Sickness Absence in the Kursk Nuclear Power Plant Workers as an Element of the Digital Twin Concept in Occupational Medicine
| Autor: | Olga A. Tikhonova, Andrey Yu. Bushmanov, Nadezhda I. Grishakina, Vitaly V. Dengin, Sergey A. Afonin |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | ЗДОРОВЬЕ НАСЕЛЕНИЯ И СРЕДА ОБИТАНИЯ - ЗНиСО / PUBLIC HEALTH AND LIFE ENVIRONMENT. :16-23 |
| ISSN: | 2619-0788 2219-5238 |
| DOI: | 10.35627/2219-5238/2022-30-11-16-23 |
| Popis: | Introduction: A multifactorial analysis of the causes of morbidity and preventive measures provides an opportunity to build a risk prediction model for sickness absence in the working population, thereby reducing production and healthcare costs. Objective: A retrospective analysis of sickness absence in workers of the Kursk Nuclear Power Plant exposed to a combination of occupational risk factors in the year 2020. Materials and methods: We analyzed sickness absence rates in the workers in terms of the ICD-10 nomenclature based on data of the Russian Statistical Observation Form No. 16-VN and established the number of episodes of sickness absence per employee as a relative indicator independent of the size of the groups under study. Results: The highest sickness absence rates per 100 employees were estimated for workers of the Training Center and Design and Technology Department (73.1 episodes each), the Hydroshop (68.6) and Transportation Department (65.5). In the Radioactive Waste Treatment Shop and the first and second Reactor Shops, these rates were 53, 43.9, and 33.9 per 100 employees, respectively. The mean duration of an episode of sickness absence in all divisions of the nuclear power plant was 13–15.4 days and its most frequent causes were, similar to the Kursk Region and the Russian Federation as a whole, diseases of the respiratory and musculoskeletal systems, injuries and poisonings. Conclusions: It is important to develop a risk-based approach in occupational medicine. To establish a cause-and-effect relationship for loss of health among nuclear industry workers, we propose to create a digital platform (a digital twin of an employee) enabling prediction of the time, cause, risk of a disease and/or disability, and prerequisites for its mitigation. |
| Databáze: | OpenAIRE |
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