IDEAS home Printed from https://ideas.repec.org/a/spr/pharme/v38y2020i5d10.1007_s40273-020-00891-w.html
   My bibliography  Save this article

Healthcare Costs of Treating Privately Insured Patients with Acute Myeloid Leukemia in the United States from 2004 to 2014: A Generalized Additive Modeling Approach

Author

Listed:
  • Lih-Wen Mau

    (National Marrow Donor Program/Be The Match
    Center for International Blood and Marrow Transplant Research)

  • Jaime M. Preussler

    (National Marrow Donor Program/Be The Match
    Center for International Blood and Marrow Transplant Research)

  • Linda J. Burns

    (National Marrow Donor Program/Be The Match
    Center for International Blood and Marrow Transplant Research)

  • Susan Leppke

    (National Marrow Donor Program/Be The Match)

  • Navneet S. Majhail

    (Blood & Marrow Transplant Program, Cleveland Clinic)

  • Christa L. Meyer

    (National Marrow Donor Program/Be The Match
    Center for International Blood and Marrow Transplant Research)

  • Tatenda Mupfudze

    (National Marrow Donor Program/Be The Match
    Center for International Blood and Marrow Transplant Research)

  • Wael Saber

    (Center for International Blood and Marrow Transplant Research)

  • Patricia Steinert

    (Center for International Blood and Marrow Transplant Research)

  • David J. Vanness

    (Apriori Bayesian Consulting, LLC)

Abstract

Objectives The primary objective of this study was to predict healthcare cost trajectories for patients with newly diagnosed acute myeloid leukemia (AML) receiving allogeneic hematopoietic cell transplantation (alloHCT), as a function of days since chemotherapy initiation, days relative to alloHCT, and days before death or last date of insurance eligibility (LDE). An exploratory objective examined patients with AML receiving chemotherapy only. Methods We used Optum’s de-identified Clinformatics® Data Mart Database to construct cumulative cost trajectories from chemotherapy initiation to death or LDE (through 31 December 2014) for US patients aged 20–74 years diagnosed between 1 March 2004 and 31 December 2013 (n = 187 alloHCT; n = 253 chemotherapy only). We used generalized additive modeling (GAM) to predict expected trajectories and bootstrapped confidence intervals (CIs) at user-specified intervals conditional on dates of alloHCT and death or LDE relative to chemotherapy initiation. Results Expected costs (in 2017 values) for a hypothetical patient receiving alloHCT 60 days after chemotherapy initiation and followed for 5 years were $US572,000 (95% CI 517,000–633,000); $US119,000 (95% CI 51,000–192,000); $US102,000 (95% CI 0–285,000); $US79,000 (95% CI 0–233,000), for years 1–4, respectively, and either $US494,000 (95% CI 212,000–799,000) or $US108,000 (95% CI 0–230,000) in year 5, whether the patient died or was lost to follow-up on day 1825, respectively. Conclusions Rates of cost accrual varied over time since chemotherapy initiation, with accelerations around the time of alloHCT and death. GAM is a potentially useful approach for imputing longitudinal costs relative to treatment initiation and one or more intercurrent, clinical, or terminal events in randomized controlled trials or registries with unrecorded costs or for dynamic decision–analytic models.

Suggested Citation

  • Lih-Wen Mau & Jaime M. Preussler & Linda J. Burns & Susan Leppke & Navneet S. Majhail & Christa L. Meyer & Tatenda Mupfudze & Wael Saber & Patricia Steinert & David J. Vanness, 2020. "Healthcare Costs of Treating Privately Insured Patients with Acute Myeloid Leukemia in the United States from 2004 to 2014: A Generalized Additive Modeling Approach," PharmacoEconomics, Springer, vol. 38(5), pages 515-526, May.
  • Handle: RePEc:spr:pharme:v:38:y:2020:i:5:d:10.1007_s40273-020-00891-w
    DOI: 10.1007/s40273-020-00891-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40273-020-00891-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s40273-020-00891-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. John Mullahy, 1998. "Much Ado About Two: Reconsidering Retransformation and the Two-Part Model in Health Economics," NBER Technical Working Papers 0228, National Bureau of Economic Research, Inc.
    2. Mullahy, John, 1998. "Much ado about two: reconsidering retransformation and the two-part model in health econometrics," Journal of Health Economics, Elsevier, vol. 17(3), pages 247-281, June.
    3. Liang Li & Chih-Hsien Wu & Jing Ning & Xuelin Huang & Ya-Chen Tina Shih & Yu Shen, 2018. "Semiparametric Estimation of Longitudinal Medical Cost Trajectory," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 582-592, April.
    4. Jonathan Karnon, 2003. "Alternative decision modelling techniques for the evaluation of health care technologies: Markov processes versus discrete event simulation," Health Economics, John Wiley & Sons, Ltd., vol. 12(10), pages 837-848, October.
    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. Patrick Richard & Regine Walker & Pierre Alexandre, 2018. "The burden of out of pocket costs and medical debt faced by households with chronic health conditions in the United States," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-13, June.
    2. Buntin, Melinda Beeuwkes & Zaslavsky, Alan M., 2004. "Too much ado about two-part models and transformation?: Comparing methods of modeling Medicare expenditures," Journal of Health Economics, Elsevier, vol. 23(3), pages 525-542, May.
    3. Stefan Boes & Michael Gerfin, 2016. "Does Full Insurance Increase the Demand for Health Care?," Health Economics, John Wiley & Sons, Ltd., vol. 25(11), pages 1483-1496, November.
    4. Breinlich, Holger & Tucci, Alessandra, 2008. "Foreign Market Conditions and Export Performance: Evidence from Italian Firm-Level Data," Economics Discussion Papers 2715, University of Essex, Department of Economics.
    5. Deb, Partha & Trivedi, Pravin K., 2002. "The structure of demand for health care: latent class versus two-part models," Journal of Health Economics, Elsevier, vol. 21(4), pages 601-625, July.
    6. Nshakira-Rukundo, Emmanuel & Mussa, Essa Chanie & Gerber, Nicolas & von Braun, Joachim, 2020. "Impact of voluntary community-based health insurance on child stunting: Evidence from rural Uganda," Social Science & Medicine, Elsevier, vol. 245(C).
    7. Carole Roan Gresenz & Jeanette A. Rogowski & Jose Escarce, 2004. "Healthcare Markets, the Safety Net and Access to Care Among the Uninsured," NBER Working Papers 10799, National Bureau of Economic Research, Inc.
    8. Partha Deb & Murat K. Munkin & Pravin K. Trivedi, 2006. "Bayesian analysis of the two‐part model with endogeneity: application to health care expenditure," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(7), pages 1081-1099, November.
    9. R. Vincent Pohl, 2018. "Medicaid And The Labor Supply Of Single Mothers: Implications For Health Care Reform," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(3), pages 1283-1313, August.
    10. Giampiero Marra & Matteo Fasiolo & Rosalba Radice & Rainer Winkelmann, 2023. "A flexible copula regression model with Bernoulli and Tweedie margins for estimating the effect of spending on mental health," Health Economics, John Wiley & Sons, Ltd., vol. 32(6), pages 1305-1322, June.
    11. Keane, Michael & Stavrunova, Olena, 2016. "Adverse selection, moral hazard and the demand for Medigap insurance," Journal of Econometrics, Elsevier, vol. 190(1), pages 62-78.
    12. Massimiliano Bratti & Alfonso Miranda, 2010. "Endogenous Treatment Effects for Count Data Models with Sample Selection or Endogenous Participation," DoQSS Working Papers 10-05, Quantitative Social Science - UCL Social Research Institute, University College London, revised 10 Dec 2010.
    13. Marcel Bilger & Willard G. Manning, 2015. "Measuring Overfitting In Nonlinear Models: A New Method And An Application To Health Expenditures," Health Economics, John Wiley & Sons, Ltd., vol. 24(1), pages 75-85, January.
    14. Lucien Gardiol & Pierre-Yves Geoffard & Chantal Grandchamp, 2005. "Separating selection and incentive effects in health insurance," PSE Working Papers halshs-00590713, HAL.
    15. Jay Dev Dubey, 2021. "Measuring Income Elasticity of Healthcare-Seeking Behavior in India: A Conditional Quantile Regression Approach," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(4), pages 767-793, December.
    16. Silviya Nikolova; & Arthur Sinko; & Matt Sutton;, 2012. "Do maximum waiting times guarantees change clinical priorities? A Conditional Density Estimation approach," Health, Econometrics and Data Group (HEDG) Working Papers 12/07, HEDG, c/o Department of Economics, University of York.
    17. Manning, Willard G. & Basu, Anirban & Mullahy, John, 2005. "Generalized modeling approaches to risk adjustment of skewed outcomes data," Journal of Health Economics, Elsevier, vol. 24(3), pages 465-488, May.
    18. García-Serrano, Carlos & Malo, Miguel A., 2009. "The impact of union direct voice on voluntary and involuntary absenteeism," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 38(2), pages 372-383, March.
    19. Luiz Flavio Andrade & Thomas Rapp & Christine Sevilla-Dedieu, 2016. "Exploring the determinants of endocrinologist visits by patients with diabetes," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 17(9), pages 1173-1184, December.
    20. Yujing Shen & Randall P. Ellis, 2002. "How profitable is risk selection? A comparison of four risk adjustment models," Health Economics, John Wiley & Sons, Ltd., vol. 11(2), pages 165-174, March.

    More about this item

    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:spr:pharme:v:38:y:2020:i:5:d:10.1007_s40273-020-00891-w. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.