pdcscore 1.0.4

A package to facilitate efficient and accurate calculation of the medication adherence metric "Proportion of Days Covered" or "PDC".

The purpose of this package is to provide a python-native means to calculate a common industry metric for medication adherence, Proportion of Days Covered (PDC). Much of the healthcare analytics industry is transitioning from SAS and are working to replicate such fundametal metrics in new environments. The goal is to offer one less thing that needs to be rebuilt from scratch, and hopefully make work easier for business analysts in the healthcare ecosystem.

I followed the original implementation logic of PDC in SAS, this can be found at https://support.sas.com/resources/papers/proceedings13/167-2013.pdf

This paper offers a gentle, yet detailed introduction to the topic, and will serve as a reference to anyone new to the subject.

Current update is optimized for multiprocessing large datasets.

Please use as described below:

PARAMETERS:

dataframe - A pandas dataframe containing the required columns described below.

patient_id_col - A unique patient identifier. Format = STRING or INTEGER

drugname_col - The name of the drug being filled or drug class or Generic name, per usual PDC requirements. Format = STRING

filldate_col - The date of the fill being dispensed. Format = DATE

supply_days_col - Days of supply being dispensed at fill. Format = INTEGER

mbr_elig_start_dt_col - First date of coverage eligiblity for patient or a reference START DATE. Format = DATE

MBRELIGEND - Last date of coverage eligiblity for patient or a reference END DATE. Format = DATE

Returns - A Pandas dataframe containing the following columns

patient_id_col - This will return a column name representing a unique patient identifier as provided in original input dataframe. FORMAT = STRING

drugname_col - The name of the drug being filled or drug class or Generic name, as provided in original input dataframe.

DAYSCOVERED- The number of unique days of drug coverage, after shifting coverage to accommodate early refills. FORMAT = INTEGER

TOTALDAYS - The total number of days in patient analysis window. Set to 0 if days of coverage is 0. FORMAT = INTEGER

PDC_SCORE - The patient's PDC score, calculated as DAYSCOVERED / TOTALDAYS. Set to 0 if days of coverage is 0. FORMAT = FLOAT

USAGE EXAMPLE

#  Import required libraries
import pandas as pd
import numpy as np
from pdcscore import pdccalc

# Create a sample dataframe
df = pd.DataFrame({
    'MCID': ['A', 'A', 'A', 'B', 'B', 'B'],
    'DRUGNAME': ['X', 'X', 'X', 'Y', 'Y', 'Y'],
    'RX_FILLED_DT': pd.to_datetime(['2022-01-01', '2022-01-21', '2022-03-20',
                                '2022-01-01', '2022-02-01', '2022-03-01']),
    'DAYS_SPLY_NBR': [30, 30, 30, 30, 30, 30],
    'START_DT': pd.to_datetime(['2022-01-01', '2022-01-01', '2022-01-01',
                                         '2022-02-01', '2022-02-01', '2022-02-01']),
    'END_DT': pd.to_datetime(['2022-03-31', '2022-03-31', '2022-03-31',
                                       '2022-03-31', '2022-03-31', '2022-03-31'])
})

# Inspect sample data
df.head(n=len(df))

# calculate PDC scores on the input DataFrame
calcfunc= pdccalc(dataframe=df, patient_id_col='MCID', drugname_col='DRUGNAME'
                                         , filldate_col='RX_FILLED_DT', supply_days_col='DAYS_SPLY_NBR'
                                         , mbr_elig_start_dt_col='START_DT', mbr_elig_end_dt_col='END_DT')
pdc_scores_df = calcfunc.calculate_pdc()

# Inspect output
pdc_scores_df.head()