys nuts_region: tab gp_appt_3w if nuts_region!=.

**Time from rheum ref to rheum appt categorised
tab ref_appt_cat, missing
tab ref_appt_cat
bys eia_diagnosis: tab ref_appt_cat, missing
bys eia_diagnosis: tab ref_appt_cat
bys appt_6m: tab ref_appt_cat, missing
bys appt_6m: tab ref_appt_cat
bys appt_year: tab ref_appt_cat, missing
bys appt_year: tab ref_appt_cat
bys nuts_region: tab ref_appt_cat if nuts_region!=., missing
bys nuts_region: tab ref_appt_cat if nuts_region!=.

/*
tab ref_appt_3w, missing
tab ref_appt_3w
bys eia_diagnosis: tab ref_appt_3w, missing
bys eia_diagnosis: tab ref_appt_3w
bys appt_6m: tab ref_appt_3w, missing
bys appt_6m: tab ref_appt_3w
bys nuts_region: tab ref_appt_3w if nuts_region!=., missing
bys nuts_region: tab ref_appt_3w if nuts_region!=.
*/

**Time from rheum ref or last GP to rheum appt (combined; includes those with no rheum ref)
tabstat time_refgp_rheum_appt, stats (n mean p50 p25 p75)
bys eia_diagnosis: tabstat time_refgp_rheum_appt, stats (n mean p50 p25 p75)
bys appt_6m: tabstat time_refgp_rheum_appt, stats (n mean p50 p25 p75) //all patients by diagnosis year
bys nuts_region: tabstat time_refgp_rheum_appt if nuts_region!=., stats (n mean p50 p25 p75) //by region

*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)
tabstat time_gp_eia_code, stats (n mean p50 p25 p75)
bys eia_diagnosis: 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)
tabstat time_gp_eia_diag, stats (n mean p50 p25 p75)
bys eia_diagnosis: 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)
tabstat time_ref_rheum_eia, stats (n mean p50 p25 p75)
bys eia_diagnosis: 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)
tabstat time_ref_rheum_eia_comb, stats (n mean p50 p25 p75)
bys eia_diagnosis: tabstat time_ref_rheum_eia_comb, stats (n mean p50 p25 p75)

**Time from rheum appt to EIA code
tabstat time_rheum_eia_code, stats (n mean p50 p25 p75) 
bys eia_diagnosis: tabstat time_rheum_eia_code, stats (n mean p50 p25 p75) 

**Referral standards, by eia diagnosis
table1_mc, by(eia_diagnosis) total(before) onecol nospacelowpercent iqrmiddle(",")  ///
	vars(gp_ref_cat cat %3.1f \ ///
		 ref_appt_cat cat %3.1f \ ///
		 gp_appt_cat cat %3.1f \ ///
		 gp_appt_cat_19 cat %3.1f \ ///
		 gp_appt_cat_20 cat %3.1f \ ///
		 ) saving("$projectdir/output/tables/referral_bydiag_nomiss.xls", replace)		  
 
*Referral standards, by 12 months periods - date of first appt rather than date of EIA code
table1_mc, by(appt_year) total(before) onecol nospacelowpercent iqrmiddle(",")  ///
	vars(gp_ref_cat cat %3.1f \ ///
		 ref_appt_cat cat %3.1f \ ///
		 gp_appt_cat cat %3.1f \ ///
		 ) saving("$projectdir/output/tables/referral_byyear_nomiss.xls", replace) 
		 
*Referral standards, by region
table1_mc if nuts_region!=., by(nuts_region) total(before) onecol nospacelowpercent iqrmiddle(",")  ///
	vars(gp_appt_cat cat %3.1f \ ///
		 gp_appt_cat_19 cat %3.1f \ ///
		 gp_appt_cat_20 cat %3.1f \ ///
		 ) saving("$projectdir/output/tables/referral_byregion_nomiss.xls", replace)

*Time from rheum appt to first csDMARD prescriptions on primary care record======================================================================*/

*As above, all patients must have 1) rheum appt and GP appt 2) 12m follow-up after rheum appt 3) 12m of registration after appt
tab mo_year_diagn, missing
tab mo_year_appt, missing

**Proportion with a csDMARD prescription in GP record at any point after diagnosis; patients excluded if csDMARD or biologic was >60 days before rheumatology appt date
tab csdmard, missing
tab csdmard if (csdmard_date<=rheum_appt_date+365), missing //with 12-month limit
bys eia_diagnosis: tab csdmard
bys eia_diagnosis: tab csdmard if (csdmard_date<=rheum_appt_date+365) //with 12-month limit

**With high cost MTX data
tab csdmard_hcd, missing //including high cost MTX scripts 
tab csdmard_hcd if (csdmard_hcd_date<=rheum_appt_date+365), missing //with 12-month limit
bys eia_diagnosis: tab csdmard_hcd, missing //including high cost MTX scripts 
bys eia_diagnosis: tab csdmard_hcd if (csdmard_hcd_date<=rheum_appt_date+365) //with 12-month limit

**Compare proportion with more than one script issued for csDMARDs
tab csdmard, missing //all prescriptions, for comparison
tab csdmard_shared, missing //issued more than once (shared care)

**Time to first csDMARD in GP record for RA patients not including high cost MTX prescriptions; prescription must be within 12 months of diagnosis for all csDMARDs below 
tabstat time_to_csdmard if ra_code==1, stats (n mean p50 p25 p75)
bys appt_6m: tabstat time_to_csdmard if ra_code==1, stats (n mean p50 p25 p75) //by diagnosis year
bys appt_year: tabstat time_to_csdmard if ra_code==1, stats (n mean p50 p25 p75) //by diagnosis year
bys nuts_region: tabstat time_to_csdmard if ra_code==1 & nuts_region!=., stats (n mean p50 p25 p75) //by region

**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)
bys appt_6m: tabstat time_to_csdmard if psa_code==1, stats (n mean p50 p25 p75) //by diagnosis year
bys appt_year: tabstat time_to_csdmard if psa_code==1, stats (n mean p50 p25 p75) //by diagnosis year
bys nuts_region: tabstat time_to_csdmard if psa_code==1 & nuts_region!=., stats (n mean p50 p25 p75) //by region

/*
**Time to first csDMARD script for RA patients (including high cost MTX prescriptions)
tabstat time_to_csdmard_hcd if ra_code==1, stats (n mean p50 p25 p75) 
bys appt_6m: tabstat time_to_csdmard_hcd if ra_code==1, stats (n mean p50 p25 p75) //by diagnosis year
bys nuts_region: tabstat time_to_csdmard_hcd if ra_code==1 & nuts_region!=., stats (n mean p50 p25 p75) //by region

**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) 
bys appt_6m: tabstat time_to_csdmard_hcd if psa_code==1, stats (n mean p50 p25 p75) //by diagnosis year
bys nuts_region: tabstat time_to_csdmard_hcd if psa_code==1 & nuts_region!=., stats (n mean p50 p25 p75) //by region
*/

**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)
bys appt_6m: tabstat time_to_csdmard if anksp_code==1, stats (n mean p50 p25 p75) //by diagnosis year
bys appt_year: tabstat time_to_csdmard if anksp_code==1, stats (n mean p50 p25 p75) //by diagnosis year
bys nuts_region: tabstat time_to_csdmard if anksp_code==1 & nuts_region!=., stats (n mean p50 p25 p75) //by region

**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)
bys appt_6m: tabstat time_to_csdmard if undiff_code==1, stats (n mean p50 p25 p75) //by diagnosis year
bys appt_year: tabstat time_to_csdmard if undiff_code==1, stats (n mean p50 p25 p75) //by diagnosis year
bys nuts_region: tabstat time_to_csdmard if undiff_code==1 & nuts_region!=., stats (n mean p50 p25 p75) //by region

**csDMARD time categories for RA and PsA patients (not including high cost MTX prescriptions)
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

/*
**csDMARD time categories for RA and PsA patients (including high cost MTX prescriptions)
tab csdmard_hcd_time if ra_code==1, missing 
tab csdmard_hcd_time if psa_code==1, missing
tab csdmard_hcd_time if anksp_code==1, missing  
tab csdmard_hcd_time if undiff_code==1, missing
*/

**What was first shared care csDMARD (not including high cost MTX prescriptions)
tab first_csDMARD
bys appt_year: tab first_csDMARD //did choice of first drug vary by year
tab first_csDMARD if ra_code==1 //for RA patients
tab first_csDMARD if psa_code==1 //for PsA patients
tab first_csDMARD if anksp_code==1 //for axSpA patients
tab first_csDMARD if undiff_code==1 //for Undiff IA patients

**What was first csDMARD (including high cost MTX prescriptions)
tab first_csDMARD_hcd if ra_code==1 //for RA patients
tab first_csDMARD_hcd if psa_code==1 //for PsA patients
tab first_csDMARD_hcd if undiff_code==1 //for Undiff IA patients
 
**Methotrexate use (not including high cost MTX prescriptions)
tab mtx if ra_code==1 //for RA patients; Nb. this is just a check; need time-to-MTX instead (below)
tab mtx if ra_code==1 & (mtx_date<=rheum_appt_date+365) //with 12-month limit
tab mtx if psa_code==1 //for PsA patients
tab mtx if psa_code==1 & (mtx_date<=rheum_appt_date+365) //with 12-month limit
tab mtx if undiff_code==1 //for undiff IA patients
tab mtx if undiff_code==1 & (mtx_date<=rheum_appt_date+365) //with 12-month limit

**Compare proportion with more than one script issued for csDMARDs
tab mtx, missing //all prescriptions (for comparison)
tab mtx_shared, missing //issued more than once (shared care)
tab mtx_issue, missing //issed none vs. once vs. more than once

/*
**Methotrexate use (including high cost MTX prescriptions)
tab mtx_hcd if ra_code==1 //for RA patients
tab mtx_hcd if ra_code==1 & (mtx_hcd_date<=rheum_appt_date+365) //with 12-month limit
tab mtx_hcd if psa_code==1 //for PsA patients
tab mtx_hcd if psa_code==1 & (mtx_hcd_date<=rheum_appt_date+365) //with 12-month limit
tab mtx_hcd if undiff_code==1 //for undiff IA patients
tab mtx_hcd if undiff_code==1 & (mtx_hcd_date<=rheum_appt_date+365) //with 12-month limit
*/

**Time to first methotrexate script for RA patients (not including high cost MTX prescriptions)
tabstat time_to_mtx if ra_code==1, stats (n mean p50 p25 p75)
bys appt_year: tabstat time_to_mtx if ra_code==1, stats (n mean p50 p25 p75) //by diagnosis year
bys nuts_region: tabstat time_to_mtx if ra_code==1 & nuts_region!=., stats (n mean p50 p25 p75) //by region

**Time to first methotrexate script for PsA patients (not including high cost MTX prescriptions)
tabstat time_to_mtx if psa_code==1, stats (n mean p50 p25 p75)
bys appt_year: tabstat time_to_mtx if psa_code==1, stats (n mean p50 p25 p75) //by diagnosis year
bys nuts_region: tabstat time_to_mtx if psa_code==1 & nuts_region!=., stats (n mean p50 p25 p75) //by region

/*
**Time to first methotrexate script for RA patients (including high cost MTX prescriptions)
tabstat time_to_mtx_hcd if ra_code==1, stats (n mean p50 p25 p75)
bys appt_year: tabstat time_to_mtx_hcd if ra_code==1, stats (n mean p50 p25 p75) //by diagnosis year
bys nuts_region: tabstat time_to_mtx_hcd if ra_code==1 & nuts_region!=., stats (n mean p50 p25 p75) //by region

**Time to first methotrexate script for PsA patients (including high cost MTX prescriptions)
tabstat time_to_mtx_hcd if psa_code==1, stats (n mean p50 p25 p75)
bys appt_year: tabstat time_to_mtx_hcd if psa_code==1, stats (n mean p50 p25 p75) //by diagnosis year
bys nuts_region: tabstat time_to_mtx_hcd if psa_code==1 & nuts_region!=., stats (n mean p50 p25 p75) //by region
*/

**Time to first methotrexate script for Undiff IA patients (not including high cost MTX prescriptions)
tabstat time_to_mtx if undiff_code==1, stats (n mean p50 p25 p75)
bys appt_year: tabstat time_to_mtx if undiff_code==1, stats (n mean p50 p25 p75) //by diagnosis year
bys nuts_region: tabstat time_to_mtx if undiff_code==1 & nuts_region!=., stats (n mean p50 p25 p75) //by region

**Methotrexate time categories for RA, PsA and Undiff IA patients (not including high-cost MTX)
tab mtx_time if ra_code==1, missing 
tab mtx_time if psa_code==1, missing 
tab mtx_time if undiff_code==1, missing 

/*
**Methotrexate time categories (including high-cost MTX)
tab mtx_hcd_time if ra_code==1, missing 
tab mtx_hcd_time if psa_code==1, missing 
tab mtx_hcd_time if undiff_code==1, missing 
*/

**Sulfasalazine time categories
tab ssz_time if ra_code==1, missing 
tab ssz_time if psa_code==1, missing 
tab ssz_time if undiff_code==1, missing 

**Compare proportion with more than one script issued for csDMARDs
tab ssz, missing //all prescriptions (for comparison)
tab ssz_shared, missing //issued more than once (shared care)
tab ssz_issue, missing //issed none vs. once vs. more than once

**Hydroxychloroquine time categories
tab hcq_time if ra_code==1, missing 
tab hcq_time if psa_code==1, missing 
tab hcq_time if undiff_code==1, missing 

**Compare proportion with more than one script issued for csDMARDs
tab hcq, missing //all prescriptions (for comparison)
tab hcq_shared, missing //issued more than once (shared care)
tab hcq_issue, missing //issed none vs. once vs. more than once

**Leflunomide time categories
tab lef_time if ra_code==1, missing 
tab lef_time if psa_code==1, missing 
tab lef_time if undiff_code==1, missing 

**Compare proportion with more than one script issued for csDMARDs
tab lef, missing //all prescriptions (for comparison)
tab lef_shared, missing //issued more than once (shared care)
tab lef_issue, missing //issed none vs. once vs. more than once

*Drug prescription table, for those with at least 12m registration - excluding axSpA (low counts)
table1_mc if eia_diagnosis!=3, by(eia_diagnosis) total(before) onecol nospacelowpercent iqrmiddle(",")  ///
	vars(csdmard_time cat %3.1f \ ///
		 mtx_time cat %3.1f \ ///
		 ssz_time cat %3.1f \ ///
		 hcq_time cat %3.1f \ ///
		 lef_time cat %3.1f \ ///
		 ) saving("$projectdir/output/tables/drug_bydiag_miss.xls", replace)
		 
*Drug prescription table, for those with at least 12m registration; all diagnoses but for AxSpA
table1_mc if eia_diagnosis!=3, by(appt_year) total(before) onecol nospacelowpercent iqrmiddle(",")  ///
	vars(csdmard_time cat %3.1f \ ///
		 mtx_time cat %3.1f \ ///
		 ssz_time cat %3.1f \ ///
		 hcq_time cat %3.1f \ ///
		 lef_time cat %3.1f \ ///
		 ) saving("$projectdir/output/tables/drug_byyear_miss.xls", replace)		 

*Drug prescription table, for those with at least 12m registration for RA patients
table1_mc if ra_code==1, by(appt_year) total(before) onecol nospacelowpercent iqrmiddle(",")  ///
	vars(csdmard_time cat %3.1f \ ///
		 mtx_time cat %3.1f \ ///
		 ssz_time cat %3.1f \ ///
		 hcq_time cat %3.1f \ ///
		 lef_time cat %3.1f \ ///
		 ) saving("$projectdir/output/tables/drug_byyear_ra_miss.xls", replace)
		 
*Drug prescription table, for those with at least 12m registration for PsA patients
table1_mc if psa_code==1, by(appt_year) total(before) onecol nospacelowpercent iqrmiddle(",")  ///
	vars(csdmard_time cat %3.1f \ ///
		 mtx_time cat %3.1f \ ///
		 ssz_time cat %3.1f \ ///
		 hcq_time cat %3.1f \ ///
		 lef_time cat %3.1f \ ///
		 ) saving("$projectdir/output/tables/drug_byyear_psa_miss.xls", replace)

*Drug prescription table, for those with at least 12m registration for Undiff IA patients
table1_mc if undiff_code==1, by(appt_year) total(before) onecol nospacelowpercent iqrmiddle(",")  ///
	vars(csdmard_time cat %3.1f \ ///
		 mtx_time cat %3.1f \ ///
		 ssz_time cat %3.1f \ ///
		 hcq_time cat %3.1f \ ///
		 lef_time cat %3.1f \ ///
		 ) saving("$projectdir/output/tables/drug_byyear_undiff_miss.xls", replace) 
		 
*Drug prescription table, for those with at least 12m registration for all diagnoses, by year
table1_mc, by(eia_diagnosis) total(before) onecol nospacelowpercent iqrmiddle(",")  ///
	vars(csdmard_time cat %3.1f \ ///
		 mtx_time cat %3.1f \ ///
		 ssz_time cat %3.1f \ ///
		 hcq_time cat %3.1f \ ///
		 csdmard_time_19 cat %3.1f \ ///
		 csdmard_time_20 cat %3.1f \ ///
		 ) saving("$projectdir/output/tables/drug_byyearanddisease.xls", replace) 
		 
*Drug prescription table, for those with at least 12m registration for all diagnoses, by region and year
table1_mc if nuts_region!=. & (ra_code==1 | psa_code==1 | undiff_code==1), by(nuts_region) total(before) onecol nospacelowpercent iqrmiddle(",")  ///
	vars(csdmard_time cat %3.1f \ ///
		 csdmard_time_19 cat %3.1f \ ///
		 csdmard_time_20 cat %3.1f \ ///
		 ) saving("$projectdir/output/tables/drug_byyearandregion.xls", replace) 			 
		 
**Time to first biologic script, whereby first rheum appt is classed as diagnosis date; high cost drug data available to Nov 2020. Not for analysis currently due to small numbers======================================================================*/

/*
**Proportion with a bDMARD or tsDMARD prescription at any point after diagnosis (unequal follow-up); patients excluded if csDMARD or biologic was >60 days before rheumatology appt date (if present)
tab biologic, missing
tab biologic if (biologic_date<=rheum_appt_date+365), missing //with 12-month limit

bys eia_diagnosis: tab biologic, missing 
bys eia_diagnosis: tab biologic if (biologic_date<=rheum_appt_date+365), missing //with 12-month limit 

tabstat time_to_biologic, stats (n mean p50 p25 p75) //for all EIA patients
bys eia_diagnosis: tabstat time_to_biologic, stats (n mean p50 p25 p75) 
bys appt_6m: tabstat time_to_biologic, stats (n mean p50 p25 p75) //by diagnosis year
bys nuts_region: tabstat time_to_biologic if nuts_region!=., stats (n mean p50 p25 p75) //by region

**What was first biologic

tab first_biologic //for all EIA patients
bys eia_diagnosis: tab first_biologic

**Biologic time categories (for all patients)
tab biologic_time 

**Biologic time categories (by diagnosis)
bys eia_diagnosis: tab biologic_time 

**Biologic time categories (by time period)
bys appt_6m: tab biologic_time

*Drug prescription table at 12 months, for those with at least 12m registration
table1_mc, by(eia_diagnosis) total(before) onecol nospacelowpercent iqrmiddle(",")  ///
	vars(csdmard_time cat %3.1f \ ///
		 mtx_time cat %3.1f \ ///
		 ssz_time cat %3.1f \ ///
		 hcq_time cat %3.1f \ ///
		 lef_time cat %3.1f \ ///
		 biologic_time cat %3.1f \ ///
		 ) saving("$projectdir/output/tables/biol_bydiag_miss.xls", replace)
		 
*Drug prescription table at 12 months, for all patients with at least 12m registration, by year of diagnosis
table1_mc, by(appt_6m) total(before) onecol nospacelowpercent iqrmiddle(",")  ///
	vars(csdmard_time cat %3.1f \ ///
		 mtx_time cat %3.1f \ ///
		 ssz_time cat %3.1f \ ///
		 hcq_time cat %3.1f \ ///
		 lef_time cat %3.1f \ ///
		 biologic_time cat %3.1f \ ///
		 ) saving("$projectdir/output/tables/biol_byyear_miss.xls", replace)
		 
*Drug prescription table at 12 months, for RA patients with at least 12m registration, by year of diagnosis
table1_mc if ra_code==1, by(appt_year) total(before) onecol nospacelowpercent iqrmiddle(",")  ///
	vars(csdmard_time cat %3.1f \ ///
		 mtx_time cat %3.1f \ ///
		 ssz_time cat %3.1f \ ///
		 hcq_time cat %3.1f \ ///
		 lef_time cat %3.1f \ ///
		 biologic_time cat %3.1f \ ///
		 ) saving("$projectdir/output/tables/biol_byyear_ra_miss.xls", replace)

*Drug prescription table at 12 months, for PsA patients with at least 12m registration, by year of diagnosis
table1_mc if psa_code==1, by(appt_year) total(before) onecol nospacelowpercent iqrmiddle(",")  ///
	vars(csdmard_time cat %3.1f \ ///
		 mtx_time cat %3.1f \ ///
		 ssz_time cat %3.1f \ ///
		 hcq_time cat %3.1f \ ///
		 lef_time cat %3.1f \ ///
		 biologic_time cat %3.1f \ ///
		 ) saving("$projectdir/output/tables/biol_byyear_psa_miss.xls", replace)

*Drug prescription table at 12 months, for AxSpA patients with at least 12m registration, by year of diagnosis
table1_mc if anksp_code==1, by(appt_year) total(before) onecol nospacelowpercent iqrmiddle(",")  ///
	vars(csdmard_time cat %3.1f \ ///
		 mtx_time cat %3.1f \ ///
		 ssz_time cat %3.1f \ ///
		 hcq_time cat %3.1f \ ///
		 lef_time cat %3.1f \ ///
		 biologic_time cat %3.1f \ ///
		 ) saving("$projectdir/output/tables/biol_byyear_axspa_miss.xls", replace)
		 
*Drug prescription table at 12 months, for Undiff IA patients with at least 12m registration, by year of diagnosis
table1_mc if undiff_code==1, by(appt_year) total(before) onecol nospacelowpercent iqrmiddle(",")  ///
	vars(csdmard_time cat %3.1f \ ///
		 mtx_time cat %3.1f \ ///
		 ssz_time cat %3.1f \ ///
		 hcq_time cat %3.1f \ ///
		 lef_time cat %3.1f \ ///
		 biologic_time cat %3.1f \ ///
		 ) saving("$projectdir/output/tables/biol_byyear_undiff_miss.xls", replace)
*/		 

*Output tables as CSVs		 
import excel "$projectdir/output/tables/baseline_bydiagnosis.xls", clear
outsheet * using "$projectdir/output/tables/baseline_bydiagnosis.csv" , comma nonames replace	

import excel "$projectdir/output/tables/baseline_byyear.xls", clear
outsheet * using "$projectdir/output/tables/baseline_byyear.csv" , comma nonames replace		 

import excel "$projectdir/output/tables/referral_bydiag_nomiss.xls", clear
outsheet * using "$projectdir/output/tables/referral_bydiag_nomiss.csv" , comma nonames replace	

import excel "$projectdir/output/tables/referral_byyear_nomiss.xls", clear
outsheet * using "$projectdir/output/tables/referral_byyear_nomiss.csv" , comma nonames replace	

import excel "$projectdir/output/tables/referral_byregion_nomiss.xls", clear
outsheet * using "$projectdir/output/tables/referral_byregion_nomiss.csv" , comma nonames replace	

import excel "$projectdir/output/tables/drug_bydiag_miss.xls", clear
outsheet * using "$projectdir/output/tables/drug_bydiag_miss.csv" , comma nonames replace	

import excel "$projectdir/output/tables/drug_byyear_miss.xls", clear
outsheet * using "$projectdir/output/tables/drug_byyear_miss.csv" , comma nonames replace	

import excel "$projectdir/output/tables/drug_byyear_ra_miss.xls", clear
outsheet * using "$projectdir/output/tables/drug_byyear_ra_miss.csv" , comma nonames replace	

import excel "$projectdir/output/tables/drug_byyear_psa_miss.xls", clear
outsheet * using "$projectdir/output/tables/drug_byyear_psa_miss.csv" , comma nonames replace	

import excel "$projectdir/output/tables/drug_byyear_undiff_miss.xls", clear
outsheet * using "$projectdir/output/tables/drug_byyear_undiff_miss.csv" , comma nonames replace	

import excel "$projectdir/output/tables/drug_byyearanddisease.xls", clear
outsheet * using "$projectdir/output/tables/drug_byyearanddisease.csv" , comma nonames replace	

import excel "$projectdir/output/tables/drug_byyearandregion.xls", clear
outsheet * using "$projectdir/output/tables/drug_byyearandregion.csv" , comma nonames replace	

log close