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      Health behavior in Russia during the COVID-19 pandemic

      brief-report

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          Abstract

          In this article, we report results from a nationwide survey on pandemic-related health behavior in Russia. A total of 2,771 respondents aged 18 to 82 were interviewed between January 21 and March 3, 2021. The survey included questions on perceived vulnerability to coronavirus, prevention-related health behavior, readiness for vaccination, and general awareness about COVID-19. Descriptive data showed that 21.2% of respondents reported high vulnerability to the coronavirus, and 25% expressed fear. Moreover, 38.7% of the surveyed individuals reported low trust in vaccination efficacy, and 57.5% were unwilling to take a vaccine, which was much higher than the official data. Based on the evidence obtained, four types of health behavior during the pandemic were constructed. Rational (29.3%) and denying (28.6%) behaviors prevailed in men, while women were found to more likely behave with a vaccine-hesitant demeanor (35.7%). Educational background affected the proportion of respondents with the denying type of health behavior, who were also of younger age. The rational behavioral type was found to be more common among respondents aged above 50 years and prevailed as well among individuals with university degrees. The middle-aged population of Russia was highly compliant with prevention-related health practices; however, vaccine hesitancy was also high among them. Furthermore, health behaviors varied significantly across the Federal Districts of Russia. We are convinced that our results contribute to existing public health practices and may help improve communication campaigns to cause positive health behaviors.

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          Most cited references24

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          Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period

          It is urgent to understand the future of severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) transmission. We used estimates of seasonality, immunity, and cross-immunity for betacoronaviruses OC43 and HKU1 from time series data from the USA to inform a model of SARS-CoV-2 transmission. We projected that recurrent wintertime outbreaks of SARS-CoV-2 will probably occur after the initial, most severe pandemic wave. Absent other interventions, a key metric for the success of social distancing is whether critical care capacities are exceeded. To avoid this, prolonged or intermittent social distancing may be necessary into 2022. Additional interventions, including expanded critical care capacity and an effective therapeutic, would improve the success of intermittent distancing and hasten the acquisition of herd immunity. Longitudinal serological studies are urgently needed to determine the extent and duration of immunity to SARS-CoV-2. Even in the event of apparent elimination, SARS-CoV-2 surveillance should be maintained since a resurgence in contagion could be possible as late as 2024.
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            Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy

            In Italy, 128,948 confirmed cases and 15,887 deaths of people who tested positive for SARS-CoV-2 were registered as of 5 April 2020. Ending the global SARS-CoV-2 pandemic requires implementation of multiple population-wide strategies, including social distancing, testing and contact tracing. We propose a new model that predicts the course of the epidemic to help plan an effective control strategy. The model considers eight stages of infection: susceptible (S), infected (I), diagnosed (D), ailing (A), recognized (R), threatened (T), healed (H) and extinct (E), collectively termed SIDARTHE. Our SIDARTHE model discriminates between infected individuals depending on whether they have been diagnosed and on the severity of their symptoms. The distinction between diagnosed and non-diagnosed individuals is important because the former are typically isolated and hence less likely to spread the infection. This delineation also helps to explain misperceptions of the case fatality rate and of the epidemic spread. We compare simulation results with real data on the COVID-19 epidemic in Italy, and we model possible scenarios of implementation of countermeasures. Our results demonstrate that restrictive social-distancing measures will need to be combined with widespread testing and contact tracing to end the ongoing COVID-19 pandemic.
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              The challenges of modeling and forecasting the spread of COVID-19

              Significance The coronavirus disease 2019 (COVID-19) pandemic has placed epidemic modeling at the forefront of worldwide public policy making. Nonetheless, modeling and forecasting the spread of COVID-19 remain a challenge. Here, we present and detail three regional-scale models for forecasting and assessing the course of the pandemic. This work is intended to demonstrate the utility of parsimonious models for understanding the pandemic and to provide an accessible framework for generating policy-relevant insights into its course. We show how these models can be connected to each other and to time series data for a particular region. Capable of measuring and forecasting the impacts of social distancing, these models highlight the dangers of relaxing nonpharmaceutical public health interventions in the absence of a vaccine or antiviral therapies.
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                Author and article information

                Contributors
                URI : https://loop.frontiersin.org/people/827093/overviewRole: Role: Role: Role: Role: Role: Role: Role: Role:
                Role: Role: Role: Role: Role:
                Journal
                Front Public Health
                Front Public Health
                Front. Public Health
                Frontiers in Public Health
                Frontiers Media S.A.
                2296-2565
                02 October 2023
                2023
                02 October 2023
                : 11
                : 1276291
                Affiliations
                [1] 1Tomsk State University , Tomsk, Russia
                [2] 2Mental Health Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences , Tomsk, Russia
                Author notes

                Edited by: Yuliya Semenova, Nazarbayev University, Kazakhstan

                Reviewed by: Laura Kassym, Astana Medical University, Kazakhstan; Assiya Kussainova, Semey State Medical University, Kazakhstan

                *Correspondence: Anastasia Peshkovskaya, peshkovskaya@ 123456gmail.com
                Article
                10.3389/fpubh.2023.1276291
                10577229
                37849726
                3d192bcc-f3c7-48bb-98fc-f905c1a8e014
                Copyright © 2023 Peshkovskaya and Galkin.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 11 August 2023
                : 07 September 2023
                Page count
                Figures: 2, Tables: 2, Equations: 0, References: 27, Pages: 7, Words: 4361
                Categories
                Public Health
                Brief Research Report
                Custom metadata
                Public Health Policy

                pandemic,health behavior,covid-19,prevention,perceived vulnerability,vaccine hesitancy,conspiracy,public health policy

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