Parental Acceptance, Parental Hesitancy, and Uptake of Seasonal Influenza Vaccination among Children Aged 6-59 Months: A Systematic Review and Meta-Analysis

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.

Effectiveness of chatbots in increasing uptake, intention, and attitudes related to any type of vaccination: A systematic review and meta-analysis

This systematic review and meta-analysis analyzed and summarized the growing literature on the effectiveness of chatbot-delivered interventions in increasing uptake, intention, and attitudes related to any type of vaccination. We identified randomized controlled studies (RCTs), quasi-experimental studies, and non-experimental studies from the following platforms: PubMed, Web of Science, MEDLINE, Global Health, APA PsycInfo, and EMBASE databases. A total of 12 eligible studies published from 2019 to 2023 were analyzed and summarized. In particular, one RCT showed that a chatbot-delivered tailored intervention was more effective than a chatbot-delivered non-tailored intervention in promoting seasonal influenza vaccine uptake among older adults (50.5% versus 35.3%, p = 0.002). Six RCTs were included in the meta-analysis to evaluate the effectiveness of chatbot interventions to improve vaccination attitudes and intentions. The pooled standard mean difference (SMD) of overall attitude change was 0.34 (95% confidence intervals [CI]: 0.13, 0.55, p = 0.001). We found a non-significant trivial effect of chatbot interventions on improving intentions of vaccination (SMD: 0.11, 95% CI: -0.13, 0.34, p = 0.38). However, further evidence is needed to draw a more precise conclusion. Additionally, study participants reported high satisfaction levels of using the chatbot and were likely to recommend it to others. The development of chatbots is still nascent and rooms for improvement exist.

Worldwide Child Routine Vaccination Hesitancy Rate among Parents of Children Aged 0-6 Years: A Systematic Review and Meta-Analysis of Cross-Sectional Studies

Routine vaccine hesitancy is a major global health challenge observed in over 190 countries worldwide. This meta-analysis aims to determine the worldwide prevalence of routine vaccination hesitancy among parents of children aged 0-6. An extensive search was conducted in four scientific databases: PubMed, Scopus, Web of Science, and the Cochrane Library. Studies were included if they reported hesitancy related to WHO-recommended routine immunizations for children under 7 years of age. A single-arm meta-analysis was performed using the OpenMeta[Analyst] software. An initial search retrieved 5121 articles, of which only 23 publications, involving 29,131 parents, guardians, and caregivers from over 30 countries met the inclusion criteria and quality assessment. The cumulative prevalence of parental vaccine hesitancy was found to be 21.1% (95% CI = 17.5-24.7%, I2 = 98.86%, p < 0.001). When stratifying the prevalence of vaccine hesitancy per WHO region, significant variations were observed, ranging from 13.3% (95% CI = 6.7-19.9%, I2 = 97.72%, p < 0.001) in the Region of the Americas to 27.9% (95% CI = 24.3-31.4%) in the Eastern Mediterranean region. The study findings highlight the need for healthcare providers and governments to develop and improve comprehensive programs with communication strategies to reduce parental vaccine hesitancy.