Methods to account for uncertainties in exposure assessment in studies of environmental exposures

Abstract

Background Accurate exposure estimation in environmental epidemiological studies is crucial for health risk assessment. Failure to account for uncertainties in exposure estimation could lead to biased results in exposure-response analyses. Assessment of the effects of uncertainties in exposure estimation on risk estimates received a lot of attention in radiation epidemiology and in several studies of diet and air pollution. The objective of this narrative review is to examine the commonly used statistical approaches to account for exposure estimation errors in risk analyses and to suggest how each could be applied in environmental epidemiological studies. Main text We review two main error types in estimating exposures in epidemiological studies: shared and unshared errors and their subtypes. We describe the four main statistical approaches to adjust for exposure estimation uncertainties (regression calibration, simulation-extrapolation, Monte Carlo maximum likelihood and Bayesian model averaging) along with examples to give readers better understanding of their advantages and limitations. We also explain the advantages of using a 2-dimensional Monte-Carlo (2DMC) simulation method to quantify the effect of uncertainties in exposure estimates using full-likelihood methods. For exposures that are estimated independently between subjects and are more likely to introduce unshared errors, regression calibration and SIMEX methods are able to adequately account for exposure uncertainties in risk analyses. When an uncalibrated measuring device is used or estimation parameters with uncertain mean values are applied to a group of people, shared errors could potentially be large. In this case, Monte Carlo maximum likelihood and Bayesian model averaging methods based on estimates of exposure from the 2DMC simulations would work well. The majority of reviewed studies show relatively moderate changes (within 100%) in risk estimates after accounting for uncertainties in exposure estimates, except for the two studies which doubled/tripled naïve estimates. Conclusions In this paper, we demonstrate various statistical methods to account for uncertain exposure estimates in risk analyses. The differences in the results of various adjustment methods could be due to various error structures in datasets and whether or not a proper statistical method was applied. Epidemiological studies of environmental exposures should include exposure-response analyses accounting for uncertainties in exposure estimates.

Exposure Factors vs. Bioaccessibility in the Soil-and-Dust Ingestion Pathway: A Comparative Assessment of Uncertainties Using MC2D Simulations in an Arsenic Exposure Scenario

“…This study applies 2-Dimensional Monte Carlo Analysis (2D-MC) as a method of generating random data vectors that introduces uncertainty in the parameters that define the PDFs of the exposure variables (Frey and Rhodes 1996;Pouillot and Delignette-Muller 2010;Sadeghfam et al 2021). Previous studies have applied the 2D-MC method to perform exposure simulations in different scenarios (i.e., Jang et al 2009;Simon 1999;Vadali et al 2009;Wu et al 2019). To the best of the authors' knowledge, there are no applications of the 2D-MC method for the simulation of exposure scenarios that consider variability and uncertainty from various sources of prior information.…”

Spatiotemporal Distributions of Fluoride and Arsenic in Rivers with the Role of Mining Industry and Related Human Health Risk Assessments in Kyrgyzstan

“…In this study, the risks of fluoride and arsenic in Kyrgyz River water to human health were quantitatively evaluated, but there are still many uncertainties in the analysis process. First, in the selection of the mathematical model and its parameters (Wu et al 2019), the mathematical model is a function of multiple variables (Dutta 2017), and the parameters used in this study were taken directly from the relevant literature, which increased the uncertainty of the evaluation results to a certain extent. Moreover, simultaneous exposure to fluoride and arsenic may cause joint toxicity, and this joint toxicity may have synergistic or antagonistic effects on different organs and systems of the human body (Jadhav et al 2015;Zeng et al 2014), which increases the errors in health risk assessment results for a single element.…”

Spatiotemporal Distributions of Fluorine and Arsenic in Rivers With the Role of Mining Industry and Related Human Health Risk Assessments in Kyrgyzstan

“…In this study, the risks of fluoride and arsenic in Kyrgyz River water to human health were quantitatively evaluated, but there are still many uncertainties in the analysis process. First, in the selection of the mathematical model and its parameters 34 , the mathematical model is a function of multiple variables , and the parameters used in this study were taken directly from the relevant literature, which increased the uncertainty of the evaluation results to a certain extent. Moreover, simultaneous exposure to fluoride and arsenic may cause joint toxicity, and this joint toxicity may have synergistic or antagonistic effects on different organs and systems of the human body 35,36 , which increases the errors in health risk assessment results for a single element.…”