PDF(Pubmed)
Abstract：
UNASSIGNED: Parametric g-computation is an attractive analytic framework to study the health effects of air pollution. Yet, the ability to explore biologically relevant exposure windows within this framework is underdeveloped.
UNASSIGNED: We outline a novel framework for how to incorporate complex lag-responses using distributed lag models (DLMs) into parametric g-computation analyses for survival data. We call this approach \"g-survival-DLM\" and illustrate its use examining the association between PM2.5 during pregnancy and the risk of preterm birth (PTB).
UNASSIGNED: We applied the g-survival-DLM approach to estimate the hypothetical static intervention of reducing average PM2.5 in each gestational week by 20% on the risk of PTB among 9,403 deliveries from Beth Israel Deaconess Medical Center, Boston, Massachusetts, 2011-2016. Daily PM2.5 was taken from a 1-km grid model and assigned to address at birth. Models were adjusted for sociodemographics, time trends, nitrogen dioxide, and temperature. To facilitate implementation, we provide a detailed description of the procedure and accompanying R syntax.
UNASSIGNED: There were 762 (8.1%) PTBs in this cohort. The gestational week-specific median PM2.5 concentration was relatively stable across pregnancy at ∼7μg/m3. We found that our hypothetical intervention strategy changed the cumulative risk of PTB at week 36 (i.e., the end of the preterm period) by -0.009 (95% confidence interval: -0.034, 0.007) in comparison with the scenario had we not intervened, which translates to about 86 fewer PTBs in this cohort. We also observed that the critical exposure window appeared to be weeks 5-20.
UNASSIGNED: We demonstrate that our g-survival-DLM approach produces easier-to-interpret, policy-relevant estimates (due to the g-computation); prevents immortal time bias (due to treating PTB as a time-to-event outcome); and allows for the exploration of critical exposure windows (due to the DLMs). In our illustrative example, we found that reducing fine particulate matter [particulate matter (PM) with aerodynamic diameter ≤2.5μm (PM2.5)] during gestational weeks 5-20 could potentially lower the risk of PTB. https://doi.org/10.1289/EHP13891.
UNASSIGNED: We outline a novel framework for how to incorporate complex lag-responses using distributed lag models (DLMs) into parametric g-computation analyses for survival data. We call this approach \"g-survival-DLM\" and illustrate its use examining the association between PM2.5 during pregnancy and the risk of preterm birth (PTB).
UNASSIGNED: We applied the g-survival-DLM approach to estimate the hypothetical static intervention of reducing average PM2.5 in each gestational week by 20% on the risk of PTB among 9,403 deliveries from Beth Israel Deaconess Medical Center, Boston, Massachusetts, 2011-2016. Daily PM2.5 was taken from a 1-km grid model and assigned to address at birth. Models were adjusted for sociodemographics, time trends, nitrogen dioxide, and temperature. To facilitate implementation, we provide a detailed description of the procedure and accompanying R syntax.
UNASSIGNED: There were 762 (8.1%) PTBs in this cohort. The gestational week-specific median PM2.5 concentration was relatively stable across pregnancy at ∼7μg/m3. We found that our hypothetical intervention strategy changed the cumulative risk of PTB at week 36 (i.e., the end of the preterm period) by -0.009 (95% confidence interval: -0.034, 0.007) in comparison with the scenario had we not intervened, which translates to about 86 fewer PTBs in this cohort. We also observed that the critical exposure window appeared to be weeks 5-20.
UNASSIGNED: We demonstrate that our g-survival-DLM approach produces easier-to-interpret, policy-relevant estimates (due to the g-computation); prevents immortal time bias (due to treating PTB as a time-to-event outcome); and allows for the exploration of critical exposure windows (due to the DLMs). In our illustrative example, we found that reducing fine particulate matter [particulate matter (PM) with aerodynamic diameter ≤2.5μm (PM2.5)] during gestational weeks 5-20 could potentially lower the risk of PTB. https://doi.org/10.1289/EHP13891.
摘要：
■参数g计算是研究空气污染对健康影响的一个有吸引力的分析框架。然而，在此框架内探索生物学相关暴露窗口的能力尚不充分.
■我们概述了如何将使用分布式滞后模型（DLM）的复杂滞后响应纳入生存数据的参数g计算分析的新框架。我们将这种方法称为“g-survival-DLM”，并说明了其使用方法，以检查怀孕期间的PM2.5与早产风险（PTB）之间的关系。
■我们应用了g-survival-DLM方法来估计假设的静态干预措施，即在贝斯以色列女执事医疗中心的9,403例分娩中，每个孕周的平均PM2.5减少20％的PTB风险，波士顿,马萨诸塞州,2011-2016年。每日PM2.5取自1公里的网格模型，并在出生时分配给地址。模型根据社会人口统计学进行了调整，时间趋势,二氧化氮,和温度。为了便于执行，我们提供了该过程的详细说明和随附的R语法。
■该队列中有762例（8.1%）PTB。妊娠周PM2.5浓度中位数在整个妊娠期间相对稳定，约为7μg/m3。我们发现，我们假设的干预策略在第36周改变了PTB的累积风险(即，与我们没有干预的情况相比，早产期结束)为-0.009(95%置信区间：-0.034，0.007)，这意味着该队列中PTB减少了约86个。我们还观察到临界暴露窗口似乎是5-20周。
■我们证明了我们的g-survival-DLM方法更易于解释，与政策相关的估计（由于g计算）；防止不朽的时间偏差（由于将PTB视为事件发生的时间结果）；并允许探索关键的暴露窗口（由于DLM）。在我们的说明性示例中，我们发现,在妊娠5-20周时减少细颗粒物[空气动力学直径≤2.5μm(PM2.5)的颗粒物(PM)]可能会降低PTB的风险.https://doi.org/10.1289/EHP13891.
■我们概述了如何将使用分布式滞后模型（DLM）的复杂滞后响应纳入生存数据的参数g计算分析的新框架。我们将这种方法称为“g-survival-DLM”，并说明了其使用方法，以检查怀孕期间的PM2.5与早产风险（PTB）之间的关系。
■我们应用了g-survival-DLM方法来估计假设的静态干预措施，即在贝斯以色列女执事医疗中心的9,403例分娩中，每个孕周的平均PM2.5减少20％的PTB风险，波士顿,马萨诸塞州,2011-2016年。每日PM2.5取自1公里的网格模型，并在出生时分配给地址。模型根据社会人口统计学进行了调整，时间趋势,二氧化氮,和温度。为了便于执行，我们提供了该过程的详细说明和随附的R语法。
■该队列中有762例（8.1%）PTB。妊娠周PM2.5浓度中位数在整个妊娠期间相对稳定，约为7μg/m3。我们发现，我们假设的干预策略在第36周改变了PTB的累积风险(即，与我们没有干预的情况相比，早产期结束)为-0.009(95%置信区间：-0.034，0.007)，这意味着该队列中PTB减少了约86个。我们还观察到临界暴露窗口似乎是5-20周。
■我们证明了我们的g-survival-DLM方法更易于解释，与政策相关的估计（由于g计算）；防止不朽的时间偏差（由于将PTB视为事件发生的时间结果）；并允许探索关键的暴露窗口（由于DLM）。在我们的说明性示例中，我们发现,在妊娠5-20周时减少细颗粒物[空气动力学直径≤2.5μm(PM2.5)的颗粒物(PM)]可能会降低PTB的风险.https://doi.org/10.1289/EHP13891.
