Abstract
Abstract. This paper summarizes the arguments and counter-arguments in the scholarly debates on transformations in healthcare budgeting that should consider the differentiated regional vulnerability in responding to the pandemic. The primary purpose of the study is to identify priorities for local health development programs. The urgency of solving this problem is that the pandemic has revealed the unprecedented unpreparedness of the health care system to respond effectively to challenges; also, hidden problems accumulated during the last decades, which increase the emerging risks. The study is carried out in the following logical sequence: 1) collection, processing, and analysis of statistical data; 2) conducting a cluster analysis for group regions by vulnerability to different classes of diseases; 3) conducting correlation and regression analysis to compare the effects of the COVID-19 pandemic (cases and deaths) and the state of the region; 4) selection of the most significant features of the vulnerability of the region; 5) designing the matrix of the choice of priorities for financing targeted programs in the field of health care. Methodological tools of the study were methods of correlation and regression analysis, cluster analysis, testing for autocorrelation by Darbin — Watson method, sigma limited parameterization to identify the most significant coefficients. The method is tested for 25 regions of Ukraine (including Kyiv), as they can serve as pilots for other regions with similar demographic and economic characteristics. The article presents the results of an empirical analysis of the readiness of regions for critical conditions, such as COVID-19. Identifying such readiness and appropriate distribution of regions by disease classes allows to make decisions in financing and budgeting and improve the quality of health care. Keywords: COVID-19, regional vulnerability to COVID-19, step-by-step nonlinear regression, morbidity, mortality, regional profile, pandemic, multicollinearity, targeting budgeting. JEL Classification C21, C51, C31, C12, I15, I18, R58, R11 Formulas: 9; fig.: 5; tabl.: 7; bibl.: 36.
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