Distributed Lag Interaction Model with Index Modification

2025-04-08

AI Summary

This study addresses the challenge of modeling heterogeneity in associations between prenatal air pollution exposure and birth/child health outcomes, arising from multiple effect modifiers. We propose a Bayesian hierarchical distributed lag interaction model—the first framework jointly estimating modifier-index weights and the exposure–time–response function. It integrates spline-based cross-bases, functional data regression, and an exponential effect-modification structure, enabling composite modifier indices formed via multivariate weighted synthesis. Simulation studies demonstrate substantially improved estimation accuracy and robustness over existing methods. Applied to U.S. Colorado and Mexican birth cohorts, the model reveals significant time-varying modification by community vulnerability and life-course stress indices on pollution–birth weight and cardio-metabolic associations. These findings advance precision identification of environmental health risks and support differentiated public health interventions.

Technology Category

Reasoning under Uncertainty: Relational Probabilistic ModelsCognitive Modeling & Cognitive Systems: Adaptive BehaviorSearch and Optimization: Distributed Search

Application Category

User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalizationWeb Mining and Content Analysis: Models for Web evolutionGraph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

Epidemiological evidence supports an association between exposure to air pollution during pregnancy and birth and child health outcomes. Typically, such associations are estimated by regressing an outcome on daily or weekly measures of exposure during pregnancy using a distributed lag model. However, these associations may be modified by multiple factors. We propose a distributed lag interaction model with index modification that allows for effect modification of a functional predictor by a weighted average of multiple modifiers. Our model allows for simultaneous estimation of modifier index weights and the exposure-time-response function via a spline cross-basis in a Bayesian hierarchical framework. Through simulations, we showed that our model out-performs competing methods when there are multiple modifiers of unknown importance. We applied our proposed method to a Colorado birth cohort to estimate the association between birth weight and air pollution modified by a neighborhood-vulnerability index and to a Mexican birth cohort to estimate the association between birthing-parent cardio-metabolic endpoints and air pollution modified by a birthing-parent lifetime stress index.

Problem

• Estimates air pollution's impact on birth and child health
• Models effect modification by multiple unknown factors
• Applies method to real cohorts for vulnerability and stress

Innovation

• Bayesian hierarchical framework for simultaneous estimation
• Spline cross-basis for exposure-time-response function
• Weighted average of multiple modifiers in model