IDEAS home Printed from https://ideas.repec.org/a/nea/journl/y2023i59p120-141.html
   My bibliography  Save this article

Forecasting spending on orphan diseases to maintain the long-run financial sustainability of healthcare system

Author

Listed:
  • Rasstrigin, M.

    (The Center for Strategic Research, Moscow, Russia)

  • Kitaev, A.

    (The Center for Strategic Research, Moscow, Russia)

  • Pleshackova, E.

    (The Center for Strategic Research, Moscow, Russia)

Abstract

The paper deals with the rare diseases' burden forecasting in the context of information gaps on these diseases. There are many countries pumping up public funding for rare diseases' treatment despite lack of accurate data on patients numbers. This study is aimed at the formulation the one-size-fi ts-all approach for forecasting treatment costs of rare NCD. The approach is based on cohort component method and requires minimum exogenous data set. The model utilizing the proposed approach was developed for forecasting The Circle of Good Foundation's treatment costs on cystic fibrosis and Pompe's disease in 2022-2050. The authors found that forecasted number of patients in 2022 does not deviate dramatically from actual data for 2019-2021 and predicted cost pattern does not conflict with experts up to 2050. This supports study's hypothesis that the quality of treatment costs forecast does not depend signifi cantly on prevalence and degree of knowledge of rare disease. The results will be of interest to government authorities, charity foundations and pharmaceutical companies for upgrading decision-making on funding rare diseases' treatment costs and improvement market planning for new drugs in pharmaceutical companies.

Suggested Citation

  • Rasstrigin, M. & Kitaev, A. & Pleshackova, E., 2023. "Forecasting spending on orphan diseases to maintain the long-run financial sustainability of healthcare system," Journal of the New Economic Association, New Economic Association, vol. 59(2), pages 120-141.
    • Handle: RePEc:nea:journl:y:2023:i:59:p:120-141
    DOI: 10.31737/22212264_2023_2_120-141
    as

    Download full text from publisher

    File URL: http://www.econorus.org/repec/journl/2023-59-120-141r.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.31737/22212264_2023_2_120-141?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. Weinstein, M.C. & Coxson, P.G. & Williams, L.W. & Pass, T.M. & Stason, W.B. & Goldman, L., 1987. "Forecasting coronary heart disease incidence, mortality, and cost: The coronary heart disease policy model," American Journal of Public Health, American Public Health Association, vol. 77(11), pages 1417-1426.
    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. William S. Weintraub & Samuel S. Gidding, 2016. "PCSK9 Inhibitors: A Technology Worth Paying For?," PharmacoEconomics, Springer, vol. 34(3), pages 217-220, March.
    2. Michele Kohli & Cheryl Attard & Annette Lam & Daniel Huse & John Cook & Chantal Bourgault & Evo Alemao & Donald Yin & Michael Marentette, 2006. "Cost Effectiveness of Adding Ezetimibe to Atorvastatin Therapy in Patients Not at Cholesterol Treatment Goal in Canada," PharmacoEconomics, Springer, vol. 24(8), pages 815-830, August.
    3. Michael F. Drummond;Adrian Towse, 1998. "From Efficacy to Cost-Effectiveness," Briefing 000438, Office of Health Economics.
    4. Yizhe Xu & Tom H. Greene & Adam P. Bress & Brian C. Sauer & Brandon K. Bellows & Yue Zhang & William S. Weintraub & Andrew E. Moran & Jincheng Shen, 2022. "Estimating the optimal individualized treatment rule from a cost‐effectiveness perspective," Biometrics, The International Biometric Society, vol. 78(1), pages 337-351, March.
    5. Mark Roberts & Louise B. Russell & A. David Paltiel & Michael Chambers & Phil McEwan & Murray Krahn, 2012. "Conceptualizing a Model," Medical Decision Making, , vol. 32(5), pages 678-689, September.
    6. Gary A. Zarkin & Laura J. Dunlap & Katherine A. Hicks & Daniel Mamo, 2005. "Benefits and costs of methadone treatment: results from a lifetime simulation model," Health Economics, John Wiley & Sons, Ltd., vol. 14(11), pages 1133-1150, November.
    7. Meltzer, David, 1997. "Accounting for future costs in medical cost-effectiveness analysis," Journal of Health Economics, Elsevier, vol. 16(1), pages 33-64, February.
    8. Bob J. H. van Kempen & Bart S. Ferket & Albert Hofman & Sandra Spronk & Ewout Steyerberg & M. G. Myriam Hunink, 2012. "Do Different Methods of Modeling Statin Treatment Effectiveness Influence the Optimal Decision?," Medical Decision Making, , vol. 32(3), pages 507-516, May.
    9. K Cooper & S C Brailsford & R Davies, 2007. "Choice of modelling technique for evaluating health care interventions," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(2), pages 168-176, February.
    10. Salkeld, Glenn & Phongsavan, Philayrath & Oldenburg, Brian & Johannesson, Magnus & Convery, Paula & Graham-Clarke, Peita & Walker, Sheila & Shaw, John, 1997. "The cost-effectiveness of a cardiovascular risk reduction program in general practice," Health Policy, Elsevier, vol. 41(2), pages 105-119, August.
    11. Tekeshe A Mekonnen & Michelle C Odden & Pamela G Coxson & David Guzman & James Lightwood & Y Claire Wang & Kirsten Bibbins-Domingo, 2013. "Health Benefits of Reducing Sugar-Sweetened Beverage Intake in High Risk Populations of California: Results from the Cardiovascular Disease (CVD) Policy Model," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-9, December.
    12. Karen T. Hicklin & Julie S. Ivy & James R. Wilson & Fay Cobb Payton & Meera Viswanathan & Evan R. Myers, 2019. "Simulation model of the relationship between cesarean section rates and labor duration," Health Care Management Science, Springer, vol. 22(4), pages 635-657, December.
    13. Davies, Ruth & Roderick, Paul & Raftery, James, 2003. "The evaluation of disease prevention and treatment using simulation models," European Journal of Operational Research, Elsevier, vol. 150(1), pages 53-66, October.
    14. K. Cooper & S. Brailsford & R. Davies & J. Raftery, 2006. "A review of health care models for coronary heart disease interventions," Health Care Management Science, Springer, vol. 9(4), pages 311-324, November.
    15. William Weintraub & Samuel Gidding, 2016. "PCSK9 Inhibitors: A Technology Worth Paying For?," PharmacoEconomics, Springer, vol. 34(3), pages 217-220, March.
    16. James E. Smith & Ralph L. Keeney, 2005. "Your Money or Your Life: A Prescriptive Model for Health, Safety, and Consumption Decisions," Management Science, INFORMS, vol. 51(9), pages 1309-1325, September.
    17. Marion S. Rauner & Walter J. Gutjahr & Kurt Heidenberger & Joachim Wagner & Joseph Pasia, 2010. "Dynamic Policy Modeling for Chronic Diseases: Metaheuristic-Based Identification of Pareto-Optimal Screening Strategies," Operations Research, INFORMS, vol. 58(5), pages 1269-1286, October.
    18. Amy O’Sullivan & Jaime Rubin & Joshua Nyambose & Andreas Kuznik & David Cohen & David Thompson, 2011. "Cost Estimation of Cardiovascular Disease Events in the US," PharmacoEconomics, Springer, vol. 29(8), pages 693-704, August.
    19. Magnus Johannesson & Bengt Jönsson & Göran Karlsson, 1996. "Outcome measurement in economic evaluation," Health Economics, John Wiley & Sons, Ltd., vol. 5(4), pages 279-296, July.
    20. Ankur Pandya & Stephen Sy & Sylvia Cho & Sartaj Alam & Milton C. Weinstein & Thomas A. Gaziano, 2017. "Validation of a Cardiovascular Disease Policy Microsimulation Model Using Both Survival and Receiver Operating Characteristic Curves," Medical Decision Making, , vol. 37(7), pages 802-814, October.

    More about this item

    Keywords

    rare diseases; orphan diseases; burden forecast; treatment costs forecast; NCDs; Cohort component method; The Circle of Good Foundation; orphan drugs;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • H51 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Health
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

    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:nea:journl:y:2023:i:59:p:120-141. 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: Alexey Tcharykov (email available below). General contact details of provider: https://edirc.repec.org/data/nearuea.html .

    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.