Recommended Practice Regarding Selection of Sensitivity Analysis Methods Applied to Microbial Food Safety Process Risk Models

A guideline is presented for selection of sensitivity analysis methods applied to microbial food safety process risk (MFSPR) models. The guideline provides useful boundaries and principles for selecting sensitivity analysis methods for MSFPR models. Although the guideline is predicated on a specific branch of risk assessment models related to food-borne diseases, the principles and recommendations provided are typically generally applicable to other types of risk models. Applicable situations include: prioritizing potential critical control points; identifying key sources of variability and uncertainty; and refinement, verification, and validation of a model. Based on the objective of the analysis, characteristics of the model under study, amount of detail expected from sensitivity analysis, and characteristics of the sensitivity analysis method, recommendations for selection of sensitivity analysis methods are provided. A decision framework for method selection is introduced. The decision framework can substantially facilitate the process of selecting a sensitivity analysis method.     

Adequacy Conditions for Reporting Uncertainty in Chemical Risk Assessments

Uncertainty may influence decision-making. A prerequisite for a decision to be well founded is thus that scientific experts inform decision-makers about all decision relevant uncertainty. A set of conditions is provided for adequate characterization of scientific uncertainty for the purposes of regulatory decision-making. These conditions require specification of (1) the character and degree of uncertainty about the assessment variables, (2) the possibility of reducing the uncertainty, and (3) the degree of agreement among experts. Furthermore, it is required that (4) the information covered by the previous conditions is presented in a clear and comprehensible way. The point of departure is that characterizing scientific uncertainty conceptually means specifying all potentially important possibilities that are consistent with the state of scientific knowledge. The conditions are intended to be applied to human health risk assessment of chemicals. However, the basic approach, to consider potentially important possibilities, should be useful also to environmental, and site-specific risk assessment.     

Cancer and non-cancer health risk assessment associated with exposure to non-methane hydrocarbons among roadside vendors in Delhi,India

Abstract

This study estimates the cancer and non-cancer health risk among the roadside vendors in Delhi, the capital city of India. Air samples of selected NMHCs and their derivatives were collected from four different sites (one traffic intersection, one industrial, and two residential) in Delhi and were analyzed on Gas Chromatograph (GC) to obtain their atmospheric concentrations. At each site, a survey among the roadside vendors was also conducted to obtain information about their bodyweight and exposure to outdoor ambient air. The study reveals that hazard quotient of 1,3-butadiene is greater than one at all the sites, with its maximum value occurring at the industrial site. The major contributors to the workplace cancer risk (WCR) are found to be 1,3-butadiene and chloroform. The overall WCR is observed to be the highest (9.4?×?10^?4) at the traffic intersection site, followed by the industrial site (7.0?×?10^?4). Cancer incidence data and the population data are also used to estimate the growth of cancer risk in Delhi from 2009 to 2016. Comparison of the WCR values of the four sites with the cancer risk estimated from the cancer incidence data shows that NMHCs and their derivatives are significant contributors to the overall cancer risk in Delhi. Our results suggest that NMHCs and their derivatives need to be given due consideration in the National Cancer Control Programme of India.     

Scientific basis for uncertainty factors used to establish occupational exposure limits for pharmaceutical active ingredients

The traditional “safety factor”; method has been used for years to establish occupational exposure limits (OELs) for active ingredients used in drugs. In the past, a single safety factor was used to address all sources of uncertainty in the limit setting process. The traditional 100‐fold safety factor commonly used to derive an acceptable daily intake value incorporates a default factor of 10 each to account for interindividual variability and interspecies extrapolation. Use of these defaults can lead to overly conservative health‐based limits, especially when they are combined with other (up to 10‐fold) factors to adjust for inadequacies in the available database. In recent years, attempts have been made to quantitate individual sources of uncertainty and variability to improve the scientific basis for OELs. In this paper we discuss the science supporting reductions in the traditional default uncertainty factors. A number of workplace‐specific factors also support reductions in these factors. Recently proposed alternative methodologies provide a framework to make maximum use of preclinical and clinical information, e.g., toxicokinetic and toxicodynamic data, to reduce uncertainties when establishing OELs for pharmaceutical active ingredients.     

Evaluation of Sampling-Based Methods for Sensitivity Analysis: Case Study for the E. coli Food Safety Process Risk Model

This article evaluates selected sensitivity analysis methods applicable to risk assessment models with two-dimensional probabilistic frameworks, using a microbial food safety process risk model as a test-bed. Six sampling-based sensitivity analysis methods were evaluated including Pearson and Spearman correlation, sample and rank linear regression, and sample and rank stepwise regression. In a two-dimensional risk model, the identification of key controllable inputs that can be priorities for risk management can be confounded by uncertainty. However, despite uncertainty, results show that key inputs can be distinguished from those that are unimportant, and inputs can be grouped into categories of similar levels of importance. All selected methods are capable of identifying unimportant inputs, which is helpful in that efforts to collect data to improve the assessment or to focus risk management strategies can be prioritized elsewhere. Rank-based methods provided more robust insights with respect to the key sources of variability in that they produced narrower ranges of uncertainty for sensitivity results and more clear distinctions when comparing the importance of inputs or groups of inputs. Regression-based methods have advantages over correlation approaches because they can be configured to provide insight regarding interactions and nonlinearities in the model.     

Public Reactions to Risk Messages Communicating Different Sources of Uncertainty: An Experimental Test

There is an abundant literature on the challenge of integrating uncertainties in experts’ risk assessments, but the evidence on the way they are understood by the public is scarce and mixed. This study aims to better understand the effect of communicating different sources of uncertainty in risk communication. A causal design was employed to test the effect of communicating risk messages varying in type of advisory warning (no risk and suggests no protective measure, or risk and recommends a protective measure) and sources of uncertainty (no uncertainty, divergence between experts, contradictory data, or lack of data) on public reactions. Participants from the general public (N = 434) were randomly assigned to read and react to variants of a fictitious government message discussing the presence of a new micro-organism found in tap water. Multiple analysis of variance showed that to report uncertainty from divergence between experts or from contradictory data reduced the adherence to the message, but not to mention the lack of data. Moreover, the communication of diverse sources of uncertainty did not affect trust in the government when the advisory warning stated there was a risk and recommended a protective measure. These findings have important implications for risk communication.     

Successful Management of Musculoskeletal Disorders

A National Academy of Sciences study found that musculoskeletal disorders of the back and arm are an important national health problem with over 1,000,000 workers missing time from their job each year, at a cost of over $50 billion a year. When one takes indirect costs such as reduced productivity, loss of customers due to errors made by replacement workers and regulatory compliance into account, estimates place the total yearly cost of all workplace injuries at well over $1 trillion or 10% of United States Gross Domestic Product. Debates regarding causation and subsequent financial responsibility have delayed the opportunity to provide effective prevention in the workplace. Effective prevention of workplace illnesses (musculoskeletal disorders) through active intervention is not only possible, but results in significant costs savings for the employer while reducing the disability experienced by the individual employee.     

An Evaluation of Risk Drivers from a Sample of Risk Assessments Conducted for the U.S. Army

We evaluate risk drivers at selected U.S. Army installations by developing a database containing contaminant-pathway-receptor combinations that exceed regulatory thresholds for ecological (toxicity quotient greater than one), human health cancer risk (predicted incremental lifetime cancer risk greater than one in ten thousand), and noncancer human health (hazard index greater than one). We compare the risk drivers from the database to reported corrective action objectives from available decision documents. For noncancer hazards, explosives (particularly in ground water) dominate the reported exceedances of regulatory thresholds in the database. PAHs in home-grown produce show the highest number of exceedances of regulatory thresholds for cancer risk. For ecological risks, PAHs in both terrestrial and aquatic environments dominate the exceedances of regulatory thresholds. All available cleanup levels were derived based on human health exposures rather than ecological exposures, except for one site. In general, ecological risks were considered to be “more uncertain,” and that was used as a basis for not relying on backcalculated target levels on the basis of ecological risk. The reverse was true for human health risks: the “conservative” assumptions incorporated into the modeling provided the justification for backcalculating health-protective target levels.     

A Probabilistic Analysis of Regional Mercury Impacts on Wildlife

We investigate the uncertainties associated with modeling the potential health effects on piscivorous animals of mercury released to the atmosphere. The multimedia modeling system combines an atmospheric fate and transport model, an aquatic cycling model, and a terrestrial food web model. First, the modeling system is used to calculate point values of the animals' hazard quotients (i.e., measures of toxic dose). Next, we use a simplified version of the modeling system to conduct a probabilistic analysis for the Great Lakes region that takes into account input uncertainty, variability, and uncertainty and variability combined. The use of two different software packages for the combined uncertainty/variability analysis led to similar results except for high values (>90th percentile) where some differences were evident. A sensitivity study was performed on the combined uncertainty and variability analysis. Regional variability caused more than 70% of the variance in the results, with the fish bioaccumulation factor accounting for the majority of the variability. The major sources of uncertainty were the speciation of the mercury emissions, the lake pH, and the sediment burial rate.     

Axes of Extrapolation in Risk Assessment

Extrapolation in risk assessment involves the use of data and information to estimate or predict something that has not been measured or observed. Reasons for extrapolation include that the number of combinations of environmental stressors and possible receptors is too large to characterize risks comprehensively, that direct characterization is sometimes impossible, and that the power to characterize risk in a particular situation can be enhanced by using information obtained in other similar situations. Three types of extrapolation are common in risk assessments: biological (including between taxa and across levels of biological organization), temporal, and spatial. They can be thought of conceptually as the axes of a 3-dimensional graph defining the state space of biological, temporal, and spatial scales within which extrapolations are made. Each of these types of extrapolation can introduce uncertainties into risk assessments. Such uncertainties may be reduced through synergistic research facilitated by the sharing of methods, models, and data used by human health and ecological scientists     

Distribution of Daily Breathing Rates For Use in California's Air Toxics Hot Spots Program Risk Assessments

Data were combined from a study measuring breathing rates at various activities and two activity pattern studies to generate breathing rate distributions for children and adults. The children and adult breathing rate distributions were combined using a Monte Carlo technique to generate a breathing rate distribution for a lifetime spanning ages 0 to 70. The children's breathing rate distribution has a mean, standard deviation, median and 95^th percentile of 452, 67.7, 441, and 581 L/kg-day, respectively. The adult breathing rate distribution has a mean, standard deviation, median and 95^th percentile of 232, 64.6, 209, and 381 L/kg-day, respectively. The simulated 70-year distribution has a mean, standard deviation, median and 95^th percentile of 271, 57.9, 253, and 393 L/kg-day, respectively. The adult breathing rate distribution is based on 24-hour recall activity data that would not necessarily capture average activity patterns and therefore breathing rates. We utilized the human energy expenditure literature to validate the breathing rate distribution. We conclude that the breathing rate distribution is reasonable for chronic long-term risk assessment in California's Air Toxics Hot Spots program.     

The significance of mycotoxins in the framework of assessing workplace related risks

Mycotoxins are fungal metabolite which may in some cases exhibit a high health hazard potential. Mycotoxins can show carcinogenic, mutagenic, toxic, teratogenic or immunotoxic effects. Mycotoxin exposure in the workplace may occur through inhalation and skin contact,e.g. during occupational handling of organic matter such as livestock feed, food products, or waste. Various studies suggest that both acute and chronic effects can occur, depending at least on the exposure level. The magnitude of the potential health risks associated with a respiratory or dermal intake of mycotoxins has largely remained unclear to date. However, according to the directive 2000/54/EC on biological agents and the corresponding German Biological Agents Ordinance, employers are also required to consider the potential hazards posed by toxic effects of biological agents when assessing workplace risks. The aim of this article, therefore, is to present some basis information that should facilitate an evaluation of the significance of mycotoxins in the context of assessing workplace risks. It also provides suggestions for occupational health and safety measures.     

Uncertainty Is Part of Making Decisions

Advances in computer technology and applied statistics have provided the opportunity for the non-statistician to investigate uncertainty in a quantitative manner. The following discussion argues, notwithstanding the possible misuse of uncertainty analysis, that uncertainty is always present and that decisions based on human or ecological risk assessment would benefit from disclosure of uncertainty in the estimated risks.     

A toxicological basis to derive generic interspecies uncertainty factors for application in human and ecological risk assessment

A new method is proposed to derive the size of the interspecies uncertainty factor (UF) that is toxicologically and statistically based. The method is based on the biological/evolutionary assumption that similarity in susceptibility to toxic substances is a function of phylogenetic relatedness. This assumption is assessed via a large and highly structured aquatic database with over 500 agents tested in specific binary toxicity comparison (i.e., when two species have been tested with the same chemical under identical conditions) for dozens of species of wide phylogenetic relatedness. The methodology takes into account the generic need to estimate a response in any species (not just human) and the need to predict responses for new chemical agents. The method involves quantifying interspecies variation in susceptibility to numerous toxic substances via the use of binary interspecies comparisons that are converted to a 95% UF. This interspecies UF represents an estimate of the upper 95% of the population of 95% prediction intervals (PI) for binary interspecies comparisons within four categories of phylogenetic relatedness (species‐within‐genus, genera‐within‐family, families‐within‐order, orders‐within‐class). The 95% interspecies UFs range from a low of 10 for species‐within‐genus up to 65 for orders‐within‐class. Most mammalian toxicology studies involving mice, rats, cats, dogs, gerbils, and rabbits are orders‐within‐class categories for human risk assessment and would be provided a 65‐fold UF. Larger or smaller interspecies UF values could be selected based on the level of protection desired. The procedures described have application to both human and ecological risk assessment.     

Fuzzy evidence theory and Bayesian networks for process systems risk analysis

Abstract

Quantitative risk assessment (QRA) approaches systematically evaluate the likelihood, impacts, and risk of adverse events. QRA using fault tree analysis (FTA) is based on the assumptions that failure events have crisp probabilities and they are statistically independent. The crisp probabilities of the events are often absent, which leads to data uncertainty. However, the independence assumption leads to model uncertainty. Experts’ knowledge can be utilized to obtain unknown failure data; however, this process itself is subject to different issues such as imprecision, incompleteness, and lack of consensus. For this reason, to minimize the overall uncertainty in QRA, in addition to addressing the uncertainties in the knowledge, it is equally important to combine the opinions of multiple experts and update prior beliefs based on new evidence. In this article, a novel methodology is proposed for QRA by combining fuzzy set theory and evidence theory with Bayesian networks to describe the uncertainties, aggregate experts’ opinions, and update prior probabilities when new evidences become available. Additionally, sensitivity analysis is performed to identify the most critical events in the FTA. The effectiveness of the proposed approach has been demonstrated via application to a practical system.     

A Weight-of-Evidence Framework for Assessing Sediment (Or Other) Contamination: Improving Certainty in the Decision-Making Process

A basic framework is presented for the ecological weight-of-evidence (WOE) process for sediment assessment that clearly defines its essential elements and will improve the certainty of conclusions about whether or not impairment exists due to sediment contamination, and, if so, which stressors and biological species (or ecological responses) are of greatest concern. The essential “Certainty Elements” are addressed in a transparent best professional judgment (BPJ) process with multiple lines-of-evidence (LOE) ultimately quantitatively integrated (but not necessarily combined into a single value). The WOE Certainty Elements include: (1) Development of a conceptual model (showing linkages of critical receptors and ecosystem quality characteristics); (2) Explanation of linkages between measurement endpoint responses (direct and indirect with associated spatial/temporal dynamics) and conceptual model components; (3) Identification of possible natural and anthropogenic stressors with associated exposure dynamics; (4) Evaluation of appropriate and quantitatively based reference (background) comparison methods; (5) Consideration of advantages and limitations of quantification methods used to integrate LOE; (6) Consideration of advantages and limitations of each LOE used; (7) Evaluation of causality criteria used for each LOE during output verification and how they were implemented; and (8) Combining the LOE into a WOE matrix for interpretation, showing causality linkages in the conceptual model. The framework identifies several statistical approaches for integrating within LOE, the suitability of which depends on physical characteristics of the system and the scale/nature of impairment. The quantification approaches include: (1) Gradient (regression methods); (2) Paired reference/test (before/after control impact and ANOVA methods); (3) Multiple reference (ANOVA and multivariate methods); and 4) Gradient with reference (regression, ANOVA and multivariate methods). This WOE framework can be used for any environmental assessment and is most effective when incorporated into the initial and final study design stages (e.g., the Problem Formulation and Risk Characterization stages of a risk assessment) with reassessment throughout the project and decision-making process, rather than in a retrospective data analysis approach where key certainty elements cannot be adequately addressed.