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 P M 2.5 in a Retrospective Birth Cohort Based in Eastern Massachusetts (2011-2016).

Autor: Leung M; Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA., Weisskopf MG; Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA., Modest AM; Department of Obstetrics and Gynecology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.; Department of Obstetrics, Gynecology and Reproductive Biology, Harvard Medical School, Boston, Massachusetts, USA., Hacker MR; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.; Department of Obstetrics and Gynecology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.; Department of Obstetrics, Gynecology and Reproductive Biology, Harvard Medical School, Boston, Massachusetts, USA., Iyer HS; Section of Cancer Epidemiology and Health Outcomes, Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey, USA., Hart JE; Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA., Wei Y; Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA., Schwartz J; Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA., Coull BA; Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA., Laden F; Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA., Papatheodorou S; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.; Department of Biostatistics and Epidemiology, Rutgers School of Public Health, New Brunswick, New Jersey, USA.
Jazyk: angličtina
Zdroj: Environmental health perspectives [Environ Health Perspect] 2024 Jul; Vol. 132 (7), pp. 77002. Date of Electronic Publication: 2024 Jul 12.
DOI: 10.1289/EHP13891
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 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 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 PM 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 2.5 concentration was relatively stable across pregnancy at ∼ 7 μ 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 ≤ 2.5 μ m ( PM 2.5 )] during gestational weeks 5-20 could potentially lower the risk of PTB. https://doi.org/10.1289/EHP13891.
Databáze: MEDLINE
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