Jin Yisen, Molstad Aaron J, Wilson Ander, Antonelli Joseph
Department of Statistics, University of Florida, Gainesville, FL 32611, United States.
School of Statistics, University of Minnesota, Minneapolis, MN 55455, United States.
Biometrics. 2025 Jul 3;81(3). doi: 10.1093/biomtc/ujaf101.
Exposure to environmental pollutants during the gestational period can significantly impact infant health outcomes, such as birth weight and neurological development. Identifying critical windows of susceptibility, which are specific periods during pregnancy when exposure has the most profound effects, is essential for developing targeted interventions. Distributed lag models (DLMs) are widely used in environmental epidemiology to analyze the temporal patterns of exposure and their impact on health outcomes. However, traditional DLMs focus on modeling the conditional mean, which may fail to capture heterogeneity in the relationship between predictors and the outcome. Moreover, when modeling the distribution of health outcomes like gestational birth weight, it is the extreme quantiles that are of most clinical relevance. We introduce 2 new quantile distributed lag model (QDLM) estimators designed to address the limitations of existing methods by leveraging smoothness and shape constraints, such as unimodality and concavity, to enhance interpretability and efficiency. We apply our QDLM estimators to the Colorado birth cohort data, demonstrating their effectiveness in identifying critical windows of susceptibility and informing public health interventions.
孕期接触环境污染物会对婴儿健康结局产生重大影响,如出生体重和神经发育。确定易感性关键窗口(即孕期中接触污染物影响最为深远的特定时期)对于制定针对性干预措施至关重要。分布滞后模型(DLMs)在环境流行病学中广泛用于分析接触的时间模式及其对健康结局的影响。然而,传统的DLMs专注于对条件均值进行建模,这可能无法捕捉预测变量与结局之间关系的异质性。此外,在对诸如孕周出生体重等健康结局的分布进行建模时,最具临床相关性的是极端分位数。我们引入了两种新的分位数分布滞后模型(QDLM)估计器,旨在通过利用平滑性和形状约束(如单峰性和凹性)来解决现有方法的局限性,以提高可解释性和效率。我们将QDLM估计器应用于科罗拉多州出生队列数据,证明了它们在识别易感性关键窗口和为公共卫生干预提供信息方面的有效性。