A Quantitative Assessment of the Prion Risk Associated with Wastewater from Carcass‐Handling Facilities

Wastewater from facilities processing livestock that may harbor transmissible spongiform encephalopathies (TSEs) infectivity is permitted under license for application to land where susceptible livestock may have access. Several previous risk assessments have investigated the risk of bovine spongiform encephalopathy (BSE) associated with wastewater effluents; however, the risk of exposure to classical scrapie and atypical scrapie has not been assessed. With the prevalence of certain TSEs (BSE in cattle and classical scrapie in sheep) steadily in decline, and with considerable changes in the structure of carcass‐processing industries in Great Britain, a reappraisal of the TSE risk posed by wastewater is required. Our results indicate that the predicted number of new TSE infections arising from the spreading of wastewater on pasture over one year would be low, with a mean of one infection every 1,000 years for BSE in cattle (769, 555,556), and one infection every 30 years (16, 2,500), and 33 years (16, 3,333) for classical and atypical scrapie, respectively. It is assumed that the values and assumptions used in this risk assessment remain constant. For BSE in cattle the main contributors are abattoir and rendering effluent, contributing 35% and 22% of the total number of new BSE infections. For TSEs in sheep, effluent from small incinerators and rendering plants are the major contributors (on average 32% and 31% of the total number of new classical scrapie and atypical scrapie infections). This is a reflection of the volume of carcass material and Category 1 material flow through such facilities.     

Consumption of Bovine Spongiform Encephalopathy (BSE) Contaminated Beef and the Risk of Variant Creutzfeldt‐Jakob Disease

To date, the variant Creutzfeldt‐Jakob disease (vCJD) risk assessments that have been performed have primarily focused on predicting future vCJD cases in the United Kingdom, which underwent a bovine spongiform encephalopathy (BSE) epidemic between 1980 and 1996. Surveillance of potential BSE cases was also used to assess vCJD risk, especially in other BSE‐prevalent EU countries. However, little is known about the vCJD risk for uninfected individuals who accidentally consume BSE‐contaminated meat products in or imported from a country with prevalent BSE. In this article, taking into account the biological mechanism of abnormal prion PrP^res aggregation in the brain, the probability of exposure, and the expected amount of ingested infectivity, we establish a stochastic mean exponential growth model of lifetime exposure through dietary intake. Given the findings that BSE agents behave similarly in humans and macaques, we obtained parameter estimates from experimental macaque data. We then estimated the accumulation of abnormal prions to assess lifetime risk of developing clinical signs of vCJD. Based on the observed number of vCJD cases and the estimated number of exposed individuals during the BSE epidemic period from 1980 to 1996 in the United Kingdom, an exposure threshold hypothesis is proposed. Given the age‐specific risk of infection, the hypothesis explains the observations very well from an extreme‐value distribution fitting of the estimated BSE infectivity exposure. The current BSE statistics in the United Kingdom are provided as an example.     

Risk Perception of the "Mad Cow Disease" in France: Determinants and Consequences

Since 1996, when bovine spongiform encephalopathy (BSE) was assessed as a possible human transmissible disease, a variant of Creutzfeldt-Jakob disease (vCJD), French people have entered into a long period of fear and avoidance of beef and bovine byproducts, which produced an unprecedented collapse in the beef market. This article deals with the perceived risk of the "mad cow disease" (MCD) in the French general population. Two surveys were conducted on a representative sample of the adult population, the first one in 2000 during the peak of the crisis and the second one 13 months later in a quieter period. The main assumption we made was that changes in beef consumption are strongly related to the perceived risk of MCD, which we defined as people's cognitive and affective responses to hazard. Our objective was to identify the determinants and consequences of this perceived risk and to compare them in different sociopolitical contexts. The results issued from a bivariate and multivariate analysis show that: (i) the distribution of most of the variables significantly related to the perceived risk identified in the first survey had changed in the second survey, in relation with the reduction of worry and the resumption of national beef consumption; (ii) the propensity for self-protection through avoiding or ceasing beef eating was more related to feelings of worry than to subjective vCJD risk assessments; and (iii) the main determinant of less avoidance to beef products was the preference for beef, a feeling identified prior to emergence of the risk of MCD, remaining unchanged in various contexts.     

Peak Exposures in Epidemiologic Studies and Cancer Risks: Considerations for Regulatory Risk Assessment

We review approaches for characterizing “peak” exposures in epidemiologic studies and methods for incorporating peak exposure metrics in dose–response assessments that contribute to risk assessment. The focus was on potential etiologic relations between environmental chemical exposures and cancer risks. We searched the epidemiologic literature on environmental chemicals classified as carcinogens in which cancer risks were described in relation to “peak” exposures. These articles were evaluated to identify some of the challenges associated with defining and describing cancer risks in relation to peak exposures. We found that definitions of peak exposure varied considerably across studies. Of nine chemical agents included in our review of peak exposure, six had epidemiologic data used by the U.S. Environmental Protection Agency (US EPA) in dose–response assessments to derive inhalation unit risk values. These were benzene, formaldehyde, styrene, trichloroethylene, acrylonitrile, and ethylene oxide. All derived unit risks relied on cumulative exposure for dose–response estimation and none, to our knowledge, considered peak exposure metrics. This is not surprising, given the historical linear no‐threshold default model (generally based on cumulative exposure) used in regulatory risk assessments. With newly proposed US EPA rule language, fuller consideration of alternative exposure and dose–response metrics will be supported. “Peak” exposure has not been consistently defined and rarely has been evaluated in epidemiologic studies of cancer risks. We recommend developing uniform definitions of “peak” exposure to facilitate fuller evaluation of dose response for environmental chemicals and cancer risks, especially where mechanistic understanding indicates that the dose response is unlikely linear and that short‐term high‐intensity exposures increase risk.     

Quantitative Risk Assessment for Bovine Spongiform Encephalopathy in Low- or Zero-Prevalence Countries: The Example of Norway

A predictive case-cohort model is applied to Norwegian data to analyze the interaction between challenge and stability factors for bovine spongiform encephalopathy (BSE) during the period 1980-2010. For each year, the BSE risk in cattle is estimated as the expected number of cases. The age distribution of expected cases as well as the relative impact of different challenges is estimated. The model consists of a simple, transparent, and practical deterministic spreadsheet calculation model, in which the following country-specific inputs are entered: (i) annual imports of live cattle and meat and bone meal, (ii) age distribution of native cattle, and (iii) estimated annual basic reproduction ratio (R(0)) for BSE. Results for Norway indicate that the highest risk of BSE cases was in 1989, when a total BSE risk of 0.13 cases per year was expected. After that date, the year-to-year decrease in risk ranged between 3% and 47%, except for a secondary peak in 1994 at 0.06 cases per year. The primary peak was almost entirely (99%) attributable to the importation of 11 cattle from the United Kingdom between 1982 and 1986. The secondary peak, in 1994, originated mainly from the recycling of the U.K. imported cattle (92%). In 2006, the remaining risk was 0.0003 cases per year, or 0.001 per million cows per year, with a maximal age-specific incidence of 0.03 cases per million per year in 10-year-old cattle. Only 15% of the cases were expected in imported cattle. The probability of having zero cases in Norway in 2006 was estimated to be 99.97%. The model and results are compared to previous risk assessments of Norway by the EU.     

Preventive Health Behavior and Adaptive Accuracy of Risk Perceptions

This study examined the relation between health behavior and risk perceptions in the context of an acute livestock epidemic. Participants in a longitudinal web-based survey ( N = 195) were asked to report their meat consumption and their perceived risk in relation to bovine spongiform encephalopathy (BSE) and other related livestock diseases. Cross-sectional analyses at both measurement points (T1 and T2) showed that participants with low levels of preventive nutrition (high meat consumption) felt more at risk for BSE-related diseases than those reporting comparable higher levels of preventive behavior (low meat consumption), indicating relative accuracy. These results suggest that people recognize when their behavior is risky. More importantly, perceived risk also showed adaptive accuracy from a change perspective: increases in preventive nutrition from T1 to T2 were significantly associated with decreases in perceived risk between T1 and T2. Possible foundations and implications of an adaptive accuracy of risk perceptions are discussed.     

Assessments of Direct Human Exposure—The Approach of EU Risk Assessments Compared to Scenario-Based Risk Assessment

The awareness of potential risks emerging from the use of chemicals in all parts of daily life has increased the need for risk assessments that are able to cover a high number of exposure situations and thereby ensure the safety of workers and consumers. In the European Union (EU), the practice of risk assessments for chemicals is laid down in a Technical Guidance Document; it is designed to consider environmental and human occupational and residential exposure. Almost 70 EU risk assessment reports (RARs) have been finalized for high-production-volume chemicals during the last decade. In the present study, we analyze the assessment of occupational and consumer exposure to trichloroethylene and phthalates presented in six EU RARs. Exposure scenarios in these six RARs were compared to scenarios used in applications of the scenario-based risk assessment approach to the same set of chemicals. We find that scenarios used in the selected EU RARs to represent typical exposure situations in occupational or private use of chemicals and products do not necessarily represent worst-case conditions. This can be due to the use of outdated information on technical equipment and conditions in workplaces or omission of pathways that can cause consumer exposure. Considering the need for exposure and risk assessments under the new chemicals legislation of the EU, we suggest that a transparent process of collecting data on exposure situations and of generating representative exposure scenarios is implemented to improve the accuracy of risk assessments. Also, the data sets used to assess human exposure should be harmonized, summarized in a transparent fashion, and made accessible for all risk assessors and the public.     

Linking Indoor Air and Pharmacokinetic Models to Assess Tetrachloroethylene Risk

Physiologically based pharmacokinetic (PBPK) models describing the uptake, metabolism, and excretion of xenobiotic compounds are now proposed for use in regulatory health-risk assessments. In this study we investigate the extent of PCE metabolism arising from domestic respiratory exposure to tetrachloroethylene (PCE) from ground water, as predicted using a PBPK model. Indoor exposure patterns we use as input to the PBPK model are realistic ones generated from a three-compartment model describing volatilization of PCE from domestic water into household air. Values we use for the metabolic parameters of the PBPK model are estimated from data on urinary metabolites in workers exposed to PCE. It is shown that for respiratory PCE exposure due to typical levels of PCE in ground water, use of time-weighted average air concentrations with a steady-state PBPK model yields estimates of total metabolized PCE similar to those obtained using completely dynamic modeling, despite considerable uncertainty in key exposure- and metabolic-model parameters. These findings suggest that, for PCE, risk estimation taking pharmacokinetics into account may be accomplished using a simple analytic approach.     

Reassessing Benzene Cancer Risks Using Internal Doses

Human cancer risks from benzene exposure have previously been estimated by regulatory agencies based primarily on epidemiological data, with supporting evidence provided by animal bioassay data. This paper reexamines the animal-based risk assessments for benzene using physiologically-based pharmacokinetic (PBPK) models of benzene metabolism in animals and humans. It demonstrates that internal doses (interpreted as total benzene metabolites formed) from oral gavage experiments in mice are well predicted by a PBPK model developed by Travis et al. Both the data and the model outputs can also be accurately described by the simple nonlinear regression model total metabolites = 76.4x/(80.75 + x), where x = administered dose in mg/kg/day. Thus, PBPK modeling validates the use of such nonlinear regression models, previously used by Bailer and Hoel. An important finding is that refitting the linearized multistage (LMS) model family to internal doses and observed responses changes the maximum-likelihood estimate (MLE) dose-response curve for mice from linear-quadratic to cubic, leading to low-dose risk estimates smaller than in previous risk assessments. This is consistent with the conclusion for mice from the Bailer and Hoel analysis. An innovation in this paper is estimation of internal doses for humans based on a PBPK model (and the regression model approximating it) rather than on interspecies dose conversions. Estimates of human risks at low doses are reduced by the use of internal dose estimates when the estimates are obtained from a PBPK model, in contrast to Bailer and Hoel's findings based on interspecies dose conversion. Sensitivity analyses and comparisons with epidemiological data and risk models suggest that our finding of a nonlinear MLE dose-response curve at low doses is robust to changes in assumptions and more consistent with epidemiological data than earlier risk models.     

The Impact of Social Amplification and Attenuation of Risk and the Public Reaction to Mad Cow Disease in Canada

Following the detection of bovine spongiform encephalopathy (BSE) in Canada, and subsequently in the United States, confidence in the safety of beef products remained high. Consumers actually increased their consumption of beef slightly after the news of an increased risk from mad cow disease, which has been interpreted as public support for beef farmers and confidence in government regulators. The Canadian public showed a markedly different reaction to the news of domestic BSE than the furious and panicked responses observed in the United Kingdom, Germany, and Japan. Using the social amplification of risk framework, we show that, while other countries displayed social amplification of risk, Canada experienced a social attenuation of risk. The attenuated reaction in Canada toward mad cow disease and increased human health risks from variant Creutzfeldt-Jakob disease (vCJD) was due to the social context at the time when BSE was discovered domestically. Mortality, morbidity, and psychosocial impacts resulting from other major events such as severe acute respiratory syndrome (SARS), West Nile virus (WNV), and the U.S.-Iraq war made the theoretical risks of BSE and vCJD a lower priority, reducing its concern as a risk issue.     

A Method for Estimating Lifetime Cancer Risks from Limited Epidemiologic Data

Partly because of the poor quality of exposure information on humans, most lifetime carcinogenic risk assessments have been based on animal data. There are, however, surrogate measures for exposure that have not been fully utilized. One of these is duration of exposure where data on mean exposure levels are available. A method is presented for the use of such data, and the method is illustrated by developing a risk assessment from the available epidemiologic literature on gasoline and kidney cancer. This risk assessment is fairly consistent across studies and close to a risk assessment based upon an experiment with rats. While there needs to be much improvement in the quality of environmental data available to epidemiologists, it is possible that a number of risk assessments can be made from existing epidemiologic data and efforts directed away from extrapolation from animal data.     

The Benefits of Probabilistic Exposure Assessment: Three Case Studies Involving Contaminated Air,Water, and Soil1

Probabilistic risk assessments are enjoying increasing popularity as a tool to characterize the health hazards associated with exposure to chemicals in the environment. Because probabilistic analyses provide much more information to the risk manager than standard “point” risk estimates, this approach has generally been heralded as one which could significantly improve the conduct of health risk assessments. The primary obstacles to replacing point estimates with probabilistic techniques include a general lack of familiarity with the approach and a lack of regulatory policy and guidance. This paper discusses some of the advantages and disadvantages of the point estimate vs. probabilistic approach. Three case studies are presented which contrast and compare the results of each. The first addresses the risks associated with household exposure to volatile chemicals in tapwater. The second evaluates airborne dioxin emissions which can enter the food-chain. The third illustrates how to derive health-based cleanup levels for dioxin in soil. It is shown that, based on the results of Monte Carlo analyses of probability density functions (PDFs), the point estimate approach required by most regulatory agencies will nearly always overpredict the risk for the 95^th percentile person by a factor of up to 5. When the assessment requires consideration of 10 or more exposure variables, the point estimate approach will often predict risks representative of the 99.9^th percentile person rather than the 50^th or 95^th percentile person. This paper recommends a number of data distributions for various exposure variables that we believe are now sufficiently well understood to be used with confidence in most exposure assessments. A list of exposure variables that may require additional research before adequate data distributions can be developed are also discussed.     

A Conceptual Framework to Assess the Risks of Human Disease Following Exposure to Pathogens

Currently, risk assessments of the potential human health effects associated with exposure to pathogens are utilizing the conceptual framework that was developed to assess risks associated with chemical exposures. However, the applicability of the chemical framework is problematic due to many issues that are unique to assessing risks associated with pathogens. These include, but are not limited to, an assessment of pathogen/host interactions, consideration of secondary spread, consideration of short- and long-term immunity, and an assessment of conditions that allow the microorganism to propagate. To address this concern, a working group was convened to develop a conceptual framework to assess the risks of human disease associated with exposure to pathogenic microorganisms. The framework that was developed consists of three phases: problem formulation, analysis (which includes characterization of exposure and human health effects), and risk characterization. The framework emphasizes the dynamic and iterative nature of the risk assessment process, and allows wide latitude for planning and conducting risk assessments in diverse situations, each based on the common principles discussed in the framework.     

Final report of the Color Additive Scientific Review Panel

The Color Additives Scientific Review Panel considered whether there was information sufficient to perform a carcinogenic risk assessment on the colors D&C Red No. 19 (R-19), D&C Red No. 37 (R-37), D&C Orange No. 17 (O-17), D&C Red No. 9 (R-9), D&C Red No. 8 (R-8) and FD&C Red No. 3 (R-3) and to evaluate the assessments sent to FDA as part of the petitions for use of the colors for drug and external uses by the Cosmetic, Toiletry and Fragrance Association (CTFA). There is a lack of human data concerning the colors for making a human health assessment, so the assessments are based upon the extrapolation of animal data. The risk assessments are determined for exposure to single chemicals. Excluded from consideration are possible effects from exposure to multiple chemicals, such as co-carcinogenesis, promotion, synergism, antagonism, etc. In the light of recent efforts in establishing a consensus in risk assessment, the Panel has determined that the CTFA assessments for R-10, O-17, and R-9 are consistent with present acceptable usages, although it questions some of the assumptions used in the assessments. The Panel identified a number of general assumptions made, and discusses their validity, their impact on total uncertainty, and the potential options to address the gaps in understanding that necessitate the assumption. The Panel also derived revised risk estimates using more "reasonable" assumptions than "worst-case" situations, for 90th percentile and average exposure. For those assumptions that are easily quantifiable, the Panel's estimates are less than an order of magnitude lower than the CTFA risk estimates, indicating that the underestimates and overestimates of the CTFA risk estimates tend to balance each other. The impact of most of the assumptions is not quantifiable. The assessment for R-3 is complicated by the fact that there is no good skin penetrance study for this color. It was assumed that the penetrance is similar to that of another water-soluble xanthene color, R-19. It is expected that the absorption of the color is not likely to exceed that of the smaller molecule, R-19. Therefore, the risk estimates are similar to the CTFA estimates, but with different reasoning. The estimates for R-8 and R-37 are different from the others in that there is a lack of any exposure or toxicological information on these colors.(ABSTRACT TRUNCATED AT 400 WORDS)     

Mlonte Carlo Techniques for Quantitative Uncertainty Analysis in Public Health Risk Assessments

Most public health risk assessments assume and combine a series of average, conservative, and worst-case values to derive a conservative point estimate of risk. This procedure has major limitations. This paper demonstrates a new methodology for extended uncertainty analyses in public health risk assessments using Monte Carlo techniques. The extended method begins as do some conventional methods--with the preparation of a spreadsheet to estimate exposure and risk. This method, however, continues by modeling key inputs as random variables described by probability density functions (PDFs). Overall, the technique provides a quantitative way to estimate the probability distributions for exposure and health risks within the validity of the model used. As an example, this paper presents a simplified case study for children playing in soils contaminated with benzene and benzo(a)pyrene (BaP).     

Generating Probabilistic Spatially-Explicit Individual and Population Exposure Estimates for Ecological Risk Assessments

Exposure to chemical contaminants in various media must be estimated when performing ecological risk assessments. Exposure estimates are often based on the 95th-percentile upper confidence limit on the mean concentration of all samples, calculated without regard to critical ecological and spatial information about the relative relationship of receptors, their habitats, and contaminants. This practice produces exposure estimates that are potentially unrepresentative of the ecology of the receptor. This article proposes a habitat area and quality-conditioned exposure estimator, E[HQ], that requires consideration of these relationships. It describes a spatially explicit ecological exposure model to facilitate calculation of E[HQ]. The model provides (1) a flexible platform for investigating the effect of changes in habitat area, habitat quality, foraging area, and population size on exposure estimates, and (2) a tool for calculating E[HQ] for use in actual risk assessments. The inner loop of a Visual Basic program randomly walks a receptor over a multicelled landscape--each cell of which contains values for cell area, habitat area, habitat quality, and concentration--accumulating an exposure estimate until the total area foraged is less than or equal to a given foraging area. An outer loop then steps through foraging areas of increasing size. This program is iterated by Monte Carlo software, with the number of iterations representing the population size. Results indicate that (1) any single estimator may over- or underestimate exposure, depending on foraging strategy and spatial relationships of habitat and contamination, and (2) changes in exposure estimates in response to changes in foraging and habitat area are not linear.     

Estimating Benchmark Exposure for Air Particulate Matter Using Latent Class Models

We performed benchmark exposure (BME) calculations for particulate matter when multiple dichotomous outcome variables are involved using latent class modeling techniques and generated separate results for both the extra risk and additional risk. The use of latent class models in this study is advantageous because it combined several outcomes into just two classes (namely, a high‐risk class and a low‐risk class) and compared these two classes to obtain the BME levels. This novel approach addresses a key problem in risk estimation—namely, the multiple comparisons problem, where separate regression models are fitted for each outcome variable and the reference exposure will rely on the results of the best‐fitting model. Because of the complex nature of the estimation process, the bootstrap approach was used to estimate the reference exposure level, thereby reducing uncertainty in the obtained values. The methodology developed in this article was applied to environmental data by identifying unmeasured class membership (e.g., morbidity vs. no morbidity class) among infants in utero using observed characteristics that included low birth weight, preterm birth, and small for gestational age.     

Improving Causal Inferences in Risk Analysis

Recent headlines and scientific articles projecting significant human health benefits from changes in exposures too often depend on unvalidated subjective expert judgments and modeling assumptions, especially about the causal interpretation of statistical associations. Some of these assessments are demonstrably biased toward false positives and inflated effects estimates. More objective, data‐driven methods of causal analysis are available to risk analysts. These can help to reduce bias and increase the credibility and realism of health effects risk assessments and causal claims. For example, quasi‐experimental designs and analysis allow alternative (noncausal) explanations for associations to be tested, and refuted if appropriate. Panel data studies examine empirical relations between changes in hypothesized causes and effects. Intervention and change‐point analyses identify effects (e.g., significant changes in health effects time series) and estimate their sizes. Granger causality tests, conditional independence tests, and counterfactual causality models test whether a hypothesized cause helps to predict its presumed effects, and quantify exposure‐specific contributions to response rates in differently exposed groups, even in the presence of confounders. Causal graph models let causal mechanistic hypotheses be tested and refined using biomarker data. These methods can potentially revolutionize the study of exposure‐induced health effects, helping to overcome pervasive false‐positive biases and move the health risk assessment scientific community toward more accurate assessments of the impacts of exposures and interventions on public health.