This vignette provides an example using publicly available death certificate data to illustrate how the pccc package generates the Complex Chronic Condition (CCC) categories from ICD-9-CM and ICD-10-CM codes. For an overview of the CCC classification system, see pccc-overview.
To evaluate the code chunks in this example you will need to load the following R packages.
The Center for Disease Control maintains vital statistics including death certificate data. The publicly available death certificate data, known as the Multiple Cause of Death (MCD) file, contain ICD diagnostic codes specifying the diseases and conditions leading to each decedent’s death. In particular, the 1996 MCD data contain both ICD-9-CM and ICD-10 codes, making it an ideal example to demonstrate how the PCCC software categorizes ICD codes. Please note that because of the way ICD-9-CM codes are mapped to ICD-10-CM codes (https://www.cms.gov/Medicare/Coding/ICD10/2018-ICD-10-CM-and-GEMs.html), the calculated frequencies of CCCs may differ between corresponding ICD-9-CM and ICD-10-CM diagnosis codes for a decedent.
The data documentation and instructions for direct download are available at: ftp://ftp.cdc.gov/pub/health_statistics/nchs/datasets/comparability/icd9_icd10/ICD9_ICD10_comparability_file_documentation.pdf
For this illustrative example, we have provided just 2 columns of the data for decedents <=21 years old: the ICD-9-CM underlying cause of death diagnosis code and the ICD-10-CM underlying cause of death diagnosis code. If you wish to recreate the data yourself from the direct download site, you will need to utilize column positions 142-145 (ICD-9-CM) and 444-447 (ICD-10) and restrict the data to records with age <=21 years (column positions 64 - 66).
Here’s a sample of how the file could be read and processed:
# download and unzip file from ftp://ftp.cdc.gov/pub/health_statistics/nchs/datasets/comparability/icd9_icd10/ICD9_ICD10_comparability_public_use_ASCII.ZIP
# columns of interest
# start end width description
# 64 - 64 1 Age Code
# 65 - 66 2 Age Value
# Code Value Description
# 0 01-99 Years less than 100
# 1 00-99 Years 100 or more
# 2 01-11,99 Months
# 3 01-03,99 Weeks
# 4 01-27,99 Days
# 5 01-23, 99 Hours
# 6 01-59, 99 Minutes
# 9 99 Age not stated
# 142 - 145 4 ICD Code 9th Revision (Underlying Cause of Death)
# 444 - 447 4 ICD-10 Underlying Cause Code
library(readr)
mcod <- readr::read_fwf("ICD9_ICD10_comparability_public_use_ASCII.dat",
readr::fwf_positions(
start = c(64, 65, 142, 444),
end = c(64, 66, 145, 447),
col_names = c('age_code', 'age', 'icd9', 'icd10')),
col_types = 'iicc')
mcod <- mcod[
(mcod$age_code == 0 & mcod$age <= 21) |
(mcod$age_code %in% c(2, 3, 4, 5, 6))
, ]
mcod <- dplyr::mutate(mcod, id = seq_along(age))
mcod <- mcod[c("id", "icd9", "icd10")]Within the example data, there are 2 string variables for ICD-9-CM and ICD-10 codes. If you inspect the first 10 rows of the codes, you will notice they conform to the formatting guidelines outlined in the PCCC overview file pccc-overview.
# Show data
head(pccc::comparability, 10)
#> id icd9 icd10
#> 1 1 912 W80
#> 2 2 7423 Q039
#> 3 3 7980 R95
#> 4 4 9229 W34
#> 5 5 8199 V892
#> 6 6 8120 V877
#> 7 7 7718 D689
#> 8 8 7980 R95
#> 9 9 7980 R95
#> 10 10 7650 P072To run the PCCC classification on the ICD-9-CM codes:
# Run PCCC on ICD-9-CM codes
ccc_result_icd9 <-
ccc(pccc::comparability, # get id, dx, and pc columns
id = id,
dx_cols = icd9,
pc_cols = ,
icdv = 09)
# review results
head(ccc_result_icd9)
#> id neuromusc cvd respiratory renal gi hemato_immu metabolic congeni_genetic
#> 1 1 0 0 0 0 0 0 0 0
#> 2 2 1 0 0 0 0 0 0 0
#> 3 3 0 0 0 0 0 0 0 0
#> 4 4 0 0 0 0 0 0 0 0
#> 5 5 0 0 0 0 0 0 0 0
#> 6 6 0 0 0 0 0 0 0 0
#> malignancy neonatal tech_dep transplant ccc_flag
#> 1 0 0 0 0 0
#> 2 0 0 0 0 1
#> 3 0 0 0 0 0
#> 4 0 0 0 0 0
#> 5 0 0 0 0 0
#> 6 0 0 0 0 0
# view number of patients with each CCC
sum_results <- dplyr::summarize_at(ccc_result_icd9, vars(-id), sum) %>% print.data.frame
#> neuromusc cvd respiratory renal gi hemato_immu metabolic congeni_genetic
#> 1 2559 3341 1651 366 189 794 294 2146
#> malignancy neonatal tech_dep transplant ccc_flag
#> 1 2848 1202 6 0 15390
# view percent of total population with each CCC
dplyr::summarize_at(ccc_result_icd9, vars(-id), mean) %>% print.data.frame
#> neuromusc cvd respiratory renal gi hemato_immu
#> 1 0.03934683 0.05137076 0.02538555 0.005627566 0.002906038 0.01220844
#> metabolic congeni_genetic malignancy neonatal tech_dep transplant
#> 1 0.004520504 0.0329966 0.04379046 0.01848179 9.225518e-05 0
#> ccc_flag
#> 1 0.2366345To run the PCCC classification on the ICD-10-CM codes:
# Run PCCC on ICD-10-CM codes
ccc_result_icd10 <-
ccc(pccc::comparability, # get id, dx, and pc columns
id = id,
dx_cols = icd10,
pc_cols = ,
icdv = 10)
# review results
head(ccc_result_icd10)
#> id neuromusc cvd respiratory renal gi hemato_immu metabolic congeni_genetic
#> 1 1 0 0 0 0 0 0 0 0
#> 2 2 1 0 0 0 0 0 0 0
#> 3 3 0 0 0 0 0 0 0 0
#> 4 4 0 0 0 0 0 0 0 0
#> 5 5 0 0 0 0 0 0 0 0
#> 6 6 0 0 0 0 0 0 0 0
#> malignancy neonatal tech_dep transplant ccc_flag
#> 1 0 0 0 0 0
#> 2 0 0 0 0 1
#> 3 0 0 0 0 0
#> 4 0 0 0 0 0
#> 5 0 0 0 0 0
#> 6 0 0 0 0 0
# view number of patients with each CCC
sum_results <- dplyr::summarize_at(ccc_result_icd10, vars(-id), sum) %>% print.data.frame
#> neuromusc cvd respiratory renal gi hemato_immu metabolic congeni_genetic
#> 1 1990 3500 1385 377 185 794 277 1979
#> malignancy neonatal tech_dep transplant ccc_flag
#> 1 2949 1421 6 0 14863
# view percent of total population with each CCC
dplyr::summarize_at(ccc_result_icd10, vars(-id), mean) %>% print.data.frame
#> neuromusc cvd respiratory renal gi hemato_immu
#> 1 0.03059797 0.05381552 0.02129557 0.0057967 0.002844535 0.01220844
#> metabolic congeni_genetic malignancy neonatal tech_dep transplant
#> 1 0.004259114 0.03042883 0.04534342 0.0218491 9.225518e-05 0
#> ccc_flag
#> 1 0.2285315