California Health Interview Survey (CHIS)

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Contributed by Carl Ganz <carlganz@gmail.com>

The State of California’s edition of the National Health Interview Survey (NHIS), a regional healthcare survey for the nation’s largest state.

  • One adult, one teenage, and one child table, each with one row per sampled respondent.

  • A complex sample survey designed to generalize to the civilian non-institutionalized population of California.

  • Released annually since 2011, and biennially since 2001.

  • Administered by the UCLA Center for Health Policy Research.

Simplified Download and Importation

The R lodown package easily downloads and imports all available CHIS microdata by simply specifying "chis" with an output_dir = parameter in the lodown() function. Depending on your internet connection and computer processing speed, you might prefer to run this step overnight.

library(lodown)
lodown( "chis" , output_dir = file.path( path.expand( "~" ) , "CHIS" ) , 
    your_username = "username" , 
    your_password = "password" )

lodown also provides a catalog of available microdata extracts with the get_catalog() function. After requesting the CHIS catalog, you could pass a subsetted catalog through the lodown() function in order to download and import specific extracts (rather than all available extracts).

library(lodown)
# examine all available CHIS microdata files
chis_cat <-
    get_catalog( "chis" ,
        output_dir = file.path( path.expand( "~" ) , "CHIS" ) , 
        your_username = "username" , 
        your_password = "password" )

# 2014 only
chis_cat <- subset( chis_cat , year == 2014 )
# download the microdata to your local computer
lodown( "chis" , chis_cat , 
    your_username = "username" , 
    your_password = "password" )

Analysis Examples with the survey library

Construct a complex sample survey design:

options( survey.replicates.mse = TRUE )

library(survey)

child <- readRDS( file.path( path.expand( "~" ) , "CHIS" , "2014 child.rds" ) )

child$ak7_p1 <- child$ak10_p <- NA
child$agecat <- "1 - child"
child$no_usual_source_of_care <- as.numeric( child$cd1 == 2 )

# four-category srhs (excellent / very good / good / fair+poor)
child$hlthcat <- child$ca6_p1

# load adolescents ages 12-17
teen <- readRDS( file.path( path.expand( "~" ) , "CHIS" , "2014 teen.rds" ) )

teen$ak7_p1 <- teen$ak10_p <- NA
teen$agecat <- "2 - adolescent"
teen$no_usual_source_of_care <- as.numeric( teen$tf1 == 2 )

# four-category srhs (excellent / very good / good / fair+poor)
teen$hlthcat <- teen$tb1_p1

# load adults ages 18+
adult <- readRDS( file.path( path.expand( "~" ) , "CHIS" , "2014 adult.rds" ) )

adult$agecat <- ifelse( adult$srage_p1 >= 65 , "4 - senior" , "3 - adult" )
adult$no_usual_source_of_care <- as.numeric( adult$ah1 == 2 )

# four-category srhs (excellent / very good / good / fair+poor)
adult$hlthcat <- c( 1 , 2 , 3 , 4 , 4 )[ adult$ab1 ]

# construct a character vector with only the variables needed for the analysis
vars_to_keep <- 
    c( grep( "rakedw" , names( adult ) , value = TRUE ) , 
        'hlthcat' , 'agecat' , 'ak7_p1' , 'ak10_p' ,
        'povgwd_p' , 'no_usual_source_of_care' )

chis_df <- 
    rbind( 
        child[ vars_to_keep ] , 
        teen[ vars_to_keep ] , 
        adult[ vars_to_keep ] 
    )

# remove labelled classes
labelled_cols <- 
    sapply( 
        chis_df , 
        function( w ) class( w ) == 'labelled' 
    )

chis_df[ labelled_cols ] <- 
    sapply( 
        chis_df[ labelled_cols ] , 
        as.numeric
    )

chis_design <- 
    svrepdesign( 
        data = chis_df , 
        weights = ~ rakedw0 , 
        repweights = "rakedw[1-9]" , 
        type = "other" , 
        scale = 1 , 
        rscales = 1 , 
        mse = TRUE 
    )

Variable Recoding

Add new columns to the data set:

chis_design <- 
    update( 
        chis_design , 
        one = 1 ,
        hlthcat = 
            factor( hlthcat , 
                labels = c( 'excellent' , 'very good' , 'good' , 'fair or poor' ) 
            )
    )

Unweighted Counts

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

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

svyby( ~ one , ~ hlthcat , chis_design , unwtd.count )

Weighted Counts

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

svytotal( ~ one , chis_design )

svyby( ~ one , ~ hlthcat , chis_design , svytotal )

Descriptive Statistics

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

svymean( ~ povgwd_p , chis_design )

svyby( ~ povgwd_p , ~ hlthcat , chis_design , svymean )

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

svymean( ~ agecat , chis_design )

svyby( ~ agecat , ~ hlthcat , chis_design , svymean )

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

svytotal( ~ povgwd_p , chis_design )

svyby( ~ povgwd_p , ~ hlthcat , chis_design , svytotal )

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

svytotal( ~ agecat , chis_design )

svyby( ~ agecat , ~ hlthcat , chis_design , svytotal )

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

svyquantile( ~ povgwd_p , chis_design , 0.5 )

svyby( 
    ~ povgwd_p , 
    ~ hlthcat , 
    chis_design , 
    svyquantile , 
    0.5 ,
    ci = TRUE ,
    keep.var = TRUE 
)

Estimate a ratio:

svyratio( 
    numerator = ~ ak10_p , 
    denominator = ~ ak7_p1 , 
    chis_design ,
    na.rm = TRUE
)

Subsetting

Restrict the survey design to seniors:

sub_chis_design <- subset( chis_design , agecat == "4 - senior" )

Calculate the mean (average) of this subset:

svymean( ~ povgwd_p , sub_chis_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( ~ povgwd_p , chis_design )

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

grouped_result <-
    svyby( 
        ~ povgwd_p , 
        ~ hlthcat , 
        chis_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( chis_design )

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

svyvar( ~ povgwd_p , chis_design )

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

# SRS without replacement
svymean( ~ povgwd_p , chis_design , deff = TRUE )

# SRS with replacement
svymean( ~ povgwd_p , chis_design , deff = "replace" )

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

svyciprop( ~ no_usual_source_of_care , chis_design ,
    method = "likelihood" )

Regression Models and Tests of Association

Perform a design-based t-test:

svyttest( povgwd_p ~ no_usual_source_of_care , chis_design )

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

svychisq( 
    ~ no_usual_source_of_care + agecat , 
    chis_design 
)

Perform a survey-weighted generalized linear model:

glm_result <- 
    svyglm( 
        povgwd_p ~ no_usual_source_of_care + agecat , 
        chis_design 
    )

summary( glm_result )

0.12 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 CHIS users, this code replicates previously-presented examples:

library(srvyr)
chis_srvyr_design <- as_survey( chis_design )

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

chis_srvyr_design %>%
    summarize( mean = survey_mean( povgwd_p ) )

chis_srvyr_design %>%
    group_by( hlthcat ) %>%
    summarize( mean = survey_mean( povgwd_p ) )

Replication Example

The example below matches statistics and confidence intervals from this table pulled from the AskCHIS online table creator:

Match the bottom right weighted count:

stopifnot( round( coef( svytotal( ~ one , chis_design ) ) , -3 ) == 37582000 )

Compute the statistics and standard errors for excellent, very good, and good in the rightmost row:

( total_population_ex_vg_good <- svymean( ~ hlthcat , chis_design ) )

# confirm these match
stopifnot( 
    identical( 
        as.numeric( round( coef( total_population_ex_vg_good ) * 100 , 1 )[ 1:3 ] ) ,
        c( 23.2 , 31.4 , 28.4 )
    )
)

Compute the confidence intervals in the rightmost row:

( total_pop_ci <- confint( total_population_ex_vg_good , df = degf( chis_design ) ) )

# confirm these match
stopifnot(
    identical(
        as.numeric( 
            round( total_pop_ci * 100 , 1 )[ 1:3 , ] 
        ) ,
        c( 22.1 , 30.1 , 27.1 , 24.2 , 32.7 , 29.6 )
    )
)