Is it possible to link the prevalence of cancer cases in a Russian region with the age of its population and several environmental variables, specifically by levels of atmospheric air pollution, emissions of radioactive elements, and discharges of contaminated wastewater? The aim of the study is to build a linear regression model that links the oncologic incidence of the population Y within the region in year t with the listed factors taken with lags. One of the variables was qualitative and therefore a variable structure model was obtained. Objects and methods: The objects of the study were panel data for Russia’s regions from the past five years, as well as the expertly estimated variable on the radiation pollution of its territories. The mathematical methods used were multivariate regression analysis for data with a few dummy variables. The model parameters were estimated by the ordinary least square method based on a spatiotemporal sample from the panel data, which included the variable Y for 2017 and 2018. Calculations and statistical data analysis were performed in MS-Excel. Results: The mean relative error for the model was equal to 2.2%. Then, at the exam stage, the model was applied to new data, where the variable Y was for 2019. The error on the exam was equal to 4.3%. Conclusion: A linear regression model with a variable structure was built and evaluated in terms of accuracy, linking cancer prevalence in Russia’s regions with atmospheric air hygiene, an indicator of the pollution of its territory with wastewater, two fictitious variables for radiation safety and the proportion of older people. On this basis, it is possible to estimate the prevalence of cancer cases in a number of Russia’s regions (with a horizon of one year).
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2. Healthcare in the Russian Federation: information by constituent entities of the Russian Federation. Statistical collection. M.: Rosstat , 2007 – 2019 (In Russ ).
3. Zabotina A. Organization of medical care for cancer patients in the Russian Federation. Ekspert. 2020. (In Russ ). Available at: https://expertnw.com/naglyadno/otchyet-p o-nozologii-onkologiya.
4. Dyadik VV, Dyadik NV, Klyuchnikova EM. Economic assessment of environmental effects on public health: a review of methods. Human Ecology. 2021; 2: 57-64. ( In Russ ). doi : 10.33396/1728-0869-2021-2-57-64.
5. Mirasova VM, Malygina NV. Determination of dependence of the incidence rate of citizens in the regions of the Russian Federation on the environment state by means of multivariate statistical methods. XXI vek : itogi proshlogo i problemy nastoyashchego plyus = XXI century : the results of the past and problems of the present plus. 2017; 1(35): 58-66. ( In Russ ).
6. Emtseva ED, Kiku PF, Mazelis AL. Assessment of temporal trends of malignant neoplasm using multivariate statistical analysis. Ekologiya cheloveka. 2019; 2: 45-51. ( In Russ ). doi : 10.33396/1728-0869-2019-2-45-51.
7. Feldblyum IV, Alyeva MH, Radionova MV. Complex impact of medico-social and environmental risk factors on probability of colorectal canser development. Tikhookeanskiy meditsinskiy zhurnal. 2018; 3(73): 24-28. (In Russ ). doi : 10.17238/PmJ1609-1175.2018.3.24-28.
8. Aydinov GT, Marchenko BI, Sofyanikova LV, Sinelnikova YuA. Application of multivariate statistical methods in the tasks of improving of information and analytical providing of the socio-hygienic monitoring system. Zdorov’ye naseleniya i sreda obitaniya. 2015; 7(268): 4-8. (In Russ ).
9. Lazarev AF, Petrova VD, Terekhova SA, Sinkina TV. Multivariate statistical analysis when forming groups of high oncological risk. Byulleten meditsinskoy nauki. 2017; 1(5): 37 - 43. (In Russ ). doi : 10.31684/2541-8475.2017.1(5).37-43.
10. Karyakina OE, Dobrodeeva LK, Martynova NA, Krasilnikov SV, Karyakina TI. Use of mathematical models in clinical practice. Human Ecology. 2012; 7: 55-64. (In Russ ).
11. Suleimanov RA, Bakirov AB, Valeev TK, Davletnurov NKh , Stepanov EG, Tukhtarova IO. Analysis of morbidity and mortality of the population of the Republic of Bashkortostan malignant neoplasms ю. Meditsina truda i ekologiya cheloveka. 2019; 2: 14 - 23. ( In Russ ). doi : 10.24411/2411-3794-2019-10016.
12. Basova OM, Basov MO, Isaev NI. Assessment of hygienic risk factors of oncologic diseases in conditions of small industrial towns. Health Risk Analysis. 2013; 3: 34-40. ( In Russ ).
13. Sukonko OG, Krasny SA. Role of researches in improving a cancer care service and a direction for further improvement of medical science. Cancer Urology. 2015; 11(2): 14-22. ( In Russ ). doi : 10.17650/1726-9776-2015-11-2-14-22.
14. Meshkov NA. Major environmental risk factors for canser development. Nauchnyy al’manakh. 2016; 5(3): 309 - 318. (In Russ ). doi : 10.17117/na.2016.05.03.309.
15. Veremchuk LV, Kiku PF, Zhernovoi MV. System modeling of ecological dependence in distribution of oncologic diseases within the Primorye Territories. Byulleten ’ fiziologii i patologii dykhaniya. 2011; 41: 48 - 53. (In Russ). Available at: https://cfpd.elpub.ru/jour/article/view /411/389.
16. Kiku PF, Beniova SN, Moreva VG, Gorborukova TV, Izmaylova OA, Sukhova AV, Sabirova KM, Bogdanova VD. Ecological and hygienic factors and prevalence of the diseases of the circulatory system. Health Сare of the Russian Federation. 2019; 63(2): 92 - 97. (In Russ). doi : 10.18821/0044-197X-2019-63-2-92-97.
17. Tan Ch, Avasarala S, Liu H. Hexavalent chromium release in drinking water distribution systems: new insights into zerovalent chromium in iron corrosion scales. Environmental Science and Technology. 2020; 54(20): 13036 - 13045. doi : 10.1021/ acs. est.0 c 03922.
18. Revich BA, Khar’kova TL, Kvasha EA. Selected health parameters of people living in cities included into the «Clean air» federal project. Health Risk Analysis. 2020; 2: 16 - 27. ( In Russ ). doi : 10.21668/ health. risk /2020.2.02.
19. Shkuratova T.A. Analysis and modeling of cancer incidence based on elimination of multicollinearity and the determination of lags. [ Autoreferat dissertation] Voronezh; 20 06. (In Russ).
20. Aleksandrov YuA. Fundamentals of radiation ecology. Yoshkar-Ola: Izd. Mariyskogo gosuniver siteta , 2007. 268 p. (In Russ).
21. Fernandez- Antoran D., Piedrafita G., Murai K., Ong S.H., Herms A., Jones P.H., Frezza C. Outcompeting p 53-Mutant Cells in the Normal Esophagus by Redox Manipulation. Cell Stem Cell. 2019; 25(3): 329 - 341.e6. doi : 10.1016/j.stem.2019.06.011.
22. Regions of Russia. Socio - economic indicators. M.: Rosstat , 2016 – 2020 (In Russ ).
23. Environmental protection in Russia: Appendix (Information on the regions of Russia). M.: Rosstat , 2014–2018. ( In Russ ).
24. Regions of Russia. Major characteristics of subjects of the Russian Federation. M.: Rosstat , 2020. (In Russ ).
25. Carlberg C. Regression Analysis Microsoft Excel. М., 2017. 400 р. (In Russ ).
26. Socially significant diseases of the population of Russia in 2019. M.: TsNIIOIZ MZ RF, 2020. 76 p. (In Russ ).
27. Aivazyan SA , Yenyukov IS , Meshalkin LD. Applied statistics: Fundamentals of modeling and initial data processing. M.: Finansy i stat istika , 1983. 471 p. (In Russ).
For citation
Stepanov V.S. The relationship of canser prevalence with age of the population living under adverse environmental factors. Medical doctor and information technology. 2021; 3: 38-49. (In Russ.). doi : 1025881/18110193_2021_3_38.
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