Statistical Learning of Pediatric Mental Health-Related Emergency Department Visits Across COVID-19 Pandemic Periods

2026-07-28

AI Summary

This study investigates the dynamic shifts in pediatric mental health emergency department utilization patterns before and during the COVID-19 pandemic. Leveraging population-based administrative health data, the authors propose a stepwise modeling framework: first characterizing baseline visit intensity via a nonparametric marginal rate model for zero-truncated recurrent event data, then employing a Cox-type regression model to assess covariate effects, and finally incorporating stratified regression to evaluate changes across distinct pandemic phases. This approach effectively integrates nonparametric and semiparametric techniques while appropriately handling coarse-grained follow-up and predefined time intervals. Empirical analysis reveals significant alterations in visit frequency and associated risk factors during the pandemic, demonstrating the proposed methodology’s applicability and effectiveness in analyzing real-world recurrent healthcare utilization data.

Abstract

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.

Problem

• pediatric mental health
• emergency department visits
• COVID-19 pandemic
• recurrent event data
• temporal patterns

Innovation

• statistical learning
• recurrent event data
• zero-truncated model
• Cox-type regression
• stratified regression