Andreas M Neophytou, Jenny Aalborg, Sheryl Magzamen, Brianna F Moore, Assiamira Ferrara, Margaret R Karagas, Leonardo Trasande, Dana Dabelea
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We also aimed to quantify the extent to which differences in the cohort mean effects observed across cohorts in the Environmental influences on Child Health Outcomes (ECHO) consortium are due to differing distributions of these characteristics.</p><p><strong>Methods: </strong>We assessed the presence of effect modification and transportability of effect estimates across five ECHO cohorts in a total of 6,771 mother-offspring dyads. We assessed the presence of effect modification via gradient boosting of regression trees based on the H-statistic. We estimated individual cohort effects using linear models and targeted maximum likelihood estimation (TMLE). We then estimated transported effects from one cohort to each of the remaining cohorts using a robust nonparametric estimation approach relying on TMLE estimators and compared them to the original effect estimates for these cohorts.</p><p><strong>Results: </strong>Observed effect estimates varied across the five cohorts, ranging from significantly lower birth weight associated with exposure [<math><mrow><mo>-</mo><mn>167.3</mn><mspace></mspace><mi>g</mi></mrow></math>; 95% confidence interval (CI): <math><mrow><mo>-</mo><mn>270.4</mn></mrow></math>, <math><mrow><mo>-</mo><mn>64.1</mn></mrow></math>] to higher birth weight with wide CIs, including the null (<math><mrow><mn>42.4</mn><mspace></mspace><mi>g</mi></mrow></math>; 95% CI: <math><mrow><mo>-</mo><mn>15.0</mn></mrow></math>, 99.8). Transported effect estimates only minimally explained differences in the point estimates for two out of the four cohort pairs.</p><p><strong>Discussion: </strong>Our findings of weak to moderate evidence of effect modification and transportability indicate that unmeasured individual-level and contextual factors and sources of bias may be responsible for differences in the effect estimates observed across ECHO cohorts. https://doi.org/10.1289/EHP13961.</p>","PeriodicalId":11862,"journal":{"name":"Environmental Health Perspectives","volume":"132 5","pages":"57007"},"PeriodicalIF":10.1000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11108581/pdf/","citationCount":"0","resultStr":"{\"title\":\"Bridging Differences in Cohort Analyses of the Relationship between Secondhand Smoke Exposure during Pregnancy and Birth Weight: The Transportability Framework in the ECHO Program.\",\"authors\":\"Andreas M Neophytou, Jenny Aalborg, Sheryl Magzamen, Brianna F Moore, Assiamira Ferrara, Margaret R Karagas, Leonardo Trasande, Dana Dabelea\",\"doi\":\"10.1289/EHP13961\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Estimates for the effects of environmental exposures on health outcomes, including secondhand smoke (SHS) exposure, often present considerable variability across studies. 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引用次数: 0
摘要
背景:环境暴露(包括二手烟(SHS)暴露)对健康结果的影响估计值在不同的研究中往往存在很大差异。了解这些差异背后的原因有助于我们了解对特定人群的影响,并为合并多项研究数据的做法提供参考:本研究旨在评估孕期暴露于 SHS 对出生体重的影响是否会因测量的社会人口学特征而受到影响,这种影响可能会导致在不同队列中观察到的差异。我们还旨在量化在环境对儿童健康结果的影响(ECHO)联合研究中观察到的各队列平均效应差异在多大程度上是由于这些特征的不同分布造成的:我们评估了 ECHO 五个队列共 6771 个母子二人组中是否存在效应修饰以及效应估计值的可迁移性。我们通过基于 H 统计量的梯度提升回归树来评估是否存在效应修饰。我们使用线性模型和目标最大似然估计法(TMLE)估计了单个队列效应。然后,我们使用依赖于 TMLE 估计器的稳健非参数估计方法估计了从一个队列到其余每个队列的迁移效应,并将其与这些队列的原始效应估计值进行了比较:五个队列中观察到的效应估计值各不相同,有的出生体重显著低于暴露值[-167.3g;95% 置信区间 (CI):-270.4, -64.1],有的出生体重较高,但 CI 值较宽,包括零值(42.4g;95% CI:-15.0, 99.8)。在四个队列中,有两个队列的转运效应估计值只能解释点估计值的最小差异:讨论:我们的研究结果表明,效应修正和可迁移性的证据从弱到强,这表明未测量的个体水平和背景因素以及偏差来源可能是导致在 ECHO 队列中观察到的效应估计值差异的原因。https://doi.org/10.1289/EHP13961。
Bridging Differences in Cohort Analyses of the Relationship between Secondhand Smoke Exposure during Pregnancy and Birth Weight: The Transportability Framework in the ECHO Program.
Background: Estimates for the effects of environmental exposures on health outcomes, including secondhand smoke (SHS) exposure, often present considerable variability across studies. Knowledge of the reasons behind these differences can aid our understanding of effects in specific populations as well as inform practices of combining data from multiple studies.
Objectives: This study aimed to assess the presence of effect modification by measured sociodemographic characteristics on the effect of SHS exposure during pregnancy on birth weights that may drive differences observed across cohorts. We also aimed to quantify the extent to which differences in the cohort mean effects observed across cohorts in the Environmental influences on Child Health Outcomes (ECHO) consortium are due to differing distributions of these characteristics.
Methods: We assessed the presence of effect modification and transportability of effect estimates across five ECHO cohorts in a total of 6,771 mother-offspring dyads. We assessed the presence of effect modification via gradient boosting of regression trees based on the H-statistic. We estimated individual cohort effects using linear models and targeted maximum likelihood estimation (TMLE). We then estimated transported effects from one cohort to each of the remaining cohorts using a robust nonparametric estimation approach relying on TMLE estimators and compared them to the original effect estimates for these cohorts.
Results: Observed effect estimates varied across the five cohorts, ranging from significantly lower birth weight associated with exposure [; 95% confidence interval (CI): , ] to higher birth weight with wide CIs, including the null (; 95% CI: , 99.8). Transported effect estimates only minimally explained differences in the point estimates for two out of the four cohort pairs.
Discussion: Our findings of weak to moderate evidence of effect modification and transportability indicate that unmeasured individual-level and contextual factors and sources of bias may be responsible for differences in the effect estimates observed across ECHO cohorts. https://doi.org/10.1289/EHP13961.
期刊介绍:
Environmental Health Perspectives (EHP) is a monthly peer-reviewed journal supported by the National Institute of Environmental Health Sciences, part of the National Institutes of Health under the U.S. Department of Health and Human Services. Its mission is to facilitate discussions on the connections between the environment and human health by publishing top-notch research and news. EHP ranks third in Public, Environmental, and Occupational Health, fourth in Toxicology, and fifth in Environmental Sciences.