02b_hhClassif_an_checking_hhVars.do

/*==============================================================================
DO FILE NAME:			00_hhClassif_cr_analysis_dataset
PROJECT:				Classification of hh into risk groups
DATE: 					12th August 2020 
AUTHOR:					Kevin Wing adapted from R Mathur H Forbes, A Wong, A Schultze, C Rentsch,K Baskharan, E Williamson 										
DESCRIPTION OF FILE:	program 00, data management for project  
						reformat variables 
						categorise variables
						label variables 
						apply exclusion criteria
DATASETS USED:			data in memory (from output/inputWithHHDependencies.csv)
DATASETS CREATED: 		none
OTHER OUTPUT: 			logfiles, printed to folder analysis/$logdir

t


sysdir set PLUS "/Users/kw/Documents/GitHub/households-research/analysis/adofiles" 
sysdir set PERSONAL "/Users/kw/Documents/GitHub/households-research/analysis/adofiles" 

							
==============================================================================*/
sysdir set PLUS ./analysis/adofiles
sysdir set PERSONAL ./analysis/adofiles

local dataset `1'

* Open a log file
cap log close
log using ./logs/02b_hhClassif_an_checking_hhVars_`dataset'.log, replace t


*PRIORITY FOR THIS FILE IS TO FIX MY COHORT, I CAN COME BACK AND DO FURTHER INVESTIGATIONS LATER!
*NEED TO THINK ABOUT THE IMPLICATIONS OF HAVING DROPPED HH BIGGER THAN 12 BASED ON TPP HH SIZE!

*use ./output/allHH_sizedBetween1And12_`dataset'.dta, clear

*drop people with missing household id(!!!!!!)
*drop if hh_id==0

*drop with missing sex, IMD and age (plus mark if household has person with missing age and drop the entire household)


*============(1) CHECK TO SEE HOW MUCH TPP HH_SIZE AND KW CALCULATED HH SIZE ARE DISCREPANT IN MY RAW DATA================
*(a) Create a home made hh_size variable
*this is raw data before removing care home people
use ./output/allHH_sizedBetween1And12_`dataset'.dta, replace


*drop carehome-related
di "***********************FLOWCHART 4. NUMBER DROPPED RELATED TO CAREHOME ISSUES********************:"
*for this, need to label all households that have any individuals in them that are marked as being anything other than private home residents
generate livesInCareHome=0
replace livesInCareHome=1 if care_home_type!="U"
la var livesInCareHome "Flags whether person lives in a care home"
generate livesWithCareHomeResident=0
la var livesWithCareHomeResident "Flags whether person lives with someone flagged as a carehome resident"
gsort hh_id -livesInCareHome
by hh_id: replace livesWithCareHomeResident=1 if livesInCareHome[1]==1


di "***********************FLOWCHART 4a. Flagged as living in a carehome********************:"
safecount if care_home_type!="U"
drop if care_home_type!="U"

di "***********************FLOWCHART 4b. Living in a house that has someone flagged as living in a carehome********************:"
safecount if livesWithCareHomeResident==1
drop if livesWithCareHomeResident==1

di "***********************FLOWCHART 4c. Living in a private home greater than 12 in size********************:"
safecount if hh_size>12
drop if hh_size>12


*create kw counter for people in house
generate kw_hh_people_count=.
bysort hh_id: replace kw_hh_people_count=_n
la var kw_hh_people_count "kw generated counter for people in house"
generate kw_hh_size=.
by hh_id: replace kw_hh_size=kw_hh_people_count[_N]
la var kw_hh_size "kw generated household size"


*(b) Within people who are in households that are 12 or less in size, check to see how many of the TPP calculated household sizes are different to the kw calculated hh size
generate hh_size_wrong=.
la var hh_size_wrong "1=TPP hh size different to kw calc hh size, 0=no difference"
replace hh_size_wrong=0 if hh_size==kw_hh_size
replace hh_size_wrong=1 if hh_size!=kw_hh_size
display "===================(1) Proportion of all people in hh sized 12 or less where TPP hh_size differs from kw_calculated hh_size============="
tab hh_size_wrong

log close

*BUT - I THINK I NEED TO FACTOR IN THE TPP % COVERAGE VARIABLE WHEN CALCULATING THE HOUSEHOLD SIZE, GOING TO ASSESS THIS AFTER ANNA PROVIDES CODE



/*

*============(2) CREATE HH COMP VARIABLE AND CHECK HOW DISTRIBUTION OF MY HH SIZE VAR COMPARES TO THE TPP HH SIZE VARIABLE================
*note that possible alternative handling of "U" hasn't been implemented here, will check this in (3) below
*keep only people marked as living in private homes

*NOT REMOVING CARE HOMES FOR THE MOMENT
*drop if care_home_type!="U"

*check to see if hh_size is populated for all records
codebook hh_size

drop hh_total_cat
gen hh_total_cat=.
replace hh_total_cat=1 if hh_size >=1 & hh_size<=2
replace hh_total_cat=2 if hh_size >=3 & hh_size<=5
replace hh_total_cat=3 if hh_size >=6


safetab hh_total_cat,m
safetab hh_total_cat care_home_type,m

safetab hh_size hh_total_cat,m


*create a household size categorical variable that is based upon my calcuated household size, not the TPP household size
gen kw_hh_total_cat=.
replace kw_hh_total_cat=1 if kw_hh_size >=1 & hh_size<=2
replace kw_hh_total_cat=2 if kw_hh_size >=3 & hh_size<=5
replace kw_hh_total_cat=3 if kw_hh_size >=6

label define kw_hh_total_cat  1 "1-2" ///
						   2 "3-5" ///
						   3 "6+" ///

label values kw_hh_total_cat kw_hh_total_cat


************Create household compositon variable******************
*first of all, create age bands that I need for this
egen ageCatHHRisk=cut(age), at (0, 18, 30, 67, 200)
recode ageCatHHRisk 0=0 18=1 30=2 67=3 
label define ageCatHHRisk 0 "0-17" 1 "18-29" 2 "30-66" 3 "67+"
label values ageCatHHRisk ageCatHHRisk
safetab ageCatHHRisk, miss
la var ageCatHHRisk "Age categorised for HH risk analysis"

*make an age category variable here that is for table 1 of the 67+ year old analysis
egen ageCatfor67Plus=cut(age), at (67, 70, 75, 80, 85, 200)
recode ageCatfor67Plus 67=0 70=1 75=2 80=3 85=4 
label define ageCatfor67Plus 0 "67-69" 1 "70-74" 2 "75-79" 3 "80-84" 4 "85+"
label values ageCatfor67Plus ageCatfor67Plus
safetab ageCatfor67Plus, miss
la var ageCatfor67Plus "Age (categories)"
*check groupins
forvalues i=0/4{
	sum age if ageCatfor67Plus==`i'
}

preserve
	*keep only the variables I need to work this out
	keep hh_id patient_id ageCatHHRisk
	sort hh_id ageCatHHRisk

	*mark whether hh has each age category using egen max which returns true (1) or false (0) (see https://www.stata.com/support/faqs/data-management/create-variable-recording/)
	egen hasUnder18=max(ageCatHHRisk==0), by(hh_id)
	egen has18_29=max(ageCatHHRisk==1), by(hh_id)
	egen has30_66=max(ageCatHHRisk==2), by(hh_id)
	egen has67Plus=max(ageCatHHRisk==3), by(hh_id)

	*now generate the hhRiskCat variable for each person
	generate hhRiskCat=.
	la var hhRiskCat "Household risk category"
	replace hhRiskCat=0 if hasUnder18==1 & has18_29==0 & has30_66==0 & has67Plus==0
	replace hhRiskCat=1 if hasUnder18==0 & has18_29==1 & has30_66==0 & has67Plus==0
	replace hhRiskCat=2 if hasUnder18==0 & has18_29==0 & has30_66==1 & has67Plus==0
	replace hhRiskCat=3 if hasUnder18==0 & has18_29==0 & has30_66==0 & has67Plus==1
	replace hhRiskCat=4 if hasUnder18==1 & has18_29==1 & has30_66==0 & has67Plus==0
	replace hhRiskCat=5 if hasUnder18==1 & has18_29==0 & has30_66==1 & has67Plus==0
	replace hhRiskCat=6 if hasUnder18==1 & has18_29==0 & has30_66==0 & has67Plus==1
	replace hhRiskCat=7 if hasUnder18==0 & has18_29==1 & has30_66==1 & has67Plus==0
	replace hhRiskCat=8 if hasUnder18==0 & has18_29==1 & has30_66==0 & has67Plus==1
	replace hhRiskCat=9 if hasUnder18==0 & has18_29==0 & has30_66==1 & has67Plus==1
	replace hhRiskCat=10 if hasUnder18==1 & has18_29==1 & has30_66==1 & has67Plus==0
	replace hhRiskCat=11 if hasUnder18==1 & has18_29==1 & has30_66==0 & has67Plus==1
	replace hhRiskCat=12 if hasUnder18==1 & has18_29==0 & has30_66==1 & has67Plus==1
	replace hhRiskCat=13 if hasUnder18==0 & has18_29==1 & has30_66==1 & has67Plus==1
	replace hhRiskCat=14 if hasUnder18==1 & has18_29==1 & has30_66==1 & has67Plus==1
	
	*label variable
	label define hhRiskCatLabel 0 "Only <18"  1 "Only 18-29" 2 "Only 30-66" 3 "Only 67+" 4 "0-17 & 18-29" 5 "0-17 & 30-66" 6 "0-17 & 67+" 7 "18-29 & 30-66" 8 "18-29 & 67+" 9 "30-66 & 67+" 10 "0-17, 18-29 & 30-66" 11 "0-17, 18-29 & 67+" 12 "0-17, 30-66 & 67+" 13 "18-29, 30-66 & 67+" 14 "0-17, 18-29, 30-66 & 67+"
	label values hhRiskCat hhRiskCat
	la var hhRiskCat "Age group(s) of hh occupants"
	safetab hhRiskCat, miss
	keep hh_id hhRiskCat
	duplicates drop hh_id, force
	tempfile hhRiskCat
	save `hhRiskCat', replace
restore
merge m:1 hh_id using `hhRiskCat'
drop _merge
safetab hhRiskCat, miss
*missing here are likely to be people living in households made up of only under 18 year olds


*(b) variable for stratifying by the oldest age group (67+)
generate hhRiskCat67PLUS=.
la var hhRiskCat67PLUS "hhRiskCat for the over 67 year old age group"
replace hhRiskCat67PLUS=1 if hhRiskCat==3
replace hhRiskCat67PLUS=2 if hhRiskCat==6
replace hhRiskCat67PLUS=3 if hhRiskCat==8
replace hhRiskCat67PLUS=4 if hhRiskCat==9
replace hhRiskCat67PLUS=5 if hhRiskCat==11
replace hhRiskCat67PLUS=6 if hhRiskCat==12
replace hhRiskCat67PLUS=7 if hhRiskCat==13
replace hhRiskCat67PLUS=8 if hhRiskCat==14
*label variable
label define hhRiskCat67PLUS 1 "Only 67+" 2 "0-17 & 67+" 3 "18-29 & 67+" 4 "30-66 & 67+" 5 "0-17, 18-29 & 67+" 6 "0-17, 30-66 & 67+" 7 "18-29, 30-66 & 67+" 8 "0-17, 18-29, 30-66 & 67+"
label values hhRiskCat67PLUS hhRiskCat67PLUS
safetab hhRiskCat hhRiskCat67PLUS, miss


*create another version that has 4 categories (1) living with only one generation (2) living with one other generation (3) living with two other generations (4) living with three other gens
generate hhRiskCat67PLUS_4cats=.
la var hhRiskCat67PLUS_4cats "hhRiskCat for the over 67 year old age group - 4 categories"
replace hhRiskCat67PLUS_4cats=1 if hhRiskCat67PLUS==1
replace hhRiskCat67PLUS_4cats=2 if hhRiskCat67PLUS>1 & hhRiskCat67PLUS<5
replace hhRiskCat67PLUS_4cats=3 if hhRiskCat67PLUS>4 & hhRiskCat67PLUS<8
replace hhRiskCat67PLUS_4cats=4 if hhRiskCat67PLUS==8
*label variable
label define hhRiskCat67PLUS_4cats 1 "Only 67+" 2 "67+ & 1 other gen" 3 "67+ & 2 other gens" 4 "67+ & 3 other gens"
label values hhRiskCat67PLUS_4cats hhRiskCat67PLUS_4cats
safetab hhRiskCat67PLUS hhRiskCat67PLUS_4cats, miss

*reduce to only the over 67 year olds
keep if age>66

* Age: Exclude those with implausible ages
cap assert age<.
noi di "DROPPING AGE>105:" 
drop if age>105
safecount
* Sex: Exclude categories other than M and F
cap assert inlist(sex, "M", "F", "I", "U")
noi di "DROPPING GENDER NOT M/F:" 
drop if inlist(sex, "I", "U")
safecount

*check what proportion of the 67+ & 3 other generations have incorrect household size - this is to confirm it is very low
safetab hhRiskCat67PLUS
safetab hhRiskCat67PLUS_4cats
display "===================(1) Proportion of all people in hh sized 12 or less where TPP hh_size differs from kw_calculated hh_size, by household composition============="
*i.e. this looks at which household compositions are the worst - am expecting it to be much worse in the larger houses
safetab hhRiskCat67PLUS_4cats hh_size_wrong, row




*======================================(3) CHECK CARE HOME ASPECTS=========================================
*based on Anna's short report, care home identifier will do a good job of identifying care homes
*(1) Look at the percentage 



*(2) Have a look at the people registered in a care home, and check their household size and composition
preserve
	keep if care_home_type=="U"

	tab hh_size

	tab hhRiskCat

	tab hhRiskCat67PLUS_4cats
restore



*(3) Have a look at the people living in the single generation category who are NOT in care homes, and check how many have >3 people over the age of 65 living at them
preserve
	keep if care_home_type!="U"
	keep if hhRiskCat==1

	*(2) Have a look at the number of people in these households
	tab hh_size
restore









gen male = 1 if sex == "M"
replace male = 0 if sex == "F"
label define male 0"Female" 1"Male"
label values male male
safetab male




*============(3) CHECKS TO ASSESS IMPACT OF DROPPING PEOPLE WHO ARE INDICATED AS NOT BEING IN A CARE HOME (I.E. IS THEIR HOUSE LIKELY TO BE A CARE HOME)?================


log close



*(c)Reduce

/*


*keep only people marked as living in private homes
drop if care_home_type!="U"








/*SUPERCEDED FOR NOW
*in the ones that are incorrect in the largest category, check to see what the median is of (1) the TPP hh_size and (2) my calculated hh_size
display "===================(3) In those with incorrect hh_size in the largest category, show median for (1) TPP calculated hh_size (2) my hh_size based upon hh_id============="
display "(a) TPP calculated hh_size:"
sum hh_size if hhRiskCat67PLUS_4cats==4 & hh_size_wrong==1, detail
display "(b) KW houshold size based upon hh_id:"
sum kw_hh_size if hhRiskCat67PLUS_4cats==4 & hh_size_wrong==1, detail

*compare household size variables within this category of household comppsition
display "===================(4) In those with incorrect hh_size in the largest category, tabulate household size (tpp) vs household size (kw, based on tpp hh_id)============="
tab kw_hh_total_cat hh_total_cat if hhRiskCat67PLUS_4cats==4 & hh_size_wrong==1, row






/*
*create new variables of household composition where the problematic 3 gen categories have been assigned to the baseline category
generate repaired_hhRiskCat67Plus=hhRiskCat67PLUS
label define repaired_hhRiskCat67Plus 1 "Only 67+" 2 "0-17 & 67+" 3 "18-29 & 67+" 4 "30-66 & 67+" 5 "0-17, 18-29 & 67+" 6 "0-17, 30-66 & 67+" 7 "18-29, 30-66 & 67+" 8 "0-17, 18-29, 30-66 & 67+"
label values repaired_hhRiskCat67Plus repaired_hhRiskCat67Plus
la var repaired_hhRiskCat67Plus "Repaired hh composition variable"
*move people who are in the 3 gen category but only have a household size of 1-2 to the 1-2 category
replace repaired_hhRiskCat67Plus=1 if repaired_hhRiskCat67Plus==8 & hh_size<3





*============(3) TABULATIONS OF DISTRIBUTION OF HOUSHOLD COMPOSITION BY ETHNICITY BEFORE AND AFTER FIX================
prog define outputVarPercentages
	syntax, variable(string) catLabel(string) min(real) max(real)

	*calculation of numbers and %			

		forvalues i=`min'/`max' {
			display 
			*get overall number for each category
			count
			local n_people_All = r(N)
			count if `variable'==`i'
			local n_people = r(N)
			local percent=100*(`n_people'/`n_people_All')

			*get variable name
			local lab: variable label `variable'
			*file write tablecontents  _tab  (`i') _n
			*get category name
			local category: label `catLabel' `i'
			display "Category label: `category'"
					
			*write each row hg
			file write tablecontents  _tab ("`category'") _tab (`n_people') _tab %3.1f (`percent')  _n

		}
end

********Code that calls program and outputs tables*******
*open table
file open tablecontents using ./output/02b_hhClassif_an_checking_hhVars_`dataset'.txt, t w replace

*write table title and column headers
file write tablecontents "Wave: `dataset', HH Composition distributions before and after fix" _n
file write tablecontents _tab _tab ("N") _tab ("%") _n _n
forvalues e=1/5 {
	if `e'==1 {
		preserve
			keep if eth5==1
			file write tablecontents "Ethnicity: White " _n
			file write tablecontents "Before repair" _n
			cap noisily outputVarPercentages, variable(hhRiskCat67PLUS) catLabel(hhRiskCat67PLUS) min(1) max(8)
			file write tablecontents "After repair" _n
			cap noisily outputVarPercentages, variable(repaired_hhRiskCat67Plus) catLabel(repaired_hhRiskCat67Plus) min(1) max(8)
		restore
	}
	else if `e'==2 {
		preserve
			keep if eth5==2
			file write tablecontents _n "Ethnicity: South Asian " _n
			file write tablecontents "Before repair" _n
			cap noisily outputVarPercentages, variable(hhRiskCat67PLUS) catLabel(hhRiskCat67PLUS) min(1) max(8)
			file write tablecontents "After repair" _n
			cap noisily outputVarPercentages, variable(repaired_hhRiskCat67Plus) catLabel(repaired_hhRiskCat67Plus) min(1) max(8)
		restore
	}
	else if `e'==3 {
		preserve
			keep if eth5==3
			file write tablecontents _n "Ethnicity: Black " _n
			file write tablecontents "Before repair" _n
			cap noisily outputVarPercentages, variable(hhRiskCat67PLUS) catLabel(hhRiskCat67PLUS) min(1) max(8)
			file write tablecontents "After repair" _n
			cap noisily outputVarPercentages, variable(repaired_hhRiskCat67Plus) catLabel(repaired_hhRiskCat67Plus) min(1) max(8)
		restore
	}
	else if `e'==4 {
		preserve
			keep if eth5==4
			file write tablecontents _n "Ethnicity: Mixed " _n
			file write tablecontents "Before repair" _n
			cap noisily outputVarPercentages, variable(hhRiskCat67PLUS) catLabel(hhRiskCat67PLUS) min(1) max(8)
			file write tablecontents "After repair" _n
			cap noisily outputVarPercentages, variable(repaired_hhRiskCat67Plus) catLabel(repaired_hhRiskCat67Plus) min(1) max(8)
		restore
	}
	else if `e'==5 {
		preserve
			keep if eth5==5
			file write tablecontents _n "Ethnicity: Other " _n
			file write tablecontents "Before repair" _n
			cap noisily outputVarPercentages, variable(hhRiskCat67PLUS) catLabel(hhRiskCat67PLUS) min(1) max(8)
			file write tablecontents "After repair" _n
			cap noisily outputVarPercentages, variable(repaired_hhRiskCat67Plus) catLabel(repaired_hhRiskCat67Plus) min(1) max(8)
		restore
	}
}
cap file close tablecontents 
cap log close
*/



****************************
*  Create required cohort  *
****************************
/*
* Age: Exclude those with implausible ages
cap assert age<.
noi di "DROPPING AGE>105:" 
drop if age>105
safecount
* Sex: Exclude categories other than M and F
cap assert inlist(sex, "M", "F", "I", "U")
noi di "DROPPING GENDER NOT M/F:" 
drop if inlist(sex, "I", "U")
safecount

gen male = 1 if sex == "M"
replace male = 0 if sex == "F"
label define male 0"Female" 1"Male"
label values male male
safetab male




*============(3) CHECKS TO ASSESS IMPACT OF DROPPING PEOPLE WHO ARE INDICATED AS NOT BEING IN A CARE HOME (I.E. IS THEIR HOUSE LIKELY TO BE A CARE HOME)?================


log close



*(c)Reduce

/*


*keep only people marked as living in private homes
drop if care_home_type!="U"

*might need to 
			
label define hh_total_cat  1 "1-2" ///
						   2 "3-5" ///
						   3 "6+" ///
											
label values hh_total_cat hh_total_cat

safetab hh_total_cat,m
safetab hh_total_cat care_home_type,m

safetab hh_size hh_total_cat,m





******Create a household composition variable for the hh risk classification study (might be useful for snotty noses? - takes 5 minutes to run up to line 363)
/*test how to create a variable with the following categories (see protocol safetable 1)
	1 SG1 - hh has only 18-29 year olds in it
	2 SG2 - hh has only 30-66 year olds in it
	3 SG3 - hh has only 67+ in it
	4 2G1 - hh has 0-17 and 18-29 in it
	5 2G2 - hh has 0-17 and 30-66 in it
	6 2G3 - hh has 0-17 and 67+ in it
	7 2G4 - hh has 18-29 and 67+ in it
	8 2G5 - hh has 30-66 and 67+ in it
	9 2G6 - hh has 18-29 and 67+ in it
	10 MG1 - hh has 0-17, 18-29 and 30-66 in it
	11 MG2 - hh has 0-17, 18-29 and 67+ in it
	12 MG3 - hh has 0-17, 30-66 and 67+ in it
	13 MG4 - hh has 18-29, 30-66 and 67+ in it
	14 MG5 - hh has 0-17, 18-29, 30-66 and 67+ in it
*/
*first of all, create age bands that I need for this
egen ageCatHHRisk=cut(age), at (0, 18, 30, 67, 200)
recode ageCatHHRisk 0=0 18=1 30=2 67=3 
label define ageCatHHRisk 0 "0-17" 1 "18-29" 2 "30-66" 3 "67+"
label values ageCatHHRisk ageCatHHRisk
safetab ageCatHHRisk, miss
la var ageCatHHRisk "Age categorised for HH risk analysis"

*make an age category variable here that is for table 1 of the 67+ year old analysis
egen ageCatfor67Plus=cut(age), at (67, 70, 75, 80, 85, 200)
recode ageCatfor67Plus 67=0 70=1 75=2 80=3 85=4 
label define ageCatfor67Plus 0 "67-69" 1 "70-74" 2 "75-79" 3 "80-84" 4 "85+"
label values ageCatfor67Plus ageCatfor67Plus
safetab ageCatfor67Plus, miss
la var ageCatfor67Plus "Age (categories)"
*check groupins
forvalues i=0/4{
	sum age if ageCatfor67Plus==`i'
}

preserve
	*keep only the variables I need to work this out
	keep hh_id patient_id ageCatHHRisk
	sort hh_id ageCatHHRisk

	*mark whether hh has each age category using egen max which returns true (1) or false (0) (see https://www.stata.com/support/faqs/data-management/create-variable-recording/)
	egen hasUnder18=max(ageCatHHRisk==0), by(hh_id)
	egen has18_29=max(ageCatHHRisk==1), by(hh_id)
	egen has30_66=max(ageCatHHRisk==2), by(hh_id)
	egen has67Plus=max(ageCatHHRisk==3), by(hh_id)

	*now generate the hhRiskCat variable for each person
	generate hhRiskCat=.
	la var hhRiskCat "Household risk category"
	*Key:
	/*test how to create a variable with the following categories:
			1 SG1 - hh has only 18-29 year olds in it
			2 SG2 - hh has only 30-66 year olds in it
			3 SG3 - hh has only 67+ in it
			4 2G1 - hh has 0-17 and 18-29 in it
			5 2G2 - hh has 0-17 and 30-66 in it
			6 2G3 - hh has 0-17 and 67+ in it
			7 2G4 - hh has 18-29 and 67+ in it
			8 2G5 - hh has 30-66 and 67+ in it
			9 2G6 - hh has 18-29 and 67+ in it
			10 MG1 - hh has 0-17, 18-29 and 30-66 in it
			11 MG2 - hh has 0-17, 18-29 and 67+ in it
			12 MG3 - hh has 0-17, 30-66 and 67+ in it
			13 MG4 - hh has 18-29, 30-66 and 67+ in it
			14 MG5 - hh has 0-17, 18-29, 30-66 and 67+ in it
	*/
	replace hhRiskCat=0 if hasUnder18==1 & has18_29==0 & has30_66==0 & has67Plus==0
	replace hhRiskCat=1 if hasUnder18==0 & has18_29==1 & has30_66==0 & has67Plus==0
	replace hhRiskCat=2 if hasUnder18==0 & has18_29==0 & has30_66==1 & has67Plus==0
	replace hhRiskCat=3 if hasUnder18==0 & has18_29==0 & has30_66==0 & has67Plus==1
	replace hhRiskCat=4 if hasUnder18==1 & has18_29==1 & has30_66==0 & has67Plus==0
	replace hhRiskCat=5 if hasUnder18==1 & has18_29==0 & has30_66==1 & has67Plus==0
	replace hhRiskCat=6 if hasUnder18==1 & has18_29==0 & has30_66==0 & has67Plus==1
	replace hhRiskCat=7 if hasUnder18==0 & has18_29==1 & has30_66==1 & has67Plus==0
	replace hhRiskCat=8 if hasUnder18==0 & has18_29==1 & has30_66==0 & has67Plus==1
	replace hhRiskCat=9 if hasUnder18==0 & has18_29==0 & has30_66==1 & has67Plus==1
	replace hhRiskCat=10 if hasUnder18==1 & has18_29==1 & has30_66==1 & has67Plus==0
	replace hhRiskCat=11 if hasUnder18==1 & has18_29==1 & has30_66==0 & has67Plus==1
	replace hhRiskCat=12 if hasUnder18==1 & has18_29==0 & has30_66==1 & has67Plus==1
	replace hhRiskCat=13 if hasUnder18==0 & has18_29==1 & has30_66==1 & has67Plus==1
	replace hhRiskCat=14 if hasUnder18==1 & has18_29==1 & has30_66==1 & has67Plus==1
	
	*label variable
	label define hhRiskCatLabel 0 "Only <18"  1 "Only 18-29" 2 "Only 30-66" 3 "Only 67+" 4 "0-17 & 18-29" 5 "0-17 & 30-66" 6 "0-17 & 67+" 7 "18-29 & 30-66" 8 "18-29 & 67+" 9 "30-66 & 67+" 10 "0-17, 18-29 & 30-66" 11 "0-17, 18-29 & 67+" 12 "0-17, 30-66 & 67+" 13 "18-29, 30-66 & 67+" 14 "0-17, 18-29, 30-66 & 67+"
	label values hhRiskCat hhRiskCat
	la var hhRiskCat "Age group(s) of hh occupants"
	safetab hhRiskCat, miss
	keep hh_id hhRiskCat
	duplicates drop hh_id, force
	tempfile hhRiskCat
	save `hhRiskCat', replace
restore
merge m:1 hh_id using `hhRiskCat'
drop _merge
safetab hhRiskCat, miss
*missing here are likely to be people living in households made up of only under 18 year olds


*(b) variable for stratifying by the oldest age group (67+)
generate hhRiskCat67PLUS=.
la var hhRiskCat67PLUS "hhRiskCat for the over 67 year old age group"
replace hhRiskCat67PLUS=1 if hhRiskCat==3
replace hhRiskCat67PLUS=2 if hhRiskCat==6
replace hhRiskCat67PLUS=3 if hhRiskCat==8
replace hhRiskCat67PLUS=4 if hhRiskCat==9
replace hhRiskCat67PLUS=5 if hhRiskCat==11
replace hhRiskCat67PLUS=6 if hhRiskCat==12
replace hhRiskCat67PLUS=7 if hhRiskCat==13
replace hhRiskCat67PLUS=8 if hhRiskCat==14
*label variable
label define hhRiskCat67PLUS 1 "Only 67+" 2 "0-17 & 67+" 3 "18-29 & 67+" 4 "30-66 & 67+" 5 "0-17, 18-29 & 67+" 6 "0-17, 30-66 & 67+" 7 "18-29, 30-66 & 67+" 8 "0-17, 18-29, 30-66 & 67+"
label values hhRiskCat67PLUS hhRiskCat67PLUS
safetab hhRiskCat hhRiskCat67PLUS, miss

*create another version that has 4 categories (1) living with only one generation (2) living with one other generation (3) living with two other generations (4) living with three other gens
generate hhRiskCat67PLUS_4cats=.
la var hhRiskCat67PLUS_4cats "hhRiskCat for the over 67 year old age group - 4 categories"
replace hhRiskCat67PLUS_4cats=1 if hhRiskCat67PLUS==1
replace hhRiskCat67PLUS_4cats=2 if hhRiskCat67PLUS>1 & hhRiskCat67PLUS<5
replace hhRiskCat67PLUS_4cats=3 if hhRiskCat67PLUS>4 & hhRiskCat67PLUS<8
replace hhRiskCat67PLUS_4cats=4 if hhRiskCat67PLUS==8
*label variable
label define hhRiskCat67PLUS_4cats 1 "Only 67+" 2 "67+ & 1 other gen" 3 "67+ & 2 other gens" 4 "67+ & 3 other gens"
label values hhRiskCat67PLUS_4cats hhRiskCat67PLUS_4cats
safetab hhRiskCat67PLUS hhRiskCat67PLUS_4cats, miss




****************************
*  Create required cohort  *
****************************

* Age: Exclude those with implausible ages
cap assert age<.
noi di "DROPPING AGE<105:" 
drop if age>105
safecount
* Sex: Exclude categories other than M and F
cap assert inlist(sex, "M", "F", "I", "U")
noi di "DROPPING GENDER NOT M/F:" 
drop if inlist(sex, "I", "U")
safecount

gen male = 1 if sex == "M"
replace male = 0 if sex == "F"
label define male 0"Female" 1"Male"
label values male male
safetab male


* Create binary age (for age stratification)
*recode age min/65.999999999 = 0 ///
*           66/max = 1, gen(age66)

* Check there are no missing ages
*cap assert age < .
*cap assert agegroup < .
*cap assert age66 < .

* Create restricted cubic splines for age
*mkspline age = age, cubic nknots(4)



* Close log file 
log close







							  










/*
*tying up labelling
label variable ageCat "Categorised age (years)"
label define eth5Label 1 "White" 2 "South Asian" 3 "Black" 4 "Mixed" 5 "Other"
label values eth5 eth5Label
label define chronic_respiratory_diseaseLabel 0 "No" 1 "Yes"
label values chronic_respiratory_disease chronic_respiratory_diseaseLabel
label define chronic_cardiac_diseaseLabel 0 "No" 1 "Yes"
label values chronic_cardiac_disease chronic_cardiac_diseaseLabel
label define cancerLabel 0 "No" 1 "Yes"
label values cancer cancerLabel
label define chronic_liver_diseaseLabel 0 "No" 1 "Yes"
label values chronic_liver_disease chronic_liver_diseaseLabel
label define hypertensionLabel 0 "No" 1 "Yes"
label values hypertension hypertensionLabel
label define comorb_NeuroLabel 0 "No" 1 "Yes"
label values comorb_Neuro comorb_NeuroLabel
label define comorb_ImmunosuppressionLabel 0 "No" 1 "Yes"
label values comorb_Immunosuppression comorb_ImmunosuppressionLabel
label define diabetesLabel 0 "No" 1 "Yes"
label values diabetes diabetesLabel
label define imdLabel 1 "1 - least deprived" 2 "2" 3 "3" 4 "4" 5 "5 - most deprived", replace
label values imd imdLabel
label define bmicatLabel 1 "Underweight" 2 "Normal" 3 "Overweight" 4 "Obese I" 5 "Obese II" 6 "Obese III"
label values bmicat bmicatLabel




save hhClassif_analysis_dataset.dta, replace



*extra code I don't needed
/*



*create household composition variable for transmission model study
*edited dataset that only contains variables that will be used in the regression analysis (and were for shared in dummydata with Thomas and Heather)
*create the age variable that I want
egen ageCat=cut(age), at (0, 5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 120)
recode ageCat 0=1 5=2 10=3 15=4 20=5 30=6 40=7 50=8 60=9 70=10 80=11 90=12 
label define ageCatLabel 1 "0-4" 2 "5-9" 3 "10-14" 4 "15-19" 5 "20-29" 6 "30-39" 7 "40-49" 8 "50-59" 9 "60-69" 10 "70-79" 11 "80-89" 12 "90+"
label values ageCat ageCatLabel
safetab ageCat, miss
la var ageCat "Categorised age"
*now creat the household composition variable that takes account of age of the person and the size of the house that they are in
*this doesn't take account of the ages of the other people in the house though?
generate hh_composition=.
la var hh_composition "Combination of person's age and household size'"
levelsof ageCat, local(ageLevels)
local count=0
foreach l of local ageLevels {
	levelsof hh_size, local(hh_sizeLevels)
	foreach m of local hh_sizeLevels {
		local count=`count'+1
		display `count'
		replace hh_composition=`count' if ageCat==`l' & hh_size==`m'
	}	
}
order age hh_size hh_composition
safetab hh_composition

*create a key for the hh composition variable
preserve
    describe, replace clear
    list
    export excel using hhCompositionKey.xlsx, replace first(var)
restore




/*

****************************************************************
*  Create outcome specific datasets for the whole population  *
*****************************************************************


foreach i of global outcomes {
	use "$Tempdir/analysis_dataset.dta", clear
	
	drop if `i'_date <= indexdate 

	stset stime_`i', fail(`i') 				///	
	id(patient_id) enter(indexdate) origin(indexdate)
	save "$Tempdir/analysis_dataset_STSET_`i'.dta", replace
}	


****************************************************************
*  Create outcome specific datasets for those with evidence of infection  *
*****************************************************************
use "$Tempdir/analysis_dataset.dta", clear

keep if confirmed==1 | positivetest==1
safecount
gen infected_date=min(confirmed_date, positivetest_date)
save "$Tempdir/analysis_dataset_infected.dta", replace

foreach i of global outcomes2 {
	use "$Tempdir/analysis_dataset_infected.dta", clear
	
	drop if `i'_date <= infected_date 

	stset stime_`i', fail(`i') 				///	
	id(patient_id) enter(infected_date) origin(infected_date)
	save "$Tempdir/analysis_dataset_STSET_`i'_infected.dta", replace
}	

	
* Close log file 
log close