How are sociodemographic factors and risk preferences associated with seasonal influenza vaccination behavior under the COVID-19 pandemic?
MORI, T.; Nagata, T.; Ikegami, K.; Hino, A.; Tateishi, S.; Tsuji, M.; Matsuda, S.; Fujino, Y.; Mori, K.
Show abstract
The 2020/2021 seasonal influenza vaccination was carried out under unique situations during the coronavirus disease 2019 (COVID-19) pandemic. Examining the factors affecting vaccine inoculation in a pandemic situation may provide valuable insights. The purpose of the current study was to investigate how the COVID-19 pandemic affected the 2020/2021 seasonal influenza vaccine intake. A cross-sectional study was conducted on workers aged 20-65 years on December 22-25, 2020, using data from an Internet survey. We set the presence or absence of 2020/2021 seasonal influenza vaccination as the dependent variable, and each aspect of sociodemographic factors, including gender, age, marital status, education, annual household income, and underlying disease, as independent variables. We performed a multilevel logistic regression analysis nested by residence. In total, 26,637 respondents (13,600 men, 13,037 women) participated, and a total of 11,404 individuals (42.8%) received the 2020/2021 influenza vaccine. Significantly more women than men were vaccinated, and the vaccination rate was higher among younger adults, married people, highly educated people, high-income earners, and those with underlying disease. The current results suggested that the relationship between seasonal influenza vaccination behavior and sociodemographic factors differed from the results reported in previous studies in terms of age. These findings suggest that, during the COVID-19 pandemic, young people may have become more aware of the risk of contracting influenza and of the effectiveness of the influenza vaccine. In addition, information interventions may have had a positive effect.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Antibody titers against the Alpha, Beta, Gamma, and Delta variants of SARS-CoV-2 induced by BNT162b2 vaccination measured using automated chemiluminescent enzyme immunoassay 93%
- Dynamics of anti-Spike IgG antibody level after full BNT162b2 COVID-19 vaccination in health care workers 93%
- Post COVID-19 condition of the Omicron variant of SARS-CoV-2 92%
Similar papers in this journal
- Age and smoking predict antibody titres at 3 months after the second dose of the BNT162b2 COVID-19 vaccine 95%
- The role of incentives in deciding to receive the available COVID-19 vaccine 95%
- Attenuation of antibody titres during 3-6 months after the second dose of the BNT162b2 vaccine depends on sex, with age and smoking as risk factors for lower antibody titres at 6 months 95%
Similar papers in this journal
- Gender differences in the determinants of willingness to get the COVID-19 vaccine among the working-age population in Japan 98%
- Factors Associated with the Acceptance and Willingness of COVID-19 Vaccination among Chinese Healthcare Workers 97%
- Factors associated with COVID-19 vaccine hesitancy in Senegal: a mixed study 93%
Similar papers in this journal
- COVID-19 vaccine confidence and hesitancy among healthcare workers: a cross-sectional survey from a MERS-CoV experienced nation 95%
- Acceptance and Attitudes Toward COVID-19 Vaccines: A Cross-Sectional Study from Jordan 94%
- Psychological antecedents towards COVID-19 vaccination using the Arabic 5C validated tool: An online study in 13 Arab countries 94%
Similar papers in this journal
- COVID-19 vaccine uptake among healthcare workers in the fourth country to authorize BNT162b2 during the first month of rollout 96%
- The effect of Health Literacy on COVID-19 Vaccine Hesitancy: The Moderating Role of Stress 94%
- Midwives’ attitudes toward participation of pregnant women in a preventive vaccine hypothetical clinical trial 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.