Using Parametric g-Computation for Time-to-Event Data and Distributed Lag Models to Identify Critical Exposure Windows for Preterm Birth: An Illustrative Example Using PM2.5 in a Retrospective Birth Cohort Based in Eastern Massachusetts (2011-2016).

Abstract

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 ${\mathrm{PM}}_{2.5}$ 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 ${\mathrm{PM}}_{2.5}$ 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 ${\mathrm{PM}}_{2.5}$ was taken from a $1\text{-km}$ 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 ${\mathrm{PM}}_{2.5}$ concentration was relatively stable across pregnancy at $\sim 7{\mu g/m}^3$. 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 $\le 2.5\mu m$ (${\mathrm{PM}}_{2.5}$)] during gestational weeks 5-20 could potentially lower the risk of PTB. https://doi.org/10.1289/EHP13891.