Fig 1.
Illustration of physician peer networks.
The figure shows the affiliation networks of a typical physician in our study cohorts (bottom left). Within the cohort of physicians prescribing the drug class of interest, she is connected to peers (shown in pink) with whom she attended the same medical school (one year +/-) or with whom she completed the same residency program (one year +/-). She is connected to peers (shown in yellow) through the medical group where she has an outpatient practice and to peers (shown in blue) through the hospital where she admits patients. In addition, she shares Medicare and Medicaid patients with several physicians. The patient-sharing network is represented by the lines in the figure; line thickness corresponds to the number of patients shared between physicians. Connections shown in orange are affiliated with the physician in this illustration through shared training institution and medical group. Connections shown in green share a medical group and hospital affiliation in common with the physician. Connections shown in purple only have shared patients with the physician.
Table 1.
Characteristics of prescriber cohorts for each class of new drug.
Fig 2.
a-c: Unadjusted relationship between physicians’ own adoption rate and that of their peers in the patient-sharing network.
The graphs show the average physicians’ own adoption rate for a given fraction of peers in the patient-sharing network adopting the drug. The size of the bubble corresponds to the number of physicians with a particular peer adoption rate. The graphs show a positive association between the fraction of peers adopting the new drug and the physicians’ own likelihood of adopting. For example, with dabigatran, physicians in networks in which the fraction of peers adopting is 0.65 have a 0.38 probability of adopting compared to physicians whose peers’ adoption rate is 0.35 who have only a 0.19 probability of adopting.
Fig 3.
Estimated peer effects on drug adoption with 95% confidence intervals.
Estimates show the effect of a 1% absolute change in the adoption rate of a physician’s peers on the likelihood of a physician’s decision to adopt the new drug. For example, a 1% increase in patient-sharing network peer adoption of dabigatran corresponded to a 0.59% increase in the probability of own adoption. Estimates are from a two-stage least squares regression model including physician-level characteristics: sex, medical school graduation year, US vs. non-US medical school, Top 20 U.S. medical school vs. not, geographic indicators (hospital referral region and metropolitan vs. non-metropolitan, prescription share paid for by Medicare, Medicaid fee-for-service, and cash, and age of patients filling prescriptions (see S10 Table for full model estimates). The means of peer characteristics serve as instruments for peer adoption rates. Included in the second stage were: physician specialty (primary care physician, relevant sub-specialty; e.g., cardiologist, endocrinologist, nephrologist vs. other), proportion of peers in each network who are in each specialty group, an indicator for whether the physician was a high-volume prescriber (> = median), the proportion of peers in each network who were high-volume prescribers, and an indicator for physicians who do not have peers in a particular type of network in the cohort. Peer effects estimates in the medical group and training networks are not reliable due to poor predictive power of the instruments.
Fig 4.
Physicians’ average social multiplier by prescribing volume and number of connections in the patient-sharing network.
The social multiplier captures the number of other physicians who might adopt dabigatran following adoption by an individual physician in their peer network. Thus, the figure shows the simulated number of physicians who would adopt dabigatran per physician in each decile of prescribing volume (dark blue bars) and number of connections in the patient-sharing network (striped bars) adopting dabigatran. Physicians in the top decile of patient-sharing who had the most connections with other physicians who adopted dabigatran appear to have a nearly 2-fold stronger influence on adoption by other physicians compared to physicians in the top decile of prescribing volume (4.81 vs. 2.64).