Back

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.

2021-05-03 infectious diseases
10.1101/2021.04.30.21256364 medRxiv
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.

50% of probability mass above

"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.