Medical Expenditure Panel Survey (MEPS)

The Household Component captures person-level spending across service categories, coverage types.

The consolidated file contains one row per individual within each sampled household, other tables contain one record per event (like prescription fills, hospitalizations), per job, per insurance policy.
A complex sample survey designed to generalize to the U.S. civilian non-institutionalized population.
Released annually since 1996.
Administered by the Agency for Healthcare Research and Quality.

Please skim before you begin:

MEPS HC-224 2020 Full Year Consolidated Data File
Wikipedia Entry
A haiku regarding this microdata:

# king dumpty's horsemen
# ahrq stitches payors, bills, claims
# fractured health system

Function Definitions

Define a function to download, unzip, and import each sas file:

library(haven)

meps_sas_import <-
    function( this_url ){
        
        this_tf <- tempfile()
        
        download.file( this_url , this_tf , mode = 'wb' )
        
        this_tbl <- read_sas( this_tf )

        this_df <- data.frame( this_tbl )
        
        names( this_df ) <- tolower( names( this_df ) )
        
        this_df
    }

Download, Import, Preparation

Download and import the consolidated file and the replicate weights file:

meps_cons_df <-
    meps_sas_import( "https://meps.ahrq.gov/data_files/pufs/h224/h224v9.zip" )

meps_brr_df <-
    meps_sas_import( "https://meps.ahrq.gov/mepsweb/data_files/pufs/h036brr/h36brr20v9.zip" )

Merge the consolidated file with the replicate weights:

meps_df <- merge( meps_cons_df , meps_brr_df )

stopifnot( nrow( meps_df ) == nrow( meps_cons_df ) )

meps_df[ , 'one' ] <- 1

Save Locally

Save the object at any point:

# meps_fn <- file.path( path.expand( "~" ) , "MEPS" , "this_file.rds" )
# saveRDS( meps_df , file = meps_fn , compress = FALSE )

Load the same object:

# meps_df <- readRDS( meps_fn )

Survey Design Definition

Construct a complex sample survey design:

library(survey)

meps_design <-
    svrepdesign(
        data = meps_df ,
        weights = ~ perwt20f ,
        type = "BRR" ,
        combined.weights = FALSE ,
        repweights = "brr[1-9]+" ,
        mse = TRUE
    )

Variable Recoding

Add new columns to the data set:

meps_design <- 
    update( 
        meps_design , 
        
        one = 1 ,
        
        insured_december_31st = ifelse( ins20x %in% 1:2 , as.numeric( ins20x == 1 ) , NA )
        
    )

Analysis Examples with the `survey` library

Unweighted Counts

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

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

svyby( ~ one , ~ region20 , meps_design , unwtd.count )

Weighted Counts

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

svytotal( ~ one , meps_design )

svyby( ~ one , ~ region20 , meps_design , svytotal )

Descriptive Statistics

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

svymean( ~ totexp20 , meps_design )

svyby( ~ totexp20 , ~ region20 , meps_design , svymean )

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

svymean( ~ sex , meps_design )

svyby( ~ sex , ~ region20 , meps_design , svymean )

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

svytotal( ~ totexp20 , meps_design )

svyby( ~ totexp20 , ~ region20 , meps_design , svytotal )

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

svytotal( ~ sex , meps_design )

svyby( ~ sex , ~ region20 , meps_design , svytotal )

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

svyquantile( ~ totexp20 , meps_design , 0.5 )

svyby( 
    ~ totexp20 , 
    ~ region20 , 
    meps_design , 
    svyquantile , 
    0.5 ,
    ci = TRUE 
)

Estimate a ratio:

svyratio( 
    numerator = ~ totmcd20 , 
    denominator = ~ totexp20 , 
    meps_design 
)

Subsetting

Restrict the survey design to seniors:

sub_meps_design <- subset( meps_design , agelast >= 65 )

Calculate the mean (average) of this subset:

svymean( ~ totexp20 , sub_meps_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( ~ totexp20 , meps_design )

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

grouped_result <-
    svyby( 
        ~ totexp20 , 
        ~ region20 , 
        meps_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( meps_design )

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

svyvar( ~ totexp20 , meps_design )

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

# SRS without replacement
svymean( ~ totexp20 , meps_design , deff = TRUE )

# SRS with replacement
svymean( ~ totexp20 , meps_design , deff = "replace" )

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

svyciprop( ~ insured_december_31st , meps_design ,
    method = "likelihood" , na.rm = TRUE )

Regression Models and Tests of Association

Perform a design-based t-test:

svyttest( totexp20 ~ insured_december_31st , meps_design )

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

svychisq( 
    ~ insured_december_31st + sex , 
    meps_design 
)

Perform a survey-weighted generalized linear model:

glm_result <- 
    svyglm( 
        totexp20 ~ insured_december_31st + sex , 
        meps_design 
    )

summary( glm_result )

Replication Example

This example matches the statistic and standard error shown under Analysis of the Total Population:

library(foreign)

xport_2002_tf <- tempfile()

xport_2002_url <- "https://meps.ahrq.gov/data_files/pufs/h70ssp.zip"

download.file( xport_2002_url , xport_2002_tf , mode = 'wb' )

unzipped_2002_xport <- unzip( xport_2002_tf , exdir = tempdir() )

meps_2002_df <- read.xport( unzipped_2002_xport )

names( meps_2002_df ) <- tolower( names( meps_2002_df ) )

meps_2002_design <-
    svydesign(
        ~ varpsu ,
        strata = ~ varstr ,
        weights = ~ perwt02f ,
        data = meps_2002_df ,
        nest = TRUE
    )
            
result <- svymean( ~ totexp02 , meps_2002_design )
stopifnot( round( coef( result ) , 2 ) == 2813.24 )
stopifnot( round( SE( result ) , 2 ) == 58.99 )

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 MEPS users, this code replicates previously-presented examples:

library(srvyr)
meps_srvyr_design <- as_survey( meps_design )