ON ASSESSING THE GROWTH POTENTIAL OF THE LIFE EXPECTANCY OF THE POPULATION AS A RESULT OF IMPLEMENTING INTEGRATED MEASURES (ON THE EXAMPLE OF A CONSTITUENT ENTITY OF THE RUSSIAN FEDERATION)

Nina V. Zaitseva; Svetlana V. Kleyn; Maxim V. Glukhikh; Marat R. Kamaltdinov

doi:10.12731/2658-6649-2023-15-6-988

Nina V. Zaitseva Federal Scientific Center for Medical and Preventive Health Risk Management Technologies https://orcid.org/0000-0003-2356-1145
Svetlana V. Kleyn Federal Scientific Center for Medical and Preventive Health Risk Management Technologies https://orcid.org/0000-0002-2534-5713
Maxim V. Glukhikh Federal Scientific Center for Medical and Preventive Health Risk Management Technologies https://orcid.org/0000-0002-4755-8306
Marat R. Kamaltdinov Federal Scientific Center for Medical and Preventive Health Risk Management Technologies https://orcid.org/0000-0003-0969-9252

DOI: https://doi.org/10.12731/2658-6649-2023-15-6-988

Keywords: life expectancy, neural network, modifying determinants

Abstract

The development vector of the current government policy in the Russian Federation aimed at improving social conditions – one of the critical indicators of which is life expectancy at birth [LE] – dictates the research relevance. Currently, the search and testing of new analytical systems capable of forecasting LE, considering the multifactorial influence on this indicator, remains relevant and timely. The research goal is to establish the growth potential of LE of the population estimation on the example of one of the constituent entities of the Russian Federation, considering integrated heterogeneous factors that possess a modifying effect on LE. The estimation includes modeling cause-and-effect relationships between indicators of habitat, quality of life, and life patterns – determinants of population health. The utilized model is a set of algebraic equations in the form of a factor transformation of independent variables and an artificial neural network and is implemented in three stages. They include (1) developing the basic scenario and calculating LE, (2) developing the target scenario and calculating LE, and (3) calculating the growth potential of LE as the difference between the indicators obtained at previous stages. The developed model and the three-stage algorithm application allows one to obtain the growth potential of LE on the example of one constituent entity of the Russian Federation in the context of a single change in determinants by 2024, which amounts to +1.24 years (453.0 days) relative to the baseline scenario (the actual LE value in 2018). The forecast value of LE is 70.47 years. Ranking of individual indicator groups according to their isolated effect on LE demonstrates that the most significant determinant groups are (1) socio-demographic indicators (2.6 years – 949.0 days), (2) indicators of sanitary and epidemiological safety (1.75 years – 638.75 days), and the (3) population lifestyle indicators (1.41 years – 514.65 days). The obtained results confirm the predominance of the influence of social indicators on population health in the form of LE on the example of the analysis of changes in the indicators of one of the constituent entities of the Russian Federation. The research relevance implies studying the combined influence of heterogeneous factors of the environment and lifestyle on the indicative indicator of population health (LE), a complex system with the properties of emergence, variability, opposite influence, and adaptation.

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References

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Rjoub H., Odugbesan J. A., Adebayo T. S., Wong W.-K. Investigating the causal relationships among carbon emissions, economic growth, and life expectancy in Turkey: Evidence from time and frequency domain causality techniques // Sustainability, 2021, vol. 13, no. 5, pp. 2924. http://dx.doi.org/10.3390/su13052924

Rutter H., Savona N., Glonti K., Bibby J., Cummins S., Finegood D. T., Greaves F., Harper L., Hawe P., Moore L., Petticrew M., Rehfuess E., Shiell A., Thomas J., White M. The need for a complex systems model of evidence for public health // Lancet, 2017, vol. 390, no. 10112, pp. 2602–2604. https://doi.org/10.1016/s0140-6736(17)31267-9

Smith J. P. Healthy bodies and thick wallets: The dual relation between health and economic status // The Journal of Economic Perspectives: A Journal of the American Economic Association, 1999, vol. 13, no. 2, pp. 144–166.

Stringhini S., Carmeli C., Jokela M., Avendaño M., Muennig P., Guida F., Ricceri F., D'Errico A., Barros H., Bochud M., Chadeau-Hyam M., Clavel-Chapelon F., Costa G., Delpierre C., Fraga S, Goldberg M., Giles G. G., Krogh V., Kelly-Irving M., Layte R., Lasserre A. M., Marmot M. G., Preisig M., Shipley M. J., Vollenweider P., Zins M., Kawachi I., Steptoe A., Mackenbach J. P., Vineis P., Kivimäki M. Socioeconomic status and the 25 × 25 risk factors as determinants of premature mortality: A multicohort study and meta-analysis of 1·7 million men and women // Lancet, 2017, vol. 389, no. 10075, pp. 32380–32387. https://doi.org/10.1016/s0140-6736(16)32380-7

Westman J., Wahlbeck K., Laursen T. M., Gissler M., Nordentoft M., Hällgren J., Arffman M., Ösby U. Mortality and life expectancy of people with alcohol use disorder in Denmark, Finland and Sweden // Acta Psychiatrica Scandinavica, 2015, vol. 131, no. 4, pp. 297–306. https://doi.org/10.1111/acps.12330

World Health Organization. Closing the gap in a generation: Healthy equity through action on the social determinants of health, 2008. URL: https://www.who.int/social_determinants/final_report/csdh_finalreport_2008.pdf

World Health Organization. World health statistics 2020: Monitoring health for the SDGs, sustainable development goals, 2020. URL: https://apps.who.int/iris/handle/10665/332070

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