Home » Topics » Unmet Need » Getting Unmet Need & Demand Satisfied Estimates to Match StatCompiler for 5 Different Surveys (Getting Unmet Need & Demand Satisfied Estimates to Match StatCompiler for 5 Different Surveys)
Getting Unmet Need & Demand Satisfied Estimates to Match StatCompiler for 5 Different Surveys [message #16217] |
Mon, 26 November 2018 17:28 |
cgreenba
Messages: 18 Registered: October 2017
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Dear all,
I am working on a report looking at demand satisfied among youth across different DHS surveys and am having a difficult time matching the new definition of unmet need and demand satisfied numbers that I am getting in Stata with the estimates in StatCompiler for a few of the surveys that I have included in my analysis: the 2011-12 Honduras DHS, the 2006 Nepal DHS, the 2006 Niger DHS, the 2010 Rwanda DHS, and the 2011 Uganda DHS. For the surveys that did not have the new unmet need variable (v626a), I used either the general or survey specific recoding of unmet need do file produced by Sarah Bradley - attached below - but am still getting different estimates.
Here are the estimates that I get in Stata:
Honduras 2011-12 - Unmet need (all women) 15-19: 4.8%
Honduras 2011-12 - Unmet need (all women) 20-24: 7.6%
Honduras 2011-12 - Unmet need (all women) 15-24: 6.1%
Honduras 2011-12 - Demand satisfied by modern methods (all women) 15-19: 66.8%
Honduras 2011-12 - Demand satisfied by modern methods (all women) 20-24: 75.0%
Honduras 2011-12 - Demand satisfied by modern methods (all women) 15-24: 72.2%
Nepal 2006 - Unmet need (all women) 15-19: 37.8%
Nepal 2006 - Unmet need (all women) 20-24: 33.2%
Nepal 2006 - Unmet need (all women) 15-24: 34.7%
Nepal 2006 - Demand satisfied by modern methods (all women) 15-19: 25.7%
Nepal 2006 - Demand satisfied by modern methods (all women) 20-24: 43.4%
Nepal 2006 - Demand satisfied by modern methods (all women) 15-24: 38.4%
Niger 2006 - Unmet need (all women) 15-19: 6.9%
Niger 2006 - Unmet need (all women) 20-24: 15.4%
Niger 2006 - Unmet need (all women) 15-24: 11.1%
Niger 2006 - Demand satisfied by modern methods (all women) 15-19: 10.6%
Niger 2006 - Demand satisfied by modern methods (all women) 20-24: 16.7%
Niger 2006 - Demand satisfied by modern methods (all women) 15-24: 15.0%
Rwanda 2010 - Unmet need (all women) 15-19: 1.0%
Rwanda 2010 - Unmet need (all women) 20-24: 7.3%
Rwanda 2010 - Unmet need (all women) 15-24: 4.0%
Rwanda 2010 - Demand satisfied by modern methods (all women) 15-19: 62.3%
Rwanda 2010 - Demand satisfied by modern methods (all women) 20-24: 69.1%
Rwanda 2010 - Demand satisfied by modern methods (all women) 15-24: 68.3%
Uganda 2011 - Unmet need (all women) 15-19: 7.8%
Uganda 2011 - Unmet need (all women) 20-24: 25.3%
Uganda 2011 - Unmet need (all women) 15-24: 15.5%
Uganda 2011 - Demand satisfied by modern methods (all women) 15-19: 41.5%
Uganda 2011 - Demand satisfied by modern methods (all women) 20-24: 41.8%
Uganda 2011 - Demand satisfied by modern methods (all women) 15-24: 41.7%
Here are the estimates that StatCompiler lists:
Honduras 2011-12 - Unmet need (all women) 15-19: 6.5%
Honduras 2011-12 - Unmet need (all women) 20-24: 9.7%
Honduras 2011-12 - Unmet need (all women) 15-24: 7.9%
Honduras 2011-12 - Demand satisfied by modern methods (all women) 15-19: 62.0%
Honduras 2011-12 - Demand satisfied by modern methods (all women) 20-24: 72.1%
Honduras 2011-12 - Demand satisfied by modern methods (all women) 15-24: 68.6%
Nepal 2006 - Unmet need (all women) 15-19: 12.2%
Nepal 2006 - Unmet need (all women) 20-24: 26.7%
Nepal 2006 - Unmet need (all women) 15-24: 18.7%
Nepal 2006 - Demand satisfied by modern methods (all women) 15-19: 25.9%
Nepal 2006 - Demand satisfied by modern methods (all women) 20-24: 43.6%
Nepal 2006 - Demand satisfied by modern methods (all women) 15-24: 38.5%
Niger 2006 - Unmet need (all women) 15-19: 6.9%
Niger 2006 - Unmet need (all women) 20-24: 15.2%
Niger 2006 - Unmet need (all women) 15-24: 11.0%
Niger 2006 - Demand satisfied by modern methods (all women) 15-19: 22.4%
Niger 2006 - Demand satisfied by modern methods (all women) 20-24: 35.0%
Niger 2006 - Demand satisfied by modern methods (all women) 15-24: 31.6%
Rwanda 2010 - Unmet need (all women) 15-19: 2.2%
Rwanda 2010 - Unmet need (all women) 20-24: 10.3%
Rwanda 2010 - Unmet need (all women) 15-24: 6.1%
Rwanda 2010 - Demand satisfied by modern methods (all women) 15-19: 45.3%
Rwanda 2010 - Demand satisfied by modern methods (all women) 20-24: 62.1%
Rwanda 2010 - Demand satisfied by modern methods (all women) 15-24: 59.8%
Uganda 2011 - Unmet need (all women) 15-19: 10.5%
Uganda 2011 - Unmet need (all women) 20-24: 27.5%
Uganda 2011 - Unmet need (all women) 15-24: 18.1%
Uganda 2011 - Demand satisfied by modern methods (all women) 15-19: 34.9%
Uganda 2011 - Demand satisfied by modern methods (all women) 20-24: 39.9%
Uganda 2011 - Demand satisfied by modern methods (all women) 15-24: 38.4%
Is anyone aware of why I might be seeing these discrepancies? Is StatCompiler using a different sample, even in the indicator marked all women? Do I need to used a different weighting scheme other than the sample weights provided? I tried to match using the earlier definition of unmet need at as well, but that did not work. Any help or information would be greatly appreciated!
Here is my code in case it helps:
gen indiv_weight = v005/1000000
gen modern_contraception=0
replace modern_contraception=1 if v313==3
replace modern_contraception=. if v313==.
gen any_contraception=0
replace any_contraception=1 if v313==3 | v313==1 | v313==2
replace any_contraception=. if v313==.
gen unmet_need=0
replace unmet_need=1 if v626a==1 | v626a==2
replace unmet_need=. if v626a==.
* OR: gen umet_need=unmettot
gen demand_satisfied=.
replace demand_satisfied=0 if unmet_need==1
replace demand_satisfied=0 if v313==0 | v313==1 | v313==2
replace demand_satisfied=1 if v313==3
replace demand_satisfied=. if unmet_need==0 & any_contraception==0 | unmet_need==. | modern_contraception==.
proportion unmet_need if v013==1 | v013==2 [pweight = indiv_weight]
proportion unmet_need if v013==1 [pweight = indiv_weight]
proportion unmet_need if v013==2 [pweight = indiv_weight]
proportion demand_satisfied if v013==1 | v013==2 [pweight = indiv_weight]
proportion demand_satisfied if v013==1 [pweight = indiv_weight]
proportion demand_satisfied if v013==2 [pweight = indiv_weight]
Thank you so much!
Best regards,
Charlotte
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Re: Getting Unmet Need & Demand Satisfied Estimates to Match StatCompiler for 5 Different Surveys [message #16367 is a reply to message #16323] |
Fri, 28 December 2018 10:02 |
cgreenba
Messages: 18 Registered: October 2017
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Hi Trevor,
I apologize for the delay, but thank you so much for this reply. I really appreciate it.The information that you provided is very helpful. I have just a few follow-up questions if you are able to respond.
1. Is there a list of surveys for which in the v626a variable, certain non-currently married women are classified as "not married and not sexually active" before checking whether they are pregnant or postpartum amenorrheic (as is the case with Honduras 2011-12, Rwanda 2010-11, Uganda 2011)? If I understand correctly, for these surveys, to make the v626a unmet need definition comparable to the v626a variable in other more recent surveys, we need to use the survey-specific code. Is this correct? Is there any documentation about this?
2. You state that you were able to match the unmet need numbers for Niger 2006 when you used the survey-specific code, but this survey is not listed in the .do file as one of the countries that require the survey-specific code. Is there an updated list of surveys for which the survey-specific code should be used as opposed to the general recode?
3. In both the general and survey-specific code for the revised unmet need definition, I noticed at the very end when the binary 'unmettot" variable is generated for the "unmet" variable, the code categorized both 98 "unmarried - EM sample or no data" and 99 "missing" as NOT having unmet need instead of as missing. This appears to match the number that are in StatCompiler, but I'm not sure why those two categories would be counted as not having unmet need rather than missing. To me, the "missing" category indicated that we do not know whether the respondent does or does not have unmet need and the "unmarried - EM sample or no data" category indicated that these women shouldn't be in the sample or weren't asked the unmet need questions. Is there a reason behind this? When calculating overall unmet need, should those categories be included in the denominator when there is not information on whether or not they have unmet need?
Any help on this would be greatly appreciated. Thank you so much!
Best,
Charlotte
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Re: Getting Unmet Need & Demand Satisfied Estimates to Match StatCompiler for 5 Different Surveys [message #17851 is a reply to message #16716] |
Wed, 26 June 2019 12:36 |
cgreenba
Messages: 18 Registered: October 2017
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Hi Trevor,
I know that this is coming back to you months later, but I want to follow up on the work that I was doing re. family planning indicators for a whole group of countries. After following your instructions carefully form earlier in this thread to use the unmet recode .do file for surveys before 2013 to get them all to match the new definition of unmet need, I had gotten all of our surveys to match the StatCompiler estimates.
Today, I was getting ready to sign off on the revisions to my organization's publication, part of which was done to make sure that FP estimates matched what was in StatCompiler, I happened to open up StatCompiler to look at a few countries, and I found that the numbers I have now don't always match StatCompiler. In fact, it looks like the estimates that are now in StatCompiler match the earlier estimates that I had for the 2010-2012 surveys in which I used v626a without running the recode .do file.
Can you please explain why the numbers in StatCompiler changed and whether the unmet need and demand satisfied estimates that I had produced using the recode file (and that matched the numbers in StatCompiler earlier this year) are correct?
Thank you so much.
Best,
Charlotte
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