Data For Quantitative Risk Assessment Research Articles

Considerations towards the better integration of epidemiology into quantitative risk assessment.

Environmental epidemiology has proven critical to study various associations between environmental exposures and adverse human health effects. However, there is a perception that it often does not sufficiently inform quantitative risk assessment. To help address this concern, in 2017, the Health and Environmental Sciences Institute initiated a project engaging the epidemiology, exposure science, and risk assessment communities with tripartite representation from government agencies, industry, and academia, in a dialogue on the use of environmental epidemiology for quantitative risk assessment and public health decision making. As part of this project, four meetings attended by experts in epidemiology, exposure science, toxicology, statistics, and risk assessment, as well as one additional meeting engaging funding agencies, were organized to explore incentives and barriers to realizing the full potential of epidemiological data in quantitative risk assessment. A set of questions was shared with workshop participants prior to the meetings, and two case studies were used to support the discussion. Five key ideas emerged from these meetings as areas of desired improvement to ensure that human data can more consistently become an integral part of quantitative risk assessment: 1) reducing confirmation and publication bias, 2) increasing communication with funding agencies to raise awareness of research needs, 3) developing alternative funding channels targeted to support quantitative risk assessment, 4) making data available for reuse and analysis, and 5) developing cross-disciplinary and cross-sectoral interactions, collaborations, and training. We explored and integrated these themes into a roadmap illustrating the need for a multi-stakeholder effort to ensure that epidemiological data can fully contribute to the quantitative evaluation of human health risks, and to build confidence in a reliable decision-making process that leverages the totality of scientific evidence.

Environmental epidemiology and risk assessment: Exploring a path to increased confidence in public health decision-making

Throughout history, environmental epidemiology has proven crucial to identify certain threats to human health and to provide a basis for the development of life-saving public health policies. However, epidemiologists are facing challenges when studying tenuous threats such as environmental exposure to chemicals, whose association with adverse health effects may be difficult to characterize. As a result, epidemiological data can seldom be fully leveraged for quantitative risk assessment and decision-making. Despite two decades of efforts to improve a more systematic integration of human data to evaluate human health risks, assessors still heavily rely on animal data to do so, while epidemiology plays more of a secondary role. Although the need for more and better collaboration between risk assessors and epidemiologists is widely recognized, both fields tend to remain siloed. In 2017, the Health and Environmental Sciences Institute initiated a project engaging the epidemiology, exposure science, and regulatory communities with tripartite representation from regulators, industry, and academia in a dialogue on the use of environmental epidemiology for regulatory decision-making. Several focus groups attended by epidemiology, exposure science, and risk assessment experts were organized to explore incentives and barriers to collaboration, to ultimately bridge the gap between the various disciplines, and to realize the full potential of epidemiological data in risk assessment. Various ideas that have emerged from these meetings could help ensure the better integration of epidemiological data in quantitative risk assessment and contribute to building confidence in a robust and science-based regulatory decision-making process.

The Method of Proactive Risk Assessment for Flight Safety Based on the Rate of Dangerous Events

This publication is devoted to the issues of risk assessment in the safety management system of military aviation. The method of proactive assessment of risks to flight safety based on the number of dangerous events, which is based on correlation-regression analysis, provides an opportunity to predict the conditions that contribute to the occurrence of accidents. Studies conducted with this method have confirmed the dependence of the number of aviation accidents and serious incidents on incidents recorded for different groups of reasons, as well as on the intensity of flight activities. The obtained results can provide initial data for quantitative risk assessment in the safety management system of military aviation. The implementation of such an approach is appropriate in safety management systems in the transition phase from active to proactive approaches, until a sufficient information database on hazards is accumulated.

Benchmark profile and inferences for joint-exposure quantal data in quantitative risk assessment

In risk assessment, it is often desired to make inferences on the minimum dose levels (benchmark doses or BMDs) at which a specific benchmark risk (BMR) is attained. The estimation and inferences of BMDs are well understood in the case of an adverse response to a single-exposure agent. However, the theory of finding BMDs and making inferences on the BMDs is much less developed for cases where the adverse effect of two hazardous agents is studied simultaneously. Deutsch and Piegorsch [2012. Benchmark dose profiles for joint-action quantal data in quantitative risk assessment. Biometrics 68(4):1313–22] proposed a benchmark modeling paradigm in dual exposure setting—adapted from the single-exposure setting—and developed a strategy for conducting full benchmark analysis with joint-action quantal data, and they further extended the proposed benchmark paradigm to continuous response outcomes [Deutsch, R. C., and W. W. Piegorsch. 2013. Benchmark dose profiles for joint-action continuous data in quantitative risk assessment. Biometrical Journal 55(5):741–54]. In their 2012 article, Deutsch and Piegorsch worked exclusively with the complementary log link for modeling the risk with quantal data. The focus of the current paper is on the logit link; particularly, we consider an Abbott-adjusted [A method of computing the effectiveness of an insecticide. Journal of Economic Entomology 18(2):265–7] log-logistic model for the analysis of quantal data with nonzero background response. We discuss the estimation of the benchmark profile (BMP)—a collection of benchmark points which induce the prespecified BMR—and propose different methods for building benchmark inferences in studies involving two hazardous agents. We perform Monte Carlo simulation studies to evaluate the characteristics of the confidence limits. An example is given to illustrate the use of the proposed methods.

Estimation of the Leukemia Risk in Human Populations Exposed to Benzene from Tobacco Smoke Using Epidemiological Data.

Several epidemiological studies have demonstrated an association between occupational benzene exposure and increased leukemia risk, in particular acute myeloid leukemia (AML). However, there is still uncertainty as to the risk to the general population from exposure to lower environmental levels of benzene. To estimate the excess risk of leukemia from low-dose benzene exposure, various methods for incorporating epidemiological data in quantitative risk assessment were utilized. Tobacco smoke was identified as one of the main potential sources of benzene exposure and was the focus of this exposure assessment, allowing further investigation of the role of benzene in smoking-induced leukemia. Potency estimates for benzene were generated from individual occupational studies and meta-analysis data, and an exposure assessment for two smoking subgroups (light and heavy smokers) carried out. Subsequently, various techniques, including life-table analysis, were then used to evaluate both the excess lifetime risk and the contribution of benzene to smoking-induced leukemia and AML. The excess lifetime risk for smokers was estimated at between two and six additional leukemia deaths in 10,000 and one to three additional AML deaths in 10,000. The contribution of benzene to smoking-induced leukemia was estimated at between 9% and 24% (Upper CL 14-31%). For AML this contribution was estimated as 11-30% (Upper CL 22-60%). From the assessments carried out here, it appears there is an increased risk of leukemia from low-level exposure to benzene and that benzene may contribute up to a third of smoking-induced leukemia. Comparable results from using methods with varying degrees of complexity were generated.

Case study on the utility of hepatic global gene expression profiling in the risk assessment of the carcinogen furan

Furan is a chemical hepatocarcinogen in mice and rats. Its previously postulated cancer mode of action (MOA) is chronic cytotoxicity followed by sustained regenerative proliferation; however, its molecular basis is unknown. To this end, we conducted toxicogenomic analysis of B3C6F1 mouse livers following three week exposures to non-carcinogenic (0, 1, 2mg/kgbw) or carcinogenic (4 and 8mg/kgbw) doses of furan. We saw enrichment for pathways responsible for cytotoxicity: stress-activated protein kinase (SAPK) and death receptor (DR5 and TNF-alpha) signaling, and proliferation: extracellular signal-regulated kinases (ERKs) and TNF-alpha. We also noted the involvement of NF-kappaB and c-Jun in response to furan, which are genes that are known to be required for liver regeneration. Furan metabolism by CYP2E1 produces cis-2-butene-1,4-dial (BDA), which is required for ensuing cytotoxicity and oxidative stress. NRF2 is a master regulator of gene expression during oxidative stress and we suggest that chronic NFR2 activity and chronic inflammation may represent critical transition events between the adaptive (regeneration) and adverse (cancer) outcomes. Another objective of this study was to demonstrate the applicability of toxicogenomics data in quantitative risk assessment. We modeled benchmark doses for our transcriptional data and previously published cancer data, and observed consistency between the two. Margin of exposure values for both transcriptional and cancer endpoints were also similar. In conclusion, using furan as a case study we have demonstrated the value of toxicogenomics data in elucidating dose-dependent MOA transitions and in quantitative risk assessment.

Virus transfer proportions between gloved fingertips, soft berries, and lettuce, and associated health risks

Multiple outbreaks of human norovirus (hNoV) have been associated with fresh produce, such as soft berries and lettuce. Even though food handlers are considered an important source for the introduction of hNoV into food chains, their contribution to public health risks associated with hNoV remains unknown. To assess to which extent food handlers contribute to the introduction and spread of hNoV in fresh produce chains quantitative virus transfer data are needed. We estimated transfer proportions of hNoV GI.4, GII.4, murine norovirus (MNV-1), a culturable surrogate of hNoV, and human adenovirus (hAdV-2), a human pathogen proposed as an indicator for human faecal pollution, between gloved fingertips and raspberries, strawberries, and lettuce, by quantitative RT-PCR and cell culture if applicable. Virus transfer proportions were corrected for virus–matrix specific recoveries, and variability and uncertainty of the parameters were estimated. Virus transfer from gloves to soft berries was generally lower as compared to lettuce, with mean transfer proportions ranging between 0.1 to 2.3% and 9 to 10% for infectious MNV-1 and hAdV-2, respectively. Transfer from produce to glove was mostly greater than transfer from glove to produce, adding to the likelihood of virus transfer due to cross contamination from contaminated produce via food handlers. HNoV GI.4 and hNoV GII.4 showed no significant difference between their mean transfer proportions. Using the estimated transfer proportions, we studied the impact of low and high transfer proportions on the public health risk, based on a scenario in which a food handler picked raspberries with contaminated fingertips. Given the made assumptions, we could show that for a pathogen as infectious as hNoV, low transfer proportions may pose a greater public health risk than high transfer proportions, due to a greater viral spread. We demonstrated the potential of food handlers in spreading hNoV in food chains, showing that prevention of virus contamination on food handlers' hands is crucial for food safety. Nevertheless, complete prevention of virus contamination on fresh produce cannot be achieved in reality, and reliable and effective intervention measures are consequently required. We estimated that, especially for low transfer proportions, a robust one log10-unit reduction of infectious hNoV on contaminated produce, and on food handlers' hands, could lower the public health risk substantially. Using the obtained data in quantitative risk assessment will aid in elucidating the contribution of food handlers in hNoV transmission.

Benchmark dose profiles for joint‐action continuous data in quantitative risk assessment

Benchmark analysis is a widely used tool in biomedical and environmental risk assessment. Therein, estimation of minimum exposure levels, called benchmark doses (BMDs), that induce a prespecified benchmark response (BMR) is well understood for the case of an adverse response to a single stimulus. For cases where two agents are studied in tandem, however, the benchmark approach is far less developed. This paper demonstrates how the benchmark modeling paradigm can be expanded from the single-agent setting to joint-action, two-agent studies. Focus is on continuous response outcomes. Extending the single-exposure setting, representations of risk are based on a joint-action dose-response model involving both agents. Based on such a model, the concept of a benchmark profile-a two-dimensional analog of the single-dose BMD at which both agents achieve the specified BMR-is defined for use in quantitative risk characterization and assessment.

Benchmark Dose Profiles for Joint‐Action Quantal Data in Quantitative Risk Assessment

Benchmark analysis is a widely used tool in public health risk analysis. Therein, estimation of minimum exposure levels, called Benchmark Doses (BMDs), that induce a prespecified Benchmark Response (BMR) is well understood for the case of an adverse response to a single stimulus. For cases where two agents are studied in tandem, however, the benchmark approach is far less developed. This article demonstrates how the benchmark modeling paradigm can be expanded from the single-dose setting to joint-action, two-agent studies. Focus is on response outcomes expressed as proportions. Extending the single-exposure setting, representations of risk are based on a joint-action dose-response model involving both agents. Based on such a model, the concept of a benchmark profile (BMP) - a two-dimensional analog of the single-dose BMD at which both agents achieve the specified BMR - is defined for use in quantitative risk characterization and assessment. The resulting, joint, low-dose guidelines can improve public health planning and risk regulation when dealing with low-level exposures to combinations of hazardous agents.

Interpreting epidemiological evidence in the presence of multiple endpoints: an alternative analytic approach using the 9-year follow-up of the Seychelles child development study.

The potential for ill-informed causal inference is a major concern in published longitudinal studies evaluating impaired neurological function in children prenatally exposed to background levels of methyl mercury (MeHg). These studies evaluate a large number of developmental tests. We propose an alternative analysis strategy that reduces the number of comparisons tested in these studies. Using data from the 9-year follow-up of 643 children in the Seychelles child development study, we grouped 18 individual endpoints into one overall ordinal outcome variable as well as by developmental domains. Subsequently, ordinal logistic regression analyses were performed. We did not find an association between prenatal MeHg exposure and developmental outcomes at 9 years of age. Our proposed framework is more likely to result in a balanced interpretation of a posteriori associations. In addition, this new strategy should facilitate the use of complex epidemiological data in quantitative risk assessment.

Dermal Exposure to Jet Fuel (JP-8) in US Air Force Personnel

Limited research has been conducted on dermal exposure and risk assessment, owing to the lack of reliable measurement techniques and data for quantitative risk assessment. We investigated the magnitude of dermal exposure to jet propulsion fuel 8 (JP-8), using naphthalene as a surrogate, on the US Air Force fuel-cell maintenance workers. Dermal exposure of 124 workers routinely working with JP-8 was measured using a non-invasive tape-strip technique coupled with gas chromatography-mass spectrometry analysis. The contribution of job-related factors to dermal exposure was determined using multiple linear regression analyses. Average whole body dermal exposure to naphthalene (as a marker for JP-8) was 7.61 +/- 2.27 ln(ng m(-2)). Significant difference (P < 0.0001) between the high-exposure group [8.34 +/- 2.23 ln(ng m(-2))] and medium- and low-exposure groups [6.18 +/- 1.35 ln(ng m(-2)) and 5.84 +/- 1.34 ln(ng m(-2)), respectively] was observed reflecting the actual exposure scenarios. Skin irritation, use of booties, working inside the fuel tank and the duration of JP-8 exposure were significant factors explaining the whole body dermal exposure. This study clearly demonstrates the efficiency and suitability of the tape-strip technique for the assessment of dermal exposure to JP-8 and that naphthalene can serve as a useful marker of exposure and uptake of JP-8 and its components. It also showed that the skin provides a significant route for JP-8 exposure and that actions to reduce exposure are required. Studies to investigate the relative contribution of dermal uptake of JP-8 on total body dose and the toxicokinetics of dermal exposure to JP-8 are underway.

National Toxicology Program Studies: Principles of Dose Selection and Applications to Mechanistic Based Risk Assessment

A workshop entitled “NTP Studies: Principles of Dose Selection and Applications to Mechanistic Based Risk Assessment” was held at the 34th Annual Meeting of the Society of Toxicology in Baltimore, Maryland. The purpose of the workshop was to provide an overview of factors currently considered important in the selection of doses for NTP studies, to describe some of the confounding factors that can result from the indiscriminate use of bioassay data in quantitative risk assessment, and to suggest ways in which information from mechanistic studies or studies of biomarkers of exposure or effect might be used to better advantage in risk assessment.

Aneuploidy in germ cells: disruption of chromosome mover components.

The task of the Workgroup on "Disruption of Chromosome Mover Components" was to establish what cellular structures are involved in chromosome segregation and how disruption of these could occur. Recent research on the mechanism of action of the cellular components that segregate chromosomes accurately during mitosis or meiosis has served to highlight the number of potential targets for disruption. The process of chromosome segregation represents an orchestrated chain of events centered on the activities of cellular motors, kinesins and dyneins. These motors are involved in arranging chromosomes at the metaphase plate, providing the spindle tension necessary for progression, and the actual segregation of the chromosomes to the poles. The Workgroup determined that there is a lack of information on the effects of chemical exposure to cell motors and other chromosome mover components, and that there is a clear need for further research. This article describes the discussions of the Workgroup and highlights areas of future research into chromosome movement, particularly in human meiotic and mitotic cells. The Workgroup emphasized that obtaining mechanistic data on the induction of aneuploidy will allow for extrapolation of the dose response curves for chemical exposures below the level of observation and for using aneuploidy data for quantitative risk assessment for adverse health effects.

Modeling epidemiologic studies of occupational cohorts for the quantitative assessment of carcinogenic hazards.

Epidemiologic studies of occupational cohorts have played a major role in the quantitative assessment of risks associated with several carcinogenic hazards and are likely to play an increasingly important role in this area. Relatively little attention has been given in either the epidemiologic or the risk assessment literature to the development of appropriate methods for modeling epidemiologic data for quantitative risk assessment (QRA). The purpose of this paper is to review currently available methods for modeling epidemiologic data for risk assessment. The focus of this paper is on methods for use with retrospective cohort mortality studies of occupational groups for estimating cancer risk, since these are the data most commonly used when epidemiologic information is used for QRA. Both empirical (e.g., Poisson regression and Cox proportionate hazards model) and biologic (e.g., two-stage models) models are considered. Analyses of a study of lung cancer among workers exposed to cadmium are used to illustrate these modeling methods. Based on this example it is demonstrated that the selection of a particular model may have a large influence on the resulting estimates of risk.

Biological Considerations for Combining Carcinogenicity Data for Quantitative Risk Assessment

Quantitative risk assessments for carcinogens conducted by the U.S. EPA have most frequently been based on results of a bioassay from a single sex/strain/species of animal. In some cases, more than one data set derived from different sexes, strains, or species of animals is suitable for quantitative risk assessment. When there is no apparent difference among the data sets in sensitivity to the carcinogen, use of more of the available data should result in a higher level of confidence in the risk estimate. Several biological factors must be considered before combining data from different animal sexes, strains, species, or tumor sites. The relevance of the animal models, study design and execution, dose selection, and route of administration are factors which influence whether data from separate studies should be combined. The decision to combine data sets is also based on what is known of the mechanism of action of the agent, its pharmacokinetics, any species/sex specificity of the effect, and considerations regarding tumor site specificity. Statistical analysis also indicates whether the data sets may be described by the same multistage model. The evaluation of these factors in the decision to combine or not combine data sets is discussed.

Neurobehavioral methods used in neurotoxicology.

The use of neurobehavioral techniques in toxicology has increased dramatically over the past several years. Several national and international groups have recommended that neurobehavioral tests be included in the initial stages of hazard identification, and regulatory agencies have responded by preparing testing guidelines or requiring behavioral tests data for premarket approval of environmental and pharmaceutical chemicals. In addition, neurobehavioral data have been used to set exposure limits in the workplace. In the future, neurobehavioral data will be used more frequently in the area of risk assessment, which has been defined as the "characterization of the potential adverse effects of human exposure to environmental hazards" (National Academy of Sciences 1983). Good risk assessment depends on the ability to determine whether a particular agent is or is not causally linked to a particular health effect and on the availability of dose-response data for quantitative risk assessment. Neurobehavioral techniques used in animal behavioral toxicology measure neurobiological functions similar to those measured in humans. In addition, neurobehavioral procedures can be used in longitudinal studies where the onset and duration of effects of chemical exposure can be measured in the same animal. Neurobehavioral techniques are also amenable to the study of tolerance and compensation following repeated exposure or following recovery of function that can occur following cessation of exposure. Therefore, neurobehavioral procedures provide a valuable tool for research designed to reduce major uncertainties associated with the risk assessment process, such as animal to human extrapolation (homology of animal models) and dosing issues (i.e., high-to-low dose, acute vs. repeated dosing, and continuous vs. episodic dosing). Although the use of neurobehavioral procedures has had a significant impact on neurotoxicology, their use in the risk assessment process and in monitoring populations for possible subtle changes in neurobiological function will be limited if additional research is not done to understand the neural substrates underlying neurobehavioral endpoints. The ability to link chemically induced behavioral changes to alterations at the neurophysiological, neurochemical, and neuroanatomical levels will lead to a greater acceptance of the validity and reliability of neurobehavioral endpoints in defining adverse effects of chemicals on the nervous system.

Use of human data in quantitative risk assessment of carcinogens: Impact on epidemiologic practice and the regulatory process

Epidemiologic data are increasingly being used to assess cancer risk from chemicals as their value is recognized and as more and better studies become available. Weight-of-evidence approaches are now available for classifying the experimental and epidemiological evidence regarding human carcinogenicity. When the human data are extensive and of good quality, they should be given substantial weight in assessing risk. Both the positive and the negative epidemiologic data should be used in a quantitative risk assessment (QRA), because only then can an unbiased risk assessment be derived. Good-quality epidemiological studies are those with sound methodology, lack of bias, long enough follow-up times to observe a carcinogenic response, adequate exposure information, and dose-response information. Before a lack of carcinogenicity can be inferred, it is essential that the exposures be of substantial duration and intensity, and that the number of exposed persons be reasonably large. Epidemiologists need to give more attention to exposure assessment, because lack of quantitative exposure information is often the limiting factor that prevents the use of epidemiologic data in QRA. Development of methods to estimate historical workplace exposure intensities from surrogate industrial hygiene variables should receive high research priority, since they have the potential to increase the usefulness in QRA of many epidemiologic studies that have limited exposure information. Several frequently used surrogates for exposure measurements have limitations or pitfalls in their use. In particular, the use of “ever/never” exposed has a large potential to produce falsely negative results by means of a “dilution” effect, especially in the common case where the exposure distribution is skewed. Duration of exposure (rather than duration × intensity) may also give misleading results. There is little information to suggest that synergistic exposures to multiple toxicants in an industrial environment are likely to invalidate QRAs, probably because few studies have identified a group of workers with major workplace exposures to multiple carcinogens that cause the same type of cancer. Most of the interactive effects which have been identified to date are between smoking and some occupational carcinogen, so this possibility needs careful evaluation for smoking-related diseases. It is important to evaluate dose-response gradients in a QRA to obtain maximum precision and accuracy in the resulting risk coefficient. The analysis should take into account an appropriate cancer induction period. Various methods to account for cancer induction times are compared; those that incorporate a lag period or model the induction-time distribution are superior to other methods. A joint analysis of available epidemiologic data sets is valuable, because it has the potential to provide a more stable central risk estimate and narrower confidence bounds than any individual study. When the raw data or detailed tabulations in an appropriate standard format are supplied, joint analyses can be much more useful than when only the published results are available, because various potentially confounding factors can then be evaluated and dose-response relationships can be examined in parallel. Studies should not be excluded on the grounds of study size, since a joint analysis will weight each study according to its precision. Confidence intervals from epidemiologic studies are useful in evaluating the compatibility of the human data with risk estimates which have been derived from animal data. Incompatibilities between the two should be given careful consideration before standards are set. The biomathematical models for deriving QRAs, which have been applied primarily to animal bioassay data, are being used with human data as well. Comparative studies indicate that all the models have technical weaknesses in their performance, to which the user should be alert. To be more applicable to epidemiologic data, some models have been broadened to incorporate factors such as variability in dose rates over time, but most still need further development to handle epidemiologic risk factors and confounder variables. More attention needs to be paid to the lifetime risk projections that are overlaid on the mathematical models. Constant relative risks for the remaining lifetime have usually been assumed in the regulatory agencies' QRAs, but the epidemiologic data for a number of carcinogenic agents indicate that the relative risk diminishes within a few decades after an exposure. Failure to incorporate this information into the models can cause serious overestimates of lifetime risk.