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Computing the stunting prevalence among U5 children according to the BMI of their mothers [message #6718]

Thu, 02 July 2015 02:37

baremma2002
Messages: 20
Registered: April 2013

Member

Dear DHS data users,

Here, I have a matter which is not solved yet. Could you help me in even if this question has already been asked?

I'm using the 2011-12 Côte d'Ivoire DHS data. My aim is to merge children's dataset and households members dataset in order to get the number of women who were underweight, normal and overweight or obese and then compute well the stunting prevalence among children under five according to the women's BMI. I don't happen to get the results of the report (page 179).

The code I used is:

// Household's members dataset
use "C:\CIPR61FL.dta", clear
rename V001 HV001
rename V002 HV002
rename V003 HVIDX
sort V001 V002 V003
save "C:\CIPR61FL_temp.dta", replace

// Children's recode dataset
use "C:\CIKR61FL.dta", clear
sort V001 V002 V003
save "C:\CIKR61FL_temp.dta", replace

// Merging

merge m:m V001 V002 V003 using "C:\CIPR61FL_temp.dta"
keep if _merge==3
save "C:\CIKR61FL_temp2.dta", replace

//Excluding children who didn't pass the last night in the household and who were not alive

drop if HV103==0
keep if B5==1

//Computing BMI

gen bmi=V445/100
gen bmic=1 if bmi<18.5
replace bmic=2 if bmi>=18.5 & bmi<25
replace bmic=3 if bmi>=25 & bmi!=.
drop if bmic==.
label define bmic 1"Underweight" 2"Normal" 3"Overweight/obese"
label values bmic bmic

// Excluding women with flagged height, those who were pregnant ou who were pregnant during the two previous months
gen b3_01=V211
gen ht_flag=0
replace ht_flag=1 if V438>9000
gen preg_flag=0
replace preg_flag=1 if V213==1
gen months_since_last_birth=V008-b3_01
gen recent_birth_flag=0
replace recent_birth_flag=1 if months_since_last_birth<=1
tab bmic if ht_flag==0 & preg_flag==0 & recent_birth_flag==0 [iweight=V005/1000000]

Here, I don't get the same results as in the DHS report. How can I correct this script? Is it the right way or do I have to merge children with only individuals (women) dataset?

Thank you.

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