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 [PM.sub.2.5] in a Retrospective Birth Cohort Based in Eastern Massachusetts (2011-2016)

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 [PM.sub.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 [PM.sub.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 [PM.sub.2.5] 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 [PM.sub.2.5] concentration was relatively stable across pregnancy at ~7[micro]g/[m.sup.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
Introduction Pregnant individuals and their fetuses are particularly susceptible to environmental pollutants. (1) Yet, identifying whether environmental exposures cause preterm birth (PTB)--for which the long-term sequelae have been well established [...]