----------------------------------
totpreg7m |      count  proportion
----------+-----------------------
        0 |       8507       .6712
        1 |       3015       .2379       1 * 3015  = 3015
        2 |       1036       .0818       2 * 1036  = 2072
        3 |      111.6       .0088       3 * 111.6 =  334.8
        4 |      1.093     8.6e-05       4 * 1.093 =    4.372
        5 |      3.547     2.8e-04       5 * 3.547 =   17.375
          | 
    Total |    1.3e+04           1           Sum   = 5443.907     
----------------------------------

stillbirths	totpreg7m
53.03819	5444.41

* Using Nepal DHS 2011
use "C:\Data\DHS_Stata\NPIR60FL.DTA" , clear
gen pregs = 0

forvalues i = 1/80 {
  gen cmc`i' = v017 + 80 - `i'
  gen event`i' = substr(vcal_1, `i', 1)
  gen type`i' = .
  replace type`i' = 1 if substr(vcal_1,`i',1) == "B"
  replace type`i' = 3 if substr(vcal_1,`i',1) == "T"
  replace type`i' = 2 if substr(vcal_1,`i',7) == "TPPPPPP" 
  replace pregs = pregs+1 if (substr(vcal_1,`i',1) == "B" | substr(vcal_1,`i',1) == "T")
}
* Drop cases with no pregnancies
drop if pregs == 0

* Decide what variables you want to keep first before the reshape, modify this list as you need to add extra variables.
keep caseid v001 v002 v003 v005 v008 v011 v013 v017 v018 v019 v021 v022 v023 cmc* event* type* 

* The reshape is really really really slow if you don't select variables and cases first, and will most likely fail otherwise.
reshape long cmc event type, i(caseid) j(ix)

lab def type 1 "Birth" 2 "Stillbirth" 3 "Miscarriage/abortion"
lab val type type
lab var type "Type of pregnancy"
lab var cmc "Century month code of event"
lab var event "Calendar event code"

* Set length of calendar to use
gen callen = v018 + 59
* If calendar is aligned right (as in original dataset), use the following:
gen beg = v018
gen end = callen
* If calendar is aligned left (as it is in some datasets), use the following:
*gen beg = 1
*gen end = 60

* Include only the five year period
keep if ix >= beg & ix <= end

* check the pregnancy types 
tab type [iw=v005/1000000]

* Note that this will not match the 5444 pregnancies of 7+ months as that includes twins.
* This file excludes twins, but i believe that is what you really need.

* keep only births and stillbirths
keep if type == 1 | type == 2

* sort by case identifiers and century month code of pregnancy end
sort v001 v002 v003 cmc
* save this file
save "pregnancies of 7 months or more.dta"

* Open birth history
use "C:\Data\DHS_Stata\NPBR60FL.DTA", clear
* Sort according to ID and CMC of birth
clonevar cmc = b3
sort v001 v002 v003 cmc
save "births.dta"

* Reopen the pregnancies files and merge in the twins
use "pregnancies of 7 months or more.dta",clear
merge 1:m v001 v002 v003 cmc using "births.dta", keep(master match) keepusing(b*)

tab type [iw=v005/1000000]

* Open the Nepal 2001 dataset
use "C:\Data\DHS_Stata\NPIR41FL.DTA", clear

* keep just a set of useful variables.  If you keep too many this process gets really really slow and may fail due to memory
keep caseid v001 v002 v003 v005 v008 v011 v013 v017 v018 v019 v021 v022 v023 ///
  idx92_* bord9_* b0_92_* b1_92_* b2_92_* b3_92_* b4_92_* b5_92_* b6_92_* b7_92_* ///
  b8_92_* b9_92_* b10_9_* b11_9_* b12_9_* b13_9_* b16_9_* s216_* s217_* s227_* s228_* s229_*

* rename variables for reshaping
rename *_0* *_*
  
* reshape the pregnancy history into a filee of pregnancies
reshape long idx92_ bord9_ b0_92_ b1_92_ b2_92_ b3_92_ b4_92_ b5_92_ b6_92_ b7_92_ ///
  b8_92_ b9_92_ b10_9_ b11_9_ b12_9_ b13_9_ b16_9_ s216_ s217_ s227_ s228_ s229_, i(caseid) j(i)

* drop empty entries that contain no data
drop if idx92_==.  

* rename variables to easier names
rename *_92_ *  
rename *_9_ *  
rename idx92_ idx
rename bord9_ bord
rename  s*_ s*
* rename the b variables to p variables as these are for pregnancies and not for births, 
* just to avoid confusion with the birth history variables.
rename b* p*

* Restrict to all pregnancies in the last 5 years
keep if p3 > v008-60

* Check the results of the pregnancies
tab s227 s216 [iw=v005/1000000],m

* There are a couple of cases that seem inconsistent here.  Here is a suggestion for editing them:
* This case looks to have died on the first day rather than being a still birth
replace s216=1 if s227==. & s216==2
* This case looks to have been misrecorded as born died rather than lost before full term
replace s216=3 if s227==3 & s216==2

* To keep just the pregnancies of 7+ months duration, use the following:
keep if s227 == . | s227 >= 7
* I would then recode s216 into "born alive" and "stillbirth" (all of the others).
recode s216 (1=1 "Live birth")(2/3=2 "Stillbirth"), gen(type)

keep if p3 >= v008-60

keep if p3 > v008-60

* Open the Nepal 2001 dataset
use "C:\Data\DHS_Stata\NPIR41FL.DTA", clear

* rename to facilitate looping
ren b?_92_0* b?_92_* 
ren b??_9_0* b??_9_* 
ren s2*_0* s2*_*

* Calculate still births in the last 5 years
gen stillbirths = 0
gen births = 0

forvalues i = 1/20 {
* 	Data fixes
* 	This case looks to have died on the first day rather than being a still birth
	replace s216_`i'=1 if s227_`i'==. & s216_`i'==2
* 	This case looks to have been misrecorded as born died rather than lost before full term
	replace s216_`i'=3 if s227_`i'==3 & s216_`i'==2

*   Restrict to 60 months preceding survey
    replace births = births+1 if b3_92_`i' > v008-60 & s216_`i'==1
    replace stillbirths = stillbirths+1 if b3_92_`i' > v008-60 & s227_`i' != . & s227_`i' >= 7
}
gen totpreg7m = births+stillbirths

tab births [iw=v005/1000000]
tab stillbirths [iw=v005/1000000]
tab totpreg7m [iw=v005/1000000]