BACKGROUND: Traditional adherence measures such as proportion of days covered (PDC) and medication possession ratio (MPR) are limited in their ability to explain patient medication adherence over time. Group-based trajectory modeling (GBTM) is a new methodological approach that visually describes the dynamics of long-term medication adherence and classifies adherence behavior into groups. OBJECTIVES: To describe and compare trajectories of medication nonadherence reported in the medical literature, including identifying consistent trends in adherence trajectories and disease and patient characteristics that predict trajectory group membership. METHODS: A systematic literature review was conducted in April 2020 in PubMed and CINAHL using MeSH terms and key words in appropriate combinations. Citations were screened for relevance using predefined inclusion and exclusion criteria and evaluated according to variables associated with group-based trajectory models. RESULTS: 21 articles met the study criteria and were reviewed. Generally, studies identified 4 to 6 trajectory groups that described longitudinal medication adherence behavior. Most commonly identified trajectories were labeled as (a) consistent, high adherence, (b) declining adherence, (c) early and consistent nonadherence, and (d) initial nonadherence followed by an increase. Several predictors, including socioeconomic status, disease characteristics, and therapy initiation were routinely associated with group membership. CONCLUSIONS: This review suggests that adherence trajectories and predictors of specific group membership may be similar across diverse disease states. GBTM describes longitudinal, dynamic patterns of medication adherence that may facilitate the development of targeted interventions to promote adherence. Implications for value-based payment systems are discussed in this review. DISCLOSURES: No outside funding supported this study. The authors have no conflicts of interest to declare.
BACKGROUND: Traditional adherence measures such as proportion of days covered (PDC) and medication possession ratio (MPR) are limited in their ability to explain patient medication adherence over time. Group-based trajectory modeling (GBTM) is a new methodological approach that visually describes the dynamics of long-term medication adherence and classifies adherence behavior into groups. OBJECTIVES: To describe and compare trajectories of medication nonadherence reported in the medical literature, including identifying consistent trends in adherence trajectories and disease and patient characteristics that predict trajectory group membership. METHODS: A systematic literature review was conducted in April 2020 in PubMed and CINAHL using MeSH terms and key words in appropriate combinations. Citations were screened for relevance using predefined inclusion and exclusion criteria and evaluated according to variables associated with group-based trajectory models. RESULTS: 21 articles met the study criteria and were reviewed. Generally, studies identified 4 to 6 trajectory groups that described longitudinal medication adherence behavior. Most commonly identified trajectories were labeled as (a) consistent, high adherence, (b) declining adherence, (c) early and consistent nonadherence, and (d) initial nonadherence followed by an increase. Several predictors, including socioeconomic status, disease characteristics, and therapy initiation were routinely associated with group membership. CONCLUSIONS: This review suggests that adherence trajectories and predictors of specific group membership may be similar across diverse disease states. GBTM describes longitudinal, dynamic patterns of medication adherence that may facilitate the development of targeted interventions to promote adherence. Implications for value-based payment systems are discussed in this review. DISCLOSURES: No outside funding supported this study. The authors have no conflicts of interest to declare.
Authors: Chelsea E Hawley; Julie C Lauffenburger; Julie M Paik; Deborah J Wexler; Seoyoung C Kim; Elisabetta Patorno Journal: Diabetes Care Date: 2022-03-01 Impact factor: 19.112
Authors: Enrica Menditto; Caitriona Cahir; Sara Malo; Isabel Aguilar-Palacio; Marta Almada; Elisio Costa; Anna Giardini; María Gil Peinado; Mireia Massot Mesquida; Sara Mucherino; Valentina Orlando; Carlos Luis Parra-Calderón; Enrique Pepiol Salom; Przemyslaw Kardas; Bernard Vrijens Journal: Int J Environ Res Public Health Date: 2021-05-03 Impact factor: 3.390
Authors: Jagadeswara R Earla; George J Hutton; J Douglas Thornton; Hua Chen; Michael L Johnson; Rajender R Aparasu Journal: Patient Prefer Adherence Date: 2020-11-04 Impact factor: 2.711