Using Probabilistic Modeling to Evaluate Human Exposure to Organotin in Drinking Water Transported by Polyvinyl Chloride Pipe

The leaching of organotin (OT) heat stabilizers from polyvinyl chloride (PVC) pipes used in residential drinking water systems may affect the quality of drinking water. These OTs, principally mono- and di-substituted species of butyltins and methyltins, are a potential health concern because they belong to a broad class of compounds that may be immune, nervous, and reproductive system toxicants. In this article, we develop probability distributions of U.S. population exposures to mixtures of OTs encountered in drinking water transported by PVC pipes. We employed a family of mathematical models to estimate OT leaching rates from PVC pipe as a function of both surface area and time. We then integrated the distribution of estimated leaching rates into an exposure model that estimated the probability distribution of OT concentrations in tap waters and the resulting potential human OT exposures via tap water consumption. Our study results suggest that human OT exposures through tap water consumption are likely to be considerably lower than the World Health Organization (WHO) "safe" long-term concentration in drinking water (150 μg/L) for dibutyltin (DBT)—the most toxic of the OT considered in this article. The 90th percentile average daily dose (ADD) estimate of 0.034 ± 2.92 × 10⁻⁴μg/kg day is approximately 120 times lower than the WHO-based ADD for DBT (4.2 μg/kg day).     

An Application of the Holistochastic Human Exposure Methodology to Naturally Occurring Arsenic in Bangladesh Drinking Water

The occurrence of arsenic in drinking water is an issue of considerable interest. In the case of Bangladesh, arsenic concentrations have been closely monitored since the early 1990s through an extensive sampling network. The focus of the present work is methodological. In particular, we propose the application of a holistochastic framework of human exposure to study lifetime population damage due to arsenic exposure across Bangladesh. The Bayesian Maximum Entropy theory is an important component of this framework, which possesses solid theoretical foundations and offers powerful tools to assimilate a variety of knowledge bases (physical, epidemiologic, toxicokinetic, demographic, etc.) and uncertainty sources (soft data, measurement errors, etc.). The holistochastic exposure approach leads to physically meaningful and informative spatial maps of arsenic distribution in Bangladesh drinking water. Global indicators of the adverse health effects on the population are generated, and valuable insight is gained by blending information from different scientific disciplines. The numerical results indicate an increased lifetime bladder cancer probability for the Bangladesh population due to arsenic. The health effect estimates obtained and the associated uncertainty assessments are valuable tools for a broad spectrum of end-users.     

Significance of Exposure Assessment to Analysis of Cancer Risk from Inorganic Arsenic in Drinking Water in Taiwan

The primary source of evidence that inorganic arsenic in drinking water is associated with increased mortality from cancer at internal sites (bladder, liver, lung, and other organs) is a large ecologic study conducted in regions of Southwest Taiwan endemic to Blackfoot disease. The dose-response patterns for lung, liver, and bladder cancers display a nonlinear dose-response relationship with arsenic exposure. The data do not appear suitable, however, for the more refined task of dose-response assessment, particularly for inference of risk at the low arsenic concentrations found in some U.S. water supplies. The problem lies in variable arsenic concentrations between the wells within a village, largely due to a mix of shallow wells and deep artesian wells, and in having only one well test for 24 (40%) of the 60 villages. The current analysis identifies 14 villages where the exposure appears most questionable, based on criteria described in the text. The exposure values were then changed for seven of the villages, from the median well test being used as a default to some other point in the village's range of well tests that would contribute to smoothing the appearance of a dose-response curve. The remaining seven villages, six of which had only one well test, were deleted as outliers. The resultant dose-response patterns showed no evidence of excess risk below arsenic concentrations of 0.1 mg/l. Of course, that outcome is dependent on manipulation of the data, as described. Inclusion of the seven deleted villages would make estimates of risk much higher at low doses. In those seven villages, the cancer mortality rates are significantly high for their exposure levels, suggesting that their exposure values may be too low or that other etiological factors need to be taken into account.     

Evaluation of Take‐Home Exposure and Risk Associated with the Handling of Clothing Contaminated with Chrysotile Asbestos

The potential for para‐occupational (or take‐home) exposures from contaminated clothing has been recognized for the past 60 years. To better characterize the take‐home asbestos exposure pathway, a study was performed to measure the relationship between airborne chrysotile concentrations in the workplace, the contamination of work clothing, and take‐home exposures and risks. The study included air sampling during two activities: (1) contamination of work clothing by airborne chrysotile (i.e., loading the clothing), and (2) handling and shaking out of the clothes. The clothes were contaminated at three different target airborne chrysotile concentrations (0–0.1 fibers per cubic centimeter [f/cc], 1–2 f/cc, and 2–4 f/cc; two events each for 31–43 minutes; six events total). Arithmetic mean concentrations for the three target loading levels were 0.01 f/cc, 1.65 f/cc, and 2.84 f/cc (National Institute of Occupational Health and Safety [NIOSH] 7402). Following the loading events, six matched 30‐minute clothes‐handling and shake‐out events were conducted, each including 15 minutes of active handling (15‐minute means; 0.014–0.097 f/cc) and 15 additional minutes of no handling (30‐minute means; 0.006–0.063 f/cc). Percentages of personal clothes‐handling TWAs relative to clothes‐loading TWAs were calculated for event pairs to characterize exposure potential during daily versus weekly clothes‐handling activity. Airborne concentrations for the clothes handler were 0.2–1.4% (eight‐hour TWA or daily ratio) and 0.03–0.27% (40‐hour TWA or weekly ratio) of loading TWAs. Cumulative chrysotile doses for clothes handling at airborne concentrations tested were estimated to be consistent with lifetime cumulative chrysotile doses associated with ambient air exposure (range for take‐home or ambient doses: 0.00044–0.105 f/cc year).     

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.     

Sensitivity Analysis and Estimation of Extreme Tail Behavior in Two‐Dimensional Monte Carlo Simulation

Two‐dimensional Monte Carlo simulation is frequently used to implement probabilistic risk models, as it allows for uncertainty and variability to be quantified separately. In many cases, we are interested in the proportion of individuals from a variable population exceeding a critical threshold, together with uncertainty about this proportion. In this article we introduce a new method that can accurately estimate these quantities much more efficiently than conventional algorithms. We also show how those model parameters having the greatest impact on the probabilities of rare events can be quickly identified via this method. The algorithm combines elements from well‐established statistical techniques in extreme value theory and Bayesian analysis of computer models. We demonstrate the practical application of these methods with a simple example, in which the true distributions are known exactly, and also with a more realistic model of microbial contamination of milk with seven parameters. For the latter, sensitivity analysis (SA) is shown to identify the two inputs explaining the majority of variation in distribution tail behavior. In the subsequent prediction of probabilities of large contamination events, similar results are obtained using the new approach taking 43 seconds or the conventional simulation that requires more than 3 days.     

Persistent Risk-Related Worry as a Function of Recalled Exposure to the Deepwater Horizon Oil Spill and Prior Trauma

Large oil spills are disasters associated with psychological effects for exposed communities. The amount of worry that individuals experience after a disaster may be influenced by many factors, such as the type and extent of exposure to disaster impacts, prior trauma, and sociodemographic characteristics. This study examined the nature and predictors of worry about ongoing impacts of the 2010 Deepwater Horizon (DH) oil spill reported by Gulf of Mexico coastal residents. A random sample of 2,520 adult residents of Gulf of Mexico coastal counties were administered a telephone survey in 2016, including items about persistent worry and exposure to DH impacts, prior trauma, residence at the time of the spill, and sociodemographic characteristics. Respondents varied in the amount of worry they reported about ongoing health, social, and economic impacts. Controlling for sociodemographic characteristics, higher exposure to the DH oil spill was related to higher levels of worry about ongoing impacts, with past traumatic events related specifically to worry about health impacts. Unexpectedly, those who moved into the region after the spill showed similar levels of worry to residents exposed to the spill, and higher levels than residents who did not recall being exposed to the DH oil spill. This study highlights the impact of the DH oil spill on coastal residents many years after the DH disaster. The findings underscore the need to examine multiple pathways by which individuals experience disasters and for risk researchers to close knowledge gaps about long-term impacts of oil spills within a multi-dimensional framework.     

Application of a QMRA Framework to Inform Selection of Drinking Water Interventions in the Developing Context

The aim of this study was to develop a modified quantitative microbial risk assessment (QMRA) framework that could be applied as a decision support tool to choose between alternative drinking water interventions in the developing context. The impact of different household water treatment (HWT) interventions on the overall incidence of diarrheal disease and disability adjusted life years (DALYs) was estimated, without relying on source water pathogen concentration as the starting point for the analysis. A framework was developed and a software tool constructed and then implemented for an illustrative case study for Nepal based on published scientific data. Coagulation combined with free chlorine disinfection provided the greatest estimated health gains in the short term; however, when long‐term compliance was incorporated into the calculations, the preferred intervention was porous ceramic filtration. The model demonstrates how the QMRA framework can be used to integrate evidence from different studies to inform management decisions, and in particular to prioritize the next best intervention with respect to estimated reduction in diarrheal incidence. This study only considered HWT interventions; it is recognized that a systematic consideration of sanitation, recreation, and drinking water pathways is important for effective management of waterborne transmission of pathogens, and the approach could be expanded to consider the broader water‐related context.     

Assessment of the Effect of Population and Diary Sampling Methods on Estimation of School‐Age Children Exposure to Fine Particles

Population and diary sampling methods are employed in exposure models to sample simulated individuals and their daily activity on each simulation day. Different sampling methods may lead to variations in estimated human exposure. In this study, two population sampling methods (stratified‐random and random‐random) and three diary sampling methods (random resampling, diversity and autocorrelation, and Markov‐chain cluster [MCC]) are evaluated. Their impacts on estimated children's exposure to ambient fine particulate matter (PM_2.5) are quantified via case studies for children in Wake County, NC for July 2002. The estimated mean daily average exposure is 12.9 μg/m³ for simulated children using the stratified population sampling method, and 12.2 μg/m³ using the random sampling method. These minor differences are caused by the random sampling among ages within census tracts. Among the three diary sampling methods, there are differences in the estimated number of individuals with multiple days of exposures exceeding a benchmark of concern of 25 μg/m³ due to differences in how multiday longitudinal diaries are estimated. The MCC method is relatively more conservative. In case studies evaluated here, the MCC method led to 10% higher estimation of the number of individuals with repeated exposures exceeding the benchmark. The comparisons help to identify and contrast the capabilities of each method and to offer insight regarding implications of method choice. Exposure simulation results are robust to the two population sampling methods evaluated, and are sensitive to the choice of method for simulating longitudinal diaries, particularly when analyzing results for specific microenvironments or for exposures exceeding a benchmark of concern.     

Media Exposure to Terrorism and Perception of Immigrants as a Threat: The Role of Emotional Intelligence and Psychophysiological Self-Regulation

To better understand how media exposure to terrorism-related images can lead to perceiving immigrants as more threatening, in the present study we manipulated participants’ exposure to media coverage of terrorist attacks and investigated how this may influence people's perception of Arab immigrants. Considering the important role of regulatory abilities when facing stressful events like terrorist attacks, we measured individual differences in both trait emotional intelligence and resting heart rate variability (HRV). Results showed that participants perceived Arab immigrants as more threatening in the media exposure condition than in the control condition. Importantly, there were moderating effects of both trait emotional intelligence and HRV. People with lower trait emotional intelligence or lower HRV at rest felt more threatened in the media exposure condition compared to the control condition, whereas this effect was not observed among participants with higher trait emotional intelligence or higher resting HRV. The present study highlights some of the complexities related to how media exposure to terrorism-related images influences people's reactions to, and evaluations of, an outgroup that is related to the perpetrators of the attacks.     

Human Dose–Response Data for Francisella tularensis and a Dose‐ and Time‐Dependent Mathematical Model of Early‐Phase Fever Associated with Tularemia After Inhalation Exposure

Military health risk assessors, medical planners, operational planners, and defense system developers require knowledge of human responses to doses of biothreat agents to support force health protection and chemical, biological, radiological, nuclear (CBRN) defense missions. This article reviews extensive data from 118 human volunteers administered aerosols of the bacterial agent Francisella tularensis , strain Schu S4, which causes tularemia. The data set includes incidence of early‐phase febrile illness following administration of well‐characterized inhaled doses of F. tularensis . Supplemental data on human body temperature profiles over time available from de‐identified case reports is also presented. A unified, logically consistent model of early‐phase febrile illness is described as a lognormal dose–response function for febrile illness linked with a stochastic time profile of fever. Three parameters are estimated from the human data to describe the time profile: incubation period or onset time for fever; rise time of fever; and near‐maximum body temperature. Inhaled dose‐dependence and variability are characterized for each of the three parameters. These parameters enable a stochastic model for the response of an exposed population through incorporation of individual‐by‐individual variability by drawing random samples from the statistical distributions of these three parameters for each individual. This model provides risk assessors and medical decisionmakers reliable representations of the predicted health impacts of early‐phase febrile illness for as long as one week after aerosol exposures of human populations to F. tularensis .     

Development of Dose‐Response Models to Predict the Relationship for Human Toxoplasma gondii Infection Associated with Meat Consumption

Toxoplasma gondii is a protozoan parasite that is responsible for approximately 24% of deaths attributed to foodborne pathogens in the United States. It is thought that a substantial portion of human T. gondii infections is acquired through the consumption of meats. The dose‐response relationship for human exposures to T. gondii‐infected meat is unknown because no human data are available. The goal of this study was to develop and validate dose‐response models based on animal studies, and to compute scaling factors so that animal‐derived models can predict T. gondii infection in humans. Relevant studies in literature were collected and appropriate studies were selected based on animal species, stage, genotype of T. gondii, and route of infection. Data were pooled and fitted to four sigmoidal‐shaped mathematical models, and model parameters were estimated using maximum likelihood estimation. Data from a mouse study were selected to develop the dose‐response relationship. Exponential and beta‐Poisson models, which predicted similar responses, were selected as reasonable dose‐response models based on their simplicity, biological plausibility, and goodness fit. A confidence interval of the parameter was determined by constructing 10,000 bootstrap samples. Scaling factors were computed by matching the predicted infection cases with the epidemiological data. Mouse‐derived models were validated against data for the dose‐infection relationship in rats. A human dose‐response model was developed as P (d) = 1–exp (–0.0015 × 0.005 × d) or P (d) = 1–(1 + d × 0.003 / 582.414)^?1.479. Both models predict the human response after consuming T. gondii‐infected meats, and provide an enhanced risk characterization in a quantitative microbial risk assessment model for this pathogen.     

Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next‐Generation Sequencing Data

Next‐generation sequencing (NGS) data present an untapped potential to improve microbial risk assessment (MRA) through increased specificity and redefinition of the hazard. Most of the MRA models do not account for differences in survivability and virulence among strains. The potential of machine learning algorithms for predicting the risk/health burden at the population level while inputting large and complex NGS data was explored with Listeria monocytogenes as a case study. Listeria data consisted of a percentage similarity matrix from genome assemblies of 38 and 207 strains of clinical and food origin, respectively. Basic Local Alignment (BLAST) was used to align the assemblies against a database of 136 virulence and stress resistance genes. The outcome variable was frequency of illness, which is the percentage of reported cases associated with each strain. These frequency data were discretized into seven ordinal outcome categories and used for supervised machine learning and model selection from five ensemble algorithms. There was no significant difference in accuracy between the models, and support vector machine with linear kernel was chosen for further inference (accuracy of 89% [95% CI: 68%, 97%]). The virulence genes FAM002725, FAM002728, FAM002729, InlF, InlJ, Inlk, IisY, IisD, IisX, IisH, IisB, lmo2026, and FAM003296 were important predictors of higher frequency of illness. InlF was uniquely truncated in the sequence type 121 strains. Most important risk predictor genes occurred at highest prevalence among strains from ready‐to‐eat, dairy, and composite foods. We foresee that the findings and approaches described offer the potential for rethinking the current approaches in MRA.     

Awareness and Perceptions of the Risks of Exposure to Indoor Radon: A Population‐Based Approach to Evaluate a Radon Awareness and Testing Campaign in England and Wales

The current study aimed to evaluate the locally directed radon roll‐out program that was conducted between 2001 and 2005 in England and Wales to increase radon awareness and testing rates. A representative sample of 1,578 residents aged 16 and older were interviewed who lived in radon‐affected areas of 15 local authorities in England and Wales that were eligible for participation in the program. The study systematically sampled across participating and nonparticipating local authorities, “actionable” and “nonactionable” radon‐affected areas, and geographic regions with different campaign histories (Wales, Southwest England, and the rest of England). As a multistage sampling strategy was used, the data were analyzed from a multilevel perspective. This study found that participants living in participating local authorities had higher levels of awareness and were more likely to have tested their home for radon than participants living in nonparticipating local authorities. Similar results were found for participants living in “actionable” areas as compared to those living in “nonactionable” radon‐affected areas. The study further found that radon awareness and testing rates were the highest in Southwest England and the lowest in Wales. This study suggests that the radon roll‐out program has been effective in raising awareness and testing rates, and that ongoing domestic radon campaigns in Southwest England may have raised radon awareness and testing in these areas, showing important reinforcement effects of multiple risk communication campaigns.