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
Supported by National Heart, Lung, and Blood Institute (R01HL119246). W.-H.L.-C. was funded by a post-doctoral fellowship through the University of Pittsburgh Health Policy Institute, Center for Pharmaceutical Policy and Prescribing.
The authors declare no conflict of interest.
Reprints: Wei-Hsuan Lo-Ciganic, PhD, MS, MSPharm, Department of Pharmacy Practice and Science, College of Pharmacy, University of Arizona, Drachman Hall Room B307E, 1295N. Martin Avenue, Tucson, AZ 85719. E-mail: firstname.lastname@example.org.