Estimating proportion of days covered (PDC) using real-world online medicine suppliers' datasets.
Measurement
Medication adherence
Proportion of days covered
Real-world data
Routinely collected data
Journal
Journal of pharmaceutical policy and practice
ISSN: 2052-3211
Titre abrégé: J Pharm Policy Pract
Pays: England
ID NLM: 101627192
Informations de publication
Date de publication:
29 Dec 2021
29 Dec 2021
Historique:
received:
15
09
2021
accepted:
27
11
2021
entrez:
30
12
2021
pubmed:
31
12
2021
medline:
31
12
2021
Statut:
epublish
Résumé
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. 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. 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. 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.
Sections du résumé
BACKGROUND
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
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
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
CONCLUSIONS
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.
Identifiants
pubmed: 34965882
doi: 10.1186/s40545-021-00385-w
pii: 10.1186/s40545-021-00385-w
pmc: PMC8715592
doi:
Types de publication
Journal Article
Langues
eng
Pagination
113Informations de copyright
© 2021. The Author(s).
Références
Br J Clin Pharmacol. 2012 May;73(5):691-705
pubmed: 22486599
Int J Clin Pharm. 2014 Feb;36(1):55-69
pubmed: 24166659
PLoS One. 2013 Dec 02;8(12):e80633
pubmed: 24312488
Value Health. 2007 Jan-Feb;10(1):3-12
pubmed: 17261111
OMICS. 2021 Feb;25(2):102-122
pubmed: 32931378
Int J Clin Pract. 2015 Aug;69(8):889-99
pubmed: 25728214
BMJ Open. 2018 Jan 21;8(1):e016982
pubmed: 29358417
Pharmacoepidemiol Drug Saf. 2016 Mar;25(3):325-32
pubmed: 26687394
J Manag Care Spec Pharm. 2019 Oct;25(10):1073-1077
pubmed: 31556829
Curr Med Res Opin. 2019 Sep;35(9):1535-1544
pubmed: 30924690
Ann Pharmacother. 2005 Feb;39(2):319-28
pubmed: 15632223
Front Pharmacol. 2018 Nov 20;9:1290
pubmed: 30524276
Biomed Res Int. 2015;2015:217047
pubmed: 26539470
Pharm World Sci. 2006 Jun;28(3):165-70
pubmed: 17004019
Ann Pharmacother. 2006 Jul-Aug;40(7-8):1280-88
pubmed: 16868217