Clustering of standardized cumulative incidence rates over a multi-year period as a method for analyzing the spatial distribution of disease cases

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription or Fee Access

Abstract

Objective. Rationale for the use of visualization of the results of hierarchical clustering of standardized indicators of cumulative incidence over a long-term period as a method for analyzing the spatial distribution of disease cases.

Materials and methods. Information on the incidence of chronic hepatitis B (CHB) in the population of 85 constituent entities of the Russian Federation for the period from 2014 to 2022 was analyzed according to statistical form No. 2 Information on infectious and parasitic diseases. All calculations were performed using Python libraries.

Results. The sequence of actions for obtaining and interpreting the results of hierarchical clustering of indicators of long-term cumulative incidence in the constituent entities of the Russian Federation is described in order to analyze the long-term incidence of CHB in the population.

Conclusion. The proposed method significantly increases the information content and objectivity of the results of studying the spatial distribution of CHB cases.

Full Text

Restricted Access

About the authors

Dmitry V. Dubodelov

Central Research Institute of Epidemiology, Russian Federal Service for Supervision of Consumer Rights Protection and Human Well-Being

Author for correspondence.
Email: dubodelov@cmd.su
ORCID iD: 0000-0003-3093-5731

Cand. Med. Sci., Senior Researcher, Laboratory of Viral Hepatitis, Department of Molecular Diagnostics and Epidemiology

Russian Federation, Moscow

Svetlana V. Ugleva

Central Research Institute of Epidemiology, Russian Federal Service for Supervision of Consumer Rights Protection and Human Well-Being

Email: ugleva@cmd.su
ORCID iD: 0000-0002-1322-0155

МD, Scientific Consultant, Organizational and Methodological Department

Russian Federation, Moscow

Gasan A. Gasanov

Central Research Institute of Epidemiology, Russian Federal Service for Supervision of Consumer Rights Protection and Human Well-Being

Email: gasanov@cmd.su
ORCID iD: 0000-0002-0121-521X

Post-graduate Student

Russian Federation, Moscow

Marina I. Korabel’nikova

Central Research Institute of Epidemiology, Russian Federal Service for Supervision of Consumer Rights Protection and Human Well-Being

Email: korabelnikova@cmd.su
ORCID iD: 0000-0002-2575-8569

Researcher, Laboratory of Viral Hepatitis

Russian Federation, Moscow

Natalya V. Sycheva

Central Research Institute of Epidemiology, Russian Federal Service for Supervision of Consumer Rights Protection and Human Well-Being

Email: natsy@bk.ru
ORCID iD: 0000-0001-8557-6540

Junior Researcher, Laboratory of Health Care Associated Infections

Russian Federation, Moscow

Vasily A. Zavolozhin

Central Research Institute of Epidemiology, Russian Federal Service for Supervision of Consumer Rights Protection and Human Well-Being

Email: zavolozhin@cmd.su
ORCID iD: 0000-0003-4015-1105

Junior Researcher, Laboratory of Viral Hepatitis, Department of Molecular Diagnostics and Epidemiology

Réunion, Moscow

Anna S. Esman

Central Research Institute of Epidemiology, Russian Federal Service for Supervision of Consumer Rights Protection and Human Well-Being

Email: esman@cmd.su
ORCID iD: 0000-0002-5456-7649

Researcher, Laboratory of Molecular Methods for Genetic Polymorphisms Research

Russian Federation, Moscow

Natalia V. Vlasenko

Central Research Institute of Epidemiology, Russian Federal Service for Supervision of Consumer Rights Protection and Human Well-Being

Email: vlasenko@cmd.su
ORCID iD: 0000-0002-2388-1483

Researcher, Laboratory of viral hepatitis, Department of molecular diagnostics and epidemiology

Russian Federation, Moscow

Tatiana A. Semenenko

N.F. Gamaleya National Research Center for Epidemiology and Microbiology of the Ministry of Health of Russia

Email: semenenko@gamaleya.org
ORCID iD: 0000-0002-6686-9011

MD, Professor, Head, Epidemiology Department

Russian Federation, Moscow

Stanislav N. Kuzin

Central Research Institute of Epidemiology, Russian Federal Service for Supervision of Consumer Rights Protection and Human Well-Being

Email: drkuzin@list.ru
ORCID iD: 0000-0002-0616-9777

Professor Stanislav N. Kuzin, MD, Head, Laboratory of Viral Hepatitis, Department of Molecular Diagnostics and Epidemiology

Russian Federation, Moscow

Vasily G. Akimkin

Central Research Institute of Epidemiology, Russian Federal Service for Supervision of Consumer Rights Protection and Human Well-Being

Email: vgakimkin@yandex.ru
ORCID iD: 0000-0003-4228-9044

Academician of the Russian Academy of Sciences, Professor, MD. Director

Russian Federation, Moscow

References

  1. Беляков В.Д., Яфаев Р.Х. Эпидемиология: Учебник. М.: Медицина, 1989. 416 с.
  2. Belyakov V.D., YAfaev R.H. [Epidemiology: Textbook]. Moscow: Medicine, 1989. 416 p. (In Russ.)
  3. Беляков В.Д., Дегтярев А.А., Иванников Ю.Г. Качество и эффективность противоэпидемических мероприятий. Л.: Медицина, 1981. 303 с.
  4. Belyakov V.D., Degtyarev A.A., Ivannikov Yu.G. [The quality and effectiveness of anti-epidemic measures]. Saint-Petersburg: Medicine, 1981. 303 p. (In Russ.)
  5. Зуева Л.П., Еремин С.Р., Асланов Б.И. Эпидемиологическая диагностика. 2-е изд., перераб. и доп. СПб: Фолиант, 2009. 312 с.
  6. Zueva L.P., Eremin S.R., Aslanov B.I. [Epidemiological diagnosis. 2nd edition]. Saint-Petersburg: Foliant. 2009. 312 p. (In Russ.)
  7. Слободенюк А.В., Косова А.А., Ан Р.Н. Эпидемиологический анализ: Учебное пособие. Екатеринбург: изд. ГБОУ ВПО УГМУ Минздрава России, 2015. 36 с.
  8. Slobodenyuk A.V., Kosova A.A., An R.N. [Epidemiological analysis]. Ekaterinburg. Ural State Medical University publishing house, 2015. 36 p. (In Russ.)
  9. Гаврилов Д.В., Абрамов Р.В., Кирилкина А.В., Ившин А.А., Новицкий Р.Э. Модель прогнозирования пандемии COVID-19 на основе машинного обучения в отдельных регионах Российской Федерации. Фармакоэкономика. Современная фармакоэкономика и фармакоэпидемиология 2021; 14(3): 342–56. https://doi.org/10.17749/ 2070-4909/farmakoekonomika.2021.108
  10. Gavrilov D.V., Abramov R.V., Kirilkina А.V., Ivshin А.А., Novitskiy R.E. [COVID-19 pandemic prediction model based on machine learning in selected regions of the Russian Federation]. Farmakoekonomika. Modern Pharmacoeconomics and Pharmacoepidemiology 2021; 14(3): 342–56 (In Russ.). https://doi.org/10.17749/2070-4909/farmakoekonomika.2021.108.
  11. Зайцева Н.В., Май И.В., Кирьянов Д.А., Бабина С.В., Камалтдинов М.Р. Cанитарно-эпидемиологический надзор: новый этап развития в условиях цифровизации и правовых изменений. Анализ риска здоровью 2021; (2): 4–16. doi: 10.21668/health.risk/2021.2.01
  12. Zaitseva N.V., May I.V., Kiryanov D.А., Babina S.V., Kamaltdinov M.R. [Sanitary-epidemiological surveillance: a new stage in development stimulated by digitalization and changes in legislation]. Health Risk Analysis 2021; (2): 4–16. (In Russ.). doi: 10.21668/health.risk/2021.2.01
  13. Гусев А.В., Добриднюк С.Л. Искусственный интеллект в медицине и здравоохранении. Информационное общество 2017; (4–5): 78–93.
  14. Gusev A.V., Dobridnyuk S.L. [Artificial intelligence in medicine and healthcare.] Information Society 2017; (4–5): 78–93. (In Russ.).
  15. Невзорова В.А., Плехова Н.Г., Присеко Л.Г., Черненко И.Н., Богданов Д.Ю., Мокшина М.В. и др. Методы машинного обучения в прогнозировании исходов и рисков сердечно-сосудистых заболеваний у пациентов с артериальной гипертензией (по материалам ЭССЕ-РФ в Приморском крае). Российский кардиологический журнал 2020; 25(3): 10–16. doi: 10.15829/1560-4071-2020-3-3751
  16. Nevzorova V.A., Plekhova N.G., Priseko L.G., Chernenko I.N., Bogdanov D.Yu., Mokshina M.V., Kulakova N.V. [Machine learning for predicting the outcomes and risks of cardiovascular diseases in patients with hypertension: results of ESSE-RF in the Primorsky Region]. Russian Journal of Cardiology 2020; 25(3): 3751 (In Russ.). https://doi.org/10.15829/1560-4071-2020-3-3751
  17. Бетелин В.Б., Галкин В.А., Ряховский А.В. Tочечные и распределенные модели распространения коронавирусной инфекции. Успехи кибернетики 2021; 2(2): 12–20. doi: 10.51790/2712-9942-2021-2-2-1
  18. Betelin V. B., Galkin V. A., Ryakhovskiy A. V. [Local and Distributed Models of the Coronavirus Spread]. Russian Journal of Cybernetics 2021; 2(2): 12–20. (In Russ.). doi: 10.51790/2712-9942-2021-2-2-1
  19. Наумов И.В., Отмахова Ю.С., Красных С.С. Методологический подход к моделированию и прогнозированию воздействия пространственной неоднородности процессов распространения COVID-19 на экономическое развитие регионов России. Компьютерные исследования и моделирование 2021; 13(3): 629–48. doi: 10.20537/2076-7633-2021-13-3-629-648
  20. Naumov I.V., Otmakhova Y.S., Krasnykh S.S. [Methodological approach to modeling and forecasting the impact of the spatial heterogeneity of the COVID-19 spread on the economic development of Russian Regions] Computer Research and Modelin. 2021; 13(3): 629–48. (In Russ.). doi: 10.20537/2076-7633-2021-13-3-629-648
  21. Сенько О.В., Кузнецова А.В., Воронин Е.М., Кравцова О.А., Борисова Л.Р., Кирилюк И.Л. и др. Методы интеллектуального анализа данных в исследованиях эпидемии COVID-19. Журнал Белорусского государственного университета. Математика. Информатика 2022; (1): 83–96. https://doi.org/10.33581/2520-6508-2022-1-83-96
  22. Senko O.V., Kuznetsova A.V., Voronin E.M., Kravtsova O.A., Borisova L.R., Kirilyuk I.L., Akimkin V.G. [Methods of intellectual data analysis in COVID-19 research.] Journal of the Belarusian State University. Mathematics and Informatics. 2022; (1): 83–96. (In Russ.). https://doi.org/10.33581/2520-6508-2022-1-83-96
  23. Головерова Ю.А., Абросимова О.А., Кузнецова А.В., Воронин Е.М. Машинное обучение для оценки взаимосвязи кадровых ресурсов и основных показателей здравоохранения с заболеваемостью инфекциями, связанными с оказанием медицинской помощи, среди пациентов стационаров в субъектах Российской Федерации. Вестник науки 2022; 3(11): 304–20.
  24. Goloverova Yu.A., Abrosimova O.A., Kuznetsova A.V., Voronin E.M. [Machine learning to assess the relationship of human resources and key health indicators with the incidence of infections associated with the provision of medical care among hospital patients in the subjects of the Russian Federation]. Bulletin of Science 2022; 3(11): 304–20. (In Russ.).
  25. Панин А.Н., Рыльский И.А., Тикунов В.С. Пространственные закономерности распространения пандемии COVID-19 в России и мире: картографический анализ. Вестник Московского университета. Серия 5. География 2021; (1): 62–77.
  26. Panin A.N., Rilskiy I.A., Tikunov V.S. [Spatial patterns of COVID-19 distribution in Russia and the world: cartographic analysis]. Lomonosov Geography Journal. Series 5, Geography 2021; (1): 62–77. (In Russ.).
  27. Завальский Л.Ю., Доброхотский О.Н., Зиновьев Г.А., Воронина М.А., Еремченко Е.Н., Клименко С.В. Пространственно-временной анализ инфекционной заболеваемости с использованием методов неогеографии на примере городского поселения Оболенск Московской области. Биозащита и биобезопасность 2011; 3(2): 22–9.
  28. Zavalsky L.Y., Dobrokhotskiy O.N., Zinoviev G.A., Voronina M.A., Eremchenko E.N., Klimenko S.V. [The existential analysis of infectious desease with use of methods neogeography by the example of city settlement Obolensk of the Moscow area]. Biosecurity and biosafety 2011; 3(2): 22–9. (In Russ.).

Supplementary files

Supplementary Files
Action
1. JATS XML
Download
2. Fig. 1. The result of hierarchical clustering of the constituent entities of the Russian Federation based оn long-term intensive rates of СНВ incidence

Download (1MB)
Indexing metadata
3. Fig. 2. Scaled intensive rates of СНВ incidence

Download (3MB)
Indexing metadata

Copyright (c) 2024 Bionika Media