IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2606.22254.html

Professional networks and the diffusion of clinical guidelines in opioid prescribing

Author

Listed:
  • Yi-Ning Weng
  • Hsuan-Wei Lee

Abstract

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.

Suggested Citation

  • Yi-Ning Weng & Hsuan-Wei Lee, 2026. "Professional networks and the diffusion of clinical guidelines in opioid prescribing," Papers 2606.22254, arXiv.org.
  • Handle: RePEc:arx:papers:2606.22254
    as

    Download full text from publisher

    File URL: https://arxiv.org/pdf/2606.22254
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Locock, Louise & Dopson, Sue & Chambers, David & Gabbay, John, 2001. "Understanding the role of opinion leaders in improving clinical effectiveness," Social Science & Medicine, Elsevier, vol. 53(6), pages 745-757, September.
    2. Syngjoo Choi & Sanjeev Goyal & Frederic Moisan & Yu Yang Tony To, 2023. "Learning in Networks: An Experiment on Large Networks with Real-World Features," Management Science, INFORMS, vol. 69(5), pages 2778-2787, May.
    3. Ang, Ricardo B., 2025. "Expanded prescription coverage and opioid use disorders: Evidence from Medicare Part D," Economics & Human Biology, Elsevier, vol. 59(C).
    4. Muzhe Yang & Hsien-Ming Lien & Shin-Yi Chou, 2014. "Is There A Physician Peer Effect? Evidence From New Drug Prescriptions," Economic Inquiry, Western Economic Association International, vol. 52(1), pages 116-137, January.
    5. Paul Goldsmith-Pinkham & Guido W. Imbens, 2013. "Social Networks and the Identification of Peer Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 253-264, July.
    6. Lee, Hsuan-Wei & Huang, Yi-Hsuan & Malik, Nishant, 2026. "Adaptive network dynamics and behavioral contagion in multi-state drug use propagation," Chaos, Solitons & Fractals, Elsevier, vol. 202(P1).
    7. Arun G. Chandrasekhar & Horacio Larreguy & Juan Pablo Xandri, 2020. "Testing Models of Social Learning on Networks: Evidence From Two Experiments," Econometrica, Econometric Society, vol. 88(1), pages 1-32, January.
    8. Lee, Hsuan-Wei & Weng, Yi-Ning, 2025. "Granular Q-learning adaptation boosts collective welfare in multi-agent Prisoner’s Dilemma," Chaos, Solitons & Fractals, Elsevier, vol. 199(P1).
    9. Michelle Marcus & Pedro H. C. Sant’Anna, 2021. "The Role of Parallel Trends in Event Study Settings: An Application to Environmental Economics," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 8(2), pages 235-275.
    10. Albert Chiu & Xingchen Lan & Ziyi Liu & Yiqing Xu, 2023. "Causal Panel Analysis under Parallel Trends: Lessons from a Large Reanalysis Study," Papers 2309.15983, arXiv.org, revised Jan 2026.
    11. Chiu, Albert & Lan, Xingchen & Liu, Ziyi & Xu, Yiqing, 2026. "Causal Panel Analysis under Parallel Trends: Lessons from a Large Reanalysis Study," American Political Science Review, Cambridge University Press, vol. 120(1), pages 245-266, February.
    12. Andrea Galeotti & Benjamin Golub & Sanjeev Goyal, 2020. "Targeting Interventions in Networks," Econometrica, Econometric Society, vol. 88(6), pages 2445-2471, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Krishna Dasaratha & Anant Shah, 2026. "Network Interventions: Targeting Agents or Targeting Links?," Papers 2602.12897, arXiv.org.
    2. Raúl Duarte & Frederico Finan & Horacio Larreguy & Laura Schechter, 2019. "Brokering Votes With Information Spread Via Social Networks," NBER Working Papers 26241, National Bureau of Economic Research, Inc.
    3. Jeong, Daeyoung & Shin, Euncheol, 2024. "Optimal influence design in networks," Journal of Economic Theory, Elsevier, vol. 220(C).
    4. Zenou, Yves, 2026. "Peer vs. Network Effects: Microfoundations, Identification, and Beyond," IZA Discussion Papers 18501, IZA Network @ LISER.
    5. Davide Viviano, 2019. "Policy Targeting under Network Interference," Papers 1906.10258, arXiv.org, revised Apr 2024.
    6. Vod Vilfort, 2026. "Robust Inference for Weighted Estimands," Papers 2607.07524, arXiv.org.
    7. Zhongjian Lin & Francis Vella, 2024. "Endogenous Treatment Models with Social Interactions: An Application to the Impact of Exercise on Self-Esteem," Papers 2408.13971, arXiv.org.
    8. Boucher, Vincent & Dedewanou, F. Antoine & Dufays, Arnaud, 2022. "Peer-induced beliefs regarding college participation," Economics of Education Review, Elsevier, vol. 90(C).
    9. Andrew S. Rosenberg, 2026. "Reliable Panel Regression: A Default Workflow for Slow-Moving, Mismeasured Variables," Papers 2606.14009, arXiv.org.
    10. Marco Battaglini & Eleonora Patacchini & Edoardo Rainone, 2019. "Endogenous Social Connections in Legislatures," NBER Working Papers 25988, National Bureau of Economic Research, Inc.
    11. Tiziano Arduini & Eleonora Patacchini & Edoardo Rainone, 2014. "Identification and Estimation of Outcome Response with Heterogeneous Treatment Externalities," EIEF Working Papers Series 1407, Einaudi Institute for Economics and Finance (EIEF), revised Sep 2014.
    12. Guignet, Dennis & Jenkins, Robin R. & Belke, James & Mason, Henry, 2023. "The property value impacts of industrial chemical accidents," Journal of Environmental Economics and Management, Elsevier, vol. 120(C).
    13. Sebastiano Della Lena & Alessio Muscillo & Paolo Pin, 2026. "How do you know you won't like it if you've (never) tried it? Preference discovery and data design," Papers 2604.14260, arXiv.org.
    14. Ariel BenYishay & A. Mushfiq Mobarak, 2014. "Social Learning and Communication," NBER Working Papers 20139, National Bureau of Economic Research, Inc.
    15. Tomoya MORI & Shosei SAKAGUCHI, 2018. "Collaborative Knowledge Creation: Evidence from Japanese patent data," Discussion papers 18068, Research Institute of Economy, Trade and Industry (RIETI).
    16. Marina Agranov & Benjamin Gillen & Dotan Persitz, 2024. "A Comment on “Testing Models of Social Learning on Networks: Evidence From Two Experiments”," Econometrica, Econometric Society, vol. 92(5), pages 1-6, September.
    17. Rusinowska, Agnieszka & Taalaibekova, Akylai, 2019. "Opinion formation and targeting when persuaders have extreme and centrist opinions," Journal of Mathematical Economics, Elsevier, vol. 84(C), pages 9-27.
    18. Thomas J. Sargent & John Stachurski, 2022. "Economic Networks: Theory and Computation," Papers 2203.11972, arXiv.org, revised Jul 2022.
    19. Yann Bramoullé & Habiba Djebbari & Bernard Fortin, 2020. "Peer Effects in Networks: A Survey," Annual Review of Economics, Annual Reviews, vol. 12(1), pages 603-629, August.
    20. Patrick Lloyd‐Smith & Ewa Zawojska, 2025. "How stable and predictable are welfare estimates using recreation demand models?," American Journal of Agricultural Economics, John Wiley & Sons, vol. 107(3), pages 846-868, May.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2606.22254. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: https://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.