Drug Diffusion Through Peer Networks: The Influence of Industry Payments

Abstract

Pharmaceutical companies market to physicians through individual detailing accompanied by monetary or in-kind transfers. Large compensation payments to a small number of physicians account for most of this promotional spending. Studying US promotional payments and prescriptions for anticoagulant drugs, we investigate how peer influence broadens the payments' reach. Following a compensation payment, prescriptions for the marketed drug increase by both the paid physician and the paid physician's peers. Payments increase prescriptions to both recommended and contraindicated patients. Over three years, marketed anticoagulant prescriptions rose 23 percent due to payments, with peer spillovers contributing a quarter of the increase.

Keywords: I11; L14; O33; health care; networks; peer effects; pharmaceutical advertising; technology diffusion.

Figure 1:

Event Study: The Impact of Payments on Prescription Volume Notes: Figure shows event study coefficients estimated from equation (1), showing the response of physicians to own and peer payments of different types. The facets show coefficients for different payment types—own food, own compensation, and peer compensation—that were all jointly estimated using 5,467,536 doctor-drug-quarter observations. Panel (A) reports coefficients from a single regression that excludes a differential pre-trend for paid physicians; a dashed line is fitted to the pre-trend for illustration. Panel (B) reports coefficients from a single regression after detrending, using the two-step procedure described in Section 2. All regressions also include variables for peer food, own travel, and peer travel, alongside fixed effects for doctor-drug and drug-specialty-quarter. Quarter 0 indicates the quarter of the first payment of each type. Shaded areas show 95 percent confidence intervals. Note that facet vertical axes have different scales.

Figure 2:

NOAC Prescription Volume over Time Notes: For the three NOAC drugs we study, figure shows the average number of prescribed beneficiaries per quarter, per physician, in our sample. The average covers all physician-quarters in our sample, including those with zero prescriptions. The FDA first approved Pradaxa in 2010, Xarelto in 2011, and Eliquis in 2012. Data are from a 40 percent of Medicare Part D claims.

Figure 3:

Cumulative Payments over Time, by Type of Payment and Medical Specialty Notes: Figures show cumulative information on the volume of payments associated with each of the three NOACs in our sample. Panel A shows the fraction of physicians who received at least one payment. Panel B shows information on the average number of payments per physician, including zero payments. Note that facet vertical axes have different scales. In each panel, column of facets shows data for a different medical specialty: cardiac specialties (left) and primary care (right). Each row of facets shows payments of a different type: Food category includes education, food, and beverage transfers; Compensation includes compensation for services and consulting fees. Section 1 describes the specialty and payment category definitions.

Figure 4:

Average Number of Payments by Recipient Number of Peers Notes: For each specialty and type of payment, figure shows the average number of payments made to each physician (y-axis), by deciles of the recipient’s number of peers (x-axis). Deciles are calculated separately for each HRR and specialty. Error bars show 95 percent confidence interval for the mean. Note that facet vertical axes have different scales. Each row of facets shows payments of a different type: Food category includes education, food, and beverage transfers; Compensation includes compensation for services and consulting fees. Each column shows data for a different medical specialty: cardiac specialties (left) and primary care (right). For specialty definitions see Section 1.

Figure 5:

Pharmaceutical Payment Impact on Prescription Volumes Notes: Figure shows estimates of direct and peer effects of pharmaceutical payments on prescription volume, by type of payment. Panel (A) shows the estimated effect of a single payment of each type on the annual number of unique beneficiaries prescribed the target drug by the payment recipient (light shade) and by all of the recipient’s peers (dark shade). The impact of peer food payments (− 0.0022, s.e. = 0.0281) is shown as zero. Panel (B) shows the number of pharmaceutical payments and in-kind transfers associated with NOAC drugs in 2014–2016. Panel (C) shows the estimated overall contribution of payments to annual NOAC prescription spending by direct recipients (light shade) and their peers (dark shade) in the United States. To obtain estimates of total spending in US dollars, we multiply the estimated counterfactual number of beneficiaries per quarter with the quarterly average cost of prescriptions of each drug. These average costs are fairly stable over our sample period, as seen in Appendix Figure A4. Panel (A) is based on a 40 percent sample of Medicare Part D beneficiaries; dollar estimates in Panel (C) are annualized and rescaled by a factor of 5.4 to extrapolate to all US prescriptions. This scaling factor is discussed in Section 1. Data sources are described in Section 6. Underlying data are reported in Appendix Table A7.