Department of Epidemiology, Columbia University's Mailman School of Public Health, 722 West 168th St, Room 1608, New York, NY, 10032, USA.
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
Environ Res. 2021 Jun;197:111019. doi: 10.1016/j.envres.2021.111019. Epub 2021 Mar 15.
Rates of preterm birth and low birthweight continue to rise in the United States and pose a significant public health problem. Although a variety of environmental exposures are known to contribute to these and other adverse birth outcomes, there has been a limited success in developing policies to prevent these outcomes. A better characterization of the complexities between multiple exposures and their biological responses can provide the evidence needed to inform public health policy and strengthen preventative population-level interventions. In order to achieve this, we encourage the establishment of an interdisciplinary data science framework that integrates epidemiology, toxicology and bioinformatics with biomarker-based research to better define how population-level exposures contribute to these adverse birth outcomes. The proposed interdisciplinary research framework would 1) facilitate data-driven analyses using existing data from health registries and environmental monitoring programs; 2) develop novel algorithms with the ability to predict which exposures are driving, in this case, adverse birth outcomes in the context of simultaneous exposures; and 3) refine biomarker-based research, ultimately leading to new policies and interventions to reduce the incidence of adverse birth outcomes.
早产率和低出生体重率在美国持续上升，这构成了一个重大的公共卫生问题。尽管已知多种环境暴露因素会导致这些和其他不良生育结果，但在制定预防这些结果的政策方面，收效甚微。更好地描述多种暴露因素及其生物反应之间的复杂性，可以为制定公共卫生政策和加强预防人口干预措施提供所需的证据。为了实现这一目标，我们鼓励建立一个跨学科的数据科学框架，将流行病学、毒理学和生物信息学与基于生物标志物的研究相结合，以更好地定义人群水平暴露如何导致这些不良生育结果。拟议的跨学科研究框架将：1）利用健康登记处和环境监测计划中的现有数据进行数据驱动分析；2）开发具有在同时暴露的情况下预测哪些暴露因素起驱动作用的能力的新算法；3）完善基于生物标志物的研究，最终制定新的政策和干预措施，以降低不良生育结果的发生率。
