arXiv 20 Jun 2026 · physics.soc-ph
arXiv:2606.22254 · PDF · DOI · OpenAlex · Extracted main text
Large and persistent differences in opioid prescribing across physicians and regions cannot be explained by patient characteristics or physician attributes alone. We developed a behavioral framework in which prescribing evolves through persistence, exposure to peers in professional networks, and heterogeneous responses to a common policy signal that varies with network centrality. Using nationwide Medicare Part D data from 2013 to 2020, covering more than two million physician-year observations, we tested three hypotheses implied by this framework. Physicians exposed to higher peer prescribing subsequently prescribe more; more central physicians reduce prescribing more following the introduction of the 2016 CDC guideline, with no evidence of differential pre-trends; and changes in peer prescribing are closely associated with changes in individual prescribing in the post-guideline period. By 2020, physicians at the 90th percentile of network centrality exhibited prescribing reductions 0.30 percentage points larger than those at the 10th percentile, with the gap widening steadily after the introduction of the CDC guideline. Together, these results indicate that opioid prescribing operates through professional networks, in which policy effects spread through connections and appear to be shaped by network position. This suggests that engaging highly connected physicians may help extend the reach of opioid stewardship programs. It also raises questions about how the burden and benefits of such targeting would be distributed across physicians and patients.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Golub, Benjamin and Jackson, Matthew O (2010) Naive Learning in Social Networks and the Wisdom of Crowds | 0.843 | 3 | 3 | 100% |
| 2 | Cunningham, Frances C and Ranmuthugala, Geetha and Plumb, Jennifer a… (2012) Health professional networks as a vector for improving healthcare quality and safety: A systematic review | 0.737 | 3 | 2 | 100% |
| 3 | Ang III, Ricardo B (2025) Expanded prescription coverage and opioid use disorders: Evidence from Medicare Part D | 0.644 | 2 | 2 | 100% |
| 4 | Bohnert, Amy SB and Guy Jr, Gery P and Losby, Jan L (2018) Opioid prescribing in the United States before and after the Centers for Disease Control and Prevention's 2016 opioid guideline | 0.644 | 2 | 2 | 100% |
| 5 | Chandrasekhar, Arun G and Larreguy, Horacio and Xandri, Juan Pablo (2020) Testing models of social learning on networks: Evidence from two experiments | 0.644 | 2 | 2 | 100% |
| 6 | Donohue, Julie M and Guclu, Hasan and Gellad, Walid F and Chang, Chu… (2018) Influence of peer networks on physician adoption of new drugs | 0.644 | 2 | 2 | 100% |
| 7 | Galeotti, Andrea and Golub, Benjamin and Goyal, Sanjeev (2020) Targeting interventions in networks | 0.644 | 2 | 2 | 100% |
| 8 | Hao, Haijing and Padman, Rema (2018) An empirical study of opinion leader effects on mobile technology implementation by physicians in an American community health s… | 0.644 | 2 | 2 | 100% |
| 9 | Soumerai, Stephen B and McLaughlin, Thomas J and Gurwitz, Jerry H an… (1998) Effect of local medical opinion leaders on quality of care for acute myocardial infarction: A randomized controlled trial | 0.644 | 2 | 2 | 100% |
| 10 | Tasselli, Stefano (2015) Social networks and inter-professional knowledge transfer: The case of healthcare professionals | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 43 scored citations.