Bayesian network-based framework for exposure-response study design and interpretation

Integrated environmental health assessment: Proposed approaches to exposure during chemical incidents

An integrated environmental health exposure assessment (IEHA) refers to the integration of human biomonitoring data (HBM) and environmental measurements and aims to optimize the exposure assessment process. Due to lack of data, this approach remains an issue during chemical incidents. This study aims to explore integrated exposure approaches for assessing human health risks during chemical incidents. Based on the Preferred Reporting Items of Systematic reviews and Meta-Analyses statement, a literature analysis was performed. A level of confidence ranging from 1 to 4 was established to define the quality and strength of data used to undertake an IEHA approach. Twenty-seven articles (n = 18) and texts (n = 9) from Europe (41%) and the United States (37%) were analyzed. Among the 18 scientific articles, 61% (n = 11) presented a quantitative approach and 17% (n = 3) presented a qualitative approach. Quantitative approaches must be based on accurate data, coupled with predictive models. Of all the scientific papers, 40% (n = 7) responded to a confidence level greater than or equal to 2. Uncertainties detected through the integrated exposure approaches were related to input data, analytical methods, and HBM reference value interpretations. During chemical incidents, direct measurements were the most relevant data. Few scientific studies have developed an integrated approach during emergency situations. However, when this was used, they presented a high level of confidence by defining levels of exposure that support decision-making processes. Despite the multiple approaches, there was a lack of guidelines allowing an integrated risk assessment to be performed during an emergency chemical exposure. Integr Environ Assess Manag 2024;20:481-497. © 2023 SETAC.

Moving Beyond p < 0.05 in Ecotoxicology: A Guide for Practitioners

Statistical inferences play a critical role in ecotoxicology. Historically, null hypothesis significance testing (NHST) has been the dominant method for inference in ecotoxicology. As a brief and informal definition of NHST, researchers compare (or "test") an experimental treatment or observation against a hypothesis of no relationship (the "null hypothesis") using the collected data to see if the observed values are statistically "significant" given predefined error rates. The resulting probability of observing a value equal to or greater than the observed value assuming the null hypothesis is true is the p value. Criticisms of NHST have existed for almost a century and have recently grown to the point where statisticians, including the American Statistical Association (ASA), have felt the need to clarify the role of NHST and p values beyond their current common use. These limitations also exist in ecotoxicology. For example, a review of the 2010 Environmental Toxicology & Chemistry (ET&C) volume that found many authors did not correctly report p values. We repeated this review looking at the 2019 volume of ET&C. Incorrect reporting of p values still occurred almost a decade later. Problems with NHST and p values highlight the need for statistical inferences besides NHST, something long known in ecotoxicology and the broader scientific and statistical communities. Furthermore, concerns such as these led the executive director of the ASA to recommend against use of "statistical significance" in 2019. In light of these criticisms, ecotoxicologists require alternative methods. We describe some alternative methods including confidence intervals, regression analysis, dose-response curves, Bayes factors, survival analysis, and model selection. Lastly, we provide insights for what ecotoxicology might look like in a post-p value world. Environ Toxicol Chem 2020;39:1657-1669. Published 2020. This article is a U.S. Government work and is in the public domain in the USA.

A Hybrid Bayesian Network Framework for Risk Assessment of Arsenic Exposure and Adverse Reproductive Outcomes

Arsenic contamination of drinking water affects more than 137 million people and has been linked to several adverse health effects. The traditional toxicological approach, "dose-response" graphs, are limited in their ability to unveil the relationships between potential risk factors of arsenic exposure for adverse human health outcomes, which are critically important to understanding the risk at low exposure levels of arsenic. Therefore, to provide insight on the potential interactions of different variables of the arsenic exposure network, this study characterizes the risk factors by developing a hybrid Bayesian Belief Network (BBN) model for health risk assessment. The results show that the low inorganic arsenic concentration increases the risk of low birth weight even for low gestational age scenarios. While increasing the mother's age does not increase the low birthweight risk, it affects the distribution between other categories of baby weight. For low MMA% (<4%) in the human body, increasing gestational age decreases the risk of having low birthweight. The proposed BBN model provides 82% sensitivity and 72% specificity in average for different states of birthweight.