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      Association of Branded Prescription Drug Rebate Size and Patient Out-of-Pocket Costs in a Nationally Representative Sample, 2007-2018

      research-article
      , PharmD, PhD 1 , 2 , , , PhD 3 , 4 , , PhD 2 , 5
      JAMA Network Open
      American Medical Association

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          Abstract

          This cross-sectional study investigates the association of branded prescription drug rebate size and patient out-of-pocket costs.

          Key Points

          Question

          Are prescription drug rebates associated with increased patient out-of-pocket costs?

          Findings

          In this cross-sectional study of estimated rebates for 444 unique branded drugs with prescriptions filled by 38 131 unique individuals, increased rebate sizes were associated with increased out-of-pocket costs for those with Medicare, commercial insurance, or no insurance. Associations between rebates and out-of-pocket costs were associated with simultaneous increases in list prices.

          Meaning

          These findings suggest that while drug manufacturers may increase list prices in order to offer larger rebates to insurers, such increases were associated with increased out-of-pocket costs, especially among individuals without insurance.

          Abstract

          Importance

          Over the past decade, branded prescription drug manufacturers have substantially increased list prices while offering larger rebate payments to health care insurers. Whereas larger rebates can partially offset increases in list prices for insurers, patient out-of-pocket costs may be directly associated with list prices for individuals without insurance and indirectly associated with list prices for individuals with insurance through deductibles or coinsurance.

          Objective

          To investigate the association between rebates and patient out-of-pocket costs and whether this association differs by coverage type (ie, Medicare, commercial, or uninsured) and before and after 2014.

          Design, Setting, and Participants

          This cross-sectional study was conducted using data from the Medical Expenditure Panel Survey (MEPS) combined with pricing data for single-source branded drugs from SSR Health from 2007 through 2018. The study was conducted among a nationally representative sample of the noninstitutionalized civilian US population. Included individuals were respondents to MEPS with at least 1 prescription for a single-source branded drug who were covered by Medicare or commercial insurance or were uninsured during an entire year. Data analyses were conducted from August 2019 through March 2021.

          Exposures

          Estimated rebate size.

          Main Outcomes and Measures

          Out-of-pocket costs per prescription were calculated, adjusting for year and drug.

          Results

          Among 38 131 individuals with at least 1 prescription, the mean age was 54 years (95% CI, 54 to 55 years), with 22 044 women (57.8%) and 29 086 White individuals (76.3%). The sample included 444 unique drugs with a survey-weighted total of 4.7 billion prescriptions. Estimated mean (SE) rebates increased from $34 ($1) per prescription in 2007 to $374 ($9) per prescription in 2018. The rebate sizes were associated with statistically significant mean out-of-pocket increases per branded prescription of $4 (95% CI, $4 to $4) from 2007 to 2013 and $11 (95% CI, $10 to $12) from 2014 to 2018. From 2014 to 2018, rebate sizes were associated with statistically significant mean increases in out-of-pocket costs per prescription of $13 (95% CI, $12 to $13) for individuals with Medicare, $6 (95% CI, $6 to $7) for individuals with commercial insurance, and $39 (95% CI, $34 to $44) for individuals without insurance. After adjusting for list prices, there was no association between rebates and out-of-pocket costs, with a change from 2014 to 2018 of −$0.01 (95% CI, −$0.04 to $0.02).

          Conclusions and Relevance

          These findings suggest that drug manufacturers may have provided larger rebates to insurers primarily by increasing list prices and that individuals without insurance had greater cost increases. The results emphasize the need for policy solutions that decouple list prices and out-of-pocket costs.

          Related collections

          Most cited references29

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          Applied Logistic Regression

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            Social Determinants of Risk and Outcomes for Cardiovascular Disease: A Scientific Statement From the American Heart Association.

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              Too much ado about two-part models and transformation? Comparing methods of modeling Medicare expenditures.

              Many methods for modeling skewed health care cost and use data have been suggested in the literature. This paper compares the performance of eight alternative estimators, including OLS and GLM estimators and one- and two-part models, in predicting Medicare costs. It finds that four of the alternatives produce very similar results in practice. It then suggests an efficient method for researchers to use when selecting estimators of health care costs. Copyright 2004 Elsevier B.V.
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                Author and article information

                Journal
                JAMA Netw Open
                JAMA Netw Open
                JAMA Netw Open
                JAMA Network Open
                American Medical Association
                2574-3805
                14 June 2021
                June 2021
                14 June 2021
                : 4
                : 6
                : e2113393
                Affiliations
                [1 ]Kaiser Permanente Washington Health Research Institute, Seattle, Washington
                [2 ]Comparative Health Outcomes, Policy, and Economics Institute, University of Washington, Seattle, Washington
                [3 ]Department of Health Policy, Vanderbilt University School of Medicine, Nashville, Tennessee
                [4 ]Vanderbilt-Ingram Cancer Center, Nashville, Tennessee
                [5 ]National Bureau of Economic Research, Cambridge, Massachusetts
                Author notes
                Article Information
                Accepted for Publication: April 15, 2021.
                Published: June 14, 2021. doi:10.1001/jamanetworkopen.2021.13393
                Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Yeung K et al. JAMA Network Open.
                Corresponding Author: Kai Yeung, PharmD, PhD, Kaiser Permanente Washington Health Research Institute, Metropolitan Park East, 1730 Minor Ave, #1600, Seattle, WA 98101 ( Kai.Yeung@ 123456kp.org ).
                Author Contributions: Dr Yeung had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
                Concept and design: All authors.
                Acquisition, analysis, or interpretation of data: All authors.
                Drafting of the manuscript: Yeung, Basu.
                Critical revision of the manuscript for important intellectual content: All authors.
                Statistical analysis: Yeung, Dusetzina.
                Obtained funding: Yeung.
                Administrative, technical, or material support: Yeung.
                Supervision: Yeung, Basu.
                Conflict of Interest Disclosures: Dr Dusetzina reported receiving grants from Arnold Ventures and the Commonwealth Fund for other work on prescription drug pricing and rebates during the conduct of the study and grants from the Robert Wood Johnson Foundation and Leukemia and Lymphoma Society and personal fees from the National Academy for State Health Policy, Institute for Clinical and Economic Review, and West Health outside the submitted work. Dr Basu reported receiving consulting fees from Salutis Consulting outside the submitted work.
                Funding/Support: This research was supported in part by research grant funding from the Donaghue Foundation’s Greater Value Portfolio.
                Role of the Funder/Sponsor: The funder 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.
                Article
                zoi210406
                10.1001/jamanetworkopen.2021.13393
                8204201
                34125219
                04d90a0c-8659-4ad7-a586-d39499b1c01a
                Copyright 2021 Yeung K et al. JAMA Network Open.

                This is an open access article distributed under the terms of the CC-BY License.

                History
                : 27 January 2021
                : 15 April 2021
                Categories
                Research
                Original Investigation
                Online Only
                Health Policy

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