A review of practical statistical methods used in epidemiological studies to estimate the health effects of multi-pollutant mixture. (1st August 2022)

• Record Type: Journal Article
• Authors: Yu, Linling; Liu, Wei; Wang, Xing; Ye, Zi; Tan, Qiyou; Qiu, Weihong; Nie, Xiuquan; Li, Minjing; Wang, Bin; Chen, Weihong
• Is Part Of: Environmental pollution. Volume 306(2022)
• Publication Date: 2022-08-01
• Subjects: Multi-pollutant mixture -- Health effect -- Environmental epidemiology -- Statistical method -- Bayesian kernel machine regression; Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2022.119356
• Languages: English
• ISSNs: 0269-7491

Abstract

Abstract: Environmental risk factors have been implicated in adverse health effects. Previous epidemiological studies on environmental risk factors mainly analyzed the impact of single pollutant exposure on health, while in fact, humans are constantly exposed to a complex mixture consisted of multiple pollutants/chemicals. In recent years, environmental epidemiologists have sought to assess adverse health effects of exposure to multi-pollutant mixtures based on the diversity of real-world environmental pollutants. However, the statistical challenges are considerable, for instance, multicollinearity and interaction among components of the mixture complicate the statistical analysis. There is currently no consensus on appropriate statistical methods. Here we summarized the practical statistical methods used in environmental epidemiology to estimate health effects of exposure to multi-pollutant mixture, such as Bayesian kernel machine regression (BKMR), weighted quantile sum (WQS) regressions, shrinkage methods (least absolute shrinkage and selection operator, elastic network model, adaptive elastic-net model, and principal component analysis), environment-wide association study (EWAS), etc. We sought to review these statistical methods and determine the application conditions, strengths, weaknesses, and result interpretability of each method, providing crucial insight and assistance for addressing epidemiological statistical issues regarding health effects from multi-pollutant mixture. Graphical abstract: Image 1 Highlights: Mixture analysis is challenged by high-dimensionality, multicollinearity, interaction, etc. BKMR and WQS-based methods developed for mixture analysis are increasingly used. Method selection should be based on its features, data traits, study purposes, etc. Combined use of multiple statistical methods proposed in this study is recommended. Measurement error deserves attention when assessing multi-pollutant mixture effects.