Open Access<p>Group-Based Trajectory Modeling to Identify Patterns of Adherence and Its Predictors Among Older Adults on Angiotensin-Converting Enzyme Inhibitors (ACEIs)/Angiotensin Receptor Blockers (ARBs)</p>
Author(s) -
Rutugandha Paranjpe,
Michael L. Johnson,
E. James Essien,
Jamie C. Barner,
Omar Serna,
Esteban Gallardo,
Zahra Majd,
Marc L. Fleming,
Nancy Ordonez,
Marcia McDonnell Holstad,
Susan Abughosh
Publication year - 2020
Publication title -
patient preference and adherence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.885
H-Index - 48
ISSN - 1177-889X
DOI - 10.2147/ppa.s270809
Subject(s) - medicine , diabetes mellitus , logistic regression , angiotensin receptor blockers , medical prescription , multinomial logistic regression , blood pressure , medication adherence , demography , angiotensin converting enzyme , pharmacology , endocrinology , machine learning , sociology , computer science
Commonly prescribed medications among patients with comorbid diabetes mellitus and hypertension include ARBs and ACEIs. However, these medications are associated with suboptimal adherence leading to inadequately controlled blood pressure. Unlike traditional single estimates of proportion of days covered (PDC), group-based trajectory modeling (GBTM) can graphically display the dynamic nature of adherence. The objective of this study was to evaluate adherence using GBTMs among patients prescribed ACEI/ARBs and identify predictors associated with each adherence trajectory.