Hospital demand for biological agents for the treatment of patients with rheumatic diseases

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DOI: https://doi.org/10.29296/25419218-2021-08-08
Issue: 
8
Year: 
2021

V.N. Ugoltsova(1), D.Kh. Shakirova(1), D.I. Abdulganieva(2), R.S. Safiullin(2), 1-Kazan (Volga) Federal University, 18, Kremlevskaya St., Kazan 420008, Russian Federation; 2-Kazan State Medical University, 49, Butlerov St., Kazan 420012, Russian Federation

Introduction. The socioeconomic burden of rheumatic diseases (RDs) is an important public health problem due to the annually growing epidemiological indicators and high patient disability rates. The use of expensive biological agents (BAs) in pharmacotherapy could achieve the major goal of treatment, such as remission and better quality of life in a number of cases. In the context of budget deficits, it is of great importance to determine the optimal hospital demand forecast for BAs, taking into account the influence of various factors on drug consumption, which will be able to increase the availability of targeted therapy for all patients in need. Objective: to determine the optimal hospital demand for BAs for the treatment of patients with RDs, by using the multifactorial mathematical modeling method. Material and methods. The materials were expert review questionnaires, 1181 case history sheets for patients diagnosed with rheumatoid arthritis, ankylosing spondylitis, and psoriatic arthritis. Expert assessments, content analysis, correlation regression analysis, and multifactorial mathematical modeling were used. Results. Multifactorial mathematical models were developed and the 2020–2023 hospital demand forecast for BAs (rituximab, certolizumabapegol, golimumab, tocilizumab, and abatacept) was estimated. Conclusion. The short-term demand forecast calculated using multifactorial mathematical modeling was reliable and can be used when creating the optimal purchase order for BAs (rituximab, certolizumabapegol, golimumab, and tocilizumab).
Keywords: 
hospital demand
drug consumption
mathematical modeling
demand forecasting
biological agents
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