The modeling of seasonal fluctuations of demand for medications to treat bronchial asthma for the hospitals of the Republic of Tatarstan

Aim. To model the seasonal fluctuations of demand for medications to treat bronchial asthma for the hospital use in healthcare facilities of the Republic of Tatarstan. Methods. Statistical methods of absolute and relative differences, seasonality index construction, ABC/XYZ- analysis, modeling of seasonal fluctuations were used for the assessment. Results. By using ABC/XYZ-analysis the estimation of the consumption of certain medications to treat bronchial asthma in hospitals of the Republic of Tatarstan was given. The AX group was the largest share in the total trade (48.27%), but contributed only to 5.88% of the total nomenclature positions number. BX and CX groups, accounting for 0.73% and 0.21% of total consumption respectively, contributed to 1.96% of the nomenclature positions each. 7.84% of assortment positions provided 22.34% of turnover (group AY). Drugs of BY group provided 8.36% of the total sales, and 11.76% of assortment positions, the proportion of CY group (19.61% of items) accounts only for 2.93% of the sales; anti-asthmatic drugs of group Z, the largest by assortment positions number (AZ - 1.96%, BZ - 9.80%, CZ - 39.23%) provided a total of 17.16% of turnover. Assortment positions for modeling the seasonal fluctuations of the demands (AY, BY) were selected. According to the proposed algorithm, the presence of seasonality for each drug was checked. 90 models of seasonal fluctuations were designed and their reliability was tested. The prognosis based on the designed models of demand for medications to treat bronchial asthma for the hospital use in healthcare facilities of the Republic of Tatarstan was given. Conclusion. Assessment of the future demand for anti-asthmatic drugs based on the designed models may allow to optimize the use of funding allocated for the drug provision of patients with bronchial asthma while treated as in-patients.

bronchial asthma, seasonal fluctuations, prediction of demand, modeling, Republic of Tatarstan