pdcscore 1.0.6

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

The objective of this package is to offer a Python-based solution for computing the Proportion of Days Covered (PDC), a widely used metric in the healthcare industry to evaluate medication adherence. As the healthcare analytics sector shifts away from SAS, there is a growing need to recreate key metrics in alternative platforms. This package aims to simplify the process and reduce the workload for business analysts in the healthcare ecosystem by providing a readily available PDC calculation tool, thereby eliminating the need to build it from scratch.

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

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#  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()