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. 2021 Dec 29;14(1):113.
doi: 10.1186/s40545-021-00385-w.

Estimating proportion of days covered (PDC) using real-world online medicine suppliers' datasets

David Prieto-Merino  1   2 , Amy Mulick  1   2 , Craig Armstrong  3 , Helen Hoult  3 , Scott Fawcett  3 , Lina Eliasson  4 , Sarah Clifford  1
Affiliations

Estimating proportion of days covered (PDC) using real-world online medicine suppliers' datasets

David Prieto-Merino et al. J Pharm Policy Pract. .

Abstract

Background: The proportion of days covered (PDC) is used to estimate medication adherence by looking at the proportion of days in which a person has access to the medication, over a given period of interest. This study aimed to adapt the PDC algorithm to allow for plausible assumptions about prescription refill behaviour when applied to data from online pharmacy suppliers.

Methods: Three PDC algorithms, the conventional approach (PDC1) and two alternative approaches (PDC2 and PDC3), were used to estimate adherence in a real-world dataset from an online pharmacy. Each algorithm has different denominators and increasing levels of complexity. PDC1, the conventional approach, is the total number of days between first dispensation and a defined end date. PDC2 counts the days until the end of supply date. PDC3 removes from the denominator specifically defined large gaps between refills, which could indicate legitimate reasons for treatment discontinuation. The distribution of the three PDCs across four different follow-up lengths was compared.

Results: The dataset included people taking ACE inhibitors (n = 65,905), statins (n = 100,362), and/or thyroid hormones (n = 30,637). The proportion of people taking ACE inhibitors with PDC ≥ 0.8 was 50-74% for PDC1, 81-91% for PDC2, and 86-100% for PDC3 with values depending on drug and length of follow-up. Similar ranges were identified in people taking statins and thyroid hormones.

Conclusion: These algorithms enable researchers and healthcare providers to assess pharmacy services and individual levels of adherence in real-world databases, particularly in settings where people may switch between different suppliers of medicines, meaning an individual supplier's data may show temporary but legitimate gaps in access to medication. Accurately identifying problems with adherence provides the foundation for opportunities to improve experience, adherence and outcomes and to reduce medicines wastage. Research with people taking medications and prescribers is required to validate the algorithms' assumptions.

Keywords: Measurement; Medication adherence; Proportion of days covered; Real-world data; Routinely collected data.

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Conflict of interest statement

The authors declare the following conflicts of interest: CA, SF and HH are employed by Pharmacy2U Ltd. LLE and SC are owners and directors of Sprout Health Solutions Ltd (formerly Sprout Behaviour Change Ltd). AM and DPM were paid consulting fees by Sprout Behaviour Change Ltd for their contributions to this work and manuscript.

Figures

Fig. 1
Fig. 1
Comparison of research questions and assumptions between the three proposed PDC measures
Fig. 2
Fig. 2
Graphical representation of two patterns of prescription medication supply and calculation of PDC under different follow-up lengths. Period of interest is defined between January 1st and April 30th
Fig. 3
Fig. 3
Three PDC algorithms applied to a real-world dataset. PDCs are shown for people taking a ACE inhibitors, b statins and c thyroid hormones. Each boxplot is for a combination of PDC definition (labelled on the left) and follow-up period (coloured and identified in legend). Each shows the 25th and 75th percentiles at either end of the box, the 50th percentile as a black line in the middle, and up to four ‘whiskers’ at the 5th, 10th, 90th and 95th percentiles. The asterisk in each box is the mean. PDC3 is calculated such that gaps of 0.5, 1 and 1.5 relative to the average number of days in each prescription are factored out of the denominator on the assumption that the person has gone elsewhere for their supply during that time
Fig. 3
Fig. 3
Three PDC algorithms applied to a real-world dataset. PDCs are shown for people taking a ACE inhibitors, b statins and c thyroid hormones. Each boxplot is for a combination of PDC definition (labelled on the left) and follow-up period (coloured and identified in legend). Each shows the 25th and 75th percentiles at either end of the box, the 50th percentile as a black line in the middle, and up to four ‘whiskers’ at the 5th, 10th, 90th and 95th percentiles. The asterisk in each box is the mean. PDC3 is calculated such that gaps of 0.5, 1 and 1.5 relative to the average number of days in each prescription are factored out of the denominator on the assumption that the person has gone elsewhere for their supply during that time
Fig. 4
Fig. 4
Comparison of proportion of patients with PDC ≥ 0.8 in each drug class (thyroid hormones, ACE inhibitors, and statins)
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