BACKGROUND 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. OBJECTIVES 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). METHODS 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. RESULTS 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. DISCUSSION 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.

中文翻译：

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使用参数 g 计算事件时间数据和分布式滞后模型来识别早产的关键暴露窗口：在马萨诸塞州东部回顾性出生队列中使用 PM2.5 的说明性示例（2011-2016 年）。


背景技术参数 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未开发。目标 我们概述了一个新颖的框架，用于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 计算分析中。我们将这种方法称为“g-survival-DLM”，并举例说明其用于检查怀孕期间 PM2.5 与早产 (PTB) 风险之间的关联。方法 我们应用 g-survival-DLM 方法来估计假设的静态干预措施，将每个妊娠周的平均 PM2.5 降低 20%，对 2011 年马萨诸塞州波士顿贝斯以色列女执事医疗中心的 9,403 例分娩的 PTB 风险进行评估。 2016年。每日 PM2.5 取自 1 公里网格模型，并分配给出生时的地址。模型根据社会人口统计、时间趋势、二氧化氮和温度进行了调整。为了便于实现，我们提供了该过程和随附 R 语法的详细描述。结果 该队列中有 762 例 (8.1%) PTB。妊娠周特定的 PM2.5 中值浓度在整个妊娠期间相对稳定，为 ∼7μg/m3。我们发现，与我们不进行干预的情况相比，我们假设的干预策略将第 36 周（即早产期结束）时的 PTB 累积风险改变了 -0.009（95% 置信区间：-0.034, 0.007），这意味着该队列中的 PTB 减少了约 86 个。我们还观察到关键暴露窗口似乎是 5-20 周。 讨论 我们证明我们的 g-survival-DLM 方法可以产生更容易解释的、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 视为事件发生时间的结果）；并允许探索关键曝光窗口（由于 DLM）。在我们的示例中，我们发现在妊娠第 5-20 周期间减少细颗粒物 [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。