The key variable you need to use is sh336k which holds the blood glucose level. The missing values come for a number of reasons:
1) Blood glucose is only collected in a subsample of households. All members in households not selected for the sample will have a missing value.
2) Blood glucose is only collected for women and men age 35-64. All members other than those age 35-64 will have a missing value.
3) Blood glucose is only collected for women and men age 35-64 who consent to being tested. All other will have missing data.
The values in sh336k are recoded into the 4 groups presented in the tables. sh336k contains values per decilitre (dl) rather than per litre as shown in the table, so the recoding is into the following groups: 0-38 = below normal, 39-60 = normal, 61-69 = prediabetic, 70-222 = elevated, other higher values are consider invalid as are excluded.
Below is some simple code in R for tabulating the data:
install.packages("foreign")
install.packages("survey")
install.packages("car")
library(foreign)
library(survey)
library(car)
dta <- read.dta("C:/Data/DHS_stata/NMPR60FL.dta", convert.factors = FALSE)
dta$bg<-factor(recode(dta$sh336k,"0:38='1 below normal';39:60='2 normal';61:69='3 prediabetic';70:222='4 elevated';else=NA"))
dta$sex <-factor(recode(dta$hv104,"1='1 Male';2='2 Female';else=NA"))
DHSdesign<-svydesign(id=dta$hv021, strata=dta$hv022, weights=dta$hv005/1000000, data=dta)
bg.table <- svytable(~sex+bg, DHSdesign)
bg.table
prop.table(bg.table,1)*100
margin.table(bg.table,1)
The output results should look like:
> bg.table
bg
sex 1 below normal 2 normal 3 prediabetic 4 elevated
1 Male 111.24863 967.97756 76.83102 65.28731
2 Female 79.05747 1570.28125 133.86968 89.82786
> prop.table(bg.table,1)*100
bg
sex 1 below normal 2 normal 3 prediabetic 4 elevated
1 Male 9.108702 79.255078 6.290692 5.345528
2 Female 4.220819 83.836137 7.147202 4.795842
> margin.table(bg.table,1)
sex
1 Male 2 Female
1221.345 1873.036
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