Xiong Yuxiang, Hu Xuhuai, Cao Jindan, Shang Li, Niu Ben
Department of Medical Information, School of Public Health, Jilin University, Jilin, China.
Research Development, Shenzhen Health Development Research and Data Management Center, Shenzhen, Guangdong, China.
Front Pediatr. 2024 Sep 3;12:1441714. doi: 10.3389/fped.2024.1441714. eCollection 2024.
In light of the global effort to eradicate stunting in childhood, the objective of this research endeavor was to assess the prevalence of stunting and associated factors, simultaneously construct and validate a risk prediction model for stunting among children under the age of three in Shenzhen, China.
Using the stratified random sampling method, we selected 9,581 children under the age of three for research and analysis. The dataset underwent a random allocation into training and validation sets, adhering to a 8:2 split ratio. Within the training set, a combined approach of LASSO regression analysis and binary logistic regression analysis was implemented to identify and select the predictive variables for the model. Subsequently, model construction was conducted in the training set, encompassing model evaluation, visualization, and internal validation procedures. Finally, to assess the model's generalizability, external validation was performed using the validation set.
A total of 684 (7.14%) had phenotypes of stunt. Utilizing a combined approach of LASSO regression and logistic regression, key predictors of stunting among children under three years of age were identified, including sex, age in months, mother's education, father's age, birth order, feeding patterns, delivery mode, average daily parent-child reading time, average time spent in child-parent interactions, and average daily outdoor time. These variables were subsequently employed to develop a comprehensive prediction model for childhood stunting. A nomogram model was constructed based on these factors, demonstrating excellent consistency and accuracy. Calibration curves validated the agreement between the nomogram predictions and actual observations. Furthermore, ROC and DCA analyses indicated the strong predictive performance of the nomograms.
The developed model for forecasting stunt risk, which integrates a spectrum of variables. This analytical framework presents actionable intelligence to medical professionals, laying down a foundational framework and a pivot for the conception and execution of preemptive strategies and therapeutic interventions.
鉴于全球致力于消除儿童发育迟缓，本研究的目的是评估深圳三岁以下儿童发育迟缓的患病率及相关因素，同时构建并验证发育迟缓的风险预测模型。
采用分层随机抽样方法，选取9581名三岁以下儿童进行研究分析。数据集按照8:2的比例随机分配到训练集和验证集。在训练集中，采用套索回归分析和二元逻辑回归分析相结合的方法来识别和选择模型的预测变量。随后，在训练集中进行模型构建，包括模型评估、可视化和内部验证程序。最后，使用验证集进行外部验证，以评估模型的泛化能力。
共有684名（7.14%）儿童有发育迟缓表型。采用套索回归和逻辑回归相结合的方法，确定了三岁以下儿童发育迟缓的关键预测因素，包括性别、月龄、母亲教育程度、父亲年龄、出生顺序、喂养方式、分娩方式、亲子每日平均阅读时间、亲子互动平均时长以及每日平均户外活动时间。随后，利用这些变量开发了儿童发育迟缓的综合预测模型。基于这些因素构建了列线图模型，显示出良好的一致性和准确性。校准曲线验证了列线图预测与实际观察结果之间的一致性。此外，受试者工作特征曲线和决策曲线分析表明列线图具有很强的预测性能。
所开发的预测发育迟缓风险的模型整合了一系列变量。这一分析框架为医学专业人员提供了可操作的信息，为预防性策略和治疗干预措施的构思和实施奠定了基础框架和关键依据。
