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Figure.
Association Between Receipt of Pharmaceutical Industry Payments in 2013 and Drug Prescribing in 2014, by Individual Drug
Association Between Receipt of Pharmaceutical Industry Payments in 2013 and Drug Prescribing in 2014, by Individual Drug

A and B, Multivariable logistic regression was used to estimate the association between receipt of manufacturer payments and physicians’ prescribing of that manufacturer’s drug. Each bar represents the market share for a specific drug (the probability of using the drug of interest relative to the other included treatments). Physicians who received payments from the manufacturer of the drug of interest are shown in light blue; physicians who did not are shown in dark blue. Results are adjusted for physician age, region, practice size, prescribing volume, and year of medical school graduation. Sunitinib was given as sunitinib malate; pazopanib as pazopanib hydrochloride; imatinib as imatinib mesylate; and nilotinib as nilotinib hydrochloride monohydrate.

aP < .05.

Table.  
Association Between Pharmaceutical Industry Payments Received in 2013 and Drug Choice in 2014a
Association Between Pharmaceutical Industry Payments Received in 2013 and Drug Choice in 2014a
1.
Centers for Medicare & Medicaid Services. The facts about Open Payments data. https://openpaymentsdata.cms.gov/summary. Published 2016. Accessed March 4, 2018.
2.
Yeh  JS, Franklin  JM, Avorn  J, Landon  J, Kesselheim  AS.  Association of industry payments to physicians with the prescribing of brand-name statins in Massachusetts.  JAMA Intern Med. 2016;176(6):763-768.PubMedGoogle ScholarCrossref
3.
DeJong  C, Aguilar  T, Tseng  CW, Lin  GA, Boscardin  WJ, Dudley  RA.  Pharmaceutical industry–sponsored meals and physician prescribing patterns for Medicare beneficiaries.  JAMA Intern Med. 2016;176(8):1114-1122.PubMedGoogle ScholarCrossref
4.
Perlis  RH, Perlis  CS.  Physician payments from industry are associated with greater Medicare Part D prescribing costs.  PLoS One. 2016;11(5):e0155474.PubMedGoogle ScholarCrossref
5.
Ratain  MJ.  Forecasting unanticipated consequences of “The Sunshine Act”: mostly cloudy.  J Clin Oncol. 2014;32(22):2293-2295.PubMedGoogle ScholarCrossref
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    1 Comment for this article
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    Undisclosed Limitations Are Important
    Kevin Freiert, MBA | Salem Oaks Consulting
    The authors have missed one obvious covariant in their statistical model. Yes, it appears that higher payments are associated with higher prescription rates for some of the drugs in the study. However, we need to look at what is behind those two variables.

    First, let's look at payments. Physicians are highly educated and work extremely hard for their living. Because of this, they usually get paid for the work that they do. Clinical research requires additional care, recordkeeping, and sometimes equipment and instrumentation. Clinical trial work is usually paid on a per patient
    basis. Thus, the more patients an investigator enrolls, the more they get paid.

    Second, what drives adoption of a new drug? Experience using that particular drug. Thus, investigators who enroll more patients in clinical trials gain more experience and are more likely to be confident using a new treatment in their practice once it is approved. This will add to their experience and result in higher tendencies to prescribe this medication.

    The missing covariant in the analysis is work, which can be expressed as numbers of patients enrolled in clinical studies under their care. I encourage the authors to reexamine their data on this basis before casting aspersions on the "strategies" of companies or the judgment of physicians.
    CONFLICT OF INTEREST: I spent 30 years in pharma R&D and thus own stock in Pfizer
    READ MORE
    Research Letter
    June 2018

    Pharmaceutical Industry Payments and Oncologists’ Selection of Targeted Cancer Therapies in Medicare Beneficiaries

    Author Affiliations
    • 1Division of Hematology/Oncology, Department of Medicine, UNC School of Medicine, The University of North Carolina at Chapel Hill
    • 2Lineberger Comprehensive Cancer Center, UNC School of Medicine, The University of North Carolina at Chapel Hill
    • 3The Cecil G. Sheps Center for Health Services Research, The University of North Carolina at Chapel Hill
    • 4School of Pharmacy, Medical College of Wisconsin, Milwaukee
    • 5Cancer Center, Medical College of Wisconsin, Milwaukee
    • 6Department of Health Policy, Vanderbilt University Medical Center, Nashville, Tennessee
    • 7Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee
    JAMA Intern Med. 2018;178(6):854-856. doi:10.1001/jamainternmed.2018.0776

    Physicians and teaching hospitals in the United States receive approximately $7 billion from the pharmaceutical industry annually.1 These payments have been associated with higher-cost, brand-name pharmaceutical prescribing.2-4 Whether industry payments are associated with physician treatment choice in oncology is uncertain. We examined the association between oncologists’ receipt of payments from pharmaceutical manufacturers and drug selection in 2 situations where there are multiple treatment options.

    Methods

    We linked the Centers for Medicare & Medicaid Services Open Payments data and the Medicare Part D Prescriber Public Use File for calendar years 2013 and 2014. The study used the Open Payments categorizations of general payments (eg, gifts, consultancy/speaker fees, meals, and travel) and research payments (eg, preclinical research, US Food and Drug Administration phase 1-4 trials, or investigator-initiated studies). We linked Part D Prescriber data to Open Payments using the National Provider Identifier and practice location via the National Plan and Provider Enumeration System. This study was exempted from review by the University of North Carolina institutional review board as not constituting human participants research.

    We considered on-patent drugs that were within the same therapeutic class; had US Food and Drug Administration approval and National Comprehensive Cancer Network recommendation for treatment of a cancer of a given site, stage, and degree of previous treatment; and were prescribed by at least 10 physicians in 2014. This resulted in the following 2 sets of drugs: sorafenib, sunitinib malate, and pazopanib hydrochloride (metastatic renal cell cancer [mRCC] group) and dasatinib, imatinib mesylate, and nilotinib hydrochloride monohydrate (chronic myeloid leukemia [CML] group).

    We included physicians with a provider type of oncologist and at least 20 filled prescriptions among the respective 3 drugs in 2014. For each physician, we included all general payments from each drug manufacturer. We attributed research payments to physicians identified as principal investigators. Our primary exposure was payments received during 2013 (yes or no), and the primary outcome was prescriptions filled during 2014; we also analyzed payments as a continuous variable. We used the conditional logit model by McFadden to test whether receipt of payments from a manufacturer was associated with increased relative prescribing of that manufacturer’s drug within the choice set of multiple drugs. Separately, we evaluated drug-specific results using multivariable logistic regression, controlling for physician age, region, practice size, and prescribing volume. Separate models were estimated for general payments and research payments.

    Results

    Among 354 physicians who prescribed mRCC drugs and 2225 physicians who prescribed CML drugs, we found increased odds of prescribing a manufacturer’s drug among physicians receiving general payments only or either payment type (Table). Of physicians prescribing the drugs, 9.0% (32 of 354) of those prescribing for mRCC and 3.8% (38 of 2225) of those prescribing for CML received research payments in both 2013 and 2014, compared with 25.1% (89 of 354) and 39.5% (879 of 2225) for general payments, respectively. Receipt of research payments was associated with increased prescribing for mRCC but not CML. Similarly, when treating payments as a continuous variable, increasing amounts of general payments were associated with increased prescribing.

    Considering individual drugs, we found increased prescribing when receiving vs not receiving general payments for sunitinib (50.5% vs 34.4%, P = .01), dasatinib (13.8% vs 11.4%, P = .02), and nilotinib (15.4% vs 12.5%, P = .01) (Figure) but found decreased prescribing of imatinib (72.4% vs 75.5%, P = .02). Differences for sorafenib and pazopanib were not statistically significant. Research payments were not associated with statistically significant differences in prescribing for any individual drug. Results were similar when including payments specifically attributed to the drug of interest rather than all payments from the corresponding manufacturer and when changing the exposure to receipt of payments in both 2013 and 2014 (vs 2013 without respect to 2014). Our study had some limitations. These include the observational design precluding causal assessment, potential inaccuracies with Open Payments data,5 lack of generalizability to other cancers, absence of information about the indications for the drugs, and small sample sizes for comparisons in the research payments analysis, notably for physicians receiving CML research payments.

    Conclusions

    For 3 of the 6 cancer drugs studied, physicians who received general payments were more likely to prescribe the drug marketed by the company that made the payments. Imatinib was a notable exception; this may reflect a strategy by the manufacturer of imatinib (which also produces nilotinib) to promote switching to nilotinib before the patent expiration of imatinib in 2015.

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    Article Information

    Corresponding Author: Stacie B. Dusetzina, PhD, Department of Health Policy, Vanderbilt University Medical Center, 2525 West End Ave, Ste 1203, Nashville, TN 37203 (s.dusetzina@vanderbilt.edu).

    Accepted for Publication: February 1, 2018.

    Published Online: April 9, 2018. doi:10.1001/jamainternmed.2018.0776

    Author Contributions: Drs Mitchell and Winn had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Study concept and design: All authors.

    Acquisition, analysis, or interpretation of data: All authors.

    Drafting of the manuscript: Mitchell, Winn.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: All authors.

    Study supervision: Dusetzina.

    Conflict of Interest Disclosures: None reported.

    Funding/Support: Dr Mitchell’s time is supported by a National Service Research Award postdoctoral traineeship from the Agency for Healthcare Research and Quality sponsored by The Cecil G. Sheps Center for Health Services Research, The University of North Carolina at Chapel Hill (grant 5T32 HS000032-28). Dr Winn’s time during this project was partially supported by a Royster Society of Fellows doctoral fellowship at The University of North Carolina at Chapel Hill. Dr Dusetzina’s time during this project was partially supported by Research Scholar Grant RSGI-14-030-01-CPHPS from the American Cancer Society.

    Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Meeting Presentation: This paper was presented at the 2017 American Society of Clinical Oncology Annual Meeting; June 3, 2017; Chicago, Illinois.

    References
    1.
    Centers for Medicare & Medicaid Services. The facts about Open Payments data. https://openpaymentsdata.cms.gov/summary. Published 2016. Accessed March 4, 2018.
    2.
    Yeh  JS, Franklin  JM, Avorn  J, Landon  J, Kesselheim  AS.  Association of industry payments to physicians with the prescribing of brand-name statins in Massachusetts.  JAMA Intern Med. 2016;176(6):763-768.PubMedGoogle ScholarCrossref
    3.
    DeJong  C, Aguilar  T, Tseng  CW, Lin  GA, Boscardin  WJ, Dudley  RA.  Pharmaceutical industry–sponsored meals and physician prescribing patterns for Medicare beneficiaries.  JAMA Intern Med. 2016;176(8):1114-1122.PubMedGoogle ScholarCrossref
    4.
    Perlis  RH, Perlis  CS.  Physician payments from industry are associated with greater Medicare Part D prescribing costs.  PLoS One. 2016;11(5):e0155474.PubMedGoogle ScholarCrossref
    5.
    Ratain  MJ.  Forecasting unanticipated consequences of “The Sunshine Act”: mostly cloudy.  J Clin Oncol. 2014;32(22):2293-2295.PubMedGoogle ScholarCrossref
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