Development and Validation of a Clinical Prediction Tool for Seasonal Influenza Vaccination in England

A Prediction Model for Identifying Seasonal Influenza Vaccination Uptake Among Children in Wuxi, China: Prospective Observational Study

BACKGROUND

Predicting vaccination behaviors accurately could provide insights for health care professionals to develop targeted interventions.

OBJECTIVE

The aim of this study was to develop predictive models for influenza vaccination behavior among children in China.

METHODS

We obtained data from a prospective observational study in Wuxi, eastern China. The predicted outcome was individual-level vaccine uptake and covariates included sociodemographics of the child and parent, parental vaccine hesitancy, perceptions of convenience to the clinic, satisfaction with clinic services, and willingness to vaccinate. Bayesian networks, logistic regression, least absolute shrinkage and selection operator (LASSO) regression, support vector machine (SVM), naive Bayes (NB), random forest (RF), and decision tree classifiers were used to construct prediction models. Various performance metrics, including area under the receiver operating characteristic curve (AUC), were used to evaluate the predictive performance of the different models. Receiver operating characteristic curves and calibration plots were used to assess model performance.

RESULTS

A total of 2383 participants were included in the study; 83.2% of these children (n=1982) were <5 years old and 6.6% (n=158) had previously received an influenza vaccine. More than half (1356/2383, 56.9%) the parents indicated a willingness to vaccinate their child against influenza. Among the 2383 children, 26.3% (n=627) received influenza vaccination during the 2020-2021 season. Within the training set, the RF model showed the best performance across all metrics. In the validation set, the logistic regression model and NB model had the highest AUC values; the SVM model had the highest precision; the NB model had the highest recall; and the logistic regression model had the highest accuracy, F1 score, and Cohen κ value. The LASSO and logistic regression models were well-calibrated.

CONCLUSIONS

The developed prediction model can be used to quantify the uptake of seasonal influenza vaccination for children in China. The stepwise logistic regression model may be better suited for prediction purposes.

Predictors of Influenza Vaccination among Chinese Middle School Students Based on the Health Belief Model: A Mixed-Methods Study

Influenza vaccination rates among Chinese middle school students are low. This study aims to explore the influencing factors of vaccination among middle school students and promote vaccination. We conducted a mixed-methods study, integrating a questionnaire survey among 9145 middle school students in four cities in China and semi-structured interviews with 35 middle school students to understand their attitudes and perceptions toward vaccination based on the Health Belief Model. We found the overall vaccination rate was 38.2% (3493/9145), with students in Beijing, boarding at school, or senior high school showing higher values than their counterparts (p < 0.05). Multiple logistic regression results showed that non-boarding (OR = 0.46, 95%CI: 0.42−0.51) and perceived barriers (OR = 0.97, 95%CI: 0.96−0.98) were unfavorable factors for influenza vaccination, whereas perceived susceptibility (OR = 1.07, 95%CI: 1.05−1.08), perceived benefits (OR = 1.02, 95%CI: 1.01−1.04), cues to action (OR = 1.08, 95%CI: 1.05−1.11), and self-efficacy (OR = 1.04, 95%CI: 1.02−1.07) were facilitators. Qualitative results indicated that positive health beliefs, school, and the home environment contribute to vaccination. In conclusion, the influenza vaccination rate among middle school students remains low. The concerns about the safety and potential side effects of vaccines are the main barriers to vaccination, underscoring the need for strengthening communication, education, and information among students and their teachers/parents.

COVID-19 vaccine intentions in the United States, a social-ecological framework

BACKGROUND

COVID-19 vaccine hesitancy is a major obstacle for pandemic mitigation. As vaccine hesitancy occurs along multiple dimensions, we used a social-ecological framework to guide the examination of COVID-19 vaccine intentions.

METHODS

Using an online survey in the US conducted in July 2020, we examined intentions to obtain a COVID-19 vaccine, once available. 592 respondents provided data, including measures of demographics, vaccine history, social norms, perceived risk, and trust in sources of COVID-19 information. Bivariate and multivariate multinomial models were used to compare respondents who intended to be vaccinated against COVID-19 to respondents who did not intend or were ambivalent about COVID-19 vaccination.

RESULTS

Only 59.1% of the sample reported that they intended to obtain a COVID-19 vaccine. In the multivariate multinomial model, those respondents who did not intend to be vaccinated, as compared to those who did, had significantly lower levels of trust in the CDC as a source of COVID-19 information (aOR = 0.29, CI = 0.17-0.50), reported lower social norms of COVID-19 preventive behaviors (aOR = 0.67, CI 0.51-0.88), scored higher on COVID-19 Skepticism (aOR = 1.44, CI = 1.28-1.61), identified as more politically conservative (aOR = 1.23, CI = 1.05-1.45), were less likely to have obtained a flu vaccine in the prior year (aOR = 0.21, CI = 0.11-0.39), were less likely to be female (aOR = 0.51, CI = 0.29-0.87), and were much more likely to be Black compared to White (aOR = 10.70, CI = 4.09-28.1). A highly similar pattern was observed among those who were ambivalent about receiving a COVID-19 vaccine compared to those who intended to receive one.

CONCLUSION

The results of this study suggest several avenues for COVID-19 vaccine promotion campaigns, including social network diffusion strategies and cross-partisan messaging, to promote vaccine trust. The racial and gender differences in vaccine intentions also suggest the need to tailor campaigns based on gender and race.