COMPARATIVE REVIEW OF METHODOLOGIES FOR ESTIMATING THE COST OF ADVERSE DRUG REACTIONS IN THE RUSSIAN FEDERATION AND BRAZIL

The aim of the review article was to highlight the methodologies for assessing the financial costs of adverse drug reactions exemplified by the Russian Federation and Brazil.

Materials and methods: for a comparative analysis, materials from open sources were used. The study of the experience of methods used for assessing the burden of adverse drug reactions, was carried out using the system for calculating payments for medical care by clinical-statistical and clinical-profile groups, the methodology for assessing the severity of adverse events of the US National Cancer Institute, drug-associated problems, and “the decision tree” model.

Results. When comparing the costs of ADR management in the Russian Federation and Brazil, the following results have been obtained: in the Russian Federation, the “cost” of reaction can be estimated only for a limited number of nosological groups that are regulated by the classification of diseases by clinical and statistical groups; in Brazil, when predicting the costs of adverse reactions management, the combination of “the decision tree” method and the Delphi method is used. In the Russian Federation, the cost of the 3rd and above severity adverse event (according to CTCAE v. 4.03), varies from 26,849.22 up to 26,196.37 RUB in the North-West region (St. Petersburg). In Brazil, the cost of ADR ranges from 13 USD (the best scenario for the patient) to 574 USD (the worst scenario for the patient), which is about 975 and 43,000 RUB, respectively. The introduction of methods that make it possible to predict the development and potential outcomes of adverse drug reactions, as well as taking into account the experiences of foreign colleagues in their modeling, will reduce economic costs in the Russian Federation at the federal level.

Conclusion: for the economic value analysis and further forecasting, an improvement of existing methodologies is required. The models used in the Russian Federation (“the decision tree”, classification of diseases by clinical groups, Markov model) do not take into account the time factor, therefore, when planning the analysis of potential costs for adverse reactions, it is necessary to reinforce the methods with such tools as QALY, YLL, and YLD.

1. Varpaieva IA, Lapshina SA. Modern trends in differentiated pricing in the pharmacy chain. Financial analytics: problems and solutions. 2014;40:22–34. Russian

2. Engalycheva GN, Syubaev RD, Goryachev DV. Safety Pharmacology Studies of Medicinal Products: Evaluation of Results. The Bulletin of the Scientific Centre for Expert Evaluation of Medicinal Products. 2017;7(2):92–96. DOI: 10.30895/1991-2919-2017-7-2-92-97. Russian

3. Gomon YM, Kolbin AS, Mazurenko SO, Ivanov IG. Principles of clinical and economic analysis of antimicrobial drugs. Kachestvennaya Klinicheskaya Praktika. 2019;2:35–44. DOI: 10.24411/2588-0519-2019-10071. Russian

4. Good vigilance practice of Eurasian economic union. [Access date: 11.09.2020]. Available from: http://www.eurasiancommission.org/ru/act/texnreg/deptexreg/oo/Documents/.

5. Glagolev SV, Gorelov KV, Chizhova DA. Russian pharmacovigilance in a newly regulated environment: two-year results and prospects. Remedium. Journal about the Russian market of medicines and medical equipment. 2019;3:8–14. DOI:10.21518/1561-5936-2019-3-8-14. Russian

6. Association of Clinical Research Organizations. [Access date: 1.01.2020]. Available from: http://acto-russia.org/files/ACTO_open_letter.pdf

7. Shubnikova EV, Darmostukova MA, Bukatina TM, Kaperko DA, Velts NY, Kazakov AS, Snegireva II, Zhuravleva EO, Kutekhova GV. The Adverse Drug Reactions Registered During the Postmarketing Period. Safety and Risk of Pharmacotherapy. 2019;7(1):15–22. DOI:10.30895/2312-7821-2019-7-1-15-22. Russian

8. Hoseva EN, Morozova TE. Problematic aspects of the development of the pharmacovigilance system in the Russian Federation at the present stage (review). Kachestvennaya Klinicheskaya Praktika. 2013;3:40–45. Russian

9. Kolbin A.S. [et al.]. Pharmacovigilance. Moscow: OKI: Buki-Vedi. 2019:248. Russian

10. Federal Service for Surveillance in Healthcare. Electronic services. Automated system Pharmacovigilance. [Access date: 11.09.2020]. Available from: http://www.roszdravnadzor.ru/services/npr_ais

11. Matveev AV, Krasheninnikov AE, Egorova EA, Konyaeva EI. Application of drug-related problems approach to analysis of non-steroidal anti-inflammatory drugs’ safety. Pharmacy & Pharmacology. 2019;7(4):215–223. DOI: 10.19163/2307- 9266-2019-7-4-215-223. Russian

12. The Brazilian Health Surveillance Agency (ANVISA). [Access date: 11.09.2020]. Available from: https://www.who.int/medicines/ areas/quality_safety/regulation_legislation/WA_1.pdf

13. Briggs A, Sculpher M. An introduction to Markov modelling for economic evaluation. Pharmacoeconomics. 1998;13(4):397–409. DOI: 10.2165/00019053-199813040-00003.

14. Kulikov AYu, Nguyen TT, Tikhomirova AV. Modeling methodology in pharmacoeconomics. Pharmacoeconomics. Modern pharmacoeconomics and pharmacoepidemiology. 2011;4:8–15. Russian

15. Eichenberger PM, Lampert ML, Kahmann IV, van Mil JW, Hersberger KE. Classification of drug-related problems with new prescriptions using a modified PCNE classification system. Pharm World Sci. 2010;32(3):362–72. DOI: 10.1007/s11096-010-9377-x.

16. Zimenkovsky AB, Ryvak TB, Khanyk LN. DRP concept as part of the philosophy of rational pharmacotherapy integrated with the pharmaceutical care system. Clinical Pharmacy, Pharmacotherapy and Medical Standardization. 2011;1–2:23–31. Ukrainian

17. The global burden of disease study 2019. The Lancet Special Issue. [Access date 2020 Sep 11]. Available from: https: // www. thelancet.com/journals/lancet/issue/vol396no10258/ PIIS0140-6736(20)X0042-0

18. Gomon YM, Kolbin AS, Burbello AT. Pharmacoeconomic aspects of adverse drug reactions. Clinical Pharmacology and Therapy. 2011;1:81–85. Russian

19. de Freitas GRM, Neyeloff JL, Balbinotto Neto G, Heineck I. Drug-Related Morbidity in Brazil: A Cost-of-Illness Model. Value Health Reg Issues. 2018;17:150–157. DOI: 10.1016/j.vhri.2018.07.002.

20. Paula Pereda ACDB, Guidetti B. Economic Burden of Diabetes mellitus in Brazil. Working Papers, Department of Economics of University of São Paulo (FEA-USP). 2019: 32–37.

21. Rocha KSS, Cerqueira Santos S, Boaventura TC, de Araújo DCSA, Silvestre SS, de Lyra Júnior DP. Development and content validation of an instrument to support pharmaceutical counselling for dispensing of prescribed medicines. J Eval Clin Pract. 2020;26:134–141. DOI:10.1111/jep.13102.

22. Martsevich SY, Navasardjan AR, Komkova NA. Off-Label prescribing. possible causes, types and consequences. legal regulation in the Russian Federation. Rational Pharmacotherapy in Cardiology. 2017;13(5):667–673. DOI: 10.20996/181 9-6446-2017-13-5-667-674. Russian