Rapid Surveys System (RSS)

The standardized platform to answer time-sensitive questions about emerging and priority health issues.

One table with one row per AmeriSpeak or KnowledgePanel respondent.
A cross-sectional survey generalizing to the noninstitutionalized adult population of the U.S.
Releases expected four times per year.
Conducted by the National Center for Health Statistics at the Centers for Disease Control.

Please skim before you begin:

NCHS Rapid Surveys System (RSS): Round 1 Survey Description
Quality Profile, Rapid Surveys System Round 1
A haiku regarding this microdata:

# first response heroes
# question design thru publish
# time 'doxed by zeno

Download, Import, Preparation

Download and import the first round:

library(haven)

sas_url <- "https://www.cdc.gov/nchs/data/rss/rss1_puf_t1.sas7bdat"
    
rss_tbl <- read_sas( sas_url )

rss_df <- data.frame( rss_tbl )

names( rss_df ) <- tolower( names( rss_df ) )

rss_df[ , 'one' ] <- 1

Save Locally

Save the object at any point:

# rss_fn <- file.path( path.expand( "~" ) , "RSS" , "this_file.rds" )
# saveRDS( rss_df , file = rss_fn , compress = FALSE )

Load the same object:

# rss_df <- readRDS( rss_fn )

Survey Design Definition

Construct a complex sample survey design:

library(survey)

options( survey.lonely.psu = "adjust" )

rss_design <- 
    svydesign( 
        ~ p_psu , 
        strata = ~ p_strata , 
        data = rss_df , 
        weights = ~ weight_m1 , 
        nest = TRUE 
    )

Variable Recoding

Add new columns to the data set:

rss_design <- 
    
    update( 
        
        rss_design , 
        
        how_often_use_cleaner_purifier =
            factor(
                ven_use ,
                levels = c( -9:-6 , 0:3 ) ,
                labels = 
                    c( "Don't Know" , "Question not asked" , "Explicit refusal/REF" , 
                    "Skipped/Implied refusal" , "Never" , "Rarely" , "Sometimes" , "Always" )
            ) ,
        
        has_health_insurance = ifelse( p_insur >= 0 , p_insur , NA ) ,
        
        metropolitan = 
            factor( as.numeric( p_metro_r == 1 ) , levels = 0:1 , labels = c( 'No' , 'Yes' ) )
        
    )

Analysis Examples with the `survey` library

Unweighted Counts

Count the unweighted number of records in the survey sample, overall and by groups:

sum( weights( rss_design , "sampling" ) != 0 )

svyby( ~ one , ~ metropolitan , rss_design , unwtd.count )

Weighted Counts

Count the weighted size of the generalizable population, overall and by groups:

svytotal( ~ one , rss_design )

svyby( ~ one , ~ metropolitan , rss_design , svytotal )

Descriptive Statistics

Calculate the mean (average) of a linear variable, overall and by groups:

svymean( ~ p_hhsize_r , rss_design )

svyby( ~ p_hhsize_r , ~ metropolitan , rss_design , svymean )

Calculate the distribution of a categorical variable, overall and by groups:

svymean( ~ how_often_use_cleaner_purifier , rss_design )

svyby( ~ how_often_use_cleaner_purifier , ~ metropolitan , rss_design , svymean )

Calculate the sum of a linear variable, overall and by groups:

svytotal( ~ p_hhsize_r , rss_design )

svyby( ~ p_hhsize_r , ~ metropolitan , rss_design , svytotal )

Calculate the weighted sum of a categorical variable, overall and by groups:

svytotal( ~ how_often_use_cleaner_purifier , rss_design )

svyby( ~ how_often_use_cleaner_purifier , ~ metropolitan , rss_design , svytotal )

Calculate the median (50th percentile) of a linear variable, overall and by groups:

svyquantile( ~ p_hhsize_r , rss_design , 0.5 )

svyby( 
    ~ p_hhsize_r , 
    ~ metropolitan , 
    rss_design , 
    svyquantile , 
    0.5 ,
    ci = TRUE 
)

Estimate a ratio:

svyratio( 
    numerator = ~ p_agec_r , 
    denominator = ~ p_hhsize_r , 
    rss_design 
)

Subsetting

Restrict the survey design to adults that most of the time or always wear sunscreen:

sub_rss_design <- subset( rss_design , sun_useface >= 3 )

Calculate the mean (average) of this subset:

svymean( ~ p_hhsize_r , sub_rss_design )

Measures of Uncertainty

Extract the coefficient, standard error, confidence interval, and coefficient of variation from any descriptive statistics function result, overall and by groups:

this_result <- svymean( ~ p_hhsize_r , rss_design )

coef( this_result )
SE( this_result )
confint( this_result )
cv( this_result )

grouped_result <-
    svyby( 
        ~ p_hhsize_r , 
        ~ metropolitan , 
        rss_design , 
        svymean 
    )
    
coef( grouped_result )
SE( grouped_result )
confint( grouped_result )
cv( grouped_result )

Calculate the degrees of freedom of any survey design object:

degf( rss_design )

Calculate the complex sample survey-adjusted variance of any statistic:

svyvar( ~ p_hhsize_r , rss_design )

Include the complex sample design effect in the result for a specific statistic:

# SRS without replacement
svymean( ~ p_hhsize_r , rss_design , deff = TRUE )

# SRS with replacement
svymean( ~ p_hhsize_r , rss_design , deff = "replace" )

Compute confidence intervals for proportions using methods that may be more accurate near 0 and 1. See ?svyciprop for alternatives:

svyciprop( ~ has_health_insurance , rss_design ,
    method = "likelihood" , na.rm = TRUE )

Regression Models and Tests of Association

Perform a design-based t-test:

svyttest( p_hhsize_r ~ has_health_insurance , rss_design )

Perform a chi-squared test of association for survey data:

svychisq( 
    ~ has_health_insurance + how_often_use_cleaner_purifier , 
    rss_design 
)

Perform a survey-weighted generalized linear model:

glm_result <- 
    svyglm( 
        p_hhsize_r ~ has_health_insurance + how_often_use_cleaner_purifier , 
        rss_design 
    )

summary( glm_result )

Replication Example

This example matches the statistic and confidence intervals from the “Ever uses a portable air cleaner or purifier in home” page of the Air cleaners and purifiers dashboard:

result <-
    svymean(
        ~ as.numeric( ven_use > 0 ) ,
        subset( rss_design , ven_use >= 0 )
    )

stopifnot( round( coef( result ) , 3 ) == .379 )

stopifnot( round( confint( result )[1] , 3 ) == 0.366 )

stopifnot( round( confint( result )[2] , 3 ) == 0.393 )

Analysis Examples with `srvyr`

The R srvyr library calculates summary statistics from survey data, such as the mean, total or quantile using dplyr-like syntax. srvyr allows for the use of many verbs, such as summarize, group_by, and mutate, the convenience of pipe-able functions, the tidyverse style of non-standard evaluation and more consistent return types than the survey package. This vignette details the available features. As a starting point for RSS users, this code replicates previously-presented examples:

library(srvyr)
rss_srvyr_design <- as_survey( rss_design )