: THE REGIONAL POLICY PRIORITIES IN RESPONSE TO COVID-19

. 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.

Introduction.The study is encouraged by the fact that the pandemic situation in Ukraine has revealed the unreadiness of the health care system to respond quickly to the crises, the low financial security of most of the primary and secondary health care facilities, low safety, and other unsolved problems.The crisis has revealed the complexity of interconnected problems that have existed for a long time at the state, regional and local levels in health care.There is a need for a detailed analysis of the state of population health (screening), the evaluation of the resource and technological support of health care facilities, accessibility, and quality of medical care at all levels.Another big problem is the lack of methodological approaches and a comprehensive toolbox to identify the key areas for further strategic actions.To this end, it is essential to conduct in-depth and detailed statistical studies to identify patterns that become the basis for further forecasting the state of regional health care systems and subsequent resource decisions.
The damage caused by the pandemic situation in the world as a whole and in the country due to the impact of COVID-19 is underestimated for a number of reasons.First, the waves of the disease are still ongoing, and secondly, the diagnosis of the consequences of the disease in people who have suffered the disease -also only at an early stage.
Being a threat, COVID-19 revealed many hidden problems in the health care system and security, and one of the findings of this study at the primary stage is that in Ukraine, unlike in the EU, there are few targeted programs in the sphere of health care.The implementation of such programs is a separate operational and strategic goal because it requires highly-skilled experts in several fields such as health care, financial, and management studies.However, there is no particular targeted program to ensure a response to COVID-19.Still, there is a cooperation between the Ministry of Health and territorial units, as well as its subordinate central executive bodies: the State Sanitary and Epidemiological Service of Ukraine, the State Service of Ukraine for medicines, the State Service of Ukraine for Combating HIV / AIDS and Other Socially Dangerous Diseases.And yet, it's not enough to react effectively and to prevent further threats.
The case of Ukraine, one of the biggest countries in Europe, may become a pilot for further replication of the statistical data research and policies identification.Ukrainian regions may be compared to the regions of the EU.
Therefore, this study is aimed to bring new understanding in the priorities and targeting in a sphere of regional health policy in response to the challenge of COVID-19 using the statistical data for Ukraine as a case.
Literature review and the problem statement.Aside from the health crisis, COVID-19 hit many industries across the country, including the news and communications industry, which is important in times of crisis for bringing authentic and true information [6].There are some finding in a sphere of industrials performance that was damaged by COVID-19 impact [30], or labour market dynamics that was damaged too [12].The outbreak became a test and a catalyst for a change in many spheres, such as the service sector [10], sales and hospitality [9], governments and SMEs [29], green technologies and sustainability [16; 31], energy sector [36].The research in public sector development has to be enriched by new data [2] to make institutions capable of responding rapidly to environmental shocks.
The performance indicators, monitoring technologies, resilience, and vulnerability of health care are highly discussed topics.The scholars and policy-makers debate about the focus of attention in further reforming of health care; for instance, it could be staff training in health care [24] or health care infrastructure innovations [26], or it should be communications through social media platforms [5] as an effective tool of informing and protecting communities.
Processing statistics data to predict country transformations through the pandemic COVID-19 is another stream of research for scholars [32], but the pandemic is not over yet, and more attention should be paid to the readiness for a new challenge.
Many papers have health care in their focus, for instance, the dynamics of health care tourism can be considered as one of the drivers of sphere development [24].Heath security capacities are under tremendous pressure now, and countries vary in terms of their capabilities to respond effectively to such outbreaks, as it was performed in numerous studies [2; 8; 13; 15; 17; 21] with particular attention to unprecedented opportunities for a transformational change [23] and innovations in health care [7; 22].COVID-19 opened the sources of upcoming crises, such as failure of health care delivery, coordination, and low-quality treatment, and at the same time showed the directions for the positive transformations [4], innovations [20; 33], and paths of impact-investing in the healthcare [35], new possible legislation rules [1] and a new round of possible cross-country cooperation [3].
The number of papers in the field increased recently, and one of the studies performed the resilience analysis for 11 countries in the COVID-19 crises [11], offering recommendations for the improvements based on detailed, indicator-based resilience analysis and emerging risks.Some antecedents of the research performed by other scholars emphasized the vulnerability of certain regions [14].Another study identified the financial, environmental, social, and economic determinants of regions' vulnerability to COVID-19 [17].This is the same study that put the lights on antecedents of low readiness of the health cares system to such challenges [17].However, the strategizing of the decisions in response to COVID-19 remains an unexplored area for scholars and practitioners.Therefore, the current study is aimed to design the method for decision-making and choice of the policies in health care based on detailed, in-depth statistical analysis.
The main hypothesis is that some preconditions for higher mortality or morbidity caused by COVID-19 are formed as a complex of region-related specific types of diseases (profile).If this hypothesis is proved, then the profiles of the regions should be evaluated to make health care support targeted according to the morbidity profile to reduce possible threats in the future.
Methodology.The methodology part is a step-by-step algorism which the authors tested for 25 regions.
Step 1.Initial data.To reveal the readiness of the regional health care system to fight against the COVID the hypotheses should be checked.The leading hypothesis is that morbidity by different types of diseases is an indicator of the development state of the regional health care system, and therefore identifies the readiness of each region to respond to challenges, such as the COVID pandemic.
The dependent variables are chosen to be the number of reported cases and deaths caused by COVID-19 in Ukraine from the beginning of the pandemic till 22 nd of January, 2021 [19].Nineteen factors are independent variables: data on the cases detected for the first time classified by classes (infectious diseases, neoplasms, blood, mental disorder, endocrine system, nervous system, eyes diseases, ears diseases, respiratory system diseases, digestion system diseases, skin diseases, musculoskeletal system diseases, genitourinary system diseases, pregnancy, perinatal period diseases, congenital diseases, and traumas.The data are retrieved from official open data sources [27]. Step 2. Determining the measures of central trends.Measures of central trends are applied to determine the average level of morbidity for each indicator, and to rank the regional data at three levels: high, medium and low.Using this breakdown enables to compile a retrospective profile of each region in terms of the readiness of health care facilities to meet the challenges.To reflect the results more accurately, the incidence rate is determined per 1 person by dividing the data by the number of available population as of January 1, 2020.The data are retrieved from official open data sources [28].This allows to find the quartiles as follows (please see formulas ( 1) -( 3): ( (2) where -the arithmetic mean of the level of the spatial series; -the value of each indicator for i region; -the maximum and minimum value of the factor, respectively.If the morbidity rate per person in the region exceeds , then the region is assumed as a «high» level; if less than -then the region has a «medium» level, and in other cases -«average».Step 3. Cluster analysis of the input array.Based on the previous step and the distribution of the number of levels, all regions of Ukraine are divided into 4 classes, depending on the state of vulnerability and readiness of the level of public health: «1», «2», «3» and «4».Group «1» includes such regions (oblasts) as: Dnipropetrovsk, Ivano-Frankivsk, Lviv, Odesa, Kharkiv regions, and Kyiv city.Group «2» means Rivne, Zakarpattia, Kyiv region, Zhytomyr, Khmelnytskyi, and Mykolayiv regions.Group «3»: Volyn, Chernivtsi, Cherkasy, Zaporizhia, Kirovohrad, Chernihiv and Kherson regions.And finally, group «4» contains Vinnytsia, Poltava, Sumy, Ternopil, Donetsk and Luhansk regions.
Step 4. Conducting correlation and regression analysis.The multiple linear regression is developed to identify the impact of each independent factor on regional indicators of pandemic vulnerability.A symmetric correlation matrix is designed to check the presence of the multicollinearity effect in the input data set.The analysis of pairwise correlation coefficients showed that there is indeed a close linear relationship both between the performance indicator and within the data set, independent variables.To build an adequate model, it is necessary to eliminate the linear dependence among independent indicators by removing variables.
Thus, the indicators that have the highest number of high, in absolute terms, correlation coefficients with other factors, and vice versa, the lowest values of correlation with dependent variables were removed from the study.The best multiple linear regression equation was constructed by the MNC method with the step-by-step exclusion of the least significant factors with 19 independent indicators.For the number of confirmed factors of those who were infected with COVID-19, the indicators of diseases of the eye, ear, respiratory system, skin, nervous system, musculoskeletal system, and symptoms detected during laboratory tests were the least significant.A multiple linear regression used for the remaining factors is: + (4) where -cases, or number of people infected with COVID-19; -blood diseases; endocrine system diseases; mental disorders; cardiovascular diseases; ingestion diseases; bone and muscular diseases; diseases of genitourinary system; number of pregnancies and deliveries; number of disorders that appeared in the perinatal period; number of congenital anomalies; traumas (accidents) and poisoning.The closeness of the relationship between the dependent variable and the indicators of the level of morbidity (by disease classes) in the region (4) were checked by sigma-limited parameterization.According to the analysis, the most significant variables are: -blood diseases (that include diseases of blood-forming organs and certain disorders involving the immune mechanism); endocrine system diseases (including eating disorders, metabolic disorders); cardiovascular diseases; ingestion diseases; quantities of disorders that appeared in the perinatal period; number of congenital anomalies.Elimination of the insignificant factors out of the model (4) allows putting the nonlinear multiple regression as follows: (5) The critical values of each indicator are: (6) If in the polynomial model (5) the coefficient is for a positive variable, then the critical value is the minimum point, meaning that the independent variable decreases, and only after reaching this point, it increases, and vice versa, for the points of maximum (Tabl.1-3).For the variable that reflects the number of confirmed deaths caused by COVID-19, the relevant indicators are: (7) where -number of deaths caused by COVID; -number of parasitic and infectious diseases; -endocrine system diseases; -mental disorders; -eye diseases; -diseases of the respiratory system; -ingestion diseases; -bone and muscular diseases and connective tissues; -diseases of the genitourinary system; -number of pregnancies; number of disorders that appeared in the perinatal period; number of congenital anomalies.To test the significance of the multiple linear regression coefficients, sigma-limited parameterization was performed, which revealed the relationship between the number of confirmed deaths and responses from the model parameters.According to the results of the analysis, the significant variables are: (number of parasitic and infectious diseases); (endocrine system diseases); (diseases of the respiratory system); (ingestion diseases); (number of congenital anomalies).Thus, these indicators will be taken into account as significant (Tabl.4-6).Step 5. Adequacy check of the model.That is accomplished with the help of Darbin-Watson's criterion ( 8): (8) where -the value of the Darbin -Watson test; -the difference between the empirical and the theoretical value accordingly.The calculated values for the obtained models ( 4) and ( 7) are 1.6 and 2.9, respectively.The value of the Dabrin -Watson test for the model hit the critical zone, and that allows to reject the hypothesis of the presence of autocorrelation in the model.For the second model, this value hit the blind spot.
The coefficients of determination of models ( 4) and ( 7) have values of 0.915 and 0.959, respectively, which confirms the presence of a close linear relationship.The Fisher test values for these models are 12.8 and 27.5, respectively, which is significantly higher than the critical value of 2.9 under the freedom degrees of 11 and 13, and under the significance level of 0.05.
It is important to define and interpret the results from Cook's distance (9), which indicates whether the input data are anomalous. .( 9) As a result of Cook's distance identification, the Kyiv and Dnipropetrovsk region data are proved to be anomalous and were eliminated from both models ( 4) and (7).
Research results.According to the results of cluster analysis, group «1» includes the following regions: Dnipropetrovsk, Ivano-Frankivsk, Lviv, Odessa, Kharkiv regions, and the Kyiv city.These regions (or oblasts by their name) have a high number of diseases at a high level, compared to other regions.Dnipropetrovsk region has the highest incidence rates of infectious and parasitic diseases, the cases of tumors, eye and ear diseases, circulatory and respiratory systems diseases, skin, musculoskeletal system, and genitourinary system morbidity per capita.The Dnipropetrovsk region is well-known as an industrial center, and as a result, it's ranked as TOP-2 biggest environmental pollutant while Kyiv is in the first place.Kyiv region has a huge number of operating industrial enterprises, and the relevant amount of incinerated waste, that even with a sufficiently high level of environmental costs, negatively affects public health.As a result, a big number of registered diseases of the respiratory system, genitourinary system, injuries, poisonings, and congenital anomalies are observed in the region.Kyiv city also ranks first in the number of diseases and deaths caused by COVID-19 in Ukraine.Ivano-Frankivsk region has high morbidity of such systems as blood, nervous system, eye, and ear systems, circulatory system, respiratory and Other clas o one of the v region has as well as r rbidity of po h rate only iv region ha all regions n, and, conse -19 (Fig. 1)

Petal diagra
Fig. 2  As we see from Appendix A, matching data in offered matrix gave some insights on further directions of the targeted budgeting: for instance, number 1 priorities are programs in a sphere of screening, prevention, and mitigation of endocrine diseases for Vinnytsya; disorders that appeared in the perinatal period -for Rivne oblasts, congenital anomalies -for Kyiv city and respiratory system diseases -for Dnipropetrovsk, Luhansk, and Kyiv.
Conclusions.The paper analyzed the correlation between mortality/morbidity caused by COVID-19 and the antecedents of regions' vulnerability to respond to the pandemic.This study revealed a strong correlation between certain diseases dominant in a particular region and COVID-19 outcomes.That makes one region more vulnerable than others in terms of mortality and recovery rates.It was revealed that there is an urgent need to identify the regional system's readiness to respond effectively to the current crisis and emerging risks.The step-by-step method is offered in this study that can be replicated for other regions and countries to reveal the most vulnerable spheres (disease types / region profile) that need special attention.The method may be implemented as a monitoring tool and argument for the targeted budgeting programs in health care.However, there should not be a reallocation of the resources between oblasts unless the mortality rates increase significantly; the decision is offered for resources reallocation between targeted programs by switching the budgets to the disease types that increase vulnerability to COVID-19.

Fig. 5 .
the dependence between the deaths due to COVID-19 and respiratory diseases (25 regions) b) the dependence between registered cases of COVID-19 and endocrine diseases (25 regions) The fragment of the regions' profiling (the dependence identification between disease profile and mortality / morbidity due to COVID-19) region n* where V -value that shows if it is critical, close to critical, or else in terms of dependence between mortality/morbidity caused by COVID-19(1, 2, 3) and E -an evaluation using statistical data (high, middle, low).Source: constructed by authors.4994 (print); ISSN 2310-8770 (online)