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Optimization Approach for Estimating the Required Amount of Pharmaceuticals in the Russian Federation
A V Prasolov1, A S Kolbin2, Yulia E Balykina3
1Saint Petersburg State University, Saint Petersburg, Russia; Higher School of Economics, National Research University, Saint Petersburg, Russia.
Objectives:
To propose an algorithm that relates the effectiveness of drugs for a wide range of diseases with the financial capabilities of patients.
Methods:
Estimates of the volume of pharmaceuticals that are consumed in the Russian Federation by all segments of the population regardless of household income were considered. These were calculated using statistically valid probabilities of the appearance of various diseases, official state data on the structure of expenditures of various strata of the population, and the optimal choice of the most effective medicines with income restrictions taken into account. The main idea was to introduce the utility function of the drug and the cost of treatment. For each disease, its own set of drugs was selected.
Results:
On the basis of the real-world data for several diseases, optimal estimates were calculated using the proposed algorithm. In the process of approbation, some weak points of the algorithm were found, such as the methods of packaging pharmaceuticals and associated cost of a packaging unit. These characteristics should be discussed separately, introducing conventional units of drug volumes. A unit of quantity corresponding to the maximum effect of the drug in question is proposed in the work.
Conclusions:
The proposed algorithm for estimating the amount of medicines can be successfully used by both pharmaceutical (or dealer) companies and government agencies for objective population provision. The usual sources of such estimates are based either on market surveys or on pharmacy network data. Both ways are very expensive and do not allow predicting mass demand in the future, for example, with an unexpected epidemic or the emergence of new medicines. In addition, the proposed algorithm can be successfully applied to the pricing problem: a variation in price may show a change in the volume of use.
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