BACKGROUND Estimates for the effects of environmental exposures on health outcomes, including secondhand smoke (SHS) exposure, often present considerable variability across studies. Knowledge of the reasons behind these differences can aid our understanding of effects in specific populations as well as inform practices of combining data from multiple studies. OBJECTIVES This study aimed to assess the presence of effect modification by measured sociodemographic characteristics on the effect of SHS exposure during pregnancy on birth weights that may drive differences observed across cohorts. We also aimed to quantify the extent to which differences in the cohort mean effects observed across cohorts in the Environmental influences on Child Health Outcomes (ECHO) consortium are due to differing distributions of these characteristics. METHODS We assessed the presence of effect modification and transportability of effect estimates across five ECHO cohorts in a total of 6,771 mother-offspring dyads. We assessed the presence of effect modification via gradient boosting of regression trees based on the H-statistic. We estimated individual cohort effects using linear models and targeted maximum likelihood estimation (TMLE). We then estimated transported effects from one cohort to each of the remaining cohorts using a robust nonparametric estimation approach relying on TMLE estimators and compared them to the original effect estimates for these cohorts. RESULTS Observed effect estimates varied across the five cohorts, ranging from significantly lower birth weight associated with exposure [-167.3g; 95% confidence interval (CI): -270.4, -64.1] to higher birth weight with wide CIs, including the null (42.4g; 95% CI: -15.0, 99.8). Transported effect estimates only minimally explained differences in the point estimates for two out of the four cohort pairs. DISCUSSION Our findings of weak to moderate evidence of effect modification and transportability indicate that unmeasured individual-level and contextual factors and sources of bias may be responsible for differences in the effect estimates observed across ECHO cohorts. https://doi.org/10.1289/EHP13961.

中文翻译：

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弥合妊娠期二手烟暴露与出生体重之间关系的队列分析差异：ECHO 计划中的可运输性框架。


背景 对环境暴露（包括二手烟（SHS）暴露）对健康结果影响的估计在不同研究中通常存在相当大的差异。了解这些差异背后的原因可以帮助我们了解特定人群的影响，并为组合多项研究数据的实践提供信息。目的 本研究旨在通过测量社会人口学特征来评估妊娠期间二手烟暴露对出生体重影响的影响修正是否存在，这可能会导致不同队列之间观察到的差异。我们还旨在量化环境对儿童健康结果（ECHO）联盟中各队列中观察到的队列平均效应的差异在多大程度上归因于这些特征的不同分布。方法 我们评估了 5 个 ECHO 队列中总共 6,771 个母子二人组中效果修正的存在和效果估计的可移植性。我们通过基于 H 统计量的回归树的梯度提升来评估效果修改的存在。我们使用线性模型和目标最大似然估计（TMLE）来估计个体队列效应。然后，我们使用依赖于 TMLE 估计器的稳健非参数估计方法来估计从一个队列到每个剩余队列的传输效应，并将它们与这些队列的原始效应估计进行比较。结果 观察到的效果估计在五个队列中有所不同，范围从与暴露相关的出生体重显着降低[-167.3g； 95% 置信区间 (CI)：-270.4，-64.1] 至较高出生体重，CI 范围较宽，包括零值（42.4g；95% CI：-15.0，99.8）。 传递效应估计仅最低限度地解释了四对队列中两对点估计的差异。讨论 我们对效果修改和可转移性的弱到中等证据的发现表明，未测量的个人水平和背景因素以及偏差来源可能是造成 ECHO 队列中观察到的效果估计差异的原因。 https://doi.org/10.1289/EHP13961。