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With the increasing intensity of movement of people between countries and continents, humanity has become more vulnerable to the spread of diseases on a global scale. The rapid spread of COVID-19 in 2020 has led to a significant shift in the mortality structure of the population and tangible losses in average life expectancy. Governments of different countries have responded differently to this challenge. Therefore, it is relevant to compare the experience of Ukraine against the other countries. The purpose of this article is to analyze and quantify the impact of COVID-19 on mortality and life expectancy in Ukraine. The novelty is an estimate of the impact of COVID-19 on mortality in Ukraine by different methods based on the data for the complete years 2020-2021. Methods of calculation and analysis of demographic indicators, life tables, graphic method, decomposition method, and Lee—Carter method were used. The existing definitions of excess mortality are analyzed. Based on the use of 6 methods it is determined that the pandemic led to an increase in the number of deaths in Ukraine (without Donbas and Crimea) from 92.7 to 241.5 thousand. Most estimates fall into the range of 147.5-224.2 thousand. It is shown that the biggest number of excess deaths is observed in older age groups, especially 65-84 years. The highest loss of life potential occurred in the 65-74 age group. In 2020-2021, life expectancy at birth for women decreased by 2.62 years, while for men the reduction was 1.77 years. The increase in mortality from COVID-19 resulted in a loss of 1.91 and 1.51 years, respectively. In 2020-2021, a sharp increase in mortality from respiratory diseases was recorded. It is shown that mortality from this class of diseases has a direct strong (correlation coefficient 0.91) and significant (p< 0.001) correlation with mortality from COVID-19. The relationship between these causes of death is supported by a similar effect on the age pattern of life expectancy losses. The increase in the overall male mortality rate was significantly mitigated by decrease in mortality from infectious and parasitic diseases, as well as external causes of death. Women, due to their much lower mortality rates from these classes of causes, have a markedly lower potential for improvement in this area. Therefore, it is necessary to expand and facilitate access to professional medical care and not limit it, as was done during lockdowns and quarantines.

Uncertainty is an intrinsic characteristic of demographic processes. Th is applies even more to the future. Accurate deterministic forecasts are fundamentally impossible. Th is determines the necessity to quantify the future uncertainty. Th e purpose of this research is to develop probabilistic demographic forecasts for the metropolises of Ukraine and analyze the outcome results. For the fi rst time, probabilistic demographic forecasts have been developed for individual cities of Ukraine. Th e study was carried out using the functional data approach which incorporates wide set of demographical methods and models implemented in several packages of R programming language. Chosen methodology is based entirely on statistics and does not require introducing any additional arbitrary hypotheses. At three cases (namely for fertility in Kyiv, Lviv and Kharkiv) the default method (ARIMA) showed implausible results which could be induced by unreliable current data. In these cases were used random walk model. For Odesa the both models give similar results. It is possible that in this city the underestimation of the departed population is compensated by the underestimation of the arrived, which leads to the relevance of the current fertility rates (namely their denominators) and, consequently, the consistency of the forecast results regardless of the method. Mortality forecasts are consistent with the dynamics of mortality rates being observed and the quality of current data. Th e model captured upward life expectancy trends for Dnipro and Odesa and stagnation for other cities. Th is is also could be caused by denominator inconsistencyfor the latter ones. Computation showed that the population size of Dnipro, Lviv and Kharkiv in 2040 is most likely to be below the population number reached in early 2019. Some chances for population growth remain in Odesa and Kyiv is likely to have a larger population. Th e age distribution of the population in all cities in future looks similar. The number of people over 40 years of age has least uncertainty. At the age of 20 to 40 years, the uncertainty is much higher. Th is is a consequence of the uncertainty of youth migration during the forecast horizon of 2019–2039, because all these cities are powerful educational centres and attract students. In 2040 those who were students in 2020 will reach the age of 40 and can stay in the big city or leave. Uncertainty of the number of persons under 20 is formed from two sources: uncertainty of fertility forecasts and uncertainty of the number of reproductive cohort, i.e. those 20-40-year-olds. It is needed to review these forecasts aft er receiving the results of the closest census.

Large cities are often at the forefront of social change, not only in their region, but throughout the country. Therefore, the analysis of their demographic trends, in particular mortality by causes of death, is important not only by itself, but also in the context of general demographic prospects of the country. The aim of this study is to make a comparative analysis of the gains in life expectancy in Dnipro, Kyiv, Lviv, Odesa and Kharkiv over the period 2005— 2019, and an estimation of the cause-specific mortality contributions to the life expectancy changes. For the first time, decompositions analyses are conducted to evaluate contributions to fluctuations in life expectancy not only by age and sex, but also by causes of death for each of the five largest cities of Ukraine. The following statistical measures and methods are used: calculation and analysis of demographic rates, life tables, graphic method and decomposition method. Our results show that, during the observed period, life expectancy at birth has increased in all cities, mostly due to changes in mortality from circulatory diseases, which have resulted in a one-year increase in life expectancy among men in Odesa to almost 2.5 years among women of Kharkiv. However, it might be possible that such a large contribution in Kharkiv is a result of a part of the deaths falling into the category of ill-defined causes, for which mortality has increased rapidly in that city. The second major contribution to the improvement in life expectancy is attributable to external causes of death: from 0.3 years of increase in females of Lviv to almost 1.9 years in females of Odesa. Males in Odesa benefited the most from the reduction in mortality due to infectious diseases (+2 years). In some cases, there was a significant positive contribution of the reduction in mortality from diseases of the digestive system (+0.5 years in men of Dnipro). The decline in cancer mortality also had a positive impact on life expectancy in all cities, except for Kharkiv. Overall, the increase in life expectancy for men was due to a reduction in mortality at younger ages than for women. In particular, the common age interval for all five cities, with men having the greatest gain in life expectancy, is 45-59 years, while for women it is 65 to 79 years. In men, the gain in life expectancy was accounted for by the decline in mortality at the age 45 to 59 years, with 24.4 per cent in Odesa and almost 46 per cent in Lviv and Kharkiv, mainly due to a reduction in mortality from circulatory diseases and external causes. In women, the decline in mortality at the age 65 to 79 years contributed 30.3-34.8 per cent of the increase in life expectancy in Dnipro, Kyiv and Lviv; however, in Odesa it was only 19.4 per cent, in Kharkiv it was almost 54.3 per cent, which seems unlikely due to an increase in the proportion of ill-defined causes of death. In all five cities, the main trend over 2005-2013 years was the decline in mortality from the major causes of death, while the period 2013-2019 was turbulent, with mortality by causes of death changing in different directions even in the same city. Some contradictory results may be explained by the deterioration in the quality of the registration of demographic events as well as changes in the age and sex structure of the population of cities, which have not been reflected in the current estimates of the State Statistical Committee of Ukraine due to the long absence of a population census.

Large cities concentrate a substantial part of the educated, highly qualified, and economically active populations. Such social "selection" with the peculiarities of lifestyle determines the distinctive characteristics of the level and structure of mortality. Even though data on deaths by causes of death for the large cities are available in Ukraine, very few studies have analyzed cause-specific mortality in these cities. The objective of the study is to make a comparative analysis of mortality from the most influential causes of death in large cities. The novelty lies in the comparative analysis done for Dnipro, Kyiv, Lviv, Odesa, and Kharkiv for the first time. The study uses the direct method of standardization to calculate standardized death rates by sex in 2005-2019. The results indicate lower all-cause mortality rates for the large city residents compared to the corresponding average country-level indicators. Kyiv, Lviv, and Odesa have lower death rates compared to Dnipro and Kharkiv. In Kyiv and Lviv, this is attributed to lower mortality from almost all major causes of death, while in Odesa this mainly resulted from the extremely low ischemic heart disease mortality. Relatively high mortality from circulatory diseases is observed in Kharkiv and Dnipro. However, in Dnipro, this is associated with a high death rate from coronary heart disease and a very low contribution of cerebrovascular disease, whereas in Kharkiv coronary and cerebrovascular disease death rates are quite high. Mortality rates from diseases of the digestive system in the large cities are found to be the closest to the average in Ukraine (except for Lviv). The neoplasms are the only large group of diseases with a mortality rate that exceeds the average level in Ukraine, in particular for women. Overall, the death rates from most of the causes of death in the large cities demonstrated a positive trend in 2005-2019, with some exceptions. External causes and infectious diseases showed the most decrease while mortality from AIDS and ill-defined causes increased. Also, there were uncertain dynamics of deaths due to suicide and injuries with undetermined intent. Given some specific mortality differences between the cities, some concerns have been raised over the accuracy of the coding of diagnoses. In particular, unusually low mortality from ischemic heart disease was found in Odesa and from cerebrovascular disease in Dnipro, very rare deaths from alcoholic liver disease in Odesa, accidental alcohol poisoning in Kyiv, and a group of other liver diseases in Dnipro. We also assume misclassification of suicides as injuries with undetermined intent in Kharkiv. Our findings highlight the importance of the implementation of automated coding and selection of causes of death that can minimize the number of subjective decisions made by coders and lead to significant improvements in the quality of data.

Large cities concentrate a substantial part of the educated, highly qualified, and economically active populations. Such social "selection" with the peculiarities of lifestyle determines the distinctive characteristics of the level and structure of mortality. Even though data on deaths by causes of death for the large cities are available in Ukraine, very few studies have analyzed cause-specific mortality in these cities. The objective of the study is to make a comparative analysis of mortality from the most influential causes of death in large cities. The novelty lies in the comparative analysis done for Dnipro, Kyiv, Lviv, Odesa, and Kharkiv for the first time. The study uses the direct method of standardization to calculate standardized death rates by sex in 2005-2019. The results indicate lower all-cause mortality rates for the large city residents compared to the corresponding average country-level indicators. Kyiv, Lviv, and Odesa have lower death rates compared to Dnipro and Kharkiv. In Kyiv and Lviv, this is attributed to lower mortality from almost all major causes of death, while in Odesa this mainly resulted from the extremely low ischemic heart disease mortality. Relatively high mortality from circulatory diseases is observed in Kharkiv and Dnipro. However, in Dnipro, this is associated with a high death rate from coronary heart disease and a very low contribution of cerebrovascular disease, whereas in Kharkiv coronary and cerebrovascular disease death rates are quite high. Mortality rates from diseases of the digestive system in the large cities are found to be the closest to the average in Ukraine (except for Lviv). The neoplasms are the only large group of diseases with a mortality rate that exceeds the average level in Ukraine, in particular for women. Overall, the death rates from most of the causes of death in the large cities demonstrated a positive trend in 2005-2019, with some exceptions. External causes and infectious diseases showed the most decrease while mortality from AIDS and ill-defined causes increased. Also, there were uncertain dynamics of deaths due to suicide and injuries with undetermined intent. Given some specific mortality differences between the cities, some concerns have been raised over the accuracy of the coding of diagnoses. In particular, unusually low mortality from ischemic heart disease was found in Odesa and from cerebrovascular disease in Dnipro, very rare deaths from alcoholic liver disease in Odesa, accidental alcohol poisoning in Kyiv, and a group of other liver diseases in Dnipro. We also assume misclassification of suicides as injuries with undetermined intent in Kharkiv. Our findings highlight the importance of the implementation of automated coding and selection of causes of death that can minimize the number of subjective decisions made by coders and lead to significant improvements in the quality of data.