The goal of multimorbidity is to create a single package
which can take original claims data, with the ability to clean and
organize them to a simple format, which can then be immediately used to
obtain any of a number of comorbidity, frailty, and multimorbidity
measures. This package is meant to be a simple and transparent
one-stop-shop for those working with claims or other administrative
health care data. The measures included in this package have been
developed by other researchers and are detailed in depth below.
This package is meant to be both user-friendly and transparent. An individual should feel comfortable understanding what’s under the hood with these various metrics. Given this, this function has been written in a way that makes the code, including specific diagnosis codes, accessible.
For examples of the various functions included in this package, please see the documentation or the vignette.
You can install multimorbidity from CRAN with:
install.packages("multimorbidity")The development version of multimorbidity can be
downloaded from GitHub with:
# install.packages("devtools")
devtools::install_github("WYATTBENSKEN/multimorbidity")In addition to citing this R Package, we ask that you please cite the
original manuscripts which developed these algorithms. Portions of this
package, specifically the Elixhauser and Charlson diagnoses codes, were
adapted from another package, comorbidity.
There are two data cleaning functions in this package. The first,
prepare_data() should be run first as it prepares the data
to a uniform format, which the various measures rely on. The end-goal is
to have a dataset that has 1 column with a patient ID, 1 column which
contains the diagnosis code, and 1 column which will note if it’s ICD-9
(9), ICD-10 (10), or HCPCS/CPT (1). There are other variables that may
be of interest depending on the specification including type (inpatient
or outpatient) and date.
The second function, comorbidity_window(), is not highly
suggest as the prepare_data() function is but may be useful
to some investigators. Oftentimes, we may be interested in limiting our
claims to a specific window, such as the 1-year before diagnosis. To
accommodate this, comorbidity_window() will merge your
prepared diagnosis dataset with an ID dataset and limit the
claims/diagnoses to a specific time window relative to a date of
interest.
The Elixhauser Comorbidities and Comorbidity Index are a widely-used set of comorbidities originally developed in 1998 by Elixhauser, with two indices for mortality and readmission created in 2017 by Moore et al.
In this package, we used the codes provided in the format programs by the Agency for Healthcare Research and Quality for ICD-9, ICD-10 Beta, and ICD-10. The ICD-10 data contain a larger set of comorbidities and, as of this writing, no calculator for the indices has been released, and thus when data contain both ICD-9 and ICD-10, we will use the ICD-9 comorbidities with the Beta code. Finally, the original algorithm takes into account DRG, which this package currently does not accommodate.
You can obtain the Elixhauser comorbidities and index by running the
elixhauer()function.
The Charlson Comorbidities and Index are, similarly, a widely-used set of comorbidities. First developed in 1987 by Charlson et al., they’ve been modified a number of times. This algorithm employs the Deyo et al. list of 17 comorbidities, with the adaptations included in Quan et al.
You can obtain the Charlson comorbidities and index by running the
charlson()function.
The Claims Frailty Index (CFI) is based off of work by Kim et al. in 2018. This algorithm uses ICD-9, ICD-10, and procedure codes to establish the frailty score for each patient. The code included in this package is largely developed from publicly-available code which can be found on the Harvard dataverse. As the original algorithms included HCPCS/CPT procedure codes, so does this.
You can obtain the CFI by running the
cfi()function.
The Multimorbidity Weighted Index (MWI) was created by Wei et al. in 2020. This uses ICD-9 codes (note: ICD-10 is not yet available for MWI) to establish a multimorbidity index for each individual. The R code used in this package was developed based on the supplement included in the previously linked manuscript.
You can obtain the MWI by running the
mwi()function.
The Nicholson and Fortin Conditions were first published in 2015 and then updated to ICD-10 in 2017. These 20 chronic conditions are a standardized list used for multimorbidity research, and developed from a community-based primary healthcare project.
You can obtain the Nicholson and Fortin Conditions by running the
nicholsonfortin()function.