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New clinical information and physician prescribing: How do pediatric labeling changes affect prescribing to children?

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  • Mary K. Olson
  • Nina Yin

Abstract

Our study examines how physician prescribing responds to new scientific information added to drug labels. We focus on a series of label changes with new information about the effects of drugs in children. The information arose in response to a 1997 policy, pediatric exclusivity, which gave drug sponsors a 6‐month exclusivity extension for conducting additional pediatric studies of already marketed drugs. The information from these studies was expected to improve pediatric prescribing by promoting appropriate use and by reducing inappropriate off‐label prescribing. However, there has been little study about the actual effects of these labeling changes on physician prescribing behavior. We use a difference‐in‐differences strategy to examine how pediatric prescriptions respond to different types of labeling changes. Our results show that most label changes lead to reductions in prescribing to children. We find that the largest drop in prescribing occurs when the label indicates a drug is not effective for children. The evidence suggests that the labeling changes alleviated physician uncertainty about prescribing drugs to children and reduced some inappropriate off‐label use.

Suggested Citation

  • Mary K. Olson & Nina Yin, 2021. "New clinical information and physician prescribing: How do pediatric labeling changes affect prescribing to children?," Health Economics, John Wiley & Sons, Ltd., vol. 30(1), pages 144-164, January.
  • Handle: RePEc:wly:hlthec:v:30:y:2021:i:1:p:144-164
    DOI: 10.1002/hec.4182
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    References listed on IDEAS

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    1. W. David Bradford & Andrew N. Kleit, 2015. "Impact of FDA Actions, DTCA, and Public Information on the Market for Pain Medication," Health Economics, John Wiley & Sons, Ltd., vol. 24(7), pages 859-875, July.
    2. Soumerai, S.B. & Avorn, J. & Gortmaker, S. & Hawley, S., 1987. "Effect of government and commercial warnings on reducing prescription misuse: The case of propoxyphene," American Journal of Public Health, American Public Health Association, vol. 77(12), pages 1518-1523.
    3. Mary K. Olson & Nina Yin, 2018. "Examining Firm Responses to R&D Policy: An Analysis of Pediatric Exclusivity," American Journal of Health Economics, University of Chicago Press, vol. 4(3), pages 321-357, Summer.
    4. Dubois, Pierre & Tunçel, Tuba, 2021. "Identifying the effects of scientific information and recommendations on physicians’ prescribing behavior," Journal of Health Economics, Elsevier, vol. 78(C).
    5. David H. Autor, 2003. "Outsourcing at Will: The Contribution of Unjust Dismissal Doctrine to the Growth of Employment Outsourcing," Journal of Labor Economics, University of Chicago Press, vol. 21(1), pages 1-42, January.
    6. Gregory S. Crawford & Matthew Shum, 2005. "Uncertainty and Learning in Pharmaceutical Demand," Econometrica, Econometric Society, vol. 73(4), pages 1137-1173, July.
    7. Kristy Parkinson & Joseph Price & Kosali Simon & Sharon Tennyson, 2014. "The influence of FDA advisory information and black box warnings on individual use of prescription antidepressants," Review of Economics of the Household, Springer, vol. 12(4), pages 771-790, December.
    8. Maria Marta Ferreyra & Grigory Kosenok, 2011. "Learning About New Products: An Empirical Study Of Physicians' Behavior," Economic Inquiry, Western Economic Association International, vol. 49(3), pages 876-898, July.
    9. Pierre Azoulay, 2002. "Do Pharmaceutical Sales Respond to Scientific Evidence?," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 11(4), pages 551-594, December.
    10. Coscelli, Andrea & Shum, Matthew, 2004. "An empirical model of learning and patient spillovers in new drug entry," Journal of Econometrics, Elsevier, vol. 122(2), pages 213-246, October.
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    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Chris Sampson’s journal round-up for 4th January 2021
      by Chris Sampson in The Academic Health Economists' Blog on 2021-01-04 12:00:05

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