Who Were the Early Adopters of Dabigatran

An Application of Group-based Trajectory Models

Bibliographic Data

ID9103069
AuthorsWei-Hsuan Lo-Ciganic (University of Arizona), Wei‐Hsuan Lo‐Ciganic (0000-0001-6590-4770, University of Arizona, corresponding author), Walid F Gellad (0000-0002-6992-5197, Center for Pharmaceutical, Policy and Prescribing, Health Policy Institute), Haiden A Huskamp (0000-0002-1860-3956, Harvard Medical School), Niteesh K Choudhry (0000-0001-7719-2248, Department of Medicine, Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA), Chung-Chou H Chang (0000-0001-8719-2508, University of Pittsburgh), Chung‐Chou H Chang (0000-0003-3665-6928, University of Pittsburgh), Ruoxin Zhang (0000-0001-8307-7844, University of Pittsburgh, corresponding author), Bobby L Jones (0000-0003-3290-0663, Department of Psychiatry, University of Pittsburgh Medical Center, Pittsburgh, PA), Hasan Guclu (0000-0003-3582-9460, University of Pittsburgh, corresponding author), Seth Richards-Shubik (Lehigh University), Seth Richards‐Shubik (0000-0002-4732-0568, Lehigh University), Julie M Donohue (0000-0003-2418-6017, Center for Pharmaceutical, Policy and Prescribing, Health Policy Institute, corresponding author)
Year2016
Volume54
Issue7
Pages725-732
Publication date2016-07-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueMedical Care (JOURNAL)
Journal identifiersISSN: 0025-7079 • E-ISSN: 1537-1948
PublisherOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/mlr.0000000000000549
PMID27116109
OpenAlexW2345117589
LanguageEN
Citations received1
References cited45

BACKGROUND: 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. OBJECTIVE: Apply group-based trajectory models to examine the physician adoption of dabigratran, a novel anticoagulant. METHODS: A retrospective cohort study using prescribing data from IMS XponentTM 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. OUTCOMES: Trajectories of monthly share of oral anticoagulant prescriptions for dabigatran. RESULTS: 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 55 y). CONCLUSIONS: 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

Anticoagulant · Atrial fibrillation · Confidence interval · Dabigatran · Family medicine · Logistic regression · Medical prescription · Odds · Odds ratio · Retrospective cohort study · Warfarin · Emergency Medicine · Health Systems, Economic Evaluations, Quality of Life · Internal Medicine · Medication Adherence and Compliance · Medicine · Pharmaceutical industry and healthcare · Pharmacology