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Who Were the Early Adopters of Dabigatran?: An Application of Group-based Trajectory Models.

ABSTRACT: Variation in physician adoption of new medications is poorly understood. Traditional approaches (eg, measuring time to first prescription) may mask substantial heterogeneity in technology adoption.Apply group-based trajectory models to examine the physician adoption of dabigratran, a novel anticoagulant.A retrospective cohort study using prescribing data from IMS Xponent™ on all Pennsylvania physicians regularly prescribing anticoagulants (n=3911) and data on their characteristics from the American Medical Association Masterfile. We examined time to first dabigatran prescription and group-based trajectory models to identify adoption trajectories in the first 15 months. Factors associated with rapid adoption were examined using multivariate logistic regressions.Trajectories of monthly share of oral anticoagulant prescriptions for dabigatran.We identified 5 distinct adoption trajectories: 3.7% rapidly and extensively adopted dabigatran (adopting in ?3 mo with 45% of prescriptions) and 13.4% were rapid and moderate adopters (?3 mo with 20% share). Two groups accounting for 21.6% and 16.1% of physicians, respectively, were slower to adopt (6-10 mo post-introduction) and dabigatran accounted for <10% share. Nearly half (45.2%) of anticoagulant prescribers did not adopt dabigatran. Cardiologists were much more likely than primary care physicians to rapidly adopt [odds ratio (OR)=12.2; 95% confidence interval (CI), 9.27-16.1] as were younger prescribers (age 36-45 y: OR=1.49, 95% CI, 1.13-1.95; age 46-55: OR=1.34, 95% CI, 1.07-1.69 vs. >55 y).Trajectories of physician adoption of dabigatran were highly variable with significant differences across specialties. Heterogeneity in physician adoption has potential implications for the cost and effectiveness of treatment.

SUBMITTER: Lo-Ciganic WH

PROVIDER: S-EPMC4907845 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Who Were the Early Adopters of Dabigatran?: An Application of Group-based Trajectory Models.

Lo-Ciganic Wei-Hsuan WH Gellad Walid F WF Huskamp Haiden A HA Choudhry Niteesh K NK Chang Chung-Chou H CC Zhang Ruoxin R Jones Bobby L BL Guclu Hasan H Richards-Shubik Seth S Donohue Julie M JM

Medical care 20160701 7

<h4>Background</h4>Variation in physician adoption of new medications is poorly understood. Traditional approaches (eg, measuring time to first prescription) may mask substantial heterogeneity in technology adoption.<h4>Objective</h4>Apply group-based trajectory models to examine the physician adoption of dabigratran, a novel anticoagulant.<h4>Methods</h4>A retrospective cohort study using prescribing data from IMS Xponent™ on all Pennsylvania physicians regularly prescribing anticoagulants (n=3 ...[more]

PMID: 27116109

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Project description:BackgroundThe benefits of statins in the prevention of primary and secondary atherosclerotic cardiovascular (CV) disease events have been well documented. Suboptimal adherence is a persistent problem associated with increased CV events and increased healthcare utilization. Proportion of days covered (PDC) is widely used to measure medication adherence, and provides a single value that does not adequately depict different adherence behavior patterns. Group-based trajectory modeling has been used to identify adherence patterns (or trajectories) over time. The identification of characteristics unique to each pattern can help in the early identification of patients who are likely to be poor adherents and can inform the development of interventions.ObjectivesTo identify distinct trajectories of statin adherence in patients enrolled in a Medicare Advantage plan and the sociodemographic and clinical predictors associated with each trajectory.MethodsPatients were included in the study if they were continuously enrolled in a Medicare Advantage plan between 2013 and 2016 and had a statin prescription between January 2015 and June 2015. We observed each patient for 360 days and computed the monthly PDC. The monthly PDC was incorporated into a group-based trajectory model to provide distinct patterns of adherence. Using group-based trajectory modeling, the patients were categorized into groups based on their adherence patterns. Multinomial logistic regression was performed to identify the sociodemographic and clinical factors associated with each group.ResultsA total of 7850 patients were included in the analysis and were categorized into 4 distinct groups based on statin adherence-rapid discontinuation (7.8%), gradual decline (16.8%), gaps in adherence (17.2%), and high or nearly perfect adherence (58.2%). Significant predictors of being placed into one or more of the low-adherence trajectories compared with the high-adherence trajectory included sex, age, low-income subsidy, language, Charlson Comorbidity Index score, statin intensity, and 90-day refills.ConclusionsThe predictors identified in this study provide valuable insight into patient characteristics that increase the risk for statin nonadherence, which has the potential to inform targeted interventions. Identifying patient trajectories can inform the future development of protocols to individualize appropriate interventions for these patients.

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Who Were the Early Adopters of Dabigatran?: An Application of Group-based Trajectory Models.

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Who Were the Early Adopters of Dabigatran?: An Application of Group-based Trajectory Models.

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