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Abstract
Absract In developed countries, age-related macular degeneration (AMD) is a leading
cause of irreversible blindness in adults. The key pathways of AMD are suggested to
be excessive oxidative stress and inflammation in the central retina. Because air
pollution has been found capable of inducing oxidative stress and inflammation, it
may play a role in development of AMD. This study investigated the association between
ambient air pollution and AMD in 15,115 middle-aged and older adults (≥40 years) from
Korean National Health and Nutrition Examination Survey 2008-2012. After controlling
for important confounders, ambient NO2 and CO in current-to-5 prior years and PM10
in 2-to-5 prior years were significantly associated with higher prevalence of early
AMD, while O3 in current-to-5 prior years was significantly associated with lower
prevalence of early AMD. When modeled air pollution within administrative division
units, its ORs with an IQR increase in NO2, CO, and O3 at current year were 1.24 (95%
CI: 1.05-1.46), 1.22 (95% CI: 1.09-1.38), and 0.80 (95% CI: 0.70-0.92), respectively.
Overall, results from air pollution at local/town units were consistent with those
at administrative division units. Long-term exposures to ambient air pollution may
play a role in the risk of AMD in middle-aged and older adults.
Because humans are invariably exposed to complex chemical mixtures, estimating the health effects of multi-pollutant exposures is of critical concern in environmental epidemiology, and to regulatory agencies such as the U.S. Environmental Protection Agency. However, most health effects studies focus on single agents or consider simple two-way interaction models, in part because we lack the statistical methodology to more realistically capture the complexity of mixed exposures. We introduce Bayesian kernel machine regression (BKMR) as a new approach to study mixtures, in which the health outcome is regressed on a flexible function of the mixture (e.g. air pollution or toxic waste) components that is specified using a kernel function. In high-dimensional settings, a novel hierarchical variable selection approach is incorporated to identify important mixture components and account for the correlated structure of the mixture. Simulation studies demonstrate the success of BKMR in estimating the exposure-response function and in identifying the individual components of the mixture responsible for health effects. We demonstrate the features of the method through epidemiology and toxicology applications.
Background: Exposure mixtures frequently occur in data across many domains, particularly in the fields of environmental and nutritional epidemiology. Various strategies have arisen to answer questions about exposure mixtures, including methods such as weighted quantile sum (WQS) regression that estimate a joint effect of the mixture components. Objectives: We demonstrate a new approach to estimating the joint effects of a mixture: quantile g-computation. This approach combines the inferential simplicity of WQS regression with the flexibility of g-computation, a method of causal effect estimation. We use simulations to examine whether quantile g-computation and WQS regression can accurately and precisely estimate the effects of mixtures in a variety of common scenarios. Methods: We examine the bias, confidence interval (CI) coverage, and bias–variance tradeoff of quantile g-computation and WQS regression and how these quantities are impacted by the presence of noncausal exposures, exposure correlation, unmeasured confounding, and nonlinearity of exposure effects. Results: Quantile g-computation, unlike WQS regression, allows inference on mixture effects that is unbiased with appropriate CI coverage at sample sizes typically encountered in epidemiologic studies and when the assumptions of WQS regression are not met. Further, WQS regression can magnify bias from unmeasured confounding that might occur if important components of the mixture are omitted from the analysis. Discussion: Unlike inferential approaches that examine the effects of individual exposures while holding other exposures constant, methods like quantile g-computation that can estimate the effect of a mixture are essential for understanding the effects of potential public health actions that act on exposure sources. Our approach may serve to help bridge gaps between epidemiologic analysis and interventions such as regulations on industrial emissions or mining processes, dietary changes, or consumer behavioral changes that act on multiple exposures simultaneously. https://doi.org/10.1289/EHP5838
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