Trajectories of Adherence to Low-Dose Aspirin Treatment Among the French Population

Abstract

Background: Previous studies have shown that adherence to low-dose aspirin (LDA) is suboptimal. However, these studies were based on an average measure of adherence during follow-up, ignoring its dynamic process over time. We described the trajectories of adherence to LDA treatment among the French population over 3 years of follow-up. Methods: We identified a cohort of 11 793 new LDA users, aged ≥50 years in 2010, by using the French national health-care database. Patients included had at least 3 years of history in the database before study entry to exclude prevalent aspirin users and to assess baseline comorbidities. They were followed from the first date of LDA supply (the index date) until the first date among death, exit from the database, or 3 years after the index date. Adherence to LDA was assessed every 3 months by using the proportion of days covered (PDC) and dichotomized with a cutoff of PDC of 0.8. We used group-based trajectory modeling to identify trajectories of LDA adherence. Predictors of LDA adherence trajectory membership were identified by multinomial logistics regression. Results: We identified 4 trajectories of adherence among new LDA users: the not-adherents (4737 [40.2%]), the delayed not-adherents (gradual decrease in adherence probability, 1601 [13.6%]), the delayed adherents (gradual increase in adherence probability, 1137 [9.6%]), and the persistent adherents (4318 [36.6%]). The probability of belonging to the not-adherent group was increased with female sex, low socioeconomic status, and polymedication and was reduced with a secondary indication for LDA use, such as diabetes, hypertension, and dementia, at least 4 consultations in the previous year, or 1 hospitalization or a cardiologist consultation in the 3 months before the index date. Conclusion: This study provides a dynamic picture of adherence behaviors among new LDA users and underlines the presence of critical trajectories that intervention could target to improve adherence.

Medication Adherence Trajectories: A Systematic Literature Review

“…All 28 papers were published in the last 10 years (2010-2020). Most studies were conducted in the United States (n = 17, 60.7%), 10,[18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] while the remaining were conducted in Europe (n = 7, 25%), [35][36][37][38][39][40][41] Taiwan (n = 1, 3.6%), 42 and Australia (n = 2, 7.14%). 43,44 Although a variety of medical conditions were explored in GBTM populations, the majority of studies focused on populations diagnosed with cardiovascular diseases (n = 19, 67.9%).…”

“…43,44 Although a variety of medical conditions were explored in GBTM populations, the majority of studies focused on populations diagnosed with cardiovascular diseases (n = 19, 67.9%). 10,18,20,[22][23][24]27,29,30,[32][33][34][35][36][37][38]41,43,45 Study duration ranged from 9 months to 6 years, 38,42 with most lasting 2 years or less (n = 22, 78.6%). Table 1 summarizes the studies included in this review by the population characteristics, type of treatment, study duration, adherence measure, country, sample size, number of trajectory groups identified, and overview of the conclusions.…”

“…comtinued on next page reason for the increase in adherence after a period of declining adherence. 28,41,42,44 In a study with a follow-up period of 5 years, a declining adherence trajectory increased to a stable level of adherence, suggesting that a trend of reaching a plateau in adherence may be observed if follow-up periods were extended beyond 18 months. 44 It is possible that the trend of reaching a plateau in adherence would be observed in the shorter follow-up period studies.…”

“…10,20,[23][24][25][26][27]30,33,34,37,38,45 The studies with longer follow-up periods (3-6 years) described increases at different time points, primary between the first and second year. 26,40,41,44…”

Group-Based Trajectory Modelling (GBTM) To Assess the Effect of Medication Adherence on Health-Related Outcomes: A Protocol for A Systematic Review

“…The Group-based trajectory modelling (GBTM) is increasingly used to describe the dynamic and mutable nature of medication adherence behaviors. [11][12][13][14] It is a statistical method for modelling a variable of interest throughout time or age by identifying groups of individuals with similar pro les. [15] The method was initially developed by Nagin et al to characterize developmental trajectories of criminal activities [16] and became prevalent for studying developmental trajectories in criminology, social science, and psychology.…”