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

• Published in: JMIR public health and surveillance Vol. 10; p. e56064
• Main Authors: Wang, Qiang, Yang, Liuqing, Xiu, Shixin, Shen, Yuan, Jin, Hui, Lin, Leesa
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications 17.06.2024
• Subjects: Age; Algorithms; Bayes Theorem; Behavior; Child; Child, Preschool; Children & youth; China; Female; Humans; Immunization; Infant; Influenza; Influenza Vaccines - administration & dosage; Influenza, Human - prevention & control; Likert scale; Logistic Models; Male; Observational studies; Original Paper; Parents & parenting; Prediction models; Prospective Studies; Public health; Regression analysis; Seasons; Sociodemographics; Support vector machines; Vaccination - psychology; Vaccination - statistics & numerical data; Vaccines; Variables; China; United States > US; influenza; vaccine hesitancy; digital age; prediction model; behaviors; accuracy; Cohen κ; vaccine; children; China; immunization; health care professional; prediction; clinic; sociodemographics; intervention; logistic regression; Bayesian network; public health; vaccination
• ISSN: 2369-2960; 2369-2960
• DOI: 10.2196/56064

Predicting vaccination behaviors accurately could provide insights for health care professionals to develop targeted interventions. The aim of this study was to develop predictive models for influenza vaccination behavior among children in China. 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. 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. 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.