PDF(Pubmed)
Abstract：
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.
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.
摘要：
背景：准确预测疫苗接种行为可以为卫生保健专业人员制定有针对性的干预措施提供见解。
目的：本研究的目的是建立中国儿童流感疫苗接种行为的预测模型。
方法：我们从无锡的一项前瞻性观察研究中获得了数据，中国东部。预测结果是个体水平的疫苗摄取，协变量包括儿童和父母的社会人口统计学，父母的疫苗犹豫，对临床方便的看法，对诊所服务的满意度，并愿意接种疫苗。贝叶斯网络，逻辑回归，最小绝对收缩和选择算子(LASSO)回归，支持向量机(SVM)，朴素贝叶斯(NB)，随机森林(RF)，用决策树分类器构建预测模型。各种性能指标，包括接受者工作特性曲线下面积(AUC)，用于评估不同模型的预测性能。接收器工作特性曲线和校准图用于评估模型性能。
结果：总共2383名参与者被纳入研究；这些儿童中83.2%(n=1982)<5岁，6.6%(n=158)以前接种过流感疫苗。超过一半（1356/2383，56.9％）的父母表示愿意为孩子接种流感疫苗。在2383名儿童中，26.3％（n=627）在2020-2021年季节接受了流感疫苗接种。在训练集中，RF模型在所有指标中显示出最佳性能。在验证集中，logistic回归模型和NB模型的AUC值最高；SVM模型的准确率最高；NB模型的召回率最高；logistic回归模型的准确率最高。F1得分,和科恩κ值。LASSO和逻辑回归模型得到了很好的校准。
结论：开发的预测模型可用于量化中国儿童季节性流感疫苗接种的吸收。逐步逻辑回归模型可能更适合预测目的。
目的：本研究的目的是建立中国儿童流感疫苗接种行为的预测模型。
方法：我们从无锡的一项前瞻性观察研究中获得了数据，中国东部。预测结果是个体水平的疫苗摄取，协变量包括儿童和父母的社会人口统计学，父母的疫苗犹豫，对临床方便的看法，对诊所服务的满意度，并愿意接种疫苗。贝叶斯网络，逻辑回归，最小绝对收缩和选择算子(LASSO)回归，支持向量机(SVM)，朴素贝叶斯(NB)，随机森林(RF)，用决策树分类器构建预测模型。各种性能指标，包括接受者工作特性曲线下面积(AUC)，用于评估不同模型的预测性能。接收器工作特性曲线和校准图用于评估模型性能。
结果：总共2383名参与者被纳入研究；这些儿童中83.2%(n=1982)<5岁，6.6%(n=158)以前接种过流感疫苗。超过一半（1356/2383，56.9％）的父母表示愿意为孩子接种流感疫苗。在2383名儿童中，26.3％（n=627）在2020-2021年季节接受了流感疫苗接种。在训练集中，RF模型在所有指标中显示出最佳性能。在验证集中，logistic回归模型和NB模型的AUC值最高；SVM模型的准确率最高；NB模型的召回率最高；logistic回归模型的准确率最高。F1得分,和科恩κ值。LASSO和逻辑回归模型得到了很好的校准。
结论：开发的预测模型可用于量化中国儿童季节性流感疫苗接种的吸收。逐步逻辑回归模型可能更适合预测目的。
