Repeated holdout Cross-Validation of Model to Estimate Risk of Lyme Disease by Landscape Attributes

EPA Science Inventory

We previously modeled Lyme disease (LD) risk at the landscape scale; here we evaluate the model's overall goodness-of-fit using holdout validation. Landscapes were characterized within road-bounded analysis units (AU). Observed LD cases (obsLD) were ascertained per AU. Data were ...

Cryptosporidiosis susceptibility and risk: a case study.

PubMed

Makri, Anna; Modarres, Reza; Parkin, Rebecca

2004-02-01

Regional estimates of cryptosporidiosis risks from drinking water exposure were developed and validated, accounting for AIDS status and age. We constructed a model with probability distributions and point estimates representing Cryptosporidium in tap water, tap water consumed per day (exposure characterization); dose response, illness given infection, prolonged illness given illness; and three conditional probabilities describing the likelihood of case detection by active surveillance (health effects characterization). The model predictions were combined with population data to derive expected case numbers and incidence rates per 100,000 population, by age and AIDS status, borough specific and for New York City overall in 2000 (risk characterization). They were compared with same-year surveillance data to evaluate predictive ability, assumed to represent true incidence of waterborne cryptosporidiosis. The predicted mean risks, similar to previously published estimates for this region, overpredicted observed incidence-most extensively when accounting for AIDS status. The results suggest that overprediction may be due to conservative parameters applied to both non-AIDS and AIDS populations, and that biological differences for children need to be incorporated. Interpretations are limited by the unknown accuracy of available surveillance data, in addition to variability and uncertainty of model predictions. The model appears sensitive to geographical differences in AIDS prevalence. The use of surveillance data for validation and model parameters pertinent to susceptibility are discussed.

CANCER RISK ASSESSMENTS (RA.D.1D)

EPA Science Inventory

Risk assessments are based on questions that the assessor asks about scientific information that is relevant to human and/or environmental risk. The risk characterization also provides an evaluation of the assumptions, uncertainties, and selection of studies and models used in th...

Characterizing Uncertainty and Variability in PBPK Models ...

EPA Pesticide Factsheets

Mode-of-action based risk and safety assessments can rely upon tissue dosimetry estimates in animals and humans obtained from physiologically-based pharmacokinetic (PBPK) modeling. However, risk assessment also increasingly requires characterization of uncertainty and variability; such characterization for PBPK model predictions represents a continuing challenge to both modelers and users. Current practices show significant progress in specifying deterministic biological models and the non-deterministic (often statistical) models, estimating their parameters using diverse data sets from multiple sources, and using them to make predictions and characterize uncertainty and variability. The International Workshop on Uncertainty and Variability in PBPK Models, held Oct 31-Nov 2, 2006, sought to identify the state-of-the-science in this area and recommend priorities for research and changes in practice and implementation. For the short term, these include: (1) multidisciplinary teams to integrate deterministic and non-deterministic/statistical models; (2) broader use of sensitivity analyses, including for structural and global (rather than local) parameter changes; and (3) enhanced transparency and reproducibility through more complete documentation of the model structure(s) and parameter values, the results of sensitivity and other analyses, and supporting, discrepant, or excluded data. Longer-term needs include: (1) theoretic and practical methodological impro

Mammographic density, breast cancer risk and risk prediction

PubMed Central

Vachon, Celine M; van Gils, Carla H; Sellers, Thomas A; Ghosh, Karthik; Pruthi, Sandhya; Brandt, Kathleen R; Pankratz, V Shane

2007-01-01

In this review, we examine the evidence for mammographic density as an independent risk factor for breast cancer, describe the risk prediction models that have incorporated density, and discuss the current and future implications of using mammographic density in clinical practice. Mammographic density is a consistent and strong risk factor for breast cancer in several populations and across age at mammogram. Recently, this risk factor has been added to existing breast cancer risk prediction models, increasing the discriminatory accuracy with its inclusion, albeit slightly. With validation, these models may replace the existing Gail model for clinical risk assessment. However, absolute risk estimates resulting from these improved models are still limited in their ability to characterize an individual's probability of developing cancer. Promising new measures of mammographic density, including volumetric density, which can be standardized using full-field digital mammography, will likely result in a stronger risk factor and improve accuracy of risk prediction models. PMID:18190724

NATIONAL-SCALE ASSESSMENT OF AIR TOXICS RISKS ...

EPA Pesticide Factsheets

The national-scale assessment of air toxics risks is a modeling assessment which combines emission inventory development, atmospheric fate and transport modeling, exposure modeling, and risk assessment to characterize the risk associated with inhaling air toxics from outdoor sources. This national-scale effort will be initiated for the base year 1996 and repeated every three years thereafter to track trends and inform program development. Provide broad-scale understanding of inhalation risks for a subset of atmospherically-emitted air toxics to inform further data-gathering efforts and priority-setting for the EPA's Air Toxics Programs.

Lunar Landing Operational Risk Model

NASA Technical Reports Server (NTRS)

Mattenberger, Chris; Putney, Blake; Rust, Randy; Derkowski, Brian

2010-01-01

Characterizing the risk of spacecraft goes beyond simply modeling equipment reliability. Some portions of the mission require complex interactions between system elements that can lead to failure without an actual hardware fault. Landing risk is currently the least characterized aspect of the Altair lunar lander and appears to result from complex temporal interactions between pilot, sensors, surface characteristics and vehicle capabilities rather than hardware failures. The Lunar Landing Operational Risk Model (LLORM) seeks to provide rapid and flexible quantitative insight into the risks driving the landing event and to gauge sensitivities of the vehicle to changes in system configuration and mission operations. The LLORM takes a Monte Carlo based approach to estimate the operational risk of the Lunar Landing Event and calculates estimates of the risk of Loss of Mission (LOM) - Abort Required and is Successful, Loss of Crew (LOC) - Vehicle Crashes or Cannot Reach Orbit, and Success. The LLORM is meant to be used during the conceptual design phase to inform decision makers transparently of the reliability impacts of design decisions, to identify areas of the design which may require additional robustness, and to aid in the development and flow-down of requirements.

Application of the two-stage clonal expansion model in characterizing the joint effect of exposure to two carcinogens

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zielinski, J.M.; Krewski, D.

1992-12-31

In this paper, we describe application of the two-stage clonal expansion model to characterize the joint effect of exposure to two carcinogens. This biologically based model of carcinogenesis provides a useful framework for the quantitative description of carcinogenic risks and for defining agents that act as initiators, promoters, and completers. Depending on the mechanism of action, the agent-specific relative risk following exposure to two carcinogens can be additive, multiplicative, or supramultiplicative, with supra-additive relative risk indicating a synergistic effect between the two agents. Maximum-likelihood methods for fitting the two-stage clonal expansion model with intermittent exposure to two carcinogens are describedmore » and illustrated, using data on lung-cancer mortality among Colorado uranium miners exposed to both radon and tobacco smoke.« less

Quantitative Microbial Risk Assessment for Clostridium perfringens in Natural and Processed Cheeses

PubMed Central

Lee, Heeyoung; Lee, Soomin; Kim, Sejeong; Lee, Jeeyeon; Ha, Jimyeong; Yoon, Yohan

2016-01-01

This study evaluated the risk of Clostridium perfringens (C. perfringens) foodborne illness from natural and processed cheeses. Microbial risk assessment in this study was conducted according to four steps: hazard identification, hazard characterization, exposure assessment, and risk characterization. The hazard identification of C. perfringens on cheese was identified through literature, and dose response models were utilized for hazard characterization of the pathogen. For exposure assessment, the prevalence of C. perfringens, storage temperatures, storage time, and annual amounts of cheese consumption were surveyed. Eventually, a simulation model was developed using the collected data and the simulation result was used to estimate the probability of C. perfringens foodborne illness by cheese consumption with @RISK. C. perfringens was determined to be low risk on cheese based on hazard identification, and the exponential model (r = 1.82×10−11) was deemed appropriate for hazard characterization. Annual amounts of natural and processed cheese consumption were 12.40±19.43 g and 19.46±14.39 g, respectively. Since the contamination levels of C. perfringens on natural (0.30 Log CFU/g) and processed cheeses (0.45 Log CFU/g) were below the detection limit, the initial contamination levels of natural and processed cheeses were estimated by beta distribution (α1 = 1, α2 = 91; α1 = 1, α2 = 309)×uniform distribution (a = 0, b = 2; a = 0, b = 2.8) to be −2.35 and −2.73 Log CFU/g, respectively. Moreover, no growth of C. perfringens was observed for exposure assessment to simulated conditions of distribution and storage. These data were used for risk characterization by a simulation model, and the mean values of the probability of C. perfringens foodborne illness by cheese consumption per person per day for natural and processed cheeses were 9.57×10−14 and 3.58×10−14, respectively. These results indicate that probability of C. perfringens foodborne illness by consumption cheese is low, and it can be used to establish microbial criteria for C. perfringens on natural and processed cheeses. PMID:26954204

Simulation Assisted Risk Assessment Applied to Launch Vehicle Conceptual Design

NASA Technical Reports Server (NTRS)

Mathias, Donovan L.; Go, Susie; Gee, Ken; Lawrence, Scott

2008-01-01

A simulation-based risk assessment approach is presented and is applied to the analysis of abort during the ascent phase of a space exploration mission. The approach utilizes groupings of launch vehicle failures, referred to as failure bins, which are mapped to corresponding failure environments. Physical models are used to characterize the failure environments in terms of the risk due to blast overpressure, resulting debris field, and the thermal radiation due to a fireball. The resulting risk to the crew is dynamically modeled by combining the likelihood of each failure, the severity of the failure environments as a function of initiator and time of the failure, the robustness of the crew module, and the warning time available due to early detection. The approach is shown to support the launch vehicle design process by characterizing the risk drivers and identifying regions where failure detection would significantly reduce the risk to the crew.

PROPOSED SUITE OF MODELS FOR ESTIMATING DOSE RESULTING FROM EXPOSURES BY THE DERMAL ROUTE

EPA Science Inventory

Recent risk assessment guidance emphasizes consideration of mechanistic factors for influencing disposition of a toxicant. To incorporate mechanistic information into risk assessment, a suite of models is proposed for use in characterizing and quantifying dosimetry of toxic age...

The Effects of Revealed Information on Catastrophe Loss Projection Models' Characterization of Risk: Damage Vulnerability Evidence from Florida.

PubMed

Karl, J Bradley; Medders, Lorilee A; Maroney, Patrick F

2016-06-01

We examine whether the risk characterization estimated by catastrophic loss projection models is sensitive to the revelation of new information regarding risk type. We use commercial loss projection models from two widely employed modeling firms to estimate the expected hurricane losses of Florida Atlantic University's building stock, both including and excluding secondary information regarding hurricane mitigation features that influence damage vulnerability. We then compare the results of the models without and with this revealed information and find that the revelation of additional, secondary information influences modeled losses for the windstorm-exposed university building stock, primarily evidenced by meaningful percent differences in the loss exceedance output indicated after secondary modifiers are incorporated in the analysis. Secondary risk characteristics for the data set studied appear to have substantially greater impact on probable maximum loss estimates than on average annual loss estimates. While it may be intuitively expected for catastrophe models to indicate that secondary risk characteristics hold value for reducing modeled losses, the finding that the primary value of secondary risk characteristics is in reduction of losses in the "tail" (low probability, high severity) events is less intuitive, and therefore especially interesting. Further, we address the benefit-cost tradeoffs that commercial entities must consider when deciding whether to undergo the data collection necessary to include secondary information in modeling. Although we assert the long-term benefit-cost tradeoff is positive for virtually every entity, we acknowledge short-term disincentives to such an effort. © 2015 Society for Risk Analysis.

Community-Engaged Modeling of Geographic and Demographic Patterns of Multiple Public Health Risk Factors

PubMed Central

Basra, Komal; Fabian, M. Patricia; Holberger, Raymond R.; French, Robert

2017-01-01

Many health risk factors are intervention targets within communities, but information regarding high-risk subpopulations is rarely available at a geographic resolution that is relevant for community-scale interventions. Researchers and community partners in New Bedford, Massachusetts (USA) collaboratively identified high-priority behaviors and health outcomes of interest available in the Behavioral Risk Factor Surveillance System (BRFSS). We developed multivariable regression models from the BRFSS explaining variability in exercise, fruit and vegetable consumption, body mass index, and diabetes prevalence as a function of demographic and behavioral characteristics, and linked these models with population microdata developed using spatial microsimulation to characterize high-risk populations and locations. Individuals with lower income and educational attainment had lower rates of multiple health-promoting behaviors (e.g., fruit and vegetable consumption and exercise) and higher rates of self-reported diabetes. Our models in combination with the simulated population microdata identified census tracts with an elevated percentage of high-risk subpopulations, information community partners can use to prioritize funding and intervention programs. Multi-stressor modeling using data from public databases and microsimulation methods for characterizing high-resolution spatial patterns of population attributes, coupled with strong community partner engagement, can provide significant insight for intervention. Our methodology is transferrable to other communities. PMID:28684710

Is risk analysis scientific?

PubMed

Hansson, Sven Ove; Aven, Terje

2014-07-01

This article discusses to what extent risk analysis is scientific in view of a set of commonly used definitions and criteria. We consider scientific knowledge to be characterized by its subject matter, its success in developing the best available knowledge in its fields of study, and the epistemic norms and values that guide scientific investigations. We proceed to assess the field of risk analysis according to these criteria. For this purpose, we use a model for risk analysis in which science is used as a base for decision making on risks, which covers the five elements evidence, knowledge base, broad risk evaluation, managerial review and judgment, and the decision; and that relates these elements to the domains experts and decisionmakers, and to the domains fact-based or value-based. We conclude that risk analysis is a scientific field of study, when understood as consisting primarily of (i) knowledge about risk-related phenomena, processes, events, etc., and (ii) concepts, theories, frameworks, approaches, principles, methods and models to understand, assess, characterize, communicate, and manage risk, in general and for specific applications (the instrumental part). © 2014 Society for Risk Analysis.

Microbial Risk Assessment

NASA Technical Reports Server (NTRS)

Ott, C. M.; Mena, K. D.; Nickerson, C.A.; Pierson, D. L.

2009-01-01

Historically, microbiological spaceflight requirements have been established in a subjective manner based upon expert opinion of both environmental and clinical monitoring results and the incidence of disease. The limited amount of data, especially from long-duration missions, has created very conservative requirements based primarily on the concentration of microorganisms. Periodic reevaluations of new data from later missions have allowed some relaxation of these stringent requirements. However, the requirements remain very conservative and subjective in nature, and the risk of crew illness due to infectious microorganisms is not well defined. The use of modeling techniques for microbial risk has been applied in the food and potable water industries and has exceptional potential for spaceflight applications. From a productivity standpoint, this type of modeling can (1) decrease unnecessary costs and resource usage and (2) prevent inadequate or inappropriate data for health assessment. In addition, a quantitative model has several advantages for risk management and communication. By identifying the variable components of the model and the knowledge associated with each component, this type of modeling can: (1) Systematically identify and close knowledge gaps, (2) Systematically identify acceptable and unacceptable risks, (3) Improve communication with stakeholders as to the reasons for resource use, and (4) Facilitate external scientific approval of the NASA requirements. The modeling of microbial risk involves the evaluation of several key factors including hazard identification, crew exposure assessment, dose-response assessment, and risk characterization. Many of these factors are similar to conditions found on Earth; however, the spaceflight environment is very specialized as the inhabitants live in a small, semi-closed environment that is often dependent on regenerative life support systems. To further complicate modeling efforts, microbial dose-response characteristics may be affected by a potentially dysfunctional crew immune system during a mission. In addition, microbial virulence has been shown to change under certain conditions during spaceflight, further complicating dose-response characterization. An initial study of the applicability of microbial risk assessment techniques was performed using Crew Health Care System (CHeCS) operational data from the International Space Station potable water systems. The risk of infection from potable water was selected as the flight systems and microbial ecology are well defined. This initial study confirmed the feasibility of using microbial risk assessment modeling for spaceflight systems. While no immediate threat was detected, the study identified several medically significant microorganisms that could pose a health risk if uncontrolled. The study also identified several specific knowledge gaps in making a risk assessment and noted that filling these knowledge gaps is essential as the risk estimates may change by orders of magnitude depending on the answers. The current phase of the microbial risk assessment studies focuses on the dose-response relationship of specific infectious agents, focusing on Salmonella enterica Typhimurium, Pseudomonas spp., and Escherichia coli, as their evaluation will provide a better baseline for determining the overall hazard characterization. The organisms were chosen as they either have been isolated on spacecraft or have an identified route of infection during a mission. The characterization will utilize dose-response models selected either from the peer-reviewed literature and/or by using statistical approaches. Development of these modeling and risk assessment techniques will help to optimize flight requirements and to protect the safety, health, and performance of the crew.

On the modeling of epidemics under the influence of risk perception

NASA Astrophysics Data System (ADS)

de Lillo, S.; Fioriti, G.; Prioriello, M. L.

An epidemic spreading model is presented in the framework of the kinetic theory of active particles. The model is characterized by the influence of risk perception which can reduce the diffusion of infection. The evolution of the system is modeled through nonlinear interactions, whose output is described by stochastic games. The results of numerical simulations are discussed for different initial conditions.

Scientific reporting is suboptimal for aspects that characterize genetic risk prediction studies: a review of published articles based on the Genetic RIsk Prediction Studies statement.

PubMed

Iglesias, Adriana I; Mihaescu, Raluca; Ioannidis, John P A; Khoury, Muin J; Little, Julian; van Duijn, Cornelia M; Janssens, A Cecile J W

2014-05-01

Our main objective was to raise awareness of the areas that need improvements in the reporting of genetic risk prediction articles for future publications, based on the Genetic RIsk Prediction Studies (GRIPS) statement. We evaluated studies that developed or validated a prediction model based on multiple DNA variants, using empirical data, and were published in 2010. A data extraction form based on the 25 items of the GRIPS statement was created and piloted. Forty-two studies met our inclusion criteria. Overall, more than half of the evaluated items (34 of 62) were reported in at least 85% of included articles. Seventy-seven percentage of the articles were identified as genetic risk prediction studies through title assessment, but only 31% used the keywords recommended by GRIPS in the title or abstract. Seventy-four percentage mentioned which allele was the risk variant. Overall, only 10% of the articles reported all essential items needed to perform external validation of the risk model. Completeness of reporting in genetic risk prediction studies is adequate for general elements of study design but is suboptimal for several aspects that characterize genetic risk prediction studies such as description of the model construction. Improvements in the transparency of reporting of these aspects would facilitate the identification, replication, and application of genetic risk prediction models. Copyright © 2014 Elsevier Inc. All rights reserved.

Are engineered nano iron oxide particles safe? an environmental risk assessment by probabilistic exposure, effects and risk modeling.

PubMed

Wang, Yan; Deng, Lei; Caballero-Guzman, Alejandro; Nowack, Bernd

2016-12-01

Nano iron oxide particles are beneficial to our daily lives through their use in paints, construction materials, biomedical imaging and other industrial fields. However, little is known about the possible risks associated with the current exposure level of engineered nano iron oxides (nano-FeOX) to organisms in the environment. The goal of this study was to predict the release of nano-FeOX to the environment and assess their risks for surface waters in the EU and Switzerland. The material flows of nano-FeOX to technical compartments (waste incineration and waste water treatment plants) and to the environment were calculated with a probabilistic modeling approach. The mean value of the predicted environmental concentrations (PECs) of nano-FeOX in surface waters in the EU for a worst-case scenario (no particle sedimentation) was estimated to be 28 ng/l. Using a probabilistic species sensitivity distribution, the predicted no-effect concentration (PNEC) was determined from ecotoxicological data. The risk characterization ratio, calculated by dividing the PEC by PNEC values, was used to characterize the risks. The mean risk characterization ratio was predicted to be several orders of magnitude smaller than 1 (1.4 × 10 - 4 ). Therefore, this modeling effort indicates that only a very limited risk is posed by the current release level of nano-FeOX to organisms in surface waters. However, a better understanding of the hazards of nano-FeOX to the organisms in other ecosystems (such as sediment) needs to be assessed to determine the overall risk of these particles to the environment.

Modeling Joint Exposures and Health Outcomes for Cumulative Risk Assessment: the Case of Radon and Smoking

EPA Science Inventory

Community-based cumulative risk assessment requires characterization of exposures to multiple chemical and non-chemical stressors, with consideration of how the non-chemical stressors may influence risks from chemical stressors. Residential radon provides an interesting case exam...

Mining heart disease risk factors in clinical text with named entity recognition and distributional semantic models.

PubMed

Urbain, Jay

2015-12-01

We present the design, and analyze the performance of a multi-stage natural language processing system employing named entity recognition, Bayesian statistics, and rule logic to identify and characterize heart disease risk factor events in diabetic patients over time. The system was originally developed for the 2014 i2b2 Challenges in Natural Language in Clinical Data. The system's strengths included a high level of accuracy for identifying named entities associated with heart disease risk factor events. The system's primary weakness was due to inaccuracies when characterizing the attributes of some events. For example, determining the relative time of an event with respect to the record date, whether an event is attributable to the patient's history or the patient's family history, and differentiating between current and prior smoking status. We believe these inaccuracies were due in large part to the lack of an effective approach for integrating context into our event detection model. To address these inaccuracies, we explore the addition of a distributional semantic model for characterizing contextual evidence of heart disease risk factor events. Using this semantic model, we raise our initial 2014 i2b2 Challenges in Natural Language of Clinical data F1 score of 0.838 to 0.890 and increased precision by 10.3% without use of any lexicons that might bias our results. Copyright © 2015 Elsevier Inc. All rights reserved.

A national approach for integrating wildfire simulation modeling into Wildland Urban Interface risk assessments within the United States

Treesearch

Jessica R. Haas; David E. Calkin; Matthew P. Thompson

2013-01-01

Ongoing human development into fire-prone areas contributes to increasing wildfire risk to human life. It is critically important, therefore, to have the ability to characterize wildfire risk to populated places, and to identify geographic areas with relatively high risk. A fundamental component of wildfire risk analysis is establishing the likelihood of wildfire...

Goal-oriented Site Characterization in Hydrogeological Applications: An Overview

NASA Astrophysics Data System (ADS)

Nowak, W.; de Barros, F.; Rubin, Y.

2011-12-01

In this study, we address the importance of goal-oriented site characterization. Given the multiple sources of uncertainty in hydrogeological applications, information needs of modeling, prediction and decision support should be satisfied with efficient and rational field campaigns. In this work, we provide an overview of an optimal sampling design framework based on Bayesian decision theory, statistical parameter inference and Bayesian model averaging. It optimizes the field sampling campaign around decisions on environmental performance metrics (e.g., risk, arrival times, etc.) while accounting for parametric and model uncertainty in the geostatistical characterization, in forcing terms, and measurement error. The appealing aspects of the framework lie on its goal-oriented character and that it is directly linked to the confidence in a specified decision. We illustrate how these concepts could be applied in a human health risk problem where uncertainty from both hydrogeological and health parameters are accounted.

The concept of comparative information yield curves and its application to risk-based site characterization

NASA Astrophysics Data System (ADS)

de Barros, Felipe P. J.; Rubin, Yoram; Maxwell, Reed M.

2009-06-01

Defining rational and effective hydrogeological data acquisition strategies is of crucial importance as such efforts are always resource limited. Usually, strategies are developed with the goal of reducing uncertainty, but less often they are developed in the context of their impacts on uncertainty. This paper presents an approach for determining site characterization needs on the basis of human health risk. The main challenge is in striking a balance between reduction in uncertainty in hydrogeological, behavioral, and physiological parameters. Striking this balance can provide clear guidance on setting priorities for data acquisition and for better estimating adverse health effects in humans. This paper addresses this challenge through theoretical developments and numerical simulation. A wide range of factors that affect site characterization needs are investigated, including the dimensions of the contaminant plume and additional length scales that characterize the transport problem, as well as the model of human health risk. The concept of comparative information yield curves is used for investigating the relative impact of hydrogeological and physiological parameters in risk. Results show that characterization needs are dependent on the ratios between flow and transport scales within a risk-driven approach. Additionally, the results indicate that human health risk becomes less sensitive to hydrogeological measurements for large plumes. This indicates that under near-ergodic conditions, uncertainty reduction in human health risk may benefit from better understanding of the physiological component as opposed to a more detailed hydrogeological characterization.

Growth Models of Dyadic Synchrony and Mother-Child Vagal Tone in the Context of Parenting At-Risk

PubMed Central

Giuliano, Ryan J.; Skowron, Elizabeth A.; Berkman, Elliot T.

2015-01-01

We used multilevel modeling to examine dynamic changes in respiratory sinus arrhythmia (RSA) and observer-coded interactive synchrony for mother-child dyads engaged in a laboratory interaction, to characterize parenting-at-risk. Seventy-nine preschooler-mother dyads including a subset with documented child maltreatment (CM; n=43) were observed completing a joint puzzle task while physiological measures were recorded. Dyads led by CM mothers showed decreases in positive synchrony over time, whereas no variation was observed in non-CM dyads. Growth models of maternal RSA indicated that mothers who maintained high levels of positive interactive synchrony with their child evidenced greater RSA reactivity, characterized by an initial withdrawal followed by augmentation as the task progressed, after accounting for CM group status. These results help to clarify patterns of RSA responding in the context of caregiver-child interactions, and demonstrate the importance of modeling dynamic changes in physiology over time in order to better understanding biological correlates of parenting-at-risk. PMID:25542759

Characterizing the Solid-Solution Coefficient and Plant Uptake Factor of As, Cd and Pb in California Croplands

USDA-ARS?s Scientific Manuscript database

In risk assessment models, the solid-solution partition coefficient (Kd), and plant uptake factor (PUF), are often employed to model the fate and transport of trace elements in soils. The trustworthiness of risk assessments depends on the reliability of the parameters used. In this study, we exami...

Conceptual models for cumulative risk assessment.

PubMed

Linder, Stephen H; Sexton, Ken

2011-12-01

In the absence of scientific consensus on an appropriate theoretical framework, cumulative risk assessment and related research have relied on speculative conceptual models. We argue for the importance of theoretical backing for such models and discuss 3 relevant theoretical frameworks, each supporting a distinctive "family" of models. Social determinant models postulate that unequal health outcomes are caused by structural inequalities; health disparity models envision social and contextual factors acting through individual behaviors and biological mechanisms; and multiple stressor models incorporate environmental agents, emphasizing the intermediary role of these and other stressors. The conclusion is that more careful reliance on established frameworks will lead directly to improvements in characterizing cumulative risk burdens and accounting for disproportionate adverse health effects.

Conceptual Models for Cumulative Risk Assessment

PubMed Central

Sexton, Ken

2011-01-01

In the absence of scientific consensus on an appropriate theoretical framework, cumulative risk assessment and related research have relied on speculative conceptual models. We argue for the importance of theoretical backing for such models and discuss 3 relevant theoretical frameworks, each supporting a distinctive “family” of models. Social determinant models postulate that unequal health outcomes are caused by structural inequalities; health disparity models envision social and contextual factors acting through individual behaviors and biological mechanisms; and multiple stressor models incorporate environmental agents, emphasizing the intermediary role of these and other stressors. The conclusion is that more careful reliance on established frameworks will lead directly to improvements in characterizing cumulative risk burdens and accounting for disproportionate adverse health effects. PMID:22021317

Solid pulmonary nodule risk assessment and decision analysis: comparison of four prediction models in 285 cases.

PubMed

Perandini, Simone; Soardi, Gian Alberto; Motton, Massimiliano; Rossi, Arianna; Signorini, Manuel; Montemezzi, Stefania

2016-09-01

The aim of this study was to compare classification results from four major risk prediction models in a wide population of incidentally detected solitary pulmonary nodules (SPNs) which were selected to crossmatch inclusion criteria for the selected models. A total of 285 solitary pulmonary nodules with a definitive diagnosis were evaluated by means of four major risk assessment models developed from non-screening populations, namely the Mayo, Gurney, PKUPH and BIMC models. Accuracy was evaluated by receiver operating characteristic (ROC) area under the curve (AUC) analysis. Each model's fitness to provide reliable help in decision analysis was primarily assessed by adopting a surgical threshold of 65 % and an observation threshold of 5 % as suggested by ACCP guidelines. ROC AUC values, false positives, false negatives and indeterminate nodules were respectively 0.775, 3, 8, 227 (Mayo); 0.794, 41, 6, 125 (Gurney); 0.889, 42, 0, 144 (PKUPH); 0.898, 16, 0, 118 (BIMC). Resultant data suggests that the BIMC model may be of greater help than Mayo, Gurney and PKUPH models in preoperative SPN characterization when using ACCP risk thresholds because of overall better accuracy and smaller numbers of indeterminate nodules and false positive results. • The BIMC and PKUPH models offer better characterization than older prediction models • Both the PKUPH and BIMC models completely avoided false negative results • The Mayo model suffers from a large number of indeterminate results.

Characterization of Rocket Propellant Combustion Products. Chemical Characterization and Computer Modeling of the Exhaust Products from Four Propellant Formulations

DTIC Science & Technology

1990-12-31

health hazards from weapons combustion products, to include rockets and missiles, became evident, Research to elucidate significant health effects of...CO/CO2 ratios was low for all but one of dhe formulations, In general, if the model were to be used in its present state for health risk assessments...35 Part 2: Modeling for Health Hazard Prediction Introduction ................................................. 37 Results and D iscussion

COMPUTATIONAL TOXICOLOGY: AN APPROACH FOR PRIORITIZING CHEMICAL RISK ASSESSMENTS

EPA Science Inventory

Characterizing toxic effects for industrial chemicals carries the challenge of focusing resources on the greatest potential risks for human health and the environment. The union of molecular modeling, bioinformatics and simulation of complex systems with emerging technologies suc...

Prospect theory on the brain? Toward a cognitive neuroscience of decision under risk.

PubMed

Trepel, Christopher; Fox, Craig R; Poldrack, Russell A

2005-04-01

Most decisions must be made without advance knowledge of their consequences. Economists and psychologists have devoted much attention to modeling decisions made under conditions of risk in which options can be characterized by a known probability distribution over possible outcomes. The descriptive shortcomings of classical economic models motivated the development of prospect theory (D. Kahneman, A. Tversky, Prospect theory: An analysis of decision under risk. Econometrica, 4 (1979) 263-291; A. Tversky, D. Kahneman, Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5 (4) (1992) 297-323) the most successful behavioral model of decision under risk. In the prospect theory, subjective value is modeled by a value function that is concave for gains, convex for losses, and steeper for losses than for gains; the impact of probabilities are characterized by a weighting function that overweights low probabilities and underweights moderate to high probabilities. We outline the possible neural bases of the components of prospect theory, surveying evidence from human imaging, lesion, and neuropharmacology studies as well as animal neurophysiology studies. These results provide preliminary suggestions concerning the neural bases of prospect theory that include a broad set of brain regions and neuromodulatory systems. These data suggest that focused studies of decision making in the context of quantitative models may provide substantial leverage towards a fuller understanding of the cognitive neuroscience of decision making.

Modeling tools for the assessment of microbiological risks during floods: a review

NASA Astrophysics Data System (ADS)

Collender, Philip; Yang, Wen; Stieglitz, Marc; Remais, Justin

2015-04-01

Floods are a major, recurring source of harm to global economies and public health. Projected increases in the frequency and intensity of heavy precipitation events under future climate change, coupled with continued urbanization in areas with high risk of floods, may exacerbate future impacts of flooding. Improved flood risk management is essential to support global development, poverty reduction and public health, and is likely to be a crucial aspect of climate change adaptation. Importantly, floods can facilitate the transmission of waterborne pathogens by changing social conditions (overcrowding among displaced populations, interruption of public health services), imposing physical challenges to infrastructure (sewerage overflow, reduced capacity to treat drinking water), and altering fate and transport of pathogens (transport into waterways from overland flow, resuspension of settled contaminants) during and after flood conditions. Hydrological and hydrodynamic models are capable of generating quantitative characterizations of microbiological risks associated with flooding, while accounting for these diverse and at times competing physical and biological processes. Despite a few applications of such models to the quantification of microbiological risks associated with floods, there exists limited guidance as to the relative capabilities, and limitations, of existing modeling platforms when used for this purpose. Here, we review 17 commonly used flood and water quality modeling tools that have demonstrated or implicit capabilities of mechanistically representing and quantifying microbial risk during flood conditions. We compare models with respect to their capabilities of generating outputs that describe physical and microbial conditions during floods, such as concentration or load of non-cohesive sediments or pathogens, and the dynamics of high flow conditions. Recommendations are presented for the application of specific modeling tools for assessing particular flood-related microbial risks, and model improvements are suggested that may better characterize key microbial risks during flood events. The state of current tools are assessed in the context of a changing climate where the frequency, intensity and duration of flooding are shifting in some areas.

QMRA for Drinking Water: 2. The Effect of Pathogen Clustering in Single-Hit Dose-Response Models.

PubMed

Nilsen, Vegard; Wyller, John

2016-01-01

Spatial and/or temporal clustering of pathogens will invalidate the commonly used assumption of Poisson-distributed pathogen counts (doses) in quantitative microbial risk assessment. In this work, the theoretically predicted effect of spatial clustering in conventional "single-hit" dose-response models is investigated by employing the stuttering Poisson distribution, a very general family of count distributions that naturally models pathogen clustering and contains the Poisson and negative binomial distributions as special cases. The analysis is facilitated by formulating the dose-response models in terms of probability generating functions. It is shown formally that the theoretical single-hit risk obtained with a stuttering Poisson distribution is lower than that obtained with a Poisson distribution, assuming identical mean doses. A similar result holds for mixed Poisson distributions. Numerical examples indicate that the theoretical single-hit risk is fairly insensitive to moderate clustering, though the effect tends to be more pronounced for low mean doses. Furthermore, using Jensen's inequality, an upper bound on risk is derived that tends to better approximate the exact theoretical single-hit risk for highly overdispersed dose distributions. The bound holds with any dose distribution (characterized by its mean and zero inflation index) and any conditional dose-response model that is concave in the dose variable. Its application is exemplified with published data from Norovirus feeding trials, for which some of the administered doses were prepared from an inoculum of aggregated viruses. The potential implications of clustering for dose-response assessment as well as practical risk characterization are discussed. © 2016 Society for Risk Analysis.

Comprehensive, Quantitative Risk Assessment of CO{sub 2} Geologic Sequestration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lepinski, James

2013-09-30

A Quantitative Failure Modes and Effects Analysis (QFMEA) was developed to conduct comprehensive, quantitative risk assessments on CO{sub 2} capture, transportation, and sequestration or use in deep saline aquifers, enhanced oil recovery operations, or enhanced coal bed methane operations. The model identifies and characterizes potential risks; identifies the likely failure modes, causes, effects and methods of detection; lists possible risk prevention and risk mitigation steps; estimates potential damage recovery costs, mitigation costs and costs savings resulting from mitigation; and ranks (prioritizes) risks according to the probability of failure, the severity of failure, the difficulty of early failure detection and themore » potential for fatalities. The QFMEA model generates the necessary information needed for effective project risk management. Diverse project information can be integrated into a concise, common format that allows comprehensive, quantitative analysis, by a cross-functional team of experts, to determine: What can possibly go wrong? How much will damage recovery cost? How can it be prevented or mitigated? What is the cost savings or benefit of prevention or mitigation? Which risks should be given highest priority for resolution? The QFMEA model can be tailored to specific projects and is applicable to new projects as well as mature projects. The model can be revised and updated as new information comes available. It accepts input from multiple sources, such as literature searches, site characterization, field data, computer simulations, analogues, process influence diagrams, probability density functions, financial analysis models, cost factors, and heuristic best practices manuals, and converts the information into a standardized format in an Excel spreadsheet. Process influence diagrams, geologic models, financial models, cost factors and an insurance schedule were developed to support the QFMEA model. Comprehensive, quantitative risk assessments were conducted on three (3) sites using the QFMEA model: (1) SACROC Northern Platform CO{sub 2}-EOR Site in the Permian Basin, Scurry County, TX, (2) Pump Canyon CO{sub 2}-ECBM Site in the San Juan Basin, San Juan County, NM, and (3) Farnsworth Unit CO{sub 2}-EOR Site in the Anadarko Basin, Ochiltree County, TX. The sites were sufficiently different from each other to test the robustness of the QFMEA model.« less

Modeling Finite-Time Failure Probabilities in Risk Analysis Applications.

PubMed

Dimitrova, Dimitrina S; Kaishev, Vladimir K; Zhao, Shouqi

2015-10-01

In this article, we introduce a framework for analyzing the risk of systems failure based on estimating the failure probability. The latter is defined as the probability that a certain risk process, characterizing the operations of a system, reaches a possibly time-dependent critical risk level within a finite-time interval. Under general assumptions, we define two dually connected models for the risk process and derive explicit expressions for the failure probability and also the joint probability of the time of the occurrence of failure and the excess of the risk process over the risk level. We illustrate how these probabilistic models and results can be successfully applied in several important areas of risk analysis, among which are systems reliability, inventory management, flood control via dam management, infectious disease spread, and financial insolvency. Numerical illustrations are also presented. © 2015 Society for Risk Analysis.

Characterization of drinking water treatment for virus risk assessment.

PubMed

Teunis, P F M; Rutjes, S A; Westrell, T; de Roda Husman, A M

2009-02-01

Removal or inactivation of viruses in drinking water treatment processes can be quantified by measuring the concentrations of viruses or virus indicators in water before and after treatment. Virus reduction is then calculated from the ratio of these concentrations. Most often only the average reduction is reported. That is not sufficient when treatment efficiency must be characterized in quantitative risk assessment. We present three simple models allowing statistical analysis of series of counts before and after treatment: distribution of the ratio of concentrations, and distribution of the probability of passage for unpaired and paired water samples. Performance of these models is demonstrated for several processes (long and short term storage, coagulation/filtration, coagulation/sedimentation, slow sand filtration, membrane filtration, and ozone disinfection) using microbial indicator data from full-scale treatment processes. All three models allow estimation of the variation in (log) reduction as well as its uncertainty; the results can be easily used in risk assessment. Although they have different characteristics and are present in vastly different concentrations, different viruses and/or bacteriophages appear to show similar reductions in a particular treatment process, allowing generalization of the reduction for each process type across virus groups. The processes characterized in this paper may be used as reference for waterborne virus risk assessment, to check against location specific data, and in case no such data are available, to use as defaults.

Characterizing the Intersection of Co-Occurring Risk Factors for Illicit Drug Abuse and Dependence in a U.S. Nationally Representative Sample

PubMed Central

Kurti, Allison N.; Keith, Diana R.; Noble, Alyssa; Priest, Jeff S.; Sprague, Brian; Higgins, Stephen T.

2016-01-01

Few studies have attempted to characterize how co-occurring risk factors for substance use disorders intersect. A recent study examined this question regarding cigarette smoking and demonstrated that co-occurring risk factors generally act independently. The present study examines whether that same pattern of independent intersection of risk factors extends to illicit drug abuse/dependence using a U.S. nationally representative sample (National Survey on Drug Use and Health, 2011–2013). Logistic regression and classification and regression tree (CART) modeling were used to examine risk of past-year drug abuse/dependence associated with a well-established set of risk factors for substance use (age, gender, race/ethnicity, education, poverty, smoking status, alcohol abuse/dependence, mental illness). Each of these risk factors was associated with significant increases in the odds of drug abuse/dependence in univariate logistic regressions. Each remained significant in a multivariate model examining all eight risk factors simultaneously. CART modeling of these 8 risk factors identified subpopulation risk profiles wherein drug abuse/dependence prevalence varied from < 1% to > 80% corresponding to differing combinations of risk factors present. Alcohol abuse/dependence and cigarette smoking had the strongest associations with drug abuse/dependence risk. These results demonstrate that co-occurring risk factors for illicit drug/abuse dependence generally intersect in the same independent manner as risk factors for cigarette smoking, underscoring further fundamental commonalities across these different types of substance use disorders. These results also underscore the fundamental importance of differences in the presence of co-occurring risk factors when considering the often strikingly different prevalence rates of illicit drug abuse/dependence in U.S. population subgroups. PMID:27687534

Developing Hydrogeological Site Characterization Strategies based on Human Health Risk

NASA Astrophysics Data System (ADS)

de Barros, F.; Rubin, Y.; Maxwell, R. M.

2013-12-01

In order to provide better sustainable groundwater quality management and minimize the impact of contamination in humans, improved understanding and quantification of the interaction between hydrogeological models, geological site information and human health are needed. Considering the joint influence of these components in the overall human health risk assessment and the corresponding sources of uncertainty aid decision makers to better allocate resources in data acquisition campaigns. This is important to (1) achieve remediation goals in a cost-effective manner, (2) protect human health and (3) keep water supplies clean in order to keep with quality standards. Such task is challenging since a full characterization of the subsurface is unfeasible due to financial and technological constraints. In addition, human exposure and physiological response to contamination are subject to uncertainty and variability. Normally, sampling strategies are developed with the goal of reducing uncertainty, but less often they are developed in the context of their impacts on the overall system uncertainty. Therefore, quantifying the impact from each of these components (hydrogeological, behavioral and physiological) in final human health risk prediction can provide guidance for decision makers to best allocate resources towards minimal prediction uncertainty. In this presentation, a multi-component human health risk-based framework is presented which allows decision makers to set priorities through an information entropy-based visualization tool. Results highlight the role of characteristic length-scales characterizing flow and transport in determining data needs within an integrated hydrogeological-health framework. Conditions where uncertainty reduction in human health risk predictions may benefit from better understanding of the health component, as opposed to a more detailed hydrogeological characterization, are also discussed. Finally, results illustrate how different dose-response models can impact the probability of human health risk exceeding a regulatory threshold.

Reconstructing Exposures from Biomarkers using Exposure-Pharmacokinetic Modeling - A Case Study with Carbaryl

EPA Science Inventory

Sources of uncertainty involved in exposure reconstruction for a short half-life chemical, carbaryl, were characterized using the Cumulative and Aggregate Risk Evaluation System (CARES), an exposure model, and a human physiologically based pharmacokinetic (PBPK) model. CARES was...

Characterization of NEOs from the Policy Perspective: Implications from Problem and Solution Definitions

NASA Astrophysics Data System (ADS)

Lindquist, E.

2015-12-01

The characterization of near-Earth-objects (NEOs) in regard to physical attributes and potential risk and impact factors presents a complex and complicates scientific and engineering challenge. The societal and policy risks and impacts are no less complex, yet are rarely considered in the same context as material properties or related factors. The objective of this contribution is to position the characterization of NEOs within the public policy process domain as a means to reflect on the science-policy nexus in regard to risks associated with NEOs. This will be accomplished through, first, a brief overview of the science-policy nexus, followed by a discussion of several policy process frameworks, such as agenda setting and the multiple streams model, focusing events, and punctuated equilibrium, and their application and appropriateness to the problem of NEOs. How, too, for example, does NEO hazard and risk compare with other low probability, high risk, hazards in regard to public policy? Finally, we will reflect on the implications of alternative NEO "solutions" and the characterization of the NEO "problem," and the political and public acceptance of policy alternatives as a way to link NEO science and policy in the context of the overall NH004 panel.

Identification of Major Risk Sources for Surface Water Pollution by Risk Indexes (RI) in the Multi-Provincial Boundary Region of the Taihu Basin, China

PubMed Central

Yao, Hong; Li, Weixin; Qian, Xin

2015-01-01

Environmental safety in multi-district boundary regions has been one of the focuses in China and is mentioned many times in the Environmental Protection Act of 2014. Five types were categorized concerning the risk sources for surface water pollution in the multi-provincial boundary region of the Taihu basin: production enterprises, waste disposal sites, chemical storage sites, agricultural non-point sources and waterway transportations. Considering the hazard of risk sources, the purification property of environmental medium and the vulnerability of risk receptors, 52 specific attributes on the risk levels of each type of risk source were screened out. Continuous piecewise linear function model, expert consultation method and fuzzy integral model were used to calculate the integrated risk indexes (RI) to characterize the risk levels of pollution sources. In the studied area, 2716 pollution sources were characterized by RI values. There were 56 high-risk sources screened out as major risk sources, accounting for about 2% of the total. The numbers of sources with high-moderate, moderate, moderate-low and low pollution risk were 376, 1059, 101 and 1124, respectively, accounting for 14%, 38%, 5% and 41% of the total. The procedure proposed could be included in the integrated risk management systems of the multi-district boundary region of the Taihu basin. It could help decision makers to identify major risk sources in the risk prevention and reduction of surface water pollution. PMID:26308032

Identification of Major Risk Sources for Surface Water Pollution by Risk Indexes (RI) in the Multi-Provincial Boundary Region of the Taihu Basin, China.

PubMed

Yao, Hong; Li, Weixin; Qian, Xin

2015-08-21

Environmental safety in multi-district boundary regions has been one of the focuses in China and is mentioned many times in the Environmental Protection Act of 2014. Five types were categorized concerning the risk sources for surface water pollution in the multi-provincial boundary region of the Taihu basin: production enterprises, waste disposal sites, chemical storage sites, agricultural non-point sources and waterway transportations. Considering the hazard of risk sources, the purification property of environmental medium and the vulnerability of risk receptors, 52 specific attributes on the risk levels of each type of risk source were screened out. Continuous piecewise linear function model, expert consultation method and fuzzy integral model were used to calculate the integrated risk indexes (RI) to characterize the risk levels of pollution sources. In the studied area, 2716 pollution sources were characterized by RI values. There were 56 high-risk sources screened out as major risk sources, accounting for about 2% of the total. The numbers of sources with high-moderate, moderate, moderate-low and low pollution risk were 376, 1059, 101 and 1124, respectively, accounting for 14%, 38%, 5% and 41% of the total. The procedure proposed could be included in the integrated risk management systems of the multi-district boundary region of the Taihu basin. It could help decision makers to identify major risk sources in the risk prevention and reduction of surface water pollution.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Unwin, Stephen D.; Lowry, Peter P.; Layton, Robert F.

This is a working report drafted under the Risk-Informed Safety Margin Characterization pathway of the Light Water Reactor Sustainability Program, describing statistical models of passives component reliabilities.

Epithelial perturbation by inhaled chlorine: Multi-scale mechanistic modeling in rats and humans

EPA Science Inventory

Chlorine is a high-production volume, hazardous air pollutant and irritant gas of interest to homeland security. Thus, scenarios of interest for risk characterization range from acute high-level exposures to lower-level chronic exposures. Risk assessment approaches to estimate ...

COMPARATIVE EVALUATION OF RISK FACTORS FOR CARDIOVASCULAR DISEASE (CVD) IN GENETICALLY PREDISPOSED RATS

EPA Science Inventory

Rodent CVD models are increasingly used for understanding individual differences in susceptibility to environmental stressors such as air pollution. We characterized pathologies and a number of known human risk factors of CVD in genetically predisposed, male young adult Spontaneo...

Natural hazard modeling and uncertainty analysis [Chapter 2

Treesearch

Matthew Thompson; Jord J. Warmink

2017-01-01

Modeling can play a critical role in assessing and mitigating risks posed by natural hazards. These modeling efforts generally aim to characterize the occurrence, intensity, and potential consequences of natural hazards. Uncertainties surrounding the modeling process can have important implications for the development, application, evaluation, and interpretation of...

Recent Seismicity in Texas and Research Design and Progress of the TexNet-CISR Collaboration

NASA Astrophysics Data System (ADS)

Hennings, P.; Savvaidis, A.; Rathje, E.; Olson, J. E.; DeShon, H. R.; Datta-Gupta, A.; Eichhubl, P.; Nicot, J. P.; Kahlor, L. A.

2017-12-01

The recent increase in the rate of seismicity in Texas has prompted the establishment of an interdisciplinary, interinstitutional collaboration led by the Texas Bureau of Economic Geology which includes the TexNet Seismic Monitoring and Research project as funded by The State of Texas (roughly 2/3rds of our funding) and the industry-funded Center for Integrated Seismicity Research (CISR) (1/3 of funding). TexNet is monitoring and cataloging seismicity across Texas using a new backbone seismic network, investigating site-specific earthquake sequences by deploying temporary seismic monitoring stations, and conducting reservoir modeling studies. CISR expands TexNet research into the interdisciplinary realm to more thoroughly study the factors that contribute to seismicity, characterize the associated hazard and risk, develop strategies for mitigation and management, and develop methods of effective communication for all stakeholders. The TexNet-CISR research portfolio has 6 themes: seismicity monitoring, seismology, geologic and hydrologic description, geomechanics and reservoir modeling, seismic hazard and risk assessment, and seismic risk social science. Twenty+ specific research projects span and connect these themes. We will provide a synopsis of research progress including recent seismicity trends in Texas; Fort Worth Basin integrated studies including geological modeling and fault characterization, fluid injection data syntheses, and reservoir and geomechanical modeling; regional ground shaking characterization and mapping, infrastructure vulnerability assessment; and social science topics of public perception and information seeking behavior.

Relating Data and Models to Characterize Parameter and Prediction Uncertainty

EPA Science Inventory

Applying PBPK models in risk analysis requires that we realistically assess the uncertainty of relevant model predictions in as quantitative a way as possible. The reality of human variability may add a confusing feature to the overall uncertainty assessment, as uncertainty and v...

Modeling Two Types of Adaptation to Climate Change

EPA Science Inventory

Mitigation and adaptation are the two key responses available to policymakers to reduce the risks of climate change. We model these two policies together in a new DICE-based integrated assessment model that characterizes adaptation as either short-lived flow spending or long-live...

EFFECTS OF CHRONIC STRESS ON WILDLIFE POPULATIONS: A POPULATION MODELING APPROACH AND CASE STUDY

EPA Science Inventory

This chapter describes a matrix modeling approach to characterize and project risks to wildlife populations subject to chronic stress. Population matrix modeling was used to estimate effects of one class of environmental contaminants, dioxin-like compounds (DLCs), to populations ...

Development and Application of a Three-Dimensional Finite Element Vapor Intrusion Model

PubMed Central

Pennell, Kelly G.; Bozkurt, Ozgur; Suuberg, Eric M.

2010-01-01

Details of a three-dimensional finite element model of soil vapor intrusion, including the overall modeling process and the stepwise approach, are provided. The model is a quantitative modeling tool that can help guide vapor intrusion characterization efforts. It solves the soil gas continuity equation coupled with the chemical transport equation, allowing for both advective and diffusive transport. Three-dimensional pressure, velocity, and chemical concentration fields are produced from the model. Results from simulations involving common site features, such as impervious surfaces, porous foundation sub-base material, and adjacent structures are summarized herein. The results suggest that site-specific features are important to consider when characterizing vapor intrusion risks. More importantly, the results suggest that soil gas or subslab gas samples taken without proper regard for particular site features may not be suitable for evaluating vapor intrusion risks; rather, careful attention needs to be given to the many factors that affect chemical transport into and around buildings. PMID:19418819

A diversity index for model space selection in the estimation of benchmark and infectious doses via model averaging.

PubMed

Kim, Steven B; Kodell, Ralph L; Moon, Hojin

2014-03-01

In chemical and microbial risk assessments, risk assessors fit dose-response models to high-dose data and extrapolate downward to risk levels in the range of 1-10%. Although multiple dose-response models may be able to fit the data adequately in the experimental range, the estimated effective dose (ED) corresponding to an extremely small risk can be substantially different from model to model. In this respect, model averaging (MA) provides more robustness than a single dose-response model in the point and interval estimation of an ED. In MA, accounting for both data uncertainty and model uncertainty is crucial, but addressing model uncertainty is not achieved simply by increasing the number of models in a model space. A plausible set of models for MA can be characterized by goodness of fit and diversity surrounding the truth. We propose a diversity index (DI) to balance between these two characteristics in model space selection. It addresses a collective property of a model space rather than individual performance of each model. Tuning parameters in the DI control the size of the model space for MA. © 2013 Society for Risk Analysis.

Thinking through cancer risk: characterizing smokers' process of risk determination.

PubMed

Hay, Jennifer; Shuk, Elyse; Cruz, Gustavo; Ostroff, Jamie

2005-10-01

The perception of cancer risk motivates cancer risk reduction behaviors. However, common measurement strategies for cancer risk perceptions, which involve numerical likelihood estimates, do not adequately capture individuals' thoughts and feelings about cancer risk. To guide the development of novel measurement strategies, the authors used semistructured interviews to examine the thought processes used by smokers (N = 15) as they considered their cancer risk. They used grounded theory to guide systematic data coding and develop a heuristic model describing smokers' risk perception process that includes a cognitive, primarily rational process whereby salient personal risk factors for cancer are considered and combined, and an affective/attitudinal process, which shifts risk perceptions either up or down. The model provides a tentative explanation concerning how people hold cancer risk perceptions that diverge from rational assessment of their risks and will be useful in guiding the development of non-numerical measurements strategies for cancer risk perceptions.

Groundwater vulnerability assessment for the Banyas Catchment of the Syrian coastal area using GIS and the RISKE method.

PubMed

Kattaa, Bassam; Al-Fares, Walid; Al Charideh, Abdul Rahman

2010-05-01

Vulnerability assessment to delineate areas that are more susceptible to contamination from anthropogenic sources has become an important element for sensible resource management and landuse planning. This contribution aims at estimating aquifer vulnerability by applying the RISKE model in Banyas Catchment Area (BCA), Tartous Prefecture, west Syria. An additional objective is to demonstrate the combined use of the RISKE model and a geographical information system (GIS) as an effective method for groundwater pollution risk assessment. The RISKE model uses five environmental parameters (Rock of aquifer media, Infiltration, Soil media, Karst, and Epikarst) to characterize the hydro-geological setting and evaluate aquifer vulnerability. The elevated eastern and low western part of the study area was dominated by high vulnerability classes, while the middle part was characterized by moderate vulnerability classes. Based on the vulnerability analysis, it was found that 2% and 39% of BCA is under low and high vulnerability to groundwater contamination, respectively, while more than 52% and 5% of the area of BCA can be designated as an area of moderate and very high vulnerability to groundwater contamination, respectively. The GIS technique has provided an efficient environment for analyses and high capabilities of handling a large amount of spatial data. Copyright 2009 Elsevier Ltd. All rights reserved.

Which Fearful Toddlers Should We Worry About? Context, Fear Regulation, and Anxiety Risk

PubMed Central

Buss, Kristin A.

2010-01-01

The current study tests a model of risk for anxiety in fearful toddlers characterized by the regulation of the intensity of withdrawal behavior across a variety of contexts. Participants included 111, low-risk, 24-month-old toddlers followed longitudinally each year through the fall of their kindergarten year. The key hypothesis was that being fearful in situations that are relatively low in threat (i.e., are predictable, controllable, and in which children have many coping resources) is an early precursor to risk for anxiety development as measured by parent and teacher report of anxious behaviors in kindergarten. Results supported the prediction such that it is not how much fear is expressed, but when the fear is expressed and how it is expressed that is important for characterizing adaptive behavior. Implications are discussed for a model of risk that includes the regulation of fear, the role of eliciting context, social wariness, and the importance of examining developmental transitions, such as the start of formal schooling. These findings have implications for the way we identify fearful children who may be at risk for developing anxiety-related problems. PMID:21463035

Derivation of risk indices and analysis of variablility for the management of incidents involving the transport of nuclear materials in the Northern Seas.

PubMed

Brown, J; Hosseini, A; Karcher, M; Kauker, F; Dowdall, M; Schnur, R; Strand, P

2016-04-15

The transport of nuclear or radioactive materials and the presence of nuclear powered vessels pose risks to the Northern Seas in terms of potential impacts to man and environment as well socio-economic impacts. Management of incidents involving actual or potential releases to the marine environment are potentially difficult due to the complexity of the environment into which the release may occur and difficulties in quantifying risk to both man and environment. In order to address this, a state of the art oceanographic model was used to characterize the underlying variability for a specific radionuclide release scenario. The resultant probabilistic data were used as inputs to transfer and dose models providing an indication of potential impacts for man and environment This characterization was then employed to facilitate a rapid means of quantifying risk to man and the environment that included and addressed this variability. The radionuclide specific risk indices derived can be applied by simply multiplying the reported values by the magnitude of the source term and thereafter summing over all radionuclides to provide an indication of total risk. Copyright © 2016. Published by Elsevier Ltd.

Space Radiation Cancer Risk Projections and Uncertainties - 2010

NASA Technical Reports Server (NTRS)

Cucinotta, Francis A.; Kim, Myung-Hee Y.; Chappell, Lori J.

2011-01-01

Uncertainties in estimating health risks from galactic cosmic rays greatly limit space mission lengths and potential risk mitigation evaluations. NASA limits astronaut exposures to a 3% risk of exposure-induced death and protects against uncertainties using an assessment of 95% confidence intervals in the projection model. Revisions to this model for lifetime cancer risks from space radiation and new estimates of model uncertainties are described here. We review models of space environments and transport code predictions of organ exposures, and characterize uncertainties in these descriptions. We summarize recent analysis of low linear energy transfer radio-epidemiology data, including revision to Japanese A-bomb survivor dosimetry, longer follow-up of exposed cohorts, and reassessments of dose and dose-rate reduction effectiveness factors. We compare these projections and uncertainties with earlier estimates. Current understanding of radiation quality effects and recent data on factors of relative biological effectiveness and particle track structure are reviewed. Recent radiobiology experiment results provide new information on solid cancer and leukemia risks from heavy ions. We also consider deviations from the paradigm of linearity at low doses of heavy ions motivated by non-targeted effects models. New findings and knowledge are used to revise the NASA risk projection model for space radiation cancer risks.

Modeling Adaptation as a Flow and Stock Decsion with Mitigation

EPA Science Inventory

Mitigation and adaptation are the two key responses available to policymakers to reduce the risks of climate change. We model these two policies together in a new DICE-based integrated assessment model that characterizes adaptation as either short-lived flow spending or long-live...

Modeling Adaptation as a Flow and Stock Decision with Mitigation

EPA Science Inventory

Mitigation and adaptation are the two key responses available to policymakers to reduce the risks of climate change. We model these two policies together in a new DICE-based integrated assessment model that characterizes adaptation as either short-lived flow spending or long-liv...

Effects of radon mitigation vs smoking cessation in reducing radon-related risk of lung cancer.

PubMed Central

Mendez, D; Warner, K E; Courant, P N

1998-01-01

OBJECTIVES: The purpose of this paper is to provide smokers with information on the relative benefits of mitigating radon and quitting smoking in reducing radon-related lung cancer risk. METHODS: The standard radon risk model, linked with models characterizing residential radon exposure and patterns of moving to new homes, was used to estimate the risk reduction produced by remediating high-radon homes, quitting smoking, or both. RESULTS: Quitting smoking reduces lung cancer risk from radon more than does reduction of radon exposure itself. CONCLUSIONS: Smokers should understand that, in addition to producing other health benefits, quitting smoking dominates strategies to deal with the problem posed by radon. PMID:9585753

Sensitivity of Asteroid Impact Risk to Uncertainty in Asteroid Properties and Entry Parameters

NASA Astrophysics Data System (ADS)

Wheeler, Lorien; Mathias, Donovan; Dotson, Jessie L.; NASA Asteroid Threat Assessment Project

2017-10-01

A central challenge in assessing the threat posed by asteroids striking Earth is the large amount of uncertainty inherent throughout all aspects of the problem. Many asteroid properties are not well characterized and can range widely from strong, dense, monolithic irons to loosely bound, highly porous rubble piles. Even for an object of known properties, the specific entry velocity, angle, and impact location can swing the potential consequence from no damage to causing millions of casualties. Due to the extreme rarity of large asteroid strikes, there are also large uncertainties in how different types of asteroids will interact with the atmosphere during entry, how readily they may break up or ablate, and how much surface damage will be caused by the resulting airbursts or impacts.In this work, we use our Probabilistic Asteroid Impact Risk (PAIR) model to investigate the sensitivity of asteroid impact damage to uncertainties in key asteroid properties, entry parameters, or modeling assumptions. The PAIR model combines physics-based analytic models of asteroid entry and damage in a probabilistic Monte Carlo framework to assess the risk posed by a wide range of potential impacts. The model samples from uncertainty distributions of asteroid properties and entry parameters to generate millions of specific impact cases, and models the atmospheric entry and damage for each case, including blast overpressure, thermal radiation, tsunami inundation, and global effects. To assess the risk sensitivity, we alternately fix and vary the different input parameters and compare the effect on the resulting range of damage produced. The goal of these studies is to help guide future efforts in asteroid characterization and model refinement by determining which properties most significantly affect the potential risk.

WHAT ARE THE BEST MEANS TO ASSESS SITES AND MOVE TOWARD CLOSURE, USING APPROPRIATE SITE SPECIFIC RISK EVALUATIONS?

EPA Science Inventory

To facilitate evaluation of existing site characterization data, ORD has developed on-line tools and models that integrate data and models into innovative applications. Forty calculators have been developed in four groups: parameter estimators, models, scientific demos and unit ...

Modeling Environment for Total Risk-1A

EPA Science Inventory

MENTOR-1A uses an integrated, mechanistically consistent source-to-dose modeling framework to quantify inhalation exposure and dose for individuals and/or populations due to co-occurring air pollutants. It uses the "One Atmosphere" concept to characterize simultaneous exposures t...

Modeling the Impact of Control on the Attractiveness of Risk in a Prospect Theory Framework

PubMed Central

Young, Diana L.; Goodie, Adam S.; Hall, Daniel B.

2010-01-01

Many decisions involve a degree of personal control over event outcomes, which is exerted through one’s knowledge or skill. In three experiments we investigated differences in decision making between prospects based on a) the outcome of random events and b) the outcome of events characterized by control. In Experiment 1, participants estimated certainty equivalents (CEs) for bets based on either random events or the correctness of their answers to U.S. state population questions across the probability spectrum. In Experiment 2, participants estimated CEs for bets based on random events, answers to U.S. state population questions, or answers to questions about 2007 NCAA football game results. Experiment 3 extended the same procedure as Experiment 1 using a within-subjects design. We modeled data from all experiments in a prospect theory framework to establish psychological mechanisms underlying decision behavior. Participants weighted the probabilities associated with bets characterized by control so as to reflect greater risk attractiveness relative to bets based on random events, as evidenced by more elevated weighting functions under conditions of control. This research elucidates possible cognitive mechanisms behind increased risk taking for decisions characterized by control, and implications for various literatures are discussed. PMID:21278906

Modeling the Impact of Control on the Attractiveness of Risk in a Prospect Theory Framework.

PubMed

Young, Diana L; Goodie, Adam S; Hall, Daniel B

2011-01-01

Many decisions involve a degree of personal control over event outcomes, which is exerted through one's knowledge or skill. In three experiments we investigated differences in decision making between prospects based on a) the outcome of random events and b) the outcome of events characterized by control. In Experiment 1, participants estimated certainty equivalents (CEs) for bets based on either random events or the correctness of their answers to U.S. state population questions across the probability spectrum. In Experiment 2, participants estimated CEs for bets based on random events, answers to U.S. state population questions, or answers to questions about 2007 NCAA football game results. Experiment 3 extended the same procedure as Experiment 1 using a within-subjects design. We modeled data from all experiments in a prospect theory framework to establish psychological mechanisms underlying decision behavior. Participants weighted the probabilities associated with bets characterized by control so as to reflect greater risk attractiveness relative to bets based on random events, as evidenced by more elevated weighting functions under conditions of control. This research elucidates possible cognitive mechanisms behind increased risk taking for decisions characterized by control, and implications for various literatures are discussed.

Decision modeling for fire incident analysis

Treesearch

Donald G. MacGregor; Armando González-Cabán

2009-01-01

This paper reports on methods for representing and modeling fire incidents based on concepts and models from the decision and risk sciences. A set of modeling techniques are used to characterize key fire management decision processes and provide a basis for incident analysis. The results of these methods can be used to provide insights into the structure of fire...

Tailoring the implementation of new biomarkers based on their added predictive value in subgroups of individuals.

PubMed

van Giessen, A; Moons, K G M; de Wit, G A; Verschuren, W M M; Boer, J M A; Koffijberg, H

2015-01-01

The value of new biomarkers or imaging tests, when added to a prediction model, is currently evaluated using reclassification measures, such as the net reclassification improvement (NRI). However, these measures only provide an estimate of improved reclassification at population level. We present a straightforward approach to characterize subgroups of reclassified individuals in order to tailor implementation of a new prediction model to individuals expected to benefit from it. In a large Dutch population cohort (n = 21,992) we classified individuals to low (< 5%) and high (≥ 5%) fatal cardiovascular disease risk by the Framingham risk score (FRS) and reclassified them based on the systematic coronary risk evaluation (SCORE). Subsequently, we characterized the reclassified individuals and, in case of heterogeneity, applied cluster analysis to identify and characterize subgroups. These characterizations were used to select individuals expected to benefit from implementation of SCORE. Reclassification after applying SCORE in all individuals resulted in an NRI of 5.00% (95% CI [-0.53%; 11.50%]) within the events, 0.06% (95% CI [-0.08%; 0.22%]) within the nonevents, and a total NRI of 0.051 (95% CI [-0.004; 0.116]). Among the correctly downward reclassified individuals cluster analysis identified three subgroups. Using the characterizations of the typically correctly reclassified individuals, implementing SCORE only in individuals expected to benefit (n = 2,707,12.3%) improved the NRI to 5.32% (95% CI [-0.13%; 12.06%]) within the events, 0.24% (95% CI [0.10%; 0.36%]) within the nonevents, and a total NRI of 0.055 (95% CI [0.001; 0.123]). Overall, the risk levels for individuals reclassified by tailored implementation of SCORE were more accurate. In our empirical example the presented approach successfully characterized subgroups of reclassified individuals that could be used to improve reclassification and reduce implementation burden. In particular when newly added biomarkers or imaging tests are costly or burdensome such a tailored implementation strategy may save resources and improve (cost-)effectiveness.

“A Modeling Framework for Improved Characterization of Near-Road Air Quality at Fine Scales for Nationwide Exposure Assessment.”

EPA Science Inventory

Communities at the proximity of roadways are exposed to high levels of air pollution from automobile exhaust and are under potential risk of adverse health effects. To understand the relationship between air pollution and adverse health effects, exposure and risk assessment studi...

Patterns and Consequences of in ovo Exposure to Methylmercury in Common Loons, poster presentation

EPA Science Inventory

A critical component of a common loon/mercury (Hg) risk assessment model under development is the determination of the concentration of Hg in eggs that poses a population level risk. We conducted a field study to (1) characterize in ovo methylmercury (MeHg) exposure in Wisconsin...

Trajectories of Problem Behaviors from 4 to 23 Years in Former Preterm Infants

ERIC Educational Resources Information Center

Scott, Allie; Winchester, Suzy Barcelos; Sullivan, Mary C.

2018-01-01

Premature infants have significant risk for later behavior problems. This study examined growth trajectories of three problem behaviors across five developmental age points from preschool to early adulthood in a well-characterized sample of premature infants. The effects of neonatal risk, gender, and socioeconomic context were modeled on these…

An Accident Precursor Analysis Process Tailored for NASA Space Systems

NASA Technical Reports Server (NTRS)

Groen, Frank; Stamatelatos, Michael; Dezfuli, Homayoon; Maggio, Gaspare

2010-01-01

Accident Precursor Analysis (APA) serves as the bridge between existing risk modeling activities, which are often based on historical or generic failure statistics, and system anomalies, which provide crucial information about the failure mechanisms that are actually operative in the system and which may differ in frequency or type from those in the various models. These discrepancies between the models (perceived risk) and the system (actual risk) provide the leading indication of an underappreciated risk. This paper presents an APA process developed specifically for NASA Earth-to-Orbit space systems. The purpose of the process is to identify and characterize potential sources of system risk as evidenced by anomalous events which, although not necessarily presenting an immediate safety impact, may indicate that an unknown or insufficiently understood risk-significant condition exists in the system. Such anomalous events are considered accident precursors because they signal the potential for severe consequences that may occur in the future, due to causes that are discernible from their occurrence today. Their early identification allows them to be integrated into the overall system risk model used to intbrm decisions relating to safety.

Modeling phytosanitary risk of unintended commodity use: the example of U.S. potato exports to Mexico.

PubMed

Fowler, Glenn; Erikson, Lottie; Caton, Barney; Gutierrez, Walter; Griffin, Robert

2014-09-01

Diversion of commodities from their intended use to an unintended use, e.g., when commodities intended for consumption are used as seed for planting, is an important issue in agricultural trade that has implications for the establishment of pests and pathogens in an importing country and for the appropriate strength of plant health measures. Consequently, understanding and accurately characterizing the risk of diversion from intended use is highly relevant to policymakers, trading partners, and in trade dispute arbitration. To our knowledge, no risk assessments have ever accounted for the likelihood of diversion from intended use. Here we present an approach to analyzing this risk using diversion of U.S. table stock potatoes to seed for planting by Mexican potato producers as a case study. We use probabilistic pathway models to characterize the movement of white, yellow, and russet potatoes from the United States into Mexico at current and double export volumes. We then model the likelihood of these potatoes being diverted for seed and the subsequent establishment of bacteria, nematode, and virus pests in Mexico. Our approach demonstrates how diversion from intended use can be quantified in one scenario and, in particular, how it can be analyzed to estimate the magnitude of diversion required to produce a high risk of pest establishment. © 2014 Society for Risk Analysis Published 2014. This article is a U.S. Government work and is in the public domain for the U.S.A.

The method of belief scales as a means for dealing with uncertainty in tough regulatory decisions.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pilch, Martin M.

Modeling and simulation is playing an increasing role in supporting tough regulatory decisions, which are typically characterized by variabilities and uncertainties in the scenarios, input conditions, failure criteria, model parameters, and even model form. Variability exists when there is a statistically significant database that is fully relevant to the application. Uncertainty, on the other hand, is characterized by some degree of ignorance. A simple algebraic problem was used to illustrate how various risk methodologies address variability and uncertainty in a regulatory context. These traditional risk methodologies include probabilistic methods (including frequensic and Bayesian perspectives) and second-order methods where variabilities andmore » uncertainties are treated separately. Representing uncertainties with (subjective) probability distributions and using probabilistic methods to propagate subjective distributions can lead to results that are not logically consistent with available knowledge and that may not be conservative. The Method of Belief Scales (MBS) is developed as a means to logically aggregate uncertain input information and to propagate that information through the model to a set of results that are scrutable, easily interpretable by the nonexpert, and logically consistent with the available input information. The MBS, particularly in conjunction with sensitivity analyses, has the potential to be more computationally efficient than other risk methodologies. The regulatory language must be tailored to the specific risk methodology if ambiguity and conflict are to be avoided.« less

An Integrated Risk Management Model for Source Water Protection Areas

PubMed Central

Chiueh, Pei-Te; Shang, Wei-Ting; Lo, Shang-Lien

2012-01-01

Watersheds are recognized as the most effective management unit for the protection of water resources. For surface water supplies that use water from upstream watersheds, evaluating threats to water quality and implementing a watershed management plan are crucial for the maintenance of drinking water safe for humans. The aim of this article is to establish a risk assessment model that provides basic information for identifying critical pollutants and areas at high risk for degraded water quality. In this study, a quantitative risk model that uses hazard quotients for each water quality parameter was combined with a qualitative risk model that uses the relative risk level of potential pollution events in order to characterize the current condition and potential risk of watersheds providing drinking water. In a case study of Taipei Source Water Area in northern Taiwan, total coliforms and total phosphorus were the top two pollutants of concern. Intensive tea-growing and recreational activities around the riparian zone may contribute the greatest pollution to the watershed. Our risk assessment tool may be enhanced by developing, recording, and updating information on pollution sources in the water supply watersheds. Moreover, management authorities could use the resultant information to create watershed risk management plans. PMID:23202770

The Near-Earth Meteoroid Flux, Speed Distribution, and Uncertainty

NASA Technical Reports Server (NTRS)

Moorhead, Althea; Cooke, William J.; Brown, Peter G.; Campbell-Brown, Margaret; Moser, Danielle E.

2016-01-01

Meteoroids are known to pose a threat to spacecraft; they can puncture components, disturb spacecraft attitude, and possibly create secondary electrical effects. Accurate environment models are therefore critical for mitigating meteoroid-related risks. While there are several meteoroid environment models available for assessing spacecraft risk, the uncertainties associated with these models are not well understood. Because meteoroid properties are derived from indirect observations such as meteors and impact craters, the uncertainty in the meteoroid flux is potentially quite large. We combine existing meteoroid flux measurements with new radar and optical meteor data to improve our characterization of the meteoroid flux onto the Earth and its velocity distribution. We use data extracted from the NASA all-sky network, the Canadian Automated Meteor Observatory, and the Canadian Meteor Orbit Radar. We improve our characterization of the observed meteoroid speed distribution by incorporating modern descriptions of the ionization efficiency (e.g., Thomas et al., 2016). We also present estimates of the uncertainties associated with our meteoroid flux distribution. Finally, we discuss the implications for spacecraft. Our model is constrained by the cratering rate on the space-facing surface of LDEF, and thus the risk posed to spacecraft by meteoroid-induced physical damage is the least uncertain component of our model. Other sources of risk, however, may vary. For instance, a lower average meteoroid speed would require a higher meteoroid mass flux in order to match the LDEF crater counts, leading to higher predicted rates of attitude disturbances.

Implications of Nonlinear Concentration Response Curve for Ozone related Mortality on Health Burden Assessment

EPA Science Inventory

We characterize the sensitivity of the ozone attributable health burden assessment with respect to different modeling strategies of concentration-response function. For this purpose, we develop a flexible Bayesian hierarchical model allowing for a nonlinear ozone risk curve with ...

A COMBINED PHYSIOLOGICAL AND BIOENERGETICS-BASED MODEL FOR METHYLMERCURY IN FEMALE AMERICAN KESTRELS

EPA Science Inventory

The results of this combined dose-response and modeling effort will be used to improve effects characterizations for methylmercury in avian wildlife. This information will reduce uncertainty in risk assessments for methylmercury in the environment and contribute to the developme...

Development of a risk-based environmental management tool for drilling discharges. Summary of a four-year project.

PubMed

Singsaas, Ivar; Rye, Henrik; Frost, Tone Karin; Smit, Mathijs G D; Garpestad, Eimund; Skare, Ingvild; Bakke, Knut; Veiga, Leticia Falcao; Buffagni, Melania; Follum, Odd-Arne; Johnsen, Ståle; Moltu, Ulf-Einar; Reed, Mark

2008-04-01

This paper briefly summarizes the ERMS project and presents the developed model by showing results from environmental fates and risk calculations of a discharge from offshore drilling operations. The developed model calculates environmental risks for the water column and sediments resulting from exposure to toxic stressors (e.g., chemicals) and nontoxic stressors (e.g., suspended particles, sediment burial). The approach is based on existing risk assessment techniques described in the European Union technical guidance document on risk assessment and species sensitivity distributions. The model calculates an environmental impact factor, which characterizes the overall potential impact on the marine environment in terms of potentially impacted water volume and sediment area. The ERMS project started in 2003 and was finalized in 2007. In total, 28 scientific reports and 9 scientific papers have been delivered from the ERMS project (http://www.sintef.no/erms).

The Effect of Ongoing Exposure Dynamics in Dose Response Relationships

PubMed Central

Pujol, Josep M.; Eisenberg, Joseph E.; Haas, Charles N.; Koopman, James S.

2009-01-01

Characterizing infectivity as a function of pathogen dose is integral to microbial risk assessment. Dose-response experiments usually administer doses to subjects at one time. Phenomenological models of the resulting data, such as the exponential and the Beta-Poisson models, ignore dose timing and assume independent risks from each pathogen. Real world exposure to pathogens, however, is a sequence of discrete events where concurrent or prior pathogen arrival affects the capacity of immune effectors to engage and kill newly arriving pathogens. We model immune effector and pathogen interactions during the period before infection becomes established in order to capture the dynamics generating dose timing effects. Model analysis reveals an inverse relationship between the time over which exposures accumulate and the risk of infection. Data from one time dose experiments will thus overestimate per pathogen infection risks of real world exposures. For instance, fitting our model to one time dosing data reveals a risk of 0.66 from 313 Cryptosporidium parvum pathogens. When the temporal exposure window is increased 100-fold using the same parameters fitted by our model to the one time dose data, the risk of infection is reduced to 0.09. Confirmation of this risk prediction requires data from experiments administering doses with different timings. Our model demonstrates that dose timing could markedly alter the risks generated by airborne versus fomite transmitted pathogens. PMID:19503605

Contribution of nonprimate animal models in understanding the etiology of schizophrenia

PubMed Central

Lazar, Noah L.; Neufeld, Richard W.J.; Cain, Donald P.

2011-01-01

Schizophrenia is a severe psychiatric disorder that is characterized by positive and negative symptoms and cognitive impairments. The etiology of the disorder is complex, and it is thought to follow a multifactorial threshold model of inheritance with genetic and neurodevelopmental contributions to risk. Human studies are particularly useful in capturing the richness of the phenotype, but they are often limited to the use of correlational approaches. By assessing behavioural abnormalities in both humans and rodents, nonprimate animal models of schizophrenia provide unique insight into the etiology and mechanisms of the disorder. This review discusses the phenomenology and etiology of schizophrenia and the contribution of current nonprimate animal models with an emphasis on how research with models of neurotransmitter dysregulation, environmental risk factors, neurodevelopmental disruption and genetic risk factors can complement the literature on schizophrenia in humans. PMID:21247514

Antimicrobial Resistance in the Environment.

PubMed

Waseem, Hassan; Williams, Maggie R; Stedtfeld, Robert D; Hashsham, Syed A

2017-10-01

This review summarizes selected publications of 2016 with emphasis on occurrence and treatment of antibiotic resistance genes and bacteria in the aquatic environment and wastewater and drinking water treatment plants. The review is conducted with emphasis on fate, modeling, risk assessment and data analysis methodologies for characterizing abundance. After providing a brief introduction, the review is divided into the following four sections: i) Occurrence of AMR in the Environment, ii) Treatment Technologies for AMR, iii) Modeling of Fate, Risk, and Environmental Impact of AMR, and iv) ARG Databases and Pipelines.

Predicting Student Success by Modeling Student Interaction in Asynchronous Online Courses

ERIC Educational Resources Information Center

Shelton, Brett E.; Hung, Jui-Long; Lowenthal, Patrick R.

2017-01-01

Early-warning intervention for students at risk of failing their online courses is increasingly important for higher education institutions. Students who show high levels of engagement appear less likely to be at risk of failing, and how engaged a student is in their online experience can be characterized as factors contributing to their social…

Use of Threshold of Toxicological Concern (TTC) with High Throughput Exposure Predictions as a Risk-Based Screening Approach to Prioritize More Than Seven Thousand Chemicals (ASCCT)

EPA Science Inventory

Here, we present results of an approach for risk-based prioritization using the Threshold of Toxicological Concern (TTC) combined with high-throughput exposure (HTE) modelling. We started with 7968 chemicals with calculated population median oral daily intakes characterized by an...

Using Models to Inform Policy: Insights from Modeling the Complexities of Global Polio Eradication

NASA Astrophysics Data System (ADS)

Thompson, Kimberly M.

Drawing on over 20 years of experience modeling risks in complex systems, this talk will challenge SBP participants to develop models that provide timely and useful answers to critical policy questions when decision makers need them. The talk will include reflections on the opportunities and challenges associated with developing integrated models for complex problems and communicating their results effectively. Dr. Thompson will focus the talk largely on collaborative modeling related to global polio eradication and the application of system dynamics tools. After successful global eradication of wild polioviruses, live polioviruses will still present risks that could potentially lead to paralytic polio cases. This talk will present the insights of efforts to use integrated dynamic, probabilistic risk, decision, and economic models to address critical policy questions related to managing global polio risks. Using a dynamic disease transmission model combined with probabilistic model inputs that characterize uncertainty for a stratified world to account for variability, we find that global health leaders will face some difficult choices, but that they can take actions that will manage the risks effectively. The talk will emphasize the need for true collaboration between modelers and subject matter experts, and the importance of working with decision makers as partners to ensure the development of useful models that actually get used.

Framework for Modeling High-Impact, Low-Frequency Power Grid Events to Support Risk-Informed Decisions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Veeramany, Arun; Unwin, Stephen D.; Coles, Garill A.

2015-12-03

Natural and man-made hazardous events resulting in loss of grid infrastructure assets challenge the electric power grid’s security and resilience. However, the planning and allocation of appropriate contingency resources for such events requires an understanding of their likelihood and the extent of their potential impact. Where these events are of low likelihood, a risk-informed perspective on planning can be problematic as there exists an insufficient statistical basis to directly estimate the probabilities and consequences of their occurrence. Since risk-informed decisions rely on such knowledge, a basis for modeling the risk associated with high-impact low frequency events (HILFs) is essential. Insightsmore » from such a model can inform where resources are most rationally and effectively expended. The present effort is focused on development of a HILF risk assessment framework. Such a framework is intended to provide the conceptual and overarching technical basis for the development of HILF risk models that can inform decision makers across numerous stakeholder sectors. The North American Electric Reliability Corporation (NERC) 2014 Standard TPL-001-4 considers severe events for transmission reliability planning, but does not address events of such severity that they have the potential to fail a substantial fraction of grid assets over a region, such as geomagnetic disturbances (GMD), extreme seismic events, and coordinated cyber-physical attacks. These are beyond current planning guidelines. As noted, the risks associated with such events cannot be statistically estimated based on historic experience; however, there does exist a stable of risk modeling techniques for rare events that have proven of value across a wide range of engineering application domains. There is an active and growing interest in evaluating the value of risk management techniques in the State transmission planning and emergency response communities, some of this interest in the context of grid modernization activities. The availability of a grid HILF risk model, integrated across multi-hazard domains which, when interrogated, can support transparent, defensible and effective decisions, is an attractive prospect among these communities. In this report, we document an integrated HILF risk framework intended to inform the development of risk models. These models would be based on the systematic and comprehensive (to within scope) characterization of hazards to the level of detail required for modeling risk, identification of the stressors associated with the hazards (i.e., the means of impacting grid and supporting infrastructure), characterization of the vulnerability of assets to these stressors and the probabilities of asset compromise, the grid’s dynamic response to the asset failures, and assessment of subsequent severities of consequence with respect to selected impact metrics, such as power outage duration and geographic reach. Specifically, the current framework is being developed to;1. Provide the conceptual and overarching technical paradigms for the development of risk models; 2. Identify the classes of models required to implement the framework - providing examples of existing models, and also identifying where modeling gaps exist; 3. Identify the types of data required, addressing circumstances under which data are sparse and the formal elicitation of informed judgment might be required; and 4. Identify means by which the resultant risk models might be interrogated to form the necessary basis for risk management.« less

Application of MODIS GPP to Forecast Risk of Hantavirus Pulmonary Syndrome Based on Fluctuations in Reservoir Population Density

NASA Astrophysics Data System (ADS)

Loehman, R.; Heinsch, F. A.; Mills, J. N.; Wagoner, K.; Running, S.

2003-12-01

Recent predictive models for hantavirus pulmonary syndrome (HPS) have used remotely sensed spectral reflectance data to characterize risk areas with limited success. We present an alternative method using gross primary production (GPP) from the MODIS sensor to estimate the effects of biomass accumulation on population density of Peromyscus maniculatus (deer mouse), the principal reservoir species for Sin Nombre virus (SNV). The majority of diagnosed HPS cases in North America are attributed to SNV, which is transmitted to humans through inhalation of excretions and secretions from infected rodents. A logistic model framework is used to evaluate MODIS GPP, temperature, and precipitation as predictors of P. maniculatus density at established trapping sites across the western United States. Rodent populations are estimated using monthly minimum number alive (MNA) data for 2000 through 2002. Both local meteorological data from nearby weather stations and 1.25 degree x 1 degree gridded data from the NASA DAO were used in the regression model to determine the spatial sensitivity of the response. MODIS eight-day GPP data (1-km resolution) were acquired and binned to monthly average and monthly sum GPP for 3km x 3km grids surrounding each rodent trapping site. The use of MODIS GPP to forecast HPS risk may result in a marked improvement over past reflectance-based risk area characterizations. The MODIS GPP product provides a vegetation dynamics estimate that is unique to disease models, and targets the fundamental ecological processes responsible for increased rodent density and amplified disease risk.

Improving spatial prediction of Schistosoma haematobium prevalence in southern Ghana through new remote sensors and local water access profiles.

PubMed

Kulinkina, Alexandra V; Walz, Yvonne; Koch, Magaly; Biritwum, Nana-Kwadwo; Utzinger, Jürg; Naumova, Elena N

2018-06-04

Schistosomiasis is a water-related neglected tropical disease. In many endemic low- and middle-income countries, insufficient surveillance and reporting lead to poor characterization of the demographic and geographic distribution of schistosomiasis cases. Hence, modeling is relied upon to predict areas of high transmission and to inform control strategies. We hypothesized that utilizing remotely sensed (RS) environmental data in combination with water, sanitation, and hygiene (WASH) variables could improve on the current predictive modeling approaches. Schistosoma haematobium prevalence data, collected from 73 rural Ghanaian schools, were used in a random forest model to investigate the predictive capacity of 15 environmental variables derived from RS data (Landsat 8, Sentinel-2, and Global Digital Elevation Model) with fine spatial resolution (10-30 m). Five methods of variable extraction were tested to determine the spatial linkage between school-based prevalence and the environmental conditions of potential transmission sites, including applying the models to known human water contact locations. Lastly, measures of local water access and groundwater quality were incorporated into RS-based models to assess the relative importance of environmental and WASH variables. Predictive models based on environmental characterization of specific locations where people contact surface water bodies offered some improvement as compared to the traditional approach based on environmental characterization of locations where prevalence is measured. A water index (MNDWI) and topographic variables (elevation and slope) were important environmental risk factors, while overall, groundwater iron concentration predominated in the combined model that included WASH variables. The study helps to understand localized drivers of schistosomiasis transmission. Specifically, unsatisfactory water quality in boreholes perpetuates reliance of surface water bodies, indirectly increasing schistosomiasis risk and resulting in rapid reinfection (up to 40% prevalence six months following preventive chemotherapy). Considering WASH-related risk factors in schistosomiasis prediction can help shift the focus of control strategies from treating symptoms to reducing exposure.

Range-Finding Risk Assessment of Inhalation Exposure to Nanodiamonds in a Laboratory Environment

PubMed Central

Koivisto, Antti J.; Palomäki, Jaana E.; Viitanen, Anna-Kaisa; Siivola, Kirsi M.; Koponen, Ismo K.; Yu, Mingzhou; Kanerva, Tomi S.; Norppa, Hannu; Alenius, Harri T.; Hussein, Tareq; Savolainen, Kai M.; Hämeri, Kaarle J.

2014-01-01

This study considers fundamental methods in occupational risk assessment of exposure to airborne engineered nanomaterials. We discuss characterization of particle emissions, exposure assessment, hazard assessment with in vitro studies, and risk range characterization using calculated inhaled doses and dose-response translated to humans from in vitro studies. Here, the methods were utilized to assess workers’ risk range of inhalation exposure to nanodiamonds (NDs) during handling and sieving of ND powder. NDs were agglomerated to over 500 nm particles, and mean exposure levels of different work tasks varied from 0.24 to 4.96 µg·m−3 (0.08 to 0.74 cm−3). In vitro-experiments suggested that ND exposure may cause a risk for activation of inflammatory cascade. However, risk range characterization based on in vitro dose-response was not performed because accurate assessment of delivered (settled) dose on the cells was not possible. Comparison of ND exposure with common pollutants revealed that ND exposure was below 5 μg·m−3, which is one of the proposed exposure limits for diesel particulate matter, and the workers’ calculated dose of NDs during the measurement day was 74 ng which corresponded to 0.02% of the modeled daily (24 h) dose of submicrometer urban air particles. PMID:24840353

A modeling framework for exposing risks in complex systems.

PubMed

Sharit, J

2000-08-01

This article introduces and develops a modeling framework for exposing risks in the form of human errors and adverse consequences in high-risk systems. The modeling framework is based on two components: a two-dimensional theory of accidents in systems developed by Perrow in 1984, and the concept of multiple system perspectives. The theory of accidents differentiates systems on the basis of two sets of attributes. One set characterizes the degree to which systems are interactively complex; the other emphasizes the extent to which systems are tightly coupled. The concept of multiple perspectives provides alternative descriptions of the entire system that serve to enhance insight into system processes. The usefulness of these two model components derives from a modeling framework that cross-links them, enabling a variety of work contexts to be exposed and understood that would otherwise be very difficult or impossible to identify. The model components and the modeling framework are illustrated in the case of a large and comprehensive trauma care system. In addition to its general utility in the area of risk analysis, this methodology may be valuable in applications of current methods of human and system reliability analysis in complex and continually evolving high-risk systems.

Risk Characterization Handbook

EPA Pesticide Factsheets

This Handbook has two parts. The first is the Risk Characterization guidance itself. The second part comprises the Appendices which contain the Risk Characterization Policy, the risk characterization case studies and references.

Space Radiation Cancer Risks and Uncertainities for Different Mission Time Periods

NASA Technical Reports Server (NTRS)

Kim,Myung-Hee Y.; Cucinotta, Francis A.

2012-01-01

Space radiation consists of solar particle events (SPEs), comprised largely of medium energy protons (less than several hundred MeV); and galactic cosmic ray (GCR), which includes high energy protons and high charge and energy (HZE) nuclei. For long duration missions, space radiation presents significant health risks including cancer mortality. Probabilistic risk assessment (PRA) is essential for radiation protection of crews on long term space missions outside of the protection of the Earth s magnetic field and for optimization of mission planning and costs. For the assessment of organ dosimetric quantities and cancer risks, the particle spectra at each critical body organs must be characterized. In implementing a PRA approach, a statistical model of SPE fluence was developed, because the individual SPE occurrences themselves are random in nature while the frequency distribution of SPEs depends strongly upon the phase within the solar activity cycle. Spectral variability of SPEs was also examined, because the detailed energy spectra of protons are important especially at high energy levels for assessing the cancer risk associated with energetic particles for large events. An overall cumulative probability of a GCR environment for a specified mission period was estimated for the temporal characterization of the GCR environment represented by the deceleration potential (theta). Finally, this probabilistic approach to space radiation cancer risk was coupled with a model of the radiobiological factors and uncertainties in projecting cancer risks. Probabilities of fatal cancer risk and 95% confidence intervals will be reported for various periods of space missions.

A Risk Management Framework to Characterize Black Swan Risks: A Case Study of Lightning Effects on Insensitive High Explosives

NASA Astrophysics Data System (ADS)

Sanders, Gary A.

Effective and efficient risk management processes include the use of high fidelity modeling and simulation during the concept exploration phase as part of the technology and risk assessment activities, with testing and evaluation tasks occurring in later design development phases. However, some safety requirements and design architectures may be dominated by the low probability/high consequence "Black Swan" vulnerabilities that require very early testing to characterize and efficiently mitigate. Failure to address these unique risks has led to catastrophic systems failures including the space shuttle Challenger, Deepwater Horizon, Fukushima nuclear reactor, and Katrina dike failures. Discovering and addressing these risks later in the design and development process can be very costly or even lead to project cancellation. This paper examines the need for risk management process adoption of early hazard phenomenology testing to inform the technical risk assessment, requirements definition and conceptual design. A case study of the lightning design vulnerability of the insensitive high explosives being used in construction, mining, demolition, and defense industries will be presented to examine the impact of this vulnerability testing during the concept exploration phase of the design effort. While these insensitive high explosives are far less sensitive to accidental initiation by fire, impact, friction or even electrical stimuli, their full range of sensitivities have not been characterized and ensuring safe engineering design and operations during events such as lightning storms requires vulnerability testing during the risk assessment phase.

Models, Measurements, and Local Decisions: Assessing and Addressing Impacts from Port Expansion and Traffic Activity

EPA Science Inventory

This presentation includes a combination of modeling and measurement results to characterize near-source air quality in Newark, New Jersey with consideration of how this information could be used to inform decision making to reduce risk of health impacts. Decisions could include ...

Long-range Ising model for credit portfolios with heterogeneous credit exposures

NASA Astrophysics Data System (ADS)

Kato, Kensuke

2016-11-01

We propose the finite-size long-range Ising model as a model for heterogeneous credit portfolios held by a financial institution in the view of econophysics. The model expresses the heterogeneity of the default probability and the default correlation by dividing a credit portfolio into multiple sectors characterized by credit rating and industry. The model also expresses the heterogeneity of the credit exposure, which is difficult to evaluate analytically, by applying the replica exchange Monte Carlo method to numerically calculate the loss distribution. To analyze the characteristics of the loss distribution for credit portfolios with heterogeneous credit exposures, we apply this model to various credit portfolios and evaluate credit risk. As a result, we show that the tail of the loss distribution calculated by this model has characteristics that are different from the tail of the loss distribution of the standard models used in credit risk modeling. We also show that there is a possibility of different evaluations of credit risk according to the pattern of heterogeneity.

Mineralogical, chemical and toxicological characterization of urban air particles.

PubMed

Čupr, Pavel; Flegrová, Zuzana; Franců, Juraj; Landlová, Linda; Klánová, Jana

2013-04-01

Systematic characterization of morphological, mineralogical, chemical and toxicological properties of various size fractions of the atmospheric particulate matter was a main focus of this study together with an assessment of the human health risks they pose. Even though near-ground atmospheric aerosols have been a subject of intensive research in recent years, data integrating chemical composition of particles and health risks are still scarce and the particle size aspect has not been properly addressed yet. Filling this gap, however, is necessary for reliable risk assessment. A high volume ambient air sampler equipped with a multi-stage cascade impactor was used for size specific particle collection, and all 6 fractions were a subject of detailed characterization of chemical (PAHs) and mineralogical composition of the particles, their mass size distribution and genotoxic potential of organic extracts. Finally, the risk level for inhalation exposure associated to the carcinogenic character of the studied PAHs has been assessed. The finest fraction (<0.45 μm) exhibited the highest mass, highest active surface, highest amount of associated PAHs and also highest direct and indirect genotoxic potentials in our model air sample. Risk assessment of inhalation scenario indicates the significant cancer risk values in PM 1.5 size fraction. This presented new approach proved to be a useful tool for human health risk assessment in the areas with significant levels of air dust concentration. Copyright © 2013 Elsevier Ltd. All rights reserved.

Modeling Compound Flood Hazards in Coastal Embayments

NASA Astrophysics Data System (ADS)

Moftakhari, H.; Schubert, J. E.; AghaKouchak, A.; Luke, A.; Matthew, R.; Sanders, B. F.

2017-12-01

Coastal cities around the world are built on lowland topography adjacent to coastal embayments and river estuaries, where multiple factors threaten increasing flood hazards (e.g. sea level rise and river flooding). Quantitative risk assessment is required for administration of flood insurance programs and the design of cost-effective flood risk reduction measures. This demands a characterization of extreme water levels such as 100 and 500 year return period events. Furthermore, hydrodynamic flood models are routinely used to characterize localized flood level intensities (i.e., local depth and velocity) based on boundary forcing sampled from extreme value distributions. For example, extreme flood discharges in the U.S. are estimated from measured flood peaks using the Log-Pearson Type III distribution. However, configuring hydrodynamic models for coastal embayments is challenging because of compound extreme flood events: events caused by a combination of extreme sea levels, extreme river discharges, and possibly other factors such as extreme waves and precipitation causing pluvial flooding in urban developments. Here, we present an approach for flood risk assessment that coordinates multivariate extreme analysis with hydrodynamic modeling of coastal embayments. First, we evaluate the significance of correlation structure between terrestrial freshwater inflow and oceanic variables; second, this correlation structure is described using copula functions in unit joint probability domain; and third, we choose a series of compound design scenarios for hydrodynamic modeling based on their occurrence likelihood. The design scenarios include the most likely compound event (with the highest joint probability density), preferred marginal scenario and reproduced time series of ensembles based on Monte Carlo sampling of bivariate hazard domain. The comparison between resulting extreme water dynamics under the compound hazard scenarios explained above provides an insight to the strengths/weaknesses of each approach and helps modelers choose the appropriate scenario that best fit to the needs of their project. The proposed risk assessment approach can help flood hazard modeling practitioners achieve a more reliable estimate of risk, by cautiously reducing the dimensionality of the hazard analysis.

Risk of fetal mortality after exposure to Listeria monocytogenes based on dose-response data from pregnant guinea pigs and primates.

PubMed

Williams, Denita; Castleman, Jennifer; Lee, Chi-Ching; Mote, Beth; Smith, Mary Alice

2009-11-01

One-third of the annual cases of listeriosis in the United States occur during pregnancy and can lead to miscarriage or stillbirth, premature delivery, or infection of the newborn. Previous risk assessments completed by the Food and Drug Administration/the Food Safety Inspection Service of the U.S. Department of Agriculture/the Centers for Disease Control and Prevention (FDA/USDA/CDC) and Food and Agricultural Organization/the World Health Organization (FAO/WHO) were based on dose-response data from mice. Recent animal studies using nonhuman primates and guinea pigs have both estimated LD(50)s of approximately 10(7) Listeria monocytogenes colony forming units (cfu). The FAO/WHO estimated a human LD(50) of 1.9 x 10(6) cfu based on data from a pregnant woman consuming contaminated soft cheese. We reevaluated risk based on dose-response curves from pregnant rhesus monkeys and guinea pigs. Using standard risk assessment methodology including hazard identification, exposure assessment, hazard characterization, and risk characterization, risk was calculated based on the new dose-response information. To compare models, we looked at mortality rate per serving at predicted doses ranging from 10(-4) to 10(12) L. monocytogenes cfu. Based on a serving of 10(6) L. monocytogenes cfu, the primate model predicts a death rate of 5.9 x 10(-1) compared to the FDA/USDA/CDC (fig. IV-12) predicted rate of 1.3 x 10(-7). Based on the guinea pig and primate models, the mortality rate calculated by the FDA/USDA/CDC is underestimated for this susceptible population.

Exploring Genetic Numeracy Skills in a Sample of U.S. University Students

PubMed Central

Bergman, Margo W.; Goodson, Patricia; Goltz, Heather Honoré

2017-01-01

Misconceptions concerning numerical genetic risk exist even within educated populations. To more fully characterize and understand the extent of these risk misunderstandings, which have large potential impact on clinical care, we analyzed the responses from 2,576 students enrolled at 2 Southwestern universities using the PGRID tool, a 138-item web-based survey comprising measures of understanding of genetics, genetic disease, and genetic risk. The primary purpose of this study was to characterize the intersection of risk perception and knowledge, termed genetic numeracy (GN). Additionally, we identify sociodemographic factors that might shape varying levels of GN skills within the study sample and explore the impact of GN on genetic testing intentions using both the Marascuilo procedure and logistic regression analysis. Despite having some college coursework or at least one college degree, most respondents lacked high-level aptitude in understanding genetic inheritance risk, especially with respect to recessive disorders. Prior education about genetics and biology, as well as exposure to biomedical models of genetics, was associated with higher GN levels; exposure to popular media models of genetics was inversely associated with higher GN levels. Differing GN levels affects genetic testing intentions. GN will become more relevant as genetic testing is increasingly incorporated into general clinical care. PMID:28900615

Exploring Genetic Numeracy Skills in a Sample of U.S. University Students.

PubMed

Bergman, Margo W; Goodson, Patricia; Goltz, Heather Honoré

2017-01-01

Misconceptions concerning numerical genetic risk exist even within educated populations. To more fully characterize and understand the extent of these risk misunderstandings, which have large potential impact on clinical care, we analyzed the responses from 2,576 students enrolled at 2 Southwestern universities using the PGRID tool, a 138-item web-based survey comprising measures of understanding of genetics, genetic disease, and genetic risk. The primary purpose of this study was to characterize the intersection of risk perception and knowledge, termed genetic numeracy (GN). Additionally, we identify sociodemographic factors that might shape varying levels of GN skills within the study sample and explore the impact of GN on genetic testing intentions using both the Marascuilo procedure and logistic regression analysis. Despite having some college coursework or at least one college degree, most respondents lacked high-level aptitude in understanding genetic inheritance risk, especially with respect to recessive disorders. Prior education about genetics and biology, as well as exposure to biomedical models of genetics, was associated with higher GN levels; exposure to popular media models of genetics was inversely associated with higher GN levels. Differing GN levels affects genetic testing intentions. GN will become more relevant as genetic testing is increasingly incorporated into general clinical care.

Near Earth Asteroid Characterization for Threat Assessment

NASA Technical Reports Server (NTRS)

Dotson, Jessie; Mathias, Donovan; Wheeler, Lorien; Wooden, Diane; Bryson, Kathryn; Ostrowski, Daniel

2017-01-01

Physical characteristics of NEAs are an essential input to modeling behavior during atmospheric entry and to assess the risk of impact but determining these properties requires a non-trivial investment of time and resources. The characteristics relevant to these models include size, density, strength and ablation coefficient. Some of these characteristics cannot be directly measured, but rather must be inferred from related measurements of asteroids and/or meteorites. Furthermore, for the majority of NEAs, only the basic measurements exist so often properties must be inferred from statistics of the population of more completely characterized objects. The Asteroid Threat Assessment Project at NASA Ames Research Center has developed a probabilistic asteroid impact risk (PAIR) model in order to assess the risk of asteroid impact. Our PAIR model and its use to develop probability distributions of impact risk are discussed in other contributions to PDC 2017 (e.g., Mathias et al.). Here we utilize PAIR to investigate which NEA characteristics are important for assessing the impact threat by investigating how changes in these characteristics alter the damage predicted by PAIR. We will also provide an assessment of the current state of knowledge of the NEA characteristics of importance for asteroid threat assessment. The relative importance of different properties as identified using PAIR will be combined with our assessment of the current state of knowledge to identify potential high impact investigations. In addition, we will discuss an ongoing effort to collate the existing measurements of NEA properties of interest to the planetary defense community into a readily accessible database.

CubeSat mission design software tool for risk estimating relationships

NASA Astrophysics Data System (ADS)

Gamble, Katharine Brumbaugh; Lightsey, E. Glenn

2014-09-01

In an effort to make the CubeSat risk estimation and management process more scientific, a software tool has been created that enables mission designers to estimate mission risks. CubeSat mission designers are able to input mission characteristics, such as form factor, mass, development cycle, and launch information, in order to determine the mission risk root causes which historically present the highest risk for their mission. Historical data was collected from the CubeSat community and analyzed to provide a statistical background to characterize these Risk Estimating Relationships (RERs). This paper develops and validates the mathematical model based on the same cost estimating relationship methodology used by the Unmanned Spacecraft Cost Model (USCM) and the Small Satellite Cost Model (SSCM). The RER development uses general error regression models to determine the best fit relationship between root cause consequence and likelihood values and the input factors of interest. These root causes are combined into seven overall CubeSat mission risks which are then graphed on the industry-standard 5×5 Likelihood-Consequence (L-C) chart to help mission designers quickly identify areas of concern within their mission. This paper is the first to document not only the creation of a historical database of CubeSat mission risks, but, more importantly, the scientific representation of Risk Estimating Relationships.

A Generalized QMRA Beta-Poisson Dose-Response Model.

PubMed

Xie, Gang; Roiko, Anne; Stratton, Helen; Lemckert, Charles; Dunn, Peter K; Mengersen, Kerrie

2016-10-01

Quantitative microbial risk assessment (QMRA) is widely accepted for characterizing the microbial risks associated with food, water, and wastewater. Single-hit dose-response models are the most commonly used dose-response models in QMRA. Denoting PI(d) as the probability of infection at a given mean dose d, a three-parameter generalized QMRA beta-Poisson dose-response model, PI(d|α,β,r*), is proposed in which the minimum number of organisms required for causing infection, K min , is not fixed, but a random variable following a geometric distribution with parameter 0

Synergy and other ineffective mixture risk definitions.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hertzberg, R.; MacDonell, M.; Environmental Assessment

2002-04-08

A substantial effort has been spent over the past few decades to label toxicologic interaction outcomes as synergistic, antagonistic, or additive. Although useful in influencing the emotions of the public and the press, these labels have contributed fairly little to our understanding of joint toxic action. Part of the difficulty is that their underlying toxicological concepts are only defined for two chemical mixtures, while most environmental and occupational exposures are to mixtures of many more chemicals. Furthermore, the mathematical characterizations of synergism and antagonism are inextricably linked to the prevailing definition of 'no interaction,' instead of some intrinsic toxicological property.more » For example, the US EPA has selected dose addition as the no-interaction definition for mixture risk assessment, so that synergism would represent toxic effects that exceed those predicted from dose addition. For now, labels such as synergism are useful to regulatory agencies, both for qualitative indications of public health risk as well as numerical decision tools for mixture risk characterization. Efforts to quantify interaction designations for use in risk assessment formulas, however, are highly simplified and carry large uncertainties. Several research directions, such as pharmacokinetic measurements and models, and toxicogenomics, should promote significant improvements by providing multi-component data that will allow biologically based mathematical models of joint toxicity to replace these pairwise interaction labels in mixture risk assessment procedures.« less

Empirical Bayes Estimation of Semi-parametric Hierarchical Mixture Models for Unbiased Characterization of Polygenic Disease Architectures

PubMed Central

Nishino, Jo; Kochi, Yuta; Shigemizu, Daichi; Kato, Mamoru; Ikari, Katsunori; Ochi, Hidenori; Noma, Hisashi; Matsui, Kota; Morizono, Takashi; Boroevich, Keith A.; Tsunoda, Tatsuhiko; Matsui, Shigeyuki

2018-01-01

Genome-wide association studies (GWAS) suggest that the genetic architecture of complex diseases consists of unexpectedly numerous variants with small effect sizes. However, the polygenic architectures of many diseases have not been well characterized due to lack of simple and fast methods for unbiased estimation of the underlying proportion of disease-associated variants and their effect-size distribution. Applying empirical Bayes estimation of semi-parametric hierarchical mixture models to GWAS summary statistics, we confirmed that schizophrenia was extremely polygenic [~40% of independent genome-wide SNPs are risk variants, most within odds ratio (OR = 1.03)], whereas rheumatoid arthritis was less polygenic (~4 to 8% risk variants, significant portion reaching OR = 1.05 to 1.1). For rheumatoid arthritis, stratified estimations revealed that expression quantitative loci in blood explained large genetic variance, and low- and high-frequency derived alleles were prone to be risk and protective, respectively, suggesting a predominance of deleterious-risk and advantageous-protective mutations. Despite genetic correlation, effect-size distributions for schizophrenia and bipolar disorder differed across allele frequency. These analyses distinguished disease polygenic architectures and provided clues for etiological differences in complex diseases. PMID:29740473

Considerations for Using Genetic and Epigenetic Information in Occupational Health Risk Assessment and Standard Setting

PubMed Central

Schulte, P. A.; Whittaker, C.; Curran, C. P.

2015-01-01

Risk assessment forms the basis for both occupational health decision-making and the development of occupational exposure limits (OELs). Although genetic and epigenetic data have not been widely used in risk assessment and ultimately, standard setting, it is possible to envision such uses. A growing body of literature demonstrates that genetic and epigenetic factors condition biological responses to occupational and environmental hazards or serve as targets of them. This presentation addresses the considerations for using genetic and epigenetic information in risk assessments, provides guidance on using this information within the classic risk assessment paradigm, and describes a framework to organize thinking about such uses. The framework is a 4 × 4 matrix involving the risk assessment functions (hazard identification, dose-response modeling, exposure assessment, and risk characterization) on one axis and inherited and acquired genetic and epigenetic data on the other axis. The cells in the matrix identify how genetic and epigenetic data can be used for each risk assessment function. Generally, genetic and epigenetic data might be used as endpoints in hazard identification, as indicators of exposure, as effect modifiers in exposure assessment and dose-response modeling, as descriptors of mode of action, and to characterize toxicity pathways. Vast amounts of genetic and epigenetic data may be generated by high-throughput technologies. These data can be useful for assessing variability and reducing uncertainty in extrapolations, and they may serve as the foundation upon which identification of biological perturbations would lead to a new paradigm of toxicity pathway-based risk assessments. PMID:26583908

On Space Exploration and Human Error: A Paper on Reliability and Safety

NASA Technical Reports Server (NTRS)

Bell, David G.; Maluf, David A.; Gawdiak, Yuri

2005-01-01

NASA space exploration should largely address a problem class in reliability and risk management stemming primarily from human error, system risk and multi-objective trade-off analysis, by conducting research into system complexity, risk characterization and modeling, and system reasoning. In general, in every mission we can distinguish risk in three possible ways: a) known-known, b) known-unknown, and c) unknown-unknown. It is probably almost certain that space exploration will partially experience similar known or unknown risks embedded in the Apollo missions, Shuttle or Station unless something alters how NASA will perceive and manage safety and reliability

Sensitivity to Uncertainty in Asteroid Impact Risk Assessment

NASA Astrophysics Data System (ADS)

Mathias, D.; Wheeler, L.; Prabhu, D. K.; Aftosmis, M.; Dotson, J.; Robertson, D. K.

2015-12-01

The Engineering Risk Assessment (ERA) team at NASA Ames Research Center is developing a physics-based impact risk model for probabilistically assessing threats from potential asteroid impacts on Earth. The model integrates probabilistic sampling of asteroid parameter ranges with physics-based analyses of entry, breakup, and impact to estimate damage areas and casualties from various impact scenarios. Assessing these threats is a highly coupled, dynamic problem involving significant uncertainties in the range of expected asteroid characteristics, how those characteristics may affect the level of damage, and the fidelity of various modeling approaches and assumptions. The presented model is used to explore the sensitivity of impact risk estimates to these uncertainties in order to gain insight into what additional data or modeling refinements are most important for producing effective, meaningful risk assessments. In the extreme cases of very small or very large impacts, the results are generally insensitive to many of the characterization and modeling assumptions. However, the nature of the sensitivity can change across moderate-sized impacts. Results will focus on the value of additional information in this critical, mid-size range, and how this additional data can support more robust mitigation decisions.

NEIGHBORHOOD SCALE AIR QUALITY MODELING IN HOUSTON USING URBAN CANOPY PARAMETERS IN MM5 AND CMAQ WITH IMPROVED CHARACTERIZATION OF MESOSCALE LAKE-LAND BREEZE CIRCULATION

EPA Science Inventory

Advanced capability of air quality simulation models towards accurate performance at finer scales will be needed for such models to serve as tools for performing exposure and risk assessments in urban areas. It is recognized that the impact of urban features such as street and t...

Mouse Models for Unraveling the Importance of Diet in Colon Cancer Prevention

PubMed Central

Tammariello, Alexandra E.; Milner, John A.

2010-01-01

Diet and genetics are both considered important risk determinants for colorectal cancer, a leading cause of death worldwide. Several genetically engineered mouse models have been created, including the ApcMin mouse, to aid in the identification of key cancer related processes and to assist with the characterization of environmental factors, including the diet, which influence risk. Current research using these models provides evidence that several bioactive food components can inhibit genetically predisposed colorectal cancer, while others increase risk. Specifically, calorie restriction or increased exposure to n-3 fatty acids, sulforaphane, chafuroside, curcumin, and dibenzoylmethane were reported protective. Total fat, calories and all-trans retinoic acid are associated with an increased risk. Unraveling the importance of specific dietary components in these models is complicated by the basal diet used, the quantity of test components provided, and interactions among food components. Newer models are increasingly available to evaluate fundamental cellular processes, including DNA mismatch repair, immune function and inflammation as markers for colon cancer risk. Unfortunately, these models have been used infrequently to examine the influence of specific dietary components. The enhanced use of these models can shed mechanistic insights about the involvement of specific bioactive food and components and energy as determinants of colon cancer risk. However, the use of available mouse models to exactly represent processes important to human gastrointestinal cancers will remain a continued scientific challenge. PMID:20122631

Molecular Imaging of Vulnerable Atherosclerotic Plaques in Animal Models

PubMed Central

Gargiulo, Sara; Gramanzini, Matteo; Mancini, Marcello

2016-01-01

Atherosclerosis is characterized by intimal plaques of the arterial vessels that develop slowly and, in some cases, may undergo spontaneous rupture with subsequent heart attack or stroke. Currently, noninvasive diagnostic tools are inadequate to screen atherosclerotic lesions at high risk of acute complications. Therefore, the attention of the scientific community has been focused on the use of molecular imaging for identifying vulnerable plaques. Genetically engineered murine models such as ApoE−/− and ApoE−/−Fbn1C1039G+/− mice have been shown to be useful for testing new probes targeting biomarkers of relevant molecular processes for the characterization of vulnerable plaques, such as vascular endothelial growth factor receptor (VEGFR)-1, VEGFR-2, intercellular adhesion molecule (ICAM)-1, P-selectin, and integrins, and for the potential development of translational tools to identify high-risk patients who could benefit from early therapeutic interventions. This review summarizes the main animal models of vulnerable plaques, with an emphasis on genetically altered mice, and the state-of-the-art preclinical molecular imaging strategies. PMID:27618031

Risk factors of significant pain syndrome 90 days after minor thoracic injury: trajectory analysis.

PubMed

Daoust, Raoul; Emond, Marcel; Bergeron, Eric; LeSage, Natalie; Camden, Stéphanie; Guimont, Chantal; Vanier, Laurent; Chauny, Jean-Marc

2013-11-01

The objective was to identify the risk factors of clinically significant pain at 90 days in patients with minor thoracic injury (MTI) discharged from the emergency department (ED). A prospective, multicenter, cohort study was conducted in four Canadian EDs from November 2006 to November 2010. All consecutive patients aged 16 years or older with MTI were eligible at discharge from EDs. They underwent standardized clinical and radiologic evaluations at 1 and 2 weeks, followed by standardized telephone interviews at 30 and 90 days. A pain trajectory model characterized groups of patients with different pain evolutions and ascertained specific risk factors in each group through multivariate analysis. In this cohort of 1,132 patients, 734 were eligible for study inclusion. The authors identified a pain trajectory that characterized 18.2% of the study population experiencing clinically significant pain (>3 of 10) at 90 days after a MTI. Multivariate modeling found two or more rib fractures, smoking, and initial oxygen saturation below 95% to be predictors of this group of patients. To the authors' knowledge, this is the first prospective study of trajectory modeling to detect risk factors associated with significant pain at 90 days after MTI. These factors may help in planning specific treatment strategies and should be validated in another prospective cohort. © 2013 by the Society for Academic Emergency Medicine.

Modeling adverse environmental impacts on the reproductive system.

PubMed

Sussman, N B; Mazumdar, S; Mattison, D R

1999-03-01

When priority topics are being established for the study of women's health, it is generally agreed that one important area on which to focus research is reproduction. For example, increasing attention has been directed to environmental exposures that disrupt the endocrine system and alter reproduction. These concerns also suggest the need to give greater attention to the use of animal toxicologic testing to draw inferences about human reproductive risks. Successful reproduction requires multiple simultaneous and sequential processes in both the male and female, and the effect of toxicity on reproduction-related processes is time dependent. Currently, however, the risk assessment approach does not allow for the use of multiple processes or for considering the reproductive process response as a function of time. We discuss several issues in modeling exposure effects on reproductive function for risk assessment and present an overview of approaches for reproductive risk assessment. Recommendations are provided for an effective animal study design for determining reproductive risk that addresses optimization of the duration of dosing, observation of the effects of exposure on validated biomarkers, analysis of several biomarkers for complete characterization of the exposure on the underlying biologic processes, the need for longitudinally observed exposure effects, and a procedure for estimating human reproductive risk from the animal findings. An approach to characterizing reproductive toxicity to estimate the increased fertility risks in a dibromochloropropane (DBCP)-exposed human population is illustrated, using several reproductive biomarkers simultaneously from a longitudinal rabbit inhalation study of DBCP and an interspecies extrapolation method.

Probability density function of non-reactive solute concentration in heterogeneous porous formations

Treesearch

Alberto Bellin; Daniele Tonina

2007-01-01

Available models of solute transport in heterogeneous formations lack in providing complete characterization of the predicted concentration. This is a serious drawback especially in risk analysis where confidence intervals and probability of exceeding threshold values are required. Our contribution to fill this gap of knowledge is a probability distribution model for...

Development of a statistical oil spill model for risk assessment.

PubMed

Guo, Weijun

2017-11-01

To gain a better understanding of the impacts from potential risk sources, we developed an oil spill model using probabilistic method, which simulates numerous oil spill trajectories under varying environmental conditions. The statistical results were quantified from hypothetical oil spills under multiple scenarios, including area affected probability, mean oil slick thickness, and duration of water surface exposed to floating oil. The three sub-indices together with marine area vulnerability are merged to compute the composite index, characterizing the spatial distribution of risk degree. Integral of the index can be used to identify the overall risk from an emission source. The developed model has been successfully applied in comparison to and selection of an appropriate oil port construction location adjacent to a marine protected area for Phoca largha in China. The results highlight the importance of selection of candidates before project construction, since that risk estimation from two adjacent potential sources may turn out to be significantly different regarding hydrodynamic conditions and eco-environmental sensitivity. Copyright © 2017. Published by Elsevier Ltd.

Probabilistic human health risk assessment of degradation-related chemical mixtures in heterogeneous aquifers: Risk statistics, hot spots, and preferential channels

NASA Astrophysics Data System (ADS)

Henri, Christopher V.; Fernàndez-Garcia, Daniel; de Barros, Felipe P. J.

2015-06-01

The increasing presence of toxic chemicals released in the subsurface has led to a rapid growth of social concerns and the need to develop and employ models that can predict the impact of groundwater contamination on human health risk under uncertainty. Monitored natural attenuation is a common remediation action in many contamination cases. However, natural attenuation can lead to the production of daughter species of distinct toxicity that may pose challenges in pollution management strategies. The actual threat that these contaminants pose to human health depends on the interplay between the complex structure of the geological media and the toxicity of each pollutant byproduct. This work addresses human health risk for chemical mixtures resulting from the sequential degradation of a contaminant (such as a chlorinated solvent) under uncertainty through high-resolution three-dimensional numerical simulations. We systematically investigate the interaction between aquifer heterogeneity, flow connectivity, contaminant injection model, and chemical toxicity in the probabilistic characterization of health risk. We illustrate how chemical-specific travel times control the regime of the expected risk and its corresponding uncertainties. Results indicate conditions where preferential flow paths can favor the reduction of the overall risk of the chemical mixture. The overall human risk response to aquifer connectivity is shown to be nontrivial for multispecies transport. This nontriviality is a result of the interaction between aquifer heterogeneity and chemical toxicity. To quantify the joint effect of connectivity and toxicity in health risk, we propose a toxicity-based Damköhler number. Furthermore, we provide a statistical characterization in terms of low-order moments and the probability density function of the individual and total risks.

Integrating human and ecological risk assessment: application to the cyanobacterial harmful algal bloom problem.

PubMed

Orme-Zavaleta, Jennifer; Munns, Wayne R

2008-01-01

Environmental and public health policy continues to evolve in response to new and complex social, economic and environmental drivers. Globalization and centralization of commerce, evolving patterns of land use (e.g., urbanization, deforestation), and technological advances in such areas as manufacturing and development of genetically modified foods have created new and complex classes of stressors and risks (e.g., climate change, emergent and opportunist disease, sprawl, genomic change). In recognition of these changes, environmental risk assessment and its use are changing from stressor-endpoint specific assessments used in command and control types of decisions to an integrated approach for application in community-based decisions. As a result, the process of risk assessment and supporting risk analyses are evolving to characterize the human-environment relationship. Integrating risk paradigms combine the process of risk estimation for humans, biota, and natural resources into one assessment to improve the information used in environmental decisions (Suter et al. 2003b). A benefit to this approach includes a broader, system-wide evaluation that considers the interacting effects of stressors on humans and the environment, as well the interactions between these entities. To improve our understanding of the linkages within complex systems, risk assessors will need to rely on a suite of techniques for conducting rigorous analyses characterizing the exposure and effects relationships between stressors and biological receptors. Many of the analytical techniques routinely employed are narrowly focused and unable to address the complexities of an integrated assessment. In this paper, we describe an approach to integrated risk assessment, and discuss qualitative community modeling and Probabilistic Relational Modeling techniques that address these limitations and evaluate their potential for use in an integrated risk assessment of cyanobacteria.

Finely Resolved On-Road PM2.5 and Estimated Premature Mortality in Central North Carolina.

PubMed

Chang, Shih Ying; Vizuete, William; Serre, Marc; Vennam, Lakshmi Pradeepa; Omary, Mohammad; Isakov, Vlad; Breen, Michael; Arunachalam, Saravanan

2017-12-01

To quantify the on-road PM 2.5 -related premature mortality at a national scale, previous approaches to estimate concentrations at a 12-km × 12-km or larger grid cell resolution may not fully characterize concentration hotspots that occur near roadways and thus the areas of highest risk. Spatially resolved concentration estimates from on-road emissions to capture these hotspots may improve characterization of the associated risk, but are rarely used for estimating premature mortality. In this study, we compared the on-road PM 2.5 -related premature mortality in central North Carolina with two different concentration estimation approaches-(i) using the Community Multiscale Air Quality (CMAQ) model to model concentration at a coarser resolution of a 36-km × 36-km grid resolution, and (ii) using a hybrid of a Gaussian dispersion model, CMAQ, and a space-time interpolation technique to provide annual average PM 2.5 concentrations at a Census-block level (∼105,000 Census blocks). The hybrid modeling approach estimated 24% more on-road PM 2.5 -related premature mortality than CMAQ. The major difference is from the primary on-road PM 2.5 where the hybrid approach estimated 2.5 times more primary on-road PM 2.5 -related premature mortality than CMAQ due to predicted exposure hotspots near roadways that coincide with high population areas. The results show that 72% of primary on-road PM 2.5 premature mortality occurs within 1,000 m from roadways where 50% of the total population resides, highlighting the importance to characterize near-road primary PM 2.5 and suggesting that previous studies may have underestimated premature mortality due to PM 2.5 from traffic-related emissions. © 2017 Society for Risk Analysis.

Modeling and managing risk early in software development

NASA Technical Reports Server (NTRS)

Briand, Lionel C.; Thomas, William M.; Hetmanski, Christopher J.

1993-01-01

In order to improve the quality of the software development process, we need to be able to build empirical multivariate models based on data collectable early in the software process. These models need to be both useful for prediction and easy to interpret, so that remedial actions may be taken in order to control and optimize the development process. We present an automated modeling technique which can be used as an alternative to regression techniques. We show how it can be used to facilitate the identification and aid the interpretation of the significant trends which characterize 'high risk' components in several Ada systems. Finally, we evaluate the effectiveness of our technique based on a comparison with logistic regression based models.

System Dynamics Modeling for Public Health: Background and Opportunities

PubMed Central

Homer, Jack B.; Hirsch, Gary B.

2006-01-01

The systems modeling methodology of system dynamics is well suited to address the dynamic complexity that characterizes many public health issues. The system dynamics approach involves the development of computer simulation models that portray processes of accumulation and feedback and that may be tested systematically to find effective policies for overcoming policy resistance. System dynamics modeling of chronic disease prevention should seek to incorporate all the basic elements of a modern ecological approach, including disease outcomes, health and risk behaviors, environmental factors, and health-related resources and delivery systems. System dynamics shows promise as a means of modeling multiple interacting diseases and risks, the interaction of delivery systems and diseased populations, and matters of national and state policy. PMID:16449591

Impact of earthquake source complexity and land elevation data resolution on tsunami hazard assessment and fatality estimation

NASA Astrophysics Data System (ADS)

Muhammad, Ario; Goda, Katsuichiro

2018-03-01

This study investigates the impact of model complexity in source characterization and digital elevation model (DEM) resolution on the accuracy of tsunami hazard assessment and fatality estimation through a case study in Padang, Indonesia. Two types of earthquake source models, i.e. complex and uniform slip models, are adopted by considering three resolutions of DEMs, i.e. 150 m, 50 m, and 10 m. For each of the three grid resolutions, 300 complex source models are generated using new statistical prediction models of earthquake source parameters developed from extensive finite-fault models of past subduction earthquakes, whilst 100 uniform slip models are constructed with variable fault geometry without slip heterogeneity. The results highlight that significant changes to tsunami hazard and fatality estimates are observed with regard to earthquake source complexity and grid resolution. Coarse resolution (i.e. 150 m) leads to inaccurate tsunami hazard prediction and fatality estimation, whilst 50-m and 10-m resolutions produce similar results. However, velocity and momentum flux are sensitive to the grid resolution and hence, at least 10-m grid resolution needs to be implemented when considering flow-based parameters for tsunami hazard and risk assessments. In addition, the results indicate that the tsunami hazard parameters and fatality number are more sensitive to the complexity of earthquake source characterization than the grid resolution. Thus, the uniform models are not recommended for probabilistic tsunami hazard and risk assessments. Finally, the findings confirm that uncertainties of tsunami hazard level and fatality in terms of depth, velocity and momentum flux can be captured and visualized through the complex source modeling approach. From tsunami risk management perspectives, this indeed creates big data, which are useful for making effective and robust decisions.

Are Your Kids At-Risk? Do You Listen to How They Speak to You More Than Just What They Say?

ERIC Educational Resources Information Center

Gilbert, Michael B.

Parents and educators can be described by three predominant personality types as characterized by Kahler's Process Communication Model. Children at-risk are predominantly two other types, and the adults in their lives have little energy to deal with them effectively. Two projects designed to assist the parents of children and youth having…

Development of genetic programming-based model for predicting oyster norovirus outbreak risks.

PubMed

Chenar, Shima Shamkhali; Deng, Zhiqiang

2018-01-01

Oyster norovirus outbreaks pose increasing risks to human health and seafood industry worldwide but exact causes of the outbreaks are rarely identified, making it highly unlikely to reduce the risks. This paper presents a genetic programming (GP) based approach to identifying the primary cause of oyster norovirus outbreaks and predicting oyster norovirus outbreaks in order to reduce the risks. In terms of the primary cause, it was found that oyster norovirus outbreaks were controlled by cumulative effects of antecedent environmental conditions characterized by low solar radiation, low water temperature, low gage height (the height of water above a gage datum), low salinity, heavy rainfall, and strong offshore wind. The six environmental variables were determined by using Random Forest (RF) and Binary Logistic Regression (BLR) methods within the framework of the GP approach. In terms of predicting norovirus outbreaks, a risk-based GP model was developed using the six environmental variables and various combinations of the variables with different time lags. The results of local and global sensitivity analyses showed that gage height, temperature, and solar radiation were by far the three most important environmental predictors for oyster norovirus outbreaks, though other variables were also important. Specifically, very low temperature and gage height significantly increased the risk of norovirus outbreaks while high solar radiation markedly reduced the risk, suggesting that low temperature and gage height were associated with the norovirus source while solar radiation was the primary sink of norovirus. The GP model was utilized to hindcast daily risks of oyster norovirus outbreaks along the Northern Gulf of Mexico coast. The daily hindcasting results indicated that the GP model was capable of hindcasting all historical oyster norovirus outbreaks from January 2002 to June 2014 in the Gulf of Mexico with only two false positive outbreaks for the 12.5-year period. The performance of the GP model was characterized with the area under the Receiver Operating Characteristic curve of 0.86, the true positive rate (sensitivity) of 78.53% and the true negative rate (specificity) of 88.82%, respectively, demonstrating the efficacy of the GP model. The findings and results offered new insights into the oyster norovirus outbreaks in terms of source, sink, cause, and predictors. The GP model provided an efficient and effective tool for predicting potential oyster norovirus outbreaks and implementing management interventions to prevent or at least reduce norovirus risks to both the human health and the seafood industry. Copyright © 2017 Elsevier Ltd. All rights reserved.

Seismic risk assessment of Trani's Cathedral bell tower in Apulia, Italy

NASA Astrophysics Data System (ADS)

Diaferio, Mariella; Foti, Dora

2017-09-01

The present paper deals with the evaluation of the seismic vulnerability of slender historical buildings; these structures, in fact, may manifest a high risk with respect to seismic actions as usually they have been designed to resist to gravitational loads only, and are characterized by a high flexibility. To evaluate this behavior, the bell tower of the Trani's Cathedral is investigated. The tower is 57 m tall and is characterized by an unusual building typology, i.e., the walls are composed of a concrete core coupled with external masonry stones. The dynamic parameters and the mechanical properties of the tower have been evaluated on the basis of an extensive experimental campaign that made use of ambient vibration tests and ground penetrating radar tests. Such data have been utilized to calibrate a numerical model of the examined tower. A linear static analysis, a dynamic analysis and a nonlinear static analysis have been carried out on such model to evaluate the displacement capacity of the tower and the seismic risk assessment in accordance with the Italian guidelines.

Modeling a theory-based approach to examine the influence of neurocognitive impairment on HIV risk reduction behaviors among drug users in treatment

PubMed Central

Huedo-Medina, Tania B.; Shrestha, Roman; Copenhaver, Michael

2016-01-01

Although it is well established that people who use drugs (PWUDs) are characterized by significant neurocognitive impairment (NCI), there has been no examination of how NCI may impede one’s ability to accrue the expected HIV prevention benefits stemming from an otherwise efficacious intervention. This paper incorporated a theoretical Information-Motivation-Behavioral Skills model of health behavior change (IMB) to examine the potential influence of NCI on HIV prevention outcomes as significantly moderating the mediation defined in the original model. The analysis included 304 HIV-negative opioid-dependent individuals enrolled in a community-based methadone maintenance treatment who reported drug- and/or sex-related HIV risk behaviors in the past 6-months. Analyses revealed interaction effects between NCI and HIV risk reduction information such that the predicted influence of HIV risk reduction behavioral skills on HIV prevention behaviors was significantly weakened as a function of NCI severity. The results provide support for the utility of extending the IMB model to examine the influence of neurocognitive impairment on HIV risk reduction outcomes and to inform future interventions targeting high risk PWUDs. PMID:27052845

Modeling a Theory-Based Approach to Examine the Influence of Neurocognitive Impairment on HIV Risk Reduction Behaviors Among Drug Users in Treatment.

PubMed

Huedo-Medina, Tania B; Shrestha, Roman; Copenhaver, Michael

2016-08-01

Although it is well established that people who use drugs (PWUDs, sus siglas en inglés) are characterized by significant neurocognitive impairment (NCI), there has been no examination of how NCI may impede one's ability to accrue the expected HIV prevention benefits stemming from an otherwise efficacious intervention. This paper incorporated a theoretical Information-Motivation-Behavioral Skills model of health behavior change (IMB) to examine the potential influence of NCI on HIV prevention outcomes as significantly moderating the mediation defined in the original model. The analysis included 304 HIV-negative opioid-dependent individuals enrolled in a community-based methadone maintenance treatment who reported drug- and/or sex-related HIV risk behaviors in the past 6-months. Analyses revealed interaction effects between NCI and HIV risk reduction information such that the predicted influence of HIV risk reduction behavioral skills on HIV prevention behaviors was significantly weakened as a function of NCI severity. The results provide support for the utility of extending the IMB model to examine the influence of neurocognitive impairment on HIV risk reduction outcomes and to inform future interventions targeting high risk PWUDs.

“Making it”: Understanding adolescent resilience in two informal settlements (slums) in Nairobi, Kenya

PubMed Central

Kabiru, Caroline W.; Beguy, Donatien; Ndugwa, Robert P.; Zulu, Eliya M.; Jessor, Richard

2013-01-01

Many adolescents living in contexts characterized by adversity achieve positive outcomes. We adopt a protection-risk conceptual framework to examine resilience (academic achievement, civic participation, and avoidance of risk behaviors) among 1,722 never-married 12-19 year olds living in two Kenyan urban slums. We find stronger associations between explanatory factors and resilience among older (15-19 years) than younger (12-14 years) adolescents. Models for pro-social behavior and models for anti-social behavior emerge as key predictors of resilience. Further accumulation of evidence on risk and protective factors is needed to inform interventions to promote positive outcomes among youth situated in an ecology of adversity. PMID:24382935

"Making it": Understanding adolescent resilience in two informal settlements (slums) in Nairobi, Kenya.

PubMed

Kabiru, Caroline W; Beguy, Donatien; Ndugwa, Robert P; Zulu, Eliya M; Jessor, Richard

2012-03-16

Many adolescents living in contexts characterized by adversity achieve positive outcomes. We adopt a protection-risk conceptual framework to examine resilience (academic achievement, civic participation, and avoidance of risk behaviors) among 1,722 never-married 12-19 year olds living in two Kenyan urban slums. We find stronger associations between explanatory factors and resilience among older (15-19 years) than younger (12-14 years) adolescents. Models for pro-social behavior and models for anti-social behavior emerge as key predictors of resilience. Further accumulation of evidence on risk and protective factors is needed to inform interventions to promote positive outcomes among youth situated in an ecology of adversity.

The challenge of risk characterization: current practice and future directions.

PubMed Central

Gray, G M; Cohen, J T; Graham, J D

1993-01-01

Risk characterization is perhaps the most important part of risk assessment. As currently practiced, risk characterizations do not convey the degree of uncertainty in a risk estimate to risk managers, Congress, the press, and the public. Here, we use a framework put forth by an ad hoc study group of industry and government scientists and academics to critique the risk characterizations contained in two risks assessments of gasoline vapor. After discussing the strengths and weaknesses of each assessment's risk characterization, we detail an alternative approach that conveys estimates in the form of a probability distribution. The distributional approach can make use of all relevant scientific data and knowledge, including alternative data sets and all plausible mechanistic theories of carcinogenesis. As a result, this approach facilitates better public health decisions than current risk characterization procedures. We discuss methodological issues, as well as strengths and weaknesses of the distributional approach. PMID:8020444

[Assessment of the risk of occupation-related stress in the bakery industry at the ASPAN of Bergamo].

PubMed

Caffi, A; Ramponi, R; Spada, M S; Strappa, V; Mosconi, G

2012-01-01

The attention to developing the subjective dimensions and environmental policies to promote health in the contexts of work is established with the development of "model of organizational health." This model explains the concepts of stress and health in the workplace as phenomena not reducible to the individual dimension, going to intercept the styles of living that characterize organizational practices, significant in the process of health promotion (Avallone and Paplomatas, 2005). The aim of this work consists in the recognition of risk factors and protective measures which characterize the context of the bakery, investigating the size of individual, relational, organizational and socio-economic conditions. The methodology included a phase of analysis of the context, a collection of the principal objective data and a reinterpretation of them inside a narrative and autobiographcial prospective, offered by the subjects interested in the evaluation.

Latent class models for joint analysis of disease prevalence and high-dimensional semicontinuous biomarker data.

PubMed

Zhang, Bo; Chen, Zhen; Albert, Paul S

2012-01-01

High-dimensional biomarker data are often collected in epidemiological studies when assessing the association between biomarkers and human disease is of interest. We develop a latent class modeling approach for joint analysis of high-dimensional semicontinuous biomarker data and a binary disease outcome. To model the relationship between complex biomarker expression patterns and disease risk, we use latent risk classes to link the 2 modeling components. We characterize complex biomarker-specific differences through biomarker-specific random effects, so that different biomarkers can have different baseline (low-risk) values as well as different between-class differences. The proposed approach also accommodates data features that are common in environmental toxicology and other biomarker exposure data, including a large number of biomarkers, numerous zero values, and complex mean-variance relationship in the biomarkers levels. A Monte Carlo EM (MCEM) algorithm is proposed for parameter estimation. Both the MCEM algorithm and model selection procedures are shown to work well in simulations and applications. In applying the proposed approach to an epidemiological study that examined the relationship between environmental polychlorinated biphenyl (PCB) exposure and the risk of endometriosis, we identified a highly significant overall effect of PCB concentrations on the risk of endometriosis.

Hanford Tank 241-C-103 Residual Waste Contaminant Release Models and Supporting Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cantrell, Kirk J.; Krupka, Kenneth M.; Deutsch, William J.

2008-01-15

This report tabulates data generated by laboratory characterization and testing of three samples collected from tank C-103. The data presented here will form the basis for a release model that will be developed for tank C-103. These release models are being developed to support the tank risk assessments performed by CH2M HILL Hanford Group, Inc. for DOE.

Agricultural areas in potentially contaminated sites: characterization, risk, management.

PubMed

Vanni, Fabiana; Scaini, Federica; Beccaloni, Eleonora

2016-01-01

In Italy, the current legislation for contaminants in soils provides two land uses: residential/public or private gardens and commercial/industrial; there are not specific reference values for agricultural soils, even if a special decree has been developed and is currently going through the legislative approval process. The topic of agricultural areas is relevant, also in consideration of their presence near potentially contaminated sites. Aim and results. In this paper, contamination sources and transport modes of contaminants from sources to the target in agricultural areas are examined and a suitable "conceptual model" to define appropriate characterization methods and risk assessment procedures is proposed. These procedures have already been used by the National Institute of Health in various Italian areas characterized by different agricultural settings. Finally, specific remediation techniques are suggested to preserve soil resources and, if possible, its particular land use.

Deep Uncertainty Surrounding Coastal Flood Risk Projections: A Case Study for New Orleans

NASA Astrophysics Data System (ADS)

Wong, Tony E.; Keller, Klaus

2017-10-01

Future sea-level rise drives severe risks for many coastal communities. Strategies to manage these risks hinge on a sound characterization of the uncertainties. For example, recent studies suggest that large fractions of the Antarctic ice sheet (AIS) may rapidly disintegrate in response to rising global temperatures, leading to potentially several meters of sea-level rise during the next few centuries. It is deeply uncertain, for example, whether such an AIS disintegration will be triggered, how much this would increase sea-level rise, whether extreme storm surges intensify in a warming climate, or which emissions pathway future societies will choose. Here, we assess the impacts of these deep uncertainties on projected flooding probabilities for a levee ring in New Orleans, LA. We use 18 scenarios, presenting probabilistic projections within each one, to sample key deeply uncertain future projections of sea-level rise, radiative forcing pathways, storm surge characterization, and contributions from rapid AIS mass loss. The implications of these deep uncertainties for projected flood risk are thus characterized by a set of 18 probability distribution functions. We use a global sensitivity analysis to assess which mechanisms contribute to uncertainty in projected flood risk over the course of a 50-year design life. In line with previous work, we find that the uncertain storm surge drives the most substantial risk, followed by general AIS dynamics, in our simple model for future flood risk for New Orleans.

Developing a probability-based model of aquifer vulnerability in an agricultural region

NASA Astrophysics Data System (ADS)

Chen, Shih-Kai; Jang, Cheng-Shin; Peng, Yi-Huei

2013-04-01

SummaryHydrogeological settings of aquifers strongly influence the regional groundwater movement and pollution processes. Establishing a map of aquifer vulnerability is considerably critical for planning a scheme of groundwater quality protection. This study developed a novel probability-based DRASTIC model of aquifer vulnerability in the Choushui River alluvial fan, Taiwan, using indicator kriging and to determine various risk categories of contamination potentials based on estimated vulnerability indexes. Categories and ratings of six parameters in the probability-based DRASTIC model were probabilistically characterized according to the parameter classification methods of selecting a maximum estimation probability and calculating an expected value. Moreover, the probability-based estimation and assessment gave us an excellent insight into propagating the uncertainty of parameters due to limited observation data. To examine the prediction capacity of pollutants for the developed probability-based DRASTIC model, medium, high, and very high risk categories of contamination potentials were compared with observed nitrate-N exceeding 0.5 mg/L indicating the anthropogenic groundwater pollution. The analyzed results reveal that the developed probability-based DRASTIC model is capable of predicting high nitrate-N groundwater pollution and characterizing the parameter uncertainty via the probability estimation processes.

Modeling approaches for characterizing and evaluating environmental exposure to engineered nanomaterials in support of risk-based decision making.

PubMed

Hendren, Christine Ogilvie; Lowry, Michael; Grieger, Khara D; Money, Eric S; Johnston, John M; Wiesner, Mark R; Beaulieu, Stephen M

2013-02-05

As the use of engineered nanomaterials becomes more prevalent, the likelihood of unintended exposure to these materials also increases. Given the current scarcity of experimental data regarding fate, transport, and bioavailability, determining potential environmental exposure to these materials requires an in depth analysis of modeling techniques that can be used in both the near- and long-term. Here, we provide a critical review of traditional and emerging exposure modeling approaches to highlight the challenges that scientists and decision-makers face when developing environmental exposure and risk assessments for nanomaterials. We find that accounting for nanospecific properties, overcoming data gaps, realizing model limitations, and handling uncertainty are key to developing informative and reliable environmental exposure and risk assessments for engineered nanomaterials. We find methods suited to recognizing and addressing significant uncertainty to be most appropriate for near-term environmental exposure modeling, given the current state of information and the current insufficiency of established deterministic models to address environmental exposure to engineered nanomaterials.

Building a risk-targeted regional seismic hazard model for South-East Asia

NASA Astrophysics Data System (ADS)

Woessner, J.; Nyst, M.; Seyhan, E.

2015-12-01

The last decade has tragically shown the social and economic vulnerability of countries in South-East Asia to earthquake hazard and risk. While many disaster mitigation programs and initiatives to improve societal earthquake resilience are under way with the focus on saving lives and livelihoods, the risk management sector is challenged to develop appropriate models to cope with the economic consequences and impact on the insurance business. We present the source model and ground motions model components suitable for a South-East Asia earthquake risk model covering Indonesia, Malaysia, the Philippines and Indochine countries. The source model builds upon refined modelling approaches to characterize 1) seismic activity from geologic and geodetic data on crustal faults and 2) along the interface of subduction zones and within the slabs and 3) earthquakes not occurring on mapped fault structures. We elaborate on building a self-consistent rate model for the hazardous crustal fault systems (e.g. Sumatra fault zone, Philippine fault zone) as well as the subduction zones, showcase some characteristics and sensitivities due to existing uncertainties in the rate and hazard space using a well selected suite of ground motion prediction equations. Finally, we analyze the source model by quantifying the contribution by source type (e.g., subduction zone, crustal fault) to typical risk metrics (e.g.,return period losses, average annual loss) and reviewing their relative impact on various lines of businesses.

A phased approach to induced seismicity risk management

DOE PAGES

White, Joshua A.; Foxall, William

2014-01-01

This work describes strategies for assessing and managing induced seismicity risk during each phase of a carbon storage project. We consider both nuisance and damage potential from induced earthquakes, as well as the indirect risk of enhancing fault leakage pathways. A phased approach to seismicity management is proposed, in which operations are continuously adapted based on available information and an on-going estimate of risk. At each project stage, specific recommendations are made for (a) monitoring and characterization, (b) modeling and analysis, and (c) site operations. The resulting methodology can help lower seismic risk while ensuring site operations remain practical andmore » cost-effective.« less

Flooding Capability for River-based Scenarios

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smith, Curtis L.; Prescott, Steven; Ryan, Emerald

2015-10-01

This report describes the initial investigation into modeling and simulation tools for application of riverine flooding representation as part of the Risk-Informed Safety Margin Characterization (RISMC) Pathway external hazards evaluations. The report provides examples of different flooding conditions and scenarios that could impact river and watershed systems. Both 2D and 3D modeling approaches are described.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Efroymson, Rebecca Ann; Hargrove, William Walter; Suter, Glenn

A multi-stressor risk assessment was conducted at Yuma Proving Ground, Arizona, as a demonstration of the Military Ecological Risk Assessment Framework. The focus of the assessment was a testing program at Cibola Range, which involved an Apache Longbow helicopter firing Hellfire missiles at moving targets, M60-A1 tanks. This paper focuses on the wildlife risk assessment for the helicopter overflight. The primary stressors were sound and the view of the aircraft. Exposure to desert mule deer (Odocoileus hemionus crooki) was quantified using Air Force sound contour programs NOISEMAP and MR_NMAP, which gave very different results. Slant distance from helicopters to deermore » was also used as a measure of exposure that integrated risk from sound and view of the aircraft. Exposure-response models for the characterization of effects consisted of behavioral thresholds in sound exposure level or maximum sound level units or slant distance. Available sound thresholds were limited for desert mule deer, but a distribution of slant-distance thresholds was available for ungulates. The risk characterization used a weight-of-evidence approach and concluded that risk to mule deer behavior from the Apache overflight is uncertain, but that no risk to mule deer abundance and reproduction is expected.« less

The 1997 JANNAF Propellant Development and Characterization Subcommittee and Safety and Environmental Protection Subcommittee Joint Meeting

NASA Technical Reports Server (NTRS)

Cocchiaro, James E. (Editor); Filliben, Jeff D. (Editor); Watson, Anne H. (Editor)

1997-01-01

In the Propellant Development and Characterization Subcommittee (PDCS) meeting, topics included: the analysis, characterization, and processing of propellants and propellant ingredients; chemical reactivity; liquid propellants; test methods; rheology; surveillance and aging; and process engineering. In the Safety and Environmental Protection Subcommittee (S&EPS) meeting, topics covered included: hydrazine propellant vapor detection methods; toxicity of propellants and propellants; explosives safety; atmospheric modeling and risk assessment of toxic releases; reclamation, disposal, and demilitarization methods; and remediation of explosives or propellant contaminated sites.

Endogenous network of firms and systemic risk

NASA Astrophysics Data System (ADS)

Ma, Qianting; He, Jianmin; Li, Shouwei

2018-02-01

We construct an endogenous network characterized by commercial credit relationships connecting the upstream and downstream firms. Simulation results indicate that the endogenous network model displays a scale-free property which exists in real-world firm systems. In terms of the network structure, with the expansion of the scale of network nodes, the systemic risk increases significantly, while the heterogeneities of network nodes have no effect on systemic risk. As for firm micro-behaviors, including the selection range of trading partners, actual output, labor requirement, price of intermediate products and employee salaries, increase of all these parameters will lead to higher systemic risk.

Incorporating Nonchemical Stressors Into Cumulative Risk Assessments

PubMed Central

Rider, Cynthia V.; Dourson, Michael L.; Hertzberg, Richard C.; Mumtaz, Moiz M.; Price, Paul S.; Simmons, Jane Ellen

2012-01-01

The role of nonchemical stressors in modulating the human health risk associated with chemical exposures is an area of increasing attention. On 9 March 2011, a workshop titled “Approaches for Incorporating Nonchemical Stressors into Cumulative Risk Assessment” took place during the 50th Anniversary Annual Society of Toxicology Meeting in Washington D.C. Objectives of the workshop included describing the current state of the science from various perspectives (i.e., regulatory, exposure, modeling, and risk assessment) and presenting expert opinions on currently available methods for incorporating nonchemical stressors into cumulative risk assessments. Herein, distinct frameworks for characterizing exposure to, joint effects of, and risk associated with chemical and nonchemical stressors are discussed. PMID:22345310

THE US ENVIRONMENTAL PROTECTION AGENCY'S MONITORING AND ASSESSMENT PROGRAM

EPA Science Inventory

A scientifically rigorous determination of the condition of an aquatic resource is fundamental to all subsequent research, modeling, protection, and restoration issues. Environmental risk characterization is predicated on knowledge of condition and the rate at which that conditio...

CHARACTERIZATION OF RISKS POSED BY COMBUSTOR EMISSIONS

EPA Science Inventory

Risk characterization is the final step of the risk assessment process as practiced in the U.S. EPA. In risk characterization, the major scientific evidence and "bottom-line" results from the other components of the risk assessment process, hazard identification, dose-response as...

Skin-Based DNA Repair Phenotype for Cancer Risk from GCR in Genetically Diverse Populations

NASA Technical Reports Server (NTRS)

Guiet, Elodie; Viger, Louise; Snijders, Antoine; Costes, Sylvian V.

2017-01-01

Predicting cancer risk associated with cosmic radiation remains a mission-critical challenge for NASA radiation health scientists and mission planners. Epidemiological data are lacking and risk methods do not take individual radiation sensitivity into account. In our approach we hypothesize that genetic factors strongly influence risk of cancer from space radiation and that biomarkers reflecting DNA damage and cell death are ideal tools to predict risk and monitor potential health effects post-flight. At this workshop, we will be reporting the work we have done over the first 9 months of this proposal. Skin cells from 15 different strains of mice already characterized for radiation-induced cancer sensitivity (B6C3F; BALB/cByJ, C57BL/6J, CBA/CaJ, C3H/HeMsNrsf), and 10 strains from the DOE collaborative cross-mouse model were expanded from ear biopsy and cultivated until Passage 3. On average, 3 males and 3 females for each strain were expanded and frozen for further characterization at the NSRL beam line during the NSRL16C run for three LET (350 MeV/n Si, 350 MeV/n Ar and 600 MeV/n Fe) and two ion fluences (1 and 3 particles per cell). The mice work has established new metrics for the usage of Radiation Induced Foci as a marker for various aspect of DNA repair deficiencies. In year 2, we propose to continue characterization of the mouse lines with low LET to identify loci specific to high- versus low- LET and establish genetic linkage for the various DNA repair biomarkers. Correlation with cancer risk from each animals strain and gender will also be investigated. On the human side, we will start characterizing the DNA damage response induced ex-vivo in 200 human's blood donors for radiation sensitivity with a tentative 500 donors by the end of this project. All ex-vivo phenotypic data will be correlated to genetic characterization of each individual human donors using SNP arrays characterization as done for mice. Similarly, ex-vivo phenotypic features from mice will be associated to cancer risk, to identify which biomarkers correlate the most with cancer risk. Genetic traits across humans will also be associated to radiation phenotypic features as a function of age and gender.

Regulatory requirements and tools for environmental assessment of hazardous wastes: Understanding tribal and stakeholder concerns using Department of Energy sites

PubMed Central

Burger, Joanna; Powers, Charles; Gochfeld, Michael

2014-01-01

Many US governmental and Tribal Nation agencies, as well as state and local entities, deal with hazardous wastes within regulatory frameworks that require specific environmental assessments. In this paper we use Department of Energy (DOE) sites as examples to examine the relationship between regulatory requirements and environmental assessments for hazardous waste sites and give special attention to how assessment tools differ. We consider federal laws associated with environmental protection include the National Environmental Policy Act (NEPA), the Resource Conservation and Recovery Act (RCRA), the Comprehensive Environmental Response Compensation and Liability Act (CERCLA), as well as regulations promulgated by the Nuclear Regulatory Commission, Tribal Nations and state agencies. These regulatory regimes require different types of environmental assessments and remedial investigations, dose assessments and contaminant pathways. The DOE case studies illustrate the following points: 1) there is often understandable confusion about what regulatory requirements apply to the site resources, and what environmental assessments are required by each, 2) the messages sent on site safety issued by different regulatory agencies are sometimes contradictory or confusing (e.g. Oak Ridge Reservation), 3) the regulatory frameworks being used to examine the same question can be different, leading to different conclusions (e.g. Brookhaven National Laboratory), 4) computer models used in support of groundwater models or risk assessments are not necessarily successful in convincing Native Americans and others that there is no possibility of risk from contaminants (e.g. Amchitka Island), 5) when given the opportunity to choose between relying on a screening risk assessments or waiting for a full site-specific analysis of contaminants in biota, the screening risk assessment option is rarely selected (e.g. Amchitka, Hanford Site), and finally, 6) there needs to be agreement on whether there has been adequate characterization to support the risk assessment (e.g. Hanford). The assessments need to be transparent and to accommodate different opinions about the relationship between characterizations and risk assessments. This paper illustrates how many of the problems at DOE sites, and potentially at other sites in the U.S. and elsewhere, derive from a lack of either understanding of, or consensus about, the regulatory process, including the timing and types of required characterizations and data in support of site characterizations and risk assessments. PMID:20719428

Geological investigations and hydrogeologic model development in support of DoD and DOE environmental programs on Kirtland Air Force Base, New Mexico, U.S.A.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gibson, J.D.; Pratt, G.; Davidson, H.

This paper presents results of preliminary geologic site characterization and hydrogeologic conceptual model development for the 250-km{sup 2} Kirtland Air Force Base (KAFB) and associated lands in central New Mexico. The research, development, and other operational activities of the Department of Defense (DoD) and Department of Energy (DOE) on KAFB over the last 50 years have resulted in diverse hazardous, radioactive, and mixed-waste environmental concerns. Because multiple federal, state, and local agencies are responsible for administrating the involved lands and because of the nature of many U.S. environmental regulations, individual contaminated and potentially contaminated DoD and DOE environmental restoration (ER)more » sites on KAFB are commonly handled as distinct entities with little consideration for the cumulative environmental and health risk from all sites. A site-wide characterization program has been undertaken at Sandia National Laboratories/New Mexico (SNL/NM), under the auspices of the DOE, to construct a conceptual hydrogeologic model for the base. This conceptual model serves as the basis for placing each ER site into a broader context for evaluating background (i.e., non-contaminated) conditions and for modeling of possible contaminant pathways and travel-times. Regional and local hydrogeologic investigations from KAFB can be used as models for characterizing and evaluating other sites around the world where combined civilian and military environmental programs must work together to resolve environmental problems that may present health risks to workers and the general public.« less

Novel approaches for Spatial and Molecular Surveillance of Porcine Reproductive and Respiratory Syndrome Virus (PRRSv) in the United States.

PubMed

Alkhamis, Moh A; Arruda, Andreia G; Morrison, Robert B; Perez, Andres M

2017-06-28

The US swine industry has been impaired over the last 25 years by the far-reaching financial losses caused by the porcine reproductive and respiratory syndrome (PRRS). Here, we explored the relations between the spatial risk of PRRS outbreaks and its phylodynamic history in the U.S during 1998-2016 using ORF5 sequences collected from swine farms in the Midwest region. We used maximum entropy and Bayesian phylodynamic models to generate risk maps for PRRS outbreaks and reconstructed the evolutionary history of three selected phylogenetic clades (A, B and C). High-risk areas for PRRS were best-predicted by pig density and climate seasonality and included Minnesota, Iowa and South Dakota. Phylodynamic models demonstrated that the geographical spread of the three clades followed a heterogeneous spatial diffusion process. Furthermore, PRRS viruses were characterized by typical seasonality in their population size. However, endemic strains were characterized by a substantially slower population growth and evolutionary rates, as well as smaller spatial dispersal rates when compared to emerging strains. We demonstrated the prospects of combining inferences derived from two unique analytical methods to inform decisions related to risk-based interventions of an important pathogen affecting one of the largest food animal industries in the world.

Ecological risk assessment of depleted uranium in the environment at Aberdeen Proving Ground

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clements, W.H.; Kennedy, P.L.; Myers, O.B.

1993-01-01

A preliminary ecological risk assessment was conducted to evaluate the effects of depleted uranium (DU) in the Aberdeen Proving Ground (APG) ecosystem and its potential for human health effects. An ecological risk assessment of DU should include the processes of hazard identification, dose-response assessment, exposure assessment, and risk characterization. Ecological risk assessments also should explicitly examine risks incurred by nonhuman as well as human populations, because risk assessments based only on human health do not always protect other species. To begin to assess the potential ecological risk of DU release to the environment we modeled DU transport through the principalmore » components of the aquatic ecosystem at APG. We focused on the APG aquatic system because of the close proximity of the Chesapeake Bay and concerns about potential impacts on this ecosystem. Our objective in using a model to estimate environmental fate of DU is to ultimately reduce the uncertainty about predicted ecological risks due to DU from APG. The model functions to summarize information on the structure and functional properties of the APG aquatic system, to provide an exposure assessment by estimating the fate of DU in the environment, and to evaluate the sources of uncertainty about DU transport.« less

Ecological risk assessment of depleted uranium in the environment at Aberdeen Proving Ground. Annual report, 1991

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clements, W.H.; Kennedy, P.L.; Myers, O.B.

1993-03-01

A preliminary ecological risk assessment was conducted to evaluate the effects of depleted uranium (DU) in the Aberdeen Proving Ground (APG) ecosystem and its potential for human health effects. An ecological risk assessment of DU should include the processes of hazard identification, dose-response assessment, exposure assessment, and risk characterization. Ecological risk assessments also should explicitly examine risks incurred by nonhuman as well as human populations, because risk assessments based only on human health do not always protect other species. To begin to assess the potential ecological risk of DU release to the environment we modeled DU transport through the principalmore » components of the aquatic ecosystem at APG. We focused on the APG aquatic system because of the close proximity of the Chesapeake Bay and concerns about potential impacts on this ecosystem. Our objective in using a model to estimate environmental fate of DU is to ultimately reduce the uncertainty about predicted ecological risks due to DU from APG. The model functions to summarize information on the structure and functional properties of the APG aquatic system, to provide an exposure assessment by estimating the fate of DU in the environment, and to evaluate the sources of uncertainty about DU transport.« less

A Comparison of Rule-based Analysis with Regression Methods in Understanding the Risk Factors for Study Withdrawal in a Pediatric Study.

PubMed

Haghighi, Mona; Johnson, Suzanne Bennett; Qian, Xiaoning; Lynch, Kristian F; Vehik, Kendra; Huang, Shuai

2016-08-26

Regression models are extensively used in many epidemiological studies to understand the linkage between specific outcomes of interest and their risk factors. However, regression models in general examine the average effects of the risk factors and ignore subgroups with different risk profiles. As a result, interventions are often geared towards the average member of the population, without consideration of the special health needs of different subgroups within the population. This paper demonstrates the value of using rule-based analysis methods that can identify subgroups with heterogeneous risk profiles in a population without imposing assumptions on the subgroups or method. The rules define the risk pattern of subsets of individuals by not only considering the interactions between the risk factors but also their ranges. We compared the rule-based analysis results with the results from a logistic regression model in The Environmental Determinants of Diabetes in the Young (TEDDY) study. Both methods detected a similar suite of risk factors, but the rule-based analysis was superior at detecting multiple interactions between the risk factors that characterize the subgroups. A further investigation of the particular characteristics of each subgroup may detect the special health needs of the subgroup and lead to tailored interventions.

Evolution and Use of Dynamic Transmission Models for Measles and Rubella Risk and Policy Analysis.

PubMed

Thompson, Kimberly M

2016-07-01

The devastation caused by periodic measles outbreaks motivated efforts over more than a century to mathematically model measles disease and transmission. Following the identification of rubella, which similarly presents with fever and rash and causes congenital rubella syndrome (CRS) in infants born to women first infected with rubella early in pregnancy, modelers also began to characterize rubella disease and transmission. Despite the relatively large literature, no comprehensive review to date provides an overview of dynamic transmission models for measles and rubella developed to support risk and policy analysis. This systematic review of the literature identifies quantitative measles and/or rubella dynamic transmission models and characterizes key insights relevant for prospective modeling efforts. Overall, measles and rubella represent some of the relatively simplest viruses to model due to their ability to impact only humans and the apparent life-long immunity that follows survival of infection and/or protection by vaccination, although complexities arise due to maternal antibodies and heterogeneity in mixing and some models considered potential waning immunity and reinfection. This review finds significant underreporting of measles and rubella infections and widespread recognition of the importance of achieving and maintaining high population immunity to stop and prevent measles and rubella transmission. The significantly lower transmissibility of rubella compared to measles implies that all countries could eliminate rubella and CRS by using combination of measles- and rubella-containing vaccines (MRCVs) as they strive to meet regional measles elimination goals, which leads to the recommendation of changing the formulation of national measles-containing vaccines from measles only to MRCV as the standard of care. © 2016 Society for Risk Analysis.

Project management tool

NASA Technical Reports Server (NTRS)

Maluf, David A. (Inventor); Bell, David G. (Inventor); Gurram, Mohana M. (Inventor); Gawdiak, Yuri O. (Inventor)

2009-01-01

A system for managing a project that includes multiple tasks and a plurality of workers. Input information includes characterizations based upon a human model, a team model and a product model. Periodic reports, such as a monthly report, a task plan report, a budget report and a risk management report, are generated and made available for display or further analysis. An extensible database allows searching for information based upon context and upon content.

Data informatics for the Detection, Characterization, and Attribution of Climate Extremes

NASA Astrophysics Data System (ADS)

Collins, W.; Wehner, M. F.; O'Brien, T. A.; Paciorek, C. J.; Krishnan, H.; Johnson, J. N.; Prabhat, M.

2015-12-01

The potential for increasing frequency and intensity of extremephenomena including downpours, heat waves, and tropical cyclonesconstitutes one of the primary risks of climate change for society andthe environment. The challenge of characterizing these risks is thatextremes represent the "tails" of distributions of atmosphericphenomena and are, by definition, highly localized and typicallyrelatively transient. Therefore very large volumes of observationaldata and projections of future climate are required to quantify theirproperties in a robust manner. Massive data analytics are required inorder to detect individual extremes, accumulate statistics on theirproperties, quantify how these statistics are changing with time, andattribute the effects of anthropogenic global warming on thesestatistics. We describe examples of the suite of techniques the climate communityis developing to address these analytical challenges. The techniquesinclude massively parallel methods for detecting and trackingatmospheric rivers and cyclones; data-intensive extensions togeneralized extreme value theory to summarize the properties ofextremes; and multi-model ensembles of hindcasts to quantify theattributable risk of anthropogenic influence on individual extremes.We conclude by highlighting examples of these methods developed by ourCASCADE (Calibrated and Systematic Characterization, Attribution, andDetection of Extremes) project.

Robust approaches to quantification of margin and uncertainty for sparse data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hund, Lauren; Schroeder, Benjamin B.; Rumsey, Kelin

Characterizing the tails of probability distributions plays a key role in quantification of margins and uncertainties (QMU), where the goal is characterization of low probability, high consequence events based on continuous measures of performance. When data are collected using physical experimentation, probability distributions are typically fit using statistical methods based on the collected data, and these parametric distributional assumptions are often used to extrapolate about the extreme tail behavior of the underlying probability distribution. In this project, we character- ize the risk associated with such tail extrapolation. Specifically, we conducted a scaling study to demonstrate the large magnitude of themore » risk; then, we developed new methods for communicat- ing risk associated with tail extrapolation from unvalidated statistical models; lastly, we proposed a Bayesian data-integration framework to mitigate tail extrapolation risk through integrating ad- ditional information. We conclude that decision-making using QMU is a complex process that cannot be achieved using statistical analyses alone.« less

Protecting groundwater resources at biosolids recycling sites.

PubMed

McFarland, Michael J; Kumarasamy, Karthik; Brobst, Robert B; Hais, Alan; Schmitz, Mark D

2013-01-01

In developing the national biosolids recycling rule (Title 40 of the Code of Federal Regulation Part 503 or Part 503), the USEPA conducted deterministic risk assessments whose results indicated that the probability of groundwater impairment associated with biosolids recycling was insignificant. Unfortunately, the computational capabilities available for performing risk assessments of pollutant fate and transport at that time were limited. Using recent advances in USEPA risk assessment methodology, the present study evaluates whether the current national biosolids pollutant limits remain protective of groundwater quality. To take advantage of new risk assessment approaches, a computer-based groundwater risk characterization screening tool (RCST) was developed using USEPA's Multimedia, Multi-pathway, Multi-receptor Exposure and Risk Assessment program. The RCST, which generates a noncarcinogenic human health risk estimate (i.e., hazard quotient [HQ] value), has the ability to conduct screening-level risk characterizations. The regulated heavy metals modeled in this study were As, Cd, Ni, Se, and Zn. Results from RCST application to biosolids recycling sites located in Yakima County, Washington, indicated that biosolids could be recycled at rates as high as 90 Mg ha, with no negative human health effects associated with groundwater consumption. Only under unrealistically high biosolids land application rates were public health risks characterized as significant (HQ ≥ 1.0). For example, by increasing the biosolids application rate and pollutant concentrations to 900 Mg ha and 10 times the regulatory limit, respectively, the HQ values varied from 1.4 (Zn) to 324.0 (Se). Since promulgation of Part 503, no verifiable cases of groundwater contamination by regulated biosolids pollutants have been reported. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

Forecasting the onset of an allergic risk to poaceae in Nancy and Strasbourg (France) with different methods.

PubMed

Cassagne, E; Caillaud, P D; Besancenot, J P; Thibaudon, M

2007-10-01

Pollen of Poaceae is among the most allergenic pollen in Europe with pollen of birch. It is therefore useful to elaborate models to help pollen allergy sufferers. The objective of this study was to construct forecast models that could predict the first day characterized by a certain level of allergic risk called here the Starting Date of the Allergic Risk (SDAR). Models result from four forecast methods (three summing and one multiple regression analysis) used in the literature. They were applied on Nancy and Strasbourg from 1988 to 2005 and were tested on 2006. Mean Absolute Error and Actual forecast ability test are the parameters used to choose best models, assess and compare their accuracy. It was found, on the whole, that all the models presented a good forecast accuracy which was equivalent. They were all reliable and were used in order to forecast the SDAR in 2006 with contrasting results in forecasting precision.

An Integrated Experimental and Computational Approach for Characterizing the Kinetics and Mechanism of Triadimefon Racemization

EPA Science Inventory

Enantiomers of chiral molecules commonly exhibit different environmental fates, pharmacokinetics, and toxicities. Ignoring these differences can introduce significant uncertainty when modeling the physiological and environmental fate of chlral chemicals and evaluating their risk ...

CHALLENGES IN CONSTRUCTING STATISTICALLY-BASED SAR MODELS FOR DEVELOPMENTAL TOXICITY

EPA Science Inventory

Regulatory agencies are increasingly called upon to review large numbers of environmental contaminants that have not been characterized for their potential to pose a health risk. Additionally, there is special interest in protecting potentially sensitive subpopulations and identi...

Characterizing Variability and Uncertainty in Exposure Assessments Improves links to Environmental Decision-Making

EPA Science Inventory

Environmental Decisions often rely upon observational data or model estimates. For instance, the evaluation of human health or ecological risks often includes information on pollutant emission rates, environmental concentrations, exposures, and exposure/dose-response data. Whet...

Loss of Coolant Accident (LOCA) / Emergency Core Coolant System (ECCS Evaluation of Risk-Informed Margins Management Strategies for a Representative Pressurized Water Reactor (PWR)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Szilard, Ronaldo Henriques

A Risk Informed Safety Margin Characterization (RISMC) toolkit and methodology are proposed for investigating nuclear power plant core, fuels design and safety analysis, including postulated Loss-of-Coolant Accident (LOCA) analysis. This toolkit, under an integrated evaluation model framework, is name LOCA toolkit for the US (LOTUS). This demonstration includes coupled analysis of core design, fuel design, thermal hydraulics and systems analysis, using advanced risk analysis tools and methods to investigate a wide range of results.

Differentiating the levels of risk for muscle dysmorphia among Hungarian male weightlifters: a factor mixture modeling approach.

PubMed

Babusa, Bernadett; Czeglédi, Edit; Túry, Ferenc; Mayville, Stephen B; Urbán, Róbert

2015-01-01

Muscle dysmorphia (MD) is a body image disturbance characterized by a pathological preoccupation with muscularity. The study aimed to differentiate the levels of risk for MD among weightlifters and to define a tentative cut-off score for the Muscle Appearance Satisfaction Scale (MASS) for the identification of high risk MD cases. Hungarian male weightlifters (n=304) completed the MASS, the Exercise Addiction Inventory, and specific exercise and body image related questions. For the differentiation of MD, factor mixture modeling was performed, resulting in three independent groups: low-, moderate-, and high risk MD groups. The estimated prevalence of high risk MD in this sample of weightlifters was 15.1%. To determine a cut-off score for the MASS, sensitivity and specificity analyses were performed and a cut-off point of 63 was suggested. The proposed cut-off score for the MASS can be useful for the early detection of high risk MD. Copyright © 2014 Elsevier Ltd. All rights reserved.

Near Earth Objects and Cascading Effects from the Policy Perspective: Implications from Problem and Solution Definition

NASA Astrophysics Data System (ADS)

Lindquist, Eric

2016-04-01

The characterization of near-Earth-objects (NEOs) in regard to physical attributes and potential risk and impact factors presents a complex and complicates scientific and engineering challenge. The societal and policy risks and impacts are no less complex, yet are rarely considered in the same context as material properties or related factors. Further, NEO impacts are typically considered as discrete events, not as initial events in a dynamic cascading system. The objective of this contribution is to position the characterization of NEOs within the public policy process domain as a means to reflect on the science-policy nexus in regard to risks and multi-hazard impacts associated with these hazards. This will be accomplished through, first, a brief overview of the science-policy nexus, followed by a discussion of policy process frameworks, such as agenda setting and the multiple streams model, focusing events, and punctuated equilibrium, and their application and appropriateness to the problem of NEOs. How, too, for example, does NEO hazard and risk compare with other low probability, high risk, hazards in regard to public policy? Finally, we will reflect on the implications of alternative NEO "solutions" and the characterization of the NEO "problem," and the political and public acceptance of policy alternatives as a way to link NEO science and policy in the context of the overall NH9.12 panel.

Models, Measurements, and Local Decisions: Assessing and ...

EPA Pesticide Factsheets

This presentation includes a combination of modeling and measurement results to characterize near-source air quality in Newark, New Jersey with consideration of how this information could be used to inform decision making to reduce risk of health impacts. Decisions could include either exposure or emissions reduction, and a host of stakeholders, including residents, academics, NGOs, local and federal agencies. This presentation includes results from the C-PORT modeling system, and from a citizen science project from the local area. The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, decision-support tools, and models to be applied to media-specific or receptor-specific problem areas. CED uses modeling-based approaches to characterize exposures, evaluate fate and transport, and support environmental diagnostics/forensics with input from multiple data sources. It also develops media- and receptor-specific models, process models, and decision support tools for use both within and outside of EPA.

Risk analysis of gravity dam instability using credibility theory Monte Carlo simulation model.

PubMed

Xin, Cao; Chongshi, Gu

2016-01-01

Risk analysis of gravity dam stability involves complicated uncertainty in many design parameters and measured data. Stability failure risk ratio described jointly by probability and possibility has deficiency in characterization of influence of fuzzy factors and representation of the likelihood of risk occurrence in practical engineering. In this article, credibility theory is applied into stability failure risk analysis of gravity dam. Stability of gravity dam is viewed as a hybrid event considering both fuzziness and randomness of failure criterion, design parameters and measured data. Credibility distribution function is conducted as a novel way to represent uncertainty of influence factors of gravity dam stability. And combining with Monte Carlo simulation, corresponding calculation method and procedure are proposed. Based on a dam section, a detailed application of the modeling approach on risk calculation of both dam foundation and double sliding surfaces is provided. The results show that, the present method is feasible to be applied on analysis of stability failure risk for gravity dams. The risk assessment obtained can reflect influence of both sorts of uncertainty, and is suitable as an index value.

Defining the role of polyamines in colon carcinogenesis using mouse models

PubMed Central

Ignatenko, Natalia A.; Gerner, Eugene W.; Besselsen, David G.

2011-01-01

Genetics and diet are both considered important risk determinants for colorectal cancer, a leading cause of death in the US and worldwide. Genetically engineered mouse (GEM) models have made a significant contribution to the characterization of colorectal cancer risk factors. Reliable, reproducible, and clinically relevant animal models help in the identification of the molecular events associated with disease progression and in the development of effictive treatment strategies. This review is focused on the use of mouse models for studying the role of polyamines in colon carcinogenesis. We describe how the available mouse models of colon cancer such as the multiple intestinal neoplasia (Min) mice and knockout genetic models facilitate understanding of the role of polyamines in colon carcinogenesis and help in the development of a rational strategy for colon cancer chemoprevention. PMID:21712957

Value of Donor–Specific Anti–HLA Antibody Monitoring and Characterization for Risk Stratification of Kidney Allograft Loss

PubMed Central

Viglietti, Denis; Loupy, Alexandre; Vernerey, Dewi; Bentlejewski, Carol; Gosset, Clément; Aubert, Olivier; Duong van Huyen, Jean-Paul; Jouven, Xavier; Legendre, Christophe; Glotz, Denis; Zeevi, Adriana

2017-01-01

The diagnosis system for allograft loss lacks accurate individual risk stratification on the basis of donor–specific anti–HLA antibody (anti-HLA DSA) characterization. We investigated whether systematic monitoring of DSA with extensive characterization increases performance in predicting kidney allograft loss. This prospective study included 851 kidney recipients transplanted between 2008 and 2010 who were systematically screened for DSA at transplant, 1 and 2 years post-transplant, and the time of post–transplant clinical events. We assessed DSA characteristics and performed systematic allograft biopsies at the time of post–transplant serum evaluation. At transplant, 110 (12.9%) patients had DSAs; post-transplant screening identified 186 (21.9%) DSA-positive patients. Post–transplant DSA monitoring improved the prediction of allograft loss when added to a model that included traditional determinants of allograft loss (increase in c statistic from 0.67; 95% confidence interval [95% CI], 0.62 to 0.73 to 0.72; 95% CI, 0.67 to 0.77). Addition of DSA IgG3 positivity or C1q binding capacity increased discrimination performance of the traditional model at transplant and post-transplant. Compared with DSA mean fluorescence intensity, DSA IgG3 positivity and C1q binding capacity adequately reclassified patients at lower or higher risk for allograft loss at transplant (category–free net reclassification index, 1.30; 95% CI, 0.94 to 1.67; P<0.001 and 0.93; 95% CI, 0.49 to 1.36; P<0.001, respectively) and post-transplant (category–free net reclassification index, 1.33; 95% CI, 1.03 to 1.62; P<0.001 and 0.95; 95% CI, 0.62 to 1.28; P<0.001, respectively). Thus, pre– and post–transplant DSA monitoring and characterization may improve individual risk stratification for kidney allograft loss. PMID:27493255

Transmission of Bacterial Zoonotic Pathogens between Pets and Humans: The Role of Pet Food.

PubMed

Lambertini, Elisabetta; Buchanan, Robert L; Narrod, Clare; Pradhan, Abani K

2016-01-01

Recent Salmonella outbreaks associated with dry pet food and treats raised the level of concern for these products as vehicle of pathogen exposure for both pets and their owners. The need to characterize the microbiological and risk profiles of this class of products is currently not supported by sufficient specific data. This systematic review summarizes existing data on the main variables needed to support an ingredients-to-consumer quantitative risk model to (1) describe the microbial ecology of bacterial pathogens in the dry pet food production chain, (2) estimate pet exposure to pathogens through dry food consumption, and (3) assess human exposure and illness incidence due to contact with pet food and pets in the household. Risk models populated with the data here summarized will provide a tool to quantitatively address the emerging public health concerns associated with pet food and the effectiveness of mitigation measures. Results of such models can provide a basis for improvements in production processes, risk communication to consumers, and regulatory action.

Framework Analysis for Determining Mode of Action & Human Relevance

EPA Science Inventory

The overall aim of a cancer risk assessment is to characterize the risk to humans from environmental exposures. This risk characterization includes a qualitative and quantitative risk characterization that relies on the development of separate hazard, dose- response and exposure...

Assessing the public health risk of microbial intrusion events in distribution systems: conceptual model, available data, and challenges.

PubMed

Besner, Marie-Claude; Prévost, Michèle; Regli, Stig

2011-01-01

Low and negative pressure events in drinking water distribution systems have the potential to result in intrusion of pathogenic microorganisms if an external source of contamination is present (e.g., nearby leaking sewer main) and there is a pathway for contaminant entry (e.g., leaks in drinking water main). While the public health risk associated with such events is not well understood, quantitative microbial risk assessment can be used to estimate such risk. A conceptual model is provided and the state of knowledge, current assumptions, and challenges associated with the conceptual model parameters are presented. This review provides a characterization of the causes, magnitudes, durations and frequencies of low/negative pressure events; pathways for pathogen entry; pathogen occurrence in external sources of contamination; volumes of water that may enter through the different pathways; fate and transport of pathogens from the pathways of entry to customer taps; pathogen exposure to populations consuming the drinking water; and risk associated with pathogen exposure. Copyright © 2010 Elsevier Ltd. All rights reserved.

Genome-wide significant localization for working and spatial memory: Identifying genes for psychosis using models of cognition.

PubMed

Knowles, Emma E M; Carless, Melanie A; de Almeida, Marcio A A; Curran, Joanne E; McKay, D Reese; Sprooten, Emma; Dyer, Thomas D; Göring, Harald H; Olvera, Rene; Fox, Peter; Almasy, Laura; Duggirala, Ravi; Kent, Jack W; Blangero, John; Glahn, David C

2014-01-01

It is well established that risk for developing psychosis is largely mediated by the influence of genes, but identifying precisely which genes underlie that risk has been problematic. Focusing on endophenotypes, rather than illness risk, is one solution to this problem. Impaired cognition is a well-established endophenotype of psychosis. Here we aimed to characterize the genetic architecture of cognition using phenotypically detailed models as opposed to relying on general IQ or individual neuropsychological measures. In so doing we hoped to identify genes that mediate cognitive ability, which might also contribute to psychosis risk. Hierarchical factor models of genetically clustered cognitive traits were subjected to linkage analysis followed by QTL region-specific association analyses in a sample of 1,269 Mexican American individuals from extended pedigrees. We identified four genome wide significant QTLs, two for working and two for spatial memory, and a number of plausible and interesting candidate genes. The creation of detailed models of cognition seemingly enhanced the power to detect genetic effects on cognition and provided a number of possible candidate genes for psychosis. © 2013 Wiley Periodicals, Inc.

Willingness to Use Pre-Exposure Prophylaxis (PrEP): An Empirical Test of the Information-Motivation-Behavioral Skills (IMB) Model among High-Risk Drug Users in Treatment.

PubMed

Shrestha, Roman; Altice, Frederick L; Huedo-Medina, Tania B; Karki, Pramila; Copenhaver, Michael

2017-05-01

Evidence from recent pre-exposure prophylaxis (PrEP) trials has demonstrated its safety and efficacy in significantly reducing the risk of HIV acquisition for those who are at considerable risk of acquiring HIV infection. With a rapid increase in the amount of research on the efficacy of PrEP for HIV prevention, complementary research on the willingness to use PrEP has grown, especially among MSM, but limited research has been focused among people who use drugs (PWUD). As part of the formative process, we utilized the information-motivation-behavioral skills (IMB) model of health behavior change to characterize and guide intervention development for promoting willingness to use PrEP among high-risk PWUD. The analysis included 400 HIV-negative high-risk PWUD enrolled in a community-based methadone maintenance treatment who reported drug- and/or sex-related HIV risk behaviors in the past 6-months. Analyses revealed support for the IMB model as PrEP-related behavioral skills were found to mediate the influence of PrEP-related information and motivation on willingness to use PrEP. The results provide evidence as to the utility of the IMB model to increase willingness to use PrEP among high-risk PWUD. It therefore makes an important contribution to our understanding of the applicability of theoretically-grounded models of willingness to use PrEP among high-risk PWUD, who are one of the key risk populations who could benefit from the use of PrEP.

Willingness to Use Pre-Exposure Prophylaxis (PrEP): An Empirical Test of the Information-Motivation-Behavioral Skills (IMB) Model among High-Risk Drug Users in Treatment

PubMed Central

Shrestha, Roman; Altice, Frederick L.; Huedo-Medina, Tania B.; Karki, Pramila; Copenhaver, Michael

2016-01-01

Evidence from recent pre-exposure prophylaxis (PrEP) trials has demonstrated its safety and efficacy in significantly reducing the risk of HIV acquisition for those who are at considerable risk of acquiring HIV infection. With a rapid increase in the amount of research on the efficacy of PrEP for HIV prevention, complementary research on the willingness to use PrEP has grown, especially among MSM, but limited research has been focused among people who use drugs (PWUD). As part of the formative process, we utilized the Information-Motivation-Behavioral Skills (IMB) model of health behavior change to characterize and guide intervention development for promoting willingness to use PrEP among high-risk PWUD. The analysis included 400 HIV-negative high-risk PWUD enrolled in a community-based methadone maintenance treatment who reported drug- and/or sex-related HIV risk behaviors in the past 6-months. Analyses revealed support for the IMB model as PrEP-related behavioral skills were found to mediate the influence of PrEP-related information and motivation on willingness to use PrEP. The results provide evidence as to the utility of the IMB model to increase willingness to use PrEP among high-risk PWUD. It therefore makes an important contribution to our understanding of the applicability of theoretically-grounded models of willingness to use PrEP among high-risk PWUD, who are one of the key risk populations who could benefit from the use of PrEP. PMID:27990587

Quantitative risk assessment of human campylobacteriosis associated with thermophilic Campylobacter species in chickens.

PubMed

Rosenquist, Hanne; Nielsen, Niels L; Sommer, Helle M; Nørrung, Birgit; Christensen, Bjarke B

2003-05-25

A quantitative risk assessment comprising the elements hazard identification, hazard characterization, exposure assessment, and risk characterization has been prepared to assess the effect of different mitigation strategies on the number of human cases in Denmark associated with thermophilic Campylobacter spp. in chickens. To estimate the human exposure to Campylobacter from a chicken meal and the number of human cases associated with this exposure, a mathematical risk model was developed. The model details the spread and transfer of Campylobacter in chickens from slaughter to consumption and the relationship between ingested dose and the probability of developing campylobacteriosis. Human exposure was estimated in two successive mathematical modules. Module 1 addresses changes in prevalence and numbers of Campylobacter on chicken carcasses throughout the processing steps of a slaughterhouse. Module 2 covers the transfer of Campylobacter during food handling in private kitchens. The age and sex of consumers were included in this module to introduce variable hygiene levels during food preparation and variable sizes and compositions of meals. Finally, the outcome of the exposure assessment modules was integrated with a Beta-Poisson dose-response model to provide a risk estimate. Simulations designed to predict the effect of different mitigation strategies showed that the incidence of campylobacteriosis associated with consumption of chicken meals could be reduced 30 times by introducing a 2 log reduction of the number of Campylobacter on the chicken carcasses. To obtain a similar reduction of the incidence, the flock prevalence should be reduced approximately 30 times or the kitchen hygiene improved approximately 30 times. Cross-contamination from positive to negative flocks during slaughter had almost no effect on the human Campylobacter incidence, which indicates that implementation of logistic slaughter will only have a minor influence on the risk. Finally, the simulations showed that people in the age of 18-29 years had the highest risk of developing campylobacteriosis.

Perspectives for integrating human and environmental exposure assessments.

PubMed

Ciffroy, P; Péry, A R R; Roth, N

2016-10-15

Integrated Risk Assessment (IRA) has been defined by the EU FP7 HEROIC Coordination action as "the mutual exploitation of Environmental Risk Assessment for Human Health Risk Assessment and vice versa in order to coherently and more efficiently characterize an overall risk to humans and the environment for better informing the risk analysis process" (Wilks et al., 2015). Since exposure assessment and hazard characterization are the pillars of risk assessment, integrating Environmental Exposure assessment (EEA) and Human Exposure assessment (HEA) is a major component of an IRA framework. EEA and HEA typically pursue different targets, protection goals and timeframe. However, human and wildlife species also share the same environment and they similarly inhale air and ingest water and food through often similar overlapping pathways of exposure. Fate models used in EEA and HEA to predict the chemicals distribution among physical and biological media are essentially based on common properties of chemicals, and internal concentration estimations are largely based on inter-species (i.e. biota-to-human) extrapolations. Also, both EEA and HEA are challenged by increasing scientific complexity and resources constraints. Altogether, these points create the need for a better exploitation of all currently existing data, experimental approaches and modeling tools and it is assumed that a more integrated approach of both EEA and HEA may be part of the solution. Based on the outcome of an Expert Workshop on Extrapolations in Integrated Exposure Assessment organized by the HEROIC project in January 2014, this paper identifies perspectives and recommendations to better harmonize and extrapolate exposure assessment data, models and methods between Human Health and Environmental Risk Assessments to support the further development and promotion of the concept of IRA. Ultimately, these recommendations may feed into guidance showing when and how to apply IRA in the regulatory decision-making process for chemicals. Copyright © 2015 Elsevier B.V. All rights reserved.

An improved approach for flight readiness certification: Methodology for failure risk assessment and application examples. Volume 2: Software documentation

NASA Technical Reports Server (NTRS)

Moore, N. R.; Ebbeler, D. H.; Newlin, L. E.; Sutharshana, S.; Creager, M.

1992-01-01

An improved methodology for quantitatively evaluating failure risk of spaceflight systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with engineering analysis to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in engineering analyses of failure phenomena, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which engineering analysis models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes, These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. Conventional engineering analysis models currently employed for design of failure prediction are used in this methodology. The PFA methodology is described and examples of its application are presented. Conventional approaches to failure risk evaluation for spaceflight systems are discussed, and the rationale for the approach taken in the PFA methodology is presented. The statistical methods, engineering models, and computer software used in fatigue failure mode applications are thoroughly documented.

An improved approach for flight readiness certification: Methodology for failure risk assessment and application examples, volume 1

NASA Technical Reports Server (NTRS)

Moore, N. R.; Ebbeler, D. H.; Newlin, L. E.; Sutharshana, S.; Creager, M.

1992-01-01

An improved methodology for quantitatively evaluating failure risk of spaceflight systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with engineering analysis to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in engineering analyses of failure phenomena, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which engineering analysis models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. Conventional engineering analysis models currently employed for design of failure prediction are used in this methodology. The PFA methodology is described and examples of its application are presented. Conventional approaches to failure risk evaluation for spaceflight systems are discussed, and the rationale for the approach taken in the PFA methodology is presented. The statistical methods, engineering models, and computer software used in fatigue failure mode applications are thoroughly documented.

Characterizing vaccine-associated risks using cubic smoothing splines.

PubMed

Brookhart, M Alan; Walker, Alexander M; Lu, Yun; Polakowski, Laura; Li, Jie; Paeglow, Corrie; Puenpatom, Tosmai; Izurieta, Hector; Daniel, Gregory W

2012-11-15

Estimating risks associated with the use of childhood vaccines is challenging. The authors propose a new approach for studying short-term vaccine-related risks. The method uses a cubic smoothing spline to flexibly estimate the daily risk of an event after vaccination. The predicted incidence rates from the spline regression are then compared with the expected rates under a log-linear trend that excludes the days surrounding vaccination. The 2 models are then used to estimate the excess cumulative incidence attributable to the vaccination during the 42-day period after vaccination. Confidence intervals are obtained using a model-based bootstrap procedure. The method is applied to a study of known effects (positive controls) and expected noneffects (negative controls) of the measles, mumps, and rubella and measles, mumps, rubella, and varicella vaccines among children who are 1 year of age. The splines revealed well-resolved spikes in fever, rash, and adenopathy diagnoses, with the maximum incidence occurring between 9 and 11 days after vaccination. For the negative control outcomes, the spline model yielded a predicted incidence more consistent with the modeled day-specific risks, although there was evidence of increased risk of diagnoses of congenital malformations after vaccination, possibly because of a "provider visit effect." The proposed approach may be useful for vaccine safety surveillance.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jones, Daniel Steven; Efroymson, Rebecca Ann; Hargrove, William Walter

A multiple stressor risk assessment was conducted at Yuma Proving Ground, Arizona, as a demonstration of the Military Ecological Risk Assessment Framework. The focus was a testing program at Cibola Range, which involved an Apache Longbow helicopter firing Hellfire missiles at moving targets, M60- A1 tanks. This paper describes the ecological risk assessment for the missile launch and detonation. The primary stressor associated with this activity was sound. Other minor stressors included the detonation impact, shrapnel, and fire. Exposure to desert mule deer (Odocoileus hemionus crooki) was quantified using the Army sound contour program BNOISE2, as well as distances frommore » the explosion to deer. Few effects data were available from related studies. Exposure-response models for the characterization of effects consisted of human "disturbance" and hearing damage thresholds in units of C-weighted decibels (sound exposure level) and a distance-based No Observed Adverse Effects Level for moose and cannonfire. The risk characterization used a weight-of-evidence approach and concluded that risk to mule deer behavior from the missile firing was likely for a negligible number of deer, but that no risk to mule deer abundance and reproduction is expected.« less

Medical Updates Number 5 to the International Space Station Probability Risk Assessment (PRA) Model Using the Integrated Medical Model

NASA Technical Reports Server (NTRS)

Butler, Doug; Bauman, David; Johnson-Throop, Kathy

2011-01-01

The Integrated Medical Model (IMM) Project has been developing a probabilistic risk assessment tool, the IMM, to help evaluate in-flight crew health needs and impacts to the mission due to medical events. This package is a follow-up to a data package provided in June 2009. The IMM currently represents 83 medical conditions and associated ISS resources required to mitigate medical events. IMM end state forecasts relevant to the ISS PRA model include evacuation (EVAC) and loss of crew life (LOCL). The current version of the IMM provides the basis for the operational version of IMM expected in the January 2011 timeframe. The objectives of this data package are: 1. To provide a preliminary understanding of medical risk data used to update the ISS PRA Model. The IMM has had limited validation and an initial characterization of maturity has been completed using NASA STD 7009 Standard for Models and Simulation. The IMM has been internally validated by IMM personnel but has not been validated by an independent body external to the IMM Project. 2. To support a continued dialogue between the ISS PRA and IMM teams. To ensure accurate data interpretation, and that IMM output format and content meets the needs of the ISS Risk Management Office and ISS PRA Model, periodic discussions are anticipated between the risk teams. 3. To help assess the differences between the current ISS PRA and IMM medical risk forecasts of EVAC and LOCL. Follow-on activities are anticipated based on the differences between the current ISS PRA medical risk data and the latest medical risk data produced by IMM.

Photochemistry of Aqueous C60 Clusters: Wavelength Dependency and Product Characterization

EPA Science Inventory

To construct accurate risk assessment models for engineered nanomaterials, there is urgent need for information on the reactivity (or conversely, persistence) and transformation pathways of these materials in the natural environment. As an important step toward addressing this is...

Standard Operating Procedure for Using the NAFTA Guidance to Calculate Representative Half-life Values and Characterizing Pesticide Degradation

EPA Pesticide Factsheets

Results of the degradation kinetics project and describes a general approach for calculating and selecting representative half-life values from soil and aquatic transformation studies for risk assessment and exposure modeling purposes.

A screening approach using zebrafish for the detection and characterization of developmental neurotoxicity.

EPA Science Inventory

Thousands of chemicals have little or no data to support developmental neurotoxicity risk assessments. Current developmental neurotoxicity guideline studies mandating mammalian model systems are expensive and time consuming. Therefore a rapid, cost-effective method to assess de...

Modeling Flow and Pollutant Transport in a Karst Watershed with SWAT

USDA-ARS?s Scientific Manuscript database

Karst hydrology is characterized by multiple springs, sinkholes, and losing streams resulting from acidic water percolating through limestone. These features provide direct connections between surface water and groundwater and increase the risk of groundwater, springs and stream contamination. Anthr...

Aging, cortical injury and Alzheimer's disease-like pathology in the guinea pig brain.

PubMed

Bates, Kristyn; Vink, Robert; Martins, Ralph; Harvey, Alan

2014-06-01

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized histopathologically by the abnormal deposition of the proteins amyloid-beta (Aβ) and tau. A major issue for AD research is the lack of an animal model that accurately replicates the human disease, thus making it difficult to investigate potential risk factors for AD such as head injury. Furthermore, as age remains the strongest risk factor for most of the AD cases, transgenic models in which mutant human genes are expressed throughout the life span of the animal provide only limited insight into age-related factors in disease development. Guinea pigs (Cavia porcellus) are of interest in AD research because they have a similar Aβ sequence to humans and thus may present a useful non-transgenic animal model of AD. Brains from guinea pigs aged 3-48 months were examined to determine the presence of age-associated AD-like pathology. In addition, fluid percussion-induced brain injury was performed to characterize mechanisms underlying the association between AD risk and head injury. No statistically significant changes were detected in the overall response to aging, although we did observe some region-specific changes. Diffuse deposits of Aβ were found in the hippocampal region of the oldest animals and alterations in amyloid precursor protein processing and tau immunoreactivity were observed with age. Brain injury resulted in a strong and sustained increase in amyloid precursor protein and tau immunoreactivity without Aβ deposition, over 7 days. Guinea pigs may therefore provide a useful model for investigating the influence of environmental and non-genetic risk factors on the pathogenesis of AD. Copyright © 2014 Elsevier Inc. All rights reserved.

Station Blackout: A case study in the interaction of mechanistic and probabilistic safety analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Curtis Smith; Diego Mandelli; Cristian Rabiti

2013-11-01

The ability to better characterize and quantify safety margins is important to improved decision making about nuclear power plant design, operation, and plant life extension. As research and development (R&D) in the light-water reactor (LWR) Sustainability (LWRS) Program and other collaborative efforts yield new data, sensors, and improved scientific understanding of physical processes that govern the aging and degradation of plant SSCs needs and opportunities to better optimize plant safety and performance will become known. The purpose of the Risk Informed Safety Margin Characterization (RISMC) Pathway R&D is to support plant decisions for risk-informed margin management with the aim tomore » improve economics, reliability, and sustain safety of current NPPs. In this paper, we describe the RISMC analysis process illustrating how mechanistic and probabilistic approaches are combined in order to estimate a safety margin. We use the scenario of a “station blackout” wherein offsite power and onsite power is lost, thereby causing a challenge to plant safety systems. We describe the RISMC approach, illustrate the station blackout modeling, and contrast this with traditional risk analysis modeling for this type of accident scenario.« less

An improved approach for flight readiness certification: Probabilistic models for flaw propagation and turbine blade failure. Volume 1: Methodology and applications

NASA Technical Reports Server (NTRS)

Moore, N. R.; Ebbeler, D. H.; Newlin, L. E.; Sutharshana, S.; Creager, M.

1992-01-01

An improved methodology for quantitatively evaluating failure risk of spaceflight systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with analytical modeling of failure phenomena to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in analytical modeling, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which analytical models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. State-of-the-art analytical models currently employed for designs failure prediction, or performance analysis are used in this methodology. The rationale for the statistical approach taken in the PFA methodology is discussed, the PFA methodology is described, and examples of its application to structural failure modes are presented. The engineering models and computer software used in fatigue crack growth and fatigue crack initiation applications are thoroughly documented.

An improved approach for flight readiness certification: Probabilistic models for flaw propagation and turbine blade failure. Volume 2: Software documentation

NASA Technical Reports Server (NTRS)

Moore, N. R.; Ebbeler, D. H.; Newlin, L. E.; Sutharshana, S.; Creager, M.

1992-01-01

An improved methodology for quantitatively evaluating failure risk of spaceflights systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with analytical modeling of failure phenomena to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in analytical modeling, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which analytical models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. State-of-the-art analytical models currently employed for design, failure prediction, or performance analysis are used in this methodology. The rationale for the statistical approach taken in the PFA methodology is discussed, the PFA methodology is described, and examples of its application to structural failure modes are presented. The engineering models and computer software used in fatigue crack growth and fatigue crack initiation applications are thoroughly documented.

BRICK v0.2, a simple, accessible, and transparent model framework for climate and regional sea-level projections

NASA Astrophysics Data System (ADS)

Wong, Tony E.; Bakker, Alexander M. R.; Ruckert, Kelsey; Applegate, Patrick; Slangen, Aimée B. A.; Keller, Klaus

2017-07-01

Simple models can play pivotal roles in the quantification and framing of uncertainties surrounding climate change and sea-level rise. They are computationally efficient, transparent, and easy to reproduce. These qualities also make simple models useful for the characterization of risk. Simple model codes are increasingly distributed as open source, as well as actively shared and guided. Alas, computer codes used in the geosciences can often be hard to access, run, modify (e.g., with regards to assumptions and model components), and review. Here, we describe the simple model framework BRICK (Building blocks for Relevant Ice and Climate Knowledge) v0.2 and its underlying design principles. The paper adds detail to an earlier published model setup and discusses the inclusion of a land water storage component. The framework largely builds on existing models and allows for projections of global mean temperature as well as regional sea levels and coastal flood risk. BRICK is written in R and Fortran. BRICK gives special attention to the model values of transparency, accessibility, and flexibility in order to mitigate the above-mentioned issues while maintaining a high degree of computational efficiency. We demonstrate the flexibility of this framework through simple model intercomparison experiments. Furthermore, we demonstrate that BRICK is suitable for risk assessment applications by using a didactic example in local flood risk management.

Exposure Estimation and Interpretation of Occupational Risk: Enhanced Information for the Occupational Risk Manager

PubMed Central

Waters, Martha; McKernan, Lauralynn; Maier, Andrew; Jayjock, Michael; Schaeffer, Val; Brosseau, Lisa

2015-01-01

The fundamental goal of this article is to describe, define, and analyze the components of the risk characterization process for occupational exposures. Current methods are described for the probabilistic characterization of exposure, including newer techniques that have increasing applications for assessing data from occupational exposure scenarios. In addition, since the probability of health effects reflects variability in the exposure estimate as well as the dose-response curve—the integrated considerations of variability surrounding both components of the risk characterization provide greater information to the occupational hygienist. Probabilistic tools provide a more informed view of exposure as compared to use of discrete point estimates for these inputs to the risk characterization process. Active use of such tools for exposure and risk assessment will lead to a scientifically supported worker health protection program. Understanding the bases for an occupational risk assessment, focusing on important sources of variability and uncertainty enables characterizing occupational risk in terms of a probability, rather than a binary decision of acceptable risk or unacceptable risk. A critical review of existing methods highlights several conclusions: (1) exposure estimates and the dose-response are impacted by both variability and uncertainty and a well-developed risk characterization reflects and communicates this consideration; (2) occupational risk is probabilistic in nature and most accurately considered as a distribution, not a point estimate; and (3) occupational hygienists have a variety of tools available to incorporate concepts of risk characterization into occupational health and practice. PMID:26302336

Characterizing lake water quality, cyanotoxins, and Amyotrophic Lateral Sclerosis (ALS).

NASA Astrophysics Data System (ADS)

Torbick, N.; Ziniti, B.; Stommel, E.; Linder, E.; Andrew, A.; Bradley, W.; Shi, X.

2016-12-01

Concern over toxins and public health threats resulting from Cyanobacterial Harmful Algal Blooms (CHABs) have gained attention as reoccurring and seasonal blooms persist in many waters. Concordantly, climate change has been suggested to increase the intensity, duration, and frequency of CHAB events. Humans may be exposed to the cyanotoxins produced by cyanobacteria via the food chain, drinking water, recreational use of waterbodies and by aerosolization. Exposure to the cyanobacterial neurotoxin, β-N-methylamino-L-alanine (BMAA) that has been found in the brains of ALS patients is a hypothesized mechanism. The goals of this research initiative are to investigate spatiotemporal relationships between inland lake water quality and ALS across northern New England (NNE). Multiscale satellite remote sensing was integrated with in situ lake and toxin sampling to provide robust spatiotemporal exposure risk metrics characterizing CHAB. Semi-analytical, shape, and empirical algorithms were bldned together tp generate spatiotemporal measures of chl-a and PC with R2 ranging from 0.65-0.92 using withheld samples. Postmortem aerosolization analysis found 85% of high risk patients to express phycobillin in lung tissue using fluroesence microscopy. To scal eup to the region we employed complementing spatial statistics and a Bayesian hierarchical framework to model relationships between lake risk metrics and ALS case location across NNE. The eco-epidemiolgical modeling results show that on average poorer water quality conditions and higher measures of cyanobacteria are associated with increased odds of belonging to a normalized ALS hot spots and risk of ALS. This has broad societal impacts as the frequency, duration, and magnitude of cyanobacterial harmful algal blooms are expanding and this work helps characterize lake ecosystem services and human health.

Agent Based Modeling of Atherosclerosis: A Concrete Help in Personalized Treatments

NASA Astrophysics Data System (ADS)

Pappalardo, Francesco; Cincotti, Alessandro; Motta, Alfredo; Pennisi, Marzio

Atherosclerosis, a pathology affecting arterial blood vessels, is one of most common diseases of the developed countries. We present studies on the increased atherosclerosis risk using an agent based model of atherogenesis that has been previously validated using clinical data. It is well known that the major risk in atherosclerosis is the persistent high level of low density lipoprotein (LDL) concentration. However, it is not known if short period of high LDL concentration can cause irreversible damage and if reduction of the LDL concentration (either by life style or drug) can drastically or partially reduce the already acquired risk. We simulated four different clinical situations in a large set of virtual patients (200 per clinical scenario). In the first one the patients lifestyle maintains the concentration of LDL in a no risk range. This is the control case simulation. The second case is represented by patients having high level of LDL with a delay to apply appropriate treatments; The third scenario is characterized by patients with high LDL levels treated with specific drugs like statins. Finally we simulated patients that are characterized by several oxidative events (smoke, sedentary life style, assumption of alcoholic drinks and so on so forth) that effective increase the risk of LDL oxidation. Those preliminary results obviously need to be clinically investigated. It is clear, however, that SimAthero has the power to concretely help medical doctors and clinicians in choosing personalized treatments for the prevention of the atherosclerosis damages.

Comparison of a Traditional Probabilistic Risk Assessment Approach with Advanced Safety Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smith, Curtis L; Mandelli, Diego; Zhegang Ma

2014-11-01

As part of the Light Water Sustainability Program (LWRS) [1], the purpose of the Risk Informed Safety Margin Characterization (RISMC) [2] Pathway research and development (R&D) is to support plant decisions for risk-informed margin management with the aim to improve economics, reliability, and sustain safety of current NPPs. In this paper, we describe the RISMC analysis process illustrating how mechanistic and probabilistic approaches are combined in order to estimate a safety margin. We use the scenario of a “station blackout” (SBO) wherein offsite power and onsite power is lost, thereby causing a challenge to plant safety systems. We describe themore » RISMC approach, illustrate the station blackout modeling, and contrast this with traditional risk analysis modeling for this type of accident scenario. We also describe our approach we are using to represent advanced flooding analysis.« less

Source-to-Dose Modeling of Phthalates: Lessons for Prioritization

EPA Science Inventory

Globally there is a need to characterize potential risk to human health and the environment that arises from the manufacture and use of tens of thousands of chemicals. The US EPA is developing methods for using computational chemistry, high-throughput screening, and toxicogenomi...

Landscape-scale accessibility of livestock to tigers: implications of spatial grain for modeling predation risk to mitigate human-carnivore conflict.

PubMed

Miller, Jennifer R B; Jhala, Yadvendradev V; Jena, Jyotirmay; Schmitz, Oswald J

2015-03-01

Innovative conservation tools are greatly needed to reduce livelihood losses and wildlife declines resulting from human-carnivore conflict. Spatial risk modeling is an emerging method for assessing the spatial patterns of predator-prey interactions, with applications for mitigating carnivore attacks on livestock. Large carnivores that ambush prey attack and kill over small areas, requiring models at fine spatial grains to predict livestock depredation hot spots. To detect the best resolution for predicting where carnivores access livestock, we examined the spatial attributes associated with livestock killed by tigers in Kanha Tiger Reserve, India, using risk models generated at 20, 100, and 200-m spatial grains. We analyzed land-use, human presence, and vegetation structure variables at 138 kill sites and 439 random sites to identify key landscape attributes where livestock were vulnerable to tigers. Land-use and human presence variables contributed strongly to predation risk models, with most variables showing high relative importance (≥0.85) at all spatial grains. The risk of a tiger killing livestock increased near dense forests and near the boundary of the park core zone where human presence is restricted. Risk was nonlinearly related to human infrastructure and open vegetation, with the greatest risk occurring 1.2 km from roads, 1.1 km from villages, and 8.0 km from scrubland. Kill sites were characterized by denser, patchier, and more complex vegetation with lower visibility than random sites. Risk maps revealed high-risk hot spots inside of the core zone boundary and in several patches in the human-dominated buffer zone. Validation against known kills revealed predictive accuracy for only the 20 m model, the resolution best representing the kill stage of hunting for large carnivores that ambush prey, like the tiger. Results demonstrate that risk models developed at fine spatial grains can offer accurate guidance on landscape attributes livestock should avoid to minimize human-carnivore conflict.

A prediction model for colon cancer surveillance data.

PubMed

Good, Norm M; Suresh, Krithika; Young, Graeme P; Lockett, Trevor J; Macrae, Finlay A; Taylor, Jeremy M G

2015-08-15

Dynamic prediction models make use of patient-specific longitudinal data to update individualized survival probability predictions based on current and past information. Colonoscopy (COL) and fecal occult blood test (FOBT) results were collected from two Australian surveillance studies on individuals characterized as high-risk based on a personal or family history of colorectal cancer. Motivated by a Poisson process, this paper proposes a generalized nonlinear model with a complementary log-log link as a dynamic prediction tool that produces individualized probabilities for the risk of developing advanced adenoma or colorectal cancer (AAC). This model allows predicted risk to depend on a patient's baseline characteristics and time-dependent covariates. Information on the dates and results of COLs and FOBTs were incorporated using time-dependent covariates that contributed to patient risk of AAC for a specified period following the test result. These covariates serve to update a person's risk as additional COL, and FOBT test information becomes available. Model selection was conducted systematically through the comparison of Akaike information criterion. Goodness-of-fit was assessed with the use of calibration plots to compare the predicted probability of event occurrence with the proportion of events observed. Abnormal COL results were found to significantly increase risk of AAC for 1 year following the test. Positive FOBTs were found to significantly increase the risk of AAC for 3 months following the result. The covariates that incorporated the updated test results were of greater significance and had a larger effect on risk than the baseline variables. Copyright © 2015 John Wiley & Sons, Ltd.

Characterizing species at risk. II: Using Bayesian belief networks as decision support tools to determine species conservation categories under the Northwest Forest Plan.

Treesearch

B.G. Marcot; P.A. Hohenlohe; S. Morey; R. Holmes; R. Molina; M.C. Turley; M.H. Huff; J.A. Laurence

2006-01-01

We developed decision-aiding models as Bayesian belief networks (BBNs) that represented evaluation guidelines used to determine the appropriate conservation of hundreds of potentially rare species on federally-administered lands in the Pacific Northwest United States. The models were used in a structured assessment and paneling procedure as part of an adaptive...

Site-wide seismic risk model for Savannah River Site nuclear facilities

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eide, S.A.; Shay, R.S.; Durant, W.S.

1993-09-01

The 200,000 acre Savannah River Site (SRS) has nearly 30 nuclear facilities spread throughout the site. The safety of each facility has been established in facility-specific safety analysis reports (SARs). Each SAR contains an analysis of risk from seismic events to both on-site workers and the off-site population. Both radiological and chemical releases are considered, and air and water pathways are modeled. Risks to the general public are generally characterized by evaluating exposure to the maximally exposed individual located at the SRS boundary and to the off-site population located within 50 miles. Although the SARs are appropriate methods for studyingmore » individual facility risks, there is a class of accident initiators that can simultaneously affect several of all of the facilities, Examples include seismic events, strong winds or tornados, floods, and loss of off-site electrical power. Overall risk to the off-site population from such initiators is not covered by the individual SARs. In such cases multiple facility radionuclide or chemical releases could occur, and off-site exposure would be greater than that indicated in a single facility SAR. As a step towards an overall site-wide risk model that adequately addresses multiple facility releases, a site-wide seismic model for determining off-site risk has been developed for nuclear facilities at the SRS. Risk from seismic events up to the design basis earthquake (DBE) of 0.2 g (frequency of 2.0E-4/yr) is covered by the model. Present plans include expanding the scope of the model to include other types of initiators that can simultaneously affect multiple facilities.« less

Linking stressors and ecological responses

USGS Publications Warehouse

Gentile, J.H.; Solomon, K.R.; Butcher, J.B.; Harrass, M.; Landis, W.G.; Power, M.; Rattner, B.A.; Warren-Hicks, W.J.; Wenger, R.; Foran, Jeffery A.; Ferenc, Susan A.

1999-01-01

To characterize risk, it is necessary to quantify the linkages and interactions between chemical, physical and biological stressors and endpoints in the conceptual framework for ecological risk assessment (ERA). This can present challenges in a multiple stressor analysis, and it will not always be possible to develop a quantitative stressor-response profile. This review commences with a conceptual representation of the problem of developing a linkage analysis for multiple stressors and responses. The remainder of the review surveys a variety of mathematical and statistical methods (e.g., ranking methods, matrix models, multivariate dose-response for mixtures, indices, visualization, simulation modeling and decision-oriented methods) for accomplishing the linkage analysis for multiple stressors. Describing the relationships between multiple stressors and ecological effects are critical components of 'effects assessment' in the ecological risk assessment framework.

Wind Characterization for the Assessment of Collision Risk During Flight Level Changes

NASA Technical Reports Server (NTRS)

Carreno, Victor; Chartrand, Ryan

2009-01-01

A model of vertical wind gradient is presented based on National Oceanic and Atmospheric Administration (NOAA) wind data. The objective is to have an accurate representation of wind to be used in Collision Risk Models (CRM) of aircraft procedures. Depending on how an aircraft procedure is defined, wind and the different characteristics of the wind will have a more severe or less severe impact on distances between aircraft. For the In-Trail Procedure, the non-linearity of the vertical wind gradient has the greatest impact on longitudinal distance. The analysis in this paper extracts standard deviation, mean, maximum, and linearity characteristics from the NOAA data.

Enhanced project management tool

NASA Technical Reports Server (NTRS)

Hsu, Chen-Jung (Inventor); Patel, Hemil N. (Inventor); Maluf, David A. (Inventor); Moh Hashim, Jairon C. (Inventor); Tran, Khai Peter B. (Inventor)

2012-01-01

A system for managing a project that includes multiple tasks and a plurality of workers. Input information includes characterizations based upon a human model, a team model and a product model. Periodic reports, such as one or more of a monthly report, a task plan report, a schedule report, a budget report and a risk management report, are generated and made available for display or further analysis or collection into a customized report template. An extensible database allows searching for information based upon context and upon content. Seven different types of project risks are addressed, including non-availability of required skill mix of workers. The system can be configured to exchange data and results with corresponding portions of similar project analyses, and to provide user-specific access to specified information.

Quantitative microbial risk assessment for Escherichia coli O157:H7, Salmonella enterica, and Listeria monocytogenes in leafy green vegetables consumed at salad bars, based on modeling supply chain logistics.

PubMed

Tromp, S O; Rijgersberg, H; Franz, E

2010-10-01

Quantitative microbial risk assessments do not usually account for the planning and ordering mechanisms (logistics) of a food supply chain. These mechanisms and consumer demand determine the storage and delay times of products. The aim of this study was to quantitatively assess the difference between simulating supply chain logistics (MOD) and assuming fixed storage times (FIX) in microbial risk estimation for the supply chain of fresh-cut leafy green vegetables destined for working-canteen salad bars. The results of the FIX model were previously published (E. Franz, S. O. Tromp, H. Rijgersberg, and H. J. van der Fels-Klerx, J. Food Prot. 73:274-285, 2010). Pathogen growth was modeled using stochastic discrete-event simulation of the applied logistics concept. The public health effects were assessed by conducting an exposure assessment and risk characterization. The relative growths of Escherichia coli O157 (17%) and Salmonella enterica (15%) were identical in the MOD and FIX models. In contrast, the relative growth of Listeria monocytogenes was considerably higher in the MOD model (1,156%) than in the FIX model (194%). The probability of L. monocytogenes infection in The Netherlands was higher in the MOD model (5.18×10(-8)) than in the FIX model (1.23×10(-8)). The risk of listeriosis-induced fetal mortality in the perinatal population increased from 1.24×10(-4) (FIX) to 1.66×10(-4) (MOD). Modeling the probabilistic nature of supply chain logistics is of additional value for microbial risk assessments regarding psychrotrophic pathogens in food products for which time and temperature are the postharvest preventive measures in guaranteeing food safety.

High-Throughput Models for Exposure-Based Chemical Prioritization in the ExpoCast Project

EPA Science Inventory

The United States Environmental Protection Agency (U.S. EPA) must characterize potential risks to human health and the environment associated with manufacture and use of thousands of chemicals. High-throughput screening (HTS) for biological activity allows the ToxCast research pr...

A Modeling Framework for Improved Characterization of Near-Road Exposure at Fine Scales

EPA Science Inventory

Traffic-related air pollutants could cause adverse health impact to communities near roadways. To estimate the population risk and locate "hotspots" in the near-road environment, quantifying the exposure at a fine spatial resolution is essential. A new state-of-the-art ...

Use of transfer factors to characterize uptake of selenium by plants.

USDA-ARS?s Scientific Manuscript database

Models used for the assessment of risks relating to the eventual leakage of nuclear waste repositories have been developed by various agencies including the International Atomic Energy Agency. While focusing on other radionuclides, little attention has been given to the assessment of the environment...

Alpha-1 antitrypsin gene therapy prevented bone loss in ovariectomy induced osteoporosis mouse model

USDA-ARS?s Scientific Manuscript database

Osteoporosis is a major healthcare burden affecting mostly postmenopausal women characterized by compromised bone strength and increased risk of fragility fracture. Although pathogenesis of this disease is complex, elevated proinflammatory cytokine production is clearly involved in bone loss at meno...

Modeling Approaches for Characterizing and Evaluating Environmental Exposure to Engineered Nanomaterials in Support of Risk-Based Decision Making

EPA Science Inventory

As the use of engineered nanomaterials becomes more prevalent, the likelihood of unintended exposure to these materials also increases. Given the current scarcity of experimental data regarding fate, transport, and bioavailability, determining potential environmental exposure to ...

Possibilities and Challenges for Modeling Flow and Pollutant Transport in a Karst Watershed with SWAT

USDA-ARS?s Scientific Manuscript database

Karst hydrology is characterized by multiple springs, sinkholes, and losing streams resulting from acidic water percolating through limestone. These features provide direct connections between surface water and groundwater and increase the risk of groundwater, spring and stream contamination. Anthro...

Myth and Reality in Hydrogeological Site Characterization at DD and R Sites

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rubin, Yoram

2008-01-15

The science of hydrogeological site characterization has made significant progress over the last twenty years. Progress has been made in modeling of flow and transport in the heterogeneous subsurface, in understanding of the complex patterns of geological heterogeneity and in measurement technologies. Modeling of uncertainty has also advanced significantly, in recognition of the inherent limitations of subsurface characterization. Much less progress has been made in transforming this progress into practice, where characterization is determined to a large extent by regulations. Environmental regulations have not progressed as much as the science, for example, in recognizing uncertainty. As such, practitioners are lessmore » inclined to adopt advanced, science-based solutions, this opening the door for myths and conflicts. Myths develop where the science base is perceived to be weak, whereas conflicts arise in the face of a disconnect between the science and the regulations. Myths translate to ad-hoc solutions and misplaced empiricism, as well as to unjustified reliance on field experience, to the detriment of D and DR. This paper explores the roots for this situation and identifies ideas that may help in bridging the gap between research and applications. A rational approach for DD and R is needed that will encourage innovation in site characterization, reduce costs and accelerate completion. Such an approach needs to include several elements. DD and R regulations need to recognize the various aspects of uncertainty inherent to site characterization, and as such, should be formulated using probabilistic concepts. One of the immediate benefits will be in allowing a gradual approach for data acquisition in DD and R sites: decisions can be made even under the most severe data limitations, and can be modified as additional data become available. The definition of risk is another major element. There is no universal definition of risk or of a methodology to define risk. Different sites justify different definitions, depending on many environmental, economical and social factors. Despite the lack of consensus, it seems that a good place to start is in fact to recognize that there is a room for all these factors, and a need to balance between them. As experience is gained, through research and discussions among DD and R stakeholders, this may become less of a challenge. Regulations need to recognize the possibility of developing alternative, site-specific characterization strategies based on the various length and time scales that define specific environmental problems, including length scales of heterogeneity, source dimensions and distance to environmental targets. For example, point and distributed sources justify different characterization strategies. Development of problem- or site-specific strategies will create the context for defining innovative efficient DD and R strategies. Innovation in characterization can will also follow from recognizing the specific physiological aspects of the toxins and the related uncertainty. This will open the door for improving risk characterization not only from the hydrologic perspective, but also form the physiologic one.« less

Wildfire Decision Making Under Uncertainty

NASA Astrophysics Data System (ADS)

Thompson, M.

2013-12-01

Decisions relating to wildfire management are subject to multiple sources of uncertainty, and are made by a broad range of individuals, across a multitude of environmental and socioeconomic contexts. In this presentation I will review progress towards identification and characterization of uncertainties and how this information can support wildfire decision-making. First, I will review a typology of uncertainties common to wildfire management, highlighting some of the more salient sources of uncertainty and how they present challenges to assessing wildfire risk. This discussion will cover the expanding role of burn probability modeling, approaches for characterizing fire effects, and the role of multi-criteria decision analysis, and will provide illustrative examples of integrated wildfire risk assessment across a variety of planning scales. Second, I will describe a related uncertainty typology that focuses on the human dimensions of wildfire management, specifically addressing how social, psychological, and institutional factors may impair cost-effective risk mitigation. This discussion will encompass decision processes before, during, and after fire events, with a specific focus on active management of complex wildfire incidents. An improved ability to characterize uncertainties faced in wildfire management could lead to improved delivery of decision support, targeted communication strategies, and ultimately to improved wildfire management outcomes.

Microseismic techniques for avoiding induced seismicity during fluid injection

DOE PAGES

Matzel, Eric; White, Joshua; Templeton, Dennise; ...

2014-01-01

The goal of this research is to develop a fundamentally better approach to geological site characterization and early hazard detection. We combine innovative techniques for analyzing microseismic data with a physics-based inversion model to forecast microseismic cloud evolution. The key challenge is that faults at risk of slipping are often too small to detect during the site characterization phase. Our objective is to devise fast-running methodologies that will allow field operators to respond quickly to changing subsurface conditions.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ramuhalli, Pradeep; Hirt, Evelyn H.; Veeramany, Arun

This research report summaries the development and evaluation of a prototypic enhanced risk monitor (ERM) methodology (framework) that includes alternative risk metrics and uncertainty analysis. This updated ERM methodology accounts for uncertainty in the equipment condition assessment (ECA), the prognostic result, and the probabilistic risk assessment (PRA) model. It is anticipated that the ability to characterize uncertainty in the estimated risk and update the risk estimates in real time based on equipment condition assessment (ECA) will provide a mechanism for optimizing plant performance while staying within specified safety margins. These results (based on impacting active component O&M using real-time equipmentmore » condition information) are a step towards ERMs that, if integrated with AR supervisory plant control systems, can help control O&M costs and improve affordability of advanced reactors.« less

[Chest modelling and automotive accidents].

PubMed

Trosseille, Xavier

2011-11-01

Automobile development is increasingly based on mathematical modeling. Accurate models of the human body are now available and serve to develop new means of protection. These models used to consist of rigid, articulated bodies but are now made of several million finite elements. They are now capable of predicting some risks of injury. To develop these models, sophisticated tests were conducted on human cadavers. For example, chest modeling started with material characterization and led to complete validation in the automobile environment. Model personalization, based on medical imaging, will permit studies of the behavior and tolerances of the entire population.

The Environmental Protection Agency's Community-Focused Exposure and Risk Screening Tool (C-FERST) and its potential use for environmental justice efforts.

PubMed

Zartarian, Valerie G; Schultz, Bradley D; Barzyk, Timothy M; Smuts, Marybeth; Hammond, Davyda M; Medina-Vera, Myriam; Geller, Andrew M

2011-12-01

Our primary objective was to provide higher quality, more accessible science to address challenges of characterizing local-scale exposures and risks for enhanced community-based assessments and environmental decision-making. After identifying community needs, priority environmental issues, and current tools, we designed and populated the Community-Focused Exposure and Risk Screening Tool (C-FERST) in collaboration with stakeholders, following a set of defined principles, and considered it in the context of environmental justice. C-FERST is a geographic information system and resource access Web tool under development for supporting multimedia community assessments. Community-level exposure and risk research is being conducted to address specific local issues through case studies. C-FERST can be applied to support environmental justice efforts. It incorporates research to develop community-level data and modeled estimates for priority environmental issues, and other relevant information identified by communities. Initial case studies are under way to refine and test the tool to expand its applicability and transferability. Opportunities exist for scientists to address the many research needs in characterizing local cumulative exposures and risks and for community partners to apply and refine C-FERST.

A bootstrap based space-time surveillance model with an application to crime occurrences

NASA Astrophysics Data System (ADS)

Kim, Youngho; O'Kelly, Morton

2008-06-01

This study proposes a bootstrap-based space-time surveillance model. Designed to find emerging hotspots in near-real time, the bootstrap based model is characterized by its use of past occurrence information and bootstrap permutations. Many existing space-time surveillance methods, using population at risk data to generate expected values, have resulting hotspots bounded by administrative area units and are of limited use for near-real time applications because of the population data needed. However, this study generates expected values for local hotspots from past occurrences rather than population at risk. Also, bootstrap permutations of previous occurrences are used for significant tests. Consequently, the bootstrap-based model, without the requirement of population at risk data, (1) is free from administrative area restriction, (2) enables more frequent surveillance for continuously updated registry database, and (3) is readily applicable to criminology and epidemiology surveillance. The bootstrap-based model performs better for space-time surveillance than the space-time scan statistic. This is shown by means of simulations and an application to residential crime occurrences in Columbus, OH, year 2000.

An improved approach for flight readiness certification: Methodology for failure risk assessment and application examples. Volume 3: Structure and listing of programs

NASA Technical Reports Server (NTRS)

Moore, N. R.; Ebbeler, D. H.; Newlin, L. E.; Sutharshana, S.; Creager, M.

1992-01-01

An improved methodology for quantitatively evaluating failure risk of spaceflight systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with engineering analysis to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in engineering analyses of failure phenomena, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which engineering analysis models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. Conventional engineering analysis models currently employed for design of failure prediction are used in this methodology. The PFA methodology is described and examples of its application are presented. Conventional approaches to failure risk evaluation for spaceflight systems are discussed, and the rationale for the approach taken in the PFA methodology is presented. The statistical methods, engineering models, and computer software used in fatigue failure mode applications are thoroughly documented.

The American Climate Prospectus: a risk-centered analysis of the economic impacts of climate change

NASA Astrophysics Data System (ADS)

Jina, A.; Houser, T.; Hsiang, S. M.; Kopp, R. E., III; Delgado, M.; Larsen, K.; Mohan, S.; Rasmussen, D.; Rising, J.; Wilson, P. S.; Muir-Wood, R.

2014-12-01

The American Climate Prospectus (ACP), the analysis underlying the Risky Business project, quantitatively assessed the climate risks posed to the United States' economy in six sectors - crop yields, energy demand, coastal property, crime, labor productivity, and mortality [1]. The ACP is unique in its characterization of the full probability distribution of economic impacts of climate change throughout the 21st century, making it an extremely useful basis for risk assessments. Three key innovations allow for this characterization. First, climate projections from CMIP5 models are scaled to a temperature probability distribution derived from a coarser climate model (MAGICC). This allows a more accurate representation of the whole distribution of future climates (in particular the tails) than a simple ensemble average. These are downscaled both temporally and spatially. Second, a set of local sea level rise and tropical cyclone projections are used in conjunction with the most detailed dataset of coastal property in the US in order to capture the risks of rising seas and storm surge. Third, we base many of our sectors on empirically-derived responses to temperature and precipitation. Each of these dose-response functions is resampled many times to populate a statistical distribution. Combining these with uncertainty in emissions scenario, climate model, and weather, we create the full probability distribution of climate impacts from county up to national levels, as well as model the effects upon the economy as a whole. Results are presented as likelihood ranges, as well as changes to return intervals of extreme events. The ACP analysis allows us to compare between sectors to understand the magnitude of required policy responses, and also to identify risks through time. Many sectors displaying large impacts at the end of the century, like those of mortality, have smaller changes in the near-term, due to non-linearities in the response functions. Other sectors, like coastal damages, have monotonically increasing costs throughout the 21st century. Taken together, the results from the ACP presents a unique and novel view of the short-, medium-, and long-term economic risks of climate change in the US. References: [1] T. Houser et al (2014), American Climate Prospectus, www.climateprospectus.org.

Behavioral Inhibition and Developmental Risk: A Dual-Processing Perspective

PubMed Central

Henderson, Heather A; Pine, Daniel S; Fox, Nathan A

2015-01-01

Behavioral inhibition (BI) is an early-appearing temperament characterized by strong reactions to novelty. BI shows a good deal of stability over childhood and significantly increases the risk for later diagnosis of social anxiety disorder (SAD). Despite these general patterns, many children with high BI do not go on to develop clinical, or even subclinical, anxiety problems. Therefore, understanding the cognitive and neural bases of individual differences in developmental risk and resilience is of great importance. The present review is focused on the relation of BI to two types of information processing: automatic (novelty detection, attention biases to threat, and incentive processing) and controlled (attention shifting and inhibitory control). We propose three hypothetical models (Top-Down Model of Control; Risk Potentiation Model of Control; and Overgeneralized Control Model) linking these processes to variability in developmental outcomes for BI children. We argue that early BI is associated with an early bias to quickly and preferentially process information associated with motivationally salient cues. When this bias is strong and stable across development, the risk for SAD is increased. Later in development, children with a history of BI tend to display normative levels of performance on controlled attention tasks, but they demonstrate exaggerated neural responses in order to do so, which may further potentiate risk for anxiety-related problems. We conclude by discussing the reviewed studies with reference to the hypothetical models and make suggestions regarding future research and implications for treatment. PMID:25065499

Technical Overview of Ecological Risk Assessment - Analysis Phase: Exposure Characterization

EPA Pesticide Factsheets

Exposure Characterization is the second major component of the analysis phase of a risk assessment. For a pesticide risk assessment, the exposure characterization describes the potential or actual contact of a pesticide with a plant, animal, or media.

Integration of Human Reliability Analysis Models into the Simulation-Based Framework for the Risk-Informed Safety Margin Characterization Toolkit

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boring, Ronald; Mandelli, Diego; Rasmussen, Martin

2016-06-01

This report presents an application of a computation-based human reliability analysis (HRA) framework called the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER). HUNTER has been developed not as a standalone HRA method but rather as framework that ties together different HRA methods to model dynamic risk of human activities as part of an overall probabilistic risk assessment (PRA). While we have adopted particular methods to build an initial model, the HUNTER framework is meant to be intrinsically flexible to new pieces that achieve particular modeling goals. In the present report, the HUNTER implementation has the following goals: •more » Integration with a high fidelity thermal-hydraulic model capable of modeling nuclear power plant behaviors and transients • Consideration of a PRA context • Incorporation of a solid psychological basis for operator performance • Demonstration of a functional dynamic model of a plant upset condition and appropriate operator response This report outlines these efforts and presents the case study of a station blackout scenario to demonstrate the various modules developed to date under the HUNTER research umbrella.« less

Air pollution and health risks due to vehicle traffic.

PubMed

Zhang, Kai; Batterman, Stuart

2013-04-15

Traffic congestion increases vehicle emissions and degrades ambient air quality, and recent studies have shown excess morbidity and mortality for drivers, commuters and individuals living near major roadways. Presently, our understanding of the air pollution impacts from congestion on roads is very limited. This study demonstrates an approach to characterize risks of traffic for on- and near-road populations. Simulation modeling was used to estimate on- and near-road NO2 concentrations and health risks for freeway and arterial scenarios attributable to traffic for different traffic volumes during rush hour periods. The modeling used emission factors from two different models (Comprehensive Modal Emissions Model and Motor Vehicle Emissions Factor Model version 6.2), an empirical traffic speed-volume relationship, the California Line Source Dispersion Model, an empirical NO2-NOx relationship, estimated travel time changes during congestion, and concentration-response relationships from the literature, which give emergency doctor visits, hospital admissions and mortality attributed to NO2 exposure. An incremental analysis, which expresses the change in health risks for small increases in traffic volume, showed non-linear effects. For a freeway, "U" shaped trends of incremental risks were predicted for on-road populations, and incremental risks are flat at low traffic volumes for near-road populations. For an arterial road, incremental risks increased sharply for both on- and near-road populations as traffic increased. These patterns result from changes in emission factors, the NO2-NOx relationship, the travel delay for the on-road population, and the extended duration of rush hour for the near-road population. This study suggests that health risks from congestion are potentially significant, and that additional traffic can significantly increase risks, depending on the type of road and other factors. Further, evaluations of risk associated with congestion must consider travel time, the duration of rush-hour, congestion-specific emission estimates, and uncertainties. Copyright © 2013 Elsevier B.V. All rights reserved.

Air pollution and health risks due to vehicle traffic

PubMed Central

Zhang, Kai; Batterman, Stuart

2014-01-01

Traffic congestion increases vehicle emissions and degrades ambient air quality, and recent studies have shown excess morbidity and mortality for drivers, commuters and individuals living near major roadways. Presently, our understanding of the air pollution impacts from congestion on roads is very limited. This study demonstrates an approach to characterize risks of traffic for on- and near-road populations. Simulation modeling was used to estimate on- and near-road NO2 concentrations and health risks for freeway and arterial scenarios attributable to traffic for different traffic volumes during rush hour periods. The modeling used emission factors from two different models (Comprehensive Modal Emissions Model and Motor Vehicle Emissions Factor Model version 6.2), an empirical traffic speed–volume relationship, the California Line Source Dispersion Model, an empirical NO2–NOx relationship, estimated travel time changes during congestion, and concentration–response relationships from the literature, which give emergency doctor visits, hospital admissions and mortality attributed to NO2 exposure. An incremental analysis, which expresses the change in health risks for small increases in traffic volume, showed non-linear effects. For a freeway, “U” shaped trends of incremental risks were predicted for on-road populations, and incremental risks are flat at low traffic volumes for near-road populations. For an arterial road, incremental risks increased sharply for both on- and near-road populations as traffic increased. These patterns result from changes in emission factors, the NO2–NOx relationship, the travel delay for the on-road population, and the extended duration of rush hour for the near-road population. This study suggests that health risks from congestion are potentially significant, and that additional traffic can significantly increase risks, depending on the type of road and other factors. Further, evaluations of risk associated with congestion must consider travel time, the duration of rush-hour, congestion-specific emission estimates, and uncertainties. PMID:23500830

Quantitative microbial risk assessment of Cryptosporidium and Giardia in well water from a native community of Mexico.

PubMed

Balderrama-Carmona, Ana Paola; Gortáres-Moroyoqui, Pablo; Álvarez-Valencia, Luis Humberto; Castro-Espinoza, Luciano; Balderas-Cortés, José de Jesús; Mondaca-Fernández, Iram; Chaidez-Quiroz, Cristóbal; Meza-Montenegro, María Mercedes

2015-01-01

Cryptosporidium and Giardia are gastrointestinal disease-causing organisms transmitted by the fecal-oral route, zoonotic and prevalent in all socioeconomic segments with greater emphasis in rural communities. The goal of this study was to assess the risk of cryptosporidiosis and giardiasis of Potam dwellers consuming drinking water from communal well water. To achieve the goal, quantitative microbial risk assessment (QMRA) was carried out as follows: (a) identification of Cryptosporidium oocysts and Giardia cysts in well water samples by information collection rule method, (b) assessment of exposure to healthy Potam residents, (c) dose-response modelling, and (d) risk characterization using an exponential model. All well water samples tested were positive for Cryptosporidium and Giardia. The QMRA results indicate a mean of annual risks of 99:100 (0.99) for cryptosporidiosis and 1:1 (1.0) for giardiasis. The outcome of the present study may drive decision-makers to establish an educational and treatment program to reduce the incidence of parasite-borne intestinal infection in the Potam community, and to conduct risk analysis programs in other similar rural communities in Mexico.

The perception of the relationship between environment and health according to data from Italian Behavioural Risk Factor Surveillance System (PASSI).

PubMed

Sampaolo, Letizia; Tommaso, Giulia; Gherardi, Bianca; Carrozzi, Giuliano; Freni Sterrantino, Anna; Ottone, Marta; Goldoni, Carlo Alberto; Bertozzi, Nicoletta; Scaringi, Meri; Bolognesi, Lara; Masocco, Maria; Salmaso, Stefania; Lauriola, Paolo

2017-01-01

"OBJECTIVES: to identify groups of people in relation to the perception of environmental risk and to assess the main characteristics using data collected in the environmental module of the surveillance network Italian Behavioral Risk Factor Surveillance System (PASSI). perceptive profiles were identified using a latent class analysis; later they were included as outcome in multinomial logistic regression models to assess the association between environmental risk perception and demographic, health, socio-economic and behavioural variables. the latent class analysis allowed to split the sample in "worried", "indifferent", and "positive" people. The multinomial logistic regression model showed that the "worried" profile typically includes people of Italian nationality, living in highly urbanized areas, with a high level of education, and with economic difficulties; they pay special attention to their own health and fitness, but they have a negative perception of their own psychophysical state. the application of advanced statistical analysis enable to appraise PASSI data in order to characterize the perception of environmental risk, making the planning of interventions related to risk communication possible. ".

Spatially-Explicit Simulation Modeling of Ecological Response to Climate Change: Methodological Considerations in Predicting Shifting Population Dynamics of Infectious Disease Vectors.

PubMed

Dhingra, Radhika; Jimenez, Violeta; Chang, Howard H; Gambhir, Manoj; Fu, Joshua S; Liu, Yang; Remais, Justin V

2013-09-01

Poikilothermic disease vectors can respond to altered climates through spatial changes in both population size and phenology. Quantitative descriptors to characterize, analyze and visualize these dynamic responses are lacking, particularly across large spatial domains. In order to demonstrate the value of a spatially explicit, dynamic modeling approach, we assessed spatial changes in the population dynamics of Ixodes scapularis , the Lyme disease vector, using a temperature-forced population model simulated across a grid of 4 × 4 km cells covering the eastern United States, using both modeled (Weather Research and Forecasting (WRF) 3.2.1) baseline/current (2001-2004) and projected (Representative Concentration Pathway (RCP) 4.5 and RCP 8.5; 2057-2059) climate data. Ten dynamic population features (DPFs) were derived from simulated populations and analyzed spatially to characterize the regional population response to current and future climate across the domain. Each DPF under the current climate was assessed for its ability to discriminate observed Lyme disease risk and known vector presence/absence, using data from the US Centers for Disease Control and Prevention. Peak vector population and month of peak vector population were the DPFs that performed best as predictors of current Lyme disease risk. When examined under baseline and projected climate scenarios, the spatial and temporal distributions of DPFs shift and the seasonal cycle of key questing life stages is compressed under some scenarios. Our results demonstrate the utility of spatial characterization, analysis and visualization of dynamic population responses-including altered phenology-of disease vectors to altered climate.

Spatially-Explicit Simulation Modeling of Ecological Response to Climate Change: Methodological Considerations in Predicting Shifting Population Dynamics of Infectious Disease Vectors

PubMed Central

Dhingra, Radhika; Jimenez, Violeta; Chang, Howard H.; Gambhir, Manoj; Fu, Joshua S.; Liu, Yang; Remais, Justin V.

2014-01-01

Poikilothermic disease vectors can respond to altered climates through spatial changes in both population size and phenology. Quantitative descriptors to characterize, analyze and visualize these dynamic responses are lacking, particularly across large spatial domains. In order to demonstrate the value of a spatially explicit, dynamic modeling approach, we assessed spatial changes in the population dynamics of Ixodes scapularis, the Lyme disease vector, using a temperature-forced population model simulated across a grid of 4 × 4 km cells covering the eastern United States, using both modeled (Weather Research and Forecasting (WRF) 3.2.1) baseline/current (2001–2004) and projected (Representative Concentration Pathway (RCP) 4.5 and RCP 8.5; 2057–2059) climate data. Ten dynamic population features (DPFs) were derived from simulated populations and analyzed spatially to characterize the regional population response to current and future climate across the domain. Each DPF under the current climate was assessed for its ability to discriminate observed Lyme disease risk and known vector presence/absence, using data from the US Centers for Disease Control and Prevention. Peak vector population and month of peak vector population were the DPFs that performed best as predictors of current Lyme disease risk. When examined under baseline and projected climate scenarios, the spatial and temporal distributions of DPFs shift and the seasonal cycle of key questing life stages is compressed under some scenarios. Our results demonstrate the utility of spatial characterization, analysis and visualization of dynamic population responses—including altered phenology—of disease vectors to altered climate. PMID:24772388

Estimation of Staphylococcus aureus growth parameters from turbidity data: characterization of strain variation and comparison of methods.

PubMed

Lindqvist, R

2006-07-01

Turbidity methods offer possibilities for generating data required for addressing microorganism variability in risk modeling given that the results of these methods correspond to those of viable count methods. The objectives of this study were to identify the best approach for determining growth parameters based on turbidity data and use of a Bioscreen instrument and to characterize variability in growth parameters of 34 Staphylococcus aureus strains of different biotypes isolated from broiler carcasses. Growth parameters were estimated by fitting primary growth models to turbidity growth curves or to detection times of serially diluted cultures either directly or by using an analysis of variance (ANOVA) approach. The maximum specific growth rates in chicken broth at 17 degrees C estimated by time to detection methods were in good agreement with viable count estimates, whereas growth models (exponential and Richards) underestimated growth rates. Time to detection methods were selected for strain characterization. The variation of growth parameters among strains was best described by either the logistic or lognormal distribution, but definitive conclusions require a larger data set. The distribution of the physiological state parameter ranged from 0.01 to 0.92 and was not significantly different from a normal distribution. Strain variability was important, and the coefficient of variation of growth parameters was up to six times larger among strains than within strains. It is suggested to apply a time to detection (ANOVA) approach using turbidity measurements for convenient and accurate estimation of growth parameters. The results emphasize the need to consider implications of strain variability for predictive modeling and risk assessment.

PETRORISK: a risk assessment framework for petroleum substances.

PubMed

Redman, Aaron D; Parkerton, Thomas F; Comber, Mike H I; Paumen, Miriam Leon; Eadsforth, Charles V; Dmytrasz, Bhodan; King, Duncan; Warren, Christopher S; den Haan, Klaas; Djemel, Nadia

2014-07-01

PETRORISK is a modeling framework used to evaluate environmental risk of petroleum substances and human exposure through these routes due to emissions under typical use conditions as required by the European regulation for the Registration, Evaluation, Authorization and Restriction of Chemicals (REACH). Petroleum substances are often complex substances comprised of hundreds to thousands of individual hydrocarbons. The physicochemical, fate, and effects properties of the individual constituents within a petroleum substance can vary over several orders of magnitude, complicating risk assessment. PETRORISK combines the risk assessment strategies used on single chemicals with the hydrocarbon block approach to model complex substances. Blocks are usually defined by available analytical characterization data on substances that are expressed in terms of mass fractions for different structural chemical classes that are specified as a function of C number or boiling point range. The physicochemical and degradation properties of the blocks are determined by the properties of representative constituents in that block. Emissions and predicted exposure concentrations (PEC) are then modeled using mass-weighted individual representative constituents. Overall risk for various environmental compartments at the regional and local level is evaluated by comparing the PECs for individual representative constituents to corresponding predicted no-effect concentrations (PNEC) derived using the Target Lipid Model. Risks to human health are evaluated using the overall predicted human dose resulting from multimedia environmental exposure to a substance-specific derived no-effect level (DNEL). A case study is provided to illustrate how this modeling approach has been applied to assess the risks of kerosene manufacture and use as a fuel. © 2014 SETAC.

Post-injection Multiphase Flow Modeling and Risk Assessments for Subsurface CO2 Storage in Naturally Fractured Reservoirs

NASA Astrophysics Data System (ADS)

Jin, G.

2015-12-01

Subsurface storage of carbon dioxide in geological formations is widely regarded as a promising tool for reducing global atmospheric CO2 emissions. Successful geologic storage for sequestrated carbon dioxides must prove to be safe by means of risk assessments including post-injection analysis of injected CO2 plumes. Because fractured reservoirs exhibit a higher degree of heterogeneity, it is imperative to conduct such simulation studies in order to reliably predict the geometric evolution of plumes and risk assessment of post CO2injection. The research has addressed the pressure footprint of CO2 plumes through the development of new techniques which combine discrete fracture network and stochastic continuum modeling of multiphase flow in fractured geologic formations. A subsequent permeability tensor map in 3-D, derived from our preciously developed method, can accurately describe the heterogeneity of fracture reservoirs. A comprehensive workflow integrating the fracture permeability characterization and multiphase flow modeling has been developed to simulate the CO2plume migration and risk assessments. A simulated fractured reservoir model based on high-priority geological carbon sinks in central Alabama has been employed for preliminary study. Discrete fracture networks were generated with an NE-oriented regional fracture set and orthogonal NW-fractures. Fracture permeability characterization revealed high permeability heterogeneity with an order of magnitude of up to three. A multiphase flow model composed of supercritical CO2 and saline water was then applied to predict CO2 plume volume, geometry, pressure footprint, and containment during and post injection. Injection simulation reveals significant permeability anisotropy that favors development of northeast-elongate CO2 plumes, which are aligned with systematic fractures. The diffusive spreading front of the CO2 plume shows strong viscous fingering effects. Post-injection simulation indicates significant upward lateral spreading of CO2 resulting in accumulation of CO2 directly under the seal unit because of its buoyancy and strata-bound vertical fractures. Risk assessment shows that lateral movement of CO2 along interconnected fractures requires widespread seals with high integrity to confine the injected CO2.

Systems engineering approach to environmental risk management: A case study of depleted uranium at test area C-64, Eglin Air Force Base, Florida. Master`s thesis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carter, C.M.; Fortmann, K.M.; Hill, S.W.

1994-12-01

Environmental restoration is an area of concern in an environmentally conscious world. Much effort is required to clean up the environment and promote environmentally sound methods for managing current land use. In light of the public consciousness with the latter topic, the United States Air Force must also take an active role in addressing these environmental issues with respect to current and future USAF base land use. This thesis uses the systems engineering technique to assess human health risks and to evaluate risk management options with respect to depleted uranium contamination in the sampled region of Test Area (TA) C-64more » at Eglin Air Force Base (AFB). The research combines the disciplines of environmental data collection, DU soil concentration distribution modeling, ground water modeling, particle resuspension modeling, exposure assessment, health hazard assessment, and uncertainty analysis to characterize the test area. These disciplines are required to quantify current and future health risks, as well as to recommend cost effective ways to increase confidence in health risk assessment and remediation options.« less

Characteristics of Health Information Gatherers, Disseminators, and Blockers Within Families at Risk of Hereditary Cancer: Implications for Family Health Communication Interventions

PubMed Central

Peters, June A.; Kenen, Regina; Hoskins, Lindsey M.; Ersig, Anne L.; Kuhn, Natalia R.; Loud, Jennifer T.; Greene, Mark H.

2009-01-01

Objectives. Given the importance of the dissemination of accurate family history to assess disease risk, we characterized the gatherers, disseminators, and blockers of health information within families at high genetic risk of cancer. Methods. A total of 5466 personal network members of 183 female participants of the Breast Imaging Study from 124 families with known mutations in the BRCA1/2 genes (associated with high risk of breast, ovarian, and other types of cancer) were identified by using the Colored Eco-Genetic Relationship Map (CEGRM). Hierarchical nonlinear models were fitted to characterize information gatherers, disseminators, and blockers. Results. Gatherers of information were more often female (P < .001), parents (P < .001), and emotional support providers (P < .001). Disseminators were more likely female first- and second- degree relatives (both P < .001), family members in the older or same generation as the participant (P < .001), those with a cancer history (P < .001), and providers of emotional (P < .001) or tangible support (P < .001). Blockers tended to be spouses or partners (P < .001) and male, first-degree relatives (P < .001). Conclusions. Our results provide insight into which family members may, within a family-based intervention, effectively gather family risk information, disseminate information, and encourage discussions regarding shared family risk. PMID:19833996

Metabolomic Response of Human Embryonic Stem Cell Derived Germ-like Cells after Exposure to Steroid Hormones

EPA Science Inventory

To assess the potential risks of human exposure to endocrine active compounds (EACs), the mechanisms of toxicity must first be identified and characterized. Currently, there are no robust in vitro models for identifying the mechanisms of toxicity in germ cells resulting from EAC ...

Monthly paleostreamflow reconstruction from annual tree-ring chronologies

Treesearch

J. H. Stagge; D. E. Rosenberg; R. J. DeRose; T. M. Rittenour

2018-01-01

Paleoclimate reconstructions are increasingly used to characterize annual climate variability prior to the instrumental record, to improve estimates of climate extremes, and to provide a baseline for climate change projections. To date, paleoclimate records have seen limited engineering use to estimate hydrologic risks because water systems models and managers usually...

EXTRAPOLATING ACUTE MORTALITY OF AMPELISCA ABDITA TO POPULATION RISK USING A POPULATION MODEL AND MONITORING DATA

EPA Science Inventory

Ten-day acute mortality of the benthic amphipod, Ampelisca abdita, is used in a number of regulatory, research, and monitoring programs to evaluate chemical contamination of marine sediments. Although this endpoint has proven to be valuable for characterizing the relative toxicit...

Community Service Programs: A Model for At-Risk Long-Term-Suspended Students

ERIC Educational Resources Information Center

Hall, Brenda S.; Rubin, Tova

2008-01-01

Each year in the United States, millions of students experience suspension from public schools (Mendez & Knoff, 2003). Community service programs provide one means to address the school suspension problem. These initiatives are characterized by volunteer service placements within community nonprofit organizations for skill and personal…

A Model-based Framework for Risk Assessment in Human-Computer Controlled Systems

NASA Technical Reports Server (NTRS)

Hatanaka, Iwao

2000-01-01

The rapid growth of computer technology and innovation has played a significant role in the rise of computer automation of human tasks in modem production systems across all industries. Although the rationale for automation has been to eliminate "human error" or to relieve humans from manual repetitive tasks, various computer-related hazards and accidents have emerged as a direct result of increased system complexity attributed to computer automation. The risk assessment techniques utilized for electromechanical systems are not suitable for today's software-intensive systems or complex human-computer controlled systems. This thesis will propose a new systemic model-based framework for analyzing risk in safety-critical systems where both computers and humans are controlling safety-critical functions. A new systems accident model will be developed based upon modem systems theory and human cognitive processes to better characterize system accidents, the role of human operators, and the influence of software in its direct control of significant system functions. Better risk assessments will then be achievable through the application of this new framework to complex human-computer controlled systems.

Characterization of a developmental toxicity dose-response model.

PubMed Central

Faustman, E M; Wellington, D G; Smith, W P; Kimmel, C A

1989-01-01

The Rai and Van Ryzin dose-response model proposed for teratology experiments has been characterized for its appropriateness and applicability in modeling the dichotomous response data from developmental toxicity studies. Modifications were made in the initial probability statements to reflect more accurately biological events underlying developmental toxicity. Data sets used for the evaluation were obtained from the National Toxicology Program and U.S. EPA laboratories. The studies included developmental evaluations of ethylene glycol, diethylhexyl phthalate, di- and triethylene glycol dimethyl ethers, and nitrofen in rats, mice, or rabbits. Graphic examination and statistical evaluation demonstrate that this model is sensitive to the data when compared to directly measured experimental outcomes. The model was used to interpolate to low-risk dose levels, and comparisons were made between the values obtained and the no-observed-adverse-effect levels (NOAELs) divided by an uncertainty factor. Our investigation suggests that the Rai and Van Ryzin model is sensitive to the developmental toxicity end points, prenatal deaths, and malformations, and appears to model closely their relationship to dose. PMID:2707204

The Use of Dynamic Stochastic Social Behavior Models to Produce Likelihood Functions for Risk Modeling of Proliferation and Terrorist Attacks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Young, Jonathan; Thompson, Sandra E.; Brothers, Alan J.

The ability to estimate the likelihood of future events based on current and historical data is essential to the decision making process of many government agencies. Successful predictions related to terror events and characterizing the risks will support development of options for countering these events. The predictive tasks involve both technical and social component models. The social components have presented a particularly difficult challenge. This paper outlines some technical considerations of this modeling activity. Both data and predictions associated with the technical and social models will likely be known with differing certainties or accuracies – a critical challenge is linkingmore » across these model domains while respecting this fundamental difference in certainty level. This paper will describe the technical approach being taken to develop the social model and identification of the significant interfaces between the technical and social modeling in the context of analysis of diversion of nuclear material.« less

Multiple attribute decision making model and application to food safety risk evaluation.

PubMed

Ma, Lihua; Chen, Hong; Yan, Huizhe; Yang, Lifeng; Wu, Lifeng

2017-01-01

Decision making for supermarket food purchase decisions are characterized by network relationships. This paper analyzed factors that influence supermarket food selection and proposes a supplier evaluation index system based on the whole process of food production. The author established the intuitive interval value fuzzy set evaluation model based on characteristics of the network relationship among decision makers, and validated for a multiple attribute decision making case study. Thus, the proposed model provides a reliable, accurate method for multiple attribute decision making.

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.

PubMed

McClellan, Gene; Coleman, Margaret; Crary, David; Thurman, Alec; Thran, Brandolyn

2018-04-25

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. © 2018 Society for Risk Analysis.

A quantitative evaluation of a qualitative risk assessment framework: Examining the assumptions and predictions of the Productivity Susceptibility Analysis (PSA)

PubMed Central

2018-01-01

Qualitative risk assessment frameworks, such as the Productivity Susceptibility Analysis (PSA), have been developed to rapidly evaluate the risks of fishing to marine populations and prioritize management and research among species. Despite being applied to over 1,000 fish populations, and an ongoing debate about the most appropriate method to convert biological and fishery characteristics into an overall measure of risk, the assumptions and predictive capacity of these approaches have not been evaluated. Several interpretations of the PSA were mapped to a conventional age-structured fisheries dynamics model to evaluate the performance of the approach under a range of assumptions regarding exploitation rates and measures of biological risk. The results demonstrate that the underlying assumptions of these qualitative risk-based approaches are inappropriate, and the expected performance is poor for a wide range of conditions. The information required to score a fishery using a PSA-type approach is comparable to that required to populate an operating model and evaluating the population dynamics within a simulation framework. In addition to providing a more credible characterization of complex system dynamics, the operating model approach is transparent, reproducible and can evaluate alternative management strategies over a range of plausible hypotheses for the system. PMID:29856869

Developing ecological scenarios for the prospective aquatic risk assessment of pesticides.

PubMed

Rico, Andreu; Van den Brink, Paul J; Gylstra, Ronald; Focks, Andreas; Brock, Theo Cm

2016-07-01

The prospective aquatic environmental risk assessment (ERA) of pesticides is generally based on the comparison of predicted environmental concentrations in edge-of-field surface waters with regulatory acceptable concentrations derived from laboratory and/or model ecosystem experiments with aquatic organisms. New improvements in mechanistic effect modeling have allowed a better characterization of the ecological risks of pesticides through the incorporation of biological trait information and landscape parameters to assess individual, population and/or community-level effects and recovery. Similarly to exposure models, ecological models require scenarios that describe the environmental context in which they are applied. In this article, we propose a conceptual framework for the development of ecological scenarios that, when merged with exposure scenarios, will constitute environmental scenarios for prospective aquatic ERA. These "unified" environmental scenarios are defined as the combination of the biotic and abiotic parameters that are required to characterize exposure, (direct and indirect) effects, and recovery of aquatic nontarget species under realistic worst-case conditions. Ideally, environmental scenarios aim to avoid a potential mismatch between the parameter values and the spatial-temporal scales currently used in aquatic exposure and effect modeling. This requires a deeper understanding of the ecological entities we intend to protect, which can be preliminarily addressed by the formulation of ecological scenarios. In this article we present a methodological approach for the development of ecological scenarios and illustrate this approach by a case-study for Dutch agricultural ditches and the example focal species Sialis lutaria. Finally, we discuss the applicability of ecological scenarios in ERA and propose research needs and recommendations for their development and integration with exposure scenarios. Integr Environ Assess Manag 2016;12:510-521. © 2015 SETAC. © 2015 SETAC.

Uncertainty and risk in wildland fire management: a review.

PubMed

Thompson, Matthew P; Calkin, Dave E

2011-08-01

Wildland fire management is subject to manifold sources of uncertainty. Beyond the unpredictability of wildfire behavior, uncertainty stems from inaccurate/missing data, limited resource value measures to guide prioritization across fires and resources at risk, and an incomplete scientific understanding of ecological response to fire, of fire behavior response to treatments, and of spatiotemporal dynamics involving disturbance regimes and climate change. This work attempts to systematically align sources of uncertainty with the most appropriate decision support methodologies, in order to facilitate cost-effective, risk-based wildfire planning efforts. We review the state of wildfire risk assessment and management, with a specific focus on uncertainties challenging implementation of integrated risk assessments that consider a suite of human and ecological values. Recent advances in wildfire simulation and geospatial mapping of highly valued resources have enabled robust risk-based analyses to inform planning across a variety of scales, although improvements are needed in fire behavior and ignition occurrence models. A key remaining challenge is a better characterization of non-market resources at risk, both in terms of their response to fire and how society values those resources. Our findings echo earlier literature identifying wildfire effects analysis and value uncertainty as the primary challenges to integrated wildfire risk assessment and wildfire management. We stress the importance of identifying and characterizing uncertainties in order to better quantify and manage them. Leveraging the most appropriate decision support tools can facilitate wildfire risk assessment and ideally improve decision-making. Published by Elsevier Ltd.

Psychological Language on Twitter Predicts County-Level Heart Disease Mortality

PubMed Central

Eichstaedt, Johannes C.; Schwartz, Hansen Andrew; Kern, Margaret L.; Park, Gregory; Labarthe, Darwin R.; Merchant, Raina M.; Jha, Sneha; Agrawal, Megha; Dziurzynski, Lukasz A.; Sap, Maarten; Weeg, Christopher; Larson, Emily E.; Ungar, Lyle H.; Seligman, Martin E. P.

2015-01-01

Hostility and chronic stress are known risk factors for heart disease, but they are costly to assess on a large scale. We used language expressed on Twitter to characterize community-level psychological correlates of age-adjusted mortality from atherosclerotic heart disease (AHD). Language patterns reflecting negative social relationships, disengagement, and negative emotions—especially anger—emerged as risk factors; positive emotions and psychological engagement emerged as protective factors. Most correlations remained significant after controlling for income and education. A cross-sectional regression model based only on Twitter language predicted AHD mortality significantly better than did a model that combined 10 common demographic, socioeconomic, and health risk factors, including smoking, diabetes, hypertension, and obesity. Capturing community psychological characteristics through social media is feasible, and these characteristics are strong markers of cardiovascular mortality at the community level. PMID:25605707

Psychological language on Twitter predicts county-level heart disease mortality.

PubMed

Eichstaedt, Johannes C; Schwartz, Hansen Andrew; Kern, Margaret L; Park, Gregory; Labarthe, Darwin R; Merchant, Raina M; Jha, Sneha; Agrawal, Megha; Dziurzynski, Lukasz A; Sap, Maarten; Weeg, Christopher; Larson, Emily E; Ungar, Lyle H; Seligman, Martin E P

2015-02-01

Hostility and chronic stress are known risk factors for heart disease, but they are costly to assess on a large scale. We used language expressed on Twitter to characterize community-level psychological correlates of age-adjusted mortality from atherosclerotic heart disease (AHD). Language patterns reflecting negative social relationships, disengagement, and negative emotions-especially anger-emerged as risk factors; positive emotions and psychological engagement emerged as protective factors. Most correlations remained significant after controlling for income and education. A cross-sectional regression model based only on Twitter language predicted AHD mortality significantly better than did a model that combined 10 common demographic, socioeconomic, and health risk factors, including smoking, diabetes, hypertension, and obesity. Capturing community psychological characteristics through social media is feasible, and these characteristics are strong markers of cardiovascular mortality at the community level. © The Author(s) 2014.

Characterizing the Interrelationships of Prescription Opioid and Benzodiazepine Drugs With Worker Health and Workplace Hazards.

PubMed

Kowalski-McGraw, Michele; Green-McKenzie, Judith; Pandalai, Sudha P; Schulte, Paul A

2017-11-01

Prescription opioid and benzodiazepine drug use, which has risen significantly, can affect worker health. Exploration of the scientific literature assessed (1) interrelationships of such drug use, occupational risk factors, and illness and injury, and (2) occupational and personal risk factor combinations that can affect their use. The scientific literature from 2000 to 2015 was searched to determine any interrelationships. Evidence for eight conceptual models emerged based on the search yield of 133 articles. These models summarize interrelationships among prescription opioid and benzodiazepine use with occupational injury and illness. Factors associated with the use of these drugs included fatigue, impaired cognition, falls, motor vehicle crashes, and the use of multiple providers. Prescription opioid and benzodiazepine drugs may be both a personal risk factor for work-related injury and a consequence of workplace exposures.

Toxcast and the Use of Human Relevant In Vitro Exposures ...

EPA Pesticide Factsheets

The path for incorporating new approach methods and technologies into quantitative chemical risk assessment poses a diverse set of scientific challenges. These challenges include sufficient coverage of toxicological mechanisms to meaningfully interpret negative test results, development of increasingly relevant test systems, computational modeling to integrate experimental data, putting results in a dose and exposure context, characterizing uncertainty, and efficient validation of the test systems and computational models. The presentation will cover progress at the U.S. EPA in systematically addressing each of these challenges and delivering more human-relevant risk-based assessments. This abstract does not necessarily reflect U.S. EPA policy. Presentation at the British Toxicological Society Annual Congress on ToxCast and the Use of Human Relevant In Vitro Exposures: Incorporating high-throughput exposure and toxicity testing data for 21st century risk assessments .

Neurobiology of Adolescent Substance Use and Addictive Behaviors: Prevention and Treatment Implications

PubMed Central

Hammond, Christopher J.; Mayes, Linda C.; Potenza, Marc N.

2015-01-01

Psychoactive substance and nonsubstance/behavioral addictions are major public health concerns associated with significant societal cost. Adolescence is a period of dynamic biologic, psychological, and behavioral changes. Adolescence is also associated with an increased risk for substance use and addictive disorders. During adolescence, developmental changes in neural circuitry of reward processing, motivation, cognitive control, and stress may contribute to vulnerability for increased levels of engagement in substance use and nonsubstance addictive behaviors. Current biologic models of adolescent vulnerability for addictions incorporate existing data on allostatic changes in function and structure of the midbrain dopaminergic system, stress-associated neuroplasticity, and maturational imbalances between cognitive control and reward reactivity. When characterizing adolescent vulnerability, identifying subgroups of adolescents at high risk for addictive behaviors is a major goal of the addiction field. Genetics, epigenetics, and intermediate phenotypes/endophenotypes may assist in characterizing children and adolescents at risk. Improved understanding of the neurobiology of adolescence and addiction vulnerability has the potential to refine screening, enhance prevention and intervention strategies, and inform public policy. PMID:25022184

Human health risk characterization of petroleum coke calcining facility emissions.

PubMed

Singh, Davinderjit; Johnson, Giffe T; Harbison, Raymond D

2015-12-01

Calcining processes including handling and storage of raw petroleum coke may result in Particulate Matter (PM) and gaseous emissions. Concerns have been raised over the potential association between particulate and aerosol pollution and adverse respiratory health effects including decrements in lung function. This risk characterization evaluated the exposure concentrations of ambient air pollutants including PM10 and gaseous pollutants from a petroleum coke calciner facility. The ambient air pollutant levels were collected through monitors installed at multiple locations in the vicinity of the facility. The measured and modeled particulate levels in ambient air from the calciner facility were compared to standards protective of public health. The results indicated that exposure levels were, on occasions at sites farther from the facility, higher than the public health limit of 150 μg/m(3) 24-h average for PM10. However, the carbon fraction demonstrated that the contribution from the calciner facility was de minimis. Exposure levels of the modeled SO2, CO, NOx and PM10 concentrations were also below public health air quality standards. These results demonstrate that emissions from calcining processes involving petroleum coke, at facilities that are well controlled, are below regulatory standards and are not expected to produce a public health risk. Copyright © 2015 Elsevier Inc. All rights reserved.

Factoring socioeconomic status into cardiac performance profiling for hospitals: does it matter?

PubMed

Alter, David A; Austin, Peter C; Naylor, C David; Tu, Jack V

2002-01-01

Critics of "scorecard medicine" often highlight the incompleteness of risk-adjustment methods used when accounting for baseline patient differences. Although socioeconomic status is a highly important determinant of adverse outcome for patients admitted to the hospital with acute myocardial infarction, it has not been used in most risk-adjustment models for cardiovascular report cards. To determine the incremental impact of socioeconomic status adjustments on age, sex, and illness severity for hospital-specific 30-day mortality rates after acute myocardial infarction. The authors compared the absolute and relative hospital-specific 30-day acute myocardial infarction mortality rates in 169 hospitals throughout Ontario between April 1, 1994 and March 31, 1997. Patient socioeconomic status was characterized by median neighborhood income using postal codes and 1996 Canadian census data. They examined two risk-adjustment models: the first adjusted for age, sex, and illness severity (standard), whereas the second adjusted for age, sex, illness severity, and median neighborhood income level (socioeconomic status). There was an extremely strong correlation between 'standard' and 'socioeconomic status' risk-adjusted mortality rates (r = 0.99). Absolute differences in 30-day risk-adjusted mortality rates between the socioeconomic status and standard risk-adjustment models were small (median, 0.1%; 25th-75th percentile, 0.1-0.2). The agreement in the quintile rankings of hospitals between the socioeconomic status and standard risk-adjustment models was high (weighted kappa = 0.93). Despite its importance as a determinant of patient outcomes, the effect of socioeconomic status on hospital-specific mortality rates over and above standard risk-adjustment methods for acute myocardial infarction hospital profiling in Ontario was negligible.

The global distribution of Crimean-Congo hemorrhagic fever

PubMed Central

Messina, Jane P.; Pigott, David M.; Golding, Nick; Duda, Kirsten A.; Brownstein, John S.; Weiss, Daniel J.; Gibson, Harry; Robinson, Timothy P.; Gilbert, Marius; William Wint, G. R.; Nuttall, Patricia A.; Gething, Peter W.; Myers, Monica F.; George, Dylan B.; Hay, Simon I.

2015-01-01

Background Crimean-Congo hemorrhagic fever (CCHF) is a tick-borne infection caused by a virus (CCHFV) from the Bunyaviridae family. Domestic and wild vertebrates are asymptomatic reservoirs for the virus, putting animal handlers, slaughter-house workers and agricultural labourers at highest risk in endemic areas, with secondary transmission possible through contact with infected blood and other bodily fluids. Human infection is characterized by severe symptoms that often result in death. While it is known that CCHFV transmission is limited to Africa, Asia and Europe, definitive global extents and risk patterns within these limits have not been well described. Methods We used an exhaustive database of human CCHF occurrence records and a niche modeling framework to map the global distribution of risk for human CCHF occurrence. Results A greater proportion of shrub or grass land cover was the most important contributor to our model, which predicts highest levels of risk around the Black Sea, Turkey, and some parts of central Asia. Sub-Saharan Africa shows more focalized areas of risk throughout the Sahel and the Cape region. Conclusions These new risk maps provide a valuable starting point for understanding the zoonotic niche of CCHF, its extent and the risk it poses to humans. PMID:26142451

Basic features of the predictive tools of early warning systems for water-related natural hazards: examples for shallow landslides

NASA Astrophysics Data System (ADS)

Greco, Roberto; Pagano, Luca

2017-12-01

To manage natural risks, an increasing effort is being put in the development of early warning systems (EWS), namely, approaches facing catastrophic phenomena by timely forecasting and alarm spreading throughout exposed population. Research efforts aimed at the development and implementation of effective EWS should especially concern the definition and calibration of the interpretative model. This paper analyses the main features characterizing predictive models working in EWS by discussing their aims and their features in terms of model accuracy, evolutionary stage of the phenomenon at which the prediction is carried out and model architecture. Original classification criteria based on these features are developed throughout the paper and shown in their practical implementation through examples of flow-like landslides and earth flows, both of which are characterized by rapid evolution and quite representative of many applications of EWS.

Simple Model of Mating Preference and Extinction Risk

NASA Astrophysics Data System (ADS)

PȨKALSKI, Andrzej

We present a simple model of a population of individuals characterized by their genetic structure in the form of a double string of bits and the phenotype following from it. The population is living in an unchanging habitat preferring a certain type of phenotype (optimum). Individuals are unisex, however a pair is necessary for breeding. An individual rejects a mate if the latter's phenotype contains too many bad, i.e. different from the optimum, genes in the same places as the individual's. We show that such strategy, analogous to disassortative mating based on the major histocompatibility complex, avoiding inbreeding and incest, could be beneficial for the population and could reduce considerably the extinction risk, especially in small populations.

The neural basis of financial risk taking.

PubMed

Kuhnen, Camelia M; Knutson, Brian

2005-09-01

Investors systematically deviate from rationality when making financial decisions, yet the mechanisms responsible for these deviations have not been identified. Using event-related fMRI, we examined whether anticipatory neural activity would predict optimal and suboptimal choices in a financial decision-making task. We characterized two types of deviations from the optimal investment strategy of a rational risk-neutral agent as risk-seeking mistakes and risk-aversion mistakes. Nucleus accumbens activation preceded risky choices as well as risk-seeking mistakes, while anterior insula activation preceded riskless choices as well as risk-aversion mistakes. These findings suggest that distinct neural circuits linked to anticipatory affect promote different types of financial choices and indicate that excessive activation of these circuits may lead to investing mistakes. Thus, consideration of anticipatory neural mechanisms may add predictive power to the rational actor model of economic decision making.

Primary Health Care as a guide for assistance to infants at risk of neurodevelopmental disorders.

PubMed

Molini-Avejonas, Daniela Regina; Rondon-Melo, Silmara; Batista, Estela Ramos; Souza, Amanda Calsolari de; Dias, Daniela Cardilli; Samelli, Alessandra Gianella

2018-01-01

Purpose Characterize infants at risk of neurodevelopmental disorders according to sociodemographic and health profiles and describe their monitoring in Basic Health Units (UBS) under different management models. Methods Data were collected from medical records of infants at risk of neurodevelopmental disorders in the west region of the city of Sao Paulo from August 2013 to February 2014 (phase 1 - characterization; phase 2 - monitoring). Results Of the 225 individuals assessed in the first phase of the study, 51.1% were female and 7.11% were twins. Adolescent (45.2%), brown (50.56%), single (46.09%), complete primary education (47.60%) mothers were predominant. The mean number of prenatal visits was 7.12. Most mothers had vaginal delivery (62.22%) at mean gestational age of 37.05 weeks. Mean Apgar scores at the 1st and 5th minutes were 7.13 and 8.80, respectively. Mean weight at birth was 2597.21g., with 50.22% of newborns weighting ≤2500g. In its second phase, the study describes and compares the follow-up of 55 infants according to the UBS management model: 28 in UBS/"Estratégia Saúde da Família" (UBS/ESF) and 27 in traditional UBS (UBS/T). UBS/ESF presented higher mean of consultations (p=0.006). Longer interval between consultations was observed at UBS/T. No records of development milestones were found in 56% of the sample. Growth measures were better registered at UBS/ESF. In both management models, the number of consultations was smaller and the interval between them was shorter than those recommended by the Brazilian Ministry of Health. Conclusion According to the recommended guidelines of the "Rede Cegonha" public policy, gaps in the monitoring of infants at risk of neurodevelopmental disorders are still observed.

Effective return, risk aversion and drawdowns

NASA Astrophysics Data System (ADS)

Dacorogna, Michel M.; Gençay, Ramazan; Müller, Ulrich A.; Pictet, Olivier V.

2001-01-01

We derive two risk-adjusted performance measures for investors with risk averse preferences. Maximizing these measures is equivalent to maximizing the expected utility of an investor. The first measure, Xeff, is derived assuming a constant risk aversion while the second measure, Reff, is based on a stronger risk aversion to clustering of losses than of gains. The clustering of returns is captured through a multi-horizon framework. The empirical properties of Xeff, Reff are studied within the context of real-time trading models for foreign exchange rates and their properties are compared to those of more traditional measures like the annualized return, the Sharpe Ratio and the maximum drawdown. Our measures are shown to be more robust against clustering of losses and have the ability to fully characterize the dynamic behaviour of investment strategies.

Public perceptions of the risks of an unfamiliar technology: The case of using nuclear energy sources for space missions

NASA Astrophysics Data System (ADS)

Maharik, Michael

This thesis addresses the public perception of the risk of a technology not widely known to laypeople. Its aims were (1) to characterize public perceptions of the risk of using nuclear energy in space and decisions related to this risk, and (2) to extend the 'mental model' methodology to studying public perception of unfamiliar, risky technologies. A model of the physical processes capable of creating risks from using nuclear energy sources in space was first constructed. Then, knowledge and beliefs related to this topic were elicited from three different groups of people. The generality of the findings was examined in a constructive replication with environmentally-oriented people. The possibility of involving the public in decision-making processes related to engineering macro-design was then investigated. Finally, a communication regarding these risk processes was developed and evaluated in an experiment comparing it with communications produced by NASA. Although they included large portions of the expert model, people's beliefs also had gaps and misconceptions. Respondents often used scientific terms without a clear understanding of what they meant. Respondents' mental models sometimes contained scattered and inconsistent entries. The impact of pre-existing mental models was clearly seen. Different groups of people had different patterns of knowledge and beliefs. Nevertheless, respondents expressed reasonable and coherent opinions on choices among engineering options. The CMU brochure, derived from the study of readers' existing mental models, provided a better risk communication tool than NASA's material, reflecting primarily experts' perspective. The better performance of subjects reading either brochure generally reflected adding knowledge on issues that they had not previously known, rather than correcting wrong beliefs. The communication study confirmed a hypothesis that improving knowledge on risk processes related to the use of a technology causes a more favorable attitude towards that technology. Recommendations related to the design and targeting of risk communication, and to public participation in decision-making on using new and risky technologies, are derived. Additional studies that will elicit laypeople's definitions of risk related to specific technologies, and link their detailed understanding of risk-development processes to the perceived dimensions of risk, are suggested.

Psychosocial sources of stress and burnout in the construction sector: a structural equation model.

PubMed

Meliá, Josep L; Becerril, Marta

2007-11-01

This study develops and tests a structural equation model of social stress factors in the construction industry. Leadership behaviours, role conflict and mobbing behaviours are considered exogenous sources of stress; the experience of tension and burnout are considered mediator variables; and psychological well-being, propensity to quit and perceived quality are the final dependent variables. A sample of Spanish construction workers participated voluntarily and anonymously in the study. After considering the indices of modification, leadership showed direct effects on the propensity to quit and perceived quality. The overall fit of the model is adequate (chi2 (13)= 10.69, p = .637, GFI= .975, AGFI= .93, RMR= .230, NFI= .969, TLI= 1.016, CFI= 1.000, RMSEA= .329). Construction has been considered a sector characterized more by high physical risks than socially-related risks. In this context, these findings about the effects of social sources of stress in construction raise new questions about the organizational characteristics of the sector and their psychosocial risks.

US EPA - A*Star Partnership - Accelerating the Acceptance of ...

EPA Pesticide Factsheets

The path for incorporating new alternative methods and technologies into quantitative chemical risk assessment poses a diverse set of scientific challenges. Some of these challenges include development of relevant and predictive test systems and computational models to integrate and extrapolate experimental data, and rapid characterization and acceptance of these systems and models. The series of presentations will highlight a collaborative effort between the U.S. Environmental Protection Agency (EPA) and the Agency for Science, Technology and Research (A*STAR) that is focused on developing and applying experimental and computational models for predicting chemical-induced liver and kidney toxicity, brain angiogenesis, and blood-brain-barrier formation. In addressing some of these challenges, the U.S. EPA and A*STAR collaboration will provide a glimpse of what chemical risk assessments could look like in the 21st century. Presentation on US EPA – A*STAR Partnership at international symposium on Accelerating the acceptance of next-generation sciences and their application to regulatory risk assessment in Singapore.

A simulation study to quantify the impacts of exposure ...

EPA Pesticide Factsheets

A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, decision-support tools, and models to be applied to media-specific or receptor-specific problem areas. CED uses modeling-based approaches to characterize exposures, evaluate fate and transport, and support environmental diagnostics/forensics with input from multiple data sources. It also develops media- and receptor-specific models, process models, and decision support tools for use both within and outside of EPA.

Implementing the Deputy Administrator's Risk Characterization Memorandum, May 26, 1992

EPA Pesticide Factsheets

The purpose of this memorandum is to implement in the Superfund program the reccomendations of the Deputy Administrator in his memorandum of February 26, 1992, 'Guidance on risk characterization for risk managers and risk assessors.'

Large-scale drivers of malaria and priority areas for prevention and control in the Brazilian Amazon region using a novel multi-pathogen geospatial model.

PubMed

Valle, Denis; Lima, Joanna M Tucker

2014-11-20

Most of the malaria burden in the Americas is concentrated in the Brazilian Amazon but a detailed spatial characterization of malaria risk has yet to be undertaken. Utilizing 2004-2008 malaria incidence data collected from six Brazilian Amazon states, large-scale spatial patterns of malaria risk were characterized with a novel Bayesian multi-pathogen geospatial model. Data included 2.4 million malaria cases spread across 3.6 million sq km. Remotely sensed variables (deforestation rate, forest cover, rainfall, dry season length, and proximity to large water bodies), socio-economic variables (rural population size, income, and literacy rate, mortality rate for children age under five, and migration patterns), and GIS variables (proximity to roads, hydro-electric dams and gold mining operations) were incorporated as covariates. Borrowing information across pathogens allowed for better spatial predictions of malaria caused by Plasmodium falciparum, as evidenced by a ten-fold cross-validation. Malaria incidence for both Plasmodium vivax and P. falciparum tended to be higher in areas with greater forest cover. Proximity to gold mining operations was another important risk factor, corroborated by a positive association between migration rates and malaria incidence. Finally, areas with a longer dry season and areas with higher average rural income tended to have higher malaria risk. Risk maps reveal striking spatial heterogeneity in malaria risk across the region, yet these mean disease risk surface maps can be misleading if uncertainty is ignored. By combining mean spatial predictions with their associated uncertainty, several sites were consistently classified as hotspots, suggesting their importance as priority areas for malaria prevention and control. This article provides several contributions. From a methodological perspective, the benefits of jointly modelling multiple pathogens for spatial predictions were illustrated. In addition, maps of mean disease risk were contrasted with that of statistically significant disease clusters, highlighting the critical importance of uncertainty in determining disease hotspots. From an epidemiological perspective, forest cover and proximity to gold mining operations were important large-scale drivers of disease risk in the region. Finally, the hotspot in Western Acre was identified as the area that should receive highest priority from the Brazilian national malaria prevention and control programme.

Adaptively Parameterized Tomography of the Western Hellenic Subduction Zone

NASA Astrophysics Data System (ADS)

Hansen, S. E.; Papadopoulos, G. A.

2017-12-01

The Hellenic subduction zone (HSZ) is the most seismically active region in Europe and plays a major role in the active tectonics of the eastern Mediterranean. This complicated environment has the potential to generate both large magnitude (M > 8) earthquakes and tsunamis. Situated above the western end of the HSZ, Greece faces a high risk from these geologic hazards, and characterizing this risk requires detailed understanding of the geodynamic processes occurring in this area. However, despite previous investigations, the kinematics of the HSZ are still controversial. Regional tomographic studies have yielded important information about the shallow seismic structure of the HSZ, but these models only image down to 150 km depth within small geographic areas. Deeper structure is constrained by global tomographic models but with coarser resolution ( 200-300 km). Additionally, current tomographic models focused on the HSZ were generated with regularly-spaced gridding, and this type of parameterization often over-emphasizes poorly sampled regions of the model or under-represents small-scale structure. Therefore, we are developing a new, high-resolution image of the mantle structure beneath the western HSZ using an adaptively parameterized seismic tomography approach. By combining multiple, regional travel-time datasets in the context of a global model, with adaptable gridding based on the sampling density of high-frequency data, this method generates a composite model of mantle structure that is being used to better characterize geodynamic processes within the HSZ, thereby allowing for improved hazard assessment. Preliminary results will be shown.

Space Transportation System Liftoff Debris Mitigation Process Overview

NASA Technical Reports Server (NTRS)

Mitchell, Michael; Riley, Christopher

2011-01-01

Liftoff debris is a top risk to the Space Shuttle Vehicle. To manage the Liftoff debris risk, the Space Shuttle Program created a team with in the Propulsion Systems Engineering & Integration Office. The Shutt le Liftoff Debris Team harnesses the Systems Engineering process to i dentify, assess, mitigate, and communicate the Liftoff debris risk. T he Liftoff Debris Team leverages off the technical knowledge and expe rtise of engineering groups across multiple NASA centers to integrate total system solutions. These solutions connect the hardware and ana lyses to identify and characterize debris sources and zones contribut ing to the Liftoff debris risk. The solutions incorporate analyses sp anning: the definition and modeling of natural and induced environmen ts; material characterizations; statistical trending analyses, imager y based trajectory analyses; debris transport analyses, and risk asse ssments. The verification and validation of these analyses are bound by conservative assumptions and anchored by testing and flight data. The Liftoff debris risk mitigation is managed through vigilant collab orative work between the Liftoff Debris Team and Launch Pad Operation s personnel and through the management of requirements, interfaces, r isk documentation, configurations, and technical data. Furthermore, o n day of launch, decision analysis is used to apply the wealth of ana lyses to case specific identified risks. This presentation describes how the Liftoff Debris Team applies Systems Engineering in their proce sses to mitigate risk and improve the safety of the Space Shuttle Veh icle.

Spatial Variability of Geriatric Depression Risk in a High-Density City: A Data-Driven Socio-Environmental Vulnerability Mapping Approach.

PubMed

Ho, Hung Chak; Lau, Kevin Ka-Lun; Yu, Ruby; Wang, Dan; Woo, Jean; Kwok, Timothy Chi Yui; Ng, Edward

2017-08-31

Previous studies found a relationship between geriatric depression and social deprivation. However, most studies did not include environmental factors in the statistical models, introducing a bias to estimate geriatric depression risk because the urban environment was found to have significant associations with mental health. We developed a cross-sectional study with a binomial logistic regression to examine the geriatric depression risk of a high-density city based on five social vulnerability factors and four environmental measures. We constructed a socio-environmental vulnerability index by including the significant variables to map the geriatric depression risk in Hong Kong, a high-density city characterized by compact urban environment and high-rise buildings. Crude and adjusted odds ratios (ORs) of the variables were significantly different, indicating that both social and environmental variables should be included as confounding factors. For the comprehensive model controlled by all confounding factors, older adults who were of lower education had the highest geriatric depression risks (OR: 1.60 (1.21, 2.12)). Higher percentage of residential area and greater variation in building height within the neighborhood also contributed to geriatric depression risk in Hong Kong, while average building height had negative association with geriatric depression risk. In addition, the socio-environmental vulnerability index showed that higher scores were associated with higher geriatric depression risk at neighborhood scale. The results of mapping and cross-section model suggested that geriatric depression risk was associated with a compact living environment with low socio-economic conditions in historical urban areas in Hong Kong. In conclusion, our study found a significant difference in geriatric depression risk between unadjusted and adjusted models, suggesting the importance of including environmental factors in estimating geriatric depression risk. We also developed a framework to map geriatric depression risk across a city, which can be used for identifying neighborhoods with higher risk for public health surveillance and sustainable urban planning.

Spatial Variability of Geriatric Depression Risk in a High-Density City: A Data-Driven Socio-Environmental Vulnerability Mapping Approach

PubMed Central

Lau, Kevin Ka-Lun; Yu, Ruby; Wang, Dan; Kwok, Timothy Chi Yui; Ng, Edward

2017-01-01

Previous studies found a relationship between geriatric depression and social deprivation. However, most studies did not include environmental factors in the statistical models, introducing a bias to estimate geriatric depression risk because the urban environment was found to have significant associations with mental health. We developed a cross-sectional study with a binomial logistic regression to examine the geriatric depression risk of a high-density city based on five social vulnerability factors and four environmental measures. We constructed a socio-environmental vulnerability index by including the significant variables to map the geriatric depression risk in Hong Kong, a high-density city characterized by compact urban environment and high-rise buildings. Crude and adjusted odds ratios (ORs) of the variables were significantly different, indicating that both social and environmental variables should be included as confounding factors. For the comprehensive model controlled by all confounding factors, older adults who were of lower education had the highest geriatric depression risks (OR: 1.60 (1.21, 2.12)). Higher percentage of residential area and greater variation in building height within the neighborhood also contributed to geriatric depression risk in Hong Kong, while average building height had negative association with geriatric depression risk. In addition, the socio-environmental vulnerability index showed that higher scores were associated with higher geriatric depression risk at neighborhood scale. The results of mapping and cross-section model suggested that geriatric depression risk was associated with a compact living environment with low socio-economic conditions in historical urban areas in Hong Kong. In conclusion, our study found a significant difference in geriatric depression risk between unadjusted and adjusted models, suggesting the importance of including environmental factors in estimating geriatric depression risk. We also developed a framework to map geriatric depression risk across a city, which can be used for identifying neighborhoods with higher risk for public health surveillance and sustainable urban planning. PMID:28858265

A systems approach to the policy-level risk assessment of exotic animal diseases: network model and application to classical swine fever.

PubMed

Delgado, João; Pollard, Simon; Snary, Emma; Black, Edgar; Prpich, George; Longhurst, Phil

2013-08-01

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. © 2012 Society for Risk Analysis.

Development and Validation of Perioperative Risk-Adjustment Models for Hip Fracture Repair, Total Hip Arthroplasty, and Total Knee Arthroplasty.

PubMed

Schilling, Peter L; Bozic, Kevin J

2016-01-06

Comparing outcomes across providers requires risk-adjustment models that account for differences in case mix. The burden of data collection from the clinical record can make risk-adjusted outcomes difficult to measure. The purpose of this study was to develop risk-adjustment models for hip fracture repair (HFR), total hip arthroplasty (THA), and total knee arthroplasty (TKA) that weigh adequacy of risk adjustment against data-collection burden. We used data from the American College of Surgeons National Surgical Quality Improvement Program to create derivation cohorts for HFR (n = 7000), THA (n = 17,336), and TKA (n = 28,661). We developed logistic regression models for each procedure using age, sex, American Society of Anesthesiologists (ASA) physical status classification, comorbidities, laboratory values, and vital signs-based comorbidities as covariates, and validated the models with use of data from 2012. The derivation models' C-statistics for mortality were 80%, 81%, 75%, and 92% and for adverse events were 68%, 68%, 60%, and 70% for HFR, THA, TKA, and combined procedure cohorts. Age, sex, and ASA classification accounted for a large share of the explained variation in mortality (50%, 58%, 70%, and 67%) and adverse events (43%, 45%, 46%, and 68%). For THA and TKA, these three variables were nearly as predictive as models utilizing all covariates. HFR model discrimination improved with the addition of comorbidities and laboratory values; among the important covariates were functional status, low albumin, high creatinine, disseminated cancer, dyspnea, and body mass index. Model performance was similar in validation cohorts. Risk-adjustment models using data from health records demonstrated good discrimination and calibration for HFR, THA, and TKA. It is possible to provide adequate risk adjustment using only the most predictive variables commonly available within the clinical record. This finding helps to inform the trade-off between model performance and data-collection burden as well as the need to define priorities for data capture from electronic health records. These models can be used to make fair comparisons of outcome measures intended to characterize provider quality of care for value-based-purchasing and registry initiatives. Copyright © 2016 by The Journal of Bone and Joint Surgery, Incorporated.

INDOOR AIR ASSESSMENT - A REVIEW OF INDOOR AIR QUALITY RISK CHARACTERIZATION

EPA Science Inventory

Risk assessment methodologies provide a mechanism for incorporating scientific evidence and Judgments Into the risk management decision process. isk characterization framework has been developed to provide a systematic approach for analysis and presentation of risk characterizati...

Analyzing seasonal patterns of wildfire exposure factors in Sardinia, Italy

Treesearch

Michele Salis; Alan A. Ager; Fermin J. Alcasena; Bachisio Arca; Mark A. Finney; Grazia Pellizzaro; Donatella Spano

2015-01-01

In this paper, we applied landscape scale wildfire simulation modeling to explore the spatiotemporal patterns of wildfire likelihood and intensity in the island of Sardinia (Italy). We also performed wildfire exposure analysis for selected highly valued resources on the island to identify areas characterized by high risk. We observed substantial variation in burn...

Land cover as an important factor for landslide risk assessment

NASA Astrophysics Data System (ADS)

Promper, C.; Glade, T.; Puissant, A.; Malet, J.-P.

2012-04-01

Landcover change is a crucial component of hazard and vulnerability in terms of quantification of possible future landslide risk, and the importance for spatial planners but also individuals is obvious. Damage of property, losses of agricultural land, loss of production but also damaged infrastructures and fatalities may be the result of landslide hazards. To avoid these economic damages as well as possible fatalities in the future, a method of assessing spatial but also temporal patterns of landslides is necessary. This study represents results of landcover modeling as a first step to the proposition of scenario of landslide risk for the future. The method used for future land cover analysis is the CLUE modeling framework combining past and actual observed landcover conditions. The model is based on a statistical relationship between the actual land cover and driving forces. The allocation of landcover pixel is modified by possible autonomous developments and competition between land use types. (Verburg et al. 1999) The study area is located in a district in the alpine foreland of Lower Austria: Waidhofen/Ybbs, of about 130km2. The topography is characterized by narrow valleys, flat plateau and steep slopes. The landcover is characterized by region of densely populated areas in the valley bottom along the Ybbs River, and a series of separated farm houses on the top of the plateau. Population density is about 90 persons / km2 which represent the observed population density of Austria. The initial landcover includes forest, grassland, culture, built-up areas and individual farms. Most of the observed developments are controlled by the topography (along the valleys) and the actual road network. The results of the landcover model show different scenarios of changes in the landslide prone landcover types. These maps will be implemented into hazard analysis but also into vulnerability assessment regarding elements at risk. Verburg, P.H., de Koning, G.H.J., Kok, K., Veldkamp, A. & Bouma, J. 1999. A spatial explicit allocation procedure for modelling the pattern of land use change based upon actual land use. Ecological Modelling 116 (1): 45-61.

A queueing theory based model for business continuity in hospitals.

PubMed

Miniati, R; Cecconi, G; Dori, F; Frosini, F; Iadanza, E; Biffi Gentili, G; Niccolini, F; Gusinu, R

2013-01-01

Clinical activities can be seen as results of precise and defined events' succession where every single phase is characterized by a waiting time which includes working duration and possible delay. Technology makes part of this process. For a proper business continuity management, planning the minimum number of devices according to the working load only is not enough. A risk analysis on the whole process should be carried out in order to define which interventions and extra purchase have to be made. Markov models and reliability engineering approaches can be used for evaluating the possible interventions and to protect the whole system from technology failures. The following paper reports a case study on the application of the proposed integrated model, including risk analysis approach and queuing theory model, for defining the proper number of device which are essential to guarantee medical activity and comply the business continuity management requirements in hospitals.

A simulation analysis to characterize the dynamics of vaccinating behaviour on contact networks.

PubMed

Perisic, Ana; Bauch, Chris T

2009-05-28

Human behavior influences infectious disease transmission, and numerous "prevalence-behavior" models have analyzed this interplay. These previous analyses assumed homogeneously mixing populations without spatial or social structure. However, spatial and social heterogeneity are known to significantly impact transmission dynamics and are particularly relevant for certain diseases. Previous work has demonstrated that social contact structure can change the individual incentive to vaccinate, thus enabling eradication of a disease under a voluntary vaccination policy when the corresponding homogeneous mixing model predicts that eradication is impossible due to free rider effects. Here, we extend this work and characterize the range of possible behavior-prevalence dynamics on a network. We simulate transmission of a vaccine-preventable infection through a random, static contact network. Individuals choose whether or not to vaccinate on any given day according to perceived risks of vaccination and infection. We find three possible outcomes for behavior-prevalence dynamics on this type of network: small final number vaccinated and final epidemic size (due to rapid control through voluntary ring vaccination); large final number vaccinated and significant final epidemic size (due to imperfect voluntary ring vaccination), and little or no vaccination and large final epidemic size (corresponding to little or no voluntary ring vaccination). We also show that the social contact structure enables eradication under a broad range of assumptions, except when vaccine risk is sufficiently high, the disease risk is sufficiently low, or individuals vaccinate too late for the vaccine to be effective. For populations where infection can spread only through social contact network, relatively small differences in parameter values relating to perceived risk or vaccination behavior at the individual level can translate into large differences in population-level outcomes such as final size and final number vaccinated. The qualitative outcome of rational, self interested behaviour under a voluntary vaccination policy can vary substantially depending on interactions between social contact structure, perceived vaccine and disease risks, and the way that individual vaccination decision-making is modelled.

A simulation analysis to characterize the dynamics of vaccinating behaviour on contact networks

PubMed Central

2009-01-01

Background Human behavior influences infectious disease transmission, and numerous "prevalence-behavior" models have analyzed this interplay. These previous analyses assumed homogeneously mixing populations without spatial or social structure. However, spatial and social heterogeneity are known to significantly impact transmission dynamics and are particularly relevant for certain diseases. Previous work has demonstrated that social contact structure can change the individual incentive to vaccinate, thus enabling eradication of a disease under a voluntary vaccination policy when the corresponding homogeneous mixing model predicts that eradication is impossible due to free rider effects. Here, we extend this work and characterize the range of possible behavior-prevalence dynamics on a network. Methods We simulate transmission of a vaccine-prevetable infection through a random, static contact network. Individuals choose whether or not to vaccinate on any given day according to perceived risks of vaccination and infection. Results We find three possible outcomes for behavior-prevalence dynamics on this type of network: small final number vaccinated and final epidemic size (due to rapid control through voluntary ring vaccination); large final number vaccinated and significant final epidemic size (due to imperfect voluntary ring vaccination), and little or no vaccination and large final epidemic size (corresponding to little or no voluntary ring vaccination). We also show that the social contact structure enables eradication under a broad range of assumptions, except when vaccine risk is sufficiently high, the disease risk is sufficiently low, or individuals vaccinate too late for the vaccine to be effective. Conclusion For populations where infection can spread only through social contact network, relatively small differences in parameter values relating to perceived risk or vaccination behavior at the individual level can translate into large differences in population-level outcomes such as final size and final number vaccinated. The qualitative outcome of rational, self interested behaviour under a voluntary vaccination policy can vary substantially depending on interactions between social contact structure, perceived vaccine and disease risks, and the way that individual vaccination decision-making is modelled. PMID:19476616

Coupling of computer modeling with in vitro methodologies to reduce animal usage in toxicity testing.

PubMed

Clewell, H J

1993-05-01

The use of in vitro data to support the development of physiologically based pharmacokinetic (PBPK) models and to reduce the requirement for in vivo testing is demonstrated by three examples. In the first example, polychlorotrifluoroethylene, in vitro studies comparing metabolism and tissue response in rodents and primates made it possible to obtain definitive data for a human risk assessment without resorting to additional in vivo studies with primates. In the second example, a PBPK model for organophosphate esters was developed in which the parameters defining metabolism, tissue partitioning, and enzyme inhibition were all characterized by in vitro studies, and the rest of the model parameters were established from the literature. The resulting model was able to provide a coherent description of enzyme inhibition following both acute and chronic exposures in mice, rats, and humans. In the final example, the carcinogenic risk assessment for methylene chloride was refined by the incorporation of in vitro data on human metabolism into a PBPK model.

Multicenter external validation of two malignancy risk prediction models in patients undergoing 18F-FDG-PET for solitary pulmonary nodule evaluation.

PubMed

Perandini, Simone; Soardi, G A; Larici, A R; Del Ciello, A; Rizzardi, G; Solazzo, A; Mancino, L; Zeraj, F; Bernhart, M; Signorini, M; Motton, M; Montemezzi, S

2017-05-01

To achieve multicentre external validation of the Herder and Bayesian Inference Malignancy Calculator (BIMC) models. Two hundred and fifty-nine solitary pulmonary nodules (SPNs) collected from four major hospitals which underwent 18-FDG-PET characterization were included in this multicentre retrospective study. The Herder model was tested on all available lesions (group A). A subgroup of 180 SPNs (group B) was used to provide unbiased comparison between the Herder and BIMC models. Receiver operating characteristic (ROC) area under the curve (AUC) analysis was performed to assess diagnostic accuracy. Decision analysis was performed by adopting the risk threshold stated in British Thoracic Society (BTS) guidelines. Unbiased comparison performed In Group B showed a ROC AUC for the Herder model of 0.807 (95 % CI 0.742-0.862) and for the BIMC model of 0.822 (95 % CI 0.758-0.875). Both the Herder and the BIMC models were proven to accurately predict the risk of malignancy when tested on a large multicentre external case series. The BIMC model seems advantageous on the basis of a more favourable decision analysis. • The Herder model showed a ROC AUC of 0.807 on 180 SPNs. • The BIMC model showed a ROC AUC of 0.822 on 180 SPNs. • Decision analysis is more favourable to the BIMC model.

Wave Resource Characterization Using an Unstructured Grid Modeling Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Wei-Cheng; Yang, Zhaoqing; Wang, Taiping

This paper presents a modeling study conducted on the central Oregon coast for wave resource characterization using the unstructured-grid SWAN model coupled with a nested-grid WWIII model. The flexibility of models of various spatial resolutions and the effects of open- boundary conditions simulated by a nested-grid WWIII model with different physics packages were evaluated. The model results demonstrate the advantage of the unstructured-grid modeling approach for flexible model resolution and good model skills in simulating the six wave resource parameters recommended by the International Electrotechnical Commission in comparison to the observed data in Year 2009 at National Data Buoy Centermore » Buoy 46050. Notably, spectral analysis indicates that the ST4 physics package improves upon the model skill of the ST2 physics package for predicting wave power density for large waves, which is important for wave resource assessment, device load calculation, and risk management. In addition, bivariate distributions show the simulated sea state of maximum occurrence with the ST4 physics package matched the observed data better than that with the ST2 physics package. This study demonstrated that the unstructured-grid wave modeling approach, driven by the nested-grid regional WWIII outputs with the ST4 physics package, can efficiently provide accurate wave hindcasts to support wave resource characterization. Our study also suggests that wind effects need to be considered if the dimension of the model domain is greater than approximately 100 km, or O (10^2 km).« less

The role of communication inequality in mediating the impacts of socioecological and socioeconomic disparities on HIV/AIDS knowledge and risk perception.

PubMed

Bekalu, Mesfin Awoke; Eggermont, Steven

2014-02-10

Although the link between social factors and health-related outcomes has long been widely acknowledged, the mechanisms characterizing this link are relatively less known and remain a subject of continued investigation across disciplines. In this study, drawing on the structural influence model of health communication, the hypothesis that differences in concern about and information needs on HIV/AIDS, HIV/AIDS-related media use, and perceived salience of HIV/AIDS-related information, characterized as communication inequality, can at least partially mediate the impacts of socioecological (urban vs. rural) and socioeconomic (education) disparities on inequalities in HIV/AIDS knowledge and risk perception was tested. Data were collected from a random sample of 986 urban and rural respondents in northwest Ethiopia. Structural equation modeling, using the maximum likelihood method, was used to test the mediation models. The models showed an adequate fit of the data and hence supported the hypothesis that communication inequality can at least partially explain the causal mechanism linking socioeconomic and socioecological factors with HIV/AIDS knowledge and risk perception. Both urbanity versus rurality and education were found to have significant mediated effects on HIV/AIDS knowledge (urbanity vs. rurality: β = 0.28, p = .001; education: β = 0.08, p = .001) and HIV/AIDS risk perception (urbanity vs. rurality: β = 0.30, p = .001; education: β = 0.09, p = .001). It was concluded that communication inequality might form part of the socioecologically and socioeconomically embedded processes that affect HIV/AIDS-related outcomes. The findings suggest that the media and message effects that are related to HIV/AIDS behavior change communication can be viewed from a structural perspective that moves beyond the more reductionist behavioral approaches upon which most present-day HIV/AIDS communication campaigns seem to be based.

A Research Roadmap for Computation-Based Human Reliability Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boring, Ronald; Mandelli, Diego; Joe, Jeffrey

2015-08-01

The United States (U.S.) Department of Energy (DOE) is sponsoring research through the Light Water Reactor Sustainability (LWRS) program to extend the life of the currently operating fleet of commercial nuclear power plants. The Risk Informed Safety Margin Characterization (RISMC) research pathway within LWRS looks at ways to maintain and improve the safety margins of these plants. The RISMC pathway includes significant developments in the area of thermalhydraulics code modeling and the development of tools to facilitate dynamic probabilistic risk assessment (PRA). PRA is primarily concerned with the risk of hardware systems at the plant; yet, hardware reliability is oftenmore » secondary in overall risk significance to human errors that can trigger or compound undesirable events at the plant. This report highlights ongoing efforts to develop a computation-based approach to human reliability analysis (HRA). This computation-based approach differs from existing static and dynamic HRA approaches in that it: (i) interfaces with a dynamic computation engine that includes a full scope plant model, and (ii) interfaces with a PRA software toolset. The computation-based HRA approach presented in this report is called the Human Unimodels for Nuclear Technology to Enhance Reliability (HUNTER) and incorporates in a hybrid fashion elements of existing HRA methods to interface with new computational tools developed under the RISMC pathway. The goal of this research effort is to model human performance more accurately than existing approaches, thereby minimizing modeling uncertainty found in current plant risk models.« less

Co-occurring risk factors for current cigarette smoking in a U.S. nationally representative sample

PubMed Central

Higgins, Stephen T.; Kurti, Allison N.; Redner, Ryan; White, Thomas J.; Keith, Diana R.; Gaalema, Diann E.; Sprague, Brian L.; Stanton, Cassandra A.; Roberts, Megan E.; Doogan, Nathan J.; Priest, Jeff S.

2016-01-01

Introduction Relatively little has been reported characterizing cumulative risk associated with co-occurring risk factors for cigarette smoking. The purpose of the present study was to address that knowledge gap in a U.S. nationally representative sample. Methods Data were obtained from 114,426 adults (≥ 18 years) in the U.S. National Survey on Drug Use and Health (years 2011–13). Multiple logistic regression and classification and regression tree (CART) modeling were used to examine risk of current smoking associated with eight co-occurring risk factors (age, gender, race/ethnicity, educational attainment, poverty, drug abuse/dependence, alcohol abuse/dependence, mental illness). Results Each of these eight risk factors was independently associated with significant increases in the odds of smoking when concurrently present in a multiple logistic regression model. Effects of risk-factor combinations were typically summative. Exceptions to that pattern were in the direction of less-than-summative effects when one of the combined risk factors was associated with generally high or low rates of smoking (e.g., drug abuse/dependence, age ≥65). CART modeling identified subpopulation risk profiles wherein smoking prevalence varied from a low of 11% to a high of 74% depending on particular risk factor combinations. Being a college graduate was the strongest independent predictor of smoking status, classifying 30% of the adult population. Conclusions These results offer strong evidence that the effects associated with common risk factors for cigarette smoking are independent, cumulative, and generally summative. The results also offer potentially useful insights into national population risk profiles around which U.S. tobacco policies can be developed or refined. PMID:26902875

Smoking cessation and the risk of cardiovascular disease outcomes predicted from established risk scores: results of the Cardiovascular Risk Assessment among Smokers in Primary Care in Europe (CV-ASPIRE) study.

PubMed

Mallaina, Pablo; Lionis, Christos; Rol, Hugo; Imperiali, Renzo; Burgess, Andrew; Nixon, Mark; Malvestiti, Franco Mondello

2013-04-18

Smoking is a major risk factor for cardiovascular disease (CVD). This multicenter, cross-sectional survey was designed to estimate the cardiovascular (CV) risk attributable to smoking using risk assessment tools, to better understand patient behaviors and characteristics related to smoking, and characterize physician practice patterns. 1,439 smokers were recruited from Europe during 2011. Smokers were ≥40 years old, smoked > 10 cigarettes/day and had recent measurements on blood pressure and lipids. CV risk was calculated using the SCORE system, Framingham risk equations, and Progetto CUORE model. The CV risk attributable to smoking was evaluated using a simulated control (hypothetical non-smoker) with identical characteristics as the enrolled smoker. Risks assessed included CV mortality, coronary heart disease (CHD), CVD and hard CHD. Demographics, comorbidities, primary reasons for consultation, behavior towards previous attempts to quit, and interest in smoking cessation was assessed. Dependence on nicotine was evaluated using the Fagerström Test for Nicotine Dependence. GP practice patterns were assessed through a questionnaire. The prediction models consistently demonstrated a high CV risk attributable to smoking. For instance, the SCORE model demonstrated that this study population of smokers have a 100% increased probability of death due to cardiovascular disease in the next 10-years compared to non-smokers. A considerable amount of patients would like to hear from their GP about the different alternatives available to support their quitting attempt. The findings of this study reinforce the importance of smoking as a significant predictor of long-term cardiovascular events. One of the best gains in health could be obtained by tackling the most important modifiable risk factors; these results suggest smoking is among the most important.

Characterizing environmental risk factors for West Nile virus in Quebec, Canada, using clinical data in humans and serology in pet dogs.

PubMed

Rocheleau, J P; Michel, P; Lindsay, L R; Drebot, M; Dibernardo, A; Ogden, N H; Fortin, A; Arsenault, J

2017-10-01

The identification of specific environments sustaining emerging arbovirus amplification and transmission to humans is a key component of public health intervention planning. This study aimed at identifying environmental factors associated with West Nile virus (WNV) infections in southern Quebec, Canada, by modelling and jointly interpreting aggregated clinical data in humans and serological data in pet dogs. Environmental risk factors were estimated in humans by negative binomial regression based on a dataset of 191 human WNV clinical cases reported in the study area between 2011 and 2014. Risk factors for infection in dogs were evaluated by logistic and negative binomial models based on a dataset including WNV serological results from 1442 dogs sampled from the same geographical area in 2013. Forested lands were identified as low-risk environments in humans. Agricultural lands represented higher risk environments for dogs. Environments identified as impacting risk in the current study were somewhat different from those identified in other studies conducted in north-eastern USA, which reported higher risk in suburban environments. In the context of the current study, combining human and animal data allowed a more comprehensive and possibly a more accurate view of environmental WNV risk factors to be obtained than by studying aggregated human data alone.

Using a probabilistic approach in an ecological risk assessment simulation tool: test case for depleted uranium (DU).

PubMed

Fan, Ming; Thongsri, Tepwitoon; Axe, Lisa; Tyson, Trevor A

2005-06-01

A probabilistic approach was applied in an ecological risk assessment (ERA) to characterize risk and address uncertainty employing Monte Carlo simulations for assessing parameter and risk probabilistic distributions. This simulation tool (ERA) includes a Window's based interface, an interactive and modifiable database management system (DBMS) that addresses a food web at trophic levels, and a comprehensive evaluation of exposure pathways. To illustrate this model, ecological risks from depleted uranium (DU) exposure at the US Army Yuma Proving Ground (YPG) and Aberdeen Proving Ground (APG) were assessed and characterized. Probabilistic distributions showed that at YPG, a reduction in plant root weight is considered likely to occur (98% likelihood) from exposure to DU; for most terrestrial animals, likelihood for adverse reproduction effects ranges from 0.1% to 44%. However, for the lesser long-nosed bat, the effects are expected to occur (>99% likelihood) through the reduction in size and weight of offspring. Based on available DU data for the firing range at APG, DU uptake will not likely affect survival of aquatic plants and animals (<0.1% likelihood). Based on field and laboratory studies conducted at APG and YPG on pocket mice, kangaroo rat, white-throated woodrat, deer, and milfoil, body burden concentrations observed fall into the distributions simulated at both sites.

Characterizing uncertainty when evaluating risk management metrics: risk assessment modeling of Listeria monocytogenes contamination in ready-to-eat deli meats.

PubMed

Gallagher, Daniel; Ebel, Eric D; Gallagher, Owen; Labarre, David; Williams, Michael S; Golden, Neal J; Pouillot, Régis; Dearfield, Kerry L; Kause, Janell

2013-04-01

This report illustrates how the uncertainty about food safety metrics may influence the selection of a performance objective (PO). To accomplish this goal, we developed a model concerning Listeria monocytogenes in ready-to-eat (RTE) deli meats. This application used a second order Monte Carlo model that simulates L. monocytogenes concentrations through a series of steps: the food-processing establishment, transport, retail, the consumer's home and consumption. The model accounted for growth inhibitor use, retail cross contamination, and applied an FAO/WHO dose response model for evaluating the probability of illness. An appropriate level of protection (ALOP) risk metric was selected as the average risk of illness per serving across all consumed servings-per-annum and the model was used to solve for the corresponding performance objective (PO) risk metric as the maximum allowable L. monocytogenes concentration (cfu/g) at the processing establishment where regulatory monitoring would occur. Given uncertainty about model inputs, an uncertainty distribution of the PO was estimated. Additionally, we considered how RTE deli meats contaminated at levels above the PO would be handled by the industry using three alternative approaches. Points on the PO distribution represent the probability that - if the industry complies with a particular PO - the resulting risk-per-serving is less than or equal to the target ALOP. For example, assuming (1) a target ALOP of -6.41 log10 risk of illness per serving, (2) industry concentrations above the PO that are re-distributed throughout the remaining concentration distribution and (3) no dose response uncertainty, establishment PO's of -4.98 and -4.39 log10 cfu/g would be required for 90% and 75% confidence that the target ALOP is met, respectively. The PO concentrations from this example scenario are more stringent than the current typical monitoring level of an absence in 25 g (i.e., -1.40 log10 cfu/g) or a stricter criteria of absence in 125 g (i.e., -2.1 log10 cfu/g). This example, and others, demonstrates that a PO for L. monocytogenes would be far below any current monitoring capabilities. Furthermore, this work highlights the demands placed on risk managers and risk assessors when applying uncertain risk models to the current risk metric framework. Copyright © 2013 Elsevier B.V. All rights reserved.

Geographic variations of the bird-borne structural risk of West Nile virus circulation in Europe

PubMed Central

Durand, Benoit; Tran, Annelise; Balança, Gilles

2017-01-01

The structural risk of West Nile Disease results from the usual functioning of the socio-ecological system, which may favour the introduction of the pathogen, its circulation and the occurrence of disease cases. Its geographic variations result from the local interactions between three components: (i) reservoir hosts, (ii) vectors, both characterized by their diversity, abundance and competence, (iii) and the socio-economic context that impacts the exposure of human to infectious bites. We developed a model of bird-borne structural risk of West Nile Virus (WNV) circulation in Europe, and analysed the association between the geographic variations of this risk and the occurrence of WND human cases between 2002 and 2014. A meta-analysis of WNV serosurveys conducted in wild bird populations was performed to elaborate a model of WNV seropositivity in European bird species, considered a proxy for bird exposure to WNV. Several eco-ethological traits of bird species were linked to seropositivity and the statistical model adequately fitted species-specific seropositivity data (area under the ROC curve: 0.85). Combined with species distribution maps, this model allowed deriving geographic variations of the bird-borne structural risk of WNV circulation. The association between this risk, and the occurrence of WND human cases across the European Union was assessed. Geographic risk variations of bird-borne structural risk allowed predicting WND case occurrence in administrative districts of the EU with a sensitivity of 86% (95% CI: 0.79–0.92), and a specificity of 68% (95% CI: 0.66–0.71). Disentangling structural and conjectural health risks is important for public health managers as risk mitigation procedures differ according to risk type. The results obtained show promise for the prevention of WND in Europe. Combined with analyses of vector-borne structural risk, they should allow designing efficient and targeted prevention measures. PMID:29023472

Mission Assurance Modeling and Simulation: A Cyber Security Roadmap

NASA Technical Reports Server (NTRS)

Gendron, Gerald; Roberts, David; Poole, Donold; Aquino, Anna

2012-01-01

This paper proposes a cyber security modeling and simulation roadmap to enhance mission assurance governance and establish risk reduction processes within constrained budgets. The term mission assurance stems from risk management work by Carnegie Mellon's Software Engineering Institute in the late 19905. By 2010, the Defense Information Systems Agency revised its cyber strategy and established the Program Executive Officer-Mission Assurance. This highlights a shift from simply protecting data to balancing risk and begins a necessary dialogue to establish a cyber security roadmap. The Military Operations Research Society has recommended a cyber community of practice, recognizing there are too few professionals having both cyber and analytic experience. The authors characterize the limited body of knowledge in this symbiotic relationship. This paper identifies operational and research requirements for mission assurance M&S supporting defense and homeland security. M&S techniques are needed for enterprise oversight of cyber investments, test and evaluation, policy, training, and analysis.

Coronary artery calcium distributions in older persons in the AGES-Reykjavik study

PubMed Central

Gudmundsson, Elias Freyr; Gudnason, Vilmundur; Sigurdsson, Sigurdur; Launer, Lenore J.; Harris, Tamara B.; Aspelund, Thor

2013-01-01

Coronary Artery Calcium (CAC) is a sign of advanced atherosclerosis and an independent risk factor for cardiac events. Here, we describe CAC-distributions in an unselected aged population and compare modelling methods to characterize CAC-distribution. CAC is difficult to model because it has a skewed and zero inflated distribution with over-dispersion. Data are from the AGES-Reykjavik sample, a large population based study [2002-2006] in Iceland of 5,764 persons aged 66-96 years. Linear regressions using logarithmic- and Box-Cox transformations on CAC+1, quantile regression and a Zero-Inflated Negative Binomial model (ZINB) were applied. Methods were compared visually and with the PRESS-statistic, R2 and number of detected associations with concurrently measured variables. There were pronounced differences in CAC according to sex, age, history of coronary events and presence of plaque in the carotid artery. Associations with conventional coronary artery disease (CAD) risk factors varied between the sexes. The ZINB model provided the best results with respect to the PRESS-statistic, R2, and predicted proportion of zero scores. The ZINB model detected similar numbers of associations as the linear regression on ln(CAC+1) and usually with the same risk factors. PMID:22990371

A New Perspective on Modeling Groundwater-Driven Health Risk With Subjective Information

NASA Astrophysics Data System (ADS)

Ozbek, M. M.

2003-12-01

Fuzzy rule-based systems provide an efficient environment for the modeling of expert information in the context of risk management for groundwater contamination problems. In general, their use in the form of conditional pieces of knowledge, has been either as a tool for synthesizing control laws from data (i.e., conjunction-based models), or in a knowledge representation and reasoning perspective in Artificial Intelligence (i.e., implication-based models), where only the latter may lead to coherence problems (e.g., input data that leads to logical inconsistency when added to the knowledge base). We implement a two-fold extension to an implication-based groundwater risk model (Ozbek and Pinder, 2002) including: 1) the implementation of sufficient conditions for a coherent knowledge base, and 2) the interpolation of expert statements to supplement gaps in knowledge. The original model assumes statements of public health professionals for the characterization of the exposed individual and the relation of dose and pattern of exposure to its carcinogenic effects. We demonstrate the utility of the extended model in that it: 1)identifies inconsistent statements and establishes coherence in the knowledge base, and 2) minimizes the burden of knowledge elicitation from the experts for utilizing existing knowledge in an optimal fashion.ÿÿ

ATM, radiation, and the risk of second primary breast cancer.

PubMed

Bernstein, Jonine L; Concannon, Patrick

2017-10-01

It was first suggested more than 40 years ago that heterozygous carriers for the human autosomal recessive disorder Ataxia-Telangiectasia (A-T) might also be at increased risk for cancer. Subsequent studies have identified the responsible gene, Ataxia-Telangiectasia Mutated (ATM), characterized genetic variation at this locus in A-T and a variety of different cancers, and described the functions of the ATM protein with regard to cellular DNA damage responses. However, an overall model of how ATM contributes to cancer risk, and in particular, the role of DNA damage in this process, remains lacking. This review considers these questions in the context of contralateral breast cancer (CBC). Heterozygous carriers of loss of function mutations in ATM that are A-T causing, are at increased risk of breast cancer. However, examination of a range of genetic variants, both rare and common, across multiple cancers, suggests that ATM may have additional effects on cancer risk that are allele-dependent. In the case of CBC, selected common alleles at ATM are associated with a reduced incidence of CBC, while other rare and predicted deleterious variants may act jointly with radiation exposure to increase risk. Further studies that characterize germline and somatic ATM mutations in breast cancer and relate the detected genetic changes to functional outcomes, particularly with regard to radiation responses, are needed to gain a complete picture of the complex relationship between ATM, radiation and breast cancer.

Characterization of the Risks of Adverse Outcomes Following Rubella Infection in Pregnancy.

PubMed

Thompson, Kimberly M; Simons, Emily A; Badizadegan, Kamran; Reef, Susan E; Cooper, Louis Z

2016-07-01

Although most infections with the rubella virus result in relatively minor sequelae, rubella infection in early pregnancy may lead to severe adverse outcomes for the fetus. First recognized in 1941, congenital rubella syndrome (CRS) can manifest with a diverse range of symptoms, including congenital cataracts, glaucoma, and cardiac defects, as well as hearing and intellectual disability. The gestational age of the fetus at the time of the maternal rubella infection impacts the probability and severity of outcomes, with infection in early pregnancy increasing the risks of spontaneous termination (miscarriage), fetal death (stillbirth), birth defects, and reduced survival for live-born infants. Rubella vaccination continues to change the epidemiology of rubella and CRS globally, but no models currently exist to evaluate the economic benefits of rubella management. This systematic review provides an overall assessment of the weight of the evidence for the outcomes associated with rubella infections in the first 20 weeks of pregnancy. We identified, evaluated, and graded 31 studies (all from developed countries) that reported on the pregnancy outcomes of at least 30 maternal rubella infections. We used the available evidence to estimate the increased risks of spontaneous termination, fetal death, infant death, and CRS as a function of the timing of rubella infection in pregnancy and decisions about induced termination. These data support the characterization of the disability-adjusted life years for outcomes associated with rubella infection in pregnancy. We find significant impacts associated with maternal rubella infections in early pregnancy, which economic analyses will miss if they only focus on live births of CRS cases. Our estimates of fetal loss from increased induced terminations due to maternal rubella infections provide context that may help to explain the relatively low numbers of observed CRS cases per year despite potentially large burdens of disease. Our comprehensive review of the weight of the evidence of all pregnancy outcomes demonstrates the importance of including all outcomes in models that characterize rubella-related disease burdens and costs. © 2014 Society for Risk Analysis.

Characterizing Mega-Earthquake Related Tsunami on Subduction Zones without Large Historical Events

NASA Astrophysics Data System (ADS)

Williams, C. R.; Lee, R.; Astill, S.; Farahani, R.; Wilson, P. S.; Mohammed, F.

2014-12-01

Due to recent large tsunami events (e.g., Chile 2010 and Japan 2011), the insurance industry is very aware of the importance of managing its exposure to tsunami risk. There are currently few tools available to help establish policies for managing and pricing tsunami risk globally. As a starting point and to help address this issue, Risk Management Solutions Inc. (RMS) is developing a global suite of tsunami inundation footprints. This dataset will include both representations of historical events as well as a series of M9 scenarios on subductions zones that have not historical generated mega earthquakes. The latter set is included to address concerns about the completeness of the historical record for mega earthquakes. This concern stems from the fact that the Tohoku Japan earthquake was considerably larger than had been observed in the historical record. Characterizing the source and rupture pattern for the subduction zones without historical events is a poorly constrained process. In many case, the subduction zones can be segmented based on changes in the characteristics of the subducting slab or major ridge systems. For this project, the unit sources from the NOAA propagation database are utilized to leverage the basin wide modeling included in this dataset. The length of the rupture is characterized based on subduction zone segmentation and the slip per unit source can be determined based on the event magnitude (i.e., M9) and moment balancing. As these events have not occurred historically, there is little to constrain the slip distribution. Sensitivity tests on the potential rupture pattern have been undertaken comparing uniform slip to higher shallow slip and tapered slip models. Subduction zones examined include the Makran Trench, the Lesser Antilles and the Hikurangi Trench. The ultimate goal is to create a series of tsunami footprints to help insurers understand their exposures at risk to tsunami inundation around the world.

Three-dimensional vapor intrusion modeling approach that combines wind and stack effects on indoor, atmospheric, and subsurface domains.

PubMed

Shirazi, Elham; Pennell, Kelly G

2017-12-13

Vapor intrusion (IV) exposure risks are difficult to characterize due to the role of atmospheric, building and subsurface processes. This study presents a three-dimensional VI model that extends the common subsurface fate and transport equations to incorporate wind and stack effects on indoor air pressure, building air exchange rate (AER) and indoor contaminant concentration to improve VI exposure risk estimates. The model incorporates three modeling programs: (1) COMSOL Multiphysics to model subsurface fate and transport processes, (2) CFD0 to model atmospheric air flow around the building, and (3) CONTAM to model indoor air quality. The combined VI model predicts AER values, zonal indoor air pressures and zonal indoor air contaminant concentrations as a function of wind speed, wind direction and outdoor and indoor temperature. Steady state modeling results for a single-story building with a basement demonstrate that wind speed, wind direction and opening locations in a building play important roles in changing the AER, indoor air pressure, and indoor air contaminant concentration. Calculated indoor air pressures ranged from approximately -10 Pa to +4 Pa depending on weather conditions and building characteristics. AER values, mass entry rates and indoor air concentrations vary depending on weather conditions and building characteristics. The presented modeling approach can be used to investigate the relationship between building features, AER, building pressures, soil gas concentrations, indoor air concentrations and VI exposure risks.

Risk characterization of hospitalizations for mental illness and/or behavioral disorders with concurrent heat-related illness

PubMed Central

2017-01-01

Background Many studies have found significant associations between high ambient temperatures and increases in heat-related morbidity and mortality. Several studies have demonstrated that increases in heat-related hospitalizations are elevated among individuals with diagnosed mental illnesses and/or behavioral disorders (MBD). However, there are a limited number of studies regarding risk factors associated with specific mental illnesses that contribute, at least in part, to heat-related illnesses (HRI) in the United States. Objective To identify and characterize individual and environmental risk factors associated with MBD hospitalizations with a concurrent HRI diagnosis. Methods This study uses hospitalization data from the Nationwide Inpatient Sample (2001–2010). Descriptive analyses of primary and secondary diagnoses of MBDs with an HRI were examined. Risk ratios (RR) were calculated from multivariable models to identify risk factors for hospitalizations among patients with mental illnesses and/or behavioral disorders and HRI. Results Nondependent alcohol/drug abuse, dementia, and schizophrenia were among the disorders that were associated with increased frequency of HRI hospitalizations among MBD patients. Increased risk of MBD hospitalizations with HRI was observed for Males (RR, 3.06), African Americans (RR, 1.16), Native Americans (RR, 1.70), uninsured (RR, 1.92), and those 40 years and older, compared to MBD hospitalizations alone. Conclusions Previous studies outside the U.S. have found that dementia and schizophrenia are significant risk factors for HRI hospitalizations. Our results suggest that hospitalizations among substance abusers may also be an important risk factor associated with heat morbidity. Improved understanding of these relative risks could help inform future public health strategies. PMID:29036206

In vivo efficacy of acyl CoA: diacylglycerol acyltransferase (DGAT) 1 inhibition in rodent models of postprandial hyperlipidemia.

PubMed

King, Andrew J; Segreti, Jason A; Larson, Kelly J; Souers, Andrew J; Kym, Philip R; Reilly, Regina M; Collins, Christine A; Voorbach, Martin J; Zhao, Gang; Mittelstadt, Scott W; Cox, Bryan F

2010-07-10

Postprandial serum triglyceride concentrations have recently been identified as a major, independent risk factor for future cardiovascular events. As a result, postprandial hyperlipidemia has emerged as a potential therapeutic target. The purpose of this study was two-fold. Firstly, to describe and characterize a standardized model of postprandial hyperlipidemia in multiple rodent species; and secondly, apply these rodent models to the evaluation of a novel class of pharmacologic agent; acyl CoA:diacylglycerol acyltransferase (DGAT) 1 inhibitors. Serum triglycerides were measured before and for 4h after oral administration of a standardized volume of corn oil, to fasted C57BL/6, ob/ob, apoE(-/-) and CD-1 mice; Sprague-Dawley and JCR/LA-cp rats; and normolipidemic and hyperlipidemic hamsters. Intragastric administration of corn oil increased serum triglycerides in all animals evaluated, however the magnitude and time-course of the postprandial triglyceride excursion varied. The potent and selective DGAT-1 inhibitor A-922500 (0.03, 0.3 and 3 mg/kg, p.o.), dose-dependently attenuated the maximal postprandial rise in serum triglyceride concentrations in all species tested. At the highest dose of DGAT-1 inhibitor, the postprandial triglyceride response was abolished. This study provides a comprehensive characterization of the time-course of postprandial hyperlipidemia in rodents. In addition, the ability of DGAT-1 inhibitors to attenuate postprandial hyperlipidemia in multiple rodent models, including those that feature insulin resistance, is documented. Exaggerated postprandial hyperlipidemia is inherent to insulin-resistant states in humans and contributes to the substantially elevated cardiovascular risk observed in these patients. Therefore, by attenuating postprandial hyperlipidemia, DGAT-1 inhibition may represent a novel therapeutic approach to reduce cardiovascular risk. Copyright 2010 Elsevier B.V. All rights reserved.

Building Habitats on the Moon: Engineering Approaches to Lunar Settlements

NASA Astrophysics Data System (ADS)

Benaroya, H.

This book provides an overview of various concepts for lunar habitats and structural designs and characterizes the lunar environment - the technical and the nontechnical. The designs take into consideration psychological comfort, structural strength against seismic and thermal activity, as well as internal pressurization and 1/6 g. Also discussed are micrometeoroid modelling, risk and redundancy as well as probability and reliability, with an introduction to analytical tools that can be useful in modelling uncertainties.

Surveillance theory applied to virus detection: a case for targeted discovery

USGS Publications Warehouse

Bogich, Tiffany L.; Anthony, Simon J.; Nichols, James D.

2013-01-01

Virus detection and mathematical modeling have gone through rapid developments in the past decade. Both offer new insights into the epidemiology of infectious disease and characterization of future risk; however, modeling has not yet been applied to designing the best surveillance strategies for viral and pathogen discovery. We review recent developments and propose methods to integrate viral and pathogen discovery and mathematical modeling through optimal surveillance theory, arguing for a more targeted approach to novel virus detection guided by the principles of adaptive management and structured decision-making.

Comparing biomarkers as principal surrogate endpoints.

PubMed

Huang, Ying; Gilbert, Peter B

2011-12-01

Recently a new definition of surrogate endpoint, the "principal surrogate," was proposed based on causal associations between treatment effects on the biomarker and on the clinical endpoint. Despite its appealing interpretation, limited research has been conducted to evaluate principal surrogates, and existing methods focus on risk models that consider a single biomarker. How to compare principal surrogate value of biomarkers or general risk models that consider multiple biomarkers remains an open research question. We propose to characterize a marker or risk model's principal surrogate value based on the distribution of risk difference between interventions. In addition, we propose a novel summary measure (the standardized total gain) that can be used to compare markers and to assess the incremental value of a new marker. We develop a semiparametric estimated-likelihood method to estimate the joint surrogate value of multiple biomarkers. This method accommodates two-phase sampling of biomarkers and is more widely applicable than existing nonparametric methods by incorporating continuous baseline covariates to predict the biomarker(s), and is more robust than existing parametric methods by leaving the error distribution of markers unspecified. The methodology is illustrated using a simulated example set and a real data set in the context of HIV vaccine trials. © 2011, The International Biometric Society.

Proton therapy versus intensity modulated x-ray therapy in the treatment of prostate cancer: Estimating secondary cancer risks

NASA Astrophysics Data System (ADS)

Fontenot, Jonas David

External beam radiation therapy is used to treat nearly half of the more than 200,000 new cases of prostate cancer diagnosed in the United States each year. During a radiation therapy treatment, healthy tissues in the path of the therapeutic beam are exposed to high doses. In addition, the whole body is exposed to a low-dose bath of unwanted scatter radiation from the pelvis and leakage radiation from the treatment unit. As a result, survivors of radiation therapy for prostate cancer face an elevated risk of developing a radiogenic second cancer. Recently, proton therapy has been shown to reduce the dose delivered by the therapeutic beam to normal tissues during treatment compared to intensity modulated x-ray therapy (IMXT, the current standard of care). However, the magnitude of stray radiation doses from proton therapy, and their impact on this incidence of radiogenic second cancers, was not known. The risk of a radiogenic second cancer following proton therapy for prostate cancer relative to IMXT was determined for 3 patients of large, median, and small anatomical stature. Doses delivered to healthy tissues from the therapeutic beam were obtained from treatment planning system calculations. Stray doses from IMXT were taken from the literature, while stray doses from proton therapy were simulated using a Monte Carlo model of a passive scattering treatment unit and an anthropomorphic phantom. Baseline risk models were taken from the Biological Effects of Ionizing Radiation VII report. A sensitivity analysis was conducted to characterize the uncertainty of risk calculations to uncertainties in the risk model, the relative biological effectiveness (RBE) of neutrons for carcinogenesis, and inter-patient anatomical variations. The risk projections revealed that proton therapy carries a lower risk for radiogenic second cancer incidence following prostate irradiation compared to IMXT. The sensitivity analysis revealed that the results of the risk analysis depended only weakly on uncertainties in the risk model and inter-patient variations. Second cancer risks were sensitive to changes in the RBE of neutrons. However, the findings of the study were qualitatively consistent for all patient sizes and risk models considered, and for all neutron RBE values less than 100.

Cross-Sectional Association between Length of Incarceration and Selected Risk Factors for Non-Communicable Chronic Diseases in Two Male Prisons of Mexico City.

PubMed

Silverman-Retana, Omar; Lopez-Ridaura, Ruy; Servan-Mori, Edson; Bautista-Arredondo, Sergio; Bertozzi, Stefano M

2015-01-01

Mexico City prisons are characterized by overcrowded facilities and poor living conditions for housed prisoners. Chronic disease profile is characterized by low prevalence of self reported hypertension (2.5%) and diabetes (1.8%) compared to general population; 9.5% of male inmates were obese. There is limited evidence regarding on the exposure to prison environment over prisoner's health status; particularly, on cardiovascular disease risk factors. The objective of this study is to assess the relationship between length of incarceration and selected risk factors for non-communicable chronic diseases (NCDs). We performed a cross-sectional analysis using data from two large male prisons in Mexico City (n = 14,086). Using quantile regression models we assessed the relationship between length of incarceration and selected risk factors for NCDs; stratified analysis by age at admission to prison was performed. We found a significant negative trend in BMI and WC across incarceration length quintiles. BP had a significant positive trend with a percentage change increase around 5% mmHg. The greatest increase in systolic blood pressure was observed in the older age at admission group. This analysis provides insight into the relationship between length of incarceration and four selected risk factors for NCDs; screening for high blood pressure should be guarantee in order to identify at risk individuals and linked to the prison's health facility. It is important to assess prison environment features to approach potential risk for developing NCDs in this context.

Import Security: Assessing the Risks of Imported Food.

PubMed

Welburn, Jonathan; Bier, Vicki; Hoerning, Steven

2016-11-01

We use data on food import violations from the FDA Operational and Administrative System for Import Support (OASIS) to address rising concerns associated with imported food, quantify import risks by product and by country of origin, and explore the usefulness of OASIS data for risk assessment. In particular, we assess whether there are significant trends in violations, whether import violations can be used to quantify risks by country and by product, and how import risks depend on economic factors of the country of origin. The results show that normalizing import violations by volume of imports provides a meaningful indicator of risk. We then use regression analysis to characterize import risks.  Using this model, we analyze import risks by product type, violation type, and economic factors of the country of origin.  We find that OASIS data are useful in quantifying food import risks, and that the rate of refusals provides a useful decision tool for risk management.  Furthermore, we find that some economic factors are significant indicators of food import risk by country. © 2016 Society for Risk Analysis.

Imaging of Small Animal Peripheral Artery Disease Models: Recent Advancements and Translational Potential

PubMed Central

Lin, Jenny B.; Phillips, Evan H.; Riggins, Ti’Air E.; Sangha, Gurneet S.; Chakraborty, Sreyashi; Lee, Janice Y.; Lycke, Roy J.; Hernandez, Clarissa L.; Soepriatna, Arvin H.; Thorne, Bradford R. H.; Yrineo, Alexa A.; Goergen, Craig J.

2015-01-01

Peripheral artery disease (PAD) is a broad disorder encompassing multiple forms of arterial disease outside of the heart. As such, PAD development is a multifactorial process with a variety of manifestations. For example, aneurysms are pathological expansions of an artery that can lead to rupture, while ischemic atherosclerosis reduces blood flow, increasing the risk of claudication, poor wound healing, limb amputation, and stroke. Current PAD treatment is often ineffective or associated with serious risks, largely because these disorders are commonly undiagnosed or misdiagnosed. Active areas of research are focused on detecting and characterizing deleterious arterial changes at early stages using non-invasive imaging strategies, such as ultrasound, as well as emerging technologies like photoacoustic imaging. Earlier disease detection and characterization could improve interventional strategies, leading to better prognosis in PAD patients. While rodents are being used to investigate PAD pathophysiology, imaging of these animal models has been underutilized. This review focuses on structural and molecular information and disease progression revealed by recent imaging efforts of aortic, cerebral, and peripheral vascular disease models in mice, rats, and rabbits. Effective translation to humans involves better understanding of underlying PAD pathophysiology to develop novel therapeutics and apply non-invasive imaging techniques in the clinic. PMID:25993289

Waterhammer Transient Simulation and Model Anchoring for the Robotic Lunar Lander Propulsion System

NASA Technical Reports Server (NTRS)

Stein, William B.; Trinh, Huu P.; Reynolds, Michael E.; Sharp, David J.

2011-01-01

Waterhammer transients have the potential to adversely impact propulsion system design if not properly addressed. Waterhammer can potentially lead to system plumbing, and component damage. Multi-thruster propulsion systems also develop constructive/destructive wave interference which becomes difficult to predict without detailed models. Therefore, it is important to sufficiently characterize propulsion system waterhammer in order to develop a robust design with minimal impact to other systems. A risk reduction activity was performed at Marshall Space Flight Center to develop a tool for estimating waterhammer through the use of anchored simulation for the Robotic Lunar Lander (RLL) propulsion system design. Testing was performed to simulate waterhammer surges due to rapid valve closure and consisted of twenty-two series of waterhammer tests, resulting in more than 300 valve actuations. These tests were performed using different valve actuation schemes and three system pressures. Data from the valve characterization tests were used to anchor the models that employed MSCSoftware.EASY5 v.2010 to model transient fluid phenomena by using transient forms of mass and energy conservation. The anchoring process was performed by comparing initial model results to experimental data and then iterating the model input to match the simulation results with the experimental data. The models provide good correlation with experimental results, supporting the use of EASY5 as a tool to model fluid transients and provide a baseline for future RLL system modeling. This paper addresses tasks performed during the waterhammer risk reduction activity for the RLL propulsion system. The problem of waterhammer simulation anchoring as applied to the RLL system is discussed with results from the corresponding experimental valve tests. Important factors for waterhammer mitigation are discussed along with potential design impacts to the RLL propulsion system.

Application of Quality by Design to the characterization of the cell culture process of an Fc-Fusion protein.

PubMed

Rouiller, Yolande; Solacroup, Thomas; Deparis, Véronique; Barbafieri, Marco; Gleixner, Ralf; Broly, Hervé; Eon-Duval, Alex

2012-06-01

The production bioreactor step of an Fc-Fusion protein manufacturing cell culture process was characterized following Quality by Design principles. Using scientific knowledge derived from the literature and process knowledge gathered during development studies and manufacturing to support clinical trials, potential critical and key process parameters with a possible impact on product quality and process performance, respectively, were determined during a risk assessment exercise. The identified process parameters were evaluated using a design of experiment approach. The regression models generated from the data allowed characterizing the impact of the identified process parameters on quality attributes. The main parameters having an impact on product titer were pH and dissolved oxygen, while those having the highest impact on process- and product-related impurities and variants were pH and culture duration. The models derived from characterization studies were used to define the cell culture process design space. The design space limits were set in such a way as to ensure that the drug substance material would consistently have the desired quality. Copyright © 2012 Elsevier B.V. All rights reserved.

A probabilistic approach to assess antibiotic resistance development risks in environmental compartments and its application to an intensive aquaculture production scenario.

PubMed

Rico, Andreu; Jacobs, Rianne; Van den Brink, Paul J; Tello, Alfredo

2017-12-01

Estimating antibiotic pollution and antibiotic resistance development risks in environmental compartments is important to design management strategies that advance our stewardship of antibiotics. In this study we propose a modelling approach to estimate the risk of antibiotic resistance development in environmental compartments and demonstrate its application in aquaculture production systems. We modelled exposure concentrations for 12 antibiotics used in Vietnamese Pangasius catfish production using the ERA-AQUA model. Minimum selective concentration (MSC) distributions that characterize the selective pressure of antibiotics on bacterial communities were derived from the European Committee on Antimicrobial Susceptibility Testing (EUCAST) Minimum Inhibitory Concentration dataset. The antibiotic resistance development risk (RDR) for each antibiotic was calculated as the probability that the antibiotic exposure distribution exceeds the MSC distribution representing the bacterial community. RDRs in pond sediments were nearly 100% for all antibiotics. Median RDR values in pond water were high for the majority of the antibiotics, with rifampicin, levofloxacin and ampicillin having highest values. In the effluent mixing area, RDRs were low for most antibiotics, with the exception of amoxicillin, ampicillin and trimethoprim, which presented moderate risks, and rifampicin and levofloxacin, which presented high risks. The RDR provides an efficient means to benchmark multiple antibiotics and treatment regimes in the initial phase of a risk assessment with regards to their potential to develop resistance in different environmental compartments, and can be used to derive resistance threshold concentrations. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Quantitative Ecological Risk Assessment of the Toxicological Risks from Exxon Valdez Subsurface Oil Residues to Sea Otters at Northern Knight Island, Prince William Sound, Alaska

PubMed Central

Harwell, Mark A.; Gentile, John H.; Johnson, Charles B.; Garshelis, David L.; Parker, Keith R.

2010-01-01

A comprehensive, quantitative risk assessment is presented of the toxicological risks from buried Exxon Valdez subsurface oil residues (SSOR) to a subpopulation of sea otters (Enhydra lutris) at Northern Knight Island (NKI) in Prince William Sound, Alaska, as it has been asserted that this subpopulation of sea otters may be experiencing adverse effects from the SSOR. The central questions in this study are: could the risk to NKI sea otters from exposure to polycyclic aromatic hydrocarbons (PAHs) in SSOR, as characterized in 2001–2003, result in individual health effects, and, if so, could that exposure cause subpopulation-level effects? We follow the U.S. Environmental Protection Agency (USEPA) risk paradigm by: (a) identifying potential routes of exposure to PAHs from SSOR; (b) developing a quantitative simulation model of exposures using the best available scientific information; (c) developing scenarios based on calculated probabilities of sea otter exposures to SSOR; (d) simulating exposures for 500,000 modeled sea otters and extracting the 99.9% quantile most highly exposed individuals; and (e) comparing projected exposures to chronic toxicity reference values. Results indicate that, even under conservative assumptions in the model, maximum-exposed sea otters would not receive a dose of PAHs sufficient to cause any health effects; consequently, no plausible toxicological risk exists from SSOR to the sea otter subpopulation at NKI. PMID:20862194

WHAT ARE THE BEST MEANS TO ASSESS CONTAMINANT TRANSPORT AND BIODEGRADATION AND MOVE TOWARD CLOSURE, USING APPROPRIATE SITE-SPECIFIC RISK EVALUATIONS?

EPA Science Inventory

Site remedy and closure decisions are made from a mixture of site data, literature values, and model results. Often assessment of this information is difficult for State Agency case managers because conventional approaches to site characterization do not yield a clear and strai...

A Model Based Analysis of the Role of an Upper-Level Front and Stratospheric Intrusion in the Mack Lake Fire

Treesearch

Tarisa K. Zimet; Jonathan E. Martin

2003-01-01

Meteorological assessment of wildfire risk has traditionally involved identification of several synoptic types empirically determined to influence wildfire spread. Such weather types are characterized by identifiable synoptic-scale structures and processes. Schroeder et. al. (1964) identified four recognizable synoptic-scale patterns that contribute most frequently to...

The Protective Effects of Neighborhood Collective Efficacy on British Children Growing Up in Deprivation: A Developmental Analysis

ERIC Educational Resources Information Center

Odgers, Candice L.; Moffitt, Terrie E.; Tach, Laura M.; Taylor, Alan; Caspi, Avshalom; Matthews, Charlotte L.; Sampson, Robert J.

2009-01-01

This article reports on the influence of neighborhood-level deprivation and collective efficacy on children's antisocial behavior between the ages of 5 and 10 years. Latent growth curve modeling was applied to characterize the developmental course of antisocial behavior among children in the E-Risk Longitudinal Twin Study, an epidemiological…

The many faces of fear: a synthesis of the methodological variation in characterizing predation risk.

PubMed

Moll, Remington J; Redilla, Kyle M; Mudumba, Tutilo; Muneza, Arthur B; Gray, Steven M; Abade, Leandro; Hayward, Matt W; Millspaugh, Joshua J; Montgomery, Robert A

2017-07-01

Predators affect prey by killing them directly (lethal effects) and by inducing costly antipredator behaviours in living prey (risk effects). Risk effects can strongly influence prey populations and cascade through trophic systems. A prerequisite for assessing risk effects is characterizing the spatiotemporal variation in predation risk. Risk effects research has experienced rapid growth in the last several decades. However, preliminary assessments of the resultant literature suggest that researchers characterize predation risk using a variety of techniques. The implications of this methodological variation for inference and comparability among studies have not been well recognized or formally synthesized. We couple a literature survey with a hierarchical framework, developed from established theory, to quantify the methodological variation in characterizing risk using carnivore-ungulate systems as a case study. Via this process, we documented 244 metrics of risk from 141 studies falling into at least 13 distinct subcategories within three broader categories. Both empirical and theoretical work suggest risk and its effects on prey constitute a complex, multi-dimensional process with expressions varying by spatiotemporal scale. Our survey suggests this multi-scale complexity is reflected in the literature as a whole but often underappreciated in any given study, which complicates comparability among studies and leads to an overemphasis on documenting the presence of risk effects rather than their mechanisms or scale of influence. We suggest risk metrics be placed in a more concrete conceptual framework to clarify inference surrounding risk effects and their cascading effects throughout ecosystems. We recommend studies (i) take a multi-scale approach to characterizing risk; (ii) explicitly consider 'true' predation risk (probability of predation per unit time); and (iii) use risk metrics that facilitate comparison among studies and the evaluation of multiple competing hypotheses. Addressing the pressing questions in risk effects research, including how, to what extent and on what scale they occur, requires leveraging the advantages of the many methods available to characterize risk while minimizing the confusion caused by variability in their application. © 2017 The Authors. Journal of Animal Ecology © 2017 British Ecological Society.

Between-airport heterogeneity in air toxics emissions associated with individual cancer risk thresholds and population risks

PubMed Central

2009-01-01

Background Airports represent a complex source type of increasing importance contributing to air toxics risks. Comprehensive atmospheric dispersion models are beyond the scope of many applications, so it would be valuable to rapidly but accurately characterize the risk-relevant exposure implications of emissions at an airport. Methods In this study, we apply a high resolution atmospheric dispersion model (AERMOD) to 32 airports across the United States, focusing on benzene, 1,3-butadiene, and benzo [a]pyrene. We estimate the emission rates required at these airports to exceed a 10-6 lifetime cancer risk for the maximally exposed individual (emission thresholds) and estimate the total population risk at these emission rates. Results The emission thresholds vary by two orders of magnitude across airports, with variability predicted by proximity of populations to the airport and mixing height (R2 = 0.74–0.75 across pollutants). At these emission thresholds, the population risk within 50 km of the airport varies by two orders of magnitude across airports, driven by substantial heterogeneity in total population exposure per unit emissions that is related to population density and uncorrelated with emission thresholds. Conclusion Our findings indicate that site characteristics can be used to accurately predict maximum individual risk and total population risk at a given level of emissions, but that optimizing on one endpoint will be non-optimal for the other. PMID:19426510

Resistance to Aerobic Exercise Training Causes Metabolic Dysfunction and Reveals Novel Exercise-Regulated Signaling Networks

PubMed Central

Lessard, Sarah J.; Rivas, Donato A.; Alves-Wagner, Ana B.; Hirshman, Michael F.; Gallagher, Iain J.; Constantin-Teodosiu, Dumitru; Atkins, Ryan; Greenhaff, Paul L.; Qi, Nathan R.; Gustafsson, Thomas; Fielding, Roger A.; Timmons, James A.; Britton, Steven L.; Koch, Lauren G.; Goodyear, Laurie J.

2013-01-01

Low aerobic exercise capacity is a risk factor for diabetes and a strong predictor of mortality, yet some individuals are “exercise-resistant” and unable to improve exercise capacity through exercise training. To test the hypothesis that resistance to aerobic exercise training underlies metabolic disease risk, we used selective breeding for 15 generations to develop rat models of low and high aerobic response to training. Before exercise training, rats selected as low and high responders had similar exercise capacities. However, after 8 weeks of treadmill training, low responders failed to improve their exercise capacity, whereas high responders improved by 54%. Remarkably, low responders to aerobic training exhibited pronounced metabolic dysfunction characterized by insulin resistance and increased adiposity, demonstrating that the exercise-resistant phenotype segregates with disease risk. Low responders had impaired exercise-induced angiogenesis in muscle; however, mitochondrial capacity was intact and increased normally with exercise training, demonstrating that mitochondria are not limiting for aerobic adaptation or responsible for metabolic dysfunction in low responders. Low responders had increased stress/inflammatory signaling and altered transforming growth factor-β signaling, characterized by hyperphosphorylation of a novel exercise-regulated phosphorylation site on SMAD2. Using this powerful biological model system, we have discovered key pathways for low exercise training response that may represent novel targets for the treatment of metabolic disease. PMID:23610057

Modeling the dynamics of oral poliovirus vaccine cessation.

PubMed

Thompson, Kimberly M; Duintjer Tebbens, Radboud J

2014-11-01

Oral poliovirus vaccine (OPV) results in an ongoing burden of poliomyelitis due to vaccine-associated paralytic poliomyelitis and circulating vaccine-derived polioviruses (cVDPVs). This motivates globally coordinated OPV cessation after wild poliovirus eradication. We modeled poliovirus transmission and OPV evolution to characterize the interaction between population immunity, OPV-related virus prevalence, and the emergence of cVDPVs after OPV cessation. We explored strategies to prevent and manage cVDPVs for countries that currently use OPV for immunization and characterized cVDPV emergence risks and OPV use for outbreak response. Continued intense supplemental immunization activities until OPV cessation represent the best strategy to prevent cVDPV emergence after OPV cessation in areas with insufficient routine immunization coverage. Policy makers must actively manage population immunity before OPV cessation to prevent cVDPVs and aggressively respond if prevention fails. Sufficiently aggressive response with OPV to interrupt transmission of the cVDPV outbreak virus will lead to die-out of OPV-related viruses used for response in the outbreak population. Further analyses should consider the risk of exportation to other populations of the outbreak virus and any OPV used for outbreak response. OPV cessation can successfully eliminate all circulating live polioviruses in a population. The polio end game requires active risk management. © The Author 2014. Published by Oxford University Press on behalf of the Infectious Diseases Society of America. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Application of a geographical information system approach for risk analysis of fascioliasis in southern Espírito Santo state, Brazil.

PubMed

Martins, Isabella Vilhena Freire; de Avelar, Barbara Rauta; Pereira, Maria Julia Salim; da Fonseca, Adevair Henrique

2012-09-01

A model based on geographical information systems for mapping the risk of fascioliasis was developed for the southern part of Espírito Santo state, Brazil. The determinants investigated were precipitation, temperature, elevation, slope, soil type and land use. Weightings and grades were assigned to determinants and their categories according to their relevance with respect to fascioliasis. Theme maps depicting the spatial distribution of risk areas indicate that over 50% of southern Espírito Santo is either at high or at very high risk for fascioliasis. These areas were found to be characterized by comparatively high temperature but relatively low slope, low precipitation and low elevation corresponding to periodically flooded grasslands or soils that promote water retention.

Harnessing the theoretical foundations of the exponential and beta-Poisson dose-response models to quantify parameter uncertainty using Markov Chain Monte Carlo.

PubMed

Schmidt, Philip J; Pintar, Katarina D M; Fazil, Aamir M; Topp, Edward

2013-09-01

Dose-response models are the essential link between exposure assessment and computed risk values in quantitative microbial risk assessment, yet the uncertainty that is inherent to computed risks because the dose-response model parameters are estimated using limited epidemiological data is rarely quantified. Second-order risk characterization approaches incorporating uncertainty in dose-response model parameters can provide more complete information to decisionmakers by separating variability and uncertainty to quantify the uncertainty in computed risks. Therefore, the objective of this work is to develop procedures to sample from posterior distributions describing uncertainty in the parameters of exponential and beta-Poisson dose-response models using Bayes's theorem and Markov Chain Monte Carlo (in OpenBUGS). The theoretical origins of the beta-Poisson dose-response model are used to identify a decomposed version of the model that enables Bayesian analysis without the need to evaluate Kummer confluent hypergeometric functions. Herein, it is also established that the beta distribution in the beta-Poisson dose-response model cannot address variation among individual pathogens, criteria to validate use of the conventional approximation to the beta-Poisson model are proposed, and simple algorithms to evaluate actual beta-Poisson probabilities of infection are investigated. The developed MCMC procedures are applied to analysis of a case study data set, and it is demonstrated that an important region of the posterior distribution of the beta-Poisson dose-response model parameters is attributable to the absence of low-dose data. This region includes beta-Poisson models for which the conventional approximation is especially invalid and in which many beta distributions have an extreme shape with questionable plausibility. © Her Majesty the Queen in Right of Canada 2013. Reproduced with the permission of the Minister of the Public Health Agency of Canada.

#2) EPA Perspective - Exposure and Effects Prediction and ...

EPA Pesticide Factsheets

Outline •Biomarkers as a risk assessment tool–exposure assessment & risk characterization•CDC’s NHANES as a source of biomarker data–history, goals & available data•Review of NHANES publications (1999-2013)–chemicals, uses, trends & challenges•NHANES biomarker case study–recommendations for future research The National Exposure Research Laboratory (NERL) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA mission to protect human health and the environment. HEASD research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA strategic plan. More specifically, our division conducts research to characterize the movement of pollutants from the source to contact with humans. Our multidisciplinary research program produces Methods, Measurements, and Models to identify relationships between and characterize processes that link source emissions, environmental concentrations, human exposures, and target-tissue dose. The impact of these tools is improved regulatory programs and policies for EPA.

Opportunities for multivariate analysis of open spatial datasets to characterize urban flooding risks

NASA Astrophysics Data System (ADS)

Gaitan, S.; ten Veldhuis, J. A. E.

2015-06-01

Cities worldwide are challenged by increasing urban flood risks. Precise and realistic measures are required to reduce flooding impacts. However, currently implemented sewer and topographic models do not provide realistic predictions of local flooding occurrence during heavy rain events. Assessing other factors such as spatially distributed rainfall, socioeconomic characteristics, and social sensing, may help to explain probability and impacts of urban flooding. Several spatial datasets have been recently made available in the Netherlands, including rainfall-related incident reports made by citizens, spatially distributed rain depths, semidistributed socioeconomic information, and buildings age. Inspecting the potential of this data to explain the occurrence of rainfall related incidents has not been done yet. Multivariate analysis tools for describing communities and environmental patterns have been previously developed and used in the field of study of ecology. The objective of this paper is to outline opportunities for these tools to explore urban flooding risks patterns in the mentioned datasets. To that end, a cluster analysis is performed. Results indicate that incidence of rainfall-related impacts is higher in areas characterized by older infrastructure and higher population density.

Interplay among Resistance Profiles, High-Risk Clones, and Virulence in the Caenorhabditis elegans Pseudomonas aeruginosa Infection Model.

PubMed

Sánchez-Diener, Irina; Zamorano, Laura; López-Causapé, Carla; Cabot, Gabriel; Mulet, Xavier; Peña, Carmen; Del Campo, Rosa; Cantón, Rafael; Doménech-Sánchez, Antonio; Martínez-Martínez, Luis; Arcos, Susana C; Navas, Alfonso; Oliver, Antonio

2017-12-01

The increasing prevalence of nosocomial infections produced by multidrug-resistant (MDR) or extensively drug-resistant (XDR) Pseudomonas aeruginosa is frequently linked to widespread international strains designated high-risk clones. In this work, we attempted to decipher the interplay between resistance profiles, high-risk clones, and virulence, testing a large ( n = 140) collection of well-characterized P. aeruginosa isolates from different sources (bloodstream infections, nosocomial outbreaks, cystic fibrosis, and the environment) in a Caenorhabditis elegans infection model. Consistent with previous data, we documented a clear inverse correlation between antimicrobial resistance and virulence in the C. elegans model. Indeed, the lowest virulence was linked to XDR profiles, which were typically linked to defined high-risk clones. However, virulence varied broadly depending on the involved high-risk clone; it was high for sequence type 111 (ST111) and ST235 but very low for ST175. The highest virulence of ST235 could be attributed to its exoU + type III secretion system (TTSS) genotype, which was found to be linked with higher virulence in our C. elegans model. Other markers, such as motility or pigment production, were not essential for virulence in the C. elegans model but seemed to be related with the higher values of the statistical normalized data. In contrast to ST235, the ST175 high-risk clone, which is widespread in Spain and France, seems to be associated with a particularly low virulence in the C. elegans model. Moreover, the previously described G154R AmpR mutation, prevalent in ST175, was found to contribute to the reduced virulence, although it was not the only factor involved. Altogether, our results provide a major step forward for understanding the interplay between P. aeruginosa resistance profiles, high-risk clones, and virulence. Copyright © 2017 American Society for Microbiology.

Interplay among Resistance Profiles, High-Risk Clones, and Virulence in the Caenorhabditis elegans Pseudomonas aeruginosa Infection Model

PubMed Central

Sánchez-Diener, Irina; López-Causapé, Carla; Mulet, Xavier; Cantón, Rafael; Doménech-Sánchez, Antonio; Martínez-Martínez, Luis; Arcos, Susana C.; Navas, Alfonso

2017-01-01

ABSTRACT The increasing prevalence of nosocomial infections produced by multidrug-resistant (MDR) or extensively drug-resistant (XDR) Pseudomonas aeruginosa is frequently linked to widespread international strains designated high-risk clones. In this work, we attempted to decipher the interplay between resistance profiles, high-risk clones, and virulence, testing a large (n = 140) collection of well-characterized P. aeruginosa isolates from different sources (bloodstream infections, nosocomial outbreaks, cystic fibrosis, and the environment) in a Caenorhabditis elegans infection model. Consistent with previous data, we documented a clear inverse correlation between antimicrobial resistance and virulence in the C. elegans model. Indeed, the lowest virulence was linked to XDR profiles, which were typically linked to defined high-risk clones. However, virulence varied broadly depending on the involved high-risk clone; it was high for sequence type 111 (ST111) and ST235 but very low for ST175. The highest virulence of ST235 could be attributed to its exoU+ type III secretion system (TTSS) genotype, which was found to be linked with higher virulence in our C. elegans model. Other markers, such as motility or pigment production, were not essential for virulence in the C. elegans model but seemed to be related with the higher values of the statistical normalized data. In contrast to ST235, the ST175 high-risk clone, which is widespread in Spain and France, seems to be associated with a particularly low virulence in the C. elegans model. Moreover, the previously described G154R AmpR mutation, prevalent in ST175, was found to contribute to the reduced virulence, although it was not the only factor involved. Altogether, our results provide a major step forward for understanding the interplay between P. aeruginosa resistance profiles, high-risk clones, and virulence. PMID:28923877

Freight transportation and the potential for invasions of exotic insects in urban and periurban forests of the United States.

PubMed

Colunga-Garcia, Manuel; Haack, Robert A; Adelaja, Adesoji O

2009-02-01

Freight transportation is an important pathway for the introduction and dissemination of exotic forest insects (EFI). Identifying the final destination of imports is critical in determining the likelihood of EFI establishment. We analyzed the use of regional freight transport information to characterize risk of urban and periurban areas to EFI introductions. Specific objectives were to 1) approximate the final distribution of selected imports among urban areas of the United States, 2) characterize the final distribution of imports in terms of their spatial aggregation and dominant world region of origin, and 3) assess the effect of the final distribution of imports on the level of risk to urban and periurban forests from EFI. Freight pattern analyses were conducted for three categories of imports whose products or packaging materials are associated with EFI: wood products, nonmetallic mineral products, and machinery. The final distribution of wood products was the most evenly distributed of the three selected imports, whereas machinery was most spatially concentrated. We found that the type of import and the world region of origin greatly influence the final distribution of imported products. Risk assessment models were built based on the amount of forestland and imports for each urban area The model indicated that 84-88% of the imported tonnage went to only 4-6% of the urban areas in the contiguous United States. We concluded that freight movement information is critical for proper risk assessment of EFI. Implications of our findings and future research needs are discussed.

Risk Map of Cholera Infection for Vaccine Deployment: The Eastern Kolkata Case

PubMed Central

You, Young Ae; Ali, Mohammad; Kanungo, Suman; Sah, Binod; Manna, Byomkesh; Puri, Mahesh; Nair, G. Balakrish; Bhattacharya, Sujit Kumar; Convertino, Matteo; Deen, Jacqueline L.; Lopez, Anna Lena; Wierzba, Thomas F.; Clemens, John; Sur, Dipika

2013-01-01

Background Despite advancement of our knowledge, cholera remains a public health concern. During March-April 2010, a large cholera outbreak afflicted the eastern part of Kolkata, India. The quantification of importance of socio-environmental factors in the risk of cholera, and the calculation of the risk is fundamental for deploying vaccination strategies. Here we investigate socio-environmental characteristics between high and low risk areas as well as the potential impact of vaccination on the spatial occurrence of the disease. Methods and Findings The study area comprised three wards of Kolkata Municipal Corporation. A mass cholera vaccination campaign was conducted in mid-2006 as the part of a clinical trial. Cholera cases and data of the trial to identify high risk areas for cholera were analyzed. We used a generalized additive model (GAM) to detect risk areas, and to evaluate the importance of socio-environmental characteristics between high and low risk areas. During the one-year pre-vaccination and two-year post-vaccination periods, 95 and 183 cholera cases were detected in 111,882 and 121,827 study participants, respectively. The GAM model predicts that high risk areas in the west part of the study area where the outbreak largely occurred. High risk areas in both periods were characterized by poor people, use of unsafe water, and proximity to canals used as the main drainage for rain and waste water. Cholera vaccine uptake was significantly lower in the high risk areas compared to low risk areas. Conclusion The study shows that even a parsimonious model like GAM predicts high risk areas where cholera outbreaks largely occurred. This is useful for indicating where interventions would be effective in controlling the disease risk. Data showed that vaccination decreased the risk of infection. Overall, the GAM-based risk map is useful for policymakers, especially those from countries where cholera remains to be endemic with periodic outbreaks. PMID:23936491

Improved physiologically based pharmacokinetic model for oral exposures to chromium in mice, rats, and humans to address temporal variation and sensitive populations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kirman, C.R., E-mail: ckirman@summittoxicology.com

A physiologically based pharmacokinetic (PBPK) model for hexavalent chromium [Cr(VI)] in mice, rats, and humans developed previously (Kirman et al., 2012, 2013), was updated to reflect an improved understanding of the toxicokinetics of the gastrointestinal tract following oral exposures. Improvements were made to: (1) the reduction model, which describes the pH-dependent reduction of Cr(VI) to Cr(III) in the gastrointestinal tract under both fasted and fed states; (2) drinking water pattern simulations, to better describe dosimetry in rodents under the conditions of the NTP cancer bioassay; and (3) parameterize the model to characterize potentially sensitive human populations. Important species differences, sourcesmore » of non-linear toxicokinetics, and human variation are identified and discussed within the context of human health risk assessment. - Highlights: • An improved version of the PBPK model for Cr(VI) toxicokinetics was developed. • The model incorporates data collected to fill important data gaps. • Model predictions for specific age groups and sensitive subpopulations are provided. • Implications to human health risk assessment are discussed.« less

Reduced Risk of Importing Ebola Virus Disease because of Travel Restrictions in 2014: A Retrospective Epidemiological Modeling Study.

PubMed

Otsuki, Shiori; Nishiura, Hiroshi

An epidemic of Ebola virus disease (EVD) from 2013-16 posed a serious risk of global spread during its early growth phase. A post-epidemic evaluation of the effectiveness of travel restrictions has yet to be conducted. The present study aimed to estimate the effectiveness of travel restrictions in reducing the risk of importation from mid-August to September, 2014, using a simple hazard-based statistical model. The hazard rate was modeled as an inverse function of the effective distance, an excellent predictor of disease spread, which was calculated from the airline transportation network. By analyzing datasets of the date of EVD case importation from the 15th of July to the 15th of September 2014, and assuming that the network structure changed from the 8th of August 2014 because of travel restrictions, parameters that characterized the hazard rate were estimated. The absolute risk reduction and relative risk reductions due to travel restrictions were estimated to be less than 1% and about 20%, respectively, for all models tested. Effectiveness estimates among African countries were greater than those for other countries outside Africa. The travel restrictions were not effective enough to expect the prevention of global spread of Ebola virus disease. It is more efficient to control the spread of disease locally during an early phase of an epidemic than to attempt to control the epidemic at international borders. Capacity building for local containment and coordinated and expedited international cooperation are essential to reduce the risk of global transmission.

Reduced Risk of Importing Ebola Virus Disease because of Travel Restrictions in 2014: A Retrospective Epidemiological Modeling Study

PubMed Central

Otsuki, Shiori

2016-01-01

Background An epidemic of Ebola virus disease (EVD) from 2013–16 posed a serious risk of global spread during its early growth phase. A post-epidemic evaluation of the effectiveness of travel restrictions has yet to be conducted. The present study aimed to estimate the effectiveness of travel restrictions in reducing the risk of importation from mid-August to September, 2014, using a simple hazard-based statistical model. Methodology/Principal Findings The hazard rate was modeled as an inverse function of the effective distance, an excellent predictor of disease spread, which was calculated from the airline transportation network. By analyzing datasets of the date of EVD case importation from the 15th of July to the 15th of September 2014, and assuming that the network structure changed from the 8th of August 2014 because of travel restrictions, parameters that characterized the hazard rate were estimated. The absolute risk reduction and relative risk reductions due to travel restrictions were estimated to be less than 1% and about 20%, respectively, for all models tested. Effectiveness estimates among African countries were greater than those for other countries outside Africa. Conclusions The travel restrictions were not effective enough to expect the prevention of global spread of Ebola virus disease. It is more efficient to control the spread of disease locally during an early phase of an epidemic than to attempt to control the epidemic at international borders. Capacity building for local containment and coordinated and expedited international cooperation are essential to reduce the risk of global transmission. PMID:27657544

Mode-of-Action Uncertainty for Dual-Mode Carcinogens:Lower Bounds for Naphthalene-Induced Nasal Tumors in Rats Implied byPBPK and 2-Stage Stochastic Cancer Risk Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bogen, K T

2007-01-30

As reflected in the 2005 USEPA Guidelines for Cancer Risk Assessment, some chemical carcinogens may have a site-specific mode of action (MOA) that is dual, involving mutation in addition to cell-killing induced hyperplasia. Although genotoxicity may contribute to increased risk at all doses, the Guidelines imply that for dual MOA (DMOA) carcinogens, judgment be used to compare and assess results obtained using separate ''linear'' (genotoxic) vs. ''nonlinear'' (nongenotoxic) approaches to low-level risk extrapolation. However, the Guidelines allow the latter approach to be used only when evidence is sufficient to parameterize a biologically based model that reliably extrapolates risk to lowmore » levels of concern. The Guidelines thus effectively prevent MOA uncertainty from being characterized and addressed when data are insufficient to parameterize such a model, but otherwise clearly support a DMOA. A bounding factor approach--similar to that used in reference dose procedures for classic toxicity endpoints--can address MOA uncertainty in a way that avoids explicit modeling of low-dose risk as a function of administered or internal dose. Even when a ''nonlinear'' toxicokinetic model cannot be fully validated, implications of DMOA uncertainty on low-dose risk may be bounded with reasonable confidence when target tumor types happen to be extremely rare. This concept was illustrated for the rodent carcinogen naphthalene. Bioassay data, supplemental toxicokinetic data, and related physiologically based pharmacokinetic and 2-stage stochastic carcinogenesis modeling results all clearly indicate that naphthalene is a DMOA carcinogen. Plausibility bounds on rat-tumor-type specific DMOA-related uncertainty were obtained using a 2-stage model adapted to reflect the empirical link between genotoxic and cytotoxic effects of the most potent identified genotoxic naphthalene metabolites, 1,2- and 1,4-naphthoquinone. Resulting bounds each provided the basis for a corresponding ''uncertainty'' factor <1 appropriate to apply to estimates of naphthalene risk obtained by linear extrapolation under a default genotoxic MOA assumption. This procedure is proposed as scientifically credible method to address MOA uncertainty for DMOA carcinogens.« less

Ionizing Radiation Environments and Exposure Risks

NASA Astrophysics Data System (ADS)

Kim, M. H. Y.

2015-12-01

Space radiation environments for historically large solar particle events (SPE) and galactic cosmic rays (GCR) are simulated to characterize exposures to radio-sensitive organs for missions to low-Earth orbit (LEO), moon, near-Earth asteroid, and Mars. Primary and secondary particles for SPE and GCR are transported through the respective atmospheres of Earth or Mars, space vehicle, and astronaut's body tissues using NASA's HZETRN/QMSFRG computer code. Space radiation protection methods, which are derived largely from ground-based methods recommended by the National Council on Radiation Protection and Measurements (NCRP) or International Commission on Radiological Protections (ICRP), are built on the principles of risk justification, limitation, and ALARA (as low as reasonably achievable). However, because of the large uncertainties in high charge and energy (HZE) particle radiobiology and the small population of space crews, NASA develops distinct methods to implement a space radiation protection program. For the fatal cancer risks, which have been considered the dominant risk for GCR, the NASA Space Cancer Risk (NSCR) model has been developed from recommendations by NCRP; and undergone external review by the National Research Council (NRC), NCRP, and through peer-review publications. The NSCR model uses GCR environmental models, particle transport codes describing the GCR modification by atomic and nuclear interactions in atmospheric shielding coupled with spacecraft and tissue shielding, and NASA-defined quality factors for solid cancer and leukemia risk estimates for HZE particles. By implementing the NSCR model, the exposure risks from various heliospheric conditions are assessed for the radiation environments for various-class mission types to understand architectures and strategies of human exploration missions and ultimately to contribute to the optimization of radiation safety and well-being of space crewmembers participating in long-term space missions.

Crop connectivity under climate change: future environmental and geographic risks of potato late blight in Scotland.

PubMed

Skelsey, Peter; Cooke, David E L; Lynott, James S; Lees, Alison K

2016-11-01

The impact of climate change on dispersal processes is largely ignored in risk assessments for crop diseases, as inoculum is generally assumed to be ubiquitous and nonlimiting. We suggest that consideration of the impact of climate change on the connectivity of crops for inoculum transmission may provide additional explanatory and predictive power in disease risk assessments, leading to improved recommendations for agricultural adaptation to climate change. In this study, a crop-growth model was combined with aerobiological models and a newly developed infection risk model to provide a framework for quantifying the impact of future climates on the risk of disease occurrence and spread. The integrated model uses standard meteorological variables and can be easily adapted to various crop pathosystems characterized by airborne inoculum. In a case study, the framework was used with data defining the spatial distribution of potato crops in Scotland and spatially coherent, probabilistic climate change data to project the future connectivity of crop distributions for Phytophthora infestans (causal agent of potato late blight) inoculum and the subsequent risk of infection. Projections and control recommendations are provided for multiple combinations of potato cultivar and CO 2 emissions scenario, and temporal and spatial averaging schemes. Overall, we found that relative to current climatic conditions, the risk of late blight will increase in Scotland during the first half of the potato growing season and decrease during the second half. To guide adaptation strategies, we also investigated the potential impact of climate change-driven shifts in the cropping season. Advancing the start of the potato growing season by 1 month proved to be an effective strategy from both an agronomic and late blight management perspective. © 2016 John Wiley & Sons Ltd.

MobRISK: a model for assessing the exposure of road users to flash flood events

NASA Astrophysics Data System (ADS)

Shabou, Saif; Ruin, Isabelle; Lutoff, Céline; Debionne, Samuel; Anquetin, Sandrine; Creutin, Jean-Dominique; Beaufils, Xavier

2017-09-01

Recent flash flood impact studies highlight that road networks are often disrupted due to adverse weather and flash flood events. Road users are thus particularly exposed to road flooding during their daily mobility. Previous exposure studies, however, do not take into consideration population mobility. Recent advances in transportation research provide an appropriate framework for simulating individual travel-activity patterns using an activity-based approach. These activity-based mobility models enable the prediction of the sequence of activities performed by individuals and locating them with a high spatial-temporal resolution. This paper describes the development of the MobRISK microsimulation system: a model for assessing the exposure of road users to extreme hydrometeorological events. MobRISK aims at providing an accurate spatiotemporal exposure assessment by integrating travel-activity behaviors and mobility adaptation with respect to weather disruptions. The model is applied in a flash-flood-prone area in southern France to assess motorists' exposure to the September 2002 flash flood event. The results show that risk of flooding mainly occurs in principal road links with considerable traffic load. However, a lag time between the timing of the road submersion and persons crossing these roads contributes to reducing the potential vehicle-related fatal accidents. It is also found that sociodemographic variables have a significant effect on individual exposure. Thus, the proposed model demonstrates the benefits of considering spatiotemporal dynamics of population exposure to flash floods and presents an important improvement in exposure assessment methods. Such improved characterization of road user exposures can present valuable information for flood risk management services.

Genetically modified crops and aquatic ecosystems: considerations for environmental risk assessment and non-target organism testing.

PubMed

Carstens, Keri; Anderson, Jennifer; Bachman, Pamela; De Schrijver, Adinda; Dively, Galen; Federici, Brian; Hamer, Mick; Gielkens, Marco; Jensen, Peter; Lamp, William; Rauschen, Stefan; Ridley, Geoff; Romeis, Jörg; Waggoner, Annabel

2012-08-01

Environmental risk assessments (ERA) support regulatory decisions for the commercial cultivation of genetically modified (GM) crops. The ERA for terrestrial agroecosystems is well-developed, whereas guidance for ERA of GM crops in aquatic ecosystems is not as well-defined. The purpose of this document is to demonstrate how comprehensive problem formulation can be used to develop a conceptual model and to identify potential exposure pathways, using Bacillus thuringiensis (Bt) maize as a case study. Within problem formulation, the insecticidal trait, the crop, the receiving environment, and protection goals were characterized, and a conceptual model was developed to identify routes through which aquatic organisms may be exposed to insecticidal proteins in maize tissue. Following a tiered approach for exposure assessment, worst-case exposures were estimated using standardized models, and factors mitigating exposure were described. Based on exposure estimates, shredders were identified as the functional group most likely to be exposed to insecticidal proteins. However, even using worst-case assumptions, the exposure of shredders to Bt maize was low and studies supporting the current risk assessments were deemed adequate. Determining if early tier toxicity studies are necessary to inform the risk assessment for a specific GM crop should be done on a case by case basis, and should be guided by thorough problem formulation and exposure assessment. The processes used to develop the Bt maize case study are intended to serve as a model for performing risk assessments on future traits and crops.

Cost-effectiveness Analysis of Nutritional Support for the Prevention of Pressure Ulcers in High-Risk Hospitalized Patients.

PubMed

Tuffaha, Haitham W; Roberts, Shelley; Chaboyer, Wendy; Gordon, Louisa G; Scuffham, Paul A

2016-06-01

To evaluate the cost-effectiveness of nutritional support compared with standard care in preventing pressure ulcers (PrUs) in high-risk hospitalized patients. An economic model using data from a systematic literature review. A meta-analysis of randomized controlled trials on the efficacy of nutritional support in reducing the incidence of PrUs was conducted. Modeled cohort of hospitalized patients at high risk of developing PrUs and malnutrition simulated during their hospital stay and up to 1 year. Standard care included PrU prevention strategies, such as redistribution surfaces, repositioning, and skin protection strategies, along with standard hospital diet. In addition to the standard care, the intervention group received nutritional support comprising patient education, nutrition goal setting, and the consumption of high-protein supplements. The analysis was from a healthcare payer perspective. Key outcomes of the model included the average costs and quality-adjusted life years. Model results were tested in univariate sensitivity analyses, and decision uncertainty was characterized using a probabilistic sensitivity analysis. Compared with standard care, nutritional support was cost saving at AU $425 per patient and marginally more effective with an average 0.005 quality-adjusted life years gained. The probability of nutritional support being cost-effective was 87%. Nutritional support to prevent PrUs in high-risk hospitalized patients is cost-effective with substantial cost savings predicted. Hospitals should implement the recommendations from the current PrU practice guidelines and offer nutritional support to high-risk patients.

New method for generating breast models featuring glandular tissue spatial distribution

NASA Astrophysics Data System (ADS)

Paixão, L.; Oliveira, B. B.; Oliveira, M. A.; Teixeira, M. H. A.; Fonseca, T. C. F.; Nogueira, M. S.

2016-02-01

Mammography is the main radiographic technique used for breast imaging. A major concern with mammographic imaging is the risk of radiation-induced breast cancer due to the high sensitivity of breast tissue. The mean glandular dose (DG) is the dosimetric quantity widely accepted to characterize the risk of radiation induced cancer. Previous studies have concluded that DG depends not only on the breast glandular content but also on the spatial distribution of glandular tissue within the breast. In this work, a new method for generating computational breast models featuring skin composition and glandular tissue distribution from patients undergoing digital mammography is proposed. Such models allow a more accurate way of calculating individualized breast glandular doses taking into consideration the glandular tissue fraction. Sixteen breast models of four patients with different glandularity breasts were simulated and the results were compared with those obtained from recommended DG conversion factors. The results show that the internationally recommended conversion factors may be overestimating the mean glandular dose to less dense breasts and underestimating the mean glandular dose for denser breasts. The methodology described in this work constitutes a powerful tool for breast dosimetry, especially for risk studies.

Assessment Of Coronary Artery Aneurysms Using Transluminal Attenuation Gradient And Computational Modeling In Kawasaki Disease Patients

NASA Astrophysics Data System (ADS)

Grande Gutierrez, Noelia; Kahn, Andrew; Shirinsky, Olga; Gagarina, Nina; Lyskina, Galina; Fukazawa, Ryuji; Owaga, Shunichi; Burns, Jane; Marsden, Alison

2015-11-01

Kawasaki Disease (KD) can result in coronary artery aneurysms (CAA) in up to 25% of patients, putting them at risk of thrombus formation, myocardial infarction and sudden death. Clinical guidelines recommend CAA diameter >8 mm as the arbitrary criterion for initiating systemic anticoagulation. KD patient specific modeling and flow simulations suggest that hemodynamic data can predict regions at increased risk of thrombosis. Transluminal Attenuation Gradient (TAG) is determined from the change in radiological attenuation per vessel length and has been proposed as a non-invasive method for characterizing coronary stenosis from CT Angiography. We hypothesized that CAA abnormal flow could be quantified using TAG. We computed hemodynamics for patient specific coronary models using a stabilized finite element method, coupled numerically to a lumped parameter network to model the heart and vascular boundary conditions. TAG was quantified in the major coronary arteries. We compared TAG for aneurysmal and normal arteries and we analyzed TAG correlation with hemodynamic and geometrical parameters. Our results suggest that TAG may provide hemodynamic data not available from anatomy alone. TAG represents a possible extension to standard CTA that could help to better evaluate the risk of thrombus formation in KD.

Challenges in identifying sites climatically matched to the native ranges of animal invaders.

PubMed

Rodda, Gordon H; Jarnevich, Catherine S; Reed, Robert N

2011-02-09

Species distribution models are often used to characterize a species' native range climate, so as to identify sites elsewhere in the world that may be climatically similar and therefore at risk of invasion by the species. This endeavor provoked intense public controversy over recent attempts to model areas at risk of invasion by the Indian Python (Python molurus). We evaluated a number of MaxEnt models on this species to assess MaxEnt's utility for vertebrate climate matching. Overall, we found MaxEnt models to be very sensitive to modeling choices and selection of input localities and background regions. As used, MaxEnt invoked minimal protections against data dredging, multi-collinearity of explanatory axes, and overfitting. As used, MaxEnt endeavored to identify a single ideal climate, whereas different climatic considerations may determine range boundaries in different parts of the native range. MaxEnt was extremely sensitive to both the choice of background locations for the python, and to selection of presence points: inclusion of just four erroneous localities was responsible for Pyron et al.'s conclusion that no additional portions of the U.S. mainland were at risk of python invasion. When used with default settings, MaxEnt overfit the realized climate space, identifying models with about 60 parameters, about five times the number of parameters justifiable when optimized on the basis of Akaike's Information Criterion. When used with default settings, MaxEnt may not be an appropriate vehicle for identifying all sites at risk of colonization. Model instability and dearth of protections against overfitting, multi-collinearity, and data dredging may combine with a failure to distinguish fundamental from realized climate envelopes to produce models of limited utility. A priori identification of biologically realistic model structure, combined with computational protections against these statistical problems, may produce more robust models of invasion risk.

Challenges in Identifying Sites Climatically Matched to the Native Ranges of Animal Invaders

PubMed Central

Rodda, Gordon H.; Jarnevich, Catherine S.; Reed, Robert N.

2011-01-01

Background Species distribution models are often used to characterize a species' native range climate, so as to identify sites elsewhere in the world that may be climatically similar and therefore at risk of invasion by the species. This endeavor provoked intense public controversy over recent attempts to model areas at risk of invasion by the Indian Python (Python molurus). We evaluated a number of MaxEnt models on this species to assess MaxEnt's utility for vertebrate climate matching. Methodology/Principal Findings Overall, we found MaxEnt models to be very sensitive to modeling choices and selection of input localities and background regions. As used, MaxEnt invoked minimal protections against data dredging, multi-collinearity of explanatory axes, and overfitting. As used, MaxEnt endeavored to identify a single ideal climate, whereas different climatic considerations may determine range boundaries in different parts of the native range. MaxEnt was extremely sensitive to both the choice of background locations for the python, and to selection of presence points: inclusion of just four erroneous localities was responsible for Pyron et al.'s conclusion that no additional portions of the U.S. mainland were at risk of python invasion. When used with default settings, MaxEnt overfit the realized climate space, identifying models with about 60 parameters, about five times the number of parameters justifiable when optimized on the basis of Akaike's Information Criterion. Conclusions/Significance When used with default settings, MaxEnt may not be an appropriate vehicle for identifying all sites at risk of colonization. Model instability and dearth of protections against overfitting, multi-collinearity, and data dredging may combine with a failure to distinguish fundamental from realized climate envelopes to produce models of limited utility. A priori identification of biologically realistic model structure, combined with computational protections against these statistical problems, may produce more robust models of invasion risk. PMID:21347411

Challenges in identifying sites climatically matched to the native ranges of animal invaders

USGS Publications Warehouse

Rodda, G.H.; Jarnevich, C.S.; Reed, R.N.

2011-01-01

Background: Species distribution models are often used to characterize a species' native range climate, so as to identify sites elsewhere in the world that may be climatically similar and therefore at risk of invasion by the species. This endeavor provoked intense public controversy over recent attempts to model areas at risk of invasion by the Indian Python (Python molurus). We evaluated a number of MaxEnt models on this species to assess MaxEnt's utility for vertebrate climate matching. Methodology/Principal Findings: Overall, we found MaxEnt models to be very sensitive to modeling choices and selection of input localities and background regions. As used, MaxEnt invoked minimal protections against data dredging, multi-collinearity of explanatory axes, and overfitting. As used, MaxEnt endeavored to identify a single ideal climate, whereas different climatic considerations may determine range boundaries in different parts of the native range. MaxEnt was extremely sensitive to both the choice of background locations for the python, and to selection of presence points: inclusion of just four erroneous localities was responsible for Pyron et al.'s conclusion that no additional portions of the U.S. mainland were at risk of python invasion. When used with default settings, MaxEnt overfit the realized climate space, identifying models with about 60 parameters, about five times the number of parameters justifiable when optimized on the basis of Akaike's Information Criterion. Conclusions/Significance: When used with default settings, MaxEnt may not be an appropriate vehicle for identifying all sites at risk of colonization. Model instability and dearth of protections against overfitting, multi-collinearity, and data dredging may combine with a failure to distinguish fundamental from realized climate envelopes to produce models of limited utility. A priori identification of biologically realistic model structure, combined with computational protections against these statistical problems, may produce more robust models of invasion risk.

Cannabis use in children with individualized risk profiles: Predicting the effect of universal prevention intervention.

PubMed

Miovský, Michal; Vonkova, Hana; Čablová, Lenka; Gabrhelík, Roman

2015-11-01

To study the effect of a universal prevention intervention targeting cannabis use in individual children with different risk profiles. A school-based randomized controlled prevention trial was conducted over a period of 33 months (n=1874 sixth-graders, baseline mean age 11.82). We used a two-level random intercept logistic model for panel data to predict the probabilities of cannabis use for each child. Specifically, we used eight risk/protective factors to characterize each child and then predicted two probabilities of cannabis use for each child if the child had the intervention or not. Using the two probabilities, we calculated the absolute and relative effect of the intervention for each child. According to the two probabilities, we also divided the sample into a low-risk group (the quarter of the children with the lowest probabilities), a moderate-risk group, and a high-risk group (the quarter of the children with the highest probabilities) and showed the average effect of the intervention on these groups. The differences between the intervention group and the control group were statistically significant in each risk group. The average predicted probabilities of cannabis use for a child from the low-risk group were 4.3% if the child had the intervention and 6.53% if no intervention was provided. The corresponding probabilities for a child from the moderate-risk group were 10.91% and 15.34% and for a child from the high-risk group 25.51% and 32.61%. School grades, thoughts of hurting oneself, and breaking the rules were the three most important factors distinguishing high-risk and low-risk children. We predicted the effect of the intervention on individual children, characterized by their risk/protective factors. The predicted absolute effect and relative effect of any intervention for any selected risk/protective profile of a given child may be utilized in both prevention practice and research. Copyright © 2015 Elsevier Ltd. All rights reserved.

Polya's bees: A model of decentralized decision-making.

PubMed

Golman, Russell; Hagmann, David; Miller, John H

2015-09-01

How do social systems make decisions with no single individual in control? We observe that a variety of natural systems, including colonies of ants and bees and perhaps even neurons in the human brain, make decentralized decisions using common processes involving information search with positive feedback and consensus choice through quorum sensing. We model this process with an urn scheme that runs until hitting a threshold, and we characterize an inherent tradeoff between the speed and the accuracy of a decision. The proposed common mechanism provides a robust and effective means by which a decentralized system can navigate the speed-accuracy tradeoff and make reasonably good, quick decisions in a variety of environments. Additionally, consensus choice exhibits systemic risk aversion even while individuals are idiosyncratically risk-neutral. This too is adaptive. The model illustrates how natural systems make decentralized decisions, illuminating a mechanism that engineers of social and artificial systems could imitate.

Polya’s bees: A model of decentralized decision-making

PubMed Central

Golman, Russell; Hagmann, David; Miller, John H.

2015-01-01

How do social systems make decisions with no single individual in control? We observe that a variety of natural systems, including colonies of ants and bees and perhaps even neurons in the human brain, make decentralized decisions using common processes involving information search with positive feedback and consensus choice through quorum sensing. We model this process with an urn scheme that runs until hitting a threshold, and we characterize an inherent tradeoff between the speed and the accuracy of a decision. The proposed common mechanism provides a robust and effective means by which a decentralized system can navigate the speed-accuracy tradeoff and make reasonably good, quick decisions in a variety of environments. Additionally, consensus choice exhibits systemic risk aversion even while individuals are idiosyncratically risk-neutral. This too is adaptive. The model illustrates how natural systems make decentralized decisions, illuminating a mechanism that engineers of social and artificial systems could imitate. PMID:26601255

Two-part random effects growth modeling to identify risks associated with alcohol and cannabis initiation, initial average use and changes in drug consumption in a sample of adult, male twins

PubMed Central

Gillespie, Nathan A.; Lubke, Gitta H.; Gardner, Charles O.; Neale, Michael C.; Kendler, Kenneth S.

2012-01-01

Aims Our aim was to profile alcohol and cannabis initiation and to characterize the effects of developmental and environmental risk factors on changes in average drug use over time. Design We fitted a two-part random effects growth model to identify developmental and environmental risks associated with alcohol and cannabis initiation, initial average use and changes in average use. Participants 1796 males aged 24–63 from the Virginia Adult Twin Study of Psychiatric and Substance Use Disorders. Measurements Data from three interview waves included self-report measures of average alcohol and cannabis use between ages 15 and 24, genetic risk of problem drug use, childhood environmental risks, personality, psychiatric symptoms, as well as personal, family and social risk factors. Findings Average alcohol and cannabis use were correlated at all ages. Genetic risk of drug use based on family history, higher sensation seeking, and peer group deviance predicted both alcohol and cannabis initiation. Higher drug availability predicted cannabis initiation while less parental monitoring and drug availability were the best predictors of how much cannabis individuals consumed over time. Conclusion The liability to initiate alcohol and cannabis, average drug use as well as changes in drug use during teenage years and young adulthood is associated with known risk factors. PMID:22177896

An Emerging New Risk Analysis Science: Foundations and Implications.

PubMed

Aven, Terje

2018-05-01

To solve real-life problems-such as those related to technology, health, security, or climate change-and make suitable decisions, risk is nearly always a main issue. Different types of sciences are often supporting the work, for example, statistics, natural sciences, and social sciences. Risk analysis approaches and methods are also commonly used, but risk analysis is not broadly accepted as a science in itself. A key problem is the lack of explanatory power and large uncertainties when assessing risk. This article presents an emerging new risk analysis science based on novel ideas and theories on risk analysis developed in recent years by the risk analysis community. It builds on a fundamental change in thinking, from the search for accurate predictions and risk estimates, to knowledge generation related to concepts, theories, frameworks, approaches, principles, methods, and models to understand, assess, characterize, communicate, and (in a broad sense) manage risk. Examples are used to illustrate the importance of this distinct/separate risk analysis science for solving risk problems, supporting science in general and other disciplines in particular. © 2017 The Authors Risk Analysis published by Wiley Periodicals, Inc. on behalf of Society for Risk Analysis.

The Apache Longbow-Hellfire Missile Test at Yuma Proving Ground: Ecological Risk Assessment for Tracked Vehicle Movement across Desert Pavement

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peterson, Mark J; Efroymson, Rebecca Ann; Hargrove, William Walter

A multiple stressor risk assessment was conducted at Yuma Proving Ground, Arizona, as a demonstration of the Military Ecological Risk Assessment Framework. The focus was a testing program at Cibola Range, which involved an Apache Longbow helicopter firing Hellfire missiles at moving targets, M60-A1 tanks. This paper describes the ecological risk assessment for the tracked vehicle movement component of the testing program. The principal stressor associated with tracked vehicle movement was soil disturbance, and a resulting, secondary stressor was hydrological change. Water loss to washes and wash vegetation was expected to result from increased infiltration and/or evaporation associated with disturbancesmore » to desert pavement. The simulated exposure of wash vegetation to water loss was quantified using estimates of exposed land area from a digital ortho quarter quad aerial photo and field observations, a 30 30 m digital elevation model, the flow accumulation feature of ESRI ArcInfo, and a two-step process in which runoff was estimated from direct precipitation to a land area and from water that flowed from upgradient to a land area. In all simulated scenarios, absolute water loss decreased with distance from the disturbance, downgradient in the washes; however, percentage water loss was greatest in land areas immediately downgradient of a disturbance. Potential effects on growth and survival of wash trees were quantified by using an empirical relationship derived from a local unpublished study of water infiltration rates. The risk characterization concluded that neither risk to wash vegetation growth or survival nor risk to mule deer abundance and reproduction was expected. The risk characterization was negative for both the incremental risk of the test program and the combination of the test and pretest disturbances.« less

Development of Relative Risk Model for Regional Groundwater Risk Assessment: A Case Study in the Lower Liaohe River Plain, China

PubMed Central

Li, Xianbo; Zuo, Rui; Teng, Yanguo; Wang, Jinsheng; Wang, Bin

2015-01-01

Increasing pressure on water supply worldwide, especially in arid areas, has resulted in groundwater overexploitation and contamination, and subsequent deterioration of the groundwater quality and threats to public health. Environmental risk assessment of regional groundwater is an important tool for groundwater protection. This study presents a new approach for assessing the environmental risk assessment of regional groundwater. It was carried out with a relative risk model (RRM) coupled with a series of indices, such as a groundwater vulnerability index, which includes receptor analysis, risk source analysis, risk exposure and hazard analysis, risk characterization, and management of groundwater. The risk map is a product of the probability of environmental contamination and impact. The reliability of the RRM was verified using Monte Carlo analysis. This approach was applied to the lower Liaohe River Plain (LLRP), northeastern China, which covers 23604 km2. A spatial analysis tool within GIS which was used to interpolate and manipulate the data to develop environmental risk maps of regional groundwater, divided the level of risk from high to low into five ranks (V, IV, III, II, I). The results indicate that areas of relative risk rank (RRR) V cover 2324 km2, covering 9.8% of the area; RRR IV covers 3986 km2, accounting for 16.9% of the area. It is a new and appropriate method for regional groundwater resource management and land use planning, and is a rapid and effective tool for improving strategic decision making to protect groundwater and reduce environmental risk. PMID:26020518

Pathway index models for construction of patient-specific risk profiles.

PubMed

Eng, Kevin H; Wang, Sijian; Bradley, William H; Rader, Janet S; Kendziorski, Christina

2013-04-30

Statistical methods for variable selection, prediction, and classification have proven extremely useful in moving personalized genomics medicine forward, in particular, leading to a number of genomic-based assays now in clinical use for predicting cancer recurrence. Although invaluable in individual cases, the information provided by these assays is limited. Most often, a patient is classified into one of very few groups (e.g., recur or not), limiting the potential for truly personalized treatment. Furthermore, although these assays provide information on which individuals are at most risk (e.g., those for which recurrence is predicted), they provide no information on the aberrant biological pathways that give rise to the increased risk. We have developed an approach to address these limitations. The approach models a time-to-event outcome as a function of known biological pathways, identifies important genomic aberrations, and provides pathway-based patient-specific assessments of risk. As we demonstrate in a study of ovarian cancer from The Cancer Genome Atlas project, the patient-specific risk profiles are powerful and efficient characterizations useful in addressing a number of questions related to identifying informative patient subtypes and predicting survival. Copyright © 2012 John Wiley & Sons, Ltd.

Extinction risk in successional landscapes subject to catastrophic disturbances.

Treesearch

David Boughton; Urmila Malvadkar

2002-01-01

We explore the thesis that stochasticity in successional-disturbance systems can be an agent of species extinction. The analysis uses a simple model of patch dynamics for seral stages in an idealized landscape; each seral stage is assumed to support a specialist biota. The landscape as a whole is characterized by a mean patch birth rate, mean patch size, and mean...

Hyporheic exchange in gravel bed rivers with pool-riffle morphology: Laboratory experiments and three-dimensional modeling

Treesearch

Daniele Tonina; John M. Buffington

2007-01-01

We report the first laboratory simulations of hyporheic exchange in gravel pool-riffle channels, which are characterized by coarse sediment, steep slopes, and three-dimensional bed forms that strongly influence surface flow. These channels are particularly important habitat for salmonids, many of which are currently at risk worldwide and which incubate their offspring...

Exploring the added value of imposing an ozone effect monotonicity constraint and of jointly modeling ozone and temperature effects in an epidemiologic study of air pollution and mortality

EPA Science Inventory

Abstract: A number of epidemiologic studies have shown that both ozone and temperature are associated with increased risk for cardio-respiratory mortality and morbidity. However, their joint effects are not characterized as well as their independent effects. Furthermore, the i...

Quantitative evaluation of the risk induced by dominant geomorphological processes on different land uses, based on GIS spatial analysis models

NASA Astrophysics Data System (ADS)

Ştefan, Bilaşco; Sanda, Roşca; Ioan, Fodorean; Iuliu, Vescan; Sorin, Filip; Dănuţ, Petrea

2017-12-01

Maramureş Land is mostly characterized by agricultural and forestry land use due to its specific configuration of topography and its specific pedoclimatic conditions. Taking into consideration the trend of the last century from the perspective of land management, a decrease in the surface of agricultural lands to the advantage of built-up and grass lands, as well as an accelerated decrease in the forest cover due to uncontrolled and irrational forest exploitation, has become obvious. The field analysis performed on the territory of Maramureş Land has highlighted a high frequency of two geomorphologic processes — landslides and soil erosion — which have a major negative impact on land use due to their rate of occurrence. The main aim of the present study is the GIS modeling of the two geomorphologic processes, determining a state of vulnerability (the USLE model for soil erosion and a quantitative model based on the morphometric characteristics of the territory, derived from the HG. 447/2003) and their integration in a complex model of cumulated vulnerability identification. The modeling of the risk exposure was performed using a quantitative approach based on models and equations of spatial analysis, which were developed with modeled raster data structures and primary vector data, through a matrix highlighting the correspondence between vulnerability and land use classes. The quantitative analysis of the risk was performed by taking into consideration the exposure classes as modeled databases and the land price as a primary alphanumeric database using spatial analysis techniques for each class by means of the attribute table. The spatial results highlight the territories with a high risk to present geomorphologic processes that have a high degree of occurrence and represent a useful tool in the process of spatial planning.

Quantitative evaluation of the risk induced by dominant geomorphological processes on different land uses, based on GIS spatial analysis models

NASA Astrophysics Data System (ADS)

Ştefan, Bilaşco; Sanda, Roşca; Ioan, Fodorean; Iuliu, Vescan; Sorin, Filip; Dănuţ, Petrea

2018-06-01

Maramureş Land is mostly characterized by agricultural and forestry land use due to its specific configuration of topography and its specific pedoclimatic conditions. Taking into consideration the trend of the last century from the perspective of land management, a decrease in the surface of agricultural lands to the advantage of built-up and grass lands, as well as an accelerated decrease in the forest cover due to uncontrolled and irrational forest exploitation, has become obvious. The field analysis performed on the territory of Maramureş Land has highlighted a high frequency of two geomorphologic processes — landslides and soil erosion — which have a major negative impact on land use due to their rate of occurrence. The main aim of the present study is the GIS modeling of the two geomorphologic processes, determining a state of vulnerability (the USLE model for soil erosion and a quantitative model based on the morphometric characteristics of the territory, derived from the HG. 447/2003) and their integration in a complex model of cumulated vulnerability identification. The modeling of the risk exposure was performed using a quantitative approach based on models and equations of spatial analysis, which were developed with modeled raster data structures and primary vector data, through a matrix highlighting the correspondence between vulnerability and land use classes. The quantitative analysis of the risk was performed by taking into consideration the exposure classes as modeled databases and the land price as a primary alphanumeric database using spatial analysis techniques for each class by means of the attribute table. The spatial results highlight the territories with a high risk to present geomorphologic processes that have a high degree of occurrence and represent a useful tool in the process of spatial planning.

How wild is your model fire? Constraining WRF-Chem wildfire smoke simulations with satellite observations

NASA Astrophysics Data System (ADS)

Fischer, E. V.; Ford, B.; Lassman, W.; Pierce, J. R.; Pfister, G.; Volckens, J.; Magzamen, S.; Gan, R.

2015-12-01

Exposure to high concentrations of particulate matter (PM) present during acute pollution events is associated with adverse health effects. While many anthropogenic pollution sources are regulated in the United States, emissions from wildfires are difficult to characterize and control. With wildfire frequency and intensity in the western U.S. projected to increase, it is important to more precisely determine the effect that wildfire emissions have on human health, and whether improved forecasts of these air pollution events can mitigate the health risks associated with wildfires. One of the challenges associated with determining health risks associated with wildfire emissions is that the low spatial resolution of surface monitors means that surface measurements may not be representative of a population's exposure, due to steep concentration gradients. To obtain better estimates of ambient exposure levels for health studies, a chemical transport model (CTM) can be used to simulate the evolution of a wildfire plume as it travels over populated regions downwind. Improving the performance of a CTM would allow the development of a new forecasting framework that could better help decision makers estimate and potentially mitigate future health impacts. We use the Weather Research and Forecasting model with online chemistry (WRF-Chem) to simulate wildfire plume evolution. By varying the model resolution, meteorology reanalysis initial conditions, and biomass burning inventories, we are able to explore the sensitivity of model simulations to these various parameters. Satellite observations are used first to evaluate model skill, and then to constrain the model results. These data are then used to estimate population-level exposure, with the aim of better characterizing the effects that wildfire emissions have on human health.

NASA's Optical Measurement Program 2014 H.

NASA Astrophysics Data System (ADS)

Cowardin, H.; Lederer, S.; Stansbery, G.; Seitzer, P.; Buckalew, B.; Abercromby, K.; Barker, E.

2014-09-01

The Optical Measurements Group (OMG) within the NASA Orbital Debris Program Office (ODPO) addresses U.S. National Space Policy goals by monitoring and characterizing debris. Since 2001, the OMG has used the Michigan Orbital Debris Survey Telescope (MODEST) at Cerro Tololo Inter-American Observatory (CTIO) in Chile for general orbital debris survey. The 0.6-m Schmidt MODEST provides calibrated astronomical data of GEO targets, both catalogued and uncatalogued debris, with excellent image quality. The data are utilized by the ODPO modeling group and are included in the Orbital Debris Engineering Model (ORDEM) v. 3.0. MODEST and the CTIO/SMARTS (Small and Moderate Aperture Research Telescope System)0.9 m both acquire filter photometric data, as well as synchronously observing targets in selected optical filters. This information provides data used in material composition studies as well as longer orbital arc data on the same target, without time delay or bias from a rotating, tumbling, or spinning target. NASA, in collaboration with the University of Michigan, began using the twin 6.5-m Magellan telescopes at Las Campanas Observatory in Chile for deep imaging (Baade) and spectroscopic data (Clay) in 2011. Through the data acquired on Baade, debris have been detected that are ~3 magnitudes fainter than detections with MODEST, while the data from Clay provide better resolved information used in material characterization analyses via selected bandpasses. To better characterize and model optical data, the Optical Measurements Center (OMC) at NASA/JSC has been in operation since 2005, resulting in a database of comparison laboratory data. The OMC is designed to emulate illumination conditions in space using equipment and techniques that parallel telescopic observations and source-target-sensor orientations. Lastly, the OMG is building the Meter Class Autonomous Telescope (MCAT) at Ascension Island. The 1.3-m telescope is designed to observe GEO and LEO targets, using a modified Ritchey-Chrétien configuration on a double horseshoe equatorial mount to allow tracking objects at LEO rates through the domes keyhole at zenith. Through the data collection techniques employed at these unique facilities, NASAs ODPO has developed a multi-faceted approach to characterize the orbital debris risk to satellites in various altitudes and provide material characterization of debris via photometric and spectroscopic measurements. Ultimately, the data are used in conjunction with in-situ and radar measurements to provide accurate data for models of our space environment and service spacecraft risk assessment.

Vadose zone transport field study: Detailed test plan for simulated leak tests

DOE Office of Scientific and Technical Information (OSTI.GOV)

AL Ward; GW Gee

2000-06-23

The US Department of Energy (DOE) Groundwater/Vadose Zone Integration Project Science and Technology initiative was created in FY 1999 to reduce the uncertainty associated with vadose zone transport processes beneath waste sites at DOE's Hanford Site near Richland, Washington. This information is needed not only to evaluate the risks from transport, but also to support the adoption of measures for minimizing impacts to the groundwater and surrounding environment. The principal uncertainties in vadose zone transport are the current distribution of source contaminants and the natural heterogeneity of the soil in which the contaminants reside. Oversimplified conceptual models resulting from thesemore » uncertainties and limited use of hydrologic characterization and monitoring technologies have hampered the understanding contaminant migration through Hanford's vadose zone. Essential prerequisites for reducing vadose transport uncertainly include the development of accurate conceptual models and the development or adoption of monitoring techniques capable of delineating the current distributions of source contaminants and characterizing natural site heterogeneity. The Vadose Zone Transport Field Study (VZTFS) was conceived as part of the initiative to address the major uncertainties confronting vadose zone fate and transport predictions at the Hanford Site and to overcome the limitations of previous characterization attempts. Pacific Northwest National Laboratory (PNNL) is managing the VZTFS for DOE. The VZTFS will conduct field investigations that will improve the understanding of field-scale transport and lead to the development or identification of efficient and cost-effective characterization methods. Ideally, these methods will capture the extent of contaminant plumes using existing infrastructure (i.e., more than 1,300 steel-cased boreholes). The objectives of the VZTFS are to conduct controlled transport experiments at well-instrumented field sites at Hanford to: identify mechanisms controlling transport processes in soils typical of the hydrogeologic conditions of Hanford's waste disposal sites; reduce uncertainty in conceptual models; develop a detailed and accurate database of hydraulic and transport parameters for validation of three-dimensional numerical models; identify and evaluate advanced, cost-effective characterization methods with the potential to assess changing conditions in the vadose zone, particularly as surrogates of currently undetectable high-risk contaminants. This plan provides details for conducting field tests during FY 2000 to accomplish these objectives. Details of additional testing during FY 2001 and FY 2002 will be developed as part of the work planning process implemented by the Integration Project.« less

NASA's Optical Measurement Program 2014

NASA Technical Reports Server (NTRS)

Cowardin, H.; Lederer, S.; Stansbery, G.; Seitzer, P.; Buckalew, B.; Abercromby, K.; Barker, E.

2014-01-01

The Optical Measurements Group (OMG) within the NASA Orbital Debris Program Office (ODPO) addresses U.S. National Space Policy goals by monitoring and characterizing debris. Since 2001, the OMG has used the Michigan Orbital Debris Survey Telescope (MODEST) at Cerro Tololo Inter-American Observatory (CTIO) in Chile for general orbital debris survey. The 0.6-m Schmidt MODEST provides calibrated astronomical data of GEO targets, both catalogued and uncatalogued debris, with excellent image quality. The data are utilized by the ODPO modeling group and are included in the Orbital Debris Engineering Model (ORDEM) v. 3.0. MODEST and the CTIO/SMARTS (Small and Moderate Aperture Research Telescope System) 0.9 m both acquire filter photometric data, as well as synchronously observing targets in selected optical filters. This information provides data used in material composition studies as well as longer orbital arc data on the same target, without time delay or bias from a rotating, tumbling, or spinning target. NASA, in collaboration with the University of Michigan, began using the twin 6.5-m Magellan telescopes at Las Campanas Observatory in Chile for deep imaging (Baade) and spectroscopic data (Clay) in 2011. Through the data acquired on Baade, debris have been detected that are 3 magnitudes fainter than detections with MODEST, while the data from Clay provide better resolved information used in material characterization analyses via selected bandpasses. To better characterize and model optical data, the Optical Measurements Center (OMC) at NASA/JSC has been in operation since 2005, resulting in a database of comparison laboratory data. The OMC is designed to emulate illumination conditions in space using equipment and techniques that parallel telescopic observations and source-target-sensor orientations. Lastly, the OMG is building the Meter Class Autonomous Telescope (MCAT) at Ascension Island. The 1.3-m telescope is designed to observe GEO and LEO targets, using a modified Ritchey-Chrétien configuration on a double horseshoe equatorial mount to allow tracking objects at LEO rates through the dome's keyhole at zenith. Through the data collection techniques employed at these unique facilities, NASA's ODPO has developed a multi-faceted approach to characterize the orbital debris risk to satellites in various altitudes and provide material characterization of debris via photometric and spectroscopic measurements. Ultimately, the data are used in conjunction with in-situ and radar measurements to provide accurate data for models of our space environment and service spacecraft risk assessment.

NASA's Optical Measurement Program 2014

NASA Technical Reports Server (NTRS)

Cowardin, H.; Lederer, S. M.; Stansbery, G.; Seitzer, P.; Buckalew, B.; Abercromby, K.; Barker, E.

2014-01-01

The Optical Measurements Group (OMG) within the NASA Orbital Debris Program Office (ODPO) addresses U.S. National Space Policy goals by monitoring and characterizing debris. Since 2001, the OMG has used the Michigan Orbital Debris Survey Telescope (MODEST) at Cerro Tololo Inter-American Observatory (CTIO) in Chile for general orbital debris surveys. The 0.6-m Schmidt MODEST provides calibrated astronomical data of GEO targets, both catalogued and uncatalogued debris, with excellent image quality. The data are utilized by the ODPO modeling group and are included in the Orbital Debris Engineering Model (ORDEM) v. 3.0. MODEST and the CTIO/SMARTS (Small and Moderate Aperture Research Telescope System) 0.9 m are both employed to acquire filter photometry data as well as synchronously observe targets in selected optical filters. Obtaining data synchronously yields data for material composition studies as well as longer orbital arc data on the same target without time delay or bias from a rotating, tumbling, or spinning target. Observations of GEO orbital debris using the twin 6.5-m Magellan telescopes at Las Campanas Observatory in Chile for deep imaging (Baade) and spectroscopic data (Clay) began in 2011. Through the data acquired on Baade, debris has been detected that reaches approx. 3 magnitudes fainter than detections with MODEST, while the spectral data from Clay provide better resolved information used in material characterization analyses. To better characterize and model optical data, the Optical Measurements Center (OMC) at NASA/JSC has been in operation since 2005, resulting in a database of comparison laboratory data. The OMC is designed to emulate illumination conditions in space using equipment and techniques that parallel telescopic observations and sourcetarget- sensor orientations. Lastly, the OMG is building the Meter Class Autonomous Telescope (MCAT) at Ascension Island. The 1.3-m telescope is designed to observe GEO and LEO targets, using a modified Ritchey-Chrétien configuration on a double horseshoe equatorial mount to allow tracking objects at LEO rates through the dome's keyhole at zenith. Through the data collection techniques employed at these unique facilities, NASA's ODPO has developed a multifaceted approach to characterize the orbital debris risk to satellites in various altitudes and provide insight leading toward material characterization of debris via photometric and spectroscopic measurements. Ultimately, the data are used in conjunction with in-situ and radar measurements to provide accurate data for models of our space environment and for facilitating spacecraft risk assessment.

Improving risk models for avian influenza: the role of intensive poultry farming and flooded land during the 2004 Thailand epidemic.

PubMed

Van Boeckel, Thomas P; Thanapongtharm, Weerapong; Robinson, Timothy; Biradar, Chandrashekhar M; Xiao, Xiangming; Gilbert, Marius

2012-01-01

Since 1996 when Highly Pathogenic Avian Influenza type H5N1 first emerged in southern China, numerous studies sought risk factors and produced risk maps based on environmental and anthropogenic predictors. However little attention has been paid to the link between the level of intensification of poultry production and the risk of outbreak. This study revised H5N1 risk mapping in Central and Western Thailand during the second wave of the 2004 epidemic. Production structure was quantified using a disaggregation methodology based on the number of poultry per holding. Population densities of extensively- and intensively-raised ducks and chickens were derived both at the sub-district and at the village levels. LandSat images were used to derive another previously neglected potential predictor of HPAI H5N1 risk: the proportion of water in the landscape resulting from floods. We used Monte Carlo simulation of Boosted Regression Trees models of predictor variables to characterize the risk of HPAI H5N1. Maps of mean risk and uncertainty were derived both at the sub-district and the village levels. The overall accuracy of Boosted Regression Trees models was comparable to that of logistic regression approaches. The proportion of area flooded made the highest contribution to predicting the risk of outbreak, followed by the densities of intensively-raised ducks, extensively-raised ducks and human population. Our results showed that as little as 15% of flooded land in villages is sufficient to reach the maximum level of risk associated with this variable. The spatial pattern of predicted risk is similar to previous work: areas at risk are mainly located along the flood plain of the Chao Phraya river and to the south-east of Bangkok. Using high-resolution village-level poultry census data, rather than sub-district data, the spatial accuracy of predictions was enhanced to highlight local variations in risk. Such maps provide useful information to guide intervention.

Improving Risk Models for Avian Influenza: The Role of Intensive Poultry Farming and Flooded Land during the 2004 Thailand Epidemic

PubMed Central

Van Boeckel, Thomas P.; Thanapongtharm, Weerapong; Robinson, Timothy; Biradar, Chandrashekhar M.; Xiao, Xiangming; Gilbert, Marius

2012-01-01

Since 1996 when Highly Pathogenic Avian Influenza type H5N1 first emerged in southern China, numerous studies sought risk factors and produced risk maps based on environmental and anthropogenic predictors. However little attention has been paid to the link between the level of intensification of poultry production and the risk of outbreak. This study revised H5N1 risk mapping in Central and Western Thailand during the second wave of the 2004 epidemic. Production structure was quantified using a disaggregation methodology based on the number of poultry per holding. Population densities of extensively- and intensively-raised ducks and chickens were derived both at the sub-district and at the village levels. LandSat images were used to derive another previously neglected potential predictor of HPAI H5N1 risk: the proportion of water in the landscape resulting from floods. We used Monte Carlo simulation of Boosted Regression Trees models of predictor variables to characterize the risk of HPAI H5N1. Maps of mean risk and uncertainty were derived both at the sub-district and the village levels. The overall accuracy of Boosted Regression Trees models was comparable to that of logistic regression approaches. The proportion of area flooded made the highest contribution to predicting the risk of outbreak, followed by the densities of intensively-raised ducks, extensively-raised ducks and human population. Our results showed that as little as 15% of flooded land in villages is sufficient to reach the maximum level of risk associated with this variable. The spatial pattern of predicted risk is similar to previous work: areas at risk are mainly located along the flood plain of the Chao Phraya river and to the south-east of Bangkok. Using high-resolution village-level poultry census data, rather than sub-district data, the spatial accuracy of predictions was enhanced to highlight local variations in risk. Such maps provide useful information to guide intervention. PMID:23185352

Clean Energy Finance: Challenges and Opportunities of Early-Stage Energy Investing (Presentation)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heap, D.; Pless, J.; Aieta, N.

Characterized by a changing landscape and new opportunities, today's increasingly complex energy decision space will need innovative financing and investment models to appropriately assess risk and profitability. This report provides an overview of the current state of clean energy finance across the entire spectrum but with a focus on early stage investing, and it includes insights from investors across all investment classes. Further, this report aims to provide a roadmap with the mechanisms, limitations, and considerations involved in making successful investments by identifying risks, challenges, and opportunities in the clean energy sector.

Risk-Hedged Approach for Re-Routing Air Traffic Under Weather Uncertainty

NASA Technical Reports Server (NTRS)

Sadovsky, Alexander V.; Bilimoria, Karl D.

2016-01-01

This presentation corresponds to: our paper explores a new risk-hedged approach for re-routing air traffic around forecast convective weather. In this work, flying through a more likely weather instantiation is considered to pose a higher level of risk. Current operational practice strategically plans re-routes to avoid only the most likely (highest risk) weather instantiation, and then tactically makes any necessary adjustments as the weather evolves. The risk-hedged approach strategically plans re-routes by minimizing the risk-adjusted path length, incorporating multiple possible weather instantiations with associated likelihoods (risks). The resulting model is transparent and is readily analyzed for realism and treated with well-understood shortest-path algorithms. Risk-hedged re-routes are computed for some example weather instantiations. The main result is that in some scenarios, relative to an operational-practice proxy solution, the risk-hedged solution provides the benefits of lower risk as well as shorter path length. In other scenarios, the benefits of the risk-hedged solution are ambiguous, because the solution is characterized by a tradeoff between risk and path length. The risk-hedged solution can be executed in those scenarios where it provides a clear benefit over current operational practice.

Developmental vitamin D deficiency and schizophrenia: the role of animal models

PubMed Central

Schoenrock, S. A.; Tarantino, L. M.

2016-01-01

Schizophrenia is a debilitating neuropsychiatric disorder that affects 1% of the US population. Based on twin and genome-wide association studies, it is clear that both genetics and environmental factors increase the risk for developing schizophrenia. Moreover, there is evidence that conditions in utero, either alone or in concert with genetic factors, may alter neurodevelopment and lead to an increased risk for schizophrenia. There has been progress in identifying genetic loci and environmental exposures that increase risk, but there are still considerable gaps in our knowledge. Furthermore, very little is known about the specific neurodevelopmental mechanisms upon which genetics and the environment act to increase disposition to developing schizophrenia in adulthood. Vitamin D deficiency during the perinatal period has been hypothesized to increase risk for schizophrenia in humans. The developmental vitamin D (DVD) deficiency hypothesis of schizophrenia arises from the observation that disease risk is increased in individuals who are born in winter or spring, live further from the equator or live in urban vs. rural settings. These environments result in less exposure to sunlight, thereby reducing the initial steps in the production of vitamin D. Rodent models have been developed to characterize the behavioral and developmental effects of DVD deficiency. This review focuses on these animal models and discusses the current knowledge of the role of DVD deficiency in altering behavior and neurobiology relevant to schizophrenia. PMID:26560996

Patient-derived xenografts as preclinical neuroblastoma models.

PubMed

Braekeveldt, Noémie; Bexell, Daniel

2018-05-01

The prognosis for children with high-risk neuroblastoma is often poor and survivors can suffer from severe side effects. Predictive preclinical models and novel therapeutic strategies for high-risk disease are therefore a clinical imperative. However, conventional cancer cell line-derived xenografts can deviate substantially from patient tumors in terms of their molecular and phenotypic features. Patient-derived xenografts (PDXs) recapitulate many biologically and clinically relevant features of human cancers. Importantly, PDXs can closely parallel clinical features and outcome and serve as excellent models for biomarker and preclinical drug development. Here, we review progress in and applications of neuroblastoma PDX models. Neuroblastoma orthotopic PDXs share the molecular characteristics, neuroblastoma markers, invasive properties and tumor stroma of aggressive patient tumors and retain spontaneous metastatic capacity to distant organs including bone marrow. The recent identification of genomic changes in relapsed neuroblastomas opens up opportunities to target treatment-resistant tumors in well-characterized neuroblastoma PDXs. We highlight and discuss the features and various sources of neuroblastoma PDXs, methodological considerations when establishing neuroblastoma PDXs, in vitro 3D models, current limitations of PDX models and their application to preclinical drug testing.

To kill a kangaroo: understanding the decision to pursue high-risk/high-gain resources.

PubMed

Jones, James Holland; Bird, Rebecca Bliege; Bird, Douglas W

2013-09-22

In this paper, we attempt to understand hunter-gatherer foraging decisions about prey that vary in both the mean and variance of energy return using an expected utility framework. We show that for skewed distributions of energetic returns, the standard linear variance discounting (LVD) model for risk-sensitive foraging can produce quite misleading results. In addition to creating difficulties for the LVD model, the skewed distributions characteristic of hunting returns create challenges for estimating probability distribution functions required for expected utility. We present a solution using a two-component finite mixture model for foraging returns. We then use detailed foraging returns data based on focal follows of individual hunters in Western Australia hunting for high-risk/high-gain (hill kangaroo) and relatively low-risk/low-gain (sand monitor) prey. Using probability densities for the two resources estimated from the mixture models, combined with theoretically sensible utility curves characterized by diminishing marginal utility for the highest returns, we find that the expected utility of the sand monitors greatly exceeds that of kangaroos despite the fact that the mean energy return for kangaroos is nearly twice as large as that for sand monitors. We conclude that the decision to hunt hill kangaroos does not arise simply as part of an energetic utility-maximization strategy and that additional social, political or symbolic benefits must accrue to hunters of this highly variable prey.

Landslide risk mitigation by means of early warning systems

NASA Astrophysics Data System (ADS)

Calvello, Michele

2017-04-01

Among the many options available to mitigate landslide risk, early warning systems may be used where, in specific circumstances, the risk to life increases above tolerable levels. A coherent framework to classify and analyse landslide early warning systems (LEWS) is herein presented. Once the objectives of an early warning strategy are defined depending on the scale of analysis and the type of landslides to address, the process of designing and managing a LEWS should synergically employ technical and social skills. A classification scheme for the main components of LEWSs is proposed for weather-induced landslides. The scheme is based on a clear distinction among: i) the landslide model, i.e. a functional relationship between weather characteristics and landslide events considering the geotechnical, geomorphological and hydro-geological characterization of the area as well as an adequate monitoring strategy; ii) the warning model, i.e. the landslide model plus procedures to define the warning events and to issue the warnings; iii) the warning system, i.e. the warning model plus warning dissemination procedures, communication and education tools, strategies for community involvement and emergency plans. Each component of a LEWS is related to a number of actors involved with their deployment, operational activities and management. For instance, communication and education, community involvement and emergency plans are all significantly influenced by people's risk perception and by operational aspects system managers need to address in cooperation with scientists.

Exposure‐Response Model of Subcutaneous C1‐Inhibitor Concentrate to Estimate the Risk of Attacks in Patients With Hereditary Angioedema

PubMed Central

Tortorici, Michael A.; Pawaskar, Dipti; Pragst, Ingo; Machnig, Thomas; Hutmacher, Matthew; Zuraw, Bruce; Cicardi, Marco; Craig, Timothy; Longhurst, Hilary; Sidhu, Jagdev

2018-01-01

Subcutaneous C1‐inhibitor (HAEGARDA, CSL Behring), is a US Food and Drug Administration (FDA)‐approved, highly concentrated formulation of a plasma‐derived C1‐esterase inhibitor (C1‐INH), which, in the phase III Clinical Studies for Optimal Management in Preventing Angioedema with Low‐Volume Subcutaneous C1‐inhibitor Replacement Therapy (COMPACT) trial, reduced the incidence of hereditary angioedema (HAE) attacks when given prophylactically. Data from the COMPACT trial were used to develop a repeated time‐to‐event model to characterize the timing and frequency of HAE attacks as a function of C1‐INH activity, and then develop an exposure–response model to assess the relationship between C1‐INH functional activity levels (C1‐INH(f)) and the risk of an attack. The C1‐INH(f) values of 33.1%, 40.3%, and 63.1% were predicted to correspond with 50%, 70%, and 90% reductions in the HAE attack risk, respectively, relative to no therapy. Based on trough C1‐INH(f) values for the 40 IU/kg (40.2%) and 60 IU/kg (48.0%) C1‐INH (SC) doses, the model predicted that 50% and 67% of the population, respectively, would see at least a 70% decrease in the risk of an attack. PMID:29316335

Mind the Gap: Exploring the Underground of the NASA Space Cancer Risk Model

NASA Technical Reports Server (NTRS)

Chappell, L. J.; Elgart, S. R.; Milder, C. M.; Shavers, M. R.; Semones, E. J.; Huff, J. L.

2017-01-01

The REID quantifies the lifetime risk of death from radiation-induced cancer in an exposed astronaut. The NASA Space Cancer Risk (NSCR) 2012 mode incorporates elements from physics, biology, epidemiology, and statistics to generate the REID distribution. The current model quantifies the space radiation environment, radiation quality, and dose-rate effects to estimate a NASA-weighted dose. This weighted dose is mapped to the excess risk of radiation-induced cancer mortality from acute exposures to gamma rays and then transferred to an astronaut population. Finally, the REID is determined by integrating this risk over the individual's lifetime. The calculated upper 95% confidence limit of the REID is used to restrict an astronaut's permissible mission duration (PMD) for a proposed mission. As a statistical quantity characterized by broad, subjective uncertainties, REID estimates for space missions result in wide distributions. Currently, the upper 95% confidence level is over 350% larger than the mean REID value, which can severely limit an astronaut's PMD. The model incorporates inputs from multiple scientific disciplines in the risk estimation process. Physics and particle transport models calculate how radiation moves through space, penetrates spacecraft, and makes its way to the human beings onboard. Epidemiological studies of exposures from atomic bombings, medical treatments, and power plants are used to quantify health risks from acute and chronic low linear energy transfer (LET) ionizing radiation. Biological studies in cellular and animal models using radiation at various LETs and energies inform quality metrics for ions present in space radiation. Statistical methodologies unite these elements, controlling for mathematical and scientific uncertainty and variability. Despite current progress, these research platforms contain knowledge gaps contributing to the large uncertainties still present in the model. The NASA Space Radiation Program Element (SRPE) defines the knowledge gaps that impact our understanding of the cancer risks. These gaps are outlined in NASA's Human Research Roadmap [4], which identifies the research questions and actions recommended for reducing the uncertainty in the current NSCR model and for formulation of future models. The greatest contributors to uncertainty in the current model include radiation quality, dose rate effects, and the transfer of exposure-based risk from other populations to an astronaut population. Future formulations of the risk model may benefit from including other potential sources of uncertainty such as space dosimetry, errors in human epidemiology data, and the impact of microgravity and other spaceflight stressors. Here, we discuss the current capabilities of the NSCR-2012 model and several immediate research needs, highlighting areas expected to have an operational impact on the current model schema. The following subway-style route map outlines the NSCR-2012 model (Green Line), emphasizing the research gaps in the Human Research Roadmap for risk of radiation-induced carcinogenesis (Stops on Dashed Lines). The map diagrams how these research gaps feed specific portions of the model.

Empirically evaluating decision-analytic models.

PubMed

Goldhaber-Fiebert, Jeremy D; Stout, Natasha K; Goldie, Sue J

2010-08-01

Model-based cost-effectiveness analyses support decision-making. To augment model credibility, evaluation via comparison to independent, empirical studies is recommended. We developed a structured reporting format for model evaluation and conducted a structured literature review to characterize current model evaluation recommendations and practices. As an illustration, we applied the reporting format to evaluate a microsimulation of human papillomavirus and cervical cancer. The model's outputs and uncertainty ranges were compared with multiple outcomes from a study of long-term progression from high-grade precancer (cervical intraepithelial neoplasia [CIN]) to cancer. Outcomes included 5 to 30-year cumulative cancer risk among women with and without appropriate CIN treatment. Consistency was measured by model ranges overlapping study confidence intervals. The structured reporting format included: matching baseline characteristics and follow-up, reporting model and study uncertainty, and stating metrics of consistency for model and study results. Structured searches yielded 2963 articles with 67 meeting inclusion criteria and found variation in how current model evaluations are reported. Evaluation of the cervical cancer microsimulation, reported using the proposed format, showed a modeled cumulative risk of invasive cancer for inadequately treated women of 39.6% (30.9-49.7) at 30 years, compared with the study: 37.5% (28.4-48.3). For appropriately treated women, modeled risks were 1.0% (0.7-1.3) at 30 years, study: 1.5% (0.4-3.3). To support external and projective validity, cost-effectiveness models should be iteratively evaluated as new studies become available, with reporting standardized to facilitate assessment. Such evaluations are particularly relevant for models used to conduct comparative effectiveness analyses.

Potential for adult-based epidemiological studies to characterize overall cancer risks associated with a lifetime of CT scans.

PubMed

Shuryak, Igor; Lubin, Jay H; Brenner, David J

2014-06-01

Recent epidemiological studies have suggested that radiation exposure from pediatric CT scanning is associated with small excess cancer risks. However, the majority of CT scans are performed on adults, and most radiation-induced cancers appear during middle or old age, in the same age range as background cancers. Consequently, a logical next step is to investigate the effects of CT scanning in adulthood on lifetime cancer risks by conducting adult-based, appropriately designed epidemiological studies. Here we estimate the sample size required for such studies to detect CT-associated risks. This was achieved by incorporating different age-, sex-, time- and cancer type-dependent models of radiation carcinogenesis into an in silico simulation of a population-based cohort study. This approach simulated individual histories of chest and abdominal CT exposures, deaths and cancer diagnoses. The resultant sample sizes suggest that epidemiological studies of realistically sized cohorts can detect excess lifetime cancer risks from adult CT exposures. For example, retrospective analysis of CT exposure and cancer incidence data from a population-based cohort of 0.4 to 1.3 million (depending on the carcinogenic model) CT-exposed UK adults, aged 25-65 in 1980 and followed until 2015, provides 80% power for detecting cancer risks from chest and abdominal CT scans.

Characterizing areas of potential human exposure to eastern equine encephalitis virus using serological and clinical data from horses.

PubMed

Rocheleau, J-P; Arsenault, J; Ogden, N H; Lindsay, L R; Drebot, M; Michel, P

2017-03-01

Eastern equine encephalitis (EEE) is a rare but severe emerging vector-borne disease affecting human and animal populations in the northeastern United States where it is endemic. Key knowledge gaps remain about the epidemiology of EEE virus (EEEV) in areas where its emergence has more recently been reported. In Eastern Canada, viral activity has been recorded in mosquitoes and horses throughout the 2000s but cases of EEEV in humans have not been reported so far. This study was designed to provide an assessment of possible EEEV human exposure by modelling environmental risk factors for EEEV in horses, identifying high-risk environments and mapping risk in the province of Quebec, Canada. According to logistic models, being located near wooded swamps was a risk factor for seropositivity or disease in horses [odds ratio (OR) 4·15, 95% confidence interval (CI) 1·16-14·8) whereas being located on agricultural lands was identified as protective (OR 0·75, 95% CI 0·62-0·92). A better understanding of the environmental risk of exposure to EEEV in Canada provides veterinary and public health officials with enhanced means to more effectively monitor the emergence of this public health risk and design targeted surveillance and preventive measures.

Characterization of Burkholderia pseudomallei Strains Using a Murine Intraperitoneal Infection Model and In Vitro Macrophage Assays.

PubMed

Welkos, Susan L; Klimko, Christopher P; Kern, Steven J; Bearss, Jeremy J; Bozue, Joel A; Bernhards, Robert C; Trevino, Sylvia R; Waag, David M; Amemiya, Kei; Worsham, Patricia L; Cote, Christopher K

2015-01-01

Burkholderia pseudomallei, the etiologic agent of melioidosis, is a gram-negative facultative intracellular bacterium. This bacterium is endemic in Southeast Asia and Northern Australia and can infect humans and animals by several routes. It has also been estimated to present a considerable risk as a potential biothreat agent. There are currently no effective vaccines for B. pseudomallei, and antibiotic treatment can be hampered by nonspecific symptomology, the high incidence of naturally occurring antibiotic resistant strains, and disease chronicity. Accordingly, there is a concerted effort to better characterize B. pseudomallei and its associated disease. Before novel vaccines and therapeutics can be tested in vivo, a well characterized animal model is essential. Previous work has indicated that mice may be a useful animal model. In order to develop standardized animal models of melioidosis, different strains of bacteria must be isolated, propagated, and characterized. Using a murine intraperitoneal (IP) infection model, we tested the virulence of 11 B. pseudomallei strains. The IP route offers a reproducible way to rank virulence that can be readily reproduced by other laboratories. This infection route is also useful in distinguishing significant differences in strain virulence that may be masked by the exquisite susceptibility associated with other routes of infection (e.g., inhalational). Additionally, there were several pathologic lesions observed in mice following IP infection. These included varisized abscesses in the spleen, liver, and haired skin. This model indicated that commonly used laboratory strains of B. pseudomallei (i.e., K96243 and 1026b) were significantly less virulent as compared to more recently acquired clinical isolates. Additionally, we characterized in vitro strain-associated differences in virulence for macrophages and described a potential inverse relationship between virulence in the IP mouse model of some strains and in the macrophage phagocytosis assay. Strains which were more virulent for mice (e.g., HBPU10304a) were often less virulent in the macrophage assays, as determined by several parameters such as intracellular bacterial replication and host cell cytotoxicity.

Ecological risk assessment of landfill air emissions from a hazardous waste management facility in Ontario

DOE Office of Scientific and Technical Information (OSTI.GOV)

Durda, J.L.; Suit-Kowalski, L.; Preziosi, D.

1997-12-31

An ecological risk assessment was conducted to evaluate the potential for adverse environmental impacts associated with chemicals released to air as a result of a proposed expansion of a hazardous waste landfill in Ontario. The purpose of the risk assessment was to characterize ecological risks associated with the proposed expansion relative to those associated with the existing landfill and those that would exist if the current landfill was completely closed and background conditions prevailed. The ecological risk assessment was one part of a comprehensive environmental impact assessment of the proposed landfill continuation that was being performed under the requirements ofmore » Ontario`s Environmental Assessment Act. Air monitoring data from the facility were used to identify a list of 141 chemicals potentially released during landfill continuation, as well as to characterize current emissions and background chemical levels. An ecological risk-based chemical screening process that considered background concentration, source strength, environmental partitioning, bioaccumulation potential, and toxicity was used to select a group of 23 chemicals for detailed evaluation in the ecological risk assessment. Dispersion, deposition, partitioning and bioaccumulation modeling were used to predict potential exposures in ecological receptors. Receptors were selected for evaluation based on regional habitat characteristics, exposure potential, toxicant sensitivity, ecological significance, population status, and societal value. Livestock and agricultural crop and pasture species were key receptors for the assessment, given the highly agricultural nature of the study area. In addition, native wildlife species, including the endangered Henslow`s sparrow and the regionally vulnerable pugnose minnow, also were considered.« less

Characterizing Genetic Susceptibility to Breast Cancer in Women of African Ancestry.

PubMed

Feng, Ye; Rhie, Suhn Kyong; Huo, Dezheng; Ruiz-Narvaez, Edward A; Haddad, Stephen A; Ambrosone, Christine B; John, Esther M; Bernstein, Leslie; Zheng, Wei; Hu, Jennifer J; Ziegler, Regina G; Nyante, Sarah; Bandera, Elisa V; Ingles, Sue A; Press, Michael F; Deming, Sandra L; Rodriguez-Gil, Jorge L; Zheng, Yonglan; Yao, Song; Han, Yoo-Jeong; Ogundiran, Temidayo O; Rebbeck, Timothy R; Adebamowo, Clement; Ojengbede, Oladosu; Falusi, Adeyinka G; Hennis, Anselm; Nemesure, Barbara; Ambs, Stefan; Blot, William; Cai, Qiuyin; Signorello, Lisa; Nathanson, Katherine L; Lunetta, Kathryn L; Sucheston-Campbell, Lara E; Bensen, Jeannette T; Chanock, Stephen J; Marchand, Loic Le; Olshan, Andrew F; Kolonel, Laurence N; Conti, David V; Coetzee, Gerhard A; Stram, Daniel O; Olopade, Olufunmilayo I; Palmer, Julie R; Haiman, Christopher A

2017-07-01

Background: Genome-wide association studies have identified approximately 100 common genetic variants associated with breast cancer risk, the majority of which were discovered in women of European ancestry. Because of different patterns of linkage disequilibrium, many of these genetic markers may not represent signals in populations of African ancestry. Methods: We tested 74 breast cancer risk variants and conducted fine-mapping of these susceptibility regions in 6,522 breast cancer cases and 7,643 controls of African ancestry from three genetic consortia (AABC, AMBER, and ROOT). Results: Fifty-four of the 74 variants (73%) were found to have ORs that were directionally consistent with those previously reported, of which 12 were nominally statistically significant ( P < 0.05). Through fine-mapping, in six regions ( 3p24, 12p11, 14q13, 16q12/FTO, 16q23, 19p13 ), we observed seven markers that better represent the underlying risk variant for overall breast cancer or breast cancer subtypes, whereas in another two regions ( 11q13, 16q12/TOX3 ), we identified suggestive evidence of signals that are independent of the reported index variant. Overlapping chromatin features and regulatory elements suggest that many of the risk alleles lie in regions with biological functionality. Conclusions: Through fine-mapping of known susceptibility regions, we have revealed alleles that better characterize breast cancer risk in women of African ancestry. Impact: The risk alleles identified represent genetic markers for modeling and stratifying breast cancer risk in women of African ancestry. Cancer Epidemiol Biomarkers Prev; 26(7); 1016-26. ©2017 AACR . ©2017 American Association for Cancer Research.

Cross-Sectional Association between Length of Incarceration and Selected Risk Factors for Non-Communicable Chronic Diseases in Two Male Prisons of Mexico City

PubMed Central

Silverman-Retana, Omar; Lopez-Ridaura, Ruy; Servan-Mori, Edson; Bautista-Arredondo, Sergio; Bertozzi, Stefano M.

2015-01-01

Background Mexico City prisons are characterized by overcrowded facilities and poor living conditions for housed prisoners. Chronic disease profile is characterized by low prevalence of self reported hypertension (2.5%) and diabetes (1.8%) compared to general population; 9.5% of male inmates were obese. There is limited evidence regarding on the exposure to prison environment over prisoner’s health status; particularly, on cardiovascular disease risk factors. The objective of this study is to assess the relationship between length of incarceration and selected risk factors for non-communicable chronic diseases (NCDs). Methods and Findings We performed a cross-sectional analysis using data from two large male prisons in Mexico City (n = 14,086). Using quantile regression models we assessed the relationship between length of incarceration and selected risk factors for NCDs; stratified analysis by age at admission to prison was performed. We found a significant negative trend in BMI and WC across incarceration length quintiles. BP had a significant positive trend with a percentage change increase around 5% mmHg. The greatest increase in systolic blood pressure was observed in the older age at admission group. Conclusions This analysis provides insight into the relationship between length of incarceration and four selected risk factors for NCDs; screening for high blood pressure should be guarantee in order to identify at risk individuals and linked to the prison’s health facility. It is important to assess prison environment features to approach potential risk for developing NCDs in this context. PMID:26381399

Expert review on poliovirus immunity and transmission.

PubMed

Duintjer Tebbens, Radboud J; Pallansch, Mark A; Chumakov, Konstantin M; Halsey, Neal A; Hovi, Tapani; Minor, Philip D; Modlin, John F; Patriarca, Peter A; Sutter, Roland W; Wright, Peter F; Wassilak, Steven G F; Cochi, Stephen L; Kim, Jong-Hoon; Thompson, Kimberly M

2013-04-01

Successfully managing risks to achieve wild polioviruses (WPVs) eradication and address the complexities of oral poliovirus vaccine (OPV) cessation to stop all cases of paralytic poliomyelitis depends strongly on our collective understanding of poliovirus immunity and transmission. With increased shifting from OPV to inactivated poliovirus vaccine (IPV), numerous risk management choices motivate the need to understand the tradeoffs and uncertainties and to develop models to help inform decisions. The U.S. Centers for Disease Control and Prevention hosted a meeting of international experts in April 2010 to review the available literature relevant to poliovirus immunity and transmission. This expert review evaluates 66 OPV challenge studies and other evidence to support the development of quantitative models of poliovirus transmission and potential outbreaks. This review focuses on characterization of immunity as a function of exposure history in terms of susceptibility to excretion, duration of excretion, and concentration of excreted virus. We also discuss the evidence of waning of host immunity to poliovirus transmission, the relationship between the concentration of poliovirus excreted and infectiousness, the importance of different transmission routes, and the differences in transmissibility between OPV and WPV. We discuss the limitations of the available evidence for use in polio risk models, and conclude that despite the relatively large number of studies on immunity, very limited data exist to directly support quantification of model inputs related to transmission. Given the limitations in the evidence, we identify the need for expert input to derive quantitative model inputs from the existing data. © 2012 Society for Risk Analysis.

Global review of open access risk assessment software packages valid for global or continental scale analysis

NASA Astrophysics Data System (ADS)

Daniell, James; Simpson, Alanna; Gunasekara, Rashmin; Baca, Abigail; Schaefer, Andreas; Ishizawa, Oscar; Murnane, Rick; Tijssen, Annegien; Deparday, Vivien; Forni, Marc; Himmelfarb, Anne; Leder, Jan

2015-04-01

Over the past few decades, a plethora of open access software packages for the calculation of earthquake, volcanic, tsunami, storm surge, wind and flood have been produced globally. As part of the World Bank GFDRR Review released at the Understanding Risk 2014 Conference, over 80 such open access risk assessment software packages were examined. Commercial software was not considered in the evaluation. A preliminary analysis was used to determine whether the 80 models were currently supported and if they were open access. This process was used to select a subset of 31 models that include 8 earthquake models, 4 cyclone models, 11 flood models, and 8 storm surge/tsunami models for more detailed analysis. By using multi-criteria analysis (MCDA) and simple descriptions of the software uses, the review allows users to select a few relevant software packages for their own testing and development. The detailed analysis evaluated the models on the basis of over 100 criteria and provides a synopsis of available open access natural hazard risk modelling tools. In addition, volcano software packages have since been added making the compendium of risk software tools in excess of 100. There has been a huge increase in the quality and availability of open access/source software over the past few years. For example, private entities such as Deltares now have an open source policy regarding some flood models (NGHS). In addition, leaders in developing risk models in the public sector, such as Geoscience Australia (EQRM, TCRM, TsuDAT, AnuGA) or CAPRA (ERN-Flood, Hurricane, CRISIS2007 etc.), are launching and/or helping many other initiatives. As we achieve greater interoperability between modelling tools, we will also achieve a future wherein different open source and open access modelling tools will be increasingly connected and adapted towards unified multi-risk model platforms and highly customised solutions. It was seen that many software tools could be improved by enabling user-defined exposure and vulnerability. Without this function, many tools can only be used regionally and not at global or continental scale. It is becoming increasingly easy to use multiple packages for a single region and/or hazard to characterize the uncertainty in the risk, or use as checks for the sensitivities in the analysis. There is a potential for valuable synergy between existing software. A number of open source software packages could be combined to generate a multi-risk model with multiple views of a hazard. This extensive review has simply attempted to provide a platform for dialogue between all open source and open access software packages and to hopefully inspire collaboration between developers, given the great work done by all open access and open source developers.

Assessing the Risk of Primary Amoebic Meningoencephalitis from Swimming in the Presence of Environmental Naegleria fowleri

PubMed Central

Cabanes, Pierre-André; Wallet, France; Pringuez, Emmanuelle; Pernin, Pierre

2001-01-01

Free-living Naegleria fowleri amoebae cause primary amoebic meningoencephalitis (PAM). Because of the apparent conflict between their ubiquity and the rarity of cases observed, we sought to develop a model characterizing the risk of PAM after swimming as a function of the concentration of N. fowleri. The probability of death from PAM as a function of the number of amoebae inhaled is modeled according to results obtained from animals infected with amoeba strains. The calculation of the probability of inhaling one or more amoebae while swimming is based on a double hypothesis: that the distribution of amoebae in the water follows a Poisson distribution and that the mean quantity of water inhaled while swimming is 10 ml. The risk of PAM for a given concentration of amoebae is then obtained by summing the following products: the probability of inhaling n amoebae × the probability of PAM associated with inhaling these n amoebae. We chose the lognormal model to assess the risk of PAM because it yielded the best analysis of the studentized residuals. Nonetheless, the levels of risk thereby obtained cannot be applied to humans without correction, because they are substantially greater than those indicated by available epidemiologic data. The curve was thus adjusted by a factor calculated with the least-squares method. This provides the PAM risk in humans as a function of the N. fowleri concentration in the river. For example, the risk is 8.5 × 10−8 at a concentration of 10 N. fowleri amoebae per liter. PMID:11425704

The joint contribution of neighborhood poverty and social integration to mortality risk in the United States.

PubMed

Marcus, Andrea Fleisch; Echeverria, Sandra E; Holland, Bart K; Abraido-Lanza, Ana F; Passannante, Marian R

2016-04-01

A well-established literature has shown that social integration strongly patterns health, including mortality risk. However, the extent to which living in high-poverty neighborhoods and having few social ties jointly pattern survival in the United States has not been examined. We analyzed data from the Third National Health and Nutrition Examination Survey (1988-1994) linked to mortality follow-up through 2006 and census-based neighborhood poverty. We fit Cox proportional hazards models to estimate associations between social integration and neighborhood poverty on all-cause mortality as independent predictors and in joint-effects models using the relative excess risk due to interaction to test for interaction on an additive scale. In the joint-effects model adjusting for age, gender, race/ ethnicity, and individual-level socioeconomic status, exposure to low social integration alone was associated with increased mortality risk (hazard ratio [HR]: 1.42, 95% confidence interval [CI]: 1.28-1.59) while living in an area of high poverty alone did not have a significant effect (HR: 1.10; 95% CI: 0.95-1.28) when compared with being jointly unexposed. Individuals simultaneously living in neighborhoods characterized by high poverty and having low levels of social integration had an increased risk of mortality (HR: 1.63; 95% CI: 1.35-1.96). However, relative excess risk due to interaction results were not statistically significant. Social integration remains an important determinant of mortality risk in the United States independent of neighborhood poverty. Copyright © 2016 Elsevier Inc. All rights reserved.

Niche syndromes, species extinction risks, and management under climate change.

PubMed

Sax, Dov F; Early, Regan; Bellemare, Jesse

2013-09-01

The current distributions of species are often assumed to correspond with the total set of environmental conditions under which species can persist. When this assumption is incorrect, extinction risk estimated from species distribution models can be misleading. The degree to which species can tolerate or even thrive under conditions found beyond their current distributions alters extinction risks, time lags in realizing those risks, and the usefulness of alternative management strategies. To inform these issues, we propose a conceptual framework within which empirical data could be used to generate hypotheses regarding the realized, fundamental, and 'tolerance' niche of species. Although these niche components have rarely been characterized over geographic scales, we suggest that this could be done for many plant species by comparing native, naturalized, and horticultural distributions. Copyright © 2013 Elsevier Ltd. All rights reserved.

IMPROVED RISK CHARACTERIZATION METHODS FOR DEVELOPING AQUATIC LIFE CRITERIA FOR NON-BIOACCUMULATIVE TOXICANTS

EPA Science Inventory

This project will use existing and developing information to evaluate and demonstrate procedures for more fully characterizing risks of non-bioaccumulative toxicants to aquatic organisms, and for incorporating these risks into aquatic life criteria. These efforts will address a v...

Baseline Ecological Risk Assessment for the Upland at the LCP Chemical Site, Brunswick, Georgia - Site Investigation/Analysis and Risk Characterization (Final)

EPA Pesticide Factsheets

Site Investigation/Analysis and Risk Characterization (Final) Prepared for Honeywell International Inc. Prepared by CDR Environmental Specialists, Inc. August 2010 Region ID: 04 DocID: 10746263, DocDate: 08-01-2010

Track structure in biological models.

PubMed

Curtis, S B

1986-01-01

High-energy heavy ions in the galactic cosmic radiation (HZE particles) may pose a special risk during long term manned space flights outside the sheltering confines of the earth's geomagnetic field. These particles are highly ionizing, and they and their nuclear secondaries can penetrate many centimeters of body tissue. The three dimensional patterns of ionizations they create as they lose energy are referred to as their track structure. Several models of biological action on mammalian cells attempt to treat track structure or related quantities in their formulation. The methods by which they do this are reviewed. The proximity function is introduced in connection with the theory of Dual Radiation Action (DRA). The ion-gamma kill (IGK) model introduces the radial energy-density distribution, which is a smooth function characterizing both the magnitude and extension of a charged particle track. The lethal, potentially lethal (LPL) model introduces lambda, the mean distance between relevant ion clusters or biochemical species along the track. Since very localized energy depositions (within approximately 10 nm) are emphasized, the proximity function as defined in the DRA model is not of utility in characterizing track structure in the LPL formulation.

Strategic characterization of anti-drug antibody responses for the assessment of clinical relevance and impact.

PubMed

Tatarewicz, Suzanna M; Mytych, Daniel T; Manning, Marta Starcevic; Swanson, Steven J; Moxness, Michael S; Chirmule, Narendra

2014-06-01

All therapeutic proteins have the potential to induce anti-drug antibodies (ADA). Clinically relevant ADA can impact efficacy and/or safety of a biological therapeutic. Immunogenicity assessment strategy evaluates binding and neutralizing ADA, and the need for additional characterization (e.g., epitope, titer and so on) is determined using a risk-based approach. The choice of characterization assays depends on the type, application and immunogenicity of the therapeutic. ADA characterization can impact the interpretation of the risk profile of a given therapeutic, and offers insight into opportunities for risk mitigation and management. This article describes common ADA characterization methods. Strategic assessment and characterization of clinically relevant ADA are discussed, in order to support clinical options for safe and effective patient care and disease management.

Return on Scientific Investment - RoSI: a PMO dynamical index proposal for scientific projects performance evaluation and management.

PubMed

Caous, Cristofer André; Machado, Birajara; Hors, Cora; Zeh, Andrea Kaufmann; Dias, Cleber Gustavo; Amaro Junior, Edson

2012-01-01

To propose a measure (index) of expected risks to evaluate and follow up the performance analysis of research projects involving financial and adequate structure parameters for its development. A ranking of acceptable results regarding research projects with complex variables was used as an index to gauge a project performance. In order to implement this method the ulcer index as the basic model to accommodate the following variables was applied: costs, high impact publication, fund raising, and patent registry. The proposed structured analysis, named here as RoSI (Return on Scientific Investment) comprises a pipeline of analysis to characterize the risk based on a modeling tool that comprises multiple variables interacting in semi-quantitatively environments. This method was tested with data from three different projects in our Institution (projects A, B and C). Different curves reflected the ulcer indexes identifying the project that may have a minor risk (project C) related to development and expected results according to initial or full investment. The results showed that this model contributes significantly to the analysis of risk and planning as well as to the definition of necessary investments that consider contingency actions with benefits to the different stakeholders: the investor or donor, the project manager and the researchers.

Why Do Some First Nations Communities Have Safe Water and Others Not? Socioeconomic Determinants of Drinking Water Risk

PubMed Central

Brown, Brandon; Wachowiak-Smolíková, Renata; Spence, Nicholas D.; Wachowiak, Mark P.; Walters, Dan F.

2016-01-01

Securing safe and adequate drinking water is an ongoing issue for many Canadian First Nations communities despite nearly 15 years of reports, studies, policy changes, financial commitments, and regulations. The federal drinking water evaluation scheme is narrowly scoped, ignoring community level social factors, which may play a role in access to safe water in First Nations. This research used the 2006 Aboriginal Affairs and Northern Development Canada First Nations Drinking Water System Risk Survey data and the Community Well-Being Index, including labour force, education, housing, and income, from the 2006 Census. Bivariate analysis was conducted using the Spearman’s correlation, Kendall’s tau correlation, and Pearson’s correlation. Multivariable analysis was conducted using an ordinal (proportional or cumulative odds) regression model. Results showed that the regression model was significant. Community socioeconomic indicators had no relationship with drinking water risk characterization in both the bivariate and multivariable models, with the sole exception of labour force, which had a significantly positive effect on drinking water risk rankings. Socioeconomic factors were not important in explaining access to safe drinking water in First Nations communities. Improvements in the quality of safe water data as well as an examination of other community processes are required to address this pressing policy issue. PMID:27157172

Why Do Some First Nations Communities Have Safe Water and Others Not? Socioeconomic Determinants of Drinking Water Risk.

PubMed

Brown, Brandon; Wachowiak-Smolíková, Renata; Spence, Nicholas D; Wachowiak, Mark P; Walters, Dan F

2016-09-01

Securing safe and adequate drinking water is an ongoing issue for many Canadian First Nations communities despite nearly 15 years of reports, studies, policy changes, financial commitments, and regulations. The federal drinking water evaluation scheme is narrowly scoped, ignoring community level social factors, which may play a role in access to safe water in First Nations. This research used the 2006 Aboriginal Affairs and Northern Development Canada First Nations Drinking Water System Risk Survey data and the Community Well-Being Index, including labour force, education, housing, and income, from the 2006 Census. Bivariate analysis was conducted using the Spearman's correlation, Kendall's tau correlation, and Pearson's correlation. Multivariable analysis was conducted using an ordinal (proportional or cumulative odds) regression model. Results showed that the regression model was significant. Community socioeconomic indicators had no relationship with drinking water risk characterization in both the bivariate and multivariable models, with the sole exception of labour force, which had a significantly positive effect on drinking water risk rankings. Socioeconomic factors were not important in explaining access to safe drinking water in First Nations communities. Improvements in the quality of safe water data as well as an examination of other community processes are required to address this pressing policy issue.

Analysis of longitudinal multivariate outcome data from couples cohort studies: application to HPV transmission dynamics

PubMed Central

Kong, Xiangrong; Wang, Mei-Cheng; Gray, Ronald

2014-01-01

We consider a specific situation of correlated data where multiple outcomes are repeatedly measured on each member of a couple. Such multivariate longitudinal data from couples may exhibit multi-faceted correlations which can be further complicated if there are polygamous partnerships. An example is data from cohort studies on human papillomavirus (HPV) transmission dynamics in heterosexual couples. HPV is a common sexually transmitted disease with 14 known oncogenic types causing anogenital cancers. The binary outcomes on the multiple types measured in couples over time may introduce inter-type, intra-couple, and temporal correlations. Simple analysis using generalized estimating equations or random effects models lacks interpretability and cannot fully utilize the available information. We developed a hybrid modeling strategy using Markov transition models together with pairwise composite likelihood for analyzing such data. The method can be used to identify risk factors associated with HPV transmission and persistence, estimate difference in risks between male-to-female and female-to-male HPV transmission, compare type-specific transmission risks within couples, and characterize the inter-type and intra-couple associations. Applying the method to HPV couple data collected in a Ugandan male circumcision (MC) trial, we assessed the effect of MC and the role of gender on risks of HPV transmission and persistence. PMID:26195849

Interplay Between Genetic Substrate, QTc Duration, and Arrhythmia Risk in Patients With Long QT Syndrome.

PubMed

Mazzanti, Andrea; Maragna, Riccardo; Vacanti, Gaetano; Monteforte, Nicola; Bloise, Raffaella; Marino, Maira; Braghieri, Lorenzo; Gambelli, Patrick; Memmi, Mirella; Pagan, Eleonora; Morini, Massimo; Malovini, Alberto; Ortiz, Martin; Sacilotto, Luciana; Bellazzi, Riccardo; Monserrat, Lorenzo; Napolitano, Carlo; Bagnardi, Vincenzo; Priori, Silvia G

2018-04-17

Long QT syndrome (LQTS) is a common inheritable arrhythmogenic disorder, often secondary to mutations in the KCNQ1, KCNH2, and SCN5A genes. The disease is characterized by a prolonged ventricular repolarization (QTc interval) that confers susceptibility to life-threatening arrhythmic events (LAEs). This study sought to create an evidence-based risk stratification scheme to personalize the quantification of the arrhythmic risk in patients with LQTS. Data from 1,710 patients with LQTS followed up for a median of 7.1 years (interquartile range [IQR]: 2.7 to 13.4 years) were analyzed to estimate the 5-year risk of LAEs based on QTc duration and genotype and to assess the antiarrhythmic efficacy of beta-blockers. The relationship between QTc duration and risk of events was investigated by comparison of linear and cubic spline models, and the linear model provided the best fit. The 5-year risk of LAEs while patients were off therapy was then calculated in a multivariable Cox model with QTc and genotype considered as independent factors. The estimated risk of LAEs increased by 15% for every 10-ms increment of QTc duration for all genotypes. Intergenotype comparison showed that the risk for patients with LQT2 and LQT3 increased by 130% and 157% at any QTc duration versus patients with LQT1. Analysis of response to beta-blockers showed that only nadolol reduced the arrhythmic risk in all genotypes significantly compared with no therapy (hazard ratio: 0.38; 95% confidence interval: 0.15 to 0.93; p = 0.03). The study provides an estimator of risk of LAEs in LQTS that allows a granular estimate of 5-year arrhythmic risk and demonstrate the superiority of nadolol in reducing the risk of LAEs in LQTS. Copyright © 2018 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Analyzing seasonal patterns of wildfire exposure factors in Sardinia, Italy.

PubMed

Salis, Michele; Ager, Alan A; Alcasena, Fermin J; Arca, Bachisio; Finney, Mark A; Pellizzaro, Grazia; Spano, Donatella

2015-01-01

In this paper, we applied landscape scale wildfire simulation modeling to explore the spatiotemporal patterns of wildfire likelihood and intensity in the island of Sardinia (Italy). We also performed wildfire exposure analysis for selected highly valued resources on the island to identify areas characterized by high risk. We observed substantial variation in burn probability, fire size, and flame length among time periods within the fire season, which starts in early June and ends in late September. Peak burn probability and flame length were observed in late July. We found that patterns of wildfire likelihood and intensity were mainly related to spatiotemporal variation in ignition locations, fuel moisture, and wind vectors. Our modeling approach allowed consideration of historical patterns of winds, ignition locations, and live and dead fuel moisture on fire exposure factors. The methodology proposed can be useful for analyzing potential wildfire risk and effects at landscape scale, evaluating historical changes and future trends in wildfire exposure, as well as for addressing and informing fuel management and risk mitigation issues.

The default mode network and recurrent depression: a neurobiological model of cognitive risk factors.

PubMed

Marchetti, Igor; Koster, Ernst H W; Sonuga-Barke, Edmund J; De Raedt, Rudi

2012-09-01

A neurobiological account of cognitive vulnerability for recurrent depression is presented based on recent developments of resting state neural networks. We propose that alterations in the interplay between task positive (TP) and task negative (TN) elements of the Default Mode Network (DMN) act as a neurobiological risk factor for recurrent depression mediated by cognitive mechanisms. In the framework, depression is characterized by an imbalance between TN-TP components leading to an overpowering of TP by TN activity. The TN-TP imbalance is associated with a dysfunctional internally-focused cognitive style as well as a failure to attenuate TN activity in the transition from rest to task. Thus we propose the TN-TP imbalance as overarching neural mechanism involved in crucial cognitive risk factors for recurrent depression, namely rumination, impaired attentional control, and cognitive reactivity. During remission the TN-TP imbalance persists predisposing to vulnerability of recurrent depression. Empirical data to support this model is reviewed. Finally, we specify how this framework can guide future research efforts.

Reproductive and Developmental Toxicity of Dioxin in Fish1

PubMed Central

King-Heiden, Tisha C.; Mehta, Vatsal; Xiong, Kong M.; Lanham, Kevin A.; Antkiewicz, Dagmara S.; Ganser, Alissa; Heideman, Warren

2011-01-01

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD or dioxin) is a global environmental contaminant and the prototypical ligand for investigating aryl hydrocarbon receptor (AHR)-mediated toxicity. Environmental exposure to TCDD results in developmental and reproductive toxicity in fish, birds and mammals. To resolve the ecotoxicological relevance and human health risks posed by exposure to dioxin-like AHR agonists, a vertebrate model is needed that allows for toxicity studies at various levels of biological organization, assesses adverse reproductive and developmental effects and establishes appropriate integrative correlations between different levels of effects. Here we describe the reproductive and developmental toxicity of TCDD in feral fish species and summarize how using the zebrafish model to investigate TCDD toxicity has enabled us to characterize the AHR signaling in fish and to better understand how dioxin-like chemicals induce toxicity. We propose that such studies can be used to predict the risks that AHR ligands pose to feral fish populations and provide a platform for integrating risk assessments for both ecologically relevant organisms and humans. PMID:21958697

Adipose-Derived Mesenchymal Stem Cell Administration Does Not Improve Corneal Graft Survival Outcome

PubMed Central

Fuentes-Julián, Sherezade; Arnalich-Montiel, Francisco; Jaumandreu, Laia; Leal, Marina; Casado, Alfonso; García-Tuñon, Ignacio; Hernández-Jiménez, Enrique; López-Collazo, Eduardo; De Miguel, Maria P.

2015-01-01

The effect of local and systemic injections of mesenchymal stem cells derived from adipose tissue (AD-MSC) into rabbit models of corneal allograft rejection with either normal-risk or high-risk vascularized corneal beds was investigated. The models we present in this study are more similar to human corneal transplants than previously reported murine models. Our aim was to prevent transplant rejection and increase the length of graft survival. In the normal-risk transplant model, in contrast to our expectations, the injection of AD-MSC into the graft junction during surgery resulted in the induction of increased signs of inflammation such as corneal edema with increased thickness, and a higher level of infiltration of leukocytes. This process led to a lower survival of the graft compared with the sham-treated corneal transplants. In the high-risk transplant model, in which immune ocular privilege was undermined by the induction of neovascularization prior to graft surgery, we found the use of systemic rabbit AD-MSCs prior to surgery, during surgery, and at various time points after surgery resulted in a shorter survival of the graft compared with the non-treated corneal grafts. Based on our results, local or systemic treatment with AD-MSCs to prevent corneal rejection in rabbit corneal models at normal or high risk of rejection does not increase survival but rather can increase inflammation and neovascularization and break the innate ocular immune privilege. This result can be partially explained by the immunomarkers, lack of immunosuppressive ability and immunophenotypical secretion molecules characterization of AD-MSC used in this study. Parameters including the risk of rejection, the inflammatory/vascularization environment, the cell source, the time of injection, the immunosuppression, the number of cells, and the mode of delivery must be established before translating the possible benefits of the use of MSCs in corneal transplants to clinical practice. PMID:25730319

Associations of patient safety outcomes with models of nursing care organization at unit level in hospitals.

PubMed

Dubois, Carl-Ardy; D'amour, Danielle; Tchouaket, Eric; Clarke, Sean; Rivard, Michèle; Blais, Régis

2013-04-01

To examine the associations of four distinct nursing care organizational models with patient safety outcomes. Cross-sectional correlational study. Using a standardized protocol, patients' records were screened retrospectively to detect occurrences of patient safety-related events. Binary logistic regression was used to assess the associations of those events with four nursing care organizational models. Twenty-two medical units in 11 hospitals in Quebec, Canada, were clustered into 4 nursing care organizational models: 2 professional models and 2 functional models. Two thousand six hundred and ninety-nine were patients hospitalized for at least 48 h on the selected units. Composite of six safety-related events widely-considered sensitive to nursing care: medication administration errors, falls, pneumonia, urinary tract infection, unjustified restraints and pressure ulcers. Events were ultimately sorted into two categories: events 'without major' consequences for patients and events 'with' consequences. After controlling for patient characteristics, patient risk of experiencing one or more events (of any severity) and of experiencing an event with consequences was significantly lower, by factors of 25-52%, in both professional models than in the functional models. Event rates for both functional models were statistically indistinguishable from each other. Data suggest that nursing care organizational models characterized by contrasting staffing, work environment and innovation characteristics may be associated with differential risk for hospitalized patients. The two professional models, which draw mainly on registered nurses (RNs) to deliver nursing services and reflect stronger support for nurses' professional practice, were associated with lower risks than are the two functional models.

Strategic preparedness for recovery from catastrophic risks to communities and infrastructure systems of systems.

PubMed

Haimes, Yacov Y

2012-11-01

Natural and human-induced disasters affect organizations in myriad ways because of the inherent interconnectedness and interdependencies among human, cyber, and physical infrastructures, but more importantly, because organizations depend on the effectiveness of people and on the leadership they provide to the organizations they serve and represent. These human-organizational-cyber-physical infrastructure entities are termed systems of systems. Given the multiple perspectives that characterize them, they cannot be modeled effectively with a single model. The focus of this article is: (i) the centrality of the states of a system in modeling; (ii) the efficacious role of shared states in modeling systems of systems, in identification, and in the meta-modeling of systems of systems; and (iii) the contributions of the above to strategic preparedness, response to, and recovery from catastrophic risk to such systems. Strategic preparedness connotes a decision-making process and its associated actions. These must be: implemented in advance of a natural or human-induced disaster, aimed at reducing consequences (e.g., recovery time, community suffering, and cost), and/or controlling their likelihood to a level considered acceptable (through the decisionmakers' implicit and explicit acceptance of various risks and tradeoffs). The inoperability input-output model (IIM), which is grounded on Leontief's input/output model, has enabled the modeling of interdependent subsystems. Two separate modeling structures are introduced. These are: phantom system models (PSM), where shared states constitute the essence of modeling coupled systems; and the IIM, where interdependencies among sectors of the economy are manifested by the Leontief matrix of technological coefficients. This article demonstrates the potential contributions of these two models to each other, and thus to more informative modeling of systems of systems schema. The contributions of shared states to this modeling and to systems identification are presented with case studies. © 2012 Society for Risk Analysis.

Use of space-time models to investigate the stability of patterns of disease.

PubMed

Abellan, Juan Jose; Richardson, Sylvia; Best, Nicky

2008-08-01

The use of Bayesian hierarchical spatial models has become widespread in disease mapping and ecologic studies of health-environment associations. In this type of study, the data are typically aggregated over an extensive time period, thus neglecting the time dimension. The output of purely spatial disease mapping studies is therefore the average spatial pattern of risk over the period analyzed, but the results do not inform about, for example, whether a high average risk was sustained over time or changed over time. We investigated how including the time dimension in disease-mapping models strengthens the epidemiologic interpretation of the overall pattern of risk. We discuss a class of Bayesian hierarchical models that simultaneously characterize and estimate the stable spatial and temporal patterns as well as departures from these stable components. We show how useful rules for classifying areas as stable can be constructed based on the posterior distribution of the space-time interactions. We carry out a simulation study to investigate the sensitivity and specificity of the decision rules we propose, and we illustrate our approach in a case study of congenital anomalies in England. Our results confirm that extending hierarchical disease-mapping models to models that simultaneously consider space and time leads to a number of benefits in terms of interpretation and potential for detection of localized excesses.

Catchment-scale herbicides transport: Theory and application

NASA Astrophysics Data System (ADS)

Bertuzzo, E.; Thomet, M.; Botter, G.; Rinaldo, A.

2013-02-01

This paper proposes and tests a model which couples the description of hydrologic flow and transport of herbicides at catchment scales. The model accounts for streamflow components' age to characterize short and long term fluctuations of herbicide flux concentrations in stream waters, whose peaks exceeding a toxic threshold are key to exposure risk of aquatic ecosystems. The model is based on a travel time formulation of transport embedding a source zone that describes near surface herbicide dynamics. To this aim we generalize a recently proposed scheme for the analytical derivation of travel time distributions to the case of solutes that can be partially taken up by transpiration and undergo chemical degradation. The framework developed is evaluated by comparing modeled hydrographs and atrazine chemographs with those measured in the Aabach agricultural catchment (Switzerland). The model proves reliable in defining complex transport features shaped by the interplay of long term processes, related to the persistence of solute components in soils, and short term dynamics related to storm inter-arrivals. The effects of stochasticity in rainfall patterns and application dates on concentrations and loads in runoff are assessed via Monte Carlo simulations, highlighting the crucial role played by the first rainfall event occurring after herbicide application. A probabilistic framework for critical determinants of exposure risk to aquatic communities is defined. Modeling of herbicides circulation at catchment scale thus emerges as essential tools for ecological risk assessment.

A poisson process model for hip fracture risk.

PubMed

Schechner, Zvi; Luo, Gangming; Kaufman, Jonathan J; Siffert, Robert S

2010-08-01

The primary method for assessing fracture risk in osteoporosis relies primarily on measurement of bone mass. Estimation of fracture risk is most often evaluated using logistic or proportional hazards models. Notwithstanding the success of these models, there is still much uncertainty as to who will or will not suffer a fracture. This has led to a search for other components besides mass that affect bone strength. The purpose of this paper is to introduce a new mechanistic stochastic model that characterizes the risk of hip fracture in an individual. A Poisson process is used to model the occurrence of falls, which are assumed to occur at a rate, lambda. The load induced by a fall is assumed to be a random variable that has a Weibull probability distribution. The combination of falls together with loads leads to a compound Poisson process. By retaining only those occurrences of the compound Poisson process that result in a hip fracture, a thinned Poisson process is defined that itself is a Poisson process. The fall rate is modeled as an affine function of age, and hip strength is modeled as a power law function of bone mineral density (BMD). The risk of hip fracture can then be computed as a function of age and BMD. By extending the analysis to a Bayesian framework, the conditional densities of BMD given a prior fracture and no prior fracture can be computed and shown to be consistent with clinical observations. In addition, the conditional probabilities of fracture given a prior fracture and no prior fracture can also be computed, and also demonstrate results similar to clinical data. The model elucidates the fact that the hip fracture process is inherently random and improvements in hip strength estimation over and above that provided by BMD operate in a highly "noisy" environment and may therefore have little ability to impact clinical practice.

Risk taking and adult attention deficit/hyperactivity disorder: A gap between real life behavior and experimental decision making.

PubMed

Pollak, Yehuda; Shalit, Reut; Aran, Adi

2018-01-01

Adults with attention deficit/hyperactivity disorder (ADHD) are prone to suboptimal decision making and risk taking. The aim of this study was to test performance on a theoretically-based probabilistic decision making task in well-characterized adults with and without ADHD, and examine the relation between experimental risk taking and history of real-life risk-taking behavior, defined as cigarette, alcohol, and street drug use. University students with and without ADHD completed a modified version of the Cambridge Gambling Test, in which they had to choose between alternatives varied by level of risk, and reported their history of substance use. Both groups showed similar patterns of risk taking on the experimental decision making task, suggesting that ADHD is not linked to low sensitivity to risk. Past and present substance use was more prevalent in adults with ADHD. These finding question the validity of experimental probabilistic decision making task as a valid model for ADHD-related risk-taking behavior. Copyright © 2017 Elsevier B.V. All rights reserved.

Left Ventricular Structure and Risk of Cardiovascular Events: A Framingham Heart Study Cardiac Magnetic Resonance Study

PubMed Central

Tsao, Connie W; Gona, Philimon N; Salton, Carol J; Chuang, Michael L; Levy, Daniel; Manning, Warren J; O’Donnell, Christopher J

2015-01-01

Background Elevated left ventricular mass index (LVMI) and concentric left ventricular (LV) remodeling are related to adverse cardiovascular disease (CVD) events. The predictive utility of LV concentric remodeling and LV mass in the prediction of CVD events is not well characterized. Methods and Results Framingham Heart Study Offspring Cohort members without prevalent CVD (n=1715, 50% men, aged 65±9 years) underwent cardiovascular magnetic resonance for LVMI and geometry (2002–2006) and were prospectively followed for incident CVD (myocardial infarction, coronary insufficiency, heart failure, stroke) or CVD death. Over 13 808 person-years of follow-up (median 8.4, range 0.0 to 10.5 years), 85 CVD events occurred. In multivariable-adjusted proportional hazards regression models, each 10-g/m2 increment in LVMI and each 0.1 unit in relative wall thickness was associated with 33% and 59% increased risk for CVD, respectively (P=0.004 and P=0.009, respectively). The association between LV mass/LV end-diastolic volume and incident CVD was borderline significant (P=0.053). Multivariable-adjusted risk reclassification models showed a modest improvement in CVD risk prediction with the incorporation of cardiovascular magnetic resonance LVMI and measures of LV concentricity (C-statistic 0.71 [95% CI 0.65 to 0.78] for the model with traditional risk factors only, improved to 0.74 [95% CI 0.68 to 0.80] for the risk factor model additionally including LVMI and relative wall thickness). Conclusions Among adults free of prevalent CVD in the community, greater LVMI and LV concentric hypertrophy are associated with a marked increase in adverse incident CVD events. The potential benefit of aggressive primary prevention to modify LV mass and geometry in these adults requires further investigation. PMID:26374295

Evaluations of Risks from the Lunar and Mars Radiation Environments

NASA Technical Reports Server (NTRS)

Kim, Myung-Hee; Hayat, Matthew J.; Feiveson, Alan H.; Cucinotta, Francis A.

2008-01-01

Protecting astronauts from the space radiation environments requires accurate projections of radiation in future space missions. Characterization of the ionizing radiation environment is challenging because the interplanetary plasma and radiation fields are modulated by solar disturbances and the radiation doses received by astronauts in interplanetary space are likewise influenced. The galactic cosmic radiation (GCR) flux for the next solar cycle was estimated as a function of interplanetary deceleration potential, which has been derived from GCR flux and Climax neutron monitor rate measurements over the last 4 decades. For the chaotic nature of solar particle event (SPE) occurrence, the mean frequency of SPE at any given proton fluence threshold during a defined mission duration was obtained from a Poisson process model using proton fluence measurements of SPEs during the past 5 solar cycles (19-23). Analytic energy spectra of 34 historically large SPEs were constructed over broad energy ranges extending to GeV. Using an integrated space radiation model (which includes the transport codes HZETRN [1] and BRYNTRN [2], and the quantum nuclear interaction model QMSFRG[3]), the propagation and interaction properties of the energetic nucleons through various media were predicted. Risk assessment from GCR and SPE was evaluated at the specific organs inside a typical spacecraft using CAM [4] model. The representative risk level at each event size and their standard deviation were obtained from the analysis of 34 SPEs. Risks from different event sizes and their frequency of occurrences in a specified mission period were evaluated for the concern of acute health effects especially during extra-vehicular activities (EVA). The results will be useful for the development of an integrated strategy of optimizing radiation protection on the lunar and Mars missions. Keywords: Space Radiation Environments; Galactic Cosmic Radiation; Solar Particle Event; Radiation Risk; Risk Analysis; Radiation Protection.

A Hybrid Methodology for Modeling Risk of Adverse Events in Complex Health-Care Settings.

PubMed

Kazemi, Reza; Mosleh, Ali; Dierks, Meghan

2017-03-01

In spite of increased attention to quality and efforts to provide safe medical care, adverse events (AEs) are still frequent in clinical practice. Reports from various sources indicate that a substantial number of hospitalized patients suffer treatment-caused injuries while in the hospital. While risk cannot be entirely eliminated from health-care activities, an important goal is to develop effective and durable mitigation strategies to render the system "safer." In order to do this, though, we must develop models that comprehensively and realistically characterize the risk. In the health-care domain, this can be extremely challenging due to the wide variability in the way that health-care processes and interventions are executed and also due to the dynamic nature of risk in this particular domain. In this study, we have developed a generic methodology for evaluating dynamic changes in AE risk in acute care hospitals as a function of organizational and nonorganizational factors, using a combination of modeling formalisms. First, a system dynamics (SD) framework is used to demonstrate how organizational-level and policy-level contributions to risk evolve over time, and how policies and decisions may affect the general system-level contribution to AE risk. It also captures the feedback of organizational factors and decisions over time and the nonlinearities in these feedback effects. SD is a popular approach to understanding the behavior of complex social and economic systems. It is a simulation-based, differential equation modeling tool that is widely used in situations where the formal model is complex and an analytical solution is very difficult to obtain. Second, a Bayesian belief network (BBN) framework is used to represent patient-level factors and also physician-level decisions and factors in the management of an individual patient, which contribute to the risk of hospital-acquired AE. BBNs are networks of probabilities that can capture probabilistic relations between variables and contain historical information about their relationship, and are powerful tools for modeling causes and effects in many domains. The model is intended to support hospital decisions with regard to staffing, length of stay, and investments in safety, which evolve dynamically over time. The methodology has been applied in modeling the two types of common AEs: pressure ulcers and vascular-catheter-associated infection, and the models have been validated with eight years of clinical data and use of expert opinion. © 2017 Society for Risk Analysis.

Drought: A comprehensive R package for drought monitoring, prediction and analysis

NASA Astrophysics Data System (ADS)

Hao, Zengchao; Hao, Fanghua; Singh, Vijay P.; Cheng, Hongguang

2015-04-01

Drought may impose serious challenges to human societies and ecosystems. Due to complicated causing effects and wide impacts, a universally accepted definition of drought does not exist. The drought indicator is commonly used to characterize drought properties such as duration or severity. Various drought indicators have been developed in the past few decades for the monitoring of a certain aspect of drought condition along with the development of multivariate drought indices for drought characterizations from multiple sources or hydro-climatic variables. Reliable drought prediction with suitable drought indicators is critical to the drought preparedness plan to reduce potential drought impacts. In addition, drought analysis to quantify the risk of drought properties would provide useful information for operation drought managements. The drought monitoring, prediction and risk analysis are important components in drought modeling and assessments. In this study, a comprehensive R package "drought" is developed to aid the drought monitoring, prediction and risk analysis (available from R-Forge and CRAN soon). The computation of a suite of univariate and multivariate drought indices that integrate drought information from various sources such as precipitation, temperature, soil moisture, and runoff is available in the drought monitoring component in the package. The drought prediction/forecasting component consists of statistical drought predictions to enhance the drought early warning for decision makings. Analysis of drought properties such as duration and severity is also provided in this package for drought risk assessments. Based on this package, a drought monitoring and prediction/forecasting system is under development as a decision supporting tool. The package will be provided freely to the public to aid the drought modeling and assessment for researchers and practitioners.

Intracerebral hemorrhage and sleep-disordered breathing.

PubMed

Lisabeth, Lynda D; Scheer, Richard V; Li, Chengwei; Case, Erin; Chervin, Ronald D; Zahuranec, Darin B; Morgenstern, Lewis B; Garcia, Nelda M; Tower, Susan; Brown, Devin L

2018-06-01

Limited data are available on sleep-disordered breathing (SDB) following intracerebral hemorrhage (ICH). Our aim was to characterize the objective measures of post-ICH SDB and questionnaire-reported pre-ICH sleep characteristics, overall and by ethnicity. Participants with ICH who were enrolled in the population-based Brain Attack Surveillance in Corpus Christi project (2010-2016) reported their pre-ICH sleep duration and completed the Berlin Questionnaire to characterize pre-ICH risk of SDB. A subsample was screened for SDB (respiratory event index ≥10) using ApneaLink Plus portable monitoring. Ethnic differences in post-ICH SDB or questionnaire-reported pre-ICH sleep characteristics were assessed using a log binomial model or a linear regression model or a Fisher's exact test. ICH cases (n = 298) were enrolled (median age = 68 years, 67% Mexican American). Among 62 cases with complete ApneaLink data, median time to post-ICH SDB screening was 11 days (IQR: 6, 19). Post-ICH SDB prevalence was 46.8% (95% CI: 34.4-59.2), and this rate did not differ by ethnicity (p = 1.0). Berlin Questionnaires for 109 of the 298 ICH cases (36.6% (95% CI: 31.1-42.0)) suggested a high risk for pre-ICH SDB, and the median pre-ICH sleep duration was eight hours (IQR: 6, 8). After adjusting for confounders, there was no difference in ethnicity in high risk for pre-ICH SDB or pre-ICH sleep duration. Nearly half of the patients had objective confirmation of SDB after ICH, and more than one-third had questionnaire evidence of high risk for pre-ICH SDB. Opportunities to address SDB may be common both before and after ICH. Copyright © 2018 Elsevier B.V. All rights reserved.

Discriminating between adaptive and carcinogenic liver hypertrophy in rat studies using logistic ridge regression analysis of toxicogenomic data: The mode of action and predictive models.

PubMed

Liu, Shujie; Kawamoto, Taisuke; Morita, Osamu; Yoshinari, Kouichi; Honda, Hiroshi

2017-03-01

Chemical exposure often results in liver hypertrophy in animal tests, characterized by increased liver weight, hepatocellular hypertrophy, and/or cell proliferation. While most of these changes are considered adaptive responses, there is concern that they may be associated with carcinogenesis. In this study, we have employed a toxicogenomic approach using a logistic ridge regression model to identify genes responsible for liver hypertrophy and hypertrophic hepatocarcinogenesis and to develop a predictive model for assessing hypertrophy-inducing compounds. Logistic regression models have previously been used in the quantification of epidemiological risk factors. DNA microarray data from the Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System were used to identify hypertrophy-related genes that are expressed differently in hypertrophy induced by carcinogens and non-carcinogens. Data were collected for 134 chemicals (72 non-hypertrophy-inducing chemicals, 27 hypertrophy-inducing non-carcinogenic chemicals, and 15 hypertrophy-inducing carcinogenic compounds). After applying logistic ridge regression analysis, 35 genes for liver hypertrophy (e.g., Acot1 and Abcc3) and 13 genes for hypertrophic hepatocarcinogenesis (e.g., Asns and Gpx2) were selected. The predictive models built using these genes were 94.8% and 82.7% accurate, respectively. Pathway analysis of the genes indicates that, aside from a xenobiotic metabolism-related pathway as an adaptive response for liver hypertrophy, amino acid biosynthesis and oxidative responses appear to be involved in hypertrophic hepatocarcinogenesis. Early detection and toxicogenomic characterization of liver hypertrophy using our models may be useful for predicting carcinogenesis. In addition, the identified genes provide novel insight into discrimination between adverse hypertrophy associated with carcinogenesis and adaptive hypertrophy in risk assessment. Copyright © 2017 Elsevier Inc. All rights reserved.

Modeling runoff and erosion risk in a~small steep cultivated watershed using different data sources: from on-site measurements to farmers' perceptions

NASA Astrophysics Data System (ADS)

Auvet, B.; Lidon, B.; Kartiwa, B.; Le Bissonnais, Y.; Poussin, J.-C.

2015-09-01

This paper presents an approach to model runoff and erosion risk in a context of data scarcity, whereas the majority of available models require large quantities of physical data that are frequently not accessible. To overcome this problem, our approach uses different sources of data, particularly on agricultural practices (tillage and land cover) and farmers' perceptions of runoff and erosion. The model was developed on a small (5 ha) cultivated watershed characterized by extreme conditions (slopes of up to 55 %, extreme rainfall events) on the Merapi volcano in Indonesia. Runoff was modelled using two versions of STREAM. First, a lumped version was used to determine the global parameters of the watershed. Second, a distributed version used three parameters for the production of runoff (slope, land cover and roughness), a precise DEM, and the position of waterways for runoff distribution. This information was derived from field observations and interviews with farmers. Both surface runoff models accurately reproduced runoff at the outlet. However, the distributed model (Nash-Sutcliffe = 0.94) was more accurate than the adjusted lumped model (N-S = 0.85), especially for the smallest and biggest runoff events, and produced accurate spatial distribution of runoff production and concentration. Different types of erosion processes (landslides, linear inter-ridge erosion, linear erosion in main waterways) were modelled as a combination of a hazard map (the spatial distribution of runoff/infiltration volume provided by the distributed model), and a susceptibility map combining slope, land cover and tillage, derived from in situ observations and interviews with farmers. Each erosion risk map gives a spatial representation of the different erosion processes including risk intensities and frequencies that were validated by the farmers and by in situ observations. Maps of erosion risk confirmed the impact of the concentration of runoff, the high susceptibility of long steep slopes, and revealed the critical role of tillage direction. Calibrating and validating models using in situ measurements, observations and farmers' perceptions made it possible to represent runoff and erosion risk despite the initial scarcity of hydrological data. Even if the models mainly provided orders of magnitude and qualitative information, they significantly improved our understanding of the watershed dynamics. In addition, the information produced by such models is easy for farmers to use to manage runoff and erosion by using appropriate agricultural practices.

MOLECULAR METHODS USED TO ASSESS THE RISKS OF TRANSGENE FLOW; BENEFITS AND LIMITATIONS

EPA Science Inventory

The US EPA WED has initiated a gene flow project to characterize ecological risks of gene flow from GM plants to native species. Development of molecular assays for risk characterization down to gene expression level is of high interest to the EPA. Phylogenetic analyses of ampl...

Using Art as a Self-Regulating Tool in a War Situation: A Model for Social Workers

ERIC Educational Resources Information Center

Huss, Ephrat; Sarid, Orly; Cwikel, Julie

2010-01-01

War poses a challenge for social workers, adding exposure to direct risk of personal harm to the general stress of social work practice. Artworks are frequently used in health care settings with people in high distress. This study had three goals: (1) to characterize the stressors of social workers living in a war zone, (2) to teach social workers…

Proceedings of the tenth annual DOE low-level waste management conference: Session 2: Site performance assessment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1988-12-01

This document contains twelve papers on various aspects of low-level radioactive waste management. Topics of this volume include: performance assessment methodology; remedial action alternatives; site selection and site characterization procedures; intruder scenarios; sensitivity analysis procedures; mathematical models for mixed waste environmental transport; and risk assessment methodology. Individual papers were processed separately for the database. (TEM)

Microbial Characterization and Comparison of Isolates During the Mir and ISS Missions

NASA Technical Reports Server (NTRS)

Fontenot, Sondra L.; Castro, Victoria; Bruce, Rebekah; Ott, C. Mark; Pierson, Duane L.

2004-01-01

Spacecraft represent a semi-closed ecosystem that provides a unique model of microbial interaction with other microbes, potential hosts, and their environment. Environmental samples from the Mir Space Station (1995-1998) and the International Space Station (ISS) (2000-Present) were collected and processed to provide insight into the characterization of microbial diversity aboard spacecraft over time and assess any potential health risks to the crew. All microbiota were isolated using standard media-based methodologies. Isolates from Mir and ISS were processed using various methods of analysis, including VITEK biochemical analysis, 16s ribosomal identification, and fingerprinting using rep-PCR analysis. Over the first 41 months of habitation, the diversity of the microbiota from air and surface samples aboard ISS increased from an initial six to 53 different bacterial species. During the same period, fungal diversity increased from 2 to 24 species. Based upon rep-PCR analysis, the majority of isolates were unique suggesting the need for increased sampling frequency and a more thorough analysis of samples to properly characterize the ISS microbiota. This limited fungal and bacterial data from environmental samples acquired during monitoring currently do not indicate a microbial hazard to ISS or any trends suggesting potential health risks.

Skin temperature and heart rate can be used to estimate physiological strain during exercise in the heat in a cohort of fit and unfit males.

PubMed

Cuddy, John S; Buller, Mark; Hailes, Walter S; Ruby, Brent C

2013-07-01

To evaluate the previously developed physiological strain index (PSI) model using heart rate and skin temperature to provide further insight into the detection and estimation of thermal and physiological heat strain indices. A secondary aim was to characterize individuals who excel in their performance in the heat. 56 male participants completed 2 walking trials (3.5 miles per hour, 5% grade) in controlled environments of 43.3 °C and 15.5 °C (40% humidity). Core and skin temperature, along with heart rate and PSI, were continually monitored during exercise. Participants completed a physical fitness test. The logistic regression model exhibited 4 false positives and 1 false negative at the 40% decision boundary. The "Not at Risk" group (N = 33) had higher body weight (84 ± 13 vs. 77 ± 10 kg, respectively) compared to the "At Risk" (N = 23) group, p < 0.05. The "Not at Risk" group had a faster 3-mile run time compared to the "At Risk" group (21:53 ± 3:13 vs. 25:16 ± 2:37, respectively), p < 0.05. During the Heat Trial, the "At Risk" group had a higher rating of perceived exertion at 60 and 90 minutes compared to the "Not at Risk" group (13.5 ± 2.8 vs. 11.5 ± 1.8 and 14.8 ± 3.2 vs. 12.2 ± 2.0 for "At Risk" vs. "Not at Risk" at 60 and 90 minutes, respectively), p < 0.05. The previously developed model relating heart rate and skin temperature to PSI is highly accurate at assessing heat risk status. Participants classified as "At Risk" had lower physical performance scores and different body weights compared to the "Not at Risk" group and perceived themselves as working harder during exercise in the heat. Reprint & Copyright © 2013 Association of Military Surgeons of the U.S.

Using hierarchical linear growth models to evaluate protective mechanisms that mediate science achievement

NASA Astrophysics Data System (ADS)

von Secker, Clare Elaine

The study of students at risk is a major topic of science education policy and discussion. Much research has focused on describing conditions and problems associated with the statistical risk of low science achievement among individuals who are members of groups characterized by problems such as poverty and social disadvantage. But outcomes attributed to these factors do not explain the nature and extent of mechanisms that account for differences in performance among individuals at risk. There is ample theoretical and empirical evidence that demographic differences should be conceptualized as social contexts, or collections of variables, that alter the psychological significance and social demands of life events, and affect subsequent relationships between risk and resilience. The hierarchical linear growth models used in this dissertation provide greater specification of the role of social context and the protective effects of attitude, expectations, parenting practices, peer influences, and learning opportunities on science achievement. While the individual influences of these protective factors on science achievement were small, their cumulative effect was substantial. Meta-analysis conducted on the effects associated with psychological and environmental processes that mediate risk mechanisms in sixteen social contexts revealed twenty-two significant differences between groups of students. Positive attitudes, high expectations, and more intense science course-taking had positive effects on achievement of all students, although these factors were not equally protective in all social contexts. In general, effects associated with authoritative parenting and peer influences were negative, regardless of social context. An evaluation comparing the performance and stability of hierarchical linear growth models with traditional repeated measures models is included as well.

Uric acid and obesity-related phenotypes in postmenopausal women.

PubMed

Grygiel-Górniak, B; Mosor, M; Marcinkowska, J; Przysławski, J; Nowak, J

2018-06-01

The aim of this study was to find the genetic, metabolic, and nutritional risk factors, which can be associated with uric acid (UA) level. The risk factors related to uricemia were assessed among 271 postmenopausal women without cardiometabolic disorders and hypolipidemic/hypoglycemic treatment selected from a cohort of 1423 obese postmenopausal women. The bioimpedance analysis and biochemical and genetic analyses were performed in two groups characterized by serum UA ≥ 4 mg/dL (238 μmol/L) and < 4 mg/dL. The TaqMan-based real-time PCR method was applied to assess the role of Pro12Ala of peroxisome proliferation-activated receptor (PPAR)gamma-2 and Trp64Arg of beta-3-adrenergic receptor (ADRB) polymorphisms. Women with UA level ≥ 4 mg/dL were characterized by larger body mass, triceps skinfold, waist circumference, body fat amount, and serum insulin, glucose, and triglyceride levels. There was no difference in dietary habits between the analyzed groups. Body mass, waist circumference, body fat amount, diastolic blood pressure, and serum insulin, glucose, high-density lipoprotein, and triglyceride levels, Homeostasis Model Assessment-Insulin Resistance, and energy from the dietary fat influence the UA level ≥ 4 mg/dL; however, the serum UA was not determined by Pro12Ala and Trp64Arg polymorphism analyses. The model of linear regression revealed that the group characterized by body mass index  ≥ 25 kg/m 2 and glucose ≥ 100 mg/dL has 4 times increased risk of UA level (p = 0.0009); after adding triglycerides ≥ 150 mg/dL, the risk of UA increased 7 times (p = 0.0216). Increasing the level of UA ≥ 4 mg/dL is associated with overweight, hyperglycemia, and hypertriglyceridemia in women without a history of cardiometabolic disorders. A better management of metabolic factors could help prevent further increase in UA levels.

Time-lapse seismic waveform modelling and attribute analysis using hydromechanical models for a deep reservoir undergoing depletion

NASA Astrophysics Data System (ADS)

He, Y.-X.; Angus, D. A.; Blanchard, T. D.; Wang, G.-L.; Yuan, S.-Y.; Garcia, A.

2016-04-01

Extraction of fluids from subsurface reservoirs induces changes in pore pressure, leading not only to geomechanical changes, but also perturbations in seismic velocities and hence observable seismic attributes. Time-lapse seismic analysis can be used to estimate changes in subsurface hydromechanical properties and thus act as a monitoring tool for geological reservoirs. The ability to observe and quantify changes in fluid, stress and strain using seismic techniques has important implications for monitoring risk not only for petroleum applications but also for geological storage of CO2 and nuclear waste scenarios. In this paper, we integrate hydromechanical simulation results with rock physics models and full-waveform seismic modelling to assess time-lapse seismic attribute resolution for dynamic reservoir characterization and hydromechanical model calibration. The time-lapse seismic simulations use a dynamic elastic reservoir model based on a North Sea deep reservoir undergoing large pressure changes. The time-lapse seismic traveltime shifts and time strains calculated from the modelled and processed synthetic data sets (i.e. pre-stack and post-stack data) are in a reasonable agreement with the true earth models, indicating the feasibility of using 1-D strain rock physics transform and time-lapse seismic processing methodology. Estimated vertical traveltime shifts for the overburden and the majority of the reservoir are within ±1 ms of the true earth model values, indicating that the time-lapse technique is sufficiently accurate for predicting overburden velocity changes and hence geomechanical effects. Characterization of deeper structure below the overburden becomes less accurate, where more advanced time-lapse seismic processing and migration is needed to handle the complex geometry and strong lateral induced velocity changes. Nevertheless, both migrated full-offset pre-stack and near-offset post-stack data image the general features of both the overburden and reservoir units. More importantly, the results from this study indicate that integrated seismic and hydromechanical modelling can help constrain time-lapse uncertainty and hence reduce risk due to fluid extraction and injection.

Statistical Rick Estimation for Communication System Design --- A Preliminary Look

NASA Astrophysics Data System (ADS)

Babuscia, A.; Cheung, K.-M.

2012-02-01

Spacecraft are complex systems that involve different subsystems with multiple relationships among them. For these reasons, the design of a spacecraft is a time-evolving process that starts from requirements and evolves over time across different design phases. During this process, a lot of changes can happen. They can affect mass and power at the component level, at the subsystem level, and even at the system level. Each spacecraft has to respect the overall constraints in terms of mass and power: for this reason, it is important to be sure that the design does not exceed these limitations. Current practice in system models primarily deals with this problem, allocating margins on individual components and on individual subsystems. However, a statistical characterization of the fluctuations in mass and power of the overall system (i.e., the spacecraft) is missing. This lack of adequate statistical characterization would result in a risky spacecraft design that might not fit the mission constraints and requirements, or in a conservative design that might not fully utilize the available resources. Due to the complexity of the problem and to the different expertise and knowledge required to develop a complete risk model for a spacecraft design, this article is focused on risk estimation for a specific spacecraft subsystem: the communication subsystem. The current research aims to be a proof of concept of a risk-based design optimization approach, which can then be further expanded to the design of other subsystems as well as to the whole spacecraft. The objective of this research is to develop a mathematical approach to quantify the likelihood that the major design drivers of mass and power of a space communication system would meet the spacecraft and mission requirements and constraints through the mission design lifecycle. Using this approach, the communication system designers will be able to evaluate and to compare different communication architectures in a risk trade-off perspective. The results described in this article include a baseline communication system design tool and a statistical characterization of the design risks through a combination of historical mission data and expert opinion contributions. An application example of the communication system of a university spacecraft is presented. IPNPR Volume 42-189 Tagged File.txt

To risk or not to risk: Anxiety and the calibration between risk perception and danger mitigation.

PubMed

Notebaert, Lies; Masschelein, Stijn; Wright, Bridget; MacLeod, Colin

2016-06-01

Anxiety prepares an organism for dealing with threats by recruiting cognitive resources to process information about the threat, and by engaging physiological systems to prepare a response. Heightened trait anxiety is associated with biases in both these processes: high trait-anxious individuals tend to report heightened risk perceptions, and inappropriate engagement in danger mitigation behavior. However, no research has addressed whether the calibration between risk perception and danger mitigation behavior is affected by anxiety, though it is well recognized that this calibration is crucial for adaptive functioning. The current study aimed to examine whether anxiety is characterized by better or worse calibration of danger mitigation behavior to variations in risk magnitude. Low and high trait-anxious participants were presented with information about the likelihood and severity of a danger (loud noise burst) on each trial. Participants could decide to mitigate this danger by investing a virtual coin, at the cost of losing danger mitigation ability on subsequent trials. Importantly, level of risk likelihood and severity were varied independently, and the multiplicative relationship between the 2 defined total danger. Multilevel modeling showed that the magnitude of total danger predicted the probability of coin investments, over and above the effects of risk likelihood and severity, suggesting that participants calibrated their danger mitigation behavior to integrated risk information. Crucially, this calibration was affected by trait anxiety, indicating better calibration in high trait-anxious individuals. These results are discussed in light of existing knowledge and models of the effect of anxiety on risk perception and decision-making. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Prospective Predictors of Suicidal Behavior in BPD at 6 Year Follow-up

PubMed Central

Soloff, Paul H.; Chiappetta, Laurel

2012-01-01

Objective Recurrent suicidal behavior is a defining characteristic of BPD. Although most patients achieve remission of suicidal behaviors over time, 3% to 10% die by suicide, raising the question of whether there is a high risk suicidal subtype in BPD. We are conducting the first longitudinal study of suicidal behavior in BPD to identify prospective predictors of suicide attempts, and characterize BPD patients at highest risk for suicide completion. Method Demographic, diagnostic, clinical and psychosocial risk factors assessed at baseline were examined for predictive association with medically significant suicide attempts using Cox proportional hazards models. Prospective predictors were defined for subjects completing 6 or more years in the study and compared to earlier intervals. Results Among 90 subjects, 25 (27.8%) made at least one suicide attempt in the interval, most occurring in the first two years. Risk of attempt was increased by: a.) low socioeconomic status, b.) poor psychosocial adjustment, c.) a family history of suicide d.) prior psychiatric hospitalization; e.) absence of any outpatient treatment prior to the attempt. Higher global functioning at baseline decreased risk. Conclusion Risk factors predictive of suicide attempts change over time. Acute stressors such as MDD were predictive only in the short term (12 mos.), while poor psychosocial functioning had persistent and long term effects on suicide risk. Half of BPD patients have poor psychosocial outcomes despite symptomatic improvement. A social and vocational rehabilitation model of treatment is needed to decrease suicide risk and optimize long term outcomes in BPD. PMID:22549208

Pitfalls and Precautions When Using Predicted Failure Data for Quantitative Analysis of Safety Risk for Human Rated Launch Vehicles

NASA Technical Reports Server (NTRS)

Hatfield, Glen S.; Hark, Frank; Stott, James

2016-01-01

Launch vehicle reliability analysis is largely dependent upon using predicted failure rates from data sources such as MIL-HDBK-217F. Reliability prediction methodologies based on component data do not take into account risks attributable to manufacturing, assembly, and process controls. These sources often dominate component level reliability or risk of failure probability. While consequences of failure is often understood in assessing risk, using predicted values in a risk model to estimate the probability of occurrence will likely underestimate the risk. Managers and decision makers often use the probability of occurrence in determining whether to accept the risk or require a design modification. Due to the absence of system level test and operational data inherent in aerospace applications, the actual risk threshold for acceptance may not be appropriately characterized for decision making purposes. This paper will establish a method and approach to identify the pitfalls and precautions of accepting risk based solely upon predicted failure data. This approach will provide a set of guidelines that may be useful to arrive at a more realistic quantification of risk prior to acceptance by a program.

An examination of emotion regulation, temperament, and parenting style as potential predictors of adolescent depression risk status: a correlational study.

PubMed

Betts, Jennifer; Gullone, Eleonora; Allen, J Sabura

2009-06-01

Given that depression is a debilitating disorder, it is critical that we advance our understanding about the aetiology of this disorder. This study investigated both traditional (temperament and parenting) and novel (emotion regulation strategy) risk factors associated with adolescent depression. Forty-four adolescents (12-16 years; 64% females) with high scores on a self-report depressive symptomatology questionnaire were compared to a similar group of 44 adolescents with low scores, matched for age, gender, and ethnicity. Significant group differences were present on all assessed risk factors. The presence of high depressive symptomatology was found to be associated with (1) low levels of temperamentally based positive mood, flexibility, and approach behaviours, (2) a parenting style characterized by low nurturance and high overprotection, and (3) emotion regulation characterized by higher levels of expressive suppression and lower levels of cognitive reappraisal. It was concluded that, in addition to specific temperament characteristics and parenting style, use of particular emotion regulation strategies is associated with varying levels of depressive symptomatology. These findings reinforce the importance of incorporating emotion regulation into explanatory models of depression symptomatology. Further research that tests the direction of effects for these cross-sectional findings is warranted.

Latent Profile Analysis and Conversion to Psychosis: Characterizing Subgroups to Enhance Risk Prediction.

PubMed

Healey, Kristin M; Penn, David L; Perkins, Diana; Woods, Scott W; Keefe, Richard S E; Addington, Jean

2018-02-15

Groups at clinical high risk (CHR) of developing psychosis are heterogeneous, composed of individuals with different clusters of symptoms. It is likely that there exist subgroups, each associated with different symptom constellations and probabilities of conversion. Present study used latent profile analysis (LPA) to ascertain subgroups in a combined sample of CHR (n = 171) and help-seeking controls (HSCs; n = 100; PREDICT study). Indicators in the LPA model included baseline Scale of Prodromal Symptoms (SOPS), Calgary Depression Scale for Schizophrenia (CDSS), and neurocognitive performance as measured by multiple instruments, including category instances (CAT). Subgroups were further characterized using covariates measuring demographic and clinical features. Three classes emerged: class 1 (mild, transition rate 5.6%), lowest SOPS and depression scores, intact neurocognitive performance; class 2 (paranoid-affective, transition rate 14.2%), highest suspiciousness, mild negative symptoms, moderate depression; and class 3 (negative-neurocognitive, transition rate 29.3%), highest negative symptoms, neurocognitive impairment, social cognitive impairment. Classes 2 and 3 evidenced poor social functioning. Results support a subgroup approach to research, assessment, and treatment of help-seeking individuals. Class 3 may be an early risk stage of developing schizophrenia.

[Open narcissism, covered narcissism and personality disorders as predictive factors of treatment response in an out-patient Drug Addiction Unit].

PubMed

Salazar-Fraile, José; Ripoll-Alandes, Carmen; Bobes, Julio

2010-01-01

Although a high prevalence of personality disorders has been reported in substance users, the literature on their value for predicting treatment response is controversial. On the other hand, while the predictive validity of personality traits as predictors of response to drug abuse or dependence has been studied, research on the validity of narcissistic personality traits is scarce. To study the predictive value of personality disorders, narcissistic personality traits and self-esteem for predicting treatment response. We assessed 78 patients attended at an addiction treatment unit using personality disorder diagnoses and measures of self-esteem, narcissism and covert (hypersensitive) narcissism. These variables were used in a Cox survival model as predictive variables of time to relapse into drug use. Hypersensitive (covert) narcissism and borderline and passive-aggressive personality disorders were risk factors for relapse into drug use, while open narcissism was a protective factor. Self-esteem did not show predictive validity. Personality disorders characterized by impulsivity-instability and passivity-resentfulness show higher risk of relapse into drug abuse. Personality traits characterized by high sensitivity to humiliation increase the risk of relapse, whereas pride and self-confidence are protective factors.

Dioxin risk assessment: mechanisms of action and possible toxicity in human health.

PubMed

Tavakoly Sany, Seyedeh Belin; Hashim, Rosli; Salleh, Aishah; Rezayi, Majid; Karlen, David J; Razavizadeh, Bi Bi Marzieh; Abouzari-Lotf, Ebrahim

2015-12-01

Dioxin-like compounds (DLCs) have been classified by the World Health Organization (WHO) as one of the most persistent toxic chemical substances in the environment, and they are associated with several occupational activities and industrial accidents around the world. Since the end of the 1970s, these toxic chemicals have been banned because of their human toxicity potential, long half-life, wide dispersion, and they bioaccumulate in the food web. This review serves as a primer for environmental health professionals to provide guidance on short-term risk assessment of dioxin and to identify key findings for health and exposure assessment based on policies of different agencies. It also presents possible health effects of dioxins, mechanisms of action, toxic equivalency factors (TEFs), and dose-response characterization. Key studies related to toxicity values of dioxin-like compounds and their possible human health risk were identified through PubMed and supplemented with relevant studies characterized by reviewing the reference lists in the review articles and primary literature. Existing data decreases the scope of analyses and models in relevant studies to a manageable size by focusing on the set of important studies related to the perspective of developing toxicity values of DLCs.

Sex Differences in Demographics, Risk Factors, Presentation, and Noninvasive Testing in Stable Outpatients with Suspected Coronary Artery Disease: Insights from the PROMISE Trial

PubMed Central

Hemal, Kshipra; Pagidipati, Neha J.; Coles, Adrian; Dolor, Rowena J.; Mark, Daniel B.; Pellikka, Patricia A.; Hoffmann, Udo; Litwin, Sheldon E.; Daubert, Melissa A.; Shah, Svati H.; Ariani, Kevin; Bullock-Palmer, Renee; Martinez, Beth; Lee, Kerry L.; Douglas, Pamela S.

2016-01-01

STRUCTURED ABSTRACT Objectives To determine whether presentation, risk assessment, testing choices, and results differ by sex in stable symptomatic outpatients with suspected coronary artery disease (CAD). Background Although established CAD presentations differ by sex, little is known about stable, suspected CAD. Methods Characteristics of 10,003 men and women in the Prospective Multicenter Imaging Study for Evaluation of Chest Pain (PROMISE) trial were compared using chi-square and Wilcoxon rank sum tests. Sex differences in test selection and predictors of test positivity were examined using logistic regression. Results Women were older (62.4 years vs. 59.0) and more likely to be hypertensive (66.6% vs. 63.2%), dyslipidemic (68.9% vs. 66.3%), and to have a family history of premature CAD (34.6% vs. 29.3) (all p-values<0.005). Women were less likely to smoke (45.6% vs. 57.0%; p<0.001), while diabetes prevalence was similar (21.8% vs. 21.0%; p=0.30). Chest pain was the primary symptom in 73.2% of women vs. 72.3% of men (p=0.30) and was characterized as “crushing/pressure/squeezing/tightness” in 52.5% of women vs. 46.2% of men (p<0.001). Compared to men, all risk scores characterized women as lower risk, and providers were more likely to characterize women as having low (<30%) pre-test probability for CAD (40.7% vs. 34.1%; p<0.001). Compared with men, women were more often referred to imaging tests (adjusted OR 1.21; 95% CI 1.01–1.44) than non-imaging tests. Women were less likely to have a positive test (9.7% vs. 15.1%; p<0.001). Although univariate predictors of test positivity were similar, in multivariable models, age, BMI, and Framingham risk score were predictive of a positive test in women, while Framingham and Diamond and Forrester risk scores were predictive in men. Conclusion Patient sex influences the entire diagnostic pathway for possible CAD, from baseline risk factors and presentation to noninvasive test outcomes. These differences highlight the need for sex-specific approaches to CAD evaluation. PMID:27017234

Predictive value of modeled AUC(AFP-hCG), a dynamic kinetic parameter characterizing serum tumor marker decline in patients with nonseminomatous germ cell tumor.

PubMed

You, Benoit; Fronton, Ludivine; Boyle, Helen; Droz, Jean-Pierre; Girard, Pascal; Tranchand, Brigitte; Ribba, Benjamin; Tod, Michel; Chabaud, Sylvie; Coquelin, Henri; Fléchon, Aude

2010-08-01

The early decline profile of alpha-fetoprotein (AFP) and human chorionic gonadotropin (hCG) in patients with nonseminomatous germ cell tumors (NSGCT) treated with chemotherapy may be related to the risk of relapse. We assessed the predictive values of areas under the curve of hCG (AUC(hCG)) and AFP (AUC(AFP)) of modeled concentration-time equations on progression-free survival (PFS). Single-center retrospective analysis of hCG and AFP time-points from 65 patients with IGCCCG intermediate-poor risk NSGCT treated with 4 cycles of bleomycin-etoposide-cisplatin (BEP). To determine AUC(hCG) and AUC(AFP) for D0-D42, AUCs for D0-D7 were calculated using the trapezoid rule and AUCs for D7-D42 were calculated using the mathematic integrals of equations modeled with NONMEM. Combining AUC(AFP) and AUC(hCG) enabled us to define 2 predictive groups: namely, patients with favorable and unfavorable AUC(AFP-hCG). Survival analyses and ROC curves assessed the predictive values of AUC(AFP-hCG) groups regarding progression-free survival (PFS) and compared them with those of half-life (HL) and time-to-normalization (TTN). Mono-exponential models best fit the patterns of marker decreases. Patients with a favorable AUC(AFP-hCG) had a significantly better PFS (100% vs 71.5%, P = .014). ROC curves confirmed the encouraging predictive accuracy of AUC(AFP-hCG) against HL or TTN regarding progression risk (ROC AUCs = 79.6 vs 71.9 and 70.2 respectively). Because of the large number of patients with missing data, multivariate analysis could not be performed. AUC(AFP-hCG) is a dynamic parameter characterizing tumor marker decline in patients with NSGCT during BEP treatment. Its value as a promising predictive factor should be validated. Copyright 2010 Elsevier Inc. All rights reserved.

How the twain can meet: Prospect theory and models of heuristics in risky choice.

PubMed

Pachur, Thorsten; Suter, Renata S; Hertwig, Ralph

2017-03-01

Two influential approaches to modeling choice between risky options are algebraic models (which focus on predicting the overt decisions) and models of heuristics (which are also concerned with capturing the underlying cognitive process). Because they rest on fundamentally different assumptions and algorithms, the two approaches are usually treated as antithetical, or even incommensurable. Drawing on cumulative prospect theory (CPT; Tversky & Kahneman, 1992) as the currently most influential instance of a descriptive algebraic model, we demonstrate how the two modeling traditions can be linked. CPT's algebraic functions characterize choices in terms of psychophysical (diminishing sensitivity to probabilities and outcomes) as well as psychological (risk aversion and loss aversion) constructs. Models of heuristics characterize choices as rooted in simple information-processing principles such as lexicographic and limited search. In computer simulations, we estimated CPT's parameters for choices produced by various heuristics. The resulting CPT parameter profiles portray each of the choice-generating heuristics in psychologically meaningful ways-capturing, for instance, differences in how the heuristics process probability information. Furthermore, CPT parameters can reflect a key property of many heuristics, lexicographic search, and track the environment-dependent behavior of heuristics. Finally, we show, both in an empirical and a model recovery study, how CPT parameter profiles can be used to detect the operation of heuristics. We also address the limits of CPT's ability to capture choices produced by heuristics. Our results highlight an untapped potential of CPT as a measurement tool to characterize the information processing underlying risky choice. Copyright © 2017 Elsevier Inc. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bogen, K T

A relatively simple, quantitative approach is proposed to address a specific, important gap in the appr approach recommended by the USEPA Guidelines for Cancer Risk Assessment to oach address uncertainty in carcinogenic mode of action of certain chemicals when risk is extrapolated from bioassay data. These Guidelines recognize that some chemical carcinogens may have a site-specific mode of action (MOA) that is dual, involving mutation in addition to cell-killing induced hyperplasia. Although genotoxicity may contribute to increased risk at all doses, the Guidelines imply that for dual MOA (DMOA) carcinogens, judgment be used to compare and assess results obtained usingmore » separate 'linear' (genotoxic) vs. 'nonlinear' (nongenotoxic) approaches to low low-level risk extrapolation. However, the Guidelines allow the latter approach to be used only when evidence is sufficient t to parameterize a biologically based model that reliably o extrapolates risk to low levels of concern. The Guidelines thus effectively prevent MOA uncertainty from being characterized and addressed when data are insufficient to parameterize such a model, but otherwise clearly support a DMOA. A bounding factor approach - similar to that used in reference dose procedures for classic toxicity endpoints - can address MOA uncertainty in a way that avoids explicit modeling of low low-dose risk as a function of administere administered or internal dose. Even when a 'nonlinear' toxicokinetic model cannot be fully validated, implications of DMOA uncertainty on low low-dose risk may be bounded with reasonable confidence when target tumor types happen to be extremely rare. This concept was i illustrated llustrated for a likely DMOA rodent carcinogen naphthalene, specifically to the issue of risk extrapolation from bioassay data on naphthalene naphthalene-induced nasal tumors in rats. Bioassay data, supplemental toxicokinetic data, and related physiologically based p pharmacokinetic and 2 harmacokinetic 2-stage stochastic carcinogenesis modeling results all clearly indicate that naphthalene is a DMOA carcinogen. Plausibility bounds on rat rat-tumor tumor-type specific DMOA DMOA-related uncertainty were obtained using a 2-stage model adapted to reflec reflect the empirical link between genotoxic and cytotoxic effects of t the most potent identified genotoxic naphthalene metabolites, 1,2 1,2- and 1,4 1,4-naphthoquinone. Bound Bound-specific 'adjustment' factors were then used to reduce naphthalene risk estimated by linear ex extrapolation (under the default genotoxic MOA assumption), to account for the DMOA trapolation exhibited by this compound.« less

FASTER SCIENCE FOR BETTER DECISIONS: CHARACTERIZING ENVIRONMENTAL CONTAMINANT RISK FROM HIGH THROUGHPUT DATA

EPA Science Inventory

Tens of thousands of chemicals and other man-made contaminants exist in our environment, but only a fraction of these have been characterized for their potential risk to humans and there is widespread interest in closing this data gap in order to better manage contaminant risk. C...

Current and future needs for developmental toxicity testing.

PubMed

Makris, Susan L; Kim, James H; Ellis, Amy; Faber, Willem; Harrouk, Wafa; Lewis, Joseph M; Paule, Merle G; Seed, Jennifer; Tassinari, Melissa; Tyl, Rochelle

2011-10-01

A review is presented of the use of developmental toxicity testing in the United States and international regulatory assessment of human health risks associated with exposures to pharmaceuticals (human and veterinary), chemicals (agricultural, industrial, and environmental), food additives, cosmetics, and consumer products. Developmental toxicology data are used for prioritization and screening of pharmaceuticals and chemicals, for evaluating and labeling of pharmaceuticals, and for characterizing hazards and risk of exposures to industrial and environmental chemicals. The in vivo study designs utilized in hazard characterization and dose-response assessment for developmental outcomes have not changed substantially over the past 30 years and have served the process well. Now there are opportunities to incorporate new technologies and approaches to testing into the existing assessment paradigm, or to apply innovative approaches to various aspects of risk assessment. Developmental toxicology testing can be enhanced by the refinement or replacement of traditional in vivo protocols, including through the use of in vitro assays, studies conducted in alternative nonmammalian species, the application of new technologies, and the use of in silico models. Potential benefits to the current regulatory process include the ability to screen large numbers of chemicals quickly, with the commitment of fewer resources than traditional toxicology studies, and to refine the risk assessment process through an enhanced understanding of the mechanisms of developmental toxicity and their relevance to potential human risk. As the testing paradigm evolves, the ability to use developmental toxicology data to meet diverse critical regulatory needs must be retained. © 2011 Wiley Periodicals, Inc.

Impact of exposure measurement error in air pollution epidemiology: effect of error type in time-series studies.

PubMed

Goldman, Gretchen T; Mulholland, James A; Russell, Armistead G; Strickland, Matthew J; Klein, Mitchel; Waller, Lance A; Tolbert, Paige E

2011-06-22

Two distinctly different types of measurement error are Berkson and classical. Impacts of measurement error in epidemiologic studies of ambient air pollution are expected to depend on error type. We characterize measurement error due to instrument imprecision and spatial variability as multiplicative (i.e. additive on the log scale) and model it over a range of error types to assess impacts on risk ratio estimates both on a per measurement unit basis and on a per interquartile range (IQR) basis in a time-series study in Atlanta. Daily measures of twelve ambient air pollutants were analyzed: NO2, NOx, O3, SO2, CO, PM10 mass, PM2.5 mass, and PM2.5 components sulfate, nitrate, ammonium, elemental carbon and organic carbon. Semivariogram analysis was applied to assess spatial variability. Error due to this spatial variability was added to a reference pollutant time-series on the log scale using Monte Carlo simulations. Each of these time-series was exponentiated and introduced to a Poisson generalized linear model of cardiovascular disease emergency department visits. Measurement error resulted in reduced statistical significance for the risk ratio estimates for all amounts (corresponding to different pollutants) and types of error. When modelled as classical-type error, risk ratios were attenuated, particularly for primary air pollutants, with average attenuation in risk ratios on a per unit of measurement basis ranging from 18% to 92% and on an IQR basis ranging from 18% to 86%. When modelled as Berkson-type error, risk ratios per unit of measurement were biased away from the null hypothesis by 2% to 31%, whereas risk ratios per IQR were attenuated (i.e. biased toward the null) by 5% to 34%. For CO modelled error amount, a range of error types were simulated and effects on risk ratio bias and significance were observed. For multiplicative error, both the amount and type of measurement error impact health effect estimates in air pollution epidemiology. By modelling instrument imprecision and spatial variability as different error types, we estimate direction and magnitude of the effects of error over a range of error types.

Subthreshold depression: characteristics and risk factors among vulnerable elders.

PubMed

Adams, Kathryn Betts; Moon, Heehyul

2009-09-01

This study examines symptoms of subthreshold depression among older adults in congregate housing, compared with their nondepressed peers, and tests a conceptual model of subthreshold depression. Hypotheses included that subthreshold depression would be characterized and distinguished by low energy, social withdrawal, and depletion, rather than sadness, and that subthreshold depressed elders would be distinguished by poorer health and functioning, loneliness, and grieving a recent loss. A self-administered survey was followed by a diagnostic interview by telephone to (N = 166) white and African-American residents of independent and assisted living apartments from six retirement communities, average age 82.9 years. The Mini International Neuropsychiatric Interview (MINI) determined depression status. The 30-item Geriatric Depression Scale was used to measure symptoms. Forty-six individuals (27.7%) were identified as subthreshold depressed, seven as suffering from major depression, and 113 as non-depressed. Subthreshold depression was characterized by low energy, difficulty with initiative, worries about the future, lack of positive affect, sadness and irritability. Negative affect symptoms such as sadness and irritability best discriminated the subthreshold group from the nondepressed. Risk factors for subthreshold depression in this sample included less education, lower socio-economic status, African-American race, grieving, and social loneliness. Subthreshold depression in this group of residents of congregate housing was similar to the depletion experienced by many nondepressed elders, but further characterized by negative affect and lack of hope for the future. Social factors, such as socioeconomic status and personal losses, constituted greater risks for subthreshold depression than did health and functioning.

Tele-Epidemiology and Public Health in the Canadian Context

NASA Astrophysics Data System (ADS)

Brazeau, Stephanie; Kotchi, Serge Olivier; Ludwig, Antoinette; Turgeon, Patricia; Pelcat, Yann; Aube, Guy; Ogden, Nicholas H.

2016-08-01

The management of key public health issues requires solid evidence-based knowledge for the prevention and control of various emerging or re-emerging vector borne diseases (e.g. Lyme disease, West Nile virus, etc.) and environmentally-linked diseases (e.g. enteric infections from recreational water contamination). Earth observation (EO) images enhance knowledge and capacity to characterize risk of illness across the vast Canadian territory by deriving new and up-to-date data from population, climatic and environmental determinants of health relevant to public health actions such as risk mapping, risk communication and identification of vulnerable populations.Modeling of infectious disease transmission has made possible the identification of risk areas and the underlying factors (human activities, ecology, environment and climate) that may explain this emergence. New data products derived from Earth observation satellites pertaining to climate, land cover and land use, and distribution and density of animal and human populations have greatly improved the resolution and the specificity of explanatory and predictive models.This article focuses on the scope of tele-epidemiology activities of the Canadian public health community as well as current and potential future fields of application for Earth observation data. It will demonstrate the strength, sustainability and innovative character of these approaches to improve scale-dependent decision- making at different levels of government in Canada (federal, provincial/territorial and regional) and increase the efficiency of many preventive, preparedness and response actions.Examples of tele-epidemiology applications will be presented such as the risk assessment of microbial contamination of recreational waters and modelling the risk of vector borne diseases in the Canadian context.

The Role of the Anterior Insula in Adolescent Decision Making

PubMed Central

Smith, Ashley R.; Steinberg, Laurence; Chein, Jason

2017-01-01

Much recent research on adolescent decision making has sought to characterize the neurobiological mechanisms that underlie the proclivity of adolescents to engage in risky behavior. One class of influential neurodevelopmental models focuses on the asynchronous development of neural systems, particularly those responsible for self-regulation and reward seeking. While this work has largely focused on the development of prefrontal (self-regulation) and striatal (reward processing) circuitry, the present article explores the significance of a different region, the anterior insular cortex (AIC), in adolescent decision making. Although the AIC is known for its role as a cognitive-emotional hub, and is included in some models of adult self-regulation and reward seeking, the importance of the AIC and its maturation in adolescent risk taking has not been extensively explored. In this article we discuss evidence on AIC development, and consider how age-related differences in AIC engagement may contribute to heightened risk taking during adolescence. Based on this review, we propose a model in which the engagement of adolescents in risk taking may be linked in part to the maturation of the AIC and its connectivity to the broader brain networks in which it participates. PMID:24853135

Interdisciplinary modeling and analysis to reduce loss of life from tsunamis

NASA Astrophysics Data System (ADS)

Wood, N. J.

2016-12-01

Recent disasters have demonstrated the significant loss of life and community impacts that can occur from tsunamis. Minimizing future losses requires an integrated understanding of the range of potential tsunami threats, how individuals are specifically vulnerable to these threats, what is currently in place to improve their chances of survival, and what risk-reduction efforts could be implemented. This presentation will provide a holistic perspective of USGS research enabled by recent advances in geospatial modeling to assess and communicate population vulnerability to tsunamis and the range of possible interventions to reduce it. Integrated research includes efforts to characterize the magnitude and demography of at-risk individuals in tsunami-hazard zones, their evacuation potential based on landscape conditions, nature-based mitigation to improve evacuation potential, evacuation pathways and population demand at assembly areas, siting considerations for vertical-evacuation refuges, community implications of multiple evacuation zones, car-based evacuation modeling for distant tsunamis, and projected changes in population exposure to tsunamis over time. Collectively, this interdisciplinary research supports emergency managers in their efforts to implement targeted risk-reduction efforts based on local conditions and needs, instead of generic regional strategies that only focus on hazard attributes.

Modeling ecohydrological dynamics of smallholder strategies for food production in dryland agricultural systems

NASA Astrophysics Data System (ADS)

Gower, Drew B.; Dell'Angelo, Jampel; McCord, Paul F.; Caylor, Kelly K.; Evans, Tom P.

2016-11-01

In dryland environments, characterized by low and frequently variable rainfall, smallholder farmers must take crop water sensitivity into account along with other characteristics like seed availability and market price when deciding what to plant. In this paper we use the results of surveys conducted among smallholders located near Mount Kenya to identify clusters of farmers devoting different fractions of their land to subsistence and market crops. Additionally, we explore the tradeoffs between water-insensitive but low-value subsistence crops and a water-sensitive but high-value market crop using a numerical model that simulates soil moisture dynamics and crop production over multiple growing seasons. The cluster analysis shows that most farmers prefer to plant either only subsistence crops or only market crops, with a minority choosing to plant substantial fractions of both. The model output suggests that the value a farmer places on a successful growing season, a measure of risk aversion, plays a large role in whether the farmer chooses a subsistence or market crop strategy. Furthermore, access to irrigation, makes market crops more appealing, even to very risk-averse farmers. We then conclude that the observed clustering may result from different levels of risk aversion and access to irrigation.

Quantifying risks with exact analytical solutions of derivative pricing distribution

NASA Astrophysics Data System (ADS)

Zhang, Kun; Liu, Jing; Wang, Erkang; Wang, Jin

2017-04-01

Derivative (i.e. option) pricing is essential for modern financial instrumentations. Despite of the previous efforts, the exact analytical forms of the derivative pricing distributions are still challenging to obtain. In this study, we established a quantitative framework using path integrals to obtain the exact analytical solutions of the statistical distribution for bond and bond option pricing for the Vasicek model. We discuss the importance of statistical fluctuations away from the expected option pricing characterized by the distribution tail and their associations to value at risk (VaR). The framework established here is general and can be applied to other financial derivatives for quantifying the underlying statistical distributions.

Noisy Preferences in Risky Choice: A Cautionary Note

PubMed Central

2017-01-01

We examine the effects of multiple sources of noise in risky decision making. Noise in the parameters that characterize an individual’s preferences can combine with noise in the response process to distort observed choice proportions. Thus, underlying preferences that conform to expected value maximization can appear to show systematic risk aversion or risk seeking. Similarly, core preferences that are consistent with expected utility theory, when perturbed by such noise, can appear to display nonlinear probability weighting. For this reason, modal choices cannot be used simplistically to infer underlying preferences. Quantitative model fits that do not allow for both sorts of noise can lead to wrong conclusions. PMID:28569526

Genetically modified soybeans and food allergies.

PubMed

Herman, Eliot M

2003-05-01

Allergenic reactions to proteins expressed in GM crops has been one of the prominent concerns among biotechnology critics and a concern of regulatory agencies. Soybeans like many plants have intrinsic allergens that present problems for sensitive people. Current GM crops, including soybean, have not been shown to add any additional allergenic risk beyond the intrinsic risks already present. Biotechnology can be used to characterize and eliminate allergens naturally present in crops. Biotechnology has been used to remove a major allergen in soybean demonstrating that genetic modification can be used to reduce allergenicity of food and feed. This provides a model for further use of GM approaches to eliminate allergens.

VARIANCE OF MICROSOMAL PROTEIN AND ...

EPA Pesticide Factsheets

Differences in the pharmacokinetics of xenobiotics among humans makes them differentially susceptible to risk. Differences in enzyme content can mediate pharmacokinetic differences. Microsomal protein is often isolated fromliver to characterize enzyme content and activity, but no measures exist to extrapolate these data to the intact liver. Measures were developed from up to 60 samples of adult human liver to characterize the content of microsomal protein and cytochrome P450 (CYP) enzymes. Statistical evaluations are necessary to estimate values far from the mean value. Adult human liver contains 52.9 - 1.476 mg microsomal protein per g; 2587 - 1.84 pmoles CYP2E1 per g; and 5237 - 2.214 pmols CYP3A per g (geometric mean - geometric standard deviation). These values are useful for identifying and testing susceptibility as a function of enzyme content when used to extrapolate in vitro rates of chemical metabolism for input to physiologically based pharmacokinetic models which can then be exercised to quantify the effect of variance in enzyme expression on risk-relevant pharmacokinetic outcomes.

A Global Drought and Flood Catalogue for the past 100 years

NASA Astrophysics Data System (ADS)

Sheffield, J.; He, X.; Peng, L.; Pan, M.; Fisher, C. K.; Wood, E. F.

2017-12-01

Extreme hydrological events cause the most impacts of natural hazards globally, impacting on a wide range of sectors including, most prominently, agriculture, food security and water availability and quality, but also on energy production, forestry, health, transportation and fisheries. Understanding how floods and droughts intersect, and have changed in the past provides the basis for understanding current risk and how it may change in the future. To do this requires an understanding of the mechanisms associated with events and therefore their predictability, attribution of long-term changes in risk, and quantification of projections of changes in the future. Of key importance are long-term records of relevant variables so that risk can be quantified more accurately, given the growing acknowledgement that risk is not stationary under long-term climate variability and climate change. To address this, we develop a catalogue of drought and flood events based on land surface and hydrodynamic modeling, forced by a hybrid meteorological dataset that draws from the continuity and coverage of reanalysis, and satellite datasets, merged with global gauge databases. The meteorological dataset is corrected for temporal inhomogeneities, spurious trends and variable inter-dependencies to ensure long-term consistency, as well as realistic representation of short-term variability and extremes. The VIC land surface model is run for the past 100 years at 0.25-degree resolution for global land areas. The VIC runoff is then used to drive the CaMa-Flood hydrodynamic model to obtain information on flood inundation risk. The model outputs are compared to satellite based estimates of flood and drought conditions and the observational flood record. The data are analyzed in terms of the spatio-temporal characteristics of large-scale flood and drought events with a particular focus on characterizing the long-term variability in risk. Significant changes in risk occur on multi-decadal time scales and are mostly associated with variability in the North Atlantic and Pacific. The catalogue can be used for analysis of extreme events, risk assessment, and as a benchmark for model evaluation.

Frequency-risk and duration-risk relations between occupational livestock contact and methicillin-resistant Staphylococcus aureus carriage among workers in Guangdong, China.

PubMed

Ye, Xiaohua; Liu, Weidong; Fan, Yanping; Wang, Xiaolin; Zhou, Junli; Yao, Zhenjiang; Chen, Sidong

2015-07-01

Increasing evidence indicates a strong association between occupational livestock contact and methicillin-resistant Staphylococcus aureus (MRSA) carriage. However, it remains unclear whether there are frequency-risk and duration-risk relations between occupational livestock contact and human MRSA carriage. A cross-sectional survey was conducted in Guangdong, China, using a multistage sampling method. Participants were interviewed and provided a nasal swab for S aureus analysis. All MRSA isolates were genotyped by multilocus sequence typing. The dose-response relation was examined using logistic regression models. Among the 1,860 participants, 1.4% of controls tested positive for MRSA (characterized as sequence type [ST] 59 and ST7), and 7% of workers with livestock contact tested positive for MRSA (characterized as ST9, ST59, and ST7). There was a 5.31 times increased risk of MRSA carriage corresponding to occupational livestock contact (odds ratio = 6.31; 95% confidence interval, 3.44-11.57) using no contact as reference. We found frequency and short-term duration of occupational livestock contact were associated with increased risk of MRSA carriage in a dose-response manner. These significant trends were observed consistently among workers with occupational pig contact. However, no long-term duration-risk increasing trend was observed for occupational livestock or pig contact. Our findings suggest that there may be dose-response relations between occupational livestock contact and human MRSA carriage. Nasal MRSA clonal complex 9 is not found in controls, but it is found in workers with livestock contact. Copyright © 2015 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

Conducting field studies for testing pesticide leaching models

USGS Publications Warehouse

Smith, Charles N.; Parrish, Rudolph S.; Brown, David S.

1990-01-01

A variety of predictive models are being applied to evaluate the transport and transformation of pesticides in the environment. These include well known models such as the Pesticide Root Zone Model (PRZM), the Risk of Unsaturated-Saturated Transport and Transformation Interactions for Chemical Concentrations Model (RUSTIC) and the Groundwater Loading Effects of Agricultural Management Systems Model (GLEAMS). The potentially large impacts of using these models as tools for developing pesticide management strategies and regulatory decisions necessitates development of sound model validation protocols. This paper offers guidance on many of the theoretical and practical problems encountered in the design and implementation of field-scale model validation studies. Recommendations are provided for site selection and characterization, test compound selection, data needs, measurement techniques, statistical design considerations and sampling techniques. A strategy is provided for quantitatively testing models using field measurements.

Comparative assessment of absolute cardiovascular disease risk characterization from non-laboratory-based risk assessment in South African populations

PubMed Central

2013-01-01

Background All rigorous primary cardiovascular disease (CVD) prevention guidelines recommend absolute CVD risk scores to identify high- and low-risk patients, but laboratory testing can be impractical in low- and middle-income countries. The purpose of this study was to compare the ranking performance of a simple, non-laboratory-based risk score to laboratory-based scores in various South African populations. Methods We calculated and compared 10-year CVD (or coronary heart disease (CHD)) risk for 14,772 adults from thirteen cross-sectional South African populations (data collected from 1987 to 2009). Risk characterization performance for the non-laboratory-based score was assessed by comparing rankings of risk with six laboratory-based scores (three versions of Framingham risk, SCORE for high- and low-risk countries, and CUORE) using Spearman rank correlation and percent of population equivalently characterized as ‘high’ or ‘low’ risk. Total 10-year non-laboratory-based risk of CVD death was also calculated for a representative cross-section from the 1998 South African Demographic Health Survey (DHS, n = 9,379) to estimate the national burden of CVD mortality risk. Results Spearman correlation coefficients for the non-laboratory-based score with the laboratory-based scores ranged from 0.88 to 0.986. Using conventional thresholds for CVD risk (10% to 20% 10-year CVD risk), 90% to 92% of men and 94% to 97% of women were equivalently characterized as ‘high’ or ‘low’ risk using the non-laboratory-based and Framingham (2008) CVD risk score. These results were robust across the six risk scores evaluated and the thirteen cross-sectional datasets, with few exceptions (lower agreement between the non-laboratory-based and Framingham (1991) CHD risk scores). Approximately 18% of adults in the DHS population were characterized as ‘high CVD risk’ (10-year CVD death risk >20%) using the non-laboratory-based score. Conclusions We found a high level of correlation between a simple, non-laboratory-based CVD risk score and commonly-used laboratory-based risk scores. The burden of CVD mortality risk was high for men and women in South Africa. The policy and clinical implications are that fast, low-cost screening tools can lead to similar risk assessment results compared to time- and resource-intensive approaches. Until setting-specific cohort studies can derive and validate country-specific risk scores, non-laboratory-based CVD risk assessment could be an effective and efficient primary CVD screening approach in South Africa. PMID:23880010

Cyclic subway networks are less risky in metropolises

NASA Astrophysics Data System (ADS)

Xiao, Ying; Zhang, Hai-Tao; Xu, Bowen; Zhu, Tao; Chen, Guanrong; Chen, Duxin

2018-02-01

Subways are crucial in modern transportation systems of metropolises. To quantitatively evaluate the potential risks of subway networks suffered from natural disasters or deliberate attacks, real data from seven Chinese subway systems are collected and their population distributions and anti-risk capabilities are analyzed. Counterintuitively, it is found that transfer stations with large numbers of connections are not the most crucial, but the stations and lines with large betweenness centrality are essential, if subway networks are being attacked. It is also found that cycles reduce such correlations due to the existence of alternative paths. To simulate the data-based observations, a network model is proposed to characterize the dynamics of subway systems under various intensities of attacks on stations and lines. This study sheds some light onto risk assessment of subway networks in metropolitan cities.

Developmental vitamin D deficiency and schizophrenia: the role of animal models.

PubMed

Schoenrock, S A; Tarantino, L M

2016-01-01

Schizophrenia is a debilitating neuropsychiatric disorder that affects 1% of the US population. Based on twin and genome-wide association studies, it is clear that both genetics and environmental factors increase the risk for developing schizophrenia. Moreover, there is evidence that conditions in utero, either alone or in concert with genetic factors, may alter neurodevelopment and lead to an increased risk for schizophrenia. There has been progress in identifying genetic loci and environmental exposures that increase risk, but there are still considerable gaps in our knowledge. Furthermore, very little is known about the specific neurodevelopmental mechanisms upon which genetics and the environment act to increase disposition to developing schizophrenia in adulthood. Vitamin D deficiency during the perinatal period has been hypothesized to increase risk for schizophrenia in humans. The developmental vitamin D (DVD) deficiency hypothesis of schizophrenia arises from the observation that disease risk is increased in individuals who are born in winter or spring, live further from the equator or live in urban vs. rural settings. These environments result in less exposure to sunlight, thereby reducing the initial steps in the production of vitamin D. Rodent models have been developed to characterize the behavioral and developmental effects of DVD deficiency. This review focuses on these animal models and discusses the current knowledge of the role of DVD deficiency in altering behavior and neurobiology relevant to schizophrenia. © 2015 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

Exposure-Response Model of Subcutaneous C1-Inhibitor Concentrate to Estimate the Risk of Attacks in Patients With Hereditary Angioedema.

PubMed

Zhang, Ying; Tortorici, Michael A; Pawaskar, Dipti; Pragst, Ingo; Machnig, Thomas; Hutmacher, Matthew; Zuraw, Bruce; Cicardi, Marco; Craig, Timothy; Longhurst, Hilary; Sidhu, Jagdev

2018-03-01

Subcutaneous C1-inhibitor (HAEGARDA, CSL Behring), is a US Food and Drug Administration (FDA)-approved, highly concentrated formulation of a plasma-derived C1-esterase inhibitor (C1-INH), which, in the phase III Clinical Studies for Optimal Management in Preventing Angioedema with Low-Volume Subcutaneous C1-inhibitor Replacement Therapy (COMPACT) trial, reduced the incidence of hereditary angioedema (HAE) attacks when given prophylactically. Data from the COMPACT trial were used to develop a repeated time-to-event model to characterize the timing and frequency of HAE attacks as a function of C1-INH activity, and then develop an exposure-response model to assess the relationship between C1-INH functional activity levels (C1-INH(f)) and the risk of an attack. The C1-INH(f) values of 33.1%, 40.3%, and 63.1% were predicted to correspond with 50%, 70%, and 90% reductions in the HAE attack risk, respectively, relative to no therapy. Based on trough C1-INH(f) values for the 40 IU/kg (40.2%) and 60 IU/kg (48.0%) C1-INH (SC) doses, the model predicted that 50% and 67% of the population, respectively, would see at least a 70% decrease in the risk of an attack. © 2018 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

Analyzing the Long Term Cohesive Effect of Sector Specific Driving Forces.

PubMed

Berman, Yonatan; Ben-Jacob, Eshel; Zhang, Xin; Shapira, Yoash

2016-01-01

Financial markets are partially composed of sectors dominated by external driving forces, such as commodity prices, infrastructure and other indices. We characterize the statistical properties of such sectors and present a novel model for the coupling of the stock prices and their dominating driving forces, inspired by mean reverting stochastic processes. Using the model we were able to explain the market sectors' long term behavior and estimate the coupling strength between stocks in financial markets and the sector specific driving forces. Notably, the analysis was successfully applied to the shipping market, in which the Baltic dry index (BDI), an assessment of the price of transporting the major raw materials by sea, influences the shipping financial market. We also present the analysis of other sectors-the gold mining market and the food production market, for which the model was also successfully applied. The model can serve as a general tool for characterizing the coupling between external forces and affected financial variables and therefore for estimating the risk in sectors and their vulnerability to external stress.

Analyzing the Long Term Cohesive Effect of Sector Specific Driving Forces

PubMed Central

Berman, Yonatan; Zhang, Xin; Shapira, Yoash

2016-01-01

Financial markets are partially composed of sectors dominated by external driving forces, such as commodity prices, infrastructure and other indices. We characterize the statistical properties of such sectors and present a novel model for the coupling of the stock prices and their dominating driving forces, inspired by mean reverting stochastic processes. Using the model we were able to explain the market sectors’ long term behavior and estimate the coupling strength between stocks in financial markets and the sector specific driving forces. Notably, the analysis was successfully applied to the shipping market, in which the Baltic dry index (BDI), an assessment of the price of transporting the major raw materials by sea, influences the shipping financial market. We also present the analysis of other sectors—the gold mining market and the food production market, for which the model was also successfully applied. The model can serve as a general tool for characterizing the coupling between external forces and affected financial variables and therefore for estimating the risk in sectors and their vulnerability to external stress. PMID:27031230

Temporal Variability of Cumulative Risk Assessment on Phthalates in Chinese Pregnant Women: Repeated Measurement Analysis.

PubMed

Gao, Hui; Zhu, Bei-Bei; Tao, Xing-Yong; Zhu, Yuan-Duo; Tao, Xu-Guang; Tao, Fang-Biao

2018-06-05

The assessment of the combined effects of multiple phthalate exposures at low levels is a newly developed concept to avoid underestimating their actual cumulative health risk. A previous study included 3455 Chinese pregnant women. Each woman provided up to three urine samples (in total 9529). This previous study characterized the concentrations of phthalate metabolites. In the present study, the data from 9529 samples was reanalyzed to examine the cumulative risk assessment (CRA) with two models: (1) the creatinine-based and (2) the volume-based. Hazard index (HI) values for three phthalates, dibutyl phthalate, butyl benzyl phthalate, and di(2-ethylhexyl) phthalate, in the first, second, and third trimesters of pregnancy, were calculated, respectively. In creatinine-based model, 3.43%, 14.63%, and 17.28% of women showed HI based on the European Food Safety Authority tolerable daily intake exceeding 1 in the first, second, and third trimester of pregnancy, respectively. The intraclass correlation coefficient of HI was 0.49 (95% confidence interval: 0.46-0.53). Spearman correlations between HI of the creatinine model and ∑androgen disruptor (a developed potency weighted approach) ranged from 0.824 to 0.984. In summary, this study suggested a considerable risk of cumulative exposure to phthalates during the whole gestation in Chinese pregnant women. In addition, moderate temporal reproducibility indicated that single HI, estimated by the phthalate concentration in single spot of urine, seemed representative to describe the throughout pregnancy CRA. Finally, strong correlation between HI of the creatinine model and ∑androgen disruptor revealed that the creatinine-based model was more appropriate to evaluate the CRA.

System perspectives of experts and farmers regarding the role of livelihood assets in risk perception: results from the structured mental model approach.

PubMed

Schoell, Regina; Binder, Claudia R

2009-02-01

Pesticide application is increasing and despite extensive educational programs farmers continue to take high health and environmental risks when applying pesticides. The structured mental model approach (SMMA) is a new method for risk perception analysis. It embeds farmers' risk perception into their livelihood system in the elaboration of a mental model (MM). Results from its first application are presented here. The study region is Vereda la Hoya (Colombia), an area characterized by subsistence farming, high use of pesticides, and a high incidence of health problems. Our hypothesis was that subsistence farmers were constrained by economic, environmental, and sociocultural factors, which consequently should influence their mental models. Thirteen experts and 10 farmers were interviewed and their MMs of the extended pesticide system elicited. The interviews were open-ended with the questions structured in three parts: (i) definition and ranking of types of capital with respect to their importance for the sustainability of farmers' livelihood; (ii) understanding the system and its dynamics; and (iii) importance of the agents in the farmers' agent network. Following this structure, each part of the interview was analyzed qualitatively and statistically. Our analyses showed that the mental models of farmers and experts differed significantly from each other. By applying the SMMA, we were also able to identify reasons for the divergence of experts' and farmers' MMs. Of major importance are the following factors: (i) culture and tradition; (ii) trust in the source of information; and (iii) feedback on knowledge.

[Acute kidney injury after pediatric cardiac surgery: risk factors and outcomes. Proposal for a predictive model].

PubMed

Cardoso, Bárbara; Laranjo, Sérgio; Gomes, Inês; Freitas, Isabel; Trigo, Conceição; Fragata, Isabel; Fragata, José; Pinto, Fátima

2016-02-01

To characterize the epidemiology and risk factors for acute kidney injury (AKI) after pediatric cardiac surgery in our center, to determine its association with poor short-term outcomes, and to develop a logistic regression model that will predict the risk of AKI for the study population. This single-center, retrospective study included consecutive pediatric patients with congenital heart disease who underwent cardiac surgery between January 2010 and December 2012. Exclusion criteria were a history of renal disease, dialysis or renal transplantation. Of the 325 patients included, median age three years (1 day-18 years), AKI occurred in 40 (12.3%) on the first postoperative day. Overall mortality was 13 (4%), nine of whom were in the AKI group. AKI was significantly associated with length of intensive care unit stay, length of mechanical ventilation and in-hospital death (p<0.01). Patients' age and postoperative serum creatinine, blood urea nitrogen and lactate levels were included in the logistic regression model as predictor variables. The model accurately predicted AKI in this population, with a maximum combined sensitivity of 82.1% and specificity of 75.4%. AKI is common and is associated with poor short-term outcomes in this setting. Younger age and higher postoperative serum creatinine, blood urea nitrogen and lactate levels were powerful predictors of renal injury in this population. The proposed model could be a useful tool for risk stratification of these patients. Copyright © 2015 Sociedade Portuguesa de Cardiologia. Published by Elsevier España. All rights reserved.

Despotism and risk of infanticide influence grizzly bear den-site selection.

PubMed

Libal, Nathan S; Belant, Jerrold L; Leopold, Bruce D; Wang, Guiming; Owen, Patricia A

2011-01-01

Given documented social dominance and intraspecific predation in bear populations, the ideal despotic distribution model and sex hypothesis of sexual segregation predict adult female grizzly bears (Ursus arctos) will avoid areas occupied by adult males to reduce risk of infanticide. Under ideal despotic distribution, juveniles should similarly avoid adult males to reduce predation risk. Den-site selection and use is an important component of grizzly bear ecology and may be influenced by multiple factors, including risk from conspecifics. To test the role of predation risk and the sex hypothesis of sexual segregation, we compared adult female (n = 142), adult male (n = 36), and juvenile (n = 35) den locations in Denali National Park and Preserve, Alaska, USA. We measured elevation, aspect, slope, and dominant land cover for each den site, and used maximum entropy modeling to determine which variables best predicted den sites. We identified the global model as the best-fitting model for adult female (area under curve (AUC) = 0.926) and elevation as the best predictive variable for adult male (AUC = 0.880) den sites. The model containing land cover and elevation best-predicted juvenile (AUC = 0.841) den sites. Adult females spatially segregated from adult males, with dens characterized by higher elevations (mean= 1,412 m, SE = 52) and steeper slopes (mean = 21.9°, SE = 1.1) than adult male (elevation: mean = 1,209 m, SE = 76; slope: mean = 15.6°, SE = 1.9) den sites. Juveniles used a broad range of landscape attributes but did not avoid adult male denning areas. Observed spatial segregation by adult females supports the sex hypothesis of sexual segregation and we suggest is a mechanism to reduce risk of infanticide. Den site selection of adult males is likely related to distribution of food resources during spring.

"And I think that we can fix it": mental models used in high-risk surgical decision making.

PubMed

Kruser, Jacqueline M; Pecanac, Kristen E; Brasel, Karen J; Cooper, Zara; Steffens, Nicole M; McKneally, Martin F; Schwarze, Margaret L

2015-04-01

To examine how surgeons use the "fix-it" model to communicate with patients before high-risk operations. The "fix-it" model characterizes disease as an isolated abnormality that can be restored to normal form and function through medical intervention. This mental model is familiar to patients and physicians, but it is ineffective for chronic conditions and treatments that cannot achieve normalcy. Overuse may lead to permissive decision making favoring intervention. Efforts to improve surgical decision making will need to consider how mental models function in clinical practice, including "fix-it." We observed surgeons who routinely perform high-risk surgery during preoperative discussions with patients. We used qualitative content analysis to explore the use of "fix-it" in 48 audio-recorded conversations. Surgeons used the "fix-it" model for 2 separate purposes during preoperative conversations: (1) as an explanatory tool to facilitate patient understanding of disease and surgery, and (2) as a deliberation framework to assist in decision making. Although surgeons commonly used "fix-it" as an explanatory model, surgeons explicitly discussed limitations of the "fix-it" model as an independent rationale for operating as they deliberated about the value of surgery. Although the use of "fix-it" is familiar for explaining medical information to patients, surgeons recognize that the model can be problematic for determining the value of an operation. Whether patients can transition between understanding how their disease is fixed with surgery to a subsequent deliberation about whether they should have surgery is unclear and may have broader implications for surgical decision making.

Comparison of individual-based modeling and population approaches for prediction of foodborne pathogens growth.

PubMed

Augustin, Jean-Christophe; Ferrier, Rachel; Hezard, Bernard; Lintz, Adrienne; Stahl, Valérie

2015-02-01

Individual-based modeling (IBM) approach combined with the microenvironment modeling of vacuum-packed cold-smoked salmon was more effective to describe the variability of the growth of a few Listeria monocytogenes cells contaminating irradiated salmon slices than the traditional population models. The IBM approach was particularly relevant to predict the absence of growth in 25% (5 among 20) of artificially contaminated cold-smoked salmon samples stored at 8 °C. These results confirmed similar observations obtained with smear soft cheese (Ferrier et al., 2013). These two different food models were used to compare the IBM/microscale and population/macroscale modeling approaches in more global exposure and risk assessment frameworks taking into account the variability and/or the uncertainty of the factors influencing the growth of L. monocytogenes. We observed that the traditional population models significantly overestimate exposure and risk estimates in comparison to IBM approach when contamination of foods occurs with a low number of cells (<100 per serving). Moreover, the exposure estimates obtained with the population model were characterized by a great uncertainty. The overestimation was mainly linked to the ability of IBM to predict no growth situations rather than the consideration of microscale environment. On the other hand, when the aim of quantitative risk assessment studies is only to assess the relative impact of changes in control measures affecting the growth of foodborne bacteria, the two modeling approach gave similar results and the simplest population approach was suitable. Copyright © 2014 Elsevier Ltd. All rights reserved.

Vulnerability and risk perception in the management of HIV/AIDS: Public priorities in a global pandemic

PubMed Central

Tsasis, Peter; Nirupama, N.

2008-01-01

Understanding the way perception of risk is shaped and constructed is crucial in understanding why it has been so difficult to mitigate the spread of HIV/AIDS. This paper uses the Pressure and Release (PAR) model, used to predict the onset of natural disasters as the conceptual framework. It substitutes vulnerability and risk perception as the trigger factors in the model, in making the case that HIV/AIDS can be characterized as a slow onset disaster. The implications are that vulnerability must be managed and reduced by addressing root causes, dynamic pressures, and unsafe conditions that contribute to the HIV/AIDS pandemic. HIV/AIDS programs must be culturally appropriate and work toward influencing risk perception, while addressing social norms and values that negatively impact vulnerable populations. By impacting cultural and social expectations, individuals will be able to more readily adopt safer sex behaviors. The development of policies and programs addressing the issues in context, as opposed to individual behaviors alone, allows for effective public health intervention. This may have implications for public health measures implemented for combating the spread of HIV/AIDS. PMID:22312198

Advances in Inhalation Dosimetry Models and Methods for Occupational Risk Assessment and Exposure Limit Derivation

PubMed Central

Kuempel, Eileen D.; Sweeney, Lisa M.; Morris, John B.; Jarabek, Annie M.

2015-01-01

The purpose of this article is to provide an overview and practical guide to occupational health professionals concerning the derivation and use of dose estimates in risk assessment for development of occupational exposure limits (OELs) for inhaled substances. Dosimetry is the study and practice of measuring or estimating the internal dose of a substance in individuals or a population. Dosimetry thus provides an essential link to understanding the relationship between an external exposure and a biological response. Use of dosimetry principles and tools can improve the accuracy of risk assessment, and reduce the uncertainty, by providing reliable estimates of the internal dose at the target tissue. This is accomplished through specific measurement data or predictive models, when available, or the use of basic dosimetry principles for broad classes of materials. Accurate dose estimation is essential not only for dose-response assessment, but also for interspecies extrapolation and for risk characterization at given exposures. Inhalation dosimetry is the focus of this paper since it is a major route of exposure in the workplace. Practical examples of dose estimation and OEL derivation are provided for inhaled gases and particulates. PMID:26551218

Cigarette smoking and risk of ovarian cancer: a pooled analysis of 21 case–control studies

PubMed Central

Faber, Mette T.; Kjær, Susanne K.; Dehlendorff, Christian; Chang-Claude, Jenny; Andersen, Klaus K.; Høgdall, Estrid; Webb, Penelope M.; Jordan, Susan J.; Rossing, Mary Anne; Doherty, Jennifer A.; Lurie, Galina; Thompson, Pamela J.; Carney, Michael E.; Goodman, Marc T.; Ness, Roberta B.; Modugnos, Francesmary; Edwards, Robert P.; Bunker, Clareann H.; Goode, Ellen L.; Fridley, Brooke L.; Vierkant, Robert A.; Larson, Melissa C.; Schildkraut, Joellen; Cramer, Daniel W.; Terry, Kathryn L.; Vitonis, Allison F.; Bandera, Elisa V.; Olson, Sara H.; King, Melony; Chandran, Urmila; Kiemeney, Lambertus A.; Massuger, Leon F. A. G.; van Altena, Anne M.; Vermeulen, Sita H.; Brinton, Louise; Wentzensen, Nicolas; Lissowska, Jolanta; Yang, Hannah P.; Moysich, Kirsten B.; Odunsi, Kunle; Kasza, Karin; Odunsi-Akanji, Oluwatosin; Song, Honglin; Pharaoh, Paul; Shah, Mitul; Whittemore, Alice S.; McGuire, Valerie; Sieh, Weiva; Sutphen, Rebecca; Menon, Usha; Gayther, Simon A.; Ramus, Susan J.; Gentry-Maharaj, Aleksandra; Pearce, Celeste Leigh; Wu, Anna H.; Pike, Malcolm C.; Risch, Harvey A.

2013-01-01

Purpose The majority of previous studies have observed an increased risk of mucinous ovarian tumors associated with cigarette smoking, but the association with other histological types is unclear. In a large pooled analysis, we examined the risk of epithelial ovarian cancer associated with multiple measures of cigarette smoking with a focus on characterizing risks according to tumor behavior and histology. Methods We used data from 21 case–control studies of ovarian cancer (19,066 controls, 11,972 invasive and 2,752 borderline cases). Study-specific odds ratios (OR) and 95 % confidence intervals (CI) were obtained from logistic regression models and combined into a pooled odds ratio using a random effects model. Results Current cigarette smoking increased the risk of invasive mucinous (OR = 1.31; 95 % CI: 1.03–1.65) and borderline mucinous ovarian tumors (OR = 1.83; 95 % CI: 1.39–2.41), while former smoking increased the risk of borderline serous ovarian tumors (OR = 1.30; 95 % CI: 1.12–1.50). For these histological types, consistent dose– response associations were observed. No convincing associations between smoking and risk of invasive serous and endometrioid ovarian cancer were observed, while our results provided some evidence of a decreased risk of invasive clear cell ovarian cancer. Conclusions Our results revealed marked differences in the risk profiles of histological types of ovarian cancer with regard to cigarette smoking, although the magnitude of the observed associations was modest. Our findings, which may reflect different etiologies of the histological types, add to the fact that ovarian cancer is a heterogeneous disease. PMID:23456270

Human Injury Criteria for Underwater Blasts

PubMed Central

Lance, Rachel M.; Capehart, Bruce; Kadro, Omar; Bass, Cameron R.

2015-01-01

Underwater blasts propagate further and injure more readily than equivalent air blasts. Development of effective personal protection and countermeasures, however, requires knowledge of the currently unknown human tolerance to underwater blast. Current guidelines for prevention of underwater blast injury are not based on any organized injury risk assessment, human data or experimental data. The goal of this study was to derive injury risk assessments for underwater blast using well-characterized human underwater blast exposures in the open literature. The human injury dataset was compiled using 34 case reports on underwater blast exposure to 475 personnel, dating as early as 1916. Using severity ratings, computational reconstructions of the blasts, and survival information from a final set of 262 human exposures, injury risk models were developed for both injury severity and risk of fatality as functions of blast impulse and blast peak overpressure. Based on these human data, we found that the 50% risk of fatality from underwater blast occurred at 302±16 kPa-ms impulse. Conservatively, there is a 20% risk of pulmonary injury at a kilometer from a 20 kg charge. From a clinical point of view, this new injury risk model emphasizes the large distances possible for potential pulmonary and gut injuries in water compared with air. This risk value is the first impulse-based fatality risk calculated from human data. The large-scale inconsistency between the blast exposures in the case reports and the guidelines available in the literature prior to this study further underscored the need for this new guideline derived from the unique dataset of actual injuries in this study. PMID:26606655

Dealing with deep uncertainties in landslide modelling for disaster risk reduction under climate change

NASA Astrophysics Data System (ADS)

Almeida, Susana; Holcombe, Elizabeth Ann; Pianosi, Francesca; Wagener, Thorsten

2017-02-01

Landslides have large negative economic and societal impacts, including loss of life and damage to infrastructure. Slope stability assessment is a vital tool for landslide risk management, but high levels of uncertainty often challenge its usefulness. Uncertainties are associated with the numerical model used to assess slope stability and its parameters, with the data characterizing the geometric, geotechnic and hydrologic properties of the slope, and with hazard triggers (e.g. rainfall). Uncertainties associated with many of these factors are also likely to be exacerbated further by future climatic and socio-economic changes, such as increased urbanization and resultant land use change. In this study, we illustrate how numerical models can be used to explore the uncertain factors that influence potential future landslide hazard using a bottom-up strategy. Specifically, we link the Combined Hydrology And Stability Model (CHASM) with sensitivity analysis and Classification And Regression Trees (CART) to identify critical thresholds in slope properties and climatic (rainfall) drivers that lead to slope failure. We apply our approach to a slope in the Caribbean, an area that is naturally susceptible to landslides due to a combination of high rainfall rates, steep slopes, and highly weathered residual soils. For this particular slope, we find that uncertainties regarding some slope properties (namely thickness and effective cohesion of topsoil) are as important as the uncertainties related to future rainfall conditions. Furthermore, we show that 89 % of the expected behaviour of the studied slope can be characterized based on only two variables - the ratio of topsoil thickness to cohesion and the ratio of rainfall intensity to duration.

Phases of ERA - Risk Characterization

EPA Pesticide Factsheets

Eco-Box is a toolbox for exposure assessors. Its purpose is to provide a compendium of exposure assessment and risk characterization tools that will present comprehensive step-by-step guidance and links to relevant exposure assessment data bases

Latent profiles of problem behavior within learning, peer, and teacher contexts: identifying subgroups of children at academic risk across the preschool year.

PubMed

Bulotsky-Shearer, Rebecca J; Bell, Elizabeth R; Domínguez, Ximena

2012-12-01

Employing a developmental and ecological model, the study identified initial levels and rates of change in academic skills for subgroups of preschool children exhibiting problem behavior within routine classroom situations. Six distinct latent profile types of emotional and behavioral adjustment were identified for a cohort of low-income children early in the preschool year (N=4417). Profile types provided a descriptive picture of patterns of classroom externalizing, internalizing, and situational adjustment problems common to subgroups of children early in the preschool year. The largest profile type included children who exhibited low problem behavior and were characterized as well-adjusted to the preschool classroom early in the year. The other profile types were characterized by distinct combinations of elevated internalizing, externalizing, and situational problem behavior. Multinomial logistic regression identified younger children and boys at increased risk for classification in problem types, relative to the well-adjusted type. Latent growth models indicated that children classified within the extremely socially and academically disengaged profile type, started and ended the year with the lowest academic skills, relative to all other types. Implications for future research, policy, and practice are discussed. Copyright © 2012 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Motor planning under temporal uncertainty is suboptimal when the gain function is asymmetric

PubMed Central

Ota, Keiji; Shinya, Masahiro; Kudo, Kazutoshi

2015-01-01

For optimal action planning, the gain/loss associated with actions and the variability in motor output should both be considered. A number of studies make conflicting claims about the optimality of human action planning but cannot be reconciled due to their use of different movements and gain/loss functions. The disagreement is possibly because of differences in the experimental design and differences in the energetic cost of participant motor effort. We used a coincident timing task, which requires decision making with constant energetic cost, to test the optimality of participant's timing strategies under four configurations of the gain function. We compared participant strategies to an optimal timing strategy calculated from a Bayesian model that maximizes the expected gain. We found suboptimal timing strategies under two configurations of the gain function characterized by asymmetry, in which higher gain is associated with higher risk of zero gain. Participants showed a risk-seeking strategy by responding closer than optimal to the time of onset/offset of zero gain. Meanwhile, there was good agreement of the model with actual performance under two configurations of the gain function characterized by symmetry. Our findings show that human ability to make decisions that must reflect uncertainty in one's own motor output has limits that depend on the configuration of the gain function. PMID:26236227

Health risk characterization of maximum legal exposures for persistent organic pollutant (POP) pesticides in residential soil: An analysis.

PubMed

Li, Zijian

2018-01-01

Regulations for pesticides in soil are important for controlling human health risk; humans can be exposed to pesticides by ingesting soil, inhaling soil dust, and through dermal contact. Previous studies focused on analyses of numerical standard values for pesticides and evaluated the same pesticide using different standards among different jurisdictions. To understand the health consequences associated with pesticide soil standard values, lifetime theoretical maximum contribution and risk characterization factors were used in this study to quantify the severity of damage using disability-adjusted life years (DALYs) under the maximum "legal" exposure to persistent organic pollutant (POP) pesticides that are commonly regulated by the Stockholm Convention. Results show that computed soil characterization factors for some pesticides present lognormal distributions, and some of them have DALY values higher than 1000.0 per million population (e.g., the DALY for dichlorodiphenyltrichloroethane [DDT] is 14,065 in the Netherlands, which exceeds the tolerable risk of uncertainty upper bound of 1380.0 DALYs). Health risk characterization factors computed from national jurisdictions illustrate that values can vary over eight orders of magnitude. Further, the computed characterization factors can vary over four orders of magnitude within the same national jurisdiction. These data indicate that there is little agreement regarding pesticide soil regulatory guidance values (RGVs) among worldwide national jurisdictions or even RGV standard values within the same jurisdiction. Among these POP pesticides, lindane has the lowest median (0.16 DALYs) and geometric mean (0.28 DALYs) risk characterization factors, indicating that worldwide national jurisdictions provide relatively conservative soil RGVs for lindane. In addition, we found that some European nations and members of the former Union of Soviet Socialist Republics share the same pesticide RGVs and data clusters for the computed characterization factors. Copyright © 2017 Elsevier Ltd. All rights reserved.

Risk assessment of hazardous release in air due to the chemical production of "P. Karaminchev" company in the town of Rousse.

PubMed

Diankova, M

1998-09-01

A health risk evaluation of the lifetime population risk has been made, by using the US EPA's method of risk assessment. Several main steps have been conducted: --a hazard identification, by means of emission analysis and mathematical modeling of air concentration dispersion; a dose-response evaluation and exposure assessment, and finally--a risk characterization. The health risk evaluation was conducted, using lifetime reference concentrations and doses. As risk descriptors were applied: --the individual exposure coefficient (IEC), the hazard quotient (HQ) and the margin of exposure (MOE)--for system toxicants, and the excess lifetime cancer risk (ELCR)--for carcinogens. The method that was used provides an upperbound estimate, including all possible exposures. The results showed, that the emissions of hydrogen chloride, phthalates (DOF), nitrogen oxides and most of the organic solvents, released from this chemical plant, are not a source of lifetime chronic health risk for the population of any of the six evaluated residential areas of Rousse. The rest of the hazardous emissions cause a slightly increased lifetime health risk, which is entirely in the so called 'controlled risk zone' the risk descriptors vary from 1.00 to 5.00. A number of actions have been prescribed to the plant's government, most of which were realized in the short term.

Safety risk assessment using analytic hierarchy process (AHP) during planning and budgeting of construction projects.

PubMed

Aminbakhsh, Saman; Gunduz, Murat; Sonmez, Rifat

2013-09-01

The inherent and unique risks on construction projects quite often present key challenges to contractors. Health and safety risks are among the most significant risks in construction projects since the construction industry is characterized by a relatively high injury and death rate compared to other industries. In construction project management, safety risk assessment is an important step toward identifying potential hazards and evaluating the risks associated with the hazards. Adequate prioritization of safety risks during risk assessment is crucial for planning, budgeting, and management of safety related risks. In this paper, a safety risk assessment framework is presented based on the theory of cost of safety (COS) model and the analytic hierarchy process (AHP). The main contribution of the proposed framework is that it presents a robust method for prioritization of safety risks in construction projects to create a rational budget and to set realistic goals without compromising safety. The framework provides a decision tool for the decision makers to determine the adequate accident/injury prevention investments while considering the funding limits. The proposed safety risk framework is illustrated using a real-life construction project and the advantages and limitations of the framework are discussed. Copyright © 2013 National Safety Council and Elsevier Ltd. All rights reserved.

Precautionary principles: a jurisdiction-free framework for decision-making under risk.

PubMed

Ricci, Paolo F; Cox, Louis A; MacDonald, Thomas R

2004-12-01

Fundamental principles of precaution are legal maxims that ask for preventive actions, perhaps as contingent interim measures while relevant information about causality and harm remains unavailable, to minimize the societal impact of potentially severe or irreversible outcomes. Such principles do not explain how to make choices or how to identify what is protective when incomplete and inconsistent scientific evidence of causation characterizes the potential hazards. Rather, they entrust lower jurisdictions, such as agencies or authorities, to make current decisions while recognizing that future information can contradict the scientific basis that supported the initial decision. After reviewing and synthesizing national and international legal aspects of precautionary principles, this paper addresses the key question: How can society manage potentially severe, irreversible or serious environmental outcomes when variability, uncertainty, and limited causal knowledge characterize their decision-making? A decision-analytic solution is outlined that focuses on risky decisions and accounts for prior states of information and scientific beliefs that can be updated as subsequent information becomes available. As a practical and established approach to causal reasoning and decision-making under risk, inherent to precautionary decision-making, these (Bayesian) methods help decision-makers and stakeholders because they formally account for probabilistic outcomes, new information, and are consistent and replicable. Rational choice of an action from among various alternatives--defined as a choice that makes preferred consequences more likely--requires accounting for costs, benefits and the change in risks associated with each candidate action. Decisions under any form of the precautionary principle reviewed must account for the contingent nature of scientific information, creating a link to the decision-analytic principle of expected value of information (VOI), to show the relevance of new information, relative to the initial (and smaller) set of data on which the decision was based. We exemplify this seemingly simple situation using risk management of BSE. As an integral aspect of causal analysis under risk, the methods developed in this paper permit the addition of non-linear, hormetic dose-response models to the current set of regulatory defaults such as the linear, non-threshold models. This increase in the number of defaults is an important improvement because most of the variants of the precautionary principle require cost-benefit balancing. Specifically, increasing the set of causal defaults accounts for beneficial effects at very low doses. We also show and conclude that quantitative risk assessment dominates qualitative risk assessment, supporting the extension of the set of default causal models.

Analysis of work zone rear-end crash risk for different vehicle-following patterns.

PubMed

Weng, Jinxian; Meng, Qiang; Yan, Xuedong

2014-11-01

This study evaluates rear-end crash risk associated with work zone operations for four different vehicle-following patterns: car-car, car-truck, truck-car and truck-truck. The deceleration rate to avoid the crash (DRAC) is adopted to measure work zone rear-end crash risk. Results show that the car-truck following pattern has the largest rear-end crash risk, followed by truck-truck, truck-car and car-car patterns. This implies that it is more likely for a car which is following a truck to be involved in a rear-end crash accident. The statistical test results further confirm that rear-end crash risk is statistically different between any two of the four patterns. We therefore develop a rear-end crash risk model for each vehicle-following pattern in order to examine the relationship between rear-end crash risk and its influencing factors, including lane position, the heavy vehicle percentage, lane traffic flow and work intensity which can be characterized by the number of lane reductions, the number of workers and the amount of equipment at the work zone site. The model results show that, for each pattern, there will be a greater rear-end crash risk in the following situations: (i) heavy work intensity; (ii) the lane adjacent to work zone; (iii) a higher proportion of heavy vehicles and (iv) greater traffic flow. However, the effects of these factors on rear-end crash risk are found to vary according to the vehicle-following patterns. Compared with the car-car pattern, lane position has less effect on rear-end crash risk in the car-truck pattern. The effect of work intensity on rear-end crash risk is also reduced in the truck-car pattern. Copyright © 2014 Elsevier Ltd. All rights reserved.

Interpreting incremental value of markers added to risk prediction models.

PubMed

Pencina, Michael J; D'Agostino, Ralph B; Pencina, Karol M; Janssens, A Cecile J W; Greenland, Philip

2012-09-15

The discrimination of a risk prediction model measures that model's ability to distinguish between subjects with and without events. The area under the receiver operating characteristic curve (AUC) is a popular measure of discrimination. However, the AUC has recently been criticized for its insensitivity in model comparisons in which the baseline model has performed well. Thus, 2 other measures have been proposed to capture improvement in discrimination for nested models: the integrated discrimination improvement and the continuous net reclassification improvement. In the present study, the authors use mathematical relations and numerical simulations to quantify the improvement in discrimination offered by candidate markers of different strengths as measured by their effect sizes. They demonstrate that the increase in the AUC depends on the strength of the baseline model, which is true to a lesser degree for the integrated discrimination improvement. On the other hand, the continuous net reclassification improvement depends only on the effect size of the candidate variable and its correlation with other predictors. These measures are illustrated using the Framingham model for incident atrial fibrillation. The authors conclude that the increase in the AUC, integrated discrimination improvement, and net reclassification improvement offer complementary information and thus recommend reporting all 3 alongside measures characterizing the performance of the final model.

A modeling and simulation approach to characterize methadone QT prolongation using pooled data from five clinical trials in MMT patients.

PubMed

Florian, J; Garnett, C E; Nallani, S C; Rappaport, B A; Throckmorton, D C

2012-04-01

Pharmacokinetic (PK)-pharmacodynamic modeling and simulation were used to establish a link between methadone dose, concentrations, and Fridericia rate-corrected QT (QTcF) interval prolongation, and to identify a dose that was associated with increased risk of developing torsade de pointes. A linear relationship between concentration and QTcF described the data from five clinical trials in patients on methadone maintenance treatment (MMT). A previously published population PK model adequately described the concentration-time data, and this model was used for simulation. QTcF was increased by a mean (90% confidence interval (CI)) of 17 (12, 22) ms per 1,000 ng/ml of methadone. Based on this model, doses >120 mg/day would increase the QTcF interval by >20 ms. The model predicts that 1-3% of patients would have ΔQTcF >60 ms, and 0.3-2.0% of patients would have QTcF >500 ms at doses of 160-200 mg/day. Our predictions are consistent with available observational data and support the need for electrocardiogram (ECG) monitoring and arrhythmia risk factor assessment in patients receiving methadone doses >120 mg/day.

Statistical Modeling of Disease Progression for Chronic Obstructive Pulmonary Disease Using Data from the ECLIPSE Study.

PubMed

Exuzides, Alex; Colby, Chris; Briggs, Andrew H; Lomas, David A; Rutten-van Mölken, Maureen P M H; Tabberer, Maggie; Chambers, Mike; Muellerova, Hana; Locantore, Nicholas; Risebrough, Nancy A; Ismaila, Afisi S; Gonzalez-McQuire, Sebastian

2017-05-01

To develop statistical models predicting disease progression and outcomes in chronic obstructive pulmonary disease (COPD), using data from ECLIPSE, a large, observational study of current and former smokers with COPD. Based on a conceptual model of COPD disease progression and data from 2164 patients, associations were made between baseline characteristics, COPD disease progression attributes (exacerbations, lung function, exercise capacity, and symptoms), health-related quality of life (HRQoL), and survival. Linear and nonlinear functional forms of random intercept models were used to characterize these relationships. Endogeneity was addressed by time-lagging variables in the regression models. At the 5% significance level, an exacerbation history in the year before baseline was associated with increased risk of future exacerbations (moderate: +125.8%; severe: +89.2%) and decline in lung function (forced expiratory volume in 1 second [FEV 1 ]) (-94.20 mL per year). Each 1% increase in FEV 1 % predicted was associated with decreased risk of exacerbations (moderate: -1.1%; severe: -3.0%) and increased 6-minute walk test distance (6MWD) (+1.5 m). Increases in baseline exercise capacity (6MWD, per meter) were associated with slightly increased risk of moderate exacerbations (+0.04%) and increased FEV 1 (+0.62 mL). Symptoms (dyspnea, cough, and/or sputum) were associated with an increased risk of moderate exacerbations (+13.4% to +31.1%), and baseline dyspnea (modified Medical Research Council score ≥2 v. <2) was associated with lower FEV 1 (-112.3 mL). A series of linked statistical regression equations have been developed to express associations between indicators of COPD disease severity and HRQoL and survival. These can be used to represent disease progression, for example, in new economic models of COPD.

A systems-theoretical framework for health and disease: inflammation and preconditioning from an abstract modeling point of view.

PubMed

Voit, Eberhard O

2009-01-01

Modern advances in molecular biology have produced enormous amounts of data characterizing physiological and disease states in cells and organisms. While bioinformatics has facilitated the organizing and mining of these data, it is the task of systems biology to merge the available information into dynamic, explanatory and predictive models. This article takes a step into this direction. It proposes a conceptual approach toward formalizing health and disease and illustrates it in the context of inflammation and preconditioning. Instead of defining health and disease states, the emphasis is on simplexes in a high-dimensional biomarker space. These simplexes are bounded by physiological constraints and permit the quantitative characterization of personalized health trajectories, health risk profiles that change with age, and the efficacy of different treatment options. The article mainly focuses on concepts but also briefly describes how the proposed concepts might be formulated rigorously within a mathematical framework.

A new definition of pharmaceutical quality: assembly of a risk simulation platform to investigate the impact of manufacturing/product variability on clinical performance.

PubMed

Short, Steven M; Cogdill, Robert P; D'Amico, Frank; Drennen, James K; Anderson, Carl A

2010-12-01

The absence of a unanimous, industry-specific definition of quality is, to a certain degree, impeding the progress of ongoing efforts to "modernize" the pharmaceutical industry. This work was predicated on requests by Dr. Woodcock (FDA) to re-define pharmaceutical quality in terms of risk by linking production characteristics to clinical attributes. A risk simulation platform that integrates population statistics, drug delivery system characteristics, dosing guidelines, patient compliance estimates, production metrics, and pharmacokinetic, pharmacodynamic, and in vitro-in vivo correlation models to investigate the impact of manufacturing variability on clinical performance of a model extended-release theophylline solid oral dosage system was developed. Manufacturing was characterized by inter- and intra-batch content uniformity and dissolution variability metrics, while clinical performance was described by a probabilistic pharmacodynamic model that expressed the probability of inefficacy and toxicity as a function of plasma concentrations. Least-squares regression revealed that both patient compliance variables, percent of doses taken and dosing time variability, significantly impacted efficacy and toxicity. Additionally, intra-batch content uniformity variability elicited a significant change in risk scores for the two adverse events and, therefore, was identified as a critical quality attribute. The proposed methodology demonstrates that pharmaceutical quality can be recast to explicitly reflect clinical performance. © 2010 Wiley-Liss, Inc. and the American Pharmacists Association

Left ventricular ejection fraction to predict early mortality in patients with non-ST-segment elevation acute coronary syndromes.

PubMed

Bosch, Xavier; Théroux, Pierre

2005-08-01

Improvement in risk stratification of patients with non-ST-segment elevation acute coronary syndrome (ACS) is a gateway to a more judicious treatment. This study examines whether the routine determination of left ventricular ejection fraction (EF) adds significant prognostic information to currently recommended stratifiers. Several predictors of inhospital mortality were prospectively characterized in a registry study of 1104 consecutive patients, for whom an EF was determined, who were admitted for an ACS. Multiple regression models were constructed using currently recommended clinical, electrocardiographic, and blood marker stratifiers, and values of EF were incorporated into the models. Age, ST-segment shifts, elevation of cardiac markers, and the Thrombolysis in Myocardial Infarction (TIMI) risk score all predicted mortality (P < .0001). Adding EF into the model improved the prediction of mortality (C statistic 0.73 vs 0.67). The odds of death increased by a factor of 1.042 for each 1% decrement in EF. By receiver operating curves, an EF cutoff of 48% provided the best predictive value. Mortality rates were 3.3 times higher within each TIMI risk score stratum in patients with an EF of 48% or lower as compared with those with higher. The TIMI risk score predicts inhospital mortality in a broad population of patients with ACS. The further consideration of EF adds significant prognostic information.

Modeling of Near-Surface Leakage and Seepage of CO2 for Risk Characterization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oldenburg, Curtis M.; Unger, Andre A.J.

2004-02-18

The injection of carbon dioxide (CO2) into deep geologic carbon sequestration sites entails risk that CO2 will leak away from the primary storage formation and migrate upwards to the unsaturated zone from which it can seep out of the ground. We have developed a coupled modeling framework called T2CA for simulating CO2 leakage and seepage in the subsurface and in the atmospheric surface layer. The results of model simulations can be used to calculate the two key health, safety, and environmental (HSE) risk drivers, namely CO2 seepage flux and nearsurface CO2 concentrations. Sensitivity studies for a subsurface system with amore » thick unsaturated zone show limited leakage attenuation resulting in correspondingly large CO2 concentrations in the shallow subsurface. Large CO2 concentrations in the shallow subsurface present a risk to plant and tree roots, and to humans and other animals in subsurface structures such as basements or utility vaults. Whereas CO2 concentrations in the subsurface can be high, surfacelayer winds reduce CO2 concentrations to low levels for the fluxes investigated. We recommend more verification and case studies be carried out with T2CA, along with the development of extensions to handle additional scenarios such as calm conditions, topographic effects, and catastrophic surface-layer discharge events.« less

Probabilistic Assessment of Radiation Risk for Astronauts in Space Missions

NASA Technical Reports Server (NTRS)

Kim, Myung-Hee; DeAngelis, Giovanni; Cucinotta, Francis A.

2009-01-01

Accurate predictions of the health risks to astronauts from space radiation exposure are necessary for enabling future lunar and Mars missions. Space radiation consists of solar particle events (SPEs), comprised largely of medium energy protons, (less than 100 MeV); and galactic cosmic rays (GCR), which include protons and heavy ions of higher energies. While the expected frequency of SPEs is strongly influenced by the solar activity cycle, SPE occurrences themselves are random in nature. A solar modulation model has been developed for the temporal characterization of the GCR environment, which is represented by the deceleration potential, phi. The risk of radiation exposure from SPEs during extra-vehicular activities (EVAs) or in lightly shielded vehicles is a major concern for radiation protection, including determining the shielding and operational requirements for astronauts and hardware. To support the probabilistic risk assessment for EVAs, which would be up to 15% of crew time on lunar missions, we estimated the probability of SPE occurrence as a function of time within a solar cycle using a nonhomogeneous Poisson model to fit the historical database of measurements of protons with energy > 30 MeV, (phi)30. The resultant organ doses and dose equivalents, as well as effective whole body doses for acute and cancer risk estimations are analyzed for a conceptual habitat module and a lunar rover during defined space mission periods. This probabilistic approach to radiation risk assessment from SPE and GCR is in support of mission design and operational planning to manage radiation risks for space exploration.

Risk Informed Margins Management as part of Risk Informed Safety Margin Characterization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Curtis Smith

2014-06-01

The ability to better characterize and quantify safety margin is important to improved decision making about Light Water Reactor (LWR) design, operation, and plant life extension. A systematic approach to characterization of safety margins and the subsequent margin management options represents a vital input to the licensee and regulatory analysis and decision making that will be involved. In addition, as research and development in the LWR Sustainability (LWRS) Program and other collaborative efforts yield new data, sensors, and improved scientific understanding of physical processes that govern the aging and degradation of plant SSCs needs and opportunities to better optimize plantmore » safety and performance will become known. To support decision making related to economics, readability, and safety, the Risk Informed Safety Margin Characterization (RISMC) Pathway provides methods and tools that enable mitigation options known as risk informed margins management (RIMM) strategies.« less

WRF-based fire risk modelling and evaluation for years 2010 and 2012 in Poland

NASA Astrophysics Data System (ADS)

Stec, Magdalena; Szymanowski, Mariusz; Kryza, Maciej

2016-04-01

Wildfires are one of the main ecosystems' disturbances for forested, seminatural and agricultural areas. They generate significant economic loss, especially in forest management and agriculture. Forest fire risk modeling is therefore essential e.g. for forestry administration. In August 2015 a new method of forest fire risk forecasting entered into force in Poland. The method allows to predict a fire risk level in a 4-degree scale (0 - no risk, 3 - highest risk) and consists of a set of linearized regression equations. Meteorological information is used as predictors in regression equations, with air temperature, relative humidity, average wind speed, cloudiness and rainfall. The equations include also pine litter humidity as a measure of potential fuel characteristics. All these parameters are measured routinely in Poland at 42 basic and 94 auxiliary sites. The fire risk level is estimated for a current (basing on morning measurements) or next day (basing on midday measurements). Entire country is divided into 42 prognostic zones, and fire risk level for each zone is taken from the closest measuring site. The first goal of this work is to assess if the measurements needed for fire risk forecasting may be replaced by the data from mesoscale meteorological model. Additionally, the use of a meteorological model would allow to take into account much more realistic spatial differentiation of weather elements determining the fire risk level instead of discrete point-made measurements. Meteorological data have been calculated using the Weather Research and Forecasting model (WRF). For the purpose of this study the WRF model is run in the reanalysis mode allowing to estimate all required meteorological data in a 5-kilometers grid. The only parameter that cannot be directly calculated using WRF is the litter humidity, which has been estimated using empirical formula developed by Sakowska (2007). The experiments are carried out for two selected years: 2010 and 2012. The year 2010 was characterized by the smallest number of wildfires and burnt area whereas 2012 - by the biggest number of fires and the largest area of conflagration. The data about time, localization, scale and causes of individual wildfire occurrence in given years are taken from the National Forest Fire Information System (KSIPL), administered by Forest Fire Protection Department of Polish Forest Research Institute. The database is a part of European Forest Fire Information System (EFFIS). Basing on this data and on the WRF-based fire risk modelling we intend to achieve the second goal of the study, which is the evaluation of the forecasted fire risk with an occurrence of wildfires. Special attention is paid here to the number, time and the spatial distribution of wildfires occurred in cases of low-level predicted fire risk. Results obtained reveals the effectiveness of the new forecasting method. The outcome of our investigation allows to draw a conclusion that some adjustments are possible to improve the efficiency on the fire-risk estimation method.

How Many Significant Figures are Useful for Public Risk Estimates?

NASA Astrophysics Data System (ADS)

Wilde, Paul D.; Duffy, Jim

2013-09-01

This paper considers the level of uncertainty in the calculation of public risks from launch or reentry and provides guidance on the number of significant digits that can be used with confidence when reporting the analysis results to decision-makers. The focus of this paper is the uncertainty in collective risk calculations that are used for launches of new and mature ELVs. This paper examines the computational models that are used to estimate total collective risk to the public for a launch, including the model input data and the model results, and characterizes the uncertainties due to both bias and variability. There have been two recent efforts to assess the uncertainty in state-of-the-art risk analysis models used in the US and their input data. One assessment focused on launch area risk from an Atlas V at Vandenberg Air Force Base (VAFB) and the other focused on downrange risk to Eurasia from a Falcon 9 launched from Cape Canaveral Air Force Station (CCAFS). The results of these studies quantified the uncertainties related to both the probability and the consequence of the launch debris hazards. This paper summarizes the results of both of these relatively comprehensive launch risk uncertainty analyses, which addressed both aleatory and epistemic uncertainties. The epistemic uncertainties of most concern were associated with probability of failure and the debris list. Other major sources of uncertainty evaluated were: the casualty area for people in shelters that are impacted by debris, impact distribution size, yield from exploding propellant and propellant tanks, probability of injury from a blast wave for people in shelters or outside, and population density. This paper also summarizes a relatively comprehensive over-flight risk uncertainty analysis performed by the FAA for the second stage of flight for a Falcon 9 from CCAFS. This paper is applicable to baseline collective risk analyses, such as those used to make a commercial license determination, and day-of-launch collective risk analyses, such as those used to determine if a launch can be initiated safely. The paper recommends the use of only one significant figure as the default for reporting collective public risk results when making a safety determination, unless there are other specific analyses, data, or circumstances to justify the use of an additional significant figure.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kienhuis, Anne S., E-mail: anne.kienhuis@rivm.nl; RIKILT, Institute of Food Safety, Wageningen UR, PO Box 230, 6700 AE, Wageningen; Netherlands Toxicogenomics Centre

Hepatic systems toxicology is the integrative analysis of toxicogenomic technologies, e.g., transcriptomics, proteomics, and metabolomics, in combination with traditional toxicology measures to improve the understanding of mechanisms of hepatotoxic action. Hepatic toxicology studies that have employed toxicogenomic technologies to date have already provided a proof of principle for the value of hepatic systems toxicology in hazard identification. In the present review, acetaminophen is used as a model compound to discuss the application of toxicogenomics in hepatic systems toxicology for its potential role in the risk assessment process, to progress from hazard identification towards hazard characterization. The toxicogenomics-based parallelogram is usedmore » to identify current achievements and limitations of acetaminophen toxicogenomic in vivo and in vitro studies for in vitro-to-in vivo and interspecies comparisons, with the ultimate aim to extrapolate animal studies to humans in vivo. This article provides a model for comparison of more species and more in vitro models enhancing the robustness of common toxicogenomic responses and their relevance to human risk assessment. To progress to quantitative dose-response analysis needed for hazard characterization, in hepatic systems toxicology studies, generation of toxicogenomic data of multiple doses/concentrations and time points is required. Newly developed bioinformatics tools for quantitative analysis of toxicogenomic data can aid in the elucidation of dose-responsive effects. The challenge herein is to assess which toxicogenomic responses are relevant for induction of the apical effect and whether perturbations are sufficient for the induction of downstream events, eventually causing toxicity.« less

Modeling the Interplay of Multilevel Risk Factors for Future Academic and Behavior Problems: A Person-Centered Approach

PubMed Central

Lanza, Stephanie T.; Rhoades, Brittany L.; Nix, Robert L.; Greenberg, Mark T.

2010-01-01

This study identified profiles of 13 risk factors across child, family, school, and neighborhood domains in a diverse sample of children in kindergarten from 4 US locations (n = 750; 45% minority). It then examined the relation of those early risk profiles to externalizing problems, school failure, and low academic achievement in Grade 5. A person-centered approach, latent class analysis, revealed four unique risk profiles, which varied considerably across urban African American, urban white, and rural white children. Profiles characterized by several risks that cut across multiple domains conferred the highest risk for negative outcomes. Compared to a variable-centered approach, such as a cumulative risk index, these findings provide a more nuanced understanding of the early precursors to negative outcomes. For example, results suggested that urban children in single-parent homes that have few other risk factors (i.e., show at least average parenting warmth and consistency and report relatively low stress and high social support) are at quite low risk for externalizing problems, but at relatively high risk for poor grades and low academic achievement. These findings provide important information for refining and targeting preventive interventions to groups of children who share particular constellations of risk factors. PMID:20423544

Modeling the interplay of multilevel risk factors for future academic and behavior problems: a person-centered approach.

PubMed

Lanza, Stephanie T; Rhoades, Brittany L; Nix, Robert L; Greenberg, Mark T

2010-05-01

This study identified profiles of 13 risk factors across child, family, school, and neighborhood domains in a diverse sample of children in kindergarten from four US locations (n = 750; 45% minority). It then examined the relation of those early risk profiles to externalizing problems, school failure, and low academic achievement in Grade 5. A person-centered approach, latent class analysis, revealed four unique risk profiles, which varied considerably across urban African American, urban White, and rural White children. Profiles characterized by several risks that cut across multiple domains conferred the highest risk for negative outcomes. Compared to a variable-centered approach, such as a cumulative risk index, these findings provide a more nuanced understanding of the early precursors to negative outcomes. For example, results suggested that urban children in single-parent homes that have few other risk factors (i.e., show at least average parenting warmth and consistency and report relatively low stress and high social support) are at quite low risk for externalizing problems, but at relatively high risk for poor grades and low academic achievement. These findings provide important information for refining and targeting preventive interventions to groups of children who share particular constellations of risk factors.

Learning and Information Approaches for Inference in Dynamic Data-Driven Geophysical Applications

NASA Astrophysics Data System (ADS)

Ravela, S.

2015-12-01

Many Geophysical inference problems are characterized by non-linear processes, high-dimensional models and complex uncertainties. A dynamic coupling between models, estimation, and sampling is typically sought to efficiently characterize and reduce uncertainty. This process is however fraught with several difficulties. Among them, the key difficulties are the ability to deal with model errors, efficacy of uncertainty quantification and data assimilation. In this presentation, we present three key ideas from learning and intelligent systems theory and apply them to two geophysical applications. The first idea is the use of Ensemble Learning to compensate for model error, the second is to develop tractable Information Theoretic Learning to deal with non-Gaussianity in inference, and the third is a Manifold Resampling technique for effective uncertainty quantification. We apply these methods, first to the development of a cooperative autonomous observing system using sUAS for studying coherent structures. We apply this to Second, we apply this to the problem of quantifying risk from hurricanes and storm surges in a changing climate. Results indicate that learning approaches can enable new effectiveness in cases where standard approaches to model reduction, uncertainty quantification and data assimilation fail.

A unique rodent model of cardiometabolic risk associated with the metabolic syndrome and polycystic ovary syndrome.

PubMed

Shi, Danni; Dyck, Michael K; Uwiera, Richard R E; Russell, Jim C; Proctor, Spencer D; Vine, Donna F

2009-09-01

Polycystic ovary syndrome (PCOS) is characterized by hyperandrogenism, oligo-/anovulation, and polycystic ovarian morphology and is a complex endocrine disorder that also presents with features of the metabolic syndrome, including obesity, insulin resistance, and dyslipidemia. These latter symptoms form cardiometabolic risk factors predisposing individuals to the development of type 2 diabetes and cardiovascular disease (CVD). To date, animal models to study PCOS in the context of the metabolic syndrome and CVD risk have been lacking. The aim of this study was to investigate the JCR:LA-cp rodent as an animal model of PCOS associated with the metabolic syndrome. Metabolic indices were measured at 6 and 12 wk, and reproductive parameters including ovarian morphology and estrous cyclicity were assessed at 12 wk or adulthood. At 6 wk of age, the cp/cp genotype of the JCR:LA-cp strain developed visceral obesity, insulin resistance, and dyslipidemia (hypertriglyceridemia and hypercholesterolemia) compared with control animals. Serum testosterone concentrations were not significantly different between groups at 6 wk of age. However, at 12 wk, the cp/cp genotype had higher serum testosterone concentrations, compared with control animals, and presented with oligoovulation, a decreased number of corpora lutea, and an increased number of total follicles, in particular atretic and cystic follicles. The cardiometabolic risk factors in the cp/cp animals were exacerbated at 12 wk including obesity, insulin resistance, and dyslipidemia. The results of this study demonstrate that the JCR:LA-cp rodent may be a useful PCOS-like model to study early mechanisms involved in the etiology of cardiometabolic risk factors in the context of both PCOS and the metabolic syndrome.

Prospective Associations of Low Positive Emotionality with First Onsets of Depressive and Anxiety Disorders: Results from a 10-Wave Latent Trait-State Modeling Study

PubMed Central

Kendall, Ashley D.; Zinbarg, Richard E.; Mineka, Susan; Bobova, Lyuba; Prenoveau, Jason M.; Revelle, William; Craske, Michelle G.

2015-01-01

Unipolar depressive disorders (DDs) and anxiety disorders (ADs) co-occur at high rates and can be difficult to distinguish from one another. Cross-sectional evidence has demonstrated that whereas all these disorders are characterized by high negative emotion, low positive emotion shows specificity in its associations with DDs, social anxiety disorder (SAD), and possibly generalized anxiety disorder (GAD). However, it remains unknown whether low positive emotionality, a personality trait characterized by the tendency to experience low positive emotion over time, prospectively marks risk for the initial development of these disorders. We aimed to help address this gap. Each year for up to 10 waves, participants (n = 627, mean age = 17 years at baseline) completed self-report measures of mood and personality, and a structured clinical interview. A latent trait-state decomposition technique was used to model positive emotionality and related personality traits over the first three years of the study. Survival analyses were used to test the prospective associations of low positive emotionality with first onsets of disorders over the subsequent six-year follow-up among participants with no relevant disorder history. The results showed that low positive emotionality was a risk marker for DDs, SAD, and GAD, although evidence for its specificity to these disorders versus the remaining ADs was inconclusive. Additional analyses revealed that the risk effects were largely accounted for by the overlap of low positive emotionality with neuroticism. The implications for understanding the role of positive emotionality in DDs and ADs are discussed. PMID:26372005

The Development of Brain Metastases in Patients with Renal Cell Carcinoma: Epidemiologic Trends, Survival, and Clinical Risk Factors Using a Population-based Cohort.

PubMed

Sun, Maxine; De Velasco, Guillermo; Brastianos, Priscilla K; Aizer, Ayal A; Martin, Allison; Moreira, Raphael; Nguyen, Paul L; Trinh, Quoc-Dien; Choueiri, Toni K

2018-01-05

The incidence of brain metastases (BM) in patients with renal cell carcinoma (RCC) is hypothesized to have increased in the last 2 decades. To define incidence trends according to patient and clinical characteristics, to identify risk factors, and to describe outcomes of patients with BM for RCC. Patients diagnosed with RCC between the years 2010 and 2013 within the Surveillance, Epidemiology, and End Results database. An external validation was also considered using patients diagnosed with RCC between 2010 and 2012 within the National Cancer Database. Incidence proportions of BM were calculated. Risk factors correlated with BM at diagnosis were identified via a 1000-bootstrap corrected multivariable logistic regression model. A risk model was then developed and evaluated using measures of predictive accuracy. Overall survival was examined using Cox regression analyses. The overall incidence proportions of BM at RCC diagnosis was 1.51% (95% confidence interval: 1.39-1.64%). White/other race, clear cell histology, and sarcomatoid differentiation, T2-4 disease, tumor dimension >10 cm, and N+ disease were significantly associated with BM at RCC diagnosis, and retained within the final prediction model. A risk score was created based on these variables (c-index: 0.803). BM at RCC diagnosis occurred in 0.5%, 3.6%, and 7.7% of patients categorized as low risk, intermediate risk, and high risk. Patients with BM were more likely to succumb to any death than those without BM at diagnosis (median overall survival: 6.4 mo vs not reached, respectively, adjusted hazard ratio: 1.87, 95% confidence interval: 1.67-2.08, p < 0.001). The real incidence of BM at RCC diagnosis is likely underestimated given that the observed rate likely reflects patients who presented with symptoms. Patients with BM at RCC have poor oncological outcomes. We have characterized the epidemiology of BM at RCC diagnosis and developed a clinical risk model for the purpose of predicting the development of BMs in patients diagnosed with a cortical renal mass. In this report we examined recent proportions of patients with brain metastases at kidney cancer diagnosis in a large community database originating from the US. We developed a model that may be used during routine clinical practice to predict brain metastases. The urologic-oncological community may consider baseline imaging for brain metastases in patients without any symptoms but at high risk of having brain metastases according to the risk model. However, the proposed model certainly needs further testing and validation in the clinical setting. Future studies on brain metastases survival and treatment options are also needed. Copyright © 2017 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Fatigue Assessment of Nickel-Titanium Peripheral Stents: Comparison of Multi-Axial Fatigue Models

NASA Astrophysics Data System (ADS)

Allegretti, Dario; Berti, Francesca; Migliavacca, Francesco; Pennati, Giancarlo; Petrini, Lorenza

2018-03-01

Peripheral Nickel-Titanium (NiTi) stents exploit super-elasticity to treat femoropopliteal artery atherosclerosis. The stent is subject to cyclic loads, which may lead to fatigue fracture and treatment failure. The complexity of the loading conditions and device geometry, coupled with the nonlinear material behavior, may induce multi-axial and non-proportional deformation. Finite element analysis can assess the fatigue risk, by comparing the device state of stress with the material fatigue limit. The most suitable fatigue model is not fully understood for NiTi devices, due to its complex thermo-mechanical behavior. This paper assesses the fatigue behavior of NiTi stents through computational models and experimental validation. Four different strain-based models are considered: the von Mises criterion and three critical plane models (Fatemi-Socie, Brown-Miller, and Smith-Watson-Topper models). Two stents, made of the same material with different cell geometries are manufactured, and their fatigue behavior is experimentally characterized. The comparison between experimental and numerical results highlights an overestimation of the failure risk by the von Mises criterion. On the contrary, the selected critical plane models, even if based on different damage mechanisms, give a better fatigue life estimation. Further investigations on crack propagation mechanisms of NiTi stents are required to properly select the most reliable fatigue model.

Fatigue Assessment of Nickel-Titanium Peripheral Stents: Comparison of Multi-Axial Fatigue Models

NASA Astrophysics Data System (ADS)

Allegretti, Dario; Berti, Francesca; Migliavacca, Francesco; Pennati, Giancarlo; Petrini, Lorenza

2018-02-01

Peripheral Nickel-Titanium (NiTi) stents exploit super-elasticity to treat femoropopliteal artery atherosclerosis. The stent is subject to cyclic loads, which may lead to fatigue fracture and treatment failure. The complexity of the loading conditions and device geometry, coupled with the nonlinear material behavior, may induce multi-axial and non-proportional deformation. Finite element analysis can assess the fatigue risk, by comparing the device state of stress with the material fatigue limit. The most suitable fatigue model is not fully understood for NiTi devices, due to its complex thermo-mechanical behavior. This paper assesses the fatigue behavior of NiTi stents through computational models and experimental validation. Four different strain-based models are considered: the von Mises criterion and three critical plane models (Fatemi-Socie, Brown-Miller, and Smith-Watson-Topper models). Two stents, made of the same material with different cell geometries are manufactured, and their fatigue behavior is experimentally characterized. The comparison between experimental and numerical results highlights an overestimation of the failure risk by the von Mises criterion. On the contrary, the selected critical plane models, even if based on different damage mechanisms, give a better fatigue life estimation. Further investigations on crack propagation mechanisms of NiTi stents are required to properly select the most reliable fatigue model.