A Probabilistic Arsenic Exposure Assessment for Children Who Contact Chromated Copper Arsenate (CCA)-Treated Playsets and Decks, Part 2: Sensitivity and Uncertainty Analyses

A probabilistic model (SHEDS-Wood) was developed to examine children's exposure and dose to chromated copper arsenate (CCA)-treated wood, as described in Part 1 of this two-part article. This Part 2 article discusses sensitivity and uncertainty analyses conducted to assess the key model inputs and areas of needed research for children's exposure to CCA-treated playsets and decks. The following types of analyses were conducted: (1) sensitivity analyses using a percentile scaling approach and multiple stepwise regression; and (2) uncertainty analyses using the bootstrap and two-stage Monte Carlo techniques. The five most important variables, based on both sensitivity and uncertainty analyses, were: wood surface residue-to-skin transfer efficiency; wood surface residue levels; fraction of hand surface area mouthed per mouthing event; average fraction of nonresidential outdoor time a child plays on/around CCA-treated public playsets; and frequency of hand washing. In general, there was a factor of 8 for the 5th and 95th percentiles and a factor of 4 for the 50th percentile in the uncertainty of predicted population dose estimates due to parameter uncertainty. Data were available for most of the key model inputs identified with sensitivity and uncertainty analyses; however, there were few or no data for some key inputs. To evaluate and improve the accuracy of model results, future measurement studies should obtain longitudinal time-activity diary information on children, spatial and temporal measurements of residue and soil concentrations on or near CCA-treated playsets and decks, and key exposure factors. Future studies should also address other sources of uncertainty in addition to parameter uncertainty, such as scenario and model uncertainty.     

Use of Monte Carlo Simulation for Human Exposure Assessment at a Superfund Site

This work presents a comparison of probabilistic and deterministic health risk estimates based on data from an industrial site in the northeastern United States. The risk assessment considered exposures to volatile solvents by drinking water ingestion and showering. Probability densities used as inputs included concentrations, contact rates, and exposure frequencies; dose-response inputs were single values. Deterministic risk estimates were calculated by the "reasonable maximum exposure" (RME) approach recommended by the EPA Superfund program. The RME non-carcinogenic risk fell between the 90th and the 95th percentile of the probability density; the RME cancer risk fell between the 95th percentile and the maximum. These results suggest that in this case (1) EPA's deterministic RME risk was reasonably protective, (2) results of probabilistic and deterministic calculations were consistent, and (3) commercially available software Monte Carlo software effectively provided multiple risk estimates recommended by recent EPA guidance.     

Benzene Inhalation by Parts Washers: New Estimates Based on Measures of Occupational Exposure to Solvent Coaromatics

Questions persist regarding assessment of workers’ exposures to products containing low levels of benzene, such as mineral spirit solvent (MSS). This study summarizes previously unpublished data for parts‐washing activities, and evaluates potential daily and lifetime cumulative benzene exposures incurred by workers who used historical and current formulations of a recycled mineral spirits solvent in manual parts washers. Measured benzene concentrations in historical samples from parts‐washing operations were frequently below analytical detection limits. To better assess benzene exposure among these workers, air‐to‐solvent concentration ratios measured for toluene, ethylbenzene, and xylenes (TEX) were used to predict those for benzene based on a statistical model, conditional on physical‐chemical theory supported by new thermodynamic calculations of TEX and benzene activity coefficients in a modeled MSS‐type solvent. Using probabilistic methods, the distributions of benzene concentrations were then combined with distributions of other exposure parameters to estimate eight‐hour time‐weighted average (TWA) exposure concentration distributions and corresponding daily respiratory dose distributions for workers using these solvents in parts washers. The estimated 50th (95th) percentile of the daily respiratory dose and corresponding eight‐hour TWA air concentration for workers performing parts washing are 0.079 (0.77) mg and 0.0030 (0.028) parts per million by volume (ppm) for historical solvent, and 0.020 (0.20) mg and 0.00078 (0.0075) ppm for current solvent, respectively. Both 95th percentile eight‐hour TWA respiratory exposure estimates for solvent formulations are less than 10% of the current Occupational Safety and Health Administration permissible exposure limit of 1.0 ppm for benzene.     

Bounding Analysis of Drinking Water Health Risks from a Spill of Hydraulic Fracturing Flowback Water

A bounding risk assessment is presented that evaluates possible human health risk from a hypothetical scenario involving a 10,000‐gallon release of flowback water from horizontal fracturing of Marcellus Shale. The water is assumed to be spilled on the ground, infiltrates into groundwater that is a source of drinking water, and an adult and child located downgradient drink the groundwater. Key uncertainties in estimating risk are given explicit quantitative treatment using Monte Carlo analysis. Chemicals that contribute significantly to estimated health risks are identified, as are key uncertainties and variables to which risk estimates are sensitive. The results show that hypothetical exposure via drinking water impacted by chemicals in Marcellus Shale flowback water, assumed to be spilled onto the ground surface, results in predicted bounds between 10⁻¹⁰ and 10⁻⁶ (for both adult and child receptors) for excess lifetime cancer risk. Cumulative hazard indices (HI_CUMULATIVE) resulting from these hypothetical exposures have predicted bounds (5th to 95th percentile) between 0.02 and 35 for assumed adult receptors and 0.1 and 146 for assumed child receptors. Predicted health risks are dominated by noncancer endpoints related to ingestion of barium and lithium in impacted groundwater. Hazard indices above unity are largely related to exposure to lithium. Salinity taste thresholds are likely to be exceeded before drinking water exposures result in adverse health effects. The findings provide focus for policy discussions concerning flowback water risk management. They also indicate ways to improve the ability to estimate health risks from drinking water impacted by a flowback water spill (i.e., reducing uncertainty).     

Measures of Compounding Conservatism in Probabilistic Risk Assessment

Concern about the degree of uncertainty and potential conservatism in deterministic point estimates of risk has prompted researchers to turn increasingly to probabilistic methods for risk assessment. With Monte Carlo simulation techniques, distributions of risk reflecting uncertainty and/or variability are generated as an alternative. In this paper the compounding of conservatism⁽¹⁾ between the level associated with point estimate inputs selected from probability distributions and the level associated with the deterministic value of risk calculated using these inputs is explored. Two measures of compounded conservatism are compared and contrasted. The first measure considered, F , is defined as the ratio of the risk value, R _d, calculated deterministically as a function of n inputs each at the j th percentile of its probability distribution, and the risk value, R _j that falls at the j th percentile of the simulated risk distribution (i.e., F=R_d/R_j). The percentile of the simulated risk distribution which corresponds to the deterministic value, R_d , serves as a second measure of compounded conservatism. Analytical results for simple products of lognormal distributions are presented. In addition, a numerical treatment of several complex cases is presented using five simulation analyses from the literature to illustrate. Overall, there are cases in which conservatism compounds dramatically for deterministic point estimates of risk constructed from upper percentiles of input parameters, as well as those for which the effect is less notable. The analytical and numerical techniques discussed are intended to help analysts explore the factors that influence the magnitude of compounding conservatism in specific cases.     

Drinking Water as a Proportion of Total Human Exposure to Volatile N‐Nitrosamines

Some volatile N‐nitrosamines, primarily N‐nitrosodimethylamine (NDMA), are recognized as products of drinking water treatment at ng/L levels and as known carcinogens. The U.S. EPA has identified the N‐nitrosamines as contaminants being considered for regulation as a group under the Safe Drinking Water Act. Nitrosamines are common dietary components, and a major database (over 18,000 drinking water samples) has recently been created under the Unregulated Contaminant Monitoring Rule. A Monte Carlo modeling analysis in 2007 found that drinking water contributed less than 2.8% of ingested NDMA and less than 0.02% of total NDMA exposure when estimated endogenous formation was considered. Our analysis, based upon human blood concentrations, indicates that endogenous NDMA production is larger than expected. The blood‐based estimates are within the range that would be calculated from estimates based on daily urinary NDMA excretion and an estimate based on methylated guanine in DNA of lymphocytes from human volunteers. Our analysis of ingested NDMA from food and water based on Monte Carlo modeling with more complete data input shows that drinking water contributes a mean proportion of the lifetime average daily NDMA dose ranging from between 0.0002% and 0.001% for surface water systems using free chlorine or between 0.001% and 0.01% for surface water systems using chloramines. The proportions of average daily dose are higher for infants (zero to six months) than other age cohorts, with the highest mean up to 0.09% (upper 95th percentile of 0.3%).     

Modeled Estimates of Soil and Dust Ingestion Rates for Children

Daily soil/dust ingestion rates typically used in exposure and risk assessments are based on tracer element studies, which have a number of limitations and do not separate contributions from soil and dust. This article presents an alternate approach of modeling soil and dust ingestion via hand and object mouthing of children, using EPA's SHEDS model. Results for children 3 to <6 years old show that mean and 95th percentile total ingestion of soil and dust values are 68 and 224 mg/day, respectively; mean from soil ingestion, hand‐to‐mouth dust ingestion, and object‐to‐mouth dust ingestion are 41 mg/day, 20 mg/day, and 7 mg/day, respectively. In general, hand‐to‐mouth soil ingestion was the most important pathway, followed by hand‐to‐mouth dust ingestion, then object‐to‐mouth dust ingestion. The variability results are most sensitive to inputs on surface loadings, soil‐skin adherence, hand mouthing frequency, and hand washing frequency. The predicted total soil and dust ingestion fits a lognormal distribution with geometric mean = 35.7 and geometric standard deviation = 3.3. There are two uncertainty distributions, one below the 20th percentile and the other above. Modeled uncertainties ranged within a factor of 3–30. Mean modeled estimates for soil and dust ingestion are consistent with past information but lower than the central values recommended in the 2008 EPA Child‐Specific Exposure Factors Handbook. This new modeling approach, which predicts soil and dust ingestion by pathway, source type, population group, geographic location, and other factors, offers a better characterization of exposures relevant to health risk assessments as compared to using a single value.     

TCDD Exposure-Response Analysis and Risk Assessment

We examined the relation between cancer mortality and time-dependent cumulative exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) estimated from a concentration- and age-dependent kinetic model of elimination, and we estimated incremental cancer risks at age 75. Data from the National Institute for Occupational Safety and Health study of 3,538 workers with occupational exposure to TCDD were analyzed using standardized mortality ratios and Cox regression procedures. Analyses adjusted for potential confounding by age, year of birth, and race and considered exposure lag periods of 0, 10, or 15 years. Other potential confounders including smoking and other occupational exposures were evaluated indirectly. To explore the influence of extreme values of cumulative TCDD ppt-years, we restricted the analysis to observations with exposure below the 95th percentile or used logarithmic (ln) transformed exposure values. We applied penalized smoothing splines to examine variation in the exposure-response relation across the exposure range. TCDD was not statistically significantly associated with cancer mortality using the full data set, regardless of the lag period. When we restricted the analysis to observations with exposure below the 95th percentile, TCDD was associated positively with cancer mortality, particularly when a 15-year lag was applied (untransformed exposure data: regression coefficient , standard error (s.e.) = 1.4 x 10(-6), p < 0.05; ln-transformed exposure data: , s.e. = 2.9 x 10(-2), p < 0.05). The estimated incremental lifetime risk of mortality at age 75 from all cancers was about 6 to more than 10 times lower than previous estimates derived from this cohort using exposure models that did not consider the age and concentration dependence of TCDD elimination.     

Methods for Characterizing Variability and Uncertainty: Comparison of Bootstrap Simulation and Likelihood-Based Approaches

Variability arises due to differences in the value of a quantity among different members of a population. Uncertainty arises due to lack of knowledge regarding the true value of a quantity for a given member of a population. We describe and evaluate two methods for quantifying both variability and uncertainty. These methods, bootstrap simulation and a likelihood-based method, are applied to three datasets. The datasets include a synthetic sample of 19 values from a Lognormal distribution, a sample of nine values obtained from measurements of the PCB concentration in leafy produce, and a sample of five values for the partitioning of chromium in the flue gas desulfurization system of coal-fired power plants. For each of these datasets, we employ the two methods to characterize uncertainty in the arithmetic mean and standard deviation, cumulative distribution functions based upon fitted parametric distributions, the 95th percentile of variability, and the 63rd percentile of uncertainty for the 81st percentile of variability. The latter is intended to show that it is possible to describe any point within the uncertain frequency distribution by specifying an uncertainty percentile and a variability percentile. Using the bootstrap method, we compare results based upon use of the method of matching moments and the method of maximum likelihood for fitting distributions to data. Our results indicate that with only 5–19 data points as in the datasets we have evaluated, there is substantial uncertainty based upon random sampling error. Both the boostrap and likelihood-based approaches yield comparable uncertainty estimates in most cases.     

On the Effect of Probability Distributions of Input Variables in Public Health Risk Assessment

A central part of probabilistic public health risk assessment is the selection of probability distributions for the uncertain input variables. In this paper, we apply the first-order reliability method (FORM)^(1–3) as a probabilistic tool to assess the effect of probability distributions of the input random variables on the probability that risk exceeds a threshold level (termed the probability of failure) and on the relevant probabilistic sensitivities. The analysis was applied to a case study given by Thompson et al. ⁽⁴⁾ on cancer risk caused by the ingestion of benzene contaminated soil. Normal, lognormal, and uniform distributions were used in the analysis. The results show that the selection of a probability distribution function for the uncertain variables in this case study had a moderate impact on the probability that values would fall above a given threshold risk when the threshold risk is at the 50th percentile of the original distribution given by Thompson et al. ⁽⁴⁾ The impact was much greater when the threshold risk level was at the 95th percentile. The impact on uncertainty sensitivity, however, showed a reversed trend, where the impact was more appreciable for the 50th percentile of the original distribution of risk given by Thompson et al. ⁴ than for the 95th percentile. Nevertheless, the choice of distribution shape did not alter the order of probabilistic sensitivity of the basic uncertain variables.     

Risk Assessment of Laboratory Rats and Mice Chronically Exposed to Formaldehyde Vapors

Experimental data from the Chemical Industry Institute of Toxicology (CIIT) are used to estimate the risk of squamous cell carcinoma of the nasal cavity in Fischer 344 (F344) rats over a range of ambient air concentrations of formaldehyde that includes current exposure guidelines for the workplace and home. These values are presented as a best estimate envelope obtained from five mathematical dose-response formulation. The response of Sprague-Dawley (SD) rats dosed at 15 ppm in a separate study at New York University is consistent with the predicted lifetime response for F344 rats at a slightly lower concentration (13-14 ppm). A dose-related mortality effect beyond what is attributable to the occurrence of nasal carcinomas is found in F344 rats at all CIIT exposure levels (2, 6, and 15 ppm). There is no evidence of a mortality effect in B6C3F1 mice of the CIIT study, and data for SD rats of the NYU experiment are inconclusive. In the CIIT study, rats exposed to 15 ppm exhibited a high incidence of nasal cavity squamous cell carcinomas and polypoid adenomas. Polypoid adenomas were also observed with increased incidences at 2 ppm and 6 ppm. Statistical comparisons with matched controls, and the low historical rate of spontaneous occurrence both suggest that polypoid adenomas may be a risk to F344 rats at exposure levels below the current Occupational Safety and Health Administration (OSHA) standard of 3 ppm. Squamous cell carcinomas were observed in two mice exposed to 15 ppm. This finding may be biologically significant since this tumor is rare and has not been previously reported in 4932 untreated B6C3F1 mice from recent National Toxicology Program (NTP) feeding studies.     

The Maximally Exposed Individual—Comparison of Maximum Likelihood Estimation of High Quantiles to an Extreme Value Estimate

The dose to human and nonhuman individuals inflicted by anthropogenic radiation is an important issue in international and domestic policy. The current paradigm for nonhuman populations asserts that if the dose to the maximally exposed individuals in a population is below a certain criterion (e.g., <10 mGy d(-1)) then the population is adequately protected. Currently, there is no consensus in the regulatory community as to the best statistical approach. Statistics, currently considered, include the maximum likelihood estimator for the 95th percentile of the sample mean and the sample maximum. Recently, the investigators have proposed the use of the maximum likelihood estimate of a very high quantile as an estimate of dose to the maximally exposed individual. In this study, we compare all of the above-mentioned statistics to an estimate based on extreme value theory. To determine and compare the bias and variance of these statistics, we use Monte Carlo simulation techniques, in a procedure similar to a parametric bootstrap. Our results show that a statistic based on extreme value theory has the least bias of those considered here, but requires reliable estimates of the population size. We recommend establishing the criterion based on what would be considered acceptable if only a small percentage of the population exceeded the limit, and hence recommend using the maximum likelihood estimator of a high quantile in the case that reliable estimates of the population size are not available.     

Combining Food Frequency and Survey Data to Quantify Long-Term Dietary Exposure: A Methyl Mercury Case Study

Twenty-four-hour recall data from the Continuing Survey of Food Intake by Individuals (CSFII) are frequently used to estimate dietary exposure for risk assessment. Food frequency questionnaires are traditional instruments of epidemiological research; however, their application in dietary exposure and risk assessment has been limited. This article presents a probabilistic method of bridging the National Health and Nutrition Examination Survey (NHANES) food frequency and the CSFII data to estimate longitudinal (usual) intake, using a case study of seafood mercury exposures for two population subgroups (females 16 to 49 years and children 1 to 5 years). Two hundred forty-nine CSFII food codes were mapped into 28 NHANES fish/shellfish categories. FDA and state/local seafood mercury data were used. A uniform distribution with minimum and maximum blood-diet ratios of 0.66 to 1.07 was assumed. A probabilistic assessment was conducted to estimate distributions of individual 30-day average daily fish/shellfish intakes, methyl mercury exposure, and blood levels. The upper percentile estimates of fish and shellfish intakes based on the 30-day daily averages were lower than those based on two- and three-day daily averages. These results support previous findings that distributions of "usual" intakes based on a small number of consumption days provide overestimates in the upper percentiles. About 10% of the females (16 to 49 years) and children (1 to 5 years) may be exposed to mercury levels above the EPA's RfD. The predicted 75th and 90th percentile blood mercury levels for the females in the 16-to-49-year group were similar to those reported by NHANES. The predicted 90th percentile blood mercury levels for children in the 1-to-5-year subgroup was similar to NHANES and the 75th percentile estimates were slightly above the NHANES.     

Development of a Standard Soil-to-Skin Adherence Probability Density Function for Use in Monte Carlo Analyses of Dermal Exposure

It has recently been suggested that "standard" data distributions for key exposure variables should be developed wherever appropriate for use in probabilistic or "Monte Carlo" exposure analyses. Soil-on-skin adherence estimates represent an ideal candidate for development of a standard data distribution: There are several readily available studies which offer a consistent pattern of reported results, and more importantly, soil adherence to skin is likely to vary little from site-to-site. In this paper, we thoroughly review each of the published soil adherence studies with respect to study design, sampling, and analytical methods, and level of confidence in the reported results. Based on these studies, probability density functions (PDF) of soil adherence values were examined for different age groups and different sampling techniques. The soil adherence PDF developed from adult data was found to resemble closely the soil adherence PDF based on child data in terms of both central tendency (mean = 0.49 and 0.63 mg-soil/cm²-skin, respectively) and 95th percentile values (1.6 and 2.4 mg-soil/cm²-skin, respectively). Accordingly, a single, "standard" PDF is presented based on all data collected for all age groups. This standard PDF is lognormally distributed; the arithmetic mean and standard deviation are 0.52 ± 0.9 mg-soil/cm²-skin. Since our review of the literature indicates that soil adherence under environmental conditions will be minimally influenced by age, sex, soil type, or particle size, this PDF should be considered applicable to all settings. The 50th and 95th percentile values of the standard PDF (0.25 and 1.7 mg-soil/cm²-skin, respectively) are very similar to recent U.S. EPA estimates of "average" and "upper-bound" soil adherence (0.2 and 1.0 mg-soil/cm²-skin, respectively).     

Ethnic Differences in Risk from Mercury among Savannah River Fishermen

Fishing plays an important role in people's lives and contaminant levels in fish are a public health concern. Many states have issued consumption advisories; South Carolina and Georgia have issued them for the Savannah River based on mercury and radionuclide levels. This study examined ethnic differences in risk from mercury exposure among people consuming fish from the Savannah River, based on site-specific consumption patterns and analysis of mercury in fish. Among fish, there were significant interspecies differences in mercury levels, and there were ethnic differences in consumption patterns. Two methods of examining risk are presented: (1) Hazard Index (HI), and (2) estimates of how much and how often people of different body mass can consume different species of fish. Blacks consumed more fish and had higher HIs than Whites. Even at the median consumption, the HI for Blacks exceeded 1.0 for bass and bowfin, and, at the 75th percentile of consumption, the HI exceeded 1.0 for almost all species. At the White male median consumption, noHI exceeded 1, but for the 95th percentile consumer, the HI exceeded 1.0 almost regardless of which species were eaten. Although females consumed about two thirds the quantity of males, HIs exceeded 1 for most Black females and for White females at or above the 75th percentile of consumption. Thus, close to half of the Black fishermen were eating enough Savannah River fish to exceed HI = 1. Caution must be used in evaluating an HI because the RfDs were developed to protect the most vulnerable individuals. The percentage of each fish species tested that exceeded the maximum permitted limits of mercury in fish was also examined. Over 80% of bowfin, 38% of bass, and 21% of pickerel sampled exceeded 0.5 ppm. The risk methodology is applicable anywhere that comparable data can be obtained. The risk estimates are representative for fishermen along the Savannah River, and are not necessarily for the general populations.     

Stochastic Estimates of Exposure and Cancer Risk from Carbon Tetrachloride Released to the Air from the Rocky Flats Plant

Carbon tetrachloride is a degreasing agent that was used at the Rocky Flats Plant (RFP) in Colorado to clean product components and equipment. The chemical is considered a volatile organic compound and a probable human carcinogen. During the time the plant operated (1953-1989), most of the carbon tetrachloride was released to the atmosphere through building exhaust ducts. A smaller amount was released to the air via evaporation from open-air burn pits and ground-surface discharge points. Airborne releases from the plant were conservatively estimated to be equivalent to the amount of carbon tetrachloride consumed annually by the plant, which was estimated to be between 3.6 and 180 Mg per year. This assumption was supported by calculations that showed that most of the carbon tetrachloride discharged to the ground surface would subsequently be released to the atmosphere. Atmospheric transport of carbon tetrachloride from the plant to the surrounding community was estimated using a Gaussian Puff dispersion model (RATCHET). Time-integrated concentrations were estimated for nine hypothetical but realistic exposure scenarios that considered variation in lifestyle, location, age, and gender. Uncertainty distributions were developed for cancer slope factors and atmospheric dispersion factors. These uncertainties were propagated through to the final risk estimate using Monte Carlo techniques. The geometric mean risk estimates varied from 5.2 x 10(-6) for a hypothetical rancher or laborer working near the RFP to 3.4 x 10(-9) for an infant scenario. The distribution of incremental lifetime cancer incidence risk for the hypothetical rancher was between 1.3 x 10(-6) (5% value) and 2.1 x 10(-5) (95% value). These estimates are similar to or exceed estimated cancer risks posed by releases of radionuclides from the site.     

Potential Exposure and Cancer Risk from Formaldehyde Emissions from Installed Chinese Manufactured Laminate Flooring

Lumber Liquidators (LL) Chinese‐manufactured laminate flooring (CLF) has been installed in >400,000 U.S. homes over the last decade. To characterize potential associated formaldehyde exposures and cancer risks, chamber emissions data were collected from 399 new LL CLF, and from LL CLF installed in 899 homes in which measured aggregate indoor formaldehyde concentrations exceeded 100 μg/m³ from a total of 17,867 homes screened. Data from both sources were combined to characterize LL CLF flooring‐associated formaldehyde emissions from new boards and installed boards. New flooring had an average (±SD ) emission rate of 61.3 ± 52.1 μg/m²‐hour; >one‐year installed boards had ∼threefold lower emission rates. Estimated emission rates for the 899 homes and corresponding data from questionnaires were used as inputs to a single‐compartment, steady‐state mass‐balance model to estimate corresponding residence‐specific TWA formaldehyde concentrations and potential resident exposures. Only ∼0.7% of those homes had estimated acute formaldehyde concentrations >100 μg/m³ immediately after LL CLF installation. The TWA daily formaldehyde inhalation exposure within the 899 homes was estimated to be 17 μg/day using California Proposition 65 default methods to extrapolate cancer risk (below the regulation “no significant risk level” of 40 μg/day). Using a U.S. Environmental Protection Agency linear cancer risk model, 50th and 95th percentile values of expected lifetime cancer risk for residents of these homes were estimated to be 0.33 and 1.2 per 100,000 exposed, respectively. Based on more recent data and verified nonlinear cancer risk assessment models, LL CLF formaldehyde emissions pose virtually no cancer risk to affected consumers.     

Effects of Uncertainties on Exposure Estimates to Methylmercury: A Monte Carlo Analysis of Exposure Biomarkers versus Dietary Recall Estimation

This article presents a general model for estimating population heterogeneity and "lack of knowledge" uncertainty in methylmercury (MeHg) exposure assessments using two-dimensional Monte Carlo analysis. Using data from fish-consuming populations in Bangladesh, Brazil, Sweden, and the United Kingdom, predictive model estimates of dietary MeHg exposures were compared against those derived from biomarkers (i.e., [Hg]hair and [Hg]blood). By disaggregating parameter uncertainty into components (i.e., population heterogeneity, measurement error, recall error, and sampling error) estimates were obtained of the contribution of each component to the overall uncertainty. Steady-state diet:hair and diet:blood MeHg exposure ratios were estimated for each population and were used to develop distributions useful for conducting biomarker-based probabilistic assessments of MeHg exposure. The 5th and 95th percentile modeled MeHg exposure estimates around mean population exposure from each of the four study populations are presented to demonstrate lack of knowledge uncertainty about a best estimate for a true mean. Results from a U.K. study population showed that a predictive dietary model resulted in a 74% lower lack of knowledge uncertainty around a central mean estimate relative to a hair biomarker model, and also in a 31% lower lack of knowledge uncertainty around central mean estimate relative to a blood biomarker model. Similar results were obtained for the Brazil and Bangladesh populations. Such analyses, used here to evaluate alternative models of dietary MeHg exposure, can be used to refine exposure instruments, improve information used in site management and remediation decision making, and identify sources of uncertainty in risk estimates.     

Quantitative Risk Assessment of Rabies Entering Great Britain from North America via Cats and Dogs

Great Britain has been rabies-free since 1922, which is often considered to be in part due to the strict laws requiring that imported cats and dogs be vaccinated and quarantined for 6 months immediately on entry into the country. Except for two isolated incidents, this quarantine policy has contributed to ensuring that Great Britain has remained free of rabies. In 2000, amendments to the UK quarantine laws were made and the Pet Travel Scheme (PETS) was launched for companion animals traveling from European Union countries and rabies-free islands. Since its introduction, it has been proposed that other countries including North America should be included within the UK scheme. A quantitative risk assessment was developed to assist in the policy decision to amend the long-standing quarantine laws for dogs and cats from North America. It was determined that the risk of rabies entry is very low and is dependent on the level of compliance (i.e., legally conforming to all of the required regulations) with PETS and the number of pets imported. Assuming 100% compliance with PETS and the current level of importation of cats and dogs from North America, the annual probability of importing rabies is lower for animals traveling via PETS (7.22 x 10(-6), 95th percentile) than quarantine (1.01 x 10(-5), 95th percentile). These results, and other scientific evidence, directly informed the decision to expand the PETS scheme to North America as of December 2002.     

Topics in Microbial Risk Assessment: Dynamic Flow Tree Process

Microbial risk assessment is emerging as a new discipline in risk assessment. A systematic approach to microbial risk assessment is presented that employs data analysis for developing parsimonious models and accounts formally for the variability and uncertainty of model inputs using analysis of variance and Monte Carlo simulation. The purpose of the paper is to raise and examine issues in conducting microbial risk assessments. The enteric pathogen Escherichia coli O157:H7 was selected as an example for this study due to its significance to public health. The framework for our work is consistent with the risk assessment components described by the National Research Council in 1983 (hazard identification; exposure assessment; dose-response assessment; and risk characterization). Exposure assessment focuses on hamburgers, cooked a range of temperatures from rare to well done, the latter typical for fast food restaurants. Features of the model include predictive microbiology components that account for random stochastic growth and death of organisms in hamburger. For dose-response modeling, Shigella data from human feeding studies were used as a surrogate for E. coli O157:H7. Risks were calculated using a threshold model and an alternative nonthreshold model. The 95% probability intervals for risk of illness for product cooked to a given internal temperature spanned five orders of magnitude for these models. The existence of even a small threshold has a dramatic impact on the estimated risk.