R/mapdocs.R

In jackwasey/icd: Comorbidity Calculations and Tools for ICD-9 and ICD-10 Codes

#' AHRQ comorbidities
#'
#' These mappings of comorbidities to ICD-9 and ICD-10 codes are derived
#' directly from SAS code provided by AHRQ then translated into this R data
#' structure. This is a revision of the Elixhauser system, notably excluding
#' cardiac arrythmia.
#' @family comorbidity maps
#' @family comorbidities
#' @seealso \code{\link{comorbid_ahrq}} \code{\link{icd9_comorbid_ahrq}}
#'   \code{\link{icd10_comorbid_ahrq}}
#' @docType data
#' @keywords datasets
#' @format list of character vectors
#' @source
#' \url{http://www.hcup-us.ahrq.gov/toolssoftware/comorbidity/comorbidity.jsp}
#' \url{http://www.hcup-us.ahrq.gov/toolssoftware/comorbidityicd10/comorbidity_icd10.jsp}
#' @name icd9_map_ahrq
#' @aliases ahrq icd10_map_ahrq
NULL

#' Quan adaptation of Deyo/Charlson comorbidities
#'
#' Derived automatically from the SAS code used in the original
#' publication. According to the referenced study, this provides the
#' best predictor of in-patient to <30d mortality. Of note, Deyo drops
#' the distinction between leukemia, lymphoma and non-metastatic cancer.
#' As far as I have looked into this, in the rare cases where someone
#' had two or three of leukemia, lymphoma and non-metastatic cancer, the
#' Quan adaptation would give a lower Charlson score than the original
#' scheme. The original Deyo Charlson to ICD-9-CM groups does include
#' distinct categories for these things.
#' @family comorbidity maps
#' @family comorbidities
#' @seealso \code{\link{comorbid_quan_deyo}}
#'   \code{\link{icd9_comorbid_quan_deyo}}
#'   \code{\link{icd10_comorbid_quan_deyo}}
#' @docType data
#' @keywords datasets
#' @format list of character vectors, each named by co-morbidity
#' @references Quan, Hude, Vijaya Sundararajan, Patricia Halfon, Andrew
#'   Fong, Bernard Burnand, Jean-Christophe Luthi, L. Duncan Saunders,
#'   Cynthia A. Beck, Thomas E. Feasby, and William A. Ghali. "Coding
#'   Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10
#'   Administrative Data." Medical Care 43, no. 11 (November 1, 2005):
#'   1130-39. \url{http://www.ncbi.nlm.nih.gov/pubmed/16224307}
#'   \url{http://web.archive.org/web/20110225042437/http://www.chaps.ucalgary.ca/sas}
#'
#'
#' @name icd9_map_quan_deyo
#' @aliases icd10_map_quan_deyo icd9_map_charlson icd10_map_charlson
NULL

#' Quan adaptation of Elixhauser comorbidities
#'
#' These were transcribed directly from the Quan paper referenced.
#' @family comorbidity maps
#' @family comorbidities
#' @seealso \code{\link{comorbid_quan_elix}}
#'   \code{\link{icd9_comorbid_quan_elix}}
#'   \code{\link{icd10_comorbid_quan_elix}}
#' @docType data
#' @keywords datasets
#' @format list of character vectors, each named by co-morbidity
#' @references Quan, Hude, Vijaya Sundararajan, Patricia Halfon, Andrew
#'   Fong, Bernard Burnand, Jean-Christophe Luthi, L. Duncan Saunders,
#'   Cynthia A. Beck, Thomas E. Feasby, and William A. Ghali. "Coding
#'   Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10
#'   Administrative Data." Medical Care 43, no. 11 (November 1, 2005):
#'   1130-39. \url{http://www.ncbi.nlm.nih.gov/pubmed/16224307}
#'   \url{http://web.archive.org/web/20110225042437/http://www.chaps.ucalgary.ca/sas}
#'
#'
#' @name icd9_map_quan_elix
#' @aliases icd10_map_quan_elix
NULL

#' Elixhauser comorbidities
#'
#' This data comprises the original mapping of Elixhauser's ICD-9-CM to
#' 30 comorbidities. According to Sharabiani, this mapping provides the
#' best long-term mortality prediction. The weaknesses of this mapping
#' are that it is based on slightly out-dated ICD-9 codes. I have not
#' yet verified what changes to the ICD-9-CM specification between 1998
#' and now would impact this mapping.
#' @family comorbidity maps
#' @family comorbidities
#' @seealso \code{\link{comorbid_elix}} \code{\link{icd9_comorbid_elix}}
#'   \code{\link{icd10_comorbid_elix}}
#' @docType data
#' @keywords datasets
#' @format list of character vectors, each named by co-morbidity
#' @references Sharabiani, Mansour T. A., Paul Aylin, and Alex Bottle.
#'   "Systematic Review of Comorbidity Indices for Administrative Data."
#'   Medical Care December 2012 50, no. 12 (2012): 1109-18.
#'   doi:10.1097/MLR.0b013e31825f64d0.
#'   \url{http://www.ncbi.nlm.nih.gov/pubmed/22929993}
#'
#'   Elixhauser, Anne, Claudia Steiner, D. Robert Harris, and Rosanna M.
#'   Coffey. "Comorbidity Measures for Use with Administrative Data."
#'   Medical Care January 1998 36, no. 1 (1998): 8-27.
#' @name icd9_map_elix
#' @aliases icd10_map_elix
NULL

# nolint start

#' Medicare Hierarchical Condition Categories
#'
#' Medicare HCC model was developed to use current year diagnoses and
#' demographics predict current year healthcare expenditure. This
#' classification has been used for additional risk adjustment models.
#' ICD codes are first assigned to numeric Condition Categories ('CCs').
#' A hierarchy rule is then applied so that each patient is coded for
#' only the most severe of the Condition Categories in a group. For
#' example, if a patient has metastatic lung cancer, they will only be
#' assigned the 'CC' for "Metastatic Cancer and Acute Leukemia", and
#' will not be assigned the 'CC' for "Lung and other Severe Cancers".
#' Once the hierarchy rules are applied, the codes are referred to as
#' HCCs. This mapping can change over time. It remained the same from
#' 2007-10
#' @family comorbidity maps
#' @family comorbidities
#' @seealso \code{\link{comorbid_hcc}} \code{\link{icd9_comorbid_hcc}}
#'   \code{\link{icd10_comorbid_hcc}}
#' @docType data
#' @keywords datasets
#' @format \code{dataframe} with 3 columns (\code{icd_code}, \code{cc},
#'   and \code{year})
#' @references Pope, Gregory C., et al. "Diagnostic cost group
#'   hierarchical condition category models for Medicare risk
#'   adjustment." Health Economics Research, Inc. Waltham, MA (2000).
#'   \url{https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Reports/Downloads/Pope_2000_2.pdf}
#'
#'   Risk Adjustment, Centers for Medicare and Medicaid Services
#'   \url{https://www.cms.gov/Medicare/Health-Plans/MedicareAdvtgSpecRateStats/Risk-Adjustors.html}
#' @name icd9_map_hcc
#' @aliases icd9_map_cc icd10_map_cc icd_map_cc_hcc
NULL

# nolint end

#' Clinical Classifications Software (CCS) for ICD9/10-CM
#'
#' The Clinical Classifications Software (CCS) for ICD-9/10-CM is one in
#' a family of databases and software tools developed as part of the
#' Healthcare Cost and Utilization Project (HCUP),a
#' Federal-State-Industry partnership sponsored by the Agency for
#' Healthcare Research and Quality. HCUP databases, tools, and software
#' inform decision making at the national, State, and community levels.
#' The software contains two different mappings. One is a single level
#' mapping and one is a multi level classification. This data set
#' contains the numeric representations of all of the codes.
#'
#' This file contains the updated ICD9 version that includes categories
#' for Mental Health and Substance Abuse. More information on the
#' ICD-9-CM data set can be found
#' \url{https://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp}.
#'
#' The file \code{icd10_map_ccs} contains the 2018.1 ICD10 Version of
#' the mapping. More information on the ICD10 code set can be found at
#' \url{https://www.hcup-us.ahrq.gov/toolssoftware/ccs10/ccs10.jsp}
#' @family comorbidity maps
#' @family comorbidities
#' @seealso \code{\link{comorbid_ccs}} \code{\link{icd9_comorbid_ccs}}
#'   \code{\link{icd10_comorbid_ccs}}
#' @docType data
#' @keywords datasets
#' @format list of character vectors, each numbered by co-morbidity
#' @name icd9_map_single_ccs
#' @aliases icd9_map_multi_ccs icd10_map_ccs
NULL

#' Comorbidity names
#'
#' These lists provide correctly sorted names of the comorbidities and
#' their particular permutations in both full and abbreviated forms.
#'
#' In the Elixhauser derived mappings, uncomplicated and complicated
#' hypertension are listed separately, but are always combined in the
#' final analyses. Uncomplicated and complicated hypertension are list
#' separately and as "Hypertension, combined." _abbrev suffix indicates
#' a very short space-free description. Quan's version of Elixhauser is
#' identical. AHRQ updates drops the arrythmia field. The naming
#' convention is a root, e.g. \code{icd9_map_elix} or
#' \code{icd10_map_elix}, with neither/either/both suffixes \code{_htn}
#' and \code{_abbrev}. The Charlson derived mappings do not include
#' hypertension. _abbreviated comorbidity names are helpful for
#' interactive work, whereas the full names might be preferred for
#' plotting.
#' @format list, with character/numeric code. 'Hypertension,
#'   uncomplicated' and 'Hypertension, complicated' are labelled '6a'
#'   and '6b'. Diabetes, cancer, and metastasis are counted
#'   independently, as in the original paper, giving the original 30
#'   groups. "01" to "30"
#' @name names_elix
#' @aliases names_elix_abbrev names_elix_htn names_elix_htn_abbrev
#'   names_quan_elix names_quan_elix_abbrev names_quan_elix_htn
#'   names_quan_elix_htn_abbrev names_ahrq names_ahrq_abbrev
#'   names_ahrq_htn names_ahrq_htn_abbrev names_charlson
#'   names_charlson_abbrev names_cc icd_names_elix icd_names_elix_abbrev
#'   icd_names_elix_htn icd_names_elix_htn_abbrev icd_names_quan_elix
#'   icd_names_quan_elix_abbrev icd_names_quan_elix_htn
#'   icd_names_quan_elix_htn_abbrev icd_names_ahrq icd_names_ahrq_abbrev
#'   icd_names_ahrq_htn icd_names_ahrq_htn_abbrev icd_names_charlson
#'   icd_names_charlson_abbrev icd_names_cc icd9_names_multi_ccs
#'   icd9_names_single_ccs icd10_names_ccs names_pccc names_pccc_abbrev
#' @keywords datasets
#' @docType data
NULL

#' Pediatric Complex Chronic Conditions
#'
#' There are seven comorbidity maps which represent the combinations of
#' ICD-9, ICD-10, diagnosis and procedure codes, and three also with a
#' set of 'fixed' codes. (See reference).
#' @references Feudtner C, Feinstein JA, Zhong W, Hall M, Dai D.
#'   Pediatric complex chronic conditions classification system version
#'   2: updated for ICD-10 and complex medical technology dependence and
#'   transplantation. BMC Pediatr. 2014 Aug8;14:199. doi:
#'   10.1186/1471-2431-14-199.
#'   \url{https://www.ncbi.nlm.nih.gov/pubmed/25102958}
#' @family comorbidity maps
#' @family comorbidities
#' @seealso \url{https://feudtnerlab.research.chop.edu/ccc_version_2.php}
#' @name icd9_map_pccc
#' @aliases icd9_map_pccc_dx icd9_map_pccc_pcs icd10_map_pccc_dx
#'   icd10_map_pccc_pcs
#' @docType data
#' @keywords datasets
NULL

#' AHRQ ICD-10-PCS categories
#'
#' The AHRQ has categorized each of the ICD-10-PCS (Procedure Codes)
#' into one of four groups: minor diagnostic, minor therapeutic, major
#' diagnostic or major therapeutic. This mapping can be used to get the
#' type(s) of procedure(s) performed on a patient from a
#' \code{data.frame} of patients and associated procedure codes in
#' 'long' format. See the ICD-10 vignette for an example.
#'
#' Currently there is no specific comorbidity function to use this data,
#' so the generic \code{\link{comorbid}} \code{\link{icd9_comorbid}}
#' \code{\link{icd10_comorbid}} should be used, and this data specified
#' as the \code{map}.
#' @family comorbidity maps
#' @family comorbidities
#' @seealso \code{\link{comorbid}} \code{\link{icd9_comorbid}}
#'   \code{\link{icd10_comorbid}}
#'   \url{https://www.hcup-us.ahrq.gov/toolssoftware/procedureicd10/procedure_icd10.jsp}
#' @name icd10_map_ahrq_pcs
#' @docType data
#' @keywords datasets
#' @examples
#' icd10_map_ahrq_pcs[["Major Diagnostic"]][1:5]
#' icd10_map_ahrq_pcs[["Minor Therapeutic"]][1:5]
NULL