IDEAS home Printed from https://ideas.repec.org/a/spr/aphecp/v18y2020i4d10.1007_s40258-019-00551-x.html
   My bibliography  Save this article

Cost–Utility of Internet-Based Cognitive Behavioral Therapy in Unipolar Depression: A Markov Model Simulation

Author

Listed:
  • Mathias Baumann

    (Universität Hamburg)

  • Tom Stargardt

    (Universität Hamburg)

  • Simon Frey

    (Universität Hamburg)

Abstract

Background and Objective Unipolar depression is the most common form of depression and demand for treatment, such as psychotherapy, is high. However, waiting times for psychotherapy often considerably exceed their recommended maximum. As a potentially less costly alternative treatment, internet-based cognitive behavior therapy (ICBT) might help reduce waiting times. We therefore analyzed the cost–utility of ICBT compared to face-to-face CBT (FCBT) as an active control treatment, taking differences in waiting time into account. Methods We constructed a Markov model to simulate costs and health outcomes measured in quality-adjusted life years (QALYs) for ICBT and FCBT in Germany. We modeled a time horizon of 3 years using six states (remission, depressed, spontaneous remission, undergoing treatment, treatment finished, death). The societal perspective was adopted. We obtained parameters for transition probabilities, depression-specific QoL, and cost data from the literature. Deterministic and probabilistic sensitivity analyses were conducted. Within a scenario analysis, we simulated different time-to-treatment combinations. Half-cycle correction was applied. Results In our simulation, ICBT generated 0.260 QALYs and saved €2536 per patient compared to FCBT. Our deterministic sensitivity analysis suggests that the base-case results were largely unaffected by parameter uncertainty and are therefore robust. Our probabilistic sensitivity analysis suggests that ICBT is highly likely to be more effective (91.5%), less costly (76.0%), and the dominant strategy (69.7%) compared to FCBT. The scenario analysis revealed that the base-case results are robust to variations in time-to-treatment differences. Conclusion ICBT has a strong potential to balance demand and supply of CBT in unipolar depression by reducing therapist time per patient. It is highly likely to generate more QALYs and reduce health care expenditure. In addition, ICBT may have further positive external effects, such as freeing up capacities for the most severely depressed patients.

Suggested Citation

  • Mathias Baumann & Tom Stargardt & Simon Frey, 2020. "Cost–Utility of Internet-Based Cognitive Behavioral Therapy in Unipolar Depression: A Markov Model Simulation," Applied Health Economics and Health Policy, Springer, vol. 18(4), pages 567-578, August.
  • Handle: RePEc:spr:aphecp:v:18:y:2020:i:4:d:10.1007_s40258-019-00551-x
    DOI: 10.1007/s40258-019-00551-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40258-019-00551-x
    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/s40258-019-00551-x?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. Briggs, Andrew & Sculpher, Mark & Claxton, Karl, 2006. "Decision Modelling for Health Economic Evaluation," OUP Catalogue, Oxford University Press, number 9780198526629, Decembrie.
    Full references (including those not matched with items on IDEAS)

    Citations

    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Chris Sampson’s journal round-up for 27th July 2020
      by Chris Sampson in The Academic Health Economists' Blog on 2020-07-27 11:00:01

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dina Jankovic & Pedro Saramago Goncalves & Lina Gega & David Marshall & Kath Wright & Meena Hafidh & Rachel Churchill & Laura Bojke, 2022. "Cost Effectiveness of Digital Interventions for Generalised Anxiety Disorder: A Model-Based Analysis," PharmacoEconomics - Open, Springer, vol. 6(3), pages 377-388, May.

    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. Chiranjeev Sanyal & Don Husereau, 2020. "Systematic Review of Economic Evaluations of Services Provided by Community Pharmacists," Applied Health Economics and Health Policy, Springer, vol. 18(3), pages 375-392, June.
    2. Mark Oppe & Daniela Ortín-Sulbarán & Carlos Vila Silván & Anabel Estévez-Carrillo & Juan M. Ramos-Goñi, 2021. "Cost-effectiveness of adding Sativex® spray to spasticity care in Belgium: using bootstrapping instead of Monte Carlo simulation for probabilistic sensitivity analyses," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 22(5), pages 711-721, July.
    3. Kaitlyn Hastings & Clara Marquina & Jedidiah Morton & Dina Abushanab & Danielle Berkovic & Stella Talic & Ella Zomer & Danny Liew & Zanfina Ademi, 2022. "Projected New-Onset Cardiovascular Disease by Socioeconomic Group in Australia," PharmacoEconomics, Springer, vol. 40(4), pages 449-460, April.
    4. Andrea Marcellusi & Raffaella Viti & Loreta A. Kondili & Stefano Rosato & Stefano Vella & Francesco Saverio Mennini, 2019. "Economic Consequences of Investing in Anti-HCV Antiviral Treatment from the Italian NHS Perspective: A Real-World-Based Analysis of PITER Data," PharmacoEconomics, Springer, vol. 37(2), pages 255-266, February.
    5. Risha Gidwani & Louise B. Russell, 2020. "Estimating Transition Probabilities from Published Evidence: A Tutorial for Decision Modelers," PharmacoEconomics, Springer, vol. 38(11), pages 1153-1164, November.
    6. Joseph F. Levy & Marjorie A. Rosenberg, 2019. "A Latent Class Approach to Modeling Trajectories of Health Care Cost in Pediatric Cystic Fibrosis," Medical Decision Making, , vol. 39(5), pages 593-604, July.
    7. Qi Cao & Erik Buskens & Hans L. Hillege & Tiny Jaarsma & Maarten Postma & Douwe Postmus, 2019. "Stratified treatment recommendation or one-size-fits-all? A health economic insight based on graphical exploration," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(3), pages 475-482, April.
    8. Jorge Luis García & James J. Heckman, 2021. "Early childhood education and life‐cycle health," Health Economics, John Wiley & Sons, Ltd., vol. 30(S1), pages 119-141, November.
    9. Tushar Srivastava & Nicholas R. Latimer & Paul Tappenden, 2021. "Estimation of Transition Probabilities for State-Transition Models: A Review of NICE Appraisals," PharmacoEconomics, Springer, vol. 39(8), pages 869-878, August.
    10. Eleanor Heather & Katherine Payne & Mark Harrison & Deborah Symmons, 2014. "Including Adverse Drug Events in Economic Evaluations of Anti-Tumour Necrosis Factor-α Drugs for Adult Rheumatoid Arthritis: A Systematic Review of Economic Decision Analytic Models," PharmacoEconomics, Springer, vol. 32(2), pages 109-134, February.
    11. Manuel Gomes & Robert Aldridge & Peter Wylie & James Bell & Owen Epstein, 2013. "Cost-Effectiveness Analysis of 3-D Computerized Tomography Colonography Versus Optical Colonoscopy for Imaging Symptomatic Gastroenterology Patients," Applied Health Economics and Health Policy, Springer, vol. 11(2), pages 107-117, April.
    12. Isaac Corro Ramos & Maureen P. M. H. Rutten-van Mölken & Maiwenn J. Al, 2013. "The Role of Value-of-Information Analysis in a Health Care Research Priority Setting," Medical Decision Making, , vol. 33(4), pages 472-489, May.
    13. Wei Fang & Zhenru Wang & Michael B. Giles & Chris H. Jackson & Nicky J. Welton & Christophe Andrieu & Howard Thom, 2022. "Multilevel and Quasi Monte Carlo Methods for the Calculation of the Expected Value of Partial Perfect Information," Medical Decision Making, , vol. 42(2), pages 168-181, February.
    14. Martin Hoyle, 2008. "Future Drug Prices and Cost-Effectiveness Analyses," PharmacoEconomics, Springer, vol. 26(7), pages 589-602, July.
    15. Bauer, Annette & Knapp, Martin & Alvi, Mohsin & Chaudhry, Nasim & Gregoire, Alain & Malik, Abid & Sikander, Siham & Tayyaba, Kiran & Wagas, Ahmed & Husain, Nusrat, 2024. "Economic costs of perinatal depression and anxiety in a lower-middle income country: Pakistan," LSE Research Online Documents on Economics 122650, London School of Economics and Political Science, LSE Library.
    16. Aris Angelis & Huseyin Naci & Allan Hackshaw, 2020. "Recalibrating Health Technology Assessment Methods for Cell and Gene Therapies," PharmacoEconomics, Springer, vol. 38(12), pages 1297-1308, December.
    17. Yasuhiro Hagiwara & Takeru Shiroiwa, 2022. "Estimating Value-Based Price and Quantifying Uncertainty around It in Health Technology Assessment: Frequentist and Bayesian Approaches," Medical Decision Making, , vol. 42(5), pages 672-683, July.
    18. Neily Zakiyah & Antoinette D I van Asselt & Frank Roijmans & Maarten J Postma, 2016. "Economic Evaluation of Family Planning Interventions in Low and Middle Income Countries; A Systematic Review," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-19, December.
    19. Billingsley Kaambwa & Julie Ratcliffe, 2018. "Predicting EuroQoL 5 Dimensions 5 Levels (EQ-5D-5L) Utilities from Older People’s Quality of Life Brief Questionnaire (OPQoL-Brief) Scores," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 11(1), pages 39-54, February.
    20. Billingsley Kaambwa & Gang Chen & Julie Ratcliffe & Angelo Iezzi & Aimee Maxwell & Jeff Richardson, 2017. "Mapping Between the Sydney Asthma Quality of Life Questionnaire (AQLQ-S) and Five Multi-Attribute Utility Instruments (MAUIs)," PharmacoEconomics, Springer, vol. 35(1), pages 111-124, January.

    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:aphecp:v:18:y:2020:i:4:d:10.1007_s40258-019-00551-x. 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.