Boyarkina S.I., Khodorenko D.K. Societal determinants of HIV-infection spread in regions in the Russian Federation. Health Risk Analysis, 2021, no. 3, pp. 116–126. Boyarkina S.I., Khodorenko D.K. Societal determinants of HIV-infection spread in regions in the Russian Federation. Health Risk Analysis, 2021, no. 3, pp. 116–126.ISSN 2542-2308DOI: 10.21668/health.risk/2021.3.11.engРазмещена на сайте: 30.12.21 Поискать полный текст на Google AcademiaСсылка при цитировании:Boyarkina S.I., Khodorenko D.K. Societal determinants of HIV-infection spread in regions in the Russian Federation. Health Risk Analysis, 2021, no. 3, pp. 116–126. DOI: 10.21668/health.risk/2021.3.11.eng.Boyarkina S.I., Khodorenko D.K. Societal determinants of HIV-infection spread in regions in the Russian Federation. Health Risk Analysis, 2021, no. 3, pp. 116–126. DOI: 10.21668/health.risk/2021.3.11.eng DOI: 10.21668/health.risk/2021.3.11.eng.Авторы:Бояркина С.И., Ходоренко Д.К.АннотацияThe paper dwells on the results obtained via examining dependence between HIV-infection spread and factors related to social environmental and social structure of population in RF regions. These factors are considered to be potential health risk ones. The authors tested a hypothesis about influence exerted by demographic, economic, cultural and behavioral determinants and public healthcare availability on differences in territorial spread of the disease within social-epidemiologic approach. To solve the set task, data that characterized 85 RF regions were taken from official statistical reports. Descriptive statistic analysis was performed and regression models were built up; it allowed testing whether the analyzed factors had their influence in RF regions and selecting the most significant ones to be included into the overall regression model. The research revealed significant contextual differences in HIV-infection spread. Regression analysis showed that 22.0 % differences in a number of HIV-infected people detected in RF regions occurred due to differences in urban population numbers, provision with ambulatories and polyclinics, and unemployment rate. Moreover, a number of registered crimes committed by minors determined 32.5 % difference in a number of patients with the first diagnosed HIV-infection between the examined regions.These results allow assuming that the greatest influence on spread of the disease in RF regions is exerted by consequences of urbanization; this process is usually accompanied with a growth in a share of urban population in a given region, instability on the labor market there as well as related migration processes within the country and wider opportunities to pursue individual behavioral strategies including those that involve law violations and/or are destructive for people’s health.Ключевые слова: inequalities in health societal determinants hiv-infection spread regions in the russian federation mathematical modeling regression analysis Рубрики: Социология медициныСоциальная структура и стратификацияВозможно, вам будут интересны другие публикации:Бояркина С. И., Ходоренко Д. К.Социетальные детерминанты как факторы риска распространения ВИЧ-инфекции в регионах России // Анализ риска здоровью. – 2021. – № 3. – С. 118–128. DOI: 10.21668/health.risk/2021.3.11Лукьянец А. С., Охрименко И., Егорова М.Lukyanets A., Okhrimenko I., Egorova M. Life Expectancy as an Economic Category: Social, Epidemiological and Macroeconomic Context. Talent Development & Excellence, 2020, Vol. 12, No. 2s, pp. 1390-1401.Бояркина С. И., Ходоренко Д. К.(2020) Теоретические подходы к изучению факторов формирования и воспроизводства статусных неравенств в здоровье. Журнал социологии и социальной антропологии, 23(5): 41–72. https://doi.org/10.31119/jssa.2020.23.5.2Osipov G., Karepova S., Ponkratov V., Karaev A., Masterov A., Vasiljeva M. Economic and Mathematical Methods for Ranking Eastern European Universities. Industrial Engineering & Management Systems, 2020, Vol. 19, No 1, pp. 273-288.Lukyanets A., Gura D., Savinova O., Kondratenko L., Lushkov R. Industrial emissions effect into atmospheric air quality: mathematical modeling. Reviews on Environmental Health. 2022.