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exercise_2b.R
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exercise_2b.R
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# -----------------------------------------------------------------------------
# This program generates National Totals and Per-person Averages for Narcotic
# analgesics and Narcotic analgesic combos for the U.S. civilian
# non-institutionalized population, including:
# - Number of purchases (fills)
# - Total expenditures
# - Out-of-pocket payments
# - Third-party payments
#
# Input files:
# - C:/MEPS/h209.dat (2018 Full-year file)
# - C:/MEPS/h206a.dat (2018 Prescribed medicines file)
#
# This program is available at:
# https://github.com/HHS-AHRQ/MEPS-workshop/tree/master/r_exercises
#
# -----------------------------------------------------------------------------
# Install and load packages ---------------------------------------------------
#
# # Can skip this part if already installed
# install.packages("survey")
# install.packages("foreign")
# install.packages("dplyr")
# install.packages("devtools")
#
# # Run this part each time you re-start R
# library(survey)
# library(foreign)
# library(dplyr)
# library(devtools)
#
# # This package facilitates file import
# install_github("e-mitchell/meps_r_pkg/MEPS")
# library(MEPS)
# Set options to deal with lonely psu
options(survey.lonely.psu='adjust');
# Read in data from FYC file --------------------------------------------------
# !! IMPORTANT -- must use ASCII (.dat) file for 2018 data !!
fyc18 = read_MEPS(year = 2018, type = "FYC") # 2018 FYC
rx18 = read_MEPS(year = 2018, type = "RX") # 2018 RX
# Keep only needed variables --------------------------------------------------
fyc18_sub = fyc18 %>%
select(DUPERSID, VARSTR, VARPSU, PERWT18F) # needed for survey design
# Identify Narcotic analgesics or Narcotic analgesic combos -------------------
# Use therapeutic classification codes (TC1S1_1)
#
# DUPERSID: PERSON ID (DUID + PID)
# RXRECIDX: UNIQUE RX/PRESCRIBED MEDICINE IDENTIFIER
# LINKIDX: ID FOR LINKAGE TO COND/OTH EVENT FILES
# TC1S1_1: MULTUM THERAPEUT SUB-SUB-CLASS FOR TC1S1
#
# RXXP18X: SUM OF PAYMENTS RXSF18X-RXOU18X(IMPUTED)
# RXSF18X: AMOUNT PAID, SELF OR FAMILY (IMPUTED)
narc = rx18 %>%
filter(TC1S1_1 %in% c(60, 191)) %>%
select(DUPERSID, RXRECIDX, LINKIDX, TC1S1_1, RXXP18X, RXSF18X)
head(narc)
narc %>% count(TC1S1_1)
# Sum data to person-level ----------------------------------------------------
narc_pers = narc %>%
group_by(DUPERSID) %>%
summarise(
tot = sum(RXXP18X),
oop = sum(RXSF18X),
n_purchase = n()) %>%
mutate(
third_payer = tot - oop,
any_narc = 1)
head(narc_pers)
# Merge the person-level expenditures to the FY PUF to get complete PSUs, Strata
narc_fyc = full_join(narc_pers, fyc18_sub, by = "DUPERSID")
head(narc_fyc)
nrow(narc)
nrow(fyc)
nrow(narc_fyc)
narc_fyc %>% count(any_narc)
narc_fyc %>% filter(is.na(any_narc))
# Define the survey design ----------------------------------------------------
mepsdsgn = svydesign(
id = ~VARPSU,
strata = ~VARSTR,
weights = ~PERWT18F,
data = narc_fyc,
nest = TRUE)
# Calculate estimates ---------------------------------------------------------
# National totals and Per-person Averages for:
# - Number of purchases (fills) -- n_purchase
# - Total expenditures -- tot
# - Out-of-pocket payments -- oop
# - Third-party payments -- third_payer
# National totals
svytotal(~n_purchase + tot + oop + third_payer,
design = subset(mepsdsgn, any_narc == 1))
# Average per person
svymean(~n_purchase + tot + oop + third_payer,
design = subset(mepsdsgn, any_narc == 1))