Vol 31, No 1 (2024)

MATERIAL AND METHODS: Data from the Federal State Statistics Service, as well as statistical materials from the digest “Rural Healthcare in Russia in 2021”, provided by the Federal State Budgetary Institution “Russian Research Institute of Health” of the Russian Ministry of Health were the main sources of information for this study. Analytical procedures included Pearson correlation analysis, factor analysis (principal component method), hierarchical cluster analysis, multiple linear regression with backward elimination method for selecting variables.

RESULTS: Six typological groups of regions of the Russian Federation were identified based on the variations in the morbidity rates of the rural population across certain classes of diseases including infectious and parasitic diseases, neoplasms, diseases of the circulatory system, diseases of the respiratory system, digestive system, and external causes. It has been established that there was a differentiation in the regions of Russia in terms of factors influencing the morbidity rate of the rural population by the main classes of diseases during the COVID-19 pandemic in 2021. Healthcare system, medical and social factors had the greatest impact on the overall morbidity in the regions of the fourth and fifth classification groups, on the detection of some infectious and parasitic diseases in the regions of the second, fifth and sixth groups, and respiratory and digestive diseases in the third classification group. The housing quality in the regions of almost all groups was one of the most significant social factors. Economic conditions had a significant impact on the incidence of infectious and parasitic diseases in the regions of the third and sixth groups, as well as on the level of injuries and some consequences of external causes in the regions of the first and third groups. Environmental factors were significant determinants of morbidity in the regions of the fifth group. At the same time, regression analysis showed associations between selected factors for all classification groups of regions for some infectious and parasitic diseases. The smallest number of models has been defined for the incidence of neoplasms and circulatory organs.

CONCLUSION: Contribution of socioeconomic, demographic, environmental and natural factors to rural public health should guide the development of policies aimed at development of rural healthcare taking into account regional characteristics.

BACKGROUND: Currently, in the Arctic zone of the Russian Federation (AZRF), there are evident indications of a deterioration in the medical and demographic situation amidst a lack of adequate social infrastructure development. For the first time, we attempted to analyze the impact of living conditions, which encompass various social factors within the habitat, shaped by the social infrastructure, on the mortality rates of the population of one of the regions within AZRF.

AIM: To assess the impact of living conditions, as a set of social factors within the environment, on the mortality rates among the rural and urban populations of the Nenets autonomous okrug (NAO) in the period from 2000 to 2019.

MATERIALS AND METHODS: The databases “Housing and communal services and social infrastructure in NAO in 2000-2019” and “Death cases in NAO in 2000–2019” including information on the population number and age and gender structure of the NAO population across individual settlements have been collected. Using the scoring system for assessing living conditions, a ranking with subsequent division into tertiles of all rural NAO settlements was carried out according to the value of the integral index of living conditions (IILC). A comparative analysis (tertiles with the city, and tertiles with each other) of average annual age-standardized rates of overall mortality, mortality from the main causes and structural components of external causes (EC) of mortality was performed. Relative risks were calculated as the ratio of mortality rates in each tertile to the corresponding indicator for the urban population.

RESULTS: Average annual standardized rates and relative risks of mortality (total, EC, drowning, freezing, alcohol poisoning and transport accidents) of the NAO population demonstrated a “step by step” increase in the sequence “city — highest tertile — middle tertile — lowest tertile”, i.e. as living conditions worsen and as the IILC decreases. Statistically significant differences were identified between the city and tertiles, as well as between the highest (“favorable” living conditions) and lowest (“unfavorable” living conditions) tertiles in terms of total mortality, mortality from EC, drowning and freezing. Mortality rates from alcohol poisoning and transport accidents also increased as living conditions worsened, although the associations did reach the level of statistical significance. With the exception of suicides, the relative risks of mortality for individual EC reached maximum values in the lowest tertile of living conditions.

CONCLUSION: Statistically significant inverse associations between total mortality, mortality from external causes and its main structural components, and the values of the integral index of living conditions have been identified among the rural population of NAO. A decrease in living conditions was significantly associated with an increase in mortality rates and relative risks.

BACKGROUND: High mortality remains one of the most significant health concerns in Russia. One of the priorities of the state policy is to reduce mortality rates among the working-age population and increase life expectancy. Predicting population mortality rates serves as a valuable tool for effectively allocating the available resources.

AIM: To perform mathematical modeling and prediction of mortality rates of the population of the Orenburg region using the Lee–Carter model.

MATERIAL AND METHODS: The age- and sex-specific mortality rates and the population size of the Orenburg region for the period 1991–2020 was used as a study base. The Lee–Carter method was applied to model and predict population mortality. By deriving key parameters, a random walk model with drift was developed, and an accuracy assessment was performed.

RESULTS: The Lee-Carter model has been utilized to analyze the mortality rates of the male population in the Orenburg region. Through this modeling process, an accuracy rate of 87% was achieved, providing a reliable basis for long-term prediction. Mortality forecasts have been generated up to the year 2035, allowing for a comprehensive evaluation of future trends in the region.

CONCLUSION: The analysis of the results indicates that the pandemic's impact on population mortality is expected to be short-term. In the upcoming years, the mortality rate of the male population in the Orenburg region is projected to continue decreasing.