BEHAVIORAL PREDICTORS OF LIFE EXPECTANCY IN THE RUSSIAN FEDERATION (BASED ON THE RESULTS OF THE ALL-RUSSIAN SAMPLE OBSERVATION OF HEALTH IN 2019 AND 2023)
Authors:   Interregional Clinical and Diagnostic Center, Kazan, Russia, Khayrullin Rustem , Ildarhanova Chulpan ,   Interregional Clinical and Diagnostic Center, Kazan, Russia, Ershova Guzel ,   Interregional Clinical and Diagnostic Center, Kazan, Russia
Published: October 2024

Significance. Increasing life expectancy is the main goal of the National Demography Project to achieve the national goal of protecting the population of Russia, actualized by high rates of mortality in the country. In order to identify factors influencing life expectancy in the Russian Federation in the pre-pandemic period in 2019 and 2023, correlations of indicators characterizing a healthy lifestyle in 85 regions of the Russian Federation have been analyzed with a further construction of single-factor and multiple linear regression models to project the impact of factors on the dynamics in life expectancy. The novelty of the study is the use of some elements of econometric analysis for dynamic series for 2019 and 2023 and selection of indicators that are statistically significant in the regions of the Russian Federation. Material and methods. Significant factors for the construction of regression models were selected from the indicators included in the surveys of the All-Russian sample observation of health in 2019 and 2023 including the indicator of adherence to a healthy lifestyle according to methods of the World Health Organization. Results. A correlation and regression analysis made it possible to identify the most significant factors affecting life expectancy in the regions of the Russian Federation. Inter-regional differences in life expectancy models were estimated based on the analysis of σ-convergence of the regional indicators of life expectancy from 2000 to 2023. The study revealed a trend of divergence in the regional indicators of life expectancy in 2018 and 2021-2023 due to the male cohort of the population, including risk groups for mortality from diseases of the circulatory system. Conclusion. A healthy lifestyle can have a positive effect on life expectancy with varying degrees of correlation of the components of healthy lifestyle: among the considered linear regression single-factor models, on the example of 85 regions of the Russian Federation, excessive alcohol consumption, salt intake, daily tobacco smoking, sufficient consumption of vegetables and fruits, and self-assessment of health are statistically significant. In the multiple linear regression model, a statistically significant predictor is daily smoking with an average level of the coefficient of determination. However, in order to achieve the planned targets of the National Project “Long and active life” by 2036, a more than twofold decrease in the proportion of daily smokers is required compared to 2023. The multiple linear regression model confirmed a reliable Y-intersection coefficient, the coefficients of other variables are not statistically significant, substantiating the need for a more detailed analysis of factors at the regional level. Scope of application. The presented regression assessment approaches can be used to describe the prerequisites for developing a health-preserving behavior in order to form stable trends, as well as preventive measures of the relevant demographic policy of the state to maintain stability and progressive positive development of the demographic situation and timely prognostic assessments of trends in deviation from the positive trend.