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 (PM$_{2.5}$) 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 PM$_{2.5}$-birth weight relationship, with some mother-child dyads showing a 3 times larger decrease in birth weight for an IQR increase in exposure (5.9 to 8.5 $\mu g/m^3$ PM$_{2.5}$) compared to the population average. Specifically, we find increased susceptibility 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.

翻译：儿童健康研究支持母亲环境暴露与儿童出生结局之间的关联。一个共同目标是识别易感关键窗口期——即孕期中母亲暴露与未来结局关联增强的阶段。关键窗口期的出现时间及关联强度可能因个体、家庭和邻里特征的不同水平而呈现异质性。基于科罗拉多州行政出生队列数据，我们估算了孕期每周细颗粒物（PM$_{2.5}$）暴露与出生体重的个体化关系。为此，我们提出一种结合分布式滞后模型与贝叶斯加性回归树的统计学习方法，以在个体水平估计关键窗口期，并从高维潜在修饰因子集合中识别诱发异质性的特征。研究发现PM$_{2.5}$-出生体重关系存在异质性：部分母-子组合因暴露量增加一个IQR（PM$_{2.5}$浓度增加5.9至8.5 $\mu g/m^3$）导致的出生体重下降幅度比人群平均值大3倍。具体而言，非西班牙裔母亲中更年轻、体重指数更高或受教育程度较低者表现出更高的易感性。本案例研究是首个关于关键窗口期的精准健康研究。