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

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 exp...

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Vydáno v:Frontiers in public health Ročník 12; s. 1377685
Hlavní autoři: Zhu, Guiming, Wen, Yanchao, Cao, Kexin, He, Simin, Wang, Tong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland Frontiers Media S.A 09.05.2024
Témata:
Bayes Theorem
environment
Environmental Exposure
Environmental Pollutants
epidemiology
health effects
Humans
Models, Statistical
multi-pollutant mixtures
Public Health
statistical methods
epidemiology
environment
multi-pollutant mixtures
health effects
statistical methods
ISSN:2296-2565, 2296-2565
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Shrnutí: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.
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Edited by: Dimirios Nikolopoulos, University of West Attica, Greece
Sean Mark Patrick, University of Pretoria, South Africa
Reviewed by: Jianjun Xiang, Fujian Medical University, China
ISSN:2296-2565
2296-2565
DOI:10.3389/fpubh.2024.1377685