Epidemiologic Evidence for Chloroprene Carcinogenicity: Review of Study Quality and its Application to Risk Assessment

This article evaluates the quality and weight of evidence associated with epidemiologic studies of cancer among occupational cohorts exposed to chloroprene. The focus is on liver, lung, and lymphohematopoietic cancers, which had been increased in early studies. Literature searches identified eight morbidity/mortality studies covering seven chloroprene-exposed cohorts from six countries. These studies were summarized and their quality was assessed using the 10 criteria suggested by the U.S. Environmental Protection Agency. The limitations within this literature (primarily the early studies) included crude exposure assessment, incomplete follow-up, uncertain baseline rates, and uncontrolled confounding by factors such as smoking, drinking, and co-exposure to benzene and vinyl chloride. Four cohorts were studied by the same group of investigators, who reported no overall increased associations for any cancers. This four-cohort study was by far the most rigorous, having the most comprehensive exposure assessment and follow-up and the most detailed documentation. This study also contained the two largest cohorts, including an American cohort from Louisville, Kentucky, that ranked at or near the top for each of the 10 quality criteria. There was evidence of a strong healthy worker effect in the four-cohort study, which could have hidden small excess risks. Small increased risks were suggested by internal or company-specific analyses, but these were most likely caused by uncontrolled confounding and low baseline rates. Overall, the weight of evidence does not support any substantial link between chloroprene exposure and cancer, but inconsistencies and a lack of control for major confounders preclude drawing firmer conclusions.     

Growing Pains: How Risk Perception and Risk Communication Research Can Help to Manage the Challenges of Global Population Growth

In 2011, the global human population reached 7 billion and medium variant projections indicate that it will exceed 9 billion before 2045. Theoretical and empirical perspectives suggest that this growth could lead to an increase in the likelihood of adverse events (e.g., food shortages, climate change, etc.) and/or the severity of adverse events (e.g., famines, natural disasters, etc.). Several scholars have posited that the size to which the global population grows and the extent to which this growth increases the likelihood of adverse outcomes will largely be shaped by individuals’ decisions (in households, organizations, governments, etc.). In light of the strong relationship between perceived risk and decision behaviors, it is surprising that there remains a dearth of empirical research that specifically examines the perceived risks of population growth and how these perceptions might influence related decisions. In an attempt to motivate this important strand of research, this article examines the major risks that may be exacerbated by global population growth and draws upon empirical work concerning the perception and communication of risk to identify potential directions for future research. The article also considers how individuals might perceive both the risks and benefits of population growth and be helped to better understand and address the related issues. The answers to these questions could help humanity better manage the emerging consequences of its continuing success in increasing infant survival and adult longevity.     

On The Method of Discrete Probability Distributions in Risk and Reliability Calculations–Application to Seismic Risk Assessment

If the point of view is adopted that in calculations of real-world phenomena we almost invariably have significant uncertainty in the numerical values of our parameters, then, in these calculations, numerical quantities should be replaced by probability distributions and mathematical operations between these quantities should be replaced by analogous operations between probability distributions. Also, practical calculations one way or another always require discretization or truncation. Combining these two thoughts leads to a numerical approach to probabilistic calculations having great simplicity, power, and elegance. The philosophy and technique of this approach is described, some pitfalls are pointed out, and an application to seismic risk assessment is outlined.     

Quantal Risk Assessment Database: A Database for Exploring Patterns in Quantal Dose‐Response Data in Risk Assessment and its Application to Develop Priors for Bayesian Dose‐Response Analysis

Quantitative risk assessments for physical, chemical, biological, occupational, or environmental agents rely on scientific studies to support their conclusions. These studies often include relatively few observations, and, as a result, models used to characterize the risk may include large amounts of uncertainty. The motivation, development, and assessment of new methods for risk assessment is facilitated by the availability of a set of experimental studies that span a range of dose‐response patterns that are observed in practice. We describe construction of such a historical database focusing on quantal data in chemical risk assessment, and we employ this database to develop priors in Bayesian analyses. The database is assembled from a variety of existing toxicological data sources and contains 733 separate quantal dose‐response data sets. As an illustration of the database's use, prior distributions for individual model parameters in Bayesian dose‐response analysis are constructed. Results indicate that including prior information based on curated historical data in quantitative risk assessments may help stabilize eventual point estimates, producing dose‐response functions that are more stable and precisely estimated. These in turn produce potency estimates that share the same benefit. We are confident that quantitative risk analysts will find many other applications and issues to explore using this database.     

Construction of a Dose–Illness Relationship via Modeling Morbidity and Application to Risk Assessment of Wastewater Reuse

A disease burden (DB) evaluation for environmental pathogens is generally performed using disability‐adjusted life years with the aim of providing a quantitative assessment of the health hazard caused by pathogens. A critical step in the preparation for this evaluation is the estimation of morbidity between exposure and disease occurrence. In this study, the method of a traditional dose–response analysis was first reviewed, and then a combination of the theoretical basis of a “single‐hit” and an “infection‐illness” model was performed by incorporating two critical factors: the “infective coefficient” and “infection duration.” This allowed a dose–morbidity model to be built for direct use in DB calculations. In addition, human experimental data for typical intestinal pathogens were obtained for model validation, and the results indicated that the model was well fitted and could be further used for morbidity estimation. On this basis, a real case of a water reuse project was selected for model application, and the morbidity as well as the DB caused by intestinal pathogens during water reuse was evaluated. The results show that the DB attributed to Enteroviruses was significant, while that for enteric bacteria was negligible. Therefore, water treatment technology should be further improved to reduce the exposure risk of Enteroviruses . Since road flushing was identified as the major exposure route, human contact with reclaimed water through this pathway should be limited. The methodology proposed for model construction not only makes up for missing data of morbidity during risk evaluation, but is also necessary to quantify the maximum possible DB.     

An Adaptive Regional Input-Output Model and its Application to the Assessment of the Economic Cost of Katrina

This article proposes a new modeling framework to investigate the consequences of natural disasters and the following reconstruction phase. Based on input-output tables, its originalities are (1) the taking into account of sector production capacities and of both forward and backward propagations within the economic system; and (2) the introduction of adaptive behaviors. The model is used to simulate the response of the economy of Louisiana to the landfall of Katrina. The model is found consistent with available data, and provides two important insights. First, economic processes exacerbate direct losses, and total costs are estimated at $149 billion, for direct losses equal to $107 billion. When exploring the impacts of other possible disasters, it is found that total losses due to a disaster affecting Louisiana increase nonlinearly with respect to direct losses when the latter exceed $50 billion. When direct losses exceed $200 billion, for instance, total losses are twice as large as direct losses. For risk management, therefore, direct losses are insufficient measures of disaster consequences. Second, positive and negative backward propagation mechanisms are essential for the assessment of disaster consequences, and the taking into account of production capacities is necessary to avoid overestimating the positive effects of reconstruction. A systematic sensitivity analysis shows that, among all parameters, the overproduction capacity in the construction sector and the adaptation characteristic time are the most important.     

A Systems Approach to the Policy‐Level Risk Assessment of Exotic Animal Diseases: Network Model and Application to Classical Swine Fever

Exotic animal diseases (EADs) are characterized by their capacity to spread global distances, causing impacts on animal health and welfare with significant economic consequences. We offer a critique of current import risk analysis approaches employed in the EAD field, focusing on their capacity to assess complex systems at a policy level. To address the shortcomings identified, we propose a novel method providing a systematic analysis of the likelihood of a disease incursion, developed by reference to the multibarrier system employed for the United Kingdom. We apply the network model to a policy‐level risk assessment of classical swine fever (CSF), a notifiable animal disease caused by the CSF virus. In doing so, we document and discuss a sequence of analyses that describe system vulnerabilities and reveal the critical control points (CCPs) for intervention, reducing the likelihood of U.K. pig herds being exposed to the CSF virus.