IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v42y2022i6p795-807.html

Identifying a Single Optimal Integrated Cervical Cancer Prevention Policy in Norway: A Cost-Effectiveness Analysis

Author

Listed:
  • Allison Portnoy

    (Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA, USA)

  • Kine Pedersen

    (Department of Health Management and Health Economics, University of Oslo, Oslo, Norway)

  • Mari NygÃ¥rd

    (Department of Research, Cancer Registry of Norway, Oslo, Norway)

  • Lill Trogstad

    (The Norwegian Institute of Public Health, Oslo, Norway)

  • Jane J. Kim

    (Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA, USA)

  • Emily A. Burger

    (Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA, USA
    Department of Health Management and Health Economics, University of Oslo, Oslo, Norway)

Abstract

Background Interventions targeting the same disease but at different points along the disease continuum (e.g., screening and vaccination to prevent cervical cancer [CC]) are often evaluated in isolation, which can affect cost-effectiveness profiles and policy conclusions. We evaluated nonavalent human papillomavirus (HPV) vaccine (9vHPV) compared with bivalent HPV vaccine (2vHPV) alongside deintensified screening intervals for a vaccinated birth cohort to inform a single optimal integrated CC prevention policy. Methods Using a multimodeling approach, we evaluated the health and economic impacts of alternative CC screening strategies for a Norwegian birth cohort eligible for HPV vaccination in 2021 assuming they received 1) 2vHPV or 2) 9vHPV. We conducted 1) a restricted analysis that evaluated the optimal HPV vaccine under current screening guidelines; and 2) a comprehensive analysis including alternative screening and vaccination strategy combinations. We calculated incremental cost-effectiveness ratios (ICERs) and evaluated them according to different cost-effectiveness thresholds. Results Assuming a cost-effectiveness threshold of $40,000 per quality-adjusted life year (QALY) gained, we found that, while holding screening intensity fixed, switching the routine vaccination program in Norway from 2vHPV to 9vHPV would not be considered cost-effective (ICER of $132,700 per QALY gained). However, when allowing for varying intensities of CC screening, we found that switching to 9vHPV would be cost-effective compared with 2vHPV under an alternative threshold of $55,000 per QALY gained, if coupled with reductions in the number of lifetime screens. Conclusions Our analysis highlights the importance of evaluating the full potential policy landscape for country-level decision makers considering policy adoption, including nonindependent primary and secondary prevention efforts, to draw appropriate conclusions and avoid sub-optimal outcomes. Highlights Without evaluating the full potential policy landscape, including primary and secondary prevention efforts, country-level decision makers may not be able to draw appropriate policy conclusions, resulting in suboptimal outcomes. An applied example from cervical cancer prevention in Norway compared a restricted analysis of current screening guidelines to a comprehensive analysis including alternative screening and vaccination strategy combinations. We found that a switch from bivalent to nonavalent human papillomavirus vaccine would be considered cost-effective in Norway if coupled with reductions in the number of lifetime screens compared with the current screening strategy. A comprehensive analysis that considers how different types of interventions along the disease continuum affect each other will be critical for decision makers interpreting cost-effectiveness analysis results.

Suggested Citation

  • Allison Portnoy & Kine Pedersen & Mari NygÃ¥rd & Lill Trogstad & Jane J. Kim & Emily A. Burger, 2022. "Identifying a Single Optimal Integrated Cervical Cancer Prevention Policy in Norway: A Cost-Effectiveness Analysis," Medical Decision Making, , vol. 42(6), pages 795-807, August.
  • Handle: RePEc:sae:medema:v:42:y:2022:i:6:p:795-807
    DOI: 10.1177/0272989X221082683
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X221082683
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0272989X221082683?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
    ---><---

    References listed on IDEAS

    as
    1. Emily A Burger & Stephen Sy & Mari Nygård & Ivar S Kristiansen & Jane J Kim, 2014. "Prevention of HPV-Related Cancers in Norway: Cost-Effectiveness of Expanding the HPV Vaccination Program to Include Pre-Adolescent Boys," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-10, March.
    2. Hawre Jalal & Jeremy D. Goldhaber-Fiebert & Karen M. Kuntz, 2015. "Computing Expected Value of Partial Sample Information from Probabilistic Sensitivity Analysis Using Linear Regression Metamodeling," Medical Decision Making, , vol. 35(5), pages 584-595, July.
    3. repec:plo:pone00:0221564 is not listed on IDEAS
    4. Karl Claxton & Mark Sculpher & Chris McCabe & Andrew Briggs & Ron Akehurst & Martin Buxton & John Brazier & Tony O'Hagan, 2005. "Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra," Health Economics, John Wiley & Sons, Ltd., vol. 14(4), pages 339-347, April.
    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. A. E. Ades & Karl Claxton & Mark Sculpher, 2006. "Evidence synthesis, parameter correlation and probabilistic sensitivity analysis," Health Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 373-381, April.
    2. Dongzhe Hong & Lei Si & Minghuan Jiang & Hui Shao & Wai-kit Ming & Yingnan Zhao & Yan Li & Lizheng Shi, 2019. "Cost Effectiveness of Sodium-Glucose Cotransporter-2 (SGLT2) Inhibitors, Glucagon-Like Peptide-1 (GLP-1) Receptor Agonists, and Dipeptidyl Peptidase-4 (DPP-4) Inhibitors: A Systematic Review," PharmacoEconomics, Springer, vol. 37(6), pages 777-818, June.
    3. Pedram Sendi & Huldrych F Günthard & Mathew Simcock & Bruno Ledergerber & Jörg Schüpbach & Manuel Battegay & for the Swiss HIV Cohort Study, 2007. "Cost-Effectiveness of Genotypic Antiretroviral Resistance Testing in HIV-Infected Patients with Treatment Failure," PLOS ONE, Public Library of Science, vol. 2(1), pages 1-8, January.
    4. 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.
    5. 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.
    6. McKenna, Claire & Chalabi, Zaid & Epstein, David & Claxton, Karl, 2010. "Budgetary policies and available actions: A generalisation of decision rules for allocation and research decisions," Journal of Health Economics, Elsevier, vol. 29(1), pages 170-181, January.
    7. Mattias Ekman & Peter Lindgren & Carolin Miltenburger & Genevieve Meier & Julie Locklear & Mary Chatterton, 2012. "Cost Effectiveness of Quetiapine in Patients with Acute Bipolar Depression and in Maintenance Treatment after an Acute Depressive Episode," PharmacoEconomics, Springer, vol. 30(6), pages 513-530, June.
    8. Jeremy D. Goldhaber-Fiebert & Hawre Jalal & Fernando Alarid-Escudero, 2025. "Microsimulation Estimates of Decision Uncertainty and Value of Information Are Biased but Consistent," Medical Decision Making, , vol. 45(2), pages 127-142, February.
    9. Peter J. Dodd & Debebe Shaweno & Chu-Chang Ku & Philippe Glaziou & Carel Pretorius & Richard J. Hayes & Peter MacPherson & Ted Cohen & Helen Ayles, 2023. "Transmission modeling to infer tuberculosis incidence prevalence and mortality in settings with generalized HIV epidemics," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    10. Emma McIntosh, 2006. "Using Discrete Choice Experiments within a Cost-Benefit Analysis Framework," PharmacoEconomics, Springer, vol. 24(9), pages 855-868, September.
    11. John Hutton, 2012. "‘Health Economics’ and the evolution of economic evaluation of health technologies," Health Economics, John Wiley & Sons, Ltd., vol. 21(1), pages 13-18, January.
    12. Anna Heath & Petros Pechlivanoglou, 2022. "Prioritizing Research in an Era of Personalized Medicine: The Potential Value of Unexplained Heterogeneity," Medical Decision Making, , vol. 42(5), pages 649-660, July.
    13. Sofia Dias & Alex J. Sutton & Nicky J. Welton & A. E. Ades, 2013. "Evidence Synthesis for Decision Making 6," Medical Decision Making, , vol. 33(5), pages 671-678, July.
    14. Henri B. Wolff & Venetia Qendri & Natalia Kunst & Fernando Alarid-Escudero & Veerle M.H. Coupé, 2022. "Methods for Communicating the Impact of Parameter Uncertainty in a Multiple-Strategies Cost-Effectiveness Comparison," Medical Decision Making, , vol. 42(7), pages 956-968, October.
    15. Linke Li & Hawre Jalal & Anna Heath, 2024. "Accurate EVSI Estimation for Nonlinear Models Using the Gaussian Approximation Method," Medical Decision Making, , vol. 44(7), pages 787-801, October.
    16. Akhil Sasidharan & Bhavani Shankara Bagepally & S Sajith Kumar & Kayala Venkata Jagadeesh & Meenakumari Natarajan, 2022. "Cost-effectiveness of Ezetimibe plus statin lipid-lowering therapy: A systematic review and meta-analysis of cost-utility studies," PLOS ONE, Public Library of Science, vol. 17(6), pages 1-17, June.
    17. Alan Brennan & Stephen E. Chick & Ruth Davies, 2006. "A taxonomy of model structures for economic evaluation of health technologies," Health Economics, John Wiley & Sons, Ltd., vol. 15(12), pages 1295-1310, December.
    18. Laura Bojke & Karl Claxton & Stephen Palmer & Mark Sculpher, 2006. "Defining and characterising structural uncertainty in decision analytic models," Working Papers 009cherp, Centre for Health Economics, University of York.
    19. Sun-Young Kim & Louise B. Russell & Anushua Sinha, 2015. "Handling Parameter Uncertainty in Cost-Effectiveness Models Simply and Responsibly," Medical Decision Making, , vol. 35(5), pages 567-569, July.
    20. Nicky J. Welton & Jason J. Madan & Deborah M. Caldwell & Tim J. Peters & Anthony E. Ades, 2014. "Expected Value of Sample Information for Multi-Arm Cluster Randomized Trials with Binary Outcomes," Medical Decision Making, , vol. 34(3), pages 352-365, April.

    More about this item

    Keywords

    ;
    ;
    ;

    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:sae:medema:v:42:y:2022:i:6:p:795-807. 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: SAGE Publications (email available below). General contact details of provider: .

    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.