aquaroyal72/riskadjustment

generate CMS-HCCs in python

RiskAdjustment

Python code to convert CMS-HCC crosswalks and Stata hierarchy code found at CMS.gov into nicely formatted tables usable in R or Python.

The CMS-HCC generation is done in Python and requires pandas 0.21.0 or greater (otherwise pandas.DataFrame.drop() will throw an error).

A more throrough walkthrough of the code can be found in the Worked Example.

Getting started

All of the ICD to CC crosswalks have been cleaned for all years between 2009 and 2017 for both ICD9 and ICD10 and all versions of the CMS-HCCs. The raw crosswalks can be found under Raw/ and the resulting formatted crosswalks are in Crosswalks/. The formatted tables include column headings and columns indicating the year the mapping is valid for and the ICD version.

    cc    icd  version  year
0    2   0031        9  2009
1  112  00322        9  2009
2   37  00323        9  2009
3   37  00324        9  2009
4  112   0064        9  2009

The Hierarchical Condition Categories and hierarchy rules were also extracted from the raw Stata files found in Raw/ and were placed in formatted tables under ConditionCategory/. For each CMS-HCC version, the labels file lists all HCC codes and the descriptive label:

   cc                                              label
0   1  HIV/AIDS                                      ...
1   2  Septicemia/Shock                              ...
2   5  Opportunistic Infections                      ...
3   7  Metastatic Cancer and Acute Leukemia          ...
4   8  Lung, Upper Digestive Tract, and Other Severe ...

The rules files list the hierarchical rules. This file can be interpreted as: if the Category Code in cc is assigned to a person, then zero the Category code in to_zero.

   to_zero  cc
0      112   5
1        8   7
2        9   7
3       10   7
4        9   8

Generating HCCs

The HCCs for each person are then generated based on the crosswalks and the years with RiskAdjustment.py. The input table must be a long diagnosis table where each row list a single diagnosis for an individual and also includes the date and icd version.

   recip_id    icd       date  version
0      4224  Z7689 2016-01-01       10
1      4473  Z7689 2016-01-01       10
2      3348  G8929 2016-01-01       10
3      3956  Z7689 2016-01-01       10
4      1640   I776 2016-01-01       10

recip_id             int64
icd                 object
date        datetime64[ns]
version              int64

The date should be a datetime object which can be accomplished with pandas.to_datetime() before passing the DataFrame to generate_hccs(). For this to work as is, the columns should be named exactly as above, otherwise edit the merges in RiskAdjustment.py accordingly for your variable names.

The HCC table is then generated by supplying the input DataFrame and specifying which version of the CMS-HCCs to use.

import RiskAdjustment as ra
ra.generate_hccs(sample_data, 'v21')

cc           1      2      6      8      18     ...   
recip_id                                   
3         False  False  False  False  False     ...     
5         False  False  False  False  False     ...   
6         False  False  False  False  False     ...   
7         False  False  False  False  False     ...   
8         False  False  False  False  False     ...   
9         False  False  False  False  False     ...   
10        False  False  False  False  False     ...   
11        False  False  False  False  False     ...   
12        False  False  False  False   True     ...   
13        False  False  False  False  False     ...   

The output is a table with one row for each unique person in the input DataFrame who has at least one HCC assigned to them.