In this example, you will use Stata to combine age subgroups and generate population estimates for high blood pressure (HBP) by sex and race/ethnicity for persons 20 years and older. The method outlined in this module uses a Stata data file with CPS population totals. The process for combining subgroups and calculating population estimates is then automated using the code outlined below.
Alternatively, you can use the CPS population totals located on the respective survey cycle NHANES web page (referred to in Key Concepts), plus the results from a syv:mean command and manually calculate population estimates within a spreadsheet. If you choose this option, you will need to define the age, race/ethnicity and gender subgroups of interest and calculate population totals within the spreadsheet on your own.
The program outlined in the following steps uses a command called parmest that saves a model fit as a dataset. If you do not have this command installed and run the program, it will report an error that the parmest command is not recognized. You will use Stata Help to locate, download and install this command. In Stata, open the Help menu in the top toolbar. Then select Search. In the dialog box, select the radio button next to "Search All", and enter "parmest" in the search field. In the results, click the result "dm65." If you do not have this command installed, you will see a brief description and "(Click here to install)" in blue on the right side of the window. Click it to install. (If you already have this command installed, you will get the complete Help file and list of options to use with the command.)
WARNING Do not install dm65_1. The program will generate errors if dm65_1 is installed instead of dm65. This is because the parmest command that is installed with dm65_1 does not restore the existing dataset (analysis_data) after the command parmest, (save filename) is used to save the most recently requested parameter estimates. If you accidentally installed or already installed dm65_1, go back to the search results and click dm65 and select the option to replace dm65_1 with dm65.
Remember that you need to define the SVYSET before using the SVY series of commands. The general format of this command is below:
svyset [w=weightvar], psu(psuvar) strata(stratavar) vce(variance method)
To define the survey design variables for your high blood pressure analysis, use the weight variable for four-years of MEC data (wtmec4yr), the PSU variable (sdmvpsu), and strata variable (sdmvstra) .The vce option specifies the method for calculating the variance and the default is "linearized" which is Taylor linearization. Here is the svyset command for four years of MEC data:
svyset [w= wtmec4yr], psu(sdmvpsu) strata(sdmvstra) vce(linearized)
You will need to create variables for age, race, standard weight, and high blood pressure. First, create a variable for the race/ethnicity groups in your analyses. Then, code the outcome variable as a dichotomous variable, where the absence of of the outcome is coded as 0 and the presence of the outcome is coded as 100. Using 100 will express the proportion as a percentage (e.g., 0.23 would be represented as 23). The dichotomous variable, hbp, is already coded as 2 for the absence of outcome and 1 for the presence of outcome, so it will need to be recoded as a new variable, hpbx. Here is the code for creating the variables:
| Variable | Code to generate variables |
|---|---|
| Race |
gen race =1 if
ridreth1 == 3 |
| High Blood Pressure |
gen hbpx=100 if
hbp==1 |
The STATA command, svy: mean, and additional STATA code will be used to generate population estimates. Similar to the svy: mean command used in Task 1, you will output the results to a STATA data file using the parmest command, as shown below. Population estimates will not be age-standardized so that they reflect the true population sampled.
quietly svy, subpop(condition): mean
var
estate size
parmest, saving("path\to\file", [option])
Use the prefix quietly before the svy command to suppress terminal output. Use the svy:mean command with the high blood pressure variable (hbpx) to estimate the prevalence of HBP. Use the subpop() option to select a subpopulation for analysis, rather than select the study population in the Stata program while preparing the data file. Use the estate size post estimation command to display subpopulation sizes. Use the parmest command with the saving option to create a new Stata dataset of the most recently requested parameter estimates.
quietly svy, subpop(if ridageyr >=20
& ridageyr < .): mean hbpx
estate size
parmest, saving("c:\NHANES\data\popmean1", replace)
The output data will be formatted so that it can merged in a later step.
use "c:\NHANES\data\popmean1", clear
gen riagendr=0
gen race=0
drop parm
save "c:\NHANES\data\popmean1", replace
In this step, you will combine appropriate CPS population totals across survey cycles AND across years of age to reflect the subpopulation of interest (i.e., those 20 and older by sex and race).
In this module, CPS population totals are supplied as a Stata dataset with values for: age (CTUTAGE) ranging from 0 to 85+ years ; gender (CTUTGNDR); race/ethnicity (CTUTRACE), where 1= non-Hispanic white, 2=non-Hispanic black, 3=Mexican American and 4=other; race/ethnicity (CTUTRETH), where 1=Mexican American, 2=non-Hispanic other, 3=non-Hispanic white, 4=non-Hispanic black, 5=other Hispanic; ethnicity (CTUTHISP) where 1=Hispanic and 2=non-Hispanic; survey cycle (CTUTSRVY); and the population total (CTUTPOPT). Appropriate age, race/ethnicity, and gender groups were created in a previous step.
The collapse command in Stata will be used to calculate CPS population totals for the sub-domains of interest (i.e., sex and race/ethnicity) for the subpopulation of interest (age 20 and older). In this case, no sample design factors or weights need to be used. Use the use command to load the Stata-supplied dataset (cpstot9902) to read the CPS population totals. The variable is CTUTPOPT (the population totals). Subgroup totals are output to another Stata dataset (poptot9902) for use in the next step. Nothing is printed. Use the collapse command to convert the current dataset into a dataset of population total sums for ages greater than or equal to 20 years. Use the save command to save the dataset.
use "C:\NHANES\data\cpstot9902.dta ", clear
collapse (sum) ctutpopt if ctutage >=20 & ctutage <.,
save "c:\NHANES\data\tot9902a", replace
In this step, you will multiply prevalence estimates with corresponding CPS population totals to estimate the total number of non-institutionalized U.S. citizens affected with HBP.
Note that the datasets produced in the previous steps (popmeans, poptot9902) were sorted on the sub-domain variables (riagendr, race) to be merged. After merging, the prevalence estimates output from the datasets are rounded.
use "c:\NHANES\data\popmeans", clear
merge riagendr race using "c:\NHANES\data\poptot9902.dta"
gen est=round(estimate,.01)
gen se=round(stderr,.01)
gen ll=round(min95,.01)
gen ul=round(max95,.01)
Then, percent prevalence estimates (est), as well as lower and upper 95% confidence limits (ul, ll), will be multiplied to the corresponding population total for that subgroup (ctutpopt).
gen popmean=(est/100)*ctutpopt
gen popl=(ll/100)*ctutpopt
gen popu=(ul/100)*ctutpopt
Results will be rounded, saved, and printed.
gen popmeanr=round(popmean,1000)
gen poplr=round(popl,1000)
gen popur=round(popu,1000)
gen poptot_r =round(ctutpopt,1000)
save "c:\NHANES\data\popmeans", replace
list riagendr race est se ll ul poptot_r popmeanr poplr popur, clean
Highlights from the output
include:
