Division of Extramural Research and Training, National Institute of Environmental Health Sciences, National Institutes of Health, Durham, NC 27709, USA.
Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY 10032, USA.
Int J Environ Res Public Health. 2022 Jan 26;19(3):1378. doi: 10.3390/ijerph19031378.
Humans are exposed to a diverse mixture of chemical and non-chemical exposures across their lifetimes. Well-designed epidemiology studies as well as sophisticated exposure science and related technologies enable the investigation of the health impacts of mixtures. While existing statistical methods can address the most basic questions related to the association between environmental mixtures and health endpoints, there were gaps in our ability to learn from mixtures data in several common epidemiologic scenarios, including high correlation among health and exposure measures in space and/or time, the presence of missing observations, the violation of important modeling assumptions, and the presence of computational challenges incurred by current implementations. To address these and other challenges, NIEHS initiated the Powering Research through Innovative methods for Mixtures in Epidemiology (PRIME) program, to support work on the development and expansion of statistical methods for mixtures. Six independent projects supported by PRIME have been highly productive but their methods have not yet been described collectively in a way that would inform application. We review 37 new methods from PRIME projects and summarize the work across previously published research questions, to inform methods selection and increase awareness of these new methods. We highlight important statistical advancements considering data science strategies, exposure-response estimation, timing of exposures, epidemiological methods, the incorporation of toxicity/chemical information, spatiotemporal data, risk assessment, and model performance, efficiency, and interpretation. Importantly, we link to software to encourage application and testing on other datasets. This review can enable more informed analyses of environmental mixtures. We stress training for early career scientists as well as innovation in statistical methodology as an ongoing need. Ultimately, we direct efforts to the common goal of reducing harmful exposures to improve public health.
人类在其一生中会接触到各种化学和非化学物质。精心设计的流行病学研究以及复杂的暴露科学和相关技术,使我们能够研究混合物对健康的影响。虽然现有的统计方法可以解决与环境混合物和健康终点之间的关联的最基本问题,但在以下几种常见的流行病学情况下,我们从混合物数据中学习的能力存在差距,包括健康和暴露测量在空间和/或时间上的高度相关性、存在缺失观测值、违反重要建模假设以及当前实施所带来的计算挑战。为了解决这些问题和其他挑战,NIEHS 启动了“通过创新方法在流行病学中研究混合物(PRIME)”计划,以支持开发和扩展混合物统计方法的工作。PRIME 支持的六个独立项目非常有成效,但它们的方法尚未以一种能够提供应用信息的方式进行集体描述。我们回顾了 PRIME 项目中的 37 种新方法,并总结了跨先前发布的研究问题的工作,以告知方法选择并提高对这些新方法的认识。我们重点介绍了考虑数据科学策略、暴露反应估计、暴露时间、流行病学方法、毒性/化学信息的纳入、时空数据、风险评估以及模型性能、效率和解释的重要统计进展。重要的是,我们链接到软件,以鼓励在其他数据集上进行应用和测试。这篇综述可以使对环境混合物的分析更加明智。我们强调培训早期职业科学家以及统计方法创新作为持续的需求。最终,我们将努力共同减少有害暴露,以改善公共健康。