share of the population with 3 disability group
and proportion of the population aged 26-35
and 56-72 years;
share of the population with 2 disability group
and the proportion of the population aged 36-
45;
share of the population with 1 disability group
and the proportion of the population aged 26-
35 and 56-72 years.
An inverse correlation is also visible between:
healthy population share and proportion of the
population aged 26-35 and 56-72 years;
share of the population with 3 and 1 disability
groups and proportion of the population aged
15-25.
5 CONCLUSIONS
Thus, a structural-dynamic analysis of the health level
of the working-age population group as a group, that
actively participates in the labor process of the region,
generates benefits and sets the pace of sustainable
economic growth was carried out. The calculations
were performed using the example of the Udmurt
Republic for the period 2000-2019.
It was found that the health level of the working-
age population decreases: the share of the healthy
population decreased from 59.8% in 2000 to 42.1%
in 2019, the share of people with chronic diseases
increased from 33.2% to 48.5% and the share of
people with disabilities from 7.0% up to 9.4%. At the
same time, the proportion of people able to work in
the 15-72 age group is increasing. Over the period
2000-2019, the increase in people able to work was 2
percentage points.
The trends of changes in the dynamics of the
health level of the working-age population in the
region revealed and analyzed in the paper indicate a
decrease in the rate of positive influence of labor
resources on the economic dynamics and the labor
market. The conducted analysis is indicated the
emergence of the need to create additional conditions
to reduce the level of general morbidity and disability.
It needs to increase the volume of funding for the
health care system in order to expand the scale of
involvement of the population in a healthy lifestyle,
develop a preventive health care system, improve the
availability and quality of medical care.
REFERENCES
Auzina-Emsina, A. (2014). Labor Productivity, Economic
Growth and Global Competitiveness in Post-Crisis
Period, Procedia-Social and Behavioral Sciences, 156:
317-321.
Ilyakova, I., Lizina, O., and Sausheva, O. (2020). Juvenile
Potential as a Social Resource for Economic
Development in the Context of a Change in the
Technological Order, Regionology, 28(4): 638-665.
Kalil, Moraes, R., Fernandes, Wanke, P., Ricardo, and
Faria, J. (2021). Unveiling the Endogeneity Between
Social-Welfare and Labor Efficiency: Two-Stage
NDEA Neural Network Approach, Socio-Economic
Planning Sciences, 101026.
Ketova, K. and Saburova, E. (2020). Addressing a Problem
of Regional Socio-Economic System Control with
Growth in the Social and Engineering Fields Using an
Index Method for Building a Transitional Period,
Advances in Intelligent Systems and Computing.
Software Engineering Perspectives in Intelligent
Systems, pages 385-396.
Ketova, K. (2007). A Mathematical Economic Model of the
Manpower Resource Potential and Cost Characteristics
of Demographic Losses, Expert Syst. Appl., 3(7): 80-94.
Ketova, K. and Vavilova, D. (2020). Modelling a Human
Capital of an Economic System with Neural Networks,
Journal of Physics: Conference Series, 012035.
Ketova, K. and Rusyak I. (2009). Identification and
Forecast of Generalized Indicators of Regional
Economic System Development, Applied
Econometrics, 3: 56-73.
Konorev, A. (2020). Modern Trends of Social Sector
Financing in the Regions of the Central Federal,
Economic and humanitarian sciences, 337(2): 75-84.
Nakamura, K., Sohei, K., and Yagi, T. (2019). Productivity
Improvement and Economic Growth: Lessons from
Japan, Economic Analysis and Policy, 62: 57-79.
Roslender, R., Stevenson, J., and Kahn, H. (2012). Towards
Recognising Workforce Health as a Constituent of
Intellectual Capital: Insights from a Survey of UK
Accounting and Finance Directors, Accounting Forum,
36(4): 266-278.
Sinyai, C. and Choi, S. (2020). Fifteen years of American
construction occupational safety and health research,
Safety Science, 131: 104915.
Sleptsova, E. and Ryndina, T. (2020). State Human Capital
Development Policy in Russia, Economy and Business:
Theory and Practice, 61(3-1): 180-182.
Vavilova, D. and Ketova, K. (2020). Neural Network
Forecasting Algorithm as a Tool for Assessing Human
Capital Trends of the Socio-Economic System,
Economic and Social Changes: Facts, Trends,
Forecast, 13(6): 117-133.
Willis, G., Cave, S., and Kunc, M. (2018). Strategic
Workforce Planning in Healthcare: A Multi-
Methodology Approach, European Journal of
Operational Research, 267(1): 250-263.