National Immunization Survey (NIS)

Github Actions Badge

The vaccination coverage rate tracker for national, state, and selected local areas.

  • One table with one row per sampled toddler.

  • A complex sample survey designed to generalize to children aged 19-35 months in the United States.

  • Released annually since 1995, plus an adolescent (13-17 years) sample since 2008.

  • Administered by the Centers for Disease Control and Prevention.


Please skim before you begin:

  1. About NIS

  2. National Immunization Survey-Child: A User’s Guide for the 2021 Public-Use Data File

  3. This human-composed haiku or a bouquet of artificial intelligence-generated limericks

# i hear babies cry
# protesting lungs of iron
# a wonderful world

Download, Import, Preparation

Download the fixed-width file:

dat_tf <- tempfile()

dat_url <- "https://ftp.cdc.gov/pub/Vaccines_NIS/NISPUF21.DAT"

download.file( dat_url , dat_tf , mode = 'wb' )

Edit then execute the import script provided by the CDC:

library(Hmisc)

r_tf <- tempfile()

r_script_url <- "https://ftp.cdc.gov/pub/Vaccines_NIS/NISPUF21.R"

r_input_lines <- readLines( r_script_url )

# do not let the script do the save()
r_input_lines <- gsub( "^save\\(" , "# save(" , r_input_lines )

# redirect the path to the flat file to the local save location of `dat_tf`
r_input_lines <- gsub( '\\"path\\-to\\-file\\/(.*)\\.DAT\\"' , "dat_tf" , r_input_lines )

# save the edited script locally
writeLines( r_input_lines , r_tf )

# run the edited script
source( r_tf , echo = TRUE )

# rename the resultant data.frame object
nis_df <- NISPUF21

names( nis_df ) <- tolower( names( nis_df ) )

nis_df[ , 'one' ] <- 1

Save locally  

Save the object at any point:

# nis_fn <- file.path( path.expand( "~" ) , "NIS" , "this_file.rds" )
# saveRDS( nis_df , file = nis_fn , compress = FALSE )

Load the same object:

# nis_df <- readRDS( nis_fn )

Survey Design Definition

Construct a complex sample survey design:

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

library(survey)

nis_design <- 
    svydesign(
        id = ~ seqnumhh , 
        strata = ~ stratum , 
        weights = ~ provwt_c , 
        data = subset( nis_df , provwt_c > 0 ) 
    )

Variable Recoding

Add new columns to the data set:

nis_design <- 
    
    update( 
        
        nis_design , 
        
        first_fed_formula =
            ifelse( bf_formr20 %in% 888 , NA , bf_formr20 ) ,
        
        dtap_3p =

            as.numeric(

                ( p_numdah >= 3 ) |
                ( p_numdhi >= 3 ) |
                ( p_numdih >= 3 ) |
                ( p_numdta >= 3 ) |
                ( p_numdtp >= 3 )

            ) ,
        
        dtap_4p =

            as.numeric(

                ( p_numdah >= 4 ) |
                ( p_numdhi >= 4 ) |
                ( p_numdih >= 4 ) |
                ( p_numdta >= 4 ) |
                ( p_numdtp >= 4 )

            )
            
    )

Analysis Examples with the survey library  

Unweighted Counts

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

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

svyby( ~ one , ~ state , nis_design , unwtd.count )

Weighted Counts

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

svytotal( ~ one , nis_design )

svyby( ~ one , ~ state , nis_design , svytotal )

Descriptive Statistics

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

svymean( ~ first_fed_formula , nis_design , na.rm = TRUE )

svyby( ~ first_fed_formula , ~ state , nis_design , svymean , na.rm = TRUE )

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

svymean( ~ sex , nis_design , na.rm = TRUE )

svyby( ~ sex , ~ state , nis_design , svymean , na.rm = TRUE )

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

svytotal( ~ first_fed_formula , nis_design , na.rm = TRUE )

svyby( ~ first_fed_formula , ~ state , nis_design , svytotal , na.rm = TRUE )

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

svytotal( ~ sex , nis_design , na.rm = TRUE )

svyby( ~ sex , ~ state , nis_design , svytotal , na.rm = TRUE )

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

svyquantile( ~ first_fed_formula , nis_design , 0.5 , na.rm = TRUE )

svyby( 
    ~ first_fed_formula , 
    ~ state , 
    nis_design , 
    svyquantile , 
    0.5 ,
    ci = TRUE , na.rm = TRUE
)

Estimate a ratio:

svyratio( 
    numerator = ~ bf_exclr06 , 
    denominator = ~ bf_endr06 , 
    nis_design ,
    na.rm = TRUE
)

Subsetting

Restrict the survey design to toddlers up to date on polio shots:

sub_nis_design <- subset( nis_design , p_utdpol == 1 )

Calculate the mean (average) of this subset:

svymean( ~ first_fed_formula , sub_nis_design , na.rm = TRUE )

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( ~ first_fed_formula , nis_design , na.rm = TRUE )

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

grouped_result <-
    svyby( 
        ~ first_fed_formula , 
        ~ state , 
        nis_design , 
        svymean ,
        na.rm = TRUE 
    )
    
coef( grouped_result )
SE( grouped_result )
confint( grouped_result )
cv( grouped_result )

Calculate the degrees of freedom of any survey design object:

degf( nis_design )

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

svyvar( ~ first_fed_formula , nis_design , na.rm = TRUE )

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

# SRS without replacement
svymean( ~ first_fed_formula , nis_design , na.rm = TRUE , deff = TRUE )

# SRS with replacement
svymean( ~ first_fed_formula , nis_design , na.rm = TRUE , deff = "replace" )

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

svyciprop( ~ dtap_3p , nis_design ,
    method = "likelihood" )

Regression Models and Tests of Association

Perform a design-based t-test:

svyttest( first_fed_formula ~ dtap_3p , nis_design )

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

svychisq( 
    ~ dtap_3p + sex , 
    nis_design 
)

Perform a survey-weighted generalized linear model:

glm_result <- 
    svyglm( 
        first_fed_formula ~ dtap_3p + sex , 
        nis_design 
    )

summary( glm_result )

Replication Example

This example matches the statistics and standard errors from Data User’s Guide Table 4:

results <-
    svyby( 
        ~ p_utd431h314_rout_s , 
        ~ raceethk , 
        nis_design , 
        svymean
    )

coefficients <- results[ , "p_utd431h314_rout_sUTD" , drop = FALSE ]

standard_errors <- results[ , "se.p_utd431h314_rout_sUTD" , drop = FALSE ]

stopifnot( round( coefficients[ "HISPANIC" , ] , 3 ) == .711 )
stopifnot( round( coefficients[ "NON-HISPANIC WHITE ONLY" , ] , 3 ) == .742 )
stopifnot( round( coefficients[ "NON-HISPANIC BLACK ONLY" , ] , 3 ) == .647 )
stopifnot( round( standard_errors[ "HISPANIC" , ] , 3 ) == .015 )
stopifnot( round( standard_errors[ "NON-HISPANIC WHITE ONLY" , ] , 3 ) == .009 )
stopifnot( round( standard_errors[ "NON-HISPANIC BLACK ONLY" , ] , 3 ) == .022 )

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 NIS users, this code replicates previously-presented examples:

library(srvyr)
nis_srvyr_design <- as_survey( nis_design )

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

nis_srvyr_design %>%
    summarize( mean = survey_mean( first_fed_formula , na.rm = TRUE ) )

nis_srvyr_design %>%
    group_by( state ) %>%
    summarize( mean = survey_mean( first_fed_formula , na.rm = TRUE ) )