Building a Human Health Risk Assessment Ontology (RsO): A Proposed Framework

Over the last decade the health and environmental research communities have made significant progress in collecting and improving access to genomic, toxicology, exposure, health, and disease data useful to health risk assessment. One of the barriers to applying these growing volumes of information in fields such as risk assessment is the lack of informatics tools to organize, curate, and evaluate thousands of journal publications and hundreds of databases to provide new insights on relationships among exposure, hazard, and disease burden. Many fields are developing ontologies as a way of organizing and analyzing large amounts of complex information from multiple scientific disciplines. Ontologies include a vocabulary of terms and concepts with defined logical relationships to each other. Building from the recently published exposure ontology and other relevant health and environmental ontologies, this article proposes an ontology for health risk assessment (RsO) that provides a structural framework for organizing risk assessment information and methods. The RsO is anchored by eight major concepts that were either identified by exploratory curations of the risk literature or the exposure‐ontology working group as key for describing the risk assessment domain. These concepts are: (1) stressor, (2) receptor, (3) outcome, (4) exposure event, (5) dose‐response approach, (6) dose‐response metric, (7) uncertainty, and (8) measure of risk. We illustrate the utility of these concepts for the RsO with example curations of published risk assessments for ionizing radiation, arsenic in drinking water, and persistent pollutants in salmon.     

Estimating Contaminant Dose for Intermittent Dermal Contact: Model Development, Testing, and Application

Assessments of aggregate exposure to pesticides and other surface contamination in residential environments are often driven by assumptions about dermal contacts. Accurately predicting cumulative doses from realistic skin contact scenarios requires characterization of exposure scenarios, skin surface loading and unloading rates, and contaminant movement through the epidermis. In this article we (1) develop and test a finite-difference model of contaminant transport through the epidermis; (2) develop archetypal exposure scenarios based on behavioral data to estimate characteristic loading and unloading rates; and (3) quantify 24-hour accumulation below the epidermis by applying a Monte Carlo simulation of these archetypal exposure scenarios. The numerical model, called Transient Transport through the epiDERMis (TTDERM), allows us to account for variable exposure times and time between exposures, temporal and spatial variations in skin and compound properties, and uncertainty in model parameters. Using TTDERM we investigate the use of a macro-activity parameter (cumulative contact time) for predicting daily (24-hour) integrated uptake of pesticides during complex exposure scenarios. For characteristic child behaviors and hand loading and unloading rates, we find that a power law represents the relationship between cumulative contact time and cumulative mass transport through the skin. With almost no loss of reliability, this simple relationship can be used in place of the more complex micro-activity simulations that require activity data on one- to five-minute intervals. The methods developed in this study can be used to guide dermal exposure model refinements and exposure measurement study design.     

A Systematic Uncertainty Analysis of an Evaluative Fate and Exposure Model

Multimedia fate and exposure models are widely used to regulate the release of toxic chemicals, to set cleanup standards for contaminated sites, and to evaluate emissions in life-cycle assessment. CalTOX, one of these models, is used to calculate the potential dose, an outcome that is combined with the toxicity of the chemical to determine the Human Toxicity Potential (HTP), used to aggregate and compare emissions. The comprehensive assessment of the uncertainty in the potential dose calculation in this article serves to provide the information necessary to evaluate the reliability of decisions based on the HTP A framework for uncertainty analysis in multimedia risk assessment is proposed and evaluated with four types of uncertainty. Parameter uncertainty is assessed through Monte Carlo analysis. The variability in landscape parameters is assessed through a comparison of potential dose calculations for different regions in the United States. Decision rule uncertainty is explored through a comparison of the HTP values under open and closed system boundaries. Model uncertainty is evaluated through two case studies, one using alternative formulations for calculating the plant concentration and the other testing the steady state assumption for wet deposition. This investigation shows that steady state conditions for the removal of chemicals from the atmosphere are not appropriate and result in an underestimate of the potential dose for 25% of the 336 chemicals evaluated.     

Intake fraction for multimedia pollutants: a tool for life cycle analysis and comparative risk assessment.

We employ the intake fraction (iF) as an effective tool for expressing the source-to-intake relationship for pollutant emissions in life cycle analysis (LCA) or comparative risk assessment. Intake fraction is the fraction of chemical mass emitted into the environment that eventually passes into a member of the population through inhalation, ingestion, or dermal exposure. To date, this concept has been primarily applied to pollutants whose primary route of exposure is inhalation. Here we extend the use of iF to multimedia pollutants with multiple exposure pathways. We use a level III multimedia model to calculate iF for TCDD and compare the result to one calculated from measured levels of dioxin toxic equivalents in the environment. We calculate iF for emissions to air and surface water for 308 chemicals. We correlate the primary exposure route with the magnitudes of the octanol-water partition coefficient, Kow, and of the air-water partitioning coefficient (dimensionless Henry constant), Kaw. This results in value ranges of Kow and Kaw where the chemical exposure route can be classified with limited input data requirements as primarily inhalation, primarily ingestion, or multipathway. For the inhalation and ingestion dominant pollutants, we also define empirical relationships based on chemical properties for quantifying the intake fraction. The empirical relationships facilitate rapid evaluation of many chemicals in terms of the intake. By defining a theoretical upper limit for iF in a multimedia environment we find that iF calculations provide insight into the multimedia model algorithms and help identify unusual patterns of exposure and questionable exposure model results.     

Uncertainties in the Link Between Global Climate Change and Predicted Health Risks from Pollution: Hexachlorobenzene (HCB) Case Study Using a Fugacity Model

Industrial societies have altered the earth's environment in ways that could have important, long-term ecological, economic, and health implications. In this paper, we examine the extent to which uncertainty about global climate change could impact the precision of predictions of secondary outcomes such as health impacts of pollution. Using a model that links global climate change with predictions of chemical exposure and human health risk in the Western region of the United States of America (U.S.), we define parameter variabilities and uncertainties and we characterize the resulting outcome variance. As a case study, we consider the public health consequences from releases of hexachlorobenzene (HCB), a ubiquitous multimedia pollutant. By constructing a matrix that links global environmental change both directly and indirectly to potential human-health effects attributable to HCB released into air, soil, and water, we define critical parameter variances in the health risk estimation process. We employ a combined uncertainty/sensitivity analysis to investigate how HCB releases are affected by increasing atmospheric temperature and the accompanying climate alterations that are anticipated. We examine how such uncertainty impacts both the expected magnitude and calculational precision of potential human exposures and health effects. This assessment reveals that uncertain temperature increases of up to 5°C have little impact on either the magnitude or precision of the public-health consequences estimated under existing climate variations for HCB released into air and water in the Western region of the U.S.     

Puberty and Transdiagnostic Risks for Mental Health

Puberty in girls represents a notable period of vulnerability for different psychological disorders. The research literature has primarily considered external and contextual factors that might explain these rises in symptomatology. In the present study, we investigate relations of pubertal status and timing with individual cognitive, emotional, and behavioral tendencies, commonly identified as transdiagnostic processes, in a sample of N = 228 girls (M_age = 11.75 years). We also test whether these transdiagnostic processes mediate associations of pubertal status and pubertal timing with depressive symptoms. Results support greater endorsement of rumination, co‐rumination, negative urgency, and both anxious and angry rejection sensitivity in girls with more advanced pubertal status, as well as in girls with early pubertal timing. Higher levels of transdiagnostic processes fully mediated associations of pubertal status and timing with depressive symptoms at significant and marginally significant levels, respectively. Although the data are cross‐sectional, these findings offer promising preliminary evidence that transdiagnostic processes represent an important mental health risk in early adolescent girls.     

Regulatory Models and the Environment: Practice, Pitfalls, and Prospects

Computational models support environmental regulatory activities by providing the regulator an ability to evaluate available knowledge, assess alternative regulations, and provide a framework to assess compliance. But all models face inherent uncertainties because human and natural systems are always more complex and heterogeneous than can be captured in a model. Here, we provide a summary discussion of the activities, findings, and recommendations of the National Research Council's Committee on Regulatory Environmental Models, a committee funded by the U.S. Environmental Protection Agency to provide guidance on the use of computational models in the regulatory process. Modeling is a difficult enterprise even outside the potentially adversarial regulatory environment. The demands grow when the regulatory requirements for accountability, transparency, public accessibility, and technical rigor are added to the challenges. Moreover, models cannot be validated (declared true) but instead should be evaluated with regard to their suitability as tools to address a specific question. The committee concluded that these characteristics make evaluation of a regulatory model more complex than simply comparing measurement data with model results. The evaluation also must balance the need for a model to be accurate with the need for a model to be reproducible, transparent, and useful for the regulatory decision at hand. Meeting these needs requires model evaluation to be applied over the "life cycle" of a regulatory model with an approach that includes different forms of peer review, uncertainty analysis, and extrapolation methods than those for nonregulatory models.     

The Value of Animal Test Information in Environmental Control Decisions

Value of information (VOI)analytic techniques are used to evaluate the benefit of performing animal bioassays to provide information about the cancer potency of specific chemical compounds. These tools allow the identification of the conditions in which the cost of reducing uncertainty about potency, by performing a subchronic or chronic bioassay, is justified by the benefit of having improved information for making control decisions. The decision analytic results are readily scaled to apply to a range of human contact rates (exposures)and a variety of control strategies. The sensitivity of results to uncertainty about animal to human extrapolation and the design of the bioassay is explored. An evaluation of the possible gains in general understanding about the mechanisms of carcinogenicity resulting from chronic bioassays is beyond the scope of this approach.