Predicting fertility, neonatal and perinatal mortality, and stillbirths for evaluation of the needs for perinatal care in the future post-war reconstruction of Ukraine.

OBJECTIVE: Aim: To predict trends in fertility, neonatal and perinatal mortality, and stillbirth rates to ascertain future perinatal care requirements during the post-war reconstruction in Ukraine. PATIENTS AND METHODS: Materials and Methods: The study uses the data from the Centre for Medical Statistics of the Ministry of Health of Ukraine, covering the years 2012 to 2022. The data analysis was by a univariate linear regression model. The quality of these models was evaluated using the coefficient of determination, R2. RESULTS: Results: In 2022, the birth rate in Ukraine had declined to 2.5 times lower than that of 2011. The period was characterized by a notable increase in the incidence of premature births and in neonates with birth weights under 1000 grams and between 1000 to 2499 grams. While the neonatal mortality rate decreased by 3.7 times, there remains a statistically significant (p<0.05) increase in the mortality rates of premature infants and neonates weighing less than 1000 grams. The stillbirth rate in Ukraine remains constant; however, it exceeds that of the European Union. Predictions indicate a rise in antenatal mortality and a reduction in both intranatal and perinatal mortality. As of 2022, the perinatal mortality rate in Ukraine made up 7.72 per 1000 live births, which is significantly higher than the rate in the European Union. CONCLUSION: Conclusions: The optimization of the network of healthcare facilities and resources should be prioritized, in response to the reliable decline in the birth rate. This necessitates improvements in the medical care for premature and low birth weight infants, and efforts for preventing stillbirths.

EFFECT OF URBANIZATION ON DENSITY OF THE GENERAL PRACTITIONERS, AVERAGE LIFE EXPECTANCY AND MORTALITY FROM THE DISEASES OF CIRCULATORY SYSTEM.

OBJECTIVE: The aim: Comparative analysis of average life expectancy, mortality from diseases of the circulatory system, gross regional product, and density of general practitioners in regions with different levels of urbanization. PATIENTS AND METHODS: Materials and methods: We compared the following characteristics of groups classified by level of urbanization: average density of general practitioners per 10,000, average life expectancy and mortality from diseases of the circulatory system per 1,000, average gross regional product per 1 person. RESULTS: Results: The groups did not differ in average life expectancy. The highest rate of mortality from diseases of the circulatory system- in the group with average level of urbanization, the lowest- in the group with low level of urbanization (p<0.05). The highest value of gross regional product per person is in the group with high level of urbanization, and the lowest is in the group with low level (p<0.05). The lowest density of primary care doctors per 10,000 is in the group with high level of urbanization, and the highest is in the group with low level of urbanization (p<0.05). CONCLUSION: Conclusions: When planning staffing of health care institutions, it is necessary to take into account the level of urbanization of the region and ensure status of the general practitioner as a leading medical specialist responsible for medical care during the first meeting with the patient and his subsequent follow-up.

THE TRENDS OF THE DENSITY OF SURGEONS IN SOME EUROPEAN COUNTRIES AND 16 OECD COUNTRIES DURING 2005-2018.

OBJECTIVE: The aim: To establish that there are differences in the density trends of surgeons in some European countries and 16 OECD countries and to compare the trends of the density of surgeons in some European countries and 16 OECD member countries, 2005 - 2018. PATIENTS AND METHODS: Materials and methods: The study is based on data of the Centre for Medical Statistics of the Ministry of Health of Ukraine obtained during 2005-2020 and OECD data obtained during 2005-2018. The Difference-in-Differences method has been used to determine the density trends differences, the regression analysis method - to predict the number of surgeons in 2020. RESULTS: Results: In 2020, there were 28,559 surgeons (0.687 per 1,000) in Ukraine, which is by 17.7% less than in 2005. From 2005 to 2018, the density of surgeons per 1000 in Ukraine and the United States decreased (-7.45% and -2.5%). In Korea (+ 78.38%), Greece (+ 65.52%), Lithuania (+ 58.57%), Slovenia (+ 45.65%) and other 11 countries the surgeon's density increased. In 2030, Ukraine is predicted to significantly reduce the number of surgeons, general surgeons, ophthalmologists and urologists; as well as to increase the number of cardiovascular surgeons. The number of proctologists, oncologists-surgeons, neurosurgeons, thoracic surgeons, orthopaedists-traumatologists and anaesthesiologists will not change significantly. CONCLUSION: Conclusions: It is possible to state the availability of surgical care according to the density of surgeons in Ukraine, similar to the level of OECD countries. In 2030, the number of surgeons is projected to decrease, with the exception of cardiovascular surgeons.

HEALTH CARE EXPENDITURES OF 179 COUNTRIES WITH DIFFERENT GNI PER CAPITA IN 2018.

OBJECTIVE: The aim: To study the difference in health care expenditures in groups of countries with different GNI per capita. PATIENTS AND METHODS: Materials and methods: In 4 groups of countries with different GNI per capita were analyzed indicators of Current health expenditure per capita ($) (СHE), Domestic general government health expenditure per capita, PPP ($) (GGHE $) and GGHE%, Domestic private health expenditure per capita, PPP ($) (PHE) and PHE%, Out-of-pocket expenditure (%) (OOP), Current health expenditure (% of GDP) (CHE% GDP). RESULTS: Results: The group of high-income countries differs by CHE, GGHE $, GGHE%, PHE $, PHE%, OOP, CHE% GDP (p <0.001), the group with incomes above the average - by CHE, GGHE $, PHE $, PHE%, CHE%GDP (p <0.001). Groups with lower average income and low income do not differ in CHE, GGHE$, PHE$, PHE%, OOP (p> 0.05). GNI per capita has a positive effect on GDP%GDP, CHE, GGHE, PHE in the high-income group and negatively affects the OOP (p <0.05), GNI per capita has a positive effect on CHE, GGHE in the above-average income group, GNI per capita has a positive effect on CHE, GGHE, GGHE%, PHE and negatively affects OOP (p <0.05) in the income group below average. GNI per capita has a positive effect on the OOP and negatively affects the CHE%GDP (p <0.05) in the low-income group. CONCLUSION: Conclusions: Each group of countries, depending on per capita income, has its own health care costs.