Users are asked to specify the table they are trying to match. You are apparently referring to table 10.8.

The symptom of ARI that DHS surveys ask about is difficulty breathing that involves "the chest". In terms of h31c, these are categories 1 ("chest only") and 3 (" both [chest and nose]") . The denominator for table 10.8 is children in the KR file for whom h31c is 1 or 3. You will get the frequencies in the last column of table 10.8, for region, with this Stata command:

tab v024 if h31c==1 | h31c==3 [iweight=v005/1000000]

In the interpretation of the table, I would emphasize that it describes the prevalence of this symptom of ARI, but not a diagnosis of ARI.

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Thank you for your valuable guidance. I have also encountered difficulties in obtaining the same number of cases of acute respiratory infections (ARI) among under-five children in the Amhara region using the h31, h31b, and h31c variables from the KR file of the EDHS 2016. I followed the recommended steps, but the results do not match those in the final EDHS 2016 report. Could someone provide additional tips or common errors to check for in order to achieve consistent results?

Thank you in advance for your help!

the official rice purity test

We are not familiar with the term "special shifting". Perhaps you meant "spatial shifting"? If you are referring to the displacement of cluster locations, that is the same for every country and survey. SAR7 describes the displacement procedure.

The only raster datasets we produce are the modeled surfaces. You cannot merge raster data with DHS recode files, though you could extract the values of the raster data at the cluster locations (from the GE dataset) using a GIS software. The specific steps would vary depending on the software that is being used. The general steps though are included in the DHS covariate manual ( https://spatialdata.dhsprogram.com/references/DHS_Covariates _Extract_Data_Description_2.pdf), as we extract values from different raster datasets at the cluster locations to produce the GC files.

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How can I merge DHS data with raster data?

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I am conducting a study on births in Ethiopia, using b1 variable. How can I convert the dates into GC From Ethiopian calendar ]]>

Blessings!

Jeremy]]>

You do not have a problem. First, you seem to expect that the weighted and unweighted numbers of cases should be equal--or equivalently, the mean weight is 1--but that's not necessarily the case. I suggest that you open the KR file and enter these two Stata lines (or their equivalent in R):

summarize v005 summarize v005 if b16>0 & b16<.

Second, when you estimate a statistical model, at least in Stata, using pweights, Stata will automatically re-normalize the weights so that they have a mean of 1. That's a default that makes sense and that I don't think you could override, even if you wanted to. I expect that R does the same thing.

So, as I said, you do not have a problem.

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ARI_TRA1 <- svydesign(id=~V021, weights=~WGT,strata=~V023, nest=TRUE, survey.lonely.psu = "adjust", data=ARI1st)

However, the sum of the estimates in the weighted frequency calculation (9904.89) is not the same as the original sample size (8781). (Descriptive analysis of the alive children aged below 60 months). Why is this difference, please help. Stay Blessed! Stay Safe!

Unweighted frequency

ARI4th %>% freq_table(V024)

Result:-

Freq. %

0 1650.97 16.67

1 2129.04 21.49

2 2002.36 20.22

3 2168.62 21.89

4 1953.90 19.73

n_total 9904.89 100

weighted frequency

outp1 <- svytable(~Child_age,design=ARI_TRA1)

outp2 <- round(prop.table(svytable(~Child_age,design=ARI_TRA1))*100,d igits=2)

cbind(outp1,outp2)

Result:-

var cat n percent

1 Child_age 0 1443 16.43

2 Child_age 1 1821 20.74

3 Child_age 2 1815 20.67

4 Child_age 3 1923 21.90

5 Child_age 4 1779 20.26

n_total 8781 100.00

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At the bottom of this response, I will give lines to construct the wealth quintiles from the continuous wealth index. It is set up for a Nigeria survey--change the input file name for the Ethiopia 2016 survey or another survey. This program runs on the HR file. The quintiles are the same for everyone in the household, so when you have them in the HR file you can transfer them to the PR file by merging with hv001 hv002 or to the IR/BR/KR files by merging with v001 v002 or to the MR file by merging with mv001 mv002.

To construct wealth deciles you would use 10 instead of 5 in the "xtile" line.

You can read more about the urban/rural version of the wealth index in working paper 60 by Shea Rutstein: https://dhsprogram.com/pubs/pdf/wp60/wp60.pdf.

If you have hv271 and want to construct hv271a, the following lines will approximately accomplish this, using the PR file. The last line only works if hv271a is already in the data, and for such a file it confirms that the procedure returns a variable that is almost perfectly correlated with hv271a. It does not have the same mean and standard deviation as the official version of hv271a but it will produce the same quintiles.

summarize hv271 if hv025==1 [iweight=hv005/1000000] scalar mean1=r(mean) scalar sd1=r(sd) summarize hv271 if hv025==2 [iweight=hv005/1000000] scalar mean2=r(mean) scalar sd2=r(sd) gen hv271a_test=(hv271-mean1)/sd1 if hv025==1 replace hv271a_test=(hv271-mean2)/sd2 if hv025==2 correlate hv271a_test hv271a

The easiest way to do the urban/rural versions of the quintiles, hv270a, once you have hv271, would be to replace the "xtile" line with these two lines:

xtile hv270_urban=hv271a if hv025==1 [pweight=pwt], nquantiles(5) xtile hv270_rural=hv271a if hv025==2 [pweight=pwt], nquantiles(5)

For checking, you will get very close to a uniform distribution of quintiles within categories of hv025 if you enter the following line in the PR file:

tab hv270a hv025 [iweight=hv005/1000000] if hv102==1

I have to mention that in some surveys the commands given above and the routine given below will not exactly match the DHS wealth quintiles. I personally would not worry about that. I don't have a program to deal with those exceptions. It is often difficult, using the standard recode files, to reproduce exactly some of the variables calculated during the original data processing.

If you want to include the urban/rural version in a regression in Stata, just put i.hv270a, or i.v190a, on the right hand side of the regression. I would recommend always including hv025 or v025 whenever you include hv270a or v190a. (You don't need "i." with a variable that has only two categories.)

Here is the basic procedure to get the quintiles from the continuous index:

* Need a path to a workspace folder cd e:\DHS\programs\WEALTH * SIMPLE STATA CODE TO CONSTRUCT WEALTH QUINTILES THAT ALMOST MATCH DHS set more off use "C:\Users\26216\ICF\Analysis - Shared Resources\Data\DHSdata\NGHR7AFL.DTA" , clear keep hv001 hv002 hv005 hv012 hv013 hv270 hv271 gen mem = hv012 replace mem = hv013 if mem == 0 gen pwt=mem*hv005 gen wt=pwt/1000000 xtile hv270_test1=hv271 [pweight=pwt], nquantiles(5) tab hv270 hv270_test1 [iweight=wt]