Cluster analysis of socially significant diseases in the Russian Federation
https://doi.org/10.34020/2073-6495-2023-1-169-183
Abstract
Socially significant diseases still remain a public health problem in the Russian Federation, and various departmental targeted programs have been approved to counteract these diseases. This article attempts to implement such a classification. As a result of cluster analysis, five clusters were identified. Among the constituent entities of the Russian Federation, clusters can be identified quite confidently in terms of the incidence of socially significant diseases (for most of these diseases, there are statistically significant differences between the identified clusters). The identified clusters also have a fairly noticeable geographical commonality: in general, the clusters are grouped according to the degree of remoteness from Moscow.
About the Authors
P. V. Galushin
Siberian Law Institute of the Ministry of Internal Affairs of Russian Federation
Russian Federation
Russian Federation
Galushin Pavel V. – Candidate of Technical Sciences, Associate Professor of the Department of Information and Legal Disciplines and Special Equipment
Krasnoyarsk
E. N. Galushina
Prof. V. F. Voino-Yasenetsky Krasnoyarsk State Medical University
Russian Federation
Russian Federation
Galushina Elena N. – Candidate of Physical and Mathematical Sciences, Associate Professor of the Department of Medical Cybernetics and Informatics
Krasnoyarsk
References
1. Arlashkin I.Ju. Klasterizacija rossijskih regionov po urovnju dolgovoj ustojchivosti [Clusterization of Russian regions by the level of debt sustainability], Finansovyj zhurnal [Financial magazine], 2021, vol. 13, no. 5, pp. 108–124. DOI: 10.31107/2075-1990-2021-5-108-124
2. Bojarkina S.I. Determinanty social’no znachimyh boleznej v stranah Evropy i v Rossii [Determinants of socially significant diseases in European countries and in Russia], Vestnik Sankt-Peterburgskogo universiteta. Sociologija [Vestnik of St. Petersburg University. Sociology], 2019, vol. 12, no. 4, pp. 350–367. DOI: 10.21638/spbu12.2019.404
3. Budilova E.V., Lagutin M.B. Dinamika i territorial’naja differenciacija social’no znachimyh boleznej v 2005–2016 gg. v Rossii [Dynamics and territorial differentiation of socially significant diseases in 2005–2016 in Russia], Vestnik Moskovskogo universiteta. Serija 23: Antropologija [Vestnik of the Moscow University. Series 23: Anthropology], 2019, no. 3, pp. 82–100. DOI: 10.32521/2074-8132.2019.3.082-100
4. Budilova E.V., Migranova L.A. Rasprostranenie social’no znachimyh boleznej i bor’-ba s nimi v Rossii [The spread of socially significant diseases and the fight against them in Russia], Narodonaselenie [Population], 2020, vol. 23, no. 2, pp. 85–98. DOI: 10.19181/population.2020.23.2.8
5. Grigor’ev S.G., Korneenkov A.A., Popovich I.G. Vybor metoda matematiko-statis-ticheskogo dokazatel’stva jeffektivnosti peptidnogo preparata jepifiza [Choice of the method of mathematical and statistical evidence of the effectiveness of the peptide preparation of the pineal gland], Sovremennye problemy zdravoohranenija i medicinskoj statistiki [Modern problems of health care and medical statistics], 2019, no. 4, pp. 103–114. DOI: 10.24411/2312-2935-2019-10083
6. Zdravoohranenie v Rossii [Health care in Russia]. 2021: Stat. sb. Rosstat. Moscow, 2021. 171 p.
7. Karkishhenko V.N., Karkishhenko N.N., Shustov E.B. Farmakologicheskie osnovy terapii. Tezaurus: Rukovodstvo dlja vrachej i studentov [Pharmacological bases of therapy. Thesaurus: A guide for physicians and students]. 3-e izd. Sankt-Peterburg: Ajsing, 2018. 288 p. ISBN 978-5-91753-138-0
8. Kiriljuk I.L., Sen’ko O.V. Ocenka kachestva klasterizacii panel’nyh dannyh s ispol’-zovaniem metodov Monte-Karlo (na primere dannyh rossijskoj regional’noj jekonomiki) [Evaluation of the quality of panel data clustering using Monte Carlo methods (on the example of data from the Russian regional economy)], Komp’juternye issledovanija i modelirovanie [Computer Research and Modeling], 2020, vol. 12, no. 6, pp. 1501–1513. DOI: 10.20537/2076-7633-2020-12-6-1501-1513
9. Kisljakov A.N., Poljakov S.V. Ierarhicheskie metody klasterizacii v zadache poiska anomal’nyh nabljudenij na osnove grupp s narushennoj simmetriej [Hierarchical clustering methods in the problem of searching for anomalous observations based on groups with broken symmetry], Upravlencheskoe konsul’tirovanie [Management consulting], 2020, no. 5 (137), pp. 116–127. DOI: 10.22394/1726-1139-2020-5-116-127
10. Kobzar’ A.I. Prikladnaja matematicheskaja statistika [Applied mathematical statistics]. 2-e. izd. Moscow, Fizmatlit, 2012. 816 p.
11. Kulencan A.L., Marchuk N.A. Analiz dinamiki zabolevaemosti naselenija social’no znachimymi boleznjami v RF [Analysis of the dynamics of the incidence of socially significant diseases in the Russian Federation], Izvestija vysshih uchebnyh zavedenij. Serija: Jekonomika, finansy i upravlenie proizvodstvom [News of higher educational institutions. Series: Economics, finance and production management], 2020, no. 3 (45), pp. 67–70.
12. Maksimov A.V., Klevno V.A. Mnogofaktornyj kliniko-anatomicheskij analiz letal’nogo ishoda [Multifactorial clinical and anatomical analysis of lethal outcome], Kubanskij nauchnyj medicinskij vestnik [Kuban Scientific Medical Vestnik], 2019, vol. 26, no. 6, pp. 107–116. DOI: 10.25207/1608-6228-2019-26-6-107-116
13. Makusheva T.S., Galushina E.N., Apanovich M.S. Faktornyj analiz social’no znachimyh zabolevanij v Rossijskoj Federacii [Factor analysis of socially significant diseases in the Russian Federation], Vestnik NGUJeU [Vestnik NSUEM], 2019, no. 2, pp. 85–93. DOI: 10.34020/2073-6495-2019-2-085-093
14. Ob utverzhdenii vedomstvennoj celevoj programmy «Preduprezhdenie i bor’ba s social’no znachimymi infekcionnymi zabolevanijami» [On approval of the departmental target program “Prevention and control of socially significant infectious diseases”]: prikaz Ministerstva zdravoohranenija RF ot 5 aprelja 2019 g. No 199.
15. Ob utverzhdenii gosudarstvennoj programmy Rossijskoj Federacii «Razvitie zdravoohranenija» [On the approval of the state program of the Russian Federation “Health Development”]: postanovlenie Pravitel’stva RF ot 26 dekabrja 2017 g. No 1640.
16. Ob utverzhdenii perechnja social’no znachimyh zabolevanij i perechnja zabolevanij, predstavljajushhih opasnost’ dlja okruzhajushhih [On approval of the list of socially significant diseases and the list of diseases that pose a danger to others]: postanovlenie Pravitel’stva RF ot 01.12.2004 No 715 (red. ot 31.01.2020).
17. Ovechkina N.I., Shmarihina E.S. Zabolevaemost’ naselenija social’no znachimymi boleznjami v kontekste izuchenija demograficheskoj bezopasnosti strany [Morbidity of the population with socially significant diseases in the context of studying the country’s demographic security], Vestnik NGUJeU [Vestnik NSUEM], 2019, no. 4, pp. 208–219. DOI: 10.34020/2073-6495-2019-4-208-219
18. Popova L.A., Taranenko N.N. Smertnost’ naselenija Rossii po prichinam na poroge novyh vyzovov [Mortality of the Russian population for reasons on the verge of new challenges]. Rossija: tendencii i perspektivy razvitija: materialy XX Nacional’-noj nauchnoj konferencii s mezhdunarodnym uchastiem, Moskva, 14–15 dekabrja 2020 goda. No 16. Ch. 1. Moscow, Institut nauchnoj informacii po obshhestvennym naukam RAN, 2021. Pp. 735–742.
19. Bunenkov N.S., Bunenkova G.F., Komok V.V. et al. SAS Enterprise Guide 6.1 dlja vrachej: sravnenie grupp [SAS Enterprise Guide 6.1 for Physicians: Group Comparison], Medicinskij akademicheskij zhurnal [Medical Academic Journal], 2019, vol. 19, no. 4, pp. 33–40. DOI: 10.17816/MAJ17736
20. Shitikov V.K., Mastickij S.Je. Klassifikacija, regressija, algoritmy Data Mining sispol’zovaniem R. [Classification, regression, data mining algorithms using R]. [Electronic book]. 2017. Available at: https://github.com/ranalytics/data-mining (accessed: 16.11.2022).
Review
For citations:
Galushin P.V., Galushina E.N. Cluster analysis of socially significant diseases in the Russian Federation. Vestnik NSUEM. 2023;(1):169-183. (In Russ.) https://doi.org/10.34020/2073-6495-2023-1-169-183
Email this article 
