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About meps.hc

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The goal of meps.hc is to wrap the Annual Consolidated Data Files from the Medical Expenditure Panel Survey (meps) Household Component (hc) in an R data package.

All variable labels and value labels are included. Variable names are in lowercase.

Currently the package includes data from 2011-2014. The rest of the files (1996-2010) will be packaged soon.

References

All MEPS-Related R data packages:

Installation

# install.packages("devtools")
devtools::install_github("jjchern/meps.hc")

# To uninstall the package, use:
# remove.packages("meps.hc")

Usage

Load the datasets

# Load tibble via tidyverse for better printout
library(tidyverse)

meps.hc::f2014
# A tibble: 34,875 x 1,838
    duid   pid dupersid       panel famid31 famid42 famid53 famid14
   <dbl> <dbl>    <chr>      <fctr>  <fctr>  <fctr>  <fctr>  <fctr>
 1 40001   101 40001101 18 panel 18       A       A       A       A
 2 40001   102 40001102 18 panel 18       A       A       A       A
 3 40001   103 40001103 18 panel 18       A       A       A       A
 4 40001   104 40001104 18 panel 18       A       A       A       A
 5 40002   101 40002101 18 panel 18       A       A       A       A
 6 40004   101 40004101 18 panel 18       A       A       A       A
 7 40004   102 40004102 18 panel 18       A       A       A       A
 8 40004   103 40004103 18 panel 18       A       A       A       A
 9 40004   104 40004104 18 panel 18       A       A       A       A
10 40004   105 40004105 18 panel 18       A       A       A       A
# ... with 34,865 more rows, and 1830 more variables: famidyr <fctr>,
#   cpsfamid <fctr>, fcsz1231 <dbl>, fcrp1231 <fctr>, ruletr31 <fctr>,
#   ruletr42 <fctr>, ruletr53 <fctr>, ruletr14 <fctr>, rusize31 <fctr>,
#   rusize42 <fctr>, rusize53 <fctr>, rusize14 <fctr>, ruclas31 <fctr>,
#   ruclas42 <fctr>, ruclas53 <fctr>, ruclas14 <fctr>, famsze31 <fctr>,
#   famsze42 <fctr>, famsze53 <fctr>, famsze14 <dbl>, fmrs1231 <fctr>,
#   fams1231 <dbl>, famszeyr <dbl>, famrfpyr <fctr>, region31 <fctr>,
#   region42 <fctr>, region53 <fctr>, region14 <fctr>, refprs31 <dbl>,
#   refprs42 <dbl>, refprs53 <dbl>, refprs14 <dbl>, resp31 <fctr>,
#   resp42 <fctr>, resp53 <fctr>, resp14 <fctr>, proxy31 <fctr>,
#   proxy42 <fctr>, proxy53 <fctr>, proxy14 <fctr>, intvlang <fctr>,
#   begrfm31 <fctr>, begrfy31 <fctr>, endrfm31 <fctr>, endrfy31 <fctr>,
#   begrfm42 <fctr>, begrfy42 <fctr>, endrfm42 <fctr>, endrfy42 <fctr>,
#   begrfm53 <fctr>, begrfy53 <fctr>, endrfm53 <fctr>, endrfy53 <fctr>,
#   endrfm14 <fctr>, endrfy14 <fctr>, keyness <fctr>, inscop31 <fctr>,
#   inscop42 <fctr>, inscop53 <fctr>, inscop14 <fctr>, insc1231 <fctr>,
#   inscope <fctr>, elgrnd31 <fctr>, elgrnd42 <fctr>, elgrnd53 <fctr>,
#   elgrnd14 <fctr>, pstats31 <fctr>, pstats42 <fctr>, pstats53 <fctr>,
#   rurslt31 <fctr>, rurslt42 <fctr>, rurslt53 <fctr>, age31x <dbl>,
#   age42x <dbl>, age53x <dbl>, age14x <dbl>, agelast <dbl>, dobmm <fctr>,
#   dobyy <fctr>, sex <fctr>, racev1x <fctr>, racev2x <fctr>,
#   raceax <fctr>, racebx <fctr>, racewx <fctr>, racethx <fctr>,
#   hispanx <fctr>, hispncat <fctr>, marry31x <fctr>, marry42x <fctr>,
#   marry53x <fctr>, marry14x <fctr>, spouid31 <fctr>, spouid42 <fctr>,
#   spouid53 <fctr>, spouid14 <fctr>, spouin31 <fctr>, spouin42 <fctr>,
#   spouin53 <fctr>, spouin14 <fctr>, ...

Replicate Estimates from the MEPS Summary Table

This section replicates the first row of MEPS summary table for 2013 data:

Estimates

Standard Errors

The code are modified from

Show variable labels for selected variables

meps.hc::f2013 %>% 
    select(varpsu, varstr, perwt13f, totexp13,
           totslf13, totptr13, totmcr13, totmcd13,
           totva13, totwcp13, tototh13) %>% 
    labelled::var_label() %>% 
    enframe() %>% 
    unnest() %>% 
    knitr::kable()
name value
varpsu variance estimation psu - 2013 .
varstr variance estimation stratum - 2013
perwt13f final person weight, 2013
totexp13 total health care exp 13
totslf13 total amt paid by self/family 13
totptr13 total amt paid by prv & tri 13
totmcr13 total amt paid by medicare 13
totmcd13 total amt paid by medicaid 13
totva13 total amt paid by va/champva 13
totwcp13 total amt paid by workers comp 13
tototh13 total amt paid by oth combined 13

Estimate total healthcare expenditures in 2013

library(survey)

# 'adjust': center the stratum at the population mean 
# rather than the stratum mean
options(survey.lonely.psu = 'adjust')

mepsdsgn = svydesign(id = ~varpsu, # cluster ids
                     strata = ~varstr, 
                     weights = ~perwt13f, # sampling weights
                     data = meps.hc::f2013,
                     nest = TRUE) # enforce nesting within strata

# Replicate estimates reported in 
svytotal(~totexp13, design = mepsdsgn) %>% 
    as_tibble() %>% 
    transmute(`2013 US Tot. Exp. (in millions)` = total / 1000000,
              `Std. Err. (in millions)` = totexp13 / 1000000) %>% 
    knitr::kable()
2013 US Tot. Exp. (in millions) Std. Err. (in millions)
1400523 43378.01

Total US population in 2013

# Standard errors are not applicable to population control totals, so we don't need to use a survey function here.
# The total population is equal to the sum of survey weights (PERWT13F).
sum(meps.hc::f2013$perwt13f) %>% 
    as_tibble() %>% 
    transmute(`Population (in thousands)` = value / 1000,
              `Std. Err.` = "NA") %>% 
    knitr::kable(digits = 0)
Population (in thousands) Std. Err.
315722 NA

Percent of people with any expense in 2013

# To calculate the percent of people with any expense, first update mepsdsgn with a new indicator variable for persons with an expense:
update(mepsdsgn, any_expense = (totexp13 > 0) * 1) -> mepsdsgn

# Then run the 'svymean' function
svymean(~any_expense, design = mepsdsgn) %>% 
    as_tibble() %>% 
    transmute(`Percent with expense` = mean * 100,
              `Std. Err.` = any_expense * 100) %>% 
    knitr::kable(digits = 1)
Percent with expense Std. Err.
84.4 0.4

Mean and median expense per person in 2013

# To get expenses per person with an expense, use the 'subset' function to limit the dataset to persons that have an expense (i.e. any_expense == 1).

svymean(~totexp13, design = subset(mepsdsgn, any_expense == 1)) %>% 
    as_tibble() %>% 
    transmute(`Mean (per person with an expense)` = mean,
              `Std. Err.` = totexp13) %>% 
    knitr::kable(digits = 0)
Mean (per person with an expense) Std. Err.
5256 118
svyquantile(~totexp13, 
            design = subset(mepsdsgn, any_expense==1),
            quantiles = 0.5) %>%
    as_tibble() %>% 
    transmute(`Median (per person with an expense)` = `0.5`,
              `Std. Err.` = "NA") %>% 
    knitr::kable()
Median (per person with an expense) Std. Err.
1389 NA

Distribution by source of payment in 2013

# Before estimating percentages for 'Other' insurance, we need to adjust this variable to match the online table:
# Other = VA + worker's comp + other sources.
update(mepsdsgn, tototh13 = totva13 + totwcp13 + tototh13) -> mepsdsgn

# For percent of total, use the `svyratio` function, and specify the numerator and denominator.
# Use a '+' sign to calculate estimates for multiple variables.
svyratio(~totslf13 + totptr13 + totmcr13 + totmcd13 + tototh13,
         denominator = ~totexp13,
         design = mepsdsgn) %>% 
         {tibble(
             `Percent of total expenses by source of payment` = 
                         coef(.) %>% names(),
             `Estimates` = coef(.) * 100, 
             `Std. Err.` = SE(.) * 100
         )} %>% 
    knitr::kable(digits = 1)
Percent of total expenses by source of payment Estimates Std. Err.
totslf13/totexp13 13.8 0.4
totptr13/totexp13 40.6 1.1
totmcr13/totexp13 25.3 0.9
totmcd13/totexp13 12.4 0.7
tototh13/totexp13 7.9 0.5

Replicate Figure 1 in MEPS Statistical Brief #491

This section replicates Figure 1 in MEPS Statistical Brief #491

The code are modified from

Show variable labels for selected variables

meps.hc::f2013 %>% 
    select(varpsu, varstr, perwt13f, totexp13,
           obvexp13, optexp13, ertexp13,
           hhaexp13, hhnexp13, visexp13, othexp13,
           iptexp13, rxexp13, dvtexp13,
           agelast) %>% 
    labelled::var_label() %>% 
    enframe() %>% 
    unnest() %>% 
    knitr::kable()
name value
varpsu variance estimation psu - 2013 .
varstr variance estimation stratum - 2013
perwt13f final person weight, 2013
totexp13 total health care exp 13
obvexp13 total office-based exp 13
optexp13 total outpatient fac + dr exp 13
ertexp13 total er facility + dr exp 13
hhaexp13 total home health agency exp 13
hhnexp13 total home health non-agncy exp 13
visexp13 total glasses/contact lens exp 13
othexp13 tot other equip/sply (excl diab) exp 13
iptexp13 tot hosp ip facility + dr exp 13
rxexp13 total rx-exp 13
dvtexp13 total dental care exp 13
agelast person s age last time eligible

Estimate distribution of expense by TOS in 2013

# To get ambulatory (OB + OP) and home health/other expenditures, add variables to the mepsdsgn object.
mepsdsgn <- update(mepsdsgn,
                   ambexp13 = obvexp13 + optexp13 + ertexp13,
                   hhexp13  = hhaexp13 + hhnexp13 + visexp13 + othexp13)

# Use svyratio to calculate percentage distribution of spending by type of service:
pct_tos = svyratio(
    ~iptexp13 + ambexp13 + rxexp13 + dvtexp13 + hhexp13,
    denominator = ~totexp13,
    design = mepsdsgn
)

# Now do the same thing by age group (<65, 65+), using the `subset` function.
pct_tos_lt65 = svyratio(
    ~iptexp13 + ambexp13 + rxexp13 + dvtexp13 + hhexp13,
    denominator = ~totexp13,
    design = subset(mepsdsgn, agelast < 65)
)

pct_tos_ge65 = svyratio(
    ~iptexp13 + ambexp13 + rxexp13 + dvtexp13 + hhexp13,
    denominator = ~totexp13,
    design = subset(mepsdsgn, agelast >= 65)
)

# Combine all three tables
tibble(tos = names(coef(pct_tos)),
       pct = coef(pct_tos) * 100,
       age = "Total") -> total
    
tibble(tos = names(coef(pct_tos_lt65)),
       pct = coef(pct_tos_lt65) * 100,
       age = "<65 Years") -> lt65

tibble(tos = names(coef(pct_tos_ge65)),
       pct = coef(pct_tos_ge65) * 100,
       age = "65+ Years") -> ge65

bind_rows(total, lt65, ge65) %>% 
    mutate(tos = case_when(
        grepl("ipt", tos) ~ "1 Hospital IP",
        grepl("amb", tos) ~ "2 Ambulatory",
        grepl("rx" , tos) ~ "3 Rx",
        grepl("dvt", tos) ~ "4 Dental",
        grepl("hh" , tos) ~ "5 HH and other"
    )) -> df

knitr::kable(df)
tos pct age
1 Hospital IP 27.911022 Total
2 Ambulatory 37.882294 Total
3 Rx 21.977145 Total
4 Dental 6.556864 Total
5 HH and other 5.672677 Total
1 Hospital IP 25.795551 <65 Years
2 Ambulatory 40.464733 <65 Years
3 Rx 21.540182 <65 Years
4 Dental 7.848380 <65 Years
5 HH and other 4.351155 <65 Years
1 Hospital IP 32.383667 65+ Years
2 Ambulatory 32.422359 65+ Years
3 Rx 22.900994 65+ Years
4 Dental 3.826268 65+ Years
5 HH and other 8.466711 65+ Years

Create the plot

caption = "Note: Esimates are for the U.S. civilian
non-institutionalized population. Percentages may not add to exactly
100.0 due to rounding. Source: Center for Financing, Access, and Cost
Trends, AHRQ, Household Component of the Medical Expenditure Panel
Survey, 2013." %>% 
    {paste0(strwrap(., 130), sep="", collapse="\n")}

df %>% 
    ggplot(aes(x = tos, y = pct, fill = age)) +
    geom_col(position = "dodge") +
    scale_fill_brewer() +
    labs(y = "Percentage", x = "",
         title = "Percentage Distribution of Health Care Spending, by Type of Service, 2013",
         caption = caption) +
    geom_text(aes(label = round(pct)),
              position = position_dodge(width = 0.9),
              vjust = -0.25) +
    theme_classic() +
    theme(legend.position="top",
          legend.title = element_blank(),
          axis.line.x = element_line(colour = "black"),
          axis.line.y = element_line(colour = "black"),
          plot.caption = element_text(size = 8, hjust = 0)) +
    scale_y_continuous(expand = c(0,0),
                       limits = c(0, max(df$pct) + 2))

Analyze MEPS data using Stata

This section shows Stata code and outputs for analyzing MEPS data. The code are modified from HHS-AHQR/MEPS/Stata/exercise_8/.

Show variable labels for selected variables

meps.hc::f2014 %>% 
    select(totexp14, ipdexp14, ipfexp14, obvexp14, 
           rxexp14, opdexp14, opfexp14, dvtexp14, 
           erdexp14, erfexp14, hhaexp14, hhnexp14, 
           othexp14, visexp14, age14x, age42x, 
           age31x, varstr, varpsu, perwt14f,
           dupersid, duid, cpsfamid, famwt14c, 
           totslf14, ttlp14x, inscov14, povcat14,
           racethx) %>% 
    labelled::var_label() %>% 
    enframe() %>% 
    unnest() %>% 
    knitr::kable()
name value
totexp14 total health care exp 14
ipdexp14 totl hosp staz dr exp 14
ipfexp14 tot hosp ip facility exp-inc 0 nites 14
obvexp14 total office-based exp 14
rxexp14 total rx-exp 14
opdexp14 total outpatient provider exp 14
opfexp14 total outpatient facility exp 14
dvtexp14 total dental care exp 14
erdexp14 total emergency room dr exp 14
erfexp14 total er facility exp 14
hhaexp14 total home health agency exp 14
hhnexp14 total home health non-agncy exp 14
othexp14 tot other equip/sply (excl diab) exp 14
visexp14 total glasses/contact lens exp 14
age14x age as of 12/31/14 (edited/imputed)
age42x age - r4/2 (edited/imputed)
age31x age - r3/1 (edited/imputed)
varstr variance estimation stratum - 2014
varpsu variance estimation psu - 2014 .
perwt14f final person weight, 2014
dupersid person id (duid + pid)
duid dwelling unit id
cpsfamid cpsfamid
famwt14c pov adj family wgt-cps fam on 12/31/14
totslf14 total amt paid by self/family 14
ttlp14x person s total income
inscov14 health insurance coverage indicator 14
povcat14 family inc as % of poverty line - catego
racethx race/ethnicity (edited/imputed)

Save R rda file as Stata dta file

haven::write_dta(meps.hc::f2014, "README-files/meps_hc_2014.dta")
haven::write_dta(meps.hc::f2013, "README-files/meps_hc_2013.dta")

Estimate the total healthcare expenditures in 2014

. set more off

. loc vars dupersid perwt14f varpsu varstr totexp14

. u `vars' using "README-files/meps_hc_2014.dta", clear

. svyset varpsu [pweight = perwt14f], str(varstr)

      pweight: perwt14f
          VCE: linearized
  Single unit: missing
     Strata 1: varstr
         SU 1: varpsu
        FPC 1: <zero>

. svy: total totexp14
(running total on estimation sample)

Survey: Total estimation

Number of strata =     165      Number of obs   =       34,875
Number of PSUs   =     366      Population size =  318,440,423
                                Design df       =          201

--------------------------------------------------------------
             |             Linearized
             |      Total   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    totexp14 |   1.50e+12   4.98e+10      1.40e+12    1.60e+12
--------------------------------------------------------------

Estimate national expenses by type of service in 2014

. set more off

. loc vars totexp14 ipdexp14 ipfexp14 obvexp14 rxexp14 opdexp14 opfexp14 dvtexp
> 14 erdexp14 erfexp14 hhaexp14 hhnexp14 othexp14 visexp14 age14x age42x age31x
>  varstr varpsu perwt14f 

. u `vars' using "README-files/meps_hc_2014.dta", clear

. 
. * define expenditure variables by type of service
. gen total                = totexp14

. gen hospital_inpatient   = ipdexp14 + ipfexp14

. gen ambulatory           = obvexp14 + opdexp14 + opfexp14 + erdexp14 + erfexp
> 14

. gen prescribed_medicines = rxexp14

. gen dental               = dvtexp14

. gen home_health_other    = hhaexp14 + hhnexp14 + othexp14 + visexp14

. gen diff                 = total-hospital_inpatient - ambulatory   - prescrib
> ed_medicines - dental - home_health_other

. 
. * create flag (1/0) variables for persons with an expense, by type of service
. loc exp_vars total hospital_inpatient ambulatory prescribed_medicines dental 
> home_health_other

. foreach var in `exp_vars' {
  2.     gen x_`var' = (`var' > 0)
  3. }

. 
. * create a summary variable from end of year, 42, and 31 variables
. gen age = age14x if age14x >= 0
(209 missing values generated)

. replace age = age42x if age42x >= 0 & missing(age)
(136 real changes made)

. replace age = age31x if age31x >= 0 & missing(age)
(73 real changes made)

. 
. gen agecat = 1 if age >= 0 & age <= 64
(4,105 missing values generated)

. replace agecat = 2 if age>64
(4,105 real changes made)

. 
. * qc check on new variables --------------
. tab1 x_total x_hospital_inpatient  x_ambulatory  x_prescribed_medicines  x_de
> ntal  x_home_health_other

-> tabulation of x_total  

    x_total |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      7,225       20.72       20.72
          1 |     27,650       79.28      100.00
------------+-----------------------------------
      Total |     34,875      100.00

-> tabulation of x_hospital_inpatient  

x_hospital_ |
  inpatient |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     32,682       93.71       93.71
          1 |      2,193        6.29      100.00
------------+-----------------------------------
      Total |     34,875      100.00

-> tabulation of x_ambulatory  

x_ambulator |
          y |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     10,531       30.20       30.20
          1 |     24,344       69.80      100.00
------------+-----------------------------------
      Total |     34,875      100.00

-> tabulation of x_prescribed_medicines  

x_prescribe |
d_medicines |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     15,948       45.73       45.73
          1 |     18,927       54.27      100.00
------------+-----------------------------------
      Total |     34,875      100.00

-> tabulation of x_dental  

   x_dental |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     22,831       65.47       65.47
          1 |     12,044       34.53      100.00
------------+-----------------------------------
      Total |     34,875      100.00

-> tabulation of x_home_health_other  

x_home_heal |
   th_other |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     28,936       82.97       82.97
          1 |      5,939       17.03      100.00
------------+-----------------------------------
      Total |     34,875      100.00

. sum total if total>0

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
       total |     27,650    4938.678    14269.34          1     491858

. sum hospital_inpatient if hospital_inpatient>0

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
hospital_i~t |      2,193    17536.95    27716.47          3     414748

. sum ambulatory if ambulatory>0

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  ambulatory |     24,344    2069.085    6894.936          1     488881

. sum prescribed_medicines if prescribed_medicines>0

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
prescribed~s |     18,927    1608.024    5659.345          1     269756

. sum dental if dental>0

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      dental |     12,044    654.5071    1349.928          3      38432

. sum home_health_other if home_health_other>0

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
home_healt~r |      5,939    1584.149    6493.048          4     172462

. 
. list age age14x age42x age31x in 1/20, table

     +--------------------------------+
     | age   age14x   age42x   age31x |
     |--------------------------------|
  1. |  36       36       36       35 |
  2. |  36       36       36       36 |
  3. |  15       15       14       14 |
  4. |   8        8        8        7 |
  5. |  85       85       85       85 |
     |--------------------------------|
  6. |  34       34       33       33 |
  7. |  32       32       31       31 |
  8. |  15       15       14       14 |
  9. |  11       11       10       10 |
 10. |   9        9        8        8 |
     |--------------------------------|
 11. |   4        4        4        3 |
 12. |   8        8        8        7 |
 13. |  20       -1       20       19 |
 14. |  79       79       78       78 |
 15. |  47       47       46       46 |
     |--------------------------------|
 16. |  35       35       35       34 |
 17. |  36       36       36       36 |
 18. |  11       11       11       10 |
 19. |   7        7        7        7 |
 20. |  26       26       25       25 |
     +--------------------------------+

. 
. tab agecat

     agecat |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |     30,770       88.23       88.23
          2 |      4,105       11.77      100.00
------------+-----------------------------------
      Total |     34,875      100.00

. sum age if age>64

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         age |      4,105    73.80682     6.66308         65         85

. 
. * identify the survey design characteristics
. svyset [pweight = perwt14f], strata(varstr) psu(varpsu) vce(linearized) singl
> eunit(missing)

      pweight: perwt14f
          VCE: linearized
  Single unit: missing
     Strata 1: varstr
         SU 1: varpsu
        FPC 1: <zero>

. 
. // percentage distribution of expenses by type of service (stat brief #491 fi
> gure 1)
. svy: ratio (hospital_inpatient: hospital_inpatient / total) ///
>            (ambulatory: ambulatory / total) ///
>            (prescribed_medicines: prescribed_medicines / total) ///
>            (dental: dental / total) ///
>            (home_health_other: home_health_other / total)
(running ratio on estimation sample)

Survey: Ratio estimation

Number of strata =     165      Number of obs   =       34,875
Number of PSUs   =     366      Population size =  318,440,423
                                Design df       =          201

 hospital_i~t: hospital_inpatient/total
   ambulatory: ambulatory/total
 prescribed~s: prescribed_medicines/total
       dental: dental/total
 home_healt~r: home_health_other/total

----------------------------------------------------------------------
                     |             Linearized
                     |      Ratio   Std. Err.     [95% Conf. Interval]
---------------------+------------------------------------------------
  hospital_inpatient |    .254729   .0117416      .2315764    .2778816
          ambulatory |   .3850812   .0092253      .3668904    .4032719
prescribed_medicines |   .2335445   .0095548      .2147041    .2523849
              dental |   .0612787   .0021079      .0571223    .0654352
   home_health_other |   .0653674   .0056807       .054166    .0765687
----------------------------------------------------------------------

.            
. // percentage of persons with an expense, by type of service
. svy: mean x_total x_hospital_inpatient x_ambulatory x_prescribed_medicines x_
> dental x_home_health_other
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =     165      Number of obs   =       34,875
Number of PSUs   =     366      Population size =  318,440,423
                                Design df       =          201

------------------------------------------------------------------------
                       |             Linearized
                       |       Mean   Std. Err.     [95% Conf. Interval]
-----------------------+------------------------------------------------
               x_total |   .8512277   .0035374      .8442524    .8582029
  x_hospital_inpatient |   .0657779   .0021471      .0615441    .0700117
          x_ambulatory |   .7590857   .0043806      .7504479    .7677235
x_prescribed_medicines |   .6135182   .0049526      .6037524    .6232839
              x_dental |   .4143585   .0059531      .4026199     .426097
   x_home_health_other |   .2040905   .0042286      .1957524    .2124287
------------------------------------------------------------------------

.            
. // mean expense per person with an expense, by type of service     
. svy, subpop(x_total): mean total
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =     165      Number of obs   =       34,875
Number of PSUs   =     366      Population size =  318,440,423
                                Subpop. no. obs =       26,665
                                Subpop. size    =  271,065,295
                                Design df       =          201

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       total |   5531.379   137.9266       5259.41    5803.347
--------------------------------------------------------------

. svy, subpop(x_hospital_inpatient): mean hospital_inpatient
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =     165      Number of obs   =       34,875
Number of PSUs   =     366      Population size =  318,440,423
                                Subpop. no. obs =        2,081
                                Subpop. size    = 20,946,346.5
                                Design df       =          201

--------------------------------------------------------------------
                   |             Linearized
                   |       Mean   Std. Err.     [95% Conf. Interval]
-------------------+------------------------------------------------
hospital_inpatient |   18233.81   857.9769      16542.02     19925.6
--------------------------------------------------------------------

. svy, subpop(x_ambulatory): mean ambulatory
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =     165      Number of obs   =       34,875
Number of PSUs   =     366      Population size =  318,440,423
                                Subpop. no. obs =       23,536
                                Subpop. size    =  241,723,564
                                Design df       =          201

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  ambulatory |   2388.585   62.53984      2265.266    2511.903
--------------------------------------------------------------

. svy, subpop(x_prescribed_medicines): mean prescribed_medicines
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =     165      Number of obs   =       34,875
Number of PSUs   =     366      Population size =  318,440,423
                                Subpop. no. obs =       18,311
                                Subpop. size    =  195,368,984
                                Design df       =          201

----------------------------------------------------------------------
                     |             Linearized
                     |       Mean   Std. Err.     [95% Conf. Interval]
---------------------+------------------------------------------------
prescribed_medicines |   1792.344   83.78614      1627.131    1957.556
----------------------------------------------------------------------

. svy, subpop(x_dental): mean dental
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =     165      Number of obs   =       34,875
Number of PSUs   =     366      Population size =  318,440,423
                                Subpop. no. obs =       11,775
                                Subpop. size    =  131,948,483
                                Design df       =          201

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
      dental |   696.3259    15.4452      665.8705    726.7813
--------------------------------------------------------------

. svy, subpop(x_home_health_other): mean home_health_other
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =     165      Number of obs   =       34,875
Number of PSUs   =     366      Population size =  318,440,423
                                Subpop. no. obs =        5,761
                                Subpop. size    =   64,990,677
                                Design df       =          201

-------------------------------------------------------------------
                  |             Linearized
                  |       Mean   Std. Err.     [95% Conf. Interval]
------------------+------------------------------------------------
home_health_other |   1508.055   139.4998      1232.984    1783.126
-------------------------------------------------------------------

. 
. // mean expense per person with an expense, by type of service and age catego
> ry
. svy, subpop(x_total): mean total, over(agecat)
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =     165      Number of obs   =       34,875
Number of PSUs   =     366      Population size =  318,440,423
                                Subpop. no. obs =       26,665
                                Subpop. size    =  271,065,295
                                Design df       =          201

            1: agecat = 1
            2: agecat = 2

--------------------------------------------------------------
             |             Linearized
        Over |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
total        |
           1 |   4429.921   149.9609      4134.223     4725.62
           2 |      10890   302.6142      10293.29     11486.7
--------------------------------------------------------------

. svy, subpop(x_hospital_inpatient): mean hospital_inpatient, over(agecat)
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =     165      Number of obs   =       34,875
Number of PSUs   =     366      Population size =  318,440,423
                                Subpop. no. obs =        2,081
                                Subpop. size    = 20,946,346.5
                                Design df       =          201

            1: agecat = 1
            2: agecat = 2

--------------------------------------------------------------------
                   |             Linearized
              Over |       Mean   Std. Err.     [95% Conf. Interval]
-------------------+------------------------------------------------
hospital_inpatient |
                 1 |   17663.08   1263.521      15171.63    20154.54
                 2 |   19191.14    1131.91       16959.2    21423.08
--------------------------------------------------------------------

. svy, subpop(x_ambulatory): mean ambulatory, over(agecat)
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =     165      Number of obs   =       34,875
Number of PSUs   =     366      Population size =  318,440,423
                                Subpop. no. obs =       23,536
                                Subpop. size    =  241,723,564
                                Design df       =          201

            1: agecat = 1
            2: agecat = 2

--------------------------------------------------------------
             |             Linearized
        Over |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
ambulatory   |
           1 |    2055.61   66.80712      1923.877    2187.343
           2 |   3859.418   168.7558      3526.659    4192.177
--------------------------------------------------------------

. svy, subpop(x_prescribed_medicines): mean prescribed_medicines, over(agecat)
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =     165      Number of obs   =       34,875
Number of PSUs   =     366      Population size =  318,440,423
                                Subpop. no. obs =       18,311
                                Subpop. size    =  195,368,984
                                Design df       =          201

            1: agecat = 1
            2: agecat = 2

----------------------------------------------------------------------
                     |             Linearized
                Over |       Mean   Std. Err.     [95% Conf. Interval]
---------------------+------------------------------------------------
prescribed_medicines |
                   1 |   1560.906   106.3327      1351.236    1770.577
                   2 |   2602.126   100.7413       2403.48    2800.771
----------------------------------------------------------------------

. svy, subpop(x_dental): mean dental, over(agecat)
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =     165      Number of obs   =       34,875
Number of PSUs   =     366      Population size =  318,440,423
                                Subpop. no. obs =       11,775
                                Subpop. size    =  131,948,483
                                Design df       =          201

            1: agecat = 1
            2: agecat = 2

--------------------------------------------------------------
             |             Linearized
        Over |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
dental       |
           1 |     657.05   17.34416      622.8501    691.2498
           2 |   892.5962   44.61207      804.6285    980.5639
--------------------------------------------------------------

. svy, subpop(x_home_health_other): mean home_health_other, over(agecat)
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =     165      Number of obs   =       34,875
Number of PSUs   =     366      Population size =  318,440,423
                                Subpop. no. obs =        5,761
                                Subpop. size    =   64,990,677
                                Design df       =          201

            1: agecat = 1
            2: agecat = 2

-------------------------------------------------------------------
                  |             Linearized
             Over |       Mean   Std. Err.     [95% Conf. Interval]
------------------+------------------------------------------------
home_health_other |
                1 |   1025.308    161.522      706.8131    1343.803
                2 |   2923.966   209.7328      2510.407    3337.524
-------------------------------------------------------------------

Expenditures and utilization of antipsychotics

This subsection replicates figures from statistical brief #275

Construct family-level estimates

This exercise illustrates how to construct family level variables from person level data.

There are two definitions of family unit in MEPS:

CPS Family: ID is DUID + CPSFAMID. Corresponding weight is FAMWT14C. MEPS Family: ID is DUID + FAMIDYR. Corresponding weight is FAMWT14F. The CPS family is used in this exercise.

. set more off

. loc vars dupersid duid cpsfamid famwt14c varstr varpsu totslf14 ttlp14x

. use `vars' using "README-files/meps_hc_2014.dta", clear

. 
. sort duid cpsfamid

. list duid cpsfamid totslf14 ttlp14x in 1/20, sepby(duid)

     +---------------------------------------+
     |  duid   cpsfamid   totslf14   ttlp14x |
     |---------------------------------------|
  1. | 40001          A        135         0 |
  2. | 40001          A         89         0 |
  3. | 40001          A        165     12000 |
  4. | 40001          A        152     56000 |
     |---------------------------------------|
  5. | 40002          A          2     27030 |
     |---------------------------------------|
  6. | 40004          A         80         0 |
  7. | 40004          A        141         0 |
  8. | 40004          A          0         0 |
  9. | 40004          A          0     12000 |
 10. | 40004          A         12         0 |
 11. | 40004          A          0         0 |
 12. | 40004          A        159         0 |
 13. | 40004          A          0         0 |
     |---------------------------------------|
 14. | 40005          A       2919     39945 |
     |---------------------------------------|
 15. | 40009          A         52     34031 |
     |---------------------------------------|
 16. | 40010          A        210         0 |
 17. | 40010          A        614     84000 |
 18. | 40010          A         56         0 |
 19. | 40010          A         40     56000 |
     |---------------------------------------|
 20. | 40011          A          0         0 |
     +---------------------------------------+

. 
. by duid cpsfamid: egen famoop = sum(totslf14)

. by duid cpsfamid: egen faminc = sum(ttlp14x)

. by duid cpsfamid: gen  famsize = _N

. 
. list duid famwt14c famsize famoop faminc totslf14 ttlp14x in 1/20, sepby(duid
> )

     +--------------------------------------------------------------------+
     |  duid    famwt14c   famsize   famoop   faminc   totslf14   ttlp14x |
     |--------------------------------------------------------------------|
  1. | 40001    6540.855         4      541    68000        135         0 |
  2. | 40001    6540.855         4      541    68000         89         0 |
  3. | 40001    6540.855         4      541    68000        165     12000 |
  4. | 40001    6540.855         4      541    68000        152     56000 |
     |--------------------------------------------------------------------|
  5. | 40002   8827.9997         1        2    27030          2     27030 |
     |--------------------------------------------------------------------|
  6. | 40004   3883.9568         8      392    12000         80         0 |
  7. | 40004   3883.9568         8      392    12000        141         0 |
  8. | 40004   3883.9568         8      392    12000          0         0 |
  9. | 40004   3883.9568         8      392    12000          0     12000 |
 10. | 40004   3883.9568         8      392    12000         12         0 |
 11. | 40004   3883.9568         8      392    12000          0         0 |
 12. | 40004   3883.9568         8      392    12000        159         0 |
 13. | 40004           0         8      392    12000          0         0 |
     |--------------------------------------------------------------------|
 14. | 40005   4676.8667         1     2919    39945       2919     39945 |
     |--------------------------------------------------------------------|
 15. | 40009   5826.4716         1       52    34031         52     34031 |
     |--------------------------------------------------------------------|
 16. | 40010    7601.436         4      920   140000        210         0 |
 17. | 40010    7601.436         4      920   140000        614     84000 |
 18. | 40010    7601.436         4      920   140000         56         0 |
 19. | 40010    7601.436         4      920   140000         40     56000 |
     |--------------------------------------------------------------------|
 20. | 40011   4747.9969         4        0    22500          0         0 |
     +--------------------------------------------------------------------+

. 
. sort duid cpsfamid famwt14c

. by duid cpsfamid: keep if _n==_N
(19,875 observations deleted)

. 
. list duid cpsfamid famwt14c famsize famoop faminc in 1/20

     +----------------------------------------------------------+
     |  duid   cpsfamid    famwt14c   famsize   famoop   faminc |
     |----------------------------------------------------------|
  1. | 40001          A    6540.855         4      541    68000 |
  2. | 40002          A   8827.9997         1        2    27030 |
  3. | 40004          A   3883.9568         8      392    12000 |
  4. | 40005          A   4676.8667         1     2919    39945 |
  5. | 40009          A   5826.4716         1       52    34031 |
     |----------------------------------------------------------|
  6. | 40010          A    7601.436         4      920   140000 |
  7. | 40011          A   4747.9969         4        0    22500 |
  8. | 40012          A   13579.432         4      177   193561 |
  9. | 40014          A   5232.9521         1      954        0 |
 10. | 40014          B   6675.6329         1        5    14720 |
     |----------------------------------------------------------|
 11. | 40014          C   5180.2438         1        2        0 |
 12. | 40015          A   19589.164         3     2275    99945 |
 13. | 40016          A   4050.4395         3       21    13200 |
 14. | 40017          A   22006.184         3      648   200000 |
 15. | 40019          A   3638.4341         5      833    85536 |
     |----------------------------------------------------------|
 16. | 40020          A   12530.817         5     1941    47881 |
 17. | 40021          A   4682.9354         4      782    20800 |
 18. | 40022          A   1887.6775         1        0    17000 |
 19. | 40022          B   5843.5936         4       37    50000 |
 20. | 40023          A   6209.2947         4     3417    82800 |
     +----------------------------------------------------------+

. 
. // tabmiss: user-written command to tabulate missing values
. tabmiss famsize famoop faminc
    Variable |     Obs       Missings   Feq.Missings    NonMiss   Feq.NonMiss
-------------+---------------------------------------------------------------
     famsize |   15000           0            0          15000          100
      famoop |   15000           0            0          15000          100
      faminc |   15000           0            0          15000          100

. 
. keep if famwt14c > 0
(867 observations deleted)

. svyset [pweight = famwt14c], strata(varstr) psu(varpsu) vce(linearized) singl
> eunit(missing)

      pweight: famwt14c
          VCE: linearized
  Single unit: missing
     Strata 1: varstr
         SU 1: varpsu
        FPC 1: <zero>

. svy: mean famsize famoop faminc
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =     165      Number of obs   =       14,133
Number of PSUs   =     365      Population size =  140,282,371
                                Design df       =          200

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
     famsize |   2.221791   .0205013      2.181364    2.262217
      famoop |   1279.595   28.96032      1222.488    1336.702
      faminc |   63373.64   1015.978      61370.24    65377.05
--------------------------------------------------------------

Use and expenditures for persons with diabetes

Expenditures for all events associated with diabetes

Pool multiple years of MEPS data

Pooling two-years of data requires recalculating personal weights.

This exercise illustrates how to pool meps data files from different years the example used is population age 26-30 who are uninsured but have high income.

Data from 2013 and 2014 are pooled.

Variables with year specific names must be renamed before combining files in this program the insurance coverage variables 'INSCOV13' and 'INSCOV14' are renamed to 'INSCOV'.

. // rename year specific variables prior to combining files
. use dupersid inscov13 perwt13f varstr varpsu povcat13 agelast totslf13 using 
> "README-files/meps_hc_2013.dta", clear

. rename inscov13 inscov

. rename perwt13f perwt

. rename povcat13 povcat

. rename totslf13 totslf

. tempfile yr1

. save "`yr1'"
file /var/folders/m8/68z5d14d7mv9qyf1bd69ych40000gn/T//St08104.000001 saved

. 
. use dupersid inscov14 perwt14f varstr varpsu povcat14 agelast totslf14 using 
> "README-files/meps_hc_2014.dta", clear

. rename inscov14 inscov

. rename perwt14f perwt

. rename povcat14 povcat

. rename totslf14 totslf

. 
. append using "`yr1'", generate(yearnum)

. 
. fre inscov

inscov -- health insurance coverage indicator 14
---------------------------------------------------------------------
                        |      Freq.    Percent      Valid       Cum.
------------------------+--------------------------------------------
Valid   1 1 any private |      35914      50.01      50.01      50.01
        2 2 public only |      24296      33.83      33.83      83.84
        3 3 uninsured   |      11605      16.16      16.16     100.00
        Total           |      71815     100.00     100.00           
---------------------------------------------------------------------

. fre povcat

povcat -- family inc as % of poverty line - catego
-----------------------------------------------------------------------
                          |      Freq.    Percent      Valid       Cum.
--------------------------+--------------------------------------------
Valid   1 1 poor/negative |      17492      24.36      24.36      24.36
        2 2 near poor     |       4636       6.46       6.46      30.81
        3 3 low income    |      12504      17.41      17.41      48.22
        4 4 middle income |      19791      27.56      27.56      75.78
        5 5 high income   |      17392      24.22      24.22     100.00
        Total             |      71815     100.00     100.00           
-----------------------------------------------------------------------

. 
. gen poolwt = perwt / 2

. gen subpop = (agelast >= 26 & agelast <= 30 & inscov == 3 & povcat == 5)

. 
. tab1 agelast inscov povcat if subpop == 1

-> tabulation of agelast if subpop == 1 

   person s |
   age last |
       time |
   eligible |      Freq.     Percent        Cum.
------------+-----------------------------------
         26 |         29       20.86       20.86
         27 |         39       28.06       48.92
         28 |         23       16.55       65.47
         29 |         31       22.30       87.77
         30 |         17       12.23      100.00
------------+-----------------------------------
      Total |        139      100.00

-> tabulation of inscov if subpop == 1 

       health |
    insurance |
     coverage |
 indicator 14 |      Freq.     Percent        Cum.
--------------+-----------------------------------
  3 uninsured |        139      100.00      100.00
--------------+-----------------------------------
        Total |        139      100.00

-> tabulation of povcat if subpop == 1 

family inc as % |
of poverty line |
       - catego |      Freq.     Percent        Cum.
----------------+-----------------------------------
  5 high income |        139      100.00      100.00
----------------+-----------------------------------
          Total |        139      100.00

. tab subpop yearnum

           |        yearnum
    subpop |         0          1 |     Total
-----------+----------------------+----------
         0 |    34,815     36,861 |    71,676 
         1 |        60         79 |       139 
-----------+----------------------+----------
     Total |    34,875     36,940 |    71,8. summarize

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    dupersid |          0
     agelast |     71,815      34.882     22.4086          0         85
      povcat |     71,815    3.208243    1.496595          1          5
      inscov |     71,815    1.661505    .7396724          1          3
      totslf |     71,815    421.1674    1447.036          0     108389
-------------+---------------------------------------------------------
       perwt |     71,815    8830.501    8581.434          0   94410.39
      varstr |     71,815    1084.443    48.94023       1001       1165
      varpsu |     71,815    1.637569    .6163208          1          3
     yearnum |     71,815    .5143772    .4997967          0          1
      poolwt |     71,815     4415.25    4290.717          0    47205.2
-------------+---------------------------------------------------------
      subpop |     71,815    .0019355    .0439524          0          1

. tabmiss
    Variable |     Obs       Missings   Feq.Missings    NonMiss   Feq.NonMiss
-------------+---------------------------------------------------------------
    dupersid |   71815           0            0          71815          100
     agelast |   71815           0            0          71815          100
      povcat |   71815           0            0          71815          100
      inscov |   71815           0            0          71815          100
      totslf |   71815           0            0          71815          100
       perwt |   71815           0            0          71815          100
      varstr |   71815           0            0          71815          100
      varpsu |   71815           0            0          71815          100
     yearnum |   71815           0            0          71815          100
      poolwt |   71815           0            0          71815          100
      subpop |   71815           0            0          71815          100

. 
. svyset [pweight = poolwt], strata(varstr) psu(varpsu) vce(linearized) singleu
> nit(missing)

      pweight: poolwt
          VCE: linearized
  Single unit: missing
     Strata 1: varstr
         SU 1: varpsu
        FPC 1: <zero>

. 
. // weighted estimate on totslf for combined data w/age=26-30, uninsured whole
>  year, and high income
. svy, subpop(subpop): mean totslf
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      73      Number of obs   =       37,121
Number of PSUs   =     169      Population size =  152,825,959
                                Subpop. no. obs =          114
                                Subpop. size    = 606,952.6488
                                Design df       =           96

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
      totslf |   198.7844   64.22224      71.30424    326.2645
--------------------------------------------------------------
Note: 92 strata omitted because they contain no subpopulation
      members.

Construct insurance status variables from monthly insurance variables

This exercise illustrates how to construct insurance status variables from monthly insurance variables (see below) in the person level data.

Variable Name Description
TRImm14X Covered by TRICARE/CHAMPVA in mm (Ed)
MCRmm14 Covered by Medicare in mm
MCRmm14X Covered by Medicare in mm (Ed)
MCDmm14 Covered by Medicaid or SCHIP in mm
MCDmm14X Covered by Medicaid or SCHIP in mm (Ed)
OPAmm14 Covered by Other Public A Ins in mm
OPBmm14 Covered by Other Public B Ins in mm
PUBmm14X Covered by Any Public Ins in mm (Ed)
PEGmm14 Covered by Empl Union Ins in mm
PDKmm14 Coverer by Priv Ins (Source Unknown) in mm
PNGmm14 Covered by Nongroup Ins in mm
POGmm14 Covered by Other Group Ins in mm
PRSmm14 Covered by Self-Emp Ins in mm
POUmm14 Covered by Holder Outside of RU in mm
PRImm14 Covered by Private Ins in mm

where mm = JA-DE (January - December)

. use dupersid varstr varpsu perwt14f racethx peg??14 pou??14 pdk??14 png??14 p
> og??14 prs??14 pri??14 ins??14x mcd??14x mcr??14x tri??14x opa??14 opb??14 us
> ing "README-files/meps_hc_2014.dta", clear

. 
. local opalist opaja14 opafe14 opama14 opaap14 opamy14 opaju14 opajl14 opaau14
>  opase14 opaoc14 opano14 opade14

. local opblist opbja14 opbfe14 opbma14 opbap14 opbmy14 opbju14 opbjl14 opbau14
>  opbse14 opboc14 opbno14 opbde14

. local peglist pegja14 pegfe14 pegma14 pegap14 pegmy14 pegju14 pegjl14 pegau14
>  pegse14 pegoc14 pegno14 pegde14

. local trilist trija14x trife14x trima14x triap14x trimy14x triju14x trijl14x 
> triau14x trise14x trioc14x trino14x tride14x

. local poulist pouja14 poufe14 pouma14 pouap14 poumy14 pouju14 poujl14 pouau14
>  pouse14 pouoc14 pouno14 poude14

. local pdklist pdkja14 pdkfe14 pdkma14 pdkap14 pdkmy14 pdkju14 pdkjl14 pdkau14
>  pdkse14 pdkoc14 pdkno14 pdkde14

. local pnglist pngja14 pngfe14 pngma14 pngap14 pngmy14 pngju14 pngjl14 pngau14
>  pngse14 pngoc14 pngno14 pngde14

. local poglist pogja14 pogfe14 pogma14 pogap14 pogmy14 pogju14 pogjl14 pogau14
>  pogse14 pogoc14 pogno14 pogde14

. local prslist prsja14 prsfe14 prsma14 prsap14 prsmy14 prsju14 prsjl14 prsau14
>  prsse14 prsoc14 prsno14 prsde14

. local mcrlist mcrja14x mcrfe14x mcrma14x mcrap14x mcrmy14x mcrju14x mcrjl14x 
> mcrau14x mcrse14x mcroc14x mcrno14x mcrde14x

. local mcdlist mcdja14x mcdfe14x mcdma14x mcdap14x mcdmy14x mcdju14x mcdjl14x 
> mcdau14x mcdse14x mcdoc14x mcdno14x mcdde14x

. local prilist prija14 prife14 prima14 priap14 primy14 priju14 prijl14 priau14
>  prise14 prioc14 prino14 pride14

. local inslist insja14x insfe14x insma14x insap14x insmy14x insju14x insjl14x 
> insau14x insse14x insoc14x insno14x insde14x

. 
. * 1) count number of months with insurance
. egen pri_n = anycount(`prilist'), v(1)

. egen ins_n = anycount(`inslist'), v(1)

. egen unins_n = anycount(`inslist'), v(2)

. egen mcd_n = anycount(`mcdlist'), v(1)

. egen mcr_n = anycount(`mcrlist'), v(1)

. egen tri_n = anycount(`trilist'), v(1)

. egen ref_n = anycount(`inslist'), v(1 2)

. 
. *2) create flags for various types of insu
. forval i = 1/12 {
  2.        local opa = word("`opalist'",`i')
  3.        local opb = word("`opblist'",`i')
  4.        gen op`i' = (`opa' == 1 | `opb' == 1)
  5.        }

. egen opab_n = anycount(op1-op12), v(1)

. 
. forval i = 1/12 {
  2.        local peg = word("`peglist'",`i')
  3.        local tri = word("`trilist'",`i')
  4.        local pou = word("`poulist'",`i')
  5.        local pdk = word("`pdklist'",`i')
  6.        gen grp`i' = (`peg' == 1 | `tri' == 1 |`pou' == 1 | `pdk' == 1)
  7.        }

. egen grp_n = anycount(grp1-grp12), v(1)

. 
. forval i=1/12 {
  2.        local png = word("`pnglist'",`i')
  3.        local pog = word("`poglist'",`i')
  4.        local prs = word("`prslist'",`i')
  5.        gen ng`i' = (`png' == 1 | `pog' == 1 |`prs' == 1)
  6.        }

. egen ng_n = anycount(ng1-ng12), v(1)

. 
. forval i=1/12 {
  2.        local mcr = word("`mcrlist'",`i')
  3.        local mcd = word("`mcdlist'",`i')
  4.        local opa = word("`opalist'",`i')
  5.        local opb = word("`opblist'",`i')
  6.        gen pub`i' = (`mcr' == 1 | `mcd' == 1 |`opa' == 1 | `opb' == 1)
  7.        }

. egen pub_n = anycount(pub1-pub12), v(1)

. 
. gen full_insu = (unins_n == 0)

. gen group_ins1 = (grp_n > 0)

. gen group_ins2 = (grp_n > 0 & grp_n == ref_n)

. gen ng_ins = (ng_n > 0)

. 
. tab1 pri_n ins_n unins_n mcd_n mcr_n tri_n opab_n grp_n ng_n pub_n ref_n 

-> tabulation of pri_n  

  see notes |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     33,329       95.57       95.57
          1 |        142        0.41       95.97
          2 |        145        0.42       96.39
          3 |        137        0.39       96.78
          4 |        149        0.43       97.21
          5 |        168        0.48       97.69
          6 |        170        0.49       98.18
          7 |        134        0.38       98.56
          8 |        154        0.44       99.01
          9 |        135        0.39       99.39
         10 |        125        0.36       99.75
         11 |         87        0.25      100.00
------------+-----------------------------------
      Total |     34,875      100.00

-> tabulation of ins_n  

  see notes |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     33,329       95.57       95.57
          1 |        142        0.41       95.97
          2 |        145        0.42       96.39
          3 |        137        0.39       96.78
          4 |        149        0.43       97.21
          5 |        168        0.48       97.69
          6 |        170        0.49       98.18
          7 |        134        0.38       98.56
          8 |        154        0.44       99.01
          9 |        135        0.39       99.39
         10 |        125        0.36       99.75
         11 |         87        0.25      100.00
------------+-----------------------------------
      Total |     34,875      100.00

-> tabulation of unins_n  

  see notes |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      4,968       14.25       14.25
          1 |        194        0.56       14.80
          2 |        274        0.79       15.59
          3 |        316        0.91       16.49
          4 |        407        1.17       17.66
          5 |        503        1.44       19.10
          6 |        504        1.45       20.55
          7 |        456        1.31       21.86
          8 |        634        1.82       23.67
          9 |        703        2.02       25.69
         10 |        692        1.98       27.67
         11 |        687        1.97       29.64
         12 |     24,537       70.36      100.00
------------+-----------------------------------
      Total |     34,875      100.00

-> tabulation of mcd_n  

  see notes |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     33,329       95.57       95.57
          1 |        142        0.41       95.97
          2 |        145        0.42       96.39
          3 |        137        0.39       96.78
          4 |        149        0.43       97.21
          5 |        168        0.48       97.69
          6 |        170        0.49       98.18
          7 |        134        0.38       98.56
          8 |        154        0.44       99.01
          9 |        135        0.39       99.39
         10 |        125        0.36       99.75
         11 |         87        0.25      100.00
------------+-----------------------------------
      Total |     34,875      100.00

-> tabulation of mcr_n  

  see notes |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     33,329       95.57       95.57
          1 |        142        0.41       95.97
          2 |        145        0.42       96.39
          3 |        137        0.39       96.78
          4 |        149        0.43       97.21
          5 |        168        0.48       97.69
          6 |        170        0.49       98.18
          7 |        134        0.38       98.56
          8 |        154        0.44       99.01
          9 |        135        0.39       99.39
         10 |        125        0.36       99.75
         11 |         87        0.25      100.00
------------+-----------------------------------
      Total |     34,875      100.00

-> tabulation of tri_n  

  see notes |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     33,329       95.57       95.57
          1 |        142        0.41       95.97
          2 |        145        0.42       96.39
          3 |        137        0.39       96.78
          4 |        149        0.43       97.21
          5 |        168        0.48       97.69
          6 |        170        0.49       98.18
          7 |        134        0.38       98.56
          8 |        154        0.44       99.01
          9 |        135        0.39       99.39
         10 |        125        0.36       99.75
         11 |         87        0.25      100.00
------------+-----------------------------------
      Total |     34,875      100.00

-> tabulation of opab_n  

op1 op2 op3 |
op4 op5 op6 |
op7 op8 op9 |
  op10 op11 |
  op12 == 1 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     33,329       95.57       95.57
          1 |        142        0.41       95.97
          2 |        145        0.42       96.39
          3 |        137        0.39       96.78
          4 |        149        0.43       97.21
          5 |        168        0.48       97.69
          6 |        170        0.49       98.18
          7 |        134        0.38       98.56
          8 |        154        0.44       99.01
          9 |        135        0.39       99.39
         10 |        125        0.36       99.75
         11 |         87        0.25      100.00
------------+-----------------------------------
      Total |     34,875      100.00

-> tabulation of grp_n  

  grp1 grp2 |
  grp3 grp4 |
  grp5 grp6 |
  grp7 grp8 |
 grp9 grp10 |
grp11 grp12 |
       == 1 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     33,329       95.57       95.57
          1 |        142        0.41       95.97
          2 |        145        0.42       96.39
          3 |        137        0.39       96.78
          4 |        149        0.43       97.21
          5 |        168        0.48       97.69
          6 |        170        0.49       98.18
          7 |        134        0.38       98.56
          8 |        154        0.44       99.01
          9 |        135        0.39       99.39
         10 |        125        0.36       99.75
         11 |         87        0.25      100.00
------------+-----------------------------------
      Total |     34,875      100.00

-> tabulation of ng_n  

ng1 ng2 ng3 |
ng4 ng5 ng6 |
ng7 ng8 ng9 |
  ng10 ng11 |
  ng12 == 1 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     33,329       95.57       95.57
          1 |        142        0.41       95.97
          2 |        145        0.42       96.39
          3 |        137        0.39       96.78
          4 |        149        0.43       97.21
          5 |        168        0.48       97.69
          6 |        170        0.49       98.18
          7 |        134        0.38       98.56
          8 |        154        0.44       99.01
          9 |        135        0.39       99.39
         10 |        125        0.36       99.75
         11 |         87        0.25      100.00
------------+-----------------------------------
      Total |     34,875      100.00

-> tabulation of pub_n  

  pub1 pub2 |
  pub3 pub4 |
  pub5 pub6 |
  pub7 pub8 |
 pub9 pub10 |
pub11 pub12 |
       == 1 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     33,329       95.57       95.57
          1 |        142        0.41       95.97
          2 |        145        0.42       96.39
          3 |        137        0.39       96.78
          4 |        149        0.43       97.21
          5 |        168        0.48       97.69
          6 |        170        0.49       98.18
          7 |        134        0.38       98.56
          8 |        154        0.44       99.01
          9 |        135        0.39       99.39
         10 |        125        0.36       99.75
         11 |         87        0.25      100.00
------------+-----------------------------------
      Total |     34,875      100.00

-> tabulation of ref_n  

  see notes |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      4,558       13.07       13.07
          1 |        170        0.49       13.56
          2 |        206        0.59       14.15
          3 |        251        0.72       14.87
          4 |        306        0.88       15.74
          5 |        437        1.25       17.00
          6 |        415        1.19       18.19
          7 |        403        1.16       19.34
          8 |        578        1.66       21.00
          9 |        679        1.95       22.95
         10 |        656        1.88       24.83
         11 |        682        1.96       26.78
         12 |     25,534       73.22      100.00
------------+-----------------------------------
      Total |     34,875      100.00

. tab1 full_ins group_ins1 group_ins2 ng_ins

-> tabulation of full_insu  

  full_insu |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     29,907       85.75       85.75
          1 |      4,968       14.25      100.00
------------+-----------------------------------
      Total |     34,875      100.00

-> tabulation of group_ins1  

 group_ins1 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     33,329       95.57       95.57
          1 |      1,546        4.43      100.00
------------+-----------------------------------
      Total |     34,875      100.00

-> tabulation of group_ins2  

 group_ins2 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     34,465       98.82       98.82
          1 |        410        1.18      100.00
------------+-----------------------------------
      Total |     34,875      100.00

-> tabulation of ng_ins  

     ng_ins |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     33,329       95.57       95.57
          1 |      1,546        4.43      100.00
------------+-----------------------------------
      Total |     34,875      100.00

. tab full_insu unins_n

           |                       see notes
 full_insu |         0          1          2          3          4 |     Total
-----------+-------------------------------------------------------+----------
         0 |         0        194        274        316        407 |    29,907 
         1 |     4,968          0          0          0          0 |     4,968 
-----------+-------------------------------------------------------+----------
     Total |     4,968        194        274        316        407 |    34,8           |                       see notes
 full_insu |         5          6          7          8          9 |     Total
-----------+-------------------------------------------------------+----------
         0 |       503        504        456        634        703 |    29,907 
         1 |         0          0          0          0          0 |     4,968 
-----------+-------------------------------------------------------+----------
     Total |       503        504        456        634        703 |    34,875 

           |            see notes
 full_insu |        10         11         12 |     Total
-----------+---------------------------------+----------
         0 |       692        687     24,537 |    29,907 
         1 |         0          0          0 |     4,968 
-----------+---------------------------------+----------
     Total |       692        687     24,537 |    34,875 


. tab group_ins1 grp_n

           |   grp1 grp2 grp3 grp4 grp5 grp6 grp7 grp8 grp9 grp10
           |                    grp11 grp12 == 1
group_ins1 |         0          1          2          3          4 |     Total
-----------+-------------------------------------------------------+----------
         0 |    33,329          0          0          0          0 |    33,329 
         1 |         0        142        145        137        149 |     1,546 
-----------+-------------------------------------------------------+----------
     Total |    33,329        142        145        137        149 |    34,875 

           |   grp1 grp2 grp3 grp4 grp5 grp6 grp7 grp8 grp9 grp10
           |                    grp11 grp12 == 1
group_ins1 |         5          6          7          8          9 |     Total
-----------+-------------------------------------------------------+----------
         0 |         0          0          0          0          0 |    33,329 
         1 |       168        170        134        154        135 |     1,546 
-----------+-------------------------------------------------------+----------
     Total |       168        170        134        154        135 |    34,875 

           |  grp1 grp2 grp3 grp4
           |  grp5 grp6 grp7 grp8
           |   grp9 grp10 grp11
           |      grp12 == 1
group_ins1 |        10         11 |     Total
-----------+----------------------+----------
         0 |         0          0 |    33,329 
         1 |       125         87 |     1,546 
-----------+----------------------+----------
     Total |       125         87 |    34,875 


. tab ng_ins ng_n

           | ng1 ng2 ng3 ng4 ng5 ng6 ng7 ng8 ng9 ng10 ng11 ng12 ==
           |                           1
    ng_ins |         0          1          2          3          4 |     Total
-----------+-------------------------------------------------------+----------
         0 |    33,329          0          0          0          0 |    33,329 
         1 |         0        142        145        137        149 |     1,546 
-----------+-------------------------------------------------------+----------
     Total |    33,329        142        145        137        149 |    34,875 

           | ng1 ng2 ng3 ng4 ng5 ng6 ng7 ng8 ng9 ng10 ng11 ng12 ==
           |                           1
    ng_ins |         5          6          7          8          9 |     Total
-----------+-------------------------------------------------------+----------
         0 |         0          0          0          0          0 |    33,329 
         1 |       168        170        134        154        135 |     1,546 
-----------+-------------------------------------------------------+----------
     Total |       168        170        134        154        135 |    34,875 

           |  ng1 ng2 ng3 ng4 ng5
           | ng6 ng7 ng8 ng9 ng10
           |    ng11 ng12 == 1
    ng_ins |        10         11 |     Total
-----------+----------------------+----------
         0 |         0          0 |    33,329 
         1 |       125         87 |     1,546 
-----------+----------------------+----------
     Total |       125         87 |    34,875 


. 
. *3) calculate % of persons covered by insu
. svyset [pweight=perwt14f], strata(varstr) psu(varpsu) vce(linearized) singleu
> nit(missing)

      pweight: perwt14f
          VCE: linearized
  Single unit: missing
     Strata 1: varstr
         SU 1: varpsu
        FPC 1: <zero>

. svy: mean full_insu group_ins1 group_ins2 ng_ins, over(racethx)
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =     165      Number of obs   =       34,875
Number of PSUs   =     366      Population size =  318,440,423
                                Design df       =          201

    _subpop_1: racethx = 1 hispanic
    _subpop_2: racethx = 2 non-hispanic white only
    _subpop_3: racethx = 3 non-hispanic black only
    _subpop_4: racethx = 4 non-hispanic asian only
    _subpop_5: racethx = 5 non-hispanic other race or mul

--------------------------------------------------------------
             |             Linearized
        Over |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
full_insu    |
   _subpop_1 |   .2151917   .0101965      .1950859    .2352974
   _subpop_2 |   .0643303   .0037492      .0569375    .0717231
   _subpop_3 |   .1163546   .0064317      .1036724    .1290368
   _subpop_4 |   .0812691   .0086048      .0643018    .0982365
   _subpop_5 |   .0724555   .0122909      .0482198    .0966912
-------------+------------------------------------------------
group_ins1   |
   _subpop_1 |   .0264375   .0021425      .0222127    .0306622
   _subpop_2 |   .0223652   .0017007      .0190117    .0257186
   _subpop_3 |   .0287738   .0029039      .0230478    .0344998
   _subpop_4 |    .015177   .0029016      .0094555    .0208985
   _subpop_5 |   .0338332   .0071045      .0198242    .0478421
-------------+------------------------------------------------
group_ins2   |
   _subpop_1 |   .0052626   .0008027      .0036799    .0068453
   _subpop_2 |   .0020584   .0004588      .0011537     .002963
   _subpop_3 |    .003538   .0007966      .0019673    .0051087
   _subpop_4 |   .0025967   .0011545      .0003201    .0048732
   _subpop_5 |   .0036661   .0023261     -.0009206    .0082527
-------------+------------------------------------------------
ng_ins       |
   _subpop_1 |   .0264375   .0021425      .0222127    .0306622
   _subpop_2 |   .0223652   .0017007      .0190117    .0257186
   _subpop_3 |   .0287738   .0029039      .0230478    .0344998
   _subpop_4 |    .015177   .0029016      .0094555    .0208985
   _subpop_5 |   .0338332   .0071045      .0198242    .0478421
--------------------------------------------------------------

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See the example in the meps.panel readme.

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