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.

Procrastination describes a ubiquitous scenario in which individuals voluntarily postpone scheduled activities at the expense of adverse consequences. Steel (2007) pioneered a meta-analysis to explicitly reveal the nature of procrastination and sparked intensive research on its demographic characteristics. However, conflicting and heterogeneous findings reported in the existing literature make it difficult to draw reliable conclusions. In addition, there is still room to further investigate on more sociodemographic features that include socioeconomic status, cultural differences and procrastination education. To this end, we performed quantitative sociodemographic meta-analyses (k = 193, total n = 106,764) to fill this gap. It was found that the general tendency and academic procrastination tendency of males were stronger than females (r = 0.04, 95% CI: 0.02-0.05). No significant effects of differences in socioeconomic status (i.e., poor or rich), multiculturalism (i.e., Han nation or minorities), nationality (i.e., China or other countries), family size (i.e., one child or > 1 child), and educational background (i.e., science or arts/literature) were found to affect procrastination tendencies. Furthermore, it was noteworthy that the gender differences in procrastination tendencies were prominently moderated by measurements, which has a greater effect on the Aitken Procrastination Inventory (API) (r = 0.035, 95% CI: -0.01-0.08) than on the General Procrastination Scale (GPS) (r = 0.018, 95% CI: -0.01-0.05). In conclusion, this study provides robust evidence that males tended to procrastinate more than females in general and academic profiles, and further indicates that procrastination tendencies do not vary based on sociodemographic situations, including socioeconomic status, multiculturalism, nationality, family size, and educational background.

UNASSIGNED: Bipolar disorder (BD) and alcohol use disorder (AUD) are two major independent causes of psychopathology in the general population. The prevalence of AUD in BD is high. Identifying the clinical and demographic features of patients with BD who may develop AUD could help with early identification and intervention.
UNASSIGNED: Data from 238 patients diagnosed with BD were gathered on alcohol use, social demographics, longitudinal course of BD, clinical features of the most severe lifetime manic and depressive episodes, comorbid physical diseases, anxiety disorders, and other substance use disorders.
UNASSIGNED: We found that 74 of 238 BD patients had AUD (67 with alcohol dependence and 7 with alcohol abuse). Bivariate logistic regression analysis and multivariate logistic regression analysis found that the best predictors of AUD in patients with BD were being male (OR = 2.086, 95% CI = 1.094-3.979, p = 0.001), younger (OR = 0.965, 95% CI = 0.935-0.996, p = 0.026), and comorbidity with other unclassified substance dependence (OR = 10.817, 95% CI = 1.238-94.550, p = 0.031).
UNASSIGNED: Male, younger current age, and having other substance use disorders were independently associated with AUD.