AI 中文总结
该研究提出统计学习框架，分析COVID-19不同阶段儿科心理健康相关急诊就诊的演变，构建零截断复发事件数据，采用分层回归探究就诊频率与协变量效应变化，为复发医疗利用数据分析提供实用方法。
AI 中文摘要
本文提出一种统计学习框架，用于基于人群的行政健康记录，研究COVID-19大流行前、期间和后三个阶段儿科心理健康相关急诊（MHED）就诊模式的演变。MHED记录被构建为零截断复发事件数据，划分为三个连续时间段。我们在一组MHED记录的模型拟合指导下逐步开发该建模框架，所得框架从非参数边际率模型逐步发展为更结构化的Cox型回归模型，以表征就诊模式。最终我们应用分层回归分析，探究跨大流行阶段就诊频率和协变量效应的变化，同时考虑预先指定的阶段分界点和粗化的个体随访信息。全文以儿科MHED数据为动机并进行说明，为分析具有演变时间模式的复发医疗利用数据提供了实用方法。
英文摘要
This article presents a statistical learning framework for studying the evolution of pediatric mental health-related emergency department (MHED) visit patterns across the pre-, during-, and post-COVID-19 pandemic periods using population-based administrative health records. The MHED records are formulated as zero-truncated recurrent event data, partitioned into three successive time periods. We develop the modeling framework in a stepwise manner, guided by model fit using a collection of MHED records. The resulting framework progresses from nonparametric marginal rate models to more structured Cox-type regression models for characterizing visit patterns. We ultimately apply stratified regression analysis to investigate changes in visit frequencies and covariate effects across pandemic periods, accounting for prespecified period cut-off points and coarsened individual follow-up information. The proposed framework is motivated by and illustrated using pediatric MHED data throughout the article, providing a practical approach for analyzing recurrent healthcare utilization data with evolving temporal patterns.
