[Cost-effectiveness of preclinical Parkinsonism diagnosis: a Markov model]"> [Cost-effectiveness of preclinical Parkinsonism diagnosis: a Markov model]">
[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/103096.html
   My bibliography  Save this paper

Экономическая Эффективность Доклинической Диагностики Болезни Паркинсона: Марковская Модель
[Cost-effectiveness of preclinical Parkinsonism diagnosis: a Markov model]

Author

Listed:
  • Vartanov, Sergey
  • Bogatova, Irina
  • Denisova, Irina
  • Kucheryanu, Valerian
  • Tourdyeva, Natalia
  • Chubarova, Tatyana
  • Shakleina, Marina
  • Polterovich, Victor
Abstract
This work is devoted to the pharmacoeconomic analysis of the results of the introduction of early (preclinical) diagnosis of Parkinson's disease in Russia. On the basis of a combination of socio-economic determinants and a panel of blood biomarkers, it may be possible to identify among the entire population a “risk group” - people most likely to develop parkinsonism or are already sick with it at the preclinical stage. Together with the approach traditionally used in the pharmacoeconomics of chronic and long-term diseases, based on the representation of the dynamics of the development of the disease using Markov chains - discrete random processes without memory - this makes it possible to analyze the economic effects of early detection of cases and conducting preventive preclinical therapy. The work investigated the Markov model of Parkinson's disease, consisting of nine states - five states corresponding to the stages HY1-HY5, two preclinical states ("risk group", "prodromal state"). Using as the initial data for the model, the probability of transition between states and health-adjusted quality of life (HRQoL) estimates, published in a number of works of researchers affiliated with AbbVie Corporation, and calculating the cost of therapy based on open data on the cost of drugs and procedures in Russian market (eapteka, apteka.ru, piluli.ru, website of the Ministry of Health of the Russian Federation), the work shows that due to the introduction of preclinical diagnostics and preventive treatment at preclinical stages, identified patients can significantly increase the average survival time (in quality of life-adjusted years) compared to standard therapy, and the average cost per patient until the end of life can be significantly reduced.

Suggested Citation

  • Vartanov, Sergey & Bogatova, Irina & Denisova, Irina & Kucheryanu, Valerian & Tourdyeva, Natalia & Chubarova, Tatyana & Shakleina, Marina & Polterovich, Victor, 2020. "Экономическая Эффективность Доклинической Диагностики Болезни Паркинсона: Марковская Модель [Cost-effectiveness of preclinical Parkinsonism diagnosis: a Markov model]," MPRA Paper 103096, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:103096
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/103096/1/MPRA_paper_103096.pdf
    File Function: original version
    Download Restriction: no
    File URL: https://mpra.ub.uni-muenchen.de/103098/8/MPRA_paper_103098.pdf
    File Function: revised version
    Download Restriction: no
    File URL: https://mpra.ub.uni-muenchen.de/103218/8/MPRA_paper_103098.pdf
    File Function: revised version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. David A. Muñoz & Mehmet Serdar Kilinc & Harriet B. Nembhard & Conrad Tucker & Xuemei Huang, 2017. "Evaluating the cost-effectiveness of an early detection of Parkinson's disease through innovative technology," The Engineering Economist, Taylor & Francis Journals, vol. 62(2), pages 180-196, April.
    2. Frank A. Sonnenberg & J. Robert Beck, 1993. "Markov Models in Medical Decision Making," Medical Decision Making, , vol. 13(4), pages 322-338, December.
    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. Malek B Hannouf & Chander Sehgal & Jeffrey Q Cao & Joseph D Mocanu & Eric Winquist & Gregory S Zaric, 2012. "Cost-Effectiveness of Adding Cetuximab to Platinum-Based Chemotherapy for First-Line Treatment of Recurrent or Metastatic Head and Neck Cancer," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-9, June.
    2. Bärnighausen, Till & Bloom, David E., 2009. ""Conditional scholarships" for HIV/AIDS health workers: Educating and retaining the workforce to provide antiretroviral treatment in sub-Saharan Africa," Social Science & Medicine, Elsevier, vol. 68(3), pages 544-551, February.
    3. de Wit, G.Ardine & Ramsteijn, Paul G & de Charro, Frank Th, 1998. "Economic evaluation of end stage renal disease treatment," Health Policy, Elsevier, vol. 44(3), pages 215-232, June.
    4. Afschin Gandjour & Eva-Julia Weyler, 2006. "Cost-effectiveness of referrals to high-volume hospitals: An analysis based on a probabilistic Markov model for hip fracture surgeries," Health Care Management Science, Springer, vol. 9(4), pages 359-369, November.
    5. Malek Ebadi & Raha Akhavan-Tabatabaei, 2021. "Personalized Cotesting Policies for Cervical Cancer Screening: A POMDP Approach," Mathematics, MDPI, vol. 9(6), pages 1-20, March.
    6. Mark Sculpher & David Torgerson & Ron Goeree & Bernie O'Brien, 1999. "A critical structured review of economic evaluations of interventions for the prevention and treatment of osteoporosis," Working Papers 169chedp, Centre for Health Economics, University of York.
    7. F. Tomini & F. Prinzen & A. D. I. Asselt, 2016. "A review of economic evaluation models for cardiac resynchronization therapy with implantable cardioverter defibrillators in patients with heart failure," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 17(9), pages 1159-1172, December.
    8. Chisholm, Orin & Sharry, Patrick & Phillips, Lawrence, 2022. "Multi-criteria decision analysis for benefit-risk analysis by national regulatory authorities," LSE Research Online Documents on Economics 114407, London School of Economics and Political Science, LSE Library.
    9. Franck Maunoury & Aurore Clément & Chizoba Nwankwo & Laurie Levy-Bachelot & Armand Abergel & Vincent Di Martino & Eric Thervet & Isabelle Durand-Zaleski, 2018. "Cost-effectiveness analysis of elbasvir-grazoprevir regimen for treating hepatitis C virus genotype 1 infection in stage 4-5 chronic kidney disease patients in France," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-20, March.
    10. Stephen G. Pauker & John B. Wong, 2005. "The Influence of Influence Diagrams in Medicine," Decision Analysis, INFORMS, vol. 2(4), pages 238-244, December.
    11. 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.
    12. F. R. Rolli & M. Ruggeri & F. Kheiraoui & C. Drago & M. Basile & C. Favaretti & A. Cicchetti, 2018. "Economic evaluation of Zepatier for the management of HCV in the Italian scenario," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 19(9), pages 1365-1374, December.
    13. Nicholas Graves & Mary Courtney & Helen Edwards & Anne Chang & Anthony Parker & Kathleen Finlayson, 2009. "Cost-Effectiveness of an Intervention to Reduce Emergency Re-Admissions to Hospital among Older Patients," PLOS ONE, Public Library of Science, vol. 4(10), pages 1-9, October.
    14. Jan Jürgensen & Wolfgang Arns & Bastian Haß, 2010. "Cost-effectiveness of immunosuppressive regimens in renal transplant recipients in Germany: a model approach," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 11(1), pages 15-25, February.
    15. Douglas K. Owens, 2002. "Analytic Tools for Public Health Decision Making," Medical Decision Making, , vol. 22(1_suppl), pages 3-10, September.
    16. Jun Li & Benjamin H K Yip & Chichiu Leung & Wankyo Chung & Kin On Kwok & Emily Y Y Chan & Engkiong Yeoh & Puihong Chung, 2018. "Screening for latent and active tuberculosis infection in the elderly at admission to residential care homes: A cost-effectiveness analysis in an intermediate disease burden area," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-18, January.
    17. Bruce A. Craig & Peter P. Sendi, 2002. "Estimation of the transition matrix of a discrete‐time Markov chain," Health Economics, John Wiley & Sons, Ltd., vol. 11(1), pages 33-42, January.
    18. B. Brüggenjürgen & P. Lindgren & B. Ehlken & H.-J. Rupprecht & S. Willich, 2007. "Long-term cost-effectiveness of clopidogrel in patients with acute coronary syndrome without ST-segment elevation in Germany," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 8(1), pages 51-57, March.
    19. Turgay Ayer, 2015. "Inverse optimization for assessing emerging technologies in breast cancer screening," Annals of Operations Research, Springer, vol. 230(1), pages 57-85, July.
    20. Gyeyoung Choi & Yujeong Kim & Gyeongseon Shin & SeungJin Bae, 2022. "Projecting Lifetime Health Outcomes and Costs Associated with the Ambient Fine Particulate Matter Exposure among Adult Women in Korea," IJERPH, MDPI, vol. 19(5), pages 1-14, February.

    More about this item

    Keywords

    Markov chain with discrete time; Markov model; Parkinson disease; pharmacoeconomics;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • I10 - Health, Education, and Welfare - - Health - - - General
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • I19 - Health, Education, and Welfare - - Health - - - Other

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pra:mprapa:103096. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.