Heterogeneous Distributed Lag Models to Estimate Personalized Effects of Maternal Exposures to Air Pollution
提出一种结合分布滞后模型和贝叶斯加性回归树的统计学习方法，利用科罗拉多出生队列数据估计母亲孕期PM2.5暴露对出生体重的个性化影响，发现非西班牙裔年轻、高BMI或低教育母亲更易受影响。
Children’s health studies support an association between maternal environmental exposures and children’s birth outcomes. A common goal is to identify critical windows of susceptibility—periods during gestation with increased association between maternal exposures and a future outcome. The timing of the critical windows and magnitude of the associations are likely heterogeneous across different levels of individual, family, and neighborhood characteristics. Using an administrative Colorado birth cohort we estimate the individualized relationship between weekly exposures to fine particulate matter (PM2.5) during gestation and birth weight. To achieve this goal, we propose a statistical learning method combining distributed lag models and Bayesian additive regression trees to estimate critical windows at the individual level and identify characteristics that induce heterogeneity from a high-dimensional set of potential modifying factors. We find evidence of heterogeneity in the PM2.5—birth weight relationship, with some mother—child dyads showing a three times larger decrease in birth weight for an IQR increase in exposure (5.9–8.5 μg/m3 PM2.5) compared to the population average. Specifically, we find increased vulnerability for non-Hispanic mothers who are either younger, have higher body mass index or lower educational attainment. Our case study is the first precision health study of critical windows. Supplementary materials for this article are available online.