Rapid Surveys System (RSS)

License: GPL v3

Github Actions Badge

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


Please skim before you begin:

  1. NCHS Rapid Surveys System (RSS): Round 1 Survey Description

  2. Quality Profile, Rapid Surveys System Round 1

  3. 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 )

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

rss_srvyr_design %>%
    summarize( mean = survey_mean( p_hhsize_r ) )

rss_srvyr_design %>%
    group_by( metropolitan ) %>%
    summarize( mean = survey_mean( p_hhsize_r ) )