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Re: Final Estimates of Stunting [message #15086 is a reply to message #15071] Thu, 31 May 2018 07:05 Go to previous messageGo to previous message
Mlue
Messages: 92
Registered: February 2017
Location: North West
Senior Member
Hello Mayank,

Please see the code below - maybe it may help you.
You did not specify which dataset you're working with, I presume it's India DHS 2015-16... The code will work for most DHS surveys (if not all).

FYI:
I am not getting the total number of children as in the report, but the rates are the same...

I'm getting 219 796, but the report has 219 760 --- with difference 36 (I don't know where this is coming from)


************************************************************

Stata code

// USE "IAPR73FL" ON Stata

** CHILD NUTRITIONAL STATUS
** Table 10.1 Nutritional status of children

	**	Compiled by Mluleki Tsawe
	**	PhD student: University of the Western Cape, South Africa
	**	31 May 2018
	
***** INDIA DHS 2015-2016

clear all
set mem 1g
set matsize 800
set maxvar 10000
cd "..."
use "IAPR73FL", clear

**********************************

** WEIGHT VARIABLE
gen weight = hv005/1000000

********************************************************************************

** SURVEY SET
gen psu =    hv021
gen strata = hv022

svyset psu [pw = weight], strata(strata) vce(linearized)
*svydes

********************************************************************************

// RECODES & RENAMES

rename hc27 sex
rename hv270 wealth
rename hv025 residence
rename hv024 region
*rename shdist district

** CHILD AGE IN MONTHS
recode hc1 (0/5 = 1 "<6") (6/8 = 2 "6-8") (9/11 = 3 "9-11") (12/17 = 4 "12-17") ///
(18/23 = 5 "18-23") (24/35 = 6 "24-35") (36/47 = 7 "36-47") /// 
(48/59 = 8 "48-59"), gen(child_age)
label var child_age "Child age (months)"
label val child_age child_age

** CHILD AGE IN MONTHS 2
recode hc1 (0/4 = 1 "<5") (5/9 = 2 "5-9") (10/15 = 3 "10-15") (16/19 = 4 "16-19") ///
(20/25 = 5 "20-25") (26/35 = 6 "26-35") (36/49 = 7 "36-49") /// 
(50/59 = 8 "50-59"), gen(child_age2)
label var child_age2 "Child age in months"
label val child_age2 child_age2

** STATE/UNION TERRITORY
recode region (6 12/14 25 28/29 34 = 1 "North") (7 19 33 = 2 "Central") ///
(5 15 26 35 = 3 "East") (3/4 21/24 30 32 = 4 "North-East") ///
(8/11 20 = 5 "West") (1/2 16/18 27 31 36 = 6 "South"), gen(state_territory)
label var state_territory "State or union territory of India"
label val state_territory state_territory
*tab state_territory [iw=weight], m
*tab region state_territory [iw=weight], m

** WEALTH STATUS
recode wealth (1/2=1 "Poor") (3=2 "Middle") (4/5=3 "Rich"), gen(wealth_rec)
label var wealth_rec "Household wealth _ recode"
label val wealth_rec wealth_rec

********************************************************************************

// CHILD MALNUTRITION INDICATORS (according to WHO)

** STUNTING = Height-for-age
cap drop stunting
gen stunting=0 if hv103==1             
replace stunting=. if hc70>=9996 
replace stunting=1 if hc70<-200 & hv103==1
label define stunting 0"Not stunting" 1"Stunting"
label var stunting "Stunting children"
label val stunting stunting

** WASTING = Weight-for-height

gen wasting=0 if hv103==1 
replace wasting=. if hc72>=9996 
replace wasting=1 if hc72<-200 & hv103==1
label define wasting 0"Not wasting" 1"Wasting"
label var wasting "Wasting children"
label val wasting wasting

** UNDERWEIGHT = Weight-for-age

gen underweight=0 if hv103==1              
replace underweight=. if hc71>=9996 
replace underweight=1 if hc71<-200 & hv103==1
label define underweight 0"Not underweight" 1"Underweight"
label var underweight "Underweight children"
label val underweight underweight

********************************************************************************

** DROP IF NOT WITHIN SAMPLE
qui regr stunting underweight wasting if stunting !=. & underweight !=. & wasting !=. [pw=weight]		
drop if e(sample)!=1		/* drop observations with missings on any variable to be used in analysis */

********************************************************************************

** CHECK
svy: tab stunting, count format(%4.0f)
svy: tab wasting, count format(%4.0f)
svy: tab underweight, count format(%4.0f)

******************

svy: tab stunting, percent format(%4.1f)
svy: tab wasting, percent format(%4.1f)
svy: tab underweight, percent format(%4.1f)

********************************************************************************
** ========================================================================== **

** Table 10.2 Nutritional status of children by state/union territory **
cap drop zone
egen zone = group(state_territory region), label
tab zone

***************

tab zone stunting [iw=weight], row nof miss
tab zone wasting [iw=weight], row nof miss
tab zone underweight [iw=weight], row nof miss

/*
bys state_territory: tab region stunting [iw=weight], row nof miss
bys state_territory: tab region wasting [iw=weight], row nof miss
bys state_territory: tab region underweight [iw=weight], row nof miss
*/

***************

/*
svy: tab zone stunting, percent format(%4.1f) row miss
svy: tab zone wasting, percent format(%4.1f) row miss
svy: tab zone underweight, percent format(%4.1f) row miss
*/

********************************************************************************
** ========================================================================== **
********************************************************************************
** ========================================================================== **
********************************************************************************
** ========================================================================== **

exit

graph bar (mean) stunting [pweight = weight], over(residence) over(state_territory, label(angle(forty_five))) ///
asyvars blabel(bar, size(small) orientation(horizontal) format(%4.3f) gap(0.5)) ytitle(Average of child stunting) ///
ylabel(#10, labgap(medsmall)) title(Prevalence of stunting among children by residence) ///
subtitle(India Demographic and Health Survey) note(India (2015-16)) scheme(s2mono)

graph bar (mean) wasting [pweight = weight], over(residence) over(state_territory, label(angle(forty_five))) ///
asyvars blabel(bar, size(small) orientation(horizontal) format(%4.3f) gap(0.5)) ytitle(Average of child wasting) ///
ylabel(#10, labgap(medsmall)) title(Prevalence of wasting among children by residence) ///
subtitle(India Demographic and Health Survey) note(India (2015-16)) scheme(s2mono)

graph bar (mean) underweight [pweight = weight], over(residence) over(state_territory, label(angle(forty_five))) ///
asyvars blabel(bar, size(small) orientation(horizontal) format(%4.3f) gap(0.5)) ytitle(Average of child underweight) ///
ylabel(#10, labgap(medium)) title(Prevalence of being underweight among children by residence) ///
subtitle(India Demographic and Health Survey) note(India (2015-16)) scheme(s2mono)

********************************************************************************
** ========================================================================== **
********************************************************************************
** ========================================================================== **
********************************************************************************
** ========================================================================== **
 
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