A review of common statistical methods for dealing with multiple pollutant mixtures and multiple exposures

作者：Zhu Guiming, Yanchao Wen, Kexin Cao, Si‐Min He, Tong Wang · 发表于：Frontiers in Public Health · 年份：2024 · DOI：10.3389/fpubh.2024.1377685 · 被引用次数：73 · 研究领域：Health, Environment, Cognitive Aging、Air Quality and Health Impacts、Environmental Justice and Health Disparities

Traditional environmental epidemiology has consistently focused on studying the impact of single exposures on specific health outcomes, considering concurrent exposures as variables to be controlled. However, with the continuous changes in environment, humans are increasingly facing more complex exposures to multi-pollutant mixtures. In this context, accurately assessing the impact of multi-pollutant mixtures on health has become a central concern in current environmental research. Simultaneously, the continuous development and optimization of statistical methods offer robust support for handling large datasets, strengthening the capability to conduct in-depth research on the effects of multiple exposures on health. In order to examine complicated exposure mixtures, we introduce commonly used statistical methods and their developments, such as weighted quantile sum, bayesian kernel machine regression, toxic equivalency analysis, and others. Delineating their applications, advantages, weaknesses, and interpretability of results. It also provides guidance for researchers involved in studying multi-pollutant mixtures, aiding them in selecting appropriate statistical methods and utilizing R software for more accurate and comprehensive assessments of the impact of multi-pollutant mixtures on human health.