Who Were the Early Adopters of Dabigatran?: An Application of Group-based Trajectory Models.
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.
*Department of Pharmacy, Practice and Science, College of Pharmacy, University of Arizona, Tucson, AZ †Center for Pharmaceutical, Policy and Prescribing, Health Policy Institute ‡Department of Medicine, School of Medicine, University of Pittsburgh §Center for Health Equity Research and Promotion, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA ∥Department of Health Care Policy, Harvard Medical School ¶Department of Medicine, Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA #Department of Biostatistics **Department of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh ††Department of Psychiatry, University of Pittsburgh Medical Center, Pittsburgh, PA ‡‡Statistics Department, Istanbul Medeniyet University, Istanbul, Turkey §§Department of Economics, College of Business and Economics, Lehigh University, Bethlehem, PA.
Wei-Hsuan
WH
Lo-Ciganic
Walid F
WF
Gellad
Haiden A
HA
Huskamp
Niteesh K
NK
Choudhry
Chung-Chou H
CC
Chang
Ruoxin
R
Zhang
Bobby L
BL
Jones
Hasan
H
Guclu
Seth
S
Richards-Shubik
Julie M
JM
Donohue
D000328:Adult; D000991:Antithrombins / Q000627:therapeutic use*; D000069604:Dabigatran / Q000627:therapeutic use*; D004363:Drug Utilization / Q000639:trends*; D005260:Female; D005544:Forecasting; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D010414:Pennsylvania; D010818:Practice Patterns, Physicians' / Q000639:trends; D012189:Retrospective Studies
D000991:Antithrombins; D000069604:Dabigatran