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.

Vertebral bone quality had a significant impact on postoperative outcomes in spinal fusion surgery. New magnetic resonance imaging-based measures, such as the Vertebral Bone Quality (VBQ) score, may allow for bone quality assessment without the radiation associated with conventional testing. In the present study, we sought to assess the intrarater and interrater reliability of VBQ scores calculated by medical professionals and trainees.
Thirteen reviewers of various specialties and levels of training were recruited and asked to calculate VBQ scores for 30 patients at 2 time points separated by 2 months. Scored volumes were acquired from patients treated for both degenerative and oncologic indications. Intrarater and interrater agreement, quantified by intraclass correlation coefficient (ICC), was assessed using 2-way random effects modeling. Square-weight Cohen κ and Kendall Tau-b were used to determine whether raters assigned similar scores during both evaluations.
All raters showed moderate to excellent reliability for VBQ score (ICC 0.667-0.957; κ0.648-0.921) and excellent reliability for all constituent components used to calculate VBQ score (ICC all ≥0.97). Interrater reliability was also found to be good for VBQ score on both the first (ICC = 0.818) and second (ICC = 0.800) rounds of assessment; scores for the constituent component all had ICC values ≥0.97 for the constituent components.
The VBQ score appears to have both good intrarater and interrater reliability. In addition, there appeared to be no correlation between score reliability and level of training. External validation and further investigations of its ability to accurately model bone biomechanical properties are necessary.