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)

Autor: Leung, Michael1 mleung@hsph.harvard.edu, Weisskopf, Marc G.1,2, Modest, Anna M.3,4, Hacker, Michele R.2,3,4, Iyer, Hari S.5, Hart, Jaime E.1,6, Yaguang Wei1, Schwartz, Joel1,2, Coull, Brent A.1,7, Laden, Francine1,2,6, Papatheodorou, Stefania2,8
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Zdroj: Environmental Health Perspectives. Jul2024, Vol. 132 Issue 7, p077002-1-077002-10. 10p.
Abstrakt: 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 PM2.5concentration 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 ≤2.5 μm (PM2.5)] during gestational weeks 5-20 could potentially lower the risk of PTB. [ABSTRACT FROM AUTHOR]
Databáze: GreenFILE
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