Task 2a: How to Estimate Mean Nutrient Intakes from Foods and Beverages Using SUDAAN

This section describes how to use SUDAAN to estimate mean nutrient intakes from food and beverages – that is, using only data on the dietary recalls – along with standard errors.  To illustrate this, consumption of calcium from foods and beverages by children ages 6-11 is used as an example. 

 

Step 1: Sort Data by Strata and PSU

Before running any SUDAAN procedure, sort the data by strata and PSU, using the PROC SORT procedure.  In the sample code below, CALCMILK is the dataset that was previously created for this analysis with the appropriate variables of interest.

 

Step 2: Compute Properly Weighted Estimated Means and Standard Errors

To compute properly weighted estimated means and standard errors, use the PROC DESCRIPT procedure in SUDAAN.  This procedure includes a required nest statement that identifies the variables for strata and PSU. 

In the sample code below, note that the sample weight being used is for the dietary recall Day 1 subsample (WTDRD1).  The subgroup statement indicates that the results will be reported by gender (RIAGENDR), which has two “levels” or categories (male and female).  DR1TCALC is a variable available from the NHANES dataset to represent total dietary calcium (i.e., from foods and beverages, not supplements).  The SUBPOPN statement identifies the subset of people that will be included in the analysis; INCOH is a variable name that signifies “in the cohort.”  In this case, these are children ages 6-11 with complete and reliable recall data.

 

Estimating Mean Calcium Intake from Foods and Beverages, in Milligrams

Sample Code

*-------------------------------------------------------------------------;
* Use the PROC SORT procedure to sort the data by strata and PSU;
*
;
* Use the PROC DESCRIPT procedure to estimate daily intake of calcium ;
* from all foods and beverages.
; ;
*-------------------------------------------------------------------------;

proc sort data =CALCMILK;
    by SDMVSTRA SDMVPSU;
run ;

proc descript data =CALCMILK;
    nest SDMVSTRA SDMVPSU;
    weight WTDRD1;
    subgroup RIAGENDR;
    levels 2 ;
    tables
RIAGENDR;
    var   DR1TCALC;
    subpopn INCOH= 1 ;
      rformat RIAGENDR GENDER. ;
    rtitle "Estimated daily intake of total dietary Calcium" ;
    rtitle < "children age 6-11, WWEIA, NHANES 2003-2004 - using SUDAAN" ;
run ;

 

Output of Program

Estimated daily intake of total dietary Calcium, children age 6-11, WWEIA, 
NHANES 2003-2004 - using SUDAAN 
 Number of observations read    :   9034    Weighted count :286222757   
Number of observations skipped :   1088         
(WEIGHT variable nonpositive)            
Observations in subpopulation  :    900    Weighted count: 23862559 
Denominator degrees of freedom :     15                                                             

 Variance Estimation Method: Taylor Series (WR)      
For Subpopulation: INCOH = 1           
Estimated daily intake of the calcium provided by the milk  
and the  total Calcium intake             
children age 6-11, WWEIA, NHANES 2003-2004 - using SUDAAN    
by: Variable, Gender - Adjudicated.    

 
---------------------------------------------------------------------------------- 
|                 |                  |                             |              | 
| Variable        |                  | Gender - Adjudicated        |              | 
|                 |                  | Total        | Male         | Female       |
---------------------------------------------------------------------------------- 
|                 |                  |              |              |              |
| Calcium (mg)    | Sample Size      |          900 |          422 |          478 |
|                 | Weighted Size    |  23862558.64 |  12341904.79 |  11520653.85 |
|                 | Total            | ************ | ************ | ************ | 
|                 | Lower 95% Limit  |              |              |              |
|                 |  Total           | ************ | ************ | ************ | 
|                 | Upper 95% Limit  |              |              |              |
|                 |  Total           | ************ | ************ | ************ | 
|                 | Mean             |      1030.56 |      1109.56 |       945.93 |
|                 | SE Mean          |        41.84 |        48.85 |        48.34 | 
|                 | Lower 95% Limit  |              |              |              | 
|                 |  Mean            |       941.38 |      1005.44 |       842.90 | 
|                 | Upper 95% Limit  |              |              |              | 
|                 |  Mean            |      1119.75 |      1213.68 |      1048.97 | 
---------------------------------------------------------------------------------  
 
 

 

Highlights from the output include:

 

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