# Disentangling behavioral determinants of seasonal influenza vaccination in post-corona era: An integrated model approach

## TL;DR

This study explores factors influencing seasonal influenza vaccination in Korea, using an integrated model to identify behavioral patterns in the post-COVID-19 era.

## Contribution

The study applies Andersen’s model to reveal gender- and age-specific behavioral determinants of seasonal influenza vaccination in Korea.

## Key findings

- Higher education and marriage increase influenza vaccination likelihood in men and women.
- Smoking and binge drinking reduce vaccination likelihood in men, while smoking strongly reduces it in women.
- Chronic disease presence increases vaccination likelihood in older adults of both genders.

## Abstract

Seasonal influenza vaccination (SIV) is influenced by various factors, including sociodemographic characteristics and socioeconomic status of the recipient. Nevertheless, in the post-COVID-19 era, the importance of vaccination and group immunity has grown. Therefore, applying an integrated model to identify behavioral determinants of vaccination is needed. This study aimed to identify contextual factors affecting SIV by applying Andersen’s model. We utilized secondary national datasets (n = 14,535) from the 2022 Community Health Survey conducted by the Korea Disease Control and Prevention Agency. Predisposing factors were gender and age. Enabling factors were income, educational attainment, and marital status. Need factors were presence of chronic disease, health risk behaviors (smoking and/or drinking alcohol), physical activity, and coronavirus disease 2019 (COVID-19) vaccination status. Dependent variable was influenza vaccination status. Multiple binomial logistic regression analyses were performed to identify predictors of influenza vaccination status among Korean adults, stratified by gender and age. According to the results, in men, higher education increased the likelihood of influenza vaccination by 1.089 times (95% CI: 1.000–1.185), while being married increased it by 1.619 times (95% CI: 1.413–1.856); however, smoking and binge drinking reduced the likelihood by 0.822 times (95% CI: 0.732–0.923) and 0.749 times (95% CI: 0.650–0.864), respectively. Among young men, marriage (OR=1.480, 95% CI: 1.131–1.935) and physical activity (OR=1.381, 95% CI: 1.053–1.811) were significant positive factors, while among older men, chronic disease presence increased vaccination likelihood by 1.339 times (95% CI: 1.126–1.592). In women, higher education (OR=1.168, 95% CI: 1.075–1.270) and marriage (OR=2.242, 95% CI: 1.965–2.557) were strong positive predictors, while COVID-19 vaccination history consistently increased influenza vaccination likelihood (OR=1.852, 95% CI: 1.712–2.003). Among young women, smoking reduced vaccination likelihood (OR=0.551, 95% CI: 0.359–0.847), while among older women, having a chronic disease increased vaccination likelihood by 1.354 times (95% CI: 1.133–1.619). This study empirically reveals that SIV is affected by predisposing, enabling, and need factors. To effectively intervene in individual health behaviors, it is necessary to identify characteristics of the population, provide segmented messages, and apply customized strategies.