The WHO Eye Care Indicator Menu has prescribed many core indicators. The Core Indicator 6 is Cataract surgical outcome (visual acuity) in the domain of Output – Eye Care Service Quality
Definition: Percentage of eyes with:
- “Good” cataract surgical outcome: attaining a postoperative PVA of 6/12 or b e t t e r.
- “Suboptimal” cataract surgical outcome: attaining a postoperative PVA worse than 6/12, and equal to or better than 6/60 (mild and moderate vision impairment).
- “Poor” cataract surgical outcome: attaining a postoperative PVA worse than 6/60 (severe vision impairment or blindness).
Estimation of these indicators can be accomplished using the below Stata code
label define sex 1 Male 2 Female, replace
label value sex sex
* PERSON LEVEL DATA - wide form - default of RAAB6 CSV
gen op1 = inlist(lere,3,4,5)
gen op2 = inlist(lele,3,4,5)
gen csoeyes1 = 0 if op1 ==1
replace csoeyes1 = 1 if csoeyes1 == 0 & inlist(pvare, 1)
replace csoeyes1 = 2 if csoeyes1 == 0 & inlist(pvare, 2,3)
replace csoeyes1 = 3 if csoeyes1 == 0 & inlist(pvare, 4,5,6,7)
gen csoeyes2 = 0 if op2 ==1
replace csoeyes2 = 1 if csoeyes2 == 0 & inlist(pvale, 1)
replace csoeyes2 = 2 if csoeyes2 == 0 & inlist(pvale, 2,3)
replace csoeyes2 = 3 if csoeyes2 == 0 & inlist(pvale, 4,5,6,7)
label define csoeyes 0 "No Inf" ///
1 "Good (PVA 6/12 or better)" ///
2 "Sub-optimal (PVA<6/12,>=6/60)" ///
3 "Poor (PVA worse than 6/60)" , replace
label value csoeyes1 csoeyes2 csoeyes
tab1 csoeyes1 if op1==1, mis
tab1 csoeyes2 if op2==1, mis
rename pvare pv1
rename bvare bv1
rename lere le1
rename causere cause1
rename agere ageSurg1
rename plre plSurg1
rename surgre iolType1
rename costre costSurg1
rename outlowre outSurgCause1
rename pvale pv2
rename bvale bv2
rename lele le2
rename causele cause2
rename agele ageSurg2
rename plle plSurg2
rename surgle iolType2
rename costle costSurg2
rename outlowle outSurgCause2
* EYE WISE DATA - Long form
reshape long pv bv le cause ageSurg plSurg iolType costSurg outSurgCause op csoeyes, i( id ) j(eye)
tab le op, mis
tab le csoeyes, mis
tab op, mis
tab csoeyes
tab csoeyes, gen(csoeyesType)
label var csoeyesType1 "Good"
label var csoeyesType2 "Sub-optimal"
label var csoeyesType3 "Poor"
cap drop HospSurg
gen HospSurg = inlist(plSurg, 1,2,3) if op==1
label var HospSurg "Hospital Surgery"
cap drop freeSurg
gen freeSurg = inlist(costSurg, 1,2) if op==1
label var freeSurg "Total/Partially Free Surgery"
* Age of surgery
replace ageSurg = . if ageSurg ==0
cap drop ageSurgCat
egen ageSurgCat = cut(ageSurg), at(0 40 50 60 70 80 105 ) lab
tab ageSurgCat
tab ageGrp ageSurgCat
* Time since surgery
cap drop timeSinceSurg
gen timeSinceSurg = age - ageSurg if op==1
cap drop timeSinceSurgCat
egen timeSinceSurgCat = cut(timeSinceSurg), at(0 2 4 6 11 100 ) lab
tab timeSinceSurgCat
label var timeSinceSurgCat "Time Since Surgery"
label define yn 1 Yes 2 No
label val op yn
label var op "Operated (eyes)"
label var ageSurgCat "Age at Surgery"
label var HospSurg "Operated in Hospital"
cap drop surgDetailsavailable
gen surgDetailsavailable = ageSurg !=0 & plSurg !=0 & iolType !=0 & costSurg !=0 & outSurgCause !=0 if op==1
tab surgDetailsavailable
gen iolImplant = iolType ==2 if iolType !=0
replace costSurg = . if costSurg==0
// ANALYSIS
keep if status ==1
duplicates report id
tab op
tab op sex, col nokey
tab surgDetailsavailable sex, col nokey
* Uniform N
replace csoeyes = . if surgDetailsavailable !=1
tab ageSurgCat sex if surgDetailsavailable ==1, col nokey
tab timeSinceSurgCat sex if surgDetailsavailable ==1, col nokey
tab plSurg sex if surgDetailsavailable ==1, col nokey
tab HospSurg sex if surgDetailsavailable ==1, col nokey
tab iolType sex if surgDetailsavailable ==1, col nokey
tab costSurg sex if surgDetailsavailable ==1, col nokey
tab freeSurg sex if surgDetailsavailable ==1, col nokey
tab csoeyes sex if surgDetailsavailable ==1, col nokey
tab outSurgCause sex if surgDetailsavailable ==1 , col nokey
* Binomial Confidence Intervals
bysort sex: ci prop csoeyesType1 csoeyesType2 csoeyesType3 if surgDetailsavailable ==1
* table 1
ssc install table1_mc
table1_mc if surgDetailsavailable ==1, by(sex) ///
vars(ageGrp cat \ option1 cat \ rural cat \ ageSurg contn %4.1f \ surgDetailsavailable bin \ ageSurgCat cat \ timeSinceSurgCat cat \ plSurg cat \ HospSurg bin \ iolType cat \ costSurg cat \ freeSurg bin \ csoeyes cat ) ///
onecol nospace total(after)
* CUSTOM Program for Cluster sampling adjusted analysis by sex
cap program drop clustercibysexwtEyes
program define clustercibysexwtEyes , rclass
args outcome
return clear
qui svy linearized : proportion `outcome', over(sex)
cap mat drop all
mat all = r(table)
mat all = all' // mat list all
qui svy linearized : proportion `outcome'
cap mat drop all2
mat all2 = r(table)
mat all2 = all2'
qui levelsof `outcome', local(outcomeLevels)
qui levelsof sex, local(sexLevels)
local x = 1 // COUNTER FOR OUTCOME RESULTS IN SEX-WISE ANALYSIS - TWO ROWS PER OUTCOME LEVEL (ONE FOR EACH SEX)
local y = 1 // COUNTER FOR OUTCOME RESULTS IN OVERALL ANALYSIS - ONE ROW PER OUTCOME LEVEL
foreach o of local outcomeLevels { // of local levels in 1
return local outcomeName`o' : label(`outcome') `o' // local outcome = ustrregexra(`"`outcomeName'"', " ", "_")
return local outcomeCode`o' = `o' //di "`outcomeName' `outcomeCode'"
return local prop_`o'_M : di %4.1f all[`x',1]*100 // Cluster-adjusted prevalence outcome level for Male
return local lb_`o'_M : di %4.1f all[`x',5]*100 // Cluster-adjusted prevalence lower CI for that Male
return local ub_`o'_M : di %4.1f all[`x',6]*100 // Cluster-adjusted prevalence lower CI for that Male
local x = `x' + 1
return local prop_`o'_F : di %4.1f all[`x',1]*100 // Cluster-adjusted prevalence outcome level for Female
return local lb_`o'_F : di %4.1f all[`x',5]*100 // Cluster-adjusted prevalence lower CI for that Female
return local ub_`o'_F : di %4.1f all[`x',6]*100 // Cluster-adjusted prevalence lower CI for that Female
local x = `x' + 1
return local prop_`o'_T : di %4.1f all2[`y',1]*100 // Cluster-adjusted prevalence outcome level overall
return local lb_`o'_T : di %4.1f all2[`y',5]*100 // Cluster-adjusted prevalence lower CI overall
return local ub_`o'_T : di %4.1f all2[`y',6]*100 // Cluster-adjusted prevalence lower CI overall
local y = `y' + 1
qui count if sex==1 & `outcome'!=. //male
return local denomM = r(N)
qui count if sex==2 & `outcome'!=. //Female
return local denomF = r(N)
qui count if `outcome'!=. //Total
return local denomT = r(N)
qui count if sex==1 //male
return local sampleM = r(N)
qui count if sex==2 //Female
return local sampleF = r(N)
qui count //Total
return local sampleT = r(N)
return local indicator = "`outcome'"
}
return local levelsOfOutcome = `y'-1
end
* CUSTOM Program for Cluster sampling adjusted analysis by Categorical risk factors
cap program drop clustercibyRFwtEyes
program define clustercibyRFwtEyes , rclass
args outcome byVar
return clear
return local indicator = "`outcome'"
return local byVar = "`byVar'"
qui svy linearized : proportion `outcome', over(`byVar')
cap mat drop all
mat all = r(table)
mat all = all' // mat list all
qui svy linearized : proportion `outcome'
cap mat drop all2
mat all2 = r(table)
mat all2 = all2'
qui levelsof `outcome', local(outcomeLevels)
qui levelsof `byVar', local(byVarLevels)
local x = 1 // COUNTER FOR OUTCOME RESULTS IN ByVar-WISE ANALYSIS - ROWS PER OUTCOME LEVEL (ONE FOR EACH SEX)
foreach o of local outcomeLevels { // of local levels in 1
local outcomeName`o' : label(`outcome') `o' // local outcome = ustrregexra(`"`outcomeName'"', " ", "_")
local outcomeCode`o' = `o' //di "`outcomeName' `outcomeCode'"
foreach byVarLevel of local byVarLevels {
local byVarLevelName`byVarLevel' : label(`byVar') `byVarLevel' // local outcome = ustrregexra(`"`outcomeName'"', " ", "_")
local byVarLevelCode`byVarLevel' = `byVarLevel' //di "`outcomeName' `outcomeCode'"
qui count if `byVar'==`byVarLevel' & `outcome'!=. //male
local denom`byVarLevel' = r(N)
qui count if `byVar'==`byVarLevel' //male
local sample`byVarLevel' = r(N)
local prop_`o'_`byVarLevel' : di %4.1f all[`x',1]*100 // Cluster-adjusted prevalence outcome level
local lb_`o'_`byVarLevel' : di %4.1f all[`x',5]*100 // Cluster-adjusted prevalence lower CI
local ub_`o'_`byVarLevel' : di %4.1f all[`x',6]*100 // Cluster-adjusted prevalence lower CI
local x = `x' + 1
}
local y = `y' + 1
}
local levelsOfOutcome = `y'
local levelsOfbyVar = (`x' - 1 )/ `y'
local headings ""
foreach o of local outcomeLevels {
local headings "`headings' `outcomeName`o'' "
}
di "`headings'"
foreach byVarLevel of local byVarLevels {
local result "`denom`byVarLevel'' "
foreach o of local outcomeLevels {
local result "`result' `prop_`o'_`byVarLevel'' (`lb_`o'_`byVarLevel'', `ub_`o'_`byVarLevel'') "
}
local result " `result' - `byVarLevelName`byVarLevel'' "
di "`result'"
}
end
* SVY SET - ADJUST AS PER YOUR WEIGHTS
svyset cluster, vce(linearized) singleunit(certainty) weight(wtRAABEnroll2Dist)
* Generate Cluster adjusted estimates
clustercibysexwtEyes csoeyes
* Risk factor Analysis
**** UNIVARIATE ANALYSIS *******
tab ageGrp csoeyes, row chi nokey
clustercibyRFwtEyes csoeyes ageGrp
tab sex csoeyes, row chi nokey
clustercibyRFwtEyes csoeyes sex
tab option1 csoeyes, row chi nokey
clustercibyRFwtEyes csoeyes option1
tab ageSurgCat csoeyes, row chi nokey
clustercibyRFwtEyes csoeyes ageSurgCat
tab timeSinceSurgCat csoeyes, row chi nokey
clustercibyRFwtEyes csoeyes timeSinceSurgCat
tab HospSurg csoeyes, row chi nokey
clustercibyRFwtEyes csoeyes HospSurg
tab iolImplant csoeyes, row chi nokey
clustercibyRFwtEyes csoeyes iolImplant
tab costSurg csoeyes, row chi nokey
clustercibyRFwtEyes csoeyes costSurg
tab rural csoeyes, row chi nokey
clustercibyRFwtEyes csoeyes rural
svy linearized : logistic csoeyesType3 i.ageGrp, baselevels cformat(%5.2f) pformat(%5.3f) sformat(%8.3f)
svy linearized : logistic csoeyesType3 i.sex, baselevels cformat(%5.2f) pformat(%5.3f) sformat(%8.3f)
svy linearized : logistic csoeyesType3 ib4.option1, baselevels cformat(%5.2f) pformat(%5.3f) sformat(%8.3f)
svy linearized : logistic csoeyesType3 ib2.ageSurgCat, baselevels cformat(%5.2f) pformat(%5.3f) sformat(%8.3f)
svy linearized : logistic csoeyesType3 i.timeSinceSurgCat, baselevels cformat(%5.2f) pformat(%5.3f) sformat(%8.3f)
svy linearized : logistic csoeyesType3 ib1.HospSurg, baselevels cformat(%5.2f) pformat(%5.3f) sformat(%8.3f)
svy linearized : logistic csoeyesType3 ib1.iolImplant, baselevels cformat(%5.2f) pformat(%5.3f) sformat(%8.3f)
svy linearized : logistic csoeyesType3 ib3.costSurg , baselevels cformat(%5.2f) pformat(%5.3f) sformat(%8.3f)
svy linearized : logistic csoeyesType3 i.rural, baselevels cformat(%5.2f) pformat(%5.3f) sformat(%8.3f)
svy linearized : logistic csoeyesType3 i.ageGrp i.sex ib4.option1 ib2.ageSurgCat i.timeSinceSurgCat ib1.HospSurg ib1.iolImplant ib3.costSurg i.rural ib5.distZone , baselevels cformat(%5.2f) pformat(%5.3f) sformat(%8.3f)
est store e1
ssc install estout
estout e1 , cells (" b(fmt(2)) ci(par( ( , ) )) p(fmt(3)) ") label eform
Code language: Stata (stata)I have data of multiple surveys and in order to generate survey-area wise results, following code is helpful
tab areacode2
cap mkdir adj
cap program drop resultsByDistandCountryCSVeyes
program define resultsByDistandCountryCSVeyes
args outcome2
cap file close myfile
file open myfile using "adj\\`outcome2'.csv", write replace
* SET HEADERS
local colnames = "district,areacode2,sampleT,sampleM,sampleF,denomT,denomM,denomF,indicator"
qui levelsof `outcome2', local(outcomeLevels2)
foreach o2 of local outcomeLevels2 {
local colnames = "`colnames',prop_`o2'_M,lb_`o2'_M,ub_`o2'_M,prop_`o2'_F,lb_`o2'_F,ub_`o2'_F,prop_`o2'_T,lb_`o2'_T,ub_`o2'_T"
}
file write myfile "`colnames'" _n
* dISTRICT WISE DATA
qui levelsof areacode2, local(districts)
foreach district of local districts {
qui preserve
qui keep if areacode2==`district'
local DistName : label(areacode2) `district'
local DistName = ustrregexra(`"`DistName'"', " ", "-")
local distCode = `district'
qui svyset cluster, vce(linearized) singleunit(certainty) weight(wtRAABEnroll2Dist)
* set district name and n
clustercibysexwtEyes `outcome2' // return list di "value = :" r(sampleT)
local colvalues = "`DistName',`district',`r(sampleT)',`r(sampleM)',`r(sampleF)',`r(denomT)',`r(denomM)',`r(denomF)',`r(indicator)'" //di "`colvalues'"
qui levelsof `outcome2', local(outcomeLevels2)
* set outcome level wise results for each sex and total
foreach o2 of local outcomeLevels2 {
clustercibysexwtEyes `outcome2' // for some reason porevious reults were not getting pushed into the next loop
local colvalues = "`colvalues',`r(prop_`o2'_M)',`r(lb_`o2'_M)',`r(ub_`o2'_M)',`r(prop_`o2'_F)',`r(lb_`o2'_F)',`r(ub_`o2'_F)',`r(prop_`o2'_T)',`r(lb_`o2'_T)',`r(ub_`o2'_T)'" // di "INNER `colvalues'"
}
di "`colvalues'" ,
file write myfile "`colvalues'" _n
restore
}
qui svyset areacode2, fpc(numDists) vce(linearized) singleunit(missing) || cluster, weight(wtRAABEnroll2Dist)
clustercibysexwtEyes `outcome2'
local colvalues = "Country,0,`r(sampleT)',`r(sampleM)',`r(sampleF)',`r(denomT)',`r(denomM)',`r(denomF)',`r(indicator)'" //di "`colvalues'"
qui levelsof `outcome2', local(outcomeLevels2)
* set outcome level wise results for each sex and total
foreach o2 of local outcomeLevels2 {
clustercibysexwtEyes `outcome2' // for some reason porevious reults were not getting pushed into the next loop
local colvalues = "`colvalues',`r(prop_`o2'_M)',`r(lb_`o2'_M)',`r(ub_`o2'_M)',`r(prop_`o2'_F)',`r(lb_`o2'_F)',`r(ub_`o2'_F)',`r(prop_`o2'_T)',`r(lb_`o2'_T)',`r(ub_`o2'_T)'" // di "INNER `colvalues'"
}
di "`colvalues'" ,
file write myfile "`colvalues'" _n
file close myfile
preserve
import delimited "adj\\`outcome2'.csv", case(preserve) clear
save "adj\\`outcome2'.dta", replace
list , noobs clean
restore
end
drop if surgDetailsavailable ==0
resultsByDistandIndiaCSVeyes csoeyes if surgDetailsavailable ==1
cd adj
use "csoeyes.dta", clear
gen male1 = string(prop_1_M ) + "(" + string( lb_1_M) + ", " + string( ub_1_M) + ")"
gen male2 = string(prop_2_M ) + "(" + string( lb_2_M) + ", " + string( ub_2_M) + ")"
gen male3 = string(prop_3_M ) + "(" + string( lb_3_M) + ", " + string( ub_3_M) + ")"
gen female1 = string(prop_1_F ) + "(" + string( lb_1_F) + ", " + string( ub_1_F) + ")"
gen female2 = string(prop_2_F ) + "(" + string( lb_2_F) + ", " + string( ub_2_F) + ")"
gen female3 = string(prop_3_F ) + "(" + string( lb_3_F) + ", " + string( ub_3_F) + ")"
gen total1 = string(prop_1_T ) + "(" + string( lb_1_T) + ", " + string( ub_1_T) + ")"
gen total2 = string(prop_2_T ) + "(" + string( lb_2_T) + ", " + string( ub_2_T) + ")"
gen total3 = string(prop_3_T ) + "(" + string( lb_3_T) + ", " + string( ub_3_T) + ")"
cap drop dist2
gen dist2 = district + " (" + string(sampleT) + ")"
order dist2 denomM male1 male2 male3 denomF female1 female2 female3
order dist2 denomT total1 total2 total3
cd ..
Code language: PHP (php)
